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Effect of internal heat source modulations on the onset of triple diffusive convection in viscoelastic liquids
The paper aims to study the dynamic behavior of a triple diffusive system subjected to sinusoidal (trigonometric cosine) and non-sinusoidal wave forms (square, sawtooth and triangular) of internal heat source modulation. The configuration of the system is such that a layer of viscoelastic liquid is heated and salted with two solutes from below. An Oldroyd-B type model is made use for viscoelastic liquids. In order to regulate the convection onset, internal heat source modulation is applied. This investigation is modelled using a linear stability analysis where a stationary convection is preferred. Venezian approach facilitates a solution by finding the eigen values of the problem. The influence of pertinent parameters which are varied for a wide range of values have been reported. It is captured via graphs that for small values of frequency of modulation, square wave form is more stable while sawtooth wave form is more stable for an increment in the values of frequency of modulation. Further, liquids such as Newtonian, Maxwell and Rivlin-Ericksen are analysed as the limiting cases of the problem. It seems worthwhile to discuss the results of the present study as it is the first work on linear theory of different wave forms of internal heat source modulation and thus paves a way for new theoretical and experimental endeavors. 2021, National Institute of Science Communication and Information Resources. All rights reserved. -
Study of Rayleigh-Benard Dynamical System Involving Newtonian and Nanofluids in Rectangular and Cylindrical Enclosures
Analyzing and#64258;uid and#64258;ow behavior in the presence of temperature gradients subjected to internal and external forces in diand#64256;erent geometries is essential for optimization newlineprocesses for various engineering applications, guiding the design of more efcient thermal systems. This thesis focuses on investigating the Rayleigh-Bard convection problems occupying rectangular and cylindrical enclosures. The linear and newlineweakly nonlinear analyses are carried out that reveal the results on regular convection, heat transport and chaotic motion for each of the problems. Steady and newlineunsteady states of the Rayleigh-Bard system are studied using the Lorenz model. The dynamical system is investigated to look for possible chaotic motion. Fluid systems can exhibit chaotic behavior, and understanding the chaotic nature of these and#64258;ows is essential for accurate predictions of their evolution over time. In view of this, the regular, chaotic, and periodic natures of the dynamical system is thoroughly analyzed. Further, the inand#64258;uence of various parameters on the indicators of chaos is explored. Additionally, the thermal performance of the system is looked into by introducing nanoparticles/nanotubes into the base and#64258;uid. newlineWith the aformentioned motivation, we now present the abstract of each of the problems considered in this thesis one-by-one. 1. Impact of boundary conditions on Rayleigh-Bard convection: stability, heat transfer and chaos In the frst problem of the thesis, discussed in Chapter 3, a comparison is made newlinebetween the results of Rayleigh-Bard convection problem for diand#64256;erent boundary combinations, namely, rigid-rigid-isothermal, rigid-free-isothermal and free-free isothermal boundaries for a Newtonian and#64258;uid. The linear and weakly-nonlinear analyses reveal that the onset of regular and chaotic motions in the case of rigid-freeisothermal boundaries happens later than that of free-free isothermal boundaries but earlier than rigid-rigid-isothermal boundaries.+ -
Revolutionizing Legal Intelligence: Advances in Neural Networks and Language Models for Legal NLP
As the legal field continues to generate vast amounts of complex text, from contracts to court rulings, machine learning and natural language processing (NLP) techniques have emerged as valuable tools to help analyze and organize this data. In this paper, a number of state-of-the-art models will be reviewed and evaluated, including transformer models like BERT, GPT, and T5, and neural network models such as LSTM and CNN-RNN hybrids. These were then tested for the legal tasks of document classification, text summarization, and entity recognition. Some of the metrics used for evaluation include Accuracy, F1-Score, ROUGE, and BLEU. Advanced models, in particular large language models (LLMs), outperform the traditional methods by a large margin since they capture the niceties of legal language and structure much more completely. Meanwhile, high-quality legal datasets remain scarce, legalese remains incomprehensible to most, and the models are still relatively unexplainable. In sum, these challenges clearly call for future research in terms of data augmentation, explainable AI techniques, and more robust training methods that would allow AI-powered tools to be integrated much more effectively within lawyers' workflows to support them in their decision-making processes. 2025 IEEE. -
Elastic circuit de-constructor: a pattern to enhance resiliency in microservices
Cloud-based workloads have proliferated with the deep penetration of the internet. Microservices based handling of high volume transactions and data have become extremely popular owing to their scalability and elasticity. The major challenge that cloud-based microservice patterns face is predicting dynamic load and failure patterns, which affect resiliency and uptime. Existing Circuit breaker patterns are biased toward denying incoming requests to maintain acceptable latency values, at the cost of availability. This paper proposes the Elastic Circuit De-Constructor (ECD) pattern to address these gaps. The proposed ECD pattern addresses this challenge by dynamically adapting to changing workloads and adjusting circuit-breaking thresholds based on real-time performance metrics. The proposed ECD pattern introduces a novel De-constructed state, that allows the ECD to identify alternate paths pre-defined by the application, ensuring user requests continue to be routed to the microservice. By leveraging Availability, Latency and Error rate as performance metrics, the ECD pattern is able to balance the fault tolerance and resiliency imperatives in the cloud-based microservices environment. The performance of the proposed ECD pattern has been verified against both no Circuit Breaker and a default Circuit Breaker setting. 2024 Informa UK Limited, trading as Taylor & Francis Group. -
