This year's government work report proposes to promote the stable growth of consumption,stimulate consumption potential,and promote new types of consumption such as digital consumption,green consumption,and health...This year's government work report proposes to promote the stable growth of consumption,stimulate consumption potential,and promote new types of consumption such as digital consumption,green consumption,and healthy consumption.展开更多
Bio-inspired computer modelling brings solutions fromthe living phenomena or biological systems to engineering domains.To overcome the obstruction problem of large-scale wind power consumption in Northwest China,this ...Bio-inspired computer modelling brings solutions fromthe living phenomena or biological systems to engineering domains.To overcome the obstruction problem of large-scale wind power consumption in Northwest China,this paper constructs a bio-inspired computer model.It is an optimal wind power consumption dispatching model of multi-time scale demand response that takes into account the involved high-energy load.First,the principle of wind power obstruction with the involvement of a high-energy load is examined in this work.In this step,highenergy load model with different regulation characteristics is established.Then,considering the multi-time scale characteristics of high-energy load and other demand-side resources response speed,a multi-time scale model of coordination optimization is built.An improved bio-inspired model incorporating particle swarm optimization is applied to minimize system operation and wind curtailment costs,as well as to find the most optimal energy configurationwithin the system.Lastly,we take an example of regional power grid in Gansu Province for simulation analysis.Results demonstrate that the suggested scheduling strategy can significantly enhance the wind power consumption level and minimize the system’s operational cost.展开更多
With increasing population and changing demographics,food consumption has experienced a significant transition in quantity and quality.However,a dearth of knowledge remains regarding its environmental impacts and how ...With increasing population and changing demographics,food consumption has experienced a significant transition in quantity and quality.However,a dearth of knowledge remains regarding its environmental impacts and how it responds to demographic dynamics,particularly in emerging economies like China.Using the two-stage Quadratic Almost Demand System(QUAIDS)model,this study empirically examines the impact of demographic dynamics on food consumption and its environmental outcomes based on the provincial data from 2000 to 2020 in China.Under various scenarios,according to changes in demographics,we extend our analysis to project the long-term trend of food consumption and its environmental impacts,including greenhouse gas(GHG)emissions,water footprint(WF),and land appropriation(LA).The results reveal that an increase in the proportion of senior people significantly decreases the consumption of grain and livestock meat and increases the consumption of poultry,egg,and aquatic products,particularly for urban residents.Moreover,an increase in the proportion of males in the population leads to higher consumption of poultry and aquatic products.Correspondingly,in the current scenario of an increased aging population and sex ratio,it is anticipated that GHG emissions,WF,and LA are likely to decrease by 1.37,2.52,and 3.56%,respectively.More importantly,in the scenario adhering to the standards of nutritional intake according to the Dietary Guidelines for Chinese Residents in 2022,GHG emissions,WF,and LA in urban areas would increase by 12.78,20.94,and 18.32%,respectively.Our findings suggest that changing demographics should be considered when designing policies to mitigate the diet-environment-health trilemma and achieve sustainable food consumption.展开更多
Despite the growing recognition of women’s increasing role in the household and corresponding empowerment programs in sub-Saharan Africa,intensive research on the relationship between women’s influence and household...Despite the growing recognition of women’s increasing role in the household and corresponding empowerment programs in sub-Saharan Africa,intensive research on the relationship between women’s influence and household food consumption is minimal.Using the most recent(2017-2018)national household survey data from Tanzania,this study examined the influence of women’s empowerment on household food consumption.First,we compared the monthly consumption of eight food categories between female-headed households(FHHs)and male-headed households(MHHs)using both descriptive statistics and the propensity score matching(PSM)method.Furthermore,we adopted the two-stage Linear Expenditure System and Almost Ideal Demand System model(LES-AIDS)to estimate income and price elasticities for the two household types.The results show that FHHs consume bread and cereals,fish,oils and fats,vegetables,and confectionery(sugar,jam,honey,chocolate,etc.)more than MHHs.Moreover,FHHs have a significantly higher income elasticity of demand for all food groups than MHHs.They are also more price elastic than MHHs in meat,fish,oils,fats,sugar,jam,honey,chocolate,etc.展开更多
