Search Results (879)

Fatigue detection in remote tower air traffic controllers is important for maintaining operational safety under sustained visual monitoring and high cognitive workload. This study proposes MFD-Net, a dual-stream multimodal fusion framework using eye-tracking and electrocardiogram (ECG) signals. The model separately encodes eye-tracking and ECG-derived temporal inputs, incorporates an ECG-derived RMSSD expert feature, and performs lightweight late fusion for fatigue-state classification. Under the mixed-subject random-window protocol, MFD-Net achieved an Accuracy of 85.20%, a Recall of 83.33%, and an AUC of 0.9337. Because overlapping windows from the same participant and scenario could appear in both training and test sets, this result should be interpreted as a potentially optimistic within-distribution estimate. Under the stricter zero-shot leave-one-subject-out (LOSO) protocol, performance decreased substantially, with an Accuracy of $70.95\pm 21.59\%$, a Recall of $22.98\pm 36.30\%$, and an AUC of $0.6025\pm 0.2984$. This low zero-shot Recall indicates limited subject-independent fatigue-detection capability. Lightweight target-subject calibration and sequential probability aggregation improved adaptation and temporal stability, although the calibration results should be interpreted cautiously because random target-subject windows were used for fine-tuning. These findings suggest that eye-tracking and ECG fusion are promising under controlled conditions, while practical deployment requires deployment-oriented calibration protocols, recall-oriented optimization, and further real-world validation. Full article

Air Traffic Control Officers (ATCOs) are responsible for controlling air traffic and ensuring the safety of the aircraft. Capacity, understood as the maximum number of aircraft that can be safely managed for one hour, is calculated based on the workload of ATCOs. This calculation normally is based on a manual and tedious data collection process that demands a high consumption of human resources. To improve and relieve human re-sources, automation tools that automatically generate a preliminary annotation of Air Traffic Control (ATC) activity have been developed. This paper focuses on the feasibility of employing data-driven approaches using neural networks to classify ATC events, as well as if it is possible to improve the performance of these ATC-activity tools. Particularly, this approach seeks to infer ATC intent for separation actions, which are the most critical in terms of ATC workload. A modular methodology has been developed to include information from different sources: flight plans, radar trajectories, trajectory prediction, conflict detection and rule-based knowledge. Different experiments are evaluated based on the different input’s combination, as well as three neural networks (Multilayer Perceptron, Convolutional Neural Network and TabNet). Results show that TabNet is the best neural network option, reaching a similar performance in task classification than current ATC tools and improving classification metrics around 4% by employing the outputs of ATC tool metrics as inputs. Full article

Trustworthy digital credential management is increasingly important in LMS-connected higher-education information systems, yet institutions still lack controlled implementation-oriented evidence on how cloud-native service decomposition and blockchain-backed trust services influence deployment performance. This study develops and evaluates a cloud-native architecture that combines containerized microservices with Hyperledger Fabric-based permissioned ledger services and a Polygon-linked public-chain anchoring path for credential issuance, learning-record verification, and record validation. Unlike largely conceptual prior work, it benchmarks four functionally aligned deployment paths in a unified Kubernetes-managed testbed: a monolithic baseline, a microservices-only baseline, a Hyperledger Fabric-integrated variant, and a Polygon-linked anchoring path. The credential-service paths were evaluated under stepped workloads from 1000 to 20,000 scheduled virtual users. Evaluation focused on service-path latency, throughput, tamper-detection accuracy, and resource utilization. The microservices-only architecture achieved the lowest baseline latency (182 ms), Hyperledger Fabric maintained stable response times for trusted institutional workflows (352 ms at baseline and 485 ms at 20,000 virtual users), and the Polygon-linked anchoring path reached the highest observed service-path throughput (228 TPS) in the tested prototype. Both blockchain-integrated variants detected tampered credentials in all successfully processed tamper cases. Overall, the results show that cloud-native decomposition and ledger-backed trust and anchoring can support scalable and trustworthy credential services when platform choice aligns with institutional governance scope, verification audience, and deployment constraints. Full article

