Search Results (48)

In recent years, generative AI and autonomous driving have been highly popular topics. Additionally, with the increasing global emphasis on carbon emissions and carbon trading, integrating autonomous driving technologies that can instantly perceive environ-mental changes with vehicle-based generative AI would enable vehicles to better under-stand their surroundings and provide drivers with recommendations for more energy-efficient and comfortable driving. This study employed You Only Look Once version11 (YOLOv11) for visual detection of the driving environment, integrating it with vehicle speed data received from the OBD-II system. All information is integrated and processed using the embedded Nvidia Jetson AGX Orin platform. For visual detection validation, part of the test set includes standard Taiwanese road signs. Experimental results show that incorporating Squeeze-and-Excitation Attention (SEAttention), into YOLOv11 improves the mAP50–95 accuracy by 10.1 percentage points. Generative AI processed this information in real time and provided the driver with appropriate driving recommendations, such as gently braking, detecting a pedestrian ahead, or warning of excessive speed. These recommendations are delivered through voice output to prevent driver distraction caused by looking at an interface. When a red light or pedestrian is detected, early deceleration is suggested, effectively reducing fuel consumption while also enhancing driving comfort, ultimately achieving the goal of energy-efficient driving. Full article

Forest fires pose severe ecological, climatic, and socio-economic threats, destroying habitats and emitting greenhouse gases. Early and timely warning is particularly challenging because fires often originate from small-scale, low-temperature ignition sources. Traditional monitoring approaches primarily rely on single-source satellite imagery and empirical threshold algorithms, and most forest fire monitoring tasks remain human-driven. Existing frameworks have yet to effectively integrate multiple data sources and detection algorithms, lacking the capability to provide continuous, automated, and generalizable fire monitoring across diverse fire scenarios. To address these challenges, this study first improves multiple monitoring algorithms for forest fire detection, including a statistically enhanced automatic thresholding method; data augmentation to expand the U-Net deep learning dataset; and the application of a freeze–unfreeze transfer learning strategy to the U-Net transfer model. Multiple algorithms are systematically evaluated across varying fire scales, showing that the improved automatic threshold method achieves the best performance on GF-4 imagery with an F-score of 0.915 (95% CI: 0.8725–0.9524), while the U-Net deep learning algorithm yields the highest F-score of 0.921 (95% CI: 0.8537–0.9739) on Landsat 8 imagery. All methods demonstrate robust performance and generalizability across diverse scenarios. Second, data-driven scheduling technology is developed to automatically initiate preprocessing and fire detection tasks, significantly reducing fire discovery time. Finally, an integrated framework of multi-source remote sensing data, advanced detection algorithms, and a user-friendly visualization interface is proposed. This framework enables all-weather, fully automated forest fire monitoring and early warning, facilitating dynamic tracking of fire evolution and precise fire line localization through the cross-application of heterogeneous data sources. The framework’s effectiveness and practicality are validated through wildfire cases in two regions of Yunnan Province, offering scalable technical support for improving early detection of and rapid response to forest fires. Full article

Low-level wind shear poses a significant hazard to aviation, especially at airports located on high plateaus and surrounded by complex terrain. In this study, we present a comprehensive analysis integrating Doppler Lidar and radiosonde measurements collected at the Xining Caojiapu Airport, situated on the northeastern Tibetan Plateau, during June 2022. The results indicate a remarkably high frequency of severe wind shear events (|Δv| ≥ 6 m/s), with an overall occurrence rate of 34% during the observation period. These events are predominantly confined to two distinct atmospheric layers: just above the surface and near the top of the convective boundary layer. The diurnal cycle of wind shear is closely associated with boundary-layer dynamics, exhibiting sharp increases after sunrise and pronounced peaks around midday, coinciding with enhanced turbulent mixing and surface heating. Case analyses further reveal that the most intense shear episodes occur at strong thermal inversions, where momentum decoupling produces thin, critical interfaces conducive to turbulence generation. In contrast, well-mixed convective conditions result in more distributed but persistent shear throughout the lower atmosphere. Diagnostic profiles of atmospheric stratification and dynamic instability, characterized by the Brunt–Väisälä frequency and Richardson number, elucidate the intricate interplay between thermal structure and vertical wind gradients. Collectively, these findings provide a robust quantitative basis for improving wind shear risk assessments and early warning systems at airports in mountainous regions, while offering new insights into the complex interactions between turbulence and atmospheric stratification. Full article

