Book Chapter

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Start date: 2020End date: 2025

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    Protecting the Traditional Knowledge of Coastal Communities for Climate Resilience
    (Rivers Unbound | Routledge, 2025-06-02) Gabriela Michael
    The increased changes in climatic conditions are not a new phenomenon, yet the repercussions can be seen in almost every sphere of human existence in recent times. Taking place at an unprecedented pace, the coasts and coastal communities are the most affected due to these changes causing severe damage to the nations. The world is on the verge of climate warfare, wherein every country is battling the multifold crisis posed by climatic conditions, adapting to climate change has become a task for everyone worldwide; thus, finding a sustainable solution has become quintessential. Coastal communities are not only the eyes and ears for the nation’s maritime security, but they also possess traditional knowledge developed over generations, offering valuable insights into climate resilience and adaptation strategies. However, this precious cultural heritage is vulnerable to erosion due to climate change, urbanization, and cultural homogenization. This research aims to record, preserve, and promote coastal communities’ traditional knowledge, emphasising their distinctive practices, beliefs, and climate adaptation solutions. The study aims to recognize and document traditional knowledge about climate adaptation, such as indigenous methods and ecosystem-based management techniques in India and other countries, as well as to examine the role of coastal communities in improving resilience to climate change and informing sustainable economic growth. Finally, the study intends to examine the policy and laws recognizing and protecting traditional knowledge in climate governance, emphasizing the necessity for a comprehensive international legal framework for protecting coastal communities’ traditional knowledge for climate resilience that benefits all countries through information sharing.
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    Application of Neural Network for Reducing Emission and Optimizing Performance of Hydrogen with Biofuel CI Engine
    (Machine Learning for Social Transformation | Springer, 2025-01-03) Atanu Roy, Sabyasachi Pramanik, Kalyan Mitra, Manashi Chakraborty
    Fuel selection influences internal combustion engine (ICE) performance and toxic emissions. However, predictive monitoring plays an imperative function in the validation and support of the machine. It optimizes engine running performance, reduces emissions, and increases efficiency. This study investigates the emission reduction and optimization of brake thermal efficiency (BTE) using a blended fuel—hydrogen, biofuel, and water on a one-cylinder compression ignition (CI) engine. Simulink simulations are used to collect data, which is then preprocessed and analyzed using advanced feature extraction to increase prediction accuracy. In this paper, a hybrid deep reinforcement learning, and artificial neural network (DRL-ANN) is initiated and designed to predict CI engine emission attributes. To optimize the prediction model, this method combines DRL and neural networks. As a result, the model achieved superior predictive accuracy compared with earlier approaches regarding accuracy (BTE 0.96851, CO 0.95124, HC 0.96624), mean-squared errors (BTE 0.00018, CO 0.00058, HC 0.00055) and R2 (BTE 0.95478, CO 0.94694, HC 0.97015). This study demonstrated the prediction model's efficacy in optimizing CI engine running characteristics and fuel types.
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    AI-Enabled Circular Economy Management for Sustainable Smart Cities: Integrating Artificial Intelligence in Resource Optimization and Waste Reduction
    (Smart Cities and Circular Economy: The Future of Sustainable Urban Development, 2024) Malla Jogarao; B. C. Lakshmanna; S. T. Naidu
    As the global community increasingly directs its attention towards sustainable urban development, integrating artificial intelligence (AI) into circular economy (CE) management within smart cities has become a potent strategy. This study aims to examine the potential influence of AI-based technologies on optimizing resources and minimizing waste, which constitute critical components of the principles underpinning the CE. The focus is mainly on applying these technologies within smart city environments. Artificial Intelligence can significantly enhance the processes of gathering, analyzing and decision-making by integrating internet of things (IoT) sensors, data analytics, machine learning algorithms and predictive analytics. This chapter explores the potential of AI in predicting trends, optimizing circular supply chains, improving waste management and recycling practices, facilitating sustainable product design, fostering citizen engagement and aiding policy development. The current research presents a comprehensive examination of the interrelated connection between the principles of CE and the advanced technology of AI. Doing so contributes significantly to our holistic comprehension of how these advancements might collectively influence the development of a more sustainable and resilient future for urban populations.
