Search Results (2,119)

The intensifying global warming and the increasing frequency of extreme weather events have created an urgent need for targeted resilience building in mountainous villages. This study focuses on three typical villages in the Hengduan Mountains region. From the perspective of individual villagers, a disaster resilience evaluation index system was constructed, encompassing four dimensions: disaster prevention capacity, disaster resistance capacity, disaster relief capacity, and recovery capacity. Using the entropy method and a village disaster resilience assessment model, the disaster resilience levels of each village were quantitatively evaluated. The results indicate the following: (1) Disaster resistance capacity is the key factor constraining the disaster resilience level of mountain villages. (2) The overall disaster resilience of mountain villages is at a medium level, with minor differences among villages. (3) Significant disparities exist in capacity dimensions across villages: Qina Village demonstrates the strongest disaster resistance capacity, while Xiamachang Village excels in disaster prevention capacity but shows relative weakness in recovery capacity. (4) Household material endowment has a significant positive impact on disaster prevention, resistance, relief, and recovery capacities, while individual self-rescue capability and individual–government collaboration capacity also significantly enhance disaster prevention, resistance, and relief capacities. We propose the following: Leveraging the rural revitalization strategy as a pivotal point, this approach promotes the diversified development of the village economy. It facilitates the increase in villagers’ income through the implementation of employment skill training programs, thereby strengthening household material foundations to enhance individual disaster resilience. By relying on the mass monitoring and mass prevention mechanism and a disaster information sharing platform, real-time exchange of disaster situation information is achieved, which enhances communication and collaboration between villagers and the government, consequently improving the synergistic efficiency between individuals and governmental bodies. Simultaneously, a villager-centered disaster prevention system is constructed. Through measures such as disaster prevention publicity and practical disaster response drills, villagers’ awareness of disasters and their capabilities for self and mutual rescue are elevated, ultimately strengthening the overall disaster resilience of rural areas in the Hengduan Mountains region. Full article

Underground mine emergencies destroy communication infrastructure when situational awareness is most critical. Current systems rely on centralized network infrastructure, which fails during emergencies when miners are trapped and require rescue coordination. This paper proposes an energy-harvesting LoRa mesh network that addresses self-powered operation, interference management, and adaptive physical layer optimization under severe underground propagation conditions. A dual-antenna architecture separates RF energy harvesting (860 MHz) from LoRa communication (915 MHz), enabling continuous operation with supercapacitor storage. The core contribution is a decentralized scheduler that derives optimal timing offsets by modeling concurrent transmissions as a Poisson collision process, exploiting LoRa’s capture effect while maintaining network coherence. A SINR-aware physical layer adapts spreading factor, bandwidth, and coding rate with hysteresis, controls recomputing timing parameters after each change. Experimental validation in Missouri S&T’s operational mine demonstrates far-field wireless power transfer (WPT) reaching 35 m. Simulations across 2000 independent trials show a 2.2× throughput improvement over ALOHA (49% vs. 22% delivery ratio at 10 nodes/hop), 64% collision reduction, and 67% energy efficiency gains, demonstrating resilient emergency communications for underground environments. Full article

The rapid growth of technology has introduced robots into daily life, necessitating navigation frameworks that enable safe, human-friendly movement while accounting for social aspects. Such methods must also scale to situations with multiple humans and robots moving simultaneously. Recent advances in Deep Reinforcement Learning (DRL) have enabled policies that incorporate these norms into navigation. This work presents a socially aware navigation framework for mobile robots operating in environments shared with humans and other robots. The approach, based on single-agent DRL, models all interaction types between the ego robot, humans, and other robots. Training uses a reward function balancing task completion, collision avoidance, and maintaining comfortable distances from humans. An attention mechanism enables the framework to extract knowledge about the relative importance of surrounding agents, guiding safer and more efficient navigation. Our approach is tested in both dynamic and static obstacle environments. To improve training efficiency and promote socially appropriate behaviors, Imitation Learning is employed. Comparative evaluations with state-of-the-art methods highlight the advantages of our approach, especially in enhancing safety by reducing collisions and preserving comfort distances. Results confirm the effectiveness of our learned policy and its ability to extract socially relevant knowledge in human–robot environments where social compliance is essential for deployment. Full article

