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                <text>Suicide and Youth: Positive Psychology Perspective</text>
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                <text>Suicidality and self-harm among adolescents and young adults need immediate attention and support. This is often seen as a cry for help where a feeling of entrapment experienced by individuals pushes them to see self-harm as a coping mechanism. Lack of social support and co-morbid emotional disorders influences suicidal ideation into planning and action. A general feeling of helplessness that gets triggered often leads to anguish. These emotions get internalized and directed inwards leading to self-directed anger which facilitates a suicidal act. Early detection and identification can prevent the loss of lives and help individuals to learn effective ways of coping. Gatekeeper Training for caregivers, teachers, and their peers will help in detecting the early signs of suicide risk. Intervention based on positive psychology will help not only in crisis management but also as preventive and maintenance therapy in holistic mental health. The concepts of hope, forgiveness, self-compassion, gratitude, and resilience can be incorporated into the intervention programs to build a better therapeutic system for youths dealing with suicidality. Practicing effective coping mechanisms every day and making it a ritual of ones daily life is the need of the hour. Integrating adaptive ways of emotional expression and learning matured means to deal with painful emotions and trauma is what needs to be incorporated into the intervention plan. The chapter focuses on these aspects and aims to make a connection between assessing and easy identification of youths in distress related to suicidality and providing a holistic intervention to help them cope with the situation.  The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023.</text>
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                <text>Banerjee T.; Reddy K.J.</text>
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                <text>Handbook of Youth Development: Policies and Perspectives from India and Beyond, pp. 327-342.</text>
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                <text>Banerjee T., Department of Psychology, CHRIST (Deemed to Be University), Bengaluru, India; Reddy K.J., Department of Psychology, CHRIST (Deemed to Be University), Bengaluru, India</text>
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                <text>Optical Spectroscopy of Classical Be Stars in The Galaxy  </text>
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                <text>A classical Be (Be hereafter) star is a special type of massive B-type main newlinesequence star surrounded by a geometrically thin, equatorial, gaseous, decretion disc orbiting the central star. Spectra of Be stars show emission lines of different elements. Studying these lines provide an excellent opportunity to understand the geometry and kinematics of the circumstellar newlinedisc and properties of the central star itself. Be stars, thus provide excellent opportunities to study circumstellar discs. However, the disc formation mechanism in Be stars the Be phenomenon is still poorly understood. The present study focuses on studying a large sample of Be stars through newlineoptical spectroscopy and using two national optical telescope facilities. We performed the spectroscopic study of all major emission lines for a sample of 115 feld Be stars in the wavelength range of 3800 - 9000 using the 2.1-m HCT facility at Ladakh. To our knowledge, this is the frst study where near simultaneous spectra covering the whole spectral range of 3800 - 9000 has been studied for over 100 feld Be stars. We, therefore, produce an atlas of emission lines for Be stars which will be a valuable resource for researchers involved in Be star research. We made use of the unprecedented capability of the Gaia mission to re-estimate the extinction parameter (AV ) for these stars. The estimated AV values are used for extinction correction in the analysis of Balmer decrement (D34 and D54) for our program stars. D34 in our sample ranges between 0.1 and 9.0, whereas the corresponding D54 value mostly (and#8776; 70%) ranges between 0.2 and 1.5, clustering somewhere near 0.8 and#8722; 1.0. Our study indicates that Be star discs are generally optically thick in nature in majority of the cases. Through comparative study with the literature, we also noticed that the Hand#945; EW values in Be stars are usually lower than -40  Further from our analysis, it appears that the emission strength of Hand#945;, P14, FeII 5169 and OI 8446 is more in early B-type stars.</text>
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                <text>Banerjee, Gourav</text>
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                <text>Christ(Deemed to be University)</text>
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                <text>Mathew, Blesson</text>
