AI-Based Risk Profiling of Online Human Trafficking Victims: A Multimodal Framework for Proactive Detection
ethical AI; Human trafficking detection; multimodal AI; social network analysis; victim risk profiling; weakly supervised learning
Human trafficking is still a major worldwide crime that is made easier by digital channels, including social media, job advertisements, and websites that offer escort services. This study offers a thorough examination of artificial intelligence (AI) approaches to human trafficking detection and prevention, with a focus on identifying the most susceptible groups. From authorship attribution and geolocation extraction to social network analysis and multimodal detection, we analyze around 20 scholarly papers that include text, photos, audio, and network data. Lack of victim-focused profiling, data scarcity, bias in AI, and ethical deployment issues are some of the main research needs. In response, we provide a conceptual AI system that uses publicly available signals to evaluate individual vulnerability by combining network analysis, natural language processing, and weakly supervised learning. This work emphasizes the importance of ethically grounded AI systems to assist NGOs, law enforcement, and policymakers proactively identify at-risk individuals and prevent exploitation before it occurs. 2025 IEEE.
Ealias, Aleena; Jayabalan, Bhuvana; Priya, Mohana
Proceedings - 2025 International Conference on Transformative Computing Technologies, ICTCT 2025;pp.50-55
Institute of Electrical and Electronics Engineers Inc.
Restricted Access; Hardcopy may be available in the library
https://doi.org/10.1109/ICTCT69201.2025.00027 https://www.scopus.com/pages/publications/105037443495?origin=resultslist
Ealias A., Christ University, Department of Computer Science, Bengaluru, India; Jayabalan B., Christ University, Department of Computer Science, Bengaluru, India; Priya M., Christ University, Department of Computer Science, Bengaluru, India
Energy Intelligence: The Smart Grid Perspective
Smart grids enable a two-way data-driven flow of electricity, allowing systematic communication along the distribution line. Smart grids utilize various power sources, automate the process of energy distribution and fault identification, facilitate better power usage, etc. Artificial Intelligence plays an important role in the management of power grids, making it even smarter. With the help of Artificial Intelligence and Internet of Things, smart grids can optimize the energy consumption, provide continuous feedback on usage, and monitor live usage statistics, thereby making the energy intelligent. Smart grids require specific hardware to continuously monitor and adapt to the requirements of the system. By enabling energy intelligence, we empower building-level and city-level optimizations that make use of green energy, thereby contributing more toward sustainable development. Thus, the multifaceted energy management system uses sustainable and renewable energy sources, combined with smart devices to provide a two-way communication system to optimize the end-to-end distribution of energy, beneficial to both suppliers and consumers. The Author(s), under exclusive license to Springer Nature Switzerland AG 2023.
Springer Science and Business Media Deutschland GmbH
Eapen N.G., CHRIST (Deemed to be University), Pune Lavasa Campus, India; Harsha K.G., CHRIST (Deemed to be University), Pune Lavasa Campus, India; Kesan A., CHRIST (Deemed to be University), Pune Lavasa Campus, India
Security Aspects for Mutation Testing in Mobile Applications
Android; GeneTic; Mobile apps; Mutation testing
Due to the increase in the number of Android Platform Devices, there are more and more applications being developed across various domains. It is interesting to see the involvement of bugs/crashes even in the deployed applications even though it has been through various test phases. Unit tests are essential in a well-trusted testing environment; however, it does not guarantee that the range of test caries every component of the application. This writes up discusses the overview of mutation testing method concerning Android Applications. Even though mutation testing is found out to be very effective in other applications, it is not that easy to implement the same for an Android Developed Application because of additional resources it would hold. Further, various measures for mutation testing are discussed with types of mutant operators, tools etc. The current studies of mutation analysis mainly focus on testing all the functionalities irrespective of the resource usage. However, the target of the future mutation tests must be also to evaluate the efficiency of the applications under the same test cases. 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.
Eapen N.G.; Rao A.R.; Samanta D.; Robert N.R.; Krishnamoorthy R.; Lokesh G.H.
Lecture Notes in Networks and Systems, Vol-291, pp. 17-27.
Eapen N.G., Department of Computer Science, CHRIST (Deemed to be University), Bengaluru, India; Rao A.R., Department of Data Science, CHRIST (Deemed to be University), Bengaluru, India; Samanta D., Department of Computer Science, CHRIST (Deemed to be University), Bengaluru, India; Robert N.R., Department of Computer Science, CHRIST (Deemed to be University), Bengaluru, India; Krishnamoorthy R., Vishwakarma University, Pune, India; Lokesh G.H., Vidyavardhaka College of Engineering, Mysuru, India
Elementary Methods for Generating Three-Dimensional Coordinate Estimation and Image Reconstruction from Series of Two-Dimensional Images
The increase in computational power in recent years has opened a new door for image processing techniques. Three-dimensional object recognition, identification, pose estimation, and mapping are becoming popular. The need for real-world objects to be mapped into three-dimensional spatial representation is greatly increasing, especially considering the heap jump we obtained in the past decade in virtual reality and augmented reality. This paper discusses an algorithm to convert an array of captured images into estimated 3D coordinates of their external mappings. Elementary methods for generating three-dimensional models are also discussed. This framework will help the community in estimating three-dimensional coordinates of a convex-shaped object from a series of two-dimension images. The built model could be further processed for increasing the resemblance of the input object in terms of its shapes, contour, and texture. 2021 Naived George Eapen et al.
Eapen N.G.; Samanta D.; Kaur M.; Al-Amri J.F.; Masud M.
Mathematical Problems in Engineering, Vol-2021
https://doi.org/10.1155/2021/9494368 https://www.scopus.com/inward/record.uri?eid=2-s2.0-85116378715&doi=10.1155%2f2021%2f9494368&partnerID=40&md5=11d5e2a6f2ad324db60d91aad1d73a1a
Eapen N.G., Department of Data Science, CHRIST (Deemed to Be University), Pune Lavasa Campus, Pune, India; Samanta D., Department of Computer Science, CHRIST Deemed to Be University, Bangalore, India; Kaur M., School of Engineering and Applied Sciences, Bennett University, Noida, India; Al-Amri J.F., Department of Information Technology, College of Computers and Information Technology, Taif University, P. O. Box 11099, Taif, 21944, Saudi Arabia; Masud M., Department of Computer Science, College of Computers and Information Technology, Taif University, P. O. Box 11099, Taif, 21944, Saudi Arabia
Model Selection Strategies for Identifying Effective Energy Storage Systems
Effective; Energy; Identifying; Model; Selection; Storage; Strategies; Systems
Energy is present in various forms in and around us. Capturing and storing energy from various sources have diverse challenges. Designing and developing energy storage systems are challenging, as various techniques are used to distribute energy from sources and to store for diverse use cases. Identifying the optimal and effective energy storage system requires the application of various model selection strategies. The success and adoption of effective energy storage systems can be identified with numerous factors, which include the systems efficiency, reliability, cost-effectiveness, and scalability. Various model selection strategies are available to compute and determine the effective energy storage mechanisms. Various researchers are planning and designing energy storage systems based on the insights from the data with the support of optimisation algorithms, mathematical models, and Artificial Intelligence (AI) and Machine Learning (ML) technologies. The chapter discusses the various model selection strategies for identifying effective models for energy storage systems. The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.
