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Toward Scalable and Sustainable Detection Systems: A Behavioural Taxonomy and Utility-Based Framework for Security Detection in IoT and IIoT
No full textResource-constrained IoT and IIoT systems require detection architectures that balance accuracy with energy efficiency, scalability, and contextual awareness. This paper presents a conceptual framework informed by a systematic review of energy-aware detection systems (XDS), unifying intrusion and anomaly detection systems (IDS and ADS) within a single framework. The proposed taxonomy captures six key dimensions: energy-awareness, adaptivity, modularity, offloading support, domain scope, and attack coverage. Applying this framework to the recent literature reveals recurring limitations, including static architectures, limited runtime coordination, and narrow evaluation settings. To address these challenges, we introduce a utility-based decision model for multi-layer task placement, guided by operational metrics such as energy cost, latency, and detection complexity. Unlike review-only studies, this work contributes both a synthesis of current limitations and the design of a novel six-dimensional taxonomy and utility-based layered architecture. The study concludes with future directions that support the development of adaptable, sustainable, and context-aware XDS architectures for heterogeneous environments
IoT integrated CNN framework for automated detection and quantification of rice and potato crop diseases
No full textIn modern precision agriculture, early and accurate identification of crop diseases is crucial for reducing yield loss and minimizing pesticide overuse. This study proposes an IoT-enabled framework that integrates convolutional neural networks (CNNs) with image processing techniques for automated classification and quantification of diseases in rice and potato crops. A custom-curated dataset was developed, comprising over 1,800 images acquired through smartphone cameras and foldscope devices under natural lighting conditions. The proposed CNN model achieved a classification accuracy of over 95%, with a disease quantification accuracy of 90.5%, calculated using pixel-level segmentation of infected regions. Experimental results revealed infection percentages ranging from 0.68% in early-stage cases to 13.98% in severely affected samples, enabling precise disease severity analysis. The framework includes a MATLAB-based graphical user interface (GUI) for real-time visualization of classification results and severity scores. Training convergence was demonstrated with a mini-batch loss reduction from 1.0879 to 0.0094 over 200 iterations, and classification confidence scores exceeding 90% for most disease categories. In addition to software implementation, the model was synthesized for hardware deployment using FPGA, demonstrating less than 5% LUT and 1% register usage for 512 × 512 images, ensuring resource-efficient performance in IoT environments. This work introduces a scalable, field-deployable tool for crop health monitoring, with potential to enhance sustainable farming practices through timely disease management
Editorial: Holistically healthy humans: championing mental and physical wellbeing in education
Get PDFThe British Journal of Music Therapy : A 25-year retrospective
No full textTo mark 25 years of the British Journal of Music Therapy (BJMT) since the millennium in 2000, we have invited editors of the journal over the past quarter century to reflect on their time in this role and offer their thoughts about the music therapy profession. Their responses are presented here with minimal editing and without commentary as a contribution to the history of BJMT and its role in the music therapy profession. Exceptionally for BJMT, this article has not been peer- reviewed, but all authors have read each other’s contributions and offered corrections of fact where needed. We are grateful to them for the time and effort they have put into responding to this initiative. Each contributor was asked to reflect on the issues they faced during their time as editor, and to choose articles published under their editorship which they felt represented significant developments in practice or thinking in the profession. They were also invited to give their thoughts on the current and future role for BJMT. Articles are not referenced in the standard way but titles, authors, year of publication and BJMT issue numbers are given, with active links in the online edition of the journal
Behavioral syndromes are associated with social plasticity and competence in a wild primate
Get PDFThe ability to optimize social behavior to varying socioecological circumstances has been termed “social competence” and relies on behavioral plasticity. Behavioral syndromes, i.e. consistent individual differences in intraindividual correlations among behavioral traits, appear to be a constraint on social competence, yet studies exploring this have largely been limited to experimental laboratory settings. Here, we tested the importance of behavioral syndromes to social competence in wild Barbary macaques (Macaca sylvanus), an endangered primate with established links between positive social relationships and survival. We studied two groups (n = 27 individuals) in which behavioral syndrome phenotypes were established in a previous study. Individuals with lower scores for the “excitable” phenotype (roughly equivalent to the “shy-bold” axis in other studies) showed greater plasticity compared to more “excitable” (i.e., “bolder”) individuals in affiliative responses to the immediate social environment, being more likely to initiate grooming with larger numbers of conspecific bystanders present. Less excitable individuals increased their grooming social network connectivity to a greater degree compared to more excitable individuals in periods of higher anthropogenic pressure. During colder weather, less excitable individuals concentrated their grooming network into fewer ties, whereas more excitable individuals slightly increased their number of connections. Any changes in network connectivity in relation to socioecology were small, reflecting the fact that stability in social network position is a common phenomenon in various taxa. Overall, we show that behavioral syndrome phenotypes influence plasticity in affiliative behavior and thus may be a key factor in individual responses to the rapidly changing socioecologies of the Anthropocene
