Search Results (78,155)

Cybersecurity operations in IoT and edge environments require fast, evidence-grounded decisions under strict resource and trust constraints. While large language models can support triage and incident analysis, their parametric knowledge may be outdated and prone to hallucination. Retrieval-augmented generation (RAG) improves grounding by conditioning responses on retrieved evidence, but also introduces new risks such as knowledge-base poisoning, indirect prompt injection, and embedding leakage. Federated learning enables collaborative adaptation without centralizing sensitive data, motivating federated RAG (FedRAG) architectures for distributed cybersecurity deployments. This study presents a deployment-oriented scoping review of FedRAG for cybersecurity. The review follows PRISMA-ScR reporting guidance and synthesizes 82 studies published between 2020 and 2026, identified through keyword search and citation snowballing over OpenAlex, arXiv, and Crossref. We develop a taxonomy that clarifies the components of federated systems, deployment locations, trust boundaries, and protected assets. We further map the combined RAG+FL attack surface, summarize practical defenses and system patterns, and distill actionable guidance for secure, privacy-preserving, and efficient FedRAG deployment in real-world IoT and edge scenarios. Our synthesis highlights recurring trade-offs among robustness, privacy, latency, communication overhead, and maintainability, and identifies open research priorities in benchmark design, governance mechanisms, and cross-silo evaluation protocols for practical deployment. Full article

Background/Objectives: Poincaré plot analysis represents a nonlinear approach to heart rate variability (HRV) assessment, but the physiological meaning of several derived parameters remains unclear. This study aimed to evaluate associations between selected Poincaré plot-derived parameters, conventional HRV indices, and cardiovascular autonomic reflex tests in a real-world clinical population. Methods: This observational study included 269 adult patients referred for evaluation of suspected autonomic dysfunction. All participants underwent short-term resting ECG, cardiovascular autonomic reflex testing, and 24 h Holter ECG monitoring. Poincaré plot-derived parameters were analyzed in relation to short- and long-term HRV measures using the Spearman correlation with false discovery rate correction, and group comparisons were performed based on reflex test results. Results: Several Poincaré plot-derived parameters showed strong correlations with long-term HRV indices. VLI and LA were primarily associated with global and long-term autonomic variability, whereas VAI and SA were more closely related to parasympathetic modulation. Associations with short-term HRV were generally weak. Lower values of selected parameters were observed in patients with abnormal parasympathetic reflex tests, while no significant differences were found in relation to orthostatic hypotension. Conclusions: Poincaré plot-derived parameters capture complementary aspects of autonomic regulation beyond conventional HRV indices and may enhance autonomic phenotyping in clinical settings. Full article

Background/Objectives: Real-world evidence on ofatumumab (OFA) beyond 12 months remains limited in relapsing–remitting multiple sclerosis (RRMS). We assessed 24-month effectiveness and safety, compared treatment-naïve and previously treated patients, and explored predictors of failure to achieve No Evidence of Disease Activity-3 (NEDA-3). Methods: This multicenter retrospective cohort study included adult RRMS patients treated with OFA in routine clinical practice. Effectiveness analyses were restricted to patients with complete 24-month follow-up and full clinical and magnetic resonance imaging (MRI) assessment (complete-case analysis). Outcomes included relapses, MRI activity, Expanded Disability Status Scale (EDSS) progression, NEDA-3, and adverse events (AEs). Exploratory multivariable logistic regression was used to assess baseline predictors of NEDA-3 non-achievement. Results: Of 258 patients who initiated OFA, 148 had completed 24-month clinical and MRI follow-up and were evaluable for effectiveness. Over 24 months, 71.5% achieved NEDA-3; relapses occurred in 15.5% of patients, MRI activity in 15.5%, gadolinium-enhancing lesions (GELs) in 4.7%, and EDSS progression in 17.6%. Disease activity was minimal during months 12–24, with relapses in 2.7%, MRI activity in 2.0%, and no GELs. In unadjusted analyses, no statistically significant differences were observed between treatment-naïve and previously treated patients. Higher baseline EDSS was associated with failure to achieve NEDA-3. In the 24-month safety subgroup, AEs were recorded in 28.4% of patients; infections occurred in 26.4% of patients (all grade 1–2), and no serious adverse events were observed. Conclusions: In this multicenter real-world cohort, OFA was associated with low inflammatory disease activity over 24 months in RRMS patients with complete follow-up. These findings should be interpreted cautiously because the effectiveness analysis was restricted to a complete-case cohort and safety data were collected retrospectively. Full article

