Search Results (676)

Timely detection of leaks is essential for the safe and reliable operation of pressure vessels used in superconducting systems, aerospace, and medical equipment. To address the lack of efficient online leak detection methods for such vessels, this paper proposes a quasi-distributed fiber Bragg grating (FBG) sensing network combined with theoretical stress analysis to diagnose vessel conditions. We analyze the stress–strain distributions of vacuum vessels under varying pressures and examine stress concentration effects induced by small holes; these analyses guided the design and placement of quasi-distributed FBG sensors around the vacuum valve for online leakage monitoring. To improve measurement accuracy, we introduce a vibration correction algorithm that mitigates pump-induced vibration interference. Comparative tests under three leakage scenarios demonstrate that when leakage occurs during vacuum extraction, the proposed system can reliably detect the approximate leak location. The results indicate that combining an FBG sensing network with stress concentration analysis enables initial localization and assessment of leak severity, providing valuable support for the safe operation and rapid maintenance of vacuum pressure vessels. Full article

Deep retrofits are one of the few pathways to decarbonize the existing building stock while simultaneously improving climate resilience. These retrofits improve insulation, airtightness, and mechanical equipment efficiency. NRCan’s Prefabricated Exterior Energy Retrofit (PEER) project developed prefabricated building envelope retrofit solutions to enable net-zero performance. The PEER process was demonstrated on two different pilot projects completed between 2017 and 2023. In 2024, in partnership with industry partners, NRCan developed new low-carbon retrofit panel designs and completed a pilot project to evaluate their performance and better understand resiliency and occupant comfort post-retrofit. The Low-Carbon Climate-Resilient (LCCR) Living Lab pilot retrofit was completed in 2024 in Ottawa, Canada, using low-carbon PEER panels. This paper outlines the design and construction for the pilot, including panel designs, the retrofitting process, and post-retrofit building and envelope commissioning. The retrofitting process included the design and installation of new prefabricated exterior retrofitted panels for the walls and the roof. These panels were insulated with cellulose, wood fibre, hemp, and chopped straw. During construction, blower door testing and infrared imaging were conducted to identify air leakage paths and thermal bridges in the enclosure. The retrofit envelope thermal resistance is RSI 7.0 walls, RSI 10.5 roof, and an RSI 3.5 floor with 0.80 W/m²·K U-factor high-gain windows. The measured normalized leakage area @10Pa was 0.074 cm²/m². The net carbon stored during retrofitting was over 1480 kg CO₂. Monitoring equipment was placed within the LCCR to enable the validation of hygrothermal models for heat, air, and moisture transport, and energy, comfort, and climate resilience models. Full article

Hospital fraud detection has often relied on periodic audits that miss evolving, internet-mediated patterns in electronic claims. An artificial intelligence and machine learning pipeline is being developed that is leakage-safe, imbalance aware, and aligned with operational capacity for large healthcare datasets. The preprocessing stack integrates four tables, engineers 13 features, applies imputation, categorical encoding, Power transformation, Boruta selection, and denoising autoencoder representations, with class balancing via SMOTE-ENN evaluated inside cross-validation folds. Eight algorithms are compared under a fraud-oriented composite productivity index that weighs recall, precision, MCC, F1, ROC-AUC, and G-Mean, with per-fold threshold calibration and explicit reporting of Type I and Type II errors. Multilayer perceptron attains the highest composite index, while CatBoost offers the strongest control of false positives with high accuracy. SMOTE-ENN provides limited gains once representations regularize class geometry. The calibrated scores support prepayment triage, postpayment audit, and provider-level profiling, linking alert volume to expected recovery and protecting investigator workload. Situated in the Future Internet context, this work targets internet-mediated claim flows and web-accessible provider registries. Governance procedures for drift monitoring, fairness assessment, and change control complete an internet-ready deployment path. The results indicate that disciplined preprocessing and evaluation, more than classifier choice alone, translate AI improvements into measurable economic value and sustainable fraud prevention in digital health ecosystems. Full article

