Search Results (4,293)

Service continuity in power systems represents one of the key factors in designing the network architecture, in selecting the protection devices and in programming the maintenance operations. Standard strategies have to face a new issue represented by the possibility that intentional electromagnetic interference (IEMI) may occur. IEMI is defined as the “Intentional malicious generation of electromagnetic energy introducing power, noise or signals into electric and electronic systems, thus disrupting, confusing or damaging these systems”, and may affect the functionality of either power or telecommunications systems. Such hostile interferences may have different purposes: in the context of a war, for terroristic aims, or common criminal objective, e.g., requests for ransom. The focus of this work is on power substations, particularly on MV-LV installations, which often have limited surveillance and are located in proximity to easily accessible areas. The main issues are presented, and guidelines are provided for designing new substations or improving the immunity of installed apparatus, such as electronic protection devices, measurement instruments, and other tools equipping smart and traditional power grids. The challenges are put in evidence, and perspectives are provided with special reference to the possible evolution of interfering technologies. Full article

Research on muscle fatigue during dynamic movement using surface electromyography (sEMG) constitutes a significant challenge within biomechanics. Despite a degree of standardization, measurements and their resultant findings continue to attract considerable debate, attributable to factors such as skin impedance, perspiration, and electrode displacement, as well as subjective fatigue perception. Further questions remain regarding signal normalization and the selection of appropriate analytical methodologies. Recent years have witnessed notable progress in dynamic fatigue research, highlighting the limitations of classical metrics (e.g., EMG Median Frequency) and introducing time–frequency methods, such as the wavelet transform (WT), which are better equipped to handle signal non-stationarity. Interest has also expanded to include non-linear metrics (e.g., entropy) and the analysis of multiple signals (EMG, accelerometers, fNIRS, EEG). The inherent complexity of conducting studies under conditions that approximate real-world sporting disciplines requires the consideration of the influence of various confounding factors. The judicious selection of relevant physical activities and the rigorous validation of the measurement apparatus are paramount for the accurate execution of the calculations. Current research is substantially predicated on artificial intelligence (AI) algorithms. The synergistic application of AI with wavelet transform, particularly in the decomposition and extraction of EMG signals, demonstrates efficacy in fatigue detection. Nevertheless, the full realization of these potential mandates requires further investigation into system generalization, the integration of data from multiple sensors, and the standardization of protocols, coupled with the establishment of publicly accessible datasets. This article delineates selected guidelines and challenges pertinent to the planning and execution of research on muscle fatigue in dynamic movement, focusing on activity selection, equipment validation, EMG signal analysis, and AI utilization. Full article

Continuous-variable quantum key distribution (CV-QKD) enables information-theoretically secure key generation between legitimate parties. To further enhance system performance, an improved two-way CV-QKD protocol has been proposed, which is accessible in practice and exhibits increased robustness against excess noise. However, in practical implementations, device nonidealities inevitably drive the optical field from the single-mode regime into the continuous-mode regime. In this work, we introduce temporal modes to characterize the evolution of optical fields in the improved two-way protocol and establish a security analysis framework for the continuous-mode scenario based on adaptive normalization with calibrated shot-noise unit. In addition, finite-size effects are taken into account in the analysis. Our results demonstrate that the improved two-way protocol retains a performance advantage over its one-way counterpart. The analysis provides useful guidance for the practical implementation and performance optimization of improved two-way CV-QKD systems. Full article

