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Leveraging Smart Prosumers for Grid Resilience under High-Impact Low-Probability Events: A Privacy-Preserving Optimization Framework
Get PDFSmart prosumers, equipped with generation, storage, and advanced communication infrastructure, have significant potential to provide grid services. However, effectively harnessing this potential in decentralized environments requires novel optimization frameworks that coordinate system operators with prosumers while preserving data privacy. To address this challenge, a two-layer hierarchical optimization structure is proposed to maximize grid service provision by smart prosumers under high-impact low-probability (HILP) events with minimal information exchange. In the first layer, smart prosumers, including Internet data centers and battery swapping stations, optimize and announce their available flexible capacities during emergencies. In the second layer, the distribution system operator (DSO) integrates these capacities into emergency operation planning, complemented by the dynamic routing of battery logistic trucks and the execution of distribution feeder reconfiguration (DFR) to restore power to customers in fault-affected areas. Implementation on the IEEE 69-bus distribution network demonstrates that the proposed hierarchical framework reduces load shedding by 44.82% and emergency operation costs by 28.2% while maintaining agent data confidentiality. These results are derived under deterministic conditions, assuming reliable communication, full prosumer participation, and accessible logistics. While uncertainties such as communication delays, partial participation, or disrupted transportation are not yet modeled, the framework provides a computationally efficient basis for decentralized resilience enhancement
Obianuju: A Journey Back to Herself: A Story of Identity, Belonging and Strength of Every Migrant Child
No full textPhysiotherapists prioritise compassionate and patient-centred care while navigating systemic constraints and ethical dilemmas in cancer rehabilitation: a mixed-methods study
Get PDFQuestion - How do physiotherapists address bioethical issues in cancer rehabilitation? What drives physiotherapists’ clinical actions regarding non-disclosure, patient autonomy, risk-benefit balance and treatment withdrawal?Design - A mixed-methods study with an explanatory sequential design.Participants - 681 Italian registered physiotherapists recruited via the National Federation of Physiotherapists’ Registers.Intervention - An online survey assessed physiotherapists’ ethical responses, followed by focus groups with participants whose survey responses aligned with key bioethical principles: beneficence/non-maleficence, self-determination, justice/equity, defensive prudence and compassionate care.Outcome measures - Quantitative data identified bioethical principles adherence patterns, while qualitative analysis explored the reasoning behind these ethical stances.Results - Quantitative findings highlighted compassionate care as the most emphasised principle (29%), followed by self-determination (26%) and defensive prudence (23%). Beneficence/non-maleficence (16%) and justice and equity (6%) were less prioritised. Qualitative analysis identified five themes: functional recovery as dignity (‘clinical good is the patient good’), patient autonomy (‘patient knows better’), equity concerns (‘everyone deserves care’), risk aversion (‘it’s better not to take risks’) and the relational nature of care (‘relationships can heal’). The mixed-methods integration showed how physiotherapists balance ethical ideals with systemic constraints, highlighting the importance of care equity, not underscored by the sole quantitative data.Conclusion - Physiotherapists working in cancer rehabilitation prioritise compassionate and patient-centred care while facing systemic constraints, risks and professional responsibilities. This study offers a framework for future research internationally and on other healthcare professionals
Surgery versus conservative management for severe pectus excavatum (RESTORE): protocol for a multicentre, randomised, controlled superiority trial
Get PDFIntroduction: Severe pectus excavatum (PE) may impair cardiopulmonary and physical function. The effectiveness of surgical treatment to correct PE and restore physical function is widely debated due to a lack of high-quality comparative evidence. The RESTORE trial aims to determine the clinical and cost-effectiveness of corrective surgery for severe PE compared with conservative management for the first time in a randomised controlled trial (RCT). Methods and analysis: RESTORE is a pragmatic, multicentre, RCT with an embedded observational cohort. 