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                <text>&lt;a href="http://hdl.handle.net/10603/547689" target="_blank" rel="noreferrer noopener"&gt;http://hdl.handle.net/10603/547689&lt;/a&gt;</text>
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                <text>Patent Number: 202041044754, Applicant: Prof.Santosh Kumar J.&#13;
Systems and methods are provided to understand mental health state of an individual by audio sensors, video sensors and log data of mobile devices. To get more accurate and reliable data machine learning module is also integrated with three input forms of data provided to the system. Once any abnormality is observed, it is reported to the caretakers with a coping strategy to solve the illness at initial stages.</text>
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                <text>Framework for Sustainable Energy Management using Smart Grid Panels Integrated with Machine Learning and IOT based Approach.</text>
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                <text>Maintaining a consistent supply of power is essential for the well-being of the economy, the public, and one's own health. The generation of energy, as well as its distribution, monitoring, and management, are all undergoing fundamental changes as a result of the implementation of a smart grid (SG), which is authorised to include communication technology and sensors into power systems. There are a lot of problems that need to be fixed before the interoperability of the smart grid can be determined. The integration of renewable energy sources and smart grid technology market size and energy management is a sustainable solution to the problem of energy demand management. The importance work quickly toward the development of an efficient Energy Management Model (EMM) that integrates smart grids and renewable energy sources. When it comes to the modelling of complex and non-linear data, machine learning (ML), Internet of Things (IoT) approaches often perform better than statistical models. So, utilizing a machine learning approach for the EMM is a good option since it simplifies the EMM by generating a single trained model to anticipate its performance characteristics across all conditions. This may be accomplished via the use of an EMM created using an ML method. It was recommended that a certain flexibility sample be used as a control mechanism for incursion into the smart grid. The outcomes of the experiment indicate that the demand-side management (DSM) device is more resistant to infiltration and is enough to lower the energy usage of the smart grid.  2024, Ismail Saritas. All rights reserved.</text>
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                <text>Bani Ahmad A.Y.A.; William P.; Uike D.; Murgai A.; Bajaj K.K.; Deepak A.; Shrivastava A.</text>
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                <text>International Journal of Intelligent Systems and Applications in Engineering, Vol-12, No. 2s, pp. 581-590.</text>
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                <text>Ismail Saritas</text>
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                <text>Bani Ahmad A.Y.A., Department of Accounting and Finance Science, Faculty of Business, Middle East University, 8788, Amman, 11831, Jordan; William P., Research and Development, Department of Information Technology, Sanjivani College of Engineering, Savitribai Phule Pune University, Pune, India; Uike D., Department of MBA, Dr. Ambedkar Institute of Management Studies and Research, Maharashtra, Nagpur, India; Murgai A., School of Business and Management, Christ University, Lavasa, Pune, India; Bajaj K.K., RNB, Global University, Rajasthan, Bikaner, India; Deepak A., Saveetha School of Engineering, Saveetha Institute of Medical and Technical Sciences, Saveetha University, Tamilnadu, Chennai, India; Shrivastava A., Saveetha School of Engineering, Saveetha Institute of Medical and Technical Sciences, Tamilnadu, Chennai, India</text>
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                <text>Banik H., Department of School of Business and Management, CHRIST (Deemed to be University), Bangalore, India; Iyer L.S., Department of School of Business and Management, CHRIST (Deemed to be University), Bangalore, India</text>
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                <text>Fraudulent activities have increased along with the new prospects of the digital economy's quick growth for both consumers and enterprises. Conventional techniques of fraud detection are insufficient to keep up with these ever-evolving fraudulent strategies. In this sense, machine learning (ML) and artificial intelligence (AI) have become potent instruments to prevent and detect fraud and guarantee the safety of online transactions. This study examines the function of AI and ML and shows how these technologies can spot irregularities and intricate patterns that would be challenging to find with conventional methods. The study includes various methods of AI-based fraud detection and analyses important ethical issues related to these practices. Furthermore, the study looks at developing technology and trends that will probably influence fraud detection in the future. In conclusion, the revolutionary potential of AI and ML in building a safer digital economy is analysed.  2024, IGI Global. All rights reserved.</text>
