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                <text>The book addresses privacy and security issues providing solutions through authentication and authorization mechanisms, blockchain, fog computing, machine learning algorithms, so that machine learning-enabled IoT devices can deliver information concealed in data for fast, computerized responses and enhanced decision-making. The main objective of this book is to motivate healthcare providers to use telemedicine facilities for monitoring patients in urban and rural areas and gather clinical data for further research. To this end, it provides an overview of the Internet of Healthcare Things (IoHT) and discusses one of the major threats posed by it, which is the data security and data privacy of health records. Another major threat is the combination of numerous devices and protocols, precision time, data overloading, etc. In the IoHT, multiple devices are connected and communicate through certain protocols. Therefore, the application of emerging technologies to mitigate these threats and provide secure data communication over the network is discussed. This book also discusses the integration of machine learning with the IoHT for analyzing huge amounts of data for predicting diseases more accurately. Case studies are also given to verify the concepts presented in the book.  2022 John Wiley &amp;amp; Sons Ltd. All rights reserved.</text>
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                <text>Internet of Healthcare Things: Machine Learning for Security and Privacy, pp. 1-304.</text>
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                <text>Sharma K., Department of CSE, Galgotias College of Engineering and Technology, Greater Noida, India; Gigras Y., Department of CSE and IT, The NorthCap University, Gurugram, India; Sharma V., Department of CSE, Galgotias College of Engineering and Technology, Greater Noida, India; Hemanth D.J., Department of ECE, Karunya University, Coimbatore, India; Poonia R.C., CHRIST (Deemed to be University), Bangalore, Karnataka, India</text>
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                <text>Internet of Healthcare Things: Machine Learning for Security and Privacy, pp. xiii-xviii.</text>
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                <text>Sharma K., Department of CSE, Galgotias College of Engineering and Technology, Greater Noida, India; Gigras Y., Department of CSE and IT, The NorthCap University, Gurugram, India; Sharma V., Department of CSE, Galgotias College of Engineering and Technology, Greater Noida, India; Hemanth D.J., Department of ECE, Karunya University, Coimbatore, India; Poonia R.C., CHRIST (Deemed to be University), Bangalore, Karnataka, India</text>
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                <text>Data Mining; Healthcare; Machine Learning classifiers; Prediction Analysis; Symptoms</text>
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                <text>In the unexpectedly evolving panorama of healthcare technology, the mixing of data mining and machine mastering gives exceptional possibilities for the advancement of sickness prediction fashions. This research paper introduces a unique Machine Learning Smart Health Procedure designed to harness the predictive energy of those era for forecasting illnesses. By meticulously reading ancient healthcare facts, which includes affected individual signs and symptoms and effects, this system leverages cutting-edge algorithms which includes Nae Bayes, Support Vector Machines (SVM), and neural networks to expect capacity health problems with accelerated accuracy. This method now not best pursuits to facilitate early and specific evaluation but also strives to noticeably enhance affected individual care and treatment consequences. Through the strategic utility of statistics mining and prediction analysis in the healthcare area, our proposed machine demonstrates the capacity to revolutionize conventional diagnostic techniques, developing a proactive and predictive healthcare model more plausible and effective than ever earlier than.  2024 IEEE.</text>
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                <text>Sharma K.; Madeswaran A.; Chandramauli A.; Narasimha V.; Jasim L.; Reeja Y.M.</text>
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                <text>2024 4th International Conference on Advance Computing and Innovative Technologies in Engineering, ICACITE 2024, pp. 968-972.</text>
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                <text>Institute of Electrical and Electronics Engineers Inc.</text>
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                <text>Sharma K., Chitkara University, Centre of Research Impact and Outreach, Punjab, Rajpura, 140401, India; Madeswaran A., Christ (Deemed to Be University), School of Business and Management, Bangalore Yeshwanthpur campus, India; Chandramauli A., Uttaranchal University, Uttaranchal Institute of Technology, Department of Civil Engineering, Dehradun, 248007, India; Narasimha V., Cmr College of Engineering &amp;amp; Technology, Department of Cse, India; Jasim L., The Islamic University, Najaf, Iraq; Reeja Y.M., Saveetha Institute of Medical and Technical Sciences, Saveetha University, Electronics and Communication Engineering, Saveetha School of Engineering, Chennai, India</text>
