The spatial scale(?shing grid) of ?sheries research af fects the observed spatial patterns of?sheries resources such as catch-per-unit-ef fort(CPUE) and ?shing ef fort. We examined the scale impact of high value(HH) c...The spatial scale(?shing grid) of ?sheries research af fects the observed spatial patterns of?sheries resources such as catch-per-unit-ef fort(CPUE) and ?shing ef fort. We examined the scale impact of high value(HH) clusters of the annual ?shing ef fort for Dosidicus gigas of fshore Peru from 2009 to 2012.For a multi-scale analysis, the original commercial ?shery data were tessellated to twelve spatial scales from 6′ to 72′ with an interval of 6′. Under these spatial scales, D. gigas clusters were identi?ed using the Anselin Local Moran's I. Statistics including the number of points, mean CPUE, standard deviation(SD),skewness, kurtosis, area and centroid were calculated for these HH clusters. We found that the z-score of global Moran's I and the number of points for HH clusters follow a power law scaling relationship from2009 to 2012. The mean ef fort and its SD also follow a power law scaling relationship from 2009 to 2012.The skewness follows a linear scaling relationship in 2010 and 2011 but ?uctuates with spatial scale in2009 and 2012; kurtosis follows a logarithmic scale relationship in 2009, 2011 and 2012 but a linear scale relationship in 2010. Cluster area follows a power law scaling relationship in 2010 and 2012, a linear scaling relationship in 2009, and a quadratic scaling relationship in 2011. Based on the peaks of Moran's I indices and the multi-scale analysis, we conclude that the optimum scales are 12′ in 2009 ? 2011 and 6′ in 2012, while the coarsest allowable scales are 48′ in 2009, 2010 and 2012, and 60′ in 2011. Our research provides the best spatial scales for conducting spatial analysis of this pelagic species, and provides a better understanding of scaling behavior for the ?shing ef fort of D. gigas in the of fshore Peruvian waters.展开更多
Density-based algorithm for discovering clusters in large spatial databases with noise(DBSCAN) is a classic kind of density-based spatial clustering algorithm and is widely applied in several aspects due to good perfo...Density-based algorithm for discovering clusters in large spatial databases with noise(DBSCAN) is a classic kind of density-based spatial clustering algorithm and is widely applied in several aspects due to good performance in capturing arbitrary shapes and detecting outliers. However, in practice, datasets are always too massive to fit the serial DBSCAN. And a new parallel algorithm-Parallel DBSCAN(PDBSCAN) was proposed to solve the problem which DBSCAN faced. The proposed parallel algorithm bases on MapReduce mechanism. The usage of parallel mechanism in the algorithm focuses on region query and candidate queue processing which needed substantive computation resources. As a result, PDBSCAN is scalable for large-scale dataset clustering and is extremely suitable for applications in E-Commence, especially for recommendation.展开更多
It is a challenging topic to develop an efficient algorithm for large scale classification problems in many applications of machine learning. In this paper, a hierarchical clustering and fixed- layer local learning （...It is a challenging topic to develop an efficient algorithm for large scale classification problems in many applications of machine learning. In this paper, a hierarchical clustering and fixed- layer local learning （HCFLL） based support vector machine（SVM） algorithm is proposed to deal with this problem. Firstly, HCFLL hierarchically dusters a given dataset into a modified clustering feature tree based on the ideas of unsupervised clustering and supervised clustering. Then it locally trains SVM on each labeled subtree at a fixed-layer of the tree. The experimental results show that compared with the existing popular algorithms such as core vector machine and decision.tree support vector machine, HCFLL can significantly improve the training and testing speeds with comparable testing accuracy.展开更多
Based on the scope economic theory of "resource curse" and industrial clusters,the scale of sugar cluster is calculated by the output of sugarcane and sugar while the scale benefit of sugar cluster is measur...Based on the scope economic theory of "resource curse" and industrial clusters,the scale of sugar cluster is calculated by the output of sugarcane and sugar while the scale benefit of sugar cluster is measured by the productivity(rate of sugar production),sales output ratio,industrial output value as well as profit margin.Positive analysis of the scale merit of sugar clusters in resource-rich area of southwestern Guangxi is conducted according to related statistics of Chongzuo City.And the primary problems of sugar clusters are pointed out.The profit created by sugar for the sugar industry