This work aims to reveal the full-field strain evolution characteristics and failure mechanisms of anisotropic coal samples under creep loading. A series of compression tests combined with digital image correlation (DIC) monitoring were employed to characterize the strain evolution process of coal specimens with bedding angles of 0°, 30°, 60°, and 90°. Testing results show that the peak strength, peak strain, and the creep loading stage of coal are significantly influenced by the bedding angle. The peak strength initially decreases and then increases as the bedding angle increases. In addition, the creep failure of coal manifests as a process of instantaneous deformation, decelerating creep, steady-state creep, accelerating creep, and failure. Under graded creep loading conditions, coal specimens exhibit distinct creep characteristics at high stress levels. Moreover, the bedding angle significantly influences the strain field evolution of the coal samples. Finally, for coal specimens with bedding angles of 0° and 90°, the final macroscopic fracture pattern upon failure is characterized by longitudinal tensile splitting. In contrast, coal samples with bedding angles of 30° and 60° tend to exhibit failure along the bedding interfaces, forming tensile-shear fractures. The results of this study will provide theoretical guidance for the prevention, early warning, and safety management of coal mine disasters. Full article

Radon anomalies have long been explored as potential geochemical precursors to seismic activity due to their responsiveness to subsurface stress variations. However, before this study, the scientific progression of this research domain had not been systematically examined through a quantitative lens. This study presents a comprehensive bibliometric analysis of 379 articles published between 1977 and 2025 and indexed in Scopus and Web of Science. Utilizing the Bibliometrix R-package and its Biblioshiny interface, the analysis investigates temporal publication trends, leading countries, institutions, international collaboration networks, and thematic evolution. The results reveal a marked increase in research output since 2010, with China, India, and Italy emerging as the most prolific contributors. Thematic mapping indicates a shift from conventional geochemical monitoring toward the integration of artificial intelligence techniques, such as decision trees and neural networks, for anomaly detection and predictive modeling. Notwithstanding this methodological evolution, core research themes remain centered on radon concentration monitoring and the analysis of environmental parameters. Overall, the findings highlight the coexistence of traditional and emerging approaches, emphasizing the importance of standardized methodologies and interdisciplinary collaboration. This bibliometric synthesis provides strategic insights to inform future research and strengthen the role of radon monitoring in seismic early warning systems. Full article

In the landslide chain from pre-disaster conditions to landslide mitigation and recovery, time is an important factor in understanding the geological hazards process and managing landsides. Static knowledge graphs are unable to capture the temporal dynamics of landslide events. To address this limitation, we propose a systematic framework for constructing a multi-temporal knowledge graph of landslides that integrates multi-source temporal data, enabling the dynamic tracking of landslide processes. Our approach comprises three key steps. First, we summarize domain knowledge and develop a temporal ontology model based on the disaster chain management system. Second, we map heterogeneous datasets (both tabular and textual data) into triples/quadruples and represent them based on the RDF (Resource Description Framework) and quadruple approaches. Finally, we validate the utility of multi-temporal knowledge graphs through multidimensional queries and develop a web interface that allows users to input landslide names to retrieve location and time-axis information. A case study of the Zhangjiawan landslide in the Three Gorges Reservoir Area demonstrates the multi-temporal knowledge graph’s capability to track temporal updates effectively. The query results show that multi-temporal knowledge graphs effectively support multi-temporal queries. This study advances landslide research by combining static knowledge representation with the dynamic evolution of landslides, laying the foundation for hazard forecasting and intelligent early-warning systems. Full article

