1,697 research outputs found
Recent advances in drug delivery technology/ Raj K. Keservani, Anil K. Sharma, and Rajesh Kumar Kesharwani [editors].
No full textIncludes bibliographical references and index."[This book] is a pivotal reference source for the latest scholarly research on the application of pharmaceutical technology to optimize techniques for drug delivery in patients"--Provided by publisher.Personalized approach in nanomedicine: understanding adverse effects and their risk assessment / Maria Vlasova, Boris V. Smirin -- Drug delivery strategies for tolerogenic therapy for autoimmune diseases in an antigen-specific manner / Kevin J. Peine [and 3 others] -- Cancer drug delivery: pharmacogenetics, biomarkers, and targeted therapies / Jai N. Patel, Jeryl Villadolid -- Genomics and proteomic approach in the treatment of various human diseases: applications of genomics and proteomics / Urmila Jarouliya, Raj K. Keservani -- Bioinformatics and its therapeutic applications / Sarvesh Kumar Gupta, Kamal Kumar Chaudhary, Nidhi Mishra -- An overview and therapeutic applications of nutraceutical and functional foods / Raj K. Keservani, Anil K. Sharma, Rajesh K. Kesharwani -- Phytoparmaceuticals and its applications in therapy / Alejandra Hernández-Ceruelos, Sergio Muñoz-Juarez, Patricia Vázquez-Alvarado -- A perspective on the phytopharmaceuticals responsible for the therapeutic applications / Rajesh K. Joshi -- Phytopharmaceutical applications of nutraceutical and functional foods / Dhan Prakash, Charu Gupta -- Cosmeceuticals: safety, efficacy and potential benefits / Long Chiau Ming [and 5 others] -- Cosmeceuticals: camel and other milk -- natural skin maintenance / Reuven Yagil -- Resealed erythrocytes as drug carriers and its therapeutic applications / Prabhakar Singh, Sudhakar Singh, Rajesh Kumar Kesharwani -- New herbal approaches for the treatment of diabetic kidney diseases and its therapeutic implications / Durgavati Yadav [and 3 others].1 online resource (509 pages)
Biocatalytic route to C-3?-azido/-hydroxy-C-4?-spiro-oxetanoribonucleosides
Full text linkThe lipase, Novozyme®-435, exclusively deacetylates the 5-O-acetyl over 4-C-acetyloxymethyl group of almost identical reactivity in 5-O-acetyl-4-C-acetyloxymethyl-3-azido-3-deoxy-1,2-O-isopropylidene-?-D-ribofuranose that led to the development of first and efficient synthesis of 3?-azido-/3?-amino-C-4?-spiro-oxetanoribonucleosides T, U, C and A in 20–24% overall yields. The X-ray study on the compound obtained by tosylation of lipase-mediated monodeacetylated product unambiguously confirmed the point of diastereoselective monodeacetylation on diacetoxy-azido-ribofuranose derivative. The capability of Novozyme®-435 for selective deacylation of 5-O-acetyl group in 5-O-acetyl-4-C-acetyloxymethyl-3-O-benzyl-1,2-O-isopropylidene-?-D-ribofuranose recently discovered by us has been successfully used for the synthesis of C-4?-spiro-oxetanoribonucleosides A and C in good yields. These results clearly indicate that the broader substrate specificity and highly selective capability of Novozyme®-435 for carrying out acetylation/deacetylation reactions can be utilized for the development of environment friendly selective methodologies in organic synthesis
PHYSIOLOGICAL ASPECT OF UPDHATU AND FORMATION OF AARTAVA FROM RASADHATU
No full textDr. Mukesh Saini*, Dr. Rajesh Kumar Sharma and Dr. Dinesh Chandra Sharm
Frontiers in industrial and applied mathematics : FIAM-2021, Punjab, India, December 21–22
No full textThis book publishes select papers presented at the 4th International Conference on Frontiers in Industrial and Applied Mathematics (FIAM-2021), held at the Sant Longowal Institute of Engineering and Technology, Longowal, Punjab, India, from 21–22 December 2021. Most of the papers deal with mathematical theory embedded with its applications to engineering and sciences. This book illustrates numerical simulation of scientific problems and the state-of-the-art research in industrial and applied mathematics, including various computational and modeling techniques with case studies and concrete examples. Graduate students and researchers, who are interested in real applications of mathematics in the areas of computational and theoretical fluid dynamics, solid mechanics, optimization and operations research, numerical analysis, bio-mathematics, fuzzy, control and systems theory, dynamical systems and nonlinear analysis, algebra and approximation theory, will find the book useful
Design and Analysis of Some Software Metrics Using Soft Computing Approaches
No full textPHD, SMCAIntroduction
Software metrics are the integral part of software engineering to quantify activities during the different phases of software development life cycle. The quantification processes consist of the measurements of entities which need to be analyzed during the software development processes. In software engineering, these entities are called software metrics, which are measured in all the phases of software development life cycle. There are defined processes and product metrics which contain various attributes to be measured. Direct measurement is not possible for some software metrics; hence these metrics are derived using a combination of different metrics attributes. For example, size metric is a direct measure and complexity is indirect measure, which depends on a combination of different factors.
