Search Results (377)

Although deep learning techniques have potential in vehicle behavior prediction, it is difficult to integrate traffic rules and environmental information. Moreover, its black-box nature leads to an opaque and difficult-to-interpret prediction process, limiting its acceptance in practical applications. In contrast, ontology reasoning, which can utilize human domain knowledge and mimic human reasoning, can provide reliable explanations for the speculative results. To address the limitations of the above deep learning methods in the field of vehicle behavior prediction, this paper proposes a front vehicle behavior prediction method that combines deep learning techniques with ontology reasoning. Specifically, YOLOv5s is first selected as the base model for recognizing the brake light status of vehicles. In order to further enhance the performance of the model in complex scenes and small target recognition, the Convolutional Block Attention Module (CBAM) is introduced. In addition, so as to balance the feature information of different scales more efficiently, a weighted bi-directional feature pyramid network (BIFPN) is introduced to replace the original PANet structure in YOLOv5s. Next, using a four-lane intersection as an application scenario, multiple factors affecting vehicle behavior are analyzed. Based on these factors, an ontology model for predicting front vehicle behavior is constructed. Finally, for the purpose of validating the effectiveness of the proposed method, we make our own brake light detection dataset. The accuracy and [email protected] of the improved model on the self-made dataset are 3.9% and 2.5% higher than that of the original model, respectively. Afterwards, representative validation scenarios were selected for inference experiments. The ontology model created in this paper accurately reasoned out the behavior that the target vehicle would slow down until stopping and turning left. The reasonableness and practicality of the front vehicle behavior prediction method constructed in this paper are verified. Full article

This study aimed to explore the impact of various ornamental plants (Heliconia psittacorum, Etlingera elatior, Spatyphilum walisii) grown in different filter media (porous river rock (PR) and tepezyl (TZ)) on the removal of pollutants in vertical-subsurface-microcosm treatment wetlands (TWs). This study also sought to assess the adaptability of these plant species to TW conditions. Twenty-four microcosm systems were utilized, with twelve containing PR and twelve containing TZ as the filter media. Each porous media type had three units planted with each species, and three were left unplanted. Rural community wastewater was treated in the TWs. The results showed no significant differences in the effects of the porous media on pollutant removal performance (p > 0.05). However, it was noted that while both porous media were efficient, TZ, a residue of construction materials, is recommended for sites facing economic constraints. Additionally, the removal efficiency was found to be independent of the type of ornamental plant used (p > 0.05); however, the measured parameters varied with plant spp. The adaptation of the plants varied depending on the species. H. psittacorum grew faster and produced a larger number of flowers compared to the other species (20–22 cm). S. wallisii typically produced 7–8 flowers. E. elatior did not produce flowers, and some plants showed signs of slight disease and pests, with the leaves turning yellow. In terms of plant biomass, the type of porous media used did not have a significant effect on the production of above (p = 0.111) or below-ground biomass (p = 0.092). The removal percentages for COD in the presence and absence of plants were in the ranges of 64–77% and 27–27.7%, respectively. For TN, the numbers were 52–65% and 30–31.8%, and for N-NO₃, they were 54–60% and 12–18%. N-NH₄ saw removal rates of 67–71% and 28–33%, while P-PO₄ saw removal rates of 60–72% and 22–25%. The difference in removal percentages between microcosms with and without plants ranged from 30 to 50%, underscoring the importance of plants in the bio-removal processes (phytoremediation). These results reveal that incorporating ornamental plants in TWs with TZ for wastewater in rural areas holds great promise for enhancing the visual appeal of these systems and ultimately gaining public approval. Our findings also enable us to offer recommendations for selecting suitable plants and substrates, as well as designing combinations for TWs. Full article

