Search Results (217)

Ambient Assisted Living (AAL) systems have become increasingly relevant as aging populations intensify the demand for technologies that promote autonomy, safety, and quality of life. However, the widespread adoption of audiovisual sensing in smart homes raises critical concerns regarding data protection, privacy, and user trust. Ensuring secure processing while maintaining accurate activity recognition remains a key challenge. This work introduces DistSense, a distributed Peer-to-Peer (P2P) system designed to enhance activity detection in domestic environments through collaborative inference among intelligent audiovisual sensors. DistSense prioritizes privacy by performing local processing, sharing only high-level events, and leveraging distributed ledger mechanisms to ensure data integrity and auditability and support cross-device validation. This collaborative strategy reduces false positives caused by occlusions, illumination variability, and acoustic noise. To assess the system, functional tests were conducted for each module, followed by two use cases evaluated in both simulated and real edge hardware environments. The trained models achieved 88% accuracy for audio and 80% for video, and the system demonstrated effective performance in detecting daily activities and domestic hazards under varying noise conditions. Results indicate that DistSense successfully balances security, user acceptance, and inference robustness, positioning it as a viable solution for privacy-preserving activity monitoring in smart home contexts. Full article

The construction industry remains among the most hazardous, with workers in horizontal transportation infrastructure facing additional risks from dynamic work zones, live traffic exposure, and variable environmental conditions. Immersive technologies such as Virtual Reality (VR) and Augmented Reality (AR) offer new approaches to accident analysis and prevention, yet their applications toward improving occupational safety in transportation construction have not been comprehensively reviewed. This paper presents a systematic review of 54 studies published between 2016 and 2025 collected from two online databases (Transportation Research International Documentation and Web of Science). This review synthesizes how immersive technologies contribute to occupational risk assessment, safety training, and real-time hazard monitoring in the construction of roads, bridges, tunnels, and work zones. Each study is classified across two dimensions: the immersive medium (VR, AR, etc.) and the operational context within the construction lifecycle (onsite tools, offsite monitoring and planning, simulation-based analysis, and workforce education). This dual classification is the first to systematically map immersive technology applications for occupational safety, specifically within horizontal transportation infrastructure. The findings of this review demonstrate the unique use cases of each immersive medium, revealing that VR is primarily used for controlled experimentation and full-immersion remote analysis, whereas AR and handheld devices are preferred for field-deployed applications. Despite these promising capabilities, widespread adoption remains limited by hardware constraints, challenging field conditions, and organizational resistance. This suggests that future work should focus on safety systems tested in real-world settings and rigorously evaluated by domain experts to enable their integration into standard workplace risk management practices. Full article

Illegal drone intrusion near airport runways poses a critical threat to civil aviation safety, creating an urgent need for runway-side vision systems that can detect intruding UAVs early enough for safety warning and collision-risk mitigation. However, the development of such detectors is severely hindered by the scarcity of annotated real-world data in this high-security scenario. To address this bottleneck, we present SynthAirDrone, the first high-fidelity synthetic dataset for UAV intrusion detection in airport runway environments, together with an intelligent data generation framework integrating scene-aware placement and multi-criteria quality assessment. The proposed method uses sky-region segmentation to guide physically plausible drone placement, and combines perspective-aware scaling, Poisson image editing, and a four-dimensional quality scoring system—covering sky overlap, lighting consistency, size plausibility, and edge continuity—to improve visual plausibility and semantic consistency. The resulting dataset comprises 6500 high-quality images, all annotated in YOLO-compatible format. Using the lightweight YOLOv11n model, we show that models trained solely on SynthAirDrone exhibit non-trivial cross-domain transfer to Anti-UAV, while mixed training with limited real data provides the strongest real-world performance under the present setting. Ablation studies further confirm that a quality threshold of $\tau =0.6$ achieves the best trade-off between diversity and fidelity. Overall, this work delivers a reproducible and efficient synthetic data solution for UAV detector development in high-security, data-scarce airport-runway scenarios. Full article

