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                <text>Circuit Breaker: A Resilience Mechanism for Cloud Native Architecture</text>
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                <text>Circuit Breaker; Cloud Native Applications; Fault Tolerance; Microservice Patterns; Resilience</text>
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                <text>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.</text>
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                <text>Norton Stanley S.A., Christ (Deemed to Be University), Department of Computer Science, Bangalore, India; Sebastian S., Christ (Deemed to Be University), Department of Computer Science, Bangalore, India</text>
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                <text>A Systematic Review on the Identification and Classification of Patterns in Microservices</text>
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                <text>microservice patterns; microservices; pattern classification; pattern identification; software architecture</text>
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                <text>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.</text>
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                <text>Norton Stanley S.A.; Sebastian S.</text>
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                <text>2023 4th International Conference for Emerging Technology, INCET 2023</text>
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                <text>Institute of Electrical and Electronics Engineers Inc.</text>
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                <text>&lt;a href="https://doi.org/10.1109/INCET57972.2023.10170375" target="_blank" rel="noreferrer noopener"&gt;https://doi.org/10.1109/INCET57972.2023.10170375&lt;/a&gt;
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                <text>ISBN: 979-835033575-0</text>
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                <text>Norton Stanley S.A., CHRIST (Deemed to Be University), Department of Computer Science, Bengaluru, India; Sebastian S., CHRIST (Deemed to Be University), Department of Computer Science, Bengaluru, India</text>
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                <text>A Review and Comparative Study on Surface Vehicle Path Planning Algorithm</text>
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                <text>Automatic Identification System (AIS); International Regulations for Preventing Collision at Sea (COLREGS); Unmanned Surface Vehicles (USVs); Vessel Intelligent Collision Avoidance Decisionmaking (VICAD)</text>
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                <text>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.</text>
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                <text>Nunia V.; Poonia R.C.</text>
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                <text>ACM International Conference Proceeding Series, pp. 106-109.</text>
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                <text>&lt;a href="https://doi.org/10.1145/3484824.3484874" target="_blank" rel="noreferrer noopener"&gt;https://doi.org/10.1145/3484824.3484874&lt;/a&gt;
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                <text>Nunia V., Department of Computer Science and Engineering, Amity University, Rajasthan, Jaipur, India; Poonia R.C., Department of Computer Science, CHRIST Deemed to Be University, Karnataka, Bangalore, India</text>
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                <text>Drivers and inhibitors of consumers adoption of AI-driven drone food delivery services</text>
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                <text>Artificial intelligence; Drone; Food delivery; Mixed methods</text>
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                <text>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</text>
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                <text>Nunkoo R.; Pillai R.; Sivathanu B.; Rana N.P.</text>
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                <text>Nunkoo R., Department of Management, University of Mauritius, Reduit, Mauritius, School of Tourism and Hospitality, University of Johannesburg, South Africa, Kyung Hee University, Campus, 26 Kyungheedae-ro, Dongdaemun-gu, South Korea, Griffith Institute for Tourism, Griffith University, Gold Coast, Australia, Copenhagen Business School, Denmark; Pillai R., Department of Management, Pune Institute of Business Management, Maharashtra, Pune, India; Sivathanu B., School of Business and Management, Christ University, Karnataka, Bengaluru, 560074, India; Rana N.P., Queen's Business School, Queen's University Belfast, Riddel Hall, 185 Stranmillis Road, Belfast, BT9 5EE, United Kingdom, Jaipuria Institute of Management Lucknow, Vineet Khand, UP, Lucknow, 226010, India</text>
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                <text>Skewed Food Policies, Distorted Inter-crop Parity, and Nutri-cereal Farmers - An Empirical Analysis</text>
