Recommendation System using Clustering and Comparing Clustering and Topic Modelling Techniques
clusters; content-filtering; filtering; Recommendation systems; topic modelling
In this paper, we have used a technique called clustering to recommend the products to the customer and also tried to compare clustering and Topic modelling to find out which technique is better for our purpose. From all the papers that have been reviewed, we observed that the greater part of the proposal approaches applied content-based filtering (55%). Collaborative-based filtering was applied by just 18% of the looked into approaches, and hybrid based by 16%. Other suggestion ideas included generalizing, thing driven proposals, and crossover suggestions. The content-based filtering approaches overwhelmingly utilized papers that the clients had made, marked, examined, or downloaded [1]. To begin with, it stays muddled which suggestion ideas and approaches are the most encouraging. For instance, analysts demonstrated different results on the presentation of content based and collaborative filtering. A portion of the time content-based filtering performed better contrasted with collaborative filtering sand a portion of the time it performed all the more regrettable. 2022 IEEE.
2022 2nd International Conference on Advance Computing and Innovative Technologies in Engineering, ICACITE 2022, pp. 2006-2011.
Institute of Electrical and Electronics Engineers Inc.
Sharma E., Christ University, Data Science Department, Pune, India; Jos J., Christ University, Data Science Department, Pune, India
Growth trajectories for executive and social cognitive abilities in an Indian population sample: Impact of demographic and psychosocial determinants
Cognitive development; Emotion recognition; Executive functions; Growth trajectory; Quantile regression; Social cognition
Cognitive abilities are markers of brain development and psychopathology. Abilities, across executive, and social domains need better characterization over development, including factors that influence developmental change. This study is based on the cVEDA [Consortium on Vulnerability to Externalizing Disorders and Addictions] study, an Indian population based developmental cohort. Verbal working memory, visuo-spatial working memory, response inhibition, set-shifting, and social cognition (faux pas recognition and emotion recognition) were cross-sectionally assessed in > 8000 individuals over the ages 623 years. There was adequate representation across sex, urban-rural background, psychosocial risk (psychopathology, childhood adversity and wealth index, i.e. socio-economic status). Quantile regression was used to model developmental change. Age-based trajectories were generated, along with examination of the impact of determinants (sex, childhood adversity, and wealth index). Development in both executive and social cognitive abilities continued into adulthood. Maturation and stabilization occurred in increasing order of complexity, from working memory to inhibitory control to cognitive flexibility. Age related change was more pronounced for low quantiles in response inhibition (??4 versus =2 for higher quantiles), but for higher quantiles in set-shifting (? > ?1 versus ?0.25 for lower quantiles). Wealth index had the largest influence on developmental change across cognitive abilities. Sex differences were prominent in response inhibition, set-shifting and emotion recognition. Childhood adversity had a negative influence on cognitive development. These findings add to the limited literature on patterns and determinants of cognitive development. They have implications for understanding developmental vulnerabilities in young persons, and the need for providing conducive socio-economic environments. 2023 Elsevier B.V.
Sharma E.; Ravi G.S.; Kumar K.; Thennarasu K.; Heron J.; Hickman M.; Vaidya N.; Holla B.; Rangaswamy M.; Mehta U.M.; Krishna M.; Chakrabarti A.; Basu D.; Nanjayya S.B.; Singh R.L.; Lourembam R.; Kumaran K.; Kuriyan R.; Kurpad S.S.; Kartik K.; Kalyanram K.; Desrivieres S.; Barker G.; Orfanos D.P.; Toledano M.; Purushottam M.; Bharath R.D.; Murthy P.; Jain S.; Schumann G.; Benegal V.
