System Dynamics Model of Shanghai Passenger Transportation Structure Evolution

AbstractBased on the data from a comprehensive transportation survey of Shanghai in 2004 and 2009, this paper analyzed the evolution of urban passenger transportation structure using the system dynamics approach. A system dynamics model of Shanghai passenger transportation structure evolution is proposed, which consists of setting modeling targets, establishing transportation system boundaries, causality analysis, establishing flow diagram, parameter estimation and model validation

Extreme Response Style: A Meta-Analysis

Extreme response style (ERS) refers to the tendency to prefer responding using extreme endpoints on rating scales. We use meta-analysis to summarize the correlates of ERS. Our findings present how one’s tendency to engage in extreme responding is related to demographic variables (e.g., age, gender, education, and race), intelligence, acquiescence, and number of points and items in a scale. We also identified a non-linear relationship between age and extreme responding. Thus, this article should be read by anyone using Likert type scales when using data from a diverse set of individuals

Individual Traits and Entrepreneurial Intentions: The Mediating Role of Entrepreneurial Self-Efficacy and Need for Cognition

The field of entrepreneurship is rapidly advancing and matures as a discipline that receives substantial amount of attention. One popular area of research in the discipline of entrepreneurship is to investigate one’s intent to start a business, which is entrepreneurial intention. This is an important construct that warrants ongoing research because entrepreneurial intention is not only a great predictor of entrepreneurial behavior but also an important step in the process of becoming an entrepreneur. The present study, based on a sample of 321 subjects along with 264 observers, makes five contributions to the entrepreneurship literature. First, I examined the psychometric property of entrepreneurial take-over intention and found that it is a construct different from entrepreneurial start-up intention. Second, the results demonstrated that risk propensity and proactive personality are positive predictors of entrepreneurial start-up and take-over intentions, whereas cognitive ability is a negative predictor of entrepreneurial start-up and take-over intentions. Rebelliousness is a positive predictor of entrepreneurial take-over intention and also has an inverted U-shaped relationship with entrepreneurial take-over intention. Third, entrepreneurial self-efficacy mediates the relationship between three individual traits (i.e., emotional intelligence, risk propensity, and proactive personality) and entrepreneurial start-up and take-over intentions. Need for cognition mediates the relationship between two individual traits (i.e., cognitive ability and proactive personality) and entrepreneurial start-up intention. Fourth, 2D:4D ratio (a proxy measure for prenatal testosterone exposure level) negatively predicts risk propensity. There also exist two two-step mediations from 2D:4D ratio to both entrepreneurial start-up and take-over intentions through risk propensity and entrepreneurial self-efficacy. Fifth, the results suggest that observer ratings of individual traits only contribute modest incremental validity above and beyond self-reported ratings of them in predicting entrepreneurial start-up and take-over intentions. I discuss implications, limitations, and future directions informed by the present study

The etiologies of post-stroke depression: Different between lacunar stroke and non-lacunar stroke

Objectives: Depression is common after both lacunar stroke and non-lacunar stroke and might be associated with lesion locations as proven by some studies. This study aimed to identify whether lesion location was critical for depression after both lacunar and non-lacunar strokes. Methods: A cohort of ischemic stroke patients was assigned to either a lacunar stroke group or a non-lacunar stroke group after a brain MRI scan. Neurological deficits and treatment response was evaluated during hospitalization. The occurrence of depression was evaluated 3 months later. Logistic regressions were used to identify the independent risk factors for depression after lacunar and non-lacunar stroke respectively. Results: 83 of 246 patients with lacunar stroke and 71 of 185 patients with non-lacunar stroke developed depression. Infarctions in the frontal cortex, severe neurological deficits, and a high degree of handicap were identified as the independent risk factors for depression after non-lacunar stroke, while lesion location was not associated with depression after lacunar stroke. Conclusion: The main determinants for depression after lacunar and non-lacunar stroke were different. Lesion location was critical only for depression after non-lacunar stroke

Context-Based Dynamic Pricing with Online Clustering

We consider a context-based dynamic pricing problem of online products which have low sales. Sales data from Alibaba, a major global online retailer, illustrate the prevalence of low-sale products. For these products, existing single-product dynamic pricing algorithms do not work well due to insufficient data samples. To address this challenge, we propose pricing policies that concurrently perform clustering over products and set individual pricing decisions on the fly. By clustering data and identifying products that have similar demand patterns, we utilize sales data from products within the same cluster to improve demand estimation and allow for better pricing decisions. We evaluate the algorithms using the regret, and the result shows that when product demand functions come from multiple clusters, our algorithms significantly outperform traditional single-product pricing policies. Numerical experiments using a real dataset from Alibaba demonstrate that the proposed policies, compared with several benchmark policies, increase the revenue. The results show that online clustering is an effective approach to tackling dynamic pricing problems associated with low-sale products. Our algorithms were further implemented in a field study at Alibaba with 40 products for 30 consecutive days, and compared to the products which use business-as-usual pricing policy of Alibaba. The results from the field experiment show that the overall revenue increased by 10.14%