Although e-bike accidents are of growing clinical relevance, there is limited large-scale research comparing injury patterns and outcomes between e-bike (EB) and conventional bicycle (CB) accidents in large patient cohorts. This study performed comparative analyses of both groups to identify patients at risk and guide future prevention and clinical management strategies. A retrospective analysis of the TraumaRegister DGU® was conducted. Patients aged 16 years or older who sustained severe injuries (AIS ≥ 3 in minimally one body region) in accidents involving conventional bicycles or e-bikes between January 2020 and December 2023 were included. Patient demographics, injury patterns, trauma severity, treatment characteristics, and clinical outcomes were analyzed. A total of 9,170 bicycle accident cases were included (EB n = 1,160; CB n = 8,010). EB riders were significantly older than CB riders (median age 63 years; IQR 53-73 vs. 57 years IQR 44-69; p < 0.001) and more frequently sustained polytrauma (16.7% vs. 12.3%; p < 0.001). Compared with CB riders, EB riders more often suffered injuries to the head (67.2% vs. 56.2%; p < 0.01), face (22.7% vs. 17.8%; p < 0.001), and chest (55.2% vs. 51.8%; p = 0.030), and were more likely to sustain injuries affecting multiple body regions (p < 0.001). Primary ICU treatment was required more frequently after EB accidents (70.3% vs. 63.5%; p < 0.001). Age-stratified analyses showed that younger EB riders were more frequently involved in nighttime and alcohol-related accidents, whereas mortality increased significantly with age, from 2.7% in patients aged 16-59 years to 18.6% in those aged ≥ 80 years. E-bike accidents are associated with a higher prevalence of head, face, and chest injuries, increased rates of polytrauma and multi-region injuries, and a greater need for ICU treatment compared with conventional bicycle accidents. These differences are particularly relevant in older riders, who represent the majority of severely injured e-bike users and experience substantially higher mortality rates. Targeted prevention strategies, improved protective measures, and age-specific risk communication may help reduce the burden of e-bike-related injuries.
While offering separation between vulnerable road users and motorized vehicles, the presence of non-motorized traffic facilities, such as bikelanes, introduces complex interaction dynamics among pedestrians, conventional bicycles, and e-bikes. Existing surrogate measures inadequately quantify conflict risks in these environments due to oversimplified assumptions and limited consideration of contextual factors. This study proposes a novel safety field-based surrogate measure (Bikelane Safety Field-based Measure, BSFM) that integrates physical dynamics (i.e., road environmental characteristics, kinematic interactions) and behavioral dynamics (i.e., psychological comfort, risk perception, evasion behaviors) to quantify bikelane conflict risk. Utilizing drone-collected trajectory data (203 conflict groups, 37,652 s) from three Tianjin intersections, a bikelane-specific safety field model was developed. Subsequently, the BSFM was proposed, and the threshold of the surrogate measure was determined using extreme value theory. Validation of the BSFM yielded the following key findings: (i) The BSFM demonstrated superior conflict identification recall (81.3%) compared to Time-to-Collision (TTC) (34.5%) and Projected Time-to-Collision (PTTC) (41.4%). (ii) Significant sensitivity to evasive actions was observed, with Kolmogorov-Smirnov and Mann-Whitney U tests confirming statistically significant changes in BSFM values during swerving and deceleration maneuvers (p < 0.001). (iii) Real-time risk tracking was effectively achieved through dynamic visualizations of the safety performance envelope. (iv) The model exhibited robust applicability across diverse conflict participants, including pedestrians, bicycles, and e-bikes. The BSFM provides a validated framework for real-time safety assessment in shared micro-mobility environments, advancing proactive traffic management strategies.