in Chongzuo City has already been near capacity.The sugar industry is big but not strong.With much governmental interfernce,there is no effective connections and inadequte competitive forces among subjects of the clusters.The extention of industrial chain is limited.Therefore,measures for developing sugar clusters in resource-rich area of southwestern Guangxi are proposed.Industrial structure is to be adjusted to improve the sugarcane cultivation techniques.The industrial chain should be extended to increase the economic benefits of sugar industry.Industrial support is to be strengthened and capital output for sicence and technology increased.Price regualtion fund of grain sugar is to be established with coordination with the superior region.The transformation from savings to investment should be quickened to evade "resource curse".展开更多
Turbulent motion could be regarded as the superposition of fluctuations with different scales. It's of great theoretical and practical importance to determine the classification of turbulent scales quantitatively ...Turbulent motion could be regarded as the superposition of fluctuations with different scales. It's of great theoretical and practical importance to determine the classification of turbulent scales quantitatively to the better description of vortex motions with different scales, and to the research of the interaction among different sclaes of vortex and the construction of better turbulent models. The mathematical method, which carries out the classification on a certain requirement, is called cluster analysis. In this paper, fuzzy cluster analysis method is used to study the classification of turbulent scales quantitatively in smooth and rough wall boundary conditions. Furthermore, the properties and interactions among all kinds of flow structures are also studied. The results are helpful to gain some insight into the properties and interactions of all kinds of turbulent scales in wall turbulent shear flow.展开更多
In modern society, dense crowd detection technology is particularly important due to the frequent occurrence of crowd scenes such as stations, shopping malls, and event sites, which are often accompanied by safety ris...In modern society, dense crowd detection technology is particularly important due to the frequent occurrence of crowd scenes such as stations, shopping malls, and event sites, which are often accompanied by safety risks, like stampede accidents. Although many studies have made progress in estimating population density, the ability to accurately identify dense areas in multi-scale scenarios still needs to be improved. To solve this problem, this paper proposed an improved multi-scale dense crowd detection method based on YOLOv5 and improved the DBSCAN clustering algorithm to identify densely crowded areas. Experiments show that the improved multi-scale dense crowd detection method can identify target crowds at multiple scales, and the accuracy of its detection results is around 70%. In addition, by calculating the crowd density under the same scale conditions and visualising the dense areas, we were able to solve the problem of dividing the crowded areas and visualise the dense areas more accurately. These improvements enhanced the applicability and reliability of the model in practical applications and provided strong technical support for security monitoring and management.展开更多
目的编制主动脉夹层术后患者症状群评估量表并进行信效度检验,为相关患者提供客观有效的评估工具。方法以症状管理模型为基础,通过文献回顾、半结构访谈构建条目池,经2轮德尔菲专家函询及预调查形成初稿。2023年9月至2024年7月,以便利...目的编制主动脉夹层术后患者症状群评估量表并进行信效度检验,为相关患者提供客观有效的评估工具。方法以症状管理模型为基础,通过文献回顾、半结构访谈构建条目池,经2轮德尔菲专家函询及预调查形成初稿。2023年9月至2024年7月,以便利抽样法选取在南京市某三级甲等医院心脏外科治疗的主动脉夹层术后患者492例为研究对象进行调查,以检验量表信效度。结果主动脉夹层术后患者症状群评估量表包括5个维度,共27个条目。量表的Cronbach’s α系数为0.940,折半信度为0.826,重测信度为0.917,量表内容效度为0.877。探索性因子分析共提取出5个公因子,累计方差贡献率为71.859%;验证性因子分析显示:卡方自由度比(χ^(2)/df)为2.121,近似误差均方根(root mean square error of approximation,RMSEA)为0.066,非规范拟合指数(Tucker-Lewis index,TLI)为0.923、增量拟合指数(incremental fit index,IFI)为0.931、比较拟合指数(comparative fix index,CFI)为0.931,模型拟合良好。结论主动脉夹层术后患者症状群评估量表具有良好的信效度,可作为主动脉夹层术后患者症状群评估的有效工具。展开更多
基金Supported by the National Natural Science Foundation of China(No.41406146)the Laboratory for Marine Fisheries Science and Food Production Processes at Qingdao National Laboratory for Marine Science and Technology of China(No.2017-1A02)the Shanghai Universities First-class Disciplines Project-Fisheries(A)