Circuit Breaker: A Resilience Mechanism for Cloud Native Architecture
Over the past decade, the utilization of cloud native applications has gained significant prominence, leading many organizations to swiftly transition towards developing software applications that leverage the powerful, accessible, and efficient cloud infrastructure. As these applications are deployed in distributed environments, there arises a need for reliable mechanisms to ensure their availability and dependability. Among these mechanisms, the circuit breaker pattern has emerged as a crucial element in constructing resilient and trustworthy cloud native applications in recent times. This research article presents a comprehensive review and analysis of circuit breaker patterns and their role within cloud-native applications. The study delves into various aspects of circuit breakers, encompassing their design, implementation, and recommended practices for their utilization in cloud native applications. Additionally, the article examines and compares different circuit breaker libraries available for employment in modern software development. The paper also presents a concept for improving the circuit breaker pattern, which will be pursued in our upcoming research. 2023 IEEE. -
A Systematic Review on the Identification and Classification of Patterns in Microservices
Determining patterns in monolithic systems to help improve the overall system development and maintenance has become quite commonplace. However, recognizing the patterns that have emerged (or are emerging) in cloud computing - especially with respect to microservices, is challenging. Although numerous patterns have been proposed through extensive research and implementation, the quality assessment tools that are currently available fall short when it comes to accurately recognizing patterns in microservices. It has been identified that a completely autonomous tool for the identification and classification of patterns in microservices has not been developed so far. Moreover, classification of services is an approach that has not been considered by researchers that are working in this field. This paper aims to perform a detailed systematic literature review that can help to explore the various possibilities of identifying and classifying the patterns in microservices. The article also briefly lists out a set of tools that is used in the industry for the implementation of patterns in microservices. 2023 IEEE. -
A Review and Comparative Study on Surface Vehicle Path Planning Algorithm
Autonomous Surface Vehicles (ASV) is very active area of robotics. There are so many projects are going on and doing research on monitoring and surveying on environment. There are significant studies on AS V's reverie, sea and coastal environments. Many algorithms are used by different researchers for path planning or route planning. Programmed recreation projects of boat route can be a useful asset for operational arranging and Layout investigations of conduits. In such a recreation framework the key undertakings of self-ruling course finding, and impact evasion are done by a reproduction program itself without or minimum interaction of a human pilot. That is from numerous points of view like programmed route frameworks in that they are intended to do self-governing route securely and proficiently without the requirement for Human intercession or to offer exhortation to the guide in regard to the best game-plan to take in certain circumstances. There are two key errands of programmed transport route frameworks: course finding and Collision evasion. 2021 ACM. -
Drivers and inhibitors of consumers adoption of AI-driven drone food delivery services
This study sheds light on the determinants of consumers adoption of artificial intelligence-driven drone food delivery service (AI-driven DFDS) using a mixed-methods approach. Interviews with hospitality industry professionals revealed several drivers and inhibitors of AI-driven DFDS adoption. Using these findings, we developed a theoretical model AI-driven DFDS adoption based on the premise of the behavioral reasoning theory and innovation resistance theory. The model was tested using data collected from 1240 consumers. The results suggest that drones relative advantage, perceived ubiquity, social influence, and green image positively influence attitudes and adoption. Risk, usage, and experience barriers have an adverse influence on attitudes and adoption. Consumers openness to new technology has a positive influence on reasons for using AI-driven DFDS. The research makes an important theoretical contribution to research on the adoption of AI-driven DFDS. The study also provides important practical implications for marketers and industry professionals. 2024 Elsevier Ltd -
Skewed Food Policies, Distorted Inter-crop Parity, and Nutri-cereal Farmers - An Empirical Analysis
Farmer profitability, cost of food production, and associated issues of nutri-cereals are analysed by leveraging a large database spanning a 35-year period. The skewed food policies being followed in India are highlighted here. An unacceptably high distortion in inter-crop parity was found, which led to loss of profitability, increased costs, and lower prices for the nutri-cereals. The policymakers must take corrective measures in several aspects, including technologies, prices, input provision, processing, storage, and distributional policies to promote the production and consumption of nutri-cereals in India. 2023 Economic and Political Weekly. All rights reserved. -
Supermarket procurement and farmgate prices in India
