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Pan-generation investigation of sustainability factors in conjunction with personality aspects influencing consumer's perception towards sustainable marketing
Sustainability has become the goal of governments and organizations worldwide since the 2030 agenda of the United Nations was declared. The large number of products has emerged in the market that claims to be green and sustainable or promise to serve a social cause. Even though numerous studies that claim consumers are increasingly conscious of their choices and consumption behaviour there has been a marked gap between this awareness and the actual behaviour exhibited. In a quest to comprehend the curious gap between the attitude and behaviour of consumers, several studies have endeavored to find a plausible explanation. Marketing specialists have come up with several strategies in order to make sustainable marketing a success but inexplicably still fail to do so. This study proposes a conceptual framework that comprehensively explains the impact of external and internal drivers for a consumer's perception of products that claim to be sustainable in nature, which may eventually explain the attitude-behaviour gap. This has been constructed using well-established consumer behaviour and personality theories. Sample size was calculated using proportion to population method and was derived as 270. Judgment and convenience sampling were used to collect the data through a structured questionnaire. The study uncovered that age influences the attitudes of consumers and both Gen X (cohort born between 1965-79) and Y (cohort born between 1980-96) bear positive attitudes towards sustainable marketing. Pricing had a major influence as well, buyers who ranked high on the 'Conventional' and 'Realistic' interests of the RIASEC personality model had a more positive perception. 2021 Ecological Society of India. All rights reserved. -
Interparental Conflict and Young Adult Romantic Relationships: A Systematic Review
In the last two decades, researchers have been progressively investigating the impact of interparental conflict (IPC) on young adults romantic relationships. This systematic review aimed to synthesize literature on IPC and romantic relationship outcomes among young adults and highlight mechanisms found in this link. Following the PRISMA protocol, 3232 studies were identified using Boolean searches on ProQuest, PubMed, EBSCOhost, Jstor, Cochrane, and Google Scholar, and 17 met the eligibility criteria. To be included, in addition to having IPC and romantic relationship outcomes as variables, studies had to be quantitative in design, have a mean sample age of 1825, include only participants in romantic relationships at the time of the study, and be published in English with full text available. The review found that IPC is associated with negative conflict management, both perpetration and victimization of aggression, worse communication, negative conflict behaviors, and poor relationship quality. Other outcomes like relationship satisfaction, commitment, as well as mediator variables in the link between IPC and young adult romantic relationship outcomes, such as attitudes towards marriage and conflict attributions, yielded varied results. Several shortcomings in the methodology of the reviewed articles, such as the research sample and measures, were discovered. To deal with the impact of IPC on offsprings romantic relationships, preventive interventions should be designed and evaluated, and more research with different variables and study designs, with more men, other ethnicities, and more representative sample frames are needed to detect crucial mediators and obtain reliable and generalizable results. The Author(s) 2022. -
Green and Sustainable Software Model for IT Enterprises
The present study is based on developing a Green and Sustainable software because in the present-day computing devices are used for all kinds of purposes and they consume a lot of energy to perform these services. The ICT sector itself consumes a lot of energy so there is a need to think of alternatives that can reduce the level of energy consumption, thus, green ICT practice can be a good option. There is, however, a scarcity of researches that explains how the maintenance of green knowledge in ICT software development may be implemented. Since we recognize that software development process (SDL) plays an essential role in enabling the ICT community, uncontrolled green knowledge in developing software that would lead to the dilemma of failing to satisfy both the community's business and environmental requirements. Therefore, this research will concentrate on presenting a methodology applying an innovative model for managing the green software development and implementation. Keeping this concern in mind the present paper is going to provide a Green and Sustainable software model which can be used in green ICT practices and will be helpful in reducing the energy consumption used by computers. 2021 IEEE. -