This paper proposes a page-turning strategy using an assistive robot that has a low-degree-of-freedom robotic hand. The robotic hand is based on human object handling characteristics, which significantly reduces the number of fingers and joints required to handle various objects. The robotic hand has right and left planar fingers that can transform their shape to handle various objects. To turn a page, the robot uses the planar fingers to push the surface of the page and then rotates the fingers. The design concept, mechanism, sensor system, strategy for page turning, and control system of the robotic hand are presented. The experimental results show that the robot can turn pages using the proposed method; however, it sometimes failed to turn the page when the robotic hand height was too low and too close to the book because the rotation of the fingers was stopped by the book. When the hand detects excessive force during page turning, the control system changes the shape of the fingers and releases the force from the book. The experimental results show the effectiveness of the control system. Full article

Congenitally corrected transposition of the great arteries (ccTGA) is an infrequent and complex congenital malformation, which accounts for approximately 0.5% of all congenital heart defects. This defect is characterized by both atrioventricular and ventriculoarterial discordance, with the right atrium connected to the morphological left ventricle (LV), ejecting blood into the pulmonary artery, while the left atrium is connected to the morphological right ventricle (RV), ejecting blood into the aorta. Due to this double discordance, the blood flow is physiologically normal. Most patients have coexisting cardiac abnormalities that require further treatment. Untreated natural course is often associated with progressive failure of the systemic right ventricle (RV), tricuspid valve (TV) regurgitation, arrhythmia, and sudden cardiac death, which occurs in approximately 50% of patients below the age of 40. Some patients do not require surgical intervention, but most undergo physiological repair leaving the right ventricle in the systemic position, anatomical surgery which restores the left ventricle as the systemic ventricle, or univentricular palliation. Various types of anatomic repair have been proposed for the correction of double discordance. They combine an atrial switch (Senning or Mustard procedure) with either an arterial switch operation (ASO) as a double-switch operation or, in the cases of relevant left ventricular outflow tract obstruction (LVOTO) and ventricular septal defect (VSD), intra-ventricular rerouting by a Rastelli procedure. More recently implemented procedures, variations of aortic root translocations such as the Nikaidoh or the half-turned truncal switch/en bloc rotation, improve left ventricular outflow tract (LVOT) geometry and supposedly prevent the recurrence of LVOTO. Anatomic repair for congenitally corrected ccTGA has been shown to enable patients to survive into adulthood. Full article

Traffic lights are the most commonly used tool to manage urban traffic to reduce congestion and accidents. However, the poor management of traffic lights can result in further problems. Consequently, many studies on traffic light control have been conducted using deep reinforcement learning in the past few years. In this study, we propose a traffic light control method in which a Deep Q-network with fuzzy logic is used to reduce waiting time while enhancing the efficiency of the method. Nevertheless, existing studies using the Deep Q-network may yield suboptimal results because of the reward function, leading to the system favoring straight vehicles, which results in left-turning vehicles waiting too long. Therefore, we modified the reward function to consider the waiting time in each lane. For the experiment, Simulation of Urban Mobility (SUMO) software version 1.18.0 was used for various environments and vehicle types. The results show that, when using the proposed method in a prototype environment, the average total waiting time could be reduced by 18.46% compared with the traffic light control method using a conventional Deep Q-network with fuzzy logic. Additionally, an ambulance prioritization system was implemented that significantly reduced the ambulance waiting time. In summary, the proposed method yielded better results in all environments. Full article

To resolve the congestion caused by imbalanced traffic at intersections, this paper establishes a model of the average delay deviation with the minimization of the average delay in the approach as the optimization objective. Then, the signal control scheme is further optimized based on the variable approach lanes setting. First, we investigate the threshold conditions for setting the VALs under different flows in a single approach direction. The results show that when the ratio of left-turn traffic exceeds the threshold range of 0.20~0.28, the function of the VALs needs to be changed from straight to left-turn. Then, based on the improved Webster’s formula, an optimal timing method that aims at minimizing the average vehicle delay, minimizing the queue length, and maximizing the capacity, is proposed. Finally, taking the actual Huangke intersection in the Hefei demonstration area as an example, three schemes are compared and analyzed in the case of a VAL at the intersection. The results show that under the cooperative optimization scheme proposed in this paper, the travel time and the efficiency of the intersection could be reduced by 18.7% and 9.9%, respectively, when compared with the original and Webster’s schemes. Full article