Industrial control systems (ICS), which manage critical infrastructure such as power grids and water treatment, are increasingly exposed to cyber threats and operational faults as their connectivity to external networks grows. AI-based anomaly detection has emerged as a key defense, yet three limitations restrict its practical deployment: (i) detected anomalies are treated uniformly without distinguishing between transient faults and intentional attacks, hindering tailored incident response; (ii) the trade-off between detection accuracy and the false-positive rate burdens experts with extensive manual triage and delays prompt action; and (iii) prevailing feature-attribution Explainable AI (XAI) techniques such as SHAP and LIME produce fragmented sensor-level explanations and fail to capture correlations among sensors in time-series data, undermining trust in model decisions. To address these gaps, this paper proposes a graph-based deep learning framework that (a) defines anomaly types in terms of the anomalous-sensor ratio measured before and after smoothing—which operationalizes the correlation-maintenance principle that faults keep coupled sensors jointly anomalous while attacks isolate them—enabling explicit separation of faults, attacks, false positives, and false negatives; (b) identifies ambiguous decisions near the detection threshold as candidate false alarms via dynamic threshold smoothing; and (c) provides correlation-aware graph visualizations for intuitive interpretation. Experiments on the Secure Water Treatment (SWaT) dataset center on this post-detection layer: built on a standard graph-based detector (F1-score 0.787 at Top-K = 10) that serves only as the substrate, the categorization separates faults from attacks, and the subsequent ambiguity analysis identifies false negatives with 83% precision and false positives with 73% precision. By separating attacks from faults and surfacing high-likelihood false alarms together with intuitive sensor-correlation explanations, the proposed approach reduces analyst workload and supports more reliable, prioritized incident response in ICS environments. Full article

High-Frequency Trading (HFT) dashboards require rapid reception, aggregation, and visualization of order book and trade update streams that may arrive at multi-million message rates. Conventional CPU-based and CPU-GPU hybrid visualization pipelines can suffer from significant delays during periods of burst due to CPU-mediated rendering, synchronization, kernel launch overhead, and copies on the host. This paper presents a visualization pipeline that is entirely resident on the graphics processor with zero-copy access to NIC accessible pinned buffers, persistent CUDA processing, fused stage execution of the parse-aggregate pipeline, and persistent CUDA OpenGL buffer interoperation. The goal is not to reach production status but rather to see whether host-to-host data movement can be decreased and whether the stages of GPU processing can be consolidated to improve latency, throughput and frame cadence in controlled HFT-style workloads. The evaluated workstation achieved a mean ingest-to-pixel latency of 6.3 ms using the proposed design compared to 29.4 ms for the current design, with sustained throughput of 10.2 million messages per second, which is 20 times greater than the current design, and a steady-state range of 185 to 192 frames per second with a burst floor of 178 frames per second for the proposed design. The improvement observed can be attributed to both the zero-copy ingestion and fused persistent kernel execution. Based on the obtained results, the proposed method of use of this technique in the implementation of real-time financial visualization under the proposed conditions is possible. More general testing is still required on other NICs, other generations of GPUs and PCIe configurations, workload traces, and actual exchange feeds. Full article

Background: Catheter-associated urinary tract infection (CAUTI) remains a major healthcare-associated infection, particularly in intensive care units (ICUs). Nurse-driven urinary catheter removal protocols can reduce catheter duration and improve patient safety; however, their implementation remains inconsistent. This study explored barriers and facilitators influencing the implementation of nurse-driven CAUTI prevention protocols in Saudi ICUs. Methods: A qualitative study guided by the Consolidated Framework for Implementation Research was conducted in two tertiary hospitals in Riyadh, Saudi Arabia. A purposive sample of 23 ICU nurses, infection control nurses, and nurse managers participated in semi-structured interviews. Data were analyzed using Braun and Clarke’s thematic analysis approach, supported by NVivo. Results: Two overarching themes emerged. Facilitators included interprofessional communication and shared decision-making, clinical experience, and professional commitment. Barriers included workload and staffing pressures, physician dominance and nurse hesitation, knowledge deficits and poor adherence to guidelines, inconsistent documentation, and reduced awareness of catheter presence. Implementation was influenced by interconnected individual, organizational, and cognitive factors. Conclusions: Implementation of nurse-driven CAUTI prevention protocols is shaped by both enabling and limiting factors. Strengthening interprofessional collaboration, supporting nurse autonomy, improving documentation, and providing ongoing education may enhance protocol uptake and sustainability, ultimately improving patient safety. Full article

