目的:研究影响脑卒中发病的危险因素,构建及应用脑卒中发病概率预测模型,为临床防治脑卒中提供依据。方法:采用病例对照研究法,收集辖区内收治的脑卒中病例162例,以内科同期收治的非脑卒中慢性病住院患者184例为对照,以多因素非条件Logi...目的:研究影响脑卒中发病的危险因素,构建及应用脑卒中发病概率预测模型,为临床防治脑卒中提供依据。方法:采用病例对照研究法,收集辖区内收治的脑卒中病例162例,以内科同期收治的非脑卒中慢性病住院患者184例为对照,以多因素非条件Logi st i c回归模型为基本依据,通过Logi st i c回归建立脑卒中发病概率预测模型,然后采用ROC曲线法对概率预测模型进行评价。本研究倾向评分法将6个因素指标融为一个综合指标-倾向评分。结果:有意义的变量数仅有6个,从变量数来看适合Logi st i c回归模型和倾向评分法,发现影响脑卒中发病的危险因素包括年龄、高血压、CHOL、TG、HDL-C、LDL-C等,预测准确率能达到90.2%。结论:Logi st i c回归模型能较为准确地预测脑卒中发病概率,而倾向评分表能较好地反映出研究对象的发病危险程度、对脑卒中进行危险评分分层,所构建的Excel脑卒中发病概率预测模型,具有较好的推广应用价值。展开更多
In equipment integrated logistics support(ILS), the supply capability of spare parts is a significant factor. There are lots of depots in the traditional support system, which makes too many redundant spare parts and ...In equipment integrated logistics support(ILS), the supply capability of spare parts is a significant factor. There are lots of depots in the traditional support system, which makes too many redundant spare parts and causes high cost of support. Meanwhile,the inconsistency among depots makes it difficult to manage spare parts. With the development of information technology and transportation, the supply network has become more efficient. In order to further improve the efficiency of supply-support work and the availability of the equipment system, building a system of one centralized depot with multiple depots becomes an appropriate way.In this case, location selection of the depots including centralized depots and multiple depots becomes a top priority in the support system. This paper will focus on the location selection problem of centralized depots considering ILS factors. Unlike the common location selection problem, depots in ILS require a higher service level. Therefore, it becomes desperately necessary to take the high requirement of the mission into account while determining location of depots. Based on this, we raise an optimal depot location model. First, the expected transportation cost is calculated.Next, factors in ILS such as response time, availability and fill rate are analyzed for evaluating positions of open depots. Then, an optimization model of depot location is developed with the minimum expected cost of transportation as objective and ILS factors as constraints. Finally, a numerical case is studied to prove the validity of the model by using the genetic algorithm. Results show that depot location obtained by this model can guarantee the effectiveness and capability of ILS well.展开更多
Fuzzy ontologics are efficient tools to handle fuzzy and uncertain knowledge on the semantic web; but there are heterogeneity problems when gaining interoperability among different fuzzy ontologies. This paper uses co...Fuzzy ontologics are efficient tools to handle fuzzy and uncertain knowledge on the semantic web; but there are heterogeneity problems when gaining interoperability among different fuzzy ontologies. This paper uses concept approximation between fuzzy ontologies based on instances to solve the heterogeneity problems. It firstly proposes an instance selection technology based on instance clustering and weighting to unify the fuzzy interpretation of different ontologies and reduce the number of instances to increase the efficiency. Then the paper resolves the problem of computing the approximations of concepts into the problem of computing the least upper approximations of atom concepts. It optimizes the search strategies by extending atom concept sets and defining the least upper bounds of concepts to reduce the searching space of the problem. An efficient algorithm for searching the least upper bounds of concept is given.展开更多
文摘目的:研究影响脑卒中发病的危险因素,构建及应用脑卒中发病概率预测模型,为临床防治脑卒中提供依据。方法:采用病例对照研究法,收集辖区内收治的脑卒中病例162例,以内科同期收治的非脑卒中慢性病住院患者184例为对照,以多因素非条件Logi st i c回归模型为基本依据,通过Logi st i c回归建立脑卒中发病概率预测模型,然后采用ROC曲线法对概率预测模型进行评价。本研究倾向评分法将6个因素指标融为一个综合指标-倾向评分。结果:有意义的变量数仅有6个,从变量数来看适合Logi st i c回归模型和倾向评分法,发现影响脑卒中发病的危险因素包括年龄、高血压、CHOL、TG、HDL-C、LDL-C等,预测准确率能达到90.2%。结论:Logi st i c回归模型能较为准确地预测脑卒中发病概率,而倾向评分表能较好地反映出研究对象的发病危险程度、对脑卒中进行危险评分分层,所构建的Excel脑卒中发病概率预测模型,具有较好的推广应用价值。
基金supported by the Science Challenge Project(TZ2018007)the National Natural Science Foundation of China(71671009+2 种基金 61871013 61573041 61573043)
文摘In equipment integrated logistics support(ILS), the supply capability of spare parts is a significant factor. There are lots of depots in the traditional support system, which makes too many redundant spare parts and causes high cost of support. Meanwhile,the inconsistency among depots makes it difficult to manage spare parts. With the development of information technology and transportation, the supply network has become more efficient. In order to further improve the efficiency of supply-support work and the availability of the equipment system, building a system of one centralized depot with multiple depots becomes an appropriate way.In this case, location selection of the depots including centralized depots and multiple depots becomes a top priority in the support system. This paper will focus on the location selection problem of centralized depots considering ILS factors. Unlike the common location selection problem, depots in ILS require a higher service level. Therefore, it becomes desperately necessary to take the high requirement of the mission into account while determining location of depots. Based on this, we raise an optimal depot location model. First, the expected transportation cost is calculated.Next, factors in ILS such as response time, availability and fill rate are analyzed for evaluating positions of open depots. Then, an optimization model of depot location is developed with the minimum expected cost of transportation as objective and ILS factors as constraints. Finally, a numerical case is studied to prove the validity of the model by using the genetic algorithm. Results show that depot location obtained by this model can guarantee the effectiveness and capability of ILS well.
基金Supported by the National Natural Science Foundation of China(60373066 , 60425206 , 90412003) , National Grand Fundamental Research 973Programof China(2002CB312000) , National Research Foundationfor the DoctoralProgramof Higher Education of China (20020286004)
文摘Fuzzy ontologics are efficient tools to handle fuzzy and uncertain knowledge on the semantic web; but there are heterogeneity problems when gaining interoperability among different fuzzy ontologies. This paper uses concept approximation between fuzzy ontologies based on instances to solve the heterogeneity problems. It firstly proposes an instance selection technology based on instance clustering and weighting to unify the fuzzy interpretation of different ontologies and reduce the number of instances to increase the efficiency. Then the paper resolves the problem of computing the approximations of concepts into the problem of computing the least upper approximations of atom concepts. It optimizes the search strategies by extending atom concept sets and defining the least upper bounds of concepts to reduce the searching space of the problem. An efficient algorithm for searching the least upper bounds of concept is given.