Power Systems;Volume;Part F914;pp.197-231
https://doi.org/10.1007/978-3-031-89459-6_11 https://www.scopus.com/pages/publications/105018106758?origin=resultslist
Eapen N.G., CHRIST (Deemed to be University), Maharashtra, Pune, India; Joseph A., CHRIST (Deemed to be University), Maharashtra, Pune, India
A Worldwide Test of the Predictive Validity of Ideal Partner Preference Matching
attraction; close relationships; human mating; ideals; matching hypothesis
Ideal partner preferences (i.e., ratings of the desirability of attributes like attractiveness or intelligence) are the source of numerous foundational findings in the interdisciplinary literature on human mating. Recently, research on the predictive validity of ideal partner preference matching (i.e., Do people positively evaluate partners who match vs. mismatch their ideals?) has become mired in several problems. First, articles exhibit discrepant analytic and reporting practices. Second, different findings emerge across laboratories worldwide, perhaps because they sample different relationship contexts and/or populations. This registered reportpartnered with the Psychological Science Acceleratoruses a highly powered design (N = 10,358) across 43 countries and 22 languages to estimate preference-matching effect sizes. The most rigorous tests revealed significant preference-matching effects in the whole sample and for partnered and single participants separately. The corrected pattern metric that collapses across 35 traits revealed a zero-order effect of ? =.19 and an effect of ? =.11 when included alongside a normative preference-matching metric. Specific traits in the level metric (interaction) tests revealed very small (average ? =.04) effects. Effect sizes were similar for partnered participants who reported ideals before entering a relationship, and there was no consistent evidence that individual differences moderated any effects. Comparisons between stated and revealed preferences shed light on gender differences and similarities: For attractiveness, mens and (especially) womens stated preferences underestimated revealed preferences (i.e., they thought attractiveness was less important than it actually was). For earning potential, mens stated preferences underestimatedand womens stated preferences overestimatedrevealed preferences. Implications for the literature on human mating are discussed. 2024 American Psychological Association
Eastwick P.W.; Sparks J.; Finkel E.J.; Meza E.M.; Adamkovi? M.; Adu P.; Ai T.; Akintola A.A.; Al-Shawaf L.; Apriliawati D.; Arriaga P.; Aubert-Teillaud B.; Ban G.; Barzykowski K.; Batres C.; Baucom K.J.; Beaulieu E.Z.; Behnke M.; Butcher N.; Charles D.Y.; Chen J.M.; Cheon J.E.; Chittham P.; Chwi?kowska P.; Cong C.W.; Copping L.T.; Corral-Frias N.S.; Adori? V.?.; Dizon M.; Du H.; Ehinmowo M.I.; Escribano D.A.; Espinosa N.M.; Expito F.; Feldman G.; Freitag R.; Armenta M.F.; Gallyamova A.; Gillath O.; Gjoneska B.; Gkinopoulos T.; Grafe F.; Grigoryev D.; Groyecka-Bernard A.; Gunaydin G.; Ilustrisimo R.; Impett E.; Ka?m P.; Kim Y.-H.; Kocur M.; Kowal M.; Krishna M.; Labor P.D.; Lu J.G.; Lucas M.Y.; Ma?ecki W.P.; Malinakova K.; Meier S.; Meier Z.; Misiak M.; Muise A.; Novak L.; Jiaqing O.; do?ru A.A.; Park H.G.; Paruzel M.; Pavlovi? Z.; Pki M.; Ribeiro G.; Roberts S.C.; Rr J.P.; Ropovik I.; Ross R.M.; Sakman E.; Salvador C.E.; Selcuk E.; Skakoon-Sparling S.; Sorokowska A.; Sorokowski P.; Spasovski O.; Stanton S.C.E.; Stewart S.L.K.; Swami V.; Szaszi B.; Takashima K.; Tavel P.; Tejada J.; Tu E.; Tuominen J.; Vaidis D.; Vally Z.; Vaughn L.A.; Villanueva-Moya L.; Wisnuwardhani D.; Yamada Y.; Yonemitsu F.; kovR.; ivnK.; Coles N.A.
Journal of Personality and Social Psychology
https://doi.org/10.1037/pspp0000524 https://www.scopus.com/inward/record.uri?eid=2-s2.0-85208261406&doi=10.1037%2fpspp0000524&partnerID=40&md5=acc575d872be7255db465099bd6640c6
Eastwick P.W., Department of Psychology, University of California, Davis, United States; Sparks J., Behavioral Decision Making Group, University of California, Los Angeles Anderson School of Management, United States; Finkel E.J., Department of Psychology, Northwestern University, United States, Kellogg School of Management, Northwestern University, United States, Institute for Policy Research, Northwestern University, United States; Meza E.M., Department of Psychology, University of California, Davis, United States; Adamkovi? M., Centre of Social and Psychological Sciences, Slovak Academy of Sciences, Slovakia, Faculty of Education, Charles University, Czech Republic, Faculty of Humanities and Social Sciences, University of Jyvkyl Finland; Adu P., Wellington Faculty of Health, School of Health, Victoria University of Wellington, New Zealand; Ai T., Department of Social Psychology, University of Kansas, United States; Akintola A.A., Department of Psychology, Redeemers University, Nigeria; Al-Shawaf L., Department of Psychology, University of Colorado, Colorado Springs, Colorado Springs, United States, Lyda Hill Institute for Human Resilience, University of Colorado, Colorado Springs, United States, Institute for Advanced Study in Toulouse (IAST), France; Apriliawati D., Department of Psychology, Universitas Islam Negeri Sunan Kalijaga, Indonesia; Arriaga P., Department of Social and Organizational Psychology, Iscte-University Institute of Lisbon, Portugal; Aubert-Teillaud B., Institut de Psychologie, UniversitParis Cit France; Ban G., Department of Educational Psychology and Psychology of Health, Pavol Jozef Safarik University, Slovakia; Barzykowski K., Faculty of Philosophy, Institute of Psychology, Jagiellonian University, Poland; Batres C., Department of Psychology, Franklin and Marshall College, United States; Baucom K.J., Department of Psychology, University of Utah, United States; Beaulieu E.Z., Department of Psychology, University of Utah, United States; Behnke M., Department of Psychology and Cognitive Science, Adam Mickiewicz University, Poland, Cognitive Neuroscience Center, Adam Mickiewicz University, Poland; Butcher N., Department of Psychology, Teesside University, United Kingdom; Charles D.Y., Department of Psychology, Christ University, India; Chen J.M., Department of Psychology, Wellesley College, United States; Cheon J.E., Department of Psychology, Yonsei University, South Korea; Chittham P., Faculty of Psychology, Chulalongkorn University, Thailand; Chwi?kowska P., Department of Psychology and Cognitive Science, Adam Mickiewicz University, Poland; Cong C.W., Department of Social Science, Faculty of Social Science and Humanities, Tunku Abdul Rahman University of Management and Technology, Malaysia; Copping L.T., Department of Psychology, Teesside University, United Kingdom; Corral-Frias N.S., Department of