The future of any business from banking,e-commerce,real estate,homeland security,healthcare,marketing,the stock market,manufacturing,education,retail to government organizations depends on the data and analytics capab...The future of any business from banking,e-commerce,real estate,homeland security,healthcare,marketing,the stock market,manufacturing,education,retail to government organizations depends on the data and analytics capabilities that are built and scaled.The speed of change in technology in recent years has been a real challenge for all businesses.To manage that,a significant number of organizations are exploring the Big Data(BD)infrastructure that helps them to take advantage of new opportunities while saving costs.Timely transformation of information is also critical for the survivability of an organization.Having the right information at the right time will enhance not only the knowledge of stakeholders within an organization but also providing them with a tool to make the right decision at the right moment.It is no longer enough to rely on a sampling of information about the organizations'customers.The decision-makers need to get vital insights into the customers'actual behavior,which requires enormous volumes of data to be processed.We believe that Big Data infrastructure is the key to successful Artificial Intelligence(AI)deployments and accurate,unbiased real-time insights.Big data solutions have a direct impact and changing the way the organization needs to work with help from AI and its components ML and DL.In this article,we discuss these topics.展开更多
This paper is the second in a two-part series that discusses the principal axes of M-DOF structures subjected to static and dynamic loads.The primary purpose of this series is to understand the magnitude of the dynami...This paper is the second in a two-part series that discusses the principal axes of M-DOF structures subjected to static and dynamic loads.The primary purpose of this series is to understand the magnitude of the dynamie response of structures to enable better design of structures and response modification devices/systems.Under idealized design condi- tions,the structural responses are obtained by using single directinn input ground motions in the direction of the intended response modification devices/systems,and by assuming that the responses of the structure is deconpleable in three mutual- ly perpendicular directions.This standard practice has been applied to both new and retrofitted structures using various seis- mic protective systems.Very limited information is available on the effects of neglecting the impact of directional couplings (cross effects of which torsion is a component)of the dynamic response of structures.In order to quantify such effects,it is necessary to examine the principal axes of structures under both static and dynamic loading.In this twn-part series,the first paper is concerned with static loading,which provides definitions and fundamental formulations,with the conclusion that cross effects of a statically loaded M-DOF structure resulting from the lack of principal axes are of insignificant magnitude. However,under dynamic or earthquake loading,a relatively small amount of energy transferred across perpendicular direc- tions is accumulated,which may result in significant enlargement of the structural response.This paper deals with a formu- lation to define the principal axes of M-DOF structures under dynamic loading and develops quantitative measures to identify cross effects resuhing from the non-existence of principal axes.展开更多
In 2015,the U.S National Institute of Standards and Technology(NIST)funded the Center of Excellence for Risk-Based Community Resilience Planning(CoE),a fourteen university-based consortium of almost 100 col-laborators...In 2015,the U.S National Institute of Standards and Technology(NIST)funded the Center of Excellence for Risk-Based Community Resilience Planning(CoE),a fourteen university-based consortium of almost 100 col-laborators,including faculty,students,post-doctoral scholars,and NIST researchers.This paper highlights the scientific theory behind the state-of-the-art cloud platform being developed by the CoE-the Interdisciplinary Networked Community Resilience Modeling Environment(IN-CORE).IN-CORE enables communities,consul-tants,and researchers to set up complex interdependent models of an entire community consisting of people,businesses,social institutions,buildings,transportation networks,water networks,and electric power networks and to predict their performance and recovery to hazard scenario events,including uncertainty propagation through the chained models.The modeling environment includes a detailed building inventory,hazard scenario models,building and infrastructure damage(fragility)and recovery functions,social science data-driven house-hold and business models,and computable general equilibrium(CGE)models of local economies.An important aspect of IN-CORE is the characterization of uncertainty and its propagation throughout the chained models of the platform.Three illustrative examples of community testbeds are presented that look at hazard impacts and recovery on population,economics,physical services,and social services.An overview of the IN-CORE technology and scientific implementation is described with a focus on four key community stability areas(CSA)that encompass an array of community resilience metrics(CRM)and support community resilience informed decision-making.Each testbed within IN-CORE has been developed by a team of engineers,social scientists,urban planners,and economists.Community models,begin with a community description,i.e.,people,businesses,buildings,infras-tructure,and progresses to the damage and loss of functions caused by a hazard scenario,i.e.,a flood,tornado,hurricane,or earthquake.This process is accomplished through chaining of modular algorithms,as described.The baseline community characteristics and the hazard-induced damage sets are the initial conditions for the recovery models,which have been the least studied area of community resilience but arguably one of the most important.Communities can then test the effect of mitigation and/or policies and compare the effects of“what if”scenarios on physical,social,and economic metrics with the only requirement being that the change much be able to be numerically modeled in IN-CORE.展开更多
文摘The future of any business from banking,e-commerce,real estate,homeland security,healthcare,marketing,the stock market,manufacturing,education,retail to government organizations depends on the data and analytics capabilities that are built and scaled.The speed of change in technology in recent years has been a real challenge for all businesses.To manage that,a significant number of organizations are exploring the Big Data(BD)infrastructure that helps them to take advantage of new opportunities while saving costs.Timely transformation of information is also critical for the survivability of an organization.Having the right information at the right time will enhance not only the knowledge of stakeholders within an organization but also providing them with a tool to make the right decision at the right moment.It is no longer enough to rely on a sampling of information about the organizations'customers.The decision-makers need to get vital insights into the customers'actual behavior,which requires enormous volumes of data to be processed.We believe that Big Data infrastructure is the key to successful Artificial Intelligence(AI)deployments and accurate,unbiased real-time insights.Big data solutions have a direct impact and changing the way the organization needs to work with help from AI and its components ML and DL.In this article,we discuss these topics.