To explore the influence of mine roof on the damage and failure of sandstone surrounding rock under different pressure rates, mechanical experiments with different strain rates were carried out on sandstone rock samples. The strength, deformation, failure, energy and damage characteristics of rock samples with different strain rates were also discussed. The research results show that with the increases in the strain rate, peak stress, and elastic modulus show a monotonically increasing trend, while the peak strain decreases in the reverse direction. At a low strain rate, the proportion of the mass fraction of complete rock blocks in the rock sample is relatively high, and the shape integrity is good, while rock samples with a high strain rate retain more small-sized fragmented rock blocks. This indicates that under high-rate loading, the bifurcation phenomenon of secondary cracks is obvious. The rock samples undergo a failure form dominated by small-sized fragments, with severe damage to the rock samples and significant fractal characteristics of the fragments. At the initial stage of loading, the primary fractures close, and the rock samples mainly dissipate energy in the forms of frictional slip and mineral fragmentation. In the middle stage of loading, the residual fractures are compacted, and the dissipative strain energy keeps increasing continuously. In the later stage of loading, secondary cracks accelerate their expansion, and elastic strain energy is released sharply, eventually leading to brittle failure of the rock sample. Under a low strain rate, secondary cracks slowly expand along the clay–quartz interface and cause intergranular failure of the rock sample. However, a high strain rate inhibits the stress relaxation of the clay, forces the energy to transfer to the quartz crystal, promotes the penetration of secondary cracks through the quartz crystal, and triggers transgranular failure. A constitutive model based on energy damage was further constructed, which can accurately characterize the nonlinear hardening characteristics and strength-deformation laws of rock samples with different strain rates. The evolution process of its energy damage can be divided into the unchanged stage, the slow growth stage, and the accelerated growth stage. The characteristics of this stage reveal the sudden change mechanism from the dissipation of elastic strain energy of rock samples to the unstable propagation of secondary cracks, clarify the cumulative influence of strain rate on damage, and provide a theoretical basis for the dynamic assessment of surrounding rock damage and disaster early warning when the mine roof comes under pressure. Full article

There are a large number of unstructured scenes and special targets in the construction machinery application scene, which brings greater interference to the environment sensing system for Construction Machinery Autonomous Operation Application. The conventional mature sensing scheme in passenger cars is not fully applicable to construction machinery. By taking the environmental characteristics and operating conditions of construction machinery into consideration, a set of environmental sensing algorithms based on LiDAR for construction machinery scenarios is studied. Real-time target detection of the environment, trajectory tracking, and prediction for dynamic targets are achieved. Decision instructions are provided for upstream detection information for the subsequent behavioral decision-making, motion planning, and other modules. To test the effectiveness of the information exchange between the proposed algorithm and the overall machine interface, the early warning and emergency braking for autonomous operation is implemented. Experiments are carried out through an excavator test platform. The superiority of the optimized detection model is verified through real-time target detection tests at different speeds and under different states. Information exchange between the environmental sensing and the machine interface based on safety warning and braking is achieved. Full article

Background/Objectives: This study aimed to enhance the usability of patient monitoring systems by integrating the Early Warning Score (EWS) function and improving user interface elements. The EWS function is expected to enable the early detection of acute deterioration and prompt medical intervention, while the optimized design supports rapid decision-making by nursing staff. Methods: Two formative usability evaluations were conducted to identify user requirements and improve the device design. A simulated usability test involved five ICU medical staff members, followed by a user preference survey with 72 ICU staff members in a real clinical setting. After incorporating feedback, a summative usability test with 23 ICU nurses was performed to evaluate the revised device. Results: Issues related to unfamiliar parameter terminology and alarm message positioning were identified, and the need for the EWS function was emphasized. The summative evaluation showed an increase in task success rate from 86% to 90% and a significant improvement in user satisfaction from 74.85 (SD: 0.88) to 89.55 (SD: 0.75) (p < 0.05). Conclusions: The integration of the EWS function and interface improvements significantly enhanced the usability of patient monitoring system. These advancements are expected to enable rapid detection of patient deterioration and support timely clinical decision-making by ICU staff. Full article