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                <text>A novel map matching algorithm for real-time location using low frequency floating trajectory data</text>
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                <text>clusters; confidence level; Euclidean distance; GAGAN; GPS trajectory; Kalman filter; map matching; perpendicular distance; signal frequency</text>
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                <text>The continuous enhancement of technologies and modern well-equipped infrastructures are necessary for easy life. Road accident and missing vehicle ratio are very challenging in preventing misshapenness because these are continually increasing due to traffic hazards. The single way to protect human life from such type of conditions that is more reliable navigation services such as correct location tracking of vehicles on the road network. The real-time location tracking methods fully depends on the map matching algorithms, which also compute a reliable path on the road network. A smart vehicle can provide more reliable tracking services during or before any misshaping using proposed map matching algorithm. This work contributes to ensure correct location for necessary action during misshaping, alert accident zone and communicate messages without wasting valuable time. The proposed approach is validated on the real tracking data and is compared against poor GPS service. Copyright  2023 Inderscience Enterprises Ltd.</text>
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                <text>Sharma K.P.; Poonia R.C.; Sunda S.</text>
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                <text>International Journal of Advanced Intelligence Paradigms, Vol-24, No. 45750, pp. 442-455.</text>
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                <text>Sharma K.P., Amity Institute of Information Technology, Amity University, Rajasthan, Jaipur, India; Poonia R.C., Department of Computer Science, CHRIST (Deemed to be University), Karnataka, Bangalore, 560029, India; Sunda S., Space Applications Centre, Indian Space Research Organisation, Ahmedabad, India</text>
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                <text>INVESTING IN WOMEN, INVESTING IN THE PLANET: QUANTIFYING THE IMPACT OF WOMEN'S EMPOWERMENT ON ENVIRONMENTAL SUSTAINABILITY; [INVESTIR NAS MULHERES, INVESTIR NO PLANETA: QUANTIFICAR O IMPACTO DO EMPODERAMENTO DAS MULHERES NA SUSTENTABILIDADE AMBIENTAL]; [INVERTIR EN LAS MUJERES, INVERTIR EN EL PLANETA: CUANTIFICAR EL IMPACTO DEL EMPODERAMIENTO DE LAS MUJERES EN LA SOSTENIBILIDAD AMBIENTAL]</text>
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                <text>Objective: This study finds out the correlation between the indicators of womens empowerment, including variables like gender parity index in tertiary education, female labour force participation and seats held by women in national parliament, and a variable of environmental sustainability such as CO2 emissions (metric tons per capita). The aim is to analyse existing datasets to know the impact of independent variables on dependent variable. Method: The study uses multiple linear regression to evaluate the effects of independent variables indicators of women's empowerment on the dependent variable, CO2 emissions, using secondary data from the World Bank covering the years 1990 to 2022. The Breusch-Pagan and Breusch-Godfrey LM tests are used to look at heteroskedasticity and autocorrelation, respectively, and VIF is used to find multicollinearity. Results and Conclusion: The study concludes that there is a statistically significant relationship between lower CO2 emissions and increases in the percentage of female seats in the national parliament (-3.73) and higher female labour force participation (-6.04). The gender parity index (GPI) in tertiary education, which is -0.2997, does not, however, appear to have a statistically significant impact on CO2 emissions. Implications: This research can serve as a cause for redesigning gender-responsive environmental initiatives and promoting a more sustainable and equitable future. Originality/Value: This study contributes empirical knowledge to the body of literature by showing the potential contribution of women's empowerment in addressing environmental issues and emphasising the significance of taking gender into account in environmental policy and decision-making processes.  2024 ANPAD - Associacao Nacional de Pos-Graduacao e Pesquisa em Administracao. All rights reserved.</text>
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                <text>Sharma L.; Salineeta; Poddar P.N.; Singhal P.B.</text>
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                <text>Revista de Gestao Social e Ambiental, Vol-18, No. 6</text>
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                <text>Sharma L., Christ University, Bengaluru, India; Salineeta, Christ University, Bengaluru, India; Poddar P.N., SIES (Nerul) College of Art, Science and Commerce, NAVI, Maharashtra, Mumbai, India; Singhal P.B., Graphic Era Hill University, Dehradun, India</text>
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                <text>The Role of ChatGPT to improve teaching and learning in higher education</text>