INDIA AND SOUTHEAST ASIA IN A CHANGING WORLD: Exploring Relationship Prospects for a Sustainable Future
This book presents a comprehensive analysis of Indias relationship with the Southeast Asian nations in the context of the changing dynamics of international relations and the emergence of Indo-Pacific as the theatre of world politics. It covers a wide range of themes, from strategic to political, economic, diplomatic and security aspects, and assesses how Indias redefining of its role in world politics unfolds through its posture towards the Southeast Asian region. The volume will be of great interest to scholars and researchers of Asian studies, both South Asian and Southeast Asian studies, and politics and international relations. It will also be useful for public policy analysts and think tanks and policymakers. 2025 selection and editorial matter, Shailza Singh, Philip Varghese, Shalini Balaiah and Sarish Sebastian. -
INTRODUCTION
World politics in the twenty-first century represents a complex arena characterised by a diversity of paradigms. These paradigms entail a dynamic interplay of conventional and newer ways of engagement. Today, the globalising world witnesses newer imaginations of space and interactions where state actors continue to enjoy a preeminent status, adopting policies based on imaginations of space in terms of connectivity and gateways while maintaining their territorial integrity. They devise a whole array of mechanisms to define, redefine and secure their interests as well as elevate their aspirations of assuming newer responsibilities and bigger roles. What we witness today is dynamic endeavours by the states to hold on to and amplify their traditional roles and carving out newer contours of forging and consolidating relationships in the global framework of international relations. This also leads to the construction of new geo-strategic and economic hotspots. This complex interplay of the traditional and the newer interactions creates both synergies and discord. The Indo-Pacific represents such a hotspot in contemporary world politics, and Indias engagement with Southeast Asia is a significant area of interest therein. 2024 Taylor & Francis. -
Harnessing Technology for a Sustainable Future in Finance: The Role of Artificial Intelligence in Promoting Environmental Responsibility
The integration of artificial intelligence (AI) into sustainable finance has become a focal point in recent years, propelled by global concerns about the environment and the pressing need for sustainable development. AI technologies, equipped with advanced capabilities, offer significant opportunities to address challenges faced by financial institutions, investors, and policymakers, ushering in the prospect of a more sustainable and inclusive economy. AI's applications in sustainable finance cover diverse areas such as environmental risk assessment, green investment analysis, climate change modeling, and the integration of Environmental, Social, and Governance (ESG) factors. By leveraging advanced data analytics and machine learning algorithms, AI empowers financial institutions to assess environmental risks associated with investments and portfolios, identifying climate-related opportunities and seamlessly integrating ESG factors into decision-making processes. Furthermore, AI-driven technologies streamline the collection, processing, and analysis of extensive data from varied sources, facilitating precise and timely sustainability reporting. These technologies contribute to identifying sustainable investment trends and play a crucial role in monitoring the progress of sustainability initiatives. AI algorithms also aid in crafting predictive models for climate-related events, assisting investors and policymakers in evaluating the long-term financial implications of climate change and formulating effective mitigation strategies. While the adoption of AI in sustainable finance offers immense potential, it is not without challenges and risks. Ethical considerations, data quality and biases, transparency, and the interpretability of AI models are among the key concerns that require careful attention. Additionally, the establishment of regulatory frameworks and industry standards is essential to ensure the responsible and ethical use of AI technologies in finance. In spite of these challenges, the integration of AI in sustainable finance holds great promise for expediting the transition towards a greener and more sustainable future. It empowers stakeholders to make well-informed decisions, advocates for responsible investment practices, and contributes significantly to the attainment of global sustainability goals. By harnessing the capabilities of AI, financial institutions and policymakers can unlock new opportunities, mitigate risks, and cultivate a financial system that is not only sustainable but also resilient. The Author(s), under exclusive license to Springer Nature Switzerland AG 2024. -