Intersection safety has been traditionally evaluated using three to five years of crash data. Recent literature suggests that connected vehicle (CV)-derived hard braking (HB) events can provide a surrogate for crashes with only a few weeks or months of data collection. This study used CV trajectories to derive HB events. Then, the HB events were normalized as the ratio of HB events to sampled CV trajectories. The normalized HB ratios were evaluated and compared at 435 signalized intersections, roundabouts, and all-way stops in Indiana. The analysis showed that signalized intersections and roundabouts had the highest counts of HB events, and all-way stops had the highest HB ratios. Through movements at signalized intersections showed the lowest HB ratios, whereas left turns at all-way stops had the highest ratios. A density analysis of the geospatial occurrence of HB events concluded that they tend to occur closest to the intersection center at all-way stops, but are more evenly distributed at signalized intersections. Additionally, a speed analysis indicated that HB events at signalized intersection through movements tend to occur at higher speeds, roughly between 26 and 36 MPH, perhaps due to the driver reaction during the onset of yellow. The findings presented in this study provide transportation agencies with insights on the occurrence of normalized HB ratios at three different intersection types. The data provided in this paper provide a framework for agencies to use HB ratios to screen different types of intersections for further evaluation. Full article

Background: The automatic detection of activities of daily living (ADL) is necessary to improve long-term home-based monitoring of Parkinson’s disease (PD) symptoms. While most body-worn sensor algorithms for ADL detection were developed using laboratory research systems covering full-body kinematics, it is now crucial to achieve ADL detection using a single body-worn sensor that remains commercially available and affordable for ecological use. Aim: to detect and segment Walking, Turning, Sitting-down, and Standing-up activities of patients with PD using a Smartwatch positioned at the ankle. Method: Twenty-two patients living with PD performed a Timed Up and Go (TUG) task three times before engaging in cleaning ADL in a simulated free-living environment during a 3 min trial. Accelerations and angular velocities of the right or left ankle were recorded in three dimensions using a Smartwatch. The TUG task was used to develop detection algorithms for Walking, Turning, Sitting-down, and Standing-up, while the 3 min trial in the free-living environment was used to test and validate these algorithms. Sensitivity, specificity, and F-scores were calculated based on a manual segmentation of ADL. Results: Sensitivity, specificity, and F-scores were 96.5%, 94.7%, and 96.0% for Walking; 90.0%, 93.6%, and 91.7% for Turning; 57.5%, 70.5%, and 52.3% for Sitting-down; and 57.5%, 72.9%, and 54.1% for Standing-up. The median of time difference between the manual and automatic segmentation was 1.31 s for Walking, 0.71 s for Turning, 2.75 s for Sitting-down, and 2.35 s for Standing-up. Conclusion: The results of this study demonstrate that segmenting ADL to characterize the mobility of people with PD based on a single Smartwatch can be comparable to manual segmentation while requiring significantly less time. While Walking and Turning were well detected, Sitting-down and Standing-up will require further investigation to develop better algorithms. Nonetheless, these achievements increase the odds of success in implementing wearable technologies for PD monitoring in ecological environments. Full article