While South Africa’s recycling chain relies heavily on informal labour, the burden of non-fatal workplace injuries among landfill-based waste pickers remains poorly characterised. This study aimed to estimate the prevalence of self-reported non-fatal workplace injuries and identify associated factors among informal waste pickers at landfill sites in Johannesburg, South Africa. We conducted a cross-sectional study at two purposively selected landfill sites in Johannesburg. Using convenience sampling, 354 waste pickers were enrolled (median age 34 years; 73.2% male). A structured questionnaire captured worker characteristics and self-reported injuries over the preceding six months. Robust (modified) Poisson regression was utilised to determine associations with self-reported workplace injury. Overall, 86.2% of participants reported at least one injury. Lacerations caused by contact with waste materials predominated (82.7%), followed by violence (20.5%) and needle-stick injuries (19.9%). Notably, 94.1% of participants reported using personal protective equipment (PPE), yet the injury prevalence was high. In the multivariable model, each additional year of landfill work experience was associated with a 1.0% higher prevalence of reported injury (adjusted prevalence ratio [aPR] 1.01; 95% CI 1.01–1.02). Conversely, pickers aged 51 years and older had a 32% lower prevalence of injury than those aged 18–28 (aPR 0.68; 95% CI 0.51–0.90). To mitigate these risks, municipal authorities should implement mandatory safety training for site entry, provide industrial-grade, puncture-resistant PPE, and formalise the integration of landfill pickers into institutional occupational health frameworks. Full article

Electric Vehicle Charging Systems (EVCSs) are increasingly connected with the Internet of Things (IoT) and smart grid infrastructure, yet they face growing cyber risks due to expanded attack interfaces. These systems are vulnerable to various attacks that potentially impact both charging operations and user privacy. Intrusion Detection Systems (IDSs) are essential for identifying suspicious activities and mitigating risks to protect EVCS networks, but conventional ML-based IDSs are often unable to achieve optimal performance due to imbalanced datasets, complex traffic distributions, and human design limitations. In practice, EVCS traffic is typically multi-class, imbalanced, and safety-critical, where both missed attacks and false alarms can lead to denial of charging, service interruption, unnecessary incident escalation, financial loss, and reduced user trust. Automated ML (AutoML) and Generative Artificial Intelligence (GAI) have emerged as promising solutions in cybersecurity. Existing GAI and augmentation methods are mostly class-frequency-driven, but this does not necessarily improve the error-prone regions where IDSs actually fail. In this paper, we propose a GAI and an AutoML-based IDS that incorporates a Conditional Generative Adversarial Network (cGAN) with the optimized XGBoost model to improve the effectiveness of intrusion detection in EVCS networks and IoT systems. The proposed framework involves two techniques: (1) a novel cGAN-based error-guided generative augmentation (EGGA) method that extracts misclassified samples and generates a more robust training set for IDS development, and (2) an optimized IDS model that automatically constructs an optimized XGBoost model based on Bayesian Optimization with Tree-structured Parzen Estimator (BO-TPE). The main algorithmic novelty lies in EGGA, which uses model errors to guide generative augmentation toward difficult decision regions, while the overall pipeline represents a practical system-level integration of EGGA, XGBoost, and BO-TPE. To the best of our knowledge, this is the first work that combines GAI and AutoML to specifically improve detection on hard samples, enabling more autonomous and reliable identification of diverse cyber attacks in EV charging networks and IoT systems. Experiments are conducted on two benchmark EVCS and cybersecurity datasets, CICEVSE2024 and CICIDS2017, demonstrating consistent and statistically meaningful improvements over state-of-the-art IDS models. This research highlights the importance of combining automation, generative balancing, and optimized learning to strengthen cybersecurity solutions for EV charging networks and IoT systems. Full article