Safety and security are major priorities in modern society. Especially for vulnerable groups of individuals, such as the elderly and patients with disabilities, providing a safe environment and adequate alerting for debilitating events and situations can be critical. Wearable devices can be effective but require frequent maintenance and can be obstructive or stigmatizing. Video monitoring by trained operators solves those issues but requires human resources, time and attention and may present certain privacy issues. We propose optical flow-based automated approaches for a multitude of situation awareness and event alerting challenges. The core of our method is an algorithm providing the reconstruction of global movement parameters from video sequences. This way, the computationally most intensive task is performed once and the output is dispatched to a variety of modules dedicated to detecting adverse events such as convulsive seizures, falls, apnea and signs of possible post-seizure arrests. The software modules can operate separately or in parallel as required. Our results show that the optical flow-based detectors provide robust performance and are suitable for real-time alerting systems. In addition, the optical flow reconstruction is applicable to real-time tracking and stabilizing video sequences. The proposed system is already functional and undergoes field trials for cases of epileptic patients. Full article

Accurate vessel track association is a key requirement for maritime traffic monitoring and collision-avoidance systems, yet the Automatic Identification System (AIS) records commonly contain noise, missing intervals, and overlapping trajectories in congested coastal waters. We propose a Dilated Residual Connection Temporal Convolutional Network (DRC-TCN) tailored to AIS sequences; residual dilated blocks with layer normalization enable stable training while capturing long-range temporal dependencies under imperfect data. Beyond kinematic inputs, we augment AIS with buoy-based meteorological variables (wind direction and speed, gust, pressure, air temperature, and sea surface temperature) via time-aligned nearest-station fusion, allowing the model to account for environmental effects on vessel motion. Experiments on New York coastal AIS data show that DRC-TCN outperforms CNN-LSTM and vanilla TCN baselines, improving F1 score by up to 99.3% and achieving 99.7% accuracy. The results indicate that environment-aware temporal modeling strengthens the robustness of track association and supports situational awareness for next-generation intelligent navigation and ocean engineering applications. Full article

Accurate identification of rear light signals in preceding vehicles is pivotal for Advanced Driver Assistance Systems (ADAS), enabling early detection of driver intentions and thereby improving road safety. In this work, we present a novel approach that leverages a meta-learning-enhanced YOLOv8 model to detect left and right turn indicators, as well as brake signals. Traditional radar and LiDAR provide robust geometry, range, and motion cues that can indirectly suggest driver intent (e.g., deceleration or lane drift). However, they do not directly interpret color-coded rear signals, which limits early intent recognition from the taillights. We therefore focus on a camera-based approach that complements ranging sensors by decoding color and spatial patterns in rear lights. This approach to detecting vehicle signals poses additional challenges due to factors such as high reflectivity and the subtle visual differences between directional indicators. We address these by training a YOLOv8 model with a meta-learning strategy, thus enhancing its capability to learn from minimal data and rapidly adapt to new scenarios. Furthermore, we developed a post-processing layer that classifies signals by the geometric properties of detected objects, employing mathematical principles such as distance, area calculation, and Intersection over Union (IoU) metrics. Our approach increases adaptability and performance compared to traditional deep learning techniques, supporting the conclusion that integrating meta-learning into real-time object detection frameworks provides a scalable and robust solution for intelligent vehicle perception, significantly enhancing situational awareness and road safety through reliable prediction of vehicular behavior. Full article

The rapid growth of urban vehicle and pedestrian flows has intensified congestion, delays, and safety concerns, underscoring the need for sustainable and intelligent traffic management in modern cities. Traditional centralized traffic signal control systems often face challenges of scalability, heterogeneity of traffic patterns, and limited real-time adaptability. To address these limitations, this study proposes a decentralized Multi-Agent Reinforcement Learning (MARL) framework for adaptive traffic signal control, where Deep Reinforcement Learning (DRL) agents are deployed at each intersection and trained on local conditions to enable real-time decision-making for both vehicles and pedestrians. A key innovation lies in the integration of Visible Light Communication (VLC), which leverages existing LED-based infrastructure in traffic lights, streetlights, and vehicles to provide high-capacity, low-latency, and energy-efficient data exchange, thereby enhancing each agent’s situational awareness while promoting infrastructure sustainability. The framework introduces a queue–request–response mechanism that dynamically adjusts signal phases, resolves conflicts between flows, and prioritizes urgent or emergency movements, ensuring equitable and safer mobility for all users. Validation through microscopic simulations in SUMO and preliminary real-world experiments demonstrates reductions in average waiting time, travel time, and queue lengths, along with improvements in pedestrian safety and energy efficiency. These results highlight the potential of MARL–VLC integration as a sustainable, resilient, and human-centered solution for next-generation urban traffic management. Full article