Software metrics measurement has been a great research interest area for last four decades. The different conventional statistical methods have been proposed for analysis and measurements of the software attributes. Regression analysis is frequently used method for software attributes analysis. Although conventional methodologies are most widely used in the industries worldwide but recently the research communities are moving towards interdisciplinary streams and have started exploring the unconventional techniques such as fuzzy logic, artificial neural network, neuro-fuzzy etc. There are some metrics attributes, which needs forecasting i.e., the occurrence in future for better process improvement in advance. Some of these metrics are software quality, reliability, maintainability, reusability, defect density and many more. There is no direct measurement possible with the fixed mathematical concept for most of these attributes. So, the term “prediction” comes into consideration in software engineering. The prediction is nothing but the forecasting and quantification of these attributes which are based on the available information during the software development process at any point of time.
The regression analysis has been used from early days for prediction of software metrics. Recently soft computing paradigms attracted the research community and foreseeing a huge potential of research using soft computing techniques in engineering disciplines. Soft computing has been used extensively in engineering and science but a great momentum has been seen in the area of software engineering during the last decade. The reason of this momentum is the predictive characteristics of soft computing approaches. There are good numbers of evidences where soft computing outperforms the conventional techniques in prediction of an attribute or future event quantification.
In the proposed study, soft computing techniques have been used to analyze software quality, maintainability, reliability, reusability, defects of software products and systems. The fuzzy logic, artificial neural network and adaptive neuro fuzzy inference system have been used in proposed study. The different soft computing techniques have different characteristic and hence some pros and cons. In literature survey, this has been observed that the proposed conventional techniques works efficiently in specific domain and in case of some variations, the conventional techniques do not perform upto expectation. The soft computing techniques tried to solve this problem and able to adapt the environment changes. The fuzzy logic is proven tool for decision making problem analysis and artificial neural networks become popular because of its learning and adaptive capability. Neuro-fuzzy combines and inherits the advantage of both.
In the proposed research work, we have used the soft computing techniques to predict the software attributes as per the applicability and importance. The fuzzy toolbox, artificial neural network simulator and adaptive neuro-fuzzy inference system of MATLABTM software is used for experimentation of the proposed soft computing approaches. Although there are plenty of research contents of the prediction of above mentioned software metrics but there is a scope of improvements of the existing approaches in terms of feasibility, efficiency, accuracy, generic framework for all domains, applicability, practical ability etc. All of them may not be feasible to include in a single approach but there can be a balance of these so that industries can adapt in their software engineering processes during the software development life cycle. Most of the soft computing techniques need some history data either to design the model or validate the model, fuzzy logic can be built with less data or even without any data. The research in software engineering using soft computing approaches is still in progress because a specific model or metric works well either in specific domain type project or with particular data set. Here, the conclusion throws out the fact that there is always a need of designing the soft computing based software metrics and model due to these limitations and practicality.
2. Objectives of the Proposed Work
In the objective, focus is to analyze the existing soft computing based software metrics and propose some soft computing based approaches to address the prediction requirements of software defects, reliability, quality, reusability, maintenance severity and maintainability as well as validate them with real project data set to show quantitative analysis. The followings are the set of objectives which are analyzed and explored in this study:
Objective 1: Study and analysis of the existing metrics and approaches.
The existing software metrics and approaches have been analyzed and explored. The conventional, nonconventional approaches and models have been thoroughly studied for software metrics such as quality, defects, reliability, reusability and maintainability. Based on analysis, the future prospects of soft computing techniques have been discussed.