Micromobility use and injuries in the USA have increased in recent years along with injuries from electric micromobility in particular. We examined USA emergency department (ED) visits between 2020 and 2024 involving e-scooters, e-bikes and bicycles overall and by motor vehicle (MV) involvement. Using National Electronic Injury Surveillance System (NEISS) data, we estimated annual weighted ED visit counts and percentages with 95% CIs for each micromobility category and by MV involvement. We also examined percentages by patient age group, sex, race and disposition. An estimated 2 532 882 ED visits involving e-scooters, e-bikes and bicycles occurred in the USA during 2020-2024, with 20.5% involving an MV. Individuals aged 18-39 years, males and Black individuals comprised a greater percentage of visits involving MVs than those not involving MVs. Hospital admission was consistently greater among MV-involved visits than those not involving an MV (eg, bicycle: 17.7% vs 9.5%). From 2020 to 2024, annual visit counts increased by 306% for e-scooters, 389% for e-bikes and 6.4% for bicycles. This study found that e-bike injuries had more MV involvement than those from e-scooters and bikes and that demographics varied by MV involvement. Our findings related to hospital admission and race align with prior literature. The growing use of micromobility vehicles, and our findings that MV involvement is associated with increased hospital admission, emphasises the need for evidence-based strategies such as helmet use and separated roadways to reduce injuries and improve safety.
The rapid increase in electric bike (e-bike) use has led to a rise in lithium-ion battery fires, which present significant hazards. Beyond thermal injury, these fires emit toxic gases such as hydrogen fluoride (HF), capable of causing severe chemical inhalation injury. The pulmonary effects of inhaled hydrofluoric acid are not well characterised in the literature. Two cases of severe lung injury occurred following indoor e-bike battery fires. The first patient sustained 32% total body surface area (TBSA) burns and developed acute respiratory distress syndrome with radiological evidence of chemical pneumonitis, necessitating prolonged mechanical ventilation and resulting in persistent pulmonary impairment. The second patient sustained 44% TBSA burns and experienced rapidly progressive respiratory failure that was disproportionate to typical smoke inhalation injury. Despite maximal supportive therapy, including extracorporeal membrane oxygenation, the patient died from catastrophic pulmonary failure. Lithium-ion battery fires present both thermal and chemical hazards, especially in enclosed environments. These cases highlight the importance of maintaining a high index of suspicion for toxic inhalation injury, promptly recognising disproportionate respiratory failure, and monitoring for biochemical indicators of hydrofluoric acid exposure as the prevalence of lithium-ion battery use increases.
Bike-sharing systems have become an essential component of sustainable urban transport, yet the resilience of system usage to changing environmental conditions remains insufficiently understood. This study provides a comprehensive spatiotemporal assessment of how weather and air quality influence bike-sharing ridership across five major U.S. cities between 2020 and 2024, and how these dynamics may evolve under future climate scenarios. Using generalized additive models, we reveal that primary weather variables mathematically dominate cycling decisions: ridership peaks around 20-25°C, while precipitation consistently suppresses usage. Conversely, air quality exerts a much weaker, secondary influence characterized by a behavioral dichotomy. Invisible, routine pollutants like ozone act as spurious proxies for pleasant weather, whereas physically perceptible hazards-such as acute wildfire smoke in San Francisco-can trigger sharp declines in usage. Hot spot analyses further show that environmental stress dynamically reconfigures spatial activity, driving a "climatic refuge" effect where cycling shifts toward waterfronts during extreme heat. Projecting these sensitivities forward, we show that future warming will enhance annual cycling suitability, particularly in seasonally cold cities, by reducing prohibitive winter days. Collectively, these results provide an integrated framework for understanding micromobility resilience, highlighting that urban cyclists respond primarily to immediate sensory environments rather than abstract health metrics.
