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Abstract Solution‐processed 2D materials hold promise for their scalable applications. However, the random, fragmented nature of the solution‐processed nanoflakes and the poor percolative conduction through their discrete networks limit the performance of the enabled devices. To overcome the problem, conduction modulation of the solution‐processed 2D materials is reported via Stark effect. Using liquid‐phase exfoliated molybdenum disulfide (MoS2) as an example, nonlinear conduction switching with a ratio of >105 is demonstrated by the local fields from the interfacial ferroelectric P(VDF‐TrFE). Through density‐functional theory calculations and in situ Raman scattering and photoluminescence spectroscopic analysis, the modulation is understood to arise from a charge redistribution in the solution‐processed MoS2. Beyond MoS2, the modulation may be shown effective for the other solution‐processed 2D materials and low‐dimensional materials. The modulation can open their electronic device applications, for instance, thin‐film nonlinear electronics and non‐volatile memories.

To enhance traffic safety on mountainous roads, this study proposes an innovative CNN-LSTM-Attention model designed for the identification of near-crash events, utilizing naturalistic driving data from the challenging terrains in Yunnan, China. A combination of a threshold method complemented by manual verification is used to label and annotate near-crash events within the dataset. The importance of vehicle motion features is evaluated using the random forest algorithm, revealing that specific variables, including x-axis acceleration, y-axis acceleration, y-axis angular velocity, heading angle, and vehicle speed, are particularly crucial for identifying near-crash events. Addressing the limitations of existing models in accurately detecting near-crash scenarios, this study combines the strengths of convolutional neural networks (CNN), long short-term memory (LSTM) networks, and an attention mechanism to enhance model sensitivity to crucial temporal and spatial features in naturalistic driving data. Specifically, the CNN-LSTM-Attention model leverages CNN to extract local features from the driving data, employs LSTM to track temporal dependencies among feature variables, and uses the attention mechanism to dynamically fine-tune the network weights of feature parameters. The efficacy of the proposed model is extensively evaluated against six comparative models: CNN, LSTM, Attention, CNN-LSTM, CNN-Attention, and LSTM-Attention. In comparison to the benchmark models, the CNN-LSTM-Attention model achieves superior overall accuracy at 98.8%. Moreover, it reaches a precision rate of 90.1% in detecting near-crash events, marking an improvement of 31.6%, 14.8%, 63.5%, 8%, 23.5%, and 22.6% compared to the other six comparative models, respectively.

The niobium oxide dihalides have recently been identified as a new class of van der Waals materials exhibiting exceptionally large second-order nonlinear optical responses and robust in-plane ferroelectricity. In contrast to second-order nonlinear processes, third-order optical nonlinearities can arise irrespective of whether a crystal lattice is centrosymmetric. Here, we report third harmonic generation (THG) in two-dimensional (2D) transition metal oxide iodides, namely NbOI2 and TaOI2. We observe a comparable THG intensity from both materials. By benchmarking against THG from monolayer WS2, we deduce that the third-order susceptibility is approximately on the same order. THG resonances are revealed at different excitation wavelengths, likely due to enhancement by excitonic states and band edge resonances. The THG intensity increases for material thicknesses up to 30 nm, owing to weak interlayer coupling. After this threshold, it shows saturation or a decrease, due to optical interference effects. Our results establish niobium and tantalum oxide iodides as promising 2D materials for third-order nonlinear optics, with intrinsic in-plane ferroelectricity and thickness-tunable nonlinear efficiency.

Abstract In order to improve the traffic safety condition of intersections, a real-time traffic conflict risk warning system (RTCRWS) is proposed for uncontrolled intersections. To evaluate the effectiveness of this system, a driving simulation experiment was designed and conducted. In this study, a virtual experimental scene including static road, traffic environment and dynamic traffic flow was constructed, and 45 drivers were recruited to complete the driving simulation experiment at 13 intersections. Three different data analysis methods were employed: (1) descriptive analysis of driving behavior characteristics; (2) descriptive analysis of physiological and psychological reactions of drivers; (3) Technique for Order Preference by Similarity to an Ideal Solution (TOPSIS) of RTCRWS. The results show that RTCRWS can effectively control the vehicle speed and reduce the driver's tension. In addition, the following conclusions are also drawn: (1) The early warning signs with better warning effect among the two types signs of RTCRWS were compared; (2) Among the elderly and young and middle-aged drivers, RTCRWS has a better warning effect on the elderly drivers. (3) Among the male and female drivers, RTCRWS has a better warning effect on female drivers.

This study proposes random-parameters multinomial logit models, with heterogeneity in means and variances, to explore the differences in the factors influencing injury severities of drivers involved in different types of two-vehicle crashes. The models are verified using crash data from the United Kingdom (UK) over three years (2016–2018). Three types of crashes are separately identified (car-car, car-truck, and truck-truck crashes). In this study, a wide variety of potential variables, including the driver, vehicle, road, and environmental characteristics, are considered, with two possible injury-severity outcomes: severe and slight injury. The results show that unobserved heterogeneity existed for young drivers in both car-car and truck-truck crash models and the 30 mph speed limit in the three separate models. Remarkably variations are observed in crashes involving different types of vehicles. The driver’s age and gender, speeding, sideswipes, presence of junctions, weekdays, unlit, and weather conditions significantly impact driver-injury severities in various types of vehicle crashes. These findings are expected to help policymakers seek to improve highway safety and implement proper safety countermeasures.