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                <text>Auxiliary Power Unit (APU); Fuel Cell Systems (FCS); Stacked Residual Gated Recurrent Unit Convolutional Neural Networks (S R-GRU-CNN)</text>
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                <text>A heavy-duty vehicle's fuel usage, and thus its carbon dioxide emissions, are significantly impacted by the driver's behavior. The average fuel economy of a car varies by about 28% between drivers. Fuel efficiency can be improved by driver education, monitoring, and feedback. Fuel efficiency-based incentives are one form of feedback that can be provided. The largest challenge for transportation companies implementing such incentive programs is how to accurately evaluate drivers' fuel consumption. The processes of preprocessing, feature extraction, and model training are all utilized in the suggested method. Principal component analysis (PCA) is widely utilized in data science's preprocessing stage. GMM is used for feature extraction. Afterwards, SR-GRU-CNN is used to train the models based on the selected features. When compared to the two most popular alternatives, CNN and SR-GRU, the proposed methodexcels.  2023 IEEE.</text>
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                <text>2nd International Conference on Automation, Computing and Renewable Systems, ICACRS 2023 - Proceedings, pp. 811-816.</text>
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                <text>Bansod P.J., Department of Mechanical Engineering, G H Raisoni College of Engineering &amp;amp;Management, Wagholi, Pune, India; Mohan U., Department of Mechanical Engineering, University of Technology and Applied Sciences, Salalah, Oman; Yadav D.K., Department of Electronics &amp;amp; Communication Engineering, IIMT College of Engineering, Greater Noida, India; Singh Y., India Meteorological Department, Mausam Bhawan, Lodhi Road, New Delhi, India; Sharmila P., Department of Science &amp;amp; Humanities, Prince Shri Venkateshwara Padmavathy Engineering College, Tamil Nadu, Chennai, India; Chauhan A., Department of Life Sciences, School of Sciences, CHRIST (Deemed to be university), Karnataka, Bengaluru, India</text>
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                <text>The objective of this paper is to propose and analyze clustering techniques for neonatal jaundice which will help in grouping the babies of similar symptoms. A variety of methods have been introduced in the literature for neonatal jaundice classification and feature selection. As far as we know, clustering techniques are not used for neonatal jaundice data set. This paper studies and proposes clustering techniques such as K-Means, Genetic K-Means and Bat K-Means for jaundice disease. To find the number of clusters elbow method is used. The clusters are validated using RMSE, SI and HI. The experimental results carried out in this paper shows bat k-means clustering performs better than K-means and genetic K-means.  2018, Springer International Publishing AG.</text>
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                <text>Advances in Intelligent Systems and Computing, Vol-633, pp. 408-419.</text>
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                <text>Banu P.K.N., Department of Computer Science, Christ University, Bengaluru, India; Own H.S., National Research Institute of Astronomy and Geophysics, Helwan, Egypt; Olariu T., Vasile Goldis Western University of Arad, Arad, Romania; Olariu I., Vasile Goldis Western University of Arad, Arad, Romania</text>
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                <text>Diabetic Retinopathy Detection Using Various Machine Learning Algorithms</text>
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                <text>The advances in technologies have paved the way to generate huge amounts of data in a variety of forms. Machine learning techniques, accompanied by Artificial Intelligence with its challenging nature help in extracting meaningful information from such data. This will have a great impact on many sectors, such as social media analytics, construction and healthcare, etc. Computer-aided clinical decision-making plays a vital role in todays medical field. Hence, a high degree of accuracy with which machine learning algorithms can detect diabetic retinopathy is really in demand. Convolutional neural networks, a deep learning technique, have been used to recognize pathological lesions from images. Image processing and analytics methods are used and have been trained to recognize the significant complications of diabetes, which cause damage to the retina, diabetic retinopathy (DR). Though this condition does not show any symptoms in its early stages, it has to be screened, diagnosed and treated at the earliest or it may lead to blindness. Deep neural networks have proved successful in screening DR from retinal images and handling the risks that may arise due to the disease. This chapter focuses on detecting diabetic retinopathy in retinal images by using efficient image processing and deep learning techniques. It also attempts to investigate the requirements of image pre-processing techniques for diabetic retinopathy. Experiments are carried out by taking a set of retinal images and predicting the level of diabetic retinopathy on a scale of 0 to 4. Deep learning techniques like CNN and DenseNet are applied and tested.  2024 Taylor &amp;amp; Francis Group, LLC.</text>
