This paper sums up four security factors after analyzing co-residency threats caused by the special multitenant environment in the cloud.To secure the factors,a multiway dynamic trust chain transfer model was proposed...This paper sums up four security factors after analyzing co-residency threats caused by the special multitenant environment in the cloud.To secure the factors,a multiway dynamic trust chain transfer model was proposed on the basis of a measurement interactive virtual machine and current behavior to protect the integrity of the system.A trust chain construction module is designed in a virtual machine monitor.Through dynamic monitoring,it achieves the purpose of transferring integrity between virtual machine.A cloud system with a trust authentication function is implemented on the basis of the model,and its practicability is shown.展开更多
Carbon materials especially with hydrogenation have attracted wide attention for their novel physical and chemical properties and broad application prospects.A systematic theoretical simulation method accurately descr...Carbon materials especially with hydrogenation have attracted wide attention for their novel physical and chemical properties and broad application prospects.A systematic theoretical simulation method accurately describing atomic interactions for hydrogen-carbon systems is crucial for the design of carbon-based materials and their industrial applications.Multiphases of hydrogenated carbon materials,from crystal to amorphous,with covalent network and diverse chemical reactions bring huge difficulties to construct a general interatomic potential under various conditions.Here,we demonstrate a transferable active machine learning scheme with separated training of sub-feature spaces and target-oriented finetuning,and construct a general-purpose pre-trained machine learning potential(MLP)for hydrogen-carbon systems.The pre-trained MLP is further efficiently transferred to three target spaces of deposition,friction and fracture with scale reliability.This work provides a robust tool for the theoretical research of hydrogen-carbon systems and a general scheme for developing transferable MLPs in multiphase systems across compositional and conditional complexity.展开更多
We present new parameterizations of the ChIMES physics informed machine-learned interatomic model for simulating carbon under conditions ranging from 300 K and 0 GPa to 10,000 K and 100 GPa,along with a new multi-fide...We present new parameterizations of the ChIMES physics informed machine-learned interatomic model for simulating carbon under conditions ranging from 300 K and 0 GPa to 10,000 K and 100 GPa,along with a new multi-fidelity active learning strategy.The resulting models show significant improvement in accuracy and temperature/pressure transferability relative to the original ChIMES carbon model developed in 2017 and can serve as a foundation for future transfer-learned ChIMES parameter sets.Applications to carbon melting point prediction,shockwave-driven conversion of graphite to diamond,and thermal conversion of nanodiamond to graphitic nanoonion are provided.Ultimately,we find the new models to be robust,accurate,and well-suited for modeling evolution in carbon systems under extreme conditions.展开更多
Wastewater-based epidemiology(WBE)is emerging as an effective tool to provide early warnings of potential disease outbreaks within communities through detecting the presence of pathogens in wastewater before clinical ...Wastewater-based epidemiology(WBE)is emerging as an effective tool to provide early warnings of potential disease outbreaks within communities through detecting the presence of pathogens in wastewater before clinical cases are reported.Nevertheless,quantitative prediction of future clinical case is challenging as uncertainties of dynamic shedding and disease transmission patterns can lead to complex correlation between wastewater viral concentration and clinical cases.Such complexities,augmented by factors such as viral variant,public behavioral change,etc.,make it challenging to develop empirical models or data-driven models to provide accurate prediction of disease case for public health policy makings.To address this gap,this study developed an iterative data-driven framework utilizing Long-Short Time Memory(LSTM)neural networks for multi-timestep real-time predictions of future clinical cases based on WBE.The proposed LSTM model structure integrates both wastewater and historical clinical data as inputs.The prediction framework enables the update of LSTM model as more WBE dataset become available to enhance its adaptability to evolving pandemic stages.This framework was applied for real-time forecasting of COVID-19 clinical cases based on dataset of Ohio Wastewater Monitoring Project from July 2020 to October 2023.The developed iterative LSTM models were proven to achieve excellent performance in making clinical case predictions at different stages of COVID-19 pandemic.Early warning threshold of viral surge was defined by moving percentile method and results showed that the model achieved over 90%accuracy in future clinical case prediction and therefore demonstrated high reliability in pre-warning of potential disease outbreaks.This framework was also found to possess strong transferability across diverse geographic regions.The impacts of social policies and events on model predictions as well as the ramification of this model for future pandemics warning are discussed.展开更多
基金supported by The National Natural Science Foundation for Young Scientists of China under Grant No.61303263the Jiangsu Provincial Research Foundation for Basic Research(Natural Science Foundation)under Grant No.BK20150201+4 种基金the Scientific Research Key Project of Beijing Municipal Commission of Education under Grant No.KZ201210015015Project Supported by the National Natural Science Foundation of China(Grant No.61370140)the Scientific Research Common Program of the Beijing Municipal Commission of Education(Grant No.KMKM201410015006)The National Science Foundation of China under Grant Nos.61232016 and U1405254and the PAPD fund
文摘This paper sums up four security factors after analyzing co-residency threats caused by the special multitenant environment in the cloud.To secure the factors,a multiway dynamic trust chain transfer model was proposed on the basis of a measurement interactive virtual machine and current behavior to protect the integrity of the system.A trust chain construction module is designed in a virtual machine monitor.Through dynamic monitoring,it achieves the purpose of transferring integrity between virtual machine.A cloud system with a trust authentication function is implemented on the basis of the model,and its practicability is shown.
