Search Results (1,501)

Today, mechatronic systems are required to operate reliably and safely. However, actuators can fail, causing the system to malfunction or, in the worst case, resulting in an accident. A clear example of this is the motors of unmanned aerial vehicles. If any of them fail, the vehicle loses control, resulting in a catastrophe and potentially leading to the partial or total loss of the system. Therefore, there is a need to design robust control strategies that allow the system to continue operating even with the loss of one of its actuators. Based on the above, this work presents a controller capable of performing output regulation while tolerating actuator faults in actuated robotic platforms. In contrast to traditional output regulation theory, where a known exosystem provides the reference signal, the proposed approach employs a High-Gain Observer (HGO) to estimate and generate the reference signal from an unknown exosystem. Additionally, an Unknown Input (UI) observer is used to estimate actuator faults, enabling the computation of a fault-tolerant control. The methodology is tested in simulation and real-time experiments on the well-known Furuta pendulum system to illustrate the effectiveness of the proposed approach. Full article

This work focuses on the possibilities of generating synthetic data for machine learning in hazard prediction in area-based speed monitoring systems. The purpose of the research conducted was to develop a methodology for generating realistic synthetic data to support the design of a continuous vehicle speed monitoring system to minimize the risk of traffic accidents caused by speeding. The SUMO traffic simulator was used to model driver behavior in the analyzed area and within a given road network. Data from OpenStreetMap and field measurements from over a dozen speed detectors were integrated. Preliminary tests were carried out to record vehicle speeds. Based on these data, several simulation scenarios were run and compared to real-world observations using average speed, the percentage of speed limit violations, root mean square error (RMSE), and percentage compliance. A new metric, the Combined Speed Accuracy Score (CSAS), has been introduced to assess the consistency of simulation results with real-world data. For this study, a basic hazard prediction model was developed using LoRaWAN sensor network data and environmental contextual variables, including time, weather, location, and accident history. The research results in a method for evaluating and selecting the simulation scenario that best represents reality and drivers’ propensities to exceed speed limits. The results and findings demonstrate that it is possible to produce synthetic data with a level of agreement exceeding 90% with real data. Thus, it was shown that it is possible to generate synthetic data for machine learning in hazard prediction for area-based speed control systems using traffic simulators. Full article

Efficient maintenance is paramount for rail transport systems to avoid catastrophic accidents. Therefore, a method that enables the early detection of defects in critical components is crucial for increasing the availability of rolling stock and reducing maintenance costs. This work’s main contribution is the proposal of a methodology for analyzing vibration signals. The vibration signals, obtained from a bogie axle on a test bench, are decomposed into intrinsic functions, to which classical signal processing techniques are then applied. Finally, decision trees are employed to characterize the axle’s state, yielding excellent results. Full article

Traditional navigation relies on visual alignment with leading lights, a task typically monitored by bridge officers over extended periods. This process can lead to fatigue-related human factor errors, increasing the risk of maritime accidents and environmental damage. To address this issue, this study explores the use of convolutional neural networks (CNNs), evaluating different training strategies and hyperparameter configurations to assist officers in identifying deviations from proper visual leading. Using video data captured from a navigation simulator, we trained a lightweight CNN capable of advising bridge personnel with an accuracy of 86% during night-time operations. Notably, the model demonstrated robustness against visual interference from other light sources, such as lighthouses or coastal lights. The primary source of classification error was linked to images with low bow deviation, largely influenced by human mislabeling during dataset preparation. Future work will focus on refining the classification scheme to enhance model performance. We (1) propose a lightweight CNN based on SqueezeNet for night-time ship navigation, (2) expand the traditional binary risk classification into six operational categories, and (3) demonstrate improved performance over human judgment in visually ambiguous conditions. Full article

Coal miners’ unsafe behavior is the primary reason for accidents. This research aims to examine the effect of male coal miners’ shared work crew identity on their safety behavior. A 2-2-1 multilevel mediation model is established based on social identity theory and safety climate theory. To validate the model, a paper-and-pencil survey with male coal miners was carried out in Henan Province, China. A total of 212 valid responses from male coal miners nested in 53 work crews were secured, and Mplus was used to analyze the data. Results show that work crew safety climate fully mediates the effect of male coal miners’ shared work crew identity on their safety behavior. In theory, the findings support that social identity brings a safety climate. In practice, the findings highlight that making safety part of work crew norms improves male coal miners’ safety behavior. Limitations and future research are also discussed. Full article