Objective 2: To design some software metrics such as maintainability, defect density, reusability, reliability for component-based systems and other domains.
The soft computing based software metrics and approaches have been proposed for prediction of software defects, reusability, maintenance severity, maintainability and reliability. The proposed approaches are defined for the appropriate software engineering paradigms as per importance and need.
Objective 3: Evaluation of software metrics by using soft computing approaches such as fuzzy logic, artificial neural network and neuro-fuzzy.
All the proposed metrics are simulated using soft computing approaches such as fuzzy logic, artificial neural network and neuro-fuzzy. The evaluation is done for these proposed metrics approach using root mean square error and error performance. The proposed defect and reliability approaches are evaluated for the generic software engineering paradigms. The reusability metrics are evaluated for component based systems. The maintenance severity is also evaluated independent of used methodology in software development. The fuzzy toolbox, artificial neural network simulator and adaptive neuro fuzzy inference system of MATLABTM software is used for experimentation of the proposed approaches.
Objective 4: Validation of proposed metrics.
The empirical validation of the defect and reliability metrics carried out using the real project data from two industrial projects which are based on mixed software engineering methodologies i.e., structural, object and component oriented for a complex telecommunication software system. The proposed reusability software metrics are empirically validated against the component based system project data collected from live project. For reusability, a comparative analysis and validation is done between different soft computing approaches as per applicability. The maintenance severity metric is validated with three project data sets of structural, component and object oriented development.
3. Thesis Outline
Thesis is structured into seven chapters as per following scheme of chapters:
Chapter 1 covers the basic understanding and introduction about the software engineering and soft computing basics. It includes some basic concepts for the need and importance of prediction of software attributes as well as the usefulness of soft computing techniques. It covers brief definitions of software engineering methodologies and the metrics proposed in the current study. It also contains the fundamental concepts of soft computing approaches i.e., fuzzy logic, artificial neural network and neuro-fuzzy techniques. This has been concluded that there is a need of prediction of software metrics, where soft computing can play an important role.
Chapter 2 contains the analysis and review of the existing conventional and nonconventional approaches. The initial section gives the introduction about the basic software metrics and need of soft computing to solve the prediction problems in software metrics. The detailed analysis of conventional metrics is carried out considering the maintainability, defect, reliability, quality, and reusability metrics. Also, the detailed analysis of soft computing approaches such as fuzzy logic technique, artificial neural network technique, neuro-fuzzy techniques and other hybrid methodologies have been carried out for the set of defined metrics. The chapter concludes with the future prospects and possibilities of soft computing intervention in prediction of software metrics and to propose the soft computing based software metrics and model.
Chapter 3 discusses the defect metric using soft computing approach. Existing work in literature regarding defects prediction has been explored in this chapter. As software defect cannot be measured directly, it needs the prediction using some technique. The defect density metric is designed using the combination of software complexity, size and number of defects observed before customer delivery. Here, the defect density metrics is designed considering any type of software engineering methodology but we need to capture the input factor values such as lines of count, complexity and defects count before release. These metrics can be designed considering the history data from any project, which can be applied to other similar projects for defect prediction. Here, we have proposed fuzzy logic and artificial neural network based defect density metric with the three independent factors. The validation is done using the data set of two different projects P1 and P2 from telecom domain, and the better accuracy has been observed through results. Project P1 is an optical telecom application project, which is used as an optical communication platform across the cities. It has been implemented using object oriented based system design as well as structural design and developed in 4 years timeframe. Project P2 is a 4G telecom application project which is mix of all three main software engineering methodologies conventional, object oriented and component based application project. The validation has been performed for proposed approach across various domain projects.
Chapter 4 proposes a unique and new quality and reliability management framework across the subsequent releases of software product. In starting sections, it gives the detailed analysis of the proposed techniques for reliability and quality relationship with defects. The basic reliability growth model is used to design the reliability and quality prediction framework. The proposed reliability and quality model is implemented using fuzzy logic and artificial neural network. The developed approach is validated using three release data from two different projects. Multiple releases of project P1 from optical telecom project are used for validation. Three release of Project P2 from 4G telecom software domain were used for cross validation across different project. The validation and experimentation is done with the different combination of fuzzy logic and artificial neural network architectural attributes. The reliability factor is calculated on the basis of predicted defects for multiple releases and the model is able to predict the defects and reliability with a good accuracy. Using the model across releases, the quality and reliability management framework is proposed which is a new concept across the subsequent releases for large and complex projects. The discussion has been organized for the practical importance and application of the developed framework in the software quality assurance and software process improvements.