Background and Objectives: The COVID-19 pandemic profoundly disrupted global healthcare systems, limiting access to diagnostic and therapeutic services for chronic diseases. Patients with decompensated liver cirrhosis were particularly vulnerable due to their fragile clinical status and dependence on continuous medical care. This study aimed to evaluate the temporal evolution of clinical, biological, and prognostic parameters in patients admitted emergently with decompensated liver cirrhosis across three distinct phases: pre-pandemic, pandemic, and post-pandemic. Materials and Methods: A retrospective, single-center study was conducted at the Department of Gastroenterology, Municipal Clinical Emergency Hospital, Timișoara, Romania, including 355 patients hospitalized between February 2018 and February 2024. Clinical, biochemical, and outcome data were collected and analyzed using univariate and multivariate logistic regression models to identify independent predictors of in-hospital mortality for each study period. Results: Significant temporal variations were observed in disease severity, management, and outcomes. The mean MELD score increased from 18.7 to 21.0 during the pandemic (p = 0.043), while endoscopic evaluations declined markedly (59.4% pre-pandemic vs. 42.7% pandemic, p = 0.037). Mortality rose from 21.7% to 30.2% during the pandemic (p = 0.044) and remained elevated post-pandemic (26.4%). Multivariate regression identified Child–Pugh, MELD, and Baveno scores as consistent mortality predictors, though their relative weight varied by period. During the pandemic, acute complications—particularly jaundice (OR = 294) and upper gastrointestinal bleeding (OR = 355)—became dominant determinants of death. Conclusions: The pandemic transformed cirrhosis from a chronic, manageable disease into an acutely unstable condition, primarily due to delayed presentation and restricted procedural access. Although post-pandemic recovery was evident, residual increases in mortality and severity indicate lasting effects of healthcare disruption, underscoring the need to strengthen system resilience and continuity of care for patients with chronic liver disease. Full article

Learning Management Systems (LMSs) remain largely static and administrative, often failing to support personalization and inclusive access to learning resources. This paper presents AI for All, a practical approach to building an adaptive, accessible, and inclusive learning experience within a mainstream LMS, demonstrated through the PREPARE project (Personalized Education Framework for AI-Enabled Adaptive and AR-Enhanced Learning) implemented in Moodle. PREPARE operationalizes an end-to-end generative AI pipeline that transforms a single authoritative PDF textbook into multimodal learning assets, including chapter summaries, structured notes and slide decks, formative quiz items, video mini-lectures with captions, podcast-style audio, and chapter-level augmented reality (AR) activities. In parallel, the system maintains a hybrid learner model by combining an initial FSLSM/ILS questionnaire with continuous behavior-based profiling derived from Moodle logs. Learner profiles drive non-prescriptive personalization through resource prioritization and recommendations, while preserving learner agency and access to all modalities. We describe the system architecture, Moodle integration mechanisms, and adaptation logic, and report an ongoing mixed-methods evaluation focusing on engagement, interaction diversity, perceived usefulness, and accessibility benefits. The system-level validation and deployment readiness suggest that AI-augmented LMS workflows can reduce instructor authoring effort while improving flexibility and inclusivity, provided that human-in-the-loop validation and privacy-aware analytics are embedded from the outset. Full article

Extended Reality (XR) has reshaped how learning can be structured, yet its integration into formal curricula continues to lag behind its technological potential. Established instructional design models such as ADDIE and ASSURE provide stable planning structures, but were not developed to address the spatial, embodied and interactive characteristics of immersive environments. The XR2Learn framework was developed to bridge this gap by combining structured instructional planning with XR-specific pedagogical considerations. This study presents a multi-round Delphi-based expert evaluation of XR2Learn, involving twenty specialists in instructional design and XR-enhanced education. Experts assessed the framework across four dimensions: validity, clarity, usability and suitability. Qualitative feedback was thematically analyzed and subsequently quantified to establish consensus. The findings show strong agreement regarding the framework’s pedagogical grounding, logical structure and alignment with established instructional design practices. At the same time, experts identified limitations related to practical enactment, accessibility and the explicit integration of XR-specific learning constructs such as presence and social interaction. Overall, the results position XR2Learn as a framework at a transitional stage, moving from conceptual formulation toward practical instructional use. The study provides the first systematic expert validation of XR2Learn and outlines targeted directions for its refinement as a robust instructional design framework for XR-based education. Full article

