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Digitalizing Sustainable Trade Corridors: A Multi-Layered Big Data Analytics Framework for Green Trade Reform
The contemporary period of rapid digital transformation has resulted in global trading routes changing from using physical means to intelligent ecosystems. One of the main ideas of this chapter is the conceptual framework that combines the power of the Internet of Things (IoT) and Big Data Analytics (BDA) to totally change the customs procedures as well as the supply chain management. However, the use of technology has only been applied in some parts of the cross- border trade sector which has created information silos and caused slow operations. This research is dedicated to showing the advantages of coupling the instantaneous IoT sensor data with powerful BDA workflows in risk reduction, and- increased transparency, and even promoting regional integration. The chapter offers a practical guide for policymakers and logistics professionals who want to connect the technical aspect of digital customs reforms with the ethical and sustainable implementation. 2026 by IGI Global Scientific Publishing. -
Unfolding the aggression and locus of control paradigm in sportspersons and non-sportspersons
The present study investigated Aggression and Locus of Control on Combat Sports Persons, Non-Combat Sports Persons, and Non-Sports Persons. In this study, a sample of 240 individuals (80 Combat sports, 80 Non-Combat Sports & 80 Non-Sportspersons) was used through purposive sampling. The tools administered were the Buss and Perry Aggression Questionnaire by Arnold H. Buss and Mark Perry and Rotters Locus of Control Scale by Julian Rotter respectively. The objective of the study was to investigate Aggression and Locus of Control in males and females from Combat, Non-Combat, and Non-Sports persons. This research also aims to explore the relationship between Aggression and Locus of Control. Mean, t-test, F-value (ANOVA), and correlation have been computed over SPSS-16. Results suggest that males from Combat have higher Aggression than people from non-sports and non-combat sports. There is also a significant difference between non-sports persons and sports people over the Locus of Control, sports persons showed internal locus of control compared to non-sports persons who were higher on external locus of control. The result also indicates a significant relationship between the anger dimension of the Aggression and Locus of Control. 2025 ARD Asociaci Espala. -
Unfolding the aggression and locus of control paradigm in sportspersons and non-sportspersons
The present study investigated Aggression and Locus of Control on Combat Sports Persons, Non-Combat Sports Persons, and Non-Sports Persons. In this study, a sample of 240 individuals (80 Combat sports, 80 Non-Combat Sports & 80 Non-Sportspersons) was used through purposive sampling. The tools administered were the Buss and Perry Aggression Questionnaire by Arnold H. Buss and Mark Perry and Rotters Locus of Control Scale by Julian Rotter respectively. The objective of the study was to investigate Aggression and Locus of Control in males and females from Combat, Non-Combat, and Non-Sports persons. This research also aims to explore the relationship between Aggression and Locus of Control. Mean, t-test, F-value (ANOVA), and correlation have been computed over SPSS-16. Results suggest that males from Combat have higher Aggression than people from non-sports and non-combat sports. There is also a significant difference between non-sports persons and sports people over the Locus of Control, sports persons showed internal locus of control compared to non-sports persons who were higher on external locus of control. The result also indicates a significant relationship between the anger dimension of the Aggression and Locus of Control. 2025 ARD Asociaci Espala. -
An Efficient Compressive Data Collection Scheme for Wireless Sensor Networks
The Compressive Data Collection (CDC) scheme is an efficient data-acquiring method that uses compressive sensing to decrease the bulk of data transmitted. Most existing schemes are modeled as Non-Uniform Sparse Random Projection (NSRP), and an NSRP-based estimator is used. These models cannot deal with anomaly readings that deviate from their standards and norms. Therefore, we provide a new CDC strategy in this study that uses an opportunistic estimator and routing. Initially, neighbor nodes are identified using the covariance function following the Gaussian process regression, and the data transfer to the neighbor node is done using the compressive sensing technique. Compressed data are then projected by using conventional random projection. Finally, the sample required to retrieve data is estimated using margin-free and maximum likelihood estimators. Results show that the sample needed to retrieve the data is less in the proposed scheme. The Author(s), under exclusive license to Springer Nature Switzerland AG 2024. -
A self-cooperative trust scheme against black hole attacks in vehicular ad hoc networks