文摘The spatial scale(?shing grid) of ?sheries research af fects the observed spatial patterns of?sheries resources such as catch-per-unit-ef fort(CPUE) and ?shing ef fort. We examined the scale impact of high value(HH) clusters of the annual ?shing ef fort for Dosidicus gigas of fshore Peru from 2009 to 2012.For a multi-scale analysis, the original commercial ?shery data were tessellated to twelve spatial scales from 6′ to 72′ with an interval of 6′. Under these spatial scales, D. gigas clusters were identi?ed using the Anselin Local Moran's I. Statistics including the number of points, mean CPUE, standard deviation(SD),skewness, kurtosis, area and centroid were calculated for these HH clusters. We found that the z-score of global Moran's I and the number of points for HH clusters follow a power law scaling relationship from2009 to 2012. The mean ef fort and its SD also follow a power law scaling relationship from 2009 to 2012.The skewness follows a linear scaling relationship in 2010 and 2011 but ?uctuates with spatial scale in2009 and 2012; kurtosis follows a logarithmic scale relationship in 2009, 2011 and 2012 but a linear scale relationship in 2010. Cluster area follows a power law scaling relationship in 2010 and 2012, a linear scaling relationship in 2009, and a quadratic scaling relationship in 2011. Based on the peaks of Moran's I indices and the multi-scale analysis, we conclude that the optimum scales are 12′ in 2009 ? 2011 and 6′ in 2012, while the coarsest allowable scales are 48′ in 2009, 2010 and 2012, and 60′ in 2011. Our research provides the best spatial scales for conducting spatial analysis of this pelagic species, and provides a better understanding of scaling behavior for the ?shing ef fort of D. gigas in the of fshore Peruvian waters.
基金National Natural Science Foundations of China( No. 61070101,No. 60875029,No. 61175048)
文摘Density-based algorithm for discovering clusters in large spatial databases with noise(DBSCAN) is a classic kind of density-based spatial clustering algorithm and is widely applied in several aspects due to good performance in capturing arbitrary shapes and detecting outliers. However, in practice, datasets are always too massive to fit the serial DBSCAN. And a new parallel algorithm-Parallel DBSCAN(PDBSCAN) was proposed to solve the problem which DBSCAN faced. The proposed parallel algorithm bases on MapReduce mechanism. The usage of parallel mechanism in the algorithm focuses on region query and candidate queue processing which needed substantive computation resources. As a result, PDBSCAN is scalable for large-scale dataset clustering and is extremely suitable for applications in E-Commence, especially for recommendation.
基金National Natural Science Foundation of China ( No. 61070033 )Fundamental Research Funds for the Central Universities,China( No. 2012ZM0061)
文摘It is a challenging topic to develop an efficient algorithm for large scale classification problems in many applications of machine learning. In this paper, a hierarchical clustering and fixed- layer local learning （HCFLL） based support vector machine（SVM） algorithm is proposed to deal with this problem. Firstly, HCFLL hierarchically dusters a given dataset into a modified clustering feature tree based on the ideas of unsupervised clustering and supervised clustering. Then it locally trains SVM on each labeled subtree at a fixed-layer of the tree. The experimental results show that compared with the existing popular algorithms such as core vector machine and decision.tree support vector machine, HCFLL can significantly improve the training and testing speeds with comparable testing accuracy.
基金Supported by Project Launched by Guangxi Education Office (201012MS212)Special Project of"Borderland Question Research"launched by Research Center of Humanities and Social Science in Guangxi(XWSKYB2010006)Research Fund of Natural Science of Guangxi Normal University for Nationalities(XYYB2010-006)
文摘Based on the scope economic theory of "resource curse" and industrial clusters,the scale of sugar cluster is calculated by the output of sugarcane and sugar while the scale benefit of sugar cluster is measured by the productivity(rate of sugar production),sales output ratio,industrial output value as well as profit margin.Positive analysis of the scale merit of sugar clusters in resource-rich area of southwestern Guangxi is conducted according to related statistics of Chongzuo City.And the primary problems of sugar clusters are pointed out.The profit created by sugar for the sugar industry in Chongzuo City has already been near capacity.The sugar industry is big but not strong.With much governmental interfernce,there is no effective connections and inadequte competitive forces among subjects of the clusters.The extention of industrial chain is limited.Therefore,measures for developing sugar clusters in resource-rich area of southwestern Guangxi are proposed.Industrial structure is to be adjusted to improve the sugarcane cultivation techniques.The industrial chain should be extended to increase the economic benefits of sugar industry.Industrial support is to be strengthened and capital output for sicence and technology increased.Price regualtion fund of grain sugar is to be established with coordination with the superior region.The transformation from savings to investment should be quickened to evade "resource curse".
