Introducing and Validating the Cranial-Dorsal-Hip Angle (∠CDH): A Method for Accurate Fetal Position Assessment in the First Trimester and Future AI Applications.

Purpose To introduce the cranial-dorsal-hip angle (∠CDH) as a novel quantitative tool for assessing fetal position in the first trimester and to validate its feasibility for future AI applications. Materials and Methods 2520 first-trimester fetal NT exams with 2582 CRL images (January-August 2022) were analyzed at a tertiary hospital as the pilot group. Additionally, 1418 cases with 1450 fetal CRL images (September-December 2022) were examined for validation. Three expert sonographers defined a standard for fetal positions. ∠CDH measurements, conducted by two ultrasound technicians, were validated for consistency using Bland-Altman plots and the intra-class correlation coefficient (ICC). This method allowed for categorizing fetal positions as hyperflexion, neutral, and hyperextension based on ∠CDH. Comparative accuracy was assessed against Ioannou, Wanyonyi, and Roux methods using the weighted Kappa coefficient (k value). Results The pilot group comprised 2186 fetal CRL images, and the validation group included 1193 images. Measurement consistency was high (ICCs of 0.993; P<0.001). The established 95% reference range for ∠CDH in the neutral fetal position was 118.3° to 137.8°. The ∠CDH method demonstrated superior accuracy over the Ioannou, Wanyonyi, and Roux methods in both groups, with accuracy rates of 94.5% (k values: 0.874, 95%CI: 0.852-0.896) in the pilot group, and 92.6% (k values: 0.838, 95%CI: 0.806-0.871) in the validation group. Conclusion The ∠CDH method has been validated as a highly reproducible and accurate technique for first-trimester fetal position assessment. This sets the stage for its potential future integration into intelligent assessment models.

Automatic segmentation of 15 critical anatomical labels and measurements of cardiac axis and cardiothoracic ratio in fetal four chambers using nnU-NetV2.

BACKGROUND: Accurate segmentation of critical anatomical structures in fetal four-chamber view images is essential for the early detection of congenital heart defects. Current prenatal screening methods rely on manual measurements, which are time-consuming and prone to inter-observer variability. This study develops an AI-based model using the state-of-the-art nnU-NetV2 architecture for automatic segmentation and measurement of key anatomical structures in fetal four-chamber view images. METHODS: A dataset, consisting of 1,083 high-quality fetal four-chamber view images, was annotated with 15 critical anatomical labels and divided into training/validation (867 images) and test (216 images) sets. An AI-based model using the nnU-NetV2 architecture was trained on the annotated images and evaluated using the mean Dice coefficient (mDice) and mean intersection over union (mIoU) metrics. The model's performance in automatically computing the cardiac axis (CAx) and cardiothoracic ratio (CTR) was compared with measurements from sonographers with varying levels of experience. RESULTS: The AI-based model achieved a mDice coefficient of 87.11% and an mIoU of 77.68% for the segmentation of critical anatomical structures. The model's automated CAx and CTR measurements showed strong agreement with those of experienced sonographers, with respective intraclass correlation coefficients (ICCs) of 0.83 and 0.81. Bland-Altman analysis further confirmed the high agreement between the model and experienced sonographers. CONCLUSION: We developed an AI-based model using the nnU-NetV2 architecture for accurate segmentation and automated measurement of critical anatomical structures in fetal four-chamber view images. Our model demonstrated high segmentation accuracy and strong agreement with experienced sonographers in computing clinically relevant parameters. This approach has the potential to improve the efficiency and reliability of prenatal cardiac screening, ultimately contributing to the early detection of congenital heart defects.

Expression characteristics and potential function of non-coding RNA in mouse cortical cells.