Psychology, Universidad de Sonora, Mexico; Adori? V.?., Department of Psychology, University of Zadar, Croatia; Dizon M., Department of Psychology, University of Edinburgh, United Kingdom; Du H., Institute of Advanced Studies in Humanities and Social Sciences, Beijing Normal University, China; Ehinmowo M.I., Department of Psychology, University of Ibadan, Nigeria; Escribano D.A., Department of Social Psychology, University of Kansas, United States; Espinosa N.M., Department of Psychology and Neuroscience, Duke University, United States; Expito F., Departament of Social Psychology, University of Granada, Spain; Feldman G., Department of Psychology, University of Hong Kong, Hong Kong; Freitag R., Departamento de Letras Vernulas, Universidade Federal de Sergipe, Brazil; Armenta M.F., Department of Law, Universidad de Sonora, Mexico; Gallyamova A., Center for Sociocultural Research, HSE University, Russian Federation; Gillath O., Department of Social Psychology, University of Kansas, United States; Gjoneska B., Devision of Neuroinformatics in Function of Sustainable Development, Macedonian Academy of Sciences and Arts, North Macedonia; Gkinopoulos T., Institute of Psychology, Jagiellonian University in Krakow, Poland; Grafe F., Department of Psychology and Psychotherapy, Witten/Herdecke University, Germany; Grigoryev D., Center for Sociocultural Research, HSE University, Russian Federation; Groyecka-Bernard A., Institute of Psychology, University of Wroclaw, Poland; Gunaydin G., Psychology Program, Faculty of Arts and Social Sciences, Sabanci University, Turkey; Ilustrisimo R., Department of Psychology, University of San Carlos, Philippines; Impett E., Department of Psychology, University of Toronto, Canada; Ka?m P., Department of Psychology, Faculty of Arts, Pavol Jozef afik University in Koice, Slovakia; Kim Y.-H., Department of Psychology, Yonsei University, South Korea; Kocur M., IDN Being Human, Institute of Psychology, University of Wroclaw, Poland; Kowal M., IDN Being Human, Institute of Psychology, University of Wroclaw, Poland; Krishna M., Karnataka, Bangalore, India; Labor P.D., Department of Psychology, University of the Philippines, Philippines; Lu J.G., Sloan School of Management, Massachusetts Institute of Technology, United States; Lucas M.Y., Department of Anthropology and Psychology, Universidad de Sonora, Mexico; Ma?ecki W.P., IDN Being Human, Institute of Polish Studies, University of Wroclaw, Poland; Malinakova K., Olomouc University Social Health Institute, PalackUniversity Olomouc, Czech Republic; Meier S., Department of Psychology and Psychotherapy, Witten/Herdecke University, Germany; Meier Z., Olomouc University Social Health Institute, PalackUniversity Olomouc, Czech Republic; Misiak M., IDN Being Human, Institute of Psychology, University of Wroclaw, Poland, School of Anthropology and Museum Ethnography, University of Oxford, United Kingdom; Muise A., Department of Psychology, York University, Canada; Novak L., Olomouc University Social Health Institute, PalackUniversity Olomouc, Czech Republic; Jiaqing O., Health and Social Sciences Cluster, Singapore Institute of Technology, Singapore; do?ru A.A., Department of Psychology, Marmara University, Turkey, Department of Psychology, kar University, Turkey; Park H.G., Department of Psychology, University of Toronto, Canada; Paruzel M., Department of Psychology and Cognitive Science, Adam Mickiewicz University, Poland; Pavlovi? Z., Faculty of Philosophy, Department of Psychology, University of Belgrade, Serbia; Pki M., Institute of Psychology, Ev Lord University, Hungary; Ribeiro G., School of Psychology, The University of Queensland, Australia, School of Law and Justice, University of Southern Queensland, Australia; Roberts S.C., IDN Being Human, Institute of Psychology, University of Wroclaw, Poland, Division of Psychology, University of Stirling, United Kingdom; Rr J.P., Department of Psychology and Psychotherapy, Witten/Herdecke University, Germany; Ropovik I., Institute for Research and Development of Education, Faculty of Education, Charles University, Czech Republic, Department of Preschool and Elementary Education and Psychology, Faculty of Education, University of Presov, Slovakia; Ross R.M., Department of Philosophy, Macquarie University, Australia; Sakman E., Department of Psychology, Bilkent University, Turkey; Salvador C.E., Department of Psychology and Neuroscience, Duke University, United States; Selcuk E., Psychology Program, Faculty of Arts and Social Sciences, Sabanci University, Turkey; Skakoon-Sparling S., Department of Psychology, Toronto Metropolitan University, Canada; Sorokowska A., Institute of Psychology, University of Wroclaw, Poland; Sorokowski P., IDN Being Human, Institute of Psychology, University of Wroclaw, Poland; Spasovski O., Department of Psychology, Faculty of Philosophy, Ss. Cyril and Methodius University in Skopje, North Macedonia; Stanton S.C.E., Department of Psychology, University of Edinburgh, United Kingdom; Stewart S.L.K., School of Psychology, University of Chester, United Kingdom; Swami V., School of Psychology, Sport, and Sensory Sciences, Anglia Ruskin University, United Kingdom, Centre for Psychological Medicine, Perdana University, Malaysia; Szaszi B., Institute of Psychology, Ev Lord University, Hungary; Takashima K., Graduate School of Human-Environment Studies, Kyushu University, Japan; Tavel P., Olomouc University Social Health Institute, PalackUniversity Olomouc, Czech Republic; Tejada J., Departamento de Psicologia, Universidade Federal de Sergipe, Brazil; Tu E., Department of Psychology, York University, Canada; Tuominen J., Department of Psychology and Speech-Language Pathology, University of Turku, Finland; Vaidis D., Department of Psychology, Centre for Learning Leadership and Excellence, Centre national de la recherche scientifique, Universitde Toulouse, France; Vally Z., Department of Clinical Psychology, Faculty of Medicine and Health Sciences, United Arab Emirates University, United Arab Emirates; Vaughn L.A., Department of Psychology, Ithaca College, United States; Villanueva-Moya L., Departament of Social Psychology, University of Granada, Spain; Wisnuwardhani D., Faculty of Psychology, Universitas Indonesia, Indonesia; Yamada Y., Faculty of Arts and Science, Kyushu University, Japan; Yonemitsu F., College of Engineering, Shibaura Institute of Technology, Japan; kovR., Olomouc University Social Health Institute, PalackUniversity Olomouc, Czech Republic; ivnK., Olomouc University Social Health Institute, PalackUniversity Olomouc, Czech Republic; Coles N.A., Center for the Study of Language and Information, Stanford University, United States
A worldwide test of the predictive validity of ideal partner preference matching.
