We study iterative methods for solving a set of sparse non-negative tensor equations （multivariate polynomial systems） arising from data mining applications such as information retrieval by query search and communit...We study iterative methods for solving a set of sparse non-negative tensor equations （multivariate polynomial systems） arising from data mining applications such as information retrieval by query search and community discovery in multi-dimensional networks. By making use of sparse and non-negative tensor structure, we develop Jacobi and Gauss-Seidel methods for solving tensor equations. The multiplication of tensors with vectors are required at each iteration of these iterative methods, the cost per iteration depends on the number of non-zeros in the sparse tensors. We show linear convergence of the Jacobi and Gauss-Seidel methods under suitable conditions, and therefore, the set of sparse non-negative tensor equations can be solved very efficiently. Experimental results on information retrieval by query search and community discovery in multi-dimensional networks are presented to illustrate the application of tensor equations and the effectiveness of the proposed methods.展开更多
A correct assessment of the landslide susceptibility component is extremely useful for the diminution of associated potential risks to local economic development, particularly in regard to land use planning and soil c...A correct assessment of the landslide susceptibility component is extremely useful for the diminution of associated potential risks to local economic development, particularly in regard to land use planning and soil conservation. The purpose of the present study was to compare the usefulness of two methods, i.e., binary logistic regression(BLR) and analytical hierarchy process(AHP), for the assessment of landslide susceptibility over a 130-km^2 area in the Moldavian Plateau(eastern Romania) region, where landslides affect large areas and render them unsuitable for agriculture. A large scale inventory mapping of all types of landslides(covering 13.7% of the total area) was performed using orthophoto images, topographical maps, and field surveys. A geographic information system database was created, comprising the nine potential factors considered as most relevant for the landsliding process. Five factors(altitude, slope angle, slope aspect, surface lithology, and land use) were further selected for analysis through the application of a tolerance test and the stepwise filtering procedure of BLR. For each predictor, a corresponding raster layer was built and a dense grid of equally spaced points was generated, with an approximately equal number of points inside and outside the landslide area, in order to extract the values of the predictors from raster layers. Approximately half of the total number of points was used for model computation, while the other half was used for validation. Analytical hierarchy process was employed to derive factor weights, with several pair-wise comparison matrices being tested for this purpose. The class weights, on a scale of 0 to 1, were taken as normalized landslide densities. A comparison of results achieved through these two approaches showed that BLR was better suited for mapping landslide susceptibility, with 82.8% of the landslide area falling into the high and very high susceptibility classes. The susceptibility class separation using standard deviation was superior to either t展开更多
目的探讨影响腰椎后路融合减压内固定术后住院时间(length of stay,LOS)的危险因素,为临床缩短患者术后LOS提供依据及相应措施。方法回顾分析在2016年5月至2017年5月接受腰椎后路融合减压内固定术的226例患者的临床资料,记录性别、年龄...目的探讨影响腰椎后路融合减压内固定术后住院时间(length of stay,LOS)的危险因素,为临床缩短患者术后LOS提供依据及相应措施。方法回顾分析在2016年5月至2017年5月接受腰椎后路融合减压内固定术的226例患者的临床资料,记录性别、年龄、体重、吸烟史、喝酒史、婚姻状况、职业状况、术前美国麻醉师协会评分(American society of anesthesiologist,ASA)、术前合并病、手术时间、融合节段、术中减压方式、估计失血量、术中补液量、术中输血量、腰室引流管留置时间、引流量、术后输血量及术后显著事件等26个可能对LOS有影响的因素,按LOS≥16d(75th LOS)定义为超长住院,应用多元逐步回归分析上述因素与LOS的关系,探讨其中的原因及相应处理措施。结果正常LOS为15d(6~34d),超长LOS患者共60例(26.5%)。多元逐步回归结果表明年龄(β=0.051,P=0.010)、术中估计失血量(estimated blood loss,EBL)(β=0.002,P<0.001)及减压方式(β=-1.603,P<0.001)与腰椎后路融合减压内固定术后LOS存在显著独立线性关系。结论年龄、术中估计失血量、椎板减压方式是影响LOS独立危险因素,控制失血量及正确选择减压方式可有助于缩短LOS。展开更多
A comprehensive understanding of spatial distribution and clustering patterns of gravels is of great significance for ecological restoration and monitoring.However,traditional methods for studying gravels are low-effi...A comprehensive understanding of spatial distribution and clustering patterns of gravels is of great significance for ecological restoration and monitoring.However,traditional methods for studying gravels are low-efficiency and have many errors.This study researched the spatial distribution and cluster characteristics of gravels based on digital image processing technology combined with a self-organizing map(SOM)and multivariate statistical methods in the grassland of northern Tibetan Plateau.Moreover,the correlation of morphological parameters of gravels between different cluster groups and the environmental factors affecting gravel distribution were analyzed.The results showed that the morphological characteristics of gravels in