This study details the design and implementation of a real-time river monitoring station established on the Sakarya River, capable of instantaneously tracking water levels and flow rates. The system comprises an ultrasonic distance sensor, a GSM module (Global System for Mobile Communications), which enables real-time wireless data transmission to a server via cellular networks, a solar panel, a battery, and a microcontroller board. The river monitoring station operates by transmitting water level data collected by the ultrasonic distance sensor to a server via a communication module developed on a microcontroller board using an Arduino program, and then sharing these data through a web interface. The developed system performs regular and continuous water level readings without the need for human intervention. During the installation and calibration of the monitoring station, laboratory and field tests were conducted, and the obtained data were validated by comparison with data from the hydropower plant located upstream. This system, mounted on a bridge, measures water levels twice per minute and sends these data to the relevant server via the GSM module. During this process, precipitation data were utilized as a critical reference point for validating measurement data for the 2023 hydrological year, with changes in precipitation directly correlated with river water levels and calculated flow values, which were analyzed accordingly. The real-time river monitoring station allows for instantaneous monitoring of the river, achieving a measurement accuracy of within 0.1%. The discharge values recorded by the system showed a high correlation (r² = 0.92) with data from the hydropower plant located upstream of the system, providing an accurate and comprehensive database for water resource management, natural disaster preparedness, and environmental sustainability. Additionally, the system incorporates early warning mechanisms that activate when critical water levels are reached, enabling rapid response to potential flood risks. By combining energy-independent operation with IoT (Internet Of Things)-based communication infrastructure, the developed system offers a sustainable solution for real-time environmental monitoring. The system demonstrates strong applicability in field conditions and contributes to advancing technologies in flood risk management and water resource monitoring. Full article

In the context of global climate change, shallow landslides induced by strong typhoons and the ensuing rainstorms have increased significantly in China’s eastern coastal areas. On 27 May 2022, more than 700 liquefied landslides were induced by the rain gush in Wuping County, Longyan City, Fujian Province, SE China. In light of their widespread occurrence and the severe damage caused, detailed field investigations, UAV surveys, trench observations, in situ tests, and numerical simulation are conducted in this work. The cascading landslides are classified as channelized landslides and hillslope landslides. Long-term rainfall, the influence of vegetation roots under wind load, and differences in the strength and structure of surficial soil are the dominant controlling factors. The sliding surface is localized to be the interface at a depth of 1–1.5 m between the fully weathered granite and the strongly weathered granite. Kinetic analysis of a channelized landslide shows that it is characterized by short runout, rapid velocity, and strong impact energy. The maximum velocity, impact energy, and impact force of the Laifu landslide are 29 m/s, 4221.35 J, and 2110 kPa. Effective excavation is usually impossible in this context. This work highlights the escalating issue of shallow landslides in eastern China’s coastal areas, exacerbated by climate change and extreme weather events like typhoons. By conducting comprehensive investigations and analyses, the research identifies key factors influencing landslide occurrence, such as rainfall patterns and soil characteristics. Understanding the dynamics and impact of these landslides is vital for improving risk assessment, developing effective early warning systems, and informing land management policies in this region. Further exploration concerning hydro-meteorological hazard early warning should be encouraged in this region. Full article

Monitoring the driver’s physical and mental state based on wearable EEG acquisition equipment, especially the detection and early warning of fatigue, is a key issue in the research of the brain–computer interface in human–machine intelligent fusion driving. Comparing and analyzing the waking (alert) state and fatigue state by simulating EEG data during simulated driving, this paper proposes a brain functional network construction method based on a phase locking value (PLV) and phase lag index (PLI), studies the relationship between brain regions, and quantitatively analyzes the network structure. The characteristic parameters of the brain functional network that have significant differences in fatigue status are screened out and constitute feature vectors, which are then combined with machine learning algorithms to complete classification and identification. The experimental results show that this method can effectively distinguish between alertness and fatigue states. The recognition accuracy rates of 52 subjects are all above 70%, with the highest recognition accuracy reaching 89.5%. Brain network topology analysis showed that the connectivity between brain regions was weakened under a fatigue state, especially under the PLV method, and the phase synchronization relationship between delta and theta frequency bands was significantly weakened. The research results provide a reference for understanding the interdependence of brain regions under fatigue conditions and the development of fatigue driving detection systems. Full article