Ideal partner preferences (i.e., ratings of the desirability of attributes like attractiveness or intelligence) are the source of numerous foundational findings in the interdisciplinary literature on human mating. Recently, research on the predictive validity of ideal partner preference matching (i.e., Do people positively evaluate partners who match vs. mismatch their ideals?) has become mired in several problems. First, articles exhibit discrepant analytic and reporting practices. Second, different findings emerge across laboratories worldwide, perhaps because they sample different relationship contexts and/or populations. This registered reportpartnered with the Psychological Science Acceleratoruses a highly powered design (N = 10,358) across 43 countries and 22 languages to estimate preference-matching effect sizes. The most rigorous tests revealed significant preference-matching effects in the whole sample and for partnered and single participants separately. The corrected pattern metric that collapses across 35 traits revealed a zero-order effect of ? =.19 and an effect of ? =.11 when included alongside a normative preference-matching metric. Specific traits in the level metric (interaction) tests revealed very small (average ? =.04) effects. Effect sizes were similar for partnered participants who reported ideals before entering a relationship, and there was no consistent evidence that individual differences moderated any effects. Comparisons between stated and revealed preferences shed light on gender differences and similarities: For attractiveness, men's and (especially) women's stated preferences underestimated revealed preferences (i.e., they thought attractiveness was less important than it actually was). For earning potential, men's stated preferences underestimatedand women's stated preferences overestimatedrevealed preferences. Implications for the literature on human mating are discussed. (PsycInfo Database Record (c) 2025 APA, all rights reserved) 2024 American Psychological Association All rights, including for text and data mining, AI training, and similar technologies, are reserved.
Eastwick, Paul W.; Sparks, Jehan; Finkel, Eli J.; Meza, Eva M.; Adamkovi?, Mat Adu, Peter; Ai, Ting; Akintola, Aderonke A.; Al-Shawaf, Laith; Apriliawati, Denisa; Arriaga, Patria; Aubert-Teillaud, Benjamin; Ban, Gabriel; Barzykowski, Krystian; Batres, Carlota; Baucom, Katherine J.; Beaulieu, Elizabeth Z.; Behnke, Maciej; Butcher, Natalie; Charles, Deborah Y.; Chen, Jane Minyan; Cheon, Jeong Eun; Chittham, Phakkanun; Chwi?kowska, Patrycja; Cong, Chin Wen; Copping, Lee T.; Corral-Frias, Nadia S.; ?ubela Adori?, Vera; Dizon, Mikaela; Du, Hongfei; Ehinmowo, Michael I.; Escribano, Daniela A.; Espinosa, Natalia M.; Expito, Francisca; Feldman, Gilad; Freitag, Raquel; Frias Armenta, Martha; Gallyamova, Albina; Gillath, Omri; Gjoneska, Biljana; Gkinopoulos, Theofilos; Grafe, Franca; Grigoryev, Dmitry; Groyecka-Bernard, Agata; Gunaydin, Gul; Ilustrisimo, Ruby; Impett, Emily; Ka?m, Pavol; Kim, Young-Hoon; Kocur, Miros?aw; Kowal, Marta; Krishna, Maatangi; Labor, Paul Danielle; Lu, Jackson G.; Lucas, Marc Y.; Ma?ecki, Wojciech P.; Malinakova, Klara; Meier, Sofia; Meier, Zden?k; Misiak, Michal; Muise, Amy; Novak, Lukas; O, Jiaqing; do?ru, Asil A.; Park, Haeyoung Gideon; Paruzel, Mariola; Pavlovi?, Zoran; Pki, Marcell; Ribeiro, Gianni; Roberts, S. Craig; Rr, Jan P.; Ropovik, Ivan; Ross, Robert M.; Sakman, Ezgi; Salvador, Cristina E.; Selcuk, Emre; Skakoon-Sparling, Shayna; Sorokowska, Agnieszka; Sorokowski, Piotr; Spasovski, Ognen; Stanton, Sarah C. E.; Stewart, Suzanne L. K.; Swami, Viren; Szaszi, Barnabas; Takashima, Kaito; Tavel, Peter; Tejada, Julian; Tu, Eric; Tuominen, Jarno; Vaidis, David; Vally, Zahir; Vaughn, Leigh Ann; Villanueva-Moya, Laura; Wisnuwardhani, Dian; Yamada, Yuki; Yonemitsu, Fumiya; kov Radka; ivn Krista; Coles, Nicholas A.
Journal of Personality and Social Psychology;Volume;128;Issue;1;pp.123-146
All Open Access; Green Open Access; Hybrid Gold Open Access
https://doi.org/10.1037/pspp0000524 https://www.scopus.com/pages/publications/85208261406?origin=resultslist
Eastwick P.W., Department of Psychology, University of California, Davis, Davis, CA, United States; Sparks J., Behavioral Decision Making Group, University of California, Los Angeles, Anderson School of Management, Los Angeles, CA, United States; Finkel E.J., Department of Psychology, Northwestern University, Evanston, IL, United States; Meza E.M., Department of Psychology, University of California, Davis, Davis, CA, United States; Adamkovi? M., Centre of Social and Psychological Sciences, Slovak Academy of Sciences, Slovakia; Adu P., Wellington Faculty of Health, School of Health, Victoria University of Wellington, Wellington, New Zealand; Ai T., Department of Social Psychology, University of Kansas, Lawrence, KS, United States; Akintola A.A., Department of Psychology, Redeemer's University, Nigeria; Al-Shawaf L., Department of Psychology, University of Colorado, Colorado Springs, CO, United States; Apriliawati D., Department of Psychology, Universitas Islam Negeri Sunan Kalijaga, Indonesia; Arriaga P., Department of Social and Organizational Psychology, Iscte-University Institute of Lisbon, Lisbon, Portugal; Aubert-Teillaud B., Institut de Psychologie, Universite Paris Cite, Paris, France; Ban G., Department of Educational Psychology and Psychology of Health, Pavol Jozef Safarik University, Slovakia; Barzykowski K., Faculty of Philosophy, Institute of Psychology, Jagiellonian University, Poland; Batres C., Department of Psychology, Franklin and Marshall College, Lancaster, PA, United States; Baucom K.J., Department of Psychology, University of Utah, Salt Lake City, UT, United States; Beaulieu E.Z., Department of Psychology, University of Utah, Salt Lake City, UT, United States; Behnke M., Department of Psychology and Cognitive Science, Adam Mickiewicz University, Poland; Butcher N., Department of Psychology, Teesside University, United Kingdom; Charles D.Y., Department of Psychology, Christ University, India; Chen J.M., Department of Psychology, Wellesley College, Wellesley, MA, United States; Cheon J.E., Department of Psychology, Yonsei University, South Korea; Chittham P., Faculty of Psychology, Chulalongkorn University, Thailand; Chwi?kowska P., Department of Psychology and Cognitive Science, Adam Mickiewicz University, Poland; Cong C.W., Department of Social Science, Faculty of Social Science and Humanities, Tunku Abdul Rahman University of Management and Technology, Malaysia; Copping L.T., Department of Psychology, Teesside University, United Kingdom; Corral-Frias N.S., Department of Psychology, Universidad de Sonora, Mexico; ?ubela Adori? V., Department of Psychology, University of Zadar, Zadar, Croatia; Dizon M., Department of Psychology, University of Edinburgh, Edinburgh, United Kingdom; Du H., Institute of Advanced Studies in Humanities and Social Sciences, Beijing Normal University, Beijing, China; Ehinmowo M.I., Department of Psychology, University of Ibadan, Ibadan, Nigeria; Escribano D.A., Department of Social Psychology, University of Kansas, Lawrence, KS, United States; Espinosa N.M., Department of Psychology and Neuroscience, Duke University, Durham, NC, United States; Expito F., Departament of Social