The intensity allocation criteria of carbon emissions permits and its influence on China's regional development are analyzed through the 30-province/autonomous region computable general equilibrium(CGE)model.Simul...The intensity allocation criteria of carbon emissions permits and its influence on China's regional development are analyzed through the 30-province/autonomous region computable general equilibrium(CGE)model.Simulation results show that:industrial intensity criteria without taking regional economic development into account deepen the unbalance of regional economic development;regional intensity criteria without taking industrial properties into account exert little negative impact on regional harmonious development,but relatively high negative influence on high-carbon emission industries.The two-step allocation scheme that the central government allocates emissions permits to provincial governments based on regional economic development and then provincial governments allocate emissions permits to emission resources or entities based on industrial properties is a feasible and operable choice.展开更多
The public health and ecological impacts of volatile organic compound(VOCs) pollution have become a serious problem in China,arousing increasing attention to emissions control.In this context,this paper analyses the e...The public health and ecological impacts of volatile organic compound(VOCs) pollution have become a serious problem in China,arousing increasing attention to emissions control.In this context,this paper analyses the effectiveness of VOC reduction policies,namely pollution charges and environmental taxes at the national and industrial sector levels.It uses a computable general equilibrium model,which connects macroeconomic variables with VOC emissions inventory,to simulate the effects of policy scenarios(with 2007 as the reference year).This paper shows that VOC emissions are reduced by 2.2% when a pollution charge equal to the average cost of engineering reduction methods-the traditional approach to regulation in China-is applied.In order to achieve a similar reduction,an 8.9% indirect tax would have to be imposed.It concludes that an environmental tax should be the preferred method of VOC regulation due to its smaller footprint on the macroeconomy.Other policies,such as subsidies,should be used as supplements.展开更多
Researching China's innovative economic and financial innovation issues under the background of the New Normal, we need to carefully analyze the internal structure and interaction of China's macroeconomics.The...Researching China's innovative economic and financial innovation issues under the background of the New Normal, we need to carefully analyze the internal structure and interaction of China's macroeconomics.The computable general equilibrium(CGE) model has outstanding advantages on predicting the external shock influences on economic system, but previous studies on forecast for China's future economy mostly considered a high growth rate which is hard to comply with the New Normal scene. By constructing China's macroeconomic dynamic CGE(DCGE) model and anticipating the economic impact of the New Normal, this paper finds that the New Normal has a certain extent inhibition on China's macro-economy and innovation. However, after adding the research and development(R&D) subsidy policy, the negative impacts of the New Normal on macro-economy can be eliminated to realize the optimization of economic structure. In addition, after combining the financial innovation promoting policy and the Ke Qiang index through the simulation of macro-economy, we find that the quality of economic growth is improved. Finally, we provide the policy recommendations for the realization of an innovative economy under China's New Normal.展开更多
BACKGROUND With the recent change in the definition(Sepsis-3 Definition)of sepsis and septic shock,an electronic search algorithm was required to identify the cases for data automation.This supervised machine learning...BACKGROUND With the recent change in the definition(Sepsis-3 Definition)of sepsis and septic shock,an electronic search algorithm was required to identify the cases for data automation.This supervised machine learning method would help screen a large amount of electronic medical records(EMR)for efficient research purposes.AIM To develop and validate a computable phenotype via supervised machine learning method for retrospectively identifying sepsis and septic shock in critical care patients.METHODS A supervised machine learning method was developed based on culture orders,Sequential Organ Failure Assessment(SOFA)scores,serum lactate levels and vasopressor use in the intensive care units(ICUs).The computable phenotype was derived from a retrospective analysis of a random cohort of 100 patients admitted to the medical ICU.This was then validated in an independent cohort of 100 patients.We compared the results from computable phenotype to a gold standard by manual review of EMR by 2 blinded reviewers.Disagreement was resolved by a critical care clinician.A SOFA score≥2 during the ICU stay with a culture 72 h before or after the time of admission was identified.Sepsis versions as V1 was defined as blood cultures with SOFA≥2 and Sepsis V2 was defined as any culture with SOFA score≥2.A serum lactate level≥2 mmol/L from 24 h before admission till their stay in the ICU and vasopressor use with Sepsis-1 and-2 were identified as Septic Shock-V1 and-V2 respectively.RESULTS In the derivation subset of 100 random patients,the final machine learning strategy achieved a sensitivity-specificity of 100%and 84%for Sepsis-1,100%and 95%for Sepsis-2,78%and 80%for Septic Shock-1,and 80%and 90%for Septic Shock-2.An overall percent of agreement between two blinded reviewers had a k=0.86 and 0.90 for Sepsis 2 and Septic shock 2 respectively.In validation of the algorithm through a separate 100 random patient subset,the reported sensitivity and specificity for all 4 diagnoses were 100%-100%each.CONCLUSION Supervised machine learning for identification of sepsis and septic shock is reliable and an efficient alternative to manual chart review.展开更多
In this paper,we study the computative structure of computable function-a structure of computative tree,and,by analysis on it,got the most general algorithm and model for computation on computable functions.
