Multiple sclerosis：integration of modeling with biology,clinical and imaging measures to provide better monitoring of disease progression and prediction of outcome

Multiple Sclerosis（MS） is a major cause of neurological disability in adults and has an annual cost of approximately $28 billion in the United States. MS is a very complex disorder as demyelination can happen in a variety of locations throughout the brain; therefore, this disease is never the same in two patients making it very hard to predict disease progression. A modeling approach which combines clinical, biological and imaging measures to help treat and fight this disorder is needed. In this paper, I will outline MS as a very heterogeneous disorder, review some potential solutions from the literature, demonstrate the need for a biomarker and will discuss how computational modeling combined with biological, clinical and imaging data can help link disparate observations and decipher complex mechanisms whose solutions are not amenable to simple reductionism.

Identification of working conditions and prediction of FeO content in sintering process of iron ore fines

The iron oxide(FeO)content had a significant impact on both the metallurgical properties of sintered ores and the economic indicators of the sintering process.Precisely predicting FeO content possessed substantial potential for enhancing the quality of sintered ore and optimizing the sintering process.A multi-model integrated prediction framework for FeO content during the iron ore sintering process was presented.By applying the affinity propagation clustering algorithm,different working conditions were efficiently classified and the support vector machine algorithm was utilized to identify these conditions.Comparison of several models under different working conditions was carried out.The regression prediction model characterized by high precision and robust stability was selected.The model was integrated into the comprehensive multi-model framework.The precision,reliability and credibility of the model were validated through actual production data,yielding an impressive accuracy of 94.57%and a minimal absolute error of 0.13 in FeO content prediction.The real-time prediction of FeO content provided excellent guidance for on-site sinter production.

A modeling system for drinking water sources and its application to Jiangdong Reservoir in Xiamen city

Drinking water sources are highly valued by authorities for safeguarding the life of a city. Models are widely applied as important and effective tools in the management of water sources. However, it is difficult to apply models in water source management because water managers are often not equipped with the professional knowledge and operational skills necessary for making use of the models. This paper introduces a drinking water source simulation and prediction system that consists of a watershed model, a hydrological model and a water quality model. This system provides methods and technical guidance for the conventional management of water sources and emergency water event response. In this study, the sub-models of the system were developed based on the data of the Jiangdong Reservoir in Xiamen, and the model validation was based on local monitoring data. The hydrological model and water quality model were integrated by computer programming, and the watershed model was indirectly integrated into the system through a network platform. Furthermore, three applications for Jiangdong Reservoir water protection utilizing the system were introduced in this paper, including a conventional simulation, an emergency simulation, and an emergency measures evaluation.