Health Internet of Things (Health IoT) systems continuously stream sensitive physiological data, making data access governance safety-critical under conflicting objectives such as privacy risk, latency, energy/resource cost, and robustness, especially when conditions change during emergencies. This paper proposes FiB-MOBA-EAFG, a hybrid blockchain–AI framework that separates on-chain accountability from off-chain decision intelligence. Off-chain, fuzzy context inference parameterizes scenario priorities, Pareto-based multi-objective search generates candidate governance policies, an emergency-aware feasibility guard filters unsafe trade-offs, and a bargaining-based selector chooses a single deployable policy. On chain, the blockchain layer records consent commitments, access events, and hashes of the selected policy and decision trace, serving as an immutable audit and accountability substrate rather than an online decision or optimization engine, while raw health data remain off-chain. Using simulation studies of home remote monitoring, clinic telehealth, and emergency triage under stochastic network variation and adversarial device behavior, FiB-MOBA-EAFG improves robustness and yields more repeatable policy selection than rule-based control and scalarized baselines within the evaluated simulation scenarios, while maintaining latency within ranges compatible with modeled edge deployment constraints through explicit emergency-aware feasibility constraints. A budget-matched random-search ablation further indicates that structured Pareto exploration is needed to reliably obtain robust, low-risk governance policies. Full article

Background: Reliable and readily accessible prognostic biomarkers for extensive-stage small-cell lung cancer (ES-SCLC) are still lacking. The neutrophil-to-lymphocyte ratio (NLR), a marker of systemic inflammation, has shown prognostic relevance in several malignancies; however, its dynamic changes during treatment have not been well characterized in SCLC. Methods: We retrospectively analyzed patients with ES-SCLC who received systemic chemotherapy between January 2010 and December 2024. Baseline NLR was calculated within 7 days before first-line treatment, and on-treatment NLR was assessed at 6 weeks. A predefined NLR cutoff value of 5 was applied, and changes in NLR (ΔNLR) were defined as the difference between 6-week and baseline values. Associations with time to treatment failure (TTF) and overall survival (OS) were evaluated. Results: A total of 176 patients were enrolled. High baseline NLR (≥5) was significantly associated with shorter TTF and OS (both p < 0.01). An increase in NLR during treatment (ΔNLR ≥ 0) was significantly associated with poorer OS. Combined assessment of baseline NLR and ΔNLR identified distinct prognostic groups, with patients exhibiting both high baseline NLR and ΔNLR ≥ 0 demonstrating markedly poor survival. In multivariate analyses, baseline NLR, ΔNLR, performance status, and immune checkpoint inhibitor combination therapy were independent predictors of survival. Baseline NLR analyzed as a continuous variable showed a significant inverse correlation with TTF and OS. Conclusions: Combined evaluation of baseline NLR and its on-treatment change provides improved prognostic stratification in patients with ES-SCLC. Full article

Introduction: HPV vaccination rates for adolescents in the United States are below recommended levels. The Adolescent Vaccination Program (AVP) guides pediatric clinics on how to implement evidence-based strategies to increase HPV vaccination rates. These strategies comprise the adoption of (1) immunization champions, (2) provider assessment and feedback, (3) continuing education, and (4) prompts, (5) parent reminders, and (6) parent education. The AVP systems-based intervention has demonstrated increased HPV vaccination rates in large urban pediatric clinic networks. The purpose of this study was to assess the feasibility of using an online decision support tool, the AVP Implementation Tool (AVP-IT), to implement AVP strategies in safety-net clinics to improve healthcare for the medically underserved in Texas. Methods: AVP immunization clinic staff champions in four urban safety-net clinics completed tailored Action Plans within the AVP-IT to guide strategy implementation, received webinar training from the research team commensurate to each AVP strategy, and participated in monthly monitoring calls with AVP-IT project staff over a 33-month period from 2022 to 2024. Results: All clinics made progress toward full implementation of AVP strategies. Interrupted time series (ITS) trend analysis demonstrated that AVP-IT implementation was associated with an immediate boost in HPV vaccine initiation rates (p < 0.001) and that long-term trends (ITS slopes) were significant for HPV, MCV4, and Tdap vaccines despite low post-COVID-19 pandemic rates (p < 0.001). Vaccination rates using raw data (mean differences) were not longitudinally significant except for older youth aged 13–17 years. Conclusion: The AVP-IT promises accessible and practical decision support to implement strategies to increase HPV vaccination rates in safety-net clinics. Scale-up in these clinics will require leadership support, technical assistance, and EHR optimization. Full article