Non-coding RNAs (ncRNAs) play essential regulatory functions in various physiological and pathological processes in the brain. To systematically characterize the ncRNA profile in cortical cells, we downloaded single-cell SMART-Seq v4 data of mouse cerebral cortex. Our results revealed that the ncRNAs alone are sufficient to define the identity of most cortical cell types. We identified 1,600 ncRNAs that exhibited cell type specificity, even yielding to distinguish microglia from perivascular macrophages with ncRNA. Moreover, we characterized cortical layer and region specific ncRNAs, in line with the results by spatial transcriptome (ST) data. By constructing a co-expression network of ncRNAs and protein-coding genes, we predicted the function of ncRNAs. By integrating with genome-wide association studies data, we established associations between cell type-specific ncRNAs and traits related to neurological disorders. Collectively, our study identified differentially expressed ncRNAs at multiple levels and provided the valuable resource to explore the functions and dysfunctions of ncRNAs in cortical cells.

Are virtual reality applications effective for construction safety training and education? A systematic review and meta-analysis.

INTRODUCTION: Virtual reality (VR) gains attention in construction safety training because it allows users to simulate real activities without the risks of real activities. However, a literary work comprehensively describing the effectiveness of VR in construction safety training and education (CSTE) is lacking. METHOD: This study provides a systematic review of the research related to VR applications for CSTE over the past decade using meta-analysis techniques. Standardized mean differences between traditional training methods and VR training were grouped by measurement. Potential moderators possibly affecting the effectiveness of VR in CSTE were analyzed. RESULTS: Results showed that VR is significantly more effective in construction training and education than traditional methods. The effectiveness of VR was 0.593, 0.432, and 0.777 higher than that of traditional methods for behaviors, skills, and experience measurements, respectively. The training context and mean work experience of trainees were two important moderators that significantly affected the effectiveness of VR in CSTE (p < 0.001). PRACTICAL APPLICATIONS: The presented results suggested the need for targeted development and management of VR technology in the construction industry and the early promotion of VR for general safety training among young, inexperienced construction workers.

Role of Trust, Risk Perception, and Perceived Benefit in COVID-19 Vaccination Intention of the Public.

COVID-19 vaccination is an effective method for dealing with the COVID-19 pandemic. This study proposed and validated a theoretical intention model for explaining the COVID-19 vaccination intention (CVI) of the public. The theoretical intention model incorporated trust in vaccines, two types of risk perception (risk perception of COVID-19 and risk perception of COVID-19 vaccination), and perceived benefit into a theory of planned behavior (TPB). Structural equation modeling was utilized to test the theoretical intention model with data collected from 816 Chinese adults in China. The results confirmed the crucial role of trust in vaccines, risk perception, and perceived benefit in shaping the CVI of the public. In addition, TPB was found to be applicable in a research context. The theoretical intention model accounted for 78.8% of the variance in CVI. Based on the findings, several practical recommendations for improving COVID-19 vaccination rates were discussed.

Automatic Diagnosis of Significant Liver Fibrosis From Ultrasound B-Mode Images Using a Handcrafted-Feature-Assisted Deep Convolutional Neural Network.

The accurate diagnosis of significant liver fibrosis ( ≥ F2) in patients with chronic liver disease (CLD) is critical, as ≥ F2 is a crucial factor that should be considered in selecting an antiviral therapy for these patients. This article proposes a handcrafted-feature-assisted deep convolutional neural network (HFA-DCNN) that helps radiologists automatically and accurately diagnose significant liver fibrosis from ultrasound (US) brightness (B)-mode images. The HFA-DCNN model has three main branches: one for automatic region of interest (ROI) segmentation in the US images, another for attention deep feature learning from the segmented ROI, and the third for handcrafted feature extraction. The attention deep learning features and handcrafted features are fused in the back end of the model to enable more accurate diagnosis of significant liver fibrosis. The usefulness and effectiveness of the proposed model were validated on a dataset built upon 321 CLD patients with liver fibrosis stages confirmed by pathological evaluations. In a fivefold cross validation (FFCV), the proposed model achieves accuracy, sensitivity, specificity, and area under the receiver-operating-characteristic (ROC) curve (AUC) values of 0.863 (95% confidence interval (CI) 0.820-0.899), 0.879 (95% CI 0.823-0.920), 0.872 (95% CI 0.800-0.925), and 0.925 (95% CI 0.891-0.952), which are significantly better than those obtained by the comparative methods. Given its excellent performance, the proposed HFA-DCNN model can serve as a promising tool for the noninvasive and accurate diagnosis of significant liver fibrosis in CLD patients.

