Search Results (51)

Healthcare faces a critical challenge: protecting sensitive medical data while enabling necessary clinical access. Evolving user behaviors, dynamic clinical contexts, and strict regulatory requirements demand adaptive access control mechanisms. Despite strict regulations, healthcare remains the most breached industry, consistently facing severe security risks related to unauthorized access. Traditional access control models cannot handle contextual variations, detect credential compromise, or provide transparent decision rationales. To address this, SAFE-GUARD (Semantic Access Control Framework Employing Generative User Assessment and Rule Decisions) is proposed as a two-layer framework that combines behavioral analysis with policy enforcement. The Behavioral Analysis Layer uses Retrieval-Augmented Generation (RAG) to detect contextual anomalies by comparing current requests against historical patterns. The Rule-Based Policy Evaluation Layer independently validates organizational procedures and regulatory requirements. Access is granted only when behavioral consistency and both organizational and regulatory policies are satisfied. We evaluate SAFE-GUARD using simulated healthcare scenarios with three LLMs (GPT-4o, Claude 3.5 Sonnet, and Gemini 2.5 Flash) achieving an anomaly detection accuracy of 95.2%, 94.1%, and 91.3%, respectively. The framework effectively identifies both compromised credentials and insider misuse by detecting deviations from established behavioral patterns, significantly outperforming conventional RBAC and ABAC approaches that rely solely on static rules. Full article

The Korean aquaculture sector relies heavily on foreign workers, who face elevated risks due to language barriers and limited safety training. This disparity necessitates data-driven safety interventions addressing specific worker vulnerabilities to ensure sustainable industry growth. This study quantitatively investigated accident characteristics and economic losses by nationality in Korean aquaculture by integrating 325 approved cases (2018–2022) from Industrial Accident Compensation Insurance (268 Korean; 57 foreign) and field survey data into the Formal Safety Assessment and Fault Tree Analysis frameworks recommended by the International Maritime Organization (IMO). The study revealed that entanglement during machinery operations accounted for 63.5% of the total cost among foreign workers. For Korean workers, slip and fall accidents were most frequent, while falls from height were the most severe in terms of unit cost and fatality. Based on the importance index and Human Element analysis, four risk control options were proposed: guarding and interlocks retrofit, multilingual training for foreign workers, and fall-protection upgrades and permit-to-work systems with lockout/tagout for Korean workers. Scenario analysis demonstrated consistent cost-saving effects. Both direct and indirect costs were incorporated into total loss estimation, with indirect costs calculated as 0.5–1.0 times the direct costs following the Ministry of Employment and Labor (2021). Full article

Football (soccer) is the world’s most widely played sport, but it carries a high incidence of traumatic injuries, particularly to the head, face, and lower limbs. Once regarded as a low-equipment discipline, the role of protective devices has expanded substantially in recent decades, both in injury prevention and in return-to-play strategies. This review provides a comprehensive overview of the historical evolution, typology, and materials of football protective equipment, with additional focus on regulatory frameworks, cultural acceptance, and illustrative cases from elite athletes. Shin guards remain the only mandatory device, yet the use of facial masks, headgear, braces, and orthoses is increasing, particularly following high-profile injuries. Advances in carbon fiber composites, thermoplastics, viscoelastic foams, and additive manufacturing have enabled lightweight, customized devices that balance protection with comfort and adherence. Beyond biomechanics, psychological reassurance, esthetics, durability, and hygiene strongly influence player compliance and perception. Despite this progress, critical challenges remain. Football lacks standardized testing protocols, clear certification pathways, and longitudinal studies on long-term outcomes. Evidence is particularly limited for youth athletes and newer categories of equipment. Looking ahead, the integration of wearable technologies, systematic hygiene and durability testing, and sustainable materials could transform protective gear into multifunctional tools for safety, monitoring, and performance optimization. Protective equipment in football has thus evolved into a multidisciplinary field at the intersection of medicine, engineering, psychology, and regulation. Future advances will depend on stronger collaboration between clinicians, researchers, governing bodies, and manufacturers to ensure safe, effective, and widely accepted protective solutions at all levels of the game. Full article

