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The Importance of Corporate Social Responsibility in Social Value Creation
The fundamental idea behind CSR is that corporations have duties that go beyond lawful execution of their economic duties (Steiner & Steiner, 2009). The waning hold of states over powerful transnational corporations and the increasing recognition of the potential of corporations to contribute to the welfare needs underline the importance of CSR in contemporary society. It is believed that a good CSR strategy would lead to significant reductions in business risks, including stakeholder, regulatory or marketplace sanctions. India being the first country to make CSR mandatory recognizes the potential of firms to synergize with the state in achieving larger developmental causes. This chapter is an attempt to review the CSR contributions toward public health during COVID-19. This chapter analyses the ways in which corporate social responsibility could contribute toward value creation in society and proposes a community-based convergent model of CSR implementation. 2024 by World Scientific Publishing Co. Pte. Ltd. -
Forecasting of Environmental Sustainability through Green Innovation of E-Vehicle Industry
E-mobility sustainability forecasting is getting more detailed with study, taking into account social cost in addition to technological, economic, or environmental factors. One solution for reducing greenhouse gas emissions is to implement green innovation in the transportation sector. The citizenrys view and acceptance of electric cars must be improved, more research into the social cost of these innovations is required. Consequently, the transportation industry might decarbonize more quickly. Another approach to do it is to advocate for more all-encompassing green innovations that can enhance sustainable development. Using Our Common Future, published in 1987 by the World Commission on Environment and Development [1], the commission emphasized the importance of sustainability while integrating social and economic development. Additionally, it recommended that governments take environmental factors into account while making decisions. The significance of sustainability was then increased and institutionalized, which meant that nations began passing laws that promoted sustainability. Consumer awareness of sustainability is rising largely from an economic and environmental standpoint. This also has an impact on the transportation industry and poses significant environmental, social, and economic difficulties. However, given that it generates close to 5% of the GDP and employs almost 11 million people, transportation is crucial from an economic standpoint. 2024 Sachi Nandan Mohanty, Preethi Nanjundan and Tejaswini Kar. -
Legal and Bioethical View of Educational Sectors and Industrial Areas of 3D Bioprinting
Recent advancements in three-dimensional printing (3D printing) within the medical field, particularly in the realm of 3D bioprinting, have shown tremendous potential in transforming various medical therapies, offering new approaches to treat organ failure and injury. However, amidst this optimism, several significant ethical and legal challenges remain unresolved before the application and transplantation of 3D bioprinted technology and organs in human subjects can become a reality. This chapter focuses on exploring the ethical and legal constraints associated with 3D bioprinting technology from both educational and industrial perspectives, recognizing their crucial roles as cornerstones for future applications. Furthermore, the analysis of 3D bioprinting technology will be conducted through the lens of the fundamental medical ethics principle, Primum non nocere; First, do no harm. Moreover, the pressing need for effective and timely standalone laws to regulate the subject of 3D printing is emphasized. This urgency arises from the grave concerns posed by the future implications of this technology on Indias scientific research and medical practice. The aim of this paper is to provide a comprehensive examination of the ethical and legal challenges posed by 3D bioprinting technology. By considering both educational and industrial perspectives, this research seeks to shed light on the complexities surrounding the application and transplantation of 3D bioprinted organs. Additionally, the analysis through the principle of Primum non nocere will contribute to the understanding of the ethical implications inherent in this innovative technology. Ultimately, this study advocates for the formulation of appropriate regulations and guidelines through the implementation of effective standalone laws, ensuring the responsible development and utilization of 3D printing technology in the realm of scientific research and medical practice in India. 2024 Scrivener Publishing LLC. -
Forecasting Demand for Paddy and Cotton in India: Empirical Analysis Using Machine Learning Models
India has a thriving and varied agricultural sector, which has long served as the foundation of the economy. Agriculture contributes significantly to Indias economy and is essential to the nations food security because a sizable percentage of the countrys agricultural population works in farming and associated industries. Indian farmers have managed to successfully produce a variety of commodities, including cash crops like cotton and sugarcane as well as staples like rice and wheat, despite confronting numerous obstacles like small landholdings, poor infrastructure, and unpredictable weather. In this context, it is crucial to examine the status of Indian agriculture at the moment, its advantages and disadvantages, and the possibilities and difficulties confronting farmers and policymakers. 2024 Sachi Nandan Mohanty, Preethi Nanjundan and Tejaswini Kar. -
