Search Results (1,331)

School buses serve as the primary mode of transportation for children traveling to and from school, and their safety measures represent a critical safeguard for children’s lives. Nevertheless, incidents in which children are left unattended on school buses—due to inadequate supervision or the children’s own actions—occur with notable frequency and can lead to fatal outcomes. To mitigate or prevent such tragedies, this paper proposes an in-vehicle thermal imaging solution based on infrared array sensors, integrated with a dedicated algorithm to detect whether a child has been left behind in the school bus. The system collects background temperature, presence temperature, and real-time temperature data inside the bus using infrared array sensors. By comparing the real-time temperature difference against a predefined presence temperature difference threshold, the algorithm determines whether a child is present under the current thermal conditions. It then verifies whether the number of positive detections within a specified temperature range meets a preset presence count threshold, thereby reaching a final decision regarding child presence. Experiments identified optimal parameters: a temperature range of 26–33 °C, a double-difference threshold (ε = 1), and a presence count threshold (P = 4). Random testing demonstrated that the proposed technical solution and algorithm achieve an overall detection success rate of 92.5%. This study develops a low-cost, easily deployable, non-contact thermal imaging method capable of identifying forgotten children on school buses with satisfactory accuracy. By detecting retention before harm occurs, the approach enhances the safety of children traveling by school bus. Full article

Introduction: River fragmentation caused by hydropower infrastructure remains a primary threat to aquatic biodiversity, creating a critical need for fish passage solutions that can adapt to high environmental variability. Although adaptive management (AM) has the potential to significantly improve longitudinal connectivity and ecological resilience, its application in real-world fishway operations is currently limited. Objective: This study aims to present and validate a flexible AM framework designed to optimize fish passage by integrating low-cost monitoring systems with automated data processing and predictive modeling. Methodology: The proposed system combines a sensor network for real-time water-level and environmental monitoring with biological performance data obtained through Passive Integrated Transponder (PIT) technology. These data were processed locally using edge computing. Over a two-year period, weekly aggregated data were used to develop Random Forest models to identify the primary drivers of fish movement. Results: The final model successfully identified five key drivers: luminosity, water temperature, and three nested hydraulic parameters at the fishway’s upstream section. Validation at a vertical-slot fishway in Vadocondes (Duero River, Spain) showed that retrospective optimization—specifically adjusting sluice-gate regulation—could increase downstream water levels and reduce drops at the first cross wall. This adjustment demonstrated a substantial increase in predicted fish passage without requiring changes to the hydropower plant’s core operation. Conclusions: The framework is highly flexible and transferable to other regulated river systems. However, its success is contingent upon the definition of clear ecological objectives and the seamless integration of monitoring results into the day-to-day operation of river infrastructure. Full article

While double-layer insulation structures are widely adopted, their thermal performance is critically dependent on the thermophysical behavior of the interstitial air cavity, a variable often oversimplified in current design practices. This article moves beyond generic material descriptions to investigate the specific mechanism of heat transfer transition within sealed air gaps sandwiched between graphite polystyrene boards. The innovation of this experiment lies in the rigorous isolation of air gap thickness as the primary independent variable within a 1 × 1 × 1 m closed building model, instrumented with high-precision GPRS temperature and humidity sensors to capture real-time thermal gradients under the authentic climate conditions of Anyang, Henan. The results demonstrate a non-monotonic relationship between gap thickness and effective thermal resistance, governed by the competition between molecular conduction and buoyancy-driven natural convection. Specifically, the data validates that a 20 mm air gap represents the statistically significant optimum, thereby maximizing insulation efficiency while minimizing radiative heat loss. Using this optimized structure reduces steady-state heat flux compared to monolithic equivalents and aligns with the energy conservation target. Unlike previous studies limited by simulation assumptions or short-term testing, this research provides empirically verified, long-term field data that bridges the gap between theoretical fluid dynamics and practical building envelope engineering. These findings offer a robust, physics-based reference for optimizing double-layer insulation systems in the Central Plains, directly supporting the low-carbon retrofitting of existing building stocks. Full article

