Invited Talks

1. Clinical BioMEMS: Future Healthcare Technology

Kanika Singh, Member, IEEE

2 A study on Smart E-Learning using Intelligence

Changduk jung ,You-Sik Hong, jangmook Kang ,,

3 Electronic State of Nanostructures &Quantum Dots

(Theoretical & Experimental Study)

Yuri V. Vorobiev, Petro M. Gorley, Vitor R. Vieira, and Paul P.,

Horley

4 Advances in polymer based micro and nano composites

Saritha . A 1

K. Jayanarayanan2

Dr. Kuruvilla Joseph

5 New Approach in Design and Engineering of Multi-junction Solar Cell Devices

Yuri V. Vorobiev, Petro M. Gorley, Jesús González-Hernández,

& Pavel Vorobiev

6 Development of Ubiquitous Health Care Systems

V.R.Singh, Fellow-IEEE

7 Analysis of Some Repairable Engineering Systems in Reliability Theory

Dr. R.K. Tuteja

8 Enhancing Global Competitiveness through Innovative Technologies, Quality and

Knowledge Management

Prof. S.K.GARG

9 Microemulsions: Drug Carriers for Delivery of Water Insoluble Drugs

Dr. Shishu, M. Pharm.

10 Health Effects of Outdoor Air Pollution due to Crop Residue Burning

Ravinder Agarwal

2 | P a g e

Clinical BioMEMS: Future Healthcare

Technology

Abstract— There is a strong demand for miniaturized, accurate, fast, inexpensive and reliable devices in the

clinical world. The miniaturizing ability has enabled MEMS (Micro-electro-mechanical systems)-based devices to be

applied recently to various engineering, biomedical and other applications. The application of bio-micro-

electromechanical systems (BioMEMS) in Biomedical engineering can be classified into diagnostics and Therapeutic.

Also, new biological materials have been used recently in the development of BioMEMS for various novel applications in

science and engineering. The BioMEMS that are used in clinical medicine are termed as ‘Clinical BioMEMS’. Recent

advances in clinical BIOMEMS, with technology, development and applications are given here. New clinical applications

of these clinical BioMEMS for both diagnostic and therapeutic treatments are discussed.

Keywords—BioMEMs, Healthcare. Clinical BioMEMS, Sensor

I. INTRODUCTION

ITH the beginning of micro-electro-mechanical systems in the early 1970s, the importance of

the biomedical applications of these miniature systems were realized [1, 2]. Biomedical or

Biological Micro- Electro-Mechanical Systems (BioMEMS) are now a heavily researched area with a

wide variety of important biomedical applications [3]. In general, BioMEMS can be defined as

‘‘devices or systems, constructed using techniques inspired from micro/nano-scale fabrication, that

are used for processing, delivery, manipulation, analysis, or construction of biological and chemical

entities [4-25].

On the other hand, BioMEMS are the biological or biomedical MEMS and are defined as the

devices or systems constructed using techniques inspired from micro/nano-scale fabrication that are

used for processing, delivery, manipulation, analysis or construction of biological and chemicals

entities [10-12, 14-18]. Clinical BioMEMS are BioMEMS used in the clinics in different configurations,

in implantable and non-implantable form. Microfluidics-based biochips have also been developed

recently which are soon expected to revolutionize clinical diagnosis. Areas of research and

applications in BioMEMS range from diagnostics, such as DNA and protein micro-arrays, to novel

materials for BioMEMS, microfluidics, tissue engineering, surface modification, implantable

BioMEMS, systems for drug delivery etc. The devices and integrated systems using BioMEMS are also

known as lab-on-chip devices and micro-TAS systems .

In this paper, detection technologies and applications having an impact on the technical and

commercial success of these devices [7,8, 11, 12 are described. Recent advances in clinical BioMEMS,

Kanika Singh has served as a Research Professor at Pusan National University , Busan and is with Indira Gandhi National Open

University, New Delhi-110068, India (corresponding author: e-mail: kstechinfo@ yahoo.comv).

Kanika Singh, Member, IEEE

W

Detection strategies

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with design, technology development and fabrication, are given. New clinical applications of these

clinical BIOMEMS for both diagnostic and therapeutic treatment purposes are discussed.

A. Detection Technologies

The choice of the detection method is generally determined by the sensitivity. Most bioMEMS

device use optical or electrical detection methods (Fig1).

Figure 1: Detection technologies

B. Mechanical detection

The cantilever type sensors are used in two modes, namely stress sensing and mass sensing. In stress

sensing mode, the biochemical reaction is performed selectively on one side of the cantilever. A

change in surface free energy results in a change in surface stress, which results in measurable

bending of the cantilever. Thus, label-free detection of bimolecular binding can be performed. In the

mass sensing mode, the cantilever is excited mechanically so that it vibrates at its resonant

frequency (using external drive or the ambient noise, for example). The resonant frequency is

measured using electrical or optical means, and compared to the resonant frequency of the

cantilever once a biological entity is captured. The change in mass can be detected by detection of

shift in resonant frequency, assuming the spring constant does not change [19, 20, 21, 25].

C. Electrical detection

Electrical or electrochemical detection techniques have also been used quite commonly in biochips

and BioMEMS sensors. These techniques can be amenable to portability and miniaturization, when

compared to optical detection techniques, however, recent advances in integration optical

components on a chip can also produce smaller integrated devices [11, 12]. Electrochemical

biosensors include three basic types , they are as follows: (i) amperometric biosensors, which

involves the electric current associated with the electrons involved in redox processes, (ii)

potentiometric biosensors, which measure a change in potential at electrodes due to ions or

chemical reactions at an electrode (such as an ion Sensitive FET), and (iii) conductometric biosensors,

which measure conductance changes associated with changes in the overall ionic medium between

the two electrodes[22-24].

Electrochemical

Optical

Electrical

Voltammetry

Impedance

Fluorescence

Chemiluminscence

Spectroscopic

FET

CMOS

Diodes

Digital

Amperometry

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D. Optical detection

Optical detection techniques are perhaps the most common due to their prevalent use in biology

and life sciences. Optical detection techniques can be based on fluorescence or chemiluminescence.

Fluorescence detection techniques are based on fluorescent markers that emit light at specific

wavelengths and the presence and enhancement, or reduction (as in Fluorescence Resonance

Energy Transfer) in optical signal can indicate a binding reaction, Recent advances in fluorescence

detection technology have enabled single molecule detection [4].

II. APPLICATION OF CLINICAL BIOMEMS

The applications of clinical BIOMEMS are broadly classified into two types (see clinical

diagnostics and clinical therapeutics (including surgery). Some of the applications are given below for

both these categories:

Design and protocol of a particular clinical diagnostic BioMEMS chip). BioMEMS hold a lot of

promise for the analysis of single cell or molecule. An example of integrated blood plasma

separation, resuspension of dried chemicals, a defined incubation time and transport to a detection

zone.

(a). Predontal disease

The gel has a gelatin-like consistency and by permitting the easy passage of smaller molecules and slowing the

passage of larger ones, it quickly separates proteins contained in the saliva. Prior to this separation, the proteins

are brought into contact with specific antibodies chosen on their ability to bind to biomarkers. The antibodies

are pre-labeled with fluorescent molecules attached to them.

(b). CardioMEMS systems (www.whistle.gatech.edu/archives/05/feb/21/mems.shtml) are new types

of testing devices to monitor heart patients. These cadio MEMS combine wireless communications

technology with micro-electromechanical systems (MEMS) fabrication. CardioMEMS provide doctors

with more information, while making testing less invasive for patients. Special endo-sensor

measures blood pressure in people who have an abdominal aortic aneurysm, a weakening in the

lower aorta. An electronics wand is waved in front of the chest of the patient. Radio frequency

activates the sensor which takes pressure measurements and then relays the information to an

external receiver and monitor.

( c). Anthrax Detection

The rare cell/disease detection, with the high speed of a MEMS-based cell sorter allows for lower

detection thresholds on diseases. Anthrax detection may be made sooner within a patient, allowing

for early detection and treatment. The most recent is based on rapid-cycle real-time PCR developed

Roche Rapid Anthrax Test [24].

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(d). Chiral and Achiral Biosensing using Nanostructured Microcantilevers

The magnitude, kinetics and reversibility of surface stresses are used caused when common

bioaffinity agents interact with microcantilevers (MCs) with nanostructured (roughened) gold

surfaces on one side are used. Exposure of nanostructured, unfunctionalized MCs to the proteins

immunoglobulin G and bovine serum albumin (BSA) gives in reversible large tensile stresses,

whereas MCs with smooth gold surfaces on one side produce reversible responses that are

considerably smaller and compressive. The response magnitude for nanostructured MCs exposed to

BSA is concentration dependent and linear calibration over the range of 1-200 mg/L in a particular

case. Stable, reusable protein bioaffinity phases based on nantioselective antibodies are created by

covalently linking monoclonal antibodies to nanostructured MC surfaces. The direct (label-free)

stereoselective detection of trace amounts of a-amino acids has been achieved based on immuno-

mechanical responses involving nanoscale bending of the cantilever [24].

(e) Cancer detection

Molecular profiling by DNA microarray technology has made significant contributions to the

understanding of many diseases, especially cancer. Cancer-specific gene sets, or disease signatures,

generated from microarray studies need to be validated using independent cancer samples and

sophisticated analytical tools. A particular MetriGenix 4D array system meets such requirements

[34]. Another system,The MGX 4D System consists of a Flow-thru Chip contained within a

microfluidic cartridge, automated hybridization and chemiluminescence detection stations, and data

analysis software. Disease-relevant gene sets are identified through extensive data mining of

comprehensive gene expression databases followed by sophisticated data analysis. Gene selection is

based on expression signatures and fold changes between normal and diseased sample groups. In

studies with these arrays, biological markers are determined for potential early detection and clinical

diagnostics in the general population using a well defined data mining strategy and an easy-to-use

validation platform.

III. CLINICAL THERAPEUTICS

Design and protocol of a particular clinical diagnostic BioMEMS chip). BioMEMS hold a lot of

promise for the analysis of single cell or molecule. An example of integrated blood plasma

separation, resuspension of dried chemicals, a defined incubation time and transport to a detection

zone.

Stem Cells Sorting for Leukemia and Vascular diseases

Fetal cells are found within samples of a mother's blood at low levels about 1 ppb. These

cells can be sorted from a blood sample rather than invasively extracted, eliminating the need for

amniocentesis.

This system has the ability to sort therapeutic stem cell doses in one to three hours. Also,

this system is to be used to isolate unique stem cell populations for the treatment of chronic heart

failure, peripheral vascular disease, leukemias, genetic enzyme deficiencies and other such diseases.

A microfluidic approach is adopted to increase the speed of cell sorting as well as provide an avenue

for a cost effective, disposable sterile fluid path that could be used on a per patient basis.

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The device entails massively parallel sorting performed in three-dimensional enclosed

microfluidic channels integrated on to a single chip. The chip and accompanying tubings are the only

parts of the system that contact the fluid or cells and the chip is designed to be disposable. Coils

wrap around a magnetic alloy to form an array of electromagnetic motors.

Obesity treatment

Therapeutic MEMS have been developed [jcp.sagepub.com/cgi/content/abstract/39/4/402]

to nonadherence treatment in the clinical management of hypercholesterolemic patients.

Monitoring of the daily compliance to a course of lipid-lowering therapy is made, using a

microelectronic device-MEMS, versus pill count. Thus, MEMS is a useful tool for monitoring

compliance in clinical practice and may possibly increase adherence to long-term lipid-lowering

therapy.

Blood pressure problem

MEMS technology uses micro-machining fabrication, similar to that originally developed for the integrated

circuit industry to build electrical and mechanical structures at the micron scale (one-millionth of a meter). he

advantage of this device compared to a hand cuff based approach is the capability of recording continuous

blood pressure data. The capacitive, membrane-based sensor device is fabricated in an industrial CMOS-

technology combined with post-CMOS micromachining. The capacitance change is detected by a ¿¿-

modulator. The modulator is operated at a sampling rate of 128kS/s and achieves a resolution of 12bit with an

external decimation filter and an OSR of 128 [3]

Cancer treatment

A limiting factor in treating cancer is the destructive effects of chemotherapy on a patient's immune

system. High purity pre-sorting of the patient's blood stem cells allows an otherwise-lethal dose of

chemotherapy to be used, followed by re-infusion of the patient's stem cells to rebuild their immune

system. Exposure to radiation or nitrogen chemicals also destroys the human immune system and

re-infusion of stem cells can be used to help these victims.

IV. RECENT RESEARCH IN CLINICAL BIOMEMS

In this section, we describe the in-house technology developed for the early detection of diseases. The section

also describes the latest trends in the Clinical BioMEMs. Finally explains protocol for smart BioMEMS.

Bio-chips and BioMEMS for early detection of disease

A novel BioMEMS chip, based on gold nanoparticles, for the detection of Osteoproteogerin

(OPG) has been developed (see Fig.6), by the authors [Singh and Kim, 2007]. This biochip is used to

evaluate the bone conditioning which is directly related to the diagnosis and prognosis of the

Osteoporosis(OP), in an effective manner. The flow visualization of the mixing capabilities were

characterized using LIF (micro-scale laser-induced fluorescence). The BioMEMs chip detection has

been based on competitive immunoassay. The monoclonal OPG antibody (anti-OPG) was

immobilized onto the AuNPs deposited conducting polymer, using covalent bonding with a

carboxylic acid group. The catalytic reduction was monitored ampereometrically at -0.4V versus

Ag/AgCl. The linear dynamic range is between 2. to 24ng/ml with the detection limit of 2ng/ml.

7 | P a g e

Figure 2. Photograph of a new Clinical BioMEMS chip using gold nanoparticles

(W: waste well, C: ounter electrode, P1-P3- tubings, R: reference electrode)

The present BioMEMS-chip consists of a PDMS (poly-dimethyl siloxane) microfluidic channel

integrated with combinatorial 2D micromixing phenomenon (combination of serpentine and chaotic

mixing), CSC mixing, and electrochemical detection technique, showing improved performance, to

enable early detection of OPG, for better healthcare[11-24]. The bioMEMS chip has been

characterized by chronoamperometry.

Lab-on-a-chip

Fully integrated laboratory-on-a-chip devices (Fig 3) for use in clinical diagnosis are more

effective. Numerous functional features such as

Fig.3. Lab-on-a-chip Devices

indicators for physical parameters and reaction chambers for cell growth and separation at micro-

and nano-scale to rapid identify diseased cells are used. Potential cells are delivered into the

microfluidic device and cultivated in-vitro followed by detection using various optical-based

detection methods. The lab-on-chip devices have several distinct advantages over the current cell

culturing and detection methods, which include ease of use for cell culture and reaction, rapid

hybridization and sensitive detection [11-24]. Thus, there is an urgent need for the development of

new smart nano-biomedical sensors, lab-on-chips and new nano-materials for the diagnosis and

therapeutic treatment of the diseases.

Lab-on-chip sensors are used in different applications. Current lab-on-a-chip products

automate only two or three analytical steps, but further advantages are realized when multiple steps

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are fully automated. There is also a need for automation to increase throughput and reproducibly.

Existing technologies, such as gel electrophoresis, and to address novel analytical problems that

cannot be solved today, can be solved. This may well come about because of the convergences of

the “micro” lab-on-a-chip systems with the increasing miniaturization of the macro-HPLC systems

world. Indeed, nano-LC systems are rapidly becoming a reality.

V. FUTURE CHALLNEGES

Explosive growth in the field of MEMS technology has resulted in significant progress in the

development of materials and fabrication technologies. With these advancements in laboratory

research, MEMS technology is now poised to deliver commercial opportunities with innovative

applications. However, a roadmap for integration of novel technologies into the commercial use is

yet to be defined. There have been several researches in the area for specific disease which also

require some advancement.

Research in BIOMEMS for asthama related problems is also important to be taken up. Study

on genetics and asthama ancilliary can be taken up to identify genetic variants that will predict

which patients in the Leukotriene Modifier or Corticosteroid or Corticosteroid-Salmeterol Trial

(LOCCS) study responded favorably to inhaled corticosteroids, montelukast or the combination of

salmeterol and corticosteroid treatment and predict which patients experience side effects. The

results of this study may enable researchers to select a priori which patients respond favorably to

these various treatments.

. Biochips scan, process, and interpret biological data vary rapidly, the technology called "lab

on a chip". As the BioMEMS (biological MicroElectroMechanical Systems) are MEMS systems and

technologies used for biotech applications, the biochips apply microchip and microelectronics

technology in the biotechnology and pharmaceutical industries. The biochips may also bring

together life sciences and information technology. These devices assist scientists to identify and

compare selected sequences of amino acids and other complex molecules. The generic term biochip

has other derivative terms such as protein chip, DNA chip, microarray, and gene chip

(www.mindbranch.com/listing/product/R350-0001.html - 12k -)

Also, the bionanotechnology will give rise to a new device and system with greater sensitivity and accuracy.

The important applications in this field of study may include synthesis of new molecules, selfless assembly of

structures from DNA, macromolecular science and engineering mimics biological assembly, drug-delivery

systems, therapeutic applications, biomolecular motors, bioelectronics, DNA computers, enabling technologies,

etc.”

VI. CONCLUSIONS

Clinical Bio-MEMS, as a new subject, has been introduced, with an overview of recent

developments in the field. The evolution of technology development of the clinical BioMEMS for

various diagnostic and therapeutic applications, in different medical fields, has been discussed.

Recent research trends and future challenges of such systems have been presented.

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Thus, as MEMS are now considered as the technology to interface the macro world to the

nanoworld, clinical BIoMEMS will also enable researchers to probe, measure and explore the nano-

machinery in the biological world as single cells, to open up new lines of research.

ACKNOWLEDGMENTS

Dr. Kanika Singh would like to convey her thanks to Prof.K. C Kim, Pusan National University, South Korea,

for his guidance and support. Thanks are also due to IGNOU, New Delhi.

REFERENCES

[1] Ko W.H, Solid-state physical transducers for biomedical research. IEEE Trans. Biomed. Eng. 1986; BME-33, no.3:153-162. [2] V.R.Singh, “Smart sensors: physics, technology and applications”, Ind. J. Pure & Appl. Phys., vol.43, pp. 7-16, 2005. [3] G.L.Cote, “Emerging biomedical sensing technologies and their applications”, IEEE Sensors Journal, vol.3, no.3, pp.251-266, 2003. [4] Klank Geschke, and Telleman, Eds., Microsystem Engineering of Lab-on-a-chip Devices, 1st ed, John Wiley & Sons, 2004. [5] E Katz, and I..Willner, “Integrated nanoparticles-biomolecule hybrid systems: synthesis, properties, and applications”,

Nanobiotechnology, Angew. Chem., vol.43, pp. 6042- 6108, 2004 [6] V.R.Singh, “New Nano-Biomedical Lab-on-Chip Sensors in Nano-Medicine”, Proc. Int Conf on Nanotecnoly in Medicine, Mumbai,

India, Oct 12-13, 2007. [7] R. Koyama, Y.Yoshida and T.Kitamori, “Hydraulic Sample/reagents handling system for disposable clinical diagnosis microchip.” Proc.

MicroTAS, 2004, 240-242.

[8] 8. E.Maeda, M.Kataoka, Y.Shinohara, N.Kaji, M.Tokeshi and Y.Baba, “Determination of total and pancreatic amylaseactivities in human blood by use of Microchip electrophoresis.Proc. 11th International conference on Miniaturized systems for Chemistry and life sciences.MicroTaS, 2007, pg 65-67.

[9] Isabella Moser, Multi-Parameter Biomems for Clinical Monitoring, Microsystems, Volume 16: BioMEMS, Springer US,2007, pp. 15-39.

[10] Proc SPIE, vol. 4982Microfluidics, BioMEMS, and Medical Microsystems, by Holger Becker, Peter Woias, Editors, January 2003, pp. 144-155

[11] Kanika Singh, Kyung Chun Kim, "BioMEMS-Early bone disease detection" Th14B002, Korean Society of Mechanical Engineers, KSME Int. conference, 30thApril, 2007, Bexco, Busan.

[12] Kanika Singh Microfractal electrodes for EEG sensing. 2nd ASM - IEEE EMBS Conference on Bio, Micro and Nanosystems, San Francisco, (USA), Jan15-16, 2006.

[13] Kanika Singh, Hyung Hoon Kim and Kyung Chun Kim, Biomems for Osteoproteogerin detection with Gold Nanoparticle", MicroTAS, 7 - 11 October 2007, Paris (France).

[14] Kanika Singh and Kyung Chun Kim, "Investigating BioMEMs techniques for early detection of Osteoporosis. Proc.29th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, Aug 23-26 2007, Lyon, (France).

[15] . Kanika Singh and Kyung Chun Kim, "Smart diagnostic BioMEMS chip for early detection of Osteoporosis. 4rth International IEEE-EMBS Summer School and Symposium on Medical devices and Bisensors 19-22nd.Aug, 2007 St. Catherine’s College, Cambridge, (UK).

[16] Kanika Singh and Kyung Chun Kim, Gold nanoparticles for amperometric immunosensor for OPG", Cross Strait Symposium on Material, Energy and Environment Sciences at POSTECH, Pohang, South Korea.

[17] Kanika Singh, "A bone Material based sensor", Proceedings of the 26th Annual International Conference of the IEEE EMBS,San Francisco, CA, USA • September 1-5, 2004.

[18] K. Singh, S..H. Lee, and K.C. Kim, “Review: osteoporosis: new biomedical engineering aspects”, J. of Mechanical Science & Technology (KSME Int.J), vol.20, no12, pp.2265-2283, 2006.

[19] Kanika Singh and K.C.Kim, Biomechanics of bone, at 7th Cross Straits Symposium on Material Energy and Environmental Sciences at Kyushu University, Japan, 1-2 Dec, 2005

[20] . Kanika Singh, and Kyung Chun Kim, Investigating the optical techniques for biological samples for disease detection. The 3rd International symposium and the 14th Workshop on Innovative Bio-physio Sensor technology,July 6-8, 2006, Center of Innovative bio-physio Center, at Jeju-do, S.Korea.

[21] Kanika Singh, Seung Geun, Lee, Sang-Gyu Kim, Donggeun Lee and Kyung Chun Kim, Osteoporosis detection for normal and abnormal biofluids by FTIR. Proc. Of The Korean Society of Visualization, workshop at Dongnae University, Busan, 1st Dec, 2006, pg109-110.

[22] Kanika Singh, Seung Geum Lee, Sang Geum Kim, Donggeum Lee and K.C Kim , "Optical Techniques for Investigation of biofluid for Early disease detection" at 8rth Cross Straits Symposium on Material (Outstanding research paper award).

[23] Kanika Singh and Kyung Chun Kim, Biochip techniques for early and rapid screening of Osteoporosis. The 2nd International symposium and the Workshop on Innovative Bio-physio Sensor technology.

[24] . ww.mindbranch.com/listing/product/R350-0001.html - 12k

Kanika Singh, M.Tech (Electr Instr Tech), IIT-Delhi, 2002, PhD (MEMS-Nano/Biomed), Pusan National Univ, Busan, South Korea, 2008; has

research/teaching experience of eight years in India (IIT-Delhi, IGNOU-Delhi) and abroad (Korea, Germany and Belgium). She is a Member

of IEEE/EMBS. She has over 35 research papers in journals/conf Proc. She is an awardee of 'IEEE Outstanding Young Engineer Award(2005-

2006)', New Delhi, 'Oustatnding Res Paper Awards: Kyushu- Japan (2005) and Postech-Korea (2007) and 'IEEE-EMBS Best Student Paper

Award , Atlanta, Georgia (1999)'. She is the receipient of the Germany DAAD Scholarship (2001-2002), AICTE-EFI Fellowship (2000-2002),

CSIR Travel Fellowship (1997), Korea Foundation Grant (2005-08), ASM-EMBS Travel Fellowship, San Francisco, USA (2006) and KU-

Leuven/IMEC Scholarship (1997-98). Presently, she is a Senior Lecturer (Electrical Engg) in Indira Gandhi National Open Univ, New Delhi,

India. Her areas of interest are Nano-Micro Sensors and Biomedical Engineering Research.

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A study on Smart E-Learning using Intelligence

Changduk jung ,You-Sik Hong, jangmook Kang ,,

Dept .of computer and information science ,korea University

Jcd1234@paran.com

Dept. of Computer Science, Sangji University.

yshong@sangji.ac.kr

Dept of computer science,Seojong university

Abstract: In this paper, experimental results are analyzed in order to further clarify and currently

prove the advantages of the IRT for the e-Learning assessment. With Item Response Theory, we

estimate the abilities of on-line learners, and recommend appropriate course works, adapted to the

learners capabilities. The difficulty degree of course work can be automatically adjusted using Item

Response Theory. Experimental results show that the IRT can provide personalized on-line learning,

based upon learner abilities, in a quickly, effective method. It is very difficult for the instructor to

distinguish anyone who understands the lecture course. In this paper, we developed adaptive

feedback algorithm for each student. Adaptive feedback algorithm confirmed according to an

analysis consequence is efficient than existent algorithm.