The main objective of the Vehicular Adhoc NETwork (VANET) is to provide secure communications for the vehicles in the network without fixed infrastructures. It inherits all the properties of the MANET. Achieving reliable routing to avoid various routing attacks is the major concern in the vehicular network. Routing attacks degrade the performance of the network. Black hole attack is one of the routing attacks, which drops the data packets without forwarding them to the destination vehicle. Different routing schemes are proposed to provide security against these attacks, which still have security issues. Hence a new self-cooperative trust scheme is proposed in this paper, to detect single as well as collaborative black hole attackers in the network. Two processes: self-detection and cooperative detection, are used to detect attackers in the network. Results show that the proposed scheme has better performance in terms of throughput, PDR and delay. Copyright 2021 Inderscience Enterprises Ltd. -
An Enhanced Secure Message Authentication Protocol for Internet of Vehicles
Internet of Vehicles (IoV) aims to transform the driving experience to the next level by ensuring communication with other vehicles, pedestrians handheld devices, Road Side Units (RSU), and other sensors used in the smart city environment. It integrates the benefits of the Internet of Things (IoT) and Vehicular Adhoc NETwork (VANET) to offer a safe and comfortable driving environment. Since communication is established through insecure channels, IoV is prone to various security attacks. Hence, there is a need for an authentication mechanism to ensure secure communications. For VANET, various authentication techniques are available, but they are not suitable for IoV due to their high computation overhead. Hence, we suggest a novel secure authentication scheme for IoV, which ensures authentication, conditional privacy preservation, message integrity, traceability, and unlinkability. It also provides security against various attacks. The proposed scheme performs better with less computation, communication, and storage overhead. The Author(s), under exclusive license to Springer Nature Switzerland AG 2026. -
Employing Artificial Intelligence and Automation in Sustainable Development Research
The rise of artificial intelligence (AI) and its increasingly widespread influence across various sectors necessitates an evaluation of its impact on the attainment of Sustainable Development Goals (SDGs). Industrialized nations have reaped the benefits of 21st-century technologies, particularly automation, which has fundamentally transformed the manufacturing and industrial production processes. The next evolutionary phase in automation is the advent of AI, characterized by machines and systems demonstrating intelligence, not only capable of performing tasks but also collaborating synergistically with humans and the environment. Among its myriad contributions, AI is poised to enhance development, foster sustainable resource utilization, and facilitate effective waste management. Intelligent systems are set to reshape various domains, including transportation, precision agriculture, biodiversity conservation, environmental modeling, public health, construction, manufacturing, and initiatives aimed at fostering prosperity on Earth. These systems will possess the ability to perceive, analyze situations, and responsively react to real-time cues such as human gestures, facial expressions, and the movement of pedestrians crossing busy streets. This research delves into the intricate relationship between AI systems and the objectives of sustainable development (SD). 2025 by Apple Academic Press, Inc. -
Employing Artificial Intelligence and Automation in Sustainable Development Research
The rise of artificial intelligence (AI) and its increasingly widespread influence across various sectors necessitates an evaluation of its impact on the attainment of Sustainable Development Goals (SDGs). Industrialized nations have reaped the benefits of 21st-century technologies, particularly automation, which has fundamentally transformed the manufacturing and industrial production processes. The next evolutionary phase in automation is the advent of AI, characterized by machines and systems demonstrating intelligence, not only capable of performing tasks but also collaborating synergistically with humans and the environment. Among its myriad contributions, AI is poised to enhance development, foster sustainable resource utilization, and facilitate effective waste management. Intelligent systems are set to reshape various domains, including transportation, precision agriculture, biodiversity conservation, environmental modeling, public health, construction, manufacturing, and initiatives aimed at fostering prosperity on Earth. These systems will possess the ability to perceive, analyze situations, and responsively react to real-time cues such as human gestures, facial expressions, and the movement of pedestrians crossing busy streets. This research delves into the intricate relationship between AI systems and the objectives of sustainable development (SD). 2025 by Apple Academic Press, Inc. -
Stability Analysis ofSalt Fingers forDifferent Non-uniform Temperature Profiles inaMicropolar Liquid
This paper describes the linear stability analysis of salt finger convection for different non-uniform temperature profiles by keeping the solutal concentration uniform throughout the system. The system consists of two parallel plates separated by a thin layer of micropolar liquid with infinite length, in which the system is heated and soluted from above the plate. Normal mode techniques are used to convert the system of partial differential equations into ordinary differential equations; further, Galerkian method is introduced to get the eigenvalue for isothermal, permeable with no-spin boundary conditions. The study also explains the effect of different micropolar parameters on the onset of convection. The phase of temperature flow for different boundary conditions explains the graphical solution of the energy equation and its gradients. It is shown that non-uniform temperature profiles, diffusivity ratio, coupling parameter, and solutal Rayleigh number influence the stability of the system. 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. -