Hydrogen is a promising clean-energy carrier, but its low ignition energy, high diffusivity, and wide flammability range demand reliable leak detection. Chemiresistive sensors based on n-type metal oxide semiconductors are attractive owing to their simple architecture, low cost, large resistance modulation, thermal robustness, and compatibility with miniaturized devices. This review focuses on n-type metal oxide semiconductor nanomaterials for hydrogen sensing, particularly ZnO, SnO₂, In₂O₃, WO₃, TiO₂, and related mixed oxides. The fundamental sensing mechanisms are examined, including oxygen chemisorption, electron-depletion-layer modulation, grain-boundary barrier control, catalytic hydrogen spillover, and hydrogen-induced surface reduction or metallization, together with the way these mechanisms compete and cooperate under different operating conditions. Recent performance-enhancement strategies are organized around morphology and porosity control, noble-metal sensitization, defect and dopant engineering, n–n heterojunctions, molecular sieving, and low-temperature activation. Density functional theory is discussed as a design tool for evaluating adsorption energetics, vacancy formation, work-function shifts, band alignment, and interfacial charge transfer, along with its current limitations for modeling humid surfaces. Finally, key challenges and future directions, including humidity tolerance, standardized reporting, device integration, and emerging materials, are summarized to guide the development of high-performance hydrogen sensors. Full article

Temperature monitoring is critical for lithium-ion battery (LIB) safety and performance, yet instrumenting every cell in a commercial pack remains impractical due to cost and wiring constraints. Existing sensorless methods rely on either physics-based thermal models requiring extensive parameterization or nonlinear recurrent estimators that cannot integrate sensor feedback when measurements become available. Motivated by these limitations, this paper proposes a Deep Koopman observer that enforces linear latent dynamics, enabling direct compatibility with Kalman filtering. The observer estimates surface temperature from four standard BMS signals and two exponential moving averages of squared current that capture thermal memory at distinct time scales, operating in two modes: fully sensorless for uninstrumented cells, or sensor-fused via a one-state EKF when a thermistor is available. Evaluated under strict cell-to-cell split across twelve drive cycles and five ambient temperatures, the open-loop observer achieves 17% lower error than the strongest reproduced CNN-LSTM baseline without online resistance identification or thermal-model simulation, and the EKF path delivers a further 35% reduction over the open-loop estimate. The evaluation is limited to a single cell chemistry and manufacturing batch; cross-chemistry and aging validation remain for future work. Full article

The adoption of lithium-ion batteries (LIBs) as secondary rechargeable batteries across many industries, including consumer electronics, electromobility, industrial tools, and electrical energy storage, is on the rise. As lithium-ion batteries approach the end of their life, there is a need to assess them for the possibility of a secondary application or reuse for a less demanding application. The extra connections of individual cells, BMS, temperature sensors, and other components to form a compact battery pack pose a challenge for second-life assessment, which usually prefers to separate individual cells for testing before discarding very bad cells for recycling and grading cells with substantive capacity based on their remaining capacity. This is a high cost for the second-life assessment. This work seeks to investigate an approach that avoids dismantling the battery pack into individual modules, cells, and BMS by including a BMS feature that allows the capacity and power ratings to be rescaled onboard after its first use. A set of cells with different chemistries was used in this work: a nickel–cobalt–aluminium oxide cathode with a silicon-doped graphite anode (NCA-GS), a nickel–cobalt–aluminium oxide cathode and graphite, and a lithium–nickel–manganese–cobalt oxide (NMC) cathode with a graphite anode (NMC-G) with various ageing states and behaviours. Their internal resistance and capacity at the beginning and end of life were compared. The scaling factor was obtained by finding the square root of the ratio of the internal resistance at EOL to that at BOL. With the current obtained by multiplying the cycling current rate by the rescaling factor, the surface temperature profile of the aged cells during cycling became the same as the temperature at the beginning of life. The relaxation voltage after discharge to 0% SOC and charge to 100% SOC was used to set the low and high cut-off voltages, respectively. This contributed significantly to reduced ageing and to a lower temperature rise in the spent cells. This set the stage for rescaling or derating battery systems without separating the individual cells, which is a huge cost for second-life use of lithium-ion batteries. BMS can be designed with configurable voltage and current limits, so that when repurposed for a second life, only a simple configuration or firmware update may be necessary. Full article

