Search Results (1,939)

Transmission lines traversing forested areas pose significant fire risks, necessitating timely and efficient inspection mechanisms. Traditional manual patrols and cloud-based UAV inspections suffer from high latency, bandwidth dependence, and delayed response times. To address these challenges, this study proposes an integrated, real-time UAV-edge computing system for the early identification of fire risks and structural hazards along transmission corridors. The system integrates a DJI M300 RTK UAV with a Manifold 2-G edge computing unit (based on NVIDIA Jetson TX2), deploying a lightweight, TensorRT-optimized YOLOv8 model. By leveraging FP16 precision quantization and operator fusion, the system achieves a real-time inference speed of 32 FPS on the embedded platform. Furthermore, a custom Payload SDK integration ensures automated image acquisition and closed-loop data transmission via a dual-mode (4G/5G + Wi-Fi) communication link. Field experiments demonstrate that the system significantly reduces data transmission latency while maintaining high detection accuracy (mAP > 94%), providing a robust and replicable solution for intelligent power grid maintenance in resource-constrained environments. Full article

The prevalence of high-rack warehouses and large-space facilities with high ceilings poses significant challenges to traditional automatic sprinkler systems, which often exhibit activation delays and limited suppression efficacy. This study investigates the spatio-temporal evolution and distribution characteristics of fire-induced thermal smoke flow through a hybrid approach combining full-scale fire experiments and numerical simulations. A physical hypothesis is proposed: the ceiling temperature field approximately follows a two-dimensional Gaussian distribution. Through parametric numerical simulations under varied ambient temperatures, fire identification criteria were calibrated, encompassing a sustained increase in the average temperature rise within high-temperature zones, the attainment of a predefined threshold, and the spatial stabilization of the Gaussian distribution center. Subsequently, a precise algorithm for rapid fire identification and source localization was developed. Experimental validation demonstrates that the proposed algorithm significantly outperforms traditional passive-activation closed sprinklers, advancing fire detection by 46–67 s. Furthermore, the fire source localization error is maintained within half of the sprinkler spacing. The algorithm also exhibits robust environmental adaptability and generalizability across a wide ambient temperature range, providing a technical foundation for active-actuation fire suppression. Full article

Due to climate change, drought periods are becoming more frequent and more intense, posing substantial stress to Central European forest stands, especially climatically sensitive conifer forests. The early detection and accurate spatial delineation of forest damage are essential for supporting adaptive forest management decisions. This study presents a two-tier, multi-step forest damage assessment approach that combines Sentinel-2 satellite-based NDVI double-difference analysis with UAV-based high-resolution photogrammetric evaluation. In the first phase, potential damaged forest patches were identified in two sample areas of the Sopron Mountains using double-difference maps derived from monthly window NDVI maxima calculated from Sentinel-2 data. In the second phase, UAV surveys were carried out over the selected forest compartments, resulting in individual-tree-level canopy segmentation and object-based NDVI analysis. The photogrammetric point clouds were combined with ground points derived from airborne laser scanning to enable the accurate generation of canopy height models. The results confirmed that NDVI double-difference analysis is suitable for the spatial detection of both gradual drought-related damage and sudden disturbances—such as forest fire—even under sequences of drought and moderate years occurring in a sporadic pattern. The UAV-based analysis corroborated the satellite observations in detail and enabled an accurate inventory of damaged trees as well as the exploration of their spatial distribution. The proposed methodology provides an efficient, cost-effective, and operational tool for multi-scale monitoring of forest damage, contributing to the timely recognition of climate-change impacts and to the substantiation of targeted forest management interventions. Full article

Forest fires are a major concern due to their significant impact on the environment, economy, and wildlife habitats. Efficient early detection systems can significantly mitigate their devastating effects. This paper provides a comprehensive review of forest fire detection (FFD) techniques and traces their evolution from basic lookout-based methods to sophisticated remote sensing technologies, including recent Internet of Things (IoT)- and Unmanned Aerial Vehicle (UAV)-based sensor network systems. Historical methods, characterized primarily by human surveillance and basic electronic sensors, laid the foundation for modern techniques. Recently, there has been a noticeable shift toward ground-based sensors, automated camera systems, aerial surveillance using drones and aircraft, and satellite imaging. Moreover, the rise of Artificial Intelligence (AI), Machine Learning (ML), and the IoT introduces a new era of advanced detection capabilities. These detection systems are being actively deployed in wildfire-prone regions, where early alerts have proven critical in minimizing damage and aiding rapid response. All FFD techniques follow a common path of data collection, pre-processing, data compression, transmission, and post-processing. Providing sufficient power to complete these tasks is also an important area of research. Recent research focuses on image compression techniques, data transmission, the application of ML and AI at edge nodes and servers, and the minimization of energy consumption, among other emerging directions. However, to build a sustainable FFD model, proper sensor deployment is essential. Sensors can be either fixed at specific geographic locations or attached to UAVs. In some cases, a combination of fixed and UAV-mounted sensors may be used. Careful planning of sensor deployment is essential for the success of the model. Moreover, ensuring adequate energy supply for both ground-based and UAV-based sensors is important. Replacing sensor batteries or recharging UAVs in remote areas is highly challenging, particularly in the absence of an operator. Hence, future FFD systems must prioritize not only detection accuracy but also long-term energy autonomy and strategic sensor placement. Integrating renewable energy sources, optimizing data processing, and ensuring minimal human intervention will be key to developing truly sustainable and scalable solutions. This review aims to guide researchers and developers in designing next-generation FFD systems aligned with practical field demands and environmental resilience. Full article

