Search Results (84)

This study investigated the determinants of entrepreneurial intentions and behavior among Saudi sports education students using the Theory of Planned Behavior. The study employed a cross-sectional survey of 372 undergraduate and graduate sports science students from Saudi universities. It extended TPB by including entrepreneurial role models as an independent variable affecting TPB antecedents—attitudes toward behavior, subjective norms and perceived behavioral control and outcomes (ENTIs and actual entrepreneurial behavior, AEB). Data were analyzed using linear and hierarchical regression with mediation testing using bootstrapping. Results showed that all TPB antecedents significantly predicted ENTI, while only ENTI and PBC influenced AEB. ERMs were significantly associated with SNs but had no direct effect on ATB, PBC, or ENTI. Mediation analyses revealed that ATB and PBC partially mediated SNs’ effect on ENTI, whereas SNs fully mediated ERMs’ influence on ATB and PBC. These findings provide theoretical and practical insights by validating the extension of TPB with role models, challenging assumptions about ERMs’ direct effects, and highlighting the importance of fostering entrepreneurial culture in universities. Integrating exposure to positive ERMs can effectively translate students’ intentions into entrepreneurial behavior, supporting the development of sports entrepreneurs. Full article

A large number of vulnerable road users such as pedestrians continue to be injured or killed in road accidents every year, and active safety systems such as automatic emergency braking systems are expected to improve the situation. However, automatic emergency braking systems for pedestrians have been tested in a variety of real-world scenarios. The purpose of this paper is to obtain typical P-AEB test scenarios that can reflect the real and collision scenarios through real pedestrian–vehicle crash data. By using the k-means clustering algorithm based on local outlier detection, the intersection data and the straight-road data are clustered and analyzed separately, with five types of typical P-AEB straight-road test scenarios and seven types of typical P-AEB intersection test scenarios. By comparing with the existing test protocols, the test scenarios proposed in this paper have good coverage and authenticity, and can play a guiding role in the construction of specific P-AEB system test scenarios. Full article

Accurate braking-distance prediction for heavy-duty multi-axle trucks remains challenging due to the large gross vehicle weight, tandem-axle interactions, and strong transient load transfer during emergency braking. Recent studies on tire–road friction estimation, commercial-vehicle braking control (EBS/AEBS), and weigh-in-motion (WIM) sensing have highlighted that unmeasured vertical-load dynamics and time-varying friction are key sources of prediction uncertainty. To address these limitations, this study proposes an integrated sensing–simulation–AI framework that combines real-time axle-load estimation, full-scale robotic braking tests, fused road-friction sensing, and physics-consistent machine-learning modeling. A micro-electro-mechanical systems (MEMS)-based load-angle sensor was installed on the leaf-spring panel linking tandem axles, enabling the continuous estimation of dynamic vertical loads via a polynomial calibration model. Full-scale on-road braking tests were conducted at 40–60 km/h under systematically varied payloads (0–15.5 t) using an actuator-based braking robot to eliminate driver variability. A forward-looking optical friction module was synchronized with dynamic axle-load estimates and deceleration signals, and additional scenarios generated in a commercial ASM environment expanded the operational domain across a broader range of friction, grade, and loading conditions. A gradient-boosting regression model trained on the hybrid dataset reproduced measured stopping distances with a mean absolute error (MAE) of 1.58 m and a mean absolute percentage error (MAPE) of 2.46%, with most predictions falling within ±5 m across all test conditions. The results indicate that incorporating real-time dynamic axle-load sensing together with fused friction estimation improves braking-distance prediction compared with static-load assumptions and purely kinematic formulations. The proposed load-aware framework provides a scalable basis for advanced driver-assistance functions, autonomous emergency braking for heavy trucks, and infrastructure-integrated freight safety management. All full-scale braking tests were carried out at approximately 60% of the nominal service-brake pressure, representing non-panic but moderately severe braking conditions, and the proposed model is designed to accurately predict the resulting stopping distance under this prescribed braking regime rather than to minimize the absolute stopping distance itself. Full article