Accurate fault diagnosis of rotating machinery in complex industrial environments demands an optimal trade-off between feature representation capability and computational efficiency. Existing single-modality models relying solely on 1D time-series signals or heavy 2D time-frequency images often fail to simultaneously capture high-frequency transient impacts and long-range degradation trends. CLiST (Complementary Lightweight Spatiotemporal Network), a novel lightweight multimodal framework driven by time-frequency fusion, was proposed to overcome this limitation. The architecture of CLiST employs a synergistic dual-stream design: a LightTS module efficiently extracts global operational trends from 1D vibration signals with linear complexity, while a structurally pruned LiteSwin integrated with Triplet Attention captures local high-frequency textures from 2D continuous wavelet transform (CWT) images. This mechanism establishes explicit cross-dimensional dependencies, effectively eliminating feature blind spots without excessive computational overhead. The experimental results show that CLiST not only achieves perfect accuracy on the fundamental CWRU benchmark but also exhibits exceptional spatial generalization when independently evaluated on non-dominant sensor axes of the XJTUGearbox dataset. Furthermore, validation on the real-world dataset (Guangzhou port) proves that the framework has excellent robustness to the attenuation of the signal transmission path and reduces the performance fluctuation between remote measurement points. Ultimately, CLiST delivers highly reliable AI-driven image and signal-processing solutions for vibration monitoring in industrial equipment. Full article

The electrical characteristics of distribution networks (DNs) are drastically changing, which is mainly due to widespread adoption of small-scale distributed energy resources (DERs) by end-users. In these cases, conventional planning models may lead to overinvestment choices. This paper presents a planning model for utility companies that explicitly incorporates a model of end-users’ energy-related decisions, considering a neighborhood energy trading scheme (NETS). The model is formulated based on the Stackelberg game (SG) approach, which guarantees the optimality of the final solution for each user and the utility. The proposed mixed-integer second-order cone programming (MISOCP) problem finds the optimal investment plan for transformers, lines, distributed generators (DGs), and energy storage systems (ESSs) for the utility, considering the scenarios of end-users’ investments in rooftop photovoltaic (PV) and battery systems that maximize their benefits. Additionally, a dynamic network charge (NC) scheme is designed to rationalize the network use. Also, Benders decomposition (BD) is used to improve the convergence of the solution algorithm. The numerical studies on a real 23-bus low voltage (LV) network in Perth, Australia, using real-world data reveals that the proposed planning model offers the lowest total cost and the highest penetration of DERs in comparison with conventional models. Full article

Artificial Intelligence (AI) has transitioned from a specialized research area to a ubiquitous socio-technical infrastructure influencing sectors from healthcare and law to manufacturing and defense. In tandem with its transformative promise, AI has created an exponentially expanding ethics literature questioning, fairness, transparency, accountability, and justice. This review synthesizes publications and key policy developments between 2019 and 2025, bringing sectoral discourses together with cross-cutting frameworks. Grounded in a systematic scoping review methodology, we frame the field along four meta-dimensions: trust and transparency, bias and fairness, governance & regulation, and justice, while we investigate their expression across diverse sectors. Special attention is dedicated to healthcare (patient trust and algorithmic bias), education (integrity and authorship), media (misinformation), law (accountability), and the industrial sector (data integrity, intellectual property protection, and environmental safety). We ground abstract principles in concrete case studies to illustrate real-world harms and mitigation strategies. Furthermore, we incorporate pluralistic ethics (e.g., Ubuntu, Islamic perspectives), environmental ethics, and emerging challenges posed by Generative AI and neuro-AI interfaces. To bridge theory and practice, we propose an operational governance framework for organizations. We contend that success involves transitioning from principles toward ethics-by-design, pluralistic governance, sustainability, and adaptive oversight. This review is intended for scholars, practitioners, and policymakers who need a comprehensive and actionable framework for navigating the complex landscape of AI ethics. Full article

