Limited resources are available on the application of wind generation systems interconnected to weak powemetworks. With the need to further interface DG （distributed generation） including WG （wind generation） to w...Limited resources are available on the application of wind generation systems interconnected to weak powemetworks. With the need to further interface DG （distributed generation） including WG （wind generation） to weak networks, it is necessary to establish a means of determining what is the most efficient quantity of WG that can be applied in order to maintain stability in the network. This paper establishes a concept that can be applied to weak networks. The aim is to estimate how much WG can be installed on weak networks as well as establishing characteristic responses to generation loss without and with faulted conditions. The main contribution is a thorough understanding of weak network limitation proved to be the most critical parameter in these calculations.展开更多
A mapping f: X→Y is called weak sequence-covering if whenever {ya} is a sequence in Y converging to y ∈ Y, there exist a subsequence {ynk} and xk∈f^-1(ynk)(k∈N) ,x∈f^-1 (y) such that xk→x. The main results are: ...A mapping f: X→Y is called weak sequence-covering if whenever {ya} is a sequence in Y converging to y ∈ Y, there exist a subsequence {ynk} and xk∈f^-1(ynk)(k∈N) ,x∈f^-1 (y) such that xk→x. The main results are: (1) Y is a sequential, Frechet, strongly Frechet space iff every weak sepuence-covering mapping onto Y is quotient, pseudo-open, countably bi-quotient respectively, (2) weak sequence-covering mapping preserves cs-network and certain k-(cs-)networks, thus some new mapping theorems on k-(cs-)notworks are proved.展开更多
Purpose: Based on the weak tie theory, this paper proposes a series of connection indicators Acof weak tie subnets and weak tie nodes to detect research topics, recognize their connections, and understand their evolut...Purpose: Based on the weak tie theory, this paper proposes a series of connection indicators Acof weak tie subnets and weak tie nodes to detect research topics, recognize their connections, and understand their evolution.Design/methodology/approach: First, keywords are extracted from article titles and preprocessed. Second, high-frequency keywords are selected to generate weak tie co-occurrence networks. By removing the internal lines of clustered sub-topic networks, we focus on the analysis of weak tie subnets’ composition and functions and the weak tie nodes’ roles.Findings: The research topics’ clusters and themes changed yearly; the subnets clustered with technique-related and methodology-related topics have been the core, important subnets for years; while close subnets are highly independent, research topics are generally concentrated and most topics are application-related; the roles and functions of nodes and weak ties are diversified.Research limitations: The parameter values are somewhat inconsistent; the weak tie subnets and nodes are classified based on empirical observations, and the conclusions are not verified or compared to other methods.Practical implications: The research is valuable for detecting important research topics as well as their roles, interrelations, and evolution trends. Originality/value: To contribute to the strength of weak tie theory, the research translates weak and strong ties concepts to co-occurrence strength, and analyzes weak ties’ functions. Also, the research proposes a quantitative method to classify and measure the topics’ clusters and nodes.展开更多
Double network(DN)hydrogels as one kind of tough gels have attracted extensive at-tention for their potential applications in biomedical and load-bearing fields.Herein,we import more functions like shape memory into t...Double network(DN)hydrogels as one kind of tough gels have attracted extensive at-tention for their potential applications in biomedical and load-bearing fields.Herein,we import more functions like shape memory into the conventional tough DN hydro-gel system.We synthesize the PEG-PDAC/P(AAm-co-AAc)DN hydrogels,of which the first network is a well-defined PEG(polyethylene glycol)network loaded with PDAC(poly(acryloyloxyethyltrimethyl ammonium chloride))strands,while the second network is formed by copolymerizing AAm(acrylamide)with AAc(acrylic acid)and cross-linker MBAA(N;N′-methylenebisacrylamide).The PEG-PDAC/P(AAm-co-AAc)DN gels exhibits high mechanical strength.The fracture stress and toughness of the DN gels reach up to 0.9 MPa and 3.8 MJ/m^3,respectively.Compared with the conventional double network hydrogels with neutral polymers as the soft and ductile second network,the PEG-PDAC/P(AAm-co-AAc)DN hydrogels use P(AAm-co-AAc),a weak polyelectrolyte,as the second network.The AAc units serve as the coordination points with Fe^3+ions and physically crosslink the second network,which realizes the shape memory property activated by the reducing ability of ascorbic acid.Our results indicate that the high mechanical strength and shape memory properties,probably the two most important characters related to the potential application of the hydrogels,can be introduced simultaneously into the DN hydrogels if the functional monomer has been integrated into the network of DN hydrogels smartly.展开更多