In night-time construction scenarios of under-construction nuclear power plants, some yellow lights and open flames exhibit highly similar visual characteristics, resulting in frequent false alarms of fire sources. Such false alarm information tends to drown out real fire alarm signals, which not only severely disrupts construction operations but also endangers fire safety. To address this problem, this paper proposes an intelligent fire risk identification method based on an enhanced YOLOv8n (named YOLO-Fire). Specifically, shallow convolutional layers embedded with a coordinate attention mechanism are integrated into the Backbone of YOLOv8n; the Neck is optimised to improve the efficiency of multi-scale feature fusion; and the Head is enhanced to strengthen the localization and classification branches. Additionally, a composite loss function combining classification loss, regression loss, and similarity loss is designed, coupled with night-scene-specific data augmentation techniques and a two-stage progressive training strategy. Experimental results show that YOLO-Fire reduces the false alarm rate by 14.3%, increases the mean average precision (mAP@0.5) for open flames by 11.3% to 75.2%, and maintains an inference speed of over 85 frames per second (FPS). This study achieves an optimal balance between false alarm control, small object detection accuracy, and real-time processing efficiency, effectively resolving the misclassification issue between open flames and lights in night-time construction scenarios, and providing precise and efficient intelligent technical support for fire risk prevention and control during the construction phase of nuclear power plants. Full article

Background: We previously established that striatal, but not cortical, dopaminergic activation stimulates movement, indicating that the crucial and original site of dopaminergic stimulation of motor function is the striatum, not the motor cortex. In the present study, we have further investigated the potential effects of the cortical and striatal dopaminergic activity on cortical pyramidal neuron physiology. Methods and Results: First, under a constant fluorescence imaging condition, we established that DA innervation and D1R and D2R expression were very low in the cerebral cortex but very high in the striatum. Second, we performed cellular neurophysiological experiments on layer 2/3 pyramidal neurons in the primary motor cortex (M1) in tyrosine hydroxylase gene knockout (TH-KO) DA-depleted mice that have hyperfunctional DA receptors. Using brain slice–whole-cell patch-clamping techniques, we found that M1 layer 2/3 pyramidal neurons had lower input resistance, stronger inward rectification, more negative RMP, and fired fewer spikes in DA-depleted TH-KO mice than in DA-intact WT mice; M1 layer 2/3 pyramidal neurons also had a diminished synaptic release function with reduced frequencies for spontaneous and miniature excitatory synaptic currents in TH-KO mice compared to WT mice. Third, we also found that when TH-KO mice were treated with L-dopa before brain slice preparation, these neurophysiological deficits of M1 layer 2/3 pyramidal neurons were reversed, but 30 min incubation of cortical brain slices with 10–20 μM DA produced no detectable effect in M1 layer 2/3 pyramidal neurons in TH-KO mice and WT mice. Fourth, Golgi staining showed that cortical pyramidal neuron morphology was indistinguishable between WT mice and TH-KO mice. Conclusions: Our results indicate that DA loss in the striatum, not in the cortex, indirectly reduces cortical pyramidal neuron membrane excitability and weakens synaptic function. Our data also indicate that (1) the normal direct effects of the cortical DA system on cortical pyramidal neurons are weak, (2) the striatal DA system is the dominant DA system in the brain, and (3) striatal DA activity can indirectly increase cortical neuron activity (spike firing and synaptic activity) and thus critically contribute to brain function. Additionally, our data suggest that in DA depletion rodent PD models, DA loss-induced effects on cortical pyramidal neurons and other neurons are functional rather than structural, such that DA replenishment restores motor function almost instantaneously. These findings provide important insights into how the brain’s dopaminergic system controls our motor and cognitive functions and indicate that the striatum is the main therapeutic target of dopaminergic drugs. Full article