Psychology, University of Granada, Granada, Spain; Feldman G., Department of Psychology, University of Hong Kong, Hong Kong; Freitag R., Departamento de Letras Vernaculas, Universidade Federal de Sergipe, Brazil; Frias Armenta M., Department of Law, Universidad de Sonora, Mexico; Gallyamova A., Center for Sociocultural Research, HSE University, Russian Federation; Gillath O., Department of Social Psychology, University of Kansas, Lawrence, KS, United States; Gjoneska B., Devision of Neuroinformatics in Function of Sustainable Development, Macedonian Academy of Sciences and Arts, North Macedonia; Gkinopoulos T., Institute of Psychology, Jagiellonian University in Krakow, Krakow, Poland; Grafe F., Department of Psychology and Psychotherapy, Witten/Herdecke University, Witten, Germany; Grigoryev D., Center for Sociocultural Research, HSE University, Russian Federation; Groyecka-Bernard A., Institute of Psychology, University of Wroclaw, Wroclaw, Poland; Gunaydin G., Psychology Program, Faculty of Arts and Social Sciences, Sabanci University, Turkey; Ilustrisimo R., Department of Psychology, University of San Carlos, Philippines; Impett E., Department of Psychology, University of Toronto, Toronto, ON, Canada; Ka?m P., Department of Psychology, Faculty of Arts, Pavol Jozef Safarik University in Kosice, Kosice, Slovakia; Kim Y.-H., Department of Psychology, Yonsei University, South Korea; Kocur M., IDN Being Human, Institute of Psychology, University of Wroclaw, Wroclaw, Poland; Kowal M., IDN Being Human, Institute of Psychology, University of Wroclaw, Wroclaw, Poland; Krishna M.; Labor P.D., Department of Psychology, University of the Philippines, Philippines; Lu J.G., Sloan School of Management, Massachusetts Institute of Technology, Cambridge, MA, United States; Lucas M.Y., Department of Anthropology and Psychology, Universidad de Sonora, Mexico; Ma?ecki W.P., IDN Being Human, Institute of Polish Studies, University of Wroclaw, Wroclaw, Poland; Malinakova K., Olomouc University Social Health Institute, Palacky University Olomouc, Olomouc, Czech Republic; Meier S., Department of Psychology and Psychotherapy, Witten/Herdecke University, Witten, Germany; Meier Z., Olomouc University Social Health Institute, Palacky University Olomouc, Olomouc, Czech Republic; Misiak M., IDN Being Human, Institute of Psychology, University of Wroclaw, Wroclaw, Poland; Muise A., Department of Psychology, York University, North York, ON, Canada; Novak L., Olomouc University Social Health Institute, Palacky University Olomouc, Olomouc, Czech Republic; O J., Health and Social Sciences Cluster, Singapore Institute of Technology, Singapore; do?ru A.A., Department of Psychology, Marmara University, Turkey; Park H.G., Department of Psychology, University of Toronto, Toronto, ON, Canada; Paruzel M., Department of Psychology and Cognitive Science, Adam Mickiewicz University, Poland; Pavlovi? Z., Faculty of Philosophy, Department of Psychology, University of Belgrade, Belgrade, Serbia; Pki M., Institute of Psychology, Eotvos Lorand University, Hungary; Ribeiro G., School of Psychology, University of Queensland, Brisbane, QLD, Australia; Roberts S.C., IDN Being Human, Institute of Psychology, University of Wroclaw, Wroclaw, Poland; Rr J.P., Department of Psychology and Psychotherapy, Witten/Herdecke University, Witten, Germany; Ropovik I., Institute for Research and Development of Education, Faculty of Education, Charles University, Czech Republic; Ross R.M., Department of Philosophy, Macquarie University, North Ryde, NSW, Australia; Sakman E., Department of Psychology, Bilkent University, Turkey; Salvador C.E., Department of Psychology and Neuroscience, Duke University, Durham, NC, United States; Selcuk E., Psychology Program, Faculty of Arts and Social Sciences, Sabanci University, Turkey; Skakoon-Sparling S., Department of Psychology, Toronto Metropolitan University, Toronto, ON, Canada; Sorokowska A., Institute of Psychology, University of Wroclaw, Wroclaw, Poland; Sorokowski P., IDN Being Human, Institute of Psychology, University of Wroclaw, Wroclaw, Poland; Spasovski O., Department of Psychology, Faculty of Philosophy, Ss. Cyril and Methodius University in Skopje, Skopje, North Macedonia; Stanton S.C.E., Department of Psychology, University of Edinburgh, Edinburgh, United Kingdom; Stewart S.L.K., School of Psychology, University of Chester, Chester, United Kingdom; Swami V., School of Psychology, Sport, and Sensory Sciences, Anglia Ruskin University, United Kingdom; Szaszi B., Institute of Psychology, Eotvos Lorand University, Hungary; Takashima K., Graduate School of Human-Environment Studies, Kyushu University, Japan; Tavel P., Olomouc University Social Health Institute, Palacky University Olomouc, Olomouc, Czech Republic; Tejada J., Departamento de Psicologia, Universidade Federal de Sergipe, Brazil; Tu E., Department of Psychology, York University, North York, ON, Canada; Tuominen J., Department of Psychology and Speech-Language Pathology, University of Turku, Turku, Finland; Vaidis D., Department of Psychology, Centre for Learning Leadership and Excellence, centre national de la recherche scientifique, Universite de Toulouse, Toulouse, France; Vally Z., Department of Clinical Psychology, Faculty of Medicine and Health Sciences, United Arab Emirates University, United Arab Emirates; Vaughn L.A., Department of Psychology, Ithaca College, Ithaca, NY, United States; Villanueva-Moya L., Departament of Social Psychology, University of Granada, Granada, Spain; Wisnuwardhani D., Faculty of Psychology, Universitas Indonesia, Indonesia; Yamada Y., Faculty of Arts and Science, Kyushu University, Japan; Yonemitsu F., College of Engineering, Shibaura Institute of Technology, Japan; kovR., Olomouc University Social Health Institute, Palacky University Olomouc, Olomouc, Czech Republic; ivnK., Olomouc University Social Health Institute, Palacky University Olomouc, Olomouc, Czech Republic; Coles N.A., Center for the Study of Language and Information, Stanford University, Stanford, CA, United States
Sustainability and Gender Equality: SDG5Gender Differences in Bargaining in the Housing Market
This chapter investigates whether gender differences exist in bargaining behaviour in the housing market. The impact of personality dispositions, location preferences and other such variables on the individuals willingness and ability to bargain and obtain a concession is studied. The variables were estimated using a 5-point Likert scale, and the final dataset was analysed by implementing logistic regression model. Findings suggest that gender, product knowledge, bargaining disposition, role of agent and reference price significantly impact bargaining behaviour. The study validates the need to attain the fifth Sustainable Development Goal, i.e. Gender Equality. The current emphasis is to ensure full participation of women in decision-making capabilities by 2030 (UN Women, Progress on the sustainable development goals: the gender snapshot, UN Women, New York, 2022), but it is found that men bargain more than women in the rental housing market. The chapter contributes to existing literature by studying gender differences in the rental housing market and justifies the findings with the help of primary data analysis. 2024 The Author(s).