Harnessing the Power of Big Data Analytics to Transform Supply Chain Management
The study aims to conduct a systematic literature review and bibliography analysis to explore the role of big data analytics in transforming supply chain management. The systematic literature review was conducted according to the PRISMA guidelines extended into a three-phase approach. The articles were reviewed from different databases like Scopus, Web of Science, and ABDC. 239 articles were reviewed through abstract screening, and 191 articles were finally selected after full-text screening. The results of the analysis reflected the publication trend from January 2011 to January 2024, keyword analysis, co-citation and network analysis, and theme identification from the domain. Moreover, the study theoretically contributes by suggesting growing trends in the field of supply chain management, and the managerial implications of the study suggest the benefits of implementing big data analytics in supply chain management. The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024. -
IOT based Green House monitoring system
With industrialization and continuously evolving climatic conditions, the urge to practice agriculture with the fusion of technology has become a necessity. In the era of Internet of Things where all eyes are witnessing the evolution of machine to machine interaction, there is also a lack of clarity in considering the type of protocol to be used in building a particular system like Green House. A green house is a regulated environment for agriculture where critical parameters like temperature, light, humidity, ph level of soil can be monitored with the help of sensor systems using Internet of Things protocols. Message Queue Telemetry Transfer protocol was chosen over Constrained Application Protocol and Extensible Messaging and Presence Protocol in the experiment conducted in terms of its light weight transmission, resource consumption and effectively providing the different quality of services to detect the temperature and humidity as well as the gas leaks encountered in a greenhouse environment. 2018 Tinu Anand Singh and J. Chandra. -
Assessment of artificial intelligence-based digital learning systems in higher education amid the pandemic using analytic hierarchy
The devastating effects of the 2020 worldwide COVID-19 virus epidemic prompted widespread lockdowns and restrictions, which will continue to be felt for decades. The repercussions of the pandemic have been most noticeable among educators and their students, which boosts the effectiveness of various AI-based learning systems in the education system. This study examines the AI-based digital learning platforms in higher education institutions based on various characteristics and uses of these systems. Several significant aspects of AI-based digital learning systems were obtained from the available literature, and significant articles were selected to properly examine various characteristics and functions of AI-based digital learning platforms used by multiple higher education institutions. The analytical hierarchy process (AHP) is employed to rank multiple AI-based learning systems based on key factors and their sub-factors. The studys outcome revealed which AI systems are effectively used in developing digital learning systems by various higher education institutions. The Author(s) under exclusive licence to The Society for Reliability Engineering, Quality and Operations Management (SREQOM), India and The Division of Operation and Maintenance, Lulea University of Technology, Sweden 2024. -
An Explainable AI-Driven Deep Learning Algorithm for Heart Disease Detection in Healthcare
The application of preprocessed Kaggle data serves as a subject of analysis to investigate heart attack prediction capabilities through machine learning models. The research examines performance outcomes of five algorithms which consist of K-Nearest Neighbors (KNN), Random Forest, Support Vector Machine (SVM), Extreme Gradient Boosting (XGBoost) and Convolutional Neural Networks (CNN). Random Forest together with XGBoost proved as the most accurate machine learning models when used for cardiovascular risk assessment. The researchers built a hybrid structure of CNN and SVM because it improved both data classification and feature extraction processes for better prediction outcomes. The training and evaluation process of models encountered difficulties because of overfitting along with high computational expenses and problems regarding optimal hyperparameter settings. The research stresses that explainable AI (XAI) methods should integrate into systems to enhance model interpretability and achieve trust from clinical professionals. Future initiatives seek real-time patient monitoring and innovative interpretability systems for heart attack prediction to enable person-specific diagnoses and optimal clinical choices in medical fields. 2025 IEEE. -
Leveraging Deep Learning for Early Detection of Alzheimer's Disease from MRI Scans
