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Automated NLP-Based Classification of Nonfunctional Requirements in Blockchain and Cross-Domain Software Systems Using BERT and Machine Learning
No full textAutomated nonfunctional requirements (NFRs) classification enhances consistency and traceability by systematically labeling requirements, saving effort, supporting early architectural and testing decisions, improving stakeholder communication, and enabling quality across diverse software domains. While prior work has applied natural language processing (NLP) and machine learning (ML) to NFR classification, existing datasets are often limited in size, domain diversity, and contextual richness. This study presents a novel dataset comprising over 2400 NFRs spanning 269 software projects across 26 software application domains, including nine blockchain projects. The raw requirements are standardized using Rupp’s boilerplate to reduce vagueness and ambiguity, and the classification of NFRs types follows ISO/IEC 25,010 definitions. We employ a range of traditional ML, deep learning (DL), and a transformer-based model (i.e., BERT-base) for automated classification of NFRs, evaluating performance across cross-domain and blockchain-specific NFRs. Results highlight that domain-aware adaptation significantly enhances classification accuracy, with traditional ML and DL models showing strong performance on blockchain requirements. This work contributes a publicly available, context-rich dataset and provides empirical insights into the effectiveness of NLP-based NFR classification in both general and blockchain-specific settings
Frequency domain manipulation of multiple copy-move forgery in digital image forensics
Get PDFCopy move forgery is a type of image forgery in which a portion of the original image is copied and pasted in a new location on the same image. The consistent illumination and noise pattern make this kind of forgery more difficult to detect. In copy-move forgery detection, conventional approaches are generally effective at identifying simple multiple copy-move forgeries. However, the conventional approaches and deep learning approaches often fall short in detecting multiple forgeries when transformations are applied to the copied regions. Motivated from these findings, a transform domain method for generating and analyzing multiple copy-move forgeries is proposed in this paper. This method utilizes the discrete wavelet transform (DWT) to decompose the original and patch image into approximate (low frequency) and detail coefficients (high frequency). The patch image approximate and details coefficients are inserted into the corresponding positions of the original image wavelet coefficients. The inverse DWT (IDWT) reconstructs the processed image planes after modification which simulates the multiple copy move forgery. In addition, this approach is tested by resizing the region of interest with varying patch sizes resulting in an interesting set of outcomes when evaluated against existing state-of-the-art techniques. This evaluation allows us to identify gaps in existing approaches and suggest improvements for creating more robust detection techniques for multiple copy-move forgeries
From Digitalization to Sustainability: Does Supply Chain Digitalization Enhance Corporate Green Transformation Performance?
No full textSupply chain digitalization (SCD) plays a critical role in accelerating corporate green innovation, reducing carbon emissions, and enhancing corporate green transformation performance (CGTP). Drawing on the practice-based view, this study examines how SCD influences CGTP and through which mechanisms, using data from Chinese A-share listed firms spanning 2013 to 2021. Applying a double machine learning approach, the results demonstrate that SCD significantly enhances CGTP. Further, heterogeneity tests reveal that this positive effect is more pronounced in firms exposed to higher technological uncertainty, led by executives with stronger green cognition, and operating in less competitive markets. Mechanism tests suggest that SCD enhances firms’ sensing, seizing, and reconfiguring capabilities, thereby facilitating CGTP. The findings enrich the understanding of digital transformation and sustainability linkages and provide practical insights for managers and policymakers seeking to leverage SCD-driven strategies to promote corporate green transformation and sustainable development
Leadership dilemmas in early childhood settings: the perceptions of early childhood leaders and the looking-glass self
Get PDF©2025, Emerald Publishing Limited. This is an author produced version of a paper published in International Journal of Educational Management uploaded in accordance with the publisher’s self- archiving policy. The final published version is available online at the link. Some minor differences between this version and the final published version may remain. We suggest you refer to the final published version should you wish to cite from it. PurposeWith widespread concerns around the funding and availability of care and education during early childhood, this article takes a social psychological approach to exploring some of the dilemmas facing early childhood leaders. Study design/methodology/approachThe study uses an interpretivist, qualitative approach involving semi-structured interviews with six early childhood leaders in England. The participants’ perspectives are analysed using the symbolic interactionist concept of the looking-glass self, neoliberal theory and the systems leadership approach.FindingsThe positioning of leaders is greatly influenced by their self-image and sense of self related to how they perceive the expectations of others about their roles as leaders and the feelings of pride, recognition or disapprobation and marginalization. The looking-glass self brings insights that systems leadership and neoliberal critiques alone do not give, including that dilemmas are the daily fare of leaders in their settings rather than problems that can be neatly solved.OriginalityExploring the perspectives of early childhood leaders through the looking-glass self generates new research agendas for early childhood leadership that contribute to the understanding of agency and structure. Our use of the looking-glass self is original in the field of leadership in that we apply it to the analysis of leadership data as well as deploying it to critique systems leadership and neoliberalism. The practical implications for leadership practice and development include the need for leaders to understand their interactions with others, how that may affect their sense of self, and how this links to macro-issues in the sector