Background: As intelligent systems increasingly operate in high-risk environments, understanding how they perceive and respond to hazards is critical for ensuring safety. Methods: In this study, we conduct a comparative analysis of 60 real-world accident reports, 30 from process control systems (PCSs) and 30 from autonomous vehicles (AVs), to examine differences in risk triggers, perception paradigms, and interaction failures between humans and artificial intelligence (AI). Results: Our findings reveal that PCS risks are predominantly internal to the system and detectable through deterministic, rule-based mechanisms, whereas AVs’ risks are externally driven and managed via probabilistic, multi-modal sensor fusion. More importantly, despite these architectural differences, both domains exhibit recurring human–AI interaction failures, including over-reliance on automation, mode confusion, and delayed intervention. In the case of PCSs, these failures are historically tied to human–automation interaction; this article extrapolates these patterns to anticipate potential human–AI interaction challenges as AI adaptation increases. Conclusions: This study highlights the need for a hybrid risk perception framework and improved human-centered design to enhance situational awareness and responsiveness. While AI has not yet been implemented in PCS incident studies, this work interprets human–automation failures in these cases as indicative of potential challenges in human–AI interaction that may arise in future AI-integrated process systems. Implications extend to developing safer intelligent systems across industrial and transportation sectors. Full article

In this research work, the authors propose an approach for analyzing a traffic accident involving a motorcycle and a pedestrian. The study was conducted under the condition that there are objects in the accident area that limit the visibility of the participants. For this purpose, a three-dimensional simulation model was developed to determine the relative positions of the pedestrian and the motorcycle-driver system at discrete moments, examining the period of time from the moment the pedestrian steps onto the roadway to the moment of contact between the participants. Data from a real traffic accident were used. Full article

Background/Objectives: Splenic torsion is a well-known and reported clinical problem. Splenic torsions after abdominal trauma represent a small group of cases that involve surgical management. They manifest primarily as abdominal pain, and the diagnosis is made based on imaging studies—ultrasound, CT, and MRI. Methods: This work aimed to analyze traumatic splenic torsions in terms of their clinical course, symptoms, timing, involvement of imaging techniques in the diagnosis, histopathological examination, and overall outcome. We searched databases using the desk research method under the keywords “splenic torsion”, “torsion”, and “spleen”, as well as in combination with “traumatic”, finding a total of eight cases, which we included in our analysis. Results: The eight cases were analyzed, comprising four females and four males, with an average age of 16.25 years (range 5–29 years). Traffic accidents were the most frequent cause of injury (five cases), while the circumstances were unclear in the remaining three. Immediate abdominal symptoms appeared in six patients. Splenic torsion was preoperatively diagnosed in five out of seven confirmed cases. A total of seven patients underwent laparotomy with splenectomy. In one case, laparoscopy converted to laparotomy with splenopexy preserved the spleen. Histopathology, performed in only two cases, confirmed splenic infarction in one patient; infarction status could not be determined in the remaining five due to missing data. Conclusions: Post-traumatic splenic torsions are a group of atypical injuries as the primary and immediate consequence of the trauma suffered is not anatomical–structural damage to the organ, such as a rupture. Mostly affecting young people, the cases described in the professional literature involve the main spleen, which was considered to be “wandering”, suggesting that this is a key predisposing factor for splenic torsion following blunt trauma and requiring diagnostic imaging for diagnosis. Full article

Concrete is a fundamental material in structural engineering, widely used in critical infrastructure such as bridges, nuclear power plants, and dams. These structures may be subjected to extreme dynamic loads resulting from natural disasters, industrial accidents, or missile impacts. Therefore, a comprehensive understanding of concrete behavior under high strain rates is essential for safe and resilient design. Experimental investigations, particularly spalling tests, have highlighted the strain-rate sensitivity of concrete in dynamic tensile loading conditions. This study presents a macroscopic 3D discrete element model specifically developed to simulate the dynamic response of concrete subjected to extreme loading. Unlike conventional continuum-based models, the proposed discrete element framework is particularly suited to capturing damage and fracture mechanisms in cohesive materials. A key innovation lies in incorporating a physically grounded strain-rate dependency directly into the local cohesive laws that govern inter-element interactions. The originality of this work is further underlined by the validation of the discrete element model under dynamic tensile loading through the simulation of spalling tests on normalstrength concrete at strain rates representative of severe impact scenarios (30–115 s⁻¹). After calibrating the model under quasi-static loading, the simulations accurately reproduce key experimental outcomes, including rear-face velocity profiles and failure characteristics. Combined with prior validations under high confining pressure, this study reinforces the capability of the discrete element method for modeling concrete subjected to extreme dynamic loading, offering a robust tool for predictive structural assessment and design. Full article

