Electric bicycle play a vital role in public transportation. It provides sustainable transportation with zero COx emissions. Further, pedaling allows the user to do physical exercise while travelling to the destinations. Electric bicycle allows the user to ride in different modes one is the complete manual physical mode and second one is the complete throttle mode without any physical effort and third one is the pedal assist mode. Pedal assist mode of riding allows the user to ride the bicycle in a comfortable manner as it senses the pedaling effort and gives the required motor speed in a proportional manner. Pedal assist mode may not suitable for many riders due to the reduced comfort. The major drawback in electrical bicycle is limited mileage sine it uses low capacity battery due to bicycle weight and regulations. Improving the mileage in an electric bicycle is a challenging task. There are many methods to improve the mileage such as regenerative braking, a dynamo attached to the cycle tube, hub dynamo etc. All these methods will give low output and will not be sufficient to charge the battery while the bicycle is in motion. Further, because of these methods, the friction will further increase on bicycle and finally there will be more losses as compared to without any of these methods. To address this, a novel friction less low rpm permanent magnet generator has been developed indigenously which will eventually provide improved bicycle mileage, more battery life, less friction and suitable for long rides. The novelty of this proposed method is that the rider can do the pedaling even in throttle mode to save the battery energy. The generator can charge the battery while the bicycle is in throttle mode and also in manual mode with a very low friction. Compared to the energy loss due to the friction, the energy recovery will be more with the proposed methodology. The hardware results also demonstrate that, the proposed methodology is a feasible solution to self-charge the battery while minimum friction on wheel.

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.

BackgroundAlthough 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.MethodsA 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.ResultsA 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.ConclusionsE-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.

The rapid adoption of electric bicycles (EB) has led to a significant increase in related injuries, posing a growing public health challenge. In Anhui Province, China, EB-related orthopedic injuries represent a major component of traffic trauma burden. However, systematic data on injury patterns, anatomical distribution, and demographic variations remain limited, hindering optimized clinical management. This study aims to characterize the clinical spectrum of orthopedic injuries associated with EB that necessitate surgical management. This single-center retrospective cohort study analyzed data from the Hospital Information System (HIS) for patients with EB-related orthopedic injuries between January, 2020, and December, 2025. Among 3,412 vehicle-related injuries, 1,735 cases met inclusion criteria. Injury types were classified into six categories (e.g., fractures, dislocations), and anatomical sites were categorized into 16 regions. Statistical analyses included descriptive statistics and chi-square tests to identify factors associated with severe injuries. The study included 1,735 patients (59.20% male; mean age 48.65 ± 15.73 years), with a bimodal age distribution peaking in the 31-44 and 45-59 groups. Fractures predominated (85.01% of cases), followed by combined injuries such as open fractures with soft tissue damage (4.67%). The most frequent anatomical sites were the clavicle, tibiofibula, and hand/foot. Female patients were significantly older than males (95% CI: 3.44, 6.38; p < 0.001), and young males had higher injury rates. EB-related orthopedic injuries predominantly affect middle-aged and elderly populations (1,735 patients; mean age 48.65 ± 15.73 years, bimodal peaks at 34.65 ± 9.41 years and 57.28 ± 6.72 years), with fractures accounting for 85.01% of cases and combined trauma (e.g., open fractures with soft tissue damage) representing 4.67%. The clavicle, tibiofibula, and hand/foot are the most commonly injured sites. These findings provide foundational insights for orthopedic clinical practice pertaining to EB-related injuries, suggesting that age-stratified triage protocols and prioritized evaluation of high-risk anatomical sites (clavicle, lower limbs) warrant further investigation to optimize resource allocation and patient outcomes in clinical settings. However, this study has several limitations, including its single-center retrospective design, absence of severity validation scores, and insufficient data on protective measures/devices/follow-up and so on. Therefore, prospective multicenter studies are warranted to validate and optimize clinical practice.

Micromobility (Electrical bicycles and scooters) craniofacial trauma and injury patterns: A single-center study.

Analysis of electric bicycle riders' visual strategies and riding performances when using a mobile phone.

Traumatic spinal cord injury (TSCI) imposes a substantial clinical and public health burden, yet contemporary epidemiological data from northern China are limited. This study aimed to describe the epidemiological characteristics of TSCI in Jinan, an urban-rural integrated city in northern China. We conducted a hospital-based retrospective observational study of patients with TSCI admitted to the 960th Hospital of the PLA and Qilu Hospital of Shandong University between 2015 and 2024. Medical records of 1134 patients were reviewed. Collected variables included sex, age, marital status, occupation, time of injury, etiology, neurological level of injury, American Spinal Injury Association grade, complications, concomitant injuries, treatments received, and length of hospital stay. Descriptive statistics were used to summarize the data. The mean (standard deviation) age was 62.0 (18.6) years (95% confidence interval, 60.9–63.1), and the male-to-female ratio was 3.69:1. Falls were the leading cause of injury (46.0%), including low-level falls (25.8%) and high-level falls (20.2%), followed by traffic accidents (40.7%). The cervical spine was the most frequently injured region. During hospitalization, 500 of 1134 patients (44.1%) developed complications, most commonly pulmonary infections (16.5%) and urinary tract infections (13.6%). Surgical intervention was performed in 88.9% (1009/1134) of patients. In this 10-year hospital-based series from Jinan, most patients with TSCI were older men, with 61 to 75 years being the most common age group. Falls and traffic accidents were the predominant etiologies. Farmers represented the highest-risk occupational group. Prevention strategies should particularly target fall risk among older adults and electric bicycle–related injuries among young men. These findings highlight the need for tailored injury-prevention measures and underscore the crucial role of rehabilitation in the long-term management of TSCI.

