Safety Systems Research Articles

Deep-Reinforcement-Learning-Based Collision Avoidance of Autonomous Driving System for Vulnerable Road User Safety

Deep-Reinforcement-Learning-Based Collision Avoidance of Autonomous Driving System for Vulnerable Road User Safety

Morphological Characteristics of Propane–Hydrogen Diffusion Flame: Experiments and Correlations

ABSTRACT This study investigated the flame height and liftoff height of turbulent diffusion jet flames of the Propane–Hydrogen mixed gas. A series of experiments have been conducted to examine their flame height and liftoff height with different concentrations of propane and hydrogen. The experimental results show that the flame height decreases with the increase in hydrogen concentration. The accuracy of the Froude number for the prediction of flame height has been verified. The liftoff height is proportional to the propane flow rate, while it was inversely proportional to the hydrogen concentration due to the increased laminar flame speed. A theoretical model incorporating laminar flame speed, heat release rate, and fuel exit velocity has been obtained. These investigations are crucial for enhancing safety in hydrogen-enriched pipeline systems, which is helpful for the use of hydrogen in energy systems and global sustainability efforts.

Clinical Practice Guidelines to Support Capacity Building in Orthopaedic Surgical Outreach: An International Consensus Building Approach.

Surgical outreach to low- and middle-income countries (LMICs) by organizations from high- income countries is on the rise to help address the growing burden of conditions warranting surgery. However, concerns remain about the impact and sustainability of such outreach. Leading organizations (e.g., the World Health Organization) advocate for a capacity-building approach to ensure the safety, quality, and sustainability of the local health-care system. Despite this, to our knowledge, no guidelines exist to inform such efforts. We aimed to develop clinical practice guidelines (CPGs) to support capacity-building in orthopaedic surgical outreach utilizing a multistakeholder and international voting panel. We followed a modified American Academy of Orthopaedic Surgeons (AAOS) CPG development process. We systematically reviewed the existing literature across 7 predefined capacity-building domains (partnership, professional development, governance, community impact, finance, coordination, and culture). A writing panel composed of 6 orthopaedic surgeons with extensive experience in surgical outreach reviewed the existing literature and developed a consensus-based CPG for each domain. We created an international voting panel of orthopaedic surgeons and administrators who have leadership roles in outreach organizations or hospitals with which outreach organizations partner. Members individually reviewed the CPGs and voted to approve or disapprove each guideline. A CPG was considered approved if >80% of panel members voted to approve it. An international voting panel of 14 surgeons and administrators from 6 countries approved all 7 of the CPGs. Each CPG provides recommendations for capacity-building in a specific domain. For example, in the domain of partnership, the CPG recommends the development of a documented plan for ongoing, bidirectional partnership between the outreach organization and the local team. In the domain of professional development, the CPG recommends the development of a needs-based curriculum focused on both surgical and nonsurgical patient care utilizing didactic and hands-on techniques. As orthopaedic surgical outreach grows, best-practice CPGs to inform capacity-building initiatives can help to ensure that resources and efforts are optimized to support the sustainability of care delivery at local sites. These guidelines can be reviewed and updated in the future as evidence that supports capacity-building in LMICs evolves.The global burden of disease warranting surgery is substantial, and morbidity and mortality from otherwise treatable conditions remain disproportionately high in low- and middle-income countries (LMICs)1,2. It is estimated that up to 2 million (about 40%) of injury-related deaths in LMICs could be avoided annually if mortality rates were reduced to the level of those in high-income countries (HICs)3. Despite this, progress toward improved access to safe, timely surgery in resource-poor areas has been slow. Historically, nongovernmental organizations (NGOs) have tried to address unmet surgical needs through short-term outreach trips; however, growing criticism has highlighted the limitations of short-term trips, including limited follow-up, an increased burden on the local workforce, and further depletion of local resources4-6. In light of ongoing concerns, public health priorities have shifted toward models that emphasize long-term capacity-building rather than short-term care delivery. Capacity-building is an approach to health-care development that builds independence through infrastructure development, sustainability, and enhanced problem-solving while taking context into account7,8.

Customizable Zr-MOF nanoantidote-based multieffective arsenic detoxification and its extended low-toxic therapy.