Chapter 5 contains the proposed fuzzy logic, artificial neural network, neuro-fuzzy based approach for prediction of component reusability. Six independent factors, which influence most to component reusability, are identified for the software components. These factors are release version, existing defects, portability, interface complexity, customizability and understandability. Several real-life components are used for the training and testing of soft computing techniques. The data for several components is collected from web sources and in-house development. These components include very simple calculator application to complex inventory management system. These components have been developed using different technologies ranging from Java beans, .Net to open source technologies. The validation is done using the components’ data and the quantified results show that adaptive fuzzy inference system performs better than other two soft computing approaches in terms of root mean square error.
Chapter 6 presents the exhaustive analysis of artificial neural network approach to design the software maintenance severity and maintainability metrics independent of component, procedural and object oriented based development. This chapter discusses soft computing based approach for maintenance severity prediction and maintainability prediction of software system modules. In the proposed maintenance severity metrics, artificial neural network approach is used. Six influencing basic metrics for maintenance severity are used as independent factors which are easily available and captured during the software development process. These factors are halstead difficulty, multiple condition count, decision count, cyclomatic complexity, design complexity and lines of count for component based, structural or object oriented software development strategy. In case of maintainability prediction, four basic metrics are considered to formulize the maintainability prediction approach. These four simple metrics are: multiple condition count, node count, percentage comments and lines code which can be easily collected by analyzing the code. The different possible combination of artificial neural network architectures and two learning algorithms are used to get the better results. For the variance in architecture and algorithm, different numbers of neurons nodes ranging from 5 to 25 are selected for the analysis. The artificial neural network approach needs a good amount of input and output data vector sets. In this case, artificial neural network is used, which is based on three projects data set to train and test the proposed metrics. The data of three projects PC5, PC4, PC2 from PROMISE repository of empirical software engineering data is used for validation and training.
The conclusion of the thesis has been presented in Chapter 7, which is summarized form of major contribution of presented work. The direction for future work has also been detailed in this chapter
Multi-Objective Optimization of Software Test Cases Using Evolutionary and Soft Computing Techniques
No full textPHD, SMCASoftware testing occurs concurrently during the software development to identify errors as early as possible and to assure that changes made in software did not affect the system pessimistically. Since software testing is a time consuming, complex, full of uncertainty and expensive activity, delivering the human safety, medical, robotics software without proper testing may lead to potentially much higher cost than that of testing. However, during the development phase, the test suite is updated and tends to increase in size. Due to the resources and time constraints for re-executing large test suites, it is important to develop techniques to reduce the effort of regression testing.
Some challenges for testing software systems are the effort, cost, complexity, ambiguity, test data adequacy involved in optimizing test cases. Optimization of the test cases before testing will surely cut down efforts, cost and improve the quality of software testing. Complexity, risks, cost, fuzziness and multi-criteria test cases fitness evaluation makes test case optimization an essential part of software testing. Several approaches have been proposed for reducing the effort, cost and improve the quality of regression testing such as test case selection, classification, filteration, prioritization, and test suite minimization. Test suite minimization techniques aim at identifying and eliminating redundant, obsolete, unfit, ambiguous test cases from the suite. Test suite minimization techniques identify a subset of test cases from suite, required to re-test the changes in the software. Test case selection is a selection of a subset of test cases from the test suite based on some test criteria. Test case prioritization techniques schedule test cases for execution in an order to increase the early fault detection, but the problem of identifying an optimized test suite with maximum coveragebility in cost efficient manner is the critical problem of software testing.
There are several objectives of test case optimization like maximum number of defect detecting capability, minimum test design efforts/cost, minimum execution cost, maximum coveragebility of codes and client requirements, maximum mutant killing score and so forth. However, most of the test cases optimization approaches are single objective, but single objective formulation of test cases optimization problem is not sufficient and not meeting the objectives of testing. In multi-objective test case optimization, some objectives are conflicting in nature such as coveragebility of one objective will effect other objective while considering all objectives concurrently.
Test cases optimization is NP-Complete, data and knowledge driven, human intensive, incomplete, vague, and multi-dimensional search space partitioning, and reduction problem.
