Multiple channel access and power control for discount-average weighting criterion over multi-sensor and Markovian fading environments

Bilateral access control ABE from lattices and application to AB-ME

A trusted industrial data space for automotive supply chain (TIDS-ASC): enabling secure and traceable data sharing

To assess whether HL7 FHIR Consent, as currently specified and deployed, is sufficient to support verifiable, regulation-aligned consent governance in distributed and cross-organisational health data sharing. We conducted a qualitative critical analysis of FHIR Consent informed by (i) peer-reviewed implementation literature, (ii) national-scale consent exchange initiatives, and (iii) accountability requirements under GDPR and the European Health Data Space (EHDS). The analysis is organized into four dimensions: semantic interpretability, consent lifecycle management, runtime enforcement, and cross-organisational trust/auditability. FHIR Consent provides an interoperable representation of authorisation intent, but large-scale deployments remain limited by (1) non-canonical semantics across implementations, (2) lack of standardized lifecycle versioning and cross-organisational revocation propagation, (3) heterogeneous translation of declarative consent into enforceable access control, and (4) limited capability for independent verification of consent provenance and historical integrity across institutional boundaries. We derive an architecture pattern that separates (a) standards-based consent representation (FHIR Consent), (b) local policy interpretation/enforcement, and (c) cross-organisational integrity verification. Cryptographic integrity anchoring is discussed as a complementary mechanism for tamper-evident verification of off-chain consent artifacts and lifecycle events, without externalizing consent semantics or personal data.

While fundamental radiological protection standards remain consistent for operating and decommissioning nuclear power plants (NPPs), the unique challenges of decommissioning require a specialized program. This study analyzes the radiological characteristics of decommissioned NPPs and proposes a tailored protection program. Key findings indicate that although total radioactive inventory decreases, radiation dose rates in work areas can fluctuate significantly during the removal and movement of structures and components. The risk of internal exposure and alpha contamination increases due to fine aerosols generated during cutting. The proposed program includes five key elements: ALARA (As Low As Reasonably Achievable), effective access control and monitoring, high-frequency radiological surveys, contamination control, and prevention of internal exposure. This approach aims to optimize radiation exposure, prioritize the removal of high-radiation sources, expand real-time monitoring, and enhance worker training. The study's results aim to bolster safety and establish a robust radiological protection system for future Korean NPP decommissioning efforts.

In the era of digital transformation characterized by the deep integration of artificial intelligence and the Internet of Things, human-machine interaction systems have become ubiquitous in smart architecture and urban security. As the primary security interface, intelligent access control systems face unprecedented challenges. Traditional biometric technologies, such as facial recognition and fingerprint scanning, not only rely heavily on external power sources but also encounter critical limitations regarding privacy risks and environmental sensitivity. To address these issues, this study develops a self-powered bimodal sensor based on a single-electrode triboelectric nanogenerator, providing a low-power, high-security, and multidimensional sensing solution. The core of the sensor lies in its sophisticated functional structural design, featuring a polydimethylsiloxane triboelectric layer patterned with a micro-pyramid array, integrated with a copper foil electrode and a polyethylene terephthalate substrate. The device leverages the coupled effects of contact electrification and electrostatic induction to convert mechanical stimuli into characteristic electrical signals rich in material electronegativity and human kinetic information. To extract latent features from these complex waveforms, a deep learning framework based on a convolutional neural network is implemented to analyze and decouple the signals. Experimental results demonstrate that the system exhibits superior recognition performance in complex environments: in single-dimensional tasks, the accuracies for material identification and user authentication reach 99.83% and 98.88%, respectively. Even under a challenging scenario, the system maintains a high recognition accuracy of 96.15%. This work provides a robust technological foundation for future smart security, flexible electronic skins, and personalized healthcare monitoring.