基金a contract from the Federal Highway Adiministration(Contract No.ETFH61-98-C-00094)a Grant from the Earthquake Education Research Centers Program of the National Science Foundation to the Multidisciplinary Center for Earthquake Engineering Research(Grant No.EEC-9701471)
文摘This paper is the second in a two-part series that discusses the principal axes of M-DOF structures subjected to static and dynamic loads.The primary purpose of this series is to understand the magnitude of the dynamie response of structures to enable better design of structures and response modification devices/systems.Under idealized design condi- tions,the structural responses are obtained by using single directinn input ground motions in the direction of the intended response modification devices/systems,and by assuming that the responses of the structure is deconpleable in three mutual- ly perpendicular directions.This standard practice has been applied to both new and retrofitted structures using various seis- mic protective systems.Very limited information is available on the effects of neglecting the impact of directional couplings (cross effects of which torsion is a component)of the dynamic response of structures.In order to quantify such effects,it is necessary to examine the principal axes of structures under both static and dynamic loading.In this twn-part series,the first paper is concerned with static loading,which provides definitions and fundamental formulations,with the conclusion that cross effects of a statically loaded M-DOF structure resulting from the lack of principal axes are of insignificant magnitude. However,under dynamic or earthquake loading,a relatively small amount of energy transferred across perpendicular direc- tions is accumulated,which may result in significant enlargement of the structural response.This paper deals with a formu- lation to define the principal axes of M-DOF structures under dynamic loading and develops quantitative measures to identify cross effects resuhing from the non-existence of principal axes.
基金The Center for Risk-Based Community Resilience Planning is a NIST-funded Center of Excellencethe Center is funded through a cooperative agreement between the U.S.National Institute of Standards and Tech-nology and Colorado State University(NIST Financial Assistance Award Numbers:70NANB15H044 and 70NANB20H008)。
文摘In 2015,the U.S National Institute of Standards and Technology(NIST)funded the Center of Excellence for Risk-Based Community Resilience Planning(CoE),a fourteen university-based consortium of almost 100 col-laborators,including faculty,students,post-doctoral scholars,and NIST researchers.This paper highlights the scientific theory behind the state-of-the-art cloud platform being developed by the CoE-the Interdisciplinary Networked Community Resilience Modeling Environment(IN-CORE).IN-CORE enables communities,consul-tants,and researchers to set up complex interdependent models of an entire community consisting of people,businesses,social institutions,buildings,transportation networks,water networks,and electric power networks and to predict their performance and recovery to hazard scenario events,including uncertainty propagation through the chained models.The modeling environment includes a detailed building inventory,hazard scenario models,building and infrastructure damage(fragility)and recovery functions,social science data-driven house-hold and business models,and computable general equilibrium(CGE)models of local economies.An important aspect of IN-CORE is the characterization of uncertainty and its propagation throughout the chained models of the platform.Three illustrative examples of community testbeds are presented that look at hazard impacts and recovery on population,economics,physical services,and social services.An overview of the IN-CORE technology and scientific implementation is described with a focus on four key community stability areas(CSA)that encompass an array of community resilience metrics(CRM)and support community resilience informed decision-making.Each testbed within IN-CORE has been developed by a team of engineers,social scientists,urban planners,and economists.Community models,begin with a community description,i.e.,people,businesses,buildings,infras-tructure,and progresses to the damage and loss of functions caused by a hazard scenario,i.e.,a flood,tornado,hurricane,or earthquake.This process is accomplished through chaining of modular algorithms,as described.The baseline community characteristics and the hazard-induced damage sets are the initial conditions for the recovery models,which have been the least studied area of community resilience but arguably one of the most important.Communities can then test the effect of mitigation and/or policies and compare the effects of“what if”scenarios on physical,social,and economic metrics with the only requirement being that the change much be able to be numerically modeled in IN-CORE.