This paper proposes and illustrates an AI embedded object-oriented methodology to formulate the computable general equilibrium (CGE) models. In this framework, a CGE model is viewed as a collection of objects embedd...This paper proposes and illustrates an AI embedded object-oriented methodology to formulate the computable general equilibrium (CGE) models. In this framework, a CGE model is viewed as a collection of objects embedded AI or namely agents in computer world, corresponding to economic agents and entities in real world, such as government, households, markets and so on. A frame representation of major objects in CGE model is used for trade and environment. Embedded Al object-oriented approach (or software agent) is used in the CGE model representation can able to narrow the gap among the semantic representation, formal CGE (mathematical) representation and computer and algorithm representation, and to improve CGE in understanding and maintenance etc. In such a system, constructing a CGE model to appear an intuitive process rather than an abstract process. This intuitive process needs more understanding of the substance of economics and the logic underlying the problem rather than mathematical notation.展开更多
This paper examined the impacts of the total energy consumption control policy and energy quota allocation plans on China′s regional economy. This research analyzed the influences of different energy quota allocation...This paper examined the impacts of the total energy consumption control policy and energy quota allocation plans on China′s regional economy. This research analyzed the influences of different energy quota allocation plans with various weights of equity and efficiency, using a dynamic computable general equilibrium(CGE) model for 30 province-level administrative regions. The results show that the efficiency-first allocation plan costs the least but widens regional income gap, whereas the outcomes of equity-first allocation plan and intensity target-based allocation plan are similar and are both opposite to the efficiency-first allocation plan′ outcome. The plan featuring a balance between efficiency and equity is more feasible, which can bring regional economic losses evenly and prevent massive interregional migration of energy-related industries. Furthermore, the effects of possible induced energy technology improvements in different energy quota allocation plans were studied. Induced energy technology improvements can add more feasibility to all allocation plans under the total energy consumption control policy. In the long term, if the policy of the total energy consumption control continues and more market-based tools are implemented to allocate energy quotas, the positive consequences of induced energy technology improvements will become much more obvious.展开更多
Changes in the energy price system will determine the direction of evolution of the energy industry structure.As a country where coal is the dominant energy source,what is the effect of coal price fluctuations on Chin...Changes in the energy price system will determine the direction of evolution of the energy industry structure.As a country where coal is the dominant energy source,what is the effect of coal price fluctuations on China’s industry development costs and energy consumption structure?To investigate this problem,this paper utilized an economy–energy–environment computable general equilibrium model.In this study,four aspects were analyzed:Energy supply side,proportion of renewable energy consumption,macroeconomy,and changes in CO_(2) emissions.The results of this study show that an increase of 10%–20%in coal prices contributes to a shift into using renewable energy,which leads to energy saving and emission reduction.Renewable energy and clean energy rose by 0.57%–4.47%in the energy structure,but this has a certain negative impact on the macroeconomy.The gross domestic product(GDP)fell by 0.07%–0.18%.As a result,the decline in coal prices became an obstacle to renewable energy substitution and energy conservation.In addition,we put forward policy suggestions according to the results in energy,economic,and environmental effects.展开更多
The challenge of meeting the ever-increasing food demand for the growing population will be further exacerbated by climate change in Ethiopia. This paper presents the simulated economy-wide impacts of climate change o...The challenge of meeting the ever-increasing food demand for the growing population will be further exacerbated by climate change in Ethiopia. This paper presents the simulated economy-wide impacts of climate change on the agriculture sector of Ethiopia using a dynamic computable general equilibrium (CGE) model. The study simulated the scenarios of agricultural productivity change induced by climate change up to the year 2050. At national level, the simulation results suggest that crop production will be adversely affected during the coming four decades and the severity will increase over the time period. Production of teff, maize and sorghum will decline by 25.4, 21.8 and 25.2 percent, respectively by 2050 compared to the base period. Climate change will also cause losses of 31.1 percent agricultural GDP at factor cost by 2050. Climate change affects more the income and consumption of poor rural households than urban rural non-farming households. The reduction in agricultural production will not be evenly distributed across agro ecological zones, and will not all be negative. Among rural residents, climate change impacts tend to hurt the income of the poor more in drought prone regions. Income from labor, land and livestock in moisture sufficient highland cereal-based will decline by 5.1, 8.8 and 15.2 percent in 2050. This study indicated that since climate change is an inevitable phenomenon, the country should start mainstreaming adaptation measures to sustain the overall performance of the economy.展开更多
Papyrus is increasingly suggested as an alternative bioenergy source to reduce the pressure on forest ecosystems. However, there are few studies on the economic viability of papyrus wetlands and the benefits for local...Papyrus is increasingly suggested as an alternative bioenergy source to reduce the pressure on forest ecosystems. However, there are few studies on the economic viability of papyrus wetlands and the benefits for local communities. We construct a village Computable General Equilibrium (CGE) model to examine whether papyrus harvesting and processing has the potential to improve local livelihoods and simultaneously counteract pressure on local forest resources. We apply the CGE model to a village in northern Zambia where overexploitation of forest resources to produce energy from firewood and charcoal poses a serious problem. The analysis is based on survey data?from 105 households collected in 2015. The model results show that papyrus briquetting would be a possible?alternative biofuel and that this technology improves household income and utility through?labor?reallocations. Higher opportunity costs lead to households switching from firewood extraction and charcoal production activities to papyrus harvesting and processing to produce bioenergy. Replacing energy supplies from firewood and charcoal with papyrus briquettes results in substitution effects between forest land and wetland and thereby reduces the pressure on local forest resources. The CGE approach allows for an economy-wide ex-ante analysis at village level and can support management decisions to ensure the success of papyrus bioenergy interventions.展开更多