The prevalence of type 2 diabetes mellitus(T2DM)is increasing rapidly worldwide.Because of the limited success of generic interventions,the focus of the disease study has shifted toward personalized strategies,particu...The prevalence of type 2 diabetes mellitus(T2DM)is increasing rapidly worldwide.Because of the limited success of generic interventions,the focus of the disease study has shifted toward personalized strategies,particularly in the early stages of the disease.Traditional Chinese medicine(TCM)is based on a systems view combined with personalized strategies and has improved our knowledge of personalized diagnostics.From a systems biology perspective,the understanding of personalized diagnostics can be improved to yield a biochemical basis for such strategies;for example,metabolomics can be used in combination with other system-based diagnostic methods such as ultra-weak photon emission(UPE).In this study,we investigated the feasibility of using plasma metabolomics obtained from 44 pre-T2DM subjects to stratify the following TCM-based subtypes:Qi-Yin deficiency,Qi-Yin deficiency with dampness,and Qi-Yin deficiency with stagnation.We studied the relationship between plasma metabolomics and UPE with respect to TCM-based subtyping in order to obtain biochemical information for further interpreting disease subtypes.Principal component analysis of plasma metabolites revealed differences among the TCM-based pre-T2DM subtypes.Relatively high levels of lipids(e.g.,cholesterol esters and triglycerides)were important discriminators of two of the three subtypes and may be associated with a higher risk of cardiovascular disease.Plasma metabolomics data indicate that the lipid profile is an essential component captured by UPE with respect to stratifying subtypes of T2DM.The results suggest that metabolic differences exist among different TCM-based subtypes of pre-T2DM,and profiling plasma metabolites can be used to discriminate among these subtypes.Plasma metabolomics thus provides biochemical insights into system-based UPE measurements.展开更多
It is discussed in this paper the spaces with σ-point-discrete N_0-weak bases. The main results are： （1） A space X has a σ-compact-finite N_0-weak base if and only if X is a k-space with a σ-point-discrete N_0-w...It is discussed in this paper the spaces with σ-point-discrete N_0-weak bases. The main results are： （1） A space X has a σ-compact-finite N_0-weak base if and only if X is a k-space with a σ-point-discrete N_0-weak base; （2） Under （CH）, every separable space with a σ-point-discrete N_0-weak base has a countable N_0-weak base.展开更多
近年来,小样本图异常检测在各个领域中引起了广泛的研究兴趣,其旨在在少量有标记训练节点(支持集)的引导下去检测出大量无标记测试节点(查询集)中的异常行为。然而,现有的小样本图异常检测算法通常假设其可以从具有大量有标记节点的训...近年来,小样本图异常检测在各个领域中引起了广泛的研究兴趣,其旨在在少量有标记训练节点(支持集)的引导下去检测出大量无标记测试节点(查询集)中的异常行为。然而,现有的小样本图异常检测算法通常假设其可以从具有大量有标记节点的训练任务(元训练任务)中学习,从而有效地推广到具有少量标记节点的测试任务(元测试任务),这一假设并不符合真实世界的应用条件。在实际应用中,用于小样本图异常检测训练的元训练任务通常只包含极其有限的有标记节点,其标签占比通常不超过0.1%,甚至更低。由于元训练和元测试任务之间存在的巨大任务差异,现有的小样本图异常检测算法很容易出现模型的过拟合问题。除此之外,现有的小样本图异常检测算法仅利用节点间的一阶邻域(局部结构信息)来学习节点的低维特征嵌入,反而忽略了节点间的长距离依赖关系(全局结构信息),进而导致学习到的低维特征嵌入的不准确性和失真问题。针对上述挑战,本文提出了极其弱监督场景下的小样本图异常检测算法——EWSFSGAD。具体来说,该方法首先提出了一个简单且有效的图神经网络框架——GLN(Global and Local Network),其能够同时有效地利用节点间的全局和局部结构信息,并进一步引入注意力机制实现节点间的信息交互,从而更加有效地学习节点鲁棒的低维特征嵌入;该方法还引入了图对比学习中的自监督重建损失,使得节点原始视图与其增强视图之间低维特征嵌入的互信息尽可能一致,为EWS-FSGAD模型的优化提供更多有效的自监督信息,进而提升模型的泛化性;为了提升模型在真实场景中小样本图异常检测任务的快速适应性,该方法引入跨网络元学习训练机制,从多个辅助网络学习可迁移元知识,为模型提供良好的参数初始化,从而能够通过在仅有很少甚至一个标记节点的目标网络上进行微调并有效泛化。在三个真实世界的数据集(Flickr、PubMed、Yelp)上的大量实验结果表明,本文所提方法的性能明显优于现有的图异常检测算法。特别是在PubMed数据集上,AUC-PR提升了28.8%~35.4%。这些实验结果强有力地证明了在极其有限标记的元训练任务引导下,本文所提方法能够更好地学习到异常节点本质特征,从而提升小样本图异常检测任务的有效性。展开更多
文摘Limited resources are available on the application of wind generation systems interconnected to weak powemetworks. With the need to further interface DG （distributed generation） including WG （wind generation） to weak networks, it is necessary to establish a means of determining what is the most efficient quantity of WG that can be applied in order to maintain stability in the network. This paper establishes a concept that can be applied to weak networks. The aim is to estimate how much WG can be installed on weak networks as well as establishing characteristic responses to generation loss without and with faulted conditions. The main contribution is a thorough understanding of weak network limitation proved to be the most critical parameter in these calculations.