While an advanced emergency braking system (AEBS) significantly improves the safety of a heavy goods vehicle (HGV), current implementations face limitations including inadequate scenario coverage for vulnerable road users (VRUs), overriding driver control and limited human–machine collaboration mechanisms, and an insufficient consideration of blind spot challenges in HGVs. To improve the adaptability of the AEBS for HGVs, this study proposes and validates a novel 2D AEBS control algorithm incorporating driver’s control and 3D visibility of VRUs. The proposed algorithm is designed to firstly identify the motion state scenarios based on the spatial relationship between the HGV and VRU. Then, based on the scenario classification result, the proposed algorithm determines whether the HGV needs to brake in the current scenario according to the 2D time to collision for both entities to reach the potential collision area while maintaining their current speeds. Finally, for situations requiring braking, it evaluates whether safety can be ensured under three conditions: the ego vehicle in free driving, the ego vehicle under driver-controlled braking (considering the 3D visibility of the VRU), and the ego vehicle under 2D AEBS-controlled braking. According to the test results, the proposed algorithm can deal with the VRU crossing scenario and leverage the driver’s control capabilities while utilizing AEBS as a safety net function. Full article

The automatic emergency braking (AEB) system plays a crucial role in reducing rear-end collisions and is mandatory on certain heavy-duty vehicles, with future regulations extending to passenger cars. However, most current AEB systems are designed based on onboard sensors such as cameras and radar, which may fail to prevent collisions in scenarios where the lead vehicle is already in a collision. To address this issue, this study proposes an enhanced AEB control method based on Vehicle-to-Vehicle (V2V) communication and onboard sensors. The method utilizes V2V communication and onboard sensors to predict obstacles ahead, applying effective braking when necessary. Simulation results in Matlab/Simulink R2022a show that the proposed V2V-based AEB control method reduces the risk of chain collisions, ensuring that the ego vehicle can avoid rear-end collisions even when the lead vehicle is involved in a crash. Three simulation scenarios were designed, where both the subject vehicle and the lead vehicle travel at 120 km/h. The following three distances between the subject vehicle and the lead vehicle were considered: 45 m, 70 m, and 30 m. When the lead vehicle detects an obstacle 30 m ahead and suddenly applies emergency braking, the lead vehicle fails to avoid a collision. In this case, the subject vehicle, equipped only with onboard sensors, is also unable to successfully avoid the crash. However, when the subject vehicle is equipped with both onboard sensors and vehicle-to-vehicle communication, it can prevent a rear-end collision with the lead vehicle, maintaining a vehicle-to-vehicle distance of 1 m, 6.8 m, and 3.1 m, respectively, during the stopping process. This control method contributes to advancing the active safety technologies of autonomous vehicles. Full article

The surface defect detection of printed circuit boards (PCBs) plays a crucial role in the field of industrial manufacturing. However, the existing PCB defect detection methods have great challenges in detecting the accuracy of tiny defects under the complex background due to its compact layout. To address this problem, we propose a novel YOLO-AMBA-EASPP-BiFPN (YOLO-AEB) network based on the YOLOv10 framework that achieves high precision and real-time detection of tiny defects through multi-level architecture optimization. In the backbone network, an adaptive multi-branch attention mechanism (AMBA) is first proposed, which employs an adaptive reweighting algorithm (ARA) to dynamically optimize fusion weights within the multi-branch attention mechanism (MBA), thereby optimizing the ability to represent tiny defects under complex background noise. Then, an efficient atrous spatial pyramid pooling (EASPP) is constructed, which fuses AMBA and atrous spatial pyramid pooling-fast (ASPF). This integration effectively mitigates feature degradation while preserving expansive receptive fields, and the extraction of defect detail features is strengthened. In the neck network, the bidirectional feature pyramid network (BiFPN) is used to replace the conventional path aggregation network (PAN), and the bidirectional cross-scale feature fusion mechanism is used to improve the transfer ability of shallow detail features to deep networks. Comprehensive experimental evaluations demonstrate that our proposed network achieves state-of-the-art performance, whose F₁ score can reach 95.7% and mean average precision (mAP) can reach 97%, representing respective improvements of 7.1% and 5.8% over the baseline YOLOv10 model. Feature visualization analysis further verifies the effectiveness and feasibility of YOLO-AEB. Full article