This article examines the pressing issue of developing a secure, specialized online platform for distance learning for children with special educational needs. The digitalization of education has opened up new opportunities for inclusion, but mainstream solutions often fail to address the specific needs of this category of students, creating digital, cognitive, and social barriers. The goal of the study is to develop a conceptual model of a secure and adaptive educational environment that comprehensively addresses accessibility, personalization, and cybersecurity. The project took into account the usability of children with various developmental disabilities, including sensory impairments, autism spectrum disorder (ASD), and attention deficit hyperactivity disorder (ADHD), and based on this, the key principles of user interface and user experience (UI/UX) design were formulated. The proposed platform architecture includes an intelligent content and interface adaptation system, personalized learning paths, and secure communication modules with pre-moderation functions. Particular attention is paid to a multi-layered security system that ensures the protection of personal data, the prevention of cyberbullying, and access control. The article is of practical value to educational technology developers, educators, and administrators of educational institutions seeking to create an inclusive digital learning environment.

Background Traditional smart lock systems frequently experience slowness and susceptibility to vulnerabilities during network interruptions due to their dependence on centralized cloud computing. This study assesses a stratified edge–cloud IoT security framework intended for robust home access control. Methods An ESP32-based edge controller was integrated with RFID and biometric sensors via MQTT protocols. Experimental validation was performed during a 14-day longitudinal study comprising 50 authentication cycles to assess accuracy, latency, and edge autonomy. Results The system attained an overall authentication accuracy of 98.0% ( n = 50 ) . Security integrity was maintained with a False Acceptance Rate (FAR) of 0.00% and a stable False Rejection Rate (FRR) of 2.86%. The average local processing response latency was optimized at 1,161.60 ms, maintaining an efficient local access control operational loop during simulated network disruptions via standalone edge-first failover logic. Conclusion The empirical findings show that a stratified edge-cloud system may successfully balance real-time local responsiveness and strong cryptographic security integrity. This dual-layer structure offers a long-lasting, cost-effective alternative for installing resilient IoT-based access control systems in volatile infrastructure situations.

High mobility group box 1 protein (HMGB1) is a central mediator of inflammation and pain, but efforts to neutralize it therapeutically have had limited clinical success. This gap suggests that the essential problem is not simply the abundance of extracellular HMGB1, but its accessibility: its availability to assemble into pathogenic complexes, engage receptors such as the receptor for advanced glycation end products (RAGE), enter cells, and deliver inflammatory cargo to the cytosol. Here, a perspective is advanced that integrates HMGB1 biology with the inflammatory reflex and the cholinergic anti-inflammatory pathway. In this framework, HMGB1 promotes inflammatory entry and amplification, whereas acetylcholine, acting through the vagus nerve and alpha7 nicotinic acetylcholine receptors, limits HMGB1 release and uptake of HMGB1-containing complexes. Vagus nerve stimulation therefore emerges as a bioelectronic strategy to restrict upstream access of danger signals to intracellular inflammatory pathways, in addition to suppressing downstream cytokine signaling. This formulation does not alter the established biology of HMGB1; rather, it places existing observations into a unifying model with direct relevance to inflammation and pain.

The increasing documentation burden in electronic health records makes it difficult to obtain arapid overview of relevant prior information in complex disease courses. Especially in highly digitized settings such as radiology and interdisciplinary case conferences, large volumes of heterogeneous documents must be reviewed under time pressure. The aim was to develop alarge language model (LLM)-based patient synopsis to accelerate information retrieval while ensuring transparency, physician oversight, and data protection. At the University Medical Center Freiburg, asystem was developed for the automated integration of clinical documents from multiple primary systems, including the electronic health record (EHR), picture archiving and communication system (PACS), and other subsystems. It uses retrieval-augmented generation (RAG), metadata harmonization, vector search, and agentic retrieval for the context-sensitive selection of relevant content. The system operates with role-based access control, end-to-end source attribution, and processing in asovereign cloud without persistent data storage. The system enables patient-specific queries, structured longitudinal summaries, and automated, source-grounded summaries to support preparation for interdisciplinary case discussions. All statements remain traceable to primary documents, while medical interpretation and decision-making remain the responsibility of the treating physician. Interoperable information systems and adata protection framework in accordance with the General Data Protection Regulation (GDPR), the German Social Code BookV (SGBV), and the EU Artificial Intelligence Act are prerequisites for implementation. LLM-based patient synopses can support text-intensive clinical workflows, provided that controlled retrieval, clear source attribution, physician validation, and an interoperable, regulation-compliant system architecture are in place. Prospective evaluations are required before routine clinical use.

