Search Results (4,002)

Aiming at the technical pain points of traditional manhole covers with low intelligence high cost and excessive power consumption, this study designs a TENG-based alarm device to enhance the safety and maintenance efficiency of urban infrastructure. The device integrates a water immersion sensor and a displacement sensor enabling real-time status monitoring through a unique TENG mechanism. The solid–liquid mode water immersion sensor detects seepage through the triboelectrification effect. Water droplets contact electrodes on the surface of FEP film and generate electric energy to trigger the detection circuit. The displacement sensor adopts the independent layer mode of TENG and combines with a mechanical tumbler mechanism to realize displacement detection. External force-induced manhole cover displacement drives internal balls to roll and rub against electrodes. Electric energy is then generated to activate the detection circuit. On the basis of the two sensors, an efficient and reliable intelligent alarm system is constructed. The system receives and analyzes displacement and water immersion-sensing signals in real time. It rapidly identifies potential safety hazards including displacement offset water accumulation and leakage. Signal analysis and early warning prompts are completed synchronously. This system provides accurate and real-time data support for public facility monitoring, pipe network operation and maintenance, and regional security in smart cities. It helps achieve early detection and early disposal of hidden dangers and improves the intelligent and refined level of smart city monitoring. Full article

Ankle–foot orthoses (AFOs) are widely used in the rehabilitation of patients with neurological or musculoskeletal disorders. However, treatment outcomes may be influenced by incorrect use of the device or by inappropriate orthosis selection. Since many types of AFOs are available, differing in materials, stiffness, and geometry, an objective evaluation tool can support clinical decision-making. This work presents the design, development, and characterization of an instrumented AFO able to quantify relevant gait parameters in an objective way. The proposed device integrates three measurement modalities in a compact wearable structure. Two longitudinal strain gauges estimate ankle plantar- and dorsiflexion angles. Two force-sensitive elements detect foot–ground contact and allow identification of stance and swing phases of the gait cycle. A single inertial measurement unit (IMU) is used to measure lateral shank inclination. The strain-gauge-based angle estimation was validated against a gold-standard motion capture system, achieving a root mean square error of approximately 1.6 degrees and showing higher accuracy than the IMU for plantar/dorsiflexion measurement, while maintaining a simple electronic architecture. The force sensors were validated using a force platform and demonstrated reliable detection of loading and unloading events. Monitoring lateral inclination through the single IMU provides additional information related to balance and potential fall risk. Data are transmitted via Bluetooth Low Energy (BLE) to a custom Python-based application for real-time visualization and recording. Overall, the results validate the electronic instrumentation and demonstrate reliable system performance, indicating that the proposed instrumented AFO represents a promising platform for objective gait assessment and future clinical applications. Full article

Extracting causal relations from complex dynamic systems has become an appealing topic for decades, especially for machine design engineering, industrial manufacturing, and equipment maintenance, which usually suffer from a large number of tangled relationships. Although many causality detection methods have been utilized, evaluating and choosing appropriate methods, and developing proper workflow remain challenges. In this paper, a causal analysis workflow designed to detect hidden patterns involved with mechanical mechanisms is presented. In particular, various causality measures are ensembled, enabling the search for refined causal mechanisms, the impact of constitutive law, and spatial distribution of causality from the entangled raw network. Based on numerical experiments, several beneficial conclusions can be drawn: Separating typical stages is necessary for a complex process; The constitutive property has a great impact on causal inference; The discrepancy of causality among different locations of monitor points mainly depends on whether it is near the fixed boundary, near to the load, or in contact with friction; Granger Causality is suitable for discovering linear dependencies among material, load, and geometry, while constraint-based and score-based algorithms excel in identifying nonlinear causality in metal plasticity, severe discontinuity in contact, impulsive dynamic load, or damping phenomenon. Full article