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                <text>This chapter critically explores the role of ChatGPT and AI in higher education, examining their effects, challenges, and contributions to teaching and learning. It reviews studies highlighting ChatGPT's ability to personalize education, enhance student engagement, and boost research. Yet, it also addresses AI-related challenges like misinformation and dependency issues. The chapter recommends a balanced AI integration, focusing on ethical use, bridging the digital divide, and promoting continuous learning for educators and students. Concluding with future perspectives, it emphasizes AI's role in enriching education while cautioning about its careful application. The chapter offers an insightful analysis of AI's intricate role in higher education and strategies for its responsible integration.  2024, IGI Global.</text>
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                <text>Sharma M.; Gupta S.</text>
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                <text>Reshaping Learning with Next Generation Educational Technologies, pp. 87-98.</text>
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                <text>Sharma M., Christ University, India; Gupta S., Christ University, India</text>
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                <text>I Can Live Without Banks, but Not Without Banking: Role of Trust on Loyalty and Evangelism</text>
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                <text>Consumers'Trust; E-Banking Evangelism; E-Banking Loyalty; India; PLS-SEM; Public Sector Banks; WEBQUAL; Website Quality</text>
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                <text>The purpose of this paper is to examine the antecedents of e-banking loyalty and evangelism via threefold construct of WEQUAL (usability, information quality, and service interaction) of public sector banks operating in India. Moreover, it also investigates the mediating role of consumers' trust on the website quality of these banks and their impact on e-banking loyalty and evangelism. The data was collected from 243 respondents through online questionnaire. In order to develop the model and test the hypotheses, partial least square structural equation modeling (PLS-SEM) was done through Smart PLS version 3.2.9. Results assert that website quality of banks positively influences the trust of consumers via usability, information quality, and service interaction. Also, consumer trust plays a mediation role between WEBQUAL constructs and e-banking loyalty and evangelism.  2021 IGI Global. All rights reserved.</text>
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                <text>International Journal of Electronic Government Research, Vol-17, No. 3, pp. 1-20.</text>
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                <text>&lt;a href="https://doi.org/10.4018/IJEGR.2021070101" target="_blank" rel="noreferrer noopener"&gt;https://doi.org/10.4018/IJEGR.2021070101&lt;/a&gt;
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                <text>Sharma N., Molecular and Nanobiotechnology Laboratory (MNBL), Department of Biochemistry, Saveetha Medical College and Hospital, Saveetha Institute of Medical and Technical Sciences (SIMATS), Thandalam, Chennai, 602105, India; Kumar V., Bioconversion and Tissue Engineering (BITE) Laboratory, Department of Community Medicine, Saveetha Medical College and Hospital, Saveetha Institute of Medical and Technical Sciences (SIMATS), Thandalam, Chennai, 602105, India; S. V., Molecular and Nanobiotechnology Laboratory (MNBL), Department of Biochemistry, Saveetha Medical College and Hospital, Saveetha Institute of Medical and Technical Sciences (SIMATS), Thandalam, Chennai, 602105, India; Umesh M., Department of Life Sciences, CHRIST (Deemed to be University), Karnataka, Bengaluru, 560029, India; Chakraborty P., School of Allied Healthcare and Sciences, Jain (Deemed To Be University), Whitefield, Karnataka, Bangalore, 560066, India; Basheer T., Waste Management Division, Integrated Rural Technology Centre (IRTC), Kerala, Palakkad, 678592, India; Sarojini S., Department of Life Sciences, CHRIST (Deemed to be University), Karnataka, Bengaluru, 560029, India; Sharma P., Department of Biochemistry, Maharshi Dayanand University, Haryana, Rohtak, 124001, India; Pasrija R., Department of Biochemistry, Maharshi Dayanand University, Haryana, Rohtak, 124001, India; Barcelo D., Department of Environmental Chemistry, Institute of Environmental Assessment and Water Research, IDAEA-CSIC, Jordi Girona, 18-26, Barcelona, 08034, Spain, Sustainability Cluster, School of Engineering, UPES, Dehradun, India</text>
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                <text>Sharma N., Department of Biochemistry, Saveetha Medical College and Hospital, Saveetha Institute of Medical and Technical Sciences (SIMATS), Thandalam, Chennai, 602105, India; Kumar V., Bioconversion and Tissue Engineering (BITE) Laboratory, Department of Community Medicine, Saveetha Medical College and Hospital, Saveetha Institute of Medical and Technical Sciences (SIMATS), Thandalam, Chennai, 602105, India; Sugumar V., Department of Biochemistry, Saveetha Medical College and Hospital, Saveetha Institute of Medical and Technical Sciences (SIMATS), Thandalam, Chennai, 602105, India; Umesh M., Department of Life Sciences, CHRIST (Deemed to Be University), Karnataka, Bengaluru, 560029, India; Sondhi S., Haryana State Pollution Control Board, Haryana, Panchkula, India; Chakraborty P., School of Allied Healthcare and Sciences, Jain (Deemed to Be) University, Whitefield, Karnataka, Bangalore, 560066, India; Kaur K., Punjab Agricultural University, Institute of Agriculture, Gurdaspur, Punjab, 143521, India; Thomas J., Department of Biotechnology, Mar Athanasius College, Kerala, India; Kamaraj C., Interdisciplinary Institute of Indian System of Medicine (IIISM), SRM Institute of Science and Technology (SRMIST), Kattankulathur, Tamilnadu, Chennai, 603203, India; Maitra S.S., Metagenomics and Bioprocess Design Laboratory, School of Biotechnology, Jawaharlal Nehru University, New Delhi, India</text>