Medication safety in the intensive care unit (ICU) is critical due to the high-risk nature of the environment and the vulnerability of patients. Implementing evidence-based guidelines and structured interventions has been shown to enhance compliance with the best practices, thereby reducing errors and improving patient safety. The aim of this project was to improve medication storage practices in the ICU of a general private hospital in Tabriz, Iran, by implementing evidence-based interventions. This evidence-based implementation project followed the JBI Evidence Implementation Framework and was conducted in seven phases. A baseline audit was conducted to identify gaps in compliance with the best practices. After the implementation of improvement strategies, a follow-up audit was conducted to measure any changes in practice. Three audit criteria, representing best-practice recommendations for safe medication storage were employed. Twenty eight nurses participated in baseline and follow-up audits. The baseline audit revealed low compliance with organizational policies (7%) and inconsistent adherence to manufacturer-recommended storage guidelines (75%). After the intervention, compliance with organizational policies and adherence to storage recommendations increased to 90%. Access control to medications remained consistent at 100% for both audits. The barriers to compliance with best practices included misalignment with the hospital's policies and guidelines due to the pharmacy's private ownership, and lack of an organizational policy and instructions for safe storage of medications. Improvement strategies included holding educational sessions and issuing and communicating medication management instructions to ICU staff. This study highlights the importance of structured organizational policies, educational initiatives, and collaborative efforts to enhance medication safety in ICU settings. It demonstrates that evidence-based audits and targeted interventions can address critical gaps in medication management, aligning with previous research on the role of policy and training in improving clinical practices and patient safety. http://links.lww.com/IJEBH/A586.

Glutarimide-containing Cereblon (CRBN) ligands are critical motifs for PROTACs, molecular glue degraders and next-generation Cereblon E3 ligase modulatory drugs (CELMoDs), which represent promising therapeutic modalities in targeted protein degradation. However, the multistep synthetic routes required to access glutarimide scaffolds continue to present formidable challenges for medicinal chemists, limiting rapid structure-activity relationship (SAR) exploration and late-stage diversification. To streamline access to these privileged motifs, modular and efficient methodologies are still highly desirable. Here, we report a unified organocatalytic synthesis platform for the rapid assembly of diverse glutarimide derivatives from readily available nitrogen heterocycles. Employing a sequence of phosphine-catalysed C-N bond formation, metal-free Giese addition and acid-mediated cyclisation, this approach provides high selectivity, broad functional group tolerance and operational simplicity under conditions amenable to both multigram synthesis and high-throughput parallel synthesis. Using this platform, we rapidly prepare CRBN binder libraries, access control analogues (for example, N‑alkylated glutarimides) and perform late‑stage functionalisation of bioactive molecules. This strategy could offer a transformative solution for the efficient and cost-effective synthesis of CRBN-targeted therapeutics and chemical biology probes, overcoming longstanding synthetic bottlenecks in the field.

Interface authorization in flexible automated mine ventilation duct production lines is difficult because heterogeneous users and services access safety-critical resources under time-varying cyber-physical risk that static RBAC/ACL rules cannot capture. This study presents HGRAD, a heterogeneous-graph risk-adaptive access control framework for industrial cyber-physical systems. HGRAD models each access event as a dynamic graph with four node types: user nodes encode operator identity, role, and history; interface nodes represent exposed PLC/MES/service access points and protocol/load states; resource nodes denote commands, records, or work-order objects with different sensitivities; and physics-informed risk nodes provide conservative structural-risk proxies for critical resources. A Temporal-HGT encoder and relation-specific hierarchical attention capture temporal context, structural semantics, and abnormal-path salience, while an intervention-inspired log-based filter, adversarial perturbation, Shapley audit weighting, and MC-Dropout uncertainty estimation support adaptive authorization rather than fixed-threshold decisions. In the TON_IoT benchmark mapping, the physics-informed node is derived only from benchmark-log proxy signals, including access intensity, operation criticality, freshness, and resource criticality; it is not generated by real mine ventilation sensors, plant-side finite-element outputs, or field digital-twin measurements. HGRAD achieves the strongest validation and held-out benchmark performance among compared baselines. The results are therefore benchmark-level proof of concept for access-control design, not evidence of completed industrial deployment or field-validated mine-safety performance.