Harnessing the Power of Climate Activism: Insights from Psychological Perspectives on Climate Change EngagementA Systematic Review
Scientific evidence has validated the inevitability of global warming and its effect in the form of climate change. There has been an increase in climate strikes and other forms of climate activism in recent years. It is important to understand the research landscape in psychological literature with regards to climate change and climate activism, to help guide future researchers. The databases of PubMed (Keywords: climate activism, climate change, psychology, n?=?1), Google Scholar (Keywords?=?climate activism, climate change, psychology, n?=?200) and Scopus database (Keywords: climate activism AND climate change AND psychology, n?=?160) were searched to create the pool of research documents. This was further filtered according to the inclusion and exclusion criteria. In the first section of this article, we have tried to explore the temporal and geographic growth trends of climate change research and collaborations using R (Bibliometric package). In the second section, we have used a text-mining approach to identify the research topics being explored in the climate change literature. R package tm along with associated packages were used to do the processing and subsequent grouping of the themes. In order to refine the classification the identified groupings were supervised by the authors. The final documents have been scoured to extract an overall understanding of the existing concepts explored so far and gauge their impact in the realm of climate change research. This systematic study casts light on the psychological views on climate activism and offers insightful information about the underlying causes that affect peoples involvement in the fight and struggle against climate change. The creation of more effective techniques for encouraging climate activism and utilizing its capacity to inspire significant action to address climate change can be influenced by an understanding of these elements. In order to address the complex issues of climate change, this chapter emphasizes the value of multidisciplinary collaboration amongst psychologists, policymakers, educators, and activists. The Author(s), under exclusive license to Springer Nature Switzerland AG 2024. -
Wireless Technology for Providing Safety in Vanets
Both the safety and efficiency of our present transportation systems might benefit greatly from the implementation of vehicular ad hoc networks (VANETs). In this book chapter, the author investigates the significance of wireless technology in improving VANET security. Before discussing the numerous wireless technologies that form the backbone of VANET security, this chapter digs into VANET architecture and communication paradigms. These can be anything from tried-and-true standards like IEEE 802.11p (WAVE) to forward-thinking concepts like 5G cellular networks ability to link vehicles to other objects. Collision avoidance, cooperative adaptive cruise control, junction collision alarms, and pedestrian safety are just some of the real-world applications of V2V communication that have been shown. The importance of security mechanisms, such as authentication, encryption, and data integrity, is discussed at length in this chapter. The narrative also delves into the fascinating intersections of VANETs with autonomous cars and AI, all while tackling issues like spectrum allocation, scalability, and privacy concerns. Case studies shed light on the concrete effects of VANET safety installations, showing how they may reduce accident rates and ease traffic congestion. This last chapter summarizes the potential of wireless technologies to usher in an era of enhanced road safety via the medium of VANETs, distills important insights, and calls for greater advances. 2024 CRC Press. -
Applications of Machine Learning and Deep Learning Models in Brain Imaging Analysis
Brain imaging is an umbrella term including many non-invasive techniques that objectively monitor brain function. Such monitoring leads to understanding how the brain works by presenting selected stimuli. More importantly, brain function monitoring allows physicians to diagnose and predict brain disorders. In the last decade, several machine learning and deep learning models have been developed by researchers to process and analyse brain imaging data for the diagnosis, detection, and prediction of brain disorders, such as stroke, schizophrenia, autism, psychosis, and Alzheimers. This chapter reviews the various applications and properties of machine learning and deep learning models for brain image analysis. The chapter also highlights the deep learning models that have either understood the test of time or shown the promise to solve challenging problems involving brain imaging data. The review also discusses various open issues yet to have practical solutions or methodologies with the help of machine learning and deep learning. The research covers a wide range of imaging modalities, disorders and models to expose researchers and practitioners in neurological disorders and machine learning and deep learning to each others field, hopefully leading to fruitful collaborations and practical solutions for processing brain images. 2024 selection and editorial matter, Anitha S. Pillai and Bindu Menon; individual chapters, the contributors. -
Internet of Things (IoT) as a Game Changer to the Education Sector