In real-world scenarios, multivariate time series data typically presents a variety of complex characteristics simultaneously, including long-term trends, multiple seasonality, sudden event disturbances and random noise. Owing to remarkable discrepancies among different variables in dimensions, periodic stability and other aspects, and the gradual evolution of these periodic characteristics over time, models are confronted with numerous challenges in handling non-stationarity, multi-scale dynamic variations and heterogeneous fusion of variables. To tackle these problems, this paper proposes a time–frequency parallel fusion framework—TFDG-Net (Time–Frequency Dual-Branch Gated Fusion Network). This framework models the prior information in the frequency domain and the temporal query network in the time domain in parallel, and introduces a channel-wise gating mechanism to achieve more flexible adaptive fusion after data inverse normalization. Such a design enables the model to operate collaboratively on the original physical scale, which not only improves the long-term prediction capability for periodically stable variables, but also effectively suppresses the interference of noise and event-driven factors, thus significantly enhancing prediction accuracy and the robustness of the training process. In multiple long-term prediction benchmark tests covering fields such as energy and finance, compared with various mainstream models, TFDG-Net reduces the mean squared error and mean absolute error by an average of 12.0% and 7.8% respectively. Full article

Cancer ranks as one of the top causes of death worldwide, and the World Health Organisation (WHO) estimates an increase of up to 55% in cases over the next 15 years, reaching 300 million cases worldwide. Current approaches to the treatment of cancer, such as chemotherapy and radiation therapy, have been used with continuous significant advancements. However, these conventional methods have harmful side effects that can last a lifetime. Today, there is growing interest in developing alternative cancer therapies from natural products or complementary medicine. One of the natural sources that has shown promise as an anticancer agent is honey, which has long been applied as a complementary medicine, and its beneficial health effects on various diseases in both animal and human models have been widely studied. Malaysian honey, such as Tualang, pineapple, Gelam, Kelulut, and Acacia, possesses a rich composition of phytochemicals, including polyphenols and flavonoids, which are reported to have promising anticancer properties. Examples of the phytochemicals highlighted in this review are phenolic acid, syringic acid, salicylic acid, p-coumaric acid, gallic acid, benzoic acid, caffeic acid, chrysin and its derivatives, kaempferol, fisetin, catechin, apigenin, quercetin, acacetin, pinocembrin, pinobanksin, hesperetin, naringenin, vitexin, isoorientin, xanthohumol, and galangin. This review highlights the anticancer mechanisms and molecular pathways of the phytochemicals found in Malaysian honey, focusing on their antioxidant effects, induction of mitochondrial-mediated apoptosis, inhibition of angiogenesis and metastasis, and suppression of cancer cell proliferation. The findings of various studies published in the past five years are collated to understand their mechanisms of action. Full article

Background: Regional anesthesia is a fundamental aspect of contemporary perioperative care. However, variability in practice, incomplete documentation, and inconsistent safety protocols continue to pose preventable risks. Although there are international checklist models for regional anesthesia and perioperative safety such as those developed by ASRA, ESAIC, and the WHO, Italy does not have a nationally endorsed checklist that is consensus-based and specifically tailored to local terminology, workflows, and legal requirements. Methods: To address this gap, we developed an evidence-based Locoregional Anesthesia Checklist (LRA Checklist) using established frameworks for healthcare checklist design. The development process included a needs assessment through a national survey of ESRA Italy members, a review of existing models, item drafting, expert consensus, and endorsement by the Board. We assessed content validity through a modified Delphi process involving 15 experts from the ESRA Italian Chapter Board. Additionally, we created a theoretical impact model to estimate the potential organizational and economic effects of implementing the checklist, using baseline institutional parameters. Results: Consensus was achieved for all checklist domains after two Delphi rounds, with minor edits to improve clarity, usability, and clinical relevance. The theoretical model indicates that adopting checklists may help reduce preventable complications, improve workflow, enhance documentation and traceability, and provide overall benefits to institutions in various scenarios. Conclusions: In conclusion, the LRA Checklist is a structured, consensus-based tool tailored for the Italian context, aimed at promoting safer and more standardized practices in regional anesthesia. To our knowledge, no prior Italian national consensus or checklist specifically dedicated to regional anesthesia has been formally published. Prospective multicenter studies are necessary to confirm its effectiveness in real-world settings and to quantify both clinical and economic outcomes. Full article