Unmanned aerial vehicle (UAV) forest fire detection is vital for forest safety. However, early-stage UAV fire scenarios often involve small targets, weak smoke signals, and strict onboard resource constraints, which pose significant challenges to existing detectors. To improve the speed and accuracy of UAV forest fire detection, this paper proposes a lightweight fire detection algorithm, AHE-YOLO, specifically designed for UAVs. The proposed method adopts a coordinated lightweight design to improve feature preservation and cross-scale representation under limited computational budgets. Specifically, the Adaptive Downsampling (ADown) module preserves shallow fire-related cues during spatial reduction, improving sensitivity to small flame and smoke targets. The high-level screening-feature fusion pyramid network (HS-FPN) introduces cross-scale attention to promote more discriminative multi-level feature interaction while reducing redundant computation. Furthermore, the Efficient Mobile Inverted Bottleneck Convolution (EMBC) module is employed to improve receptive-field efficiency and feature selectivity under lightweight constraints, further enhancing detection accuracy and inference speed. Finally, the performance of AHE-YOLO is comprehensively evaluated through ablation and comparative experiments on the same dataset. The final experimental results show that YOLO-AHE achieves a mean average precision (mAP) of 94.8% while reducing model parameters by 39.7%, decreasing FLOPs by 27.0%, and shrinking the model size by 36.4%. In addition, its inference speed improves by 16.5%. Beyond detection performance, the proposed framework supports sustainable forest monitoring by enabling early fire warning with reduced computational and energy demands, showing strong potential for real-time deployment on resource-constrained UAV and edge platforms. Full article

In August 2023, a large forest fire burned more than 60% of the Dadia–Lefkimi–Soufli Forest National Park in northeastern Greece, following another large fire in 2022. To quantify the effects of these fires on local herpetofauna, we analyzed community composition, abundance, and diversity before and after the 2023 event. Standardized visual encounter surveys were conducted across 29 sites between 2015 and 2024, spanning burned and unburned areas. Species richness, abundance, and diversity metrics, together with Bray–Curtis community dissimilarities, were compared across sampling periods and fire-severity classes. Amphibian assemblages showed high postfire persistence, with 82% of regional species still detected and no significant changes in diversity indices, likely reflecting the buffering role of perennial streams and other hydrologically stable refugia. In contrast, reptile communities showed clear compositional shifts and experienced severe declines: overall reptile species richness decreased to 30% of prefire levels and diversity indices dropped significantly. Tortoises (i.e., Testudo graeca, T. hermanni) declined by nearly 90% relative to prefire estimates, indicating high vulnerability of low-mobility, long-lived species. Snakes were not detected in any burned sites, whereas only a few small-bodied lizards and the freshwater turtle Mauremys rivulata persisted locally. These findings demonstrate that extreme, landscape-scale fires can restructure reptile communities in Mediterranean forests, particularly where long-term habitat change and drought had already reduced population resilience. The study underscores the need for targeted postfire restoration, conservation planning for slow-dispersing taxa, and long-term biodiversity monitoring under increasingly frequent fire regimes. Full article

To improve diagnostics and prediction of changes caused by increased impact of anthropogenic activity, it is necessary to increase the comparative analysis of measurements and modeling of ozone—one of the climatically important atmospheric gases due to the decisive influence of stratospheric ozone on the radiation balance of the Earth-atmosphere system and the role of tropospheric ozone, the third most significant anthropogenic factor contributing to the greenhouse effect. This task is particularly relevant for Russia, as its geographical location makes it more vulnerable to climate change than other countries, whereas its regional tendencies in ozone variability have not yet been studied in sufficient detail. An analysis of IKFS-2 tropospheric ozone content (TrOC) measurements for 2015–2022 revealed that in Siberian, Far Eastern, North Caucasian, and Southern federal districts of Russia TrOC maximum, caused by photochemical formation of ground-level ozone, is observed in July (up to 30–35 DU for monthly means in surface-400 hPa layer). In Northwestern federal district, TrOC maximum (up to 25–30 DU), determined by meridional transport, is observed in late spring. No statistically significant linear trends in TrOC are detected. The WRF-Chem model qualitatively describes the seasonal variations of TrOC as well as the anomalous increase in TrOC caused by forest fires. The variability of total ozone content (TOC) is analyzed by OMI (2005–2023) and IKFS-2 (2015–2022) measurements as well as by SOCOLv3 simulations. Ozone negative anomalies in spring (up to 15% for monthly means) are generally observed with positive Arctic oscillation index values and a westerly phase of Quasi-biennial oscillations. For the 2008–2022 period, a statistically significant increase in TOC (+1.6–1.7% per year) is obtained for European Russia and Western and Central Siberia in November. Full article