200 participants aged ≥12 years with severe PE will be recruited at around 12 National Health Service cardiothoracic surgical centres in England. Participants will be randomised 1:1 to receive either surgery within 3 months of randomisation (intervention arm) or no surgery until after the primary outcome measurement at 1 year (comparator arm). The primary outcome is change in physical functioning from baseline to 1 year as measured by the Short Form Health Survey (SF-36v2) physical function score. The primary economic outcome is cost-effectiveness. The key secondary outcome is change in % predicted VO2peak at 1 year measured by cardiopulmonary exercise test (CPET). Outcomes will be assessed at 1 year post-randomisation in the comparator arm and 1 year post-surgery in the intervention arm. The primary analyses will be undertaken on an intention-to-treat population using a linear mixed-effects model, adjusted for stratification variables via a binary covariate. Other secondary outcomes will include change from baseline of cardiopulmonary function (CPET and spirometry), health-related quality of life using the EuroQol 5 Dimension 5 Level (EQ-5D-5L) and SF-36v2 questionnaires, Hospital Anxiety and Depression Scale and disease specific symptoms (Phoenix Comprehensive Assessment for Pectus Excavatum Symptoms and Pectus Excavatum Evaluation Questionnaire). Adverse events, complications from surgery and operative technical success (Haller and Compression Indices from preoperative and postoperative CT scans) will also be assessed. Health economic analysis will estimate the incremental cost per quality adjusted life year at 1 year. Ethics and dissemination: The trial was approved by East of Scotland Research and Ethics Service (24/ES/0034). Participants who are ≥16 years of age will be required to provide written informed consent. For participants <16 years of age who are not judged to be Gillick competent, written assent and written informed consent from a parent/guardian will be required. Results will be submitted for publication in peer-reviewed journals and shared with participants, clinicians and commissioners. Trial registration number: ISRCTN11359779
A Scoping Review of Long COVID and Menopause
Get PDFBackground: According to the National Institute for Health and Care Excellence (NICE), long COVID refers to symptoms persisting for four weeks or more after acute infection, with over 100 identified, including fatigue, cognitive dysfunction, and breathlessness. Women aged 45–54 are disproportionately affected, overlapping with the typical age for perimenopause and menopause. This scoping review aimed to provide an overview of existing research on the intersection between long COVID and the menopausal transition. Methods: Five database (CINAHL ultimate, MEDLINE, ScienceDirect, Cochrane, and Scopus) searches yielded 387 articles; after removing 40 duplicates and screening 347 titles and abstracts, fourteen studies were reviewed in full, with seven meeting the inclusion criteria (examined both long COVID and menopause in their scope and are written in English language). Results: This scoping review identified a significant symptomatic overlap between long COVID and menopause reported by participants, particularly fatigue, cognitive difficulties, mood changes, and sleep disturbances. Preliminary evidence also suggests that hormonal fluctuations may influence symptom severity, though biological mechanisms remain insufficiently understood. Methodological limitations restrict generalisability, underscoring the need for longitudinal symptom tracking, diverse samples, and biomarker-informed studies. Recognising the intersection of long COVID and menopausal transition is essential for improving assessment, management, and targeted care for affected women
Adaptive Swin Transformer V2-Tiny Based Model for Classification of Bacteria, Fungus, Virus, and Healthy Fruit and Leaf Images
Get PDFThe classification of fruits and leaves affected by bacteria, viruses, and fungi has made significant progress in the fields of artificial intelligence and image processing. However, most methods focus on particular categories of fruit and leaf diseases, but not on both fruit and leaf diseases caused by bacteria, viruses, and fungi. This study aimed to develop a model for the classification of the initial, intermediate, and final stages of bacterial, viral, and fungal diseases, irrespective of fruit and leaf types. To achieve this goal, inspired by the accomplishments of the Swin Transformer, the Swin Transformer V2-Tiny was explored for the classification of 10 classes, which included healthy and three stages of bacteria, virus, and fungus images of fruits and leaves. The stages of Swin Transformer V2-Tiny divide the image into patches, namely, linear projection, Window Multi-Head Self-Attention (W-MSA), and Shifted Window Multi-Head Self-Attention (SW-MSA) for local and global features, which were adapted to perform the plant disease classification. Experiments on authors’ curated and standard datasets and a comparative study with recent methods demonstrate effective classification and superiority over existing methods. To the best of our knowledge, this is the first study on the classification of fruit and leaf pathogens caused by bacteria, viruses, and fungi based on their development stages. The proposed model achieved an average classification rate of 91.04% on fruit datasets and 94.07% on leaf datasets, outperforming recent benchmark methods. It also demonstrated strong