Search Results (65)

Background/Objectives: Our goal was to evaluate the effectiveness of tract sealing agents in reducing pneumothorax and chest drainage insertion following CT-guided lung biopsy (CLB), and to assess the certainty of supporting evidence. Methods: A systematic review and meta-analysis were conducted according to PRISMA 2020 guidelines (PROSPERO: CRD42024608747). Four health science databases (ScienceDirect, PubMed, Scopus, and Cochrane Library) were searched up to 13 October 2025. Randomized controlled trials and cohort studies reporting tract sealing after CLB were included. Outcomes were post-procedural pneumothorax and pleural drainage insertion. Both were analyzed as dichotomous variables using random-effects meta-analysis with the Mantel–Haenszel method. Statistical heterogeneity was assessed using the I² statistic. Results were considered statistically significant for p < 0.05. Study quality was assessed using the Revised Cochrane risk-of-bias tool for randomized trials (RoB 2) and the Risk Of Bias In Non-randomized Studies—of Interventions, Version 2 (ROBINS-I V2) tool for cohort studies. Results: A total of 3328 records were initially retrieved, with 37 studies (13,107 patients, 7161 male and 4526 female) meeting the inclusion criteria. Sealing agents included saline solution, hydrogel plug, gelatin sponge, autologous blood patch, saline + rapid roll-over, hemocoagulase, gelatin sponge + hemocoagulase, and fibrin glue. Meta-analysis demonstrated significant reductions in pneumothorax and drainage insertion with saline solution (pneumothorax: OR = 0.35; 95% CI 0.25–0.48; p < 0.00001; drainage: OR = 0.22, 95% CI 0.11–0.43; p < 0.00001), gelatin sponge (pneumothorax: OR = 0.44, 95% CI 0.37–0.53; p < 0.00001; drainage: OR = 0.40, 95% CI 0.29–0.54; p < 0.00001), autologous blood patch (pneumothorax: OR = 0.50, 95% CI 0.40–0.62; p < 0.00001; drainage: OR = 0.40, 95% CI 0.27–0.59; p < 0.00001), and hydrogel plug (pneumothorax: OR = 0.65, 95% CI 0.50–0.85; p = 0.001; drainage: OR = 0.44, 95% CI 0.25–0.76; p < 0.004). Conclusions: Saline solution, hydrogel plug, gelatin sponge, and autologous blood patch are sealing agents that are effective at lowering the risk of pneumothorax and drainage insertion following CLB. Full article

Forefoot offloading footwear is widely used in postoperative care, trauma management, and the prevention of diabetic foot ulceration, where redistribution of plantar load must be achieved without compromising gait stability. This study evaluated plantar pressure and centre of pressure characteristics of a new side-specific forefoot offloading footwear design in comparison with commonly used clinical and retail footwear. Twelve healthy adults completed treadmill walking trials at 4.0 km/h under five footwear conditions. Plantar pressure data were collected using an in-shoe pressure measurement system and analysed for peak pressure, average pressure, force–time impulse, centre of pressure velocity, and centre of pressure excursion index across seven anatomically defined plantar regions. Across all conditions, consistent left–right asymmetry in plantar loading was observed, although overall variability between footwear designs was modest. The experimental footwear demonstrated pressure and impulse distributions comparable to retail and universal offloading footwear, without increasing hallux loading. Centre of pressure measures were generally consistent between side variability, indicating controlled rollover and preserved gait stability. These findings suggest that side-specific sole geometry can support balanced forefoot load management without introducing instability in healthy walking and provide a foundation for future bilateral testing in clinical populations at risk. Full article

Ground vehicles navigating uneven terrain must simultaneously guarantee motion safety and efficiency. Safety requires that the planned waypoints lie in highly traversable terrain, while ensuring vehicle reachability to these waypoints, which must be kinematically feasible. Efficiency demands fewer detours and smoother paths that avoid excessive vehicle acceleration and steering. However, existing path planning research for uneven terrain fails to comprehensively integrate vehicle kinematic constraints, terrain factors, path smoothness, rollover risk, and total path length. To address this problem, this paper proposes a novel navigation framework. It first integrates terrain slope, flatness, elevation variation, and sparsity to generate a 2D global terrain traversability cost map. Subsequently, a three-phase path planning algorithm integrates A*, guided Rapidly-exploring Random Tree (RRT), and our proposed Kinematic and Terrain-Aware Probabilistic Roadmap (KT-PRM) local re-planning algorithm, which jointly considers multiple factors including ground vehicle kinematic constraints, terrain factors, path smoothness, rollover risk, and path length. This three-phase combination delivers safe, smooth, and short global paths over uneven terrain within a relatively short planning time. Finally, Nonlinear Model Predictive Control (NMPC) is employed for path tracking in the framework. Experiments were conducted in both simulated and real-world uneven terrain environments. The results demonstrated that the three-phase path planning algorithm integrated with our proposed KT-PRM algorithm achieves comprehensive performance in generating safer, smoother, and shorter paths. Our proposed navigation framework achieves safer and more efficient navigation compared with existing navigation frameworks. Full article