Supermarkets have gained in importance in the food systems of many developing countries, with profound implications for smallholder farmers. Several studies analyzed effects of selling to supermarkets on smallholder productivity and income. However, no previous work systematically analyzed effects of supermarkets on farmgate prices, even though prices are important determinants of farmers profits and livelihoods. Here, we use data from smallholder vegetable growers in India to compare output prices received in supermarket and traditional market channels. We also quantify farmers transport and transaction costs in both channels. Even after controlling for quality differences, prices are significantly higher in supermarket channels. Positive price effects are confirmed through hedonic price models and propensity score matching. Average effects of supermarkets on farmgate prices are in a magnitude of 20% or more. Higher farmgate prices are due to fewer intermediaries and lower transaction costs in supermarket channels. In the absence of binding contracts, supermarkets also need to pay higher prices to ensure regular supply of high-quality vegetables. These results suggest that the rise of supermarkets can contribute to increased market efficiency with positive effects on farmgate prices and revenues. 2020 The Authors -
Prediction of Campus Energy Consumption Patterns Using Machine Learning Techniques
The exponential increase in campus energy consumption results from the rise in population density, leading to urbanisation and the use of higher energy-intensive devices within the environment. This study explored high-performance data analytics techniques to visualise energy consumption across buildings using datasets obtained from a load audit of the entire distribution network within the Federal University of Technology, Owerri (FUTO). Advanced time series models were used to predict and forecast the consumption patterns for a year. Visualisations for this research provided detailed insights into the energy profile across all the clusters, while the SARIMA, ARIMA, and Prophet models predicted the energy demands. The heatmap for the correlation matrix reveals a constant energy scale throughout the week (weekend average energy usage is at least 40% of the weekday). A comparative performance was done to analyse the scalability and predictive abilities of the individual models. Results from the study indicate that SARIMA has the lowest mean square error (4.4896) and the highest R2 score (0.8362). The study concludes that the adoption of machine learning models for energy forecasting and prediction is vital for modern-day energy management in the University. 2025 IEEE. -
Comparative Analysis of Machine Learning Models for Uterine Cancer Prediction Using Clinical and Genomic Data
Uterine cancer prediction accuracy is important in clinical decision-making because it improves the overall chances of patient recovery. Several machine learning models, such as Decision Tree, Random Forest, XGBoost Regressor, and Support Vector Regressor, were explored to determine which is more effective in predicting uterine cancer. Attributes such as mutation counts, diagnosis age, and MSI score, were used for the analysis. The different models were tested using the standard performance metrics such as the Mean Absolute Error (MAE), Root Mean Squared Error (RMSE), and R2 Score. Random Forest showed the highest predictive performance with an R2 score of 0.655, followed by XGBoost regressor, which was relatively close to the R2 score of Random Forest. Support Vector Regressor performed very poorly as the R2 score was negative, implying that the model is not suitable for such prediction. Ensemble-based models, which include Random Forest and XGBoost Regressor, have proven to be more effective in handling medical prediction tasks, and this is because of their robustness and their ability when it comes to handle overfitting. Though model generalizability was affected due to small data size and the absence of hyperparameter tuning. The future work will focus on expanding the dataset, implementing hyperparameter tuning, integrating deep learning, and leveraging explainable AI (XAI). The research has provided valuable insight for clinicians who wish to use machine learning for uterine cancer prognosis. 2025 IEEE. -
Cellular agriculture research progress and prospects: Insights from bibliometric analysis
World agriculture is facing a daunting task to feed the burgeoning population against multiple production and environmental threats. The alarming growth in population vis-vis current food production is expected to increase the global food insecurity levels. Inter alia, cellular agriculture an incipient technology is being considered as a potential alternative to cater for the growing demand for food and nutrition. The technology aims to develop edible agricultural products including meat with reduced environmental footprint against conventional farm production. In this context, an attempt has been made to review the progress of cellular agriculture research in four decades (19812020) through a bibliometric analysis and to suggest a roadmap for future research. The study sourced data from the Web of Science during October 2020. Using keywords, the database showed 212 searches pertaining to cellular agriculture from 135 journals worldwide. Of the journals, seven had at least five published articles and 33 had two articles each. Subsequently, the bibliographic coupling among the identified journals was carried out. It is found that the Journals: Appetite, Meat Science, and Journal of Agricultural and Environmental Ethics had the largest circles corresponding to their respective number of publications coupled with notable linkages with other journals. Also, a detailed analysis was performed on categories, growth trend, keywords, institutions, regions and leading researchers of cellular agriculture. The findings indicate that the Appetite Journal followed by the Journal of Agricultural and Environmental Ethics had published a significant percentage of articles on cellular agriculture, and Environmental Science and Technology was identified as the highly cited journal. The USA, England and the Netherlands were identified as the progressive regions in cellular agriculture research. The bibliometric analysis points to sluggish progress in cellular agriculture research and production despite its potential benefits. Future research should focus on the cost-effectiveness of the technology, consumer willingness to buy, development of food safety protocols on its merit and regional policy governance coupled with popularising its paybacks in the context of ensuring food security. 2021 The Author(s) -
IOT based intelligent traffic management system /
Patent Number: 202131062286, Applicant: Bikas Mondal.