Alzheimer's disease (AD) remains shrouded in mystery, with its early detection posing a significant challenge. This research paper delves into the cutting-edge realm of deep learning, exploring its potential to explore the brain's secrets and revolutionize AD diagnostics using Magnetic Resonance Imaging (MRI) data. Upon comprehensively reviewing the performance of six state-of-the-art models and studying their strengths and limitations on MRI data, this paper proposes a novel deep-learning architecture based on the InceptionV3 model for Alzheimer's Disease prediction using MRI data. The proposed architecture leverages convolutional neural networks (CNNs) to extract subtle brain structure and function patterns, potentially identifying early AD signatures before noticeable cognitive decline. The proposed model is validated on a large-scale MRI dataset that comprises four stages of dementia, demonstrating more insights. Inception V3 base model yielded 82% accuracy, measured using the metric Area Under the Curve (AUC), on the dataset, and an improved AUC of 87% was achieved by performing data augmentation to remove the class imbalance in the dataset. The proposed deep learning model built on top of Inception V3 exhibited an improved performance with an AUC of 88% underlining the potential of deep learning models in early AD detection. The paper's findings will contribute to the ongoing effort to revolutionize AD diagnosis and accelerate the development of personalized treatment strategies. Grenze Scientific Society, 2025. -
Deep Learning for Uncovering of Fraud: A Design for Automated Financial Protection
Leveraging the unparalleled adaptability and hierarchical feature stratification capabilities of deep learning, this study constructs a sophisticated framework for fraud detection, seamlessly integrating convolution and recurrent neural architectures with advanced anomaly detection algorithms to decode complex, nonlinear transactional patterns within heterogeneous financial datasets, thereby enabling real-time fraud identification while addressing pivotal challenges of algorithmic interpretability, adversarial resilience, regulatory compliance, scalability, and data confidentiality, ultimately redefining the paradigm of automated financial security in an increasingly digitized global economy. The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2026. -
Condensate phases of nuclear matter from AdS hardwall models
This work develops our previous study of confined phases at finite densities in AdS/QCD by systematically exploring the possibility of baryonic condensates. Using phenomenologically motivated boundary conditions in an AdS hardwall model, we show that both baryonic and quark-type condensates dominate the phase diagram at low temperatures. We also undertake a careful scan of the parameter space to extract robust conclusions. (2025), (American Physical Society). All rights reserved. -
Inclusive Pedagogy in Computer Science Education: A Critical Pedagogical Framework for Computational Access
Computer science (CS) inclusive teaching provides an opportunity for everyone to learn in a way that is fair and accessible. Through the traditional approaches, neurodiverse and disadvantaged learners are often cut off due to the strictness of the instruction and the surrounding limitations on support. UDL (Universal Design for Learning) and CRP (Culturally Responsive Pedagogy) are the two major strategies for inclusive teaching that facilitate flexible, engaging, and relevant learning. UDL allows you to choose different ways to learn and to express your understanding, and CRP connects the content with the students' cultural backgrounds. Gallaudet University and AccessCSforAll are some of the initiatives that have been proving that learners are more engaged when using assistive tools and through the application of adaptive methods. Supported by educating CS for all, U.N. SDG 4, inclusivity in computer sciences education not only narrows the digital gap but also opens up the door for every student to thrive in a diverse and tech-savvy world. Copyright 2026 by IGI Global Scientific Publishing. -
Advancing Supply Chain and Logistics With Emerging Technologies
Abstract The digital era is transforming industries, requiring businesses to innovate for cost- efficiency and scalability. This chapter explores emerging technologies like cloud computing, AI, automation, blockchain, and IoT across healthcare, retail, finance, and logistics. It examines digital transformation's impact on traditional business models, industry trends, and strategies for adaptation. Technology integration enhances competitive advantage through big data analytics, digital marketing, and continuous innovation. Sector- specific insights cover IoT and blockchain in supply chains, AI diagnostics in healthcare, omnichannel retail, and digital finance solutions. The chapter provides recommendations for fostering innovation, investing in technology, and forming digital partnerships. Concluding with key takeaways and a future outlook, it serves as a resource for executives, entrepreneurs, and industry professionals navigating digital transformation for efficiency. 2026 by IGI Global Scientific Publishing. All rights reserved. -