This study employed Virtual Reality (VR) technology to investigate the influence of corridor directional configuration on evacuation wayfinding behavior in underground spaces. The study designed two virtual underground space fire evacuation scenarios with different forms of intersections, and recruited 115 volunteers to participate in the experiment.The results indicated that corridor directional configuration significantly affected participants’ fire evacuation wayfinding behavior. At Y-shaped and T-shaped intersections with left and right turning options, participants showed a preference for choosing the right-side corridor. At ┡-shaped and ┩-shaped intersections with straight and turning options, participants tended to choose the straight path. Individual factors (such as gender, evacuation experience, and professional background) did not demonstrate significant effects on wayfinding choices in this study, though they may produce different evacuation outcomes in various scenarios. In practical evacuation design, corridor directional configuration should be organically integrated with other environmental factors to reinforce directional preferences and more effectively guide evacuation. The findings provide scientific evidence for underground space evacuation route design, which can be used to optimize evacuation signage and path configuration, thereby improving evacuation efficiency and safety. Future research could be conducted in more complex environments, considering additional variables to gain a more comprehensive understanding of evacuation behavior. Full article

Novice drivers often face challenges such as misjudgment, inappropriate steering control, distraction, and insufficient speed control when making left turns at intersections, leading to safety hazards. Installing intersection guide lines offers a solution by providing clear path directions, mitigating safety concerns associated with novice drivers’ left-turn actions. This study explored the impact of intersection guide line configurations on the driving behavior of novice drivers during left turns, utilizing large, medium, and small typical intersections to create six categories of left-turn simulation scenarios in a driving simulator. Data on vehicle trajectory, steering angle, steering speed, and eye-tracking were collected and analyzed. The study revealed that guide line arrangement significantly influences novice drivers’ left-turning behavior, enhancing path guidance while reducing trajectory and steering angle fluctuations, speed variations, and driver attention dispersion. This improvement in stability is particularly notable as intersection size and the number of left-turn lanes increase. The study’s findings offer robust theoretical support and guidance for the development and widespread adoption of intersection guide lines. Full article

Stage IV grade C localized periodontitis (pre-puberal localized aggressive periodontitis/LPP), an extremely rare form of periodontal disease, occurs in otherwise healthy individuals (no signs of dental plaque/calculus) due a hyper-aggressive auto-immune response to high periodontopathic bacteria levels. Methods: A 4-year-old Caucasian girl with unusually high mobility of the deciduous lower left canine and localized gingival inflammation was misrecognized by multiple clinicians (initially diagnosed with hypophosphatasia, genetic and metabolic disorders, all turning negative), over a period of 4–6 months, despite initial radiographs showing clear pathognomonic signs. The LPP diagnostic was made by the last clinician, but by then the tooth was lost. Similar inflammation signs appeared around the lower deciduous right canine. X-ray examination showed similar bone and periodontal loss as previously seen, while periodontopathic bacteria tested highly positive. The patient received both mechanical cleaning and ten days of systemic antibiotic treatment (Augmentin and Metronidazole). Results: Two months later, inflammation signs disappeared, with periodontal regeneration radiologically present, and only small periodontopathic bacteria precursor concentrations. Conclusions: Despite initial periodontal loss, an adequate treatment can keep under control an LPP disease. Moreover, bone and periodontal regeneration appears if periodontopathic bacteria scores are kept lower, showing the importance of fast adequate diagnostic and treatment. Full article

Pilot behavior is crucial for aviation safety. This study aims to investigate the EEG characteristics of pilots, refine training assessment methodologies, and bolster flight safety measures. The collected EEG signals underwent initial preprocessing. The EEG characteristic analysis was performed during left and right turns, involving the calculation of the energy ratio of beta waves and Shannon entropy. The psychological workload of pilots during different flight phases was quantified as well. Based on the EEG characteristics, the pilots’ psychological workload was classified through the use of a support vector machine (SVM). The study results showed significant changes in the energy ratio of beta waves and Shannon entropy during left and right turns compared to the cruising phase. Additionally, the pilots’ psychological workload was found to have increased during these turning phases. Using support vector machines to detect the pilots’ psychological workload, the classification accuracy for the training set was 98.92%, while for the test set, it was 93.67%. This research holds significant importance in understanding pilots’ psychological workload. Full article