Traditional one size fits all pharmacotherapy often yields suboptimal clinical outcomes, preventable adverse drug reactions (ADRs), and significant drug waste, imposing substantial economic and ecological burdens on healthcare systems. This review evaluates the transformative potential of pharmacogenomics (PGx) testing, particularly cytochrome P450 (CYP) gene variants, as a foundation for an ecosystem-centric accountability framework for green pharmacy and links human metabolic variability to specific environmental outcomes. Personalized CYP profiling is shown to minimize the environmental release of unused drugs and potentially ecotoxic metabolites into aquatic ecosystems, in contrast to standard uniform drug use approaches. The limitations of ethnicity-based dosing models, which rely on population genetic variation, are examined in the context of increasing global genetic admixture. It is argued that individual genetic profiling, conceptualized as a PGx-Green Passport, provides a reliable safety standard that accounts for individual differences, thereby enhancing efficiency and well-being in a globalized society. By integrating clinical data, including real-world evidence on hospital utilization, with sustainability frameworks, this review demonstrates that PGx-guided therapy is not only a tool for clinical efficiency but also a fundamental requirement for systematically achieving environmentally sustainable healthcare. Full article

Since At-Turaif’s inscription as a World Heritage Site in 2010, Al-Diriyah and its peripheries have witnessed massive urban development. With the recently proposed Wadi Safar project, the expansion of Al-Diriyah has taken another turn, as it is conceptualized as a luxury driven mixed-use district, integrating cultural experiences that are rooted in the past. This research examines the urban development of Al-Diriyah through the lens of the Experience Economy Model (1998), in which value is derived not just from objects or spaces but from the memorable and immersive experiences they tend to incorporate. This study employs a qualitative-case study methodology structured through a five-phase analytical framework that spans from 2010 to 2025/2030. Utilizing a deductive qualitative approach, the analysis demonstrates a differentiated application of the four experiential realms of the Experience Economy Model across the study sites. While At-Turaif predominantly engages two experiential dimensions and the broader regeneration of Al-Diriyah incorporates three, the planned development of Wadi Safar is designed to encompass all four dimensions of the Experience Economy. This configuration produces a balanced spectrum of active and passive participation as well as absorption and immersion, positioning Wadi Safar within Al-Diriyah’s broader transformation into the world’s largest heritage-led urban development. The findings contribute to the theme of a thriving economy of KSA Vision 2030 by advancing heritage-oriented experience as a pathway towards economic diversification. Full article

Background: Artificial intelligence in nephrology has largely focused on predictive models for outcomes such as acute kidney injury (AKI), chronic kidney disease (CKD) progression, and transplant complications. Although these models demonstrate technical performance, their real-world clinical impact has remained limited because prediction alone rarely translates into coordinated clinical action. Clinical artificial intelligence agents represent workflow-native systems that operate in real time, interact bidirectionally with clinical environments, adapt to evolving patient and workflow states, and support coordinated clinical action rather than generating isolated predictions. This review proposes clinical artificial intelligence agents as a new paradigm for integrating artificial intelligence directly into nephrology workflows. Methods: We conducted a narrative synthesis of emerging literature on artificial intelligence systems, agentic artificial intelligence architectures, clinical decision support, and digital health infrastructures relevant to kidney care. Drawing from interdisciplinary sources in medicine, health informatics, and artificial intelligence research, we developed a conceptual framework describing the architecture, governance requirements, and evaluation principles of clinical artificial intelligence agents in nephrology. Results: Clinical artificial intelligence agents represent workflow-integrated systems capable of continuously perceiving patient data, reasoning under clinical constraints, planning tasks, and supporting coordinated clinical actions over time. We describe a layered architecture consisting of perception, cognition, planning and control, action, and learning components. Potential applications span the nephrology care continuum, including CKD management, AKI monitoring, dialysis and continuous renal replacement therapy (CRRT) optimization, kidney transplantation care coordination, glomerulonephritis management, and supervised patient-facing systems. Conclusions: Clinical artificial intelligence agents shift the role of artificial intelligence from isolated prediction toward longitudinal clinical orchestration. Future evaluation should prioritize workflow integration, time-to-action, clinician oversight, safety, and patient-centered outcomes rather than relying solely on traditional model performance metrics. This roadmap provides a conceptual foundation for the responsible development and clinical integration of agentic artificial intelligence systems in nephrology. Full article

