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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.

Energy dissipation damping technology is usually used for infrastructure construction in seismic regions. In this study, a lever-type lead viscoelastic node damper (LTLVND), which can capture small rotational displacements of the infrastructure under seismic excitation, is innovatively proposed based on the leverage effect. The characteristics of energy-absorbing capacity of the LTLVND and its mitigation effect on the dynamics of the structure under seismic excitation are studied. Testing and modelling results show that a satisfactory energy dissipation effect can be observed for the innovative lead viscoelastic damper (LTLVND). Finally, a seismic analysis of a concrete frame structure with LTLVNDs is carried out. Pushover analysis and dynamic elastoplastic analysis are included. It is shown that a significant improvement in structural performance under seismic conditions can be achieved with the addition of LTLVNDs at appropriate locations. [ABSTRACT FROM AUTHOR]

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 [...]

Biodegradable iron alloy implants have become one of the most ideal possible candidates because of their biocompatibility and comprehensive mechanical properties. Iron alloy's impact on chondrocytes is still unknown, though. This investigation looked at the biocompatibility and degradation of the Fe30Mn0.6N alloy as well as how it affected bone formation and chondrocyte autophagy. In vivo implantation of Fe30Mn0.6N and Ti6Al4V rods into rabbit femoral cartilage and femoral shaft was carried out to evaluate the degradation of the alloy and the cartilage and bone response at different intervals. After 8 weeks of implantation, the cross-sectional area of the Fe30Mn0.6N alloys lowered by 50.79 ± 9.59 %. More Ca and P element deposition was found on the surface Fe30Mn0.6N rods by using energy dispersive spectroscopy (EDS) and scanning electron microscopy (P < 0.05). After 2, 4, and 8 weeks of implantation, no evident inflammatory infiltration was seen in peri-implant cartilage and bone tissue of Fe30Mn0.6N and Ti6Al4V alloys. Also, implantation of Fe30Mn0.6N alloy promoted autophagy in cartilage by detecting expression of LC3-II compared with Ti6Al4V after implantation (P < 0.05). Fe30Mn0.6N alloy also stimulated early osteogenesis at the peri-implant interface compared with Ti6Al4V after implantation (P < 0.05). In the in vitro test, we found that low concentrations of Fe30Mn0.6N extracts had no influence on cell viability. 15% and 30% extracts of Fe30Mn0.6N could upregulate autophagy compared to the control group by detecting beclin-1, LC3, Atg3, and P62 on the basis of WB and IHC (P < 0.05). Also, the PI3K-AKT-mTOR signaling pathway mediated in the upregulation of autophagy of chondrocytes resulting in exposure to extract of Fe30Mn0.6N alloy. It is concluded that Fe30Mn0.6N showed degradability and biocompatibility in vivo and upregulated autophagy activity in chondrocytes. [ABSTRACT FROM AUTHOR]

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.

Logistic regression model is the most popular regression technique, available for modeling categorical data especially for dichotomous variables. Classic logistic regression model is typically used to interpret relationship between response variables and explanatory variables. However, in real applications, most data sets are collected in follow-up, which leads to the temporal correlation among the data. In order to characterize the different variables correlations, a new method about the latent variables is introduced in this study. At the same time, the latent variables about AR (1) model are used to depict time dependence. In the framework of Bayesian analysis, parameters estimates and statistical inferences are carried out via Gibbs sampler with Metropolis-Hastings (MH) algorithm. Model comparison, based on the Bayes factor, and forecasting/smoothing of the survival rate of the tree are established. A simulation study is conducted to assess the performance of the proposed method and a pika data set is analyzed to illustrate the real application. Since Bayes factor approaches vary significantly, efficiency tests have been performed in order to decide which solution provides a better tool for the analysis of real relational data sets.

Objectives. Acupoint catgut embedding therapy has shown effectiveness in treating functional constipation; however, relevant, high-quality clinical evidence is scarce. This study aimed to systematically assess the effectiveness and safety of acupoint catgut embedding in treating poststroke constipation. Methods. Correlative randomized controlled trials were identified through a comprehensive literature search of PubMed, Cochrane Library/Cochrane Central Register of Controlled Trials, Web of Science, Embase, China National Knowledge Internet, Chinese Biomedical Literature Database, Wanfang, and VIP databases from inception until February 2022. Meta-analysis was performed using RevMan 5.3 software. Results. Fifteen trials involving 1084 patients were identified. The meta-analysis revealed that the acupoint catgut embedding group was significantly superior to the non-catgut embedding group with regard to the efficacy rate (RR = 1.27, 95% CI (1.19, 1.37), P < 0.05), the first defecation time (MD = −3.08, 95% CI (−4.53, −1.63), P < 0.05), the defecation sensation score (MD = −0.44, 95% CI (−0.61, −0.26), P < 0.05), the degree of difficulty in defecation (MD = −0.73, 95% CI (−1.10, −0.37), P < 0.05), the PAC-QOL scale score (MD = −10.06, 95% CI (−13.47, −6.64), P < 0.05), and the symptom integral (MD = −3.15, 95% CI (−3.60, −2.71), P < 0.05). However, there was no significant difference in the stool property score (MD = 0.06, 95% CI (−0.39, 0.50), P > 0.05) as well as the incidence of adverse reactions (RD = 0.01, 95% CI (−0.01, 0.03), P > 0.05) between the two groups. Conclusions. The results showed that acupoint catgut embedding is probably an effective and safe acupuncture treatment strategy for poststroke constipation. Nevertheless, more rigorously designed, standardized, large-sample, and multicenter randomized controlled designs are warranted to further verify the findings of this study. [ABSTRACT FROM AUTHOR]