The coexistence of double Fano interference originating from anti-phased and in-phased lattice resonances is observed in a novel plasmonic lattice array, which being consists of a horizontal double-stacked rectangular metal-insulator block. Based on a classic model analysis of coupled harmonic oscillators, the observed double Fano interference is revealed as a result of the interaction between a bright broad resonance and a dark narrow one as well as the coupling of narrow and broad spectral lines. This allows for active tuning of the double Fano resonance to a desired spectral position by a controlled size variation of the metal-insulator block arrays. After optimizing the parameters, they display two narrow absorbance peaks with high-quality factors of 84.92 and 214.1, respectively. This is because of the utmost enhancements of local electric fields in the gap between two opposing rectangular metal blocks. We expect that double Fano interference character may gain more significant potential applications in biomedical sensing.

The real-time traffic conflict risk warning system (RTCRWS) is proposed as a new proactive crash prevention and control strategy for intersections designed to reduce traffic on the main road to rural unsignalized intersections when a vehicle enters the access roads. This study aims at evaluating the effectiveness of the RTCRWS with different locations based on a driving simulation experiment. In this study, four types of the RTCRWS installation location schemes (i.e., no installation, 50 m/100 m/150 m away from the unsignalized intersection) are designed. Twenty-two experienced drivers participated in the driving simulation experiment, and seven evaluating indicators representing driving behavior data are proposed. Two methods to analyze the data are applied: (1) descriptive analysis of driving behavior characteristics different location schemes of the RTCRWS and (2) entropy weight-fuzzy comprehensive evaluation of the RTCRWS. The results show that the RTCRWS has a significant effect on slowing vehicles when approaching the rural unsignalized intersections. If the location of the RTCRWS is 50 m, 100 m, and 150 m from the intersection, the comprehensive score of fuzzy evaluation is 75.82, 74.91, and 77.22, respectively, which implies that the scheme with the RTCRWS 150 m ahead of the intersection is the most effective.

Pavement temperature is the main factor determining road icing, and accurate and timely pavement temperature prediction is of significant importance to regional traffic safety management and preventive maintenance. The prediction of pavement temperature at the micro-scale has been a challenge to be tackled. To solve this problem, a bidirectional extended short-term memory network model based on the attention mechanism (Att-BiLSTM) was proposed to improve the prediction performance by using the time series features of pavement temperature and meteorological factors. Pavement temperature data and climatic data were collected from a road weather station in Yunnan, China. The results show that the MAE, MSE, and MAPE of the proposed Att-BiLSTM model were 0.330, 0.339, and 10.1%, respectively, which were better than the other baseline models. It was shown that 93.4% of the predicted values had an error less than 1 °C, and 82.1% had an error less than 0.5 °C, indicating that the proposed Att-BiLSTM model enables significant performance improvement. In addition, this paper quantified and analyzed the effects of parameters such as the size of the sliding window, the number of hidden layer neurons, and the optimizer on the performance of the prediction model.

Understanding the factors that contribute to traffic crashes can help provide a fundamental basis to plan and develop appropriate countermeasures for road safety issues emerging in particular on two-lane rural roads. However, most of the studies have focused on urban roadways and freeway systems, and few studies have investigated the issue of heterogeneity on two-lane rural roads. The purpose of this study is to uncover the risk factors influencing crash severity on two-lane rural roads in China. A sample of 1490 traffic crashes occurring on two-lane rural roads between 2012 and 2017 was collected from the Mouding County Highway Bureau in Yunnan, China. A random-parameter ordered probit model was estimated using these data to capture underlying unobserved characteristics in personal traits, vehicle attributes, roadway conditions, environmental factors, and crash attribute. To better understand the effect of critical factors on crash severity outcome probability, an elasticity analysis was then introduced. The results show that six factors such as driver’s attribution, illegal driving behaviour, access segment, day of week, vehicle type, and crash form have a significant impact on the injury severity, and the impacts of driving behaviours, access segment, and vehicle-fixed object crashes had significant variation across observations. Besides, the correlations between critical factors and the probability of serious injury sustained in traffic crashes are identified and discussed. The local driver indicator has more positive impact on the crash severity than nonlocal driver, and nonaccess segment appears a higher probability of serious or vicious collisions. It is worth mentioning that motorcycle-involved crashes do show an obvious correlation with crash injury severity. As for crash forms, vehicle-vehicle crashes are more likely to lead to severe crash injury. Besides, high-risk driving behaviour (e.g., fatigue driving, speeding, and converse driving), weekends, and holidays are found to have significant contribution to increasing the probability of traffic crash injuries and fatalities on two-lane rural roads.