Left Main Coronary Dissection Masquerading in Pediatric Polytrauma: A Diagnostic Challenge.

Hydrofluoric acid inhalation injury after electric bike battery fire.

Electric-bicycles and speed-related trauma in pediatrics: Risk of internal injury and hospitalization.

Life cycle and net environmental impact assessment of a shared e-bike service: application to the case of Madrid

Powered micromobility devices (PMDs), including electric scooters (e-scooters) and electric bicycles (e-bikes), can operate at speeds approaching those of low-speed motorcycles. Yet PMDs are treated as low-energy mechanisms in trauma surveillance systems and are not classified as distinct mechanisms of injury. Although prior studies report higher head injury rates in PMD crashes than bicycle crashes, injury severity and health care utilization relative to motorcycle crashes are not known. We hypothesized that e-scooter and e-bike crashes demonstrate distinct injury and resource utilization patterns compared with motorcycles and bicycles. We performed a retrospective cohort study using the 2023 Florida Agency for Health Care Administration emergency department and inpatient databases. Encounters for motorcycle, bicycle, e-scooter, or e-bike crashes were identified. Patients meeting National Trauma Data Standard criteria were classified as trauma registry-eligible. Multivariable regression evaluated associations between mechanism and severe head and facial injury. Of 50,889 encounters, 10,522 (20.7%) were trauma registry-eligible. Median ISS was highest for motorcyclists [10 (interquartile range: 4-22)], followed by bicyclists [9 (1-14)], e-bike [8 (2-17)], and e-scooter riders [6 (1-15)] (p<0.001). On adjusted analyses, e-scooter and e-bike crashes had no significant differences in adjusted odds ratios (aORs) of severe head injury relative to motorcycle crashes, while e-bike crashes were associated with a higher aOR of severe facial injury (aOR: 1.78, 95% confidence interval: 1.15-2.74). E-bike injuries demonstrated the second-highest intensive care unit utilization and per-encounter hospital charges after motorcycles (both p<0.001). E-scooter and e-bike crashes demonstrated injury and resource utilization patterns distinct from both traditional bicycles and motorcycles. Head injury risk among PMD riders was similar to that of motorcyclists, while facial injury risk among e-bike riders exceeded that of motorcyclists. Findings highlight the need for improved injury surveillance and evidence-based helmet standards for PMD users, including evaluation of whether full-face protection may be warranted. Prognostic/Epidemiological; Level III.

BACKGROUND AND OBJECTIVES:The rapid rise of electric and mechanical bikes and scooters has transformed urban transportation, but their neurosurgical consequences remain underexplored. This study aimed to evaluate micromobility-related injuries over time, examining mechanisms of injury, patient risk factors, injury patterns, and associated clinical outcomes at a Level-1 trauma center over a 5-year period.METHODS:We performed a retrospective review of patients who sustained micromobility-related injuries and presented to the Bellevue Hospital Center between 2018 and 2023. The cohort included riders of electric or mechanical bikes and scooters, as well as pedestrians struck by these devices. Key clinical variables and outcomes were compared across device types, both before and after propensity score matching. Unlike national database studies, this hospital-based analysis provides detailed clinical and neurosurgical outcome data.RESULTS:A total of 914 patients presented with micromobility-related injuries, accounting for 6.9% of all trauma admissions. Annual case volume and electric device involvement increased over time. The most common mechanism was collision with a motor vehicle (49.9%). Most patients (68.7%) required admission; 30.2% required intensive care. The median length of hospital stay was 3 days [IQR 1-5]. Half underwent a surgical intervention or procedure, and the overall mortality was 1.2%. Helmet use was low (31.7%). Pedestrians experienced the most severe outcomes, particularly when struck by electric devices. Injuries clustered during evening hours, suggesting modifiable environmental and behavioral risk factors.CONCLUSION:Micromobility-related trauma imposes a substantial neurosurgical burden, with frequent traumatic brain injury, intensive care unit utilization, and operative intervention. Unlike previous database studies, this hospital-based analysis provides detailed neurosurgical outcome data and identifies prevention targets—including helmet use, intoxication, and urban infrastructure—to reduce morbidity and resource utilization.