1. Introduction

According to the analysis by several large, prestigious corporations, the worldwide corporate e-

learning market will exceed US $24 billion by 2004. The reason for this extraordinary growth is that

it gives a convenient and efficient way to learn anytime and anywhere. Many large corporations are

using e-learning for on-line employee training. Nowadays, most systems consider learner/user

preferences and interests when designing an educational system. Therefore, considering learner

ability and limiting information in order to prevent overload, can promote the best learning

performance. Item Response Theory (IRT) is usually applied to the Computerized Adaptive Test (CAT)

domain to select the most appropriate test items based upon individual ability. This e-Learning

system, based upon a user profile, prevents the learner from becoming lost in the course material,

resulting in more efficient and effective learning. However, CAT was not provided smart learning

environment to a student for adaptive learning. So, our goal of the research is to assess the

students’ knowledge in various topics using IRT and intelligence method. As a result, our proposed

system will provide a smart learning environment to a student in anytime and anywhere.

11 | P a g e

2. Related Work The amount and quality of feedback provided to the learner has an impact on learner satisfaction.

Feedback is especially significant to the efficient transmission of e-learning courses. E-learning

delivery methods such as web-based instruction can provide obstacles to conventional type

schoolroom feedback. For instance, in a web - based course learner cannot simply raise a hand and

ask for clarification about a point made by the instructor. Hence, the design and integration of

feedback mechanisms affect the learners experience and level of satisfaction.

According to Neal & Ingram (1999)[4] distance learners do not receive the day-today feedback

available in conventional schoolroom environments. Instructor-student feedback is significant as it

serves the instructor to gauge the level of student satisfaction regarding a topic or a whole course.

By reason of the loss of conventional schoolroom feedback in e-learning situations, other methods

to assess learner satisfaction need to be supervised. Learner feedback during and after the learning

event is important to successfully measure levels of satisfaction. E-learning courses, due to the

insufficiency of face-to-face contact between instructor and student, require special efforts in order

to obtain information regarding learner pleasure. For example, e-learning courses don't allow the

instructor to gauge levels of learner satisfaction using traditional methods such as facial expressions

or body language. Neal and Ingram (1999) proposed that problems related to the efficiency of what

students have learned and their level of satisfaction with distance learning courses remain largely

unresolved until the conventional end-of-course evaluation forms are completed and reviewed.

Exceptional consideration must be given to steadily gain student feedback in e-learning.

Sherry, Fulford, and Zhang (1998)[3] conducted studies on two different measures of distance

learners' satisfaction with instruction. The researches were held at a major University known for its

early consistent involvement in distance education. The courses were produced through live two-

way audio and video technology. The first study analyzed the accuracy of a short, written survey

designed to obtain learner perceptions for opportunity to interact in the distance education course.

The survey included questions regarding interaction between the instructor and learner-to-leaner

interaction. Results revealed that instructor-to-class interaction is positively and moderately

correlated with perception of learner-to-learner interaction. The second study by Sherry et al.

examined the utility and feasibility of the Small Group Instructional Diagnostic (SGID) evaluation

process in distance education. SGID is an interactive evaluation process tested at the University of

Massachusetts. The SGID examines broad views of the instructional environment. In the SGID

evaluation process, course instructors volunteer for a facilitated mid-semester evaluation. A trusted

colleague who usually has experience in faculty development conducts the evaluation. As a

consequence, growing level of students, it is essentiality to providing a feedback frequently as well

as finding a level of students. In order to providing a smart learning system, we proposed an e-

Learning system using IRT and intelligence course.

3. Item Response Theory This e-learning system estimates the abilities of on-line learners, and recommends appropriate

course materials, adapted to the learners' abilities. Course material difficulty can be automatically

adjusted using the collaborative voting approach. Experimental results show that the IRT can provide

personalized on-line learning, based upon learner abilities, in a fast, efficient manner. To solve these

12 | P a g e

problems examining, we wish to present method that measure problem degree of difficulty as

following. These are student's percentages who speculate right answer among whole students that

apply for an exam. Equations that calculate degree of difficulty is as following.

100N

Rp

N : Whole examination candidate's number

R : Person's number who guess right answer of problem

Table 1 Degree of difficulty in a test

item N R P

① 200 10 .05

② 200 80 .4

③ 200 50 .25

④ 200 180 .9

⑤ 200 100 .5

Item 1 in table 1 is the most hard. 10 people among 200 an examination candidate set answer of a problem. A

problem degree of difficulty is 0.05. Problem 4 is the easiest. Because 180 people among 200 subjects set

answer of problem, problem degree of difficulty is 0.9.

Cangelosi (1990) presented evaluation base by problem degree of difficulty with table 2.

Table 2 Item evaluation for item difficulty

Problem degree of difficult Problem evaluation

below .25 Hard problem

.25-.75 Suitable problem

more than .75 easy problem

It produces by correlation coefficient of the problem analysis. If a student is a high total score, let's suppose that

the student in each subject is high averagely. That is, if correlation coefficient between two points is high,

discrimination the problem may be high. Formula that looks for correlation coefficient is as following.

Y

PP

S

MMr

t

WRbis

)1(

MR : Student's score average (reaction to right answer

MW : Student's score average (reaction to incorrect)

Si : Standard deviation of whole point distribution

P : Whole student's the right answer rat

Y : In formality distribution curve P and 1 - P division

If a student is the more acknowledgements, possibility to get good awareness is high. A person who

solves easily hard problem supposes that solve easily problem of an easy degree of difficulty. A

problem degree of difficulty is come for 10 on present example and number of persons is 2 people

13 | P a g e

out of 20 people. Person belonging to 10 supposes almost all problems that are resolvable to whole

number of persons. As a result, expectation point is 100. When this person solves next problem, a

person heightens degree of difficulty. 200 students solved 5 problems.

Table 3 Calculation of score using item difficulty and number of

item

step

difficult

rate

(%)

number of

problem

score of the

problem

Scorer by

degree of

difficulty

Point

by

setting

a

problem

unit

Scorer's

number

by

setting

a

problem

unit

0 10 1 10 2 100 2

1 30 2 20 6 90 4

2 40 2 20 8 ⇒ 70 2

3 50 1 10 10 50 2

4 70 3 30 14 40 4

5 90 1 10 18 10 4

6 NONE 0 0 20 0 2

Table 3 finds out point distribution and ascertained head count by point distribution using a degree of difficulty.

Because scorer's number of percentage is a ratio that dominates in degree of difficulty, degree of difficulty is

decided according to scorer's number. The Identification problem about a person that takes an examination in

on-line estimation is the most important point in estimation of a cyber education system. Cyber researching

estimation method is as following.

System manage & security

Quiz system management

E-Learning note creating

Quiz & Evaluation & Feedback

Administrator

Student

Teacher

Board

Quiz & Lecture note

Fig. 1 Course of smart e-Learning system

In fig.1, administrator conducts system management and security in e-Learning system as well as

quiz system management. Instructors creates a lot of quiz and lecture notes in lots of topic. A

student solves the quiz via the computer system to increase his/her level in specific subject. In

accordance with his/her score, they need a various feedback related his/her subject.

14 | P a g e

Fig.2.Test result for full duplex learning -A

As can be seen figure 2, it presents a result of estimation in virtual university using a IRT concept and

intelligence method. .

Begin Course Take System

Study Chapter #i

Quiz #i

End Course

Level #1 Level #2 Level #3 Level #4

Personal level

Quiz #iPass (Level +1)Fail (Study content)

Fig.3.flowchart of adaptive feedback engine

As can be recognized from the figure 3, it explained a flow diagram of an adaptive feedback engine in a smart e

e-Learning system.

4. Fuzzy algorithm for both direction studying

In this paper, we present level analyzing of each person using a neural network and a fuzzy expert system. In

addition to, demand estimate process that we use is as following. X shank is time and Y shaft is value (data

value past) of variable.

...3322110 XXXY

Last point that consider degree of difficulty

X1 : Element 1 that influence in dependent variable

X2 : Element 2 that influence in dependent variable

X3 : Element 3 that influence in dependent variable

15 | P a g e

X4 : Element 4 that influence in dependent variable

X5 : Element 5 that influence in dependent variable

Table.4. Input data for neural network

Neural network early input condition

1. Learner test score during past 1 month small Big

2. The incorrectness rate of exam small Big

3. The right answer rate of exam Big Small

4. Degree of difficulty of exam Big Small

5. Learner attitude/attendance during past 1

month

Small Big

Table 4 is speaking an estimate process in 5 different conditions that serve to prediction. It is an important

problem that set up value of a neural network analyzing. It reduces analyzing error and accelerates analyzing a

process that chooses value appropriately early. Usually, neural network's studying begins in value specification

early. The analyzing rate how we decide parameter value is decided. Therefore, we choose suitable parameter to

data that wish to analyze. So, consider all cases according to each extent 0.1, 0.3, 0.5, 0.7, 0.9 with (kappa, theta,

phi, mu) and tried an experiment in free case.

And, it is limited class by each 500 number of times.

① Study test data with 10 different condition using neural network.

② Calculate test data and error of estimate data after predict about 10 test data.

Fig. 4 Structure of neural network

As can be seen figure 4, it presents the structure of a neural network for e-Learning system in our

experiment.

16 | P a g e

Fig. 5 Calculation of final score using Fuzzy rule

Fuzzy relation makes concept of relation that use in mathematics in fuzzy. For example, 'X and Y

resembled very', relation called 'X is more active than Y' gets into fuzzy relation. Fuzzy relation

becomes important method to express fuzzy condition in fuzzy inference. Express by position

function ),( yxR about relation of x and y.

Usually, we can mark fuzzy relation by fuzzy graph and fuzzy procession as we display relation by

graph and procession. Fuzzy graph expresses using vertex and arc and arc means strength of

relation.

In figure5 displays a student point about 4 people. Examination marks means a high position student from 80

points to 100 points, and an average student can mark by 0.5 - 07 from 50 points to 70 points. Finally, a low

rank student corresponds to 0.1 to 0.4 less than 40 points. Here, P1, P2 and P3 are denoting last results point that

considers degree of difficulty. Number registered to tie the line here means degree of difficulty and student

studying state condition. Therefore, we produce a point that is corrected for evaluation about a student who

gains a same point.

Fig. 6 E-learning system simulation 1

17 | P a g e

Fig.6 is shown the test screen of proposed e-learning system. Especially, we developed automatically

check attendance of students in cyber e-learning. In order to develop this function, we applied RFID

tag in proposed system. In this paper, we develop new two-way leaning simulation function that

estimates student not only based on their grade but also shows the weakness as well. If the two-way

leaning test is developed for each subject, it makes teacher analyze both of student’s grade and

weakened subject at the same time every end of class. Therefore, it could give intensive course for

the top ranked students who understand the lesson, and some students who are lack of

understanding can repeat the lesson; good learning model would be developed.

5. Conclusion

An e-learning system is not only with good teaching strategy and better learning resources but the

also proper assessment model. In this paper, we proposed analysis feedback for recently e-learning

environments. There are several appropriate feedbacks for instructors, students, and learning

control systems. The feedback could provide suitable teaching, learning resource delivering and

learning advance suggestions. With the approach, estimation propels the learning effort in e-

learning. Adaptive feedback algorithm helped in results elevation more than existent learning

system. The purpose of this paper is to discuss the ways in which we might use on-line assessment

and feedback with students. With fast development in e-learning, assessment plays an important

role between teaching and learning. A good e-learning system is not only with good teaching

strategy and better learning resources but also proper assessment model. In this paper, we

proposed analysis feedback for recently e-learning environments. There are several proper

feedbacks for teachers, students, and learning management systems. The feedback could provide

proper teaching, learning resource delivering and learning progress suggestions. With the approach,

assessment prompts the learning effort in e-learning. Adaptive feedback algorithm aided in results

elevation more than existent studying method.

Acknowledgment

This work was supported by the Korea Research Foundation Grant funded by the Korean

Government (MOEHRD, Basic Research Promotion Fund) (KRF-2007—D00306-I00563)"

References

1. Athabasca University, Theory and Practice of Online Learning, E-Book under Creative Commons License

2. Thomas Toth (2003), Technology for Trainers, ASTD Press. ISBN 1562863215

18 | P a g e

3. Neal, L., & Ingram, D. (1999). Asynchronous distance learning for corporate education: Experiences with

lotus learningspace [On-line]. Available: http://www.lucent.com/cedl/neal_formatted.html.

4. Sherry, A. C., Fulford, C. P., & Zhang, S. (1998). Assessing distance learners’ satisfaction with instruction: A

quantitative and a qualitative measure. The American Journal of Distance Education. 12(3), 5-28.

5. Hulin, C.L., Drasgow, F., & Parsons, C.K. (1983). Item response theory. Homewood, IL: Dow Jones-Irwin.

6. BAKER, F. B. (1992). Item Response Theory: Parameter Estimation Techniques. NY:Marcel Dekker, Inc.

7. HAMBLETON, R. K, & Swaminathan, H. (1985). Item Response Theory: Principles and Applications.

Boston, MA: Kluwer Academic Publishers.

8. Kreitzberg, Charles, et al. "Computerized Adaptive Testing: Principles and Directions," Computers and

Education. 1978, 2, 4, pp. 319-329.

9. Garrison, D. and Anderson, T. 2003. E-Learning in the 21st Century. London: Routledge Falmer. ISBN

0415263468

10.Klir, J. & Harmanec, D. (1997). Types and Measures of Uncertainty, in J. Kacprzyk, H. Nurmi & M. Fedrizzi

(eds), Consensus under Fuzziness, Kluwer Academic, pp. 29--51.

Electronic States of Nanostructures and Quantum Dots (Theoretical and Experimental Study)

19 | P a g e

Abstract— A methodology is developed to obtain analytical solution of Schrödinger equation where the boundaries

(“walls”) of a quantum dot are treated as mirrors. The results obtained allowed calculation of the space probability

distribution and energy spectrum of electron confined in 2D and 3D nanostructures of different geometries, triangular,

hexagonal or pyramidal in particular. Comparison of our methodology with the traditional one using “impenetrable

walls” or “periodical” boundary conditions shows that the former can be considered as particular case of our new

“mirror” case, and there is close relation between the “mirror” conditions and the periodical ones. The calculated energy

spectra contain no adjustable parameters, and have a reasonable agreement with experiment.

Index Terms— Optical materials, quantum dots, quantum well devices, semiconductor devices.

VII. INTRODUCTION

HE optical properties of nanostructures with a pronounced quantum confinement effect (zero-

dimensional quantum dots, one-dimensional quantum wires, two-dimensional quantum wells)

are evidently defined by the corresponding energy dependence of the electrons’ density of states

(which is reduced to the discreet energy spectrum in case of a quantum dot QD). This problem was

treated from the early stages of the development of quantum mechanics (a classic “particle in a

box” problem *1+); appearance of artificial semiconductor nanostructures stimulated great amount

of new publications on the subject (for example, [2-5]). There exist many types of shapes of

nanosystems (QDs); however, at present only a few geometries of the “boxes” (like sphere or

rectangular prism) are well treated, and in many papers (see [6, 7]) the QDs of quite complicated

shape are modeled on the basis of the three-dimensional rectangular prism.

An important element of the quantum mechanical treatment of nanostructures is the boundary

conditions. In many approximations, the impenetrable walls conditions are used implying that the

wave-function is zero at the wells (dots) boundaries. However, these conditions could only be

applied to QDs of simplest geometry: for example, triangular-shaped or pyramidal well (dot) could

not be treated in this manner. On the other hand, these conditions are not realistic since they do

Manuscript received ….., 2009. This work was supported in part by the CONACYT through the projects 33901 and 48792. Yu. V.

Vorobiev is with CINVESTAV-IPN, Unidad Queretaro, Libramiento Norponiente No. 2000, Fracc. Real de Juriquilla, Queretaro 76230,

QRO., MEXICO. Corresponding autor. Phone 52442-2119916, FAX 52442-2119938. e-mail: vorobiev@qro.cinvestav.mx P. M. Gorley is

with Department of Electronics and Energy Engineering, Chernivtsi National University, 58012 Chernivtsi, V. R. Vieira is with Centro de

Física das Interacções Fundamentais (CFIF), 1049-001 Lisboa, Portugal P. P. Horley is with Centro de Física das Interacções

Fundamentais (CFIF), 1049-001 Lisboa, Portugal

Electronic States of Nanostructures and Quantum

Dots (Theoretical and Experimental Study)

Yuri V. Vorobiev, Petro M. Gorley, Vitor R. Vieira, and Paul P. Horley

T

20 | P a g e

not take into account the character of the interaction of a particle (electron) with the boundary.

There are many experimental evidences (like [8]) that this interaction frequently is a reflection,

giving a clear pattern of standing de-Broglie waves formed by interference of incident and reflected

ones. Thus it seems natural to treat the QD boundaries as mirrors.

Here we present an attempt to introduce the “mirror” boundary conditions in the quantum

mechanical treatment of a particle confined in QDs of different geometry, and comparison of the

results with those related to traditional conditions. Some experimental data are compared with the

results of calculations, showing a reasonable agreement.

VIII. THEORETICAL CONSIDERATION

The stationary Schrödinger equation for a particle in a QD with zero potential energy inside the latter, has the

form Ψ + k2 Ψ = 0, with wave-vector square k

2 = 2mE/2

, E is a particle energy, m – its mass; Ψ is a wave

function of a particle with radius-vector r. If the QD has certain symmetry properties, it is possible to apply

variable separation and look for the solution as superposition of plane waves along different axes:

j

jjjjjj

j

jj xikBxikAx ))exp()exp(()( (1)

where xj and kj are components of r and k vectors.

All traditional boundary conditions (with the only exception of periodic or Born - von Karman

ones) demand specification of dot’s boundaries in analytical form, which could be easily done only

for simplest shapes like rectangle (rectangular prism) or sphere. To account for reflection of a

particle from the walls of quantum system, we assume that for any point inside the well we could

find corresponding points reflected by all the walls, and write the “mirror” boundary conditions as

equivalency of Ψ-functions in real and reflected points:

imagereal (2)

Since the actual physical meaning has a square of the Ψ-function module, we can re-formulate

the mirror boundary conditions stating that the Ψ-function in a “reflected” point should be equal

either to the positive or to the negative value of the Ψ-function in real point (we shall call the

former case “even mirror boundary conditions”, and the latter – “odd” ones). It is evident that the

odd boundary conditions are equivalent to the impenetrable wall conditions since the value of the

Ψ-function at the boundary in this case turns to zero.

The concept of a mirror-like boundary of a quantum system was already used in the literature (so-

called “quantum billiard” problem *9+), but the analytical form of the boundary conditions used was

much more complicated (for example, one of the form stated that the flux of the particles to the

boundary is equal to zero). The form which we introduce is fairly simple, and in many cases allows

getting the solution without analytical specification of the boundary. Below the consideration is

given of several geometries of QDs.

A. Rectangular Prism

This is the easiest case well investigated in the text books. For simplicity, we shall treat two-

dimensional quantum box with the dimensions a, b (a < b, to be specific) placed in the Cartesian

system as shown in Fig. 1. An arbitrary point in the box is shown by cross, and its reflections from

walls-mirrors – by dots. One could see that such an approach will lead to a quasi-periodic structure

21 | P a g e

formed by the initial well and its multiple reflections. Taking into account the first reflections (i.e.

reflections of a real particle from the walls), we get the even boundary conditions in the form

Ψ(x, y) = Ψ(x, y) = Ψ(x, y) = Ψ(2a x, y) = Ψ(x, 2b y)

(3)

Having applied the first two of them to the general solution (1), we get

Ψ(x, y) = A cos kx x cos ky y.

The last two give kx a = nx and kyb = ny, which lead to the energy spectrum

E = (h2/8m) (nx2/a2 + ny

2/b2). (4)

The values of quantum numbers are integers 1, 2, 3 etc. It should be noted that the impenetrable

walls conditions give for this case the same spectrum. We also see that the conditions of mirror-like

boundaries are in this case equivalent to the periodic conditions, but with the period doubled in

relation to the initial well size (the conditions Ψ(x, y) = Ψ(x ± 2a, y) = Ψ(x, y ± 2b) give the same

solution as that determined by (4)).

The energy spectrum (4) describes two independent systems (at “x” and “y” directions) of the de-

Broglie standing waves formed by the wave reflections from the opposite walls, and the allowed

values of kx, ky show that at each length a, b an even number of corresponding half-wavelengths

could be placed (with wavelength x,y = 2/kx,y).

In a three-dimensional rectangular prism, the corresponding expression for energy levels in a QD

with walls-mirrors will be

E = h2/8m (nx2/a2 + ny

2/b2 + nz2/c2). (4a)

Evidently, the one-dimensional quantum box of the size a, will have the energy spectrum

E = h2 n2/(8ma2)

(4b)

22 | P a g e

Fig. 1. Rectangular and triangular (bilateral) 2D quantum dots with the

quasiperiodic structures (see text)

We also note that the application of the odd mirror boundary conditions to this case (i.e.

thatimagereal ) gives the solution as a product of corresponding sinuses, with the same energy

spectrum.

B. Rectangular Bilateral Triangle

This triangle with the two sides of length “a” is shown in the same Fig. 1; the arbitrary point with

the coordinates x, y is indicated by the same cross, and the dashed crosses show the reflections of

this point by the walls-mirrors; one can see that a quasi-periodic structure similar to the previous

one could also be drawn. The even mirror boundary conditions can be written as

Ψ(x, y) = Ψ(x, y) = Ψ(x, y) = Ψ(a y, a x) (5)

The symmetry of the system, as of the previous one, allows application of separation of variables.

The solution, as in the first case, is a product of cos kx x and cos ky y. Application of the conditions (5)

gives

kx = ky = (/a) n, where n – any integer starting from 1.

The set of energy levels is

E = (h2/4ma2) n2.

(6)

23 | P a g e

This spectrum was described in [10] where the orientation of the triangle in relation to the

coordinate system (and therefore the form of the boundary conditions) was different. We can see

that it does not affect the solution, as it is expected.

The odd mirror boundary conditions in this case give again the solution in sinusoidal form, with

the result that kxky, and the same energy spectrum.

C. Equilateral Triangle

Contrary to the previous cases, for the symmetry reasons (see Fig. 2) now we cannot apply the

variable separation method. Instead, we look for the wave function as a sum of the waves in three

main directions normal to the triangle’s sides, namely:

i i

iiii ikBikAr )exp()exp()( rr , ki = k ei,

where ei is the unit vector of the corresponding direction. It gives the following function

)2

3

2(

2

)2

3

2(

10

)2

3

2(

2

)2

3

2(

10),,(

yx

ikyx

ikikx

yx

ikyx

ikikx

eBeBeB

eAeAeAzyx

(7)

If we consider now the mirror reflections of an arbitrary point (cross in the figure) by all three

sides of a triangle, and state the boundary conditions as equivalence of the actual point and its

images in relation to -function (even mirror boundary conditions), it gives:

22

3

2,

2

3

322),(

22

3

2,

2

3

322),(

),3

(),(

yxayaxyx

yxayaxyx

ya

xyx

Having applied these conditions to the solution (7), we obtain the following relations among its

coefficients:

3exp;

32exp;

32

;3

exp;32

exp;32

exp

32exp;

32exp;

3exp

112020

221010

212100

ikaBA

ikaAB

ikaBA

ikaBA

ikaAB

ikaBA

ikaAB

ikaBA

ikaBA

24 | P a g e

It follows finally that ,, 210210 BBBAAA and k = .3

4n

a

The corresponding energy spectrum is

E = 2

2

2

3

2n

ma

h

(8)

In [11] we published the treatment of the odd mirror case (including the calculation of the

distributions of the squared wave function), with the same energy spectrum. Besides, our analysis of

the solution of Schrödinger equation for a hexagonal 2D QD is given is [12].

Fig. 2. Equilateral triangle with reflecting walls

D. Spherical QDs

To introduce the mirror boundary conditions in analysis of the case (a sphere with the radius a), we employ

the laws of spherical optics to find the position (“x”) of the reflection of the point with the radius “r” nearby the

wall. Using the standard expression for spherical mirror, we get

(r – a)-1 + (x – a)-1 = – 2/a,

which gives x = a r/(2r – a).

According to the classical treatment [1, 2], the wave function in polar coordinates has a form

r,, = R(r) Y(,)

25 | P a g e

with the angular part Yl,m similar to that of hydrogen atom. The energy spectrum is determined by

the solution of the radial equation, which is expressed in spherical Bessel functions of half-odd-

integer order of the new variable = r; for our purposes, it will be sufficient to analyze the first of

them:

j0() = sin/. (9)

Here /r = = ħ-1 (2mE)1/2.