Easwar S.; Walimbe S.; Vishnu K.; Pramila R.M.
Technical and Vocational Education and Training, Vol-38, pp. 76-89.
Easwar S., CHRIST (Deemed to be University), Maharashtra, Pune, India; Walimbe S., CHRIST (Deemed to be University), Maharashtra, Pune, India; Vishnu K., NMIMS Deemed-to-be-University, Mumbai, India; Pramila R.M., CHRIST (Deemed to be University), Maharashtra, Pune, India
Information and communication technology integration in education by harare secondary school teachers in relation to school technology culture and their educational leader competencies
Information and Communication Technology (ICT) integration in education refers newlineto using technology in teaching and learning processes. The use of Information and Communication Technology (ICT) in the classroom would solve a lot of the problems related to Zimbabwean secondary school education. It motivates students to learn by providing a variety of learning activities such as watching videos, viewing pictures and models and online quizzes. This interests students who are digital natives and is more applicable to their everyday experiences. newlineStudent participation increases when ICT is used in the classroom as students are newlineconversant with the use of technology. ICT integration in education also allows students to learn in circumstances where there are shortages of teachers. The culture which exists in a school and the competencies of the educational leaders cannot be ignored when planning for and assessing the outcome of ICT integration programs. This study focused on Information and Communication newlineTechnology Integration in Education by Harare Secondary School Teachers, in relation to School Technology Culture and their Educational Leader Competencies . A survey was carried out in government and private secondary schools in Harare, Zimbabwe during the period from October 2012 to mid-2013. The population was all the secondary school teachers in Harare district of Zimbabwe. According to the data obtained from the Ministry of Education in October 2012, the target population of secondary school teachers in Harare was 4 244. The sample size was calculated based on an equation cited from Camorin and Calmorin (2007:230). This newlinegave a sample of 248 teachers from Harare secondary schools. 140 were females and 108 males. Four research instruments were used for data collection: a proforma on demographics, the ICT Integration questionnaire (ICTIQ), the School Technology Culture (STC) Scale and the Educational Leader Competency (ELC) scale.
Thomas, Kennedy Andrew and Nyaruwata, Leonorah Tendayi
Information and communication technology integration in education by harare secondary school teachers in relation to school technology culture and their educational leader competencies /
Spectroscopic Study of Late-type Emission-line Stars Using the Data from LAMOST DR6
Line: identification; Stars: activity; Stars: late-type
Low-mass emission-line stars belong to various evolutionary stages, from pre-main-sequence young stars to evolved stars. In this work, we present a catalog of late-type (F0 to M9) emission-line stars from the LAMOST Data Release 6. Using the scipy package, we created a Python code that finds the emission peak at H? in all late-type stellar spectra. A data set of 38,152 late-type emission-line stars was obtained after a rigorous examination of the photometric quality flags and the signal-to-noise ratio of the spectra. Adopting well-known photometric and spectroscopic methods, we classified our sample into 438 infrared (IR) excess sources, 4669 post-main-sequence candidates, 9718 Fe/Ge/Ke sources, and 23,264 dMe sources. From a crossmatch with known databases, we found that 29,222 sources, comprising 65 IR excess sources, 7899 Fe/Ge/Ke stars, 17,533 dMe stars, and 3725 PtMS candidates, are new detections. We measured the equivalent width of the major emission lines observed in the spectra of our sample of emission-line stars. Furthermore, the trend observed in the line strengths of major emission lines over the entire late-type spectral range is analyzed. We further classified the sample into four groups based on the presence of hydrogen and calcium emission lines. This work presents a large data set of late-type emission-line stars, which can be used to study active phenomena in late-type stars. 2024 National Astronomical Observatories, CAS and IOP Publishing Ltd. All rights, including for text and data mining, AI training, and similar technologies, are reserved.
Edwin D.; Mathew B.; Shridharan B.; Valsan V.; Nidhi S.; Bhattacharyya S.; Kartha S.S.; Robin T.
Research in Astronomy and Astrophysics, Vol-24, No. 12
https://doi.org/10.1088/1674-4527/ad8f9b https://www.scopus.com/inward/record.uri?eid=2-s2.0-85211070102&doi=10.1088%2f1674-4527%2fad8f9b&partnerID=40&md5=be6d900bd3d42d47afbb3fda2b91a4db
Edwin D., Department of Physics and Electronics, CHRIST (Deemed to be University, Hosur Main Road, Bangalore, 560029, India; Mathew B., Department of Physics and Electronics, CHRIST (Deemed to be University, Hosur Main Road, Bangalore, 560029, India; Shridharan B., Department of Physics and Electronics, CHRIST (Deemed to be University, Hosur Main Road, Bangalore, 560029, India, Department of Astronomy and Astrophysics, Tata Institute of Fundamental Research, Mumbai, 400005, India; Valsan V., Department of Physics and Electronics, CHRIST (Deemed to be University, Hosur Main Road, Bangalore, 560029, India; Nidhi S., Department of Physics and Electronics, CHRIST (Deemed to be University, Hosur Main Road, Bangalore, 560029, India, The Oxford College of Science, 17th, 32, 19th Main road, Sector 4, HSR Layout, Karnataka, Bengaluru, 560102, India; Bhattacharyya S., Department of Physics and Electronics, CHRIST (Deemed to be University, Hosur Main Road, Bangalore, 560029, India; Kartha S.S., Department of Physics and Electronics, CHRIST (Deemed to be University, Hosur Main Road, Bangalore, 560029, India; Robin T., Department of Physics and Electronics, CHRIST (Deemed to be University, Hosur Main Road, Bangalore, 560029, India
Machine Learning Algorithms for Predictive Maintenance in Hybrid Renewable Energy Microgrid Systems
Energy Management; Hybrid Renewable Energy Systems; Machine Learning; Microgrid; Predictive Maintenance; Remaining Useful Life (RUL)
The rapid expansion of hybrid renewable energy microgrid systems presents new challenges in maintaining system reliability and performance. This paper explores the application of machine learning algorithms for predictive maintenance in such systems, focusing on the early detection of potential failures to optimize operational efficiency and reduce downtime. By integrating real-time data from solar, wind, and storage components, the proposed models predict the remaining useful life (RUL) of critical components. The results demonstrate significant improvements in predictive accuracy, offering a robust solution for enhancing the reliability and longevity of renewable energy microgrids. The Authors, published by EDP Sciences.