Petri nets represent a highly versatile mathematical formalism for modeling discrete event and hybrid systems. For the development of modern complex production processes for Industry 4.0, integrating these formal models with industrial communication standards is an appropriate and effective option. The main aim of the proposed article is to design a new web-based software tool for the modeling, simulation, and control of mechatronic systems with OPC Unified Architecture support. To accomplish this task, an original software solution called PetriLink is proposed. This platform leverages an intuitive graphical interface and significantly expands the formalism by combining hybrid Petri nets with Colored Petri Nets (CPN) data extensions and a reactive OPC UA subscription model. These new features greatly expand the area of systems that can be modeled and controlled, bridging the gap between theoretical academic tools and practical industrial automation. Furthermore, the structural flexibility of the implemented Petri net models enables the explicit representation of symmetric cyber-physical architectures, as well as the design of asymmetric, event-driven control strategies (e.g., using inhibitor and reset arcs) for enhanced system robustness. The platform was evaluated on a reference net of 5000 places and 2500 transitions, where an incremental dirty-flag evaluation mechanism keeps the per-step engine cost below 1 ms for sparse industrial markings and at about 350 µs for a moderate workload of one hundred concurrent tokens, yielding a speed-up of up to roughly three orders of magnitude over naive full re-evaluation and confirming consistent soft real-time behavior on commodity hardware. Offering a graphical environment for the design of discrete event and hybrid system control algorithms, it can be used for education, research and practice in cyber-physical systems (Industry 4.0). Full article

Background/Objectives: Despite well-documented chemical and physical hazards in healthcare settings, existing reviews of occupational reproductive risks have largely focused on single-agent risk estimation and have rarely translated occupational hygiene evidence into clinical decision-making frameworks for reproductive counseling and surveillance. This narrative review synthesizes evidence across multiple occupational exposure categories—antineoplastic agents, high-level disinfectants (HLDs), sterilants, and work-organization factors—and proposes an integrated, clinically operational framework for preconception counseling, pregnancy-sensitive risk stratification, exposure-control verification, and reproductive health surveillance among women healthcare workers. Methods: A structured narrative literature search was conducted across PubMed/MEDLINE, Scopus, Web of Science, and Embase from database inception through January 2025 and updated in March 2026. The review was guided by a Population–Exposure–Comparison–Outcome (PECO) framework and structured using Search–Appraisal–Synthesis–Analysis (SALSA) principles and the Scale for the Assessment of Narrative Review Articles (SANRA). Evidence quality was summarized using a modified hierarchy-of-evidence classification provided as a reader aid. This narrative review employed structured transparency tools but does not claim the methodological status of a systematic review. Quantitative meta-analytic pooling was not performed owing to substantial heterogeneity across study designs, exposure assessment methods, and outcome definitions; findings were synthesized narratively by exposure category. Results: The strongest and most consistent evidence was identified for occupational exposure to antineoplastic agents, which has been associated with spontaneous abortion, stillbirth, congenital abnormalities, impaired fecundability, and selected cancer-related concerns. HLDs and sterilants represent exposure categories warranting precautionary attention, with some evidence suggesting possible adverse effects on fecundability and early pregnancy maintenance; however, findings are considerably more heterogeneous, context-dependent, and reliant on self-reported exposure assessment than those for antineoplastic agents. Broader workplace factors, including shift work, prolonged working hours, physical workload, and mixed exposures, may further contribute to reproductive risk. The synthesis supports task-specific occupational history taking, exposure-control verification, and pregnancy-sensitive risk stratification. Conclusions: This review provides a multi-exposure, clinically operational framework that bridges occupational hygiene evidence with reproductive healthcare delivery, offering practical decision-support tools for clinicians managing women healthcare workers during preconception, pregnancy, and lactation. The framework includes structured occupational history-taking questions, a clinical decision pathway with evidence-tier classification, and a prevention matrix linking exposure sources to workplace controls and clinical actions. Integrating task-specific occupational history taking into routine reproductive care may improve detection of preventable workplace risks and support timely accommodation, while clinicians should calibrate recommendation strength to the underlying evidence quality for each exposure category. Full article