Background: Work-related musculoskeletal disorders (WMSDs) are prevalent among orthopaedic surgeons as a result of prolonged exposure to non-neutral postures and forceful manual tasks during surgery. Although working height is a key determinant of trunk and upper-limb posture, the systematic evaluation of ergonomic working-height recommendations in orthopaedic surgery remains limited. Methods: A simulated left total knee arthroplasty (TKA) was divided into twelve critical surgical steps and analysed across four commonly used surgeon positions (A–D). Two conditions were compared: uncorrected working height (N) and working height corrected according to Canadian Centre for Occupational Health and Safety (CCOHS) recommendations (C). Joint angles were measured from standardized photographs using Kinovea software, and postural load was quantified with the Rapid Entire Body Assessment (REBA) method. Two trained evaluators conducted three independent assessments, yielding 288 REBA scores. Results: Mean REBA scores decreased across all surgeon positions following ergonomic correction, with statistically significant reductions observed in positions A, B, and D. When pooled across all position–step combinations (n = 48), the mean reduction was 0.92 REBA points (95% CI 0.50–1.33; p < 0.001). Notably, 27 of the 48 position–step comparisons exceeded the minimal detectable change threshold. The largest reductions occurred during force-intensive surgical steps, including bone cutting, drilling, and implant impaction. Conclusions: Adjusting working height in accordance with CCOHS ergonomic recommendations reduces surgeons’ postural load during TKA. These findings support the integration of evidence-based ergonomic adjustments into routine orthopaedic surgical practice. Full article

Background: Work at heights is a high-risk occupational activity, with falls being a leading cause of fatal accidents in construction and industrial maintenance. Conventional safety training often does not fully prepare workers for real-world hazards. Immersive virtual reality (IVR) has emerged as a promising training tool, providing controlled and realistic simulations of hazardous scenarios. This hypothesis-generating pilot study evaluates the feasibility and effectiveness of IVR in enhancing practical skills, safety perception, and physiological responses during work-at-height training. Methods: This controlled trial will recruit first-time trainees from the National Learning Service (SENA) of Colombia. Participants will be assigned to an intervention group, receiving IVR training before field-based practical sessions, or a control group, receiving standard theoretical instruction. Outcomes include practical skill acquisition, ergonomic risk, cognitive performance, and physiological responses, including heart rate variability measured with validated devices. Assessments will be performed using standardized tools, and data will be analyzed with repeated-measures ANOVA and regression models to compare groups. Conclusions: By integrating practical, cognitive, ergonomic, and physiological measures, this study will provide evidence on whether IVR improves the effectiveness of work-at-height training beyond conventional methods. Findings may inform future strategies to enhance occupational safety training in high-risk work environments. Full article

The integration of social sustainability clauses into port terminal concession agreements is essential for fostering a more inclusive, balanced, and resilient framework for port operation and development. Such integration addresses the needs of both external and internal stakeholders, namely local communities and society at large, as well as port employees who seek to safeguard their interests. This paper examines how negative externalities can be mitigated or minimized, and how positive externalities can be reinforced, through the inclusion of social sustainability measures and monitoring mechanisms within concession frameworks. Building upon an analytical review of existing literature, available concession agreements and guidelines for port concessions, as well as relevant best practices, the paper identifies and classifies a comprehensive set of social sustainability indicators applicable to the port sector. The results of the analysis are presented through two categories of indicators: external indicators, encompassing stakeholder engagement, community development, and participation of society in economic benefits; and internal indicators, covering employment conditions, training, diversity, occupational health and safety, and the working environment. These indicators, aggregated and structured for the first time within the context of port literature, form a scientific foundation for embedding social sustainability considerations into concession agreements. Full article

To address the challenges of coordinated optimization in building integrated energy systems (IES) under the dual-carbon targets—characterized by strong multi-energy coupling, significant uncertainty in renewable generation, and stringent safety constraints—a novel safe deep reinforcement learning algorithm, Safe-DDPG, is proposed. Traditional deep reinforcement learning methods often suffer from high constraint-violation risk and limited policy reliability due to coupled objectives in building IES optimization. To overcome these limitations, a dual-channel critic architecture is designed to independently evaluate and decouple economic and safety objectives. In addition, a dynamic safety–penalty mechanism based on logarithmic barrier functions is introduced, together with an adaptive exploration strategy, enabling dynamic balancing between economic cost and constraint satisfaction according to system states during training. Experimental results demonstrate that, compared with mainstream algorithms, Safe-DDPG achieves substantial improvements across multiple key performance indicators: safety violations are reduced by up to 96.7%, average daily operating costs decrease by 18.5%, and cumulative rewards increase by more than 30%. Ablation studies further confirm the effectiveness and necessity of each core component. Two DRL methods from reference papers are reproduced, and their performance is compared with the proposed method in the existing experimental results, showing that the proposed method has significant advantages in reward value and economic cost. This work provides a safe, reliable, and efficient reinforcement-learning-based approach for optimization and scheduling of building energy systems under complex operational constraints. Full article