Current generative AI systems, despite extraordinary progress, face fundamental limitations in temporal reasoning, contextual understanding, and ethical decision-making. These systems process information statistically without authentic comprehension of experiential time or intentional context, limiting their applicability in security-critical domains where reasoning about past experiences, present situations, and future implications is essential. We present Phase 3 of the Sophimatics framework: Super Time-Cognitive Neural Networks (STCNNs), which address these limitations through complex-time representation T ∈ ℂ where chronological time (Re(T)) integrates with experiential dimensions of memory (Im(T) < 0), present awareness (Im(T) ≈ 0), and imagination (Im(T) > 0). The STCNN architecture implements philosophical constraints through geometric parameters α and β that bound memory accessibility and creative projection, enabling neural systems to perform temporal-philosophical reasoning while maintaining computational tractability. We demonstrate STCNN’s effectiveness across five security-critical applications: threat intelligence (AUC 0.94, 1.8 s anticipation), privacy-preserving AI (84% utility at ε = 1.0), intrusion detection (96.3% detection, 2.1% false positives), secure multi-party computation (ethical compliance 0.93), and blockchain anomaly detection (94% detection, 3.2% false positives). Empirical evaluation shows 23–45% improvement over baseline systems while maintaining temporal coherence > 0.9, demonstrating that integration of temporal-philosophical reasoning with neural architectures enables AI systems to reason about security threats through simultaneous processing of historical patterns, current contexts, and projected risks. Full article

Food loss and food waste (FLFW) constitute a major global challenge with profound economic, environmental, and social consequences. This study examines how businesses in Greece’s food service and retail sectors perceive and manage food waste, integrating the triple bottom line framework—economic, environmental, and social sustainability—as the guiding analytical lens. The research aims to: (1) analyze perceptions, practices, and barriers to food waste reduction among businesses; (2) explore the relationship between awareness, business policies, technological adoption, and consumer-oriented strategies; and (3) situate the Greek experience within broader European and international contexts to identify transferable lessons for policy and business innovation. Drawing on a structured survey of 250 industry representatives and comparative international evidence, the study finds that although awareness of food waste is widespread, it remains weakly connected to structured policies, technology adoption, or operational practices. Businesses recognize economic opportunities in waste reduction—such as supply chain optimization and near-expiry discounting—but these remain underexploited due to a lack of strong regulatory and financial incentives. The findings highlight that addressing food waste is not only an environmental and ethical necessity but also a strategic opportunity to enhance economic resilience, competitiveness, and sustainability within the agri-food sector. Full article

In Hungary, the consumption of any alcoholic beverage before driving is illegal. A person is considered drunk if they have a blood alcohol concentration of 0.5 g per liter or more. The situation regarding drug use is also disappointing. This research analyses these effects on transport and their “outcome” by evaluating analyses based on police data, driver training data, and experimental data. The research aims to further raise awareness of the public health importance of this problem through a case–control study. Descriptive and correlational, statistical calculations were performed with a significance value of p < 0.05. Between 2019 and 2023, there were 10–13.000 drunk driving offenses and 1.000–1.300 drunk-driving accidents on the roads each year, most of which occurred in the capital and caused minor injuries. The results will be used to discover synergies to improve road safety. Full article

Teleoperation in agricultural environments requires enhanced situational awareness for effective architectural scouting and decision-making for orchard management applications. The dynamic complexity of orchard structures presents challenges for remote visualization during architectural scouting operations. This study presents an adaptive streaming and rendering pipeline for real-time point cloud visualization in Virtual Reality (VR) teleoperation systems. The proposed method integrates selective streaming that localizes teleoperators within live maps, an efficient point cloud parser for Unity Engine, and an adaptive Level-of-Detail rendering system utilizing dynamically scaled and smoothed polygons. The implementation incorporates pseudo-coloring through LiDAR reflectivity fields to enhance the distinction between materials and geometry. The pipeline was evaluated using datasets containing LiDAR point cloud scans of orchard environments captured during spring and summer seasons, with testing conducted on both standalone and PC-tethered VR configurations. Performance analysis demonstrated improvements of 10.2–19.4% in runtime performance compared to existing methods, with a framerate enhancement of up to 112% achieved through selectively streamed representations. Qualitative assessment confirms the method’s capability to maintain visual continuity at close proximity while preserving the geometric features discernible for architectural scouting operations. The results establish the viability of VR-based teleoperation for precision agriculture applications, while demonstrating the critical relationship between Quality-of-Service parameters and operator Quality of Experience in remote environmental perception. Full article