According to data from the World Federation of Societies of Anesthesiologists, numerous countries across Asia and Africa have fewer than one anaesthesiologist per 100,000 people. Upskilling nurse anaesthetists in these regions is critical to improving clinical outcomes, and interactive virtual patient simulators offer a safe environment to explore complex clinical scenarios. This paper introduces an advanced general anaesthesia patient simulator engineered to bridge the accessibility gap left by existing platforms, which often require expert programming knowledge and restrict users to manual titration. Our simulator features an intuitive graphical user interface optimised for clinical education and natively supports both manual and closed-loop anaesthesia administration. The platform includes a suite of pre-designed controllers, specifically standard PIDs and two distinct fractional-order FO-PID variants, highlighting a novel robust FO-PID framework engineered to mitigate high patient variability. The deployment of these embedded controllers is demonstrated via a Depth of Hypnosis regulation case study and validated across a diverse cohort of 19 virtual patients. Closed-loop evaluation reveals that while the standard PID achieves a lower average mean squared error during the maintenance phase, the fractional-order alternatives deliver significantly superior robustness and inter-patient consistency. Ultimately, integrating this simulator into clinical training frameworks offers a viable pathway to reduce nursing workload and enhance patient safety through optimised automated drug delivery. Full article

Secure data collaboration among mutually distrustful organizations requires more than encrypted storage: it also needs accountable ownership control, auditable access governance, privacy-preserving transaction execution, and reliable settlement when data are exchanged as digital assets. This paper proposes TrustTrade, a unified multi-party secure data management and transaction framework designed for cross-organization data sharing, trading, and compliance-sensitive analytics. TrustTrade integrates policy-bound data capsules, a tamper-evident provenance ledger, adaptive threshold escrow, verifiable data-payment settlement, and selective audit with revocation rebinding. On four real-dataset-derived workloads, TrustTrade reaches a $90.4$–$94.8\%$ settlement rate, with a $92.5\%$ average that is $6.4$ percentage points higher than the strongest baseline average. Under adversarial request injection, TrustTrade reduces unauthorized release to $0.31\%$ and atomicity violation to $0.38\%$, corresponding to $93.6\%$ and $93.0\%$ reductions compared with Plain-Market, respectively; compared with Fixed-Escrow, unauthorized release is reduced by $77.4\%$. TrustTrade also achieves $96.7\%$ dispute-resolution accuracy while maintaining practical settlement latency. These results indicate that jointly designing secure data management and secure data transaction protocols offers a practical path toward trustworthy multi-party data ecosystems. Full article

