Search Results (5,637)

This research examines the risk factors that influence injury severity in individual motorcycle accidents, utilising a dataset of 5253 incidents. Five machine learning algorithms—multinomial logistic regression, classification trees, random forests, XGBoost, and neural networks—were used to classify the results into three groups: Death (13.48%), Injury (80.14%), and No injury (6.38%). In all models, passenger presence was the most important predictor of injury. Motorcycle accidents involving passengers do not always have more serious consequences for several overlapping reasons. On the one hand, a motorcycle with a passenger has a significantly higher mass, which increases the braking distance and kinetic energy at the moment of collision, hindering quick defensive manoeuvres, cornering, and reactions to sudden hazards. Often, the rider also refrains from sudden movements to prevent the passenger from losing their balance. In the case of single-rider motorcycle accidents on roadways, approximately 5% of those involved with a passenger were fatalities, while approximately 48% were uninjured; in the case of those without a passenger, no one was uninjured. It follows from the above that the presence of a passenger increases the rider’s sense of responsibility. Other factors that significantly increased risk were single-lane carriageways, vehicle overturning, contaminated road surfaces, and collisions with complex objects, e.g., like trees. The multinomial logistic regression model had an overall accuracy of 69.2% on the test set. The Recurrent Neural Network achieved the best overall accuracy of 79.56%. Balanced accuracy, as the average between sensitivity and specificity of the RNN model for the “death” class was 68.15%, for the “injury” class—72.6%, and for the “no injury” class—96.61%. The Area Under the ROC Curve of the Recurrent Neural Networks model for “no injury” was 0.97, indicating it was very good at distinguishing between this class and the other classes. Even though it was easy to tell which cases did not involve injuries, it was still hard to tell the difference between fatal and non-fatal injuries in all models. The results support interventions tailored to specific situations, such as improved road lighting and speed control in rural areas, as well as helmet enforcement and safety measures at intersections in cities. Full article

To address the performance degradation of the traditional Extended Kalman Filter (EKF) in state estimation for sensorless brushless DC motor (BLDC) control under dynamic operating conditions, such as sudden speed and load changes—a degradation caused primarily by model mismatches—this paper proposes an Adaptive Extended Kalman Filter (AEKF) algorithm. The proposed algorithm incorporates a robust weighting strategy based on the Mahalanobis distance and a dynamically adjusted adaptive forgetting factor. This integration establishes an estimation mechanism capable of online updating of the innovation covariance, thereby enhancing the state observer’s adaptability to system uncertainties and external disturbances. Simulation results demonstrate that, compared to the traditional EKF, the designed AEKF algorithm significantly improves the estimation accuracy of rotor position and speed under various operating conditions, including low-speed start-up, speed step changes, and sudden load applications. Furthermore, it accelerates dynamic response, suppresses overshoot, and enhances the system’s disturbance rejection robustness. This work provides an effective state estimation solution for high-dynamic performance sensorless control of BLDC. Full article

This paper studies mixed impulsive boundary value problems involving tempered $\mathsf{\Psi }$-fractional derivatives of Caputo type. By introducing exponential tempering into the fractional framework, the proposed model effectively captures systems with fading memory—an improvement over conventional power-law kernels that assume long-range dependence. The generalized tempered $\mathsf{\Psi }$-operator unifies several existing fractional derivatives, offering enhanced flexibility for modeling complex dynamical phenomena. Impulsive effects and integral boundary conditions are incorporated to describe processes subject to sudden changes and historical dependence. The problem is reformulated as a Volterra integral equation, and fixed-point theory is employed to establish analytical results. Existence and uniqueness of solutions are proven using the Banach Contraction Mapping Principle, while the Leray–Schauder nonlinear alternative ensures existence in non-contractive cases. The proposed framework provides a rigorous analytical basis for modeling phenomena characterized by both fading memory and sudden perturbations, with potential applications in physics, control theory, population dynamics, and epidemiology. A numerical example is presented to illustrate the validity and applicability of the main theoretical results. Full article

This article presents the results of research into the operation of retractable-type fall arresters used for fall protection in conjunction with flexible anchor points. The purpose of these devices is to enable the user to move freely in the vertical direction and safely arrest any fall from the workstation. Reports from users of such protective equipment and previous studies have indicated the occurrence of specific situations in which retractable-type fall arresters did not work properly. It was manifested by the sudden locking and unlocking of the device’s retractable lanyard, which means that the falling person was not stopped at the required distance. This is a highly dangerous phenomenon known as “jump action” that can cause serious injury or even death. Therefore, three different designs of retractable-type fall arresters and three loading conditions were investigated to analyze the jump action phenomenon. Based on the experimental results, a modification of the retractable type fall arresters was proposed in the form of an added electronic control. The proposed programmable control system will make it possible to eliminate the risks associated with “jump action” and expand the functionality of the studied fall arresters. Full article

