Based on the demand of carbon peak and carbon emission reduction strategy, divinyl-terminated polydimethylsiloxane (ViPDMSVi), poly(methylhydrosiloxane) (PMHS), divinyl-terminated polymethylvinylsiloxane (ViPMVSVi), and fumed silica were used as primary raw materials, polydimethylsiloxane (PDMS) synthetic leather coating was in situ constructed by thermally induced hydrosilylation polymerization on the synthetic leather substrate. The effect of the viscosity of ViPDMSVi, the active hydrogen content of PMHS, the molar ratio of vinyl groups to active hydrogen, the dosage of ViPMVSVi and fumed silica on the performance of PDMS polymer coating, including mechanical properties, cold resistance, flexural resistance, abrasion resistance, hydrophobic and anti-fouling properties were investigated. The results show that ViPDMSVi with high vinyl content and PMHS with low active hydrogen content is more conducive to obtaining organosilicon coating with better mechanical properties, the optimized dosage of ViPMVSVi and fumed silica was 7 wt% and 40 wt%, respectively. In this case, the tensile strength and the broken elongation of the PDMS polymer coating reached 5.96 MPa and 481%, showing reasonable mechanical properties for leather coating. Compared with polyurethane based or polyvinyl chloride based synthetic leather, the silicon based synthetic leather prepared by this method exhibits excellent cold resistance, abrasion resistance, super hydrophobicity, and anti-fouling characteristics. Graphical Abstract

Herein, double slope solar still (DSSS) performance is accurately forecast with the aid of four different machine learning (ML) models, namely, artificial neural network (ANN), random forest (RF), support vector regression (SVR), and linear SVR. Furthermore, the tuning of ML models is optimized using the Bayesian optimization algorithm (BOA) to get the optimal performance of all models and identify the best predictive one. All the models are trained, tested, and validated depending on experimental data acquired under Egyptian climatic conditions. The results reveal that ML models can be a powerful tool to forecast DSSS performance. Among them, RF is the most potent ML model obtaining the highest determination coefficient (R2) and the lowest absolute error percentage of 0.997% and 2.95%, respectively. Furthermore, the experimental results also show that the mean value of accumulated (daily) freshwater productivity from DSSS is 4.3 L m−2.

This paper focuses on modeling mixed traffic flow that comprises human-driven vehicles (HV), adaptive cruise control (ACC) vehicles, and cooperative adaptive cruise control (CACC) vehicles in the off-ramp diverging area. The car-following behaviors of HVs, ACC vehicles, and CACC vehicles are modeled using an intelligent driver model (IDM), ACC car-following model, and CACC car-following model, respectively. The lane-changing behaviors of different types of vehicles in off-ramp diverging areas are modeled using the anticipatory lane change (ALC) model and the mandatory lane change (MLC) model. These models are important for describing the interaction among different types of vehicles in mixed traffic. The safety and efficiency of mixed traffic flow are analyzed by integrating the developed car-following models and lane-changing models in numerical simulation. A one-way, two-lane scenario is established for the simulation. The results reveal that when the proportion of CACC vehicles is about 0.6, the safety and general operating efficiency of mixed traffic flow in the off-ramp area deteriorate significantly. Increasing the conservative MLC zone length can improve the average speed of traffic flow. Guiding drivers in changing lanes is one way to improve the efficiency of traffic flow.

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