文摘A mapping f: X→Y is called weak sequence-covering if whenever {ya} is a sequence in Y converging to y ∈ Y, there exist a subsequence {ynk} and xk∈f^-1(ynk)(k∈N) ,x∈f^-1 (y) such that xk→x. The main results are: (1) Y is a sequential, Frechet, strongly Frechet space iff every weak sepuence-covering mapping onto Y is quotient, pseudo-open, countably bi-quotient respectively, (2) weak sequence-covering mapping preserves cs-network and certain k-(cs-)networks, thus some new mapping theorems on k-(cs-)notworks are proved.
基金funded by the National Social Science Youth Project “Study on the Interdisciplinary Subject Identification and Prediction” (Grant No.:14CTQ033)
文摘Purpose: Based on the weak tie theory, this paper proposes a series of connection indicators Acof weak tie subnets and weak tie nodes to detect research topics, recognize their connections, and understand their evolution.Design/methodology/approach: First, keywords are extracted from article titles and preprocessed. Second, high-frequency keywords are selected to generate weak tie co-occurrence networks. By removing the internal lines of clustered sub-topic networks, we focus on the analysis of weak tie subnets’ composition and functions and the weak tie nodes’ roles.Findings: The research topics’ clusters and themes changed yearly; the subnets clustered with technique-related and methodology-related topics have been the core, important subnets for years; while close subnets are highly independent, research topics are generally concentrated and most topics are application-related; the roles and functions of nodes and weak ties are diversified.Research limitations: The parameter values are somewhat inconsistent; the weak tie subnets and nodes are classified based on empirical observations, and the conclusions are not verified or compared to other methods.Practical implications: The research is valuable for detecting important research topics as well as their roles, interrelations, and evolution trends. Originality/value: To contribute to the strength of weak tie theory, the research translates weak and strong ties concepts to co-occurrence strength, and analyzes weak ties’ functions. Also, the research proposes a quantitative method to classify and measure the topics’ clusters and nodes.
基金supported by the National Natural Science Foundation of China (No.51273189)the National Science and Technology Major Project of the Ministry of Science and Technology of China (No.2016ZX05016),the National Science and Technology Major Project of the Ministry of Science and Technology of China (No.2016ZX05046)
文摘Double network(DN)hydrogels as one kind of tough gels have attracted extensive at-tention for their potential applications in biomedical and load-bearing fields.Herein,we import more functions like shape memory into the conventional tough DN hydro-gel system.We synthesize the PEG-PDAC/P(AAm-co-AAc)DN hydrogels,of which the first network is a well-defined PEG(polyethylene glycol)network loaded with PDAC(poly(acryloyloxyethyltrimethyl ammonium chloride))strands,while the second network is formed by copolymerizing AAm(acrylamide)with AAc(acrylic acid)and cross-linker MBAA(N;N′-methylenebisacrylamide).The PEG-PDAC/P(AAm-co-AAc)DN gels exhibits high mechanical strength.The fracture stress and toughness of the DN gels reach up to 0.9 MPa and 3.8 MJ/m^3,respectively.Compared with the conventional double network hydrogels with neutral polymers as the soft and ductile second network,the PEG-PDAC/P(AAm-co-AAc)DN hydrogels use P(AAm-co-AAc),a weak polyelectrolyte,as the second network.The AAc units serve as the coordination points with Fe^3+ions and physically crosslink the second network,which realizes the shape memory property activated by the reducing ability of ascorbic acid.Our results indicate that the high mechanical strength and shape memory properties,probably the two most important characters related to the potential application of the hydrogels,can be introduced simultaneously into the DN hydrogels if the functional monomer has been integrated into the network of DN hydrogels smartly.