Axial optimization of biomimetic nanoenzyme catalysts applied to oxygen reduction reactions.

Inspired by the bio-oxygen oxidation/reduction processes of hemoglobin, iron-based transition metal-like enzyme catalysts have been explored as oxygen reduction reaction (ORR) electrocatalysts. We synthesized a chlorine-coordinated monatomic iron material (FeN4Cl-SAzyme) via a high temperature pyrolysis method as a catalyst for the ORR. The half-wave potential (E1/2) was 0.885 V, which exceeded those of Pt/C and the other FeN4X-SAzyme (X = F, Br, I) catalysts. Furthermore, through density functional theory (DFT) calculations, we systematically explored the better performance reason of FeN4Cl-SAzyme. This work offers a promising approach toward high-performance single atom electrocatalysts.

A deep learning model with data integration of ultrasound contrast-enhanced micro-flow cines, B-mode images, and clinical parameters for diagnosing significant liver fibrosis in patients with chronic hepatitis B.

OBJECTIVE: To develop and investigate a deep learning model with data integration of ultrasound contrast-enhanced micro-flow (CEMF) cines, B-mode images, and patients' clinical parameters to improve the diagnosis of significant liver fibrosis (≥ F2) in patients with chronic hepatitis B (CHB). METHODS: Of 682 CHB patients who underwent ultrasound and histopathological examinations between October 2016 and May 2020, 218 subjects were included in this retrospective study. We devised a data integration-based deep learning (DIDL) model for assessing ≥ F2 in CHB patients. The model contained three convolutional neural network branches to automatically extract features from ultrasound CEMF cines, B-mode images, and clinical data. The extracted features were fused at the backend of the model for decision-making. The diagnostic performance was evaluated across fivefold cross-validation and compared against the other methods in terms of the area under the receiver operating characteristic curve (AUC), with histopathological results as the reference standard. RESULTS: The mean AUC achieved by the DIDL model was 0.901 [95% CI, 0.857-0.939], which was significantly higher than those of the comparative methods, including the models trained by using only CEMF cines (0.850 [0.794-0.893]), B-mode images (0.813 [0.754-0.862]), or clinical data (0.757 [0.694-0.812]), as well as the conventional TIC method (0.752 [0.689-0.808]), APRI (0.792 [0.734-0.845]), FIB-4 (0.776 [0.714-0.829]), and visual assessments of two radiologists (0.812 [0.754-0.862], and 0.800 [0.739-0.849]), all ps < 0.01, DeLong test. CONCLUSION: The DIDL model with data integration of ultrasound CEMF cines, B-mode images, and clinical parameters showed promising performance in diagnosing significant liver fibrosis for CHB patients. KEY POINTS: • The combined use of ultrasound contrast-enhanced micro-flow cines, B-mode images, and clinical data in a deep learning model has potential to improve the diagnosis of significant liver fibrosis. • The deep learning model with the fusion of features extracted from multimodality data outperformed the conventional methods including mono-modality data-based models, the time-intensity curve-based recognizer, fibrosis biomarkers, and visual assessments by experienced radiologists. • The interpretation of the feature attention maps in the deep learning model may help radiologists get better understanding of liver fibrosis-related features and hence potentially enhancing their diagnostic capacities.

Modeling faults among mountainous highway fatal crash-involved drivers with different route familiarity levels.