The electrification of personal transport has transformed urban mobility, but the rapid adoption of e-bikes and e-scooters has introduced distinct fatal crash risks. Existing research on micromobility safety is often limited to non-fatal injuries and relies on standard crash databases that often lack the granular detail needed, for instance, to distinguish between vehicle types, ownership (private vs. rental), or to quantify the severity of alcohol intoxication. We conducted a retrospective analysis of all fatal crashes involving conventional bicyclists (n = 152), e-cyclists (n = 34), and e-scooterists (n = 18) recorded in Sweden's unique in-depth fatal crash database (2016-2024). This national-level data, compiled by multidisciplinary teams, allowed for an unprecedented comparative analysis of crash typologies, vehicle characteristics, and rider profiles. The three micromobility modes showed different fatal crash profiles. Conventional bicyclists were old (median age 71.0) involved in multi-road-user crashes during weekdays. In contrast, e-scooterist fatalities involved middle-aged riders (median age 47.5) in single-rider crashes, occurred on weekends and at night, and showed a high prevalence of alcohol intoxication (44.4%). Interestingly, the majority of e-scooterist crashes (66.7%), particularly those involving alcohol, occurred on privately-owned vehicles. E-cyclists occupied an intermediate crash and rider profile, sharing characteristics with both modes. Across all modes, head injuries were the dominant cause of death, while helmet use was critically low or absent. The unique crash profiles suggest that a vehicle-agnostic regulatory approach may be a missed opportunity to develop appropriate safety interventions. The findings highlight that safety interventions must extend beyond shared fleets to ensure private e-scooterists are not overlooked. The high prevalence of severe alcohol intoxication and lack of helmet use indicate clear areas for intervention. This study provides a detailed, evidence-based resource for policymakers to develop targeted regulations, safer infrastructure, and create awareness campaigns that address the risks unique to different micromobility modes.
In response to the increasingly serious traffic safety issues and regulatory challenges of electric bicycles (e-bikes), this study proposes an advanced multi-task detection model called P-YOLOv10. The model aims to achieve end-to-end unified recognition of riding safety factors and fine-grained regional attributes of license plates. To address inaccurate small-object detection and difficulty in distinguishing fine-grained features in complex real-world scenes, P-YOLOv10 introduces systematic optimizations based on the latest YOLOv10 architecture. First, it integrates the Selective Channel-Spatial Attention (SCSA) module to enhance the network's ability to capture key local features. Second, it adopts the minimum point distance intersection over union (MPDIoU) loss function to improve bounding box regression accuracy, especially for small objects such as license plates. Finally, it uses the Gaussian error linear unit (GELU) activation function to improve nonlinear representation and training stability. This study trains and evaluates the model on a self-built dataset with 2,237 images. The dataset covers diverse scenes in Guangzhou and Foshan and includes new fine-grained regional annotations. The experimental results show that P-YOLOv10 achieves excellent performance. Its overall mean average precision (mAP) reaches 96.5%, which is 1% higher than the baseline YOLOv10. It also achieves high accuracy on the newly added license plate region recognition task. The results of this study confirm the effectiveness of the integrated optimization strategy. They provide a more accurate and more comprehensive technical solution for intelligent traffic regulation systems.
Urban congestion is simultaneously influenced by heterogeneous spatio-temporal travel demands, the topology and spatial characteristics of road networks, and the interplay between multiple travel modes. As a critical component of solutions towards a greener and more sustainable transportation, bike-sharing systems have great potential in reducing carbon emissions, improving public health, and alleviating congestion by substituting short-distance motorized trips. Benefiting from flexible accessibility and usage, dockless bike-sharing has gained wide popularity and revived the fashion of cycling in cities. In this study, we reveal that the widely adopted detour ratio alone cannot effectively reflect congestion levels at the route level. Using large-scale dockless bike-sharing data and taxi trajectory data in Beijing, we quantitatively examine the relationships between cycling flow, motor vehicle traffic and road network structure. In addition, the proposed cycling-traffic-weighted detour ratio can prescreen potentially inefficient cycling routes, which can assist targeted infrastructure optimization and evidence-based urban planning.