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                <text>Machine Intelligence: Computer Vision and Natural Language Processing, pp. 113-128.</text>
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                <text>This study aims to formulate strategies that impact the vaccine supply chain (VSC). This study measures the VSC performance using the proposed strategy concerning stakeholders theory. From the literature review and experts consent, the strategies are classified into six broad strategies as-VSC traceability, VSC visibility, VSC velocity, digitalising VSC, localising VSC, and vaccine inventory. A questionnaire is developed for surveying healthcare organisations and hospitals. All six proposed hypotheses got accepted. The developed model satisfies all the model fit parameters. Strategies like VSC traceability, VSC visibility, VSC velocity, digitalising VSC, localising VSC, and vaccine inventory have positively impacted vaccine supply chain performance. This research will be helpful for healthcare professionals and organisations for the faster delivery of the vaccine. This research will also help policymakers in improving the performance of VSC. This study is also the first to use the stakeholder theory approach for measuring VSC performance. Copyright  2024 Inderscience Enterprises Ltd.</text>
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                <text>Baral M.M., Department of Operations, GITAM School of Business, GITAM (Deemed to be University), Andhra Pradesh, Visakhapatnam, India; Mukherjee S., Department of Operations, GITAM School of Business, GITAM (Deemed to be University), Andhra Pradesh, Visakhapatnam, India; Nagariya R., School of Business and Management, CHRIST (Deemed to be University), Delhi-NCR, Uttar Pradesh, Ghaziabad, India; Chittipaka V., School of Management Studies, Indira Gandhi National Open University, Delhi, India</text>
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                <text>This research aims to determine critical success components for implementing the vendor-managed inventory (VMI) and test their influence on the inventory in Indian hospitals. The independent and dependent components of the research are identified from the extensive literature review. The independent variables are top management commitment, supply chain strategy, business process integration, continuous improvement, resource sharing, and information technologies adoption. The dependent variable identified is the adoption of vendor-managed inventory. The study results suggest that the proposed latent variables significantly impact the VMI and significantly contribute to VMIs implementation and sustainability. The study highlights the importance of VMI in Indian hospitals, and therefore, it will help the management focus on the VMI for enhanced operational efficiency. Previous studies have not empirically tested the impact of the suggested practices for VMI in Indian hospitals. The analysis would help evaluate VMI adoption in Indian hospitals. Copyright  2024 Inderscience Enterprises Ltd.</text>
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                <text>International Journal of Procurement Management, Vol-21, No. 2, pp. 180-197.</text>
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                <text>Baral M.M., Department of Operations, GITAM School of Business, GITAM (Deemed to be University), Andhra Pradesh, Visakhapatnam, India; Mukherjee S., Department of Operations, GITAM School of Business, GITAM (Deemed to be University), Andhra Pradesh, Visakhapatnam, India; Nagariya R., School of Business and Management, CHRIST (Deemed to be University), Delhi-NCR, Uttar Pradesh, Ghaziabad, India; Chittipaka V., School of Management Studies, Indira Gandhi National Open University, Delhi, India</text>
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                <text>Analysis of factors impacting firm performance of MSMEs: lessons learnt from COVID-19</text>
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                <text>COVID-19; Firm performance; MSMEs; Organizational resilience; Supply chain disruptions</text>
