Zeolitic-imidazole frameworks (ZIFs), as novel porous materials, are attracting much attention in several fields due to their special advantages such as large specific surface area, versatile porosity and well-connected networks. Here, we develop a porous ZIF-derived catalytic thin film, which was coated on the conducting glass as a counter electrode (CE) to substitute costly platinum for quantum dot-sensitized solar cells (QDSSCs). A ZIF layer is first prepared by coating ZIF-67 powders on the conducting glass, followed by the careful calcination treatments in sulfur vapour (sulfuration) or nitrogen gas (carbonization). The structure and morphologies of the derived porous film are characterized by the measurements of XRD, SEM and BET, and the electrochemical properties in the polysulfide solution are evaluated by the measurements of Tafel curves and electrochemical impedance spectroscopies. The derived porous film is used as a CE to fabricate QDSSC with CdSe quantum dot-sensitized TiO2 nanocrystalline thin film and the polysulfide solution. Compared with the photovoltaic performance of CdSe QDSSCs based on the CE prepared by the different sulfuration conditions, QDSSC based on the CE derived by the sulfuration for 30 min shows an excellent light-to-electric conversion efficiency of 3.77%, it is even higher than that of QDSSC based on Pt CE (2.98%). This work will open a new avenue to design a facile, low-cost and renewable CE for QDSSC.
Pedestrian violations pose a danger to themselves and other road users. Most previous studies predict pedestrian violation behaviors based only on pedestrians’ demographic characteristics. In practice, in addition to demographic characteristics, other factors may also impact pedestrian violation behaviors. Therefore, this study aims to predict pedestrian crossing violations based on pedestrian attributes, traffic conditions, road geometry, and environmental conditions. Data on the pedestrian crossing, both in compliance and in violation, were collected from 10 signalized intersections in the city of Jinhua, China. We propose an illegal pedestrian crossing behavior prediction approach that consists of a logistic regression model and a Markov Chain model. The former calculates the likelihood that the first pedestrian who decides to cross the intersection illegally within each signal cycle, while the latter computes the probability that the subsequent pedestrians who decides to follow the violation. The proposed approach was validated using data gathered from an additional signalized intersection in Jinhua city. The results show that the proposed approach has a robust ability in pedestrian violation behavior prediction. The findings can provide theoretical references for pedestrian signal timing, crossing facility optimization, and warning system design.
Providing high-quality public transport services and enhancing passenger experiences require efficient urban rail transit connectivity; however, passengers’ perceived transfer distance at urban rail transit stations may differ from the actual transfer distance, resulting in inconvenience and dissatisfaction. To address this issue, this study proposed a novel machine learning framework that measured the perceived transfer distance in urban rail transit stations and analyzed the significance of each influencing factor. The framework introduced the Ratio of Perceived Transfer Distance Deviation (R), which was evaluated using advanced XGBoost and SHAP models. To accurately evaluate R, the proposed framework considered 32 indexes related to passenger personal attributes, transfer facilities, and transfer environment. The study results indicated that the framework based on XGBoost and SHAP models can effectively measure the R of urban rail transit passengers. Key factors that affected R included the Rationality of Signs and Markings, Ratio of Escalators Length, Rationality of Traffic Organization outside The Station, Ratio of Stairs Length, and Degree of Congestion on Passageways. These findings can provide valuable theoretical references for designing transfer facilities and improving transfer service levels in urban rail transit stations.
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