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]

A broadband dual‐polarized end‐fire millimeter‐wave antenna array without ground clearance is proposed in this paper for 5G communication terminals. Horizontally polarized (HP) and vertically polarized (VP) radiation are realized using a quasi‐Yagi antenna and a substrate‐integrated waveguide cavity antenna, respectively. A multilayer dielectric plate for which the relative dielectric constant increases from top to bottom is used to achieve the HP antenna's end‐fire radiation without ground clearance. The VP antenna is implemented using the HP antenna, GND, and three rows of metallic vias. The HP and VP antennas are located in the same area, making the integrated design very compact. An inverted L‐shaped feeding structure was used to broaden the VP antenna's bandwidth with a low profile (0.12λL). Finally, a 4 × 1 array was processed to verify the proposed antenna. The measured results show that the overlapping bandwidth is 24 to 29.5 GHz (20.6%) for two polarization modes. The maximum gain varies between 7.6 to 9.1 and 6.5 to 9.1 dBi for the HP and VP modes, respectively. [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]

Unpaired image translation is a challenging problem in computer vision, while existing generative adversarial networks (GANs) models mainly use the adversarial loss and other constraints to model. But the degree of constraint imposed on the generator and the discriminator is not enough, which results in bad image quality. In addition, we find that the current GANs-based models have not yet been implemented by adding an auxiliary domain, which is used to constrain the generator. To solve the problem mentioned above, we propose a multiscale and multilevel GANs (MMGANs) model for image translation. In this model, we add an auxiliary domain to constrain generator, which combines this auxiliary domain with the original domains for modelling and helps generator learn the detailed content of the image. Then we use multiscale and multilevel feature matching to constrain the discriminator. The purpose is to make the training process as stable as possible. Finally, we conduct experiments on six image translation tasks. The results verify the validity of the proposed model. [ABSTRACT FROM AUTHOR]

Constrained spectral clustering (CSC) method can greatly improve the clustering accuracy with the incorporation of constraint information into spectral clustering and thus has been paid academic attention widely. In this paper, we propose a fast CSC algorithm via encoding landmark-based graph construction into a new CSC model and applying random sampling to decrease the data size after spectral embedding. Compared with the original model, the new algorithm has the similar results with the increase of its model size asymptotically; compared with the most efficient CSC algorithm known, the new algorithm runs faster and has a wider range of suitable data sets. Meanwhile, a scalable semisupervised cluster ensemble algorithm is also proposed via the combination of our fast CSC algorithm and dimensionality reduction with random projection in the process of spectral ensemble clustering. We demonstrate by presenting theoretical analysis and empirical results that the new cluster ensemble algorithm has advantages in terms of efficiency and effectiveness. Furthermore, the approximate preservation of random projection in clustering accuracy proved in the stage of consensus clustering is also suitable for the weighted k-means clustering and thus gives the theoretical guarantee to this special kind of k-means clustering where each point has its corresponding weight. [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]

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. [ABSTRACT FROM AUTHOR]

Pedestrian detection with large intraclass variations is still a challenging task in computer vision. In this paper, we propose a novel pedestrian detection method based on Random Forest. Firstly, we generate a few local templates with different sizes and different locations in positive exemplars. Then, the Random Forest is built whose splitting functions are optimized by maximizing class purity of matching the local templates to the training samples, respectively. To improve the classification accuracy, we adopt a boosting-like algorithm to update the weights of the training samples in a layer-wise fashion. During detection, the trained Random Forest will vote the category when a sliding window is input. Our contributions are the splitting functions based on local template matching with adaptive size and location and iteratively weight updating method. We evaluate the proposed method on 2 well-known challenging datasets: TUD pedestrians and INRIA pedestrians. The experimental results demonstrate that our method achieves state-of-the-art or competitive performance. [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]