Search Results (958)

To address the issues of small-target missed detection, false alarms from cloud/fog interference, and low computational efficiency in traditional wildfire detection for transmission line corridors, this paper proposes a YOLOv11_MDS detection model by integrating Multi-Scale Convolutional Attention (MSCA) and Distribution-Shifted Convolution (DSConv). The MSCA module is embedded in the backbone and neck to enhance multi-scale dynamic feature extraction of flame and smoke through collaborative depth strip convolution and channel attention. The DSConv with a quantized dynamic shift mechanism is introduced to significantly reduce computational complexity while maintaining detection accuracy. The improved model, as shown in experiments, achieves an mAP@0.5 of 88.21%, which is 2.93 percentage points higher than the original YOLOv11. It also demonstrates a 3.33% increase in recall and a frame rate of 242 FPS, with notable improvements in detecting small targets (pixel occupancy < 1%). Generalization tests demonstrate mAP improvements of 0.4% and 0.7% on benchmark datasets, effectively resolving false/missed detection in complex backgrounds. This study provides an engineering solution for real-time wildfire monitoring in transmission lines with balanced accuracy and efficiency. Full article

Electric submersible pumps (ESPs) are among the most widely used artificial lifting systems, and their operational stability is crucial to the production capacity and lifespan of oil wells. However, during the operation of ESP systems, they often face complex flow issues such as gas lock and insufficient liquid carry. Traditional control strategies relying on liquid level monitoring and electrical parameter alarms exhibit obvious latency, making it difficult to effectively guide the adjustments of key operating parameters such as pump frequency, valve opening, and on/off strategies. To monitor the flow state of ESP systems and optimize it in a timely manner, this paper proposes an innovative profile recognition method based on multiphase flow fitting in the wellbore, aimed at reconstructing the flow state at the pump’s intake. This method identifies flow abnormalities and, in conjunction with flow characteristics, designs targeted operating parameter optimization logic to enhance the stability and efficiency of ESP systems. Research shows that this optimization method can significantly improve the pump’s operational performance, reduce failure rates, and extend equipment lifespan, thus providing an effective solution for optimizing production in electric pump wells. Additionally, this method holds significant importance for enhancing oil well production efficiency and economic benefits, providing a scientific theoretical foundation and practical guidance for future oil and gas exploration and management. Full article

Insects use chemical compounds to communicate with conspecifics and other organisms. The corn leafhopper, Dalbulus maidis (Hemiptera: Cicadellidae) (DeLong & Wolcott), is an important pest in Brazilian maize crops due to its role as a vector of phytopathogens. Despite its economic importance, the chemical communication between sexes in this species remains to be elucidated. This research aimed to unveil whether D. maidis produces chemical compounds that influence the behavior of the opposite sex and may act as sex pheromones. To evaluate the influence of these volatiles, olfactometer bioassays were conducted as dynamic headspace volatile collections from live insects. Results showed that both male and female leafhoppers emit volatile compounds; however, no sex-specific compounds were detected. Females were attracted to male odors and male aeration extracts, suggesting males produce sex-specific volatiles. Interestingly, males avoided odors from non-acclimated females, which may indicate possible alarm pheromone release. Although the compounds were not identified, this is the first study to demonstrate intraspecific chemical communication in D. maidis mediated by volatiles, and the first such record in Membracoidea. These results contribute to understanding the pest’s biology and support the development of monitoring and control strategies in maize crops. Full article

