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Due to the large passenger flow and the high traffic density of the Chinese high-speed railway network, the number of train delay incidents is increasing in recent years. In a railway network, an initial train delay may bring a cascade effect of secondary delays over the following trains. In this paper, we analyse the delay propagation among trains on a single railway line and aim to establish a train delay propagation algorithm using the max-plus algebra method. Then we illustrate the proposed algorithm by an example. The study presented here could be potentially useful in large-scale railway networks, and may serve as a useful component in decision support systems to assist train dispatchers.

We introduce and compare two prediction systems on the task of replicating human decisions regarding patient admittance in a typical American emergency department. The data-set used describes the patient trajectories in a 65,000 patient per-year emergency department in the United States. Among the descriptive attributes those of prime importance are the severity of the patient’s condition and the time they waited to be admitted from the waiting room to the department proper. A recurrent neural network (RNN) is developed to learn the task of selecting the next patient from the waiting-room/queue to be admitted for treatment which is then compared to a heuristic-based selection algorithm currently used in industry for hospital simulation applications. We demonstrate achievable accuracies of 75.29% and 84.97% using the RNN, depending on the type of the data preprocessing used. These accuracies are only potentially and theoretically achievable, respectively. The former’s validity hinges on whether certain "anomalous cases" are outliers or not, the second is achieved with the assumed existence of a method for labeling these same cases as anomalous as part of the RNN’s input, which may or may not be achievable, pending further consultation with industry experts. Our conclusions hinge on whether or not such cases are outliers though in either case a more sophisticated data-set is desired. If they are not outliers then a more detailed data-set is likely necessary to apply machine learning, or at least our methods, meaningfully to this prediction problem for use in simulated, or real world, hospitals.

Finance is the core and lifeblood of the modern market economy. And good financial services platform is a prerequisite for the success of high-tech industrial parks. In the process of the development of industrial parks, how to effectively play the role of promoting the industry by using the tool of finance has become a key problem for the development of many high-tech industrial parks. In this paper, starting from how to effectively promote the butt joint of finance and industry in industrial parks, it, combined with the functions positioning and construction principles of the financial services platform, builds the models of the financial services platform in industrial parks, and carries out certain discussions on the operation mode of the platform.

The government-enterprise cooperation in high-tech industrial park is a useful exploration of the development models in the industrial parks. In the process of the construction and development of the park, how to effectively promote the government and enterprises and other organizations to work together to jointly improve the development of industrial park has become the most important issue of the development of many government-enterprise cooperation industrial parks. In this study, taking China Electronic Xi'an Industrial Park for example, it elaborates the effective implementation strategies of systematic operation of the government-enterprise cooperation in high-tech industrial parks from the perspective of how to effectively promote the industrial development of the park, which has certain reference significance to the construction and development of the government-enterprise cooperation industrial park or other parks.

By combining theoretical analysis and practical test, this paper present an dynamic dimming control method of highway tunnel LED lighting based on controllability of LED light, and undertook a comprehensive analysis of dynamic control method and Principle. And developed a highway tunnel LED lighting dynamic dimming control system. Through the demonstration analysis on tunnel LED lighting system of demonstration project, it indicated that the research results can significantly save the lighting electricity cost of highway tunnel lighting system under safety, on-demand lighting conditions. Which will provide a energy saving dynamic dimming control scheme for highway tunnel LED lighting system in our country.

Research on the emergency organization which is helpful to the reply of emergencies is an important direction in the field of emergency management. Computational and mathematical organization theory (CMOT) is a scientific area in which research members study organizations by using computational and mathematical methodologies. The application of CMOT to the study of emergency organizations is proposed in this paper, and studying the organization problems in the field of emergency management with computational and mathematical methodologies and formal models is discussed. The description model of the emergency organization system is presented. Then, the description model is applied in a case. Finally, the optimal design methodology of the emergency organization system is proposed. This paper presents a new approach and solution to study the emergency organization, the description model and optimal design methodology provide quantitative and scientific basis for contingency planning, and computing framework for the construction of the emergency organization system.