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Green finance is considered to be the financial support for green growth. It acts on the environmental protection industry through financial activities to reduce greenhouse gas emissions. It is a new financial model that combines financial and environmental protection activities. In order to explore the origin and development of this new financial model, namely, green finance, this study adopts the method of bibliometrics. Based on the database based on the Science Citation Index Expanded and Social Sciences Citation Index databases, the historical evolution of green finance and the classification of research hotspots are explored. The results show that green finance research has gone through three stages: the budding stage, the stable development stage, and the booming development stage. The research topics have also changed from the establishment of the green financial system to the difficulties faced by the green financial systems of various countries to the empirical evidence of the vigorous development of green finance. In addition, we have also established a three-dimensional framework (i.e., purpose, process, and participants, or 3P) of research hotspots in green finance to show a road map for future research directions.

Nowadays, an express/local mode has be studied and applied in the operation of urban rail transit, and it has been proved to be beneficial for the long-distance travel. The optimization of train patterns and timetables is vital in the application of the express/local mode. The former one has been widely discussed in the various existing works, while the study on the timetable optimization is limited. In this study, a timetable optimization model is proposed by minimizing the total passenger waiting time at platforms. Further, a genetic algorithm is used to solve the minimization problems in the model. This study uses the data collected from Guangzhou Metro Line 14 and finds that the total passenger waiting time at platforms is reduced by 9.3%. The results indicate that the proposed model can reduce the passenger waiting time and improve passenger service compared with the traditional timetable., Author(s): Renjie Zhang [1]; Shisong Yin [1]; Mao Ye (corresponding author) [1]; Zhiqiang Yang [2]; Shanglu He [1] 1. Introduction Urban rail transit system has been adopted as a main [...]

Modern trams have been widely used around the world, especially in China. This paper explores the main influencing factors of modern trams' passenger flow at the early operational stage. The system dynamics model is adopted for dealing with the problem on hand. Tram Line 1 in Huai'an, Jiangsu Province, China is selected as the case study. Data are collected using the RP and SP survey. The sensitivity test and extreme condition test are performed. The simulation results demonstrate that four variables (i.e., land development intensity, fares, service level, and transfer efficiency) significantly affect passenger flow. Land development intensity is the most significant factor, and the effect of service level on passenger flow is higher than that of the fares. The departure interval of 10 minutes is the maximum psychological limit that passengers can bear, and 2 RMB is a reasonable price. Such conclusions can provide guidance for the planning and design of modern trams and address the problem of shortage of passengers at an early stage., 1. Introduction The modern tram is a low-floor electric vehicle guided by electric traction wheel rails. It runs on a dedicated track and is a medium and low-volume rail transit [...]

Factor analysis models with continuous and ordinal responses are a useful tool for assessing relations between the latent variables and mixed observed responses. These models have been successfully applied to many different fields, including behavioral, educational, and social-psychological sciences. However, within the Bayesian analysis framework, most developments are constrained within parametric families, of which the particular distributions are specified for the parameters of interest. This leads to difficulty in dealing with outliers and/or distribution deviations. In this paper, we propose a Bayesian semiparametric modeling for factor analysis model with continuous and ordinal variables. A truncated stick-breaking prior is used to model the distributions of the intercept and/or covariance structural parameters. Bayesian posterior analysis is carried out through the simulation-based method. Blocked Gibbs sampler is implemented to draw observations from the complicated posterior. For model selection, the logarithm of pseudomarginal likelihood is developed to compare the competing models. Empirical results are presented to illustrate the application of the methodology.

Many urban streets are designed with on-street bike lanes to provide right-of-way for bicycle traffic. However, when bicycle flow is large, extensive passing maneuvers could occupy vehicle lanes and thus cause interferences to vehicle traffic. The primary objective of this study is to evaluate how bicycle traffic affects vehicle operation on urban streets with bike lanes. Data were collected on six street segments in Nanjing, China. The cumulative curves were constructed to extract traffic flow information including individual bicycle and vehicle speeds and aggregated traffic parameters such as flow and density. The results showed that as bicycle density on bike lanes continuously increases faster bicycles may run into vehicle lanes causing considerable reductions in vehicle speeds. A generalized linear model was estimated to predict the vehicle delay. Results showed that vehicle delay increases as bicycle flow and vehicle flow increase. Number of vehicle lanes and width of bike lane also have significant impact on vehicle delay. Findings of the study are helpful to regions around the world in bike infrastructure design in order to improve operations of both bicycles and vehicles., 1. Introduction In the past decade, there has been an increasing use of bicycles in many countries around the world [1-6]. Bicycle has been widely used in people's daily trips [...]

