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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. -
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. -
Developing an Advanced Cybersecurity Framework and Blueprint: A Contemporary Approach to Counter Hacking Through Reverse Engineering Techniques
Recently many of the world's most secure networks have been breached by hackers, resulting in damage, information theft, data corruption, and threats to both national and international security. Protection experts are now doubting the dependability and efficacy of the current protection measures against hacking assaults in light of this dire situation. This research will use a variety of accepted practice models, global standards, information security frameworks, and best practices to achieve this goal of developing a framework and blueprint for a specialized hacking countermeasure. The deliverable outcome is a technical and administrative hacking countermeasure framework and blueprint because the study will concentrate on technological management practices in addition to hacking countermeasure techniques and tools. The framework and the blueprint were validated and authorized, and the effectiveness and reliability of the study deliverable outcome were confirmed using questionnaire and interview surveys, finding that it fully met the established objectives and scope of work. Furthermore, the validation has demonstrated that the introduced solutions for the Defense-in-Breadth and the deception and concealment strategies can further improve the hacker countermeasure and the development of SNORT rules, the construction of a prototype, and the execution of live testing with the ultimate goal of closing the security gap created by hacker countermeasures in the present defense-in-depth-based security models. The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025. -
Establishing the Cornerstones of Ethical AI in Education
Establishing the cornerstones of ethical AI in education is essential for fostering an inclusive and equitable learning environment. Ethical AI can enhance personalization, improve access to resources, and empower educators with data-driven insights. Key principles include transparency, accountability, fairness, and privacy. Transparency ensures that AI algorithms are understandable and their decisions are explainable, allowing educators and students to trust the technology. Accountability involves clear guidelines on who is responsible for AI's actions and decisions. Fairness seeks to prevent biases that can adversely affect marginalized groups, ensuring equal opportunities for all students. Privacy is crucial to protect sensitive data and safeguard students' rights. Educators, policymakers, and technologists must collaborate to establish frameworks that prioritize these ethical foundations, promoting responsible AI integration. By embedding these cornerstones into the educational system, we can harness AI's potential while safeguarding the rights and dignity of all learners. Copyright 2026, IGI Global Scientific Publishing. Copying or distributing in print or electronic forms without written permission of IGI Global Scientific Publishing is prohibited. Use of this chapter to train generative artificial intelligence (AI) technologies is expressly prohibited. The publisher reserves all rights to license its use for generative AI training and machine learning model development. -
The Influence of Leadership on Organizational Resilience
Leadership plays a crucial role in shaping organizational resilience, influencing how a company responds to challenges and changes. Leaders set the tone for organizational culture, instilling values that promote adaptability, innovation, and collaboration. When leaders demonstrate a clear vision, they empower employees to embrace uncertainty and find solutions, fostering an environment where resilience thrives. Effective leaders communicate openly and inspire trust, which is essential for navigating crises. They create a supportive atmosphere that encourages risktaking and learning from failures. Additionally, leaders who prioritize professional development equip their workforce with the skills necessary to adapt to evolving market conditions. Strategic decision-making, anchored in a deep understanding of both internal and external environments, enhances resilience. Ultimately, the influence of leadership on organizational resilience is profound, as strong leaders cultivate a culture that not only withstands pressures but also flourishes in the face of challenges. 2026 by IGI Global Scientific Publishing. -
An overview of AI applications in wildlife conservation
The integration of artificial intelligence (AI) into wildlife conservation has revolutionized methodologies for monitoring species, enhancing habitat management, and combating poaching. This chapter examines various AI applications that contribute to the protection and preservation of biodiversity. Remote sensing technologies, powered by machine learning algorithms, assist in assessing habitat health and tracking changes over time. AI- driven image recognition tools enable the identification of individual animals from camera trap photos, facilitating more accurate population estimates and behavioral studies. Moreover, predictive analytics play a crucial role in forecasting human- wildlife conflicts and informing proactive management strategies. This synthesis of AI technologies demonstrates their potential to enhance conservation efforts, optimize resource allocation, and ultimately foster more effective wildlife protection initiatives. The ongoing advancement of AI in this field promises to create innovative solutions to some of the most pressing challenges. 2025, IGI Global Scientific Publishing. All rights reserved. -