The role of the construction industry in economic growth has been widely discussed in the extant literature,but existing studies have not investigated the disaggregated impact of construction investments on the produc...The role of the construction industry in economic growth has been widely discussed in the extant literature,but existing studies have not investigated the disaggregated impact of construction investments on the production and social sectors.This study examines the disaggregated effect of construction investments on the Saudi economy.The study uses a social accounting matrix of Saudi Arabia and constructs a dynamic computable general equilibrium model.The findings reveal that construction investments significantly boosted GDP and aggregate investments in the first two periods;however,the growth declined in the following three periods.This finding underlines the importance of long-term investments in the construction sector and calls for continuous monitoring and updating of the investment policy for sustainable development.This study also presents the disaggregated impact of investments on the value-added by each sector of the economy.The ranking of sectors exhibits that mining and quarry activities underwent a high increase in value-added,second to construction activities.Other economic activities also experienced growth in value-added and some of them changed their ranks within the five years.展开更多
With growing regional economic integration,transportation systems have become critical to regional development and economic vitality but vulnerable to disasters.However,the regional economic ripple effect of a disaste...With growing regional economic integration,transportation systems have become critical to regional development and economic vitality but vulnerable to disasters.However,the regional economic ripple effect of a disaster is difficult to quantify accurately,especially considering the cumulated influence of traffic disruptions.This study explored integrating transportation system analysis with economic modeling to capture the regional economic ripple effect.A state-of-the-art spatial computable general equilibrium model is leveraged to simulate the operation of the economic system,and the marginal rate of transport cost is introduced to reflect traffic network damage post-disaster.The model is applied to the 50-year return period flood in2020 in Hubei Province,China.The results show the following.First,when traffic disruption costs are considered,the total output loss of non-affected areas is 1.81 times than before,and non-negligible losses reach relatively remote zones of the country,such as the Northwest Comprehensive Economic Zone(36%of total ripple effects).Second,traffic disruptions have a significant hindering effect on regional trade activities,especially in the regional intermediate input—about three times more than before.The industries most sensitive to traffic disruptions were transportation,storage,and postal service(5 times),and processing and assembly manufacturing(4.4 times).Third,the longer the distance,the stronger traffic disruptions'impact on interregional intermediate inputs.Thus,increasing investment in transportation infrastructure significantly contributes to mitigating disaster ripple effects and accelerating the process of industrial recovery in affected areas.展开更多
Noncohesive particle clusters are identified and tracked in turbulent flows to determine the breakdown and time evolution of cluster statistics and their implications for interscale mass transfer,which has connections...Noncohesive particle clusters are identified and tracked in turbulent flows to determine the breakdown and time evolution of cluster statistics and their implications for interscale mass transfer,which has connections to the classical turbulent energy cascade and its mass cascade counterpart running in parallel.In particular,the formation and dynamics of sediment and larvae clusters are of interest to coral larvae settlement in coastal regions and particularly the resilience of green-gray coastal protection solutions.Analogous cluster behavior is relevant to cloud microphysics and precipitation initiation,radiation transport and light transmission through colloids and suspensions,heat and mass transfer in particle-laden flows,and viral and pollutant transmission.Following a comparison between various clustering techniques,we adopt a density-based cluster identification algorithm based on its simplicity and efficiency,where particles are clustered based on the number of neighboring particles in their individual spheres of influence.We establish parallels with lattice-based percolation theory,as evident in the power-law scaling of the cluster size distribution near the percolation threshold.The degree of discontinuity of the phase transition associated with this percolation threshold is observed to broaden with larger Stokes numbers and thereby large-scale clustering.The sensitivity of our findings to the employed clustering algorithm is discussed.A novel cluster tracking algorithm is deployed to determine the interscale transfer rate along the particle-number phase-space dimension via accounting of cluster breakup and merger events,extending previous work on the bubble breakup cascade beneath surface breaking waves.Our findings shed light on the interaction between particle clusters and their carrier turbulent flows,with an eye toward transport models incorporating cluster characteristics and dynamics.展开更多
Biomass-based hydrocarbon fuels,as one of the alternatives to traditional fossil fuels,have attracted considerable attention in the energy field due to their renewability and environmental benefits.This article provid...Biomass-based hydrocarbon fuels,as one of the alternatives to traditional fossil fuels,have attracted considerable attention in the energy field due to their renewability and environmental benefits.This article provides a systematic review of recent research progress in the chemical synthesis of biomass-based hydrocarbon fuels.It outlines the conversion pathways using feedstocks such as lipids,terpenoids,cellulose/hemicellulose,and lignin.Depending on the feedstock,various products with distinct structural characteristics can be prepared through reactions such as cyclization,condensation,and catalytic hydrogenation.Throughout the synthesis process,three key factors play a critical role:efficient catalyst development,production process optimization,and computational-chemistry-based molecular design.Finally,the article discusses future perspectives for biomass-based hydrocarbon fuel synthesis research.展开更多
The capture of atmospheric carbon dioxide by adsorbents is an important strategy to deal with the greenhouse effect.Compared with traditional CO_(2) adsorption materials like activated carbon,silica gel,and zeolite mo...The capture of atmospheric carbon dioxide by adsorbents is an important strategy to deal with the greenhouse effect.Compared with traditional CO_(2) adsorption materials like activated carbon,silica gel,and zeolite molecular sieves,covalent organic frameworks(COFs)have excellent thermal and chemical stabilities and can be produced in many different forms.Using their different possible construction units,ordered structures for specific applications can be produced,giving them broad prospects in fields such as gas storage.This review analyzes the different types of COFs that have been synthesized and their different methods of CO_(2) capture.It then discusses different ways to increase CO_(2) adsorption by changing the internal structure of COFs and modifying their surfaces.The limitations of COF-derived carbon materials in CO_(2) capture are reviewed and,finally,the key role of machine learning and computational simulation in improving CO_(2) adsorption is mentioned,and the current status and future possible uses of COFs are summarized.展开更多