OBJECTIVE: With the constrained topography and road geometry, adverse weather conditions and restricted roadway facilities, mountainous highway crash rates and fatality rates are much higher. Considering the potential influence of driver's route familiarity level on driving behavior and fault assignment, this research investigates high- and low- route familiarity level drivers (HRF and LRF drivers) fault assignment in mountainous highway fatal crashes in Yunnan Province of China by examining factors of driver, crash/environment and pre-crash behaviors. METHODS: Yunnan Province is famous for its tourism, and tourism can also bring in many drivers with low-route familiarity levels. Spatial distance away from residence-based method is used for identifying route familiarity levels of the drivers in this study. We employed two separate binary logistic regression models to investigate the effects of the explanatory variables on the likelihoods that the HRF or LRF drivers were at fault in the mountainous highway fatal crashes. RESULTS: The results show that driver under alcohol influence, sharp turn, dawn/dusk and left turning are 4 common factors that significantly influence both HRF and LRF drivers' fault assignments. Factors including driver age, driver seatbelt use, weather condition, road type, section type, lighting condition and pre-crash behaviors have different or opposite influences on HRF and LRF drivers' fault assignments. HRF drivers are much easier to be distracted under the conditions that are without the need of extra attention. LRF drivers have much more difficulties in figuring out, responding to and making timely driving behavior adjustment to ensure their driving safety on the high-risk sections like tunnels, continuous long downhills and sharp turns. Street parking/backing and left turning of the LRF drivers are very serious problems on the mountainous highways in China. CONCLUSIONS: There is a large difference of significant factors contributing to the fault assignment of HRF and LRF drivers in mountainous highway fatal crashes. Some more effective and targeted countermeasures are put forward for HRF and LRF drivers and transportation managers to improve mountainous highway traffic safety.

Analyzing the impact of curve and slope on multi-vehicle truck crash severity on mountainous freeways.

Many studies examine the road characteristics that impact the severity of truck crash accidents. However, some only analyze the effect of curves or slopes separately, ignoring their combination. Therefore, there are nine types of the combination of curve and slope in this study. The combination of curve and slope factor that affected the injury severity of truck crashes on mountainous freeways was examined using a correlated random parameter logit model. This method is applied to evaluate the correlation between the random parameters and those that exhibit unobserved heterogeneity. Also, the multinomial logit model and traditional random parameter logit model are used. The study's data were collected from multi-vehicle truck crashes on mountainous freeways in China. The results showed that the correlated random parameters logit model was better than the others. In addition, they demonstrated a correlation between the random parameters. Based on the estimation coefficients and marginal effects, the combination of curve and slope has a great influence on the injury severity of truck crashes. The main finding is that curve with medium radius and medium slope will significantly increase the probability of medium severity comparing to curve with high radius and flat slope. On the other hand, the injury severity of truck accidents was significantly impacted by crash type, vehicle type, surface condition, time of day, season, lighting condition, pavement type, and guardrail. Variables such as sideswipe, head-on, medium trucks, morning, dawn or dusk and summertime reduced the probability of truck crashes. Rollover, winter, gravel, and guardrail variables increased the risk of truck crashes. Correlations were also discovered between a rollover and dry surface condition and rollover and gravel pavement type. The research findings will help traffic officials determine effective countermeasures to decrease the severity of truck crashes on mountainous freeways.

Analysis of Factors Contributing to the Injury Severity of Overloaded-Truck-Related Crashes on Mountainous Highways in China.

Overloaded transport can certainly improve transportation efficiency and reduce operating costs. Nevertheless, several negative consequences are associated with this illegal activity, including road subsidence, bridge collapse, and serious casualties caused by accidents. Given the complexity and variability of mountainous highways, this study examines 1862 overloaded-truck-related crashes that happened in Yunnan Province, China, and attempts to analyze the key factors contributing to the injury severity. This is the first time that the injury severity has been studied from the perspective of crashes involving overloaded trucks, and meanwhile in a scenario of mountainous highways. For in-depth analysis, three models are developed, including a binary logit model, a random parameter logit model, and a classification and regression tree, but the results show that the random parameter logit model outperforms the other two. In the best-performing model, a total of fifteen variables are found to be significant at the 99% confidence level, including random variables such as freeway, broadside hitting, impaired braking performance, spring, and evening. In regards to the fixed variables, it is likely that the single curve, rollover, autumn, and winter variables will increase the probability of fatalities, whereas the provincial highway, country road, urban road, cement, wet, and head-on variables will decrease the likelihood of death. Our findings are useful for industry-related departments in formulating and implementing corresponding countermeasures, such as strengthening the inspection of commercial trucks, increasing the penalties for overloaded trucks, and installing certain protective equipment and facilities on crash-prone sections.