Harnessing Deep Learning with AlexNet for Tomato Leaf Disease Detection in the Indian Himalayan Terrain
No full textAgriculture is essential for living in the Indian Himalayan region (IHR), as it functions as the main occupation and source ofincome. 0erefore, monitoring crops and detecting diseases at early stages becomes essential. Our research leverages deep learning(DL), a dynamically growing technology pro8cient in handling large datasets. In this work, convolutional neural networks(CNNs) are used to solve real-world problems using the computer vision technique. 0is paper proposes a modi8ed AlexNetarchitecture for detecting diseases through images at an early stage and to increase the production rate by cultivating good qualitycrops. 0is model combines steps such as tomato leaf image data collection, image data preprocessing, and classi8cation anddetection of disease. In this work, a total of 5500 tomato leaf images have been taken from Kaggle, and an additional 1650photographs have been collected from a variety of 8eld sites, thereby creating a comprehensive dataset that will facilitate thedevelopment and assessment of a robust model. In the classi8cation and detection of tomato plant leaf diseases, our proposedmodel achieves an accuracy of 94.80%. Using DL technology, we hope to give farmers an accurate way to monitor and protect theircrops, ultimately enhancing agricultural quality and output
Perinatal Imaging in Partnership with Families (PIPKIN): Longitudinal cohort study protocol
Get PDFWhile advances in behavioural and neuroimaging methods suitable for use with infants have greatly increased our understanding of infant brain function, cognition and behaviour in recent years, relatively little is known about the rapid period of development during the last trimester of pregnancy and first weeks and months after birth, as well as the roles that the social environment and stress play in shaping this development. This protocol paper outlines The UK Perinatal Imaging in Partnership with Families (PIPKIN) Study, a unique, multi-method, longitudinal cohort study investigating the early development of fetal and infant neurocognitive function and behaviour, and how the infant's social and family environment shapes this development. The study follows families from a range of socio-economic backgrounds who participate at ten timepoints, from the third trimester of pregnancy until their infant is nine months old, with three visits taking place during the infant's first postnatal month. The study harnesses recent methodological advances coupled with the drive for more ecologically valid data collection by undertaking many of these visits in families' homes. Methods include measures of fetal behaviour using 4D ultrasound scanning; infant brain imaging using fNIRS and EEG; a full-day video recording of the home environment from the infant's perspective, with physiological measures; measures of recent stress in both infant and mother; questionnaires relating to the home environment as well as parents' feelings, attitudes, health and parenting routines; and standardised measures of infant behaviour and development. Specific aims are to investigate: i) individual differences in basic sensory, behavioural and motor processing between late prenatal and early postnatal periods; ii) rapid change in cortical functions over the first month, particularly for brain networks that support social behaviour; iii) effects of social interaction on developing brain function; and iv) individual differences in developmental trajectories associated with poverty-related contextual factors
Sources of occupational stress in UK construction projects: an empirical investigation and agenda for future research
No full textPurpose While stress, anxiety and depression rank as the second leading cause of work-related ill health in the UK construction sector, there exists a scarcity of empirical studies explicitly focused on investigating the sources of occupational stress among construction workers and professionals at both the construction project and supply chain levels. This study seeks to identify and investigate the primary stressors (sources of stress) in UK construction projects and to propose effective strategies for preventing or reducing stress in this context. Design/methodology/approach The study adopted a qualitative multi-methods research approach, comprising the use of a comprehensive literature review, case study interviews and a focus group. It utilised an integrated deductive-inductive approach theory building using NVivo software. In total, 19 in-depth interviews were conducted as part of the case-study with a well-rounded sample of construction professionals and trade supervisors, followed by a focus group with 12 policy influencers and sector stakeholders to evaluate the quality and transferability of the findings of the study. Findings The results reveal seven main stressors and 35 influencing factors within these 7 areas of stress in a UK construction project, with “workflow interruptions” emerging as the predominant stressor. In addition, the results of the focus-group, which was conducted with a sample of 12 prominent industry experts and policy influencers, indicate that the findings of the case study are transferrable and could be applicable to other construction projects and contexts. It is, therefore, recommended that these potential stressors be addressed by the project team as early as possible in construction projects. Additionally, the study sheds empirical light on the limitations of the critical path method and identifies “inclusive and collaborative planning” as a proactive strategy for stress prevention and/or reduction in construction projects. Research limitations/implications The findings of this study are mainly based on the perspectives of construction professionals at managerial and supervisory levels. It is, therefore, suggested that future studies are designed to focus on capturing the experiences and opinions of construction