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                <text>Endocrine disrupting chemicals or carcinogens have been known for decades for their endocrine signal disruption. Endocrine disrupting chemicals are a serious concern and they have been included in the top priority toxicants and persistent organic pollutants. Therefore, researchers have been working for a long time to understand their mechanisms of interaction in different human organs. Several reports are available about the carcinogen potential of these chemicals. The presented review is an endeavor to understand the hazard identification associated with endocrine disrupting carcinogens in relation to the human body. The paper discusses the major endocrine disrupting carcinogens and their potency for carcinogenesis. It discusses human exposure, route of entry, carcinogenicity and mechanisms. In addition, the paper discusses the research gaps and bottlenecks associated with the research. Moreover, it discusses the limitations associated with the analytical techniques for detection of endocrine disrupting carcinogens.  2024 Elsevier B.V.</text>
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                <text>Sharma N., Department of Biochemistry, Saveetha Medical College and Hospital, Saveetha Institute of Medical and Technical Sciences (SIMATS), Chennai, Thandalam, 602105, India; Kumar V., Biomaterials &amp;amp; Tissue Engineering (BITE) Laboratory, Department of Community Medicine, Saveetha Medical College and Hospital, Saveetha Institute of Medical and Technical Sciences (SIMATS), Chennai, Thandalam, 602105, India; Vimal S., Department of Biochemistry, Saveetha Medical College and Hospital, Saveetha Institute of Medical and Technical Sciences (SIMATS), Chennai, Thandalam, 602105, India; Umesh M., Department of Life Sciences, CHRIST (Deemed to be University), Karnataka, Bengaluru, 560029, India; Sharma P., Department of Biochemistry, Maharshi Dayanand University, Haryana, Rohtak, 124001, India; Thazeem B., Waste Management Division, Integrated Rural Technology Centre (IRTC), Kerala, Palakkad, 678592, India; Kaur K., Punjab Agricultural University, Institute of Agriculture, Punjab, Gurdaspur, 143521, India; Thomas J., Department of Biotechnology, Mar Athanasius College, Kerala, India; Pasrija R., Department of Biochemistry, Maharshi Dayanand University, Haryana, Rohtak, 124001, India; Utreja D., Department of Chemistry, Punjab Agricultural University, Punjab, Ludhiana, 141004, India</text>
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                <text>Being socially responsible: How green self-identity and locus of control impact green purchasing intentions?</text>
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                <text>Attribution theory; External environmental locus of control; Green self-identity; IPMA; PLS-SEM; Pro-environmental locus of control</text>
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                <text>This paper investigates the influence of green self-identity (GSI) and two attributes of locus of control, namely external environmental locus of control (ExLOC) and pro-environmental locus of control (PELOC), to predict perceived consumers effectiveness (PCE) on green purchase intentions (GPI) using attribution theory. For this study, data from 391 Indian consumers were analyzed using PLS-SEM via SMARTPLS version 3.2.9. Results show that GSI positively influences both ExLOC and PELOC. Furthermore, both aspects of locus of control are significant positive predictors of PCE and have partial mediation roles. The results not only imply comprehensively expound the process of green buying intentions of consumers through self-identity but also addresses the process of attribution. The study applied the Importance Performance Map Analysis (IPMA) to compare the relative importance and performance of three antecedents (i.e., ELOC, GSI, and PCE). The finding is of utmost importance for practitioners and public authorities to design more focused strategies to increase GPI among the masses to enhance the sales of green products.  2022 The Authors</text>
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                <text>Sharma N.; Lal M.; Goel P.; Sharma A.; Rana N.P.</text>
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                <text>Journal of Cleaner Production, Vol-357</text>
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                <text>ISSN: 9596526; CODEN: JCROE</text>
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                <text>Sharma N., School of Commerce, Christ (Deemed to be University), NCR, Delhi, India; Lal M., Department of Commerce, University of Delhi, India; Goel P., Department of Commerce, Shaheed Bhagat Singh College, University of Delhi, India; Sharma A., Chandragupt Institute of Management Patna, Bihar, Patna, 800001, India; Rana N.P., College of Business and Economics, Qatar University, P.O. Box 2713, Doha, Qatar</text>