https://doi.org/10.1016/j.ajp.2023.103475 https://www.scopus.com/inward/record.uri?eid=2-s2.0-85147231129&doi=10.1016%2fj.ajp.2023.103475&partnerID=40&md5=9be5e435d7cab30b5a3a0103886494c7
Sharma E., Department of Child and Adolescent Psychiatry, National Institute of Mental Health and Neurosciences, Bengaluru, India; Ravi G.S., Department of Health Data Science, University of Liverpool, United Kingdom; Kumar K., Department of Mental Health and Clinical Psychology, National Institute of Mental Health and Neurosciences, Bengaluru, India; Thennarasu K., Department of Biostatistics, National Institute of Mental Health and Neurosciences, Bengaluru, India; Heron J., Population Health Sciences, Bristol Medical School, University of Bristol, United Kingdom; Hickman M., Population Health Sciences, Bristol Medical School, University of Bristol, United Kingdom; Vaidya N., PONS Centre, CharitMental Health, Department of Psychiatry and Psychotherapy, CharitUniversitsmedizin Berlin, Germany; Holla B., Department of Integrative Medicine, National Institute of Mental Health and Neurosciences, Bengaluru, India; Rangaswamy M., Department of Psychology, CHRIST (Deemed to be University), Bengaluru, India; Mehta U.M., Department of Psychiatry, National Institute of Mental Health & Neurosciences, Bangalore, India; Krishna M., Foundation for Research and Advocacy in Mental Health, Mysuru, India; Chakrabarti A., ICMR-Centre on Non-Communicable Diseases, Kolkata, India; Basu D., Department of Psychiatry, Post Graduate Institute of Medical Education and Research, Chandigarh, India; Nanjayya S.B., Department of Psychiatry, Post Graduate Institute of Medical Education and Research, Chandigarh, India; Singh R.L., Department of Psychiatry, Regional Institute of Medical Sciences, Imphal, India; Lourembam R., Department of Psychology, Regional Institute of Medical Sciences, Imphal, India; Kumaran K., MRC Lifecourse Epidemiology Unit, University of Southampton, United Kingdom & Epidemiology Research Unit, CSI Holdsworth Memorial Hospital, Mysuru, India; Kuriyan R., Division of Nutrition, St John's Research Institute, Bengaluru, India; Kurpad S.S., Department of Psychiatry & Department of Medical Ethics, St. John's Medical College & Hospital, Bengaluru, India; Kartik K., Rishi Valley Rural Health Centre, Madanapalle, Chittoor, India; Kalyanram K., Rishi Valley Rural Health Centre, Madanapalle, Chittoor, India; Desrivieres S., Centre for Population Neuroscience and Precision Medicine, Institute of Psychology, Psychiatry & Neuroscience, MRC SGDP Centre, King's College London, United Kingdom; Barker G., Department of Neuroimaging, Institute of Psychology, Psychiatry & Neuroscience, King's College London, United Kingdom; Orfanos D.P., NeuroSpin, CEA, UniversitParis-Saclay, Paris, France; Toledano M., MRC Centre for Environment and Health, School of Public Health, Imperial College, London, United Kingdom; Purushottam M., Molecular Genetics Laboratory, National Institute of Mental Health and Neurosciences, Bengaluru, India; Bharath R.D., Department of Neuroimaging and Interventional Radiology, National Institute of Mental Health and Neurosciences, Bengaluru, India; Murthy P., Department of Psychiatry, National Institute of Mental Health and Neurosciences, Bengaluru, India; Jain S., Department of Psychiatry, National Institute of Mental Health and Neurosciences, Bengaluru, India; Schumann G., PONS Centre, CharitMental Health, Department of Psychiatry and Psychotherapy, CharitUniversitsmedizin Berlin, Germany, Centre for Population Neuroscience and Precision Medicine (PONS), Institute for Science and Technology of Brain-inspired Intelligence (ISTBI), Fudan University, Shanghai, China; Benegal V., Centre for Addiction Medicine, Department of Psychiatry, National Institute of Mental Health and Neurosciences, Bengaluru, India
AI and Machine Learning Applications in Predicting Energy Market Prices and Trends
AI; energy prices; forecasting; machine learning
The worldwide energy market is intricate and unstable, shaped by several aspects including geopolitical occurrences, supply-demand variations, and regulatory modifications. Precisely forecasting energy prices and trends is essential for stakeholders, such as energy producers, dealers, and policymakers. This study investigates the utilization of artificial intelligence (AI) and machine learning (ML) to improve energy price forecasting models. Conventional forecasting methods frequently fail to account for the dynamic and non-linear characteristics of energy markets; however, AI/ML techniques, including neural networks, decision trees, and reinforcement learning, provide enhanced prediction precision. By including external variables such as meteorological conditions and economic metrics, AI models can produce more accurate and useful insights. Case studies illustrate the effective implementation of AI in energy markets, showcasing its capacity to surpass traditional methods. This article addresses difficulties such as data quality and computing expenses while delineating potential developments in AI-driven energy market forecasts. The Authors, published by EDP Sciences.