Modeling accident investigation reports is crucial for elucidating accident causation mechanisms, analyzing risk evolution processes, and formulating effective accident prevention strategies. However, such reports are typically long, hierarchically structured, and information-dense, posing unique challenges for existing language models. To address these domain-specific characteristics, this study proposes SAFE-Transformer, a Structure-Aware and Format-Enhanced Transformer designed for long-document modeling in the emergency safety context. SAFE-Transformer adopts a dual-stream encoding architecture to separately model symbolic section features and heading text, integrates hierarchical depth and format types into positional encodings, and introduces a dynamic gating unit to adaptively fuse headings with paragraph semantics. We evaluate the model on a multi-label accident intelligence classification task using a real-world corpus of 1632 official reports from high-risk industries. Results demonstrate that SAFE-Transformer effectively captures hierarchical semantic structure and outperforms strong long-text baselines. Further analysis reveals an inverted U-shaped performance trend across varying report lengths and highlights the role of attention sparsity and label distribution in long-text modeling. This work offers a practical solution for structurally complex safety documents and provides methodological insights for downstream applications in safety supervision and risk analysis. Full article

The Dangerous Goods Transportation (DGT) presents significant challenges, requiring a strong and systematic risk assessment framework to ensure the safety and efficiency of the supply chain. This study addresses a critical gap by integrating a deterministic and holistic approach to risk assessment and management. Utilizing Geographic Information Systems (GIS), meteorological data, and material-specific information, the research develops a data-driven approach to identify analyze, evaluate, and mitigate risks associated with DGT. The main objectives include monitoring dangerous goods flows to identify critical risk areas, optimizing emergency response using a shared model, and providing targeted training for stakeholders involved in DGT. The study leverages Information and Communication Technologies (ICT) to systematically collect, interpret, and evaluate data, producing detailed risk scenario maps. These maps are instrumental in identifying vulnerable areas, predicting potential accidents, and assessing the effectiveness of risk management strategies. This work introduces an innovative GIS-based risk assessment model that combines static and dynamic data to address various aspects of DGT, including hazard identification, accident prevention, and real-time decision support. The results contribute to enhancing safety protocols and provide actionable insights for policymakers and practitioners aiming to improve the resilience of technological systems for road transport networks handling dangerous goods. Full article

Background: The consequences of injuries resulting from accidents are among the most common health disorders in children. A scar forms at the site of the injury. In the treatment of scars, not all methods used in adults can be used in children. The authors attempted to assess the effectiveness of using KT kinesiology taping on scars in children. The aim of the work is to assess the effect of KT on the treatment of keloid, hypertrophic scars, and postoperative adhesions in children. Methods: The study included 30 patients aged 4 to 10 years. The subjects were divided into three groups: group G1-9 patients with keloid scars, group G2-14 with hypertrophic scars, group G3-7 with postoperative adhesions. The patients underwent kinesiology taping for 8 weeks. The analyzed parameters were determined using the VSS scale and ultrasonography. Results: The analysis of the VSS scale results in relation to the type of scars showed a significant (p < 0.001) downward trend in the measured parameters for keloid and hypertrophic scars. Analysis of ultrasound results in relation to the type of scars showed a significant (p < 0.001) downward trend in the measured parameters, comparing parameters I and II for all types of scars. Conclusions: Kinesiology taping significantly changes the following scar parameters: deformability, pigmentation, and perfusion in the case of keloid and hypertrophic scars. Full article