Arsenic (As) poisoning has become a global public problem threatening human health. Chelation therapy (CT) is the preferred treatment for arsenic poisoning. Nevertheless, efficient and safe arsenic removal in vivo remains a daunting challenge due to the limitations of chelators, including weak affinity, poor cell membrane penetration, and short half-life. Herein, a mercapto-functionalized and size-tunable hierarchical porous Zr-MOF (UiO-66-TC-SH) is developed, which possesses abundant arsenic chemisorption sites, effective cell uptake ability, and long half-life, thereby efficiently removing toxic arsenic in vivo. Moreover, the strong binding affinity of UiO-66-TC-SH for arsenic reduces systemic toxicity caused by off-target effects. In animal trials, UiO-66-TC-SH decreases the blood arsenic levels of acute arsenic poisoning mice to a normal value within 48 h, and the efficacy is superior to clinical drugs 2,3-dimercaptopropanesulfonic acid sodium salt (DMPS). Meanwhile, UiO-66-TC-SH also significantly mitigates the arsenic accumulation in the metabolic organs of chronic arsenic poisoning mice. Surprisingly, UiO-66-TC-SH also accelerates the metabolism of arsenic in organs of tumor-bearing mice and alleviates the side effects of arsenic drugs antitumor therapy. STATEMENT OF SIGNIFICANCE: Arsenic (As) contamination has become a global problem threatening public health. The present clinical chelation therapy (CT) still has some limitations, including the weak affinity, poor cell membrane permeability and short half-life of hydrophilic chelators. Herein, a metal-organic framework (MOF)-based multieffective arsenic removal strategy in vivo is proposed for the first time. Mercapto-functionalized and size-tunable hierarchical porous Zr-MOF nanoantidote (denoted as UiO-66-TC-SH) is accordingly designed and synthesized. After injection, UiO-66-TC-SH can form Zr-O-As bonds and As-S bonds with arsenic, thus enhancing arsenic adsorption capacity, cycling stability and systemic safety simultaneously. The acute arsenic poisoning model results indicate that UiO-66-TC-SH shows superior efficacy to the clinical drug sodium dimercaptopropanesulfonate (DMPS). More meaningfully, we find that UiO-66-TC-SH also accelerates the metabolism of arsenic in organs of tumor-bearing mice and alleviates side effects of arsenic drugs anti-tumor therapy.

Monitoring the Center of Gravity of a Vehicle Seat to Detect the Occupant Position

Deploying an airbag when a vehicle occupant is too close to it can cause injury. An adaptive Airbag Control Unit (ACU) would improve the effectiveness of the safety system, provided it is aware of the actual position of the occupants once the crash is going to occur. Occupants can be monitored with vision-based and radar-based sensing in the vehicle, but the research question is whether other reliable devices exist. In this research, a real seat is equipped with four sensors in the supports from the floor, as well as an Inertial Measurement Unit (IMU) and a microcontroller. The device is capable of identifying correct position or different Out of Position (OP) conditions and inform an adaptive ACU. The paper presents the seat layout in detail and its testing in extensive driving experiments with multiple participants. Depending on the position of the driver, the identification is correct 45–100% of the time. Monitoring the occupant position by a sensorized seat is feasible and can improve the reliability of the onboard safety system when integrated with other occupant monitoring devices.

Automating Fault Test Cases Generation and Execution for Automotive Safety Validation via NLP and HIL Simulation

The complexity and the criticality of automotive electronic implanted systems are steadily advancing and that is especially the case for automotive software development. ISO 26262 describes requirements for the development process to confirm the safety of such complex systems. Among these requirements, fault injection is a reliable technique to assess the effectiveness of safety mechanisms and verify the correct implementation of the safety requirements. However, the method of injecting the fault in the system under test in many cases is still manual and depends on an expert, requiring a high level of knowledge of the system. In complex systems, it consumes time, is difficult to execute, and takes effort, because the testers limit the fault injection experiments and inject the minimum number of possible test cases. Fault injection enables testers to identify and address potential issues with a system under test before they become actual problems. In the automotive industry, failures can have serious hazards. In these systems, it is essential to ensure that the system can operate safely even in the presence of faults. We propose an approach using natural language processing (NLP) technologies to automatically derive the fault test cases from the functional safety requirements (FSRs) and execute them automatically by hardware-in-the-loop (HIL) in real time according to the black-box concept and the ISO 26262 standard. The approach demonstrates effectiveness in automatically identifying fault injection locations and conditions, simplifying the testing process, and providing a scalable solution for various safety-critical systems.