If the point determined by the r-value (i.e. the position of a particle) is very close to the wall, we

can take r = a being much less than a. Then from the expression just found we obtain x ≈ a

(it means that at very small distances, a spherical mirror is not much different from the plane

one). Thus the even mirror boundary condition has the form

=

Using for the radial eigenfunction the spherical Bessel function given above, we obtain the

condition cos a = 0, where = k = ħ-1 (2mE)1/2. That gives a = 0.5 (2n + 1), and the energy

spectra

E = h2 (2n + 1)2 /(32m a2), n = 0, 1, 2, ... (10)

The expression just obtained is not much different from the classical one [1, 2]: in the latter one has

the same coefficient h2/(32ma2) multiplied by squares of all the even integers whereas in (10) – by

squares of all odd integers. For large quantum numbers it is practically the same, but the difference is

essential for small “n” values. It is interesting to mention that application of the odd mirror boundary

conditions in this case leads to the classical spectrum, in agreement with the equivalence of these

conditions and the impenetratable walls ones.

E. A Pyramid

We consider a pyramid (Fig. 3) formed by the planes x = 0, y = 0, z = 0, and x + y + z = (a√2)/2; its

facets are one equilateral triangle with the side equal to a (pyramid’s bottom plane, or base) and

three rectangular bilateral triangles. The basic directions corresponding to the reflections of the

waves de Broglie are normal to all these facets.

However, the waves reflected from “zero-planes” (pyramid’s walls x = 0, y = 0, z = 0) are deflected

after reflection from pyramid’s base and therefore cannot form a standing wave pattern. On the

contrary, the waves traveling normally to the “base” (i.e. in *111+ direction) are reflected in direction

26 | P a g e

parallel to the incident one and thus can make standing waves forming the energy spectrum of the

system.

Fig. 3. A pyramid formed in the Cartesian coordinate system by the plane normal to the direction [111]. The auxiliary vertical plane

ABC is shown, to facilitate the finding of the position of the arbitrary point’s reflections.

Therefore we choose Ψ-function as a combination of the waves normal to the base:

3

)(

3

)( zyxikzyxik

BeAe

(11)

Analysis of the pyramid’s geometry allows us to write the “even” mirror boundary conditions in the

following form:

)(3

2

33

2),(

3

2

33

2),(

3

2

33

2

),,(),,(),,(),,(

yxza

zxya

zyxa

zyxzyxzyxzyx

The last of these equations corresponds to the reflection from the pyramid’s base which, as we

have pointed out earlier, is important for the formation of a standing wave pattern. Having applied it

to the function (11), we get A = B, and the following condition for the wave vector values:

nka 23

2

That gives the energy spectrum

2

2

2

4

3n

ma

hE

(12)

Some comments in relation to validity of our boundary conditions. When A = B,

= A*cos b(x+y+z), where b = ik/3. Then we can write:

27 | P a g e

cos (bx+by+bz) = cos bx cos (by+bz) – sin bx sin (by+bz), etc. (*)

Near the plane-boundary x = 0 (i.e. where the condition (x,y,z) = (x,y,z) has to be applied), cos

bx is close to 1, and sin bx is close to zero. Thus, the second term in (*) vanishes, and the boundary

condition holds in a good approximation.

Evidently, near the plane y = 0 we rewrite

cos (bx+by+bz) = cos by cos (bx+bz) – sin by sin (bx+bz), with the same argumentation and

conclusion.

So, we see that the first three conditions (reflections from the three Cartesian’s planes, which we

actually are not using for the solution) are valid for the region close to the boundary, the same as it

was in case of a sphere; the last from the conditions that is most important is valid without

approximations.

IX. SOME EXPERIMENTAL DATA

Using the expressions obtained, we calculated the energy spectra for organic dye molecules which

could be modeled as 2-dimensional QDs of different shapes (rectangle, bilateral and equilateral

triangle etc. [13]); a model of spherical QW was applied to CdSe and porous Si nanostructures

surfaces. Here we include a non-trivial case of a rectangular bilateral triangle where a classic

approach could not be used. Fig. 4 shows molecule of a dye Tartrazine which evidently could be

treated as rectangular triangle. On the basis of real interatomic bonds lengths, we get a = 1.6 nm.

Table 1 shows the calculated energy values E according to (6) for the lowest quantum numbers, and

the transition energies E, together with the experimental values Eexp. There is a reasonable

agreement, as it was in other investigated cases.

28 | P a g e

Fig. 4. A scheme of tartrazine molecule approximated by bilateral rectangular triangle

TABLE I

CALCULATED AND EXPERIMANTAL ENERGY LEVELS FOR TARTRAZINE MOLECULE APPROXIMATED AS BILATERAL RECTANGULAR

TRIANGLE

n E, eV E Eexp

1 0.29

2 2.62 2.33 2.6

3 4.65 4.36 4.14

X. CONCLUSION

A new type of boundary conditions is used in treatment of a classic “particle in a box” quantum

mechanical problem. The conditions imply that the waves of de-Broglie representing the particle

have a specular reflection from the walls of quantum system. It is shown that for system of

relatively high symmetry these conditions are equivalent to periodic (Born – von Karman) ones and

lead to the same solution as in the case of impenetrable walls; in other cases new solutions can be

obtained in relatively simple way. In all cases studied, we obtained a reasonable agreement

between theory and experiment, without adjustable parameters. The method developed is

applicable to a variety of Quantum dots and other nanosystems exhibiting relatively large well

potentials.

REFERENCES

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[27] A. I. Kopylov, A. V. Prinz, V. Ya. Prinz, “Novel type of quantum dots based on bell-shaped nanoshells: modeling, fabrication, and properties”, presented at the 2006 14th International Symposium “Nanostructures: Physics and Technology”, St. Petersburg, Russia, June 26-30, pp. 103-104.

[28] N. Vukmirovid, Z. Iconid, D. Indjin, P. Harrison, “The use of hexagonal symmetry for the calculation of single-particle states in III-nitride quantum dots”- presented at the 2006 14th International Symposium “Nanostructures: Physics and Technology”, St. Petersburg, Russia, June 26-30, pp. 140-141.

[29] P. Mohan, J. Motohisa, T. Fukui, “Fabrication of InP/InAs/InP core-multishell heterostructure nanowires by selective area metalorganic vapor phase epitaxy” Appl. Phys. Lett, vol. 88, paper 133105, March 2006.

[30] J. L. Liu, W. G. Wu, A. Balandin, G. L. Jin, K. L. Wang, “Intersubband absorption in boron-doped multiple Ge quantum dots” Appl. Phys. Lett., vol. 74, pp. 185-187, Jan. 1999.

[31] M. Grundman, F. Heinrichsdorff, N. N. Ledentsov, C. Ribbat, D. Bimberg, A. E. Zhukov, A. R. Kovsh, M. V. Maximov, Y. M. Shernyakov, D. A. Lifshits, V. M. Ustinov and Zh. I. Alferov, “Progress in Quantum Dot Laser: 1100 nm, 1300 nm, and High Power Applications”, Jpn. J. Appl. Phys., vol. 39, pp. 2341-2343, Apr. 2000.

[32] R. L. Liboff, J. Greenberg, “The hexagon quantum billiard”, J. Stat. Phys. Vol. 105, pp. 389-402, Oct. 2001.

[33] M. Schmid, S. Crampin, P. Varga, “STM and STS of bulk electron scattering by subsurface objects”, J. Electron Spectr. and Rel.

Phenomena, Vol. 109, pp. 71-84, Jan. 2000.

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[34] Y. V. Vorobiev, P. P. Horley, P. M. Gorley, V. R. Vieira, J. F. Louvier-Hernandez, G. Luna-Bárcenas, J. González-Hernández,

“Calculation of electronic spectra of semiconductor nanostructures using the mirror” boundary conditions”, Appl. Surf. Sci., vol. 255,

pp. 665-668, 2008.

[35] V. R. Vieira, Y. V. Vorobiev, P. P. Horley, P. M. Gorley, “Theoretical description of energy spectra of nanostructures assuming specular reflection of electron from the structure boundary”, Phys. Stat. Sol. C, vol. 5, pp.3802-3805, Sept. 2008.

[36] Y. V. Vorobiev, V. R. Vieira, P. P. Horley, P. M. Gorley, “Energy spectrum of an electron confined in the hexagon-shaped quantum well”, Science in China Series E: Technological Sciences, vol. 52, pp. 15-18, Jan. 2009.

[37] L. L. Díaz-Flores, J. F. Pérez-Robles, P. Vorobiev, P. P. Horley, R. V. Zakharchenko, J. González-Hernández, Y. V. Vorobiev, “Structure and Optical Properties of Nanocomposites Prepared by the Incorporation of Organic Dyes into a SiO2 and SiO2-PMMA Glassy Matrix”, Inorganic Materials, vol. 39, pp. 631-639, 2003.

30 | P a g e

Advances in polymer based micro and nano

composites

Saritha . A 1 K. Jayanarayanan2 Dr. Kuruvilla Joseph1

1. Department of Chemistry, Indian Institute of Space Science and Technology ISRO. PO,

Thiruvananthapuram, 695022, Kerala, India

Tel: +91-471-2564806, Fax: +91-471-2564806

e-mail:kjoseph.iist@gmail.com

2. Department of Chemical Engineering and Materials Science, Amrita Vishwa Vidyapeetham,

Coimbatore 641 105, Tamil Nadu, India

Abstract

Polymer composites are promising systems for a variety of applications due to their

outstanding improvements in material properties. These types of property enhancements

can be imparted by the physical presence of the nano fillers like titania, layered silicates,

carbon nanotubes , their interaction with the polymer matrix, the state of dispersion etc.

The processing of immiscible polymers in which the dispersed phase forms in situ reinforced

fibers is another excellent route to achieve good mechanical properties for the resultant

compound. This method is extensively used in the blending of homopolymers with liquid

crystalline polymers (LCP s) as potential in-situ reinforcing materials. The ultimate properties

of fibre-reinforced composites based on crystallizable thermoplastics are determined by the

crystalline morphology of the polymer matrix which in turn depends on the rates of

nucleation and crystal growth that define the crystallization kinetics. In recent years

nanocomposites have attracted a great deal of interest, both in academia and in industry,

because they often exhibit remarkable improvements in material properties when

compared with virgin polymer or conventional macro and micro composites. These

materials exhibit behavior different from conventional composite materials with micro scale

structure due to small size of structural unit and high surface to volume ratio. As compared

to micron size filler particles the nano size filler particles are able to occupy substantially

greater number of sites in the polymer matrix. The significant increase in specific surface

area of filler particles contributes to the enhanced physical property of the polymer matrix.

Nanocomposites containing a wide variety of fillers with different particle morphology and

size prepared using varying techniques like melt processing, solution mixing etc. exhibit

excellent mechanical, thermal and barrier properties which make them appropriate for

industrial as well as space oriented applications.

Key words

Barrier properties, Fibre-reinforced composites, Nanocomposites, Solution mixing

31 | P a g e

INTRODUCTION

Composite materials have attracted a great deal of interest, both in academia and in

industry, because they often exhibit remarkable improvements in properties. The structure

of the composite depends on the extend to which the organic and inorganic components

are compatible. Particle additives with a variety of particle morphologies and compositions

have become commercially available in recent years. Such mechanical property

improvements have resulted in major interest in composite materials in numerous

automotive and general/industrial applications. The extent of property enhancement

depends on many factors including the aspect ratio of the filler, its degree of dispersion and

orientation in the matrix, and the adhesion at the filler-matrix interface. Generally, inorganic

materials neither have good interaction with organic polymers to achieve good dispersion

nor adequate adhesion, and, as a result, surface treatments are common [1]–[4]. Due to

their nanometer phase dimensions, polymer nanocomposites (PNCs) exhibit unique

properties even by the addition of just a low weight percentage (<5 wt %), not shared by

their micro counterparts or conventional filled polymers [5[-[7]. The primary advantage of

polymer/clay nanocomposites, especially with exfoliated morphology, is dramatic

improvement in gas barrier properties. Some important rubber engineering products

containing high pressure air, for example tire inner-tubes, air springs and cure bladders, etc.

[8] demand a high barrier to gas permeation. Several rubber/clay nanocomposites, such as

natural rubber (NR)/clay, nitrile rubber (NBR)/clay, ethylene– propylene–diene rubber

(EPDM)/clay and styrene butadiene rubber (SBR)/clay, have been successfully prepared [9]-

[12] The blending of immiscible fibre forming semi crystalline thermoplastics to produce

microfibrillar composites (MFCs) has received considerable interest in recent years [13]-[20].

The fiber formation of the dispersed phase requires elongation of the dispersed phase

particles rather than their breakup. MFC is characterized by an isotropic thermoplastic

matrix reinforced by fibrils of another thermoplastic material (dispersed phase) which are

generated insitu during processing. Thus they are different from the conventional

composites which are made by the blending of the constitutive components (matrix and

fibre). Evstatiev et al [16] prepared microfibrillar polymer-polymer composites from LDPE

and recycled PET. The resultant MFCs were found to have tensile properties better than

LDPE filled with glass spheres. Li et al [17] developed HDPE/PET MFCs by hot stretching

which exhibited significantly enhanced tensile properties. It was found that the draw ratio

employed during processing has a profound effect on tensile properties of the resultant

MFCs. The significance of the long microfibrils on the improvement in the tensile and

flexural properties of the MFCs was established in a recent study [20].

EXPERIMENTAL

The polymers used for the preparation of microcomposites were isotactic PP (Repol-

H110MA, Reliance, India, MFI: 11.0g/10min, Tm: 167.7°C) and PET (940400-B, Futura

Polymers, India, Intrinsic viscosity: 0.814dl/g, Tm: 246.4°C). After drying PET for 12 hours at

32 | P a g e

100ºC it was tumble mixed with PP at a constant weight ratio of 15/85. The mixture was

then melt blended in a single screw extruder (Screw Diameter-20mm, L/D Ratio-30)

provided with a strand die of diameter 2mm at a set temperature profile of

225,235,250,255,260ºC. Subsequently the strands were taken to self designed orientation

unit downstream the die, the hot air oven of which was maintained at 100°C. The melt

blending was carried out for draw ratios 1, 2, 5, 8, 10. For the preparation of

nanocomposites the chlorobutyl rubber (CBK 150) Mooney viscosity [ML(1+8)@1250C]

45,with Chlorine content 1.2 used in this study is from Nizhnekamsk,Russia. The layered

silicate , Closite 15 A ( Organic modifier used are dimethyl ,dehydrogenated tallow and

quarternary ammonium)with a density 1.66 g/cc and cation exchange capacity 125meq/

100g clay was obtained from Southern Clay products.. The samples for analysis were

prepared by a solution mixing method. The nanocomposites so prepared were tested for

the improvement in mechanical and gas barrier properties.

CHARACTERIZATION METHODS

The morphology of the microcomposites was studied using a JEOL JSM 840 SEM

with an acceleration voltage of 20kV. To extract the PET phase from the specimens a

mixture of phenol/1, 1, 2, 2, tetra chloroethane in 60/40 wt. % was used as the solvent.

Similarly, to extract PP, the specimens were treated with hot xylene. The specimens were

coated with a thin gold layer prior to the SEM analysis. Storage and loss modulii (G′ and G′ ′

) and mechanical loss factor (tan δ) were investigated as function of angular frequency (ω)

ranging from 0.6 to 100 rad/s at 205°C. In the case of nanocomposites the extend of

exfoliation or intercalation of clay particles in the matrix of chlorobutyl rubber was analysed

by XRD. Gas permeability values were measured using Lyssy Manometric gas permeability

tester with a flow rate of 500 ml per minuteand the mechanical properties were studied

using a Universal Testing Machine (Instron 4411; England) at a cross-head speed of 500

mm/min and 100 mm/min.

RESULTS AND DISCUSSION

Injection moulding at temperatures above the Tm of PP but below that of PET leads to the

melting and loss of orientation of PP, but the fibrillar morphology of PET is preserved. After

injection moulding, these fibrils lose their orientation and are randomly distributed in the PP

matrix. Since the PET fibrils are exposed to temperatures above the Tm of PP (which is much

higher than the glass transition temperature of PET) during injection moulding, the cross

sectional dimensions of the PET fibrils in the moulded samples are not uniform along their

length in comparison with corresponding drawn samples. This phenomenon is due to the

‘break up behaviour’ *21 of the fibrils during relaxation at elevated temperatures which is

manifested as a reduction in their aspect ratio. The aspect ratio of the fibres is further

reduced due to the high shear forces during injection moulding.This is clearly indicated in

the SEM micrographs shown in figure 1. The reinforcing effect in a composite (matrix and

33 | P a g e

fibre) system is related to amount of fiber, length of the fiber, the length/diameter ratio,

length distribution of the fiber, direction of the fiber, amount of entangling points of the

fibers, and the adhesion between the fiber and the matrix. There is a strong possibility for

the formation of a transcrystalline layer of PP around PET in the case of H5I and H8I. The

long microfibrils of PET in H5I and H8I act as nucleating agents for the transcrystallization of

PP which improves the adhesion between the two phases. H10I exhibits poor tensile

properties as shown in figure 2, which may be attributed to the low aspect ratio of the fibrils

as evidenced from the micrographs. The loss modulus (G00) values at 205 _C were also

found to increase with frequency (x) as shown in figure 3. However, at frequency nearing

100 rad/s the difference in loss modulus for the various MFCs is negligible. This indicates the

viscous behaviour at higher frequencies is identical for the MFCs irrespective of the stretch

ratio. The loss modulus of NBI is greater than H2I and H10I at low frequencies whereas it is

lesser at higher frequencies. The difference in G00 values of H5I with H2I and H10I at low

frequencies is lower than the corresponding difference in the G0 values. This indicates that

the PET microfibrils have a more significant effect on the elastic behaviour than the viscous

behaviour of the composite.

The enhancement of mechanical properties of the nanocomposites can be attributed

to the high rigidity and aspect ratio together with the favouring affinity between the

polymer and organoclay. For instance strong interface interactions significantly reduce the

stress concentration point upon repeat distortion which easily occurs in conventional

composites. In the nanocomposites from the XRD profiles shown in figure 5 it is evident that

exfoliation is taking place at lower loadings of clay since there is absence of peak in the XRD

profile. As the amount of filler increases the extend of exfoliation decreases and the

nanocomposites exhibit more or less an intercalated structure .This is clearly depicted from

the decrease in the 2θ value .The extend of intercalation increases with filler loading upto

10 phr and then agglomeration of clay is evident from the intense peaks appearing at a

slightly higher 2θ value.

The presence of silicate layers are expected to cause a decrease in permeability of gases

because of more tortuous paths for the diffusing molecules that must bypass impenetrable

platelets (Figure 6). This phenomenon is significant when the filler is of nanometer size with

high aspect ratio. Each platelet has high strength and stiffness and can be regarded as a rigid

inorganic polymer whose molecular weight is much greater than that of typical polymers.

The figure 7 shows the decrease in the permeability of chlorobutyl rubber nanocomposites

with various gases like oxygen, carbon dioxide and nitrogen.

CONCLUSION

Microfibrillar composites were prepared from the blends of polypropylene and

polyethylene terephthalate by continuous drawing followed by injection moulding. Scanning

electron microscopy (SEM) studies showed that the extruded blends were isotropic, but

both phases possessed highly oriented fibrils in the stretched blends, which were generated

34 | P a g e

insitu during drawing. The PET fibrils with the lowest mean diameter during stretching (4.1

m) were obtained at draw ratio of 8. Beyond stretch ratio 8, the breakage of the fibrils was

observed during stretching which produced very short randomly distributed fibrils after

injection moulding. After injection moulding at a temperature below the melting point of

PET, fibrils with high aspect ratio were obtained for samples drawn at stretch ratio 5 and 8.

The tensile and properties were found increasing with stretch ratio up to an optimized level

between 5 and 8 beyond which it declined. The fibrillar morphology of the PET phase

hastens the crystallization of PP. The long and oriented PET fibrils of the stretched blend

have greater heterogeneous nucleating effect for the crystallization of PP than the short PET

fibrils in the MFC. The storage modulus and loss modulus values were the highest for MFC

prepared at stretch ratio 5 and 8 (H5I and H8I) as revealed from dynamic rheology studies.

The dynamic viscosity values were found to be higher for H5I and H8I. The randomly

distributed PET microfibrils can form a physical network with the PP matrix which has a

significant effect on the elastic behaviour than the viscous behaviour of the composite.

Chlorobutyl rubber nanocomposites were prepared using organically modified Closite 15 A

as filler at different loadings ( 2,5,10 and 20 ).The mechanical properties of the

nanocomposites are superior when compared to the gum vulcanizates as well as

conventional composites at relatively low filler loadings. The tensile strength and tear

strength increases with filler loading upto 10 phr of clay and then decreases which might be

due to the agglomeration of clay at higher loadings. The reinforcing effect is presumed to

occur because of intercalated/exfoliated layered silicates are covered by highly cross linked

rubber molecular chains with strong interfacial interactions in between the phases. Finally

the extremely low gas permeation values shows that the nanocomposites can be

effectively used for applications in packaging and automotive industries.

REFERENCE

1. A. Okada and A. Usuki, Mater. Sci. Eng., C3, 109 9. A 2. B. M. Novak, Adv. Mater., 5, 422 (1993). O. Kamigaito, U.S. Pat. 4,889,885 (1989). 3. E. P. Giannelis, Adv. Mater., 8, 29 (1996). 10. K. Fukumori, A. Usuki, N. Sato, A.

Okada, and T. 4. R. A. Vaia, K. D. Jandet, E. J. Kramer, and E. P. Kurauchi, Proceedings of the 2nd Japan

InternaGiannelis,Macromolecules, 28, 8080 (1995). tional SAMPE Symposium, 1991, p. 89.

5. Krishnamoorti R, Vaia RA. Polymer nanocomposites:synthesis, characterization and modelling. In: KrishnamoortiR, Vaia RA, editors. American Chemical SocietySymposium. Washington, Inc., 2001.

6. Pinnavaia TJ, Beall GW. In: Pinnavaia TJ, Beall GW,editors, New York,Inc.: John Wiley & Sons; 2000.

7. Giannelis EP, Krishnamoorti R, Manias E. , Adv Polym Sci 1999;138:107–47. 8. C. Nah, H.J. Ryu, W.D. Kim, S.-S. Choi, Polymers for Advanced Technologies 13

(2002) 649–652.

9. A. Usuki, A. Tukigase, M. Kato, Polymer 43 (2002) 2185– 2189.

10. S. Varghese, J. Karger-Kocsis, Polymer 44 (2003) 4921–4927.

35 | P a g e

11. M. Alexandre, P. Dubois, Materials Science and Engineering 28 (2000) 1–63.

12. L.Q. Zhang, Y.Z. Wang, Y.Q. Wang, et al., Journal of Applied Polymer Science 78

(2000) 1873–1878.

13. Evstatiev M, Schultz JM, Petrovich S, Georgiev G, Fakirov S, Friedrich K. , , J Appl Poly Sci. 1998; 67(4):723-737.

14. Krumova M, Fakirov S, Balta Calleja FJ, Evstatiev M. , J. Mater. Sci. 1998; 33(11): 2857-2868.

15. Sapoundjieva D, Denchev Z, Evstatiev M, Fakirov S, Stribeck N, Stamm M. , J. Mater. Sci. 1999; 34(13): 3063-3066.

16. Evstatiev M, Schultz JM, Fakirov S, Friedrich K, Polym Eng Sci 2001; 41(2): 192 - 204. 17. Li ZM, Yang MB, Huang R, Yang W, Feng JM , Polym. - Plast. Technol. Eng. 2002;

41(1): 19–32. 18. Huang WY , Shen JW, Chen XM, J. Mater. Sci. 2003; 38(3): 541- 547. 19. Sarkissova M, Harrats C, Groeninckx G, Thomas S , Part A 2004; 35(4):489-499. 20. Garmabi H, Naficy S, J .Appl. Poly. Sci 2007; 106(5): 3461- 3467. 21. Lin QH, Jho J, Yee AF, Polym Eng Sci 1993;33(13):789–98

ACKNOWLEDGEMENT

A part of this work has been financially supported by the Indian Space Research

Organisation (ISRO/RES/3/587/2007-08).

Figures

36 | P a g e

Fig: 1 SEM images of injection moulded (isotropized) PP/PET blends (a) injection moulded

neat blend with PP phase extracted, (b–e) isotropized drawn blends at stretch ratios 2, 5, 8

and 10, respectively, with PP phase extracted.

Fig: 2 Stress–strain curves for injection moulded neat blend and microfibrillar composites

prepared at stretch ratios 2, 5, 8 and 10.

37 | P a g e

Fig: 3 Variation of loss modulus with frequency for PP, injection moulded neat blend and

microfibrillar composites prepared at drawratios 2, 5, 8, 10 carried out at 205 _C.

0 100 200 300 400 500 600

0

2

4

6

8

10

12

14

Str

ess (

MP

a)

Strain (%)

Gum

1 phr

2 phr

5 phr

10 phr

20 phr

Fig: 4 Stress strain curves of Chlorobutyl rubber nanocomposites ontaining cloisite 15A

38 | P a g e

Fig: 5 XRD plots of cloisite 15A (LS) and chlorobutyl rubber nanocomposites containing

cloisite 15A

Tortous path in layered silicate nanocomposite

Fig 6: Schematic representation of gas permeation through conventional microcomposite

(left) and layered silicate nanocomposite (right)

39 | P a g e

Gum 1 phr 2 phr 5 phr 10 phr 20 phr

0

10

20

30

40

50

60

70

80

Perm

ea

bili

ty m

l/m

2/d

ay

Clay loading

Oxygen

Carbon dioxide

Nitrogen

Fig: 7 Effect of nanoclay loading on the permeability of Chlorobutyl rubber nanocomposites

containing cloisite 15A

40 | P a g e

New Approach in Design and Engineering

of Multi-junction Solar Cell Devices

Abstract—A non-traditional approach is proposed in design of multi-junction solar devices: the different cells are

electrically independent, which gives the possibility of different connection between them and additional degrees of

freedom in the election of the cells’ materials, in the sequence of “p” and “n” layers and in general design of the system.