Reflective writing skills among pre service teachers: a scoping review
Reflection is a soul-searching process. It is an innate ability to delve down the memory lane to judge a reaction to a particular situation as right or wrong as a response. The positive reactions are reinforced and the ineffective negative ones are relinquished. Developing reflective skills among preservice teachers include regular reflective practice sessions. They have to painstakingly record all their reflections after the delivery of each lesson as part of their curriculum along with other reflective practice opportunities. This effort should lead to evolution of professional practitioner in the long run. Although, there are factors affecting its development, preservice teachers seem to do it more monotonously without much reflective learning. Their reflective writing skills are way behind the expected level. This study adopts the research design outline advocated by Arksey and OMalley. The study appraised the research studies conducted from 2015 to 2024 as a part of scoping review. The study throws light on the various aspects related to the teacher-trainees reflective writing skills. Future studies may focus on empirical validation of the reflective writing skills among preservice teachers. 2025 Institute of Advanced Engineering and Science. All rights reserved. -
Performance Analysis of Different Classifiers to Build a Classification Model and to Improve the Vigilance Skills in Crime Detection Using Data Mining Techniques
International Journal of Advanced Research in Computer Science, Vol-3 (7), pp. 314-317. ISSN-0976-5697 -
Quantum-Driven Finance Transforming Banking Through Next-Generation Technologies
The swift progress of quantum computing is set to revolutionize the financial sector, especially in the fields of risk management and portfolio optimization. Existing financial models, though effective in some measure, are unable to handle the huge complexities of today's markets, where high-frequency trading, nonlinear interdependencies, and complex risk factors require advanced computational capabilities. Quantum finance, a new multidisciplinary research area, uses quantum computing concepts to improve financial decision-making, investment strategy optimization, and risk reduction more effectively than traditional techniques. This chapter discusses how quantum computing is revolutionizing risk management and portfolio optimization using quantum mechanics-based algorithms like quantum annealing, quantum Monte Carlo simulations, and variational quantum eigensolvers (VQEs). These methods enable financial institutions to resolve high-dimensional optimization problems exponentially quicker, detect more optimal risk-adjusted portfolios, and build predictive models with higher accuracy. 2026 by IGI Global Scientific Publishing. All rights reserved. -
Real-Time Data Fusion Algorithm for Multi-Modal Environmental Sensor Networks Using Kalman Filtering and IoT Integration
Fusion of heterogeneous, noisy, and asynchronous multimodal data streams is essential to environmental sensor networks, given the computational, memory, and energy constraints of IoT devices. This paper introduces a real-time data fusion framework integrating hybrid adaptive Kalman filtering, distributed edge computing, and seamless IoT connectivity. The proposed framework incorporates three key innovations. First, a hybrid adaptive Kalman filtering mechanism employs the Unscented Kalman Filter (UKF) sigma-point technique, augmented with Long Short-Term Memory (LSTM) neural networks and fuzzy logic, for dynamic noise correction and robust nonlinear state estimation. Second, a three-tier distributed fusion architecture employs edge computing for local data processing, reducing network latency, communication overhead, and energy consumption. Third, a modular Service-Oriented Architecture enables seamless IoT integration, remote data access, and adaptive system reconfiguration. The framework also incorporates multi-criteria fault detection that combines chi-square tests, sequential probability ratio tests, and LSTM-based predictive compensation during sensor failures. Experimental validation employed 150 sensors for urban air-quality monitoring, industrial facility surveillance, and water-quality measurement. Sensor nodes utilized ESP32-S3 microcontrollers with LoRa communication, while Raspberry Pi 4 devices served as edge gateways connected to AWS IoT infrastructure. Compared to standard Kalman filtering, the proposed method achieved: (i) 25.2% reduction in root mean square estimation error, (ii) 41% energy reduction driven by 70% communication savings through predictive transmission and edge compression, (iii) sub-100 ms end-to-end latency representing 54% improvement, and (iv) robust performance maintaining below 10% degradation at 15% sensor failure rates. 2026 Taylor & Francis Group, LLC. -