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    Sustainable Supply Chains and Digital Transformation
    (CRC Press, 2025) Rai, Akshat Kumar; Bangar Raju, T
    Integrating blockchain technology into supply chain management offers a revolutionary approach to enhancing transparency, traceability, and security within the industry. Despite its potential to address inefficiencies and trust issues, widespread adoption faces significant barriers. This chapter uses both qualitative and quantitative data to systematically analyze and rank the challenges to blockchain adoption in supply chains. In this study, utilizing the Fuzzy Making use of the Decision-Making Trial and Evaluation Laboratory (DEMATEL) and the Fuzzy Analytical Hierarchy Process (AHP), key issues are identified with reference to technical integration, regulatory issues, and concerns over scalability, and environmental effects. This research emphasizes the need for understanding technological propositions, regulation assurance, and business practices for adopting blockchain supply chain integration; the influence of the TOE framework is found to be insufficiently explored across different contexts. The chapter adds to the body of academic literature and provides recommendations for blockchain implementation, stressing multistakeholder cooperation to address these issues. Hence, the chapter provides a roadmap for further research in the complex interplays of blockchain in supply chain networks. It provides actionable insights for industry practitioners aiming to harness its transformative potential.
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    Data analysis on automation of purging with IoT in FRAMO cargo pump
    (Routledge, 2024-05-06) Kumar, R. Prasanna; Devi, V. Ajantha
    Oil and chemical tankers use the FRAMO system to transport cargo from ship to shore. FRAMO cargo pumps, which are installed inside cargo tanks, are an essential component of the system. The results of the purging operation are used to determine whether or not these cargo pumps are operationally ready. The purging procedures confirm the integrity of the sealing arrangements on both the cargo and hydraulic systems. The cofferdam that separates the cargo from the hydraulic fluid collects any leakage from the FRAMO cargo pump’s seals. The AUDRINO control board and electronic control of various solenoid-signaled hydraulic actuated valves are recommended for this auto- mated purging procedure. Control signals from the shore control center or the inbuilt timer circuit can be delivered via IoT. This control board also oversees the purging sequence. The leak-off liquid is lifted from the cofferdam space to the sample container by automated purging. The identification of the liquid is essential for obtaining the purging result. The method proposed in this paper employs three distinct sensors to identify the liquid in the cofferdam space in the autonomous ship environ- ment. The three parameters are density meter, pH meter, and a color sensor. These three characteristics distinguish the cargo liquid from the hydraulic oil used in the system. This test result is useful for cargo operation plan- ning. The content received in the cofferdam is revealed by comparing the measured data set to the preloaded database. The major goal of this research is to use physicochemical data to predict oil content. Two distinct data sets were obtained in this investigation. These data sets include three major cargo and hydraulic oil physicochemical properties. Using the random forests algo- rithm, the instances were effectively identified as cargo oil or hydraulic oil with an accuracy of 98.6229%. The detection of both cargo oil and hydraulic oil was then classified using three distinct data mining techniques: k-near- est-neighbor, support vector machines, and random forests. The random for- ests algorithm provided the best accurate classification.
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    Issues and challenges in Indian academic libraries after COVID-19 period
    (2020) Devendrappa, T.M.; Nageswara Rao, K. N; Mayuri, Naga Krishna A.; Jain, Rishabh Kumar
    The purpose of writing of this book chapter is to create the awareness among the library professionals during period of covid-19 instant what all the changes happened in academic library environment, issues and challenges faced by library professionals. How the professionals are tackled the situation to came out from the issues and challenges to provide the library services for users community. How the library professionals shown the importance of library to academic institution and they make users to realize the importance of library in academic activities. The various new kinds of services and techniques brought in use by various library professionals examples are presented for future use for other professionals in India and across the world. In this article, some of the initiatives taken are observed for providing free information to general public usage by government and other non government organizations.
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    Hongkong convention: impact on environment due to refrigeration waste in ship recycling: an Indian perspective
    (Interdisciplinary Research in Technology and Management, 2021-09) Das, Krishnendu.; Sivasami, K.; Thangalakshmi, S.; Das, N.
    Considering strict environmental concerns and compliances to protect our Globe, sustainable developments in maritime domains comprises of ship building process, transport by shipping and ship recycling process. All three phases having concerns to environmental impacts, but evidence of the impacts of present ship recycling process undermines the Maritime Education’s contribution to sustainable development. Ship breaking process includes complete scrapping or partially dismantling of vessels. At the yard, various parts, equipment, hull sections, superstructure materials, pipes, gears etc. are segregated after dismantling. These segregated scraps are recycled and re-use in various industry which is the most important business for ship breaking industry. About 85% ship recycling of the globe are mainly in Bangladesh, India, China and Pakistan, where environmental impacts and health hazards are serious concerns same mentioned by Vally Athanasopoulou. In India, ship recycling process from long time is following in primitive way which is unsafe and has tremendous health hazards.