Genomic surveillance is a cornerstone of pandemic response; it has helped guide public health interventions worldwide. However, North Africa stands between limited surveillance resources and efforts to address the data gap in this strategic geographic region that links sub-Saharan Africa and Europe. In this study, we present the first comprehensive evolutionary investigation of Algerian SARS-CoV-2 genomes, revealing their phylogeny, continuous phylogeography within the country, mutation analysis, and a super-spreading event through haplotype network analysis. We characterized the genetic diversity and unique mutation pattern of 449 Algerian sequences, revealing multiple independent introductions into the country since the first reported case on the 25th of February 2020 followed by numerous local transmissions that facilitated the virus’s rapid propagation. This study highlights both the importance of molecular epidemiology and equitable access to resources in implementing genomic epidemiology and in increasing sequencing efforts to strengthen pandemic preparedness. Full article

(1) Background: Induced After-Death Communication (IADC) therapy is a brief intervention facilitating grief resolution through a perceived experience of communication with the deceased. Despite growing evidence of its efficacy, little is known about which individual characteristics may influence treatment responsiveness. (2) Methods: This pre–post study investigated psychological predictors of IADC outcomes in 73 bereaved adults. Standardized measures assessed grief severity, alexithymia, dissociation, attachment dimensions, and Big Five personality traits. Changes in grief-related distress and continuing bonds were analyzed using paired-sample t-tests and hierarchical regressions. (3) Results: IADC therapy produced substantial reductions in grief-related distress and enhanced continuing bonds. Dissociation, demographic variables, and most personality traits were unrelated to outcomes. Neuroticism showed a marginally negative association, whereas Openness predicted greater improvement. Alexithymia negatively predicted clinical gains, suggesting that limited emotional awareness may interfere with the therapeutic phase of abreaction and, in turn, limit access to the receptive state. Among attachment dimensions, only Need for Approval significantly predicted poorer outcomes, consistent with performance anxiety and self-evaluative control interfering with spontaneous mental processes. (4) Conclusions: IADC therapy appears highly effective across diverse individual profiles. Screening for alexithymia and Need for Approval may help identify these potential sources of therapeutic failure and be followed by targeted strategies aimed at counteracting their impact and mitigating their effects. Full article

Inequitable access to affordable, nutritious food is partly sustained because markdowns on perishable products are often delayed until quality deterioration becomes visible, through which affordability gains are limited and waste is increased. In this study, the extent to which Internet of Things (IoT) real-time quality monitoring enables quality-triggered markdowns that reduce waste while improving food equity is examined. An analytical pricing and markdown model for perishables with quality-sensitive demand is developed, and optimal decisions under IoT-enabled quality observability and under a baseline setting without IoT are compared. Convexity is established for the retailer’s problem, and closed-form solutions are derived for the optimal regular price, markdown timing, and markdown depth. Under continuous quality visibility, earlier markdown initiation within the selling horizon is shown to be optimal while product quality remains acceptable, and a deeper markdown than in the non-IoT setting is shown to be optimal. Through numerical experiments, increased sell-through before products become unsalable is demonstrated, waste reduction is quantified, and an expanded time window is shown in which price-sensitive consumers can purchase acceptable-quality food at a lower price. Overall, improved food equity is supported by proactive, quality-aligned pricing policies without retailer profitability being sacrificed. Full article

Background: Kidney biopsy is the diagnostic gold standard for characterizing glomerular disease and other intrarenal pathologies. Despite its clinical importance, epidemiological trends in kidney biopsy incidence remain poorly understood in many developed healthcare systems. This study characterizes temporal and demographic trends in kidney biopsy utilization in Germany between 2006 and 2023, providing crucial data for resource allocation in renal pathology services. Methods: Data on all kidney biopsies (OPS code 1-465.0) performed in German hospitals were extracted from the Federal Statistical Office database and stratified by age and sex. Population denominators were obtained from national census data. Incidence rates per 100,000 inhabitants per year were calculated, and temporal trends were analyzed using Poisson regression with year as a continuous predictor variable. Separate models were fitted for overall population incidence, age-stratified incidence, and sex-stratified incidence. Results: The incidence of kidney biopsies increased 96.6% over 18 years, from 8.59 per 100,000 inhabitants in 2006 to 16.89 per 100,000 in 2023 (IRR: 1.0296 per year, 95% CI: 1.0287–1.0305; p < 0.0001). Age-stratified analysis revealed pronounced heterogeneity, with the oldest patients (>80 years) experiencing the steepest increase of 7.74% annually, while the youngest age group (<20 years) showed no significant temporal change. Sex-stratified analysis demonstrated similar increases in both males and females (3.36% and 3.04% annually, respectively). Conclusion: The substantial increase in kidney biopsy utilization in Germany over nearly two decades mirrors international patterns and suggests a global shift toward more liberal biopsy utilization in aging populations. Multiple factors likely contributed to this increase, including demographic aging, improved procedural safety and accessibility, evolving diagnostic guidelines, and expanding therapeutic options for glomerular disease. These findings underscore the need for national registry systems to optimize resource allocation for renal pathology and ensure equitable diagnostic access across healthcare systems. Full article