Implementation of Recent Advancements in Cyber Security Practices and Laws in India
In the past few decades, a large number of scholars and experts have found that wireless connectivity technologies and systems are susceptible to many kinds of cyber attacks. Both governmental organizations and private firms are harmed by these attacks. Cybersecurity law is a complex and fascinating area of law in the age of information technology. This essay aims to outline numerous cyber hazards as well as ways to safeguard against them. In both local and international economic contexts, it is critical to establish robust regulatory and legal structures that address the growing concerns about fraud on the internet, security of information, and intellectual property protection. Additionally, it covers cybercrime's different manifestations and security in a global perspective. Due to recent technical breakthroughs and a growth in access to the internet, cyber security is now utilized to safeguard not just a person's workstation but also their own mobile devices, including tablets and mobile phones, that have grown into crucial tools for data transmission. The community of security researchers, which includes members from government, academia, and industry, must collaborate in order to comprehend the new risks facing the computer industry. The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025. -
Separate Electorate
In colonial India, the category of Depressed Classes was approached through two distinct lenses as a socio-political issue and a moral and cultural issue. Two towering figures represented these perspectives B. R. Ambedkar and M. K. Gandhi. While Gandhi earnestly sought to address untouchability and other socio-cultural challenges faced by Depressed Classes through moral teachings, his approach was contested for being quite ineffective. Ambedkar, on the contrary, viewed the problem as deeply entrenched within religion and culture, advocating for socio-political empowerment as the solution. This chapter maps the historical events leading to Ambedkars successfully lobbying of British India for a separate electorate for Depressed Classes. It examines Gandhis vehement objection to the same based on his understanding that such provision would be detrimental to Depressed Classes, Hinduism, and national unity. Consequently, this chapter analyses how Ambedkar was forced to come to an agreement with Gandhi and sign the Poona Pact that put an end to proposed separate electorate but granted additional reserved seats to Depressed Classes. This chapter provides historical contexts and discusses the debates around the political representation of the minorities to address the larger question of political share of Depressed Classes in Indian electorate. 2026 selection and editorial matter, Mahitosh Mandal and Sanjiv Kondekar; individual chapters, the contributors. -
Artificial Intelligence in Language Teaching: Exploring Translanguaging, Eco-Linguistics, and Community-Based Learning
This chapter critically examines the role of Artificial Intelligence (AI) in second and foreign language teaching through the lens of eco-linguistics, with a particular focus on translanguaging. It explores three key dimensions of AI's impact: the theory of language, the theory of language learning, and the evolving role of the teacher. Using an argumentative and experimental methodology, the chapter integrates theoretical insights from eco-linguistics and translanguaging to evaluate AI-assisted tools. It highlights the opportunities these tools provide, such as personalized learning and adaptive feedback, while addressing their limitations, including challenges to emotional connection, language standardization, and the diminishing role of human interaction. The chapter proposes a hybrid model that combines AI with community- based language teaching to mitigate these issues, preserving the communication ecology and fostering more holistic language learning practices. 2026, IGI Global Scientific Publishing. -
Perspectives Envisaging Employee Loyalty : A Case Analysis
Journal of Management Research Vol. 12, No. 2, pp 100-112, ISSN No. 0974-455X -
Stacked Ensemble Method of Multi Class Malware Detection Using PE Header and Section Attributes
Malware has now become sophisticated. The type of attacks has changed, too. To identify and remove them is now a great challenge. This paper presents a machine learning model for malware detection in windows. The Malware is detected based on the static collection of features, which includes the Portable Executable (PE) Header and Section data. Several classifiers were trained on a balanced dataset, including Logistic Regression, K-Nearest Neighbour, Support Vector Machine, Multi-Layer Perceptron, XGBoost, and Stacked Ensemble. The proposed stacking method utilises SVM, MLP, and XGBoost, with XGBoost serving as the meta-learner. The model delivered the best performance when compared with all the baseline models for an accuracy of 96.25% and an AUC of 0.9978. 2025 IEEE. -