In this study, a deep learning-based multimodal framework is presented for forest fire detection using RGB images, which synthetically generates night-vision-like, white-hot, and green-hot pseudo-thermal representations. The synthetic modalities are derived directly from RGB data and integrated into a hardware-independent multimodal learning pipeline to increase visual diversity without relying on additional sensing hardware. Each modality is processed using an ImageNet-pretrained convolutional backbone, and modality-specific feature vectors are combined through feature-level concatenation before classification. The proposed framework was evaluated using multiple backbone architectures, including ResNet18, EfficientNet-B0, and DenseNet121, which were assessed independently under a unified experimental protocol. Experiments were conducted on two datasets with substantially different scales and characteristics: the FLAME dataset (39,375 images, binary classification) and the FireStage dataset (791 images, three-class classification). For both datasets, stratified 80–20% training–validation splits were employed, and online stochastic data augmentation was applied exclusively to the training sets. On the FLAME dataset, the proposed framework achieved consistently high performance across different backbone and modality configurations. The best-performing models reached an accuracy of 99.66%, precision of 99.80%, recall of 99.66%, F1-score of 99.73%, and ROC AUC value of 0.9998. On the more challenging FireStage dataset, the framework demonstrated stable performance despite limited data availability, achieving an accuracy of 93.71% for RGB-only configurations and up to 93.08% for selected multimodal combinations, while macro-averaged F1-scores exceeded 0.92, and ROC AUC values reached up to 0.9919. Per-class analysis further indicates that early-stage fire (Start Fire) patterns can be discriminated, achieving ROC AUC values above 0.96, depending on the backbone and modality combination. Overall, the results suggest that synthetic-modality-based multimodal learning can provide competitive performance for both large-scale and data-limited fire detection scenarios, offering a flexible and hardware-independent alternative for forest fire monitoring applications. Full article

People affected by vision impairment experience significant challenges in mobility and daily life activities. In this paper, a smart assistive navigation system is proposed to address mobility challenges and to enhance the independence of visually impaired individuals. Three modules are integrated into the proposed system. The vision module detects obstacles and interactive objects such as doors, chairs, people, fire extinguishers, etc. The depth cam-based distance module provides the distance of detected objects and obstacles. The voice module provides auditory feedback to visually impaired individuals about the detected objects and obstacles that fall under the pre-defined threshold distance. Finally, the proposed system is optimized in terms of performance and user experience. Jetson Nano is used to reduce the cost of the overall system; however, it has compatibility issues with many of the latest object detection models. The YOLOv5n model is used considering compatibility for object detection, but it has low Mean Average Precision (mAP) and frame rate. To improve the performance of the vision module, various hyperparameters of YOLOv5n are fine-tuned along with transfer learning to enhance the mAP@50 from the original 0.457 to 0.845 and mAP@50-95 from 0.28 to 0.593. Tensor-RT optimization is employed to increase the frame rate to deploy the model in a real scenario. The real-time experimentation shows that the proposed system successfully alerts users to key objects, hazards, and obstacles which enables independent and confident navigation. Full article

Taiwan’s rapid demographic shift toward a super-aged society has heightened demand for long-term care, yet limited staffing creates safety risks from fires; heating, ventilation, and air conditioning failures; and health incidents. To address this, we propose an IoT-based intelligent environmental monitoring and early-warning system designed for care facilities. The three-layer architecture integrates sensors for temperature, humidity, light, air quality, and noise; employs ESP-NOW and wireless fidelity mesh for reliable networking; and supports user interfaces with real-time anomaly alerts. Using PCA and Isolation Forest for efficient anomaly detection, the modular, node-based design enhances safety, reduces manpower burden, and enables scalable smart services. Full article