Sharma G.; Dhore M.L.; Jansirani D.; Jeshurun S.B.; Sathi G.; Sherje N.; Vivek V.
https://doi.org/10.1051/e3sconf/202459101002 https://www.scopus.com/inward/record.uri?eid=2-s2.0-85211823746&doi=10.1051%2fe3sconf%2f202459101002&partnerID=40&md5=4d1b251c6e717004c761bbc278ba6bda
Sharma G., Institute of Business Management, GLA University, Mathura, India; Dhore M.L., Department of Computer Engineering, Vishwakarma Institute of Technology, Pune, India; Jansirani D., Department of IT, New Prince Shri Bhavani College of Engineering and Technology, Tamil nadu, Chennai, 600073, India; Jeshurun S.B., School of Business and Management, CHRIST University, Bangaluru, India; Sathi G., Department of MECH, Prince Shri Venkateshwara Padmavathy Engineering College, Chennai, 127, India; Sherje N., Dr. D. Y. Patil Institute of Technology, Pimpri, Pune, India; Vivek V., Department of Computer Science and Engineering, AAA College of Engineering and Technology, Tamil Nadu, Sivakasi, 626005, India
Sharma H.; Saraswat M.; Kumar S.; Bansal J.C.
Lecture Notes on Data Engineering and Communications Technologies, Vol-61, pp. v-vi.
Springer Science and Business Media Deutschland GmbH
Sharma H., Department of Computer Science and Engineering, Rajasthan Technical University, Kota, Rajasthan, India; Saraswat M., Department of Computer Science & Engineering and Information Technology, Jaypee Institute of Information Technology, Noida, Uttar Pradesh, India; Kumar S., Department of Computer Science and Engineering, CHRIST (Deemed to be University), Bangalore, Karnataka, India; Bansal J.C., Department of Mathematics, South Asian University, New Delhi, Delhi, India
Application and challenges of optimization in Internet of Things (IoT)
Nature-Inspired Optimization Algorithms and Soft Computing: Methods, Technology and Applications for IoTs, Smart Cities, Healthcare and Industrial Automation, pp. 147-172.
Institution of Engineering and Technology
Sharma H., Sheffield Hallam University, United Kingdom; Srivastava S., Christ (Deemed to be University), Delhi, India; Gupta V., Christ (Deemed to be University), Delhi, India
Breeding distrust during artificial intelligence (AI) era: howtechnological advancements, jobinsecurity and job stress fuel organizational cynicism?
Job insecurity; Job involvement; Job stress; Layoff; Organizational cynicism; Psychological capital; Technological advancements
Purpose: This study examines how technological advancements and psychological capital contribute to job stress. Furthermore, the paper examines how job insecurity, job stress and job involvement influence the cynicism of recently laid-off employees. Despite various research studies, there is a lack of understanding of employees views on their work future and its probable influence on their job behaviors in this era of technology. Design/methodology/approach: A quantitative method was used to collect a sample of 403 recently laid-off employees. The research tool of this study was a questionnaire, and the sampling technique was stratified random sampling. IBM SPSS and AMOS software were utilized to ensure the trustworthiness and accuracy of constructs via factor analysis. The proposed hypotheses were tested using structural equation modeling. Findings: The analysis showed that technological advancements, specifically in job-related stress, job involvement and job insecurity, significantly affect organizational cynicism. Job involvement is negatively associated with employees cynicism. Practical implications: The current study adds to the comprehension of shifts in the perceived behavior of employees toward their organizations due to factors like the adoption of new technology in the organization, job stress, job insecurity and job involvement. Accordingly, there will be a need to form a favorable working atmosphere so that employees can perform their jobs with positive psychology and without any insecurity or stress. Originality/value: The study is thought to contribute to the literature in terms of measuring organizational cynicism while layoffs continue due to AI advancements. 2024, Emerald Publishing Limited.