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                <text>Purpose: The micro, small and medium scale enterprises (MSMEs) faced various challenges in the ongoing COVID-19 pandemic, making it challenging to remain competitive and survive in the market. This research develops a model for MSMEs to cope with the current pandemic's operational and supply chain disruptions and similar circumstances. Design/methodology/approach: The exhaustive literature review helped in identifying the constructs, their items and five hypotheses are developed. The responses were collected from the experts working in MSMEs. Total 311 valid responses were received, and the structural equation modeling (SEM) approach was used for testing and validating the proposed model. Findings: Critical constructs identified for the study are-flexibility (FLE), collaboration (COL), risk management culture (RMC) and digitalization (DIG). The statistical analysis indicated that the four latent variables, flexibility, digitalization, risk management culture and collaboration, contribute significantly to the firm performance of MSMEs. Organizational resilience (ORS) mediates the effects of all the four latent variables on firm performance (FP) of MSMEs. Practical implications: The current study's findings will be fruitful for the manufacturing MSMEs and other firms in developing countries. It will enable them to identify the practices that significantly help in achieving the firm performance. Originality/value: The previous researches have not considered the effect of organizational resilience on the firm performance of MSMEs. This study attempts to fill this gap.  2022, Emerald Publishing Limited.</text>
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                <text>Baral M.M.; Mukherjee S.; Nagariya R.; Singh Patel B.; Pathak A.; Chittipaka V.</text>
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                <text>Benchmarking, Vol-30, No. 6, pp. 1942-1965.</text>
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                <text>Baral M.M., Department of Operations, GITAM Institute of Management, GITAM (Deemed to be University), Visakhapatnam, India; Mukherjee S., Department of Operations, GITAM Institute of Management, GITAM (Deemed to be University), Visakhapatnam, India; Nagariya R., School of Business and Management, CHRIST University, Delhi NCR Campus, Ghaziabad, India; Singh Patel B., Department of Operations Management, Thiagarajar School of Management, Madurai, India; Pathak A., Department of Management, Bule Hora University, Bule Hora, Ethiopia; Chittipaka V., School of Management Studies, Indira Gandhi National Open University, New Delhi, India</text>
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                <text>The aim of the present study was to translate the Interpersonal Emotion Regulation Questionnaire (IERQ) into the Tamil language and examine its psychometric properties in the Indian cultural context. Data were collected from a dyadic sample of 340 married heterosexual couples (N = 680) currently residing in India. The mean age of husbands was 39.57 (SD = 6.10; 26 ? range ? 58), and the wives was 35.33 (SD = 5.72; 23 ? range ? 54). Descriptive results indicated that husbands and wives reported similar levels of interpersonal emotion regulation. Confirmatory factor analysis showed a 20-item model with four factorsenhancing positive affect, perspective-taking, soothing and social modeling, similar to the original version, fits the data well. Furthermore, the multiple-group analysis indicated robust measurement invariance across gender (husbands vs. wives), family type ( joint vs. nuclear) and marriage type (arranged vs. love), indicating that the Tamil version of the IERQ operates similarly across these groups. Besides, the Tamil version of the IERQ showed good convergent and discriminant validity with measures of dyadic coping and relationship satisfaction. Implications for research and couples therapy in the Indian cultural context are discussed.  2022, PsychOpen. All rights reserved.</text>
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                <text>Interpersona, Vol-16, No. 1, pp. 36-55.</text>
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                <text>ISSN: 19816472</text>
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                <text>Barani Kanth D., Department of Applied Psychology, Pondicherry University, Pondicherry, India; Indumathy J., Department of Applied Psychology, Pondicherry University, Pondicherry, India, Department of Psychology, Christ University, Bangalore, India</text>
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                <text>The rapid proliferation of digital currencies has ignited significant interest in understanding investor behaviour and the regulatory challenges within this evolving financial landscape. The study seeks to elucidate the factors that influence investor behaviour in the cryptocurrency/digital currency market and explore the regulatory implications associated with digital currency investments. A systematic approach was adopted to identify, review, and analyze relevant scholarly articles from reputable databases. The selected articles span various dimensions of the cryptocurrency/digital currency ecosystem, including investor behaviour, market efficiency, technological innovation, and regulatory measures. It reveals that investor behaviour in the digital currency market is influenced by a complex interplay of emotions, market sentiment, and information asymmetry. Additionally, market efficiency theories are being reconsidered in light of the highly volatile nature of cryptocurrencies. Regulatory challenges encompass issues related to fraud, pump-and-dump schemes, and legal ambiguity.  2024, IGI Global. All rights reserved.</text>