A growing interest in producing and sharing computable biomedical knowledge artifacts(CBKs) is increasing the demand for repositories that validate, catalog, and provide shared access to CBKs. However, there is a lack...A growing interest in producing and sharing computable biomedical knowledge artifacts(CBKs) is increasing the demand for repositories that validate, catalog, and provide shared access to CBKs. However, there is a lack of evidence on how best to manage and sustain CBK repositories. In this paper, we present the results of interviews with several pioneering CBK repository owners. These interviews were informed by the Trusted Repositories Audit and Certification(TRAC) framework. Insights gained from these interviews suggest that the organizations operating CBK repositories are somewhat new, that their initial approaches to repository governance are informal, and that achieving economic sustainability for their CBK repositories is a major challenge. To enable a learning health system to make better use of its data intelligence, future approaches to CBK repository management will require enhanced governance and closer adherence to best practice frameworks to meet the needs of myriad biomedical science and health communities. More effort is needed to find sustainable funding models for accessible CBK artifact collections.展开更多
In this paper, we prove that if a c.e. Turing degree d is non-low2, then there are two left-c.e, reals β0,β1 in d, such that, if β0 is wtt-reducible to a left-c.e, real a, then β1 is not computable Lipschitz （cl-...In this paper, we prove that if a c.e. Turing degree d is non-low2, then there are two left-c.e, reals β0,β1 in d, such that, if β0 is wtt-reducible to a left-c.e, real a, then β1 is not computable Lipschitz （cl-） reducible to a. As a corollary, d contains a left-c.e, real which is not cl-reducible to any complex （wtt-complete） left-c.e, real.展开更多
Organic electrochemical transistor(OECT)devices demonstrate great promising potential for reservoir computing(RC)systems,but their lack of tunable dynamic characteristics limits their application in multi-temporal sca...Organic electrochemical transistor(OECT)devices demonstrate great promising potential for reservoir computing(RC)systems,but their lack of tunable dynamic characteristics limits their application in multi-temporal scale tasks.In this study,we report an OECT-based neuromorphic device with tunable relaxation time(τ)by introducing an additional vertical back-gate electrode into a planar structure.The dual-gate design enablesτreconfiguration from 93 to 541 ms.The tunable relaxation behaviors can be attributed to the combined effects of planar-gate induced electrochemical doping and back-gateinduced electrostatic coupling,as verified by electrochemical impedance spectroscopy analysis.Furthermore,we used theτ-tunable OECT devices as physical reservoirs in the RC system for intelligent driving trajectory prediction,achieving a significant improvement in prediction accuracy from below 69%to 99%.The results demonstrate that theτ-tunable OECT shows a promising candidate for multi-temporal scale neuromorphic computing applications.展开更多
Vehicle Edge Computing(VEC)and Cloud Computing(CC)significantly enhance the processing efficiency of delay-sensitive and computation-intensive applications by offloading compute-intensive tasks from resource-constrain...Vehicle Edge Computing(VEC)and Cloud Computing(CC)significantly enhance the processing efficiency of delay-sensitive and computation-intensive applications by offloading compute-intensive tasks from resource-constrained onboard devices to nearby Roadside Unit(RSU),thereby achieving lower delay and energy consumption.However,due to the limited storage capacity and energy budget of RSUs,it is challenging to meet the demands of the highly dynamic Internet of Vehicles(IoV)environment.Therefore,determining reasonable service caching and computation offloading strategies is crucial.To address this,this paper proposes a joint service caching scheme for cloud-edge collaborative IoV computation offloading.By modeling the dynamic optimization problem using Markov Decision Processes(MDP),the scheme jointly optimizes task delay,energy consumption,load balancing,and privacy entropy to achieve better quality of service.Additionally,a dynamic adaptive multi-objective deep reinforcement learning algorithm is proposed.Each Double Deep Q-Network(DDQN)agent obtains rewards for different objectives based on distinct reward functions and dynamically updates the objective weights by learning the value changes between objectives using Radial Basis Function Networks(RBFN),thereby efficiently approximating the Pareto-optimal decisions for multiple objectives.Extensive experiments demonstrate that the proposed algorithm can better coordinate the three-tier computing resources of cloud,edge,and vehicles.Compared to existing algorithms,the proposed method reduces task delay and energy consumption by 10.64%and 5.1%,respectively.展开更多
In the field of edge computing,achieving low-latency computational task offloading with limited resources is a critical research challenge,particularly in resource-constrained and latency-sensitive vehicular network e...In the field of edge computing,achieving low-latency computational task offloading with limited resources is a critical research challenge,particularly in resource-constrained and latency-sensitive vehicular network environments where rapid response is mandatory for safety-critical applications.In scenarios where edge servers are sparsely deployed,the lack of coordination and information sharing often leads to load imbalance,thereby increasing system latency.Furthermore,in regions without edge server coverage,tasks must be processed locally,which further exacerbates latency issues.To address these challenges,we propose a novel and efficient Deep Reinforcement Learning(DRL)-based approach aimed at minimizing average task latency.The proposed method incorporates three offloading strategies:local computation,direct offloading to the edge server in local region,and device-to-device(D2D)-assisted offloading to edge servers in other regions.We formulate the task offloading process as a complex latency minimization optimization problem.To solve it,we propose an advanced algorithm based on the Dueling Double Deep Q-Network(D3QN)architecture and incorporating the Prioritized Experience Replay(PER)mechanism.Experimental results demonstrate that,compared with existing offloading algorithms,the proposed method significantly reduces average task latency,enhances user experience,and offers an effective strategy for latency optimization in future edge computing systems under dynamic workloads.展开更多
基金supported by National Natural Sci- ence Foundation of China(No.71173212,41101556 and 71203215)the President Fund of GUCAS(No Y1510RY00)
文摘The intensity allocation criteria of carbon emissions permits and its influence on China's regional development are analyzed through the 30-province/autonomous region computable general equilibrium(CGE)model.Simulation results show that:industrial intensity criteria without taking regional economic development into account deepen the unbalance of regional economic development;regional intensity criteria without taking industrial properties into account exert little negative impact on regional harmonious development,but relatively high negative influence on high-carbon emission industries.The two-step allocation scheme that the central government allocates emissions permits to provincial governments based on regional economic development and then provincial governments allocate emissions permits to emission resources or entities based on industrial properties is a feasible and operable choice.