Sharma K.; Davidson B.G.J.; George J.P.; Muttungal P.V.
https://doi.org/10.1108/EBHRM-05-2024-0159 https://www.scopus.com/inward/record.uri?eid=2-s2.0-85208940841&doi=10.1108%2fEBHRM-05-2024-0159&partnerID=40&md5=cfea09b4cf9eb519699c5e88c51355f4
Sharma K., School of Business and Management, Christ University, Bangalore, India; Davidson B.G.J., School of Business, University of the Fraser Valley, Abbotsford, Canada, Department of Marketing, Strategy, and Entrepreneurship, University Canada West, Vancouver, Canada, Department of Business Administration, Acsenda School of Management, Vancouver, Canada; George J.P., School of Business and Management, Christ University, Bangalore, India; Muttungal P.V., Department of Psychology, Christ University, Bangalore, India
Internet of healthcare things: Machine learning for security and privacy
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 & Sons Ltd. All rights reserved.
Sharma K.; Gigras Y.; Sharma V.; Hemanth D.J.; Poonia R.C.
Internet of Healthcare Things: Machine Learning for Security and Privacy, pp. 1-304.
https://doi.org/10.1002/9781119792468 https://www.scopus.com/inward/record.uri?eid=2-s2.0-85147901582&doi=10.1002%2f9781119792468&partnerID=40&md5=3d88019e9cd72ae98f31ab3fb4ba7e8a
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
Internet of Healthcare Things: Machine Learning for Security and Privacy, pp. xiii-xviii.
https://doi.org/10.1002/9781119792468.fmatter https://www.scopus.com/inward/record.uri?eid=2-s2.0-85147917780&doi=10.1002%2f9781119792468.fmatter&partnerID=40&md5=a0d0b0fd25033e1903aa8c6d4f2578ae
A Deep Assessment of ML Based Procedure used as a Classifiers in the Clinical Field
Data Mining; Healthcare; Machine Learning classifiers; Prediction Analysis; Symptoms
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.
Sharma K.; Madeswaran A.; Chandramauli A.; Narasimha V.; Jasim L.; Reeja Y.M.
2024 4th International Conference on Advance Computing and Innovative Technologies in Engineering, ICACITE 2024, pp. 968-972.
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 & 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
A novel map matching algorithm for real-time location using low frequency floating trajectory data
clusters; confidence level; Euclidean distance; GAGAN; GPS trajectory; Kalman filter; map matching; perpendicular distance; signal frequency
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.
International Journal of Advanced Intelligence Paradigms, Vol-24, No. 45750, pp. 442-455.
https://doi.org/10.1504/IJAIP.2023.129188 https://www.scopus.com/inward/record.uri?eid=2-s2.0-85153758955&doi=10.1504%2fIJAIP.2023.129188&partnerID=40&md5=36461fbcaa00d6e784ba341e31df7368
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
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]
Co2 Emissions; Environmental Sustainability; Gender Parity Index; Labour Force Participation; Women's Empowerment
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.
Sharma L.; Salineeta; Poddar P.N.; Singhal P.B.
Revista de Gestao Social e Ambiental, Vol-18, No. 6
ANPAD - Associacao Nacional de Pos-Graduacao e Pesquisa em Administracao
All Open Access; Hybrid Gold Open Access
https://doi.org/10.24857/RGSA.V18N6-096 https://www.scopus.com/inward/record.uri?eid=2-s2.0-85193513840&doi=10.24857%2fRGSA.V18N6-096&partnerID=40&md5=e2cf6c9f7613c632a7e7b0b1313adfe0
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
The Role of ChatGPT to improve teaching and learning in higher education
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.
