This research describes a quantitative,rapid,and low-cost methodology for debris flow susceptibility evaluation at the basin scale using open-access data and geodatabases.The proposed approach can aid decision makers ...This research describes a quantitative,rapid,and low-cost methodology for debris flow susceptibility evaluation at the basin scale using open-access data and geodatabases.The proposed approach can aid decision makers in land management and territorial planning,by first screening for areas with a higher debris flow susceptibility.Five environmental predisposing factors,namely,bedrock lithology,fracture network,quaternary deposits,slope inclination,and hydrographic network,were selected as independent parameters and their mutual interactions were described and quantified using the Rock Engineering System(RES)methodology.For each parameter,specific indexes were proposed,aiming to provide a final synthetic and representative index of debris flow susceptibility at the basin scale.The methodology was tested in four basins located in the Upper Susa Valley(NW Italian Alps)where debris flow events are the predominant natural hazard.The proposed matrix can represent a useful standardized tool,universally applicable,since it is independent of type and characteristic of the basin.展开更多
The"omics"revolution has transformed the biomedical research landscape by equipping scientists with the ability to interrogate complex biological phenomenon and disease processes at an unprecedented level.Th...The"omics"revolution has transformed the biomedical research landscape by equipping scientists with the ability to interrogate complex biological phenomenon and disease processes at an unprecedented level.The volume of"big"data generated by the different omics studies such as genomics,transcriptomics,proteomics,and metabolomics has led to the concurrent development of computational tools to enable in silico analysis and aid data deconvolution.Considering the intensive resources and high costs required to generate and analyze big data,there has been centralized,collaborative efforts to make the data and analysis tools freely available as"Open Source,"to benefit the wider research community.Pancreatology research studies have contributed to this"big data rush"and have additionally benefitted from utilizing the open source data as evidenced by the increasing number of new research findings and publications that stem from such data.In this review,we briefly introduce the evolution of open source omics data,data types,the"FAIR"guiding principles for data management and reuse,and centralized platforms that enable free and fair data accessibility,availability,and provide tools for omics data analysis.We illustrate,through the case study of our own experience in mining pancreatitis omics data,the power of repurposing open source data to answer translationally relevant questions in pancreas research.展开更多
The selection of suitable models and solutions is a fundamental requirement for con-ducting energy flow analysis in integrated energy systems(IES).However,this task is challenging due to the vast number of existing mo...The selection of suitable models and solutions is a fundamental requirement for con-ducting energy flow analysis in integrated energy systems(IES).However,this task is challenging due to the vast number of existing models and solutions,making it difficult to comprehensively compare scholars'studies with current work.In this paper,we aim to address this issue by presenting a comprehensive overview of mainstream IES models and clarifying their relationships,thereby providing guidance for scholars in selecting appro-priate models.Additionally,we introduce several widely used solvers for solving algebraic and differential equations,along with their detailed implementations in the energy flow analysis of IES.Furthermore,we conduct extensive testing and demonstration of these models and methods in various cases to establish benchmarking datasets.To facilitate reproducibility,verification and comparisons,we provide open‐source access to these datasets,including system data,analysis settings and implementations of the various solvers in the mainstream models.Scholars can utilise the provided datasets to reproduce the results,verify the findings and perform comparative analyses.Moreover,they have the flexibility to customise these settings according to their specific requirements.展开更多
文摘This research describes a quantitative,rapid,and low-cost methodology for debris flow susceptibility evaluation at the basin scale using open-access data and geodatabases.The proposed approach can aid decision makers in land management and territorial planning,by first screening for areas with a higher debris flow susceptibility.Five environmental predisposing factors,namely,bedrock lithology,fracture network,quaternary deposits,slope inclination,and hydrographic network,were selected as independent parameters and their mutual interactions were described and quantified using the Rock Engineering System(RES)methodology.For each parameter,specific indexes were proposed,aiming to provide a final synthetic and representative index of debris flow susceptibility at the basin scale.The methodology was tested in four basins located in the Upper Susa Valley(NW Italian Alps)where debris flow events are the predominant natural hazard.The proposed matrix can represent a useful standardized tool,universally applicable,since it is independent of type and characteristic of the basin.
基金supported by the Stanford Diabetes Research Center(no.P30DK116074)and mentored by SPARK Translational Research Program,Stanford University.
文摘The"omics"revolution has transformed the biomedical research landscape by equipping scientists with the ability to interrogate complex biological phenomenon and disease processes at an unprecedented level.The volume of"big"data generated by the different omics studies such as genomics,transcriptomics,proteomics,and metabolomics has led to the concurrent development of computational tools to enable in silico analysis and aid data deconvolution.Considering the intensive resources and high costs required to generate and analyze big data,there has been centralized,collaborative efforts to make the data and analysis tools freely available as"Open Source,"to benefit the wider research community.Pancreatology research studies have contributed to this"big data rush"and have additionally benefitted from utilizing the open source data as evidenced by the increasing number of new research findings and publications that stem from such data.In this review,we briefly introduce the evolution of open source omics data,data types,the"FAIR"guiding principles for data management and reuse,and centralized platforms that enable free and fair data accessibility,availability,and provide tools for omics data analysis.We illustrate,through the case study of our own experience in mining pancreatitis omics data,the power of repurposing open source data to answer translationally relevant questions in pancreas research.
基金The National Science Fund for Distinguished Young Scholars,Grant/Award Number:52325703IEEE Power and Energy Society Working Group on Test Systems for Economic Analysis。
文摘The selection of suitable models and solutions is a fundamental requirement for con-ducting energy flow analysis in integrated energy systems(IES).However,this task is challenging due to the vast number of existing models and solutions,making it difficult to comprehensively compare scholars'studies with current work.In this paper,we aim to address this issue by presenting a comprehensive overview of mainstream IES models and clarifying their relationships,thereby providing guidance for scholars in selecting appro-priate models.Additionally,we introduce several widely used solvers for solving algebraic and differential equations,along with their detailed implementations in the energy flow analysis of IES.Furthermore,we conduct extensive testing and demonstration of these models and methods in various cases to establish benchmarking datasets.To facilitate reproducibility,verification and comparisons,we provide open‐source access to these datasets,including system data,analysis settings and implementations of the various solvers in the mainstream models.Scholars can utilise the provided datasets to reproduce the results,verify the findings and perform comparative analyses.Moreover,they have the flexibility to customise these settings according to their specific requirements.
