Steel production involves the transfer and transformation of material and energy at different levels, structures, and scales, and this process incurs substantial information in the material and energy dimensions. Give...Steel production involves the transfer and transformation of material and energy at different levels, structures, and scales, and this process incurs substantial information in the material and energy dimensions. Given the black-box feature of iron and steel production processes, process visualization plays an important role and inevitably benefits parameter correction, technical support decision-making, personnel training, and other aspects of the steel metallurgy industry. The technological characteristics of the entire process in the steel industry were analyzed in this study, a visualization technology route based on virtual reality(VR) was built, and the important components of visual simulation system for steel industry and the important technical points needed to realize the system were proposed. On the foundation, a visual simulation model for the process scheduling of the iron and steel enterprise raw materials' field, slab, and hot rolling processes was built, and a visualization simulation platform of the iron and steel metallurgy plant-wide process, including ironmaking, steelmaking, hot rolling, and cold rolling, was developed. Lastly, the effectiveness of platform was illustrated by practical application.展开更多
With the increasing market demand for optical complex surface parts,the application of multi-axis ultraprecision single-point diamond turning is increasing.A tool path generation method is very important to decrease m...With the increasing market demand for optical complex surface parts,the application of multi-axis ultraprecision single-point diamond turning is increasing.A tool path generation method is very important to decrease manufacturing time,enhance surface quality,and reduce cost.Compared with the tool path generation of the traditional multi-axis milling,that of the ultra-precision single-point diamond turning requires higher calculation accuracy and efficiency.This paper reviews the tool path generation of ultra-precision diamond turning,considering several key issues:cutter location(CL)points calculation,the topological form of tool path,interpolation mode,and G code optimization.展开更多
A profound transition of energy system is central to China's"3060"goal since the bulk of anthropogenic emissions arises from energy use.Covering the production,conversion,transmission,distribution and co...A profound transition of energy system is central to China's"3060"goal since the bulk of anthropogenic emissions arises from energy use.Covering the production,conversion,transmission,distribution and consumption of energy,energy system has complex internal structure and close linkages with the surrounding economic and social systems.Energy system transition towards carbon neutrality has presented tremendous challenges in a wide range of aspects,including technology,finance market,business models,governance structure,policy regime,etc.A better understanding of the dynamics and mechanism underlying energy system transition is critical to the transition management of energy systems.展开更多
Disaster relief logistics is a significant element in the management of disaster relief operations.In this paper,the operational decisions of relief logistics are considered in the distribution of resources to the aff...Disaster relief logistics is a significant element in the management of disaster relief operations.In this paper,the operational decisions of relief logistics are considered in the distribution of resources to the affected areas to include scheduling,routing,and allocation decisions.The proposed mathematical model simultaneously captures many aspects relevant to real life to face the challenging situation of disasters.Characteristics such as multiple uses of vehicles and split delivery allow for better use of vehicles as one of the primary resources of disaster response.A multi-period multi-criteria mixed-integer programming model is introduced to evaluate and address these features.The model utilizes a rolling horizon method that provides possibilities to adjust plans as more information becomes available.Three objectives of efficiency,effectiveness,and equity are jointly considered.The augmented epsilon constraint method is applied to solve the model,and a case study is presented to illustrate the potential applicability of our model.Computational results show that the model is capable of generating efficient solutions.展开更多
In this paper,we consider approximation algorithms for optimizing a generic multivariate polynomial function in discrete(typically binary)variables.Such models have natural applications in graph theory,neural networks...In this paper,we consider approximation algorithms for optimizing a generic multivariate polynomial function in discrete(typically binary)variables.Such models have natural applications in graph theory,neural networks,error-correcting codes,among many others.In particular,we focus on three types of optimization models:(1)maximizing a homogeneous polynomial function in binary variables;(2)maximizing a homogeneous polynomial function in binary variables,mixed with variables under spherical constraints;(3)maximizing an inhomogeneous polynomial function in binary variables.We propose polynomial-time randomized approximation algorithms for such polynomial optimizationmodels,and establish the approximation ratios(or relative approximation ratios whenever appropriate)for the proposed algorithms.Some examples of applications for these models and algorithms are discussed as well.展开更多
