Standard & Poor’s Small Business Portfolio Model introduces a potential new tool for community development loan risk analysis

The Small Business Portfolio Evaluator™ analytical model helps issuers and underwriters to assess the gross default and prepayment risk of small business loan portfolios using a Monte Carlo simulation. This new tool provides an important first step to securitizing existing community development loan portfolios.

Research on the Relationship Between Perceived Social Support and Subjective Well Being of Left Behind Children

In this study, “perceived social support scale (SPSS)”, “happiness” and “emotional index scale: positive emotion, negative emotion and emotional balance” as the measurement tool of left-behind children understand the relationship between social support and subjective well-being were studied. The results showed that: (a) left-behind children and non left-behind children perceived social support have significant differences, which left the perceived social support level of children is significantly lower than the non left-behind children; (b) left-behind children and non left-behind children in emotional well-being, life satisfaction index, there is a significant difference, performance for the left-behind children in happiness and emotion index, life satisfaction scores were significantly lower than those of non left-behind children; (c) left behind children between perceived social support and subjective well-being are positively related. In particular, friends and family support the greatest impact on happiness. Through the regression analysis, the results showed that friends and family support had a significant predictive effect on the well-being of left behind children

A Relation Research on the Self-Worth and Personality of Rural Junior High School Left-Behind Students

Using two questionnaires, the research investigates the self-worth and the personality of 303 rural Junior High School left-behind students and 365 Left-behind Junior High School students. The results show that: (a) The development level of self-worth of rural Left-behind Junior High School students is less than normal left-behind students in N scale and SS1.（b）There is higher of grade one than grade three in GI. (c) There is significant of SI3 and P scale and N scale in gender. (d) There is significant correlation between personality and self-worth for rural Left-behind Junior High School students. (e) Personality traits could predict most dimensions of self-worth

An Intelligent Model for Stock Investment with Buffett Strategy, Classifier System, Neural Network and Linear Programming

“The Intelligent Model for Stock Investment with Buffett Strategy, Classifier System, Neural Network and Linear Programming” was studied for developing an intelligent model which can learn more knowledge regarding to stock investment with artificial intelligence technology. Classifier system, neural network, fundamental financial investment factors and linear programming are the fundamental components for the research. Knowledge transformation and genetic evolution capability was discussed in the article, too. Furthermore, the investment strategy developed by Warren E. Buffett[17], the great financial investment master, was the major knowledge which was practiced in the article. For realizing more detail about learning system, a lot of topics regarding to artificial intelligence were discussed in advanced, including “A Market-Based Rule Learning System” [1], “Dynamic Trading Strategy Learning Model using Learning Classifier System” [2], “Nonlinear Index Prediction” [3], “Financial Decision Support with Hybrid Genetic and Neural Based Modeling Tool” [4] and “Fuzzy Interval methods in Investment risk Appraisal” [5]. According to the study mentioned above, the ideas to give intelligent model, especially with genetic algorithm, bring the direction for the advanced financial investment strategy and operation. Therefore, it was why a novel intelligent model with Buffett strategy, classifier system, neural network and linear programming proposed in the article

HSP90 inhibitors stimulate DNAJB4 protein expression through a mechanism involving N6-methyladenosine.

Small-molecule inhibitors for the 90-kDa heat shock protein (HSP90) have been extensively exploited in preclinical studies for the therapeutic interventions of human diseases accompanied with proteotoxic stress. By using an unbiased quantitative proteomic method, we uncover that treatment with three HSP90 inhibitors results in elevated expression of a large number of heat shock proteins. We also demonstrate that the HSP90 inhibitor-mediated increase in expression of DNAJB4 protein occurs partly through an epitranscriptomic mechanism, and is substantially modulated by the writer, eraser, and reader proteins of N6-methyladenosine (m6A). Furthermore, exposure to ganetespib leads to elevated modification levels at m6A motif sites in the 5'-UTR of DNAJB4 mRNA, and the methylation at adenosine 114 site in the 5'-UTR promotes the translation of the reporter gene mRNA. This m6A-mediated mechanism is also at play upon heat shock treatment. Cumulatively, we unveil that HSP90 inhibitors stimulate the translation of DNAJB4 through an epitranscriptomic mechanism

Applying Minimum-Risk Criterion to Stochastic Hub Location Problems

AbstractThis paper presents a new class of two-stage stochastic hub location (HL) programming problems with minimum-risk criterion, in which uncertain demands are characterized by random vector. Meanwhile we demonstrate that the twostage programming problem is equivalent to a single-stage stochastic P-model. Under mild assumptions, we develop a deterministic binary programming problem by using standardization, which is equivalent to a binary fractional programming problem. Moreover, we show that the relaxation problem of the binary fractional programming problem is a convex programming problem. Taking advantage of branch-and-bound method, we provide a number of experiments to illustrate the efficiency of the proposed modeling idea

Adaptive Multi-Feature Budgeted Profit Maximization in Social Networks

Online social network has been one of the most important platforms for viral marketing. Most of existing researches about diffusion of adoptions of new products on networks are about one diffusion. That is, only one piece of information about the product is spread on the network. However, in fact, one product may have multiple features and the information about different features may spread independently in social network. When a user would like to purchase the product, he would consider all of the features of the product comprehensively not just consider one. Based on this, we propose a novel problem, multi-feature budgeted profit maximization (MBPM) problem, which first considers budgeted profit maximization under multiple features propagation of one product. Given a social network with each node having an activation cost and a profit, MBPM problem seeks for a seed set with expected cost no more than the budget to make the total expected profit as large as possible. We consider MBPM problem under the adaptive setting, where seeds are chosen iteratively and next seed is selected according to current diffusion results. We study adaptive MBPM problem under two models, oracle model and noise model. The oracle model assumes conditional expected marginal profit of any node could be obtained in O(1) time and a (1-1/e) expected approximation policy is proposed. Under the noise model, we estimate conditional expected marginal profit of a node by modifying the EPIC algorithm and propose an efficient policy, which could return a (1-exp({\epsilon}-1)) expected approximation ratio. Several experiments are conducted on six realistic datasets to compare our proposed policies with their corresponding non-adaptive algorithms and some heuristic adaptive policies. Experimental results show efficiencies and superiorities of our policies.Comment: 12 pages, 6 figure