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                <text>The impact of eco-innovation ongreen buying behaviour: the moderating effect of emotional loyalty and generation</text>
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              <elementText elementTextId="108716">
                <text>Buying behaviour; Eco-innovation; Emotional loyalty; Environmental concern; Generation; Generativity; Structural equation modelling</text>
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                <text>Purpose: This study intends to contribute to the literature of eco-innovation by examining the pro-environmental intentions and behaviour among consumers through their understanding of eco-innovation. Thus, the relationship among eco-innovation, general pro-social attitude, generativity, environmental concern, purchasing intentions and buying environmentally friendly products and the differences of the relationship between high and low emotional loyalty and Generation Y and Z were investigated via structural equation modelling (SEM). Design/methodology/approach: Data were collected through an online questionnaire directed to Indian consumers, and analysis was done through partial least square structural equation modelling (PLS-SEM) in two stages, i.e. measurement model and structural model. Findings: Results confirm the relationships established in the proposed model, and some differences were found between the levels of emotional loyalty and the Generations Y and Z. The research shows that individualistic norms and perceived marketplace influence play a purposeful role in transforming environmental concerns into buying behaviour towards eco-innovation-driven products. Practical implications: From a policy and management perspective, the results not only imply the importance of continuous performance and environmental improvement but also those policies hindering diffusion and adoption need to be addressed. Green buying is an elusive task but can be opportunely attained by marketers by adding elements of eco-innovations and understanding mindsets of consumers to create winwin situations for themselves and consumers. Originality/value: The results reinforced that emotional loyalty and Generations Y and Z vitally impact consumers' green buying decision within the framework of eco-innovation and cognitive factors.  2022, Emerald Publishing Limited.</text>
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                <text>Sharma N.; Pa A.; Kautish P.</text>
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                <text>Management of Environmental Quality: An International Journal, Vol-33, No. 4, pp. 1026-1045.</text>
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                <text>Emerald Group Holdings Ltd.</text>
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                <text>&lt;a href="https://doi.org/10.1108/MEQ-11-2021-0267" target="_blank" rel="noreferrer noopener"&gt;https://doi.org/10.1108/MEQ-11-2021-0267&lt;/a&gt;
&lt;br /&gt;&lt;br /&gt;&lt;a href="https://www.scopus.com/inward/record.uri?eid=2-s2.0-85126212717&amp;amp;doi=10.1108%2FMEQ-11-2021-0267&amp;amp;partnerID=40&amp;amp;md5=12efef328aa843f07281b38f6e7e6028" target="_blank" rel="noreferrer noopener"&gt;https://www.scopus.com/inward/record.uri?eid=2-s2.0-85126212717&amp;amp;doi=10.1108%2fMEQ-11-2021-0267&amp;amp;partnerID=40&amp;amp;md5=12efef328aa843f07281b38f6e7e6028&lt;/a&gt;</text>
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                <text>ISSN: 14777835</text>
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                <text>Sharma N., School of Commerce, Finance and Accountancy, CHRIST University, Bangalore, India; Pa A., University of Beira Interior, Covilha, Portugal; Kautish P., Department of Marketing, Institute of Management, Nirma University, Ahmedabad, India</text>
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          <element elementId="50">
            <name>Title</name>
            <description>A name given to the resource</description>
            <elementTextContainer>
              <elementText elementTextId="96562">
                <text>Option or necessity: Role of environmental education as transformative change agent</text>
              </elementText>
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          </element>
          <element elementId="49">
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            <description>The topic of the resource</description>
            <elementTextContainer>
              <elementText elementTextId="96563">
                <text>Environmental concern; Environmental education; Pro-environmental behaviour; Structural equation modelling; Sustainability; Transformative learning; University</text>
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              <elementText elementTextId="96564">
