Browse Items (2832 total)
Sort by:
-
Harnessing Digital Finance for Sustainability and Equity in Marine and Seaside Economies
The integration of digital finance into marine and seaside economies offers transformative potential to foster sustainability, equity, and economic growth. This chapter explores the role of financial technology (FinTech) in addressing the unique challenges faced by coastal and marine industries, including resource depletion, economic instability, and socio-environmental disparities. It highlights how innovations like blockchain, artificial intelligence (AI), and mobile financial platforms can enhance financial inclusion for coastal communities, streamline investment in sustainable marine projects, and promote transparent governance. Through case studies and data-driven analysis, the chapter investigates global best practices where digital finance has enabled resource optimisation, resilience in the face of climate change, and support for blue economy initiatives. By examining the interplay of technology, finance, and sustainability, this chapter aims to provide actionable insights into leveraging digital finance for building resilient and equitable marine ecosystems, contributing to a sustainable blue economy. The Author(s), under exclusive license to Springer Nature Switzerland AG 2025. -
Blockchain in Drone Systems: Advancements, Security Implications and Community Acceptance
Use of drones is an indication of urbanization. There are many societal acceptance factors that need to be assessed for urban drones. This work also emphasizes on the future of blockchain as a novel technology in the upcoming decade. Acceptance of this novel technology will substantially increase the effectiveness and efficiency of future delivery options. The studys methodology will be determined after performing a detailed literature survey on the topic of drone and blockchain technology usage acceptance and community engagement. This study provides a comprehensive and detailed analysis about the knowledge of drone technology among the diversified population. The primary goal of the study is to analyse the general acceptance of drones in day-to-day activities. The research also focuses on understanding the need to educate people about drone technology in plausible areas of applications. With the emergence of this technology, it is evident that drone have a great prospect to grow in various sectors and industries. The Author(s), under exclusive license to Springer Nature Switzerland AG 2025. -
Transforming Food Waste Management with Blockchain: A Sustainable, Consensus Driven Framework
Food waste management is a stern issue that is prevalent around the word, affecting multiple countries and cultures. In a technology driven era, blockchain has gained intense attention because of its various beneficial facets. Blockchain is defined as a technology with its features like security, transparency and immutability, where only the authorized members in a network are given access. The decentralized technology enables transparency and traceability which can be used in food supply chain in conjunction with the various consensus mechanisms to validate transactions. This would ensure consistency and reliability, ensuring the stakeholders to track vivid stages such as food production, processing and distribution. Wastage of food is a cosmopolitan issue conducive to social, economic and environmental challenges. Our proposed work provides substantial benefits in a way to tackle inefficiencies in the food supply chain, Blockchain based business process reengineering can further automate these processes. The paper presents five popular consensus mechanisms that can be used for a sustainable food waste management. The work focuses on providing a blockchain based solution that is low powered and scalable, which in turn increases sustainability and reduces global food waste. The Author(s), under exclusive license to Springer Nature Switzerland AG 2025. -
Human Resource (HR) Analytics and Its Modus OperandiAn Explorative Study
People management immensely benefited from predictive HR analytics that provide deeper insight into employee data for effective decision-making. HR analytics has contributed to formulating and implementing data-driven strategies across various industries and organizations. The main objective of this conceptual paper is to explain the concept of HR analytics and describe its modus operandi. We reviewed the literature on various facets of HR analytics from 2016 to 2024. We have explained the modus operandi of HR analytics through the key aspects of strategic HRM. This paper is significant because HR analytics is evolving as a more technical discipline with modern technologies such as machine learning and artificial intelligence. The Author(s), under exclusive license to Springer Nature Switzerland AG 2025. -
Epileptic Seizure Detection Contribution in Healthcare Sustainability
This study describes a sustainable EEG data methodology. Classification using Discrete Wavelet Transform (DWT) for feature extraction, with the objective of reducing the computational efforts while keeping accurate neural signal analysis. DWT decomposes the EEG signal into timefrequency specific components which allows extraction of ten key wavelet features, including wavelet energy, entropy, maximal coefficients, zero-crossing counts, and dominant frequency. These features capture essential timefrequency features of EEG signals, providing a comprehensive yet computationally efficient representation. By streamlining feature extraction, this approach reduces data dimensionality and minimizes computational processing time, aligning with sustainable technology objectives. The resulting feature vectors serve as robust inputs for classification models, effectively supporting EEG data interpretation with reduced energy and less resource utilization. This study demonstrates that targeted feature extraction can achieve high classification performance in EEG analysis while adhering to principles of sustainability and resource efficiency. The Author(s), under exclusive license to Springer Nature Switzerland AG 2025. -