With the global expansion of hydrogen infrastructure, the safe and efficient transportation of hydrogen is becoming more important. In this study, several technical factors, including material degradation, pressure variations, and monitoring effectiveness, that influence hydrogen transportation using pipelines are examined using system dynamics. The results show that hydrogen embrittlement, which is the result of microstructural trapping and limited diffusion in certain steels, can have a profound effect on pipeline integrity. Material incompatibility and pressure fluctuations deepen fatigue damage and leakage risk. Moreover, pipeline monitoring inefficiency, combined with hydrogen’s high flammability and diffusivity, can raise serious safety issues. An 80% decrease in monitoring efficiency will result in a 52% reduction in the total hydrogen provided to the end users. On the other hand, technical risks such as pressure fluctuations and material weakening from hydrogen embrittlement also affect overall system performance. It is essential to understand that real-time detection using hydrogen monitoring is particularly important and will lower the risk of leakage. It is crucial to know where hydrogen is lost and how it impacts transport efficiency. The model offers practical insights for developing stronger and more reliable hydrogen transport systems, thereby supporting the transition to a low-carbon energy future. Full article

High-frequency vibrations in annular gap type pressure-regulating valves of wind tunnels can induce fatigue, fracture, and operational failures, posing challenges to safe and reliable operation. This study proposes a hybrid leakage fault diagnosis framework that integrates wavelet packet-based signal analysis with advanced deep learning techniques. Time-domain acceleration signals collected from multiple sensors are processed to extract maximum component energy and its variation rate, identified as sensitive and robust indicators for leakage detection. A fluid–solid coupled finite element model of the valve system further validates the reliability of these indicators under different operational scenarios. Based on this foundation, a Vision Transformer (ViT)-based model is trained on a dedicated database encompassing multiple leakage conditions and sensor arrangements. Comparative evaluation demonstrates that the ViT model outperforms conventional deep learning architectures in terms of accuracy, stability, and predictive reliability. The integrated framework enables fast, automated, and robust leakage diagnosis, providing a comprehensive solution to enhance the monitoring, maintenance, and operational safety of wind tunnel valve systems. Full article

Open-pit mining often induces geological hazards such as slope instability, surface subsidence, and ground fissures. To support sustainable mine operations and safety, high-resolution monitoring and mechanism-based interpretation are essential tools for early warning, risk management, and compliant reclamation. This study focuses on the Baorixile open-pit coal mine in Inner Mongolia, China, where 48 Sentinel-1 images acquired between 3 March 2017 and 23 April 2021 were processed using the Small-Baseline Subset and Distributed-Scatterer Interferometric Synthetic Aperture Radar (SBAS-DS-InSAR) technique to obtain dense and reliable time-series deformation. Furthermore, a Trend–Periodic–Residual Subspace-Constrained Regression (TPRSCR) method was developed to decompose the deformation signals into long-term trends, seasonal and annual components, and residual anomalies. By introducing Distributed-Scatterer (DS) phase optimization, the monitoring density in low-coherence regions increased from 1055 to 338,555 points (approximately 321-fold increase). Deformation measurements at common points showed high consistency ($R^2$ = 0.97, regression slope = 0.88; mean rate difference = −0.093 mm/yr, standard deviation = 3.28 mm/yr), confirming the reliability of the results. Two major deformation zones were identified: one linked to ground compaction caused by transportation activities, and the other associated with minor subsidence from pre-mining site preparation. In addition, the deformation field exhibits a superimposed pattern of persistent subsidence and pronounced seasonality. TPRSCR results indicate that long-term trend rates range from −14.03 to 14.22 mm/yr, with a maximum periodic amplitude of 40 mm. Compared with the Seasonal-Trend decomposition using LOESS (STL), TPRSCR effectively suppressed “periodic leakage into trend” and reduced RMSEs of total, trend, and periodic components by 48.96%, 93.33%, and 89.71%, respectively. Correlation analysis with meteorological data revealed that periodic deformation is strongly controlled by precipitation and temperature, with an approximately 34-day lag relative to the temperature cycle. The proposed “monitoring–decomposition–interpretation” framework turns InSAR-derived deformation into sustainability indicators that enhance deformation characterization and guide early warning, targeted upkeep, climate-aware drainage, and reclamation. These metrics reduce downtime and resource-intensive repairs and inform integrated risk management in open-pit mining. Full article