In particular, sun-tracking “self-concentrating” multi-junction device is considered where the solar cells with the largest

band gap material act as mirrors reflecting the part of solar spectrum not absorbed in the cells onto the other cells with

smaller gap, or to the high-temperature converting stage in a hybrid system.

Index Terms— Optical materials, semiconductor devices, solar cells.

XI. INTRODUCTION

OWADAYS we witness a quick growing of the solar photovoltaic (PV) modules production and

application in the whole world, together with a growing demand for modules of higher

efficiency and lower cost caused by heavy problems with shortage of fossil fuel as well as by the

serious ecological aspects. It is evident that for mass application of solar PV modules the cost is the

first important factor; however, there are cases (like all kind of transport units – autos, buses, trains

etc.) where the efficiency goes in the first place due to the restriction of the surface to be used.

Many speculations were made about the third generation of PV solar cells. This one could be

based on the nanotechnology (multi-junction tandem devices using materials for which band gap is

defined by quantum confinement effects, so that the whole device can be made of one material but

with the layers having different crystallite size [1, 2]). Other suggestions were based on the new

exotic multi-bands materials [3, 4] or some version of multi-junction devices, for example, using

“vary-zone” semiconductors *5+.

All the devices mentioned above impose specific (sometimes, very specific) demands upon the necessary

materials and the technology related, so it is difficult to expect that they will have an acceptable cost in near

Manuscript received ….., 2009. This work was supported in part by the

CONACYT through the projects 33901 and 48792.

Yu. V. Vorobiev is with CINVESTAV-IPN, Unidad Queretaro, Libramiento Norponiente No. 2000, Fracc. Real de Juriquilla, Queretaro

76230, QRO., MEXICO. Corresponding autor. Phone 52442-2119916, FAX 52442-2119938. e-mail: vorobiev@qro.cinvestav.mx

P. M. Gorley is with Department of Electronics and Energy

Engineering, Chernivtsi National University, 58012 Chernivtsi, Ukraine

J. González-Hernández is with CIMAV, Miguel de Cervantes

120, 31109 Chihuahua, México

P. Vorobiev is with Moscow State University of Railway Engineering, Moscow, Novosuschevskaya 22, ed. 4, C.P. 127030, Moscow,

Russia

Yuri V. Vorobiev, Petro M. Gorley, Jesús González-Hernández, and Pavel Vorobiev

N

41 | P a g e

future. Besides, it is worth to mention that the tandem multi-junction devices (both traditional and the “vary-

zone” ones) have inherent limitations upon the conversion efficiency. Thus in the traditional (series-connected)

tandem solar devices, the current is determined by the lowest one from all the cells connected; in “vary-zone”

(parallel connected) device, the voltage is determined by the lowest band gap present. Both limitations can be

overcome with a possibility to enhance the conversion efficiency, if we make the cells of a tandem electrically

independent although connected optically in series; some of the arising possibilities are discussed below.

XII. DESCRIPTION OF THE 2-JUNCTION DEVICE

In Fig. 1 the energy band diagram is given for the proposed two-junction solar cell device at

equilibrium conditions (i.e. no illumination, no photo voltage, the Fermi level EF is the same in all

parts: p-i-n junction, insulating layer, and n-i-p junction, from left to right). The band gaps of the two

semiconductor materials must be chosen to utilize in optimal way the solar spectrum, i.e. to have

approximately equal numbers of photons absorbed by the top junction (GaxAl1-xAs or CdSe, for

example) and the bottom one (could be Si or Ge). In this case, the photo current generated by the

top and the bottom cells is more or less the same. No tunnel junction is present. The two cells are

connected optically in series, but are independent electrically. The contacts to each of the cells'

active layers (shown by arrows) provide the possibility to connect the individual cells in a different

way.

As illustrated by Fig. 2, the two-junction version of the solar cell device includes the two p-i-n

junctions having the opposite sequence of n and p layers, with an insulating layer between the two

active cells. The top cell consists of the p-layer 1, the i-layer 2 and the n-layer 3. After the insulating

layer 4 follows the n-layer of the bottom cell 5, then the i-layer 6, and the p-layer 7. The top contact

8 to the upper p-layer 1 of the top cell serves for electrical connections; the transparent conductive

layer (or heavily doped one) can be introduced between the p-layer 1 and the contact 8 (not

shown). The electric contact to the n-layer 3 of the top cell is denoted as 9; 11 is the electric contact

to the n-layer 5 of the bottom cell, and 10 is the electric contact to the p-layer 7 of the bottom cell.

As in the case of the contact 8, all three contacts 9-11 can be added with the transparent conductive

layer on the surface of the corresponding semiconductor layer forming the part of a p-n junction.

The two external contacts (indicated as 8 and 10 in Fig. 1, the contacts to the p-layers of the top and

the bottom cells) under illumination are charged positively, the other two (9 and 11) are charged

negatively.

42 | P a g e

Fig. 1. Energy band diagram of a 2-junction device

Fig. 2. Construction scheme of a 2-junction device with electrically

independent cells

The possible ways of the electrical connections of the contacts are shown in Fig. 3. Fig. 3A refers

to the case when there is only one working two-junction solar cell device. Since the two cells

generate the different photo voltage (the larger is the band gap, the larger the potential barrier, and

the larger voltage), the only possible way of connection is in series, which is illustrated by the Fig.

3A: the negative contact of one cell is connected to the positive contact of the other one, and the

other two contacts are used to connect the device into external circuit.

When several devices of this type are working (in a solar module), there are many options of

connection, which might be chosen to provide the necessary voltage of the module. Fig. 3B gives

one example of the cells' interconnection in a module consisting of 5 two-junction solar cell devices,

corresponding to the case when the photo voltage of the top cell is V1 = 1.55 V and the photo

voltage of the bottom cell is V2 = 0.93 V. All 5 bottom cells and one of the top cells are connected in

series producing the voltage 5 V2 + V1 = 6.2 V; the other 4 top cells connected in series produce the

same voltage: 4 V1 = 6.2 V. These two arrays must be connected in parallel, to double the photo

current. For the larger amount of the devices in a module, there are more options in electrical

connections.

It is evident that the order of layers can be reversed (i.e. the device of the type (n-i-p)1-insulating

layer-(p-i-n)2 can be formed, with the same characteristics but the opposite charge on the contacts

compared to the case described above).

To reduce the solar light reflection from the device’s surface, antireflection layer might be added

to the top cell. In this respect, our multi-junction device is not different from the traditional multi-

junction devices. To reduce the reflection losses at the interface between semiconductor and

insulating layer, the insulating layer material with large refractive index n must be chosen: for

example, using the TiO2 for the insulating layer (n = 2.5) and semiconductor of GaAs type (n = 3.5),

we shall have the interface reflection coefficient less than 3 %. Having smaller losses than the

traditional two-junction solar cell device (no tunnel junctions in our device), our device is capable to

have higher efficiency, and has more options for optimization.

43 | P a g e

Fig. 3. Electrical connections in 2-junction device working alone (A)

and in a 5-cells module (B).

XIII. SEMICONDUCTOR MATERIALS FOR NEW DEVICES

Except for the multi-junction solar cell devices, there are many other options of efficient and cost-effective

utilization of solar energy, if we do not restrict ourselves with its direct conversion to electricity. It is sufficient

to mention the hybrid PV/Thermal systems producing electricity and hot water (air) with the total efficiency of

50 – 60 % and acceptable cost which are already widely used (see, for example, [6-8]). The other hybrid

systems were also considered [9] where one part (“optical”) of solar spectrum is used directly for electricity

generation by semiconductor material with band gap Eg < h, whereas another one (“thermal” with h Eg)

being concentrated to give sufficiently high temperature for the second conversion stage, is used to drive a heat

engine (like Stirling engine) with an electric generator, or can be transformed directly to electricity with

thermoelectric generator TEG.

The general energetic and entropic analysis of such an idealized two-stage hybrid system

(assuming coupled photoelectric and thermal converters, the latter as Carnot Engine) was

performed in [10]. It was shown that the total conversion efficiency could be very high (up to 86.8 %

for infinite amount of band gaps), being at the same time strictly equivalent to the efficiency of

photovoltaic or solar thermal devices working alone. On the other hand, the two-stage hybrid

system has more degrees of freedom and allows for a most optimum design. Thus the hybrid

systems of this kind could be considered as alternative for tandem solar energy conversion devices

with better allowance for the design and cost optimization.

It is essential that for the two-stage hybrid system mentioned as well as for a tandem device of

electrically independent cells, the different cell design is needed (without the surface texturization,

to start with) as well as a wider range of semiconductor materials (in particular, those with the band

gap larger than that corresponding to the one-material cell maximum efficiency). Among them,

CdSe could be considered as very promising semiconductor: with its band gap of approximately 1.8

eV, it absorbs almost 50 % of solar radiation with the AM1.5 spectrum, leaving another 50 % for a

second stage of conversion, which could be another cell in a tandem, or a thermal device of some

kind. Being a direct-band gap semiconductor, CdSe can be used for photovoltaic applications in thin

film form which is an additional advantage. We have succeeded in development of a production

method of CdSe films by ammonia free chemical bath deposition [11], and now are attempting to

make solar cells with it.

Here we present our estimations of the expected efficiency of solar energy conversion with these

cells and the corresponding two-stage hybrid system described above (or 3-junction solar cell

44 | P a g e

device). For the cell efficiency, we use the traditional approach [12] based upon the graphical

analysis of the photons density distribution in solar spectrum taking that all photons with the energy

exceeding the band gap (“optical” ones according to definition above) are absorbed by

semiconductor and converted into electron-hole pairs. It gives for the band gap of 1.8 eV and the

AM1.5 solar spectrum without concentration the “ideal” conversion efficiency of 20 %. As a practical

efficiency, we take for our estimations the value of 15 %, i.e. ¾ of the ideal efficiency. This choice is

based upon the fact that in case of Si solar cells, the calculated “ideal” efficiency is 30 % and the

practically achieved value - 24.7 %, or 82 % of the ideal value; our estimate (75 %) is much more

modest.

XIV. HYBRID SYSTEMS AND 3-JUNCTION SOLAR CELL DEVICE

The hybrid system, as it was mentioned, should use concentration of “thermal” part of solar

spectrum not absorbed in semiconductor upon the second conversion stage. This can be done with

lenses; we find more attractive the “mirror concentration” option, see a scheme in Fig. 4. Here the

highest band gap solar cells (like CdSe, denoted as I in the figure) are positioned in a solar-tracking

2-axis base (III; our tracking system was designed and described earlier [13]). The cells ought to have

metallic back contact with a mirror finish, so that it reflects the radiation not absorbed by a

semiconductor p-n junction. The radiation reflected by each cell is directed to a high-temperature

second stage (II), thus the amount of the cells I determines the degree of concentration, and finally

the working temperature of the second stage.

Fig. 4. Scheme of the hybrid system (for the details, see text).

The cells possess antireflection coating optimized for the cell working region (1.8 – 3 eV). The

corresponding optical thickness (i.e. the product of the geometrical thickness d and the refractive

index n) must be a quarter wavelengths; for average photon energy 2.4 eV (500 nm), it is

approximately 500/4 = 125 nm. Taking n = 2.9 of CdSe for this region, we get d = 43 nm. With this

thickness, in “thermal” spectral region (average wavelength 1000 nm, average n = 2.2) optical

thickness of 95 nm will be much smaller than the quarter wavelength (250 nm), so the interference

conditions are close to constructive ones, and the antireflection coating of the cell will effectively

reflect “thermal” radiation to the second stage. The mirror back contact reflects the radiation that

entered the cell bulk but was not absorbed. It is worth to note that for the “optical” radiation, the

45 | P a g e

effective cell’s thickness is doubled because of this reflecting back contact, so the actual material

thickness in this design could be reduced by 50 %.

The working temperature of the stage II is determined by the radiation flux and the heat losses

through convection and radiation (the detailed analysis was presented in [9]. The total conversion

efficiency of the hybrid system discussed could be written in the following form:

tot = PV + f K T/Th (1)

Here PV is the efficiency of the solar photovoltaic cell; T is the temperature difference between

the hot and cold terminals of the stage II device, and Th is its hot terminal temperature (thus, T/Th

is the efficiency of a Carnot engine working in the corresponding temperature regime, and K is the

coefficient showing how close to the Carnot efficiency is the stage II). The coefficients and

represent the optical and thermal losses related to the stage II; “f” is the percentage of “thermal”

radiation in the solar spectrum (50 % in our case).

The optical losses coefficient () is determined by the quality of the concentrating system, and

can be easily taken equal to 0.9. For the thermal losses we take the expression [9]

fCIa

TTTh

o

roomhT

)(1

44

(2)

It takes into account in an evident manner the relation between heat fluxes from the stage II

(convection characterized by the coefficient h with the value typical for natural convection of 10

W/m2K, and the thermal radiation exchange with the ambient, according to the Stephen-Boltzman

law with the coefficient = 5.67X10-8 W/m2K4) and to it (“thermal” radiation with its percentage f of

the total insolation I concentrated with the degree C and the optical losses ). For the insolation we

take I = 1000 W/m2.

The calculated total conversion efficiency of the hybrid system as a function of hot terminal

temperature is shown in Fig. 5. The starting point at 300 K gives a cell’s estimated efficiency; the

curves 1 and 2 corresponds to concentration C = 100, the “ideality” coefficient K of the stage II in

the first case is 0.5 and in the second one – 0.8 (these values can be reached in existing Stirling

engines, and could be expected in future thermoelectric generators). The highest curve 3 is for K =

0.8 and C = 200, it shows how important is the concentration degree. It could be seen that the

hybrid system analyzed can have efficiency compared to that of the existing (and very expensive)

multi-stage tandem solar energy converters, without application of semiconductor high technology

and consequently with much lower cost.

46 | P a g e

300 400 500 600

10

15

20

25

30

Effic

iency, %

Th, K

Fig. 5. Estimated temperature dependence of the

total efficiency of hybrid

system. Lowest curve: C = 100, K = 0.5. Intermediate curve: C = 100,

K = 0.8. Highest curve: C = 200, K = 0.8.

With smaller amount of high-gap cells, we shall not get a high temperature of a stage II, so the

system discussed will not be effective as a hybrid one. In this case, a double-junction solar cell

device described above could serve as stage II. The cell I and the stage II device must be connected

electrically in parallel; for that, their photo voltages ought to be practically the same. This equality

can be achieved by the choice of cells’ materials together with the doping levels of the cells’ active

layers. As a result, we shall have a 3-junction solar cell device without the tunnel junctions. In the

traditional multi-junction solar cell device, the current of all cells must be adjusted, which is done by

the choice of the band gaps of the corresponding materials, and leave very little freedom for

optimization. In our case, we adjust the voltage of the two parts of the device, which can be done by

operating of many variables (not just the choice of materials, but also the different layers doping

levels). This makes our device much more flexible and therefore more capable of optimization to

obtain the higher conversion efficiency.

To estimate the potential conversion efficiency of the device proposed, we have made calculations

following the traditional method [12]: assuming that semiconductor absorbs all solar radiation with photon

energies larger than the band gap (h > Eg) and is transparent for photons of smaller energy (h < Eg). The

results (Fig. 6) shows that in many cases, the expected efficiency exceeds by 50 – 60 % the ideal one-gap

efficiency, so the device is really promising.

1

2 3

47 | P a g e

0.6 0.7 0.8 0.9 1.0 1.1

44

45

46

47

,

%

Eg3

, eV

Fig. 6. Ideal conversion efficiency calculated

for 3-junction device as

a function of minimal band gap. The values of 2 others are 1.7 y 1.3 eV

(1), 2.0 y 1.4 eV (2), 1.9 y 1.3 eV (3), 1.8 y 1.3 eV (4), 2.1 y 1.4 eV (5).

XV. CONCLUSION

On the basis of theoretical and practical analysis, we conclude that the proposed version of the multi-

junction solar cell device has additional degrees of freedom in the election of the cells’ semiconductor materials

and in the interconnections between individual cells. These features could allow for a device of this type to

achieve a better efficiency in comparison with the traditional tandem solar energy converters, with the more

reasonable cost.

REFERENCES

[1] M. Green, Third Generation Photovoltaic: Ultra-High Efficiency at

Low Cost, Springer-Verlag, Berlin, Germany, 2003.

[2] G. Conibeer, M. Green, R. Corkish, Y. Cho, Eun-Chel Cho, Chu-Wei Jiang, T. Fangsuwannarak, E. Pink, Y. Huang, T. Puzzer, T. Trupke, B.

Richards, A. Shalav, K. Lin “Silicon Nanostructures for third generation solar cells”, Thin Solid Films , vol. 511-512, pp. 654-662, July

2006.

*3+ A. Luque, A. Marti, “A metallic intermediate band high efficiency solar cell”, Prog. Photovoltaic, vol. 9, pp. 73-86, 2001.

[4] A. Martí, N. López, E. Antolín, E. Cánovas, C. Stanley, C. Farmer, L. Cuadra, A. Luque, “Novel semiconductor solar cell structures: The

quantum doy intermediate band solr cell”, Thin Solid Films , vol. 511-512, pp. 638-644, July 2006.

*5+ I.M. Dharmadasa, “Third generation multi-layer tandem solar cells for achieving high conversion efficiencies”, Sol. En. Mater. Solar

Cells, vol. 85, pp. 293-300, Jan. 2005.

[6] Y. Tripanagnostopoulos, M. Souliotis, R. Battisti, A. Corrado, “Performance, cost and life-cycle assessment study of hybrid PVT/AIR

Solar System”, Prodr. Photovolt: Res. Appl., vol. 14, pp. 65-76, 2006 Boulder, CO, private communication, May 1995.

[7] PVT-roadmap, www.pvtforum.org

[8] B. Robles-Ocampo, E. Ruíz-Vasquez, H. Canseco-Sánchez, R. C. Cornejo-Meza, G. Trápaga-Martínez, F. J. García-Rodriguez, J. González-

Hernández, Y. V. Vorobiev, “Thermal-photovoltaic solar hybrid system for efficient solar energy conversion”, Sol. En. Mater. Solar

Cells, vol. 91, pp. 1117-1131, 2007.

[9] Yu. Vorobiev, J. González-Hernández, P. Vorobiev and L. Bulat, “Thermal-photovoltaic solar hybrid system for efficient solar energy

conversion”, Solar Energy, vol. 80, pp. 170-176, 2006.

*10+ A. Luque, A. Marti, “Limiting efficiency of coupled thermal and photovoltaic converters”, Sol. En. Mater. Solar Cells, vol. 58, pp. 147-

165, 1999.

1

2 3

4

5

48 | P a g e

[11] H. Esparza-Ponce, J. Hernández-Borja, A. Reyes-Rojas, M. Cervantes-Sánchez, Y. V. Vorobiev, R. Ramirez-Bon, J. F. Pérez-Robles, J.

González-Hernández, “Growth technology, X-ray and optical properties of CdSe thin films”, Materials Chemistry and Physics, vol.

113, pp. 824-828, 2009.

*12+ C. H. Henry, “Limiting efficencies of ideal single and multiple energy gap terrestrial solar colls”, J. Appl. Phys., vol. 51, pp. 4494-4500,

Aug. 1980

[13] Yu. Vorobiev, P. Vorobiev, P. Horley, J. González-Hernández, “Experimental and Theoretical Evaluation of the Solar Energy Collection

by Tracking and Non-Tracking Photovoltaic Panel”, in Proceedings of 2005 Solar World Congress (ISBN-0-89553-177-1), August 6 -

12, Orlando, FL, USA, 2005.

49 | P a g e

Abstract— New trends in ubiquitous health care systems’ technologies are discussed here. Main emphasis is placed on

U-health care technologies like e-health, biomedical sensors and networking, by using super highways, internet, RF-ID

technology, etc. The research and development in U-health care systems, are given in brief, to promote health care

mainly in remote areas for elderly people. New ultrasonic sensors are developed with newly developed piezo-

composite materials for ubiquitous health care applications. Thus, development of new RFID chips, nano-scale or

sensor-enabled radio technologies and better sensor networks will assist in the cure of unexplored diseases, for better

health care.

Index Terms—UHealth care, ultrasonic sensors, wireless sensors, embedded systems

I. INTRODUCTION

T With the advancement of technology, a great progress in Information, Computers and

Telecommunication (ICT) has been made for getting newer and newer applications in different fields

of science, technology and medicine. Ubiquitous captures the convergence between a number of

technological fields as well as their implications for the economic, political and social aspects of

society. The major possible modalities include low cost radio frequency identification (RFID) chips,

mobile phones and computers.

The ubiquitous computing involves computer devices embedded in everyday objects invisibly at

work and the environment, in which intelligent, intuitive interfaces make computer devices simple

to use and unobtrusive, and in which communication networks connect these devices together to

facilitate anywhere, anytime, always-on communications. Now, mobile communications and the

Internet have made this work successfully. On the other hand, RFID promises a shift in the

computing paradigm such that not only people and their communication devices are to be

connected to global networks, but also a large number of inanimate objects say from tires to

razorblades. RFID systems assist in the automatic and autonomous collection of data about the

objects visible in the environment, thereby creating truly intelligent and ambient network spaces.

Other RFID applications are in public transport, toll collection, contactless payment cards, and in

health care monitoring.

V.R.Singh is with the PDM College of Engineering, Bahadurgarh- 124507, India (email: vrsingh@ieee.org)

Development of Ubiquitous Health Care

Systems

V.R.Singh, Fellow-IEEE

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Thus the growth of ICT with mobile and fixed line subscribers, and Internet users is at a rapid speed

worldwide, while India has the fastest growth of technology and users. Although, the Indian

advantage of technological lead is just new, but now there is thrust on tapping newer sub-systems

such as nano technologies, bioinformatics and smart appliances to set them to make ubiquitous. In

thousands of towns and villages across India, the next stage of the country momentous is revolution

in telecommunications. India is the fastest-growing mobile phone market in the world. The

subscribers are increasing day by day even in the rural areas.

The Indian software sector has built now the strength to tap the potential of the emerging market in

medicine, genetics, micro technologies and biomedical research, with better technology skills, to

meet the requirements of people. With the Indian Ministry of Communications and Information

Technology, has introduced broadband, for better connectivity of the network. Thus, now, it is a

new digital India with ubiquitous broadband connectivity, both wireless and wired, teeming with an

always-connected young generation that is mobile and empowered. Agriculture in India is unique in

its characteristics, where over 250 different crops are cultivated in its varied agro-climatic regions,

with 25 to 30 crops grown. The use of various sources of power from the humble arm of the farmer

to the mightiest of tractors is ubiquitous. India is the largest producer of tractors in the world and

has emerged as a net exporter of food grains and continues to forge ahead in the adoption and

indigenization of advanced technologies. These are now under U-green studies, and case studies

are available on the experiences of applications of RFID in India: for say delegate management at

NASSCOM and livestock management at Chitale Dairy Farms. In India, the use of prenatal care in

Ladakh has increased in terms of ecological and cultural factors. However, attempts to manage the

outcome of pregnancy are ubiquitous among human societies. Those practices are becoming

standardized as prenatal care under a biomedically trained practitioner to characterize the formal

management of pregnancy.

Extensive use of ICT has boosted further biomedical technologies for better health care in the

country. Since India is leading in IT (Information Technology), newer and newer research findings

have assisted in the health care of patients in remote areas and hills. Studies are actively being

carried out in e-health systems, tele-health monitoring, biotelemetry, bioinformatics, bio-computing,

U-health, etc, with wireless sensor networking, RFID or ubiquitous networking. The potential

benefits of RFID chips are better and efficient medical care.

In this presentation, an overview of biomedical ubiquitous studies in India is given, with main

emphasis on U-health care technologies namely, e-health, biomedical sensors and networking, by

using super highways, internet, RF-ID technology, etc. Some research programmes in U-health care,

being pursued by Ministry of Health, Ministry of Environment, Ministry of IT (Information

Technology, ISRO (Indian Space Research Organisation), Ministry of Communication, IITs (Indian

Institutes of Technology), Universities, Industry and other R & D establishments, are cited in brief, to

promote health care mainly in remote areas for elderly. Development of new RFID chips, nano-scale

or sensor-enabled radio technologies and better sensor networks will take care of unexplored

diseases, as well as quality control of medicines, drugs, equipment and monitoring of physiological

parameters for better diagnosis and therapeutic treatment. For better biomedical ubiquitous, next

51 | P a g e

generation networks require international coordination in different areas including standardization,

both of technical interfaces and product New ultrasonic sensors are developed with newly

developed piezo-composite materials for ubiquitous health care applications codes, frequency

allocation, allocation, etc.