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                <text>Digital Currency Assets and Challenges to Financial System Stability, pp. 144-177.</text>
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                <text>Initial coin offerings as a form of alternative financing for start-ups: A systematic literature review and bibliometric analysis</text>
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                <text>This chapter aims to provide a comprehensive overview of the field of initial coin offerings ICOs as a form of alternative financing for start-ups. Through a systematic literature review and bibliometric analysis, the study explores the current understanding of ICOs, the potential uses of fractional ownership of digital assets, and the opportunities and challenges associated with this form of fundraising. The study also highlights the importance of study, which provided the first comprehensive explanation of ICOs and their characteristics. The findings of the research suggest that ICOs have the potential to revolutionize the way start-ups raise funds, but they also present significant challenges and risks. The study encourages further research to continue exploring the opportunities and challenges presented by ICOs, as well as the potential impact of regulatory and legal frameworks on the ICO market. Overall, this chapter provides valuable insights into the complex and evolving topic of ICOs and their implications for start-ups, investors, and the broader economy.  2024, IGI Global. All rights reserved.</text>
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                <text>This study aims to examine the mediating factors of learning environment, technology, good behaviour, and effective communication, sports, media, problem-based case study, and psychological influence on student motivation and behaviour. The study employs secondary data based on peer-reviewed articles, conference papers, and books collected from a variety of sources such as Scopus, WoS, J-Store, EBSCO, ProQuest, etc. between 1982 and 2022. The 103 articles have been selected based on the keywords which related to study. The results evidenced that the mediating factors have a significant positive impact on student motivation and behaviour. This study contributes to the existing literature by providing a com- prehensive understanding of the relationship between the mediating factors and student motivation and behaviour. The findings of this study have implications for educational institutions, policy makers, and educators in creating effective learning environments that can enhance student motivation and behaviour.  2023, IGI Global. All rights reserved.</text>
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                <text>Baranidharan S.</text>
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                <text>Handbook of Research on Creating Motivational Online Environments for Students, pp. 260-289.</text>
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                <text>Baranidharan S., CHRIST University (Deemed), India</text>
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                <text>Driving sustainable development through climate finance in India: A case study of the National Clean Energy Fund (NCEF)</text>
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                <text>This case study examines the national clean energy fund (NCEF) as a climate finance policy in India. The NCEF was established with the objective of promoting renewable energy projects and sustainable development in the country. The study explores the background and context of climate finance, providing an overview of the NCEF's goals and implementation. The case study analyzes the impact of the NCEF by examining its funding allocations and utilization over the years. It highlights the challenges faced in effectively utilizing the funds, such as administrative hurdles, limited capacity, policy uncertainties, project development barriers, financial constraints, and governance issues. Furthermore, the case study discusses the socio-economic impacts of the NCEF, including job creation, clean energy adoption, and environmental benefits. It also explores the lessons learned from the NCEF implementation, identifying areas for improvement and providing recommendations for enhancing climate finance mechanisms in India. This chapter creates a contribution to renewable energy development in India.  2023, IGI Global. All rights reserved.</text>
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                <text>Financing Regions Toward Sustainability in the Midst of Climate Change Risks and Uncertainty, pp. 194-220.</text>
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