northern region(cluster C)and southern region(cluster B)of the Tibetan Plateau were similar,with a low gravel coverage,small gravel diameter,and elongated shape.These regions were mainly distributed in high mountainous areas with large topographic relief.The central region(cluster A)has high coverage of gravels with a larger diameter,mainly distributed in high-altitude plains with smaller undulation.Principal component analysis(PCA)results showed that the gravel distribution of cluster A may be mainly affected by vegetation,while those in clusters B and C could be mainly affected by topography,climate,and soil.The study confirmed that the combination of digital image processing technology and SOM could effectively analyzed the spatial distribution characteristics of gravels,providing a new mode for gravel research.展开更多
Re-vegetation plays a fundamental role for erosion control and plant recovery in lands affected by gully erosion. Bioengineered practices facilitate the gullies rehabilitation. Objectives of the research were: 1) Iden...Re-vegetation plays a fundamental role for erosion control and plant recovery in lands affected by gully erosion. Bioengineered practices facilitate the gullies rehabilitation. Objectives of the research were: 1) Identify taxonomically the pioneer vegetation on each gully section; 2) Characterize vegetation distribution preferences and 3) Assess structural/functional traits to recognize erosion control key species. Bioengineering was applied in a watershed belonging to Sierra Madre del Sur, at Oaxaca, Mexico, on eight gullies, with local support and minimal investment. "La Mixteca" is a poor ecological and socio-economic region, comparable to other regions of the world. The Initial Floristic Composition(IFC) inventory is the baseline of the successional process. The transect method was used to determine the colonization of species. Cover abundance of registered species was estimated using the semi-quantitative scale of Braun-Blanquet. This procedure was repeated in five different positions(floor, hillslopes and tops), in the cross section of the gully. Throughcorrespondence analysis and clustering, the distribution of species was analyzed. Adequate responses were obtained in soil retention(quantity) and plant cover(existence and diversity); as measurable indicators of the bioengeneering works efficiency. Occupation of soil by native species from the Tropical Deciduous Forest was favored using live barriers. We detected species guilds with spatial distribution preferences in the gullies cross section. Plant cover characterization includes: native colonizer species, herbaceous, shrubby and trees of the forest community bordering the gully area, with cover abundance and structural/functional traits, useful to protect degraded areas. This spatial occupation process of plants responds to a secondary succession in gullies, where the proposed IFC model is correctly represented through bioengineering. Natural establishment of plants was successful by traits of species such as extensive root system and sexual/vegetative r展开更多
In this study, a multivariate local quadratic polynomial regression(MLQPR) method is proposed to design a model for the sludge volume index(SVI). In MLQPR, a quadratic polynomial regression function is established to ...In this study, a multivariate local quadratic polynomial regression(MLQPR) method is proposed to design a model for the sludge volume index(SVI). In MLQPR, a quadratic polynomial regression function is established to describe the relationship between SVI and the relative variables, and the important terms of the quadratic polynomial regression function are determined by the significant test of the corresponding coefficients. Moreover, a local estimation method is introduced to adjust the weights of the quadratic polynomial regression function to improve the model accuracy. Finally, the proposed method is applied to predict the SVI values in a real wastewater treatment process(WWTP). The experimental results demonstrate that the proposed MLQPR method has faster testing speed and more accurate results than some existing methods.展开更多
文摘We study iterative methods for solving a set of sparse non-negative tensor equations （multivariate polynomial systems） arising from data mining applications such as information retrieval by query search and community discovery in multi-dimensional networks. By making use of sparse and non-negative tensor structure, we develop Jacobi and Gauss-Seidel methods for solving tensor equations. The multiplication of tensors with vectors are required at each iteration of these iterative methods, the cost per iteration depends on the number of non-zeros in the sparse tensors. We show linear convergence of the Jacobi and Gauss-Seidel methods under suitable conditions, and therefore, the set of sparse non-negative tensor equations can be solved very efficiently. Experimental results on information retrieval by query search and community discovery in multi-dimensional networks are presented to illustrate the application of tensor equations and the effectiveness of the proposed methods.