Email continues to serve as a primary vector for cyber-attacks, with phishing, spoofing, and polymorphic malware evolving rapidly to evade traditional defences. Conventional email security systems, often reliant on static, signature-based detection struggle to identify zero-day exploits and protect user privacy in increasingly data-driven environments. This paper introduces TwinGuard, a privacy-preserving framework that leverages digital twin technology to enable adaptive, personalised email threat detection. TwinGuard constructs dynamic behavioural models tailored to individual email ecosystems, facilitating proactive threat simulation and anomaly detection without accessing raw message content. The system integrates a BERT–LSTM hybrid for semantic and temporal profiling, alongside federated learning, secure multi-party computation (SMPC), and differential privacy to enable collaborative intelligence while preserving confidentiality. Empirical evaluations were conducted using both synthetic AI-generated email datasets and real-world datasets sourced from Hugging Face and Kaggle. TwinGuard achieved 98% accuracy, 97% precision, and a false positive rate of 3%, outperforming conventional detection methods. The framework offers a scalable, regulation-compliant solution that balances security efficacy with strong privacy protection in modern email ecosystems. Full article

In Low Earth Orbit (LEO) satellite communication systems, providing reliable data transmission for ultra-high-speed mobile terminals faces severe challenges from dramatic Doppler effects and fast time-varying channels. Orthogonal Time Frequency Space (OTFS) modulation is a promising technique for high-mobility Low Earth Orbit (LEO) satellite communications, but its performance is often limited by inaccurate Channel State Information (CSI) prediction and suboptimal resource allocation, particularly in dynamic channels with coupled parameters like SNR, Doppler, and delay. To address these limitations, this paper proposes an adaptive OTFS frame configuration scheme based on multi-domain channel prediction. We utilize a Long Short-Term Memory (LSTM) network to jointly predict multi-dimensional channel parameters by leveraging their temporal correlations. Based on these predictions, the OTFS transmitter performs two key optimizations: dynamically adjusting the pilot guard bands in the Delay-Doppler domain to reallocate guard resources to data symbols, thereby improving spectral efficiency while maintaining channel estimation accuracy; and performing optimal power allocation based on predicted sub-channel SNRs to minimize the system’s Bit Error Rate (BER). The simulation results show that our proposed scheme reduces the required SNR for a BER of $1\times {10}^{-3}$ by approximately 1.5 dB and improves spectral efficiency by 10.5% compared to baseline methods, demonstrating its robustness and superiority in high-mobility satellite communication scenarios. Full article

A high-precision coal seam model is crucial to improving the adaptability of unmanned mining technology to geological conditions. However, the accuracy of a coal seam model constructed with boreholes and geophysical data is far from the required accuracy of unmanned mining (sub-decimeter level). Therefore, it is necessary to collect geological data revealed by mining and to update the coal seam model dynamically. As a solution to this problem, this paper proposes a new method for conducting off-site geological surveying of longwall faces by integrating multi-source monitoring data. The spatial attitudes of hydraulic supports are monitored to estimate the local dip angles of longwall face. A roof line calculation model was established, which integrates the local inclination angle of the longwall face, the number of hydraulic supports, and the roof elevation of the two roadways. Meanwhile, the local coal–rock columns at the camera observation point are extracted automatically using image segmentation and a proportional relationship between the picture and the actual scene. Coal and rock walls and a support guarding plate in the longwall face image are identified accurately using the coal-rock support segmentation model trained with U-net. Then, the height of the coal (or rock) wall above the coal–rock interface is estimated automatically according to the image segmentation and the similar proportion equation of actual longwall face and longwall face image. Combined with mining height information, the local coal–rock column can be extracted. Finally, the geological surveying profile of longwall face can be obtained by integrating the estimated roof line and local coal–rock columns. The field test demonstrated the efficacy of the method. This study helps to address a long-standing limitation of insufficient geological adaptability of intelligent mining technology. Full article

Satellite networks face escalating cybersecurity threats from evolving attack vectors and systemic complexities. This paper proposes SatGuard, a novel framework integrating a three-dimensional penetration testing methodology and a nonlinear risk assessment mechanism tailored for satellite security. To address limitations of conventional tools in handling satellite-specific vulnerabilities, SatGuard employs large language models (LLMs) like GPT-4 and DeepSeek-R1. By leveraging their contextual reasoning and code-generation abilities, SatGuard enables semi-automated vulnerability analysis and exploitation. Validated in a simulated ground station environment, the framework achieved a 73.3% success rate (22/30 attempts) across critical ports, with an average of 5.5 human interactions per test. By bridging AI-driven automation with satellite-specific risk modeling, SatGuard advances cybersecurity for next-generation space infrastructure through scalable, ethically aligned solutions. Full article