基金supported by the National Natural Science Foundation of China(Grant Nos.52225502,52305200)New Cornerstone Science Foundation through the XPLORER PRIZE,National Key Research and Development Program of China(2024YFB3410201,2018YFB0704300)+1 种基金Scientific and Technological Project of Yunnan Precious Metals Laboratory(YPML-20240502088)Key Research and Development Program of Yunnan Province(202203ZA080001).
文摘Carbon materials especially with hydrogenation have attracted wide attention for their novel physical and chemical properties and broad application prospects.A systematic theoretical simulation method accurately describing atomic interactions for hydrogen-carbon systems is crucial for the design of carbon-based materials and their industrial applications.Multiphases of hydrogenated carbon materials,from crystal to amorphous,with covalent network and diverse chemical reactions bring huge difficulties to construct a general interatomic potential under various conditions.Here,we demonstrate a transferable active machine learning scheme with separated training of sub-feature spaces and target-oriented finetuning,and construct a general-purpose pre-trained machine learning potential(MLP)for hydrogen-carbon systems.The pre-trained MLP is further efficiently transferred to three target spaces of deposition,friction and fracture with scale reliability.This work provides a robust tool for the theoretical research of hydrogen-carbon systems and a general scheme for developing transferable MLPs in multiphase systems across compositional and conditional complexity.
基金supported by LLNL LDRD 23-SI-006.LLNL-JRNL-861515.
文摘We present new parameterizations of the ChIMES physics informed machine-learned interatomic model for simulating carbon under conditions ranging from 300 K and 0 GPa to 10,000 K and 100 GPa,along with a new multi-fidelity active learning strategy.The resulting models show significant improvement in accuracy and temperature/pressure transferability relative to the original ChIMES carbon model developed in 2017 and can serve as a foundation for future transfer-learned ChIMES parameter sets.Applications to carbon melting point prediction,shockwave-driven conversion of graphite to diamond,and thermal conversion of nanodiamond to graphitic nanoonion are provided.Ultimately,we find the new models to be robust,accurate,and well-suited for modeling evolution in carbon systems under extreme conditions.
基金supported by the US National Science Foundation(No.1638320).
文摘Wastewater-based epidemiology(WBE)is emerging as an effective tool to provide early warnings of potential disease outbreaks within communities through detecting the presence of pathogens in wastewater before clinical cases are reported.Nevertheless,quantitative prediction of future clinical case is challenging as uncertainties of dynamic shedding and disease transmission patterns can lead to complex correlation between wastewater viral concentration and clinical cases.Such complexities,augmented by factors such as viral variant,public behavioral change,etc.,make it challenging to develop empirical models or data-driven models to provide accurate prediction of disease case for public health policy makings.To address this gap,this study developed an iterative data-driven framework utilizing Long-Short Time Memory(LSTM)neural networks for multi-timestep real-time predictions of future clinical cases based on WBE.The proposed LSTM model structure integrates both wastewater and historical clinical data as inputs.The prediction framework enables the update of LSTM model as more WBE dataset become available to enhance its adaptability to evolving pandemic stages.This framework was applied for real-time forecasting of COVID-19 clinical cases based on dataset of Ohio Wastewater Monitoring Project from July 2020 to October 2023.The developed iterative LSTM models were proven to achieve excellent performance in making clinical case predictions at different stages of COVID-19 pandemic.Early warning threshold of viral surge was defined by moving percentile method and results showed that the model achieved over 90%accuracy in future clinical case prediction and therefore demonstrated high reliability in pre-warning of potential disease outbreaks.This framework was also found to possess strong transferability across diverse geographic regions.The impacts of social policies and events on model predictions as well as the ramification of this model for future pandemics warning are discussed.