Diagnosis of significant liver fibrosis in patients with chronic hepatitis B using a deep learning-based data integration network.

BACKGROUND AND AIMS: Chronic hepatitis B virus (CHB) infection remains a major global health burden and the non-invasive and accurate diagnosis of significant liver fibrosis (≥ F2) in CHB patients is clinically very important. This study aimed to assess the potential of the joint use of ultrasound images of liver parenchyma, liver stiffness values, and patients' clinical parameters in a deep learning model to improve the diagnosis of ≥ F2 in CHB patients. METHODS: Of 527 CHB patients who underwent US examination, liver elastography and biopsy, 284 eligible patients were included. We developed a deep learning-based data integration network (DI-Net) to fuse the information of ultrasound images of liver parenchyma, liver stiffness values and patients' clinical parameters for diagnosing ≥ F2 in CHB patients. The performance of DI-Net was cross-validated in a main cohort (n = 155) of the included patients and externally validated in an independent cohort (n = 129), with comparisons against single-source data-based models and other non-invasive methods in terms of the area under the receiver-operating-characteristic curve (AUC). RESULTS: DI-Net achieved an AUC of 0.943 (95% confidence interval [CI] 0.893-0.973) in the cross-validation, and an AUC of 0.901 (95% CI 0.834-0.945) in the external validation, which were significantly greater than those of the comparative methods (AUC ranges: 0.774-0.877 and 0.741-0.848 for cross- and external validations, respectively, ps < 0.01). CONCLUSION: The joint use of ultrasound images of liver parenchyma, liver stiffness values, and patients' clinical parameters in a deep learning model could significantly improve the diagnosis of ≥ F2 in CHB patients.

Multiparametric Quantitative US Examination of Liver Fibrosis: A Feature-Engineering and Machine-Learning Based Analysis.

Quantitative ultrasound (QUS), which attempts to extract quantitative features from the US radiofrequency (RF) or envelope data for tissue characterization, is becoming a promising technique for noninvasive assessments of liver fibrosis. However, the number of feature variables examined and finally used in the existing QUS methods is typically small, limiting the diagnostic performance. Therefore, this paper devises a new multiparametric QUS (MP-QUS) method which enables the extraction of a large number of feature variables from US RF signals and allows for the use of feature-engineering and machine-learning based algorithms for liver fibrosis assessment. In the MP-QUS, eighty-four feature variables were extracted from multiple QUS parametric maps derived from the RF signals and the envelope data. Afterwards, feature reduction and selection were performed in turn to remove the feature redundancy and identify the best combination of features in the reduced feature set. Finally, a variety of machine-learning algorithms were tested for fibrosis classification with the selected features, based on the results of which the optimal classifier was established. The performance of the proposed MP-QUS method for staging liver fibrosis was evaluated on an animal model, with histologic examination as the reference standard. The mean accuracy, sensitivity, specificity and area under the receiver-operating-characteristic curve achieved by MP-QUS are respectively 83.38%, 86.04%, 80.82%, and 0.891 for recognizing significant liver fibrosis, and 85.50%, 88.92%, 85.24%, and 0.924 for diagnosing liver cirrhosis. The proposed MP-QUS method paves a way for its future extension to assess liver fibrosis in human subjects.

Applying a Bayesian multivariate spatio-temporal interaction model based approach to rank sites with promise using severity-weighted decision parameters.