基金supported by the National Basic Research Program(973 Program)of China:[Grant Number2012CB955800]the National Natural Science Foundation(863 Program)of China:[Grant Number 2012 AA063101]the "Strategic Priority Research Program" of the Chinese Academy of Sciences[Grant Number XDB05050200]
文摘The public health and ecological impacts of volatile organic compound(VOCs) pollution have become a serious problem in China,arousing increasing attention to emissions control.In this context,this paper analyses the effectiveness of VOC reduction policies,namely pollution charges and environmental taxes at the national and industrial sector levels.It uses a computable general equilibrium model,which connects macroeconomic variables with VOC emissions inventory,to simulate the effects of policy scenarios(with 2007 as the reference year).This paper shows that VOC emissions are reduced by 2.2% when a pollution charge equal to the average cost of engineering reduction methods-the traditional approach to regulation in China-is applied.In order to achieve a similar reduction,an 8.9% indirect tax would have to be imposed.It concludes that an environmental tax should be the preferred method of VOC regulation due to its smaller footprint on the macroeconomy.Other policies,such as subsidies,should be used as supplements.
基金the National Natural Science Foundation of China(Nos.71373153,71262022 and 71003068)the Shanghai Philosophy and Social Science Fund Project(No.2014BJB001)the“Shuguang Program”Supported by Shanghai Education Development Foundation and Shanghai Municipal Education Commission(No.14SG32)
文摘Researching China's innovative economic and financial innovation issues under the background of the New Normal, we need to carefully analyze the internal structure and interaction of China's macroeconomics.The computable general equilibrium(CGE) model has outstanding advantages on predicting the external shock influences on economic system, but previous studies on forecast for China's future economy mostly considered a high growth rate which is hard to comply with the New Normal scene. By constructing China's macroeconomic dynamic CGE(DCGE) model and anticipating the economic impact of the New Normal, this paper finds that the New Normal has a certain extent inhibition on China's macro-economy and innovation. However, after adding the research and development(R&D) subsidy policy, the negative impacts of the New Normal on macro-economy can be eliminated to realize the optimization of economic structure. In addition, after combining the financial innovation promoting policy and the Ke Qiang index through the simulation of macro-economy, we find that the quality of economic growth is improved. Finally, we provide the policy recommendations for the realization of an innovative economy under China's New Normal.
文摘BACKGROUND With the recent change in the definition(Sepsis-3 Definition)of sepsis and septic shock,an electronic search algorithm was required to identify the cases for data automation.This supervised machine learning method would help screen a large amount of electronic medical records(EMR)for efficient research purposes.AIM To develop and validate a computable phenotype via supervised machine learning method for retrospectively identifying sepsis and septic shock in critical care patients.METHODS A supervised machine learning method was developed based on culture orders,Sequential Organ Failure Assessment(SOFA)scores,serum lactate levels and vasopressor use in the intensive care units(ICUs).The computable phenotype was derived from a retrospective analysis of a random cohort of 100 patients admitted to the medical ICU.This was then validated in an independent cohort of 100 patients.We compared the results from computable phenotype to a gold standard by manual review of EMR by 2 blinded reviewers.Disagreement was resolved by a critical care clinician.A SOFA score≥2 during the ICU stay with a culture 72 h before or after the time of admission was identified.Sepsis versions as V1 was defined as blood cultures with SOFA≥2 and Sepsis V2 was defined as any culture with SOFA score≥2.A serum lactate level≥2 mmol/L from 24 h before admission till their stay in the ICU and vasopressor use with Sepsis-1 and-2 were identified as Septic Shock-V1 and-V2 respectively.RESULTS In the derivation subset of 100 random patients,the final machine learning strategy achieved a sensitivity-specificity of 100%and 84%for Sepsis-1,100%and 95%for Sepsis-2,78%and 80%for Septic Shock-1,and 80%and 90%for Septic Shock-2.An overall percent of agreement between two blinded reviewers had a k=0.86 and 0.90 for Sepsis 2 and Septic shock 2 respectively.In validation of the algorithm through a separate 100 random patient subset,the reported sensitivity and specificity for all 4 diagnoses were 100%-100%each.CONCLUSION Supervised machine learning for identification of sepsis and septic shock is reliable and an efficient alternative to manual chart review.
