As far as the minimal spanning tree problem for the digraph with asymmetric weightsis concerned, an explicit integer programming model is proposed, which could be solved successfullyusing the integer programming packa...As far as the minimal spanning tree problem for the digraph with asymmetric weightsis concerned, an explicit integer programming model is proposed, which could be solved successfullyusing the integer programming packages such as LINDO, and furthermore this model is extendedinto the stochastic version, that is, the minimal spanning tree problem for the digraph with theweights is not constant but random variables. Several algorithms are also developed to solve themodels. Finally, a numerical demonstration is given.展开更多
The comparison of networks with different orders strongly depends on the stability analysis of graph features in evolving systems. In this paper, we rigorously investigate the stability of the weighted spectral distri...The comparison of networks with different orders strongly depends on the stability analysis of graph features in evolving systems. In this paper, we rigorously investigate the stability of the weighted spectral distribution（i.e., a spectral graph feature） as the network order increases. First, we use deterministic scale-free networks generated by a pseudo treelike model to derive the precise formula of the spectral feature, and then analyze the stability of the spectral feature based on the precise formula. Except for the scale-free feature, the pseudo tree-like model exhibits the hierarchical and small-world structures of complex networks. The stability analysis is useful for the classification of networks with different orders and the similarity analysis of networks that may belong to the same evolving system.展开更多
针对基本的快速搜索随机树(rapidly-exploring random tree,RRT)算法用于路径规划时存在的树扩展无导向性、密集障碍物区域规划效率低、局部区域节点聚集等问题,提出一种新的RRT改进算法。该算法采用增强的目标偏向策略,并引入可变的权...针对基本的快速搜索随机树(rapidly-exploring random tree,RRT)算法用于路径规划时存在的树扩展无导向性、密集障碍物区域规划效率低、局部区域节点聚集等问题,提出一种新的RRT改进算法。该算法采用增强的目标偏向策略,并引入可变的权值系数,提高随机树扩展的导向性和灵活性;同时采用局部节点过滤机制,过滤局部区域内聚集的节点;最后,使用节点直连策略对初始路径进行优化处理。仿真实验的结果表明,改进的RRT算法规划路径的速度更快且生成的路径质量更高,充分证明了改进算法的有效可行性。展开更多
目的分析低出生体重儿(low-birth-weight infant,LBWI)的影响因素,并将决策树算法和logistic回归模型相结合,提高影响因素分析的准确性。方法本研究将2019年5月—2021年12月于广西壮族自治区妇幼保健院构建的出生队列中活产单胎12686例...目的分析低出生体重儿(low-birth-weight infant,LBWI)的影响因素,并将决策树算法和logistic回归模型相结合,提高影响因素分析的准确性。方法本研究将2019年5月—2021年12月于广西壮族自治区妇幼保健院构建的出生队列中活产单胎12686例孕妇作为研究对象,收集人口统计学资料、孕期检查资料、新生儿相关指标等,采用单因素分析、CART决策树算法和logistic回归模型进行建模,分析LBWI的影响因素。结果孕妇平均年龄为(31.69±4.48)岁,平均分娩孕周为(38.64±1.59)周。LBWI有777例,占比6.1%,正常出生体重儿11909例,占比93.9%。决策树模型显示影响LBWI的前3个最重要因素依次是孕周、孕期增重、孕前体重指数(body mass index,BMI),模型灵敏度为80.9%,特异度为96.5%。logistic回归显示孕周小于37周(OR=35.215),孕期增重不足(OR=1.974),孕前BMI过低(OR=1.460),妊娠期高血压(OR=2.025)是低体重发生的危险因素,灵敏度为66.5%,特异度为94.9%。两种模型的结果指标和展现形式存在差异,决策树算法能够提供更直观、可解释的结果,而logistic回归模型能够更好地分析变量之间的关系。结论孕周、孕前BMI、孕期增重、产次、妊娠期高血压、户籍、新生儿性别均可对新生儿体重产生影响。决策树算法和logistic回归模型相结合的方法,可以提高影响因素分析的准确性和可解释性,对于临床保健和公共卫生部门决策具有一定的指导意义。展开更多
文摘As far as the minimal spanning tree problem for the digraph with asymmetric weightsis concerned, an explicit integer programming model is proposed, which could be solved successfullyusing the integer programming packages such as LINDO, and furthermore this model is extendedinto the stochastic version, that is, the minimal spanning tree problem for the digraph with theweights is not constant but random variables. Several algorithms are also developed to solve themodels. Finally, a numerical demonstration is given.
基金Project supported by the National Natural Science Foundation of China(Grant Nos.61402485,61303061,and 71201169)
文摘The comparison of networks with different orders strongly depends on the stability analysis of graph features in evolving systems. In this paper, we rigorously investigate the stability of the weighted spectral distribution（i.e., a spectral graph feature） as the network order increases. First, we use deterministic scale-free networks generated by a pseudo treelike model to derive the precise formula of the spectral feature, and then analyze the stability of the spectral feature based on the precise formula. Except for the scale-free feature, the pseudo tree-like model exhibits the hierarchical and small-world structures of complex networks. The stability analysis is useful for the classification of networks with different orders and the similarity analysis of networks that may belong to the same evolving system.
文摘针对基本的快速搜索随机树(rapidly-exploring random tree,RRT)算法用于路径规划时存在的树扩展无导向性、密集障碍物区域规划效率低、局部区域节点聚集等问题,提出一种新的RRT改进算法。该算法采用增强的目标偏向策略,并引入可变的权值系数,提高随机树扩展的导向性和灵活性;同时采用局部节点过滤机制,过滤局部区域内聚集的节点;最后,使用节点直连策略对初始路径进行优化处理。仿真实验的结果表明,改进的RRT算法规划路径的速度更快且生成的路径质量更高,充分证明了改进算法的有效可行性。
文摘目的分析低出生体重儿(low-birth-weight infant,LBWI)的影响因素,并将决策树算法和logistic回归模型相结合,提高影响因素分析的准确性。方法本研究将2019年5月—2021年12月于广西壮族自治区妇幼保健院构建的出生队列中活产单胎12686例孕妇作为研究对象,收集人口统计学资料、孕期检查资料、新生儿相关指标等,采用单因素分析、CART决策树算法和logistic回归模型进行建模,分析LBWI的影响因素。结果孕妇平均年龄为(31.69±4.48)岁,平均分娩孕周为(38.64±1.59)周。LBWI有777例,占比6.1%,正常出生体重儿11909例,占比93.9%。决策树模型显示影响LBWI的前3个最重要因素依次是孕周、孕期增重、孕前体重指数(body mass index,BMI),模型灵敏度为80.9%,特异度为96.5%。logistic回归显示孕周小于37周(OR=35.215),孕期增重不足(OR=1.974),孕前BMI过低(OR=1.460),妊娠期高血压(OR=2.025)是低体重发生的危险因素,灵敏度为66.5%,特异度为94.9%。两种模型的结果指标和展现形式存在差异,决策树算法能够提供更直观、可解释的结果,而logistic回归模型能够更好地分析变量之间的关系。结论孕周、孕前BMI、孕期增重、产次、妊娠期高血压、户籍、新生儿性别均可对新生儿体重产生影响。决策树算法和logistic回归模型相结合的方法,可以提高影响因素分析的准确性和可解释性,对于临床保健和公共卫生部门决策具有一定的指导意义。