MSGC-YOLO: An Improved Lightweight Traffic Sign Detection Model under Snow Conditions

Traffic sign recognition plays a crucial role in enhancing the safety and efficiency of traffic systems. However, in snowy conditions, traffic signs are often obscured by particles, leading to a severe decrease in detection accuracy. To address this challenge, we propose an improved YOLOv8-based model for traffic sign recognition. Initially, we introduce a Multi-Scale Group Convolution (MSGC) module to replace the C2f module in the YOLOv8 backbone. Data indicate that MSGC enhances detection accuracy while maintaining model lightweightness. Subsequently, to improve the recognition ability for small targets, we introduce an enhanced small target detection layer, which enhances small target detection accuracy while reducing parameters. In addition, we replaced the original BCE loss with the improved EfficientSlide loss to improve the sample imbalance problem. Finally, we integrate Deformable Attention into the model to improve the detection efficiency and performance of complex targets. The resulting fused model, named MSGC-YOLOv8, is evaluated on an enhanced dataset of snow-covered traffic signs. Experimental results show that the MSGC-YOLOv8 model is used for snow road traffic sign recognition. Compared with the YOLOv8n model mAP@0.5:0.95, mAP@0.5:0.95 is increased by 17.7% and 18.1%, respectively, greatly improving the detection accuracy. Compared with the YOLOv8s model, while the parameters are reduced by 59.6%, mAP@0.5 only loses 1.5%. Considering all aspects of the data, our proposed model shows high detection efficiency and accuracy under snowy conditions.

Drone Safety and Security Surveillance System (D4S)

Drones offer significant safety and security advantages by enhancing situational awareness across various fields. However, realizing these benefits hinges on well-designed drone systems. This study builds upon previous research on drone deployment challenges and proposes the Drone Safety and Security Surveillance System (D4S). D4S aims to standardize similar drone-based systems, enhancing situational awareness and supporting decision-making processes. While initially tailored for safety and security, D4S holds potential for broader applications. Two system architectures have been proposed and evaluated with positive feedback from safety and security professionals. D4S has the potential to revolutionize safety practices, improve situational awareness, and facilitate timely decision making in critical scenarios.

Safe Autonomous Driving with Latent Dynamics and State-Wise Constraints

Autonomous driving has the potential to revolutionize transportation, but developing safe and reliable systems remains a significant challenge. Reinforcement learning (RL) has emerged as a promising approach for learning optimal control policies in complex driving environments. However, existing RL-based methods often suffer from low sample efficiency and lack explicit safety constraints, leading to unsafe behaviors. In this paper, we propose a novel framework for safe reinforcement learning in autonomous driving that addresses these limitations. Our approach incorporates a latent dynamic model that learns the underlying dynamics of the environment from bird’s-eye view images, enabling efficient learning and reducing the risk of safety violations by generating synthetic data. Furthermore, we introduce state-wise safety constraints through a barrier function, ensuring safety at each state by encoding constraints directly into the learning process. Experimental results in the CARLA simulator demonstrate that our framework significantly outperforms baseline methods in terms of both driving performance and safety. Our work advances the development of safe and efficient autonomous driving systems by leveraging the power of reinforcement learning with explicit safety considerations.

Преимущества интеграции автоматизированных систем управления технологическим процессом угольных шахт с системами определения местоположения

Coal mining companies, especially those involved in underground operations, are currently at different stages on their way to digitalization and face a number of challenges, which are described in the article. One group of such problems is the difficulties concerned with the coal mine dispatching control. The paper shows how a properly selected approach, i.e. the integration of various automated process control systems with positioning systems, provides a solution to these problems and offers significant advantages both regarding the industrial safety and maximizing the economic effect. Examples of such integration are given for the conveyor transport control system, for the air and mine gas control systems as well as a CCTV systems with positioning systems within the Smart Mine® complex. An assessment of the current technical basis for the implementation of this approach is given, and the advantages of using a single platform for subsystems of the multi-functional safety system and production management systems are shown.