Unpaid caregivers in Hong Kong, China (Hong Kong) are known to be under tremendous stress. The government of the Hong Kong Special Administrative Region (SAR) has been funnelling resources to non-profit organisations to support these caregivers in recent years. Since 2020, the British government has provided British National (Overseas) passport holders with a pathway to gain citizenship in Britain, and many Hong Kongers, especially young families, have migrated to Britain. This migration includes many former caregivers of older adults who remain in Hong Kong. How do these left-behind elderly parents comprehend the loss of their main caregivers, an extreme case of empty nest? And how do faith-based, especially Evangelical Christian, organisations and churches, support these older adults and their adult children in transnational caregiving? This study employs an ethnographic approach through on-site fieldwork and semi-structured interviews with older adults whose children migrated abroad, social workers at faith-based organisations, and church pastors. These field observations and interviews are supplemented by case studies and interviews published in news outlets. Through this group study, though limited in sample size, this article argues for the importance of faith identity and religious community in supporting both older adults and their caregivers, whether situated locally or remotely, and how faith-based organisations support transnational caregiving through connecting both parties. Full article

Master–slave bilateral teleoperation systems face several practical challenges, including model uncertainties, time-varying communication delays, and environment-induced force disturbances. To address these issues, this paper proposes an adaptive super-twisting sliding-mode control scheme to achieve high-precision position tracking and real-time force-feedback synchronization. First, joint-space dynamic models are established for both the master and the slave manipulators, and a passive impedance model is adopted to characterize the interaction dynamics at the operator–master and environment–slave interfaces. Second, to attenuate measurement noise in the environment interaction force, a first-order low-pass filter is used to preprocess the raw force measurements, and a radial basis function neural network (RBFNN) is employed to approximate the environment torque online. Furthermore, a super-twisting sliding-mode controller is developed and combined with an adaptive law to compensate online for system uncertainties, including dynamic parameter variations and environment-induced force disturbances. The stability of the resulting closed-loop system is rigorously analyzed using Lyapunov stability theory. Finally, the effectiveness of the proposed method is validated through numerical simulations, virtual experiments conducted in the MuJoCo physics engine, and real-world hardware experiments. The results show that the proposed strategy achieves accurate position synchronization and force tracking while maintaining stable haptic interaction in the presence of bounded time-varying delays, parameter uncertainties, and external disturbances. Full article

Early detection of battery degradation is essential for ensuring the safety and reliability of electric vehicle (EV) systems under real-world operating variability. This paper proposes a physics-guided multi-sensor learning framework, termed SensorFusion-Former (SFF), for early warning of short-term EV battery performance degradation. The proposed approach integrates a physics-based baseline model for operational normalization, a multi-sensor fusion attention mechanism to model cross-modality interactions, and a lightweight transformer architecture for efficient temporal representation learning. Weak supervision is derived from physics-consistent residual analysis with temporal smoothing, enabling scalable training without dense manual annotations. To support reliable deployment, evidential uncertainty modeling and conformal calibration are incorporated to obtain statistically controlled decision thresholds. Experiments conducted on a real driving cycle dataset from IEEE DataPort demonstrate that SFF consistently outperforms classical machine learning methods, deep neural networks, and standard transformer models in terms of early-warning lead time, false alarm rate, and inference efficiency while maintaining competitive discriminative performance. Cross-scenario evaluations under diverse thermal conditions further confirm the robustness and generalization capability of the proposed framework. Full article