DNPFL : A Federated Deep Learning Framework for Enhanced Network Intrusion Detection With Privacy Preservation
No full textIn the face of increasing cybersecurity threats, effective network intrusion detection systems are crucial for safeguarding sensitive data and maintaining the integrity of digital infrastructures. This research paper presents the DNPFL (Deep Neural Powered Federated Learning) framework, which integrates advanced deep learning techniques with a federated learning paradigm to enhance the detection of various network attacks, including DoS, DDoS, Probe, R2L, and U2R. The proposed method utilizes a hybrid architecture that combines decentralized data processing through federated learning with advanced deep learning techniques, enabling efficient feature selection and classification of various network attack types while ensuring data privacy and security. Utilizing prominent datasets such as NSL‐KDD, CICIDS 2017, UNSW, and DARPA 1998/1999, the model demonstrates remarkable performance metrics, achieving high accuracy. The DNPFL model effectively minimizes false positives while accurately identifying both common and complex attack types. The integration of federated learning ensures data privacy by enabling decentralized model training, while the deep learning architecture captures intricate patterns associated with malicious activities. The outcome highlight the model's robustness and adaptability, pointing it as a valuable solution based on real‐time intrusion detection systems in evolving cyber threat landscapes
Environmental, social and governance (ESG) practices and organizational resilience in Brazilian companies
No full textPurpose Over the past two decades, various exogenous shocks pushed companies to enhance their organizational resilience capabilities. This study aims to identify mechanisms used by Brazilian companies to reduce the impact of exogenous shocks and antecedents contributing to the development of organizational resilience. Design/methodology/approach The study particularly examines the influence of ESG performance and disclosures on organizational resilience, which, as a latent construct, was measured using long-term growth and financial volatility and stressed by an ordinary least squares regression with random effects and robust standard errors. Findings Our results demonstrate that ESG performance reduces the financial volatility of Brazilian non-financial listed companies and that ESG disclosures have a significant impact on long-term growth and the reduction of financial volatility during periods when companies are exposed to exogenous shocks, thereby contributing to their resilience. Research limitations/implications Our study was limited to the long term. Future studies investigate the impact of ESG performance and disclosure on the trade-off between short and long-term growth in emerging market countries. Practical implications The practical implications of the study are to observe how managers, investors and regulators can use ESG practices as a mechanism for building organizational resilience and managing crises. Social implications The study demonstrates the impact on building resilience in the communities and regions in which they operate by using ESG practices to build their resilience. Originality/value This study, therefore, corroborates the influence of ESG performance and disclosure in the development of proactive and reactive organizational resilience capabilities
Design and Implementation of Real-Time Tomato Plant Growth Monitoring System using Deep Learning based YOLO and Raspberry Pi
Get PDFTomatoes are widely used in India for daily meals and are very sensitive to environmental changes, pests, and diseases, which can have a significant impact on the growth and productivity of the crop. To address these issues, it is important to monitor plant growth. This paper presents the design and implementation of a real-time tomato growth-monitoring system based on a deep learning YOLO model and Raspberry Pi. The system leverages the YOLOv8 architecture for accurate detection and classification of the tomato plant growth stage and anomalies in real time. The Raspberry Pi serves as the central processing unit, integrating camera sensor data and image analysis to provide a cost-effective, portable, and scalable solution. This system incorporates high-resolution cameras to capture real-time images. The proposed system demonstrated high accuracy in detecting various growth stages and facilitating timely interventions. This technology provides an efficient and automated approach to precision agriculture, enabling farmers to optimize resource utilization, improve yields, and reduce the environmental footprint of tomato cultivation. The performance and feasibility of the system were validated through extensive testing in controlled and open-field environments, highlighting its potential for adoption in smart agriculture
Navigating Services in the UK: The Lived Experiences of Families Affected by 22q11.2 Deletion Syndrome
Get PDFFamilies affected by 22q11.2 deletion syndrome (22q) face complex health and mental health challenges, yet their lived experiences, particularly within the UK, remain underexplored. This study aimed to understand how families navigate care systems, mental health provision, and the transition to adulthood. Using a participatory action research (PAR) framework, five young adults with 22q and six parents were interviewed, with a steering group co-developing the research questions to ensure relevance and accessibility. Thematic analysis revealed five key themes: lack of professional awareness and diagnostic delays; fragmented and generic care pathways; emotional burden of parental advocacy; systemic gaps during transition to adulthood; and the enabling role of supportive relationships and environments. These experiences highlight a need for holistic, collaborative models of care, improved professional training, and inclusive support systems tailored to the unique needs of individuals with 22q. By centring family voices, this study offers critical insights into systemic barriers and facilitators in the UK, with implications for policy, practice, and future research