Maxillofacial trauma is a common condition with varying incidence and causes across regions due to geographical and temporal factors. Epidemiological research is essential for improving quality of care, guiding clinical priorities, and developing effective treatment and prevention strategies. This study evaluated the epidemiology of operatively treated maxillofacial fractures at a tertiary trauma center in the Netherlands over a 5-year period. This retrospective case series was conducted at the Department of Oral and Maxillofacial Surgery, University Medical Center Utrecht, the Netherlands. The study sample included subjects who underwent operative treatment for maxillofacial fractures, excluding those with prior treatment elsewhere, pathological fractures, or objection to data use. NA. NA. Demographic variables included age, sex, body mass index, and American Society of Anesthesiologists physical status classification. Trauma-related variables included trauma etiology, injury context, helmet use, intoxication status, associated injuries, and temporal patterns. Fracture-related variables included fracture location, laterality, and single or multiple fracture status. Treatment-related variables included operative treatment type, time to treatment, hospital stay, intensive care unit admission, and discharge destination. Descriptive statistics were used to summarize the study variables. Categorical variables were reported as n (%) and continuous variables as mean (standard deviation) or median (interquartile range). Exploratory comparisons were performed using Pearson's χ2 test and Student's two-tailed t-test. The study sample included 338 subjects; 256 subjects (76%) were male, and the mean age was 38.9 years (standard deviation, 19.5 years). The largest age group was 21 to 30 years (73 subjects, 22%). Road traffic accidents (RTAs) were the most common trauma cause (167 subjects, 50%), including 129 bike-related accidents (77% of RTAs). Zygomatic and mandibular fractures were the most common fracture locations, occurring in 163 (48%) and 162 subjects (48%), respectively. Additional injuries were documented in 198 subjects (59%). RTAs were the most common cause of operatively treated maxillofacial fractures in this study sample, primarily involving bike-related accidents. These findings emphasize the need for targeted traffic safety and bicycle-related injury prevention strategies in the Netherlands aimed at reducing RTAs and their associated injuries.
This paper presents a comprehensive methodology for optimizing electric bike powertrains to address the operational challenges of permanent magnet (PM) machines in electric vehicles (EVs), particularly under wide speed ranges and high-temperature conditions that can induce irreversible demagnetization. To mitigate risks in the field weakening region, a multi-speed transmission (MST) system is proposed to confine the machine's operation to targeted speed and torque intervals. The PM machine's design parameters and transmission gear ratios are jointly optimized to minimize demagnetization risk across all driving scenarios. Comparative analysis indicates that the adoption of multi-speed transmission architecture significantly enhances system reliability by reducing the time spent in the field weakening region from 57 to 10%. Furthermore, energy assessments based on the WLTP Class 3 drive cycle demonstrate that any additional losses due to gearbox weight depend on the drive cycle and driver behavior. This study delivers a holistic solution to prolong the service life and economic viability of PM machines in EV applications by leveraging advanced powertrain design to suppress demagnetization phenomena.
This study investigated whether field-based intermittent tests performed under fatigued conditions better predict Olympic cross-country mountain biking (XCO-MTB) performance, whether prior prolonged exercise reduces power output during these tests, and whether the ability to maintain power output during intermittent tests under fatigue distinguishes competitive level in mountain bikers. Twenty-five male XCO-MTB athletes were tested under "fresh" and fatigued conditions in randomized order, separated by 72 h. Within each condition, participants performed three field-based intermittent tests with work: recovery formats of 30 s:15 s (30/15), 10 s:20 s (10/20), and 3 min:2 min (3/2), in randomized order and separated by 24 h. In the fatigued condition, each intermittent test was immediately preceded by a 140-min fatigue protocol that included repeated efforts at 105%-110% of critical power. Participants were classified as high-performance (HP) or low-performance (LP) based on XCO-MTB individual time-trial (ITT) performance, assessed 72 h after the last test session. All intermittent tests showed large to nearly perfect correlations with XCO-MTB ITT performance (n = 24; r = -0.53 to -0.95; p = 0.007 to p < 0.001), with stronger associations under fatigued conditions. The fatigued 10/20 test expressed relative to body mass was the strongest predictor, explaining 89% of performance variance. Power output decreased across all three tests after the fatigue protocol (n = 25; all p < 0.001), with greater declines in the LP group (≈15%-20%) than in the HP group (≈6%-10%; p = 0.008 to p < 0.001). In conclusion, these findings suggest that the ability to sustain power output during repeated intermittent efforts under fatigue is relevant to XCO-MTB ITT performance and competitive level.