II. U-HEALTH CARE: STRATEGIES AND TECHNOLOGY TRENDS

The ubiquitous healthcare is the real-time response of always being on. Ubiquitous healthcare

consumers can send out appropriate and accurate information from embedded, wearable and

mobile devices in a sentient, context-aware, ambient and pervasive manner and can receive

appropriate medical information, either on the Internet in the Ubiquitous Healthcare Information

System (UHIS) and gain continuous support from ubiquitous healthcare professionals or physicians.

Also, the UHIS encourages ubiquitous healthcare consumers to share their many hidden concerns

with physicians who have virtual presence, and enables them to become active participants in self

care Fig1).

Ubiquitous healthcare strategies include designing, planning and implementing non-traditional u-

healthcare delivery modalities. Basically, a strategic u-healthcare framework may be thought of as a

unique coupling of organizations' u-health business structures to satisfy the identified business

needs or to leverage strategic opportunities with a set of value propositions.

Popular e-business structures include business-to-consumer (B2C) or business-to-business (B2B)

service models whereas the value propositions can be some or a combination of specific

performance goals such as achieving greater efficiency, convenience, effectiveness, affordability,

accessibility, and intelligence.

The u-healthcare market system facilitates bi-directional and synchronized access to information for

all stakeholders involved in u-healthcare processes and in the u-healthcare marketplace. These

stakeholders are u-healthcare consumers, u-healthcare payers, u-healthcare clinics and physicians,

u-healthcare providers, uhealthcare vendors and u-healthcare insurers.

Tthe benefits of ubiquitous healthcare are the goals to achieve in u-healthcare processes and in the

u-healthcare marketplace. They are, first, providing real-time availability and accessibility of

healthcare knowledge and expertise on a more equitable basis to underserved rural and urban areas

regardless of time, specialty, and geographic location. Second, savings in procedural, travel, and

claim processing costs through reduced use of traditional emergency services, improved non-

emergency services, and decreased waiting time for non-emergency services. And third,

comprehensive availability of ubiquitous clinical services and timely access to critical information will

be available in the event of emergencies through greater awareness of services among rural and

remote residents and caregiver

52 | P a g e

A ubiquitous service shall satisfy the criteria of ubiquity (A.T.S.A.T.): Availability, Transparency,

Seamlessness, Awareness, and Trustworthiness. And it shall meet the criteria of ubiquitous

computing (S.C.A.L.E.): Scalability, Connectivity, Adaptability, Liability and Ease-of-Use.

The status of u-healthcare consumers takes different steps in medical examination or consultation,

diagnosis, treatment and monitoring. Accordingly, u-healthcare consumers have a different u-

healthcare service.

Methodologically, u-healthcare physician consultations begin with a u-healthcare consumer having a

health problem, for example, arthritis, hair loss, back pain, or some other symptom. A request for a

u-consultation is initiated when the patient logs into the clinic's web site. The web site can prompt

first-time visitors for their medical history. Even when consulting in person, the physician depends

on the honesty and accuracy of patient self-reports in order to dispense the proper treatment. Of

course, visible physical ailments can be detected in person but are difficult and often impossible to

perceive in u-consultations. And ubiquitous technologies such as inexpensive, interactive web-based

videoconferencing and remote vital sign detection diminish this difference between physical and

virtual consultations.

Another step is for the patient to provide acceptable information on means of payment. After the

payment information is received, the fees are displayed. Once the patient has been accepted for u-

consultation and has agreed to the fee structure, he or she will be asked to describe current medical

problems, including precise information about symptoms - that is, how often, where, and when the

problems occur; what solutions have already been tried, if any; what makes the symptoms worse;

what medications have previously alleviated the symptoms; and what treatments have been

arranged to resolve the symptoms. This information completes the initial u-healthcare consumer

record.

III. ULTRASONIC U-HEALTH CARE SENSOR

Ultrasound is used effectively for various communication applications, in addition to biomedical,

industrial and engineering applications. Ultrasonic sensors with different frequencies and

configurations are used in transmitting and receiving modes, in single element or in array forms. PZT

(Lead Zirconium Titanate), quartz and other such piezoelectric materials are, generally, used for the

generation of ultrasound waves. However, these days, there are developments in nano-ultrasonic

techniques which give improved resolution measurements of say smaller structures. The ultrasonic

sensors find useful applications in sensor networking in U-health care systems also. Thus, new

materials are developed for better sensor performance with better sensitivity, directivity and

stability, etc [1-7]. Such piezoelectric sensors are also developed from biological materials like bone,

teeth, etc. Design, development and performance evaluation results of these these sensors have

been discussed, and sensitivity and other performance parameters are found to be better for such

sensors. Diagnostic and therapeutic applications in biotelemetry, telehealth and other U-health care

systems.

53 | P a g e

IV. METHOD AND MATERIALS

Generally, piezoelectric composites and polymers have been widely studied and used in ultrasonic

transducers. Piezocomposite with 0-3 connectivity made from a ceramic powder dispersed in a

polymer or piezoelectric polymers and copolymers are used in thin layers for high frequency

biomedical imaging systems. Nano- composite materials as a combination of piezoelectric ceramics

and composites like polymers have been used here, due to better acoustic impedance. Piezoceramic

materials developed in the laboratory locally have been used with PVDF films (make PolySciences

Inc, USA) having specific gravity of 1.78 and melting point 170 degree C, and acoustic impedance of

3.87 Mega Ohm..

V. RESULTS AND DISCUSSIONS

Acoustic impedance has been found to be reduced. The transducer surface reflects back incident

energy to a lesser extent, resulting into reduced reverberations in the near field. Unwanted surface

waves propagating laterally over the transducer surface are suppressed by use of composite

materials. Piezocomposite material also helps to allow more control over the trade off between the

sensitivity and the bandwidth, with better electromechanical coupling. Conductance values are

shown at different frequencies for particular samples. Newly developed sensor finds applications in

ubiquitous health care systems. The electrical output of the sensor is amplified and telemetersed

through conventional RF (radio frequency) communication and the data can be received by the user

for necessary analysis. A micro-capsule can be designed with such sensor for physiological

parameters like blood pressure, blood flow and body temperature, etc. [7]. Thus, a very low-

powered consumption wireless sensing system is developed for real time monitoring for health care.

New wearable sensors by using new piezo-composite material is possible to be used in ubiquitous

environment. A new biotelemetry system with smart sensors is small enough to be implanted in

laboratory animals, with a miniaturized biotelemetry transmitter.Multi channel bio-telemetry,

development of high data density ultrasound/acoustic systems, bio-technology with internet,

wireless technology and mobiles are good for better healthcare to the distant community. Plans and

strategies to introduce the concept of health robot to conquer the tyranny of distance by activating

the device through voice commands for desired operations is another added advantage this system.

VI. APPLICATIONS AND CASE STUDIES

Health care systems are designed to meet the health care needs of target populations. There are a wide variety

of health care systems around the world. In some countries, the health care system has evolved and has not been

planned, whereas in others a concerted effort has been made by governments, trade unions, charities, religious,

or other co-ordinated bodies to deliver planned health care services targeted to the populations they serve [7-9].

Bar Code Technology

54 | P a g e

Hospitals and Clinics have implemented recently wireless bar coding technology for all the inpatient

and outpatient care areas, and the equipment has been on the basis of evaluation and field trials by

the staff nurses.Future technology systems will depend on the bedside staff nurse input, as these are

the personnel who will use the technology.

Robotic Surgery

Precise fingertip control of fully articulating robotic surgery instruments allow for motion scaling

and tremor reduction, enhanced technique and capability in complex minimally invasive surgeries.

The surgeons also experience improved precision, range of motion, dexterity, visualization and

access. Patients experience shorter hospital stays, pain, less risk of infection, less blood loss, fewer

transfusions, less scarring, faster recovery. One main misconception of this system is that it is not a

robot that performs autonomous programmed procedures. It works on real time and is not

programmable and cannot make its own decisions, it moves just like a surgeon. It interposes a

computer between the surgeon’s hands and the tips of the micro instrument; relaying some

feedback to accommodate for loss of tactile sensation, and this is augmented by the enhanced vision

provided by the high resolution 3D view.

Sensor Grid Gateway

Researchers nowadays are trying to implement u-Healthcare (ubiquitous Healthcare) systems for real-time monitoring and analysis of patients' health status regardless of time and space through a low-cost and low-power wireless sensor network. u-Healthcare system should provide reliable and fast medical services for patients by transmitting to doctors, nurses and other caregivers a large quantity of real-time vital signs collected from sensor network. Currently existing u-Healthcare systems can merely monitor patients' health status. However, they do not derive physiologically meaningful results by analyzing vital signs. In order to solve the problem we introduce a Grid computing technology for deriving the results by analyzing rapidly the vital signs collected from the sensor network. Since both sensor network and Grid computing use different protocols, a gateway is needed. To build an advanced u-Healthcare system by using these two technologies most efficiently, design and implementationof a SensorGrid gateway are to connect transparently the sensor network and the Grid network. Also, a middleware for control and management of the sensor network is implemented as a mobile monitoring system for observing patient's health status on the move.

VII. CONCLUSION

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A new piezo-composite based sensor has been developed for biotelemetry and other ubiquitous

healthcare applications. Combining a piezoelectric ceramic and a passive polymer to form a

piezocomposite allows the transducer to have many advantages over the conventional piezoelectric

ceramics and polymers, with enhanced electromechanical coupling and acoustic impedance close to

that of tissue. These advantages yield transducers for medical ultrasonic imaging with high sensitivity

and compact impulse response, with focusing ultrasonic beam. Proper design of the rod spacing

yields materials which exhibit low cross talk between array elements formed by patterning the

electrode alone, without cutting between the elements. In this way, curved annular arrays may be

made that provide high quality clinical images of substantial diagnostic value to physicians

highlighting the role of piezocomposites in ultrasonic imaging transducers. Applications and

technology trends have also been discussed for u-health care systems.

REFERENCES

[1] V.R.Singh and R. Parshad, “Transducers for Biotelemetry”, Biotelemetry II (ed P.A.Neukomm), S. Kargel, Basel,

Switzerland, pp 28-29, 1974.

*2+ V.R.Singh, S.Yadav and A. Ahmed, “A Piezoelectric Bone Hydrophone for Medical Ult4rsound Applications”, Proc. 10th

Int IEEE-EMBS Conf, New Orleans (USA), pp. 755-756. 1988.

*3+ V.R. Singh, ‘Portable ultrasonic lithotripters”, Proc. IEEE-EMBS Asia-Pacific Conf on BioMed Engg, Hangzhou (China),

Sept. 26-28, p. 883, 2000.

*4+ V.R.Singh, “Mobile ultrasonic lithotripters: evolution in

lithotripters”, Proc. Nat Conf. On Biomed Engg, Roorkee, pp.

387-398, April 21-22, 2000.

*5+ S. Yadav and V.R.Singh, “Development of a Bone Piezoelectric

Microphone Pick-up for Vibration Measurements”, ITBM (Innov

et Tech en Bioliog et Med), 11 (no10), pp 89-95, 1990.

*6+ V.R. Singh, “A Piezo-electric Bone Sensor for Biomedical

Applications”, J. Acoust Soc Ind., vol.28 (no.1-4), pp. 207-209,

2000.

[7] K. Singh, “Biotelemetry: Could Technological Developments

Assist Healthcare in Rural India,” The International Electronic

Journal of Rural and Remote Health Research , Education,

Practice and Policy, ISSN 1445-6054

[8]http://www.cs.joensuu.fi/~thlaine/research/wp-

content/uploads/2008/05/sensorplanetdiagram.png

[9] Se-Jin Oh Chae-Woo Lee “u-Healthcare Sensor Grid Gateway

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for connecting Wireless G. O. Young, “Synthetic structure of

industrial plastics (Book style with paper title and editor),” in

Plastics, 2nd ed. vol. 3, J. Peters, Ed. New York: McGraw-Hill,

1964, pp. 15–64.

Prof. V.R.Singh, Ph.D. (IIT-Delhi), 1974: Fellow-IEEE/EMBS-IMS, F-IETE, F-IE-I, F-ASI/USI, F-IFUMB has 35 yrs of research/teaching

experience in India and abroad; has been working at National Physical Lab, New Delhi, as a Director-Scientist./Distinguished Prof/ Head-

Instrumentation, Sensors and Biomedical Measurements & Standards; has over 250 papers, 150 talks, 4 books, 14 patents, 30

consultancies and 22 PhDs. He is Associate Editor of IEEE Trans on Instrum & Measurements and is on Editorial Boards of Sensors &

Transducers J (Europe) & Int J Onlinne Engg (Austria); as well as on the Editorial Review Committees of various other journals like Sensors

& Actuators (Switzerland), IEEE Trans, J Computers in Elect Engg (USA), J.Instn Electr Telecom Engrs, J.Instn Engrs -India, Ind J Pure & Appl

Physics, J.of Instrm Soc Ind, J. Pure & Appl Ultrasonics, J. Life Science Engg, etc He is the recipient of awards by INSA 1974, NPL 1973,

Thapar Trust 1983, ICMR 1984; Japan Soc. Ultr in Medicine 1985, Asian Fed Soc Ultr in Med & Biology 1987, IE-I 1988/ 1991 and IEEE-

EMBS 1999. He is the Chair of IEEE-EMBS/IMS-Delhi chapters and Vice President of IEEE-Delhi Section. Presently, Dr Singh is a

Distinguished Professor at NPL, New Delhi, India, as well as Director, PDM College of Engg, Bahadurgarh, India. He has also served as a

visiting Professor at Korea University, South Korea. His main areas of interest are sensors and transducers, biomedical instrumentation and

electronics & communication engineering.

57 | P a g e

Analysis of Some Repairable Engineering Systems in

Reliability Theory

Dr. R.K. Tuteja

Director(Academic), N.C. Institute of Computer Sciences, Israna

Email: rk_tuteja2006@yahoo.co.in, zeba_ncce@rediffmail.com

Abstract

The present globalization and market economy have brought out quality and

reliability of products as an important driver to gain competitive advantage. Reliability and

quality are the core issues to be addressed during the design and operation of engineering

systems like nuclear power plants, security systems, transportative systems, software systems

and systems of strategic importance. These issues are the key to the growth of the economy

in the industrialized world.

The reliability and profit of one-unit system have been analyzed in the present

paper. On the failure of the unit, an ordinary repairman comes immediately who first

carries out inspection to see whether the unit is repairable or not. Two models have been

discussed. If the ordinary repairman declares that the unit is irreparable then in the first

model it is replaced with a new one, whereas in the second model an expert opinion is

sought to confirm whether the unit is actually not repairable. The unit is then repaired or

replaced according as it found repairable or irreparable. System is analyzed by making use

of semi-Markov processes and regenerative point technique various measures of system

effectiveness have been obtained. Graphical study is also made. Various generalizations of

the models have been discussed.

Introduction

More and more automation is being introduced by the industries in their industrial

processes and more complex and sophisticated systems are being developed in order to

meet the ever increasing demands of society. However, occurrence of undesirable events

or failures during lifetime of the system is an inevitable phenomenon. Then what do we do?

Feel utterly desperate or fight it with renewed vigour, what do we get? if not absolutely

success, a certain minimization of failures. Minimization of failures and improvement in the

operational use of the systems and increase in the available operating time can be achieved

by reliability and maintainability.

Reliability is an important consideration in the planning, design and operation of the

system and is concerned with random occurrence of failures. Reliability of a system / device

is the probability of the system / device performing its anticipated purpose adequately for

the intended period of time under the given operating conditions.

58 | P a g e

Growth of reliability has been motivated by various factors including the increased

complexity and sophistication of systems, public awareness and insistence on product

quality, new laws and regulations concerning product liability, government contractual

requirements to meet reliability performance specifications and profit considerations

resulting from the high cost of failures, their repairs and warranty programs. The

probabilistic theory of reliability has grown out of the demands of modern technology and

particularly out of the experiences in world war II with complex military systems.

Complexity and automation of equipments used in the war resulted in several problems of

maintenance and repair. ‘Life testing’ and ‘electronic and missiles reliability’ problems

started to receive a great deal of attention both from statisticians and engineers in early

1950.

In 1952, the US Department of Defence had established the Advisory Group on

Reliability of Electronic Equipment (AGREE). This group published its first report on

reliability in 1957. Davis (1952) discussed failure data and goodness of fit tests for various

competing failure distributions. Epstein and Sobel (1954) published a fundamental paper on

life testing which laid the foundation of classical reliability analysis. Epstein and Sobel

(1955), Epstein (1958) worked in the field of life testing with assumption of exponential

distribution. After these papers, the exponential failure distribution acquired a unique

position in life testing and reliability analysis.

Therefore, besides finding reliability of the system, investigations had been carried

out to evaluate other measures. Barlow and Hunter (1960), Gaver (1963), Myers (1964),

Barlow and Proschan (1965), Rau (1970), Beron (1974) and Kontoleon et al. (1974) widely

discussed the concept of availability. Srinivasan and Gopalan (1973) concentrated on

regenerative point technique. Nakagawa and Osaki (1975) considered stochastic behaviour

of a two-unit priority standby redundant system with repair. Nakagawa (1976) considered

the replacement of the unit at a certain level of damage while Arora (1977), Mine and

Kaiwal (1979) enhanced the system reliability by assigning priority repair discipline.

Nakagawa (1980) studied an inspection policy for a standby unit by taking a standby electric

generator as an example. Murari and Goyal (1983) studied a two-unit cold standby system

with two types of repair facility. Murari and Maruthachalam (1984) considered a two-unit

system with two different interlinkings in two different periods. Goel et al. (1985) dealt with

a two-unit cold standby system under different weather conditions. Goel et al. (1986)

analysed the reliability of a system subject to random shocks and preventive maintenance.

Mahmoud (1989) worked on two-unit system with two types of failure and preventive

maintenance. Guo Tong De (1989) studied stochastic behaviour of a system with

preparation for repair. Mokaddis et al. (1989) gave the profit analysis of two-unit priority

system with administrative delay in repair. Gopalan et al. (1991) carried out the cost

analysis of a system subject to on-line preventive maintenance and repair. Tuteja and

Taneja (1991, 92, 93) investigated reliability and profit analysis of two-unit standby system

introducing the concepts of two identical repairmen, minor repair, partial failure and

59 | P a g e

random inspection. Goel et al. (1992) gave the idea of random change of operative unit.

Rander et al. (1991, 92) discussed a system with major and minor failures and preparation

time in case of major failure and a system with imperfect assistant repairman and perfect

master repairman.

Gupta et al. (1993) dealt with the profit analysis of a two-unit priority standby

system subject to degradation and random shocks. Singh and Mishra (1994) evaluated

profit for a two-unit standby system with two operating modes. Saini and Kumar (1994)

analysed a two-unit cold standby system under the influence of earthquakes.

The concept of instruction in the literature of reliability was first introduced by

Kumar et al. (1985). Gupta et al. (1997) dealt with the analysis of a system with three non-

identical units (Super-priority, priority and ordinary) with arbitrary distributions. Mokkaddis

et al. (1997) analysed a two-unit warm standby system subject to degradation. Attahiru and

Zhao (1998) studied the stochastic analysis of a repairable system with three-units and

repair facilities. Sehgal (2000) studied some reliability models with partial failure, accidents

and various types of repair. Siwach et al. (2001) studied two-unit cold standby system with

instruction and accident. Taneja et al. (2001) discussed a system with two types of

repairman wherein the expert repairman may not always be available. Taneja and Vandana

(2003) studied reliability of expert models with patience time and chances of non-

availability of expert repairman. Taneja and Nanda (2003) incorporated the idea of adopting

one of the two repair policies-repeat repair policy or resume repair policy by the expert

repairman after the try made by the ordinary repairman.

These researches, while making the analysis through graphs, took the assumed

values of failure, repair and other rates i.e. the real data on these rates were not taken into

consideration.

Taneja (2004) collected the real data on failure and repair rates of 232

programmable logic controllers (PLC) and studied a single unit PLC considering the four

types of failure. Taneja (2005) discussed reliability and profit analysis of a system which

consists of one main unit (used for manufacturing) and two PLCs (used for controlling).

Initially, one of the PLCs is operative and the other is hot standby.

k-out-of-n structure is also a very popular type of redundancy and is applied in

industrial and military systems. Reliability and / or availability or such systems have been

analysed by various researchers including Chiang and Niu (1981), McGrady (1985), Ksir and

Boushaba (1993), Li and Chen (2004).

None of the researchers in the field of reliability has carried out the profit analysis

for k-out-of-n system on the basis of real data on failure / repair / replacement times. Our

attempt is to analyse reliability and profit for a 2-out-of-3 unit system as such systems are

widely used by various industries. A practical example of such a system is an Ash Water

Pump System consisted in and Ash handling plant. The Ash Water Pump System has three

60 | P a g e

pumps. Out of three, two pumps are in working and third is standby. The purpose of the

system is disposal of the ash generating during the combustion of coal.

Since large capacity thermal power stations are being installed in India, the need of

reliable and efficient ash handling system is well recognized. It becomes more significant as

coal with high ash content is being supplied to power stations. Tons of ash is produced

when coal with 45% ash content is used in these thermal stations. If the system does not

operate properly, it caused accumulation of ash at collection point which may result in

failure or shut down of unit. Therefore a reliable system which can handle such a large

quantity of ash is required.

We, in the present thesis, collected data on failure / repair times and on some other

parameters of a 2-out-of-3 Ash Water Pump system from Panipat unit of National Fertilizer

Limited (NFL) and analyse it by doing modeling for practically existing situation in the plant

and also for some other situations / assumptions. Comparative study amongst these

different situations has also been made to arrive at very important / useful conclusions.

We now discuss some fundamental concepts related to reliability and to the

performance measures of the systems of interest:

Reliability

Reliability of a system / device is the probability of the system / device performing its

anticipated purpose adequately for the intended period of time under the given operating

conditions.

Quantitatively, reliability of a device in time ‘t’ is the probability that it will not fail in

a given environment before time t. If T is a random variable representing the time till the

failure of the device starting with an initial operable condition at t = 0, then reliability R(t) of

device is given by

R(t) = P [T > t] = 1 – P [T < t] = 1 – F(t)

Thus, reliability is always a function of time. It also depends on environmental conditions

which may or may not vary with time. Following assumptions are made:-

(i) R(0) = 1 since the device is assumed to be operable at t = 0.

(ii) R() = 0 since no device can work forever without failure. (iii) R(t) = is non-increasing function between limits 0 and 1

Instantaneous Hazard Rate (or Failure Rate)

It is defined as the conditional probability that the system fails during the time interval (t, t +

t) given that it was operating during [0, t].

61 | P a g e

Unit 1 Unit 2 Unit n Unit 3 CAUSE EFFECT

Let r(t)t = probability that the device has life time between t and t + t,

given that it has functioned upto time t.

= Pr[t < T < t + t | T > t]

= ]tT[P

]tT[P]tδtT[P

]tT[P

]tδtTt[P

= )t(R

)t(R)tδt(R

)t(R

)]t(R1[)]tδt(R1[

Now, the instantaneous failure rate or hazard rate r(t) at time t is defined as

r(t) = )t(R

)t(f

)t(R

)t('R

tδ)t(R

)t(R)tδt(Rlim

0tδ

where f(t) is the p.d.f. of the device life time.

It can be seen that

F(t) =

t

f (u)du = R(t) = exp [ – )u(ft

0

du]

f(t) = r(t) exp [ – )u(ft

0

du ]

XVI. SYSTEM CONFIGURATIONS

By a system, we mean an arbitrary device having several units / subsystems /

components assuming that their reliabilities are known. It is now important that the system

structures be known. Various system structures have been considered as follows: -

(i) Series Configuration

A system having n-units is said to have series of configuration if the failure of an

arbitrary unit (say ith unit) causes the entire system failure. The examples of the series

configuration are:

The aircraft electronic system consists of mainly a sensor subsystem, a guidance

subsystem, computer subsystem and the fire control subsystem. This system can only

operate successfully if all these operate simultaneously.

Deepawali or Christmas glow bulbs where if one bulb fails the whole lead fails. The

block diagram of a series system configuration is shown as follows: -

62 | P a g e

Fig. Series configuration

Let Ri(t) be the reliability of ith components, then the system reliability is given by

R(t) = Pr (T>t] = Pr{min(T1,T2,T3,….,Tn) > t]

=

n

1i

P [ Ti > t] =

n

1i

Ri (t)

where Ti is life time of the ith unit of the system. The system hazard rate, therefore,

is

r(t) = n

l

i )t(r

where ri(t) is the instantaneous failure rate of the ith unit.

(ii) Parallel Configuration

In this configuration, all the units in a system are connected in parallel i.e. failure of

the system occurs only when all the units of system fail. For example, four engined aircraft,

which is still able to fly with only two engines working. Block diagram representing a parallel

configuration is shown in fig.