Heavy metal contamination poses significant environmental risks to groundwater and soil, necessitating efficient early-warning technologies for leakage detection. This study proposes a novel early-warning approach for heavy metal leakage using the self-potential (SP) method. A coupled numerical model integrating seepage, ion diffusion, and electric potential fields was developed within the COMSOL Multiphysics platform in order to elucidate the dynamic response mechanism of SP signals to advancing seepage fronts. Key findings reveal that the SP signal responds 1.5 h earlier than the contaminant diffusion front (Case 1), providing a critical early-warning window. The leakage process exhibits a distinct bipolar SP anomaly pattern (negative upstream/positive downstream), with the most significant response observed at the downstream toe area. Consequently, an optimized monitoring strategy prioritizing downstream deployment is proposed and validated using a representative landfill model. This SP-based technology offers a promising solution for real-time environmental risk monitoring, particularly in ecologically sensitive zones. Full article

In order to enhance leakage detection accuracy in water distribution networks (WDNs) while reducing sensor deployment costs, an intelligent algorithm for the optimal deployment of water network monitoring sensors based on the automatic labelling and graph neural network (ALGN) was proposed for the optimal deployment of WDN monitoring sensors. The research aims to develop a data-driven, topology-aware sensor deployment strategy that achieves high leakage detection performance with minimal hardware requirements. The methodology consisted of three main steps: first, the dung beetle optimization algorithm (DBO) was employed to automatically determine optimal parameters for the DBSCAN clustering algorithm, which generated initial cluster labels; second, a customized graph neural network architecture was used to perform topology-aware node clustering, integrating network structure information; finally, optimal pressure sensor locations were selected based on minimum distance criteria within identified clusters. The key innovation lies in the integration of metaheuristic optimization with graph-based learning to fully automate the sensor placement process while explicitly incorporating the hydraulic network topology. The proposed approach was validated on real-world WDN infrastructure, demonstrating superior performance with 93% node coverage and 99.77% leakage detection accuracy, surpassing state-of-the-art methods by 2% and 0.7%, respectively. These results indicate that the ALGN framework provides municipal water utilities with a robust, automated solution for designing efficient pressure monitoring systems that balance detection performance with implementation cost. Full article

Depression relapse is a common issue during long-term care. We introduce a privacy-aware explainable personalized federated learning (PFL) framework that incorporates layer-wise relevance propagation and Shapley value analysis to provide patient-specific interpretable predictions from EEG. The study is conducted with the publicly available Healthy Brain Network (HBN) dataset, with analysis conducted for n = 100 subjects with resting-state 128-channel EEG with accompanying psychometric scores, and subject-wise 10-fold cross-validation is used to assess the performance of the model. Multi-channel EEG features and standardized symptom scales are jointly modeled to both increase the clinical context of the model and avoid leakage issues. This results in overall accuracy, precision, recall, and F1-score values of 92%, 91%, 93%, and 90.5%, respectively. The attribution maps from the model suggest region-anchored spectral patterns that are associated with relapse risk, providing clinical interpretability, and the federated setup of the model allows for a privacy-aware training setup that is more easily adaptable to multi-site deployment. Together, these results suggest a scalable and clinically feasible approach to trustworthy relapse monitoring with earlier intervention. Full article