文摘A correct assessment of the landslide susceptibility component is extremely useful for the diminution of associated potential risks to local economic development, particularly in regard to land use planning and soil conservation. The purpose of the present study was to compare the usefulness of two methods, i.e., binary logistic regression(BLR) and analytical hierarchy process(AHP), for the assessment of landslide susceptibility over a 130-km^2 area in the Moldavian Plateau(eastern Romania) region, where landslides affect large areas and render them unsuitable for agriculture. A large scale inventory mapping of all types of landslides(covering 13.7% of the total area) was performed using orthophoto images, topographical maps, and field surveys. A geographic information system database was created, comprising the nine potential factors considered as most relevant for the landsliding process. Five factors(altitude, slope angle, slope aspect, surface lithology, and land use) were further selected for analysis through the application of a tolerance test and the stepwise filtering procedure of BLR. For each predictor, a corresponding raster layer was built and a dense grid of equally spaced points was generated, with an approximately equal number of points inside and outside the landslide area, in order to extract the values of the predictors from raster layers. Approximately half of the total number of points was used for model computation, while the other half was used for validation. Analytical hierarchy process was employed to derive factor weights, with several pair-wise comparison matrices being tested for this purpose. The class weights, on a scale of 0 to 1, were taken as normalized landslide densities. A comparison of results achieved through these two approaches showed that BLR was better suited for mapping landslide susceptibility, with 82.8% of the landslide area falling into the high and very high susceptibility classes. The susceptibility class separation using standard deviation was superior to either t
文摘目的探讨影响腰椎后路融合减压内固定术后住院时间(length of stay,LOS)的危险因素,为临床缩短患者术后LOS提供依据及相应措施。方法回顾分析在2016年5月至2017年5月接受腰椎后路融合减压内固定术的226例患者的临床资料,记录性别、年龄、体重、吸烟史、喝酒史、婚姻状况、职业状况、术前美国麻醉师协会评分(American society of anesthesiologist,ASA)、术前合并病、手术时间、融合节段、术中减压方式、估计失血量、术中补液量、术中输血量、腰室引流管留置时间、引流量、术后输血量及术后显著事件等26个可能对LOS有影响的因素,按LOS≥16d(75th LOS)定义为超长住院,应用多元逐步回归分析上述因素与LOS的关系,探讨其中的原因及相应处理措施。结果正常LOS为15d(6~34d),超长LOS患者共60例(26.5%)。多元逐步回归结果表明年龄(β=0.051,P=0.010)、术中估计失血量(estimated blood loss,EBL)(β=0.002,P<0.001)及减压方式(β=-1.603,P<0.001)与腰椎后路融合减压内固定术后LOS存在显著独立线性关系。结论年龄、术中估计失血量、椎板减压方式是影响LOS独立危险因素,控制失血量及正确选择减压方式可有助于缩短LOS。
基金funded by the National Natural Science Foundation of China(41971226,41871357)the Major Research and Development and Achievement Transformation Projects of Qinghai,China(2022-QY-224)the Strategic Priority Research Program of the Chinese Academy of Sciences(XDA28110502,XDA19030303).