This paper presents an experimental study of the Automatic Emergency Braking (AEB) system, focusing on three essential testing phases: verifying the match between calculated and actual brake actuator operation time, validating the forecasted vs. real-time stabilized deceleration onset duration, and comparing the theoretically computed braking distance derived from mathematical models with its actual measurement. Standard instrumentation coupled with an original test procedure was utilized during the experiments. A full-scale experimental campaign was conducted on a specialized proving ground, thus substantiating the validity and robustness of the computational models used for assessing the AEB system parameters. The empirical outcomes confirmed that current-generation AEB systems offer dependable predictions regarding braking dynamics and exhibit prompt responsiveness to imminent collisions. However, it should be noted that variations in road conditions, driver behavior, and sensor precision may affect their performance. Consequently, additional efforts aimed at optimizing existing AEB solutions are required to minimize potential errors and enhance overall reliability. Finally, the significance of complying with design specifications and continuously upgrading AEB systems to meet evolving road safety standards is emphasized. Full article

This paper evaluates Advanced Driver Assistance Systems (ADASs) in test scenarios derived from naturalistic driving and crash data, mapped to ISO 26262, ISO/PAS 21448 (SOTIF), and ISO 34502. From eight high-risk scenarios, it is validated for left turns across oncoming traffic on a proving ground using a Škoda Superb iV against a soft Global Vehicle Target. ODD and spatiotemporal thresholds are parameterized and speed/acceleration profiles from GNSS/IMU data are analyzed. AEB and FCW performance varies across nominally identical runs, driven by human-in-the-loop variability and target detectability. In successful interventions, peak deceleration reached −0.64 g, meeting UNECE R152 criteria; in other runs, late detection narrowed TTC below intervention thresholds, leading to contact. Limitations in current protocols are identified and argue for scenario catalogs with realistic context (weather, surface, masking) and latency-aware metrics. The results motivate extending validation beyond standard tracks toward mixed methods linking simulation, scenario databases, and instrumented field trials. Full article

Predicting potential collisions with leading vehicles is a fundamental capability of autonomous and assisted driving systems. In particular, automatic emergency braking (AEB) demands reaction times on the order of microseconds. A key limitation of existing approaches lies in their update rate, which is constrained by the sampling speed of conventional sensors. Event-based Dynamic Vision Sensors (DVSs), with their microsecond temporal resolution and high dynamic range, offer a promising alternative to frame-based cameras in challenging driving environments. In this work, we investigate the integration of DVS into autonomous driving pipelines, focusing specifically on AEB scenarios. Building on our earlier work, where a YOLO-based detection model was trained on real-world DVS data, we extend the approach to CARLA’s simulated DVS environment. We publish a CARLA-compatible 2-channel DVS dataset aligned with our detection model, bridging the gap between real-world recordings and simulation. Through a series of simulated AEB scenarios, we demonstrate how DVS enables earlier and more reliable detection compared to RGB cameras, resulting in improved braking performance. Full article