Feasibility and safety report on robotic assistance for cervical pedicle screw fixation: a cadaveric study

This cadaveric study aimed to evaluate the safety and usability of a novel robotic system for posterior cervical pedicle screw fixation. Three human cadaveric specimens and C2-T3 were included. Freshly frozen human cadaver specimens were prepared and subjected to robot-assisted posterior cervical pedicle screw fixation using the robotic system. The accuracy of screw placement, breach rate, and critical structure violations were evaluated. The results were statistically compared with those of previous studies that used different robotic systems for cervical pedicle screw fixation. The robotic system demonstrated a high accuracy rate in screw placement. A significant number of screws were placed within predetermined safe zones. The total entry offset was 1.08 ± 0.83 mm, the target offset was 1.86 ± 0.50 mm, and the angle offset was 2.14 ± 0.77°. Accuracy rates comparable with those of previous studies using different robotic systems were achieved. The system was also feasible, allowing precise navigation and real-time feedback during the procedure. This cadaveric study validated the safety and usability of the novel robotic system for posterior cervical pedicle screw fixation. The system exhibited high precision in screw placement, and the results support the extension of the indications for robot-assisted pedicle screw fixation from the lumbar spine to the cervical spine.

Targeted degradation of oncogenic BCR-ABL by silencing the gene of NEDD8 E3 ligase RAPSYN

Tyrosine kinase inhibitors have been the standard treatment for patients with Philadelphia chromosome-positive (Ph+) leukemia. However, a series of issues, including drug resistance, relapse and intolerance, are still an unmet medical need. Here, we report the targeted siRNA-based lipid nanoparticles in Ph+ leukemic cell lines for gene therapy of Ph+ leukemia, which specifically targets a recently identified NEDD8 E3 ligase RAPSYN in Ph+ leukemic cells to disrupt the neddylation of oncogenic BCR-ABL. To achieve the specificity for Ph+ leukemia therapy, a single-chain fragment variable region (scFv) of anti-CD79B monoclonal antibody was covalently conjugated on the surface of OA2-siRAPSYN lipid nanoparticles to generate the targeted lipid nanoparticles (scFv-OA2-siRAPSYN). Through effectively silencing RAPSYN gene in leukemic cell lines by the nanoparticles, BCR-ABL was remarkably degraded accompanied by the inhibition of proliferation and the promotion of apoptosis. The specific targeting, therapeutic effects and systemic safety were further evaluated and demonstrated in cell line-derived mouse models. The present study has not only addressed the clinical need of Ph+ leukemia, but also enabled gene therapy against a less druggable target.Graphical

An Edge Computing System with AMD Xilinx FPGA AI Customer Platform for Advanced Driver Assistance System

The convergence of edge computing systems with Field-Programmable Gate Array (FPGA) technology has shown considerable promise in enhancing real-time applications across various domains. This paper presents an innovative edge computing system design specifically tailored for pavement defect detection within the Advanced Driver-Assistance Systems (ADASs) domain. The system seamlessly integrates the AMD Xilinx AI platform into a customized circuit configuration, capitalizing on its capabilities. Utilizing cameras as input sensors to capture road scenes, the system employs a Deep Learning Processing Unit (DPU) to execute the YOLOv3 model, enabling the identification of three distinct types of pavement defects with high accuracy and efficiency. Following defect detection, the system efficiently transmits detailed information about the type and location of detected defects via the Controller Area Network (CAN) interface. This integration of FPGA-based edge computing not only enhances the speed and accuracy of defect detection, but also facilitates real-time communication between the vehicle’s onboard controller and external systems. Moreover, the successful integration of the proposed system transforms ADAS into a sophisticated edge computing device, empowering the vehicle’s onboard controller to make informed decisions in real time. These decisions are aimed at enhancing the overall driving experience by improving safety and performance metrics. The synergy between edge computing and FPGA technology not only advances ADAS capabilities, but also paves the way for future innovations in automotive safety and assistance systems.