Enhanced mechanical properties of CNT/Graphene reinforced PLA-based composites fabricated via fused deposition modelling
This study addresses the mechanical properties of polylactic acid (PLA) composite materials reinforced by CNTs and graphene, produced using the Fused Deposition Modeling process. The collaborative impact of graphene and CNTs upon the primary mechanical attributes including UTS, yield strength, modulus of elasticity, and impact resistance has been investigated. Three distinct CNT's weight percentages of 0.5, 1, and 1.5 have been fabricated under constant graphene content at 0.5 wt%. These findings revealed that the UTS of pure PLA were 28 MPa, whereas for the composite with 1.5 wt% CNT and 0.5 wt% graphene, it was raised to 48 MPa. From 2.6 GPa to 4 GPa the young's modulus enhancement is seen and the yield strength enhancement is seen up to 28 MPa for the composite from 20 MPa of pure PLA. The impact strength was greatly enhanced from 1.2 J for pure PLA to as high as 4 J for the composite comprising 1.5 wt percentage CNT and 0.5 wt percentage graphene. 2025 The Author(s) -
A Comprehensive Study on Parametric Optimization of Plasma-Sprayed Cr2C3 Coatings on Al6061 Alloy
Plasma spray, a widely employed thermal spray method, is known for enhancing coatings with heightened microhardness, density, and bonding strength. In this study, Taguchis approach was applied to optimize processing parameters for plasma spray-coated surfaces, aiming to reduce porosity, increase hardness, and fortify the connection between Cr2C3 coatings. The design of experiments method facilitated the optimization of process parameters, utilizing signal-to-noise ratios and ANOVA analysis to assess the significance of each processing parameter and identify optimal parameter combinations. Powdered feed rate and stand-off distance emerged as the two most critical processing variables influencing permeability and hardness, contingent on signal-to-noise ratios. S/N ratio analysis was employed to determine the optimal processing parameters for permeability, hardness, and bonding strength. For porosity, the optimal stand-off distance, powdered feed rate, and current density were identified as 60rpm, 50g/min, and 460ampsmm/s, respectively. Exemplary process conditions for hardness included a powdered feed rate of 60g/min, a stand-off distance of 80rpm, and a current density of 480 amps. Lastly, for strength properties, the ideal process variables were a stand-off distance of 80rpm, a current density of 480amps, and a powdered feed rate of 60g/min. Despite small differences between projected R2 and modified R2 values in statistical data on permeability, hardness, and bonding strength, the proximity to the one emphasizing the fit of the linear regression used for analysis was evident. Fracture results from the binding strength test postulate mixed adhesion-cohesion type failures in the Cr2C3 coatings. The Institution of Engineers (India) 2024. -
Estimation of System Reliability Based on Inverted Exponentiated Pareto Distribution Under a Progressively First-Failure Censored Scheme With Application
This article explores and derives the estimation of the multicomponent stressstrength (MSS) reliability parameter, assuming that the samples are coming from the inverted exponentiated Pareto distribution using a progressively first-failure censored scheme. To estimate the MSS reliability, both classical and Bayesian approaches are adopted. In the classical approach, the maximum likelihood and the asymptotic confidence interval estimation methods are used. The Bayes estimates with their corresponding highest posterior density credible interval estimates are obtained under the Bayesian approach, under the linear exponential loss function under both the noninformative and gamma informative priors. In addition, to compute the Bayes estimates, Markov chain Monte Carlo methods are used. To compare the efficacy of the different estimation strategies adopted in this paper, a Monte Carlo simulation study is carried out. To demonstrate the applicability of the proposed methodology, two real-life scenarios resulting/arising from two different carbon fiber data sets arere-analyzed. 2025 The Author(s). Quality and Reliability Engineering International published by John Wiley & Sons Ltd. -
Flexible Ni-Zn aqueous battery based on Ni?S?/NiO composite cathode with high energy-density and durability