Ethical Integrity and Performance in Isolation: An Intrinsic Motivation Mediated Focus
This research investigates the mediating influence of intrinsic motivation on employee isolation and job performance of work-from-anywhere Information Technology professionals in India. Intrinsic motivation and ethical integrity are fundamentally intertwined, each enhancing and reinforcing the other. The lenses of the self-determination theory underpin the study. The research questions covered in the study are: (a) Does employee isolation affect job performance? (b) Does intrinsic motivation during employee isolation affect job performance? (c) Does intrinsic motivation mediate the connection between employee isolation and job performance? The SEM based approach survey was conducted to collect data from 410 IT employees who work from anywhere for a minimum of one or more days per week. The findings indicate that (a) employee isolation has a negative influence on job performance, (b) intrinsic motivation significantly influences job performance during employee isolation, and (c) intrinsic motivation mediates indirect-only (full mediation) the link between employee isolation and job performance. The employers need to build upon intrinsic motivation tools for work-from-anywhere Information Technology professionals, which ensures employees relatedness by giving rewards, a sense of purpose towards assignments, autonomy through intrinsic motivation and ethical integrity, and developing IT expertise, which significantly increases performance among employees in work isolation situations. Through the lens of self-determination theory, the research contributes to the work-from-anywhere literature by exploring the relations between employee isolation, job performance, and the mediating influence of intrinsic motivation on IT professionals in India in the current scenario. The Author(s), under exclusive license to Springer Nature Switzerland AG 2025. -
Nanomaterials Synthesized from Mangroves and Their Associates
Nanotechnology has great potential for developing nano-enabled equipment and products in a variety of industries, including personal care, medical, food, and agriculture. Despite the increasing use of metal nanoparticles in various domains, concerns concerning biological and environmental safety during manufacture remain. Traditional commercial methods for generating nanoparticles often entail chemical procedures and high-energy physical approaches that are both environmentally damaging and expensive. As an alternative, green synthesis employing plants has arisen, which reduces the requirement for toxic chemicals and severe reaction conditions in nanoparticle synthesis. The utilization of mangrove plants for nanoparticle synthesis has recently gained popularity due to their abundance of unique phytochemicals that aid in nanoparticle synthesis. Microorganisms in mangroves and enzymatic activities in plants can be utilized for a range of biotechnological and environmental uses. Bioactive compounds from mangrove resources show potential for creating bionanomaterials that can be utilized in environmental and biomedical fields. Bionanomaterials created from mangroves are incredibly effective in medical uses and cleaning up the environment. Bionanomaterials are produced by utilizing mangrove and various biomolecules obtained from mangrove plants as substances for the creation of nanoparticles. Bionanomaterials made from biomolecules offer benefits for the sustainable use of mangroves because of their large surface area, biocompatibility, and minimal toxicity. Here focuses on the potential of mangroves as a natural resource for producing bionanomaterials in various applications, promoting an eco-friendly approach. This chapter investigates various types of mangrove species and their elements utilized in creating nanoparticles, as well as the applications of the nanoparticles in therapy, agriculture, and industry. It also investigates the obstacles hindering the extensive utilization of plant-based nanoparticle synthesis. Springer Nature Switzerland AG 2026. -
Bioactive Compounds and Biological Activities of Mangrove-Associated Bacteria
Mangroves are used by folklore in indigenous medicine for the treatment of diseases. They contain an array of pharmacologically significant bioactive compounds. The endophytes of the mangroves have the capability of producing biologically active compounds which may be similar to their host plant. They are also able to produce novel and unique bioactive compounds which can be used in therapeutics. Bacillus and Streptomyces are the major genera of bacterial endophytes found in the mangroves. The major groups of bioactive compounds produced by the bacterial endophytes of mangroves include terpenoids, alkaloids, polyketides, etc. The bioactive compounds produced by the endophytes possesses biological activities, such as antibacterial, cytotoxic, antioxidant activity, etc. These compounds have profound applications in the discovery of drugs. The present chapter focuses on the bacterial endophytes found in the mangroves, the bioactive molecules produced by them, and the pharmacological activities associated with these endophytes. Springer Nature Switzerland AG 2026. -
Novel Secondary Metabolites from Mangrove Flora: Chemistry and Bioactivity
Secondary metabolites found in abundance in mangrove plants play a vital role in enabling these plants to withstand challenging environmental circumstances. Over the years, studies on the isolation and characterization of secondary compounds from mangroves have shown that they include a vast number of novel compounds that have not been previously described. Mangrove secondary compounds have been shown to feature unique carbon skeletons, unique ring systems, or peculiar structural moieties. These new substances have also shown a range of biological activity. We reviewed a variety of new compounds in this review, along with their structural variations and biological activity. Springer Nature Switzerland AG 2026. -
Phytochemicals and Biological Activities of Lumnitzera racemosa Willd.