Median crossover crashes are among the most severe roadway safety events due to their high-energy nature and strong association with fatal and incapacitating injuries, posing a substantial public health burden. This study develops a multimodal intelligent analytics framework to evaluate the cable median barrier performance in Tennessee by integrating structured crash data, roadway and traffic characteristics, post-impact vehicle responses, and unstructured police narratives. Across 6094 crashes on 576 cable barrier segments, 1196 involved barrier impacts and 914 included complete post-impact response information. Deep learning-based text mining using a BERT transformer model was applied to narrative descriptions from fatal, serious injury, and minor injury crashes to extract contextual indicators of loss of control, impact dynamics, and injury mechanisms. Safety effectiveness evaluation using Empirical Bayes methods showed substantial reductions after installation, including a 96% decrease in fatal crashes and an 88% reduction in serious-injury crashes. Vehicle–barrier interactions—classified as containment, redirection, rollover, or penetration—were modeled using a multinomial logit framework with marginal effects to assess the influence of geometric, operational, and vehicle-related factors. Reduced barrier offset, narrow shoulders, high traffic volumes, outer-lane departures, and heavy-vehicle involvement significantly increased the likelihood of rollover and penetration events, which are strongly linked to higher injury severity. Through fusing multimodal data and combining explainable statistical models with deep learning text analysis, this study provided a scalable, trustworthy approach to characterizing injury risk, aligning transportation safety analytics with emerging intelligent healthcare and big-data methodologies aimed at preventing severe and fatal trauma. Full article

In this study, a three-dimensional (3D) model is developed based on an actual small forestry crawler tractor, to analyze its overturning and rollover behaviors, and a corresponding simulation model is constructed. The accuracy of the 3D model is validated by comparing its dimensions and center of gravity with those of the physical tractor, and the fidelity of the simulation model is verified using static sidelong falling angle, minimum turning radius, and driving tests. The developed simulation framework was employed to investigate the dynamic behavior of the small forestry crawler tractor, focusing on roll and pitch angular velocities across different obstacle heights, slope angles, and driving speeds. Backward rollover was not observed within the tractor’s realistic operating speed range, indicating that backward rollover is not the dominant risk mode. In contrast, lateral overturning occurs under all driving scenarios, and increases in driving speed and obstacle height lead to higher roll angular velocities, increasing the risk of lateral overturning. Across all conditions, the likelihood of lateral overturning surges when the roll angular velocity enters the 80–100°/s range, with obstacle height exerting the greatest influence. In conclusion, the small forestry crawler tractor is more prone to lateral overturning than backward rollover when driving on inclined surfaces. A distinct threshold roll angular velocity is identified as the onset point of lateral overturning, which will vary according to the tractor’s specifications. This study is a quantitative study of a small forestry crawler tractor and does not correlate with a full-scale tractor. While angular velocity values vary during lateral overturning and backward rollover, this study was conducted to identify trends under various driving conditions. Further work is required to apply the proposed analysis methodology to full-scale agricultural and forestry machinery and validate it with real-world operational data. Full article