Fig. Parallel configuration

Suppose Ri(t) and Ti be the reliability of ith component and the life time in time t

respectively, then the system reliability is given by

Unit 1

Unit 2

Unit n

CAUSE EFFECT

63 | P a g e

R(t) = Pr (T>t} = Pr[min(T1,T2,T3,….,Tn) > t]

= 1-P (T1<t, T2<t,T3<t,….,Tn < t]

If the units function independently, then

R(t) =1- [1- R1(t)][1- R2(t)] [1- R3(t)+….*1- Rn(t)]

=

n

li

i )t(R1[1

(iii) Standby Redundant Configuration

Redundancy is a device to improve reliability of a system. In a redundant system,

more units are made available than which are necessary. There are two types of

redundancy:-

(a) Active Redundancy (b) Passive Redundancy

(a) Active Redundancy

In this case of Redundancy, the system has a positive probability of failure even

when it is not in operation. This may happen due to the effect of temperature,

environment condition etc.

Active redundancy can further be classified as hot redundancy and warm

redundancy:-

(i) If the off-line unit can fail and is loaded in exactly the same way as the operating unit, it is called hot standby unit.

(ii) If the off-line unit can fail and can diminish the load, it is called warm standby unit. The probability of failure for a warm standby is less than that of failure for operative unit.

(b) Passive or Cold Standby Redundancy

This is that form of redundancy in which the off-line unit cannot fail and is

completely unloaded.

64 | P a g e

INPUT

OUTPUT

Fig. Standby redundant configuration

Reliability R(t) of an n-unit standby system at any time instant t is given by

R(t) = P ]tT[n

1i

i

where Ti is the life time of ith unit and all the n-units are independent.

A standby system functions as long as one of the units is available for the task on

hand. A block diagram of such system is shown in fig.

(iv) k-out-of-n configuration

In many problems the system operates if at least k-out-of-n units function, e.g. a

bridge supported by n-cables, k of which are necessary to support the maximum load. If

each of n-units is identical with the same reliability then the system reliability becomes

n

R(t) =

n

ki

intλtiλi

n )e1(eC

There exists many other configurations such as series-parallel, parallel-series, mixed

parallel, etc. which are used by the industries.

Stochastic Process

Unit 1

Unit 2

Unit n

65 | P a g e

A stochastic process is a family of random variables indexed by a parameter set

realising values on another set known as the state space. Both the parameter set and the

state space can be either discrete or continuous.

In a stochastic process {X(t), t T}, where X(t), t and T respectively are the state

space, parameter (generally taken to be time) and the index set. If T=,0,1,2,3,…-, then the

stochastic process is said to be discrete parameter process and if T = {t: – < T < } or T =

{t: t > – 0}, the stochastic process is said to be continuous parametric process. The state

space is classified if it consists of an interval on the real line. In the present study, we have

only dealt with discrete state space continuous time parameter stochastic process.

Markov Process

A stochastic process is said to be Markov Process if the future development is completely

determined by the present state and is independent of the way in which the present state

has developed, If {X(t), t T} is a stochastic process such that, given the value of X (t), t > s

do not depend on the values of X(u), u < s, i.e. for t > s, is

Pr [X(t) =i | X(u),0 < u <s] = Pr[X(t) = I | X(s)]

Then the process {X (t), t T} is a Markov process.

Stochastic Processes whish do not posses the Markovian property are said to be non

markovian.

Markov Chain

A Markov Process with discrete state space is said to be a Markov Chain Mathematically, a

stochastic process {Xn; n = 0, 1, 2,….- is called a Markov Chain if, for j, k, j1, j2, j3,…,jn-1]

Pr.[Xn = k | Xn-1 = j, Xn-2 = j1,……… X0 = jn-1 ]

= Pr.[Xn = k | Xn-1 = j] pjk (say)

If the transition probabilities pij are independent of n, the Markov chain is said to be

homogeneous and if it is dependent on n the chain is said to be homogeneous.

Renewal Process

Suppose we have repairable system which starts operation at t = 0. If X1 denotes the

time to first failure and Y1 denotes the time from first failure to next system operation (after

repair) then t1 = X1 + Y1 denotes the time of first renewal. Similarly, if X2 denotes the time to

first renewal to second failure and Y2 denotes the time from second failure to second

renewal then t2 = X2 + Y2 and the time of second renewal is t1 + t2. In general, ti = Xi + Y1

(inter-arrival) time between the (i-1)th and ith renewal) for i = 1,2,3,….If we define

66 | P a g e

S0 = 0, Sn = t1 + t2 + ….tn

= epoch of nth renewal,

and N(t) = number of renewals during (0,t]

then the process {N(t), t > 0} is called renewal process.

Markov Renewal process

Let the states of a process be denoted by the set E = ,0, 1, 2, …. -, and let the

transition of the process occur at epochs t0 (= 0), t1, t2, …,tn (tn< tn+1). If

Pr{ Xn+1 = k, tn+1 –tn < t | X0 = i0,……, Xn = in : t0, t1,…..tn}

= Pr(Xn+1 = k, tn+1 – tn < t | Xn = in}

Then { Xn, tn}, n = 0,1,2,…., constitutes a Markov Renewal Process with state space E.

Semi-Markov Process

In the above, if we assume that the process is the time homogeneous, i.e.

= Pr(Xn+1 = j, tn+1 – tn > j | Xn = i} = Qij (t), i, j s

Is independent of n, then there exist limiting transition probabilities

Pij = t

lim Qij (j) = Pr (Xn+1 = j | Xn = i}

Then { Xn, }, n=0,1,2,,….- constitutes a Markov chain with state space E and transition

probability matrix (t.p.m) is given by

P = [Pij]

The continuou8s parameter stochastic process Y(t) with state space E defined by

Y(t) = Xn, tn < t < tn+1

is called a semi-Markov process.

In other words, we define the semi-Markov process in which transition from one state to

another is governed by the transition probabilities of Markov process but the time spent in

each state before a transition occurs is random variable depending upon the last transition

made. Thus at transition instants the semi-Markov behaves just like a Markov process.

However, the times at which transition occur are governed by a different probability

mechanism.

67 | P a g e

Regenerative Process

Regenerative stochastic process was defined by smith (1955 ) and has been crucial in

analysis of complex system . In this, we take a time point at which a system history prior to

the time point is irrelevant to system conditions. These points are called regeneration

points. Let X(t) be the state of system at epoch t. If t1,t2,.…are epochs at which the process

probabilistically restarts, then these epoch are called regenerative epoch and the process

{X(t),t=t1,t2,….- is called the regenerative process.

Transforms and Convolutions

(a) Laplace Transform Let f(t) be a function of positive real variable t. Then rhe Laplacce Transform(L.T.) of f(t)

is defined as

L[f(t) = f* (s) =

0

e–st f(t) dt

For the range of value of s for which the integral exists. Here f(t) is called an inverse Laplace

Transform of f*(s) and we write f(t)= l{f*(s)}. The following are some important properties of

Laplace transform:

(i) L

n

li

*ii

n

1i

ii )s(fc)]t(fc[

(ii) Ln

*nnn

ds

)s(fd)1()]t(ft[

(iii) Ls

)s(f)]t(F[L]du)u(f[

*t

0

(iv) 0t

lim

f(t) = s

lim sf*(s) (initial value theorem)

(v) )s(sflim)t(Flim *

0st (final value problem)

(vi) 0s

lim

f*(s) = 1 if f* (s) is L.T. of a.p.d.f.

(b) Laplace Stieltjes Transforms

Let X be a non-negative random variable with distribution function

F(x) = Pr [X < x]

68 | P a g e

then Laplace Stieltjes Transforms (L.S.T.) of F(x) is defined , for s > 0 by

F** (s) =

0

sx )x(dFe

Therefore, we have

F** (s) =

0

*sx )s(fdx)x(dfe

where f(x) = dx

)x(dF

Convolution

Let f(t) and g(t) be two real value non-negative continuous function of t, then the integral

t

0

t

0

du)u(f)ut(gdu)u(g)ut(f

= f(t) © g(t) = L–1 [f*(s).g*(s)]

is called the Laplace convolution of the functions f(t) and g(t).

If f(t) and g(t) be two real value distribution functions defined for t > 0, the resulting

convolution is again distribution function and the integral

t

0

F (t – u) dG(u) = t

0

G (t – u)dF(u) = F(t) G (t)

is known as Stieltjes convolution of f(t) and g(t).

First Passage Time

Suppose that a system starts with a state j, then the time taken to reach a given state k for

the first time from state j is called first passage time. In general, first passage time is a

measure of how long it takes to reach a given state from another state.

Mean Sojourn Time in a State

The expected time taken by the system in a particular state before transiting to another

state is known as the mean Sojourn Time or mean survival time in that state. If T be Sojourn

Time in state i, then mean Sojourn Time in i is

Mean Time to System Failure (MTSF)

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The average duration between successive system failure, i.e. MTSF is defined as expected

time for which a system is in operation before it completely fails.

Suppose the reliability function for a system is given by R(t)= 1-F(t), where F(t) is a failure

time distribution function and F(t)=dF(t)/(dT) is a failure time density function. The mean

time to system failure is given by

MTSF =

0

t f(t) dt

=

0

t dtdt

)t(dR

= [t R(t) 0]

0

R(t)dt

=

0

R(t) dt = 0s

lim

R*(s).

Let 0(t) be the cumulative distribution function of the first passage time from initial state to

a failed state, then

R*(s) = s

)s(φ1 **0

Thus, we have

MTSF = s

)s(φ1lim

**0

0s

Availability

When a system is often unavailable due to breakdowns in concerning department becomes

interested to put it back into operation after each break down with proper repairs. In fact, it

is concerned with availability equally as it does with reliability because of additional cost and

inconvenience incurred when the system is not available. The differences between the

measures reliability and availability are as follows:

(i) The reliability is an interval function while the availability is a point function describing the behaviour of the system at a specified epoch.

(ii) The reliability function precludes the failure of the system during the interval under consideration, while availability function does not impose any such restriction on the behaviour of the system.

70 | P a g e

We may categorize availability as:

(i) Instantaneous (Point wise) Availability This is the probability that the system will be able to operate within the tolerance at a given

instant of time.

Let X(t)=1,if the system is operable at time t, and X(t)=0,when it is not operable. The

availability A(t) of the system at time t is given by

A(t)=P[X(t)=1| X(0)=1]

(ii) Average (Interval) Availability It is the expected fraction of a given interval of time that the system will be able to operate

within tolerances.

Suppose the given interval of time is (0,T]. en interval availability H(0,T]=A(T) for this

interval is given by

A(T) = T

0

dt)t(AT

1

(iii) Steady State (Limited Interval) Availability It is expected fraction of time in long run that the system operates satisfactory. To obtain

steady state availability we simply compute

TTlim)T,0(Hlim A(T)

Maintainability

Maintainability is associated with the system under repair. It is the probability that the

system will be restored to operational effectiveness within a specified time when the

maintenance action is taken in accordance with prescribed conditions. Maintenance is on of

the effective ways of increasing the reliability of a system. Maintenance of a system is of

two types:

(i) Preventive Maintenance (PM) (ii) Corrective Maintenance (CM)

PM includes actions such as Lubrications, replacement of a nut or a screw or some part of a

system, refueling, cleaning, etc., while CM involves minor repairs that may crop up between

inspections.

On the failure of a unit, it is sent to a repair facility if available, otherwise it queues

up for repair. There may be three types of repair policies as follows:

(i) Resume Repair Policy

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The repair of a failed component is terminated before completion due to one reason

or the other. When it begins again, it is started from the stage where it was prior to the

termination of repair.

(ii) Repeat Repair Policy(Type-I)

Due to certain reasons the repair of a failed unit has to be stopped. When the repair

is begun again, it starts all over again. To this let us call Repeat Repair Policy(Type-I)

(iii) Repeat Repair Policy(Type-II)

During the process of repair their may be one of the possibilities that the unit

damages in the sense that the repair is begun again from much earlier stage than the stage

from which it had started. To this let us call Repeat Repair Policy (Type-II).

Profit Analysis

Availability of the system leads to revenue where as the busy period of repairman,

expected number of visits by the repairman, expected numbers of replacement, etc. lead to

the cost of maintenance and spares. The revenue and cost function lead to profit function of

a firm, as the profit is excess of revenue over the cost of production .The profit function

takes the form

P(t) = Expected revenue in (0,t] – expected total cost in (0, t]

In general, the optimal policies can more easily be derived for an infinite time span

as compared to a finite span. The profit per unit time is expressed as

t

)t(Plimt

i.e. profit per unit time = total revenue per unit time – total cost per unit time

For example, the profit equation may be given as

Pi or Pij = C0A0 – C1I0 – C2B0 – C3B0e – C4V0 – C5V0

e – C6RT0

where

Pi=Profit per unit up time of the model in the ith chapter

Pij= Profit per unit up time of the jth model of the ith chapter

C0=revenue per unit up time of the system

A0=Total fraction of the time for which system is up

C1=Cost per unit time for which the ordinary repairman is busy for inspection

72 | P a g e

I0= the total fraction of time for which the ordinary repairman is busy

C2= cost per unit time for which the ordinary repairman is busy in repairing the

failed unit

B0= the total fraction of time for which the ordinary repairman is busy

C3= cost per unit time for which the expert repairman is busy in repairing the

failed unit

B0e= the total fraction of time for which the expert repairman is busy

C4= cost per visit by the ordinary repairman

V0= expected number of visits of the ordinary repairman

C5= cost per visit by the expert repairman

V0e= expected number of visits of the expert repairman

C6= cost per replacement with a new one

RT0= expected number of replacements of Type-I failure

Distribution Used

In the present work, the failure time distribution is assumed to be an exponential

distribution. The family of exponential distribution is the best known and most thoroughly

explored, largely through the work of Epstein(1958) and his associates. Exponential

distribution plays an important role in reliability studies. Besides a number of desirable

mathematical properties, it has a very important memoryless property i.e. if the life length T

of a structure has the exponential distribution, previous use does not effect its future life

length.

Exponential distribution is defined as follows:

A continuous random variable having the range 0 < t < is said to have an exponential

distribution if it has the probability density function of the form

f(t) =

0t,0

t0,eλ tλ

where is a positive constant. The corresponding distribution function is

F(t) =

0t,0

t0,e1 tλ

73 | P a g e

we in the present paper, analyse the reliability and profit for a one-unit system on the

failure of unit, an ordinary repairman comes immediately who first carries out inspection to

see whether the unit is repairable or not. Two models are discussed. If the ordinary

repairman declares that the unit is irreparable then in the first model it is replaced with a

new one whereas in the second model an expert opinion is taken to confirm whether the

unit is actually not repairable. If he finds that it is repairable, then it is repaired by the expert

himself, otherwise it is replaced with new one by the ordinary repairman. The expressions

for various measures of system effectiveness have been evaluated. Comparative study of

both the models have been made graphically.

Notations :

: constant failure rate of operative unit, p1 : probability that unit is

repairable p2 : probability that unit is irreparable

h1(t), H1(t) : p.d.f. and c.d.f. of time to inspection for detecting reparability of

a failed unit

h2(t), H2(t) : p.d.f. and c.d.f. of replacement time

g(t), G(t) : p.d.f. and c.d.f. of repair time of ordinary repairman

he,(t), He(t) : p.d.f. and c.d.f. of inspection time of expert repairman

ge(t), Ge(t) : p.d.f and c.d.f. of repair time of expert repairman

Symbols for the States of the System

o : operative unit

Fui : failed unit under inspection by ordinary repairman

Fur : : failed unit under repair of ordinary repairman

Frep : failed unit under replacement of ordinary repairman

Fuie : failed unit under inspection by expert repairman

Fre : failed unit under repair of expert repairman

Model-1

In this Model, it is assumed that if the ordinary repairman declares the failed unit is

irreparable then it is replaced with new one. The transition diagram showing the various

states of the system is shown as in figure. The epochs of entry into states 0, 1, 2, and 3 are

regeneration points and thus 0, 1, 2 and 3 are regenerative stats. State 1, 2 and 3 are failed

states.

74 | P a g e

The transition probabilities are as follows:

dQ01 = et dt

dQ12 = p1h1(t) dt

dQ13 = p2 h1(t)dt

dQ20 = g(t)dt

dQ30 = h2(t)dt

The non-zero elements pij = 0s

Lim

)s(q*ij

p01 = 1λs

λLim

0s

p12 = 0s

Lim

p1 h1*(s) = p1h1*(0) = p1

p13 = 0s

Lim

p2 h2*(s) = p2

p20 = 0s

Lim

g*(s) = g*(0) = 1

p30 = 0s

Lim

h2*(s) = h2*(0) = 1

The mean sojourn time (i) in state i are as follows

1 =

0 0

1st

0s1 dt))t(ht(eLimdt)t(ht

=

)s(*h

ds

d)1(Lim 1

0s

= )0(*'h1

0 1 2 3 H2(t)

g(t)

p1h1(t) (Fur) (Fup)

75 | P a g e

2 =

0

)0(*'gdt)t(gt

3 =

0

22 )0(*'hdt)t(ht

0 = 1/

The unconditional time taken by system to transit for any regenerative state ‘j’ when it is

counted from epoch of entrance in to state ‘i’ is given by

mij = )0('q*ij

m01 = 0s

Lim 2)λs(

λ

= 1/ = 0

m12 = 0s

Lim

p1 h1*(s) = p1h1*(0) = p11

m13 = 0s

Lim

p2 h1*(s) = p2 h1*(0) = p2 1

m20 = 0s

Lim

g*(0) = g*(0) = 2

m30 = 0s

Lim

h2*(s) = h2*(0) = 3

Mean time to System Failure

Taking L.S.T. (Laplace-Stieltjes) we get

0(t) = Q01(t),

0**(s) = Q01**(s)

MTSF = 0s

Lim s

)s(φ1 **0

= )s('φ **01 = 0

Availability Analysis

A0(t) = q01(t) A1(t) + M0(t)

A1(t) = q12(t) A2(t) + q13(t) A3(t)

A2(t) = q20(t) A0(t)

A3(t) = q30(t) A0(t) M0(t) = et

76 | P a g e

Taking L.T. of above equation and on solving

A0 q01 A1 + 0A2 + 0A3 = M0

0A0 + A1 q12 A2 q13A3 = 0

q20 A0 + 0A1 + A2 + 0A3 = 0

q30 A0 + 0A1 + 0A2 + A3 = 0

D1(s) =

100q

010q

qq10

00q1

30

20

1312

01

= 1 q01 [q12 q20 + q13 q30]

D1(s) = q01 [q12 q20 + q13 p30] q01 [q12 q20 + q12 q20 + q13 q30 + q13 q30)

D1(0) = m01 [p12 p20+p13 p30]+p01 [m12 p20+p12 + p12 m20 + p30 m13 + p13 m30]

= m01[p1 .1 + p2 .1] + p01 [m12 + p1 m20 + m13 + p2 m30]

= m01 + p01 [(m12 + m13) + p1 m20 + p2 m20]

= 0 + p01 [1 (p1 + p2) + p1 2 + p2 3]

D1(0) = 0 + p01 [1 + p12 2 + p13 3] = D1

N1(s) =

1000

0100

qq10

00qM

1312

010

M0*(s)

N1(0) = M0*(0) = 1/ = 0 = N1

A0 = 1

1

0s D

N

)s(D

)s(NsLim

where

D1 = 0 + 1 + p12 2 + p13 3

N1 = 0

77 | P a g e

Similarly

Busy period of ordinary repairman (Repair Time only) (B0) = N2/D1

Busy period of ordinary repairman (Inspection Time only) (BI0) = N3/D1

Busy period of ordinary repairman (Replacement Time) = N4/D1

Expected Number of Visits by ordinary repairman (V0) = N5/D1

Expected number of Replacements (R0) = N6/D1

where

N2 = p12 2

N3 = 1

N4 = p13 3

N5 = 1

N6 = p13

and D1 is already specified.

Profit of system in steady-state is given by

P1 = C0A0 C1B0 C2BI0 C3BR0 C4V0 C5R0

Busy Period Analysis of Ordinary Repairman (Inspection Time Only)

The total fraction of the time for which the system is under repair of ordinary

repairman. In steady-state, is given by

BI0 = 1

3

D

N

where N3 = 1

Busy Period Analysis (Replacement Time Only)

In steady-state, the total fraction of the time for which the system is under

replacement is given by

BR0 = 1

4

D

N

where N4 = p13 3

78 | P a g e

Expected Number of Visits

In steady-stat, the total number of visits per unit time by ordinary repairman is given

by

V0 = 1

5

D

N

where N5 = 1

Expected Number of Replacements

In steady-state, total number of expected replacements is given by

R0 = 1

6

D

N

where N6 = p13

Profit Analysis

The expected total profit incurred to the system in steady-state is given by

P1 = C0A0 C1B0 C2BI0 C3BR0 C4V0 C5R0

where

C0 = revenue per unit up time of the system

C1 = cost per unit time for which the repairman is busy for repairing the failed unit.

C2 = cost per unit time for which the repairman is busy in inspecting the failed unit

C3 = cost per unit time for which the repairman is busy in replacing the failed unit

C4 = cost per visit of the repairman

C5 = cost per replacement with a new one

Model-2

This model is discussed with an additional assumption that whenever the ordinary

repairman finds that the failed unit is not repairable, an expert opinion is taken to confirm

whether the unit is actually not repairable and then repaired or replaced accordingly. Figure

shows various states of the transition of the system.

79 | P a g e

g(t)

p1h1(t)

(Fui) (Fur)

ge(t) h2(t)

(Fre)

p2h1(t)

p1he(t) (Fuie)

(Frep) p2he(t)

The epochs of entry into states 0, 1, 2, 3, 4 and 5 are regeneration points and thus 0, 1, 3, 4

and 5 are the regenerative state. States 1, 2, 3, 4 and 5 are failed states.

The non-zero elements pij = )s(qlim *ij

0s are given as follows: -

Model-2

The transition probability are as follows.

dQ01 = et dt

dQ12 = p1 h1(t) dt

dQ13 = p2 h1(t)dt

dQ20 = g(t)dt

dQ34 = p1 h2(t)dt

dQ35 = p2 he(t)dt

dQ40 = ge(t)dt

0 1 2

3 4

5

80 | P a g e

dQ50 = h2(t)dt

The non-zero elements pij = )s(qLim *ij

0s are given by

p01 = 1λs

λLim

0s

p12 = 1*11

*11

0sp)0(hp)s(hpLim

p13 = p2

p20 = 1

p34 = p1

p35 = p2

p40 = 1

p50 = 1

The mean sojourn time (i) given by

1 =

0

t h1(t)dt = h1*(0) 0 = 1/

2 =

0

t g(t)dt = g*(0)

3 =

0

t he(t)dt = he*(0)

4 =

0

t ge(t)dt = ge*(0)

5 =

0

t h2(t)dt = h2*(0)

Now

mij = qij*(0)

m01 = )λs(

λLim

0s

= 1/ = 0

81 | P a g e

m12 = 0s

Lim

p1 h1*(s) = p1 h1*(0) = p11

m13 = 0s

Lim

p2 h1*(s) = p2 h1*(0) = p2 1

m20 = g*(0) = 2

m34 = p1 he*(0) = + p1 3

m35 = p2 he*(0) = + p2 3

m40 = ge*(0) = 4

m50 = h2(0) = 5

m12 + m13 = (p1 + p2) 1 = 1

m34 + m36 = (p1 + p2) 3 = 3

MTSF

0(t) = Q01(t)

0**(s) = Q01**(s)

MTSF = 0s

Lim

s

)s(φ1 **0

= 0

Availability Analysis

A0(t) = M0(t) + q01(t) A1(t)

A1(t) = q12(t) A2(t) + q13(t) A3(t)

A2(t) = q20(t) A0(t)

A3(t) = q34(t) A4(t) + q35(t) A5(t)

A4(t) = q40(t) A0(t)

A5(t) = q50(t) A0(t) M0(t) = et

Taking L.T. of above equations and solving

A0 q01 A1 + 0A2 + 0A3 + 0A4 + 0A5 = M0

0A0 + A1 q12 A2 q13 A3 + 0A4 + 0A5 = 0

q20A0 + 0A1 + 0A2 + 0A3 + 0A4 + 0A5 = 0

82 | P a g e

0A0 + 0A1 + 0A2 + A3 q34 A4 q35 A5 = 0

q40 A0 + 0A1 + 0A2 + 0A3 + A4 + 0A5 = 0

q50 A0 + 0A1 + 0A2 + 0A3 + 0A4 + A5 = 0

D1(s) =

10000q

01000q

qq1000

00010q

00qq10

0000q1

50

40

3534

20

1312

11

D(s) = 1 q01 q12 q20 q01 q13 q34 q40 q01 q13 q35 q50

D1(s) = q01 q12 q20 q01 q12q20 q01 q12 q20 q01 q13 q34 q40q01 q13 q34 q40

q01q13q34 q40 q01q13q34 q10 q01q13q35q50 q01 q13 q35 q50

q01 q13 q35 q50 q01 q13 q35 q50

D1(s) = m01p12p20 + p01p20 m12 + p01 p12m20 + p13 p34 p40 m01

+ p01 m13 p34 p40 + p01 p13 p40 m34 + p01 p13 p34 m40

+ p13 p35 p50 m01 + p01 p35 p50 m13 + p01 p13 p50 m35

+ p01 p13 p35 m50

= m01 p12 + m12 + p12 m20 + p13 p34 m01

+ m13 p34 + p13 m34 + p13 p34 m40

+ p13 p35 m01 + p35 m13 + p13 m35 + p13 p35 m50

= 0 p10 + p10 1 + p12 2 + p2 p1 0 + p1 p2 1

+ p2 p1 3 + p2 p1 4 + p2 p2 0 + p2 p2 1

+ p2p2 3 + p2 p2 5

= 0 (p1 + p1 p2 + 22p ) + (p1 1 + p1 p2 1 + 2

2p 1)

+ (p2 p1 + 22p ) 3 + p2 p1 4 + 2

2p 5

= 0 + 1 + p2 3 + p2 p1 4 + 22p 5

83 | P a g e

D1(0) = 0 + 1 + p13 3 + p13 p34 4 + p13 p35 5 = D1

N1(s) =

100000

010000

qq1000

000100

00qq10

0000qM

3534

1312

010

= M0*(s)

N1(0) = M0*(0) = 1/ = 0 = N1

A0 = )s(D

)s(NsLim

0s =

1

1

D

N

where

D1 = 0 + 1 + p13 3 + p13 p34 4 + p13 p35 5

N1 = 0

Similarly,

Busy period of ordinary repairman (Repair time only) B0 = N2/D1

Busy period of ordinary repairman (Inspection time only) BI0 = N3/D1

Busy period of ordinary repairman (Replacement time only) BR0 = N4/D1

Expected number of visits by ordinary repairman (V0) = N5/D1

Expected number of replacements R0 = N6/D1

Busy period of expert repairman (Repair time) B0e = N7/D1

Busy period of expert repairman (Inspection) BI0e = N8/D1

Expected number of visits by expert repairman V0e = N7/D1

where

N2 = p12 2

N3 = 1

N4 = p13 p35 5

N5 = p13 p35 + 1

84 | P a g e

N6 = p13 p35

N7 = p13 p34 4

N8 = p13 3

N9 = p13

Profit P2 = C0A0 C1B0 C2BI0 C2BR0 C4V0 C5R0 C6 B0e C7BI0

e C8V0e

Comparison between Model 1 and Model 2

Comparative study is made for the particular cases assuming all the general

distributions as exponential, i.e.,

g(t) 1 t

1α

e

, ge(t) = 2 t

2α

e

, h1(t) = 1 t

1γ

e

,

he(t) = 2 t

2γ

e

, h2(t) = 1 et

The numerical values assumed and given to various rates/costs have been mentioned along

with the graphs.