Existing building management systems face critical limitations in real-time data integration, primarily relying on static models that lack dynamic updates from IoT sensors. To address this gap, this study proposes a novel system integrating MQTT over WebSocket with Three.js visualization, enabling real-time sensor-data binding to Building Information Models (BIM). The architecture leverages MQTT’s lightweight publish-subscribe protocol for efficient communication and employs a TCP-based retransmission mechanism to ensure 99.5% data reliability in unstable networks. A dynamic topic-matching algorithm is introduced to automate sensor-BIM associations, reducing manual configuration time by 60%. The system’s frontend, powered by Three.js, achieves browser-based 3D visualization with sub-second updates (280–550 ms latency), while the backend utilizes SpringBoot for scalable service orchestration. Experimental evaluations across diverse environments—including high-rise offices, industrial plants, and residential complexes—demonstrate the system’s robustness: Real-time monitoring: Fire alarms triggered within 2.1 s (22% faster than legacy systems). Network resilience: 98.2% availability under 30% packet loss. User efficiency: 4.6/5 satisfaction score from facility managers. This work advances intelligent building management by bridging IoT data with interactive 3D models, offering a scalable solution for emergency response, energy optimization, and predictive maintenance in smart cities. Full article

Unrecognized mechanical faults and emergency sounds in vehicles can compromise safety, particularly for individuals with hearing impairments and in sound-insulated or autonomous driving environments. As intelligent transportation systems (ITSs) evolve, there is a growing need for inclusive, non-intrusive, and real-time diagnostic solutions that enhance situational awareness and accessibility. This study introduces an interpretable, sound-based machine learning framework to detect vehicle faults and emergency sound events using acoustic signals as a scalable diagnostic source. Three purpose-built datasets were developed: one for vehicular fault detection, another for emergency and environmental sounds, and a third integrating both to reflect real-world ITS acoustic scenarios. Audio data were preprocessed through normalization, resampling, and segmentation and transformed into numerical vectors using Mel-Frequency Cepstral Coefficients (MFCCs), Mel spectrograms, and Chroma features. To ensure performance and interpretability, feature selection was conducted using SHAP (explainability), Boruta (relevance), and ANOVA (statistical significance). A two-phase experimental workflow was implemented: Phase 1 evaluated 15 classical models, identifying ensemble classifiers and multi-layer perceptrons (MLPs) as top performers; Phase 2 applied advanced feature selection to refine model accuracy and transparency. Ensemble models such as Extra Trees, LightGBM, and XGBoost achieved over 91% accuracy and AUC scores exceeding 0.99. SHAP provided model transparency without performance loss, while ANOVA achieved high accuracy with fewer features. The proposed framework enhances accessibility by translating auditory alarms into visual/haptic alerts for hearing-impaired drivers and can be integrated into smart city ITS platforms via roadside monitoring systems. Full article

Sudden Infant Death Syndrome (SIDS) is one of the leading causes of postnatal mortality, with the prone sleeping position identified as a critical risk factor. This article presents the design, implementation, and validation of a low-cost embedded system for unobtrusive, real-time monitoring of infant posture. The system acquires data from a pressure mat on which the infant rests, converting the pressure matrix into an image representing the postural imprint. A Convolutional Neural Network (CNN) has been trained to classify these images and distinguish between prone and supine positions with high accuracy. The trained model was optimized and deployed in a data acquisition and processing system (DAQ) based on the Raspberry Pi platform, enabling local and autonomous inference. To prevent false positives, the system activates a visual and audible alarm upon detection of a sustained risk position, alongside remote notifications via the MQTT protocol. The results demonstrate that the prototype is capable of reliably and continuously identifying the infant’s posture when used by people who are not technology experts. We conclude that it is feasible to develop an autonomous, accessible, and effective monitoring system that can serve as a support tool for caregivers and as a technological basis for new strategies in SIDS prevention. Full article

The Lamb wave-based damage detection method shows great potential for composite impact failure assessments. However, the traditional single signal feature-based methods only depend on partial structural state monitoring information, without considering the inconsistency of damage sensitivity and detection capability for different signal features. Therefore, this paper proposes a damage fusion detection method based on distance analysis and evidence theory for composite laminate panels. Firstly, the signal features of different dimensions are extracted from time–frequency domain perspectives. Correlational analysis and cluster analysis are applied to achieve feature reduction and retain highly sensitive signal features. Secondly, the damage detection results of highly sensitive features and the corresponding basic probability assignments (BPAs) are acquired using distance analysis. Finally, the consistent damage detection result can be acquired by applying evidence theory to the decision level to fuse detection results for highly sensitive signal features. Impact tests on ten composite laminate panels are implemented to validate the proposed fusion detection method. The results show that the proposed method can accurately identify the delamination damage with different locations and different areas. In addition, the classification accuracy is above 85%, the false alarm rate is below 25% and the missing alarm rate is below 15%. Full article