It is now recognized that nanoscale particulate matter(PM)represents a substantial health hazard for our society,including PM from restaurant smoke.In this study,we explored the use of a transient pulsed plasma in con...It is now recognized that nanoscale particulate matter(PM)represents a substantial health hazard for our society,including PM from restaurant smoke.In this study,we explored the use of a transient pulsed plasma in conjunction with an applied DC bias to treat oil aerosols that closely resemble restaurant(i.e.,charbroiler)smoke emissions.For polyaromatic olefin PAO-4 and soybean oil,we found that a three-order-of-magnitude reduction in particulates(i.e.,99.9%remediation)could be achieved with this system.Here,the plasma discharge was produced in a 4-in.-diameter cylindrical reactor with a 5-10 ns high voltage(30 kV)pulse generator together with applied DC bias voltages up to 10 kV.The distribution of nanoparticle sizes was measured using a scanning mobility particle sizer(SMPS)with diameter centered around 225 nm.Here,the main mechanism of remediation occurs in a two-step process in which the oil nanoparticles are first ionized by the free electrons and free radicals in the plasma and then the charged particles are swept out to the sidewalls of the reactor by the applied DC potential.We believe this general approach opens up new degrees of freedom in the design of electrostatic oil aerosol pollution control devices.展开更多
To battle with economic challenges during the COVID-19 pandemic,the US government implemented various measures to mitigate economic loss.From issuance of stimulus checks to reopening businesses,consumers had to consta...To battle with economic challenges during the COVID-19 pandemic,the US government implemented various measures to mitigate economic loss.From issuance of stimulus checks to reopening businesses,consumers had to constantly alter their behavior in response to government policies.Using anonymized card transactions and mobile device-based location tracking data,we analyze the factors that contribute to these behavior changes,focusing on stimulus check issuance and state-wide reopening.Our finding suggests that stimulus payment has a significant immediate effect of boosting spending,but it typically does not reverse a downward trend.State-wide reopening had a small effect on spending.Foot traffic increased gradually after stimulus check issuance,but only increased slightly after reopening,which also coincided or preceded several policy changes and confounding events(e.g.,protests)in the US.We also find differences in the reaction to these policies in different regions in the US.Our results may be used to inform future economic recovery policies and their potential consumer response.展开更多
Having the ability to forecast cyberattacks before they happen will unquestionably change the landscape of cyber warfare and cyber crime.This work predicts specific types of attacks on a potential victim network befor...Having the ability to forecast cyberattacks before they happen will unquestionably change the landscape of cyber warfare and cyber crime.This work predicts specific types of attacks on a potential victim network before the actual malicious actions take place.The challenge to forecasting cyberattacks is to extract relevant and reliable signals to treat sporadic and seemingly random acts of adversaries.This paper builds on multi-faceted machine learning solutions and develops an integrated system to transform large volumes of public data to aggregate signals with imputation that are relevant and predictive of cyber incidents.A comprehensive analysis of the individual parts and the integrated whole demonstrates the effectiveness and trade-offs of the proposed approach.Using 16-months of reported cyber incidents by an anonymized victim organization,the integrated approach achieves up to 87%,90%,and 96% AUC for forecasting endpoint-malware,malicious-destination,and malicious-email attacks,respectively.When assessed month-by-month,the proposed approach shows robustness to perform consistently well,achieving F-Measure between 0.6 and 1.0.The framework also enables an examination of which unconventional signals are meaningful for cyberattack forecasting.展开更多
1 Introduction Megaprojects are a critical aspect of socio-economic development that can have huge effects on local communities,the environment,society,politics,or locals9 way of life(Zeng et al.,2015;Denicol et al.,2...1 Introduction Megaprojects are a critical aspect of socio-economic development that can have huge effects on local communities,the environment,society,politics,or locals9 way of life(Zeng et al.,2015;Denicol et al.,2020).Megaproject social responsibility(MSR)refers to “the policies and practices of stakeholders through the whole project lifecycle that reflect responsibilities for the well-being of the wide society”(Zeng et al.,2015).MSR governance refers to socially responsible actions of relevant stakeholders to alleviate and eliminate a megaprojecfs negative effects on socio-economic and environmental outcomes during the megaprojecfs entire lifecycle(Lin et al.,2017;Ma et al.,2017).展开更多