Advancing Cycling Safety: On-Bike Alert System Utilizing Multi-Layer Radar Point Cloud Clustering for Coarse Object Classification

Cyclists are considered to be vulnerable road users (VRUs) and need protection from potential collisions with cars and other vehicles induced by unsafe driving, dangerous road conditions, or weak cycling infrastructure. Integrating mmWave radars into cycling safety measures presents an efficient solution to this problem given their compact size, low power consumption, and low cost compared to other sensors. This paper introduces an mmWave radar-based bike safety system designed to offer real-time alerts to cyclists. The system consists of a low-power radar sensor affixed to the bicycle, connected to a micro-controller, and delivering a preliminary classification of detected obstacles. An efficient two-level clustering based on the accumulation of radar point clouds from multiple frames with a temporal projection from previous frames into the current frame is proposed. The clustering is followed by a coarse classification algorithm in which we use relevant features extracted from the resulting clusters. An annotated RadBike dataset composed of radar point cloud data synchronized with RGB camera images is developed to evaluate our system. The two-level clustering outperforms the DBSCAN algorithm, achieving a v-measure score of 0.91, compared to 0.88 with classical DBSCAN. Different classifiers, including decision trees, random forests, support vector machines (SVMs), and AdaBoost, have been assessed, with an overall accuracy of 87% for the three main object classes: four-wheeled, two-wheeled, and others. The system has the ability to improve rider safety on the road and substantially reduce the frequency of incidents involving cyclists.

Optimizing Insulator Defect Detection with Improved DETR Models

With the increasing demand for electricity, the power grid is undergoing significant advancements. Insulators, which serve as protective devices for transmission lines in outdoor high-altitude power systems, are widely employed. However, the detection of defects in insulators captured under challenging conditions, such as rain, snow, fog, sunlight, and fast-moving drones during long-distance photography, remains a major challenge. To address this issue and improve the accuracy of defect detection, this paper presents a novel approach: the Multi-Scale Insulator Defect Detection Approach using Detection Transformer (DETR). In this study, we propose a multi-scale backbone network that effectively captures the features of small objects, enhancing the detection performance. Additionally, we introduce a self-attention upsampling (SAU) module to replace the conventional attention module, enhancing contextual information extraction and facilitating the detection of small objects. Furthermore, we introduce the insulator defect (IDIoU) loss, which mitigates the instability in the matching process caused by small defects. Extensive experiments were conducted on an insulator defect dataset to evaluate the performance of our proposed method. The results demonstrate that our approach achieves outstanding performance, particularly in detecting small defects. Compared to existing methods, our approach exhibits a remarkable 7.47% increase in the average precision, emphasizing its efficacy in insulator defect detection. The proposed method not only enhances the accuracy of defect detection, which is crucial for maintaining the reliability and safety of power transmission systems but also has broader implications for the maintenance and inspection of high-voltage power infrastructure.

Online Exam Portal

An online test is one that is conducted via the internet or an intranet utilizing a computer system. It is a web-based exam system. It works well as a mass education evaluation solution. Our system for online exams is built on a Browser/Server architecture. The multiple choice questions that are fed into the system are examined and automatically graded by the system. Even while it is comfortable, the classic pen and paper exam format has drawbacks in the current digital learning environment. IT students find these limits even more noticeable because they are used to using internet resources and platforms. In response, this proposal suggests creating an efficient, user-friendly, and safe online assessment system especially for IT students. This project goes beyond practicality. It increases assessment quality with interesting formats, empowers students with freedom, and promotes efficiency with quicker administration and grading. Teachers receive insightful data-driven information for bettering their curricula. In the end, it welcomes the digital age and uses cutting-edge evaluation techniques to get IT students ready for success

The safety and efficacy of the Stryker OptaBlate™ Bone Tumor Ablation system for vertebral body metastases