Accurate precipitation estimates are essential for hydrological modeling and flood forecasting, particularly in regions like Ireland where rainfall patterns are highly variable and extreme events are becoming more frequent. This study evaluates the performance of two widely used gridded precipitation datasets, ERA5 reanalysis and GPM IMERG (Early, Late, and Final run) precipitation products, against ground-based observations from 25 synoptic stations operated by Met Éireann, Ireland’s national meteorological service, over the period of 2014–2021. A grid-to-point matching method was applied to ensure spatial alignment between gridded and point-based data. The datasets were assessed using seven statistical and categorical metrics across hourly and daily timescales, meteorological seasons, and rainfall intensity classes. Results show that ERA5 consistently outperforms IMERG across most evaluation metrics, particularly for low-to-moderate intensity rainfall associated with winter frontal systems, and demonstrates strong temporal agreement and low bias in coastal regions. However, it tends to underestimate short-duration, high-intensity events and displays higher false alarm rates at the hourly scale. In contrast, IMERG-Final exhibits improved detection of extreme rainfall events, especially during summer, and performs more reliably at daily resolution. Its spatial performance is stronger than the Early and Late runs but still limited in Ireland’s western regions due to complex climatological settings. IMERG-Early and Late generally follow similar trends but tend to overestimate rainfall in mountainous regions. This study provides the first systematic intercomparison of ERA5 and IMERG datasets over Ireland and supports the recommendation of adopting a hybrid approach of combining ERA5’s seasonal consistency with IMERG-Final’s event responsiveness for enhanced rainfall monitoring and hydrological applications. Full article

Monitoring multimode industrial processes presents significant challenges due to varying operating conditions, nonlinear dynamics, and mode-dependent feature distributions. This paper proposes a novel process monitoring framework that integrates an enhanced Gaussian Mixture Model (GMM) for mode identification with Label Consistent K-SVD (LC-KSVD) for sparse dictionary learning. The improved GMM employs a parallelized Expectation–Maximization algorithm to achieve accurate and scalable mode partitioning in high-dimensional environments. Subsequently, the LC-KSVD then learns label-consistent, discriminative sparse representations, enabling effective monitoring across modes. The proposed method is evaluated through a simulation study and the widely used Continuous Stirred Tank Heater (CSTH) benchmark. Comparative results with traditional techniques such as LNS-PCA and FGMM demonstrate that the proposed method achieves superior fault detection rates (FDRs) and significantly lower false alarm rates (FARs), even under complex mode transitions and mild fault scenarios. Furthermore, the method also provides interpretable fault isolation through reconstruction-error-guided variable contribution analysis. These findings confirm that the proposed LC-KSVD-based scheme offers a reliable solution for fault detection and isolation in multimode process systems. Full article

Air pollution has emerged as one of the most critical public health challenges globally, with an astonishing 96% of the world’s population breathing air below the health standards. This study investigates the amount and distribution of six major air pollutants, PM₁₀, PM_2.5, O₃, SO₂, NO₂, and CO, at numerous air monitoring stations across Iran from 2016 to 2021. The primary objectives were to identify the cities with the highest pollution levels, and to assess the spatiotemporal evolution of air pollution across the country, aiming to provide a comprehensive overview and climatology of air quality. The results indicate that cities such as Zabol and Ahvaz consistently rank among the most polluted, with annual average PM₁₀ concentrations exceeding 190 µg m⁻³ and PM_2.5 reaching alarming levels up to 116.7 µg m⁻³. Furthermore, O₃ and SO₂ amounts were high in Zabol too, classifying it as the most polluted city in Iran. In addition, Tehran exhibits high NO₂, SO₂, and CO concentrations due to high industrial activity and vehicular emissions. Seasonal analysis reveals significant variations in pollutant levels, with PM concentrations peaking during specific months over various parts of the country, particularly driven by local and distant dust events. By integrating MERRA-2 reanalysis pollution data and ground measurements, this research provides a robust framework for understanding pollution dynamics, thereby facilitating more effective policy-making and public health interventions. The results underscore the necessity for immediate action to mitigate the adverse effects of air pollution on public health, particularly in areas prone to industrial activities (i.e., Tehran, Isfahan) and dust events (Zabol, Ahvaz). Full article

