Leaf trait networks(LTNs)visualize the intricate linkages reflecting plant trait-functional coordination.Typical karst vegetation,developed from lithological dolomite and limestone,generally exhibits differential comm...Leaf trait networks(LTNs)visualize the intricate linkages reflecting plant trait-functional coordination.Typical karst vegetation,developed from lithological dolomite and limestone,generally exhibits differential communities,possibly due to habitat rock exposure,soil depth,and soil physicochemical properties variations,leading to a shift from plant trait variation to functional linkages.However,how soil and habitat quality affect the differentiation of leaf trait networks remains unclear.LTNs were constructed for typical dolomite and limestone habitats by analyzing twenty-one woody plant leaf traits across fifty-six forest subplots in karst mountains.The differences between dolomite and limestone LTNs were compared using network parameters.The network association of soil and habitat quality was analyzed using redundancy analysis(RDA),Mantle's test,and a random forest model.The limestone LTN exhibited significantly higher edge density with lower diameter and average path length when compared to the dolomite LTN.It indicates LTN differentiation,with the limestone network displaying a more compact architecture and higher connectivity than the dolomite network.The specific leaf phosphorus and leaf nitrogen contents of dolomite LTN,as well as the leaf mass and leaf carbon contents of limestone LTN,significantly contributed to network degree and closeness,serving as crucial node traits regulating LTN connectedness.Additionally,both habitat LTNs significantly correlated with soil nitrogen and phosphorus,stoichiometric ratios,pH,and organic carbon,as well as soil depth and rock exposure rates,with soil depth and rock exposure showing greater relative importance.Soil depth and rock exposure dominate trait network differentiation,with the limestone habitat exhibiting a more compact network architecture than the dolomite habitat.展开更多
Powerful storage, high performance and scalability are the most important issues for analytical databases. These three factors interact with each other, for example, powerful storage needs less scalability but higher ...Powerful storage, high performance and scalability are the most important issues for analytical databases. These three factors interact with each other, for example, powerful storage needs less scalability but higher performance, high performance means less consumption of indexes and other materializations for storage and fewer processing nodes, larger scale relieves stress on powerful storage and the high performance processing engine. Some analytical databases （ParAccel, Teradata） bind their performance with advanced hardware supports, some （Asterdata, Greenplum） rely on the high scalability framework of MapReduce, some （MonetDB, Sybase IQ, Vertica） highlight performance on processing engine and storage engine. All these approaches can be integrated into an storage-performance-scalability （S- P-S） model, and future large scale analytical processing can be built on moderate clusters to minimize expensive hardware dependency. The most important thing is a simple software framework is fundamental to maintain pace with the development of hardware technologies. In this paper, we propose a schemaaware on-line analytical processing （OLAP） model with deep optimization from native features of the star or snowflake schema. The OLAP model divides the whole process into several stages, each stage pipes its output to the next stage, we minimize the size of output data in each stage, whether in central processing or clustered processing. We extend this mechanism to cluster processing using two major techniques, one is using NetMemory as a broadcasting protocol based dimension mirror synchronizing buffer, the other is predicatevector based DDTA-OLAP cluster model which can minimize the data dependency of star-join using bitmap vectors. Our OLAP model aims to minimize network transmission cost （MINT in short） for OLAP clusters and support a scalable but simple distributed storage model for large scale clustering processing. Finally, the experimental results show the speedup and scalability performance.展开更多
基金supported by the National Natural Science Foundation of China(NSFC:32260268)the Science and Technology Project of Guizhou Province[(2021)General-455]the Guizhou Hundred-level Innovative Talents Project[Qian-ke-he platform talents(2020)6004-2].
文摘Leaf trait networks(LTNs)visualize the intricate linkages reflecting plant trait-functional coordination.Typical karst vegetation,developed from lithological dolomite and limestone,generally exhibits differential communities,possibly due to habitat rock exposure,soil depth,and soil physicochemical properties variations,leading to a shift from plant trait variation to functional linkages.However,how soil and habitat quality affect the differentiation of leaf trait networks remains unclear.LTNs were constructed for typical dolomite and limestone habitats by analyzing twenty-one woody plant leaf traits across fifty-six forest subplots in karst mountains.The differences between dolomite and limestone LTNs were compared using network parameters.The network association of soil and habitat quality was analyzed using redundancy analysis(RDA),Mantle's test,and a random forest model.The limestone LTN exhibited significantly higher edge density with lower diameter and average path length when compared to the dolomite LTN.It indicates LTN differentiation,with the limestone network displaying a more compact architecture and higher connectivity than the dolomite network.The specific leaf phosphorus and leaf nitrogen contents of dolomite LTN,as well as the leaf mass and leaf carbon contents of limestone LTN,significantly contributed to network degree and closeness,serving as crucial node traits regulating LTN connectedness.Additionally,both habitat LTNs significantly correlated with soil nitrogen and phosphorus,stoichiometric ratios,pH,and organic carbon,as well as soil depth and rock exposure rates,with soil depth and rock exposure showing greater relative importance.Soil depth and rock exposure dominate trait network differentiation,with the limestone habitat exhibiting a more compact network architecture than the dolomite habitat.
文摘Powerful storage, high performance and scalability are the most important issues for analytical databases. These three factors interact with each other, for example, powerful storage needs less scalability but higher performance, high performance means less consumption of indexes and other materializations for storage and fewer processing nodes, larger scale relieves stress on powerful storage and the high performance processing engine. Some analytical databases （ParAccel, Teradata） bind their performance with advanced hardware supports, some （Asterdata, Greenplum） rely on the high scalability framework of MapReduce, some （MonetDB, Sybase IQ, Vertica） highlight performance on processing engine and storage engine. All these approaches can be integrated into an storage-performance-scalability （S- P-S） model, and future large scale analytical processing can be built on moderate clusters to minimize expensive hardware dependency. The most important thing is a simple software framework is fundamental to maintain pace with the development of hardware technologies. In this paper, we propose a schemaaware on-line analytical processing （OLAP） model with deep optimization from native features of the star or snowflake schema. The OLAP model divides the whole process into several stages, each stage pipes its output to the next stage, we minimize the size of output data in each stage, whether in central processing or clustered processing. We extend this mechanism to cluster processing using two major techniques, one is using NetMemory as a broadcasting protocol based dimension mirror synchronizing buffer, the other is predicatevector based DDTA-OLAP cluster model which can minimize the data dependency of star-join using bitmap vectors. Our OLAP model aims to minimize network transmission cost （MINT in short） for OLAP clusters and support a scalable but simple distributed storage model for large scale clustering processing. Finally, the experimental results show the speedup and scalability performance.