This study examines the use of internet of things (IoT) technology in the field of education, concentrating on its uses, advantages, and difficulties. The current educational system frequently fails to provide individualized learning opportunities since it is characterized by traditional classroom settings and teacher-centered learning. But the emergence of IoT and its companion technologies, including big data, artificial intelligence, and network communication, offers fresh chances to transform education. The IoT architecture in the education sector is covered in the opening section of the paper, with an emphasis on the function of IoT devices in building a networked environment. These tools, such as smart HVAC (heating, ventilation, and air conditioning) systems, make it easier to gather and analyze enormous volumes of data. Institutions of higher learning can get important insights and create individualized learning strategies, thanks to the integration of big data and artificial intelligence. The report also examines a variety of IoT uses in education. It emphasizes the importance of IoT in remote learning, which has become more popular recently. It also demonstrates how the internet of things has influenced the development of smart campuses with interactive whiteboards and other IoT gadgets. The importance of personalized learning in contemporary education is also discussed, with IoT acting as a catalyst for experiences that are specifically suited for students. The study also looks at how IoT might benefit students with disabilities and improve staff and student health monitoring. The use of augmented reality and virtual reality tools in teaching is also investigated. The study explores Edutech-based IoT solutions, concentrating on their function in the processes of teaching, learning, and evaluation. It examines management and government initiatives on both a national and international scale, including those from Ireland's Future Schools, Jharkhand's DigiSAT, and Assam's online job advisory portal. The Kajeet Smart Bus, C-Pen, and Ipevo VZ-X Wireless Document Camera are just a few examples of IoT deployments in education that are highlighted in the study. These instances highlight the concrete contribution of IoT to improving educational practices. IoT tools are also examined in relation to several educational contexts, such as primary, secondary, and higher secondary education. The study also examines the distinct needs of special schools and universities and emphasizes the importance of IoT in STEAM teaching at the university level. The chapter discusses the advantages, disadvantages, possibilities, and difficulties that players in the education sector would have when implementing IoT. It highlights how crucial it is to take advantage of the capabilities of big data, artificial intelligence, and network communication to enhance teaching and learning results. The article also highlights problems with the research and suggests potential fixes, noting areas that could use more investigation. This chapter's conclusion highlights the IoT's disruptive potential in the field of education. Education can be revolutionized by integrating IoT devices, utilizing big data and artificial intelligence, and utilizing network communication. It makes it possible to create individualized, engaging, and data-driven learning experiences that get students ready for the digital era. 2024 selection and editorial matter, Alex Khang, Vugar Abdullayev, Vladimir Hahanov and Vrushank Shah; individual chapters, the contributors. -
Assessment of Enablers for Adoption of Blockchain Technology in the Indian Hospitality Industry
Purpose The chapter attempts to analyse various enablers for implementing blockchain technology in the Indian hospitality sector and examine the appropriate set of facilitators through the causal interactions among the enablers. Design/methodology/approach To analyse the enablers for the adoption of blockchain, the tool used is the decision-making trial and evaluation laboratory (DEMATEL), which captures the judgements provided by the experts in the field for the cause-and-effect enablers and the interaction effect among these enablers. Findings The literature suggests fifteen enablers classified into cause-and-effect enabler groups and interactions (i.e., enabling and enabled) among each blockchain adoption practice. The study reveals a reduction in cost and transparency as the most significant cause enablers and the effect variables as trust and database security. Research limitations/implications The results generate various enablers that can be focused upon for bringing out various significant interventions in the field. The study, however, provides an understanding of the enablers for this specific industry in the Indian context. Practical implications The results may be useful for devising policies and managerial implications related to adopting blockchain technology in the hospitality sector. Originality/value Very few researchers have integrated the role of grey DEMATEL techniques in the hospitality industry. 2024 selection and editorial matter, Park Thaichon, Pushan Kumar Dutta, Pethuru Raj Chelliah and Sachin Gupta; individual chapters, the contributors. -
Demand Forecasting Methods: Using Machine Learning to Predict Future Sales
To thrive in the market today, businesses must increase the effectiveness, dependability, and accessibility of their services. Sales estimation and operative demand scheduling definitely impact the end result of the organizations, influencing their procurement process, production, delivery, supply chain, marketing communications, etc. 2024 Sachi Nandan Mohanty, Preethi Nanjundan and Tejaswini Kar. -