With the rapid development of intelligent driving technology, in-vehicle bus networks face increasingly stringent requirements for real-time performance and data transmission. Traditional bus network technologies such as LIN, CAN, and FlexRay are showing significant limitations in terms of bandwidth and response speed. In-Vehicle Ethernet, with its advantages of high bandwidth, low latency, and high reliability, has become the core technology for next-generation in-vehicle communication networks. This study focuses on bandwidth waste caused by guard bands and the limitations of Frame Pre-Emption in fully utilizing available bandwidth in In-Vehicle Ethernet. It aims to optimize TSN scheduling mechanisms by enhancing scheduling flexibility and bandwidth utilization, rather than modeling system-level vehicle functions. Based on the Time-Sensitive Networking (TSN) protocol, this paper proposes an innovative Adaptive Frame Segmentation (AFS) algorithm. The AFS algorithm enhances the performance of In-Vehicle Ethernet message transmission through flexible frame segmentation and efficient message scheduling. Experimental results indicate that the AFS algorithm achieves an average local bandwidth utilization of 94.16%, improving by 4.35%, 5.65%, and 30.48% over Frame Pre-Emption, Packet-Size Aware Scheduling (PAS), and Improved Qbv algorithms, respectively. The AFS algorithm demonstrates stability and efficiency in complex network traffic scenarios, reducing bandwidth waste and improving In-Vehicle Ethernet’s real-time performance and responsiveness. This study provides critical technical support for efficient communication in intelligent connected vehicles, further advancing the development and application of In-Vehicle Ethernet technology. Full article

To achieve automation at the inner corner guard installation station in a steel coil packaging production line and enable automatic docking and installation of the inner corner guard after eye position detection, this paper proposes a binocular vision method based on deep learning for eye position detection of steel coil rolls. The core of the method involves using the Mask R-CNN algorithm within a deep-learning framework to identify the target region and obtain a mask image of the steel coil end face. Subsequently, the binarized image of the steel coil end face was processed using the RGB vector space image segmentation method. The target feature pixel points were then extracted using Sobel edges, and the parameters were fitted by the least-squares method to obtain the deflection angle and the horizontal and vertical coordinates of the center point in the image coordinate system. Through the ellipse parameter extraction experiment, the maximum deviations in the pixel coordinate system for the center point in the u and v directions were 0.49 and 0.47, respectively. The maximum error in the deflection angle was 0.45°. In the steel coil roll eye position detection experiments, the maximum deviations for the pitch angle, deflection angle, and centroid coordinates were 2.17°, 2.24°, 3.53 mm, 4.05 mm, and 4.67 mm, respectively, all of which met the actual installation requirements. The proposed method demonstrates strong operability in practical applications, and the steel coil end face position solving approach significantly enhances work efficiency, reduces labor costs, and ensures adequate detection accuracy. Full article

In the context of the global energy landscape restructuring driven by the “dual-carbon” goals, new energy storage technologies have emerged as a critical enabler for energy transformation and the development of a new power system. However, as these technologies advance and the market expands, ensuring safety remains a significant and long-term challenge. This paper focuses on the safety risk prevention and control of new energy storage systems. It systematically reviewed various new energy storage technology pathways and their associated potential risks. Furthermore, it analyzed the challenges and difficulties faced in safety risk prevention and control across different stages of new energy storage projects, including large-scale application, pilot demonstration, and R&D reserves. Considering the technical uncertainties in the future development of new energy storage, this study evaluated potential safety risks and proposed corresponding strategies and measures for risk management. By addressing these challenges, this study aims to safe-guard the security and reliability of new energy storage technologies, thereby supporting the construction of a robust and sustainable new power system. Full article

With the extensive deployment and application of the Internet of Things (IoT), 5G and 6G technologies and edge intelligence, the volume of data generated by IoT and the number of intelligence applications derived from these data are rapidly growing. However, the absence of effective mechanisms to safeguard the vast data generated by IoT, along with the security and privacy of edge intelligence applications, hinders their further development and adoption. In recent years, Trusted Execution Environment (TEE) has emerged as a promising technology for securing cloud data storage and cloud processing, demonstrating significant potential for ensuring data and application confidentiality in more scenarios. Nevertheless, applying TEE technology to enhance security in IoT and edge intelligence scenarios still presents several challenges. This paper investigates the technical challenges faced by current TEE solutions, such as performance overhead and I/O security issues, in the context of the resource constraints and data mobility that are inherent to IoT and edge intelligence applications. Using Intel Software Guard Extensions (SGX) technology as a case study, this paper validates these challenges through extensive experiments. The results provide critical assessments and analyses essential for advancing the development and usage of TEE in IoT and edge intelligence scenarios. Full article