This paper presents a novel methodology for predicting the tire–road friction coefficient in real-time under challenging climatic conditions based on a fuzzy logic inference system. The core innovation of the proposed approach lies in the integration and probabilistic weighting of a diverse set of input data, which includes signals from ambient temperature and precipitation intensity sensors, activation events of the anti-lock braking system (ABS) and electronic stability control (ESP), windshield wiper operation modes, and road marking recognition via a front-facing camera. This multi-sensor data fusion strategy significantly enhances prediction accuracy compared to traditional methods that rely on limited data sources (e.g., temperature and precipitation alone), especially in transient or non-uniform road conditions such as compacted snow or shortly after rainfall. The reliability of the fuzzy-logic-based predictor was experimentally validated through extensive road tests on dry asphalt, wet asphalt, and wet basalt (simulating packed snow). The results demonstrate a high degree of convergence between predicted and actual values, with a maximum modeling error of less than 10% across all tested scenarios. The developed methodology provides a robust and adaptive solution for enhancing the performance of Advanced Driver Assistance Systems (ADASs), particularly Automatic Emergency Braking (AEB), by enabling more accurate braking distance calculations. Full article

In this paper, three alternative solutions to the problem of detecting and cleaning anomalies in soil signal time series, involving the use of artificial neural networks deployed on in situ data measurement end devices, are proposed and investigated. These models are designed to perform calculations on MCUs, characterized by significantly limited computing capabilities and a limited supply of electrical power. Training of neural network models is carried out based on data from multiple sensors in the supporting computing cloud instance, while detection and removal of anomalies with a trained model takes place on the constrained end devices. With such a distribution of work, it is necessary to achieve a sound compromise between prediction accuracy and the computational complexity of the detection process. In this study, neural-primed heuristic (NPH), autoencoder-based (AEB), and U-Net-based (UNB) approaches were tested, which were found to vary regarding both prediction accuracy and computational complexity. Labeled data were used to train the models, transforming the detection task into an anomaly segmentation task. The obtained results reveal that the UNB approach presents certain advantages; however, it requires a significant volume of training data and has a relatively high time complexity which, in turn, translates into increased power consumption by the end device. For this reason, the other two approaches—NPH and AEB—may be worth considering as reasonable alternatives when developing in situ data cleaning solutions for IoT measurement systems. Full article

To address the growing need for field calibration of the optical properties of pedestrian targets used in autonomous emergency braking (AEB) tests, a novel three-dimensional multi-faceted standard body (TDMFSB) was developed. A camera-based analytical algorithm was proposed to evaluate the bidirectional reflectance distribution function (BRDF) characteristics of pedestrian targets. Additionally, a field calibration method applied in AEB testing scenarios (CPFAO and CPLA protocols) on one new and one aged typical pedestrian target of the same type revealed a 21% decrease in the BRDF uniformity of the aged target compared to the new one, confirming optical degradation due to repeated “crash–scatter–reassembly” cycles. The surface wear of the aged target on the side facing the vehicle produced a smoother surface, increasing its BRDF magnitude by 25% compared to the new target and making it easily detectable by the vehicle’s perception system. This led to “reverse scoring,” a safety risk in performance evaluation, necessitating timely calibration of AEB pedestrian targets to ensure reliable test results. The findings provide valuable insights into the development of regulatory techniques, evaluation standards, and technical specifications for test targets and offer a practical path toward full-life-cycle traceability and quality control. Full article

In the field of active safety technology for intelligent connected vehicles (ICVs), the reliability and safety of the Automatic Emergency Braking (AEB) system is recognized as critical to driving safety. However, existing evaluation methods have been constrained by the inadequacy of static weight assessments in adapting to diverse driving conditions, as well as by the disconnect between conventional evaluation frameworks and experimental validation. To address these limitations, a comprehensive Vehicle-in-the-Loop (VIL) evaluation system based on the dynamic weight analytic hierarchy process (DWAHP) was proposed in this study. A two-tier dynamic weighting architecture was established. At the criterion level, a bivariate variable–weight function, incorporating the vehicle speed and road surface adhesion coefficient, was developed to enable the dynamic coupling modeling of road environment parameters. At the scheme level, a five-dimensional indicator system—integrating braking distance, collision speed, and other key metrics—was constructed to support an adaptive evaluation model under multi-condition scenarios. By establishing a dynamic mapping between weight functions and driving condition parameters, the DWAHP methodology effectively overcame the limitations associated with fixed-weight mechanisms in varying operating conditions. Based on this framework, a dedicated AEB system performance test platform was designed and developed. Validation was conducted using both VIL simulations and real-world road tests, with a Volvo S90L as the test vehicle. The experimental results demonstrated high consistency between VIL and real-world road evaluations across three dimensions: safety (deviation: 0.1833/9.5%), reliability (deviation: 0.2478/13.1%), and riding comfort (deviation: 0.05/2.7%), with an overall comprehensive score deviation of 0.0707 (relative deviation: 0.51%). This study not only verified the technical advantages of the dynamic weight model in adapting to complex driving environments and analyzing multi-parameter coupling effects but also established a systematic methodological framework for evaluating AEB system performance via VIL. The findings provide a robust foundation for the testing and assessment of AEB system, offer a structured approach to advancing the performance evaluation of advanced driver assistance systems (ADASs), facilitate the safe and reliable validation of ICVs’ commercial applications, and ultimately contribute to enhancing road traffic safety. Full article