Extracting hydrocarbons from complex, ultra-deepwater and high-pressure/high-temperature wells requires precise control of hydrostatic pressure to avoid well control problems. Among these, a gas kick is one of the most serious events, as it can quickly develop into a blowout with severe consequences for both safety and project cost. Traditionally, the industry has depended on reactive surface-based indicators, such as pit volume and delta flow, for early kick detection (EKD). However, these methods are often limited by data transmission delays and frequent false alarms. This review goes beyond a conventional summary by critically examining the key weaknesses of current EKD technologies. In particular, it highlights major challenges in modern sensor systems, including the difficulty of interpreting ultrasonic signals in multiphase flow and the way formation leakage can hide or distort kick indicators. It also provides a detailed and original link between specific Artificial Intelligence (AI) models and the drilling signals they are designed to analyze. Although recent studies have shown progress in downhole sensing and predictive algorithms, a significant gap still exists between theoretical models and the highly dynamic, multiphase conditions found in real wellbores. This makes it necessary to evaluate EKD technologies considering actual field demands rather than idealized assumptions. To address these limitations, this review proposes several practical directions for future work. These include the development of dynamic, multiphase, acoustic computational fluid dynamics (CFD) models to improve ultrasonic signal interpretation, the standardization of unsupervised AI models supported by synthetic data generation, the integration of unified leakage detection frameworks, the mechanical standardization of Managed Pressure Drilling (MPD) systems, and the adoption of rig-based edge computing to enable faster and more reliable real-time decision-making. Full article

The UHV dry-type bushing plays a critical role in power transmission by enabling electrical connection, electrical insulation, and mechanical support, making it a core component for ensuring the safe and stable operation of UHV direct current (DC) transmission projects. Epoxy resin, serving as the fundamental insulating material for the bushing core, undergoes significant residual strain during high-temperature curing due to chemical shrinkage and thermal strain, which directly affects the molding quality and service reliability of the component. This paper investigates the curing process of a large-thickness epoxy material, which is on the same scale as a UHV bushing. An in situ monitoring system combining fiber Bragg grating (FBG) sensors and thermocouples, together with COMSOL Multiphysics simulations, is employed to systematically study the evolution of the temperature field and residual strain throughout the entire curing process, considering the demolding effect. The results show that during the curing stage, the internal temperature distribution is non-uniform, with a maximum temperature difference of 65 °C between the center and the edge. The residual strain is dominated by chemical shrinkage (accounting for 73.25%) and exhibits a pronounced radial gradient. Mold constraint and demolding cause abrupt changes in the strain. The developed thermo-chemo-mechanical coupled model shows good agreement between simulations and experimental measurements. Thermal cycling relaxes the residual stress, achieving a reduction of 3.89–5.77%. This study provides support for process optimization and defect prevention in large-scale epoxy insulation components. Full article

Reliable fault detection and diagnosis (FDD) plays a key role in inverter-driven permanent magnet synchronous motor (PMSM) systems, especially in applications where operational continuity cannot be compromised. In this work, a hybrid deep learning framework is developed by combining one-dimensional convolutional neural networks (CNN), bidirectional long short-term memory networks (BiLSTM), and a multi-head self-attention mechanism. The model targets multi-class fault classification in a three-phase PMSM inverter system. Its effectiveness is evaluated on a publicly available experimental dataset consisting of 10,892 multi-sensor samples collected under nine operating conditions, including normal operation, open-circuit faults, short-circuit faults, and half-bridge overheating scenarios. To avoid temporal data leakage, a block-aware chronological splitting strategy is applied. Model hyperparameters are determined through a validation process involving 24 different configurations. The proposed CNN–BiLSTM–Attention model achieves a macro F1-score of 0.9681 ± 0.0195, accuracy of 0.9810 ± 0.0102, Matthews correlation coefficient (MCC) of 0.9757 ± 0.0130, and ROC-AUC of 0.9996 ± 0.0003 over five independent runs, achieving the highest accuracy and MCC among all evaluated models; although the Random Forest baseline attains a marginally higher macro F1 score (0.9747) by operating on temporally aggregated features without temporal modelling, the proposed model provides superior discrimination across the full confusion matrix structure alongside end-to-end temporal interpretability via SHAP. Model interpretability is provided through SHAP (SHapley Additive exPlanations) GradientExplainer analysis, revealing that temperature-related features dominate fault discrimination, particularly for over-heating conditions, while current imbalance features are critical for distinguishing open- and short-circuit faults. Full article