Adaptive Mesh Networking Protocol for Self-Healing Electrochemical Sensor Networks in Environmental Monitoring Applications
Sensor networks for environmental monitoring must be robust, flexible, and long-lasting, and comprehensive reviews and evaluations of adaptive mesh networking protocols for self-healing to enable autonomous operation under challenging environmental conditions are needed. The purpose of this study was to conduct an extensive review and assessment of adaptive mesh networking protocols for the self-healing of electrochemical sensor networks used in environmental monitoring. The Adaptive Mesh Networking Protocols enable the distributed autonomous sensors (distributed over vast areas or through obstructions) to dynamically route their collected data, recover when nodes fail, and extend their life (in real-time). In evaluating adaptive mesh networking protocols, we reviewed several key features, including self-healing mechanisms, adaptive routing algorithms (including their mathematical representations), methods for achieving energy efficiency, and mechanisms for securing data collection from autonomous sensor networks. Our simulation results show that our proposed adaptive mesh networking protocol achieves greater than 98% packet delivery success, even with up to 30% of nodes lost. Furthermore, we have shown that our approach can reduce the energy consumption of autonomous sensors by up to 87.5% compared to existing non-adaptive approaches. Our demonstration of real-time monitoring dashboards and a comprehensive performance analysis of the autonomous sensor networks demonstrates the feasibility of implementing adaptive mesh networking protocols into large-scale environmental monitoring projects. A significant area of focus for future research will be sensor-level self-correction to address bio-fouling remediation. 2026 Taylor & Francis Group, LLC. -
Real-Time Video Text Spotting with OpenCV and OCR Powered by Deep Learning
The identification of text in video places huge challenges, and translating them into target form demands high-level expert skills in computer vision and deep learning. This system can grab and supervise the real-time video process of text elements by utilizing OpenCV for text recognition and extraction. The proposed model was employed diverse machine translation models to guarantee high-quality results for translation. Based on wide testing and assessment, the goal is to make the apporach fast and precise, offering valuable tool for instructors, content creators, and overall users. This novel approach solves problems relating to language difficulty problems. Key elements include video processing, text detection and recognition, and machine translation. In addition to these essential functionalities, sophisticated preprocessing methods are applied to make text stand out from diverse backgrounds to render high performance in diverse environments. Deep learning algorithms improves the accuracy of text detection, especially for occluded or distorted characters. Finally, the cloud-based translation service offers real-time multilingual support to enable maximal adaptability to user needs. For the first time, this innovative technology finally enables streamlining access to the content and facilitates cross-cultural communication in multimedia contexts with nigh-guaranteed linguistic barriers broken down. 2025 IEEE. -
Adversarial Shadows in Digital Forensics: New Insights Into File Fragment Classification Vulnerabilities and Defenses
The paper is a comprehensive survey of adversarial attacks on file fragment classification (FFC) models - a relatively unexplored area in digital forensics, given the increasing application of machine learning techniques. Unlike image or text classification adversarial attacks, adversarial attacks on FFC exploit statistical and structural properties at the byte level in systems that lack semantic or perceptual knowledge. Such properties necessitate the use of domain-specific defense strategies, as the defense strategies adopted from other domains are typically not effective for the problems of FFC. The survey comprehensively evaluates attack mechanisms relevant to FFC, including evasion and poisoning attacks, and discusses their impact on forensic reliability. It highlights the absence of domain-specific benchmarks, robust evaluation protocols, and systematic research on the adversarial robustness of FFC. The paper also discusses the different types of byte level perturbations that can happen in fragment data, and it sets specific research priorities for raising the reliability of machine learning-based digital evidence recovery and security. The paper provides building blocks for future work, offering practical insights for development in ensuring file fragment classification systems utilized in forensics are secure. 2013 IEEE. -
Attention and Representation Learning in Byte-Level Digital Forensics: A Survey of Methods, Challenges, and Applications