With the accelerated low-carbon transition of the global energy mix, offshore wind power (OWP) is one of the fastest-growing renewable resources and is often integrated with conventional thermal units into a bundled export transmission system. Under sudden large disturbances, the lack of inertia support makes rotor-angle instability prone to occur, which undermines sustainable operation. Battery energy storage systems (BESS) provide fast emergency power support, and an effective control strategy can enhance transient rotor-angle stability while improving operational sustainability. Accordingly, equivalent-circuit models of the regional export system are established for the before-fault, during-fault, and after-fault stages. Building on the extended equal area criterion (EEAC) and the low-voltage ride-through (LVRT) capability of OWP, the stabilizing mechanism of BESS participation is examined from the perspectives of optimal power and timing, thereby yielding an optimal BESS control strategy for improving transient rotor-angle stability in regional renewable export systems. Finally, a regional renewable export system is implemented in MATLAB/Simulink R2022b, where severe contingencies are imposed to validate the effectiveness of the proposed BESS control strategy. Full article

During coal mining, the development of joint fractures in overlying rock strata is one of the key factors that degrade the mechanical properties of rock masses, form water-conducting fracture zones, and induce safety hazards. To investigate the fracture evolution characteristics of overlying strata during coal extraction under thick and hard roof conditions, this study established a mining physical model based on similarity simulation technology, tracked the fracture evolution process, and performed quantitative analysis using fractal theory. The results show that fracture development is significantly correlated with the mining advance distance: the fractal dimension of fractures is small in the initial mining stage and gradually increases as the working face advances. When the mining width exceeds the ultimate span of the roof, local fractures expand rapidly with a sharp rise in the fractal dimension to 1.436; further increasing the mining width triggers large-scale sudden fracture expansion, resulting in severe degradation of rock mass integrity, with the maximum fractal dimension reaching 1.445. The research findings provide theoretical references for safety management and disaster prevention in coal mining under thick and hard roof conditions. Full article

To address the challenges of strong electromechanical coupling, nonlinear friction, and poor disturbance rejection in semi-direct-drive scraper conveyor systems under complex coal mining conditions, this paper aims to propose a high-performance drive control strategy that balances dynamic response speed with steady-state operational smoothness. First, an integrated electromechanical coupling dynamic model incorporating Permanent Magnet Synchronous Motor (PMSM) vector control and the time-varying meshing stiffness of a two-stage planetary gear train is established. Subsequently, a Sliding Mode Control (SMC) strategy optimized with a saturation boundary layer is designed and compared with traditional Proportional-Integral (PI) control under multiple operating conditions. Time-frequency domain analysis indicates that SMC significantly enhances the dynamic stiffness of the drive system. Under sudden load change conditions, the speed recovery time is shortened by approximately 76%, and the steady-state error is reduced by 37% compared to PI control. Microscopic characteristic evaluation based on FFT and Total Variation (TV) metrics reveals that SMC achieves active disturbance rejection through spectral broadening of the electromagnetic torque. Crucially, the steady-state cumulative control effort of SMC is equivalent to that of PI, implying no additional mechanical stress burden, while the equivalent dynamic transmission force fluctuation in the mechanical chain is reduced by about 3%. The study confirms that the proposed strategy successfully achieves a synergistic optimization of “macroscopic rapid response” and “microscopic smooth operation,” providing a theoretical basis for the high-precision control of heavy-duty underground transmission equipment. Full article

Robotic grippers play a crucial role in pick-and-place tasks, as their performance directly affects the robot’s operational efficiency, stability, and safety. In industrial applications, such as coal gangue sorting, the target objects have irregular shapes and sharp surfaces, which pose challenges to the gripper’s grasping ability. To solve these problems, an adaptive under-actuated gripper based on rope control is designed. The gripper is simple to control and combines the excellent features of both rigid and flexible grippers. To analyze the characteristics of the gripper, both mathematical analysis and holding force experiments are conducted. The results show that the gripper can generate a greater holding force when grasping larger objects with a constant input air pressure. Furthermore, irregularly shaped testing objects, including coal lumps and ores, are selected to conduct grasping experiments. The gripper achieves a 100% grasping success rate with a load of up to four times the object’s weight suspended beneath it and shows the ability to reliably grasp irregularly shaped objects in high-speed pick-and-place tasks with a payload of four times the object’s weight. Meanwhile, the gripper has a passive anti-collision ability due to the special outer contour of the distal finger when subjected to unexpected, sudden force. Full article