generalization on unseen public datasets with over 93% accuracy. Received: 5 May 2025 | Revised: 15 August 2025 | Accepted: 17 October 2025 Conflicts of Interest Shivakumara Palaiahnakote is the Editor-in-Chief for Artificial Intelligence and Applications, and he was not involved in the editorial review or the decision to publish this article. The authors declare that they have no conflicts of interest to this work. Data Availability Statement Data sharing is not applicable to this article as no new data were created or analyzed in this study. Author Contribution Statement Poornima Basatti Hanuma Gowda: Software, Data curation, Writing – original draft, Visualization. Basavanna Mahadevappa: Formal analysis, Investigation, Supervision, Project administration. Shivakumara Palaiahnakote: Conceptualization, Methodology. Muhammad Hammad Saleem: Validation, Writing – review & editing. Niranjan Mallappa Hanumanthu: Resources
Does regularity quality enhance sustainability performance? International evidence
Get PDFRegulations play an important role in holding companies accountable to society. This study, therefore, examines the relationship between regulatory quality and sustainability performance. We used panel data from 383 financial firms across 12 countries, covering the period from 2012 to 2022. A fixed effects model is employed for the regression analysis. The results indicate that higher regulatory quality enhances a company’s sustainability performance, suggesting that stricter regulations encourage firms to engage in sustainable practices. Economically, a one-unit improvement in regulatory quality results in an 18.8 percent increase in sustainability performance. These findings remain consistent after robustness testing and highlight important policy implications. Managers and policymakers can enhance regulatory compliance to promote stable, long-term performance
AI‐Driven Dynamic Resource Allocation for IoT Networks Using Graph‐Convolutional Transformer and Hybrid Optimization
Get PDFEffective resource allocation is a fundamental challenge for software systems in Internet of Things (IoT) networks, influencing their performance, energy consumption, and scalability in dynamic environments. This study introduces a new framework, DRANet-graph convolutional network (GCN)+, which integrates GCNs, transformer architectures, and reinforcement learning (RL) with adaptive metaheuristics to improve real-time decision making in IoT resource allocation. The framework employs GCNs to model spatial relationships among heterogeneous IoT devices, transformer-based architectures to capture temporal patterns in resource demands, and RL with fairness-aware reward functions to dynamically optimize allocation strategies. Unlike previous approaches, DRANet-GCN+ addresses computational overhead through efficient graph partitioning and parallel processing, making it suitable for resource-constrained environments. Comprehensive evaluation includes sensitivity analysis of key parameters and benchmarking against recent hybrid approaches, including GCN-RL and attention-enhanced multiagent RL (MARL) methods. Performance evaluation on real-world and large-scale synthetic datasets (up to 5000 nodes) demonstrates the frame-work's capabilities under varied conditions, achieving 93.2% resource allocation efficiency, 50 ms average latency with 12 ms standard deviation, and 990 Mbps throughput while consuming 15% less energy than baseline approaches. These findings establish DRANet-GCN+ as a robust solution for intelligent resource management in heterogeneous IoT networks, with detailed quantifi-cation of computational overhead, scalability limitations, and fairness-energy-throughput trade-offs
Harmony Bot: Well-being Enhancement through Computer Interaction Monitoring
Get PDFAffective traits, including extraversion and emotion regulation, are important considerations in clinical psychology due to their associations with the occurrence of affective disorders. Previously, emotional real-world scenes have been shown to influence visual search. However, it is currently unknown whether extraversion and emotion regulation can influence visual search towards neutral targets embedded within real-world scenes, or whether these traits can impact the effect of emotional stimuli on visual search. An opportunity sample of healthy individuals had trait levels of extraversion and emotion regulation recorded before completing a visual search task. Participants more accurately identified search targets in neutral images compared to positive images, whilst response times were slower in negative images. Importantly, individuals with higher trait levels of expressive suppression displayed faster identification of search targets regardless of the emotional valence of the stimuli. Extraversion and cognitive reappraisal did not influence visual search. These findings add to our understanding regarding the influence of extraversion, cognitive reappraisal, and expressive suppression on our ability to allocate attention during visual search when viewing real-world scenes
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