China's pawn-broking industry and the puzzle of losses during the global financial crisis in 2008-2009

Pawnbrokers in China, like those in many other countries, not only provide financial intermediation services to individuals and households to finance the shortfall between consumption and income in the short term, but also serve as a supplementary financing channel for private entrepreneurs and small- and medium-sized enterprises (SMEs) that face difficulty in obtaining formal credit. This article uses first-hand 2008–2009 survey data from Zhejiang province—one of the pioneering regions in China to develop the pawnbroking business—to study the special characteristics of the pawnbroking industry and explain why it has become a viable and useful financing instrument in transitional China. It also examines the puzzle of widespread losses and serious setbacks in the industry during the 2008–2009 global financial crisis, to which the authors attributed the increased default rate of export-oriented SMEs that reduced their demand for pawnbroking loans. The pawnbroking industry’s strict regulations and low litigation efficiency in China also increased pawnshops’ operation costs of handling defaults, leading to a temporary setback of an otherwise rapidly growing pawnbroking business during the 2008–2009 crisis. However, the pawnbroking industry will continue to play an important role in financing SMEs in China in the foreseeable future

Anti-Skid Characteristics of Asphalt Pavement Based on Partial Tire Aquaplane Conditions

This study presented a finite element model of radial tire-asphalt pavement interaction using ABAQUS 6.14 software to investigate the skid resistance properties of asphalt pavement under partial tire aquaplane conditions. Firstly, the pavement profile datum acquired by laser scanning were imported to Finite Element Analysis (FEA) software to conduct the pavement modeling. Secondly, a steady state rolling analysis of a tire on three types of asphalt pavements under drying conditions was carried out. Variation laws of the friction coefficient of the radial tire on different pavements with different pavement textures, tire pressures, and loads on the tire were examined. Subsequently, calculation results of the steady state rolling analysis were transmitted to dynamic explicit analysis, and an aquaplane model of a radial tire on asphalt pavements was built by inputting the flow Euler grids. The tire-pavement adhesive characteristics under partial aquaplane conditions are discussed regarding the aquaplane model. Influences of the thickness of water film, the texture of asphalt pavement, and the rolling speed of the tire on the vertical pavement-tire contact force are analyzed. It is found that the vertical contact force between open graded friction course (OGFC) pavement and tire is the highest, followed by stone mastic asphalt (SMA) pavement and dense graded asphalt concrete (AC) pavement surface. The vertical contact force between tire and pavement will be greatly reduced, even with increasing speed or water film thickness. As tire speed increases from 70 km/h to 130 km/h, the tire-pavement contact force is reduced by about 25%. Moreover, when the thickness of water film increases from 0 (dry condition) to 4 mm and then to 12 mm, the vertical contact force reduced 50% and 15%, respectively, compared with under the dry contact condition. This study provided a key theoretical reference for safe driving on wet pavements

Radial Basis Function Neural Network with Particle Swarm Optimization Algorithms for Regional Logistics Demand Prediction

Regional logistics prediction is the key step in regional logistics planning and logistics resources rationalization. Since regional economy is the inherent and determinative factor of regional logistics demand, it is feasible to forecast regional logistics demand by investigating economic indicators which can accelerate the harmonious development of regional logistics industry and regional economy. In this paper, the PSO-RBFNN model, a radial basis function neural network (RBFNN) combined with particle swarm optimization (PSO) algorithm, is studied. The PSO-RBFNN model is trained by indicators data in a region to predict the regional logistics demand. And the corresponding results indicate the model’s applicability and potential advantages

A Dynamical Innovation Diffusion Model with Fuzzy Coefficient and Its Application to Local Telephone Diffusion in China

This paper studies the innovation diffusion problem with the affection of urbanization, proposing a dynamical innovation diffusion model with fuzzy coefficient, and uses the shifting rate of people from rural areas stepping into urban areas to show the process of urbanization. The numerical simulation shows the diffusion process for telephones in China with Genetic Algorithms and this model is effective to show the process of innovation diffusion with the condition of urbanization process

The mechanism of traditional medicine in alleviating ulcerative colitis: regulating intestinal barrier function

Ulcerative colitis (UC) is an idiopathic inflammatory disease mainly affects the large bowel and the rectum. The pathogenesis of this disease has not been fully elucidated, while the disruption of the intestinal barrier function triggered by various stimulating factors related to the host genetics, immunity, gut microbiota, and environment has been considered to be major mechanisms that affect the development of UC. Given the limited effective therapies, the treatment of this disease is not ideal and its incidence and prevalence are increasing. Therefore, developing new therapies with high efficiency and efficacy is important for treating UC. Many recent studies disclosed that numerous herbal decoctions and natural compounds derived from traditional herbal medicine showed promising therapeutic activities in animal models of colitis and have gained increasing attention from scientists in the study of UC. Some of these decoctions and compounds can effectively alleviate colonic inflammation and relieve clinical symptoms in animal models of colitis via regulating intestinal barrier function. While no study is available to review the underlying mechanisms of these potential therapies in regulating the integrity and function of the intestinal barrier. This review aims to summarize the effects of various herbal decoctions or bioactive compounds on the severity of colonic inflammation via various mechanisms, mainly including regulating the production of tight junction proteins, mucins, the composition of gut microbiota and microbial-associated metabolites, the infiltration of inflammatory cells and mediators, and the oxidative stress in the gut. On this basis, we discussed the related regulators and the affected signaling pathways of the mentioned traditional medicine in modulating the disruption or restoration of the intestinal barrier, such as NF-κB/MAPK, PI3K, and HIF-1α signaling pathways. In addition, the possible limitations of current studies and a prospect for future investigation and development of new UC therapies are provided based on our knowledge and current understanding. This review may improve our understanding of the current progression in studies of traditional medicine-derived therapies in protecting the intestinal barrier function and their roles in alleviating animal models of UC. It may be beneficial to the work of researchers in both basic and translational studies of UC

Contrastive Counterfactual Learning for Causality-aware Interpretable Recommender Systems

There has been a recent surge in the study of generating recommendations within the framework of causal inference, with the recommendation being treated as a treatment. This approach enhances our understanding of how recommendations influence user behaviour and allows for identification of the factors that contribute to this impact. Many researchers in the field of causal inference for recommender systems have focused on using propensity scores, which can reduce bias but may also introduce additional variance. Other studies have proposed the use of unbiased data from randomized controlled trials, though this approach requires certain assumptions that may be difficult to satisfy in practice. In this paper, we first explore the causality-aware interpretation of recommendations and show that the underlying exposure mechanism can bias the maximum likelihood estimation (MLE) of observational feedback. Given that confounders may be inaccessible for measurement, we propose using contrastive SSL to reduce exposure bias, specifically through the use of inverse propensity scores and the expansion of the positive sample set. Based on theoretical findings, we introduce a new contrastive counterfactual learning method (CCL) that integrates three novel positive sampling strategies based on estimated exposure probability or random counterfactual samples. Through extensive experiments on two real-world datasets, we demonstrate that our CCL outperforms the state-of-the-art methods.Comment: conferenc