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                <text>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.</text>
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                <text>Economic and Political Weekly, Vol-58, No. 34, pp. 54-63.</text>
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&lt;br /&gt;&lt;br /&gt;&lt;a href="https://www.scopus.com/inward/record.uri?eid=2-s2.0-85171281103&amp;amp;partnerID=40&amp;amp;md5=38ec0c14d87868ee529b733106ef6b6a" target="_blank" rel="noreferrer noopener"&gt;https://www.scopus.com/inward/record.uri?eid=2-s2.0-85171281103&amp;amp;partnerID=40&amp;amp;md5=38ec0c14d87868ee529b733106ef6b6a&lt;/a&gt;</text>
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                <text>Nuthalapati C.S., Cornell University, New Delhi, India; Beero S.K., Christ Deemed University, Delhi NCR Campus, India; Kumar A., International Food Policy Research Institute, South Asia Office, New Delhi, India; Sonkar V., International Food Policy Research Institute, South Asia Office, New Delhi, India; Areef M., Mulla Areef is with the IEG, New Delhi, India</text>
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                <text>Supermarket procurement and farmgate prices in India</text>
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                <text>Hedonic price models; India; Small-scale farmers; Supermarkets; Transaction costs</text>
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                <text>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</text>
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                <text>Nuthalapati C.S.R.; Sutradhar R.; Reardon T.; Qaim M.</text>
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                <text>World Development, Vol-134</text>
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&lt;br /&gt;&lt;br /&gt;&lt;a href="https://www.scopus.com/inward/record.uri?eid=2-s2.0-85086737315&amp;amp;doi=10.1016%2Fj.worlddev.2020.105034&amp;amp;partnerID=40&amp;amp;md5=9d9a760f9ef03869ea7c43cd7f07c87c" target="_blank" rel="noreferrer noopener"&gt;https://www.scopus.com/inward/record.uri?eid=2-s2.0-85086737315&amp;amp;doi=10.1016%2fj.worlddev.2020.105034&amp;amp;partnerID=40&amp;amp;md5=9d9a760f9ef03869ea7c43cd7f07c87c&lt;/a&gt;</text>
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                <text>All Open Access; Hybrid Gold Open Access</text>
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                <text>ISSN: 0305750X</text>
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                <text>Nuthalapati C.S.R., Institute of Economic Growth, University of Delhi Enclave, New Delhi, India; Sutradhar R., Christ University, Bangalore, India; Reardon T., Michigan State University, East Lansing, MI, United States; Qaim M., University of Goettingen, Goettingen, Germany</text>
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              <elementText elementTextId="274581">
                <text>Nwibo, Ezekiel Gabriel; Iwendi, Celestine; Sharma, Vandana; Nwigwe, Simon; Ojo, Olayinka Anthony; Odirichukwu, Jacinta Chioma</text>
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                <text>Prediction of Campus Energy Consumption Patterns Using Machine Learning Techniques</text>
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                <text>2025 12th International Conference on Reliability, Infocom Technologies and Optimization ,Trends and Future Directions, ICRITO 2025;</text>
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                <text>Nwibo E.G., School of Arts and Creative Technologies, University of Greater Manchester, Department of Computing, Bolton, United Kingdom; Iwendi C., School of Arts and Creative Technologies, University of Greater Manchester, Department of Computing, Bolton, United Kingdom; Sharma V., Christ University, Computer Science Department, Bengaluru, India; Nwigwe S., Doctoral College, University of Greater Manchester, Department of Research, Bolton, United Kingdom; Ojo O.A., University of Greater Manchester, Department of Computing, Bolton, United Kingdom; Odirichukwu J.C., Federal University of Technology Owerri, Department of Computer Science, Nigeria</text>
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                <text>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.</text>
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                <text>ARIMA; Artificial Intelligence; Energy Management; Machine Learning; Prophet; Renewable Energy; SARIMA</text>
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                <text>Institute of Electrical and Electronics Engineers Inc.</text>
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                <text>Nwigwe S., University of Greater Manchester, Centre of Intelligence of Things, Bolton, United Kingdom; Iwendi C., University of Greater Manchester, Centre of Intelligence of Things, Bolton, United Kingdom; Sharma V., Christ University, Computer Science Department, Bengaluru, India; Ojo O.A., University of Greater Manchester, Centre of Intelligence of Things, Bolton, United Kingdom; Uwah S.E., University of Greater Manchester, Centre of Intelligence of Things, Bolton, United Kingdom; Nwibo E.G., University of Greater Manchester, Centre of Intelligence of Things, Bolton, United Kingdom</text>