Lumnitzera racemosa Willd. is a mangrove plant with a broad distribution, spanning from the coastal regions of East Africa to Southeast Asia, New Guinea, and Australia. Various parts of this plant have been traditionally used to treat a wide range of ailments, including infertility, asthma, diabetes, snake bites, and skin conditions, such as herpes, pruritus, scabies, sores, leprosy, and thrush. The therapeutic properties of L. racemosa are believed to be due to its diverse bioactive compounds, including flavonoids, lignans, phenolics, sulfur-containing compounds, tannins, terpenoids, and glycosides. A literature review has identified 101 distinct compounds isolated from this plant, many of which have demonstrated significant biological activities, such as antimicrobial, antioxidant, antidiabetic, cytotoxic, anti-inflammatory, and antihypertensive effects. In addition to these, extracts from L. racemosa exhibit anti-allergic, anti-angiogenic, anticoagulant, antimalarial, and larvicidal properties. This review highlights the traditional uses of the plant, the bioactive compounds isolated from it, and their pharmacological properties. Springer Nature Switzerland AG 2026. -
Phytochemicals and Biological Activities of Ceriops tagal (Perr.). C. B. Rob.
Plants have been used for medicines since ancient times as they serve critical needs and are easily accessible. In recent years, various nations have seen a major increase in the use of plant-based treatments, resulting in a significant rise in the global demand for herbal products. This chapter describes Ceriops tagal, a mangrove species with excellent potential for bioactive components and biological activity. The majority of the distinctive secondary metabolites and their analogs reported in this plant are di-, tri-, and tetra-terpenoids (dolabrane, lupane, oleanane, dammarane, and pimarane), phenolics, and steroids from the hypocotyls, roots, and aerial parts. Various studies reported 97 terpenoids and 14 other metabolites. Many biological activities have already been identified from various extracts, including anticancer, antidiabetic, antioxidant, anti-inflammatory, antibacterial, and neurotrophic activities. In this chapter, we explored the biological potential of C. tagal, particularly its anticancer and neuroprotective activities, and it may be valuable for young researchers looking into the potential drug for chemotherapeutic and neurotrophic properties for the treatment and prevention of cancerous and neurological disorders. Springer Nature Switzerland AG 2026. -
Specialized Metabolites of Mangroves and Their Biological Activities
Mangroves are woody plants that are found in intertidal zones, where land meets the sea, especially in the tropical and subtropical regions of the world. They synthesize and accumulate diverse specialized metabolites that fall into major categories such as phenolics, terpenes, and alkaloids. Mangrove-derived chemical compounds have also been shown to exhibit a variety of biological properties including anticancer, antidiabetic, anti-inflammatory, antibacterial, antioxidant, and neuroprotective activities. In this chapter, we present the chemistry and biological activities of the mangrove-specialized metabolites. Springer Nature Switzerland AG 2026. -
Interconnected Intelligence: Navigating Through Power Quality Checking and Control Using Smart Intelligence-Based Methods
Globally, power quality issues incur substantial costs. In the United States, power quality problems contribute to a $150 billion annual cost, covering lost productivity, equipment damage, and safety hazards. Smart intelligence-based methods can potentially cut these costs by up to 50%. In India, power quality disturbances result in a $10 billion annual cost involving equipment damage, productivity losses, and customer dissatisfaction. The adoption of smart intelligence-based power quality methods in India is projected to grow annually by 25% for the next 5years due to increasing grid demands. In todays intricate power landscape, dependable electrical systems are crucial. Power quality disturbances, including voltage variations, harmonics, and flicker, can disrupt sensitive equipment, resulting in financial losses and safety risks. Addressing these challenges, smart intelligence-based methods emerge as promising solutions. This chapter systematically explores the application of artificial intelligence, machine learning, and data analytics for elevated power quality monitoring, assessment, and regulation. Such intelligent approaches optimise power system performance, reduce downtimes, and ensure a consistent supply of high-quality electrical energy. The assimilation of smart intelligence-based methods emerges as a promising avenue to address these challenges effectively. Harnessing the capabilities of these intelligent paradigms empower power systems to attain optimal performance, curtail downtimes, and ensure a steadfast provision of high-grade electrical energy. The Author(s), under exclusive license to Springer Nature Switzerland AG 2025. -