In this paper we propose a finite element method for solving elliptic equations with observational Dirichlet boundary data which may subject to random noises.The method is based on the weak formulation of Lagrangian m...In this paper we propose a finite element method for solving elliptic equations with observational Dirichlet boundary data which may subject to random noises.The method is based on the weak formulation of Lagrangian multiplier and requires balanced oversampling of the measurements of the boundary data to control the random noises.We show the convergence of the random finite elemen t error in expec tat ion and,when the noise is subGaussian,in the Orlicz^2-norm which implies the probability that the finite element error estimates are viola ted decays exponentially.Numerical examples are included.展开更多
Polynomial chaos expansions(PCEs)have been used in many real-world engineering applications to quantify how the uncertainty of an output is propagated from inputs by decomposing the output in terms of polynomials of t...Polynomial chaos expansions(PCEs)have been used in many real-world engineering applications to quantify how the uncertainty of an output is propagated from inputs by decomposing the output in terms of polynomials of the inputs.PCEs for models with independent inputs have been extensively explored in the literature.Recently,different approaches have been proposed for models with dependent inputs to expand the use of PCEs to more real-world applications.Typical approaches include building PCEs based on the Gram–Schmidt algorithm or transforming the dependent inputs into independent inputs.However,the two approaches have their limitations regarding computational efficiency and additional assumptions about the input distributions,respectively.In this paper,we propose a data-driven approach to build sparse PCEs for models with dependent inputs without any distributional assumptions.The proposed algorithm recursively constructs orthonormal polynomials using a set of monomials based on their correlations with the output.The proposed algorithm on building sparse PCEs not only reduces the number of minimally required observations but also improves the numerical stability and computational efficiency.Four numerical examples are implemented to validate the proposed algorithm.The source code is made publicly available for reproducibility.展开更多
The U.S.National Institute of Standards and Technology(NIST)published the Community Resilience Planning Guide in 2016.The NIST Guide advocates for a participatory process for developing a performance measurement frame...The U.S.National Institute of Standards and Technology(NIST)published the Community Resilience Planning Guide in 2016.The NIST Guide advocates for a participatory process for developing a performance measurement framework for the jurisdiction’s resilience against a scenario hazard.The framework centers around tables of expected and desired recovery times for selected community assets,such as electricity,water,and natural gas infrastructures.The NIST Guide does not provide a method for estimating the expected recovery times.However,building high-fidelity computer models for such estimations requires substantial resources that even larger ju-risdictions cannot cost-justify.The most promising approach to recovery time estimation is to systematically use data elicited from people to tap into the wisdom of the(knowledgeable)crowd.This paper describes a novel research-through-design project to enable the computer-supported elicitation of recovery time series data.This work is the first in the literature to examine people’s ability to estimate recovery curves and how design in-fluences such estimations.Its main contribution to resilience planning is three-fold:development of a new elicitation tool called Restimate,understanding its potential user base,and providing insights into how it can facilitate resilience planning.Restimate is the first tool to enable evidence-based expert elicitation in any community with limited resources for resilience planning.Beyond resilience planning,those who facilitate high-stakes planning activities under large uncertainties(e.g.,mission-critical system design and planning)will benefit from a similar research-through-design process.展开更多
The design of materials and identification of optimal processing parameters constitute a complex and challenging task,necessitating efficient utilization of available data.Bayesian Optimization(BO)has gained popularit...The design of materials and identification of optimal processing parameters constitute a complex and challenging task,necessitating efficient utilization of available data.Bayesian Optimization(BO)has gained popularity in materials design due to its ability to work with minimal data.However,many BO-based frameworks predominantly rely on statistical information,in the form of input-output data,and assume black-box objective functions.In practice,designers often possess knowledge of the underlying physical laws governing a material system,rendering the objective function not entirely black-box,as some information is partially observable.In this study,we propose a physics-informed BO approach that integrates physics-infused kernels to effectively leverage both statistical and physical information in the decision-making process.We demonstrate that this method significantly improves decision-making efficiency and enables more data-efficient BO.The applicability of this approach is showcased through the design of NiTi shape memory alloys,where the optimal processing parameters are identified to maximize the transformation temperature.展开更多