Edwin Prabhakar P.B.; Rajarajeswari S.; Antad S.; Jeshurun S.B.; Badhoutiya A.; Chandrika S.; Suresh Babu D.
https://doi.org/10.1051/e3sconf/202459105002 https://www.scopus.com/inward/record.uri?eid=2-s2.0-85211764825&doi=10.1051%2fe3sconf%2f202459105002&partnerID=40&md5=f9f970983ec03a1283b410acacecd651
Edwin Prabhakar P.B., Department of CSE, New Prince Shri Bhavani College of Engineering and Technology, Tamil nadu, Chennai, 600073, India; Rajarajeswari S., Department of S&H, Prince Shri Venkateshwara Padmavathy Engineering College, Chennai, 127, India; Antad S., Department of Computer Engineering, Vishwakarma Institute of Technology, Pune, India; Jeshurun S.B., School of Business and Management, CHRIST University, Bangaluru, India; Badhoutiya A., Department of Electrical Engineering, GLA University, Mathura, India; Chandrika S., Department of Mechanical Engineering, Dr.D.Y.Patil Institute of Technology, Pimrpi, Pune, India; Suresh Babu D., Department of EEE, School of Engineering, Mohan Babu University, Andhra Pradesh, Tirupati, India
Big Data Analytics Tools and Applications for Modern Business World
Big Data; Business intelligence; data analytics; Database; Deep Learning; Finance marketing
In the modern world, data is the unavoidable word. The digital environment in almost all our day to day life is linked with digital data. Effective data management is one of the important tasks. The gradual growth of technology in recent years, the generation of data has increased exponentially. Everything, ranging from sending a mail to simply browsing the internet generates data and this is collected and stored. This data has countless uses in various fields such as medicine, business, agriculture and marketing, but most of the time it goes unused. Business intelligence is a key factor in the current business world. Business growth is purely depending on technology. Technology is not only used in manufacturing it is applied to getting the customer. The data analytics is still in its earlier stages and has a long way to go before it yields favourable results. It is a good time as any to start working in this domain to utilize its prowess. This article has discussed the opportunities and growth of data analytics in the research domain. It can face soon when it reaches its advance stages. The big data is handling a larger amount of data in a conventional and non-conventional manner. Technology is playing a vital role to handle larger data from the database. This article is to discuss data analytics application in modern industry. In the technical perspective, big data Map-reduce is an advanced tool and for simulation part, R tool is used. 2020 IEEE.
Proceedings of the International Conference on Electronics and Sustainable Communication Systems, ICESC 2020, pp. 587-592.
https://doi.org/10.1109/ICESC48915.2020.9155704 https://www.scopus.com/inward/record.uri?eid=2-s2.0-85090853340&doi=10.1109%2fICESC48915.2020.9155704&partnerID=40&md5=24e3c23b8e1f664e28cd463cc7a666bb
Ekka S., Cse, Christ Deemed to Be University Kengeri Campus, Bangalore, India; Jayapandian N., Cse, Christ Deemed to Be University Kengeri Campus, Bangalore, India
Towards Smarter Warehouse Layouts: Simulation-Driven Insights on Congestion and Forklift Flow Patterns
Behavioral Flow Modeling; Discrete Event Simulation; Forklift Logistics; Replication-Based Analysis; Warehouse Slotting Optimization
In high-mix, make-to-order warehouse environments, slotting decisions in constrained warehouse settings affect flow dynamics, yet their behavioral implications remain underexplored. This study employs discrete event simulation (DES) using the software FlexSim to evaluate three slotting strategies: baseline reflecting current operations, a benchmark informed by heuristic frequency-based clustering, and a proposed layout based on a learned configuration within a forklift-operated warehouse characterized by narrow aisles, unidirectional traffic, and spatial contention. Instead of emphasizing throughput alone, the study takes a closer look at how forklifts operate on a day-to-day basis, specifically how their time is divided between active movement, waiting due to blockages, and idling with no task assigned. Each strategy was tested over 20 simulation replications. Notably, the proposed layout cut blocked time by more than 30% and allowed forklifts to remain idle (and ready) more often, without reducing overall utilization. These patterns held consistently across runs. Statistical analysis confirmed that the differences in forklift behavior were significant, and Levenes tests showed that performance didnt become more erratic. These findings demonstrate that the improvements are systematic, not random. The work presents a simulation-based method for diagnosing layout effectiveness by looking at behavior, not just outputs, connecting slotting choices to real operational flow and system stability. This approach supports more resilient warehouse designs in settings with limited space and high product mix. 2025 IEEE.
El Baz, Hicham; Parag Jose, C.; Bhat, Vinayambika S.; Wang, Yong
Proceedings of 2025 International Conference on Intelligent Systems and Pioneering Innovations in Robotics and Electric Mobility: Transforming Mobility and Automation Through Intelligent Engineering, INSPIRE 2025;pp.578-582
https://doi.org/10.1109/INSPIRE67328.2025.11300596 https://www.scopus.com/pages/publications/105032428880?origin=resultslist
El Baz H., School of Systems Science and Industrial Engineering, Binghamton University, Binghamton, United States; Parag Jose C., Department of Electrical and Electronics Engineering, Christ University, Karnataka, India; Bhat V.S., Department of Electronics & Communication Engineering, Mangalore Institute of Technology & Engineering, Karnataka, India; Wang Y., School of Systems Science and Industrial Engineering, Binghamton University, Binghamton, United States
Pluronic f127 encapsulated titanium dioxide nanoparticles: Evaluation of physiochemical properties for biological applications
Biomedical application; Green synthesis; Nanomaterials; Optical properties; Structural analysis
The infections caused by bacteria that are resistant to drugs are very bad for human health and kill thousands of people every year. Also, both human actions and natural processes make surface waters more likely to have drug-resistant bacteria grow and spread in the environment. Because of this, researchers need to find new ways to treat bacterial infections quickly becoming resistant to drugs as soon as possible. Drug delivery systems based on nanoparticles have enhanced biocompatibility, biocidal properties, pharmacokinetics, tumor targeting, and stability while exhibiting non-toxicity to normal cells and overcoming drug resistance. In the present work, the pluronic-F127 encapsulated titanium dioxide (PF127/TiO2) nanoparticles (NPs) were prepared by a green process using Morinda citrifolia leaf extract. X-ray diffraction patterns (XRD) revealed that synthesized NPs exhibit an anatase structure. FESEM and TEM images of synthesized PF127/TiO2 NPs showed a polymorphic structure and an average particle size of 5060 nm. The chemical composition of the prepared NPs, which included elements like carbon, titanium, and oxygen, was identified using the EDAX spectrum. With the DLS spectrum, the hydrodynamic sizes of PF127/TiO2 NPs were 176 nm. In the FTIR spectrum, the metal oxide stretching bands like O-Ti-O were located at 664 cm?1 for PF127/TiO2 NPs. The surface defects, including Ti and O vacancies, were studied using the photoluminescence spectrum. The prepared PF127/TiO2 NPs exhibited significant microbial activity for inhibiting hospital pathogenic bacterial and fungal strains, specifically (Staphylococcus aureus) S. aureus, (Streptococcus pneumoniae) S. pneumonia, (Klebsiella pneumoniae) K. pneumonia, (Shigella dysenteriae) S. dysenteriae and (Candida albicans) C. albicans. In addition, PF127/TiO2 NPs had highly anti-cancer properties against human blood cancer (MOLT-4) cell lines. Furthermore, we found that synthesized PF127/TiO2 NPs exhibited anti-inflammatory activity. 2023
Elamin N.Y.; Indumathi T.; Ranjith Kumar E.
