This study modeled the effects of structural and dimensional manipulations on hydrodynamic behavior of a bench vertical current classifier. Computational fluid dynamics （CFD） approach was used as modeling method, an...This study modeled the effects of structural and dimensional manipulations on hydrodynamic behavior of a bench vertical current classifier. Computational fluid dynamics （CFD） approach was used as modeling method, and turbulent intensity and fluid velocity were applied as system responses to predict the over- flow cut size variations. These investigations showed that cut size would decrease by increasing diameter and height of the separation column and cone section depth, due to the decrease of turbulent intensity and fluid velocity. As the size of discharge gate increases, the overflow cut-size would decrease due to freely fluid stream out of the column. The overflow cut-size was significantly increased in downward fed classifier compared to that fed by upward fluid stream. In addition, reforming the shape of angular overflow outlet＇s weir into the curved form prevented stream inside returning and consequently unselec- tire cut-size decreasing.展开更多
The coronavirus disease 2019(COVID-19)continues to have a huge impact on health care and economic systems around the world.The first question to ponder is to understand the flow of COVID-19 in the spatial and temporal...The coronavirus disease 2019(COVID-19)continues to have a huge impact on health care and economic systems around the world.The first question to ponder is to understand the flow of COVID-19 in the spatial and temporal dimensions.We collected 7 Omicron clusters outbreaks in China since the outbreak of COVID-19 as of August 2022,selected outbreak cases from different provinces and cities,and collected variable indicators that affect spillover outcomes,such as distance,migration index,PHSM index,daily reported cases number and so on.First,variables influencing spillover outcome events were assessed and analyzed retrospectively by constructing an infectious disease dynamics model and a classifier model,and secondly,the association between explanatory variables and spillover outcome events was constructed by fitting a logistics function.This study incorporates 7 influencing factors and classifies the spillover risk level into 3 levels.If different outbreak sites could be classified into different levels of spillover,it may reduce the pressure of epidemic prevention in some districts due to the lack of a uniform standard,which might be more conducive to achieving the goal of"dynamic zero".展开更多
The sophistication of cyberattacks penetrating into enterprise networks has called for predictive defense beyond intrusion detection,where different attack strategies can be analyzed and used to anticipate next malici...The sophistication of cyberattacks penetrating into enterprise networks has called for predictive defense beyond intrusion detection,where different attack strategies can be analyzed and used to anticipate next malicious actions,especially the unusual ones.Unfortunately,traditional predictive analytics or machine learning techniques that require training data of known attack strategies are not practical,given the scarcity of representative data and the evolving nature of cyberattacks.This paper describes the design and evaluation of a novel automated system,ASSERT,which continuously synthesizes and separates cyberattack behavior models to enable better prediction of future actions.It takes streaming malicious event evidences as inputs,abstracts them to edge-based behavior aggregates,and associates the edges to attack models,where each represents a unique and collective attack behavior.It follows a dynamic Bayesian-based model generation approach to determine when a new attack behavior is present,and creates new attack models by maximizing a cluster validity index.ASSERT generates empirical attack models by separating evidences and use the generated models to predict unseen future incidents.It continuously evaluates the quality of the model separation and triggers a re-clustering process when needed.Through the use of 2017 National Collegiate Penetration Testing Competition data,this work demonstrates the effectiveness of ASSERT in terms of the quality of the generated empirical models and the predictability of future actions using the models.展开更多
The sophistication of cyberattacks penetrating into enterprise networks has called for predictive defense beyond intrusion detection,where different attack strategies can be analyzed and used to anticipate next malici...The sophistication of cyberattacks penetrating into enterprise networks has called for predictive defense beyond intrusion detection,where different attack strategies can be analyzed and used to anticipate next malicious actions,especially the unusual ones.Unfortunately,traditional predictive analytics or machine learning techniques that require training data of known attack strategies are not practical,given the scarcity of representative data and the evolving nature of cyberattacks.This paper describes the design and evaluation of a novel automated system,ASSERT,which continuously synthesizes and separates cyberattack behavior models to enable better prediction of future actions.It takes streaming malicious event evidences as inputs,abstracts them to edge-based behavior aggregates,and associates the edges to attack models,where each represents a unique and collective attack behavior.It follows a dynamic Bayesian-based model generation approach to determine when a new attack behavior is present,and creates new attack models by maximizing a cluster validity index.ASSERT generates empirical attack models by separating evidences and use the generated models to predict unseen future incidents.It continuously evaluates the quality of the model separation and triggers a re-clustering process when needed.Through the use of 2017 National Collegiate Penetration Testing Competition data,this work demonstrates the effectiveness of ASSERT in terms of the quality of the generated empirical models and the predictability of future actions using the models.展开更多
基金financially supported by INVENTIVE~ Mineral Processing Research Center of Iran
文摘This study modeled the effects of structural and dimensional manipulations on hydrodynamic behavior of a bench vertical current classifier. Computational fluid dynamics （CFD） approach was used as modeling method, and turbulent intensity and fluid velocity were applied as system responses to predict the over- flow cut size variations. These investigations showed that cut size would decrease by increasing diameter and height of the separation column and cone section depth, due to the decrease of turbulent intensity and fluid velocity. As the size of discharge gate increases, the overflow cut-size would decrease due to freely fluid stream out of the column. The overflow cut-size was significantly increased in downward fed classifier compared to that fed by upward fluid stream. In addition, reforming the shape of angular overflow outlet＇s weir into the curved form prevented stream inside returning and consequently unselec- tire cut-size decreasing.