The responsive light intensity dynamic range (DR) of the human eye far exceeds that of existing visual systems, and the development of a biomimetic retinal detecting unit is currently an important challenge in the field of machine vision. Here, a two-terminal Au-contacted VO₂/WSe₂ heterojunction photodetector with the same adaptive DR as retinal cells is developed. It is revealed that the VO₂/WSe₂ heterojunction part-mimics the cone cell for strong light detection with photoresponsivity (R) of 320 mA W⁻¹ and the Au/WSe₂ Schottky contact part-mimics the rod cell for weak light detection with an R of 217 A W⁻¹ and noise equivalent power (NEP) as low as 248.2 fW/$\surd \mathrm{H}\mathrm{z}$. The dual-mode photodetector shows a fast response speed of less than 39.28 μs. Image fusion by the cone mode and rod mode shows enhanced recognition. These results demonstrate that contact engineering enables a photodetector with the functionality of both rod and cone cells, and the resulting visual imaging system can achieve performance comparable to that of the human eye in certain operating conditions. Full article

Vacuum degree inside vacuum interrupter (VI) deteriorates due to cracks from long-term operation of VI, gas emitted from internal arc heat, leakage through the joint, etc. Partial discharge occurs between the two contacts inside the VI or between the contact and floating shield, which leads to dielectric breakdown and electrical accidents of high voltage apparatus. In this paper, the study on the vacuum degree monitoring of distribution class vacuum interrupter according to non-contact method of coupling capacitor based on partial discharge was performed. In order to monitor the partial discharge between two contacts inside VI with high accuracy, a partial discharge sensing electrode (PDDE) was designed using the 3D finite element method (FEM). In addition, after calculating the internal capacitance according to the structure and size characteristics inside VI, the capacity of the coupling capacitor to detect the signal was calculated. The partial discharge characteristics according to the vacuum degree were analyzed by applying PDDE and a coupling capacitor. As results, it was found that the partial discharge characteristics inside VI differ depending on the voltage type. In addition, it was confirmed that even if VI has the same internal structure and size, the partial discharge characteristics appear differently. Based on the experimental results, we proposed maintenance criteria for VI for each voltage type. Full article

Tree contact discharge is a key contributing factor to wildfires caused by medium-voltage insulated conductors. Prolonged abrasion of the insulation layer by branches gradually creates weak points in the insulation. When subjected to lightning strikes, these areas are prone to forming lightning-induced pinholes, which can subsequently trigger partial discharge and even ignition. This study systematically investigates the discharge-induced ignition mechanism for 10 kV overhead insulated conductors in tree contact scenarios by establishing an experimental platform integrated with high-speed imaging, ultraviolet detection, and simulation methods. Three types of typical defects were set up in the experiments: complete insulation abrasion, lightning puncture holes accompanied by localized abrasion, and lightning puncture holes without abrasion. The development process and characteristics of different discharge forms were observed and analyzed. The results indicate that the tree contact discharge ignition mechanism can be categorized into two types: thermal accumulation and direct arcing. The former occurs when insulation abrasion or composite defects exist, where sustained partial discharge or a high-resistance current leads to gradual heat accumulation, resulting in an ignition delay lasting tens of seconds. The latter occurs when only small defects such as lightning puncture holes exist in the insulation layer. A concentrated arc forms due to gap breakdown under high voltage, leading to a millisecond-level ignition process. The study found that different discharge forms produce significantly distinct ablation and carbonization patterns on both the insulation layer and the branch surface, reflecting differences in energy transfer pathways. Simulation analysis further indicated that the thickness of the insulation layer affects the electric field distribution in the tree contact gap, with the initial discharge field strength decreasing as the thickness increases. This study provides experimental evidence and classification guidance for tree contact fault monitoring, insulation condition assessment, and wildfire prevention and control in medium-voltage distribution networks. Full article