The development of high-performance flexible aqueous zinc batteries requires innovative manufacturing protocols, suitable electrode composition and large area, three-dimensional architecture that results in exceptional electrochemical properties even under mechanical deformation. This work presents a unique approach for synthesizing flexible alkaline Ni-Zn batteries utilizing Ni?S?/NiO composite cathode over highly flexible and durable micro/nano-textured stainless steel mesh with excellent electrochemical performance. The binder-free composite cathode was developed through nickel electrodeposition followed by hydrothermal processing to create Ni?S?/NiO composite nanowires. The innovative anode design employs zinc composite coated on a pressed copper foam, significantly outperforming conventional zinc foil configuration. The Ni3S2/NiO/SSE composite cathode delivers an impressive specific capacity of 452 mAh/g, demonstrating excellent electrochemical properties at the electrode level. This advanced electrode architecture also enables the assembled device to showcase outstanding performance, delivering 316 mAh/g specific capacity at 1 A/g, a remarkable energy density of 553 Wh/kg, and exceptional power density of 11.19 kW/kg, substantially exceeding most reported aqueous zinc battery systems. Further, the device maintains stable electrochemical performance across various bending conditions, demonstrating superior mechanical flexibility essential for the advanced electronics and flexible energy storage applications. 2025 Elsevier B.V. -
Sustainability Reporting
Today, with the increased awareness among the various stakeholders, the success and growth of companies are not gauged by their financial performance but by the impact of their business operation on the environment and society. Companies are under immense pressure from different stakeholders to undertake sustainability practices and publish sustainability reports. Due to this, sustainability reporting has transformed from a voluntary exercise to a strategic imperative for companies. This chapter aims to explain the concept of sustainability reporting (SR), various drivers, and its benefits for the various stakeholders. It also provides a brief overview of the evolution of the notion of SR and the discourse of voluntary versus mandatory approach to SR. Further, this chapter discusses prominent global SR guidelines, frameworks, and challenges in the adoption of sustainability reporting practices. 2026 Elsevier Inc. All rights are reserved, including those for text and data mining, AI training, and similar technologies. -
What Influences Companies to Go Beyond Mandatory Corporate Social Responsibility Rule? Empirical Evidence from India
This study aims to empirically investigate how corporate governance (CG) characteristics influence firms to go beyond the mandatory minimum corporate social responsibility (CSR) expenditure rule to contribute towards sustainable development. It employs the panel regression technique for analysis of top seventy-five listed companies of the NIFTY100 index at Indian National Stock Exchange (NSE) for the years from 201415 to 202021. The empirical results revealed that CG attributes like large board size, large independent directors, and female directors significantly influence the CSR performance of the companies. However, no significant evidence was found in case of the impact of board meeting frequency and CSR practices of the companies. This chapter enables a better understanding of self-induced CSR practices to policymakers, regulators, practitioners, and other stakeholders. The findings suggest that various stakeholders should concentrate on specific CG attributes to focus on CSR performance. It is one of the first studies that determines what influence the adoption of self-induced CSR practices especially against the backdrop of major CG mechanism and CSR reforms in India. It provides additional empirical evidence to the extant body of literature on the CG and CSR practices from the perspective of emerging economies. The Author(s), under exclusive license to Springer Nature Switzerland AG 2025. -
Analysis of Market Communication and Informatization Services: A Data-Driven Study Based on SDMX Statistics
The market communication and informatization services sector plays a crucial role in modern economic development, facilitating digital transformation and connectivity. This study leverages official statistical data to analyze trends, growth patterns, and the economic impact of communication services in Uzbekistan. Using the SDMX dataset, we evaluate sectoral contributions, regional disparities, and the role of technological advancements. The findings provide insights into investment efficiency and policy recommendations for sustainable sectoral growth. 2025 IEEE. -
SECURITY VIEWPOINT IN ARTIFICIAL INTELLIGENCE-BASED SYSTEMS
Artificial intelligence (AI) is playing a key role in recent times linked with automation in almost all the fields on human interference and inventions. Industry 4.0 also signifies automation as one of the major aspects from its allies. However, on the other hand, issues and security concerns are at high priority in these systems and often are seen ignored as the AI systems are evolving as state-of-the-art technology. In this chapter, we focus on elaborating security vulnerabilities in AI-based infrastructure, explain the relation between AI attacks and cyber-attacks, and describe the sustained AI systems with inherited resilience. Case study is also considered to elicit threats and vulnerable issues associated with AI-based systems. Securing artificial intelligence and its associated techniques help in better outcomes which are tightly coupled with human monitored and controlled environments. AI algorithms need to be checked for accuracy in prediction model contexts on the evolution of technology for sustaining adversarial attacks. 2026 by Apple Academic Press, Inc. -
Effect of impulse buying on socioeconomic factors and retail categories /
Indian Journal of Marketing, Vol.46, Issue 9, ISSN: 0973-8703.