                <text>There is a consensus around the importance of environmental education in mitigating the ill effects of environmental problems and preserving the natural environment and promoting green behaviours. The present paper studies the role of environmental education based on transformative learning theory. It intends to present and test a model proposal using sequential mediation analysis of several constructs as the Environmental Education Support (EES) and Volunteer Attitude (VA). A quantitative study was carried out by using data obtained through online questionnaires from several Indian and Brazilian Higher Education Institutions. A multivariate statistical method was employed to analyse the data by using partial least squares structural equation modelling. The results demonstrated that environmental education positively influences students environmental concern, willingness to be environmentally friendly, and volunteer attitude. As a novelty, it reports that environmental education beliefs, concern for the environment and willingness to be environmentally friendly sequentially mediate the relationship between environmental education support and volunteering attitude.  2023 Elsevier Ltd</text>
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              <elementText elementTextId="96565">
                <text>Sharma N.; Pa A.; Upadhyay D.</text>
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              <elementText elementTextId="96566">
                <text>Evaluation and Program Planning, Vol-97</text>
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              <elementText elementTextId="96567">
                <text>Elsevier Ltd</text>
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                <text>2023-01-01</text>
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                <text>&lt;a href="https://doi.org/10.1016/j.evalprogplan.2023.102244" target="_blank" rel="noreferrer noopener"&gt;https://doi.org/10.1016/j.evalprogplan.2023.102244&lt;/a&gt;
&lt;br /&gt;&lt;br /&gt;&lt;a href="https://www.scopus.com/inward/record.uri?eid=2-s2.0-85147814908&amp;amp;doi=10.1016%2Fj.evalprogplan.2023.102244&amp;amp;partnerID=40&amp;amp;md5=cb7afd816ab6220ef493d0c062fb9330" target="_blank" rel="noreferrer noopener"&gt;https://www.scopus.com/inward/record.uri?eid=2-s2.0-85147814908&amp;amp;doi=10.1016%2fj.evalprogplan.2023.102244&amp;amp;partnerID=40&amp;amp;md5=cb7afd816ab6220ef493d0c062fb9330&lt;/a&gt;</text>
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              <elementText elementTextId="96570">
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                <text>ISSN: 1497189; PubMed ID: 36716606; CODEN: EPPLD</text>
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              <elementText elementTextId="96573">
                <text>English</text>
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                <text>Sharma N., International Management Institute New Delhi, India; Pa A., Universidade da Beira Interior, NECE-UBI (Research Centre for Business Sciences), R. Marqu D'ila e Bolama, Covilh 6201-001, Portugal; Upadhyay D., School of Commerce, Finance &amp;amp; Accountancy, CHRIST (Deemed to be University), Bangalore, India</text>
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            <element elementId="50">
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          <element elementId="50">
            <name>Title</name>
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              <elementText elementTextId="122663">
                <text>Relating the role of green self-concepts and identity on green purchasing behaviour: An empirical analysis</text>
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          </element>
          <element elementId="49">
            <name>Subject</name>
            <description>The topic of the resource</description>
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              <elementText elementTextId="122664">
                <text>emerging economies; green purchasing behaviour; green purchasing intentions; green self-concept; product self-concept; self-identity</text>
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                <text>At present, consumers in emerging economies are becoming more conscious about environmental well-being. Therefore, organizations compete to make their products and practices more eco-friendly. Several studies have tried to explain the relationship between green consumerism and an individual's buying behaviour using traditional theories. However, there is quite a challenge in understanding the influence of green self-concept (GSC) and green self-identity (GSI) in predicting the green purchase intention (GPI) of consumers. Therefore, the authors developed six hypotheses to assess the relation between self-concept and the GPI. The survey was conducted, and the responses were evaluated through the partial least square (PLS) method. The authors analysed the measurement model results (n = 717) and the direct and indirect mediating effect of the latent variable contributing to GPI. The measurement model results show that a significant relationship exists in the proposed model, namely, GSCs ? green purchasing intentions, product self-concept (PSC) ? green purchasing intentions and GSI ? green purchasing intentions. Further, the GSI acted as a mediator for the measurement model. The implications of the study can be used to understand the green consumer behavior in developing new strategies and policies for the organizational practice in emerging economies.  2020 ERP Environment and John Wiley &amp;amp; Sons Ltd.</text>
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              <elementText elementTextId="122666">
                <text>Sharma N.; Saha R.; Sreedharan V.R.; Paul J.</text>