Large signal perturbations, such as abrupt load changes and sudden voltage surges, pose significant challenges to the stable operation of DC microgrids. Various control strategies have been explored to mitigate these disturbances. Among them, terminal sliding mode control (TSMC) offers a rapid dynamic response, but its inherent chattering and limited disturbance rejection capabilities hinder its effectiveness in DC microgrid applications. To address these issues, this paper proposes a novel adaptive smooth gain terminal sliding mode control (ASTSMC) strategy for the outer voltage loops of DC-DC converters. In addition, a reduced-order error-based extended state observer (REESO) is integrated to enhance disturbance estimation, mitigate the effects of disturbances, and improve the computational efficiency. The proposed ASTSMC-REESO approach effectively tackles the inherent chattering issues of TSMC, achieving superior voltage control performance under severe disturbances. Experimental results demonstrate that the proposed strategy significantly improves the performance of voltage regulation in DC microgrids. Full article

Heavy precipitation events are characterized by sudden onset, limited spatiotemporal scales, rapid evolution, and high disaster potential, posing long-standing challenges in weather forecasting. With the development of deep learning, an increasing number of researchers have leveraged its powerful feature representation and non-linear modeling capabilities to address the challenge of precipitation nowcasting. Despite recent advances in deep learning for precipitation nowcasting, most existing methods do not explicitly separate precipitation from non-precipitation regions. This often leads to the extraction of redundant or irrelevant features, thereby causing models to learn misleading patterns and ultimately reducing their predictive capability for heavy precipitation events. To address this issue, we propose a novel dynamic region attention (DRA) mechanism, and an improved model TPDTC-Net-DRA, based on our previously introduced TPDTC-Net. The proposed TPDTC-Net-DRA applies the DRA mechanism and incorporates its two key components: a dynamic region module and a weight control module. The dynamic region module generates a mask matrix that is applied to the feature maps, guiding the attention mechanism to focus only on precipitation areas. Meanwhile, the weight control module produces a location-sensitive weight matrix to direct the model’s attention toward regions with intense precipitation. Extensive experiments demonstrate that TPDTC-Net-DRA achieves superior performance for heavy precipitation, outperforming current state-of-the-art methods, and indicate that the proposed DRA mechanism exhibits strong generalization ability across diverse model architectures. Full article

Background: Aortic dissection (AoD) is a life-threatening medical emergency characterized by the separation of the layers of the aortic wall. The typical clinical presentation of AoD includes intense thoracic pain in the anterior chest or interscapular region, often described as migratory and tearing in nature. However, in rare cases, AoD can present without classic signs but with neurological symptoms, including seizures. Case Presentation: A 60-year-old patient experienced a sudden loss of consciousness followed by a tonic–clonic seizure and subsequently developed right-sided weakness. He had a medical history of hypertension and smoking. Although the symptoms quickly resolved, the brain imaging revealed signs of an acute ischemic stroke located in the left hemisphere. The seizures resumed, blood D-dimer levels were found to be highly elevated, and subsequent thoracic and abdominal computed tomography angiography revealed the presence of AoD, which originated at the proximal part of the ascending aorta. The patient received symptomatic treatment to alleviate his symptoms and prevent complications and was quickly transferred for surgical intervention, resulting in a favorable outcome. Conclusions: This case demonstrates that a tonic–clonic seizure can be the first clinical manifestation of AoD. Such atypical symptoms highlight the diverse and misleading nature of AoD presentations, underscoring the challenges in the diagnostic process. This emphasizes the need for increased clinical vigilance when treating a patient experiencing their first seizure episode. Full article

Phacoemulsification is performed within a highly dynamic intraocular environment, in which fluid exchange, pressure regulation, and tissue biomechanics interact continuously. Although modern cataract surgery is considered safe and efficient, disruption of this delicate intraoperative microenvironment remains a major source of complications. Among fluidics-related events, post-occlusion break surge represents one of the most critical destabilizing factors of the anterior chamber. A surge occurs when the sudden release of an occluded aspiration port generates an abrupt pressure–volume imbalance that cannot be immediately compensated by infusion, leading to a transient collapse of the intraocular environment. This narrative review integrates current experimental and clinical evidence on the pathophysiology, quantification, and technological control of surge, framing it as a model of environmentally driven intraoperative stress. The evolution of phacoemulsification fluidics, from gravity-based systems to active, adaptive, and predictive platforms, is analyzed in relation to their ability to preserve a stable and physiologic intraocular environment. Comparative data from contemporary devices are reviewed, highlighting differences in surge volume, recovery time, and pressure restitution. Special emphasis is placed on the impact of surge on the microenvironments of both the anterior and posterior segments, including endothelial stress, capsular instability, vitreoretinal traction, and macular perfusion. Emerging strategies such as handpiece-integrated pressure sensors, predictive fluidics algorithms, intraoperative imaging, and artificial intelligence are reshaping environmental control during surgery. Despite substantial technological progress, the complete elimination of surge remains an unmet need. Continued innovation, standardized biomechanical models, and robust clinical validation will be essential to further protect the intraoperative intraocular environment and improve long-term visual outcomes. Full article