A 14-year-old boy sustained a distal clavicle physeal fracture after an e-bike crash. Examination showed swelling without threatened skin, restricted motion, and intact neurovascular status. Anteroposterior radiographs suggested a transphyseal fracture; however, axial imaging showed posteroinferior displacement of the medial metaphyseal fragment relative to the distal physeal-epiphyseal component. Closed reduction under general anesthesia, 4 weeks of sling immobilization, and early physiotherapy restored full, pain-free function by 10 weeks. Our report describes a successful outcome after closed reduction under general anesthesia without internal fixation for a distal clavicle physeal fracture with posteroinferior displacement of the medial metaphyseal fragment.
This cross-sectional study assessed differences in physical activity and dietary habits among adolescents in Indonesia. Multistage cluster design was used with a representative sample of 375 students, 234 female participants, 141 male participants, aged 11-18. The instruments used were PAQ-A and AFHC. Statistical analysis used the Pearson Chi-square test. This study found that adolescents with an overweight BMI were more common among female adolescents, with 30 participants having a family history of obesity (26.9%). Most participants attended public schools, with a higher number of female adolescents (49.9%). This lack of physical activity/exercise duration was found more frequently in female adolescents (49.3%). This study has found a greater difference in the choice of PAQ-A items#3,#5,#6, and#7. Where the choice of PAQ-A items in#3 female adolescents have chosen more"sat down (talking, reading, doing schoolwork)"as much as 42.1%, in items #5"not doing physical activity/sports during the week as much as 21.1%, items#6"doing physical activity/sports only 1 time a week as much as 22.8%, and items#7 have chosen more"I sometimes (1-2 times last week) did physical things in my free time (e.g played sport, went running, swimming, bike riding, did aerobics)"as much as 30.8%. and although statistical analysis results did not showed differences, the percentage of AFHC items selected by participants showed differences between items #1 and #23. The large risk of physical inactivity and inadequate eating habits in adolescent girls, means that lifestyle education is needed to promote healthy body mass targeting adolescents and must consider gender factors.
Cardiorespiratory fitness (CRF) is a common risk factor for cardiometabolic diseases, but the causal relation of CRF with chronic obstructive pulmonary disease (COPD) and its interplay with genetic risk remain unknown. We integrated genetic susceptibility and causal inference methods to evaluate the protective roles of CRF in COPD development. We included 68,288 White British individuals from the UK Biobank (aged 40-79 years) without prevalent COPD at the baseline (2006-13). CRF was assessed using heart rate responses to submaximal bike tests. Genetic risk for COPD was quantified using a polygenic risk score constructed from 71 uncorrelated single nucleotide polymorphisms. Cox regression was used to estimate the hazard of COPD. Causal inference was evaluated via two-sample Mendelian randomisation (MR). Potential reverse causation was assessed using a bidirectional MR. Within an MR framework, we found that higher genetically predicted CRF is causally associated with lower risk of COPD. Observational analysis found: (1) compared with low CRF (bottom tertile), hazard ratios (95% CI) of COPD were 0.80 (0.72-0.89) and 0.71 (0.64-0.80) for medium and high CRF, respectively; (2) compared to the high CRF-low genetic risk group, COPD hazards were higher for individuals who had medium or high genetic risk combined with low or medium CRF but not for those who had medium genetic risk but high CRF; (3) low CRF combined with any levels of genetic risk showed consistently higher COPD hazards relative to high CRF and low genetic risk combination. Being more aerobically fit may prevent or delay the onset of COPD. Improving CRF has the potential to attenuate the increased risk of COPD associated with elevated genetic risk. Public health initiatives should prioritise making measurable improvements in cardiorespiratory fitness (beyond merely being active or exercising more) as a promising intervention target for reducing the risk of COPD.