Early detection of fire-related risks in lithium-ion batteries (LIBs) remains a critical challenge, as conventional protection mechanisms typically activate only after irreversible degradation or macroscopic failure occurs. In this study, an innovative sensor-based diagnostic framework is proposed for proactive fire prediction in LIBs by simultaneously monitoring low-frequency and high-frequency electrical signatures generated during battery charge–discharge processes. An electromagnetic (EM) antenna sensor and a high-frequency current transformer (HFCT) sensor were employed to capture complementary voltage- and current-based transient signals associated with internal degradation phenomena. Cell-level experiments were conducted under various C-rates and temperature conditions, including high-stress environments, while module-level validation was performed on a 4-series, 1-parallel (4S1P) configuration at a 2C-rate under ambient temperature. Time–frequency characteristics of the measured signals were systematically evaluated using MATLAB-based continuous wavelet transform (CWT) and discrete wavelet transform (DWT) techniques. The results reveal that degradation-induced transient events exhibit non-stationary, impulsive voltage and current signatures with distinct frequency-band localization, which intensify with increasing C-rate, elevated temperature, and aging progression. At the module level, although signal amplitudes were partially attenuated due to current redistribution, characteristic wavelet energy patterns and time–frequency concentrations remained clearly distinguishable, demonstrating the scalability of the proposed approach. The combined EM antenna–HFCT sensing strategy, together with multi-resolution wavelet analysis, enables effective phenomenological differentiation between normal operational noise and incipient internal fault signatures well before conventional thermal or capacity-based indicators become evident. These findings demonstrate feasibility of the proposed method for early-stage fault diagnosis and highlight its potential applicability to advanced battery management systems for proactive fire prevention in large-scale energy storage and electric vehicle applications. Unlike conventional voltage-, temperature-, or gas-based diagnostics, the proposed approach enables the detection of incipient degradation phenomena at the microsecond scale by exploiting complementary low- and high-frequency electrical signatures. This study provides experimental evidence that wavelet-based EM and HFCT sensing can identify MISC-related precursors significantly earlier than conventional battery management indicators. Full article

This article addresses issues related to power supply reliability for electronic security systems (ESSs) during their operational lifetime. ESS are deployed both in enclosed building structures, where environmental conditions are stabilised, and across large open areas exposed to natural environmental conditions, such as transport depots, airports, railway stations, ports, and other similar facilities. Laboratory tests on selected power supply units used in ESSs have been conducted by the authors, as well as a theoretical analysis of the reliability of the power supply process. The reliability analysis of the power supply took into account the reliability of delivering electrical energy with specified parameters to all components forming a system aimed at ensuring the safety of electronic security systems (ESSs). Power supply is essential for the correct operation of all modules, components, devices, and alarm control panels (ACPs) within ESSs. In addition to meeting the basic requirements for the provision of electrical power, the system designer must also give particular consideration to power supply reliability, especially in facilities classified as part of the state critical infrastructure (CI). This issue is particularly significant in the case of Fire Detection and Alarm Systems (FASs), which constitute the most critical safety systems responsible for protecting human life and health. Accordingly, this article discusses selected aspects of power supply for representative electronic security systems (ESSs). The subsequent part of this paper presents operational tests of selected ESS power supply units. A further topic addressed in the article is the definition of models of the operational process of power supply systems and the execution of computer simulations. The analysis of the operational process of ESS power supply units, expressed as models and graphs and supported by computer simulations, enabled the formulation of conclusions regarding reliability. The conclusions drawn from this article may be applied in the design, routine maintenance, and operation of ESSs. Full article

New glass-free low-temperature co-fired microwave dielectric composites with compositions (1–4x/3)Ba₃(VO₄)₂–xBaWO₄–(2x/3)Li₃VO₄ (x = 0.3–0.7) were fabricated by reactive liquid-phase sintering of (1–x)Ba₃(VO₄)₂–xLi₂WO₄ mixtures at 850 °C. During sintering, Li₂WO₄ is fully consumed by reacting with Ba₃(VO₄)₂ to form BaWO₄ and Li₃VO₄ while providing a transient liquid phase that promotes densification. As a result, the sintered ceramics achieve high relative densities of ≈94–98% at 850 °C. The relative fractions of Ba₃(VO₄)₂, BaWO₄, and Li₃VO₄ can be systematically tailored by adjusting the initial Li₂WO₄ content, enabling effective control of the temperature coefficient of the resonant frequency (${\tau }_f$) and the quality factor (Q × f). With increasing Li₂WO₄ content, the ${\tau }_f$ values shift from +23.97 to −45.48 ppm/°C, owing to the increasing contributions of the negative ${\tau }_f$ phases BaWO₄ and Li₃VO₄, while the Q × f values increase moderately from 44,300 to 47,300 GHz. The optimal microwave dielectric properties are obtained for x = 0.5, meaning ${\epsilon }_r$ = 9.19, Q × f = 45,900 GHz, and ${\tau }_f$ = −1.15 ppm/°C when sintering at 850 °C for 1 h. Chemical compatibility tests confirmed that the composites exhibit no detectable reaction with Ag electrodes, indicating that the Ba₃(VO₄)₂–BaWO₄–Li₃VO₄ system is a promising glass-free dielectric for LTCC applications requiring low firing temperature, near-zero thermal drift, and reliable electrode compatibility. Full article