Green fintech meets DeFi: A path to digital sustainability
This chapter explores the interconnection between Green Finance and Digital Ecology, highlighting their potential in advancing sustainability through technological innovation. Emphasis is placed on how green FinTech can mitigate these challenges by designing low- carbon financial systems and promoting sustainable investment practices. The methodology involves a review of scholarly literature to assess how digital finance and ecology contribute to global sustainability efforts. Findings suggest that the integration of green FinTech, digital money, and ecological practices can enhance transparency, reduce costs, and expand access to sustainable finance, ultimately leading to more resilient financial ecosystems. The chapter calls for increased awareness, financial literacy, and robust regulatory frameworks to manage associated risks. 2026 by IGI Global Scientific Publishing. All rights reserved. -
Corporate social responsibility of Tata company - A public relations strategy /
Tata Group, a global enterprise was founded by Jamshedji Tata in the year 1868. The conglomerate is headquartered in India. The company comprises over 100 liberated functioning businesses. Tata Sons is the main parent and major investment stock company and also the promoter of various other Tata companies. Tata as a company believes in providing quality products and services to its customers. But at the same time they also maintain good employee relationship through loyalty programs and initiatives. -
Investigating system vulnerabilities in digital environments
[No abstract available] -
Impact of Macroeconomic Integration in Hybrid GARCH-GRU Volatility Modelling on Nifty Bank
In countries like India, where banking systems are closely tied to macroeconomic swings, being able to forecast volatility is critical for managing financial risk. Sudden changes in interest rates, exchange movements, or growth expectations can unsettle banks much faster than in mature markets. Econometric tools such as the Generalised Autoregressive Conditional Heteroskedasticity (GARCH) model remain popular because they capture volatility clustering well, but they fall short when the data exhibit nonlinear patterns. Neural networks-particularly Gated Recurrent Units (GRUs)-handle time-series dynamics more effectively, though they tend to miss traits specific to financial volatility. In this work, we put forward a hybrid GARCH-GRU framework that blends the traditional strengths of econometric models with the pattern-learning ability of neural networks, while also folding in key macroeconomic indicators. The framework is applied to the Nifty Bank index and draws on daily records spanning March 2010 to December 2022. Altogether, the dataset includes just over three thousand observations, covering more than a decade of varied market conditions. The framework uses a two-step design: conditional volatility from a GJR-GARCH(1,1) model is first estimated and then used as input, along with macroeconomic variables such as repo rates, exchange rates (USD/INR, CNY/INR, EUR/INR), oil prices, and GDP growth, for the GRU network. Our results indicate that the hybrid model performs noticeably better, cutting the Mean Absolute Error by about a quarter. The error falls from 0.000263 in the baseline GARCH model to 0.000199 under the hybrid design. Among the different factors considered, movements in exchange rates and changes in repo rates stand out most strongly, showing how these macroeconomic signals feed directly into risk management for Indian banks. 2025 IEEE. -
Research on Big Data for Industry 4.0 Cyber-Physical Systems
The objective of the revolution known as Industry 4.0 seeks to optimize goods creation based on consumer requirements, specifications for quality, and financial viability. Big data collected by the Internet of Things (IoT)-based commercial Cyber-Physical Systems (CPS) plays an essential part in boosting platform operation efficiency to promote throughput with improved consumer encounters in Industry 4.0. This study shows big databases derived from IoT-based Optical-Wireless CPS (OWCPSs) for optimizing the functioning of maintenance networks in the electronics-manufacturing Industry 4.0. This research collected and analyzed big databases including five parameters: data delivery, delay, overload, throughput, and package error percentage in OWCPSs. The information gathered is important for optimizing the functioning of service systems in the production of electronic goods Industry 4.0. 2024 selection and editorial matter, Prof. (Dr.) Dorota Jelonek, Prof. (Dr.) Narendra Kumar, Prof. (Dr.) Mamta Chahar, Prof. (Dr.) Rusudan Kinkladze and Prof. (Dr.) Lilla Knop; individual chapters, the contributors.