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                <text>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.</text>
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                <text>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)</text>
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                <text>Current Research in Biotechnology, Vol-3, pp. 215-224.</text>
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                <text>Nyika J., Technical University of Kenya, Nairobi, Kenya; Mackolil J., CHRIST (Deemed to be University), Bangalore, India; Workie E., School of Environmental Science and Engineering, Shanghai Jiao Tong University, China; Adhav C., ICAR-National Dairy Research Institute, Haryana, India; Ramadas S., ICAR-Indian Institute of Wheat and Barley Research, Haryana, India</text>
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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.</text>
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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.</text>
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                <text>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.</text>
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                <text>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.</text>
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&lt;br /&gt;&lt;br /&gt;&lt;a href="https://www.scopus.com/inward/record.uri?eid=2-s2.0-85142198148&amp;amp;doi=10.1016%2Fj.jenvp.2022.101887&amp;amp;partnerID=40&amp;amp;md5=ab6eb9f31eaddf74f5ac5e6b1f574298" target="_blank" rel="noreferrer noopener"&gt;https://www.scopus.com/inward/record.uri?eid=2-s2.0-85142198148&amp;amp;doi=10.1016%2fj.jenvp.2022.101887&amp;amp;partnerID=40&amp;amp;md5=ab6eb9f31eaddf74f5ac5e6b1f574298&lt;/a&gt;</text>
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                <text>Ogunbode C.A., School of Psychology, University of Nottingham, United Kingdom; Doran R., Department of Psychosocial Science, Faculty of Psychology, University of Bergen, Norway; Hanss D., Department of Social Sciences, Darmstadt University of Applied Sciences, Germany; Ojala M., School of Law, Psychology and Social Work, ebro University, Sweden; Salmela-Aro K., Department of Educational Sciences, University of Helsinki, Finland; van den Broek K.L., Copernicus Institute of Sustainable Development, Utrecht University, Netherlands, Research Centre for Environmental Economics, Heidelberg University, Germany; Bhullar N., School of Psychology, University of New England, NSW, Australia; Aquino S.D., Psychology Department, Pontifical Catholic University of Rio de Janeiro, Brazil; Marot T., Psychology Department, Pontifical Catholic University of Rio de Janeiro, Brazil; Schermer J.A., Department of Management and Organisational Studies, Faculty of Social Science, University of Western Ontario, Canada; Wlodarczyk A., Escuela de Psicolog, Facultad de Humanidades, Universidad Catica del Norte, Chile; Lu S., School of Applied Social Sciences, De Montfort University, United Kingdom; Jiang F., Department of Human Resources and Organisational Behaviour, University of Greenwich, United Kingdom; Maran D.A., Department of Psychology, University of Torino, Italy; Yadav R., School of Business and Management, Christ University, India; Ardi R., Department of Psychology, Universitas Airlangga, Indonesia; Chegeni R., Department of Psychosocial Science, Faculty of Psychology, University of Bergen, Norway; Ghanbarian E., Department of Educational Psychology and Counselling, University of Tehran, Iran; Zand S., Department of Psychology, University of Milano-Bicocca, Italy; Najafi R., Department of General Psychology, University of Padova, Italy; Park J., Nagoya University of Commerce and Business, Japan; Tsubakita T., Nagoya University of Commerce and Business, Japan; Tan C.-S., Department of Psychology and Counselling, Faculty of Arts and Social Science, Universiti Tunku Abdul Rahman, Malaysia; Chukwuorji J.C., Department of Psychology, University of Nigeria, Nigeria; Ojewumi K.A., Department of Psychology, Obafemi Awolowo University, Nigeria; Tahir H., Department of Psychosocial Science, Faculty of Psychology, University of Bergen, Norway; Albzour M., Department of Social and Behavioural Science, Birzeit University, Palestine; Reyes M.E.S., Department of Psychology, College of Science, University of Santo Tomas, Philippines; Lins S., Faculty of Psychology and Education Sciences, University of Porto, Portugal; Enea V., Department of Psychology, Alexandru Ioan Cuza University of Iasi, Romania; Volkodav T., Faculty of Pedagogy, Psychology and Communicology, Kuban State University, Russian Federation; Sollar T., Department of Psychological Sciences, Faculty of Social Sciences and Healthcare, Constantine the Philosopher University in Nitra, Slovakia; Navarro-Carrillo G., Department of Psychology, University of Ja, Spain; Torres-Mar J., Department of Research Methods in Behavioural Sciences, University of Granada, Spain; Mbungu