Chronic subdural hematoma (CSDH) in frail older adults is increasingly recognized as a sentinel event, with mortality often driven by medical complications rather than neurosurgical factors. We report a failure-to-rescue case in which rapid postoperative deterioration occurred after burr-hole drainage for bilateral CSDH in a frail older adult with diabetes. A clinical picture consistent with sepsis was suspected, and a gastrointestinal source was considered, but the infectious focus could not be confirmed due to limited diagnostic work-up. On admission, chest-computed tomography showed mild right lower-lobe pneumonia, and incidental transverse colonic dilatation was also visible. Burr-hole drainage was uneventful and oxygenation rapidly normalized on room air. On postoperative day (POD) 3, the patient developed a high fever (39 °C), rising C-reactive protein (CRP; 14 mg/dL), abrupt leukopenia (15,300 → 3300/µL), and, several hours later, profuse watery diarrhea. At that time, an evaluation for an infectious source and escalation of therapy (e.g., blood cultures, serum lactate, and abdominal imaging) were not performed. In the early hours of POD 4, he suffered sudden desaturation, shock, and cardiac arrest, and died despite resuscitation. A portable radiograph after intubation showed no new diffuse pulmonary infiltrates but marked colonic gas distension. This case highlights the need to reassess diagnostic framing when discordant postoperative “red flags” emerge and proposes practical triggers for early sepsis evaluation and escalation—prioritizing early recognition and timely rescue rather than a definitive determination of the cause of death—in high-risk CSDH patients. Full article

Background/Objectives: Coenzyme Q10 (CoQ10) is crucial for mitochondrial bioenergetics and redox balance and has been studied in hearing disorders. Its clinical use ranges from genetic mitochondrial deafness to acquired hearing loss associated with oxidative stress. This review aimed to map human clinical evidence on CoQ10 in hearing issues and differentiate its therapeutic roles based on underlying causes. Methods: This review was conducted following the PRISMA Extension for Scoping Reviews (PRISMA-ScR). A systematic search of PubMed, Europe PubMed Central, the Directory of Open Access Journals (DOAJ), and ClinicalTrials.gov was performed. Human clinical studies evaluating CoQ10 or water-soluble CoQ10 formulations with hearing-related outcomes were included and synthesized descriptively. Results: Fourteen studies met the inclusion criteria, including randomized controlled trials, non-randomized clinical studies, case series, and case reports. Two distinct therapeutic roles of CoQ10 emerged: in primary mitochondrial hearing disorders caused by defects in mitochondrial DNA or CoQ10 biosynthesis pathways, CoQ10 acted as a replacement therapy and was consistently linked to stabilization or prevention of progressive sensorineural hearing loss. Conversely, in acquired or age-related conditions—including presbycusis, noise-induced hearing loss, ototoxicity, tinnitus, and sudden sensorineural hearing loss—CoQ10 was used as an antioxidant or neuroprotective supplement, with outcomes showing functional preservation, symptom reduction, or decreased cochlear injury. Internal validity varied across studies: most evidence for replacement therapy was derived from observational designs, and antioxidant applications were mainly supported by small or preliminary clinical trials. Conclusions: The available evidence suggests two distinct clinical roles of CoQ10 in hearing disorders: (i) replacement therapy in genetically defined mitochondrial deafness and (ii) adjunctive antioxidant/neuroprotective use in acquired conditions. Given heterogeneity and limited study quality, further well-designed trials are needed before broad clinical recommendations can be made. Full article

Large language models (LLMs) have shown remarkable results when tasked with the analysis and production of texts or images and for captioning images. Aerial images differ from other images since they exhibit many natural objects that have a highly variable color range and no clear contours. This paper reports to what extent an LLM, i.e., Llama-4, can be tasked with the identification and captioning in aerial images of natural objects, such as tree categories, uncultivated land, and some man-made objects, such as roads. This valuable automation is needed to scan large areas and detect the parts for which a sudden maintenance or an emergency intervention is due. Tests on the chosen LLM were performed against a custom image dataset built to overcome the limited availability of such a domain-specific aerial image set. To evaluate the identification and captioning results, the accuracy, precision and recall metrics were computed. The results given by a cutting-edge variant of Llama-4, namely Maverick, reveal its strengths and weaknesses in this context. Although it is remarkable that an out-of-the-box tool can give assistance in such a complex observation and detection task, substantial progress is needed for such a model to improve accuracy and constitute a reliable support, as accuracy is at most 58.6% and recall is at most 56.1%. Full article