Infectious diseases manifesting in the form of epidemics or pandemics do not only cause devastating impacts on public health systems but also disrupt the functioning of the socio-economic structure. Further, risks associated with pandemics and epidemics become exacerbated with coincident compound hazards. This study aims to develop a framework that captures key elements and components of epidemic and pandemic preparedness and response systems, focusing on a multi-hazard context. A systematic literature review was used to collect data through peer-reviewed journal articles using three electronic databases, and 17 experts were involved in the validation. Epidemiological surveillance and early detection, risk and vulnerability assessments, preparedness, prediction and decision making, alerts and early warning, preventive strategies, control and mitigation, response, and elimination were identified as key elements associated with epidemic and pandemic preparedness and response systems in a multi-hazard context. All elements appear integrated within three interventional phases: upstream, interface, and downstream. A holistic approach focusing on all interventional phases is required for preparedness and response to pandemics and epidemics to counter their cascading and systemic effects. Further, a paradigm shift in the preparedness for multi-hazards during an epidemic or pandemic is essential due to the multiple challenges posed by concurrent hazards. Full article

This systematic review aims to synthesise Web-GIS evidence for managing natural hazards to share state-of-the-art practices and policies. A comprehensive search in SCOPUS, among other databases, identified 1775 articles published between 2014 and 2023. Following a selection process based on the PRISMA model, 65 articles met the inclusion criteria. The analysis revealed a growing trend over the past decade, with most research concentrated in the last three years. Eight crucial subtopics within the Web-GIS domain have emerged: Integrated Spatial Analysis and Modelling, Technologies and Infrastructure, Visualisation and User Interface Design, Decision Support Systems, Real-time Monitoring and Early Warning, Disaster Recovery and Resilience, Citizen and Social Media Integration, and Multi-Stakeholder Collaboration. A substantial contribution of the literature has been identified in Decision Support Systems and Integrated Spatial Analysis, reflecting their vital role in strategising and predicting hazard impacts. Furthermore, a geographical distribution analysis revealed significant Web-GIS applications in countries like Italy and China, alongside a deficit in low- and middle-income countries. It also highlights potential gaps in hazard studies, including the need to prioritise heatwave management in the face of climate change. This research calls for policymakers and practitioners to leverage evidence-informed decision making and foster community collaboration for enhanced natural disaster resilience. Full article

Citizen science reinforces the development of emergent tools for the surveillance, monitoring, and early detection of biological invasions, enhancing biosecurity resilience. The contribution of farmers and farm citizens is vital, as volunteers can strengthen the effectiveness and efficiency of environmental observations, improve surveillance efforts, and aid in delimiting areas affected by plant-spread diseases and pests. This study presents a robust, user-friendly, and cost-effective smart module for citizen science that incorporates a cutting-edge developed hyperspectral imaging (HI) module, integrated in a single, energy-independent device and paired with a smartphone. The proposed module can empower farmers, farming communities, and citizens to easily capture and transmit data on crop conditions, plant disease symptoms (biotic and abiotic), and pest attacks. The developed HI-based module is interconnected with a smart embedded system (SES), which allows for the capture of hyperspectral images. Simultaneously, it enables multimodal analysis using the integrated environmental sensors on the module. These data are processed at the edge using lightweight Deep Learning algorithms for the detection and identification of Tuta absoluta (Meyrick), the most important invaded alien and devastating pest of tomato. The innovative Artificial Intelligence (AI)-based module offers open interfaces to passive surveillance platforms, Decision Support Systems (DSSs), and early warning surveillance systems, establishing a seamless environment where innovation and utility converge to enhance crop health and productivity and biodiversity protection. Full article