Solitary fibrous tumor (SFT) is a rare mesenchymal neoplasm primarily arising from the pleura. While the majority exhibit benign biological behavior, their clinical presentation is often indolent. Solitary fibrous tumor of the pleura (SFTP) is frequently an incidental finding during physical examinations or imaging for unrelated conditions, as patients are often asymptomatic in the early stages. A 37-year-old previously healthy male, a delivery driver, suffered blunt chest and abdominal trauma following an electric bike accident with right-sided chest and abdominal pain rapidly progressing to altered consciousness and circulatory failure. On arrival, he was in hemorrhagic shock (blood pressure 70/40 mmHg, heart rate 120 beats/min). Bedside ultrasonography demonstrated a large right pleural effusion. Emergency tube thoracostomy drained >1000 mL of bright red blood. and subsequent chest computed tomography revealed massive right pleural effusion and a giant heterogeneous mass (approximately 16.4 ×14.5×15.6 cm) in the right lower hemithorax adjacent to the mediastinum, with marked mediastinal shift and cardiac compression. Given ongoing shock and suspected rupture of an intrathoracic lesion, emergent right thoracotomy was performed. A pedunculated giant tumor (20×15×12cm) arising from the right diaphragmatic pleura had partially torn with active bleeding from the pedicle; the mass was completely resected. Despite aggressive resuscitation, including open pericardium and direct cardiac massage for intraoperative cardiac arrest, the patient died postoperatively from multiple organ failure following massive blood loss and prolonged low-flow time. Histopathology and immunohistochemistry (CD34+, STAT6+) supported the diagnosis of SFT. SFTP may remain clinically silent even when extremely large. Blunt trauma may cause catastrophic tumor vessel rupture and fatal hemothorax,accompanied by sudden circulatory failure.In unstable patients with massive hemothorax and an intrathoracic mass, rupture of a hypervascular pleural tumor including SFTP should be considered, We review the relevant literature to enhance clinical recognition and management strategies for giant SFTPs with atypical presentations.
Background: Plantar pressure analysis provides insight into load distribution at the foot-pedal interface during cycling; however, its modulation by pedaling power, cadence, and overuse injury status remains poorly understood by professional cyclists. It is unclear whether common overuse injuries, such as Achilles tendinopathy, patellofemoral pathology, and iliotibial band syndrome, are associated with distinct plantar loading patterns. This study aimed to characterize plantar pressure distribution in elite cyclists and determine how power, cadence, and injury status influence this pattern. Methods: Professional cyclists completed a single integrated protocol using a high-resolution in-shoe pressure system. Plantar forces were recorded across nine anatomical regions and grouped into the transverse and longitudinal segments of the foot. Three phases were included: absolute power manipulation (100 and 200 W), cadence manipulation (80 and 100 rpm) at fixed power, and an ecological combined protocol using relative power (1.5 and 3 W·kg-1) with individualized cadence. The cyclists used their habitual bike setups. Participants were classified into the non-pathological (NP), AT, PFP, or ITBS groups. Mixed repeated-measures ANOVAs were used to analyze the effects of power, cadence, zone, foot, and injury status. Results: The plantar pressure distribution was consistently dominated by the medial forefoot. Increasing the absolute power from 100 to 200 W increased the maximal plantar pressures by 84.74% (p < 0.001), whereas increasing the cadence from 80 to 100 rpm at a fixed power increased the pressures by 15.90% (p = 0.003). Under individualized conditions, increasing relative power from 1.5 to 3 W·kg-1 increased pressures by 39.59% (p < 0.001), whereas cadence had no global main effect but significantly altered the regional pressure distribution (p < 0.001). Injury groups showed pathology-specific deviations, including higher overall pressures and asymmetry in Achilles tendinopathy, bilateral asymmetry in patellofemoral pathology, and asymmetric loading patterns in iliotibial band syndrome. Conclusions: Power is the main determinant of plantar pressure, and cadence modulates load distribution. Overuse injuries induce pathology-specific pressure patterns, supporting plantar pressure analysis for injury prevention and performance optimization in athletes.