BackgroundMetastatic spinal lesions are a significant cause of morbidity and decreased quality of life in those with a high tumor burden. Despite treatment modalities such as medical therapy (e.g., chemotherapy, steroids), spinal augmentation procedures, and radiation therapy, many patients still experience refractory back pain due to neoplastic infiltration of the vertebral body and/or pathologic compression fractures. With the aim to address refractory pain in patients who have exhausted conventional treatment options, Stryker developed the OptablateTM Bone Tumor Ablation system (BTA; Stryker Corporation, Kalamazoo, MI), which delivers radiofrequency energy to pathologic vertebral body lesions. In this preliminary single-institution study, we characterize the use of the BTA system in 11 patients undergoing kyphoplasty for pathologic spinal lesions with the goal to demonstrate the impact of this novel technology on refractory pain in this challenging clinical setting.MethodsA single-center retrospective chart review was performed on all patients identified as those receiving tumor ablation/kyphoplasty for spinal neoplasms using the OptablateTM BTA system performed by a single surgeon at the University of Oklahoma Medical Center. Sex, age, primary lesion type, presenting symptomatology, spinal level, time of follow-up, and outcome were obtained from the electronic medical record (EMR).ResultsEleven patients (4 males, 7 females) with a mean age of 62 (range, 38–82) years had an average follow-up time of 6 months. Presenting symptoms attributed to spinal pathology included back pain (n = 11, 100%), pathologic fracture (n = 6, 55%), and lower extremity weakness (n = 3, 27%). A total of 20 lesions were ablated at 12 vertebral levels. Eight patients (73%) had improved pain. No complications were reported.ConclusionThis preliminary study documents the safety of the BTA system, in addition to its diverse use across many levels. The majority of patients reported improvement in their pain. Further study is required to fully characterize the use of the BTA system in those with neoplastic spinal pathology.

Movie recommendation and classification system using block chain

Recommender Systems are mainly used in various e-commerce applications, especially online stores threatening users’ privacy. The privacy issues can be overcome by using security solutions, which include blockchain technology for privacy applications. The fusion of the Internet of Things and blockchain technology has fully improved modern distributed systems. The combination guarantees the safety and scalability of the recommender system. We aim to create an authorized secure exchange device using blockchain-enabled multiparty computation by adding smart contracts to the core blockchain protocol. The recommendation structure and Blockchain technology make online shopping more convenient and private. We propose a blockchain-related recommender system using the “movielens” data. The case study includes a smart contract model that recommends movies to buyers. Initially, we tested the model on a small “movielens dataset” and extended it to a 3M movielens dataset. We developed a classifier model for movielens and proposed a Dual light graph convolutional network for movielens data classification. Our results, including ablation analysis, show that blockchain strategies and Dual light graph convolutional networks can effectively improve recommender systems’ privacy. Furthermore, the suggested blockchain technique can be stretched by similar procedures.

Application of LuTan-1 SAR Data in Railway Subsidence Monitoring

Abstract. InSAR technology is currently a crucial tool for large-scale surface deformation monitoring, particularly excelling in regional subsidence monitoring. In this study, focusing on the Jinan section of the Shandong railway, newly launched LUTAN-1 satellite SAR data was employed. DInSAR and Stacking techniques were applied to analyze the subsidence in response to the long-term operation load of high-speed trains and surrounding human activities. Through the analysis of data from June 2023 to December 2023, regional subsidence was identified in this section, with a subsidence rate reaching 8 cm/year. Comparative analysis between DInSAR technology monitoring results and precise leveling monitoring results showed consistency in the subsidence deformation trend in the region, achieving centimeter-level deformation monitoring in the study area.The LuTan-1 satellite provided robust SAR data support for railway subsidence monitoring, offering substantial reliability and accuracy to deepen the understanding of railway subsidence issues. These research findings hold significant practical implications for adopting timely maintenance and repair measures, ensuring the safety and reliable operation of the railway system.

Exploring Motion Stability of a Novel Semi-Submersible Platform for Offshore Wind Turbines

The stability of offshore floating wind turbine foundation platforms is a fundamental requirement for the efficiency and safety of wind power generation systems. This paper proposes a novel small-diameter float-type semi-submersible platform to improve system stability. To evaluate the superior motion stability of the proposed floating platform, a comprehensive frequency–domain response analysis and experimental study were conducted in comparison with the OC4-DeepCwind platform developed by the National Renewable Energy Laboratory (NREL). The respective comparison of the frequency–domain response analysis and the experimental results demonstrated that the proposed floating wind turbine platform shows better hydrodynamic characteristics and resonance avoidance capability. This not only reduces the Response Amplitude Operators (RAOs), but also enhances the system stability, namely, effectively avoiding the regions of concentrated wave loading and low-frequency ranges. Furthermore, the proposed small-diameter semi-submersible platform has the potential to reduce manufacturing costs, providing valuable insights for the manufacturing of offshore floating wind turbine systems.