Networks for military communications frequently function in settings where reliable connectivity cannot be ensured. By allowing nodes to store, transfer, and forward messages up to the availability of communication links, Disruption Tolerant Networks (DTNs) provide a solution to such circumstances. Nonetheless, maintaining data privacy and enforcing access control procedures DTNs is a difficult task. Ciphertext Policy Attribute-Based Encryption (CP-ABE) is seen to be a promising technique for dispersed networks' fine-grained access control. This study presents a CP-ABE-based secure data retrieval approach for decentralized, disruption-tolerant military networks. Several key authorities independently handle attribute keys in the proposed approach. The encrypted data contains embedded access policies. Confidential information can be securely stored and retrieved thanks to the system, which only permits authorized workers with the necessary credentials. the established access guidelines for data decryption. The architecture that is suggested strengthens the flexible property, lowers important escrow risks, and improves data confidentiality. Revocation procedures. The method demonstrates how CP-ABE can be used to secure critical military data in a setting with sporadic connectivity.

IntroductionSurgical systems in Southeast Asia were already experiencing significant supply shortages before the COVID-19 pandemic, which further exacerbated these deficits. With the aim of identifying modifiable factors for improvement, our study benchmarks surgical system efficiencies of eight Southeast Asian Hospital and Health Systems’ (HHS) in combating COVID-19, with an analysis spanning three levels: country, city and HHS.MethodsBased on 18-month data (January 2020 to June 2021), we developed a two-stage frontier benchmarking approach that applies efficiency benchmarking techniques to evaluate how close organisations operate relative to an efficiency frontier (representing the maximum potential output achievable with a given set of inputs). Stage 1 involves data envelopment analysis, which yields monthly national efficiency scores for five countries based on country-level panel data. Stage 2 involves stochastic frontier analysis, followed by evaluating HHS-level efficiency with HHS-level data over 64 possible model configurations, adjusting for stage 1 national-level efficiency scores as well as city-level responses.ResultsAmong the 64 possible stage 2 model configurations, two model clusters comprising 36 plausible models demonstrated superior fit to the observed data, thereby providing robust insights through majority voting. Policy measures related to access control and workplace closure were positively associated with recovery rates. Elective surgery volumes showed a negative association and emergency surgery volumes a positive association with efficiency. Our analysis indicates lags of 0–1 month between input changes and effects on the surgical system.ConclusionsInsights from the benchmarking of HHS efficiencies will help inform surgical system policy and responses. Access control and workplace closure policies are associated with COVID-19 recovery rates. Emergency surgery volumes are associated with higher efficiency and elective surgery volumes with lower efficiency. System responses to policy measures can manifest with lags of up to a month. Considering insights from multiple plausible models makes these conclusions more robust.

