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                <text>Exploring digital age influences on undergraduate students mental health through social media, academic pressure and digital literacy</text>
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                <text>Sharma E., Symbiosis International University, Pune, India; Nigam N., School of Business and Management, Christ University, NCR, Delhi, India</text>
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                <text>The research aims to measure the impact of usage of social media, academic pressure, and digital literacy, on mental health. It also aims to measure the mediating role of perceived stress on mental health of undergraduate students. Survey method was used for collecting data from a sample of 565 undergraduate students from state and private universities of Tamil Nadu, Karnataka, Andhra Pradesh, Telangana, and Kerala. EFA and Path analysis was used for testing and validating the conceptual model. The results showed that Social Media Usage increases Perceived Stress and negatively impacts Mental Health Outcomes both directly and indirectly through Perceived Stress. Academic Pressure increases Perceived Stress, which negatively impacts Mental Health Outcomes indirectly. Digital Literacy reduces Perceived Stress and has a positive effect on Mental Health Outcomes both directly and indirectly through reduced stress. Perceived Stress was found to have a significantly negative impact on the Mental Health Outcomes. The demographic variables namely; age, gender, living status, family type, and course type were found to have a significant impact on the usage of social media, academic pressure, digital literacy, perceived stress, and mental health scores of undergraduate students. The study also came up with interventions for managing mental health of under graduate students.  The Author(s) 2025.</text>
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                <text>Sharma E., Symbiosis International University, India; Nigam N., Christ University, India</text>
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                <text>The digital era is providing new methods of learning, thinking, and problem-solving. Introducing the foundational ideas of AI to young learners helps them adjust to a technology-driven environment more effectively. This study explores effective ways to make young learners aged 6-12 aware of basic AI concepts, including logic, patterns, and algorithms. By utilizing secondary data, the study aims to explore effective pedagogical strategies and challenges in early education related to AI, and provide guidelines for parents and educators. The aim of the study is to enable curriculum designers and educators to design an inclusive stakeholders' model that can be used in primary education to introduce AI literacy in a way that enhances the critical thinking, creativity, and problem-solving abilities of young learners. Copyright  2026, IGI Global Scientific Publishing. Copying or distributing in print or electronic forms without written permission of IGI Global Scientific Publishing is prohibited.</text>
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                <text>Sharma H., Graphic Era University, India; Singhal A., Graphic Era University, India; Ali A., Graphic Era University, India; Ghai S., Graphic Era University, India; Singh M., Graphic Era University, India; Saluja K., Christ University, Bangalore, India</text>
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                <text>In this modern world all the organizations are adopting the new technology and making the use of modern technologies to match the HR procedures with the strategic Big Data and AI enhance HR decision-making by providing insights into workforce demographics, performance patterns, and employee behavior. Together, they automate tasks like hiring, training, and performance reviews, while addressing skill gaps, talent acquisition, and retention with unmatched precision. The study examines several important applications, including predictive analysis for workforce planning, AI driven recruitment system and real-time employee sentiment analysis. Additionally, it looks at data protection issues, ethical issues and the necessity of HR personnel being skilled in order to use these technologies effectively. The study demonstrates how Big Data and AI have the ability to change SHRM from a reactive role into a proactive, value-creating discipline by examining case-studies and new trends.  2025 by IGI Global Scientific Publishing. All rights reserved.</text>
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                <text>Systematic Literature Review Methodology and Enabling Systems: A Rigorous Framework for Evidence Synthesis</text>
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                <text>Innovative Approaches to Social Research Challenges;pp.213-254</text>
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                <text>Sharma I., Christ University, India; Sangal S., Christ University, India</text>
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                <text>The exponential rise in academic publications has made synthesizing knowledge and identifying research gaps increasingly challenging. The Systematic Literature Review (SLR) offers a rigorous, transparent, and reproducible approach to evidence synthesis. This chapter discusses the methodological foundations and digital systems that support SLRs, distinguishing them from narrative and scoping reviews. It details key phases such as question formulation, protocol design, search, screening, data extraction, quality appraisal, and synthesis, referencing frameworks like PRISMA 2020. Additionally, it explores the role of AI-driven tools (e.g., EPPI-Reviewer, DistillerSR, Covidence, Rayyan) in improving efficiency and scalability. The chapter concludes with challenges, ethical concerns, and future directions, emphasizing AI integration and living review methodologies.  2026 by IGI Global Scientific Publishing. All rights reserved.</text>
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                <text>Sharma M., Amity School of Architecture and Planning, Amity University, Rajasthan, Jaipur, India; Verma T., Dean School of Architecture and Design, K R Mangalam University, Haryana, Gurugram, India; Kamal A., Architecture Section, Aligarh Muslim University, Aligarh, India; Keskar Y., Amity school of Architecture and Planning, Rajasthan, Jaipur, India; Upreti K., Department of Computer Science, Christ University, Delhi NCR, Ghaziabad, India</text>
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                <text>Sharma M.K., Christ University, Uttar Pradesh, Ghaziabad, India; Alapatt B.P., Christ University, Bengaluru, India; Sharma V., Christ University, Bengaluru, India; Shanbhog M., Christ University, Bengaluru, India</text>