The integration of computational fluid dynamics (CFD) with deep learning in tunnel fire research is currently constrained by excessive reliance on manual operations and low overall efficiency. To address these limitations, this study presents a multi-agent collaborative framework driven by large language models, which enables full automation of the fire source characteristic inversion process. This framework reorganizes the conventional research pipeline into four dedicated, specialized agents: physical modeling, data governance, model training, and evaluation analysis. As a typical automated verification task, five deep learning models are systematically benchmarked under 45 experimental configurations to implement multi-task continuous regression inversion, which fully demonstrates the framework’s capability of automated, reproducible and large-scale comparative experiments. The experimental results demonstrated that the CNN-LSTM model outperforms other models in extracting spatiotemporal correlation features from temperature time-series data, enabling high-precision prediction of multiple fire parameters. With a 6 s observation window and 10 m sensor spacing, the average R² attains 0.942, an improvement of 2% over the baseline LSTM model, and the RMSE decreases by 28.8%. For sparse sensor deployment at 30 m spacing, the average R² remains at 0.917, confirming the effectiveness of integrating spatial feature extraction with temporal modeling. This study provides an efficient technical pathway for intelligent tunnel fire identification and advances the research paradigm by shifting the traditional manual optimization process to a multi-agent system-based optimization workflow. Full article

Tool wear in CNC milling increases friction and torque demand at the tool-workpiece interface, which is reflected in spindle motor current. This study develops a non-intrusive tool wear condition classification method using spindle motor current monitoring during practical CNC milling of commercial medium-carbon steel workpieces (JIS S50C/AISI SAE 1050-equivalent; as-received and non-heat-treated; nominal laboratory hardness approximately 4.3 HRC). Experiments were performed on a Tongtai MDV-508 vertical machining center at fixed cutting conditions (3000 rpm spindle speed, 2 mm axial depth of cut, 5 mm cutting width, and 300 mm/min feed rate) using eight TiAlN-coated fine-grain WC–Co solid carbide end mills (10 mm diameter, four flutes; nominal Co binder approximately 10 wt%). An oil-based HS Highstart/HS-SSHS-BH10 cutting fluid was applied through the machine external coolant nozzle in flood mode at an estimated nominal flow rate of approximately 3 L/min and near-room coolant temperature (25 ± 2 °C), and was used as supplied without dilution. A clamp-type AC current sensor was installed on one phase line supplying the spindle motor, and current was acquired using an NI-9221 module at 20 kHz. Cutting intervals were isolated by envelope-based segmentation, concatenated, and divided into 1 s windows (0.5 s overlap) for feature extraction. Three feature sets were evaluated: time-domain statistics, frequency-domain statistics, and an FFT→PCA hybrid representation. Tool states (New, Mid-life, Old) were labeled using post-process surface roughness Ra thresholds supported by microscope observation. The PCA transformation was fitted only on training data and then applied to the held-out test data. A logistic regression classifier achieved 97.44% test accuracy (152/156 windows; 95% Wilson CI: 93.59–99.00%) with the PCA-hybrid features, outperforming time-domain (89.74%) and frequency-domain (94.87%) models. The results support spindle current monitoring as a low-cost approach for quality-aligned tool condition monitoring, while the external validity remains limited to the tested machine, material, tool, coolant, and cutting-parameter combination. Full article

Agriculture currently faces multiple challenges associated with climate change, the reduction in arable land, and the need to produce food more efficiently in terms of water and nutrient use. This study evaluated an Internet of Things (IoT)-based hydroponic monitoring system with threshold-controlled recirculation for lettuce (Lactuca sativa) under open-field thermal stress conditions, comparing it with a conventional closed recirculating PVC pipe-based hydroponic system operated using fixed pump timing. The architecture integrated an ESP32 microcontroller, sensors for nutrient solution temperature, pH, total dissolved solids (TDS), turbidity voltage, dissolved oxygen (DO), and electrical conductivity (EC), Wi-Fi/HTTPS connectivity, a PHP–MySQL server, and a web interface for near-real-time monitoring. During the growing period, 241,797 readings were recorded between 21 January and 13 February 2026. The threshold-based logic activated the pump mainly according to nutrient solution temperature and DO, while pH, EC, TDS, and relative turbidity voltage were monitored as operational indicators. The sensor-instrumented system operated with pump activation during approximately 28.5% of the monitoring period, while temperature exhibited high variability and peaks of 40.19 °C. Visual crop monitoring showed greater canopy uniformity in the sensor-instrumented system, supporting the technical feasibility of low-cost IoT-based monitoring and threshold-controlled recirculation for open-field hydroponic production of lettuce. Full article