https://doi.org/10.1016/j.molliq.2023.121655 https://www.scopus.com/inward/record.uri?eid=2-s2.0-85150429762&doi=10.1016%2fj.molliq.2023.121655&partnerID=40&md5=eca3a4703f59133f7ba87dbb9d596a7a
Elamin N.Y., Department of Chemistry, College of Science, Imam Mohammad Ibn Saud Islamic University (IMSIU), Riyadh, 11623, Saudi Arabia, Department of Chemistry, Sudan University of Science and Technology, Khartoum, Sudan; Indumathi T., Department of Chemistry, CHRIST (Deemed to be University), 560029 Karnataka, Bangalore, India; Ranjith Kumar E., Department of Physics, KPR Institute of Engineering and Technology, Tamil Nadu, Coimbatore, 641407, India
Evaluation of physicochemical and biological properties of SnO2 and Fe doped SnO2 nanoparticles
Anticancer; Doping concentration; Green synthesis; Nanoparticles; Structural analysis
In recent decades, nanoparticle synthesis has been used for various physical and chemical methods. However, different toxic chemicals are used during this synthesis process to address these concerns, which has multiple effects on environmental toxicity and high cost. To avoid these problems, we need a cost-effective and environmentally friendly approach. In this study, green synthesis was used to make tin oxide (SnO2) and ferrous doped tin oxide (SFO) nanoparticles (NPs) from Morinda citrifolia leaf extracts. The X-ray diffraction patterns of SnO2 and SFO NPs reveal a tetragonal crystalline structure. From the FESEM image of synthesized SnO2 and SFO NPs, their spherical structure and chemical composition were identified by EDX spectrum. Through the DLS spectrum, the hydrodynamic size was observed at 66 and 61 nm for SnO2 and SFO NPs, respectively. In the FTIR spectrum, the OSnO stretching vibration peak arises at (606 & 509 cm?1 for SnO2 NPs) and (613 & 538 cm?1 for SFO NPs). Photoluminescence is used in materials to detect surface defects and impurity levels. The antibacterial activity of the SnO2, SFO NPs, and conventional antibiotics like amoxicillin NPs is effectively inhibited against S. aureus and E. coli bacterial strains. SFO NPs exhibit a higher antibacterial activity as compared to SnO2 and amoxicillin. The anticancer efficacy of increased SFO NPs compared to SnO2 NPs was tested against (MDA-MB-237) human breast cancer cells. These results suggest that Fe ions modified SnO2 NPs could be used in healthcare industrial applications to improve human health. 2022 Elsevier Ltd and Techna Group S.r.l.
Ceramics International, Vol-49, No. 2, pp. 2388-2393.
https://doi.org/10.1016/j.ceramint.2022.09.211 https://www.scopus.com/inward/record.uri?eid=2-s2.0-85139025089&doi=10.1016%2fj.ceramint.2022.09.211&partnerID=40&md5=b2364d9c92201c5d935a29b12127d0b4
Elamin N.Y., Department of Chemistry, College of Science, Imam Mohammad Ibn Saud Islamic University (IMSIU), Riyadh, 11623, Saudi Arabia, Department of Chemistry, Sudan University of Science and Technology, Khartoum, Sudan; Indumathi T., Department of Chemistry, CHRIST (Deemed to be University), Karnataka, Bangalore, 560029, India; Ranjith Kumar E., Department of Physics, KPR Institute of Engineering and Technology, Tamil Nadu, Coimbatore, 641407, India
Salutogenesis: A Paradigm for Organizational Health And Sustainability- A Metaphorical analysis.
https://www.researchgate.net/publication/262936244_Salutogenesis_A_Paradigm_for_Organisational_Health_and_Sustainability_-_A_Metaphorical_Analysis
Journal of Management Studies Coimbatore
Mediation of Perceived Innovation Characteristics on ERP Adoption in Industrial Cluster
absorptive capacity; Cluster institutional isomorphic pressures; ERP adoption; industrial cluster; perceived innovation characteristics
An industrial cluster, due to its close network of institutions, will experience various pressures that force the industry to have a homogeneous structure, norms, and practices. These pressures can also lead to adoption of innovative technologies. Enterprise Resource Planning (ERP) is perceived as a sophisticated technology and diffuses across the cluster by its innovative characteristics. However, firms in the industrial cluster will have different rate of adoption of technologies due to the varying level of knowledge spillover and a heterogeneous absorptive capacity. This study empirically tests how absorptive capacity mediates the institutional forces and the perceived innovation characteristics towards the ERP adoption in an industrial cluster. Mediation effect and the model validity are tested using SEM technique. The results show that absorptive capacity complements the forces of institutional pressure and the perceived innovation characteristics on ERP adoption. The implications of managing the absorptive capacity for better ERP adoption are discussed. 2016 World Scientific Publishing Company.
International Journal of Innovation and Technology Management, Vol-13, No. 3
Elangovan N., Department of Management, Christ University, Benguluru, India
Adapting Case Study Pedagogy for Non-Residential Business Schools: Strategies for Implementation
Case study pedagogy is widely recognized as a powerful teaching approach in business education programs. However, its implementation in non-residential business schools poses distinct challenges. Optimizing case study pedagogy to the unique needs and circumstances of non-residential students necessitates a specific strategy. This chapter delves into various strategies essential for the effective implementation of case study pedagogy in non-residential business schools. First, an overview of 2024 by IGI Global. All rights reserved.
Elangovan N.; Dilipchandra S.; Kapse M.; Sundaravel E.
Utilizing Case Studies in Business Education, pp. 31-58.
Elangovan N., Christ University, India; Dilipchandra S., Christ University, India; Kapse M., Symbiosis Centre for Management and Human Resource Development, Symbiosis International University (Deemed), India; Sundaravel E., Christ University, India