The growing adoption of wearable devices creates a critical need for robust and secure Internet of Things solutions to manage biometric data streams. Current architectures often lack emphasis on seamless data capture, secure cloud storage and integrated dashboard visualization. This research addresses these gaps by investigating and evaluating an IoT framework leveraging lightweight communication and real-time visualization for improved healthcare monitoring. Drawing primarily on recent peer-reviewed journals and reputable conference proceedings, we evaluate an IoT architecture that securely integrates wearable biometric data into a cloud-based dashboard. The system utilizes encrypted advertising packets (e.g., AES-128-CCM) to broadcast biometric signals, eliminating the need for permanent device pairing and minimizing energy consumption. These packets are captured by our prototype ESP32-based (Espressif Systems, Shanghai, China) gateway node, decrypted and forwarded to a secure cloud environment that ensures persistent storage and accessibility. The cloud-based dashboard provides medical staff and end-users with real-time insights and long-term data tracking. Emphasis was placed on evaluating the system’s low latency performance, energy efficiency and data confidentiality. System evaluation demonstrates that encrypted advertising packets can securely transmit biometric signals, while drastically reducing energy consumption and latency. System evaluation demonstrates that encrypted BLE advertising serves as a superior alternative to traditional pairing-based methods for long-term medical monitoring. By implementing a dual-optimization strategy that balances data confidentiality with power efficiency, the proposed system achieved a 33-fold increase in operational autonomy compared with standard permanent BLE connections. These results represent a significant advancement in battery longevity for the IoMT ecosystem, providing a scalable solution for continuous, secure biometric signal transmission with minimal energy overhead. Full article

Telemedicine has the potential to substantially improve the care of children and adolescents with chronic respiratory diseases, including asthma, cystic fibrosis, bronchiectasis, and chronic respiratory failure. Digital health interventions—such as remote monitoring, virtual consultations, adherence-support tools, and educational platforms—can enhance disease control, continuity of care, and access to specialized services. Despite these opportunities, the implementation of telemedicine in pediatric respiratory care remains fragmented and uneven across healthcare systems. A central barrier to progress is the marked heterogeneity of outcome measures used to evaluate telemedicine interventions. Inconsistent definitions, variable endpoints, and limited follow-up reduce comparability across studies, hinder evidence synthesis, and impede translation into clinical guidelines, reimbursement models, and policy decisions. Consequently, telemedicine is often confined to isolated pilot projects rather than embedded within standard care pathways. This narrative review issues a Call to Action for the coordinated implementation and harmonization of telemedicine in pediatric chronic respiratory diseases. We advocate for the urgent development and adoption of a Core Outcome Set (COS) to standardize outcome measurement across clinical trials and real-world evaluations. In addition, we highlight the importance of integrating implementation science, economic evaluation, ethical oversight, and equity considerations into telemedicine research and deployment. Addressing regulatory fragmentation, ensuring interoperability, and aligning accreditation with reimbursement and Health Technology Assessment requirements are essential for sustainable scale-up. Finally, we emphasize the need for international collaboration among clinicians, researchers, policymakers, payers, technology developers, and patient advocacy groups to accelerate learning and promote equitable, evidence-based digital care models. Through coordinated action, telemedicine can evolve from a promising innovation into a reliable and accessible standard of care for children with chronic respiratory diseases. Full article