This paper presents the reliability assessment of an IoT-based sensor node designed for detecting combustible gas leaks in residential environments. Building on a previously published design that integrates low-power micromachined (Micro-Electro-Mechanical Systems, MEMS) pellistors and electrochemical Volatile Organic Compounds (VOC) sensors, this study evaluates the node’s long-term robustness and stability under both realistic and accelerated operating conditions. The system employs a dual-sensor strategy in which the VOC sensor acts as a sentinel, activating the pellistor only when necessary, thereby optimizing power consumption and extending battery life. BLE and LoRa communication capabilities support flexible deployment and real-time data transmission. To ensure suitability for safety-critical applications, we conducted comprehensive reliability testing, including accelerated life tests and environmental stress testing in compliance with IEC 60068 standards. The results confirm the system’s ability to maintain consistent performance and data integrity under thermal, mechanical, and chemical stress, demonstrating its robustness for prolonged operation in demanding environments. Overall, this work underscores the importance of rigorous reliability validation for IoT-based safety devices and positions the proposed solution as a significant step toward enhancing residential gas safety, with potential applications in broader industrial monitoring scenarios. Full article

The large lakes and reservoirs of the northeastern Tibetan Plateau play a key role in regional water resources, yet their influence on terrestrial water storage (TWS) changes at different spatial scales remains unclear. This study employed the constrained forward modeling (CFM) method to correct leakage errors in level-2 spherical harmonic (SH) coefficients from the Gravity Recovery and Climate Experiment and its follow-on missions (GRACE/GRACE-FO) at three spatial scales: two circular regions covering 90,000 km² and 200,000 km², respectively, and a 220,000 km² region based on the shape of mass concentration (Mascon). TWS changes derived from SH solutions after leakage correction through CFM were compared with level-3 Mascon solutions. Individual water storage components, including lake and reservoir water storage (LRWS), groundwater storage (GWS), and soil moisture storage (SMS), were quantified, and their relationships with precipitation were assessed. From 2003 to 2022, the CFM method effectively mitigated signal leakage, revealing an overall upward trend in TWS at all spatial scales. Signals from Qinghai Lake and Longyangxia Reservoir dominated the long-term trend and amplitude variations of LRWS, respectively. LRWS explained more than 47% of the TWS changes, and together with GWS, accounted for over 85% of the changes. Both CFM-based and Mascon-based TWS changes indicated a consistent upward trend from January 2003 to September 2012, followed by declines from November 2012 to May 2017 and October 2018 to December 2022. During the decline phases, GWS contributions increased, while LRWS contributions and component exchange intensity decreased. LRWS, SMS, and TWS changes were significantly correlated with precipitation, with varying time lags. These findings underscore the value of GRACE/GRACE-FO data for monitoring multiscale TWS dynamics and their climatic drivers in lake- and reservoir-dominated regions. Full article

This study constructed an integrated underwater pipeline monitoring system, which combines pipeline posture sensing modules and pipeline leakage detection modules. The proposed system can achieve the real-time monitoring of pipeline posture and the comprehensive assessment of pipeline damage. By deploying pipeline posture sensing and leakage detection modules in array configurations along an underwater pipeline, information related to pipeline posture and flow variations is continuously collected. An array of inertial sensor nodes that form the pipeline posture sensing system is used for real-time pipeline posture monitoring. The system measures underwater motion signals and obtains bending and buckling postures using posture algorithms. Pipeline leakage is evaluated using flow and water temperature data from Hall sensors deployed at each node, assessing pipeline health while estimating the location and area of pipeline damage based on the flow values along the nodes. The human–machine interface designed in this study for underwater pipelines supports automated monitoring and alert functions, so as to provide early warnings for pipeline postures and the analysis of damage locations before water supply abnormalities occur in the pipelines. Underwater experiments validated that this system can precisely capture real-time postures and damage locations of pipelines using sensing modules. By taking flow changes at these locations into consideration, the damage area with an error margin was estimated. In the experiments, the damage areas were 8.04 cm² to 25.96 cm², the estimated results were close to the actual area trends (R² = 0.9425), and the area error was within 5.16 cm² (with an error percentage ranging from −20% to 26%). The findings of this study contribute to the management efficiency of underwater pipelines, enabling more timely maintenance while effectively reducing the risk of water supply interruption due to pipeline damage. Full article