Despite decades of research, autonomous forklifts remain deployed at a small scale (2–50 vehicles), while industrial warehouses require coordinating hundreds of vehicles in environments shared with human workers. This systematic review analyzes forklift-specific autonomous technologies published between 2010 and 2025 across major robotics databases (including IEEE Xplore, ACM, Elsevier, and related venues) to identify deployment barriers. Following the PRISMA guidelines, we systematically selected 122 peer-reviewed papers addressing forklift-specific challenges across eight subsystems: vehicle modeling, localization, planning, control, vision-based manipulation, multi-vehicle coordination, and safety. We synthesized 80 methods through 8 standardized comparison tables with quality assessment based on validation rigor. State-of-the-art approaches demonstrate strong laboratory performance: localization achieving ±1.4 mm accuracy, control enabling sub-centimeter manipulation, planning reducing mission times by 2–55%, vision reaching 98%+ recognition, and safety frameworks cutting rollover risk by 53–59%. However, validation predominantly occurs at laboratory scale, revealing a critical deployment gap. These achievements do not scale to industrial environments due to fleet coordination complexity, payload variability, and unpredictable human behavior. Our contributions include the following: (1) performance rankings with technology selection guidance, (2) systematic gap characterization, and (3) research priorities addressing mixed-fleet coordination, learning-enhanced control, and human-aware safety. This review was not prospectively registered. Full article

Forestry logging is among the most hazardous economic activities, so identifying where hazards and risks concentrate supports targeted prevention. This scoping review mapped evidence on logging hazards and risks, their co-occurrence with operations, and preventive measures. PRISMA-ScR was followed. Only peer-reviewed journal articles (2015–2025) in English on occupational hazards/risks, risk-assessment methods or preventive measures in logging were included, found in Scopus, Web of Science, Inspec and Dimensions (last search 15 September 2025). Independent data screening and extraction were performed by two reviewers, with a third reviewer resolving any disagreements. No formal risk-of-bias appraisal was conducted. Forty-two studies were included. Hazards and risks concentrated in three phases—chainsaw/manual cutting, skidding/cable yarding, and loading/short-haul transport—where acute injury mechanisms (struck-by events, slips/trips/falls, rollovers, lacerations) coexisted with chronic exposures (musculoskeletal strain, noise, vibration, diesel exhaust). Preventive measures emphasised engineering and organisational controls, complemented by raining and PPE, but were inconsistently specified and evaluated. Evidence was heterogeneous and geographically concentrated in few countries, limiting generalisability. A small set of tasks consistently concentrates acute and chronic risks; prevention should integrate accident control and health protection, prioritising engineering/organisational measures supported by training and PPE. Future studies should standardise descriptors and outcome metrics to enable comparisons. Full article

Ensuring the safe, efficient, and economically viable operation of irrigation and drainage infrastructures is essential for long-term system resilience. This field-based study presents a comparative evaluation of the semi-quantitative William T. Fine (WF) method and a simplified probability–consequence (SM) approach applied in the Lis Valley Irrigation and Drainage Association (Leiria, Portugal). Monthly on-site observations of routine maintenance and conservation activities were conducted between January 2023 and December 2024, covering eight main operation types and resulting in 87 distinct occupational risk scenarios (N = 87). The mean Hazard Risk Score (HRS) was 88.9 ± 51.1, corresponding predominantly to “Substantial” risk levels according to the William T. Fine classification (HRS = 70–200). Both methods consistently identified the highest-risk activities—tractor rollover, work at height, and boat-based removal of aquatic plants. Quantitative differences emerged for medium and chronic hazards; WF produced a wider dispersion of risk scores across tasks, while the SM aggregated most hazards into a limited number of intervention classes (74% classified as Intervention Level II and 26% as Level III). These differences reflect complementary methodological limitations; WF requires greater data input and expert judgment but offers finer prioritization, whereas SM enables rapid field application but tends to group ergonomic and low-intensity hazards when consequences are not immediately observable. Based on these findings, a combined assessment framework is proposed, integrating the discriminative capacity of WF with the operational simplicity of SM. Recommended mitigation measures include targeted personal protective equipment, task rotation, focused training, and technology-assisted monitoring to reduce worker exposure. The methodology is readily replicable for Water Users’ Associations with similar operational contexts and supports evidence-based decision-making for sustainable irrigation management. From a sustainability perspective, this integrated risk assessment framework supports safer working conditions, more efficient maintenance planning, and informed policy decisions for the long-term management of irrigation and drainage infrastructures. Full article

This study addresses the accurate estimation of safe driving speeds for multi-axle trucks negotiating curved road segments by explicitly incorporating dynamic axle load transfer and load-sensitive tire–road friction characteristics. Conventional standards that assume a constant friction coefficient fail to capture wheel-specific load variations, leading to underestimation of rollover and skidding risks. To overcome these limitations, a load-sensitive friction model is integrated with the friction ellipse and static rollover threshold (SRT), and a forward–backward algorithm is applied to compute dynamically feasible speed trajectories. The proposed framework is demonstrated through accident reconstruction of a ramp rollover scenario using TruckSim–Simulink co-simulation with reported geometric and vehicle parameters. The results reveal that neglecting load sensitivity systematically overestimates safe speeds and underestimates lateral deviation. Furthermore, SRT variation analysis illustrates a trade-off between structural stability and frictional constraints, where rollover dominates under low stability and skidding under high stability conditions. These findings emphasize the necessity of accounting for dynamic load distribution and load-sensitive friction in truck safety speed estimation, providing a foundation for autonomous truck speed control strategies and enhanced road design standards. Full article