The approaches presented in the literature have various drawbacks and limitations such as resources, time, etc. In this work, multi-objective optimization of test cases with an emphasis on assessment of fitness and ambiguity of test cases on several parameters concurrently, by integrating evolutionary and soft computing approaches is presented. New three-tier framework has been proposed for multi-faceted test cases classification and selection using fuzzy synthesis, fuzzy entropy and ant colony optimization approach to overcome resource limitations.
The experimental study has been done to identify an optimal solution of multi-faceted test cases classification and selection. In this empirical study, we have also measured the efficiency of all three steps and analyzed the size of regression test suite. For validation of proposed framework, simulation of proposed framework is done in MATLAB™ software. Data sets used for experimentation is taken from the Software Infrastructure Repository (SIR).
The results of empirical study clearly show that the precision, recall, F1 score and accuracy of the algorithms increases as we move in next stage. The third stage of proposed framework has highest precision, recall, F1 score and accuracy values among all three stages for all the case studies. Results of experimental study shows that proposed conduit framework reduces the cost and efforts, uncertainty, and the number of test cases in test suite to be exercised. The results of empirical evaluation show that our three-tier framework efficiently finds out an optimal subset of test cases from large pool of test cases. The proposed framework enhances the quality of software testing by reducing the ambiguity, efforts, and size of the test suite
Collected Papers (Papers of Mathematics or Applied Mathematics), Volume V
No full textThis volum includes 37 papers of mathematics or applied mathematics written by the author alone or in collaboration with the following co-authors: Cătălin Barbu, Mihály Bencze, Octavian Cira, Marian Niţu, Ion Pătraşcu, Mircea E. Şelariu, Rajan Alex, Xingsen Li, Tudor Păroiu, Luige Vlădăreanu, Victor Vlădăreanu, Ştefan Vlăduţescu, Yingjie Tian, Mohd Anasri, Lucian Căpitanu, Valeri Kroumov, Kimihiro Okuyama, Gabriela Tonţ, A. A. Adewara, Manoj K. Chaudhary, Mukesh Kumar, Sachin Malik, Alka Mittal, Neetish Sharma, Rakesh K. Shukla, Ashish K. Singh, Jayant Singh, Rajesh Singh, V.V. Singh, Hansraj Yadav, Amit Bhaghel, Dipti Chauhan, V. Christianto, Priti Singh, and Dmitri Rabounski
Evidence for a role of nitric oxide in iron homeostasis in plants
No full textNitric oxide (NO), once regarded as a poisonous air pollutant, is now understood as a regulatory molecule essential for several biological functions in plants. In this review, we summarize NO generation in different plant organs and cellular compartments, and also discuss the role of NO in iron (Fe) homeostasis, particularly in Fe-deficient plants. Fe is one of the most limiting essential nutrient elements for plants. Plants often exhibit Fe deficiency symptoms despite sufficient tissue Fe concentrations. NO appears to not only up-regulate Fe uptake mechanisms but also makes Fe more bioavailable for metabolic functions. NO forms complexes with Fe, which can then be delivered into target cells/tissues. NO generated in plants can alleviate oxidative stress by regulating antioxidant defense processes, probably by improving functional Fe status and by inducing post-translational modifications in the enzymes/proteins involved in antioxidant defense responses. It is hypothesized that NO acts in cooperation with transcription factors such as bHLHs, FIT, and IRO to regulate the expression of enzymes and proteins essential for Fe homeostasis. However, further investigations are needed to disentangle the interaction of NO with intracellular target molecules that leads to enhanced internal Fe availability in plants.RKT is grateful to Department of Science and Technology-Science and Engineering Research Board (DST-SERB) New Delhi, for a Teachers Associateship for Research Excellence (TAR/2019/000064).Tewari, RK (corresponding author), Univ Lucknow, Dept Bot, Lucknow 226007, Uttar Pradesh, India.
[email protected]
Internet and its Impact on the Patient-Physician Relationship Patient Visiting Various Dental Clinics in Northern India
Full text linkINTRODUCTION: Readily available health-related information over the internet has led to increased patient awareness, and this might be a possible factor straining the patient-physician relationship.
AIM: To assess the impact of the internet on the patient-physician relationship amongst patient visiting various dental clinics in Northern India.