The problem of the foul odor caused by H₂S in livestock farms has become a major complaints. In this study, optimal sulfur-oxidizing bacteria (SOB) strains were screened from dairy farm wastewater and the adjacent soil for odor treatment. The strains and physiological functions were determined by identification and genome comparison, and the optimal operating conditions were determined by experiments under different conditions. The identification results showed that the strain that had the highest homology with Halomonas mongoliensis was Halomonas sp. AEB2. The comparative genomic results showed that the average nucleotide identity and DNA–DNA hybridization value were 95.8% and 68.6%, respectively. The optimization results were as follows: sodium succinate-carbon (10 g/L) and ammonium chloride-nitrogen (0.07 g/L). The optimal operating conditions were as follows: seeding rate 4%, temperature 30 °C, stirring speed 90 rpm, and pH 8. The oxidation products of AEB2 were mainly elemental sulfur and tetrathionate, and the metabolic pathway of AEB2 was constructed accordingly. This study suggests a feasible path to reduce H₂S emissions from dairy farms, and it provides theoretical support for the restoration of livestock environment and sustainability. Full article

Background/Objectives: Over the past decade, numerous studies have explored the bidirectional relationship between obesity and mental health, mainly eating disorders (EDs). This study aimed to assess the prevalence and characteristics of altered eating behaviors (AEBs) in a cohort of people with obesity (PwO) using the validated Eating Behaviors Assessment for Obesity (EBA-O). Methods: We conducted a cross-sectional study from May 2023 to April 2024, recruiting consecutive PwO seeking weight loss. Participants completed the 18-item EBA-O questionnaire, which focuses on five primary eating behaviors: night eating, food addiction, sweet eating, hyperphagia, and binge eating. Unlike other validated tools, the EBA-O is specifically designed to capture these behaviors in PwO and is easy for patients to self-administer. We also collected sociodemographic and clinical data. Results: A total of 127 participants were included (76 women, median age 52 years, median BMI 42.9 kg/m²). We found a significant prevalence of AEBs: 33.1% for sweet eating, 23.6% for hyperphagia, 15.7% for food addiction, 14.2% for binge eating, and 7.1% for night eating. The EBA-O scores correlated positively with BMI (r = 0.201, p = 0.024) and increased across BMI categories (p = 0.001). Males had higher scores for night eating and hyperphagia (p = 0.01), and active smokers had higher hyperphagia scores (p = 0.043) than ex-smokers and non-smokers. The night eating scores were inversely correlated with sleep hours (r = −0.197, p = 0.026), and food addiction was positively correlated with age (r = 0.261, p = 0.003); conversely, hyperphagia (r = −0.198, p = 0.025) and binge eating (r = −0.229, p = 0.010) were inversely correlated with age. PwO without diabetes had higher scores for food addiction (p = 0.01) and binge eating (p = 0.004) compared to those with diabetes. Conclusions: These results highlight the potential to characterize PwO based on their AEBs, offering new opportunities to tailor treatment strategies for PwO by targeting specific eating behaviors. Full article