Orbital maneuver detection is critical for space situational awareness, yet it remains challenging due to the complex and dynamic nature of satellite behaviors. This paper proposes a novel Multi-Level Firing Spiking Neural Network (MLF-SNN) for detecting orbital maneuvers based on changes in satellite orbital parameters. The MLF-SNN incorporates multiple firing thresholds and a leaky integrate-and-fire (LIF) neuron model to enhance temporal feature extraction and classification performance. The MLF-SNN encodes time-dependent input features, which include variations in orbital elements, and subsequently processes these features through a multi-layer spiking architecture. A surrogate gradient approach is adopted during training to enable end-to-end backpropagation through the spiking layers. Experimental results on real satellite data demonstrate that the proposed method achieves improved recall in maneuver detection compared to conventional approaches, effectively reducing false alarms and missed detections. The work highlights the potential of MLF-SNN in processing time-series spatial data and offers a robust solution for autonomous satellite behavior analysis. Full article

As the number of natural and man-made catastrophes has increased in recent years, there has been an increasing need for quicker and more efficient disaster response. Information from traditional sources, such as radio, television, and websites, is sometimes incomplete or delayed. While mobile applications provide a means of enhancing real-time crisis communication, a secure mobile app-based solution has not been fully explored yet. In this paper, we propose a secure and scalable cross-layer disaster management system architecture. To validate the system performance, we developed a user-centred, scalable mobile application known as the disaster emergency events application (DEAPP) for real-time disaster reporting and visualization including disaster notifications and observing the affected areas on an interactive map. The solution connects a web-based backend, cloud database, and native Android mobile app via a cross-layer architecture. Role-based access control, HTTPS connection, and verified event publication all contribute to security. Moreover, Redis caching is employed to expedite data access in emergency situations. The need to verify publicly filed reports to prevent false alarms, safeguard real-time data transfer without slowing down the system, and create an intuitive user interface for individuals in high-stress circumstances are some of the issues that the project attempts to solve. The results obtained show that a mobile system that is secure, scalable, and easy to use can enhance catastrophe awareness and facilitate quicker emergency responses. For developers, researchers, and emergency organisations looking to leverage mobile technology for disaster preparedness, the findings provide helpful insights. Full article

Background/Objectives: The demand for Emergency Medical Services (EMS) has increased over the past few years, increasing the EMS burden. Public utilization of EMS for non-emergency cases is a major global issue contributing to this burden. This study explores the public’s ability to accurately recognize emergency situations and appropriately request ambulance services. Methods: This cross-sectional study utilized a survey to explore public awareness among residents of Riyadh, Saudi Arabia. The survey was developed from the relevant literature and panel discussions, followed by validation through a pilot study. Recruitment was conducted in different publicly accessible places to capture the diverse demographics of the residents. The sample size was statistically calculated using the Raosoft sample size calculator to identify significant differences. Results: This study included 522 respondents, predominantly females (79%) aged 18–34 (42%) and 35–54 (41%) years. Both males and females correctly identified around two-thirds of the total emergency cases, with means of 6.49 (SD = 1.27) and 6.55 (SD = 1.32), respectively. Appropriate ambulance requests were made in less than one-third of the emergencies by both males and females, with means of 2.29 (SD = 1.29) and 2.38 (SD = 1.32), respectively. Stroke and older adults with hip pain were the most accurately recognized emergency cases at 92.5% and 90%, respectively, while mild chest pain and child head hematoma were the least accurately recognized at 36.6% and 38.5%. Women in labor and objects in the ear canal were the most misidentified as emergencies at 97.3% and 87.7%, respectively. Conclusions: This study highlights the prevalence of unrecognized emergency situations and the underutilization of EMS for real emergency cases. The findings recommend the need for national training programs and provide valuable insights for EMS dispatcher training programs regarding public perceptions of emergency and non-emergency situations. While the findings provide insights into targeted preventive measures to alleviate the EMS burden, they also demonstrate the critical role of public awareness in enhancing public health safety concerning emergency situations. Full article

The Irbene single-baseline radio interferometer (ISBI), operated by the Ventspils International Radio Astronomy Centre (VIRAC), offers a rare and versatile configuration in modern radio astronomy. Combining the 32-m and 16-m fully steerable parabolic radio telescopes separated by an 800-m baseline, this system possesses a unique capability for high-sensitivity, time-domain interferometric observations. Unlike large interferometric arrays optimized for sub-arcsecond resolution imaging, the Irbene system is tailored for studies that require high temporal resolution and a strong signal-to-noise ratio. This paper reviews key scientific applications of the Irbene interferometer, including simultaneous methanol maser and radio continuum variability studies, high-cadence monitoring of quasi-periodic pulsations (QPPs) in stellar flares, ionospheric diagnostics using GNSS signals, orbit determination of navigation satellites and forward scatter radar techniques for space object detection. These diverse applications demonstrate the scientific potential of compact interferometric systems in an era dominated by large-scale observatories. Full article