基金supported by National Key Research and Development Program of China(2021YFC2301604)the Fundamental Research Funds for the Central Universities(20720230001).
文摘The coronavirus disease 2019(COVID-19)continues to have a huge impact on health care and economic systems around the world.The first question to ponder is to understand the flow of COVID-19 in the spatial and temporal dimensions.We collected 7 Omicron clusters outbreaks in China since the outbreak of COVID-19 as of August 2022,selected outbreak cases from different provinces and cities,and collected variable indicators that affect spillover outcomes,such as distance,migration index,PHSM index,daily reported cases number and so on.First,variables influencing spillover outcome events were assessed and analyzed retrospectively by constructing an infectious disease dynamics model and a classifier model,and secondly,the association between explanatory variables and spillover outcome events was constructed by fitting a logistics function.This study incorporates 7 influencing factors and classifies the spillover risk level into 3 levels.If different outbreak sites could be classified into different levels of spillover,it may reduce the pressure of epidemic prevention in some districts due to the lack of a uniform standard,which might be more conducive to achieving the goal of"dynamic zero".
基金This research is supported by NSF Award#1526383.
文摘The sophistication of cyberattacks penetrating into enterprise networks has called for predictive defense beyond intrusion detection,where different attack strategies can be analyzed and used to anticipate next malicious actions,especially the unusual ones.Unfortunately,traditional predictive analytics or machine learning techniques that require training data of known attack strategies are not practical,given the scarcity of representative data and the evolving nature of cyberattacks.This paper describes the design and evaluation of a novel automated system,ASSERT,which continuously synthesizes and separates cyberattack behavior models to enable better prediction of future actions.It takes streaming malicious event evidences as inputs,abstracts them to edge-based behavior aggregates,and associates the edges to attack models,where each represents a unique and collective attack behavior.It follows a dynamic Bayesian-based model generation approach to determine when a new attack behavior is present,and creates new attack models by maximizing a cluster validity index.ASSERT generates empirical attack models by separating evidences and use the generated models to predict unseen future incidents.It continuously evaluates the quality of the model separation and triggers a re-clustering process when needed.Through the use of 2017 National Collegiate Penetration Testing Competition data,this work demonstrates the effectiveness of ASSERT in terms of the quality of the generated empirical models and the predictability of future actions using the models.
文摘The sophistication of cyberattacks penetrating into enterprise networks has called for predictive defense beyond intrusion detection,where different attack strategies can be analyzed and used to anticipate next malicious actions,especially the unusual ones.Unfortunately,traditional predictive analytics or machine learning techniques that require training data of known attack strategies are not practical,given the scarcity of representative data and the evolving nature of cyberattacks.This paper describes the design and evaluation of a novel automated system,ASSERT,which continuously synthesizes and separates cyberattack behavior models to enable better prediction of future actions.It takes streaming malicious event evidences as inputs,abstracts them to edge-based behavior aggregates,and associates the edges to attack models,where each represents a unique and collective attack behavior.It follows a dynamic Bayesian-based model generation approach to determine when a new attack behavior is present,and creates new attack models by maximizing a cluster validity index.ASSERT generates empirical attack models by separating evidences and use the generated models to predict unseen future incidents.It continuously evaluates the quality of the model separation and triggers a re-clustering process when needed.Through the use of 2017 National Collegiate Penetration Testing Competition data,this work demonstrates the effectiveness of ASSERT in terms of the quality of the generated empirical models and the predictability of future actions using the models.