Objective: To evaluate the feasibility and effectiveness of wrist-worn wearable devices combined with machine learning (ML) approaches for detecting a diverse array of seizure types beyond generalized tonic–clonic (GTC), including focal, generalized, and subclinical seizures. Materials and Methods: Twenty-eight patients undergoing inpatient video-EEG monitoring at Mayo Clinic were concurrently monitored using Empatica E4 wrist-worn devices. These devices captured accelerometry, blood volume pulse, electrodermal activity, skin temperature, and heart rate. Seizures were annotated by neurologists. The data were preprocessed to experiment with various segment lengths (10 s and 60 s) and multiple feature sets. Three ML strategies, XGBoost, deep learning models (LSTM, CNN, Transformer), and ROCKET, were evaluated using leave-one-patient-out cross-validation. Performance was assessed using area under the receiver operating characteristic curve (AUROC), seizure-wise recall (SW-Recall), and false alarms per hour (FA/h). Results: Detection performance varied by seizure type and model. GTC seizures were detected most reliably (AUROC = 0.86, SW-Recall = 0.81, FA/h = 3.03). Hyperkinetic and tonic seizures showed high SW-Recall but also high FA/h. Subclinical and aware-dyscognitive seizures exhibited the lowest SW-Recall and highest FA/h. MultiROCKET and XGBoost performed best overall, though no single model was optimal for all seizure types. Longer segments (60 s) generally reduced FA/h. Feature set effectiveness varied, with multi-biosignal sets improving performance across seizure types. Conclusions: Wrist-worn wearables combined with ML can extend seizure detection beyond GTC seizures, though performance remains limited for non-motor types. Optimizing model selection, feature sets, and segment lengths, and minimizing false alarms, are key to clinical utility and real-world adoption. Full article

As a high-frequency and essential type of special electromechanical equipment, a vertical elevator has a significant societal implication for their safe operation. The load-weighing module, serving as the core component for overload warning, is susceptible to precision degradation due to the nonlinear deformation of rubber buffers installed at the base of the elevator car. This deformation arises from the coupled effects of environmental factors such as temperature, humidity, and material aging, leading to potential safety risks including missed overload alarms and false empty status detections. To address the issue of accuracy deterioration in elevator load-weighing systems, this study proposes an online self-calibration method based on multimodal information fusion. A reference detection model is first constructed to map the relationship between applied load and the corresponding relative compression of the rubber buffers. Subsequently, displacement data from a draw-wire sensor are integrated with target detection model outputs, enabling real-time extraction of dynamic rubber buffers’ deformation characteristics under empty conditions. Based on the above, a displacement-based compensation term is derived to enhance the accuracy of load estimation. This is further supported by a dynamic error compensation mechanism and an online computation framework, allowing the system to self-calibrate without manual intervention. The proposed approach eliminates the dependency on manual tuning inherent in traditional methods and forms a highly robust solution for load monitoring. Field experiments demonstrate the effectiveness of the proposed method and the stability of the prototype system. The results confirm that the synergistic integration of multimodal perception and adaptive calibration technologies effectively resolves the challenge of load-weighing precision degradation under complex operating conditions, offering a novel technical paradigm for elevator safety monitoring. Full article