The knotter, as a core module for the knotting function of a rice electric binder, has structural parameters and spatial configurations that significantly impact the efficiency and quality of rice collection, making the in-depth analysis and optimization of these parameters, and their spatial relationships, crucial for enhancing the operational quality of the rice electric binder. At present, rice binders still face the issues of a low bundling efficiency and quality, which affect the progress of rice harvesting during the harvest season. Through theoretical analysis and calculation, this study determined the main parameters affecting the knotter’s knotting process and their value ranges. Based on the ADAMS software, a simulation model of the knotter operation was constructed. Using the Box–Behnken design (BBD) method and response surface analysis of variance, a regression prediction model for knotter operation evaluation indicators was established, and the multi-objective optimization of the knotter’s operation quality was performed. The prediction results showed that, under the optimal structural parameter combination of a 30.23° angle between the knotting pincer and rope guard axes, a −3.75 mm rope clamping board position, and a 40.75° inclination angle of the knotting pincer convex platform, the knotter’s knotting quality reached the best state, with an average knot end protrusion of 9.10 mm and a maximum tension of 134.25 N on the knotting rope. The field tests results showed an average knot end protrusion of 9.60 mm and a maximum tension of 127.87 N on the knotting rope, with average relative errors of 5.82% and 4.72% compared to the theoretical values, respectively. After optimizing the knotter, the average knot end protrusion increased by 14.48% and the maximum tension of the knot rope was reduced by 11.27%. Meanwhile, the knotter achieved an average bundling rate as high as 99.3%. The bundling success rate also increased by 2.7%. These results fully verify the reliability and accuracy of the regression model, and demonstrate the reasonableness of the knotter structural parameter optimization design, providing a theoretical basis and reference for improving the operational quality of the rice electric binder. Full article

When developing a distributed application, several issues need to be handled, and software components should include some mechanisms to make their execution resilient when network faults, delays, or tampering occur. For example, synchronous calls represent a too-tight connection between a client requesting a service and the service itself, whereby potential network delays or temporary server overloads would keep the client side hanging, exposing it to a domino effect. The proposed approach assists developers in dealing with such issues by providing an automatic tool that enhances a distributed application using simple blocking calls and makes it robust in the face of adverse events. The proposed devised solution consists in automatically identifying the parts of the application that connect to remote services using simple synchronous calls and substituting them with a generated customized snippet of code that handles potential network delays or faults. To accurately perform the proposed transformation, the devised tool finds application code statements that are data-dependent on the results of the original synchronous calls. Then, for the dependent statements, a solution involving guarding code, proper synchronization, and timeouts is injected. We experimented with the analysis and transformation of several applications and report a meaningful example, together with the analysis of the results achieved. Full article

With the rapid popularity of unmanned aerial vehicles (UAVs), airspace safety is facing tougher challenges, especially for the identification of non-cooperative target UAVs. As a vital approach for non-cooperative target identification, radar signal processing has attracted continuous and extensive attention and research. The constant false alarm rate (CFAR) detector is widely used in most current radar systems. However, the detection performance will sharply deteriorate in complex and dynamical environments. In this paper, a novel truncated statistics- and neural network-based CFAR (TSNN-CFAR) algorithm is developed. Specifically, we adopt a right truncated Rayleigh distribution model combined with the characteristics of pattern recognition using a neural network. In the simulation environments of four different backgrounds, the proposed algorithm does not need guard cells and outperforms the traditional mean level (ML) and ordered statistics (OS) CFAR algorithms. Especially in high-density target and clutter edge environments, since utilizing 19 statistics obtained from the numerical calculation of two reference windows as the input characteristics, the TSNN-CFAR algorithm has the best adaptive decision ability, accurate background clutter modeling, stable false alarm regulation property and superior detection performance. Full article

With the increase of mining height at the working face, the influence range of roof fractures in the goaf increases, the advanced supporting pressure on the coal wall increases, ground pressure becomes more intense, and roof support becomes more difficult. Based on the analysis of ground pressure behavior in the first mining and caving stage, the normal mining stage, and the final mining breakthrough stage of the fully-mechanized mining face near 12,404, the relationship between conveyor current and coal speed is studied and compared. Based on the intelligent control system of the fully-mechanized mining face with a super-high mining height of 12,404 and the structure of the football team, the “playing football” roof control mode of the fully-mechanized mining face with super-high mining height under the background of 5G+ big data is put forward. The conclusions are as follows: In 12,404, the ground pressure was first mined. During normal mining, when the roof with a buried depth of more than 200 m is broken, the speed of the coal machine is kept within 12 m/min, and the full guard defends and controls the roof, pulling the lead frame through the area with severe ground pressure. When the roof is good, it is necessary to speed up the coal cutting and get rid of the pressure. When it is less than 200 m, it will overcome the local weighting, and show an offensive trend to speed up and increase production. In the final mining breakthrough stage, the speed of the coal machine should be controlled within 8 m/min, with attention to defense, guarding against roof leakage, and reducing waste rock. Full article