Ranking sites with promise is an essential step for cost-effective engineering improvement on roadway traffic safety. This study proposes a Bayesian multivariate spatio-temporal interaction model based approach for ranking sites. The severity-weighted crash frequency and crash rate are used as the decision parameters. The posterior expected rank and posterior mean of the decision parameters are adopted as the statistical criteria. The proposed approach is applied to rank road segments on Kaiyang Freeway in China, which is conducted via programming in the freeware WinBUGS. The results of Bayesian estimation and assessment indicate that incorporating spatio-temporal correlations and interactions into the crash frequency model significantly improves the overall goodness-of-fit performance and affects the identified crash-contributing factors and the estimated safety effects for each severity level. With respect to the ranking results, significant differences are found between those generated from the proposed approach and those generated from the naïve ranking approach and a Bayesian approach based on the multivariate Poisson-lognormal model. Besides, the ranks under the posterior mean criterion are found generally consistent with those under the posterior expected rank criterion.

Investigating hazardous factors affecting freeway crash injury severity incorporating real-time weather data: Using a Bayesian multinomial logit model with conditional autoregressive priors.

INTRODUCTION: It has been demonstrated that weather conditions have significant impacts on freeway safety. However, when employing an econometric model to examine freeway crash injury severity, most of the existing studies tend to categorize several different adverse weather conditions such as rainy, snowy, and windy conditions into one category, "adverse weather," which might lead to a large amount of information loss and estimation bias. Hence, to overcome this issue, real-time weather data, the value of meteorological elements when crashes occurred, are incorporated into the dataset for freeway crash injury analysis in this study. METHODS: Due to the possible existence of spatial correlations in freeway crash injury data, this study presents a new method, the spatial multinomial logit (SMNL) model, to consider the spatial effects in the framework of the multinomial logit (MNL) model. In the SMNL model, the Gaussian conditional autoregressive (CAR) prior is adopted to capture the spatial correlation. In this study, the model results of the SMNL model are compared with the model results of the traditional multinomial logit (MNL) model. In addition, Bayesian inference is adopted to estimate the parameters of these two models. RESULT: The result of the SMNL model shows the significance of the spatial terms, which demonstrates the existence of spatial correlation. In addition, the SMNL model has a better model fitting ability than the MNL model. Through the parameter estimate results, risk factors such as vertical grade, visibility, emergency medical services (EMS) response time, and vehicle type have significant effects on freeway injury severity. Practical Application: According to the results, corresponding countermeasures for freeway roadway design, traffic management, and vehicle design are proposed to improve freeway safety. For example, steep slopes should be avoided if possible, and in-lane rumble strips should be recommended for steep down-slope segments. Besides, traffic volume proportion of large vehicles should be limited when the wind speed exceeds a certain grade.

A new method of temporal and spatial risk estimation for lane change considering conventional recognition defects.

Risky lane change behavior of drivers normally will pose some negative impacts on traffic safety. To ensure a lane change safe and prevent potential accidents, it is important to recognize some lane-changing conditions with potential risks in advance. Despite the researches on traffic safety assessment have been developed for decades, most of the existing researches are mainly interesting in how to estimate the overall safety of the lane-changing process based on historical data. These methods tend to ignore the interactive impacts between lane-changing vehicle and its surrounding vehicles, and have the disadvantages of the long-term evaluation period and single evaluation index. To address these gaps, this study presents a temporal and spatial risk estimation (TSRE) to recognize lane-changing risk in real-time. However, this study concentrates on the instantaneous risk for a lane change event, considering temporal and spatial dimensions for the current lane change circumstance on the highway. After processing the realistic vehicle trajectory dataset, this study extracted 1444 groups of lane change samples, and then incorporates the temporal risk level (TRL) and spatial risk level (SRL) into a comprehensive risk index by applying fault tree analysis. Furthermore, SRL and comprehensive risk index are both used to determine whether the traffic condition of a lane change is safe, and it can effectively overcome the conventional recognition defects that existed in other methods. To achieve a better evaluation effect, the sensitivity tests of recognition accuracy for various risk threshold combinations were carried out. Ultimately, experimental results showed that the proposed TSRE model achieved 97.45 %, 97.79 %, 84.1 % and 85.05 % accuracy rate in terms of classifying the risky lane change samples, traffic conflicts, risky frames and safe frames, when an appropriate risk threshold combination was selected. This encouraging finding can provide the basis for algorithm design of the lane change warning system for connected vehicles.