Due to a prolonged operation time and low mass transfer efficiency, the primary challenge in the aeration process of non-Newtonian fluids is the high energy consumption, which is closely related to the form and rate o...Due to a prolonged operation time and low mass transfer efficiency, the primary challenge in the aeration process of non-Newtonian fluids is the high energy consumption, which is closely related to the form and rate of impeller, ventilation, rheological properties and bubble morphology in the reactor. In this perspective, through optimal computational fluid dynamics models and experiments, the relationship between power consumption, volumetric mass transfer rate(kLa) and initial bubble size(d0) was constructed to establish an efficient operation mode for the aeration process of non-Newtonian fluids. It was found that reducing the d0could significantly increase the oxygen mass transfer rate, resulting in an obvious decrease in the ventilation volume and impeller speed. When d0was regulated within 2-5 mm,an optimal kLa could be achieved, and 21% of power consumption could be saved, compared to the case of bubbles with a diameter of 10 mm.展开更多
Studying user electricity consumption behavior is crucial for understanding their power usage patterns.However,the traditional clustering methods fail to identify emerging types of electricity consumption behavior.To ...Studying user electricity consumption behavior is crucial for understanding their power usage patterns.However,the traditional clustering methods fail to identify emerging types of electricity consumption behavior.To address this issue,this paper introduces a statistical analysis of clusters and evaluates the set of indicators for power usage patterns.The fuzzy C-means clustering algorithm is then used to analyze 6 months of electricity consumption data in 2017 from energy storage equipment,agricultural drainage irrigation,port shore power,and electric vehicles.Finally,the proposed method is validated through experiments,where the Davies-Bouldin index and profile coefficient are calculated and compared.Experiments showed that the optimal number of clusters is 4.This study demonstrates the potential of using a fuzzy C-means clustering algorithmin identifying emerging types of electricity consumption behavior,which can help power system operators and policymakers to make informed decisions and improve energy efficiency.展开更多
Transient memories,which can physically disappear without leaving traceable remains over a period of normal operation,are attracting increasing attention for potential applications in the fields of data security and g...Transient memories,which can physically disappear without leaving traceable remains over a period of normal operation,are attracting increasing attention for potential applications in the fields of data security and green electronics.Resistive random access memory(RRAM)is a promising candidate for next-generation memory.In this context,biocompatible l-carrageenan(l-car),extracted from natural seaweed,is introduced for the fabrication of RRAM devices(Ag/l-car/Pt).Taking advantage of the complexation processes between the functional groups(C–O–C,C–O–H,et al.)and Ag metal ions,a lower migration barrier of Ag ions and a high-speed switching(22.2 ns for SET operation/26 ns for RESET operation)were achieved,resulting in an ultralow power consumption of 56 fJ.And the prepared Ag/l-car/Pt RRAM devices also revealed the capacities of multilevel storage and flexibility.In addition,thanks to the hydrophilic groups of l-car molecule,the RRAM devices can be rapidly dissolved in deionized(DI)water within 13 minutes,showing excellent transient characteristics.This work demonstrates that l-car based RRAM devices have great potential for applications in secure storage applications,flexible electronics and transient electronics.展开更多
In light of the rapid expansion of China s economy,there has been a notable shift in the consumption patterns of urban and rural residents.This is evident in the increased consumption of agricultural products,with fru...In light of the rapid expansion of China s economy,there has been a notable shift in the consumption patterns of urban and rural residents.This is evident in the increased consumption of agricultural products,with fruit consumption representing a particularly pronounced trend.The 2023 Beijing Consumption Statistics indicate that demand for fruit is increasing,accompanied by heightened competition for quality and brands.The paper is based on research that examines the current situation of fruit consumption among urban and rural residents in Beijing.The results indicate that when urban and rural residents in Beijing purchase fruits,price is the most significant factor influencing fruit consumption.Taste,appearance,and variety are also considered to be important influencing factors,while the influence of factors such as fruit type,consumption pattern,and fruit brand is relatively minor.展开更多