Carbon dioxide (CO₂) is a non-toxic asphyxiant gas that, once released, can pose severe risks, including suffocation, poisoning, frostbite, and even death. As a critical component of carbon capture, utilization, and storage (CCUS) technology, CO₂ pipeline transportation requires reliable leakage detection and precise localization to safeguard the environment, ensure pipeline operational safety, and support emergency response strategies. This study proposes an inversion model that integrates wireless sensor networks (WSNs) with the Gaussian plume model for CO₂ pipeline leakage monitoring. The WSN is employed to collect real-time CO₂ concentration data and environmental parameters around the pipeline, while the Gaussian plume model is used to simulate and invert the dispersion process, enabling both leak source localization and emission rate estimation. Simulation results demonstrate that the proposed model achieves a source localization error of 12.5% and an emission rate error of 3.5%. Field experiments further confirm the model’s applicability, with predicted concentrations closely matching the measurements, yielding an error range of 3.5–14.7%. These findings indicate that the model satisfies engineering accuracy requirements and provides a technical foundation for emergency response following CO₂ pipeline leakage. Full article

Open-mouth panting in broiler chickens is a visible and critical indicator of heat stress and compromised welfare. However, detecting this behavior in densely populated cages is challenging due to the small size of the target and frequent occlusions and cluttered backgrounds. To overcome these issues, we proposed an enhanced object detection method based on the lightweight YOLOv8n framework, incorporating four key improvements. First, we add a dedicated P2 detection head to improve the recognition of small targets. Second, a space-to-depth grouped convolution module (SGConv) is introduced to capture fine-grained texture and edge features crucial for panting identification. Third, a bidirectional feature pyramid network (BIFPN) merges multi-scale feature maps for richer representations. Finally, a squeeze-and-excitation (SE) channel attention mechanism emphasizes mouth-related cues while suppressing irrelevant background noise. We trained and evaluated the method on a comprehensive, full-cycle broiler panting dataset covering all growth stages. Experimental results show that our method significantly outperforms baseline YOLO models, achieving 0.92 mAP@50 (independent test set) and 0.927 mAP@50 (leakage-free retraining), confirming strong generalizability while maintaining real-time performance. The initial evaluation had data partitioning limitations; method generalizability is now dually validated through both independent testing and rigorous split-then-augment retraining. This approach provides a practical tool for intelligent broiler welfare monitoring and heat stress management, contributing to improved environmental control and animal well-being. Full article

Urban rivers, as crucial components of ecosystems, serve multiple functions, including flood control, drainage, and landscape services. However, with the acceleration of urbanization, factors such as industrial wastewater discharge, domestic sewage leakage, and surface runoff pollution have led to increasingly severe degradation of water quality in urban rivers. Unmanned aerial vehicle (UAV) remote sensing technology, with its sub-meter spatial resolution and operational flexibility, demonstrates significant advantages in the detailed monitoring of complex urban water systems. This study proposes an Ensemble Self-Attention Enhanced Mixture Density Network (ESA-MDN), which integrate an ensemble learning framework with a mixture density network and incorporates a self-attention mechanism for feature enhancement. This approach better captures the nonlinear relationships between water quality parameters and remote sensing features, achieving high-precision modeling of water quality parameter distributions. The resulting spatiotemporal distribution maps provide valuable support for pollution source identification and management decision making. The model successfully retrieved five water quality parameters, Chl-a, TSS, COD, TP, and DO, and validation metrics such as R², RMSE, MAE, MSE, MAPE, bias, and slope were utilized. Key metrics for the ESA-MDN test set were as follows: Chl-a (R² = 0.98, RMSE = 0.31), TSS (R² = 0.93, RMSE = 0.27), COD (R² = 0.93, RMSE = 0.39), TP (R² = 0.99, RMSE = 0.02), and DO (R² = 0.88, RMSE = 0.1). The results indicated that ESA-MDN can effectively extract water quality parameters from multispectral remote sensing data, with the generated spatiotemporal water quality distribution maps providing crucial support for pollution source identification and emergency response decision making. Full article