This study introduces a computational framework that formalizes rollover risk in heavy-duty vehicles by integrating simulation-informed physical modeling with sensor-driven decision logic. The approach combines high-fidelity fluid–structure interaction modeling (via CFD) with multibody vehicle dynamics simulations to capture the complex behavior of rotating, partially filled mixer tanks under dynamic conditions. Rollover thresholds were identified by extracting the maximum safe speeds across a range of maneuvers (e.g., steady-state turning and step steering), using tire lift-off as the critical indicator. These thresholds were then formalized into decision rules using onboard sensor data, such as lateral acceleration, steering input, and tank rotation speed, allowing a real-time rollover warning system to continuously compare current vehicle states against critical limits. By systematically extracting critical force and moment responses and translating them into limit values provided by conventional onboard sensors (lateral acceleration, roll angle, steering input), the framework bridges high-fidelity simulation and real-time monitoring. A concrete truck mixer is used as a case study to demonstrate the utility of this approach in formalizing rollover thresholds for real-world decision support. Beyond the specific vehicle type, this work contributes to the broader discourse on how computational methods can contribute to new control or assistance strategies for safety-critical systems. Full article

With the growing adoption of electric buses in urban transportation systems, ensuring the safety and structural integrity of their battery systems under accident scenarios has become increasingly important. Among potential accidents, rollover events pose a particular risk, as they can lead to the penetration or deformation of the battery pack and, consequently, trigger thermal runaway. In this context, this study evaluates the structural performance of rechargeable energy storage systems (REESS) in electric buses under rollover conditions, following the guidelines of United Nations Economic Commission for Europe (UNECE) Regulations No. 100 and No. 66. The analysis focuses on the structural safety of uniformly distributing the battery pack beneath the vehicle floor during rollover scenarios. The methodology adopted includes detailed finite element modeling to accurately represent the vehicle structure and battery modules, as well as virtual instrumentation using accelerometers. Simulations were conducted to evaluate structural deformations, battery retention integrity, and acceleration levels within the REESS compartments under rollover impact conditions. The results demonstrated compliance with both regulations and highlighted the importance of properly positioning and securing the battery module to the vehicle floor. The findings contribute to the improvement of design and validation criteria for electric buses, reinforcing the need to align technological innovation with international safety standards. Finally, this research supports the development of safer and more reliable vehicles, promoting sustainable mobility solutions for urban transportation systems. Full article

Mechanised agricultural operations are often performed individually, under minimal supervision and across a wide range of unfavourable working conditions, resulting in a complex mixture of hazards and external stressors that severely affect safety conditions. Socio-technical and environmental constraints significantly affect safety culture and require continuous performance adjustments to overcome timing pressures, resource limitations, and unstable weather conditions. This study introduces a FRAM-based safety culture model that embeds the thoroughness-efficiency trade-off (ETTO) in four distinct operational modes that adhere to specific safety cultures, namely, thoroughness, risk awareness, compliance, and efficiency. This model has been instantiated for mechanised ploughing: foreground task functions were coupled with background functions that represent socio-technical constraints and environmental variability, while severity classes for potential incidents were derived from the US OSHA accident database. The framework was also supported by a semi-quantitative Resonance Index based on severity and coupling strength, the Total Resonance Index (TRI), to assess how variability propagates in foreground functions and to identify hot-spot functions where small adjustments can escalate into high resonance and hazardous conditions. Results showed that the negative effects on functional resonance generated by safety detriment on TRI observed between compliance and effective working modes were three times larger than the drift between risk awareness and compliance, demonstrating that efficiency comes with a much higher cost than keeping safety at compliance levels. Extending the proposed approach with quantitative assessments could further support the management of socio-technical and environmental drivers in mechanised farming, strengthening the role of safety as a competitive asset for enhancing resilience and service quality. Full article