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                <text>This project is aimed to solve the problem of flight delay prediction. This problem does not only affect airlines but it can cause multiple problems in different sectors i.e., commercial (Cargo aviation), passenger aviation, etc. There are a number of reasons why flights can be delayed, with weather being the main one. Our goal in this study is to forecast flight delays resulting from a variety of reasons, such as inclement weather, delayed aircraft, and other issues. The dataset gives itemized data on flight appearances and postponements for U.S. air terminals, classified via transporters. The information incorporates metrics such as the number of arriving flights, delays over 15 minutes, cancellation and diversion counts, and the breakdown of delays attributed to carriers, weather, NAS (National Airspace System), security, and late aircraft arrivals. For the purpose of predicting flight delays, the outcomes of several machine learning algorithms are examined, including Ridge, Lasso, Random Forest, Decision Tree, and Linear regression. With the lowest RMSE score of 0.0024, the Random Forest regressor performed the best across all scenarios. A deep learning model using a dense neural network is built to check how accurate a deep learning model will be while predicting the delay and the result was an RMSE score of 0.1357.   2024 IEEE.</text>
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                <text>Sharma N., Christ (Deemed to Be University), Department of Data Science, Maharashtra, Pune, India; Vijayalakshmi S., Christ (Deemed to Be University), Department of Data Science, Maharashtra, Pune, India</text>
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                <text>Enhancing the performance of renewable biogas powered engine employing oxyhydrogen: Optimization with desirability and D-optimal design</text>
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                <text>Biogas; D-optimal; Desirability; Optimization; Oxyhydrogen; Renewable</text>
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                <text>The performance and exhaust characteristics of a dual-fuel compression ignition engine were explored, with biogas as the primary fuel, diesel as the pilot-injected fuel, and oxyhydrogen as the fortifying agent. The trials were carried out with the use of an RSM-based D-optimal design. ANOVA was used to create the relationship functions between input and output. Except for nitrogen oxide emissions, oxyhydrogen fortification increased biogas-diesel engine combustion and decreased carbon-based pollutants. For each result, RSM-ANOVA was utilized to generate mathematical formulations (models). The output of the models was predicted and compared to the observed findings. The prediction models showed robust prediction efficiency (R2 greater than 99.21%). The optimal engine operating parameters were discovered by desirability approach-based optimization to be 24 crank angles before the top dead center, 10.88 kg engine loading, and 1.1 lpm oxyhydrogen flow rate. All outcomes were within 3.75% of the model's predicted output when the optimized parameters were tested experimentally. The current research has the potential to be widely used in compression ignition engine-based transportation systems.  2023 Elsevier Ltd</text>
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                <text>Sharma P.; Balasubramanian D.; Thanh Khai C.; Papla Venugopal I.; Alruqi M.; Josephin JS F.; Sonthalia A.; Geo Varuvel E.; Khalife E.; Ravikumar R.; Wae-Hayee M.</text>
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                <text>Fuel, Vol-341</text>
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                <text>&lt;a href="https://doi.org/10.1016/j.fuel.2023.127575" target="_blank" rel="noreferrer noopener"&gt;https://doi.org/10.1016/j.fuel.2023.127575&lt;/a&gt;
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                <text>ISSN: 162361; CODEN: FUELA</text>
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                <text>Sharma P., Mechanical Engineering Department, Delhi Skill and Entrepreneurship University, Delhi, 110089, India; Balasubramanian D., Department of Mechanical Engineering, Mepco Schlenk Engineering College, Tamilnadu, India; Thanh Khai C., Faculty of Technology, Dong Nai Technology University, Dong Nai, Bien Hoa, Viet Nam; Papla Venugopal I., Department of Mechanical Engineering, Mepco Schlenk Engineering College, Tamilnadu, India; Alruqi M., Department of Mechanical Engineering, College of Engineering, Shaqra University, Al Riyadh, 11911, Saudi Arabia; Josephin JS F., Department of Computer Engineering, Faculty of Engineering and Natural Sciences, Istinye University, Istanbul, Turkey; Sonthalia A., Department of Automobile Engineering, SRM Institute of Science and Technology, NCR Campus, Modi Nagar, 201204, India; Geo Varuvel E., Department of Mechanical Engineering, Faculty of Engineering and Natural Sciences, Istinye University, Istanbul, Turkey; Khalife E., Department of Civil Engineering, Cihan University-Erbil, Kurdistan Region, Iraq; Ravikumar R., Department of Mechanical and Automobile Engineering, CHRIST University, Bengalore, India; Wae-Hayee M., Department of Mechanical and Mechatronics Engineering, Faculty of Engineering, Prince of Songkla University, Songkhla, Hatyai, 90110, Thailand</text>