Background/Objectives: Bloodstain pattern analysis (BPA) is widely used in crime scene investigation (CSI), yet its practical application, evidential limits, and interpretive role are often discussed in fragmented or technique-focused terms. This systematic literature review examines how BPA is used in CSI, with emphasis on its operational functions, interpretive scope, and scientific robustness. Methods: The review followed PRISMA 2020 guidelines. A comprehensive search was conducted in Scopus using predefined Boolean strings. After screening, eligibility assessment, and manual review, 18 peer-reviewed research articles published between 1996 and 2026 were included. Data were extracted systematically and analysed using thematic synthesis. Results: The findings show that BPA is applied in CSI as an integrated evidential pathway rather than as a single analytical procedure. Its uses include bloodstain detection and documentation, geometric reconstruction through trajectory and area-of-origin analysis, differentiation of mechanisms and sources to prevent misclassification, activity-level inference based on transfer and contact phenomena, and temporal reasoning related to trace formation. The review also highlights the role of validation infrastructures, including blood substitutes, animal analogues, and computational methods, which support training, experimentation, and reproducibility under ethical and practical constraints. Across the literature, reconstruction accuracy is shown to be sensitive to documentation quality, measurement assumptions, environmental conditions, and contextual limitations. Conclusions: Overall, BPA contributes to CSI by enabling structured, context-aware interpretation of blood evidence while remaining subject to measurement assumptions, contextual influences, and cognitive factors that may affect reconstruction outcomes. Its evidential value lies not only in reconstructing events, but also in supporting transparent, testable, and defensible forensic reasoning. Full article

Wind turbine blades are subjected to complex environmental conditions during long-term operation, which may lead to structural degradation and performance loss. To ensure structural integrity, fatigue testing prior to deployment is essential. This paper proposes a vision-based method for measuring the full-cycle breathing deformation of wind turbine blades during fatigue testing. The method captures dynamic image sequences of the blade’s hotspot cross-section using industrial cameras and employs a feature-based template matching approach to reconstruct the three-dimensional coordinates of target points. Through coordinate transformation, the deformation trajectories are obtained, enabling quantitative analysis of the blade’s dynamic responses in both flapwise and edgewise directions. A dedicated hardware–software system was developed and validated through full-scale fatigue experiments. Quantitative comparison with strain gage measurements shows that the proposed method achieves mean absolute deviations of 0.84 mm and 0.93 mm in two independent experiments, respectively, with closely matched deformation trends under typical loading conditions. These results demonstrate that the proposed method can reliably capture the global deformation behavior of the blade with millimeter-level accuracy, while significantly reducing instrumentation complexity compared to conventional contact-based approaches. The proposed method provides an effective and practical solution for full-field dynamic deformation measurement in blade fatigue testing, offering strong potential for structural health monitoring and early damage detection in wind turbine systems. Full article

Slaughterhouse detection of lesions compatible with bovine tuberculosis represents a key passive surveillance component in Santa Catarina, Brazil, yet subsequent trace-back investigations often fail to identify infected farms. This study developed a quantitative framework to prioritize epidemiological investigations by estimating the probability of infection associated with each farm connected to PCR-confirmed cases. Using official movement records and historical diagnostic data, we reconstructed the lifetime contact networks of slaughtered cattle presenting confirmed Mycobacterium bovis lesions (n = 502). For each sentinel animal–farm interaction (n = 1452), infection probability was estimated through a non-homogeneous Poisson process incorporating exposure duration and the time-weighted average herd size as determinants of infectious pressure. After evaluating stochastic variability through Monte Carlo simulation, a deterministic model using the mean infectious-pressure parameter was applied to classify farms into high-, medium-, and low-risk categories. Model performance was assessed using validated field diagnostic outcomes within a three-year temporal window. High-risk farms represented most validated contacts (58%) and demonstrated a relative risk of 3.48 compared with lower-risk category. These findings indicate that a standardized risk-based classification can substantially improve the prioritization of trace-back investigations, offering a practical decision-support tool to enhance bovine tuberculosis surveillance and contribute to eradication strategies in Santa Catarina. Full article