This paper presents a novel system designed to enhance pedestrian safety in urban environments by utilizing real-time video analysis and machine learning techniques. With a focus on the bustling streets of Macao, known for its high pedestrian traffic and complex road conditions, the proposed model alerts drivers to the presence of pedestrians, significantly reducing the risk of accidents. Leveraging the You Only Look Once algorithm, this research demonstrates how timely alerts can be generated based on risk assessments derived from video footage. The model is rigorously tested against diverse driving scenarios, providing robust accuracy in detecting potential hazards. A comparative analysis of various machine learning algorithms, including Gradient Boosting and Logistic Regression, underscores the effectiveness and reliability of the system. The key finding of this research indicates that dataset refinement and enhanced feature differentiation could lead to improved model performance. Ultimately, this work seeks to contribute to the development of smart city initiatives that prioritize safety through advanced technological solutions. This approach exemplifies a vision for more responsive and responsible urban transport systems. Full article

As aging urban expressways become more pronounced, maintenance and construction work on these roadways is increasingly necessary. Some lanes may need to be closed during maintenance and construction, decreasing driving safety and comfort in the work zone. This situation often leads to traffic congestion and a higher risk of traffic accidents. Notably, 80% of work zone traffic accidents occur in the warning and upstream transition areas (or simply warning and transition areas). Therefore, it is crucial to appropriately determine the lengths of these areas to enhance both safety and comfort for drivers. In this study, we examined three different warning lengths (1800 m, 2000 m, and 2200 m) and three transition lengths (120 m, 140 m, and 160 m) using the entropy weighting method to create nine simulation scenarios on a two-way, six-lane urban expressway. We selected various metrics for driving safety and comfort, including drivers’ eye movement, electroencephalogram, and driving behavior indicators. A total of 45 participants (mean age = 23.9 years, standard deviation = 1.8) were recruited for the driving simulation experiment, and each participant completed all 9 simulation scenarios. After eliminating 5 invalid datasets, we obtained valid data from 40 participants. We employed a combination of the analytic network process and entropy weighting method to calculate the comprehensive weights of the eight evaluation indicators. Additionally, we introduced the fuzzy theory, utilizing a trapezoidal membership function to evaluate the membership matrix values of the indicators and the comprehensive evaluation grade eigenvalues. The ranking of the experimental scenarios was determined using these eigenvalues. The results indicated that more extended warning lengths correlated with increased safety and comfort. Specifically, the best driver safety and comfort levels were observed in Scenario I, which featured a 2200 m warning length × 160 m transition length. However, the difference in safety and comfort across different transition lengths diminished as the warning length increased. Therefore, when road space is limited, a thoughtful combination of reasonable lengths can still provide high driving safety and comfort. Full article

Detecting alcohol intoxication is crucial for preventing accidents and enhancing public safety. Traditional intoxication detection methods rely on direct blood alcohol concentration (BAC) measurement via breathalyzers and wearable sensors. These methods require the user to purchase and carry external hardware such as breathalyzers, which is expensive and cumbersome. Convenient, unobtrusive intoxication detection methods using equipment already owned by users are desirable. Recent research has explored machine learning-based approaches using smartphone accelerometers to classify intoxicated gait patterns. While neural network approaches have emerged, due to the significant challenges with collecting intoxicated gait data, gait datasets are often too small to utilize such approaches. To avoid overfitting on such small datasets, traditional machine learning (ML) classification is preferred. A comprehensive set of ML features have been proposed. However, until now, no work has systematically evaluated the performance of various categories of gait features for alcohol intoxication detection task using traditional machine learning algorithms. This study evaluates 27 signal processing features handcrafted from accelerometer gait data across five domains: time, frequency, wavelet, statistical, and information-theoretic. The data were collected from 24 subjects who experienced alcohol stimulation using goggle busters. Correlation-based feature selection (CFS) was employed to rank the features most correlated with alcohol-induced gait changes, revealing that 22 features exhibited statistically significant correlations with BAC levels. These statistically significant features were utilized to train supervised classifiers and assess their impact on alcohol intoxication detection accuracy. Statistical features yielded the highest accuracy (83.89%), followed by time-domain (83.22%) and frequency-domain features (82.21%). Classifying all domain 22 significant features using a random forest model improved classification accuracy to 84.9%. These findings suggest that incorporating a broader set of signal processing features enhances the accuracy of smartphone-based alcohol intoxication detection. Full article