基金supported by National Natural Science Foundation of China.
文摘In this paper,we study the computative structure of computable function-a structure of computative tree,and,by analysis on it,got the most general algorithm and model for computation on computable functions.
文摘This paper proposes and illustrates an AI embedded object-oriented methodology to formulate the computable general equilibrium (CGE) models. In this framework, a CGE model is viewed as a collection of objects embedded AI or namely agents in computer world, corresponding to economic agents and entities in real world, such as government, households, markets and so on. A frame representation of major objects in CGE model is used for trade and environment. Embedded Al object-oriented approach (or software agent) is used in the CGE model representation can able to narrow the gap among the semantic representation, formal CGE (mathematical) representation and computer and algorithm representation, and to improve CGE in understanding and maintenance etc. In such a system, constructing a CGE model to appear an intuitive process rather than an abstract process. This intuitive process needs more understanding of the substance of economics and the logic underlying the problem rather than mathematical notation.
基金National Natural Science Foundation of China(No.41101556,71173212,71203215)
文摘This paper examined the impacts of the total energy consumption control policy and energy quota allocation plans on China′s regional economy. This research analyzed the influences of different energy quota allocation plans with various weights of equity and efficiency, using a dynamic computable general equilibrium(CGE) model for 30 province-level administrative regions. The results show that the efficiency-first allocation plan costs the least but widens regional income gap, whereas the outcomes of equity-first allocation plan and intensity target-based allocation plan are similar and are both opposite to the efficiency-first allocation plan′ outcome. The plan featuring a balance between efficiency and equity is more feasible, which can bring regional economic losses evenly and prevent massive interregional migration of energy-related industries. Furthermore, the effects of possible induced energy technology improvements in different energy quota allocation plans were studied. Induced energy technology improvements can add more feasibility to all allocation plans under the total energy consumption control policy. In the long term, if the policy of the total energy consumption control continues and more market-based tools are implemented to allocate energy quotas, the positive consequences of induced energy technology improvements will become much more obvious.
基金financial support from the Humanities and Social Science Fund of Ministry of Education of China(Project No.18YJAZH138)the National Natural Science Foundation of China(No.71403163)National Social Science Foundation of China(No.20BJL036).
文摘Changes in the energy price system will determine the direction of evolution of the energy industry structure.As a country where coal is the dominant energy source,what is the effect of coal price fluctuations on China’s industry development costs and energy consumption structure?To investigate this problem,this paper utilized an economy–energy–environment computable general equilibrium model.In this study,four aspects were analyzed:Energy supply side,proportion of renewable energy consumption,macroeconomy,and changes in CO_(2) emissions.The results of this study show that an increase of 10%–20%in coal prices contributes to a shift into using renewable energy,which leads to energy saving and emission reduction.Renewable energy and clean energy rose by 0.57%–4.47%in the energy structure,but this has a certain negative impact on the macroeconomy.The gross domestic product(GDP)fell by 0.07%–0.18%.As a result,the decline in coal prices became an obstacle to renewable energy substitution and energy conservation.In addition,we put forward policy suggestions according to the results in energy,economic,and environmental effects.
文摘The challenge of meeting the ever-increasing food demand for the growing population will be further exacerbated by climate change in Ethiopia. This paper presents the simulated economy-wide impacts of climate change on the agriculture sector of Ethiopia using a dynamic computable general equilibrium (CGE) model. The study simulated the scenarios of agricultural productivity change induced by climate change up to the year 2050. At national level, the simulation results suggest that crop production will be adversely affected during the coming four decades and the severity will increase over the time period. Production of teff, maize and sorghum will decline by 25.4, 21.8 and 25.2 percent, respectively by 2050 compared to the base period. Climate change will also cause losses of 31.1 percent agricultural GDP at factor cost by 2050. Climate change affects more the income and consumption of poor rural households than urban rural non-farming households. The reduction in agricultural production will not be evenly distributed across agro ecological zones, and will not all be negative. Among rural residents, climate change impacts tend to hurt the income of the poor more in drought prone regions. Income from labor, land and livestock in moisture sufficient highland cereal-based will decline by 5.1, 8.8 and 15.2 percent in 2050. This study indicated that since climate change is an inevitable phenomenon, the country should start mainstreaming adaptation measures to sustain the overall performance of the economy.
文摘Papyrus is increasingly suggested as an alternative bioenergy source to reduce the pressure on forest ecosystems. However, there are few studies on the economic viability of papyrus wetlands and the benefits for local communities. We construct a village Computable General Equilibrium (CGE) model to examine whether papyrus harvesting and processing has the potential to improve local livelihoods and simultaneously counteract pressure on local forest resources. We apply the CGE model to a village in northern Zambia where overexploitation of forest resources to produce energy from firewood and charcoal poses a serious problem. The analysis is based on survey data?from 105 households collected in 2015. The model results show that papyrus briquetting would be a possible?alternative biofuel and that this technology improves household income and utility through?labor?reallocations. Higher opportunity costs lead to households switching from firewood extraction and charcoal production activities to papyrus harvesting and processing to produce bioenergy. Replacing energy supplies from firewood and charcoal with papyrus briquettes results in substitution effects between forest land and wetland and thereby reduces the pressure on local forest resources. The CGE approach allows for an economy-wide ex-ante analysis at village level and can support management decisions to ensure the success of papyrus bioenergy interventions.