Spatial efficiency drives the adoption of integrated station–bridge structures in maglev transit, yet the rigid coupling between track and station poses inherent challenges to vibration serviceability. This study isolates the impact of support constraints, specifically contrasting rigid connections with pinned supports, on the dynamic performance of a five-story maglev station. Using a unified, high-fidelity 3D coupled model that incorporates electromagnetic suspension nonlinearity, we evaluated structural responses under train speeds of 60–120 km/h. Simulations identify a critical operational threshold: while the waiting hall remains compliant with standard comfort criteria (DIN 4150-3), the platform floor exceeds the 1.5% g acceleration limit during dual-track operations at speeds ≥ 100 km/h. Beyond standard safety checks, the main scientific innovation of this study is revealing the mechanical transmission paths of structure-borne vibrations at the track-frame interface. The results demonstrate that rigid connections create full mechanical coupling, directly passing train-induced bending moments into the station frame. Conversely, pinned supports release the rotational degrees of freedom, which physically cuts off the primary energy transmission route. By explaining this structural decoupling mechanism, this work moves beyond a specific engineering case study to provide a fundamental theoretical framework for vibration control in complex maglev hubs. Full article

Industrial automation laboratories often face limitations related to restricted access to industrial equipment, safety constraints, and limited scalability for hands-on experimentation. To address these challenges, this work proposes a real-time multi-layer Digital Twin architecture integrating a physical Siemens S7-1500 PLC, an immersive Unity-based virtual environment, HMI supervision, and IoT-enabled remote monitoring within a unified communication framework. The architecture is structured into physical, digital, and integration layers, enabling modular scalability and bidirectional synchronization between the physical process and its virtual representation through Ethernet TCP/IP communication. System performance was evaluated using synchronization metrics including communication latency, jitter, deterministic timing deviation, and event synchronization accuracy. Experimental results demonstrated stable PLC–Digital Twin communication with average latencies below 15 ms and jitter below 0.5 ms, ensuring reliable real-time interaction during continuous operation. A comparative evaluation with engineering students also showed improved learning conditions, achieving high perceived usability (SUS = 86/100) and reduced cognitive workload (NASA-TLX = 34/100). These results confirm the effectiveness of the proposed architecture as a scalable platform for Industry 4.0 training environments. Full article

Background: Breakpoints in the pre-examination processes and at organizational interfaces are a significant source of failures in specimen identification and tracking in anatomic pathology. While ISO 15189 emphasizes end-to-end traceability and risk-based quality management, implementing these principles in complex, multi-actor specimen pathways remains challenging. This study explores expert perspectives on specimen process chains, tracking mechanisms, and ISO 15189-oriented quality and risk management in pathology. Methods: We conducted 10 semi-structured expert interviews across three settings. Interviews were audio-recorded, transcribed, pseudonymized, and analyzed using structured qualitative content analysis (Mayring) supported by MAXQDA. A deductive category system derived from the theoretical framework and interview guide comprised six main categories and twelve subcategories. Results: Across 512 coded text segments, participants identified several factors as critical for effective implementation, including: (i) interface management along the specimen pathway, with recurrent vulnerabilities at handovers between operating theater/ward/transport and accessioning; (ii) the central role of barcode-based identification and the need for closed-loop traceability; (iii) the importance of measurable quality indicators and incident learning systems to operationalize risk management; (iv) persistent paper–digital handoffs and heterogeneous IT landscapes that undermine data integrity; (v) the need for clearly assigned responsibilities, training, and SOP governance; and (vi) implementation barriers including resources, change management, and vendor integration, alongside practical enablers such as incremental roll-out and cross-professional governance. Conclusions: Experts converge on a pragmatic ISO 15189-aligned roadmap: prioritize interface risks, standardize identifiers and handover rules, define a minimal KPI set for tracking and misidentification events, and reduce paper–digital handoffs by interoperable IT. Future work should quantify baseline error rates and evaluate the impact of digital tracking interventions on patient safety and turnaround times. Full article