The ability to make informed decisions in complex scenarios is crucial for intelligent automotive systems. Traditional expert rules and other methods often fall short in complex contexts. Recently, reinforcement learning has garnered significant attention due to its superior decision-making capabilities. However, there exists the phenomenon of inaccurate target network estimation, which limits its decision-making ability in complex scenarios. This paper mainly focuses on the study of the underestimation phenomenon, and proposes an end-to-end autonomous driving decision-making method based on an improved TD3 algorithm. This method employs a forward camera to capture data. By introducing a new critic network to form a triple-critic structure and combining it with the target maximization operation, the underestimation problem in the TD3 algorithm is solved. Subsequently, the multi-timestep averaging method is used to address the policy instability caused by the new single critic. In addition, this paper uses Carla platform to construct multi-vehicle unprotected left turn and congested lane-center driving scenarios and verifies the algorithm. The results demonstrate that our method surpasses baseline DDPG and TD3 algorithms in aspects such as convergence speed, estimation accuracy, and policy stability. Full article

Introduction: Multisystem inflammatory syndrome in children (MIS-C) is a new disease entity occurring in the pediatric population two to six weeks after coronavirus exposure due to a systemic arteritis. We investigated post-hospital-discharge arterial function at short- and mid-term intervals using pulse wave velocity. We assessed associations between arterial function, left ventricular diastolic and systolic function and left ventricular mass. Materials and methods: Retrospective data collection was carried out on 28 patients with MIS-C with at least two outpatient pediatric cardiology clinic visits post hospital admission. The patients underwent assessment of systemic arterial function and cardiac function. Data included pulse wave velocity between carotid and femoral arteries and echocardiographic assessment of left ventricular systolic function (shortening and ejection fraction, longitudinal strain), diastolic function and left ventricular mass. Results: Pulse wave velocity significantly decreased from visit 1 to visit 2 (5.29 ± 1.34 m/s vs. 4.51 ± 0.91 m/s, p = 0.009). Left ventricular mass significantly decreased from visit 1 to visit 2 (42 ± 9 g/m^2.7 vs. 38 ± 7 g/m^2.7, p = 0.02). There was a significant negative correlation between the pulse wave velocity and E/A mitral inflow (−0.41, p < 0.05). Conclusions: Children have elevated pulse wave velocity and left ventricular mass in the short-term relative to mid-term values after hospital discharge. These results suggest that MIS-C is associated with transient systemic arterial dysfunction, which, in turn, may play a role in cardiac changes. Full article

The continuous increase in the penetration rate of autonomous vehicles in highway traffic flow has become an irreversible development trend; in this paper, a novel hybrid prediction model of deep sequence learning and an integrated decision tree is proposed for human–machine mixed driving heterogeneous traffic flow scenarios, so as to realize the accurate prediction of the driving intention of the target vehicle in the traffic environment by autonomous vehicles (AVs). Firstly, the hybrid model uses the attention mechanism-based double-layer gated network model (Bilayer-GRU-Att) to effectively capture the time sequence dependence of the target vehicle’s driving state, and then accurately calculate its trajectory data in different prediction time-domains (t_pred). Furthermore, the hybrid model introduces the eXtreme Gradient Boosting decision tree optimized by the Grey Wolf Optimization model (GWO-XGBoost) to identify the lane-changing intention of the target vehicle, because the prediction information of the future trajectory data of the target vehicle by the aforementioned Bilayer-GRU-Att model is properly integrated. The GWO-XGBoost model can accurately predict the lane-changing intention of the target vehicle in different prediction time-domains. Finally, the efficacy of this hybrid model was tested using the HighD dataset for training, validation, and testing purposes. The results of a benchmark analysis indicate that the hybrid model proposed in this paper has the best error evaluation index and balanced prediction time consuming index under the six prediction time-domains. Meanwhile, the hybrid model demonstrates the best classifying performance in predicting the lane-changing intentions of “turning left”, “going straight”, and “turning right” driving behaviors. Full article