Introduction. Chronic non-cancer pain is highly prevalent and profoundly diminishes quality of life. While pharmacological and interventional treatments are central, its psychosocial and spiritual dimensions remain under-addressed. This pilot study assessed the feasibility of a therapist-led support group intervention for patients with chronic non-cancer pain and explored preliminary psychospiritual outcomes. Methods. A two-arm, non-randomized pilot feasibility study was conducted among 58 outpatients of a university pain management clinic in Poland. Feasibility was assessed through recruitment, retention, attendance, and safety, while preliminary psychological and spiritual outcomes were evaluated using validated self-report instruments. The intervention group (n = 29) participated in eight group sessions combining psychoeducation, mindfulness-based techniques, and supportive dialogue inspired by the Simonton Method. The control group (n = 29) received standard care. Participants completed the Numeric Rating Scale to measure pain intensity, the Satisfaction with Life Scale, the Positive and Negative Affect Schedule, the WHOQOL-BREF, the Spiritual Well-Being Scale, the Generalized Anxiety Disorder Scale, and the Patient Health Questionnaire-9. Results. The intervention was feasible in terms of physician workload; however, patients adherence varied significantly. At baseline, the control group showed a significantly higher positive affect and existential well-being than did the intervention group. In exploratory within-group analyses, participants in the intervention group showed improved positive affect and reduced anxiety (p < 0.05), whereas existential well-being showed a trend toward improvement (p < 0.06). However, the self-selection design limits causal inferences. Nevertheless, participants reported social connectedness, meaning-making, and enhanced vitality. Discussion. This pilot feasibility study provides preliminary evidence that a therapist-led support group intervention integrating psychoeducation, mindfulness, and supportive components is practicable within multidisciplinary pain management. Further research in a larger, randomized trial is needed to evaluate adherence and safety, as well as clinical effects, more rigorously. Full article

As robotic systems evolve toward large-scale distributed architectures and cloud-edge collaboration, communication middleware has become a critical infrastructure that impacts system real-time performance and scalability. The traditional Robot Operating System 1 (ROS 1) communication architecture, which relies on a centralized master node, has limitations in dynamic network environments. Robot Operating System 2 (ROS 2) achieves decentralized communication through the introduction of DDS. However, the single Data Distribution Service (DDS) mechanism remains inadequate for cross-network communication and high-performance local data exchange. Addressing the current issue in ROS communication research: the coexistence of multiple mechanisms without a unified analytical framework or guidance for selection. This paper systematically traces the evolution of the ROS communication architecture from centralized to distributed systems. It constructs a unified analytical framework covering two dimensions: communication models and data transmission paths. Crucially, to overcome the unreliability of cross-protocol comparisons based on heterogeneous literature, this paper designs and executes a set of unified benchmark experiments on a controlled testbed. These experiments systematically evaluate the performance of two mainstream DDS implementations (CycloneDDS and FastDDS) across five key metrics: latency, throughput, jitter, scalability, and packet loss rate under load. Additionally, a comprehensive comparative analysis of the performance of three transmission modes is conducted. Based on this comprehensive evaluation, this paper summarizes the performance characteristics of different mechanisms and further proposes an optimization-based middleware selection method for quantitative communication mechanism selection under different workload and application requirements. This paper provides a systematic reference for the design and optimization of ROS communication systems and offers guidance for promoting the application of multi-middleware collaborative architectures in robotic systems. Full article

The rapid proliferation of cloud data centers has intensified concerns over carbon emissions, energy efficiency, and sustainability. Virtual machine (VM) placement is a pivotal control lever, yet existing methods rarely couple carbon intensity signals with computationally tractable multi-objective optimization. In this paper, we propose CASO (Carbon-Aware Surrogate-Guided Optimization), a novel framework that integrates an online adaptive Radial Basis Function (RBF) surrogate model with a self-adaptive hybrid PSO-DE swarm optimizer for real-time VM placement in geo-distributed edge cloud environments. CASO simultaneously minimizes carbon emissions, energy consumption, SLA violation rate, and network latency under strict host capacity and Quality-of-Service (QoS) constraints. Three key innovations differentiate CASO: (i) an online surrogate update mechanism that refines fitness approximations incrementally as workload patterns evolve; (ii) a carbon intensity weighting scheme anchored to real-time Grid Emission Factor (GEF) signals; and (iii) an adaptive parameter controller that autonomously tunes swarm exploration–exploitation trade-offs without hand-crafting. Experiments on the publicly available Alibaba Cluster Trace (cluster-trace-v2026-GenAI) dataset within a CloudSim-Plus environment show that CASO reduces carbon emissions by up to 31.4%, energy consumption by 27.9%, and SLA violations by 18.8% compared to the strongest baseline while converging 3.8× faster than the strongest baseline (ADEDL). Full article