文摘The role of the construction industry in economic growth has been widely discussed in the extant literature,but existing studies have not investigated the disaggregated impact of construction investments on the production and social sectors.This study examines the disaggregated effect of construction investments on the Saudi economy.The study uses a social accounting matrix of Saudi Arabia and constructs a dynamic computable general equilibrium model.The findings reveal that construction investments significantly boosted GDP and aggregate investments in the first two periods;however,the growth declined in the following three periods.This finding underlines the importance of long-term investments in the construction sector and calls for continuous monitoring and updating of the investment policy for sustainable development.This study also presents the disaggregated impact of investments on the value-added by each sector of the economy.The ranking of sectors exhibits that mining and quarry activities underwent a high increase in value-added,second to construction activities.Other economic activities also experienced growth in value-added and some of them changed their ranks within the five years.
基金supported by the National Natural Science Foundation of China(Grant Nos.42177448 and 41907393)。
文摘With growing regional economic integration,transportation systems have become critical to regional development and economic vitality but vulnerable to disasters.However,the regional economic ripple effect of a disaster is difficult to quantify accurately,especially considering the cumulated influence of traffic disruptions.This study explored integrating transportation system analysis with economic modeling to capture the regional economic ripple effect.A state-of-the-art spatial computable general equilibrium model is leveraged to simulate the operation of the economic system,and the marginal rate of transport cost is introduced to reflect traffic network damage post-disaster.The model is applied to the 50-year return period flood in2020 in Hubei Province,China.The results show the following.First,when traffic disruption costs are considered,the total output loss of non-affected areas is 1.81 times than before,and non-negligible losses reach relatively remote zones of the country,such as the Northwest Comprehensive Economic Zone(36%of total ripple effects).Second,traffic disruptions have a significant hindering effect on regional trade activities,especially in the regional intermediate input—about three times more than before.The industries most sensitive to traffic disruptions were transportation,storage,and postal service(5 times),and processing and assembly manufacturing(4.4 times).Third,the longer the distance,the stronger traffic disruptions'impact on interregional intermediate inputs.Thus,increasing investment in transportation infrastructure significantly contributes to mitigating disaster ripple effects and accelerating the process of industrial recovery in affected areas.
文摘Noncohesive particle clusters are identified and tracked in turbulent flows to determine the breakdown and time evolution of cluster statistics and their implications for interscale mass transfer,which has connections to the classical turbulent energy cascade and its mass cascade counterpart running in parallel.In particular,the formation and dynamics of sediment and larvae clusters are of interest to coral larvae settlement in coastal regions and particularly the resilience of green-gray coastal protection solutions.Analogous cluster behavior is relevant to cloud microphysics and precipitation initiation,radiation transport and light transmission through colloids and suspensions,heat and mass transfer in particle-laden flows,and viral and pollutant transmission.Following a comparison between various clustering techniques,we adopt a density-based cluster identification algorithm based on its simplicity and efficiency,where particles are clustered based on the number of neighboring particles in their individual spheres of influence.We establish parallels with lattice-based percolation theory,as evident in the power-law scaling of the cluster size distribution near the percolation threshold.The degree of discontinuity of the phase transition associated with this percolation threshold is observed to broaden with larger Stokes numbers and thereby large-scale clustering.The sensitivity of our findings to the employed clustering algorithm is discussed.A novel cluster tracking algorithm is deployed to determine the interscale transfer rate along the particle-number phase-space dimension via accounting of cluster breakup and merger events,extending previous work on the bubble breakup cascade beneath surface breaking waves.Our findings shed light on the interaction between particle clusters and their carrier turbulent flows,with an eye toward transport models incorporating cluster characteristics and dynamics.
基金Support by National Natural Science Foundation of China(22127802,22573091)the HY Action(62402010305)。
文摘Biomass-based hydrocarbon fuels,as one of the alternatives to traditional fossil fuels,have attracted considerable attention in the energy field due to their renewability and environmental benefits.This article provides a systematic review of recent research progress in the chemical synthesis of biomass-based hydrocarbon fuels.It outlines the conversion pathways using feedstocks such as lipids,terpenoids,cellulose/hemicellulose,and lignin.Depending on the feedstock,various products with distinct structural characteristics can be prepared through reactions such as cyclization,condensation,and catalytic hydrogenation.Throughout the synthesis process,three key factors play a critical role:efficient catalyst development,production process optimization,and computational-chemistry-based molecular design.Finally,the article discusses future perspectives for biomass-based hydrocarbon fuel synthesis research.
文摘The capture of atmospheric carbon dioxide by adsorbents is an important strategy to deal with the greenhouse effect.Compared with traditional CO_(2) adsorption materials like activated carbon,silica gel,and zeolite molecular sieves,covalent organic frameworks(COFs)have excellent thermal and chemical stabilities and can be produced in many different forms.Using their different possible construction units,ordered structures for specific applications can be produced,giving them broad prospects in fields such as gas storage.This review analyzes the different types of COFs that have been synthesized and their different methods of CO_(2) capture.It then discusses different ways to increase CO_(2) adsorption by changing the internal structure of COFs and modifying their surfaces.The limitations of COF-derived carbon materials in CO_(2) capture are reviewed and,finally,the key role of machine learning and computational simulation in improving CO_(2) adsorption is mentioned,and the current status and future possible uses of COFs are summarized.