文摘The prevalence of type 2 diabetes mellitus(T2DM)is increasing rapidly worldwide.Because of the limited success of generic interventions,the focus of the disease study has shifted toward personalized strategies,particularly in the early stages of the disease.Traditional Chinese medicine(TCM)is based on a systems view combined with personalized strategies and has improved our knowledge of personalized diagnostics.From a systems biology perspective,the understanding of personalized diagnostics can be improved to yield a biochemical basis for such strategies;for example,metabolomics can be used in combination with other system-based diagnostic methods such as ultra-weak photon emission(UPE).In this study,we investigated the feasibility of using plasma metabolomics obtained from 44 pre-T2DM subjects to stratify the following TCM-based subtypes:Qi-Yin deficiency,Qi-Yin deficiency with dampness,and Qi-Yin deficiency with stagnation.We studied the relationship between plasma metabolomics and UPE with respect to TCM-based subtyping in order to obtain biochemical information for further interpreting disease subtypes.Principal component analysis of plasma metabolites revealed differences among the TCM-based pre-T2DM subtypes.Relatively high levels of lipids(e.g.,cholesterol esters and triglycerides)were important discriminators of two of the three subtypes and may be associated with a higher risk of cardiovascular disease.Plasma metabolomics data indicate that the lipid profile is an essential component captured by UPE with respect to stratifying subtypes of T2DM.The results suggest that metabolic differences exist among different TCM-based subtypes of pre-T2DM,and profiling plasma metabolites can be used to discriminate among these subtypes.Plasma metabolomics thus provides biochemical insights into system-based UPE measurements.
基金Supported by the National Natural Science Foundation of China (10971185, 11171162, 11201053)China Postdoctoral Science Foundation funded project (20090461093, 201003571)+1 种基金Jiangsu Planned Projects for Teachers Overseas Research FundsTaizhou Teachers College Research Funds
文摘It is discussed in this paper the spaces with σ-point-discrete N_0-weak bases. The main results are： （1） A space X has a σ-compact-finite N_0-weak base if and only if X is a k-space with a σ-point-discrete N_0-weak base; （2） Under （CH）, every separable space with a σ-point-discrete N_0-weak base has a countable N_0-weak base.
文摘近年来,小样本图异常检测在各个领域中引起了广泛的研究兴趣,其旨在在少量有标记训练节点(支持集)的引导下去检测出大量无标记测试节点(查询集)中的异常行为。然而,现有的小样本图异常检测算法通常假设其可以从具有大量有标记节点的训练任务(元训练任务)中学习,从而有效地推广到具有少量标记节点的测试任务(元测试任务),这一假设并不符合真实世界的应用条件。在实际应用中,用于小样本图异常检测训练的元训练任务通常只包含极其有限的有标记节点,其标签占比通常不超过0.1%,甚至更低。由于元训练和元测试任务之间存在的巨大任务差异,现有的小样本图异常检测算法很容易出现模型的过拟合问题。除此之外,现有的小样本图异常检测算法仅利用节点间的一阶邻域(局部结构信息)来学习节点的低维特征嵌入,反而忽略了节点间的长距离依赖关系(全局结构信息),进而导致学习到的低维特征嵌入的不准确性和失真问题。针对上述挑战,本文提出了极其弱监督场景下的小样本图异常检测算法——EWSFSGAD。具体来说,该方法首先提出了一个简单且有效的图神经网络框架——GLN(Global and Local Network),其能够同时有效地利用节点间的全局和局部结构信息,并进一步引入注意力机制实现节点间的信息交互,从而更加有效地学习节点鲁棒的低维特征嵌入;该方法还引入了图对比学习中的自监督重建损失,使得节点原始视图与其增强视图之间低维特征嵌入的互信息尽可能一致,为EWS-FSGAD模型的优化提供更多有效的自监督信息,进而提升模型的泛化性;为了提升模型在真实场景中小样本图异常检测任务的快速适应性,该方法引入跨网络元学习训练机制,从多个辅助网络学习可迁移元知识,为模型提供良好的参数初始化,从而能够通过在仅有很少甚至一个标记节点的目标网络上进行微调并有效泛化。在三个真实世界的数据集(Flickr、PubMed、Yelp)上的大量实验结果表明,本文所提方法的性能明显优于现有的图异常检测算法。特别是在PubMed数据集上,AUC-PR提升了28.8%~35.4%。这些实验结果强有力地证明了在极其有限标记的元训练任务引导下,本文所提方法能够更好地学习到异常节点本质特征,从而提升小样本图异常检测任务的有效性。
