Features of SAS Enterprise Guide for probabilistic modeling system, macroeconomic analysis and forecasting

This paper addresses to the problem of using SAS Enterprise Guide 6.1 as a means for building probabilistic models and as optimum method of modeling gross domestic product in terms of the economic crisis and social threats is proposed. Today in a complex socio-political and economic situation growing influence of external factors, presence of uncertainties and risks there exists a problem of anticipating potential threats in the humanitarian and social spheres and ways to overcome them aiming to provide food security and controllability of ecological situation. All these problems, as reported in the NATO program “Science for Peace and Security”, are of high priority for the countries that need to take into account threats to security, including Ukraine. That is why in the framework of the project NUKR. SFPP G4877 “Modeling and Mitigation of Social Disasters Caused by Catastrophes and Terrorism” the problems of scientific prediction of national economy for the period to 2030 as one of the measures preventing growth of social tension in the country are disclosed

Data based identification and prediction of nonlinear and complex dynamical systems

We thank Dr. R. Yang (formerly at ASU), Dr. R.-Q. Su (formerly at ASU), and Mr. Zhesi Shen for their contributions to a number of original papers on which this Review is partly based. This work was supported by ARO under Grant No. W911NF-14-1-0504. W.-X. Wang was also supported by NSFC under Grants No. 61573064 and No. 61074116, as well as by the Fundamental Research Funds for the Central Universities, Beijing Nova Programme.Peer reviewedPostprin

The safety case and the lessons learned for the reliability and maintainability case

This paper examine the safety case and the lessons learned for the reliability and maintainability case

Structural and Functional Imaging Correlates of Visual Hallucinations in Parkinson's Disease

PURPOSE OF REVIEW: To review recent structural and functional MRI studies of visual hallucinations in Parkinson's disease. RECENT FINDINGS: Previously, neuroimaging had shown inconsistent findings in patients with Parkinson's hallucinations, especially in studies examining grey matter volume. However, recent advances in structural and functional MRI techniques allow better estimates of structural connections, as well as the direction of connectivity in functional MRI. These provide more sensitive measures of changes in structural connectivity and allow models of the changes in directional functional connectivity to be tested. We identified 27 relevant studies and found that grey matter imaging continues to show heterogeneous findings in Parkinson's patients with visual hallucinations. Newer approaches in diffusion imaging and functional MRI are consistent with emerging models of Parkinson's hallucinations, suggesting shifts in attentional networks. In particular, reduced bottom-up, incoming sensory information, and over-weighting of top-down signals appear to be important drivers of visual hallucinations in Parkinson's disease

A bayesian nework based risk model for volume loss in soft soils in mechanized bored tunnels

Volume loss is one of the most important risks when boring a tunnel. This is particularly true when a tunnel is being constructed in soft soils. The risk of excessive volume loss, if materialised can lead to large consequences such as damage in buildings on the surface. This paper describes the development and use of a Bayesian based risk model containing more than forty relevant risk factors associated with the occurrence of volume loss in soft soils in mechanized bored tunnels. The developed risk model takes into account additional factors other than those normally used in analytical methods to estimate volume loss. The considered risk factors involve issues related to the excavation process, design, monitoring, human factors as well as variables associated with ground conditions. By means of data elicited from tunnel experts and the analysis of the importance of the various factors using sensitivity analysis, the model is evaluated and its ability to provide information to derive specific risk measures is verified

Data-Driven Process Mining Framework for Risk Management in Construction Projects

Construction Projects are exposed to numerous risks due to their complex and uncertain nature, threatening the realization of the project objectives. However, Risk Management (RM) is a less efficient realm in the industry than other knowledge areas given the manual and time-consuming nature of its processes and reliance on experience-based subjective judgments. This research proposes a Process Mining-based framework for detecting, monitoring, and analysing risks, improving the RM processes using evidence-based event logs, such as Risk Registers and Change-Logs within previous projects' documents. Process Mining (PM) is a data- driven methodology, well established in other industries, that benefits from Artificial Intelligence(AI) to identify trends and complex patterns among event logs. It performs well while intaking large amounts of data and predicting future outputs based on historical data. Therefore, this research proposes a Bayesian Network (BN)-based Process Mining framework for graphical representation of the RM processes, intaking the conditional dependence structure between Risk variables, and continuous and automated risk identification and management. A systematic literature review on RM, PM, and AI forms the framework theoretical basis and delineates the integration areas for practical implementation. The proposed framework is applied to a small database of 20 projects as the case study, the scope of which can be tailored to the enterprise requirements. It contributes to creating a holistic theoretical foundation and practical workflow applicable to construction projects and filling the knowledge gap in inefficient and discrete conventional RM methods, which ignore the interdependencies between risk variables and assess each risk isolated

Identifying Causal Structure in Dynamical Systems

We present a method for automatically identifying the causal structure of a dynamical control system. Through a suitable experiment design and subsequent causal analysis, the method reveals, which state and input variables of the system have a causal influence on each other. The experiment design builds on the concept of controllability, which provides a systematic way to compute input trajectories that steer the system to specific regions in its state space. For the causal analysis, we leverage powerful techniques from causal inference and extend them to control systems. Further, we derive conditions that guarantee discovery of the true causal structure of the system and show that the obtained knowledge of the causal structure reduces the complexity of model learning and yields improved generalization capabilities. Experiments on a robot arm demonstrate reliable causal identification from real-world data and extrapolation to regions outside the training domain