Integrated driving risk surrogate model and car-following behavior for freeway risk assessment.

Drivers' risk perception plays a crucial role in understanding vehicle interactions and car-following behavior under complex conditions and physical appearances. Therefore, it is imperative to evaluate the variability of risks involved. With advancements in communication technology and computing power, real-time risk assessment has become feasible for enhancing traffic safety. In this study, a novel approach for evaluating driving interaction risk on freeways is presented. The approach involves the integration of an interaction risk perception model with car-following behavior. The proposed model, named the driving risk surrogate (DRS), is based on the potential field theory and incorporates a virtual energy attribute that considers vehicle size and velocity. Risk factors are quantified through sub-models, including an interactive vehicle risk surrogate, a restrictions risk surrogate, and a speed risk surrogate. The DRS model is applied to assess driving risk in a typical scenario on freeways, and car-following behavior. A sensitivity analysis is conducted on the effect of different parameters in the DRS on the stability of traffic dynamics in car-following behavior. This behavior is then calibrated using a naturalistic driving dataset, and then car-following predictions are made. It was found that the DRS-simulated car-following behavior has a more accurate trajectory prediction and velocity estimation than other car-following methods. The accuracy of the DRS risk assessments was verified by comparing its performance to that of traditional risk models, including TTC, DRAC, MTTC, and DRPFM, and the results show that the DRS model can more accurately estimate risk levels in free-flow and congested traffic states. Thus the proposed risk assessment model provides a better approach for describing vehicle interactions and behavior in the digital world for both researchers and practitioners.

Cost-Effective Fabrication of Uniformly Aligned Silver Nanowire Microgrid-Based Transparent Electrodes with Higher than 99% Transmittance.

Silver nanowire (Ag NW)-based transparent electrodes (TEs) are promising alternatives to indium tin oxide (ITO) for next-generation flexible optoelectronic devices. Although many different constructs of Ag NW networks and post-treatment methods have been developed for TE applications, trade-offs between optical and electrical performance still remain. Herein, aided by electrohydrodynamic (EHD) printing, we present a cost-effective strategy to fabricate aligned Ag NW microgrids in a large area with excellent uniformity, resulting in superior optoelectronic properties. Guided by the percolation theory and simulation, we demonstrated that by confining aligned Ag NWs into a microgrid arrangement, the percolation threshold can be reduced significantly and adequate electrical conducting pathways can be achieved with an optimized combination of sheet resistance and optical transparency, which surpass conventional random Ag NW networks and random aligned Ag NW networks. The resulting TEs exhibit an ultrahigh transmittance of 99.1% at a sheet resistance of 91 Ω sq-1 with extremely low nanowire usage, an areal mass density of only 8.3 mg m-2, and uniform spatial distribution. Based on this TE design, we demonstrated transparent heaters exhibiting rapid thermal response and superior uniformity in heat generation. Using UV-curable epoxy, highly flexible Ag NW-embedded TEs were fabricated with superior mechanical stabilities and low surface roughness of 2.6 nm. Bendable organic light-emitting diodes (OLEDs) are directly fabricated on these flexible Ag NW electrodes, with higher current efficiency (27.7 cd A-1) than ITO devices (24.8 cd A-1).

A High Precision Real-time Premature Ventricular Contraction Assessment Method based on the Complex Feature Set.

This paper presents a high precision and low computational complexity premature ventricular contraction (PVC) assessment method for the ECG human-machine interface device. The original signals are preprocessed by integrated filters. Then, R points and surrounding feature points are determined by corresponding detection algorithms. On this basis, a complex feature set and feature matrices are obtained according to the position feature points. Finally, an exponential Minkowski distance method is proposed for PVC recognition. Both public dataset and clinical experiments were utilized to verify the effectiveness and superiority of the proposed method. The results show that our R peak detection algorithm can substantially reduce the error rate, and obtained 98.97% accuracy for QRS complexes. Meanwhile, the accuracy of PVC recognition was 98.69% for the MIT-BIH database and 98.49% for clinical tests. Moreover, benefiting from the lightweight of our model, it can be easily applied to portable healthcare devices for human-computer interaction.