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                <text>This study aims to get an insight into the intellectual structure, current research themes, and future research directions on digital currency, cryptocurrency, and blockchain. Bibliometric analysis coupled with performance analysis and cluster analysis has been conducted on the digital currency articles, published between the years 2011 and 2023, filtered using PRISMA protocol, and extracted from the Web of Science and Scopus databases. Network analysis was carried out in the Biblioshiny package of R software and VOSviewer. The study highlights that the research of digital currency is classified into four broad categories: "emerging technology", "cryptocurrencies portfolios", "cryptocurrencies as a medium of exchange and an asset class", and "cryptocurrencies and financial risk". The chapter presents an innovative model focusing on productive avenues for future research by synthesizing the latest research articles extracted from the databases, related to digital currency through bibliometric analysis.  2024 by IGI Global. All rights reserved.</text>
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                <text>Sharma P.; Chandel J.K.; Sinha D.; Nagariya R.; Saxena A.</text>
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                <text>Integrating RegTech Solutions for Industry 4.0, pp. 96-135.</text>
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                <text>&lt;a href="https://doi.org/10.4018/979-8-3693-3322-8.ch006" target="_blank" rel="noreferrer noopener"&gt;https://doi.org/10.4018/979-8-3693-3322-8.ch006&lt;/a&gt;
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                <text>Seed, being a truly static period of the plant's existence, is exposed to a variety of biotic and abiotic shocks during dormancy that causes many cellular alterations. To improve its germination and vigor, the seed industry employs a variety of invigoration techniques, which are commonly referred to as seed priming procedures. The treatment with an exogenous H2S donor such as sodium hydrosulfide (NaHS) has been proven to improve seed germination. The H2S molecule is not only a key contributor to the signal transduction pathway meant for the sensation of seed exposure to various biotic and abiotic stresses but also contribute toward the alleviation of different abiotic stress. Although it was initially recognized as a toxic molecule, later its identification as a third gaseous transmitter molecule unveiled its potential role in seed germination, root development, and opening of stomata. Its involvement in cross talks with several other molecules, including plant hormones, also guides numerous physiological responses in the seeds, such as regulation of gene expression and enzymatic activities, which contribute to reliving various biological and non-biological stresses. However, the other metabolic pathways that could be implicated in the dynamics of the germination process when H2S is used are unclear. These pathways possibly may contribute to the seed germinability process with improved performance and stress tolerance. The present review briefly addresses the signaling and physiological impact of H2S in improving seed germination on exposure to various stresses. Graphical abstract: [Figure not available: see fulltext.]  2022, The Author(s), under exclusive licence to Springer Nature B.V.</text>
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                <text>Plant Growth Regulation, Vol-98, No. 3, pp. 401-420.</text>
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                <text>Sharma P., Department of Microbiology, CCS Haryana Agricultural University, Haryana, Hisar, 125004, India; Meyyazhagan A., Department of Life Science, CHRIST (Deemed to Be University), Karnataka, Bengaluru, 560076, India; Easwaran M., Nutritional Improvement of Crops, International Centre for Genetic Engineering and Biotechnology, Aruna Asaf Ali Marg, New Delhi, 110067, India; Sharma M.M.M., Department of Agriculture and Life Industry, Kangwon National University, Gangwon-DO, Chuncheon, 24341, South Korea; Mehta S., School of Agricultural Sciences, K.R. Mangalam University, Haryana, Sohna Rural, 122103, India; Pandey V., School of Comparative Indic Studies &amp;amp; Tribal Science, Kalinga Institute of Social Sciences (KISS-DU), Odisha, Bhubaneswar, 751024, India; Liu W.-C., Department of Animal Science, College of Agriculture, Guangdong Ocean University, Guangdong, Zhanjiang, 524088, China; Kamyab H., Malaysia-Japan International Institute of Technology Universiti Teknologi Malaysia, Jalan Sultan Yahya Petra, Kuala Lumpur, 54100, Malaysia, Department of Biomaterials, Saveetha Dental College and Hospital, Saveetha Institute of Medical and Technical Sciences, Chennai, 600 077, India; Balasubramanian B., Department of Food Science and Biotechnology, College of Life Sciences, Sejong University, Seoul, 05006, South Korea; Baskaran R., Department of Bioinformatics and Medical Engineering, Asia University, Taichung, Taiwan; Kleme J.J., Sustainable Process Integration Laboratory (SPIL), Faculty of Mechanical Engineering, NETME Centre, Brno University of Technology - VUT Brno, Technick2896/2, 616 00, Brno, Czech Republic; Mesbah M., Engineering Department, Razak Faculty of Technology and Informatics, Universiti Teknologi Malaysia, Jln sultan Yahya Petra, Kuala Lumpur, 54100, Malaysia; Chelliapan S., Engineering Department, Razak Faculty of Technology and Informatics, Universiti Teknologi Malaysia, Jln sultan Yahya Petra, Kuala Lumpur, 54100, Malaysia</text>
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