High-quality 3D perception is essential for autonomous vehicles, urban analytics, and the development of intelligent transportation systems. However, existing LiDAR datasets are limited in their representation of fine-grained roadway and pedestrian infrastructure, and geographic diversity, particularly for environments common in North American cities. This paper introduces YEG3D, a large-scale, point-wise annotated mobile laser scanning (MLS) dataset comprising more than 682 million points collected across 14 km of urban roadway in Edmonton, Canada. The dataset includes a fine-grained taxonomy of 18 semantic classes, with an emphasis on detailed pedestrian, cyclist, and roadway infrastructure rarely distinguished in existing benchmarks. We additionally present a comprehensive baseline evaluation using five state-of-the-art semantic segmentation models, including PointNet++, DGCNN, KPConv, KPConvX, and Point Transformer v3. Among the evaluated models, Point Transformer V3 achieves the strongest overall performance, attaining 81.8% overall accuracy, 46.2% mean Intersection over Union (mIoU), and 56.8% mean F1 score, outperforming all other architectures across both global and class-level metrics. Detailed confusion matrix analysis reveals that while large structural classes are segmented reliably, fine-grained elements such as markings, bike lanes, and crosswalks remain challenging due to sparsity, occlusion, and class imbalance. YEG3D provides a new foundation for advancing research in 3D semantic segmentation, urban perception, and infrastructure-aware autonomous systems, and will be expanded in future releases to broaden its geographic and semantic coverage.
Mountain biking is increasingly popular but carries a large risk of severe trunk and spinal injuries. However, realistic crash scenarios for back protector design remain poorly characterized. This study aimed to define trunk impact conditions during mountain biking crashes. A multi-body model for mountain bike accident reconstruction was developed, and its kinematics were validated against real-world crash video footage. The model was then used to assess the influence of initial conditions (speed, slope, crash cause, etc.) on trunk impact kinematics (velocities, forces, pseudo-energy) and spinal loading indicators during forward crashes. Across 288 simulated crashes, the median normal trunk impact velocity (4.61 m/s) and pseudo-energy (48 J) aligned with current test standards, while substantial tangential (5.97 m/s) and rotational (4.90 rad/s) components were also observed. Three main impact types emerged: head-thorax impacts (43.5%), involving a head impact followed by chest impact (Vn: 5.42 m/s, Emax: 59 J); tumbling (25.1%), featuring a head impact followed by back impact (Vn: 3.98 m/s, Emax: 57 J); and overflip-back impacts (20.7%), involving direct back contact (Vn: 3.35 m/s, Emax: 47 J). This study's results define trunk impact conditions during MTB crashes, informing on realistic boundary conditions for testing and designing back protectors.
Hands-on motor characterization is essential in engineering education, but commercial dynamometers often exceed USD 15,000. This paper presents an open-source BLDC motor test bed that reduces cost by combining off-the-shelf e-bike components, ESC-native telemetry, an ESP32 controller, and a floating-caliper torque measurement mechanism. The Votol EM-50 ESC provides voltage, current, speed, and temperature telemetry, reducing the need for external electrical instrumentation, while a low-cost load cell and HX711 amplifier measure braking torque through a calibrated lever arm. Beyond hardware implementation, the platform integrates experimental validation with a dq-axis-based BLDC motor model to support interpretation of speed response, torque estimation, and transmission losses. Validation showed electrical measurement errors below 3%, torque error below 5%, expanded torque uncertainty below 0.5%, and model-versus-experiment speed-response agreement with an RMSE of 14.8 rpm. The complete system costs USD 370-488 and is supported by open firmware, wiring diagrams, bill of materials, experimental data, assembly documentation, and supplementary videos archived on Zenodo and GitHub.
Polymer-based dielectrics are widely employed in electrostatic energy storage capacitors serving as pulse power supply owing to their lightweight nature and rapid charge-discharge capability. However, their intrinsically low dielectric constant severely limits energy storage density. Although high-dielectric-constant nanofillers are commonly incorporated to enhance permittivity, organic-inorganic interfacial incompatibility often induces particle agglomeration and structural defects. In this work, we propose a confined co-doping strategy for structured polymer dielectrics, wherein BaTiO3 and Al2O3 nanoparticles are co-doed within the ferroelectric core P(VDF-HFP) of coaxial fibers and undergo self-assembly. This approach simultaneously enhances both energy density and charge-discharge efficiency. As a result, the 1 wt% BaTiO3/1 wt% Al2O3 core co-doping composite dielectric achieves a discharged energy density of 19.2 J/cm3 and a charge-discharge efficiency of 81.0%, and maintains stable performance over 1 × 105 cycles under an electric field of 400 kV/mm. This confined co-doping strategy thus provides an effective and scalable route for developing polymer-based dielectrics with high energy density and high reliability.