Railway traffic monitoring requires robust detection technologies capable of operating reliably under real-world vibration and environmental conditions. In this work, we present the design and validation of an optical vibration sensor based on a Single-mode–Multimode–Single-mode (SMS) fiber structure for Light Rail Vehicle (LRV) detection. The sensing mechanism relies on multimodal interference in the multimode fiber (MMF), where rail-induced vibrations modify the guided mode distribution and, consequently, the transmitted optical intensity. The optical signal is converted to voltage and processed through an embedded acquisition system. Additionally, we conducted tests with freight trains and maintenance trains in order to evaluate the applicability of the sensor in other types of trains besides the LRV. We conducted laboratory experiments to assess mechanical stability, sensibility, and packaging strategies, followed by supervised field tests on an operational LRV line. The recorded time-domain signal exhibited clear modulation during train passage, and first-derivative and sliding-window variance analyses were applied to reliably identify vibration events, even in the presence of slow baseline drift. In addition, frequency-domain analysis was performed by applying the Fast Fourier Transform (FFT) to the measured signal, enabling the identification of characteristic low-frequency spectral components induced by train passage. A quantitative sensitivity assessment was further carried out by correlating the integrated spectral energy (0–12 Hz) with vehicle weight, yielding a linear response with a sensitivity of 0.0017 a.u./t and coefficient of determination $R^2=0.933$. The proposed solution demonstrated stable operation using commercially available low-cost components, confirming the feasibility of SMS-based optical sensing for railway monitoring. These results indicate strong potential for future deployment in traffic safety systems and distributed sensing networks. Full article

Foreign object intrusion in railway tracks is a major threat to train operation safety, yet current detection methods face challenges in identifying small distant targets and adapting to low-light conditions. Moreover, existing systems often lack the ability to assess intrusion risk levels, limiting real-time warning and graded response capabilities. To address these gaps, this paper proposes a novel method for intrusion detection and risk assessment based on keypoint spatial discrimination. First, an XS-BiSeNetV2-based track segmentation network is developed, incorporating cross-feature fusion and spatial feature recalibration to improve track extraction accuracy in complex scenes. Second, an enhanced STI-YOLO detection model is introduced, integrating a Shuffle attention mechanism for better feature interaction, a high-resolution Transformer detection head to improve small-target sensitivity, and the Inner-IoU loss function to refine bounding box regression. Detected targets’ bottom keypoints are then analyzed relative to track boundaries to determine intrusion direction. By combining lateral distance and motion state features, a multi-level risk classification system is established for quantitative threat assessment. Experiments on the RailSem19 and GN-rail-Object datasets show that the method achieves a track segmentation mIoU of 88.19% and a detection mAP of 82.6%. The risk assessment module effectively quantifies threats across scenarios and maintains stable performance under low-light and strong-glare conditions. This work offers a quantifiable risk assessment solution for intelligent railway safety systems. Full article

In autonomous driving, deep convolutional neural networks face a core conflict between computational efficiency and safety-critical robustness on resource-constrained onboard computing units. Dominant structural pruning, based on weight magnitude or geometric statistics, fails in long-tailed traffic scenarios by equating parameter magnitude with feature importance and pruning critical filters in the tail classes. To address this, we propose a structural pruning framework that evaluates the semantic utility of features using weighted copula entropy rather than relying solely on their magnitude. Our novel approach integrates Elastic Net regularization for inducing sparsity and weighted copula entropy for unbiased information-theoretic feature selection. By incorporating inverse class frequency weighting into empirical Copula estimation, we decouple feature relevance from sample abundance, ensuring the preservation of rare-class discriminators based on their information content rather than occurrence frequency. Furthermore, this metric is embedded into an enhanced max-relevance and min-redundancy algorithm to eliminate semantic redundancy while maintaining representational diversity. Extensive experiments on the BDD100K dataset with YOLOv5l and YOLOv8l architectures demonstrate that, at a 50% pruning rate, the proposed method reduces FLOPs and parameters by nearly 50%, with only 0.09% mAP@0.5 loss for YOLOv5l and 0.14% mAP@0.5 loss for YOLOv8l, while significantly improving the mAP of the extreme tail class Train from 0% to 3.84% and 2.76% to 5.12%, respectively. It achieves a more favorable trade-off between detection accuracy and computational efficiency than mainstream pruning approaches. This work provides a lightweight scheme for autonomous driving perception models and a new information-theoretic perspective for structured network pruning. Full article