workers/operatives on the site. Practical implications The findings from this study have the potential to assist decision-makers in the prevention of stress within construction projects, ultimately enhancing workforce performance. It is suggested that the findings could be adapted for use as Construction Supply Chain Management Standards to improve occupational stress management and productivity in construction projects. The study also provides decision-makers and practitioners with a conceptual framework that includes a list of effective strategies for stress prevention or reduction at both project and organisational levels. It also contributes to practice by offering novel ideas for incorporating occupational stress and mental health considerations into production planning and control processes in construction. Originality/value To the best of the authors’ knowledge, this is the first, or one of the very few studies, to explore the concept of occupational stress in construction at the project and supply chain levels. It is also the first study to reveal “workflow” as a predominant stressor in construction projects. It is, therefore, suggested that both academic and industry efforts should focus on finding innovative ways to enhance workflow and collaboration in construction projects, to improve the productivity, health and well-being of their workforce and supply chain. Further, it is suggested that policymakers should consider the potential for incorporating “workflow” into the HSE's Management Standards for stress prevention and management
Computer-Vision- and Edge-Enabled Real-Time Assistance Framework for Visually Impaired Persons with LPWAN Emergency Signaling
Get PDFIn recent decades, various assistive technologies have emerged to support visually impaired individuals. However, there remains a gap in terms of solutions that provide efficient, universal, and real-time capabilities by combining robust object detection, robust communication, continuous data processing, and emergency signaling in dynamic environments. In many existing systems, trade-offs are made in range, latency, or reliability when applied in changing outdoor or indoor scenarios. In this study, we propose a comprehensive framework specifically tailored for visually impaired people, integrating computer vision, edge computing, and a dual-channel communication architecture including low-power wide-area network (LPWAN) technology. The system utilizes the YOLOv5 deep-learning model for the real-time detection of obstacles, paths, and assistive tools (such as the white cane) with high performance: precision 0.988, recall 0.969, and mAP 0.985. Implementation of edge-computing devices is introduced to offload computational load from central servers, enabling fast local processing and decision-making. The communications subsystem uses Wi-Fi as the primary link, while a LoRaWAN channel acts as a fail-safe emergency alert network. An IoT-based panic button is incorporated to transmit immediate location-tagged alerts, enabling rapid response by authorities or caregivers. The experimental results demonstrate the system’s low latency and reliable operations under varied real-world conditions, indicating significant potential to improve independent mobility and quality of life for visually impaired people. The proposed solution offers cost-effective and scalable architecture suitable for deployment in complex and challenging environments where real-time assistance is essential
Securing the Road Ahead: A Survey on Internet of Vehicles Security Powered by a Conceptual Blockchain‐Based Intrusion Detection System for Smart Cities
No full textThe Internet of Vehicles (IoV) is a critical component of the smart city. Various nodes exchange sensitive data for urban mobility, such as identification, position, messages, speed, and traffic statistics. Along with developing smart cities come threats to privacy and security through networks. Security is of the highest priority, considering various security‐privacy risks from the wellness, safety, and confidentiality of men and women inside the vehicle. This survey presents a detailed analysis of state‐of‐the‐art and evolving security challenges to IoV systems. It handles security challenges, such as data integrity and privacy. It also includes a critical review of the literature to identify gaps in current security mechanisms. It uses complete mathematical modeling and case studies to show the practical effectiveness of the proposed solutions. It aims to guide future development and implementation of more secure, efficient, and resilient IoV systems, particularly in smart city environments. It also introduces a novel Intrusion Detection System (IDS) with Artificial Intelligence (AI), smart contracts, and blockchain technology. These smart contracts ensure instant security with the utmost level of vulnerability through blockchain technology. In addition, we proposed a hybrid multi‐layered framework using Fog to conserve the resources at the vehicle level. We used mathematical proof to assess this framework. Merging blockchain, smart contracts, and AI into IoVs could increase human security by removing significant vulnerabilities
Wild chimpanzees share fermented fruits
No full textThe use of fermented foods and drinks by humans is so widespread as to be considered ubiquitous, with their use largely linked to dietary benefits and social bonding . The discovery of a molecular adaptation in an alcohol dehydrogenase enzyme that greatly increased ethanol metabolism in the common ancestor of African apes suggests that the incorporation of fermented fruit in the human diet has ancient origins . However, little is known about the inclusion of ethanolic foods in the diet of nonhuman great apes. Here, we document for the first time the repeated ingestion and sharing of naturally fermented African breadfruit (Treculia africana) with confirmed ethanol (alcohol), by wild chimpanzees (Pan troglodytes verus) in Cantanhez National Park, Guinea-Bissau. Widespread plant food sharing in great apes and the recent confirmation of ethanol presence in diverse fruit species suggest the sharing, and dietary incorporation, of ethanol-containing foods is extensive and may have played a long-standing role in hominoid societies. [Abstract copyright: Copyright © 2025 The Authors. Published by Elsevier Inc. All rights reserved.