Neck injuries are significant causes of morbidity and mortality, and their chronic forms due to repetitive or sustained physical acts(e.g., prolonged use of mobile phone with a dropped head) are becoming increasingly ...Neck injuries are significant causes of morbidity and mortality, and their chronic forms due to repetitive or sustained physical acts(e.g., prolonged use of mobile phone with a dropped head) are becoming increasingly more prevalent. Many injuries are preventable but the prevention and control requires a clear basic understanding of the neck biomechanics. In this paper, we describe a first-of-its-kind study that integrates a gamut of state-of-the-art imaging modalities(dynamic radiography, computed tomography(CT), and magnetic resonance imaging(MRI)) and biodynamic measurements(motion capture, electromyography(EMG), force sensing), thereby investigating holistically the in vivo responses of the neck and its various interconnected musculoskeletal components during functional activities. We present a sample of findings to illustrate how the integrations at multiple levels can enable creating truly subject-specific neck musculoskeletal models and attaining novel insights that otherwise would be unattainable by a singular or subset of approaches.展开更多
This study combines multi-regional inputoutput(MRIO)model with linear programming(LP)model to explore economic structure adjustment strategies for the reduction of carbon dioxide(CO_(2))emissions.A particular feature ...This study combines multi-regional inputoutput(MRIO)model with linear programming(LP)model to explore economic structure adjustment strategies for the reduction of carbon dioxide(CO_(2))emissions.A particular feature of this study is the identification of the optimal regulation sequence of final products in various regions to reduce CO_(2)emissions with the minimum loss in gross domestic product(GDP).By using China's MRIO tables 2017 with 28 regions and 42 economic sectors,results show that reduction in final demand leads to simultaneous reductions in GDP and CO_(2)emissions.Nevertheless,certain demand side regulation strategy can be adopted to lower CO_(2)emissions at the smallest loss of economic growth.Several key final products,such as metallurgy,nonmetal,metal,and chemical products,should first be regulated to reduce CO_(2)emissions at the minimum loss in GDP.Most of these key products concentrate in the coastal developed regions in China.The proposed MRIO-LP model considers the inter-relationship among various sectors and regions,and can aid policy makers in designing effective policy for industrial structure adjustment at the regional level to achieve the national environmental and economic targets.展开更多
基金financially supported by the Major International Joint Research Project of the National Natural Science Foundation of China (No.71520107004)the Major Program of National Natural Science Foundation of China (No.71790614)the 111 Project (No.B16009)。
文摘Steel production involves the transfer and transformation of material and energy at different levels, structures, and scales, and this process incurs substantial information in the material and energy dimensions. Given the black-box feature of iron and steel production processes, process visualization plays an important role and inevitably benefits parameter correction, technical support decision-making, personnel training, and other aspects of the steel metallurgy industry. The technological characteristics of the entire process in the steel industry were analyzed in this study, a visualization technology route based on virtual reality(VR) was built, and the important components of visual simulation system for steel industry and the important technical points needed to realize the system were proposed. On the foundation, a visual simulation model for the process scheduling of the iron and steel enterprise raw materials' field, slab, and hot rolling processes was built, and a visualization simulation platform of the iron and steel metallurgy plant-wide process, including ironmaking, steelmaking, hot rolling, and cold rolling, was developed. Lastly, the effectiveness of platform was illustrated by practical application.
基金supports of the Funds for the National Natural Science Foundation of China [grant numbers 51575386,51275344]
文摘With the increasing market demand for optical complex surface parts,the application of multi-axis ultraprecision single-point diamond turning is increasing.A tool path generation method is very important to decrease manufacturing time,enhance surface quality,and reduce cost.Compared with the tool path generation of the traditional multi-axis milling,that of the ultra-precision single-point diamond turning requires higher calculation accuracy and efficiency.This paper reviews the tool path generation of ultra-precision diamond turning,considering several key issues:cutter location(CL)points calculation,the topological form of tool path,interpolation mode,and G code optimization.
文摘A profound transition of energy system is central to China's"3060"goal since the bulk of anthropogenic emissions arises from energy use.Covering the production,conversion,transmission,distribution and consumption of energy,energy system has complex internal structure and close linkages with the surrounding economic and social systems.Energy system transition towards carbon neutrality has presented tremendous challenges in a wide range of aspects,including technology,finance market,business models,governance structure,policy regime,etc.A better understanding of the dynamics and mechanism underlying energy system transition is critical to the transition management of energy systems.