As the population grows and there are more cars on the roads, a large number of people may visit a site. It may be difficult for those who work there to keep track of everyone who comes in. Cloud-based interconnection between vehicles will benefit traffic cops by allowing them to monitor traffic and flow patterns without having to get out and do anything. Customers with valid driver's licences can only use a vehicle with an automatic ignition that is solely based on biometrics. -
Photonic crystal based piston type micro pressure sensor /
Patent Number: 201741047023, Applicant: Dr. Preeta Sharan.
A pressure sensor comprising, A two piston shaped slab structure placed invertedly, this two piston are ket such that there is physical gap between them. Further the micro pressure is applied from top and bottom end. -
A novel moems sensor design simulation and analysis with MEEP /
International Journal Of Engineering Technology Science And Research, Vol.2, Issue 8, pp.319-325, ISSN No: 2394-3386. -
Online Health Information Behavior: A study based on PLS-SEM
In this digital era, internet provides a speedy, economical and convenient platform for seeking information on health. Moreover, the presence of audio visual resources for health and option to get expert opinion directly makes online health information seeking behaviour more adaptable among the health consumers. The major purpose of this study is to investigate the relationship between online health information seeking behaviour and the consequences of post-search. For doing the analysis, Smart PLS2 is used to execute structural equation modelling technique to understand the relationship between variables under study. The results of the study recommend that one's intention to search health information online is a significant predictor of post-search behavior in terms of altering health condition, visiting physician or sharing the same information with others. The present study gives a strong indication to the health care practitioners to understand the mechanism of desires and intentions of a healthcare consumer towards online health information seeking behavior. 2021 IEEE. -
Cooperation affects NGO staff performance patterns
In order to optimise employee productivity and overall profitability, non-profits must invest heavily in their human resources. Contrarily, the focus of this study will be on the value of cooperation and the strategies the non-governmental organisation (NGO) should use to improve the performance of the bank as a whole. Once the data have been collected using quantitative and qualitative techniques, SPSS descriptive statistics will be utilised to maintain the findings and support the research hypothesis. According to the study, qualities like trust, camaraderie, job happiness, and benefits directly impact employees productivity at the bank. The degree of teamwork among co-workers directly affects how productive an employee is. Using the statistical program SPSS, managers and staff of NGOs were surveyed; the results revealed a favourable correlation between employee performance and NGO cooperation. When employees cooperate at work, their productivity increases, and the efficacy of the organisations they work for rises. Good news for charitable organisations. Because of this, the collaborative NGO outperforms the non-collaborative NGO in terms of productivity. It was found that better communication results in greater cooperation amongst NGOs. Copyright 2023 Inderscience Enterprises Ltd. -
Evaluating the Role of Economic Factors in Sustainable Consumption Behaviour
The research paper investigates the intricate nexus between economic factors and sustainable consumption behaviour, providing a nuanced exploration of how such considerations shape an individuals choice in the realm of environmental responsibility. Drawing on established theories of consumer behaviour and sustainability, this study employs a positivist approach encompassing quantitative surveys and analysis. It scrutinises the effects of prices, expectations and other broader economic conditions on the adoption of sustainable practices. The findings contribute significantly to the existing body of knowledge by providing a holistic understanding of the economic levers instrumental in driving towards sustainable consumption like taste and preferences and price or hindering the shift towards sustainable consumption like societal behaviour and availability of alternatives. This research aims to inform policymakers, businesses and consumers alike, facilitating the development of targeted interventions and initiatives that foster a harmonious convergence of economic and ecological goals. 2024 IOS Press BV. All rights reserved.