Refugees frequently face language and access barriers to preventive oral health information. Brief multilingual digital interventions may help reduce such barriers in shelter settings. This randomized controlled trial evaluated whether a multilingual digital microlearning video improved plaque control and selected self-reported oral health-related behaviors among adults living in refugee shelters. A 2-arm, parallel-group randomized controlled trial was conducted among 86 adults living in 2 municipal refugee shelters in Germany. Participants were randomized (1:1) to receive either a multilingual 4-minute oral hygiene microlearning video or delayed access (control group). Plaque index and gingival index were assessed clinically at baseline and at 2-month follow-up. Secondary outcomes included questionnaire-based measures of oral health literacy-related cognitions and self-reported oral health behaviors. Between-group differences in change scores were analyzed using 2-sided tests; exploratory multivariable regression analyses were conducted to assess potential effect modifiers. Follow-up was completed by 83 (97%) of 86 participants. Plaque index decreased more in the intervention group than in the control group (mean change -0.21, SD 0.27 vs mean change -0.04, SD 0.17; P=.002). Gingival index decreased in both groups, but the between-group difference was not significant. Among questionnaire-based outcomes, toothbrushing frequency increased substantially, whereas the remaining oral health literacy-related items showed small numerical changes that did not reach statistical significance or remained stable. Approximately three-quarters of participants in the intervention group (32/42, 76%) reported reviewing the video at least once. Brief multilingual digital microlearning improved plaque control and self-reported toothbrushing frequency in refugee shelters. Effects on broader oral health literacy-related outcomes were limited and should be interpreted cautiously. Larger, prospectively powered trials with longer follow-up periods and blinded outcome assessment are warranted.

Embedding LLMs in the patient portal to summarize acute minor illness information: a three-arm experimental study.

Crohn's Disease (CD) and Ulcerative Colitis (UC) are chronic Inflammatory Bowel Diseases (IBD) characterized by complex immune dysregulation and multifactorial pathogenesis. Conventional therapies often show limited efficacy and are associated with substantial adverse effects. This review highlights recent advancements in targeted molecular therapies for IBD, emphasizing biologics, small molecules, and emerging delivery technologies. A comprehensive literature search was conducted across the PubMed, Scopus, and Web of Science databases, focusing on studies published within the last 7 years. Relevant data on therapeutic mechanisms, clinical efficacy, safety, and cost-effectiveness were systematically extracted and critically analyzed. Biologic therapies, including anti-TNF agents (infliximab, adalimumab), integrin inhibitors (vedolizumab), and interleukin inhibitors (ustekinumab, risankizumab), have demonstrated significant improvements in remission and mucosal healing. However, challenges such as immunogenicity, loss of response, and high treatment costs persist. Small-molecule drugs, such as JAK inhibitors (tofacitinib, upadacitinib) and S1P receptor modulators (ozanimod), provide oral alternatives with rapid onset but entail systemic safety concerns. Novel therapeutic avenues, including TYK2 and HDAC inhibitors, as well as nanotechnology-based delivery systems, show encouraging potential in early trials. Accessibility and affordability remain major obstacles, particularly in low- and middle-income regions, highlighting the need for biosimilars and international policy support. Targeted molecular therapies have revolutionized IBD management by enabling precision treatment with enhanced efficacy and tolerability. Future efforts should focus on mitigating drug resistance, reducing costs, and integrating personalized medicine approaches to ensure global accessibility and sustainable disease control.

A multimodal dataset for environmental occupancy detection

ABSTRACT With the rapid advancement of digital technologies, intelligent employment and entrepreneurship service systems have demonstrated significant potential in enhancing user experience and operational efficiency. However, cybersecurity challenges have emerged as a critical bottleneck for their development. These platforms accumulate vast amounts of user data—including resumes, job preferences, and emotional intelligence metrics. Our research has developed an emotion recognition‐based data collection system that analyzes real‐time facial expressions, voice patterns, and text content to extract users' emotional states. By applying machine learning algorithms to analyze and mine this data, we established an emotion classification model integrated into the intelligent platform, enabling personalized recommendations and emotional support services. For cybersecurity, our approach combines data encryption, access control, and identity authentication with blockchain technology for secure storage of emotional data. Additionally, adversarial training and federated learning methods were employed to enhance model robustness and privacy protection. Experimental results show that the intelligent system effectively captures users' emotional fluctuations through emotion recognition and machine learning, significantly improving engagement and satisfaction. The application of encryption and blockchain technologies ensures data integrity, confidentiality, and availability while preventing leaks and tampering. The overall performance and security of the system have been substantially enhanced. Integrating emotion recognition technology with machine learning and implementing robust cybersecurity measures allows employment and entrepreneurship service systems to deliver more personalized, secure, and reliable user experiences.