Silicone food contact materials (FCMs) pose potential health risks due to the migration of siloxanes. This study presents a comprehensive migration profiling of 35 siloxanes (cyclic D3–D22 and linear L2–L14) from 30 silicone FCMs, with migration tests rigorously conducted under worst-case intended-use scenarios to ensure conservative and reliable exposure estimates. Methodological innovations include an expanded analytical scope, age-stratified exposure assessment across seven age groups, and a multi-tiered risk evaluation framework. The results reveal that migration behaviors were affected by simulant polarity, siloxane solubility, and silicone thermal stability. The risk evaluation framework integrates aggregate migration limits for total cyclic (D3–D13) and total cyclic plus linear siloxanes (D3–D13, L3–L13), complemented by individual siloxane assessment via Risk Quotient (RQ) and Threshold of Toxicological Concern (TTC) approaches. While the total migration of cyclic siloxanes exceeded the proposed action limit of 12 mg/kg for adults in several samples and 2 mg/kg for children in most samples, granular assessment revealed divergent risks: Cyclic D4 and D5 showed negligible risk (RQ < 5). In contrast, D3 migration posed a potential concern (RQ > 5), especially for individuals aged >13 years. Notably, the estimated exposures to 14 siloxanes with low molecular weight (<1000 Da), including highly prevalent D6 and L12 with detection frequency >90%, exceeded the TTC threshold across all age groups, highlighting unaddressed risks that are not captured by aggregate action limits. This work underscores the need for substance-specific, age-specific risk evaluations and regulatory updates for silicone FCMs. Full article

This paper presents a non-contact depth detection method for corrugated heat exchanger plates, aiming to improve measurement efficiency and accuracy. The system integrates a line laser sensor with a precision linear guide rail, enabling continuous acquisition of high-resolution 2D surface profiles as the sensor moves along the plate. To reduce data redundancy while preserving geometric features, a multi-stage data reduction strategy is proposed. This strategy combines the angle–chord height criterion with spline-based filtering to identify key regions of curvature and eliminate unnecessary point cloud data. For depth extraction, a two-stage feature recognition algorithm is designed. First, a coarse analysis locates candidate peaks and valleys by identifying local extrema in the reduced 2D data. Then, a fine detection process is applied: local B-spline fitting is performed near each candidate point, and a binary search algorithm is used to accurately determine the spline extrema. By computing the vertical distance between precisely located peaks and valleys, the system rapidly extracts the corrugation depth parameters. This method achieves a high balance between speed and precision, offering a practical and reliable solution for automated surface morphology inspection in heat exchanger manufacturing. Full article

Infrared thermography is a non-contact tool for monitoring inflammatory processes in the diabetic foot, but quantitative bedside use remains challenging with low-cost thermal infrared cameras due to radiometric drift, non-uniformity (vignetting), geometric distortions, and visible–thermal parallax. This paper presents an end-to-end clinical and instrumental framework built around a cheap thermal camera to ensure reproducible acquisition and physically consistent temperature estimation. The approach combines a standardized mobile acquisition setup and measurement protocol, extraction of embedded radiometric data from raw images, radiometric inversion with atmospheric correction, vignette correction performed in the radiometric domain, and geometric calibration of both visible and infrared sensors using dedicated (thermal) calibration targets. Accurate visible–infrared registration is obtained from hybrid heated markers, enabling reliable overlay and downstream analysis. The full processing chain yields quantitative thermograms with radiometric errors below 0.15 °C and sub-pixel multimodal alignment, supporting the detection of clinically relevant plantar temperature asymmetries and paving the way for routine calibrated low-cost thermography in diabetic foot care. Full article