This paper addresses inherent limitations in unmanned aerial vehicle (UAV) undercarriages hindering vertical takeoff and landing (VTOL) capabilities on uneven slopes and obstacles. Robotic landing gear (RLG) designs have been proposed to address these limitations; however, existing designs are typically limited to ground slopes of 6–15°, beyond which rollover would happen. Moreover, articulated RLG concepts come with added complexity and weight penalties due to multiple drivetrain components. Previous research has highlighted that even a minor 3-degree slope change can increase the dynamic rollover risks by 40%. Therefore, the design optimization of robotic landing gear for enhanced VTOL capabilities requires a multidisciplinary framework that integrates static analysis, dynamic simulation, and control strategies for operations on complex terrain. This paper presents a novel, hybrid, compliant, belt-driven, three-legged RLG system, supported by a multidisciplinary design optimization (MDO) methodology, aimed at achieving enhanced VTOL capabilities on uneven surfaces and moving platforms like ship decks. The proposed system design utilizes compliant mechanisms featuring a series of three-flexure hinges (3SFH), to reduce the number of articulated drivetrain components and actuators. This results in a lower system weight, improved energy efficiency, and enhanced durability, compared to earlier fully actuated, articulated, four-legged, two-jointed designs. Additionally, the compliant belt-driven actuation mitigates issues such as backlash, wear, and high maintenance, while enabling smoother torque transfer and improved vibration damping relative to earlier three-legged cable-driven four-bar link RLG systems. The use of lightweight yet strong materials—aluminum and titanium—enables the legs to bend 19 and 26.57°, respectively, without failure. An animated simulation of full-contact landing tests, performed using a proportional-derivative (PD) controller and ship deck motion input, validate the performance of the design. Simulations are performed for a VTOL UAV, with two flexible legs made of aluminum, incorporating circular flexure hinges, and a passive third one positioned at the tail. The simulation results confirm stable landings with a 2 s settling time and only 2.29° of overshoot, well within the FAA-recommended maximum roll angle of 2.9°. Compared to the single-revolute (1R) model, the implementation of the optimal 3R Pseudo-Rigid-Body Model (PRBM) further improves accuracy by achieving a maximum tip deflection error of only 1.2%. It is anticipated that the proposed hybrid design would also offer improved durability and ease of maintenance, thereby enhancing functionality and safety in comparison with existing robotic landing gear systems. Full article

Post-op shoes (POSs) are commonly used after forefoot surgery to protect the surgical site. However, there are insufficient data on their impact on forefoot load during the rollover phase of walking. This study aims to analyze the effects of a commonly used POS on plantar pressures under the forefoot and to assess whether improper usage could affect pressure patterns. Sixteen healthy volunteers underwent three different walking tests on a straight tartan track. The test setting included walking barefoot, as well as normal walking and a modified heel-accentuated “limping” gait while wearing a common POS. The pressure distribution over the forefoot regions of interest was measured using sensor insoles and a pressure-measuring plate on the ground. Results show that only the heel-accentuated “limping” gait in the POS led to a significant reduction in pressure values over all anatomical regions compared to the normal barefoot gait. Furthermore, higher pressure values were found over the lesser toes during normal walking in the POS compared to normal barefoot walking. The findings highlight that the protective function of a POS relies on proper use, specifically the correct gait pattern. If used incorrectly, POS may even have unfavorable effects on the pressure on the operated forefoot and possibly even increase the risk of delayed healing or complications in comparison to barefoot walking. Therefore, strategies such as patient training in proper walking techniques should be incorporated into postoperative care. Full article

In response to the 2023 mandate requiring electronic stability control (ESC) for trucks in South Korea, domestic manufacturers have called for a relaxation of the maximum safe slope angle to reduce production costs. However, limited research exists on the quantitative relationship between ESC implementation and vehicle rollover stability under relaxed safety standards. This study addresses this gap by conducting dynamic simulations of standardized rollover tests to evaluate the static stability factor (SSF) and by developing a machine-learning-based model for predicting rollover risk. The model incorporates planned path curvature and driving speed to compute lateral acceleration, which serves as a key input for predicting the lateral load transfer ratio (LTR), a critical indicator of vehicle stability. Among several models tested, the recurrent neural network (RNN) achieved the highest accuracy in LTR prediction. The results highlight the effectiveness of integrating data-driven models into dynamic stability assessment frameworks, offering practical insights for optimizing route planning and speed control—particularly in autonomous freight vehicle applications. Full article