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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. -
A Study on Work Engagement among the School Teachers
Work engagement is a measurable degree of an persons positive or negative emotional attachment to their job, colleagues and organization which profoundly influences their willingness to learn and perform at work. Now a days it is observed that the commitment and dedication of the teachers in their profession as decreasing. It is also seen that teacher turnover is also becoming high. Even though the teachers in the schools were paid good, the turn over seems to be increasing. The study here tries to investigate the relationship with the work engagement and the socio demographic characteristics of teachers where the demographic variables could explain the relationship between the dimensions of work engagement. Descriptive research design is being used in the study . This design is helpful to identify the socio demographic characteristics and its relationship between the Work engagement among School teachers . The sample consisted of 100 school teachers who having more than one years of experience and the sample was selected by using the convenient sampling method. The study was done using the UWES Scale developed by Wilmar Schaufeli and Arnold Bakker in 2003 and nineteen other questionnaires were developed to know the other factors contributing to work engagement. The resourceful work environment can foster teachers work engagement. Consequently, the study shows that the older experienced married teachers shows the high level of work engagement where the educational qualification has no much role in it. This means that the young generation is not much interested in the profession with a a passion rather than they themselves consider it as a job. The work engagement can be increased among them through making interventions like improving and enhancing effective job and personal resources. -
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. -
Beyond the Surface, Delving into du?kha: Buddhist Insights into the Lives of Married Women in a South Indian Metropolis
The modern era has brought women unparalleled opportunities in various fields. However, they still encounter numerous challenges that impede their well-being. This qualitative study explores how Buddhist wisdom can be applied to understand the well-being challenges experienced by married women aged 2539years and residing in Bengaluru, a metropolitan city in India. The study involved conducting semi-structured interviews with eight married women. A thematic analysis of these interviews revealed three main themes, that is, my body, my mind, and my relationship, each with sub-themes falling under the overarching global theme, echoes of unease: unveiling du?khas reflections. The factors driving unsatisfactoriness among the participants were then analysed by applying the Buddhist doctrine of dependent arising to understand the origins of du?kha. This study suggests that Buddhist philosophy provides valuable insights for unravelling the intricacies of the modern womans du?kha and may serve as a potential pathway for women to tap into their inner reservoir of wisdom and compassion, enhancing their overall well-being. The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2025. -
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Interaction of Nanomaterials with Plant Macromolecules: Nucleic Acid, Proteins and Hormones
Nanotechnology has the ability to change a wide range of industrial and agricultural operations. To harness these possibilities, it is essential to construct nano-materials that have minimum impact on the human body, plant systems as well as the environment. Using different materials can up-or down-regulate diverse genes of plants, create stimulating or stressful conditions and even cause production of metabolites that affect plant-associated microbes. The same nanoparticle can promote one plant species growth and be toxic to another. A small change in the concen-trations could cause either flourishment or senescence. It is crucial to understand how nanomaterials interact with nucleic acids, the most fundamental plant macro-molecule, as well as with the proteins and hormones made by biochemical processes. This chapter explores the basics of nanotechnology, with a brief classification and notes on some of the most recently used nanomaterials in agriculture such as metals and their oxides, quantum dots, graphene, arabinoxylan and chitosan nanoparticles, single and multi-walled carbon nanotubes. Interactions with these above-mentioned macromolecules are explored, along with futuristic applications in plants that are currently being tested, like nanocarriers and nanovalves. Through this work, it is hoped that the field will further be extended through proper understanding of the environmental implications of nanomaterials, and that green technology will become the norm. The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerland AG 2023. -
SCSLnO-SqueezeNet: Sine Cosine-Sea Lion Optimization enabled SqueezeNet for intrusion detection in IoT
Security and privacy are regarded as the greatest priority in any real-world smart ecosystem built on the Internet of Things (IoT) paradigm. In this study, a SqueezeNet model for IoT threat detection is built using Sine Cosine Sea Lion Optimization (SCSLnO). The Base Station (BS) carries out intrusion detection. The Hausdorff distance is used to determine which features are important. Using the SqueezeNet model, attack detection is carried out, and the network classifier is trained using SCSLnO, which is developed by combining the Sine Cosine Algorithm (SCA) with Sea Lion Optimization (SLnO). BoT-IoT and NSL-KDD datasets are used for the analysis. In comparison to existing approaches, PSO-KNN/SVM, Voting Ensemble Classifier, Deep NN, and Deep learning, the accuracy value produced by devised method for the BoT-IoT dataset is 10.75%, 8.45%, 6.36%, and 3.51% higher when the training percentage is 90. 2023 Informa UK Limited, trading as Taylor & Francis Group. -
Impact of domestic investment, market size, and trade openness on outward fdi: A panel data analysis on brics
The recent phenomenal increase in the outward foreign direct investment (FDI) of emerging countries has raised concerns among policymakers. One school of thought argues that when multinational firms relocate production facilities abroad, it reduces the likelihood of concurrent investments in the home country, resulting in reduced domestic output. In this case, the outward FDI would harm the domestic investments. The other argues that the outward FDI would be more advantageous for the domestic investment when firms internationalize for entering into new markets and/or to import intermediate goods, wherein outward investments boost the returns in the home country, leading to a positive impact of outward FDI on domestic investment. The influence of the outward FDI on the domestic investment of any country or a region state cannot be generalized as each country is unique, and the drivers of investments would differ for different countries at the different development phases of each country. An attempt was made in this study to empirically trace the impact of the domestic investment, market size, and trade openness of the BRICS's members on the BRICS's outward FDI as a group. The results of the panel least square method highlighted that the variables - domestic investment and trade openness of BRICS had a positive effect on the outward FDI; whereas, the market size of BRICS was inversely related to outward FDI of BRICS. The data were tested for stationarity and Hausman test validated the results. 2019, Associated Management Consultants Pvt. Ltd.. All rights reserved. -
On ideal sumset labelled graphs
The sumset of two sets A and B of integers, denoted by A + B, is defined as (formula presented). Let X be a non-empty set of non-negative integers. A sumset labelling of a graph G is an injective function (Formula Presented) such that the induced function (Formula Presented) is defined by (Formula presented). In this paper, we introduce the notion of ideal sumset labelling of graph and discuss the admissibility of this labelling by certain graph classes and discuss some structural characterization of those graphs. 2021 Jincy P. Mathai, Sudev Naduvath, and Satheesh Sreedharan. This is an open access article distributed under the terms of the Creative Commons License, which permits unrestricted use and distribution provided the original author and source are credited. -
Beyond Grand Narratives of the Sacred A Postmodern Outlook in The Chosen
[No abstract available]