Reshaping Learning with Next Generation Educational Technologies, pp. 87-98.
https://doi.org/10.4018/979-8-3693-1310-7.ch006 https://www.scopus.com/inward/record.uri?eid=2-s2.0-85196252484&doi=10.4018%2f979-8-3693-1310-7.ch006&partnerID=40&md5=1a183e8d5fd4ea39adfdaf05ffbafeca
Sharma M., Christ University, India; Gupta S., Christ University, India
I Can Live Without Banks, but Not Without Banking: Role of Trust on Loyalty and Evangelism
Consumers'Trust; E-Banking Evangelism; E-Banking Loyalty; India; PLS-SEM; Public Sector Banks; WEBQUAL; Website Quality
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.
International Journal of Electronic Government Research, Vol-17, No. 3, pp. 1-20.
https://doi.org/10.4018/IJEGR.2021070101 https://www.scopus.com/inward/record.uri?eid=2-s2.0-85110515086&doi=10.4018%2fIJEGR.2021070101&partnerID=40&md5=33ece48fd0561a224c7b9d55bba0b43a
Sharma N., Christ University, Deemed, India; Goel P., Shaheed Bhagat Singh College, University of Delhi, India; Sharma A., Chandragupt Institute of Management, Patna, India
Microplastic residues in clinical samples: A retrospection on sources, entry routes, detection methods and human toxicity
Analytical methods; Clinical samples; Human organ; Human samples; Microplastics; Toxicity
Microplastics (MPs) are emerging toxicants which have been detected in varying environments. Despite MPs adverse effects, reports on MPs detection from human clinical samples are only a few. This is due to several reasons such as inefficiency of current MPs detection techniques to detect them from human clinical samples, lack of understanding about the MPs toxicity to human organs and ethical regulations that restricts study with human placental exposure to MPs. This review gives a comprehensive outlook on the major sources MPs sources and routes into human system and their human toxicity mechanisms. Further an in-depth discussion on the significance and limitations of various MPs detection methods is elaborated in the review. Challenges in current research framework for detection of MPs from human clinical samples and the possible future directions in this imperative research domain are also focused in this review. 2024 Elsevier B.V.
Sharma N.; Kumar V.; S. V.; Umesh M.; Chakraborty P.; Basheer T.; Sarojini S.; Sharma P.; Pasrija R.; Barcelo D.
TrAC - Trends in Analytical Chemistry, Vol-173
https://doi.org/10.1016/j.trac.2024.117618 https://www.scopus.com/inward/record.uri?eid=2-s2.0-85187793038&doi=10.1016%2fj.trac.2024.117618&partnerID=40&md5=f5208ba0029d82d163610dda29632441
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
A comprehensive review on the need for integrated strategies and process modifications for per- and polyfluoroalkyl substances (PFAS) removal: Current insights and future prospects
PFAS degradation; PFAS removal; PFAS removal strategies; PFAS toxicity; PFAS transport
Alarming concern over the persistence and toxicity of per- and polyfluoroalkyl substances (PFAS) in the environment has created an imperative need for designing and redesigning strategies for their detection and remediation. Conventional PFAS removal technologies that uses physical, chemical, or biological methods. Increase in the diversity and quantity of PFAS entering the environment has necessitated the need for developing more advanced and integrated strategies for their removal. Despite of the advances reported in this domain, there exist a huge research gap that need to be mentored to tackle the problems associated with mitigation of combined toxicity of wide variety of PFAS in the environment. The possibility of PFAS to combine with other emerging contaminants poses an additional threat to the existing treatment methods thereby stressing the need for a continuous monitoring and updating the treatment processes. This review work aims at understanding the structure, entry, and fate of different types of PFAS in to the environment. Further an in-depth discussion regarding the different levels of toxicity associated with PFAS is elaborated in the review. The process description of recent PFAS remediation techniques along with their significance, limitations and possibility of integration is discussed in detail. Further a detailed outlook on the advantages and limitations of PFAS removal methods and an insight into the recently developed PFAS removal methods is outlined in this review. 2024 The Authors
Sharma N.; Kumar V.; Sugumar V.; Umesh M.; Sondhi S.; Chakraborty P.; Kaur K.; Thomas J.; Kamaraj C.; Maitra S.S.