A Multilayered Feed-Forward Neural Network Architecture for Rainfall Forecasting
The amount of rain received in a particular demographic region in a given time interval is called the rainfall. Rainfall is a natural and complex process and has significance in different domains including agriculture, transport, disaster management, and natural calamities resilience [1]. Abnormal rainfall affects every facet of humans and all other living beings of the world and also has a great impact in wellbeing and financial disruptions of a country. Accurate rainfall predictions at regular time intervals are always important to issue warnings about likelihood of any disaster about to happen. This also provides people a time for strategic planning in their work and precautions at time of adversity [2]. It is worth noting that rainfall forecasting does not only have an impact in day-to-day life, but more importantly for tropical countries like India where the chief occupation being agriculture and also for various other industries. It largely helps in disaster management and recovery process as well. The rainfall being a variable over time, geography and atmospheric conditions makes the forecasting considerably difficult [3]. Rainfall forecasting keeps a person informed about the likelihood of rainfall the forthcoming day, week, or month which enable long-time planning and on the other way; hourly prediction helps for shortterm planning such as enforcing traffic measures. Literature has seen various studies in this domain using predictive machine learning (ML) algorithms such as neural networks (NNs), Genetic algorithms, and Fuzzy-based systems [4]. 2024 Sachi Nandan Mohanty, Preethi Nanjundan and Tejaswini Kar. -
Genetic Algorithms for Wireless Network Security
[No abstract available] -
Analysis and Prediction of Suitable Model for Coconut Production Estimates in South Indian States
The study attempts to forecast coconut production in major coconut-producing states in India. The future projections on coconut production have been calculated based on yearly data for 73 years (194950 to 202122) accessed from the database of Indiastat (2022). We have used prominent forecasting techniques for the purpose and a suitable model has been chosen based on the lowest results of MAPE. The damped linear trend has been chosen for forecasting coconut production in Karnataka whereas Differenced first-order Auto Regressive model with drift has been adopted for Kerala and Karnataka. This study has considered a large dataset compared to other existing works and has chosen states that produce coconut on a large scale in India. Along with this, this study also attempts to find which state will produce more nuts for the Indian coconut industry, which can help the concerned stakeholders to take necessary decisions. Future projections depict that Kerala will continue to be the largest producer of coconut and Karnataka will show remarkable performance in coconut production during the upcoming four years post-study period. The Author(s), under exclusive license to Springer Nature Switzerland AG 2024. -
Augmented Reality-Enabled IoT Devices for Wireless Communication
[No abstract available] -
Agricultural Internet of Things (AIoT) Architecture, Applications, and Challenges
The internet of things (IoT) is a system that involves adding sensors, software, and network connectivity to physical devices, enabling them to collect and exchange data. This technology has the potential to bring significant advancements to various sectors, including agriculture. In farming, the agricultural internet of things (AIoT) utilizes IoT to improve efficiency, sustainability, and productivity. Through the real-time collection and analysis of data, AIoT can optimize growing conditions, prevent diseases and pests, and ultimately increase crop yields. By monitoring factors such as soil moisture, temperature, and nutrient levels, AIoT technology can effectively track crop health and detect potential issues in advance. In this way, AIoT technology is helping farmers to make more informed decisions and take more effective actions to improve crop yields, reduce waste, and lower costs. AIoT in agriculture finds practical applications in smart irrigation systems, precision agriculture, livestock monitoring systems, and climate control systems. Smart irrigation systems utilize weather data and soil moisture sensors to efficiently manage water consumption. Precision agriculture employs sensors and data analysis techniques to optimize planting, fertilization, and pest control practices. Livestock monitoring systems aid in monitoring and managing the well-being of farm animals. Climate control systems utilize AIoT to regulate and optimize environmental conditions for crops and livestock. Livestock monitoring systems use sensors to track the health and well-being of animals. Climate control systems for greenhouses and barns use AIoT devices to monitor temperature, humidity, and other environmental factors to optimize growing conditions. Sensors can be used to monitor various environmental factors in a farm, by connecting the sensors to a cloud-based platform for storing and analyzing data. The wireless sensor networks can be used to calculate the dew point on leaves and adjust the greenhouse environment to prevent and control plant diseases. Drones equipped with sensors, cameras, and other imaging technology can also be used to monitor crop conditions, as this allows farmers to take proactive measures to address these issues, preventing crop loss and reducing the need for pesticides and other chemicals. IoT/sensor nodes are vital components in precision agriculture as they gather real-time data. Integrating data analytics and machine learning into the agricultural system improves its practicality and efficiency. Real-time data availability enhances precision in agriculture, and combining data analytics with this information leads to notable progress in the field. However, AIoT technology is gradually advancing in agriculture, but there is a need for a more rigorous research approach in this area. Additionally, the current literature lacks coherence and solid research on the interconnectedness of technology and agriculture. 2024 selection and editorial matter, Alex Khang, Vugar Abdullayev, Vladimir Hahanov and Vrushank Shah; individual chapters, the contributors. -