Empowering Solar Power Generation: The Z-Source Inverter Approach
The Z-source inverter model is a revolutionary design obtainable in this study for solar power conversion systems that do away with the traditional intermediary DC/DC converter. The competitive pricing of renewable energy bases in the market has drawn a percentage of attention in recent times. Government funding and technological advancements are to blame. Astral photovoltaic system is a created green technology that requires no upkeep, requires less time to install, and has grid parity. Systems for solar PV supply are categorized based on the phases of conversion. In order to maximize the amount of power created by solar energy, the conventional boost converter is utilized as an intermediary power conversion circuit. Voltage source inverters, or VSIs, are frequently employed to provide a controlled AC voltage at the output. However, the inability of VSIs to control current properly results in overcurrent problems during fault conditions. The size, weight, and switching losses of the filter circuit are condensed by the suggested converter. To solve the aforementioned issues, a Z-source inverter (ZSI) is replaced as an alternative of the voltage source inverter (VSI) in variable speed drive systems. One type of single-stage buck-boost inverter is the Z-source inverter. It functions similarly to a conventional VSI in buck mode, with six active vectors, and adds an additional switching state in boost mode, known as the shoot-through state, through utilizing a resistance Z-network. The resistivity network is regarded as an appealing solution for a number of applications since it raises the DC link current to the necessary level. In comparison to the traditional two-stage influence adaptation, the developed Z-source inverter extracts greater power from photovoltaic arrays. The Author(s), under exclusive license to Springer Nature Switzerland AG 2025. -
Power Consumption Forecasting with AI and IOT
Electricity plays a fundamental and indispensable role in modern society, driving progress, development, and the overall quality of life. Electricity is profoundly ingrained in daily life. It powers homes, providing lighting, heating, cooling, and appliances that support, comfort, and convenience. From cooking meals to powering electronic devices and entertainment systems, electricity is vital for modern living, enhancing our quality of life and enabling various activities. Power forecasting is critical to the effective management and optimization of power generation, consumption, and distribution. Power consumption forecasting has evolved significantly with the introduction of advanced technologies such as Artificial Intelligence (AI) and the Internet of Things (IoT). AI techniques, such as machine learning and deep learning, make use of the massive amounts of data produced by IoT devices like smart meters and energy monitoring devices. These devices continuously gather real-time data on power consumption, weather conditions, grid performance, and other relevant factors. AI algorithms can find patterns and correlations and provide accurate forecasts and important insights for power forecasting by processing and analyzing data. Machine learning algorithms, such as regression models, neural networks, and ensemble approaches, are trained using historical power consumption data and the features that have been chosen. The models discover the underlying patterns and correlations between input features and power consumption. These forecasts can be used for short-term load balancing, energy procurement planning, demand response management, and optimizing energy distribution. AI and IoT power usage projections give valuable data for decision-making and energy optimization techniques. These projections can be used by energy suppliers, grid operators, building managers, and consumers to plan energy usage, distribute resources efficiently, optimize demand response programs, and discover possibilities for energy saving. The Author(s), under exclusive license to Springer Nature Switzerland AG 2025. -
Smart and Secured Ways of E-Payment: Design of New Frameworks