This paper presents Resilient Edge-IVA (Intelligent Video Analytics), an integrated framework designed to ensure real-time inference stability and high-speed embedding-based similarity search in resource-constrained edge computing environments. Conventional systems often face Quality of Experience (QoE) degradation caused by computational overhead and hardware-level bottlenecks. To address these challenges, this study proposes a “Whole-cycle” methodology employing a perception-driven, three-tier adaptive control algorithm. This algorithm dynamically modulates encoding parameters, such as resolution and bitrate, by utilizing real-time inference latency and CPU utilization as feedback signals. Furthermore, the framework incorporates an event-density-based Data Diet mechanism. This mechanism selectively adjusts video quality based on object detection results, preserving high-fidelity imagery for critical events while significantly reducing data volume during static intervals. The backend implements a hybrid storage architecture combining the Milvus vector database for CLIP-based high-dimensional visual embeddings with a PostgreSQL relational database for structured metadata. These systems are linked via a deterministic hash key to ensure data atomicity and facilitate high-speed, multi-dimensional embedding-based retrieval. Experimental evaluations conducted on a Raspberry Pi 5 and Hailo-8 NPU demonstrate that the proposed framework maintains a frame drop rate below 0.3% even under extreme workloads, providing a 13-fold improvement in operational stability over static configurations. The results also confirm a 54.2% reduction in total storage occupancy and a Hash Mapping Consistency (HMC) score of 0.89. These findings validate the framework’s effectiveness in reconciling real-time processing stability with storage efficiency. Building upon this baseline, future research will extend the framework to multi-class environments, targeting applications such as Intelligent Transport Systems (ITS). Full article

Artificial intelligence is reshaping warehouse work through algorithmic task allocation, scanner-based monitoring, KPI feedback, dynamic scheduling, and real-time performance control. Although these systems can improve coordination and operational visibility, they also create governance risks related to fairness, transparency, autonomy, privacy, workload pressure, trust, and employee resistance. This study develops a human-centered decision framework for prioritizing algorithmic management governance packages in third-party logistics (3PL) warehousing. The main contribution is to translate employee-level governance concerns into a scenario-sensitive decision model that helps managers select appropriate governance packages under different operational pressures. The study uses survey data from 380 warehouse employees to examine key psychological and behavioral mechanisms, including procedural fairness, transparency, system/information quality, autonomy, privacy concern, workload, trust, acceptance, and resistance/disengagement. These survey-supported constructs are then converted into six governance criteria: procedural fairness, transparency and contestability clarity, system and information quality, autonomy support, privacy boundary governance, and workload protection. A seven-expert panel evaluates five governance packages under three scenarios: peak season surge, labor shortage/high turnover, and audit pressure/compliance scrutiny. Methodologically, the framework combines Dynamic Multi-Facet Fuzzy Sets to capture membership, non-membership, hesitancy, engagement, and resistance; Bayesian Network weighting to reflect dependencies among governance criteria; and PCA-based ranking optimization to generate scenario-specific and robust rankings. Comparative validation with SAW and TOPSIS is also used to assess ranking consistency. The findings show that effective algorithmic management governance is not a fixed compliance solution. Transparency, workload protection, autonomy support, privacy boundary governance, and procedural fairness become more or less important depending on the operational scenario. A2, which combines transparency, workload protection, and autonomy support, emerges as the strongest robust package. A1 performs best under labor shortage/high turnover, while A3 performs best under audit pressure/compliance scrutiny. These results suggest that 3PL warehouses should adopt adaptive governance routines that combine explainability, contestability, workload safeguards, privacy boundaries, and employee voice mechanisms. The study contributes to the literature on AI in socio-technical systems by showing how human, organizational, and ethical concerns can be embedded into an interpretable decision framework for responsible algorithmic management in logistics work environments. Full article