Fig. shows the behaviour of difference between profits P2 (Model 2) and P1 (Model 1) with

respect to repair rate (2) for different values of probability that unit is not repairable.

Following conclusions are drawn:

(i) If p2 = 0.1, then P22 P21 > or = < 0 according as 2 > or = or < 5.45. Hence the

Model 2 is better or worse than Model 1 according as 2 > or < 5.45. Both the

models are equally good if 2 = 5.45.

(ii) If p2 = 0.5, then P2 P1 > or = or < 0 according as 2 > or = or < 16.05. Hence

Model 2 is better or worse than Model 1 according as 2 > or < 16.05. Both the

models are equally good if 2 = 16.05.

(iii) If p2 = 0.9, then P2 P1 < 0 irrespective of the values of repair rate. Hence, it is concluded that Model 1 is better than Model 2 for p2 = 0.9.

(iv) It can also be concluded that if the chances of non-reparability become more, Mode 1 becomes more profitable than Model 2.

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88 | P a g e

Abstract: The present trends of fast changes in technologies, enhanced requirements of customers for good quality

products and services, competitive costs and fast response along with the globalization has created a need for

competitive processes at every stage of engineering and business. In this environment, drivers of competitiveness are

not physical assets or size of the organization but intellectual workers who are creative and innovative. The focus has

shifted on service rather than products. Use of computational packages has taken the place of analytical tools due to the

increased complexity. The decision making is knowledge based along with data based due to increased uncertainty and

fast change.

The innovative technologies like information and computational technologies are facilitating the industry by way of their

capabilities and impact in making e manufacturing possible. These technologies are characterized by capabilities like

transactional, automatical, analytical, informational, sequential and tracking. The last decade has seen vast applications

for the automation of information systems and the next phase of applications will be for the automation of the

manufacturing systems and infrastructure systems. To reduce the cost and counter the fast attrition rate of knowledge

workers, knowledge management needs to be integrated in the organization structures.

Keywords: Innovative Technologies, Knowledge Management, Global Competitiveness

XVII. INTRODUCTION

Various debates and discussions in 70’s and 80’s have made population as one of the main reasons

for poverty in India. But the last two decades has seen that this curse getting repackaged as

demographic dividend that will drive our growth rather than impede it.

After liberalization of economy in early 1990s, Indian industry has seen an unparallel growth primarily driven

by the boom in information technology. During this period, the trends have been from traditional economy to

knowledge base economy, from manufacturing to service sector, diminishing importance to size, local to global

economy etc. Under these global and national changes, the innovation technologies in five areas namely space

technology, food technology, bio technology, information technology and nano technology are going to

influence the economy and the power of nations. India has a unique demographic dividend as the only country

growing younger in rapidly ageing world. 25% of the world’s new workers in the next 5 years will be Indians.

XVIII. NEED OF INNOVATIVE TECHNOLOGIES

Due to global competitiveness, the present day business is customer centric and thus the invention

and development technologies should start with identifying the needs of the customer, generally

Prof S.K.Garg is Professor and Associate Head with Department of Mechanical Engineering, Delhi College of Engineering, Bawana

Road, Delhi-110042. (e-mail: skgarg63@yahoo.co.in).

Enhancing Global Competitiveness through

Innovative Technologies, Quality and

Knowledge Management

Prof. S.K.GARG

89 | P a g e

called “voice of the customer”. The voice can be explicit or implicit. Explicit means the needs and

requirements are explicitly narrated by the customer and the job of the product or service provider

is to fulfill that, whereas, the implicit requirements are difficult to know and understand. But the

maximum value additions and gains in competitiveness are possible through understanding the

customer’s implicit needs. Generally, the needs are three fold. Like from an apple tree, the

expectations are sweet apple, plenty of apples and low hanging apples; the customer of products or

services, expects good quality at competitive cost and delivered at place and time of his choice. This

is referred as QCD of competitiveness. Figure 1 represents a model for emergence of innovative

technologies.

The 21st century needs thinking by the companies beyond QCD as all good organization are able to

achieve this and QCD have become hygiene factor, in absence of which you cannot enter in the

market. Then what additional is required to be order winning? It is innovations in product/service

features. Supply Chain Management is recent comprehensive thinking and philosophy of meeting

the customers requirement on all fronts through bringing agility in the various entities of the supply

chain and then integrating them by proper use of outsourcing and information and computational

technologies (ICT).

XIX. INNOVATIVE TECHNOLOGIES IN SCM

SCM in a concept that helps to integrate the various entities in the value chain responsible for

providing goods and services of the customer. An efficient supply chain requires management of:

a) Flow of material; b) Flow of money; and c) Flow of information.

Several innovative technologies in the form of ICT are facilitating the SCM environment. Some of

these are Enterprise Resource Planning (ERP), e- commerce, e-business,

Emergence of Innovative Technologies

Figure 1: Emergence of Innovative Technologies

Global

Manufacturing

and Logistics

Quick

Response and

Quality

Demanding

Customers for

Variety and

innovations

Greater

Environmental

Uncertainty

90 | P a g e

Customers Relationship management (CRM), Point of Sale (POS) information, Poka Yoke, Flexibility

and Flexible Manufacturing System (FMS) etc. Out of these, in this paper, the last three approaches

are briefly discussed here.

1. POS Information: Traditionally production planning is carried out based on demand forecast. The quality of planning, in such case, depends upon the forecast. The system is called push system. Under this system, due to changes in market environment or wrong prediction, either surplus inventory is created or shortages and backorders exist. With the help of ICT, it has been made possible to capture the point of sale (POS) information and planning is done based on actual demand. The cash register of the retail outlets is connected to production planning and control (PPC) department. The system is called pull system. Actual customer demand creates trigger for dispatches, dispatch create a trigger for final assembly, final assembly creates a trigger for fabrication of components and which in turn creates trigger for the purchase of raw material and components. In this way, high level of customer response with minimum inventory is achieved.

2. Poka Yoke: Poka Yoke is Japanese word means fool proofing. This is an approach towards zero defects and also helps in reducing inventories. Under this approach, with the help of mechatronics, the processes are designed in such a way that if right conditions of manufacturing are missing, machine will not operate and thus production of defectives is ruled out. Consider a welding operation in a car manufacturing system. In fabrication line at one stage, let us assume twenty components are to be welded to the chassis by spot weld. In this, operator positions 20 components and then allow jig to come down from top and spot weld the components. This

process is carried out at rate of 120 units/hr i.e. cycle time of 30 sec. Think of

situation, worker(s) forget to place one of these components or kept it misaligned, a good

quality car is not possible. Any amount of training or quality control will never be able to

produce 100% good quality. Sensors and actuators are used, which prevent the jig to come

down to weld, unless all the components at right position are placed; thus achieving a

process with zero defects. Lot of such automation is visible in industry as well as in

appliances of daily use.

Poka Yoke is also used to stop the process, when the right designed conditions of the process

are achieved. Here the inspection process is integrated with the operator through closed loop

system.

3. Flexibility and FMS: It has been seen that complexities in manufacturing system leads to the need of flexibilities which in turns leads to the improvements in performance and level of competitiveness. Flexible Manufacturing System (FMS) in any organization can totally change the concept of traditional business unit and if designed and implemented properly, will result in cost effectiveness and greater flexibilities in manufacturing, improved quality, lower unit cost and reduced lead time

The Complexity-Flexibility-Performance analysis (C-F-P) for the design of FMS is as shown in Figure 2.

For C-F-P analysis the first step is identification of the variables which make the manufacturing

system complex. These variables can be from the following categories:-

Product related

Process related

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Customer related

Market related

Supplier related

Logistics related

After identifying these variables, they are described for the conditions of low complexity and high

complexity at five levels. A score of 0 is given for negligible complexity and 1.0 if variable is highly

complex. After having this format, a company can be mapped and its complexity score and

dimensions of complexity can be identified. Based on this diagnosis, a plan for incorporating suitable

dimensions of flexibility can be prepared. The proposed plan can be simulated to see its impact on

the key performance areas of the organization.

Based on simulation analysis and other observations, a decision table is prepared as given in table 1.

The three categories are examined and eight different scenarios are found to exist, these scenarios

decide the type of FMS needed, based on the complexity of each category and the relationship

within each variable. The first scenario is when market is low, technology is low and production is

also low. In this case there

is no need for any FMS. The second scenario is when technology is low, market is also low but

production is high, it is required to limit the automation processes to improve the competitiveness.

The third scenario is when technology is low, market is high and production is low, in this case the

design of

Innovative Technologies

to

Innovative Products

New Materials New Processes Reduce

Uncertainty Automation of

Information, Manufacturing and Infrastructure

Flexibility

Product and process

Customer

Market

Item

Supplier

Logistics

Performance

Cost

Lead time

timeliness

Complexity

Operational level

System level

Market level

Environment

Figure 2: The C-F-P analysis of the design of FMS

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FMS is need based. The fourth scenario is when technology is low and market and production is high,

here since the technology is low we have to go for the various indigenous strategies and technology

management processes to remain competitive.

The fifth scenario is when technology is high, market is low and production is high, in this case it is

seen that the management usually go for full automation in their manufacturing system. In the sixth

and seventh scenario i.e. when technology is high, market is low and production is low; and

technology is high, market is high and production is low, in both these cases the design of FMS is

need based. In the final scenario i.e. when technology, market and production all are high then the

management is required to go for full FMS to survive in the market. The variable Government

policies have an indirect and superficial affinity to all the three categories. If the value of this variable

increases then the decision making process regarding the FMS design will be complex and difficult

and if the value is low then the decision making process will be simple and easy.

Table 1: FMS Design Decision Table

Need of FMS due to

Comment Technology

Condition

Market

Condition

Production

Condition

Low Low Low No need for FMS

Low Low High Limited automation

Low High Low Need base FMS as FMS technology is not

available or expensive

Low High High

Strategies for manufacturing to improve

competition (like JIT, SCM, Kanban, Kaizan,

simulation etc.)

High Low High Full automation and exploitation of easy

availability of flexibility

High Low Low Need base FMS as the market and is very low

High High Low Need base FMS as the production is low and

market is high

High High High

Full FMS as the conditions are very conducive,

the technology, market and production all are

high

XX. CONCLUSION

In this paper, the importance of innovative technologies especially information and computation technology

(ICT) in the context of Supply Chain Management is discussed. The role of ICT is very vital to meet the ever

changing, ever increasing demands of the customers

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REFERENCES

1. Adegoke, O., 2003, Drivers of Volume Flexibility Requirements in Manufacturing Plants, International Journal of Operations and Production Management, Vol. 23, No. 12, pp. 1497-1513.

2. Albino, V. and Garavelli, A.C., 1998, Some Effects of Flexibility and Dependability on Cellular Manufacturing System Performance, Computers in Industrial Engineering, Vol. 35, No.3-4, pp.491-494

3. Arias, D., 2003, Service Operations Strategy, Flexibility and Performance in Engineering Consulting Firms, International Journal of Operations and Production Management, Vol. 23, No. 11, pp. 1401-23.

4. Baldwin, L. P., Eldabi, T. and Paul, R. J., 2005, “Business Process Design: Flexible Modeling with Multiple Levels of Detail”, Business Process Management Journal, Vol. 11 No.1, Pp. 22-36.

5. Bengtsson, J., 2001, Manufacturing Flexibility and Real Options: A review, International Journal of Production Economics, Vol. 74, pp. 213–224.

6. Beyers, W.B. and Lindahl, D.P., 1999, Workplace Flexibilities in the Producer Services, The Service Industries Journal, Vol. 19, No. 1, pp. 35-60.

7. Chang, S.C., Lin, N.P. and Sheu, C., 2002, Aligning Manufacturing Flexibility with Environmental Uncertainty in High-Tech Industry, International Journal of Production Research, Vol. 40, No. 18, pp. 4765-80.

8. Gerwin, D., 1987, An Agenda for Research on the Flexibility of Manufacturing Processes, International Journal of Operations and Production Management, Vol. 7, No. 1, pp. 38-49.

9. Harvey, J., Lefebvre, L.A. and Lefevbre, E., 1997, Flexibility and Technology in Services: A Conceptual Model, International Journal of Operations and Production Management, Vol. 17, No. 1, pp. 29-45.

10. Saygin, P. and Kilic, C., 1999, Integrating Flexible Process Plans with Scheduling in Flexible Manufacturing System, International Journal of Advance Manufacturing Systems, Vol. 15, No. 4, pp. 268–280.

11. Shang, J. and Sueyoshi, T., 1995, A Unified Framework for the Selection of A Flexible Manufacturing System, European Journal of Operational Research, Vol. 85, No. 2, pp. 297-316.

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Abstract-During the last couple of decades, the science of formulation development has undertaken remarkable strides

in the development and successive implementation of diverse types of novel drug delivery systems such as liposomes,

niosomes, microemulsions, organogels, and nanocapsules to resolve the problems of low solubility and low

bioavailability associated with many drugs. These self-organizing systems often lead to improvement in the therapeutic

index of the lipophilic drugs through increased solubilization and modification of their pharmacokinetic profiles. A

microemulsion is defined as a system of water, oil and surfactants, which is a transparent, single optically isotropic and

thermodynamic stable liquid solution. Microemulsions possess unique characteristics; the thermodynamic stability,

supersolvency, small droplet size and the use of food grade, pharmacologically inactive excipients that make them ideal

formulation candidates for delivery of poorly water soluble- low permeability drugs. It is considered that the improved

absorption from microemulsion is due to the incorporation of drug into microemulsion droplets and increased surface

area which results in enhanced contact with biomembranes. We have investigated the promising potential of

microemulsion systems for bioavailability enhancement of some hydrophobic molecules and developed their

formulations. The present paper gives a brief overview of experimental work performed in our laboratories on the

development of microemulsion based formulations of some water insoluble drugs.

Index Terms: Microemulsions, poorly soluble drugs, drug delivery systems, novel drug carriers

1. INTRODUCTION

Ideally a successful pharmaceutical formulation should deliver the active substance to the target

organ at therapeutically relevant levels, with negligible discomfort and side effects to the patient. In

order to achieve this goal lot of research is going on and many new pharmaceutical dosage forms are

under development to deliver physicochemically different molecules.

A microemulsion is defined as a system of water, oil and surfactants, which is a transparent, single

optically isotropic and thermodynamic stable liquid solution [1]. Under certain conditions the oil

droplets can be made so small that they do not refract light, hence form transparent dispersion. This

transparent dispersion is called microemulsion due to its small droplet size (<100 nm).

Microemulsions: Drug Carriers for Delivery of Water Insoluble Drugs

Dr. Shishu, M. Pharm., Ph. D. (Pharmaceutics)

University Institute of Pharmaceutical Sciences, Panjab University, Chandigarh

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Microemulsions are thermodynamically stable which implies that they form spontaneously at certain

concentrations of oil, water and surfactant and the formation is limited only by diffusion of the

molecules.

Depending on the characteristics of the components involved, microemulsions can appear over a

wide range of oil-water-surfactant compositions and the region of existence is typically presented in

pseudo-ternary phase diagrams, as ratios between oil, water and a fixed mixture of surfactant-co-

surfactant (Fig. 1). The primary determinant for the range of microemulsion formation is the

physico-chemical properties of the aqueous phase, oil phase and surfactants. The physico-chemical

interaction between the components is too complex to provide a functional general mathematical

guideline for prediction of microemulsion formation as a function of component properties;

however, a few essential conditions like, the production of a very low interfacial tension at water-oil

interface, formation of highly fluid interfacial surfactant film and the penetration and association of

the molecules of the oil phase with the interfacial surfactant film have been described by Schulman

et al. [2]. The lowering of the interfacial tension and fluidization of the interfacial surfactant film is

usually done by introducing a short chain co-surfactant to the surfactant film.

Structure of Microemulsions

The mixture of oil, water and surfactants is able to form a wide variety of structures and phases.

Besides microemulsions, structural examinations can reveal the existence of regular emulsions,

anisotropic crystalline hexagonal or cubic phases, and lamellar structures depending on the ratio of

the components. Most of these different phases and structures are easily recognized by simple

visual inspection of the compositions due to their physical appearance (e.g., emulsions are

nontransparent and phases separate after a while; lamellar structures and cubic phases are high

viscous) or can be revealed by inspection with polarized light (crystalline phases), and thereby

discerned from actual microemulsions. The microemulsions structure is greatly influenced by the

physico-chemical properties of the components used, and the ratios between the components.

Preparation of Microemulsions

The major advantage microemulsions possess over other colloidal carrier systems is the ease of

preparation. Most microemulsion systems can be spontaneously formed by blending oil, water,

surfactant and cosurfactant with mild agitation. This can be done by using simple equipments at a

minimum cost. The initial method of microemulsion preparation consists of initial coarse emulsion

and then titrating it to the point of clarity by the addition of cosurfactant.

The most common method of preparation consists of dissolution of the surfactants in oil and

subsequently adding the solution to the aqueous phase with gentle shaking. The solution becomes

translucent first and then optically clears in a few seconds. When non-ionic surfactants are

employed, the surfactant may be dissolved in water first.

The order of mixing components is generally considered not to be critical since microemulsions form

spontaneously. However, although microemulsification is a spontaneous process, the driving forces

96 | P a g e

are small and time taken for these systems to reach an equilibrium interfacial tension can be long.

Large transitory fluctuations in interfacial tension can occur during the microemulsion formation, as

the components arrange themselves in such a way that the resulting interfacial tension and bulk

microstructures lead to an overall minimum free energy.

Characterization and Evaluation of Microemulsions

Microemulsions have been characterized using a wide variety of techniques. The characterization of

microemulsions is a difficult task due to their complexity, variety of structure and components

involved in these systems as well as limitations associated with each technique but such knowledge

is essential for their successful commercial exploitation. The characterization methods should be

sensitive to the key parameters of microemulsion performance and should avoid artifacts. Following

characteristics are monitored for the prepared microemulsion systems.

Morphology and structure [3], [4]

Particle size and Zeta potential [5] Nuclear magnetic resonance studies [6] Interfacial Tension, Electrical Conductivity and Viscosity Measurements [7]

Applications of Microemulsions

Microemulsions have been the subject of extensive research over the last two decades primarily

because of their scientific and technological importance. Microemulsions have potential applications

whenever it is necessary to mix oil and water, and when a large interface is required for e.g., in oil

recovery, detergency, agrochemicals, environmental remediation and detoxification, bioseparation

etc. such systems have been used for around 100 years in the chemical industry and currently their

scope has been expanded to include a broad area of pharmaceutical applications.

Pharmaceutical applications

The new research trends reveal that microemulsions are attaining significance in both basic

researches as well as in industry. This can be owed to the unique properties, namely, ultralow

interfacial tension which results in easy formation, large interfacial area, remarkable environmental

and thermodynamic stability and the ability to solubilize otherwise immiscible liquids. Therefore, the

microemulsions are better placed as compared to the other systems like micelles or emulsions which

usually suffer from low solubilization capacity and instability respectively.

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Microemulsions are promising delivery systems to allow sustained or controlled drug release

for percutaneous, peroral, topical, transdermal, ocular and parenteral administration [8-10].

Enhanced absorption of drugs, modulation of the kinetics of the drug release and decreased toxicity

are several advantages in the delivery process.

The dispersed phase, lipophilic or hydrophilic (o/w or w/o type) can act as a potential

reservoir of lipophilic or hydrophilic drugs that can be partitioned between the dispersed and the

continuous phases. The drug can easily cross the semipermeable membrane, such as skin or mucous

membrane, exhibiting easy transport through the barrier [9].

Microemulsions having low viscosity suitably accompanied with suitable protein compatible

surfactants can be used as injection solutions, for they are miscible with blood in any ratio. In

contrast to emulsions, microemulsions cause minimum immune-reactions or fat embolism. Proteins

are not denatured in microemulsions although they are unstable at high or low temperatures.

The total dose of the drug can be reduced when administered/applied as microemulsion and

thus side effects can be minimized. However, the toxicity, bio-incompatibility of some surfactants

and cosurfactants, sometimes requirement of high concentrations of surfactants/cosurfactants for

formulations and other relevant factors such as maintenance of thermodynamic stability in the

temperature range between 0o C and 40o C, salinity, constant pressure during storage, low

solubilizing capacity for high molecular weight drug (and oil), limit the uses of microemulsions in the

pharmaceutical and medicinal fields.

An interesting and specific practical application of o/w microemulsion in the pharmaceutical

industry is the use of strongly hydrophobic fluorocarbons (as oils) to produce short-time blood

plasma substitutes to maintain the supply of oxygen in the living systems. The components to be

used must have low allergic potential, good physiological compatibility and high biocompatibility.

The biocompatibility requirements of the amphiphiles are fulfilled by lecithins, non-ionic surfactants

(Brijs, Arlacel 186, Spans, Tweens and AOT).

The microemulsion drug delivery system has also been explored for delivery of different

types of drugs[9], viz. antineoplastics/antitumour agents (doxorubicin, idarubicin, tetrabenzamidine

derivative), peptide drugs (cyclosporine, insulin, vassopressin), sympatholytics (bupranolol, timolol,

levobunolol, propanolol), local anesthetics (lidocaine, benzocaine, tetracaine, heptacaine), steroids

(testosterone, testosterone propionate, testosterone enanthate, progesterone,

medroxyprogestorane acetate), anxiolytics(benzodiazepines), antiinfective drugs(cloitrimazole,

ciclopirox olamine, econazole nitrate, tetracycline hydrochloride), vitamins (menadione, ascorbic

acid), anti-inflammatory drugs (butibufen, indomethacin), and dermological products (tyrocine,

azelaic acid, octyl dimethyl PABA, 2-ethyl hexyl p-methoxy cinnamate).

Enzyme doped silica nanoparticles (ceramic drug carrier) in the aqueous core of reverse

micelles and microencapsulation of diospyrin, a plant-derived bis-napthoquinol of potential

chemotherapeutic activity is also reported [11].

2. INVESTIGATIONS CARRIED OUT AT U.I.P.S.

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The solubility enhancing and the permeability improvement potential of microemulsion based drug

delivery systems due to very low surface tension and enormous interfacial area due to nanosized

droplets of the microemulsion was explored for development of topical delivery systems of two

poorly soluble antifungal drugs, namely, griseofulvin and itraconazole.

Griseofulvin

Griseofulvin is the treatment of choice for fungal infections of skin and nails due to

Microsporum, Trichophyton, Tinea and Epidermophyton sp. [12], [13]. Griseofulvin is a BCS

Class II drug, practically insoluble in water, therefore shows poor oral bioavailability. The oral

treatment regimen is associated with low patient compliance due to long term treatment and

the systemic side effects such as headaches, gastrointestinal disturbances, blood

dyscrasias, hepatotoxicity and gynaecomastia [14]. Therefore, topical delivery of

griseofulvin may be advantageous as it would result in targeting of drug to affected sites,

minimize systemic side effects and enhance patient compliance.