High-voltage bushings, the fault-prone key electrical components of transformers, are critical for real-time and high-accuracy fault monitoring and management. Intelligent fault detection via infrared images is plagued by low classification accuracy due to massive interference from similar tubular objects and small target characteristics. This study proposes a lightweight deep learning model, MTrip–YOLO, an improved YOLO11n integrated with Mamba-2 (Structured State Space Duality, SSD) and Triplet Attention, to achieve efficient fault monitoring in complex backgrounds. The training and validation dataset comprises open-source images, on-site data from a substation, and field-collected infrared images, categorized into four types: normal bushings, poor contact, oil shortage, and high dielectric loss faults. Mamba-2 captures the long-range global context of infrared features with its linear-complexity long-range modeling capability to enhance feature extraction, while Triplet Attention suppresses complex background radiation noise through cross-dimensional interaction without dimensionality reduction, enabling the model to focus on small targets and accurately classify bushings from morphologically similar strip-shaped objects. Experimental results show that MTrip–YOLO achieves a top mAP50 of 91.6% and a minimal parameter count of 1.9 M, outperforming Faster R-CNN, RT-DETR, and YOLO26n across all evaluated metrics and being potentially suitable for edge deployment on UAV-mounted or handheld infrared platforms, pending hardware validation on embedded computing devices. Ablation experiments verify the independent contributions of Mamba-2 (0.8027% mAP50 improvement) and Triplet Attention (0.89327% mAP50 improvement), with a synergistic effect from their combination. MTrip–YOLO provides a potential edge-deployable solution for high-voltage bushing fault monitoring, offering important application value for the intelligent operation and maintenance of substations. Full article

The detection of ammonia (NH₃) at room temperature is of significant importance for environmental monitoring, industrial safety and early disease diagnosis. In this work, a novel room-temperature ammonia sensor was developed by combining graphene oxide with WO₃ quantum dots. The as-fabricated sensor exhibited excellent comprehensive sensing performance, including high sensitivity, rapid response, outstanding selectivity, and reliable long-term stability. Specifically, when exposed to 10 ppm NH₃, the sensor based on 1.5% GO@WO₃ nanocomposites achieved a frequency shift of 578 Hz, which was 6.4 times that of the pure WO₃ QDs sensor. The theoretical limit of detection (LOD) of the sensor was calculated to be 60 ppb, enabling ppb-level NH₃ detection. In addition, the sensor demonstrated good long-term stability over a two-week period. The enhanced performance of the GO@WO₃ nanocomposite sensor is attributed to the formation of an ohmic contact between GO and WO₃, which eliminates charge transfer barriers, promotes oxygen adsorption, and amplifies the sensing signal. This work provides a simple, efficient, and practical solution for room-temperature NH₃ detection, offering significant advantages over traditional single-component sensors. Full article

Impact artificial seismic sources are gaining popularity in reflection seismic exploration. However, challenges arise due to the uncertain delay between the hammer’s acceleration and its impact on the interface, as well as the strong vibrations or pulsed magnetic fields produced during the acceleration process. These factors complicate the synchronous triggering methods typically used in traditional explosive and sledgehammer artificial seismic sources, often resulting in temporal misalignment of the acquired data. To tackle this issue, this study introduces a high-precision synchronous triggering method based on non-contact audio detection. Utilizing an STM32F4 microcontroller as the core hardware, the system collects ambient audio and extracts 39-dimensional acoustic features via Mel-frequency cepstrum coefficients (MFCC). A lightweight convolutional neural network (CNN) model is employed to accurately identify hammer impact events. Additionally, a synchronization time compensation mechanism is implemented to address system processing delays. Results from 300 field tests conducted in three environments—open ground, construction site, and mining tunnel—demonstrate that the system achieves a triggering accuracy of up to 94.6%, with compensated triggering time errors controlled within ±125 μs, thereby meeting the minimum tolerable synchronous triggering error requirement. This study significantly enhances the reliability of impact-type Artificial Seismic Source exploration data and offers insights for the application of sound recognition in engineering surveying and other related fields. Full article