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                <text>A picture is worth a thousand words, uploading images immediately attract attention a comments or tweets. Images are able to trigger multiple emotions and are effectively used for propaganda. Visual propaganda stands for usage of images to fulfil propaganda goals. These images carry meaning, emotion and a purpose.</text>
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                <text>The digital revolution has transformed how brands interact with consumers, creating new opportunities and challenges for brand equity management. In the era of convergence between the physical and digital realms, commonly referred to as the "Phygital Era," brands must navigate a complex landscape to build and sustain their value. This chapter explores strategies for managing brand equity in the digital age, focusing on how brands can create cohesive, engaging, and personalized experiences that seamlessly bridge physical and digital touchpoints. It discusses the importance of omnichannel marketing, data-driven decision-making, and the role of social media and online communities in shaping consumer perceptions. Additionally, the chapter examines how digital transformation impacts brand loyalty, customer engagement, and the evolving expectations of consumers. By understanding the interplay between digital innovation and traditional brand-building practices, businesses can successfully position themselves for long-term growth and success in a rapidly evolving market.  2025, IGI Global Scientific Publishing.</text>
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                <text>Sharma P., Service Now, United States; Logeshwaran J., Christ University, Department of Computer Science, Karnataka, Bengaluru, 560029, India</text>
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                <text>IoT devices have seen explosive growth, causing a data explosion, which makes it almost impossible to manage and monitor these networks. This has led to a demand for solutions providing real-time monitoring and detection of anomalies in cloud-based Internet of Things (IoT) networks. In cybersecurity, the term real-time monitoring denotes the ongoing analysis of data and network performance to detect potential problems. It allows for the identification of anomalies and possible weaknesses in the system. In contrast, anomaly detection is the process of finding deviations from what is considered normal for a system or data. Within cloud-based IoT networks, this might involve identifying abnormal traffic patterns or unusual activity from devices. To solve these challenges, we propose a cloud computing and machine learning-based solution. IoT devices generate a massive amount of data, which is processed in real-time and stored on the cloudbased infrastructure. Many machine learning algorithms analyze these data algorithms to identify anomalies or threats to security. This solution provides an active early warning detection of security breaches from the network management perspective, along with timely response in the case of abnormal behaviour. It will ultimately result in improved cloud-based networks for IoT devices with regard to reliability, security, and performance. This solution can be a key factor in driving the mass implementation of IoT in various sectors.   2025 IEEE.</text>
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                <text>Sharma R., Christ University, India; Chaudhary B., Christ University, India</text>
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                <text>The supply networks form the backbones of the global economy as a flow of goods, services, and information across industries and geographies. Meanwhile, globalization and digitalization that promoted the increasing complexity of supply chains highlight even greater urgency for sustainable and responsible supply chain management. This chapter considers the manner in which multi- stakeholder collaboration can become an important enabler of sustainability within the supply chains since no individual stakeholder has a chance of unilaterally solving all of its various problems. From the case studies conducted on Sedex and the automotive sector, this study has shown that sustainability strategies take a diversified view-from corporate management to government bodies, NGOs, and local communities- to align priorities, bridge cultural gaps, and develop collective solutions that will drive systemic change. Given the nature of conflicting priorities, the research finds concentration on cross- industry cooperation and the role of external organizations in promoting responsible practices.  2025, IGI Global Scientific Publishing. All rights reserved.</text>
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                <text>Next Energy;Volume;11;Issue;;Article No.;100514;</text>
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                <text>Sharma S., Computer Science Engineering Department, Maharishi Markandeshwar University (MMU), Haryana, Mullana, India; S.S. N., Department of Computer Science, Christ University, Bangalore, India; Arya P., Department of Artificial Intelligence &amp;amp; Machine Learning, School of Engineering &amp;amp; Technology, IIMT University, Meerut, India; Prakash C., Amity School of Engineering and Technology, Amity University Punjab, Punjab, Mohali, India</text>
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                <text>The indicators of electric vehicle performance such as state of charge (SOC), remaining useful life (RUL), and charge demand need to be accurately forecasted to ensure maximum energy control and battery life. The models used are usually not able to capture the spatial and temporal correlation of battery data and be robust to the presence of noisy measurements. In this study, we model a sequential attention-based deep learning structure with convolutional neural networks, gated recurrent units, and an attention mechanism that can ultimately understand the local features, temporal relationships, and dynamic significance of various features in sequential battery data. The hybrid architecture of this model allows it to extract local spatial features, long-term sequential dependencies and dynamically find the importance of the critical time steps. We also develop a hybrid loss that is an accumulation of Huber loss and Mean Squared Error, which is much more resilient to outliers and at the same time has high prediction accuracy. It is experimentally proven that the proposed model has R2 values of 0.9575, 0.9558, and 0.9199 on SOC, RUL, and charge demand, respectively, which are better than the current single-architecture methods.  2026 The Authors</text>
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