Byte-level analysis has become an essential capability in digital forensics, enabling content-based investigation when file system metadata, headers, or structural information are unavailable or unreliable. Recent advances in deep learning allow forensic systems to learn discriminative features directly from raw byte streams; however, the growing diversity of representation strategies, architectural designs, and attention mechanisms makes it difficult to assess their relative effectiveness and practical suitability. This study presents a structured survey of representation learning and attention-based approaches for byte-level digital forensic analysis. We examine statistical, embedding-based, image-based, sequential, and hybrid representations, and analyze how architectural choices and attention mechanisms influence performance, robustness, and scalability. Across the literature, hybrid representations combined with lightweight convolutional backbones and selective attention mechanisms consistently provide a favorable balance between accuracy and computational efficiency. The survey also reviews key forensic applications, including file fragment classification, malware and binary analysis, network payload forensics, and encrypted or compressed data triage. In addition, we critically discuss challenges related to distribution shift, dataset bias, adversarial vulnerability, interpretability, and reproducibility, along with practical considerations for deployment in large-scale forensic pipelines. By synthesizing architectural trends, operational constraints, and reliability concerns, this work identifies critical research gaps and provides a structured foundation for the development of robust and trustworthy byte-level forensic learning systems. (2026), (Science and Information Organization). All rights reserved. -
Attention-based CNN for Adversarial File Fragment Detection Against Padding and Bit-Flip Attacks
File fragment classification represents a critical task within digital forensics and cybersecurity that aims to recover fragmented files when their metadata is not available. Even though cutting-edge deep learning models achieve 77-79% accuracy on clean fragments, none of the existing file fragment classification systems currently include detection mechanisms against adversarial attacks, thus remaining defenseless against attackers using byte-level perturbations. This paper addresses this gap by proposing the first adversarial detection framework for file fragment classification. This paper presents an attention-based CNN that combines byte embeddings with both spatial and channel attention mechanisms to detect byte-level perturbations before actual classification. Evaluated over 30.72 million fragments across 75 file types, the detector reaches an accuracy of 91.44% against five attack strategies: null-byte padding, random-byte padding, cross-file padding, random bit-flipping, and header-targeted bit-flipping, at 91.34% recall, 95.46% specificity, and 0.9819 AUC-ROC. With 1.31 M parameters and 1 ms inference time per fragment, the detector enables practical deployment as a preprocessing filter within two-stage forensic pipelines screening suspicious fragments before reaching standard classifiers. This foundational work sets up the first comprehensive benchmark for adversarial robustness evaluation specifically in file fragment classification. 2025 IEEE. -
File fragment classification: A comprehensive survey of research advances
A crucial task in digital forensics is file fragment classification, which involves classifying file fragments into their respective types based on their content. It is integral to digital forensics and data recovery, where investigators reconstruct and analyze fragmented files to gather evidence in criminal cases, data breaches, or other cybercrimes. This comprehensive survey paper offers insights into the different methodologies used for file fragment classification, including but not restricted to specialized approaches, hierarchical classification, and neural networks. The paper also highlights the challenges in file fragment classification, such as the need for format standardization, limited training data, scalability, and noise and ambiguity. A research gap analysis of the existing literature was conducted, and it was identified that further research could be done to explore the effectiveness of different approaches for file fragment classification, including transfer learning, ensemble methods, and so on. 2025 Scrivener Publishing LLC. All rights reserved. -
From Dharma to Dialogue: A Scoping Review of Couple Interventions Based on Buddhist Wisdom
There has been a surge of interest in interventions based on Buddhist traditions in the domain of relational therapy research. Our scoping review aimed to present a comprehensive overview of the current research landscape on this topic. Through systematic selection criteria, we identified 16 studies. We discovered that these interventions predominantly focused on mindfulness or compassiontwo pillars taken from the Buddhist tradition. Although the findings are varied, the collated evidence indicates that Buddhism-based interventions are promising in improving physical, mental, and relational health for individuals and dyads. However, the sustainability of these benefits needs to be examined. A point of concern is the possible dilution of the practices effectiveness when stripped of their comprehensive, traditional Buddhist context. We conclude from this review that while interventions such as mindfulness- and compassion-based programs can positively affect well-being, their efficacy might be constrained when these practices are detached from their broader, original Buddhist context. Therefore, future research should expand the field to develop intervention programs that maintain the integrity of holistic Buddhist wisdom to enhance relationship health and well-being. The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2025.