Case Studies in Chemical and Environmental Engineering, Vol-9
https://doi.org/10.1016/j.cscee.2024.100623 https://www.scopus.com/inward/record.uri?eid=2-s2.0-85182878208&doi=10.1016%2fj.cscee.2024.100623&partnerID=40&md5=2dfb842c05f9d530babe6395d37a0537
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
Hazard identification of endocrine-disrupting carcinogens (EDCs) in relation to cancers in humans
Carcinogenesis; Carcinogens; Endocrine disruption; Hazard identification; Toxicity assessment
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.
Sharma N.; Kumar V.; Vimal S.; Umesh M.; Sharma P.; Thazeem B.; Kaur K.; Thomas J.; Pasrija R.; Utreja D.
Environmental Toxicology and Pharmacology, Vol-109
ISSN: 13826689; PubMed ID: 38825092; CODEN: ETOPF
https://doi.org/10.1016/j.etap.2024.104480 https://www.scopus.com/inward/record.uri?eid=2-s2.0-85195076333&doi=10.1016%2fj.etap.2024.104480&partnerID=40&md5=06f3f95fa387f9cc22da8fc267761620
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 & 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
Being socially responsible: How green self-identity and locus of control impact green purchasing intentions?
Attribution theory; External environmental locus of control; Green self-identity; IPMA; PLS-SEM; Pro-environmental locus of control
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
Sharma N.; Lal M.; Goel P.; Sharma A.; Rana N.P.
https://doi.org/10.1016/j.jclepro.2022.131895 https://www.scopus.com/inward/record.uri?eid=2-s2.0-85129530761&doi=10.1016%2fj.jclepro.2022.131895&partnerID=40&md5=04172efefdec9c516c97c1f1a79b398a
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
The impact of eco-innovation ongreen buying behaviour: the moderating effect of emotional loyalty and generation
Buying behaviour; Eco-innovation; Emotional loyalty; Environmental concern; Generation; Generativity; Structural equation modelling
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.
Management of Environmental Quality: An International Journal, Vol-33, No. 4, pp. 1026-1045.
https://doi.org/10.1108/MEQ-11-2021-0267 https://www.scopus.com/inward/record.uri?eid=2-s2.0-85126212717&doi=10.1108%2fMEQ-11-2021-0267&partnerID=40&md5=12efef328aa843f07281b38f6e7e6028
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
Option or necessity: Role of environmental education as transformative change agent
Environmental concern; Environmental education; Pro-environmental behaviour; Structural equation modelling; Sustainability; Transformative learning; University
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
ISSN: 1497189; PubMed ID: 36716606; CODEN: EPPLD
https://doi.org/10.1016/j.evalprogplan.2023.102244 https://www.scopus.com/inward/record.uri?eid=2-s2.0-85147814908&doi=10.1016%2fj.evalprogplan.2023.102244&partnerID=40&md5=cb7afd816ab6220ef493d0c062fb9330
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 & Accountancy, CHRIST (Deemed to be University), Bangalore, India
Relating the role of green self-concepts and identity on green purchasing behaviour: An empirical analysis
emerging economies; green purchasing behaviour; green purchasing intentions; green self-concept; product self-concept; self-identity
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 & Sons Ltd.
Sharma N.; Saha R.; Sreedharan V.R.; Paul J.
Business Strategy and the Environment, Vol-29, No. 8, pp. 3203-3219.
https://doi.org/10.1002/bse.2567 https://www.scopus.com/inward/record.uri?eid=2-s2.0-85087707573&doi=10.1002%2fbse.2567&partnerID=40&md5=6b4c0953e27f6bc606fcaa08d44a969d
Sharma N., School of Commerce, Christ (Deemed to be) UniversityNCR Campus, Ghaziabad, India; Saha R., School of Management, IILM University, Gurugram, Haryana, India; Sreedharan V.R., Rabat Business School, UniversitInternationale de Rabat, Rabat, Morocco; Paul J., Graduate School of Business Administration, University of Puerto Rico, San Juan, Puerto Rico