Traditional landslide monitoring systems struggle to capture the spatiotemporal dynamics of rainfall-induced hydro-mechanical processes, with a significant risk of signal loss during critical “unsaturated-saturated” state transitions. To address this issue, we propose an integrated framework that utilizes FLAC3D numerical simulation to dynamically optimize multi-sensor deployments. Through coupled seepage-stress analysis under different rainfall scenarios in China’s Tianshan Mountains, this study achieved the following objectives: (1) risk-based sensor deployment by precisely identifying shallow shear strain concentration zones (5–15 m) through FLAC3D simulation (with FBG density of 0.5 m/point in the core sliding belt and GNSS spacing ≤ 50 m); (2) establishment of a multi-parameter cooperative early warning system (displacement > 50 mm/h, pore water pressure > 0.4 MPa, strain > 6400 με), where red alerts are triggered when at least two parameters exceed thresholds, reducing false alarm rates; and (3) development of an adaptive sampling framework based on three rainfall intensity scenarios, which increases measurement frequency during heavy rainfall to capture transient critical points (GNSS sampling rate enhanced to 10 Hz). This approach significantly enhances the capture capability of critical hydro-mechanical transition processes while reducing the monitoring redundancy. The framework provides a scientifically robust and reliable solution for slope disaster-risk prevention and management. Full article

Background/Objectives: Adolescent idiopathic scoliosis (AIS) often requires prolonged brace use to prevent curve progression. However, adherence is challenging due to discomfort, mobility restrictions, and psychosocial stressors. This study evaluated the feasibility and clinical utility of a mobile health (mHealth) system for real-time tracking of brace adherence and treatment-related experiences in adolescents with AIS. Methods: Thirty adolescents with AIS (mean age = 12.9, SD = 1.8) undergoing brace treatment at a tertiary care center used a custom app for 90 days. The app collected daily self-reports on brace wear duration, discomfort, movement limitations, emotional distress, and social challenges. A clinical alarm system alerted providers when patient input indicated potential concerns. Primary outcomes were feasibility (adherence to daily use and usability ratings) and brace adherence. Secondary outcomes included the app’s capacity to identify treatment-related challenges and its association with changes in stress, quality of life, anxiety, and depression. Results: Participants reported meeting recommended brace wear time (≥16 h/day) on 84.8% of days. The app triggered 186 clinical alarms, with the most frequent related to emotional distress (23.1%) and pain (15.6%). Alarm frequency declined over time. Improvements of ≥20% in psychological outcomes were observed in 20–26.7% of participants, while group-level changes were nonsignificant. Conclusions: mHealth-based monitoring appears feasible and acceptable for digitally engaged adolescents with AIS. The app supported early detection of treatment barriers and prompted timely clinical responses. Despite limitations, it shows promise as a tool to improve treatment engagement and address psychosocial challenges in scoliosis care. Full article

In deep-sea mining operations, rigid risers operate in a complex and uncertain ocean environment where vessel–riser interactions present significant structural challenges. This study develops a coupled dynamic modeling framework that integrates vessel motions and environmental loads to evaluate the probabilistic risk of riser failure. Using frequency-domain RAOs derived from AQWA and time-domain simulations in OrcaFlex 11.0, we analyze the riser’s effective tension, bending moment, and von Mises stress under a range of wave heights, periods, and directions, as well as varying current and wind speeds. A Monte Carlo simulation framework based on Latin hypercube sampling is used to generate 10,000 sea state scenarios. The response distributions are approximated using probability density functions to assess structural reliability, and global sensitivity is evaluated using a Sobol-based approach. Results show that the wave height and period are the primary drivers of riser dynamic response, both with sensitivity indices exceeding 0.7. Transverse wave directions exert stronger dynamic excitation, and the current speed notably affects the bending moment (sensitivity index = 0.111). The proposed methodology unifies a coupled time-domain simulation, environmental uncertainty analysis, and reliability assessment, enabling clear identification of dominant factors and distribution patterns of extreme riser responses. Additionally, the workflow offers practical guidance on key monitoring targets, alarm thresholds, and safe operation to support design and real-time decision-making. Full article