The rapid development in technology and the rapid growth of e-commerce have paved the way for changes in the method of payment. It also gave rise to various hazards while making the e-payment when compared to the normal or standard payment methods it is necessary to make a secured payment. Various initiatives are taken by the government for providing a secure payment system which will be more useful for all the commercial activities done through online. Already various e-payment systems are used by the consumers for paying the amount for the materials purchased. The increasing need of foreign exchange with an effective and efficient electronic payment system is required for making the low value payment. The framework that is been used in the global market and also in virtual marketplace require a complete legal structure which should also have impact on the economy of the mediaeval trade. Rapid development of e-commerce during the recent years has made more changes in the financial and non-financial transactions. In e-commerce, the payment gateway plays an important role in the exchange in ensuring that the transactions occur without any disputes and also maintains the security of the system. Most of the payment gateways used in the e-commerce are provided by rusted third party who will provide monetary information. Due to the increased use of e-commerce and online payment system, there is also any increase in security breaches during the past few years. So, it is necessary to build a new framework that will provide a secured platform for the e-payment system through which the consumers can directly connect to their merchants securely. Most of the third-party providers are also asking for the identity of the customers while making the payment which might even have change of loss of person information of the customer. The new framework should contain an improved security and the data collected should be confident, proper authentication method should be used, and availability of the data and integrity of the data should be maintained. The Author(s), under exclusive license to Springer Nature Switzerland AG 2025. -
Effective Models for Computing Optimized Storage Systems for Energy
This chapter investigates effective modeling techniques for designing optimized storage systems that minimize energy consumption. We explore various models capturing the interplay between storage performance, capacity, and energy efficiency, focusing on computational methods to enhance effectiveness. As the demand for renewable energy sources continues to increase, the need for reliable and efficient storage solutions becomes increasingly crucial. We discuss the design and implementation of optimized storage systems for energy, highlighting computational models role in improving efficiency. Starting with an overview of the energy storage system, we examine different modeling approaches such as mathematical optimization, machine learning, and simulation techniques. Each approach offers a unique approach to addressing the complexities of energy storage. Additionally, we discuss optimization models, ensuring that energy storage solutions are both technically efficient and economically viable. In summary, this section emphasizes the importance of computational modeling in developing efficient energy storage systems, which are crucial for meeting energy integration demands and ensuring stability and sustainability. The Author(s), under exclusive license to Springer Nature Switzerland AG 2025. -
Model Selection Strategies for Identifying Effective Energy Storage 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. -
Model Selection Strategies for Identifying Effective Energy Storage 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. -
Smart Intelligence Aided Power and Energy Management
The Artificial Intelligence (AI) has become a revolutionary technology in power and energy management, providing exceptional prospects for improving efficiency, reliability, and sustainability. This study delves into the incorporation of AI methodologies into smart intelligence-driven systems for power and energy management. It delves into how AI algorithms, encompassing machine learning and optimization approaches, are utilized to enhance energy generation, distribution, and consumption across a range of environments, including smart grids, microgrids, and intelligent buildings. The abstract examines the primary challenges and factors to consider when implementing AI-driven solutions for power and energy management, which encompass issues such as data quality, privacy, security, and scalability. It emphasizes the crucial role of transparency and interpretability in AI algorithms to cultivate trust among stakeholders and secure user acceptance. Additionally, it addresses the importance of upholding ethical standards and regulatory requirements to address societal apprehensions and mitigate potential risks linked to the deployment of AI in energy systems. Moreover, the abstract highlights AIs contribution to advancing energy efficiency and sustainability through dynamic demand response, incorporating renewable resources, and the optimization of grid operations. It underscores the importance of on-going monitoring and evaluation of AI-driven energy management systems to pinpoint areas for enhancement and mitigate unintended repercussions. In summary, this paper offers perspectives on AIs potential to transform power and energy management methodologies, leading to more intelligent, robust, and eco-friendly energy systems. The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.