文摘Disaster relief logistics is a significant element in the management of disaster relief operations.In this paper,the operational decisions of relief logistics are considered in the distribution of resources to the affected areas to include scheduling,routing,and allocation decisions.The proposed mathematical model simultaneously captures many aspects relevant to real life to face the challenging situation of disasters.Characteristics such as multiple uses of vehicles and split delivery allow for better use of vehicles as one of the primary resources of disaster response.A multi-period multi-criteria mixed-integer programming model is introduced to evaluate and address these features.The model utilizes a rolling horizon method that provides possibilities to adjust plans as more information becomes available.Three objectives of efficiency,effectiveness,and equity are jointly considered.The augmented epsilon constraint method is applied to solve the model,and a case study is presented to illustrate the potential applicability of our model.Computational results show that the model is capable of generating efficient solutions.
基金supported in part by Hong Kong General Research Fund(No.CityU143711)Zhening Li was supported in part by Natural Science Foundation of Shanghai(No.12ZR1410100)+1 种基金Ph.D.Programs Foundation of Chinese Ministry of Education(No.20123108120002)Shuzhong Zhang was supported in part by U.S.National Science Foundation(No.CMMI-1161242).
文摘In this paper,we consider approximation algorithms for optimizing a generic multivariate polynomial function in discrete(typically binary)variables.Such models have natural applications in graph theory,neural networks,error-correcting codes,among many others.In particular,we focus on three types of optimization models:(1)maximizing a homogeneous polynomial function in binary variables;(2)maximizing a homogeneous polynomial function in binary variables,mixed with variables under spherical constraints;(3)maximizing an inhomogeneous polynomial function in binary variables.We propose polynomial-time randomized approximation algorithms for such polynomial optimizationmodels,and establish the approximation ratios(or relative approximation ratios whenever appropriate)for the proposed algorithms.Some examples of applications for these models and algorithms are discussed as well.
文摘It is now recognized that nanoscale particulate matter(PM)represents a substantial health hazard for our society,including PM from restaurant smoke.In this study,we explored the use of a transient pulsed plasma in conjunction with an applied DC bias to treat oil aerosols that closely resemble restaurant(i.e.,charbroiler)smoke emissions.For polyaromatic olefin PAO-4 and soybean oil,we found that a three-order-of-magnitude reduction in particulates(i.e.,99.9%remediation)could be achieved with this system.Here,the plasma discharge was produced in a 4-in.-diameter cylindrical reactor with a 5-10 ns high voltage(30 kV)pulse generator together with applied DC bias voltages up to 10 kV.The distribution of nanoparticle sizes was measured using a scanning mobility particle sizer(SMPS)with diameter centered around 225 nm.Here,the main mechanism of remediation occurs in a two-step process in which the oil nanoparticles are first ionized by the free electrons and free radicals in the plasma and then the charged particles are swept out to the sidewalls of the reactor by the applied DC potential.We believe this general approach opens up new degrees of freedom in the design of electrostatic oil aerosol pollution control devices.
文摘To battle with economic challenges during the COVID-19 pandemic,the US government implemented various measures to mitigate economic loss.From issuance of stimulus checks to reopening businesses,consumers had to constantly alter their behavior in response to government policies.Using anonymized card transactions and mobile device-based location tracking data,we analyze the factors that contribute to these behavior changes,focusing on stimulus check issuance and state-wide reopening.Our finding suggests that stimulus payment has a significant immediate effect of boosting spending,but it typically does not reverse a downward trend.State-wide reopening had a small effect on spending.Foot traffic increased gradually after stimulus check issuance,but only increased slightly after reopening,which also coincided or preceded several policy changes and confounding events(e.g.,protests)in the US.We also find differences in the reaction to these policies in different regions in the US.Our results may be used to inform future economic recovery policies and their potential consumer response.
基金Intelligence Advanced Research Projects Activity(IARPA)with contract number FA875016C0114.