文摘A comprehensive understanding of spatial distribution and clustering patterns of gravels is of great significance for ecological restoration and monitoring.However,traditional methods for studying gravels are low-efficiency and have many errors.This study researched the spatial distribution and cluster characteristics of gravels based on digital image processing technology combined with a self-organizing map(SOM)and multivariate statistical methods in the grassland of northern Tibetan Plateau.Moreover,the correlation of morphological parameters of gravels between different cluster groups and the environmental factors affecting gravel distribution were analyzed.The results showed that the morphological characteristics of gravels in northern region(cluster C)and southern region(cluster B)of the Tibetan Plateau were similar,with a low gravel coverage,small gravel diameter,and elongated shape.These regions were mainly distributed in high mountainous areas with large topographic relief.The central region(cluster A)has high coverage of gravels with a larger diameter,mainly distributed in high-altitude plains with smaller undulation.Principal component analysis(PCA)results showed that the gravel distribution of cluster A may be mainly affected by vegetation,while those in clusters B and C could be mainly affected by topography,climate,and soil.The study confirmed that the combination of digital image processing technology and SOM could effectively analyzed the spatial distribution characteristics of gravels,providing a new mode for gravel research.
基金World Wildlife Fund (WWF) for providing financial support for the conduction of the research through Oaxaca Community Foundationthe National Council for Science and Technology supported the first author through grant for two years
文摘Re-vegetation plays a fundamental role for erosion control and plant recovery in lands affected by gully erosion. Bioengineered practices facilitate the gullies rehabilitation. Objectives of the research were: 1) Identify taxonomically the pioneer vegetation on each gully section; 2) Characterize vegetation distribution preferences and 3) Assess structural/functional traits to recognize erosion control key species. Bioengineering was applied in a watershed belonging to Sierra Madre del Sur, at Oaxaca, Mexico, on eight gullies, with local support and minimal investment. "La Mixteca" is a poor ecological and socio-economic region, comparable to other regions of the world. The Initial Floristic Composition(IFC) inventory is the baseline of the successional process. The transect method was used to determine the colonization of species. Cover abundance of registered species was estimated using the semi-quantitative scale of Braun-Blanquet. This procedure was repeated in five different positions(floor, hillslopes and tops), in the cross section of the gully. Throughcorrespondence analysis and clustering, the distribution of species was analyzed. Adequate responses were obtained in soil retention(quantity) and plant cover(existence and diversity); as measurable indicators of the bioengeneering works efficiency. Occupation of soil by native species from the Tropical Deciduous Forest was favored using live barriers. We detected species guilds with spatial distribution preferences in the gullies cross section. Plant cover characterization includes: native colonizer species, herbaceous, shrubby and trees of the forest community bordering the gully area, with cover abundance and structural/functional traits, useful to protect degraded areas. This spatial occupation process of plants responds to a secondary succession in gullies, where the proposed IFC model is correctly represented through bioengineering. Natural establishment of plants was successful by traits of species such as extensive root system and sexual/vegetative r
文摘In this study, a multivariate local quadratic polynomial regression(MLQPR) method is proposed to design a model for the sludge volume index(SVI). In MLQPR, a quadratic polynomial regression function is established to describe the relationship between SVI and the relative variables, and the important terms of the quadratic polynomial regression function are determined by the significant test of the corresponding coefficients. Moreover, a local estimation method is introduced to adjust the weights of the quadratic polynomial regression function to improve the model accuracy. Finally, the proposed method is applied to predict the SVI values in a real wastewater treatment process(WWTP). The experimental results demonstrate that the proposed MLQPR method has faster testing speed and more accurate results than some existing methods.