W., Department of Civil and Water Resources Engineering, School of Engineering and Technology, Sokoine University of Agriculture, Tanzania; Ayanian A.H., Institute for Interdisciplinary Research on Conflict and Violence, Bielefeld University, Germany; Ghorayeb J., Department of Psychology, Zayed University, Dubai, United Arab Emirates; Onyutha C., Department of Civil and Environmental Engineering, Kyambogo University, Uganda; Lomas M.J., School of Health and Society, University of Salford, United Kingdom; Helmy M., Department of Psychology, Sultan Qaboos University, Oman, Psychology Department, Faculty of Arts, Menuofia University, Egypt; Martez-Buelvas L., Universidad Tecnolica de Bolar, Cartagena, Colombia; Bayad A., Institute for Interdisciplinary Research on Conflict and Violence, Bielefeld University, Germany, Van YcYil University, Turkey; Karasu M., Institute for Interdisciplinary Research on Conflict and Violence, Bielefeld University, Germany</text>
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                <text>The rapidly changing quality of irrigation water is a pressing issue that needs to be addressed in order to understand and predict the long-term effects on soils and crops in a world that is facing increasing water stress. The use of irrigation in agriculture is becoming increasingly reliant on sources of water that are poorly understood and largely unmonitored. This trend has led to a decline in water and soil quality in many areas. While soil salinization and reduced crop productivity have traditionally been the main concerns when it comes to the quality of irrigation water, there is now evidence that geogenic contaminants, such as trace elements and an increase in the use of wastewater, are also affecting irrigation water quality. The ability to measure extremely small concentrations of biologically-active organic contaminants, including plasticizers, pharmaceuticals, personal care products, and steroid hormones, in various irrigation water sources allows us to evaluate their uptake and occurrence in crops. However, it does not address questions related to food safety or the potential health effects on humans. Additionally, natural and synthetic nanoparticles are now known to be present in many water sources, which may alter plant growth and impact food standards.   2023 Author(s).</text>
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                <text>Ojha S.S., Department of Civil Engineering, National Institute of Technology Patna, Bihar, 800005, India; Bhushan M., Department of Civil Engineering, GEC Khagaria, Bihar, 848203, India; Sankalp S., Department of Civil Engineering, Centurion University of Technology and Management, Odisha, 761211, India; Sahoo B.B., Department of Agricultural Engineering, Centurion University of Technology and Management, 761211, India; Pradhan A., Department of Civil Engineering, CHRIST (Deemed to Be University), Karnataka, Bangalore, 560074, India</text>
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                <text>Recycling carbon tax for inclusive green growth: A CGE analysis of India</text>
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                <text>In this decade, India has been pursuing a low carbon inclusive growth strategy. However, carbon tax, the most direct price instrument to reduce carbon emissions, has not found favour with policymakers because of its supposed detrimental effects on economic growth and income distribution. In the Indian context, the literature indicates that though carbon tax is extremely effective in abating carbon emissions, it simultaneously leads to reductions in GDP. There is, thus, an undesirable trade-off between economic growth and climate change mitigation. However, in trying to overcome this trade-off through a double-dividend from carbon tax, these studies have not really explored all possible options. Whether the carbon tax will yield a double-dividend or not, will depend upon how the carbon tax revenue is recycled. The present paper fills this gap in the literature on recycling carbon tax for inclusive green growth by exploring the consequences of using carbon tax revenue for investment to build capacity in all sectors or exclusively in the clean energy sectors and to execute transfers to households to improve the distribution of income. This analysis has been done with a recursively dynamic India-specific CGE model having a disaggregated energy sectors and an endogenous income distribution module.  2020 Elsevier Ltd</text>
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                <text>Ojha V.P.; Pohit S.; Ghosh J.</text>
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                <text>Energy Policy, Vol-144</text>
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                <text>ISSN: 3014215; CODEN: ENPYA</text>
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                <text>Ojha V.P., Christ (Deemed to be University), Delhi-NCR, India; Pohit S., National Council of Applied Economic Research, India; Ghosh J., Policy Modelling Association for Inclusive Development (PMAID), Delhi, India</text>
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