文摘Turbulent motion could be regarded as the superposition of fluctuations with different scales. It's of great theoretical and practical importance to determine the classification of turbulent scales quantitatively to the better description of vortex motions with different scales, and to the research of the interaction among different sclaes of vortex and the construction of better turbulent models. The mathematical method, which carries out the classification on a certain requirement, is called cluster analysis. In this paper, fuzzy cluster analysis method is used to study the classification of turbulent scales quantitatively in smooth and rough wall boundary conditions. Furthermore, the properties and interactions among all kinds of flow structures are also studied. The results are helpful to gain some insight into the properties and interactions of all kinds of turbulent scales in wall turbulent shear flow.
文摘随着算力网络中计算资源与虚拟化设备的广泛应用,在算力网络虚拟化中,针对云集群弹性伸缩策略基于阈值的响应式触发过程中存在的弹性滞后问题,提出一种基于Transformer的预测式云集群资源弹性伸缩方法(Predictive Cloud Cluster Resource Elastic Scaling Method Based on Transformer,Cloudformer).该方法利用序列分解模块将云集群数据分解为趋势项和季节项,趋势项采用双系数网络分别对输入空间预测的均值和方差进行归一化和反归一化,季节项采用融合傅里叶变换的频域自注意力模型进行预测,并在模型训练过程中使用指数移动平均模型动态调整训练损失的误差范围.实验结果表明,对比最先进的五种预测式弹性伸缩算法,本文所提出的方法在保持较低的模型训练和推理时间下,不同预测窗口单变量与多变量预测均方误差分别降低了10.07%和10.01%.
文摘In modern society, dense crowd detection technology is particularly important due to the frequent occurrence of crowd scenes such as stations, shopping malls, and event sites, which are often accompanied by safety risks, like stampede accidents. Although many studies have made progress in estimating population density, the ability to accurately identify dense areas in multi-scale scenarios still needs to be improved. To solve this problem, this paper proposed an improved multi-scale dense crowd detection method based on YOLOv5 and improved the DBSCAN clustering algorithm to identify densely crowded areas. Experiments show that the improved multi-scale dense crowd detection method can identify target crowds at multiple scales, and the accuracy of its detection results is around 70%. In addition, by calculating the crowd density under the same scale conditions and visualising the dense areas, we were able to solve the problem of dividing the crowded areas and visualise the dense areas more accurately. These improvements enhanced the applicability and reliability of the model in practical applications and provided strong technical support for security monitoring and management.
文摘目的编制主动脉夹层术后患者症状群评估量表并进行信效度检验,为相关患者提供客观有效的评估工具。方法以症状管理模型为基础,通过文献回顾、半结构访谈构建条目池,经2轮德尔菲专家函询及预调查形成初稿。2023年9月至2024年7月,以便利抽样法选取在南京市某三级甲等医院心脏外科治疗的主动脉夹层术后患者492例为研究对象进行调查,以检验量表信效度。结果主动脉夹层术后患者症状群评估量表包括5个维度,共27个条目。量表的Cronbach’s α系数为0.940,折半信度为0.826,重测信度为0.917,量表内容效度为0.877。探索性因子分析共提取出5个公因子,累计方差贡献率为71.859%;验证性因子分析显示:卡方自由度比(χ^(2)/df)为2.121,近似误差均方根(root mean square error of approximation,RMSEA)为0.066,非规范拟合指数(Tucker-Lewis index,TLI)为0.923、增量拟合指数(incremental fit index,IFI)为0.931、比较拟合指数(comparative fix index,CFI)为0.931,模型拟合良好。结论主动脉夹层术后患者症状群评估量表具有良好的信效度,可作为主动脉夹层术后患者症状群评估的有效工具。