Keeping in view the above mentioned facts the topical microemulsion (ME) based formulations were

developed using combination of oil, surfactant, cosurfactant and penetration enhancers (PE) and

triple distilled water. These were then evaluated for drug content, pH, globule size distribution,

polydispersity index and zeta potential, viscosity measurement, morphological characterization, ex

vivo permeability through mice skin, skin retention, histopathology, anti-fungal activity and stability.

The results of ex vivo permeation studies as shown in Fig. 2 revealed that 152.24± 2.47 µg/cm2,

164.96± 0.89 µg/cm2 and 173.02± 0.86 µg/cm2 of drug was permeated in 24 h from different ME

formulations whereas only 7.61±0.001 µg/cm2 was released from aqueous suspension and 106.42±

2.4 µg/cm2 from conventional emulsion. Similarly, almost 10 to 24 times increase in rate of

permeation (flux) were observed when compared with control formulation (Table 1). Also skin

retention of griseofulvin was more from ME formulations (Table 1). The results of microbiological

studies against fungal strain Microsporum gypsum (MTCC ACC no. 2830) as presented in Table 2

indicate the effectiveness of prepared ME formulations. Further dermatological safety and nontoxic

property of the formulation was checked by histological studies. These ME formulations were found

to be stable at three different temperatures 4oC, 25oC and 40oC w.r.t. their drug content, feel and

transparency for a period of over five months.

Itraconazole

Itraconazole is a new, orally active, broad-spectrum antifungal agent and is currently marketed

under the brand names Sporanox®, Trisporal® and Sempera®. It is a Class II drug characterized by low

water solubility (nearly 1 ng/ml at neutral pH) and high permeability (Log P>5) [15]. In spite of its

high antifungal activity bioavailability of itraconazole is low due to poor dissolution and the oral

99 | P a g e

route of administration also suffers the problem of large inter individual variations in bioavailability.

Some other disadvantages of itraconazole oral delivery include patient non-compliance as the oral

dose is 2-3 times a day for 3-6 months. Moreover, due to nausea, vomiting, gastrointestinal

disturbances patients find it difficult to continue the treatment. Apart from these minor side effects

some major side effects like hepatotoxicity and cardiac failure are also reported. Therefore, a few

attempts were made for localized delivery of itraconazole for eg., vaginal creams [16], extruded

hydroxypropylcellulose based films [17] and ocular preparations [18].

This study was undertaken with an aim to probe the promising potential of the MEs, to deliver

itraconazole topically in therapeutically effective concentration in treatment of superficial fungal

infections of skin and nails.

The topical MEs were prepared and evaluated for different parameters already mentioned under

griseofulvin ME.

The ex vivo permeation studies revealed that there was no permeation from aqueous suspension of

itraconzole, where as 22.58±0.45 µg/cm2, 46.59± 0.31µg/cm2, 42.73± 0.50 µg/cm2 and 62.69± 3.70

µg/cm2 was permeated from microemulsion I (control-without any PE), microemulsion II (menthol as

PE), microemulsion III (propylene glycol as PE) and microemulsion IV (menthol and propylene glycol

as PE) respectively over a period of 24 h (Fig. 3). The skin retention studies revealed that 0.052±0.03

µg/cm2, 59.53±0.01 µg/cm2, 34.68±0.04 µg/cm2 and 134.97±0.04 µg/cm2 was retained in the mice

skin after the permeation studies from ME I, II, III and IV respectively. The microbiological studies

were performed against two fungal strains: Microsporum gypsum (MTCC Acc no. 2830) and

Aspergillus candidus (MTCC ACC no. 2202) showed that the amount permeated in vitro was

sufficient enough to achieve minimal inhibitory concentration ranging from 0.01 to 1 µg/ml (Table 3

& 4).

The histological studies revealed the safety of the formulation ingredients and the microemulsions

were found to be stable at three different temperatures 4oC, 25oC and 40oC w.r.t. their drug content,

feel and transparency for a period of over two months.

Conclusion: The microemulsion based delivery systems can be effectively and safely used for the

delivery of hydrophobic drugs. These systems are more bioavailable, efficacious, patient compliant

and help in drug targeting.

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REFERENCES

[1]. L. Danielsson, and B. Lindman, “The definition of microemulsion,” Colloid. Surf. 3,

pp. 391-392, 1981.

[2]. J. H. Schulman, W. Stoeckenius, and L. M. Prince, “Mechanism of formation and

structure of microemulsions by electron microscopy,” J. Phys. Chem. 63, pp. 1677-

1680, 1959.

[3]. A. J. Domb, L. Bergelson, and S. Maselem, “Lipospheres for controlled delivery of

substances in: Benita, S. (Ed.), Microencapsulation methods and industrial

applications,” Marcel Dekker Inc., New York, p. 377- 410, 1996.

[4]. N. Garti, and A. Aserin, “Pharmaceutical emulsions, double emulsions and

microemulsions in: Benita, S. (Ed.), Microencapsulation methods and industrial

applications,” Vol. 73. Marcel Dekker Inc., New York, p. 411-534, 1996.

[5]. W. Mehnert, and K. Mader, “Solid lipid nanoparticles- Production, characterization

and application,” Adv. Drug Del. Rev. 47, pp. 165-196, 2001.

[6]. M. Krielgaard, “Dermal pharmacokinetics of microemulsion formulation determined

by in vivo microdialysis,” Pharm. Res. 18, pp. 367, 2001.

[7]. G. Ktistis, “A viscosity study on oil in water microemulsions,” Int. J. Pharm. 60, pp.

213-218, 1990. [8]. P. Kumar, and K. L. Mittal, (eds), Handbook of Microemulsion Science and Technology,

Marcel Dekker Inc., New York, 1999; Malmsten, M., pp. 755–771; Guo, R. and Zhu, X., pp. 483–497; Osseo-Asare, K., pp. 549–603; Candau, F., pp. 679–712; Bunton, C. A. and Romsted, L. S., pp. 457–482.

[9]. C. Solans, and H. Kunieda, (eds), Industrial Applications of Microemulsions, Marcel Dekker Inc., New York, 1997; Tadros, Th. F., p. 199; Dungan, S. R., pp. 147–174; Gasco, M. R., pp. 97–122; Garcia-Celma, M. J., pp. 123–145; Holmberg, K., pp. 69–95.

[10]. D. Attwood, in Colloidal Drug Delivery System (ed. Kreuter, J.), Marcel Dekker, New York, 1994, 31; Aboofazeli, R. and Lawrence, M. J., Int. J. Pharm., 1993, 93, 161.

[11]. T. K. Jain, I. Roy, T. K. De, and A. N. Maitra, J. Am. Chem. Soc., 120, 11092, 1998. [12]. [12] G. Arthur, and K. Night, “The activity of various topical griseofulvin preparations

and the appearance of oral griseofulvin in the stratum corneum,” Br J Dermatol. 91, pp. 49-55, 1974.

[13]. K. S. Post, and J. R, “Topical Treatment of Experimental Ringworm in Guinea Pigs with Griseofulvin in Dimethylsulfoxide,” J. Can. vet. 20, pp. 45-48, 1979.

[14]. [14] W. A. Ritschel, and A. S. Hussain, “In vitro skin penetration of griseofulvin in rat and human skin from an ointment dosage form,” Arzneimittel Forschung. 38, pp. 1622–1630, 1988.

[15]. G. L. Amidon, H. Lennernas, V. P. Shah, J. R. Crison, “A theoretical basis for a biopharmaceutic classification: the correlation of in vitro drug product dissolution and in vivo bioavailability”, Pharm Res. 12, pp. 413-420, 1995.

[16]. M. Francois, E. Snoeckx, P. Putteman, F. Wouters, E. De Proost, U. Delaet, J. Peeters, and M. E. Brewster, “A mucoadhesive, cyclodextrin based vaginal cream formulation of itraconazole”, AAPS Pharm. Sci. 5(1), pp. E5, 2003.

[17]. S. M. Trey, D. A. Wicks, P. K. Mididoddi, and M. A. Repka, “Delivery of itraconazole from extruded HPC films”, Drug Dev Ind Pharm. 33, pp. 727-735, 2007.

[18]. P. K. Agarwal, P. Roy, A. Das, A. Banerjee, P. K. Maity, and A. R. Banerjee, “Efficacy of topical and systemic itraconazole as a broad-spectrum antifungal agent in

mycotic corneal ulcer- A preliminary study,” Ind J Opthalmol. 49, pp. 173-176, 2001.

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Table 1: Comparison of rate of permeation (flux), skin retention from various formulations of

griseofulvin

Formulation Code Flux value (µg/cm2/h) Skin retention (µg/cm2)

Aqueous suspension (Control) - -

Emulsion 3.48±0.03 15.43±0.38

ME I (without enhancer) 9.91±0.41 27.05±1.84

ME II (NMP* as enhancer) 18.38±0.30 3.51±0.57

ME III (Menthol as enhancer) 24.02±0.21 2.68±0.36

* NMP: N-methyl-2-pyrrolidone

Table 2: Zone of inhibition against M. gypsum and average amount of drug diffused for various ME

of griseofulvin

Formulation code Zone of inhibition

(mm)

Average amount of drug

diffused (µg)

ME I 38.73±0.74 35.45±1.32

ME II 37.88±0.25 32.94±1.27

ME III 39.03±0.32 36.39±1.27

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Table 3: Zone of inhibition against M. gypsum and average amount of drug diffused for various ME

of itraconazole

Formulation code Zone of inhibition

(mm)

Average amount of drug

diffused (µg)

ME II 27.50±1.91 47.44±0.38

ME III 29.50±1.29 68.75±1.04

ME IV 29.50±1.00 68.75±0.32

Table 4: Zone of inhibition against A. candidus and average amount of drug diffused for various

ME of itraconazole

Formulation code Zone of inhibition

(mm)

Average amount of drug

diffused (µg)

ME II 35.50±0.57 33.61±0.29

ME III 36.75±1.25 39.85±0.32

ME IV 41.75±1.89 78.78±0.35

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Fig. 1 Ternary phase diagram showing various region and compositions

0

40

80

120

160

200

0 5 10 15 20 25

Time (h)

Me

an c

um

ula

tive

am

ou

nt

pe

rme

ate

d

( g/

cm2)

Dispersion Emulsion ME I ME II ME III

Fig. 2: Comparison of ex vivo permeation profiles from different formulations of griseofulvin

104 | P a g e

0

10

20

30

40

50

60

70

0 5 10 15 20 25 30

Time (h)

Me

an c

um

ula

tive

am

ou

nt

pe

rme

ate

d (

g/cm

2)

ME I ME II ME III ME IV

Fig. 3: Comparison of ex vivo permeation profiles from different microemulsion formulations of

itraconazole

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Health Effects of Outdoor Air Pollution due to

Crop Residue Burning

Ravinder Agarwal

Thapar University, Patiala (India)Abstract - Outdoor air pollution due to agriculture crop residue

burning in the north part of India is a significant public health concern. Burning of agricultural

residue materials increase the suspended particulate matter level and release variety of gas

products into the atmosphere like carbon monoxide, carbon dioxide, volatile chemicals etc. Besides

fully combusted materials, the smoke plume contains particulates of partially combusted materials,

which affects the quality of air we breathe. The primary pollutants of concern are particulate matter.

These emissions have significant potential impact on the health and well being of humans. These

microscopic particles enter into the respiratory system through nasal air filtering system. It is

difficult for the body to dislodge them from the respiratory tract. These fine particles aggravate

chronic heart and lung diseases.

In the current study the impact of agriculture crop residue burning on human health, some of the

pulmonary function tests were carried out using electronic Spirometer. This study brings forward

the state of lung function of the normal selected persons of lower, middle and aged group

category. Pulmonary Function Tests (PFT’s) like FVC, FEV1, PEF, FEF25-75%, etc. showed that

Suspended Particulate Matter (SPM) and Particulate Matter (PM) of microscopic size even affect

the healthy persons. Pulmonary function parameters of lower and upper age groups are more

affected as compared to the middle age group. Results showed that the public exposed to

relatively high levels of pollutants during exhaustive burning period of wheat and rice residue

influence the PFTs of even healthy inhabitants.

Keywords: Crop residue burning, air pollution, Pulmonary Function Tests, Suspended Particulate

Matter.

1. INTRODUCTION

With the advent of mechanized harvesting, farmers have been burning large quantities of crop

residues, particularly in areas with high yield potential. As the crop residues may interfere with

tillage and seeding operations for the next crop, many farmers prefer to burn the residues left in the

field [1-4]. The burning of these residues (which is not at all a sustainable practice) leads many

problems. Air pollution (particularly due to the release of CO2 , nitrous oxide, ammonia and

particulate matter in the atmosphere), which farms environment and contributes to global climate

change. Also, SPM level increases. It is reported that 40 to 80% of the nitrogen in wheat crop residue

106 | P a g e

is lost as ammonia when it is burned in the field. The ash left on the soil surface after burning crop

residues causes nitrogen losses from soil and applied fertilizer. Deterioration of soil physical

properties (crop residue, being an organic material, leads to an improvement in soil structure and

fertility, whereas burning residues leads to a corresponding loss in soil fertility). Residue burning can

have a beneficial short term effect on the nitrogen supply to subsequent crops but has negative long

- term effects on overall N supply and soil carbon levels. Tests indicate that, on an average 90% of

smoke particles from crop residue burning and causes air pollution with PM10 and 70% are PM2.5. It

damages the lung functioning. Moreover, visibility conditions are affected by scattering and

absorption of light by particles and gases. The fine particles most responsible for visibility

impairment are sulfates, nitrates, organic compounds and soil dust. Fine particles are more efficient

per unit mass than coarse particles at scattering light [5-6]. Light scattering efficiencies also go up as

humidity rises, due to water adsorption on fine particles, which allow the particles to grow to sizes

comparable to the wavelength of light.

II. METHODOLOGY

A study was undertaken to find the extent of agriculture crop residue burning on human health by

studying their pulmonary functions using due to increase in pollutions level in ambient air of Patiala

city. Pulmonary Function Tests (PFT’s) like Force Vital Capacity (FVC), Force Expiratory Volume in 1

second (FEV1), Peak Expiratory Flow (PEF), Force Expiratory Flow (FEF25-75%), etc. were measured by

using transportable Spirometer on 51 normal persons of different age group and gender. At the

same time Suspended Particulate Matter (SPM) and Particulate Matter (PM) of microscopic size

were also measured using High Volume Samplers (HVS) and Cascade Impactor respectively to

correlated the PFT parameters to see the effect on healthy on normal persons in Patiala.

III. RESULTS AND DISCUSSION

The effect of change in environment pollution level during crop residue burning period data SPM, PM

and PFTs was studied and analyzed. Two seasons (1 rice crops and 1 wheat crop) SPM levels data and,

one rice season PM10 and PM2.5 sampling data was collected and analyzed with PFTs parameters from

April 2007 to March 2008. Monthly averaged results of SPM indicate a clear contribution of crop

residue burning.

In 2007 during wheat crop residue burning, the levels rose from 170 gm-3 in March to about 370

gm-3 in April. The SPM levels rose from 136 gm-3 to 440 gm-3 during rice crop residue burning

period, thereby indicating a clear contribution from the crop residue burning on the SPM levels as

shown in Fig. 1.

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Fig. 1: Monthly averages of SPM levels in Patiala

Sampling of PM10 particles was done (during August 2007 to January) once in a month during non

burning season and twice in a month during the burning season. Concentration of PM10 and PM2.5

was found to be higher in the month of October and November 2007 as compared to other months

of the year. From Fig. 2 it is clear that concentration of PM10 and PM2.5 was less in August, increases

up to October and then decreases from November 2007 to January 2008. But the values obtained in

November were higher as compared to that in September. Maximum percentages of PM2.5 (58%)

were obtained in the month of October 2007 and in November (52%).

0

20

40

60

80

100

Aug Sep Oct Nov Dec Jan

Sampling Months

Co

ncen

tratr

ion

(u

gm

-3) PM10 PM2.5

Fig. 2: Variation of PM10 & PM2.5 levels in Patiala from August 2007 to January 2008

Respiratory data subjects are categorized into three age groups i.e., lower age group (less than 18

years), middle age group (between 18 to 40 years) and higher age group (greater than 40 years).

Various respiratory parameters FVC, PEF, FEF25-75%, FEF25%, FEF75%, FEF50% and FEV1 / VC were

measured.

1.5

1.75

2

2.25

2.5

2.75

3

3.25

3.5

3.75

4

4.25

Apr May Jun Jul Aug Sep Oct Nov Dec Jan Feb Mar

Month

FVC

(L)

FVC value of lower age group (<18 years)FVC value of middle age group (18 to 40years)FVC value of higher age group (>40)

Fig. 3: Variation in FVC of three age groups

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It is seen that in all the three age groups the value of FVC is less during the crop residue-burning

period and after the crop residue-burning period it shows an increase. Fig. 3 represents the variation of

FVC during April - May 2007, the FVC value for the lower age group is less in comparison to that in

June 2007. After May 2007 the value of FVC increased gradually up to July 2007 and after that very

small decrease is observed up to September. Significant decrease is seen during October - November

2007 and then the value recovers in the December 2007. After December 2007 the value continue to

increase up to March 2008. In the middle age group, almost same trend is noticed i.e. the value shows

a significant decrease in the month of April and October 2007. In higher age group, the values of FVC

in April 2007 were low and then increased slowly up to June 2007. FVC value decreases slightly up to

September 2007. Significant decrease is seen in the month of October 2007. After this its value

recovers in November 2007 and increases up to December 2007. Thereafter, FVC value decreased in

January and remains high up to March 2008. It indicates that the ability to exhale air forcefully by all

the age group decreases in the burning period. Values are lowest in the month of October 2007 being

the rice straw burning period.

1.5

1.7

1.9

2.1

2.3

2.5

2.7

2.9

3.1

3.3

3.5

Apr May Jun Jul Aug Sep Oct Nov Dec Jan Feb Mar

Month

FEV1

(L)

FEV1 value of lower age group (<18 years)FEV1 value of middle age group (18 to 40years)FEV1 value of higher age group (>40)

Fig. 4: Variation in FEV1 of three age groups

Similarly for parameters FEV1, FEF25-75% , FEF50% (Fig 4-6), same trend as in FVC that its value

decreases in burning period months and after this value increases slowly. In all the three age groups,

value of FEV1 in April 2007 is less in comparison to May 2007. In lower age group, values of FEV1

increases in May 2007 and then remain almost same up to July 2007 and then there is a small decrease

in August followed by an increases in September 2007. In October a significant decrease in the value

of FEV1 is seen due to increases in SPM by the rice residue burning. After October 2007 there is an

increase in value of FEV1 in November and then a decrease in December and then increases up to

March 2008. For the middle age group, the value FEV1 is small in April 2007 then there is increase in

its value up to June, after this its value show a small decrease up to September 2007. Significant

decrease is again seen in the month of October in which rice straw burning occurred. After this FEV1

increase in November and then a small decrease in its value is observed in December. After

December, the value increases up to March-2008. For the higher age group, trend is same as that of

lower age group i.e., the value of FEV1 is small in the crop residue burning period and after this its

value recovers .These observation indicate that amount of air expired forcefully in one second

decrease in the burning season and almost same trend is observed in all the three age groups. The

variation in the respiratory parameter in lower and higher age group is large as comparison to that

middle age group.

1

1.25

1.5

1.75

2

2.25

2.5

2.75

3

3.25

3.5

3.75

4

4.25

Apr May Jun Jul Aug Sep Oct Nov Dec Jan Feb Mar

Month

FEF2

5-75

% (L

/s)

FEF25-75% value of lower age group (<18 years)FEF25-75% value of middle age group (18 to 40years)FEF25-75% value of higher age group (>40)

Fig. 5: Variation in FEF25-75% of three age groups

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0

0.5

1

1.5

2

2.5

3

3.5

4

4.5

Apr May Jun Jul Aug Sep Oct Nov Dec Jan Feb Mar

Month

FE

F50

(L

/s)

FEF50% value of lower age group (<18 years)FEF50% value of middle age group (18 to 40years)FEF50% value of higher age group (>40)

Fig. 6: Variation in FEF50% of three age groups

3

3.5

4

4.5

5

5.5

6

6.5

7

7.5

Apr May Jun Jul Aug Sep Oct Nov Dec Jan Feb Mar

Month

PEF

(L/s

)

PEF value of lower age group (<18 years)

PEF value of middle age group (18 to 40years)PEF value of higher age group (>40)

Fig. 7 Variation in PEF of three age groups

In Fig. 6 for PEF almost same trend is noted as in FVC, FEV1 etc.. In PEF, lower age group, value is

less in April 2007 then its value increases up to June 2007, a small decrease in its value in July 2007

and thereafter, PEF increases up to September 2007. A significant decrease is seen in October 2007

which continues in November2007. PEF recovered in December and increases up to January 2008 and

then its value decreases in February 2008 and again increase in March 2008. In middle age group, the

trend is almost same that its value is small in April 2007 and followed by an increase up to June 2007

and then there is a small decrease in its value up to September 2007 but significant decrease in its

value in the month of October 2007 then its value increases up to March 2008. In higher age group the

same trend as in other parameters i.e., its value decreases in the month of April 2007 and in the month

October 2007. It indicates that rate of air flow attained during forced expiration is affected by the

burning period of all the age group. From all observation there is an indication that the values of

almost all parameters show a significant change during the burning period. Respiratory parameters

which are under investigation show generally negative correlation with SPM concentration.

IV Conclusion

Monitoring of physiological parameters like FVC, FEV1, PEF, FEF25-75%, FEF25%, FEF50%, FEF75%, etc.

were carried out from April 2007 to March 2008 by using Spirometers. The FVC values were found

lowest during the crop residue-burning period and then rise in the subsequent months. Similar results

were observed for all the age groups. The FVC values of higher age group during rice crop residue

burning period rose from about 2.4 L in October to 2.7 L in November and December 2007. During

wheat crop period the FVC values rise from 2.7 L in April to 2.9 L in May 2007 in higher age group.

These results indicate a clear-cut impact of crop residue burning on the respiratory system of human

beings. These results are true for all the age groups. Similar inferences can be drawn from other

parameters like PEF, FEF25-75%, FEF25%, FEF50%, FEF75%, FEV1/VC and MVV.

ACKNOWLEDGEMENT

Author is thankful Department of Science & Technology, New Delhi (India) for providing financial

support to carry out research in this area.

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REFERENCES

1. C.E.J Cuijpers, G.M.H. Swaen, G. Wesseling,.; G. Hoek, F. Sturmans, and E.F.M Wouters, Acute respiratory effects of low level summer smog in primary school children, Journal of European Respiratory,vol. 8, pp: 967–975, 1995

2. P.K. Gupta, S. Sahai, N. Singh, C.K. Dixit, D.P. Singh, C. Sharma, M.K. Tiwari, R.K. Gupta and S. C.Garg, Residue burning in rice–wheat cropping system: Causes and implications. Current Science, vol. 87, pp. 1713-1717, 2004

3. J. Kim, D.H. Lim J.K. Kim, S.J. Jeong and B.K. Son, Effects of Particulate Matter (PM10) on the pulmonary function of middle school children. Journal of Korean Medical Science, vol. 20, pp. 42-45, 2005

4. S. Vedal, J. Petkau, R. White and J. Blair, Acute effects of ambient inhalable particles in asthmatic and non-asthmatic children. American Journal of Respiratory and Critical Care Medicine, vol. 157, pp. 1034-1043, 1998

5. S. Yang, H. He, S. Lu D. Chen and J. Zhu, Quantification of crop residue burning in the field and its influence on ambient air quality in Suqian, China. Atmospheric Environment, vol. 42, pp. 1961-1969, 2008

6. Susheel Mittal, Nirankar Singh, Ravinder Agarwal, Amit Awasthi and Prabhat Kumar Gupta, Ambient air quality during wheat and rice crop stubble burning episodes in Patiala Atmospheric Environment, vol. 43 , pp 243 - 244 , 2009

Dr. Ravinder Agarwal did his Ph.D. from National Physical Laboratory, New Delhi in 1991. Dr. Agarwal is

currently Associate Professor in the Department of Electrical and Instrumentation Engineering and Head of

University Science Instrumentation Centre at Thapar University, Patiala. Dr. Agarwal has more than twenty

years of research and teaching experience in the area of biomedical instrumentation. He has published 34

research papers in reviewed international journals of repute and about 100 papers in various national and

international conferences to his credit. His current areas of research include biomedical instrumentation, sensors,

characterization of biological materials, environment monitoring instrumentation etc. He is a Fellow of IETE,

USI, MSI and life member of ISI.

Fig. 2.

Experimental

Apparatus for

Visualization of

droplet flow

Fig. 1. Images of

fabrication-

completed micro

chemical plant

18th

June’2009

19th

June’20

09