Predicting NO_(x)in the sintering process of iron ore powder in advance was helpful to adjust the denitrification process in time.Taking NO_(x)in the sintering process of iron ore powder as the object,the boxplot,empi...Predicting NO_(x)in the sintering process of iron ore powder in advance was helpful to adjust the denitrification process in time.Taking NO_(x)in the sintering process of iron ore powder as the object,the boxplot,empirical mode decomposition algorithm,Pearson correlation coefficient,maximum information coefficient and other methods were used to preprocess the sintering data and naive Bayes classification algorithm was used to identify the sintering conditions.The regression prediction model with high accuracy and good stability was selected as the sub-model for different sintering conditions,and the sub-models were combined into an integrated prediction model.Based on actual operational data,the approach proved the superiority and effectiveness of the developed model in predicting NO_(x),yielding an accuracy of 96.17%and an absolute error of 5.56,and thereby providing valuable foresight for on-site sintering operations.展开更多
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 v...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.展开更多
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 pot...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.展开更多
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 a...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.展开更多
基金financially supported by the Natural Science Basic foundation of China(Program No.52174325)the Key Research and Development Program of Shaanxi(Grant No.2020GY-166 and Program No.2020GY-247)the Shaanxi Provincial Innovation Capacity Support Plan(Grant No.2023-CX-TD-53).
文摘Predicting NO_(x)in the sintering process of iron ore powder in advance was helpful to adjust the denitrification process in time.Taking NO_(x)in the sintering process of iron ore powder as the object,the boxplot,empirical mode decomposition algorithm,Pearson correlation coefficient,maximum information coefficient and other methods were used to preprocess the sintering data and naive Bayes classification algorithm was used to identify the sintering conditions.The regression prediction model with high accuracy and good stability was selected as the sub-model for different sintering conditions,and the sub-models were combined into an integrated prediction model.Based on actual operational data,the approach proved the superiority and effectiveness of the developed model in predicting NO_(x),yielding an accuracy of 96.17%and an absolute error of 5.56,and thereby providing valuable foresight for on-site sintering operations.
文摘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.
基金the National Natural Science Foundation of China(52174325)the Key Research and Development Program of Shaanxi(Grant Nos.2020GY-166 and 2020GY-247)the Shaanxi Provincial Innovation Capacity Support Plan(Grant No.2023-CX-TD-53).
文摘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.
文摘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.