MATERIALS AND METHODS: Of the 600 pre-tested online questionnaires distributed, a total of 456 (response rate 76%) adequately filled questionnaires were analysed for the impact of internet on the patient-physician relationship. Responses were subsequently tabulated and analysed using SPSS Version 21.0. Statistical significance was kept as p≤0.05.
RESULTS: A statistically significant difference (p=.04) was seen amongst males and females regarding their internet usage with a higher proportion of health information being seeked by males. Most internet users (66.6%) followed their physician’s advice before they began using the internet with behavioural changes seen mostly in the 18-30 years age group (75.64%), yet only 14.38% of them informing their physician about such changes.
CONCLUSION: It is important that people be advised about the potential risks of believing in sources from the internet with physicians also being advised to spend more quality time with their patients to alleviate them of their fears and doubts
Agent Based Efficient Consistency Control and Replica Management in Data Grids
No full textPh. D. ThesisData Grids provide services and infrastructure for distributed data-intensive applications accessing massive geographically distributed datasets. An important technique to speed up the access to _les in data grids is replication, which create multiple copies of a file. The work presented in this dissertation is focused on the problem area of data grids, mainly on the challenges posed by data replication. The distinct problem areas addressed in this work are efficient management of replica and consistency management. Therefore, to achieve the set objectives of proposed work, an extensive literature review on replica management has been done. The state of the art techniques in the area of replica manageable in data grid have been explored. The comprehensive study of asynchronous replica management in data grids has been carried out to identify their inherent limitations. From the literature survey, it is apparent that the biggest challenge confronting data grid is related to replica creation, placement, selection and consistency maintenance. To address the replica management challenge, an agent based model, namely “Replica Management Model" (RMM), has been proposed and implemented for efficient management of distributed files across the data grid. The model considers various characteristics of data grid such as scalability, availability, and dynamic nature. The model is layered in nature. The proposed model divides the entire functionality across the different layers. The layered architecture bears negligible overhead in terms of communication cost. Evaluation of the proposed hierarchical model is done on two factors: (i) topology and (ii) availability. The performance analysis of the proposed model has been carried out in simulated environment. The comparative analysis of the hierarchical approach with centralized approach has shown that RMM model provide more scalability in the system.
Moreover, increased availability of the files in the system increases the efficiency of the system. An agent based Replica Creation and Placement (RCP) strategy has been proposed and implemented. The RCP strategy aimed at achieving faster data access to files, efficient utilization of network resources and optimal number of replication.
A popularity-driven dynamic RCP strategy for hierarchically structured RMM model balances access latency of the _le and storage space utilization. RCP dynamically adapt to the frequency and degree of replication on the basis of certain parameters such as average access frequency, available storage capacities, replication cost, placement cost etc. Simulation results has shown that the effectiveness
of RCP strategy is dependent on various file access patterns, scheduling strategies and varied number of jobs. The optimal number of replica creation helps in enhancing the usefulness of the system. RCP utilizes the network resources in very efficient manner. Finally, the performance of proposed strategy with number of relevant strategies from the literature has done and demonstrates the effectiveness of the system.
To enhance the utility of RMM model, a replica selection strategy, namely, Efficient Dynamic Replication using Agent (EDRA) has been proposed and implemented. A user submits a job to the grid, the selection of best replica which helps in execution of job is a crucial decision. EDRA strategy helps in finding the best replica from among the pool of replicas. EDRA has been applied at two levels i.e. region and sub-region for selection of best replica. At first level, the region level optimizer allocates jobs to a particular region, whereas at second level sub-region optimizer allocates jobs to particular node. Dynamic mapping has been employed with the help of access frequencies and availability of the files to maximize the hit ratio. The effectiveness of this approach is evaluated in Optorsim with varying jobs and different scenarios. Results observed from comparative analysis with existing replica selection strategies shows the efficiency of the EDRA strategy in term of execution time, storage utilization, computing utilization, network resource usage, hit ratio and transfer time.
Lastly, the Replica Consistency and Conflict Resolution (RCCR) strategy is implemented to handle writeable replicas. A hybrid strategy is used, which take advantage of both pessimistic and optimistic approach. In RCCR strategy, consistencies of the replicas are handled at two levels i.e. local and global level. The local level consistency is attained by using pessimistic approach at region level. At global level optimistic approach is used for obtaining consistency and resolving conflicts among two different networks. Simulation results depicts that RCCR
strategy handles write request in an efficient way compared with other existing approaches
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