Soil moisture content plays a crucial role in precision agriculture and geological hazard monitoring, driving the need for stable, reliable, and high-precision sensors. Capacitive soil moisture sensors based on Frequency Domain Reflectometry (FDR) are widely adopted due to their favorable measurement performance, yet their accuracy is highly susceptible to environmental interferences such as temperature, salinity (electrical conductivity), and soil type. This paper systematically reviews current calibration strategies addressing these three factors, classifying them into hardware-based compensation and software-based calibration (including conventional mathematical and machine learning models). Furthermore, it critically analyzes the trade-offs of these approaches in terms of robustness, scalability, and field applicability. To break through current technical limitations, this review argues that future research must prioritize the physical decoupling of multi-parameter interferences under extreme conditions. Additionally, to overcome the generalization crisis of current data-driven models, adaptive strategies utilizing techniques like transfer learning are essential. Finally, implementing Edge-AI on resource-constrained hardware is crucial for achieving calibration-free or real-time online calibration strategies, ensuring long-term measurement accuracy. Full article

This paper presents a dual-axis sun sensor employing a cross-slit aperture in conjunction with a four-quadrant trapezoidal photodiode layout. The cross-slit configuration enhances angular sensitivity and resolution, while the trapezoidal photodiode geometry preserves a high signal-to-noise ratio at both near-normal incidence and large Sun angles, maintaining reliable directional discriminability around normal incidence. Compared with conventional quad-triangle photodiode layouts, the proposed trapezoidal geometry avoids the rapid collapse of the illuminated area near the triangular apex at large incidence angles, thereby preserving signal margin near the field-of-view boundary. System-level optical verification demonstrates that, after calibration, the proposed sensor achieves an angular accuracy of ±0.3° (3$\sigma$). To mitigate performance variations induced by temperature drift, an embedded shielded dummy photodiode is incorporated to provide a dark-current reference for compensation. Unlike compensation approaches that mainly rely on pre-characterization or offline calibration, the embedded shielded dummy photodiode provides an in situ, real-time dark-current reference for compensating for temperature-induced signal drift in the actual operating environment. Experimental results under dark conditions indicate that the embedded dummy photodiode served as a dark-current reference for compensating the temperature-dependent dark-current variation in the active photodiodes, reducing the peak-to-peak dark-signal variation by 96% over a temperature range from 20 °C to 120 °C. Furthermore, a pyramid-type sun-sensor architecture is proposed by integrating the dual-axis fine sun sensor with four wide-field coarse sun sensors. This system-level configuration extends the effective Sun field of view from the conventional 120°–180° range to approximately 280°, enabling near-hemispherical Sun-angle observability for enhanced attitude determination robustness. Full article

This work presents a comprehensive analysis of a Palladium (Pd)-gated graphene nanoribbon field-effect transistor (GNRFET) as a high-sensitivity potential hydrogen sensor under idealized conditions, focusing on the structural and environmental control of multimetric sensitivity. Hydrogen adsorption is modeled through pressure-dependent work-function modulation and interface coverage, including competition with oxygen. For hydrogen gas at a pressure of $P_{{\mathrm{H}}_2}={10}^{-6}$ Torr without O₂, the sensor exhibits a maximum threshold voltage sensitivity of about 300 mV, which is reduced to roughly 40 mV under an oxygen partial pressure of 152 Torr, quantifying the impact of background gas on response. Band diagrams, transmission spectra, local density of states, and transfer characteristics are examined over wide ranges of H₂ pressure, temperature, gate length, and nanoribbon width. Sensitivity is evaluated using drain current change, threshold voltage shift, and average subthreshold swing variation. Results showed that the sensitivity based on current is high for ultralow hydrogen pressures, whereas it is low in higher levels of pressure compared to the sensitivity based on subthreshold. Also, uncertainty analysis revealed that the threshold voltage metric remains largely geometry-independent and thus tolerant to fabrication variations. Full article