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This paper presents the results of acase study on the causes and effects of typical service disruptions in aHigh-speed rail (HSR) system in China–Wuhan–Guangzhou High-speed railway (WH-GZ HSR). With acause-specific approach, seven delay causalities leading to primary delays (PDs) are identified, and the properties and consequences of each primary delay (PD) factor is derived. The comparison of candidate distributional forms shows that the Log-normal distribution model can approximate better the length of all identified PDs. For each PD cause, the distribution of delay duration is estimated and tuned. Next, cause-specific distributional models for PDs severity are discussed. The models for the number of affected trains are presented in the form of inverse regression models with specific domains. Then, comparing five different kinds of candidate models, the results show that the Cubic is the best to approximate the distributions of total-affected time. [ABSTRACT FROM AUTHOR]

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This paper presents a cost-benefit based approach to the problem of finding the optimal location and density of road weather information system (RWIS) stations over a regional road network. The novelty of the proposed method lies in the models that can be used to estimate the benefits of RWIS information, including reduced maintenance costs and collisions, thus determining the optimal number and location of a RWIS network. A case study based on the existing RWIS network in Northern Minnesota in the US is used to show the application of the proposed approach. Linear regression models are developed for the annual maintenance costs and the expected number of collisions that could occur on two types of highways delineated by the existence of a RWIS station nearby. The calibrated models are then applied to individual highway segments defined on the basis of a uniform grid system to determine the expected benefit of having a RWIS station installed. These benefits along with RWIS installation and maintenance costs are converted into net benefits and then the net present value, which are then used in determining the optimal number of RWIS stations and prioritizing the candidate locations. It was found from the case study that a total of 45 stations would provide the best return of investment with a 25 year net benefit of approximately $6.5 million and a life-cycle benefit-to-cost ratio of 3.5. [ABSTRACT FROM AUTHOR]

Abstract: Impulsivity is a pathological hallmark of drug addiction. However, little is known about the neuropsychological underpinnings of this impaired impulsive control network on drug addiction. Twenty two abstinent heroin dependent (HD) subjects and 15 cognitively normal (CN) subjects participated in this study. Resting-state functional connectivity MRI was employed to measure abnormalities in the intrinsic amygdala functional connectivity (iAFC) network activity and the Barratt Impulsive Scale, 11th version was used to measure impulsivity. Linear regression analysis was applied to detect the neural constructs underlying impulsivity by correlating iAFC network activity with impulsive scores. In the HD group, higher impulsivity scores and significantly enhanced iAFC network activity were found, especially in bilateral thalamus, right insula, and inferior frontal gyrus. Markedly decreased anticorrelated iAFC network activity was seen in the left precuneus, and even switched to positive correlation pattern in right precuneus, relative to the CN group. The iAFC network strengths in the HD group were positively correlated with impulsivity in the right subcallosal gyrus, insula, thalamus and posterior cingulate cortex, and negatively correlated in left fusiform area. In the CN group, the left pre-somamotor area-amygdala connectivity was positively correlated, and right orbital frontal cortex-amygdala and precuneus-amygdala connectivity were negatively correlated with impulsivity scores. Our study demonstrates different constructs of the impulsive network in HD and CN subjects. Altered iAFC network connectivity in HD subjects may contribute to the loss of impulsive control. This further facilitates our understanding of the neural underpinnings of behavior dysfunction in addiction. [Copyright &y& Elsevier]