The current unsupervised cross‐domain detection methods need source domain data to retrain the detection model in target domain. However, the source domain data may be unavailable due to privacy, decentralization, or computation resource restrictions. A natural idea is to optimize the parameters of the source domain model by self‐supervised learning based on pseudo labels. We propose another approach from the viewpoint of noise perturbation without pseudo‐labeling. It can be assumed that the source and target domains are actually derived from a domain invariant space through domain‐specific perturbations, respectively. A super target domain can be constructed by augmenting more target domain perturbations to the target domain images. The optimal direction of the target domain to the domain invariant space can be approximated as the alignment direction from the super target domain to the target domain. Based on this idea, we propose a novel method called SOAP (SOurce data‐free domain Adaptation through domain Perturbation) which can remove domain perturbation from the target domain. The image‐level, instance‐level, and category consistency regularizations based on Mean Teacher structure are proposed to learn the correct alignment direction. Specifically, the category consistency can also further improve the classification accuracy. Extensive experiments on multiple domain adaptation scenarios demonstrate that SOAP achieves better performance surpassing the baseline (Faster R‐CNN) and multiple state‐of‐the‐art domain adaptation methods which need to access source domain data. [ABSTRACT FROM AUTHOR]

For the text‐to‐image synthesis task, most discriminators in existing generative adversarial networks based methods tend to fall into a local suboptimal state too early in the training process, resulting in the poor quality of generated images. To address the above problems, a hybrid augmented loss discriminator is designed. In this designed discriminator, to reduce the sensitivity of the discriminator classification recognition, make it pay attention to the semantic and structural changes, we add the loss value of the fake sample to the loss value of the real sample. Moreover, to indirectly guide the generator to generate samples, the loss value of the real sample is added to the fake sample. The loss value mixed with real and fake samples actually augments signal transmission. It perturbs parameter update of the discriminator during optimization and prevents the discriminator from falling into the local suboptimal state prematurely. Whereafter, we apply the proposed discriminator to two kinds of text‐to‐image synthesis tasks. Experimental results show that the proposed method can help the baseline models to improve performance. [ABSTRACT FROM AUTHOR]

Because of its positive effects on dealing with the curse of dimensionality in big data, random projection for dimensionality reduction has become a popular method recently. In this paper, an academic analysis of influences of random projection on the variability of data set and the dependence of dimensions has been proposed. Together with the theoretical analysis, a new fuzzy c-means (FCM) clustering algorithm with random projection has been presented. Empirical results verify that the new algorithm not only preserves the accuracy of original FCM clustering, but also is more efficient than original clustering and clustering with singular value decomposition. At the same time, a new cluster ensemble approach based on FCM clustering with random projection is also proposed. The new aggregation method can efficiently compute the spectral embedding of data with cluster centers based representation which scales linearly with data size. Experimental results reveal the efficiency, effectiveness, and robustness of our algorithm compared to the state-of-the-art methods. [ABSTRACT FROM AUTHOR]

Machine olfaction is an intelligent system that combines a cross-sensitivity chemical sensor array and an effective pattern recognition algorithm for the detection, identification, or quantification of various odors. Data collected by the sensor array are the multivariate time series signals with a complex structure, and these signals become more difficult to analyze due to sensor drift. In this work, we focus on improving the classification performance under sensor drift by using the deep learning method, which is popular nowadays. Compared with other methods, our method can effectively tackle sensor drift by automatically extracting features, thus not only removing the complexity of designing the hand-made features but also making it pervasive for a variety of application in machine olfaction. Our experimental results show that the deep learning method can learn the features that are more robust to drift than the original input and achieves high classification accuracy. [ABSTRACT FROM AUTHOR]