文摘Having the ability to forecast cyberattacks before they happen will unquestionably change the landscape of cyber warfare and cyber crime.This work predicts specific types of attacks on a potential victim network before the actual malicious actions take place.The challenge to forecasting cyberattacks is to extract relevant and reliable signals to treat sporadic and seemingly random acts of adversaries.This paper builds on multi-faceted machine learning solutions and develops an integrated system to transform large volumes of public data to aggregate signals with imputation that are relevant and predictive of cyber incidents.A comprehensive analysis of the individual parts and the integrated whole demonstrates the effectiveness and trade-offs of the proposed approach.Using 16-months of reported cyber incidents by an anonymized victim organization,the integrated approach achieves up to 87%,90%,and 96% AUC for forecasting endpoint-malware,malicious-destination,and malicious-email attacks,respectively.When assessed month-by-month,the proposed approach shows robustness to perform consistently well,achieving F-Measure between 0.6 and 1.0.The framework also enables an examination of which unconventional signals are meaningful for cyberattack forecasting.
基金This research is supported in part by the National Natural Science Foundation of China(Grant Nos.71942006 and 71620107004)Humanities and Social Sciences Research Project of the Ministry of Education of China(Grant No.20YJC630099).
文摘1 Introduction Megaprojects are a critical aspect of socio-economic development that can have huge effects on local communities,the environment,society,politics,or locals9 way of life(Zeng et al.,2015;Denicol et al.,2020).Megaproject social responsibility(MSR)refers to “the policies and practices of stakeholders through the whole project lifecycle that reflect responsibilities for the well-being of the wide society”(Zeng et al.,2015).MSR governance refers to socially responsible actions of relevant stakeholders to alleviate and eliminate a megaprojecfs negative effects on socio-economic and environmental outcomes during the megaprojecfs entire lifecycle(Lin et al.,2017;Ma et al.,2017).
基金This work was supported in part by the National Center for Mathematics and Interdisciplinary Sciences,CAS and China NSF under the grant 118311061 and 11501551.
文摘In this paper we propose a finite element method for solving elliptic equations with observational Dirichlet boundary data which may subject to random noises.The method is based on the weak formulation of Lagrangian multiplier and requires balanced oversampling of the measurements of the boundary data to control the random noises.We show the convergence of the random finite elemen t error in expec tat ion and,when the noise is subGaussian,in the Orlicz^2-norm which implies the probability that the finite element error estimates are viola ted decays exponentially.Numerical examples are included.
基金This work was supported in part by the U.S.National Science Foundation(NSF grants CMMI-1824681,DMS-1952781,and BCS-2121616).
文摘Polynomial chaos expansions(PCEs)have been used in many real-world engineering applications to quantify how the uncertainty of an output is propagated from inputs by decomposing the output in terms of polynomials of the inputs.PCEs for models with independent inputs have been extensively explored in the literature.Recently,different approaches have been proposed for models with dependent inputs to expand the use of PCEs to more real-world applications.Typical approaches include building PCEs based on the Gram–Schmidt algorithm or transforming the dependent inputs into independent inputs.However,the two approaches have their limitations regarding computational efficiency and additional assumptions about the input distributions,respectively.In this paper,we propose a data-driven approach to build sparse PCEs for models with dependent inputs without any distributional assumptions.The proposed algorithm recursively constructs orthonormal polynomials using a set of monomials based on their correlations with the output.The proposed algorithm on building sparse PCEs not only reduces the number of minimally required observations but also improves the numerical stability and computational efficiency.Four numerical examples are implemented to validate the proposed algorithm.The source code is made publicly available for reproducibility.
基金support of the U.S.National Science Foundation(NSF grants CMMI-1824681,BCS-2121616,&CMMI-2211077)。
文摘The U.S.National Institute of Standards and Technology(NIST)published the Community Resilience Planning Guide in 2016.The NIST Guide advocates for a participatory process for developing a performance measurement framework for the jurisdiction’s resilience against a scenario hazard.The framework centers around tables of expected and desired recovery times for selected community assets,such as electricity,water,and natural gas infrastructures.The NIST Guide does not provide a method for estimating the expected recovery times.However,building high-fidelity computer models for such estimations requires substantial resources that even larger ju-risdictions cannot cost-justify.The most promising approach to recovery time estimation is to systematically use data elicited from people to tap into the wisdom of the(knowledgeable)crowd.This paper describes a novel research-through-design project to enable the computer-supported elicitation of recovery time series data.This work is the first in the literature to examine people’s ability to estimate recovery curves and how design in-fluences such estimations.Its main contribution to resilience planning is three-fold:development of a new elicitation tool called Restimate,understanding its potential user base,and providing insights into how it can facilitate resilience planning.Restimate is the first tool to enable evidence-based expert elicitation in any community with limited resources for resilience planning.Beyond resilience planning,those who facilitate high-stakes planning activities under large uncertainties(e.g.,mission-critical system design and planning)will benefit from a similar research-through-design process.