From bean to brain: Coffee, gray matter, and neuroprotection in neurological disorders spectrum
Coffee is a popular drink enjoyed around the world, and scientists are very interested in studying how it affects the human brain. This chapter looks at lots of different studies to understand how drinking coffee might change the brain and help protect it from neurodegenerative disorders especially like schizophrenia. With the help of available literature a link between the coffee mechanism and neurodegenerative disorders is established in this chapter. Researchers have found that drinking coffee can change the size of certain parts of the brain that control things like thinking and mood. Scientists also study how coffee's ingredients, especially caffeine, can change how the brain works. They think these changes could help protect the brain from diseases. This chapter focuses on how coffee might affect people with schizophrenia as hallucination is caused during and after excess consumption of caffeine. There's still a lot we don't know, but researchers are learning more by studying how different people's brains respond to coffee over time. Overall, this chapter shows that studying coffee and the brain could lead to new ways to help people with brain disorders. This study also draws ideas for future research and ways to help people stay healthy. 2024 Elsevier B.V. -
Evaluation of Consumer Experiences by Extended AIDUA Framework in the World of the Metaverse the Future of Next-Gen Hospitality
The hospitality industry is continuously exploring novel ways to enhance consumer experiences, and the advent of the metaverse presents exciting prospects for transforming guest interactions. The metaverse can provide an immersive, 3D social experience of a virtual world by using technologies like virtual reality and augmented reality, which helps bridge the gap between the real and virtual worlds. This study explores consumer experiences in the metaverse in the hospitality context. Using the extended AIDUA (artificial intelligence device usage acceptance) model, the research aims to comprehensively analyze customers willingness to accept the metaverse in the dynamic digital landscape. The objective of this study is to investigate how the metaverse revolutionizes consumer experiences in the hospitality sector, specifically in the context of room and amenity booking, in-house events, and virtual tours. In this study, a three-step cognitive appraisal process with utilitarian motivation and the attitude of the customers that determines customers willingness and objection to utilizing AI devices is evaluated through secondary data. The metaverse empowers guests to optimize in-house event participation, seamlessly navigate their stay through taking a virtual walkthrough to explore a higher-end suite, and touring the city virtually. Practical implications for industry practitioners and researchers seeking to exploit the potential of the metaverse to create an immersive and unforgettable consumer experience in the ever-evolving landscape of hospitality are explored in this chapter. 2024 selection and editorial matter, Park Thaichon, Pushan Kumar Dutta, Pethuru Raj Chelliah and Sachin Gupta; individual chapters, the contributors. -
Forecasting the Stock Market Index Using Artificial Intelligence Techniques
If the stock market would have a predictable to maximum accuracy, then every stockbroker and investor would have been billionaire. But it is not the ground truth. In a one-to-one interaction with stock analysts, who mention that the stock market is unpredictable and that is why their role is important, else everything would have been black and white. 2024 Sachi Nandan Mohanty, Preethi Nanjundan and Tejaswini Kar. -
Business Forecasting and Error Handling Using AI
Business forecasting is the technique of accurately predicting the future of a business and outcomes using historical data and present trends. To evaluate historical data and find patterns, trends, and other elements that might be used to forecast future events, a variety of analytical tools and techniques are used. Business forecasting is a crucial component of strategic planning because it enables businesses to foresee market changes, spot possible risks and opportunities that may arise in the future, and make wise resource allocation and investment decisions. Businesses that use effective business forecasting can plan and carry out their programs that help them stay competitive, expand their operations, and meet their objectives. According to Glueck [1], Forecasting is a formal process of predicting future events that will significantly affect the functioning of an enterprise.. 2024 Sachi Nandan Mohanty, Preethi Nanjundan and Tejaswini Kar. -
Genetic Algorithms for Graph Theoretic Problems
[No abstract available]