Injury severity analysis of familiar drivers and unfamiliar drivers in single-vehicle crashes on the mountainous highways.

Mountainous highways suffer from high crash rates and fatality rates in many countries, and single-vehicle crashes are overrepresented along mountainous highways. Route familiarity has been found greatly associated with driver behaviour and traffic safety. This study aimed to investigate and compare the contributory factors that significantly influence the injury severities of the familiar drivers and unfamiliar drivers involved in mountainous highway single-vehicle crashes. Based on 3037 cases of mountainous highway single-vehicle crashes from 2015 to 2017, the characteristics related to crash, environment, vehicle and driver are included. Random-effects generalized ordered probit (REGOP) models were applied to model injury severities of familiar drivers and unfamiliar drivers that are involved in the single-vehicle crashes on the mountainous highways, given that the single-vehicle crashes had occurred. The results of REGOP models showed that 8 of the studied factors are found to be significantly associated with the injury severities of the familiar drivers, and 10 of the studied factors are found to significantly influence the injury severities of unfamiliar drivers. These research results suggest that there is a large difference of significant factors contributing to the injury severities between familiar drivers and unfamiliar drivers. The results shed light on both the similar and different causes of high injury severities for familiar and unfamiliar drivers involved in mountainous highway single-vehicle crashes. These research results can help develop effective countermeasures and proper policies for familiar drivers and unfamiliar drivers targetedly on the mountainous highways and alleviate injury severities of mountainous highway single-vehicle crashes to some extent. Based on the results of this study, some potential countermeasures can be proposed to minimize the risk of single-vehicle crashes on different mountainous highways, including tourism highways with a large number of unfamiliar drivers and other normal mountainous highways with more familiar drivers.

The Influence of Road Geometry on Vehicle Rollover and Skidding.

This paper analyzes the influence of single and combined unfavorable road geometry on rollover and skidding risks of D-class mid-sized sport utility vehicles (SUVs) with front-wheel drive for roads with design speeds at 80 km/h. A closed-loop simulation model of human-vehicle-road interactions is established to examine the systematic influence of road geometry on vehicle rollover and skidding. The effects of different road geometry on rollover and skidding on SUVs are studied for pavement surface with good and poor friction when vehicles are in the action of steady state cornering. The rollover and skidding risks of the most unfavorable road segments are assessed. The critical wheel is defined by the threshold of skidding during curve negotiation. The results found that SUVs are not easy to rollover on the most unfavorable roads, regardless of good or poor friction of pavement surface. The safety margin of rollover is greater than that of skidding. The safety margin of skidding is minimal on poor friction roads. Therefore, for the sake of driving safety, it is not recommended to design the roads with these unfavorable road geometry combinations.

Effect of Music Listening on Physiological Condition, Mental Workload, and Driving Performance with Consideration of Driver Temperament.

This paper presents the study on the association between in-vehicle music listening, physiological and psychological response, and driving performance, using the driving simulator approach, with which personality (temperament) was considered. The performance indicators considered were the standard deviation of speed, lane crossing frequency, perceived mental workload, and mean and variability of heart rate. Additionally, effects of the presence of music and music genre (light music versus rock music) were considered. Twenty participants of different personalities (in particular five, four, seven, and four being choleric, sanguine, phlegmatic, and melancholic, respectively) completed a total of 60 driving simulator tests. Results of mixed analysis of variance (M-ANOVA) indicated that the effects of music genre and driver character on driving performance were significant. The arousal level perceived mental workload, standard deviation of speed, and frequency of lane crossing were higher when driving under the influence of rock music than that when driving under the influence of light music or an absence of music. Additionally, phlegmatic drivers generally had lower arousal levels and choleric drivers had a greater mental workload and were more likely distracted by music listening. Such findings should imply the development of cost-effective driver education, training, and management measures that could mitigate driver distraction. Therefore, the safety awareness and safety performance of drivers could be enhanced.