Global transition toward sustainable micro-mobility is an essential aspect of Saudi Vision 2030; however, high car dependency remains a significant barrier to public health and safety targets. In this context, this study explores the factors determining the adoption of electric bicycles (e-bikes) in Al-Qunfudhah, Saudi Arabia. The present research used a convenience sampling strategy through an online survey conducted via social media and texting, utilizing a designed questionnaire of 10 sections delivered to 171 participants, alongside a 5-point Likert scale. Additionally, the scientific validation and analysis were conducted utilizing internal consistency, validity and scale reliability via statistical analysis. The findings indicated a significant intention–action disparity; while respondents demonstrate a strong psychological intention to adopt e-bikes within 12 months (an average of 3.51), real household ownership was relatively low at 11.1%. In addition, a significant 71.9% of participants use private vehicles for short-distance travel (&lt;5 km), influenced by an average bus stop distance of 21.22 km. The hierarchy of barriers indicates infrastructure and security as the main barrier, particularly the absence of dedicated bike lanes, and concerns regarding traffic safety. In contrast, a perception of physical fitness, and interpersonal interaction behave as significant facilitators. Public health data reveals an average weekly activity of 109.77 min, significantly lower than worldwide recommendations; however, 66.7% of individuals believe e-bikes may address the difference. The statistical evaluation acknowledged the questionnaire’s robustness, with significant Pearson correlation coefficients (p &lt; 0.01) demonstrating internal consistency validity and Cronbach’s alpha values between 0.71 and 0.88 indicating high scale reliability, demonstrating a scientifically stable framework for assessing the measured behavioral determinants. The research recommends the establishment of shaded, dedicated micro-mobility networks and the enforcement of safety regulations to promote a healthy, multi-modal urban ecosystem.

The growing trend of electric bicycle use as an environmentally friendly transportation solution in Jepara indicates a shift in public preference toward more modern, efficient vehicles that support a sustainable lifestyle. This study aims to analyze the influence of brand awareness, brand image, and influencer marketing on electric bicycle purchasing decisions in Jepara. The research method used a quantitative approach with purposive sampling techniques involving 100 respondents who are electric bicycle users in the Jepara area. The data were analyzed using the Partial Least Squares-Structural Equation Modeling (PLS-SEM) method through SmartPLS 4 software. The results showed that brand awareness, brand image, and influencer marketing had a positive and significant effect on purchasing decisions with a p-value &lt; 0.05. Influencer marketing has the most dominant influence, followed by brand awareness and brand image. The research model is able to explain 65% of the variation in electric bicycle purchase decisions. This study provides a theoretical contribution in strengthening consumer behavior theory and a practical contribution for business actors in developing marketing strategies based on brand strength and collaboration with relevant influencers.

With the widespread use of electric bicycles and batteries, fire accidents caused by batteries have become increasingly serious, especially in closed indoor environments. Traditional fire prevention methods often rely on static monitoring and simple sensors, which can suffer from delayed responses or fail to accurately identify fire hazards. To address this issue, this paper proposes an innovative monitoring system for preventing fire hazards caused by batteries or electric bicycles in indoor environments, based on an STMicroelectronics 32-bit Microcontroller (STM32) and Open-source Machine Vision module (OpenMV). The system uses deep learning to train a neural network to recognize the image information of batteries or electric bicycles. The key innovation of this system lies in several aspects: firstly, it utilizes the OpenMV module for real-time image processing, enabling efficient and accurate recognition of batteries and electric bicycles; secondly, the integration with the STM32 microcontroller enhances the system’s data processing capabilities and enables flexible communication and responses with external devices; finally, the system features high-efficiency serial communication, ensuring the real-time transmission and processing of monitoring data for swift responses to potential fire risks. Experimental results show that the system can accurately identify batteries or electric bicycles in indoor environments and respond in a timely manner, significantly reducing fire hazards. In addition, the system's design is not limited to preventing battery-related fire hazards in indoor environments. Compared to traditional methods, this study's innovation lies in combining deep learning and embedded control technology for fire prevention, providing a practical and scalable solution for battery-related fire risk prevention.

Injury severity analysis of e-bike crashes: An age-stratified study of riders aged 40 and above.

Understanding consumers' resistance intention towards battery swapping services for electric bikes: Insights from PLS-SEM and ANN

Strap it up! Decoding shared electric bike users’ helmet use behavior through a social ecological lens: A configurational approach

In this paper, a wireless charging system architecture is proposed and designed to charge the receiving coil installed on the hollow frame of the electric bicycle (EB) body through the cylindrical charging coils on both sides. Among them, the size of the transmitting coils is designed and optimized to enhance tolerance to three-dimensional misalignment. A novel auxiliary coil position reconstruction (ACPR) method is proposed to actively offset the misalignment effect by adjusting the position of an auxiliary coil nested on the primary coil. The parameters of the resonant network are reconstructed by adjusting the position of the auxiliary coil when the EBs are parked in a misaligned state. The topology enables the voltage reconstruction of an equivalent T/S constant voltage topology, even effectively eliminating the voltage variation caused by the misalignment with simply moving the auxiliary coil. A prototype of the proposed system is built. The misalignment tolerance against output voltage changes in different dimensions is verified through the proposed ACPR method, efficiency of the system is demonstrated to be maintained at around 90%.