文摘A growing interest in producing and sharing computable biomedical knowledge artifacts(CBKs) is increasing the demand for repositories that validate, catalog, and provide shared access to CBKs. However, there is a lack of evidence on how best to manage and sustain CBK repositories. In this paper, we present the results of interviews with several pioneering CBK repository owners. These interviews were informed by the Trusted Repositories Audit and Certification(TRAC) framework. Insights gained from these interviews suggest that the organizations operating CBK repositories are somewhat new, that their initial approaches to repository governance are informal, and that achieving economic sustainability for their CBK repositories is a major challenge. To enable a learning health system to make better use of its data intelligence, future approaches to CBK repository management will require enhanced governance and closer adherence to best practice frameworks to meet the needs of myriad biomedical science and health communities. More effort is needed to find sustainable funding models for accessible CBK artifact collections.
文摘In this paper, we prove that if a c.e. Turing degree d is non-low2, then there are two left-c.e, reals β0,β1 in d, such that, if β0 is wtt-reducible to a left-c.e, real a, then β1 is not computable Lipschitz （cl-） reducible to a. As a corollary, d contains a left-c.e, real which is not cl-reducible to any complex （wtt-complete） left-c.e, real.
基金supported by the National Key Research and Development Program of China under Grant 2022YFB3608300in part by the National Nature Science Foundation of China(NSFC)under Grants 62404050,U2341218,62574056,62204052。
文摘Organic electrochemical transistor(OECT)devices demonstrate great promising potential for reservoir computing(RC)systems,but their lack of tunable dynamic characteristics limits their application in multi-temporal scale tasks.In this study,we report an OECT-based neuromorphic device with tunable relaxation time(τ)by introducing an additional vertical back-gate electrode into a planar structure.The dual-gate design enablesτreconfiguration from 93 to 541 ms.The tunable relaxation behaviors can be attributed to the combined effects of planar-gate induced electrochemical doping and back-gateinduced electrostatic coupling,as verified by electrochemical impedance spectroscopy analysis.Furthermore,we used theτ-tunable OECT devices as physical reservoirs in the RC system for intelligent driving trajectory prediction,achieving a significant improvement in prediction accuracy from below 69%to 99%.The results demonstrate that theτ-tunable OECT shows a promising candidate for multi-temporal scale neuromorphic computing applications.
基金supported by Key Science and Technology Program of Henan Province,China(Grant Nos.242102210147,242102210027)Fujian Province Young and Middle aged Teacher Education Research Project(Science and Technology Category)(No.JZ240101)(Corresponding author:Dong Yuan).
文摘Vehicle Edge Computing(VEC)and Cloud Computing(CC)significantly enhance the processing efficiency of delay-sensitive and computation-intensive applications by offloading compute-intensive tasks from resource-constrained onboard devices to nearby Roadside Unit(RSU),thereby achieving lower delay and energy consumption.However,due to the limited storage capacity and energy budget of RSUs,it is challenging to meet the demands of the highly dynamic Internet of Vehicles(IoV)environment.Therefore,determining reasonable service caching and computation offloading strategies is crucial.To address this,this paper proposes a joint service caching scheme for cloud-edge collaborative IoV computation offloading.By modeling the dynamic optimization problem using Markov Decision Processes(MDP),the scheme jointly optimizes task delay,energy consumption,load balancing,and privacy entropy to achieve better quality of service.Additionally,a dynamic adaptive multi-objective deep reinforcement learning algorithm is proposed.Each Double Deep Q-Network(DDQN)agent obtains rewards for different objectives based on distinct reward functions and dynamically updates the objective weights by learning the value changes between objectives using Radial Basis Function Networks(RBFN),thereby efficiently approximating the Pareto-optimal decisions for multiple objectives.Extensive experiments demonstrate that the proposed algorithm can better coordinate the three-tier computing resources of cloud,edge,and vehicles.Compared to existing algorithms,the proposed method reduces task delay and energy consumption by 10.64%and 5.1%,respectively.
基金supported by the National Natural Science Foundation of China(62202215)Liaoning Province Applied Basic Research Program(Youth Special Project,2023JH2/101600038)+4 种基金Shenyang Youth Science and Technology Innovation Talent Support Program(RC220458)Guangxuan Program of Shenyang Ligong University(SYLUGXRC202216)the Basic Research Special Funds for Undergraduate Universities in Liaoning Province(LJ212410144067)the Natural Science Foundation of Liaoning Province(2024-MS-113)the science and technology funds from Liaoning Education Department(LJKZ0242).
文摘In the field of edge computing,achieving low-latency computational task offloading with limited resources is a critical research challenge,particularly in resource-constrained and latency-sensitive vehicular network environments where rapid response is mandatory for safety-critical applications.In scenarios where edge servers are sparsely deployed,the lack of coordination and information sharing often leads to load imbalance,thereby increasing system latency.Furthermore,in regions without edge server coverage,tasks must be processed locally,which further exacerbates latency issues.To address these challenges,we propose a novel and efficient Deep Reinforcement Learning(DRL)-based approach aimed at minimizing average task latency.The proposed method incorporates three offloading strategies:local computation,direct offloading to the edge server in local region,and device-to-device(D2D)-assisted offloading to edge servers in other regions.We formulate the task offloading process as a complex latency minimization optimization problem.To solve it,we propose an advanced algorithm based on the Dueling Double Deep Q-Network(D3QN)architecture and incorporating the Prioritized Experience Replay(PER)mechanism.Experimental results demonstrate that,compared with existing offloading algorithms,the proposed method significantly reduces average task latency,enhances user experience,and offers an effective strategy for latency optimization in future edge computing systems under dynamic workloads.