基金Research was sponsored by the Army Research Laboratory and was accomplished under Cooperative Agreement Number W911NF-22-2-0106The views and conclu-sions contained in this document are those of the authors and should not be interpreted as representing the official policies,either expressed or implied,of the Army Research Laboratory or the U.S.Government.The U.S.Government is authorized to reproduce and distribute reprints for Government Purposes notwithstanding any copyright notation herein.Department of Energy(DOE)ARPA-E ULTIMATE Program through Project DE-AR0001427+2 种基金DK acknowledges the support of NSF through Grant No.CDSE-2001333.RA acknowledges the support from Grants No.NSF-CISE-1835690 and NSF-DMREF-2119103Part of this research was carried out at the Jet Propulsion Laboratory(JPL),California Institute of Technology,under a contract with the National Aeronautics and Space Administration(80NM0018D0004)This research was supported by the JPL Strategic University Research Partnership(SURP)program.
文摘The design of materials and identification of optimal processing parameters constitute a complex and challenging task,necessitating efficient utilization of available data.Bayesian Optimization(BO)has gained popularity in materials design due to its ability to work with minimal data.However,many BO-based frameworks predominantly rely on statistical information,in the form of input-output data,and assume black-box objective functions.In practice,designers often possess knowledge of the underlying physical laws governing a material system,rendering the objective function not entirely black-box,as some information is partially observable.In this study,we propose a physics-informed BO approach that integrates physics-infused kernels to effectively leverage both statistical and physical information in the decision-making process.We demonstrate that this method significantly improves decision-making efficiency and enables more data-efficient BO.The applicability of this approach is showcased through the design of NiTi shape memory alloys,where the optimal processing parameters are identified to maximize the transformation temperature.
基金supported by a research grant from the Centers for Disease Control and Prevention/National Institute for Occupational Safety and Health (Grant No. R01OH010587)。
文摘Neck injuries are significant causes of morbidity and mortality, and their chronic forms due to repetitive or sustained physical acts(e.g., prolonged use of mobile phone with a dropped head) are becoming increasingly more prevalent. Many injuries are preventable but the prevention and control requires a clear basic understanding of the neck biomechanics. In this paper, we describe a first-of-its-kind study that integrates a gamut of state-of-the-art imaging modalities(dynamic radiography, computed tomography(CT), and magnetic resonance imaging(MRI)) and biodynamic measurements(motion capture, electromyography(EMG), force sensing), thereby investigating holistically the in vivo responses of the neck and its various interconnected musculoskeletal components during functional activities. We present a sample of findings to illustrate how the integrations at multiple levels can enable creating truly subject-specific neck musculoskeletal models and attaining novel insights that otherwise would be unattainable by a singular or subset of approaches.
基金This work is supported by the National Research Foundation,Prime Ministers Office,Singapore under its Campus for Research Excellence and Technological Enterprise(CREATE)programme,and by the Energy and Environmental Sustainability for Megacities(E2S2)Phase II program of the National Research Foundation,Prime Ministers Office,Singapore under its CREATE programme。
文摘This study combines multi-regional inputoutput(MRIO)model with linear programming(LP)model to explore economic structure adjustment strategies for the reduction of carbon dioxide(CO_(2))emissions.A particular feature of this study is the identification of the optimal regulation sequence of final products in various regions to reduce CO_(2)emissions with the minimum loss in gross domestic product(GDP).By using China's MRIO tables 2017 with 28 regions and 42 economic sectors,results show that reduction in final demand leads to simultaneous reductions in GDP and CO_(2)emissions.Nevertheless,certain demand side regulation strategy can be adopted to lower CO_(2)emissions at the smallest loss of economic growth.Several key final products,such as metallurgy,nonmetal,metal,and chemical products,should first be regulated to reduce CO_(2)emissions at the minimum loss in GDP.Most of these key products concentrate in the coastal developed regions in China.The proposed MRIO-LP model considers the inter-relationship among various sectors and regions,and can aid policy makers in designing effective policy for industrial structure adjustment at the regional level to achieve the national environmental and economic targets.