To address the issues of limited demand response data,low generalization of demand response potential evaluation,and poor demand response effect,the article proposes a demand response potential feature extraction and ...To address the issues of limited demand response data,low generalization of demand response potential evaluation,and poor demand response effect,the article proposes a demand response potential feature extraction and prediction model based on data mining and a demand response potential assessment model for adjustable loads in demand response scenarios based on subjective and objective weight analysis.Firstly,based on the demand response process and demand response behavior,obtain demand response characteristics that characterize the process and behavior.Secondly,establish a feature extraction and prediction model based on data mining,including similar day clustering,time series decomposition,redundancy processing,and data prediction.The predicted values of each demand response feature on the response day are obtained.Thirdly,the predicted data of various characteristics on the response day are used as demand response potential evaluation indicators to represent different demand response scenarios and adjustable loads,and a demand response potential evaluation model based on subjective and objective weight allocation is established to calculate the demand response potential of different adjustable loads in different demand response scenarios.Finally,the effectiveness of the method proposed in the article is verified through examples,providing a reference for load aggregators to formulate demand response schemes.展开更多
Missing value is one of the main factors that cause dirty data.Without high-quality data,there will be no reliable analysis results and precise decision-making.Therefore,the data warehouse needs to integrate high-qual...Missing value is one of the main factors that cause dirty data.Without high-quality data,there will be no reliable analysis results and precise decision-making.Therefore,the data warehouse needs to integrate high-quality data consistently.In the power system,the electricity consumption data of some large users cannot be normally collected resulting in missing data,which affects the calculation of power supply and eventually leads to a large error in the daily power line loss rate.For the problem of missing electricity consumption data,this study proposes a group method of data handling(GMDH)based data interpolation method in distribution power networks and applies it in the analysis of actually collected electricity data.First,the dependent and independent variables are defined from the original data,and the upper and lower limits of missing values are determined according to prior knowledge or existing data information.All missing data are randomly interpolated within the upper and lower limits.Then,the GMDH network is established to obtain the optimal complexity model,which is used to predict the missing data to replace the last imputed electricity consumption data.At last,this process is implemented iteratively until the missing values do not change.Under a relatively small noise level(α=0.25),the proposed approach achieves a maximum error of no more than 0.605%.Experimental findings demonstrate the efficacy and feasibility of the proposed approach,which realizes the transformation from incomplete data to complete data.Also,this proposed data interpolation approach provides a strong basis for the electricity theft diagnosis and metering fault analysis of electricity enterprises.展开更多
This study proposes a prediction model considering external weather and holiday factors to address the issue of accurately predicting urban taxi travel demand caused by complex data and numerous influencing factors.Th...This study proposes a prediction model considering external weather and holiday factors to address the issue of accurately predicting urban taxi travel demand caused by complex data and numerous influencing factors.The model integrates the Complete Ensemble Empirical Mode Decomposition with Adaptive Noise(CEEMDAN)and Convolutional Long Short Term Memory Neural Network(ConvLSTM)to predict short-term taxi travel demand.The CEEMDAN decomposition method effectively decomposes time series data into a set of modal components,capturing sequence characteristics at different time scales and frequencies.Based on the sample entropy value of components,secondary processing of more complex sequence components after decomposition is employed to reduce the cumulative prediction error of component sequences and improve prediction efficiency.On this basis,considering the correlation between the spatiotemporal trends of short-term taxi traffic,a ConvLSTM neural network model with Long Short Term Memory(LSTM)time series processing ability and Convolutional Neural Networks(CNN)spatial feature processing ability is constructed to predict the travel demand for urban taxis.The combined prediction model is tested on a taxi travel demand dataset in a certain area of Beijing.The results show that the CEEMDAN-ConvLSTM prediction model outperforms the LSTM,Autoregressive Integrated Moving Average model(ARIMA),CNN,and ConvLSTM benchmark models in terms of Symmetric Mean Absolute Percentage Error(SMAPE),Root Mean Square Error(RMSE),Mean Absolute Error(MAE),and R2 metrics.Notably,the SMAPE metric exhibits a remarkable decline of 21.03%with the utilization of our proposed model.These results confirm that our study provides a highly accurate and valid model for taxi travel demand forecasting.展开更多
With the development of intelligent and interconnected traffic system,a convergence of traffic stream is anticipated in the foreseeable future,where both connected automated vehicle(CAV)and human driven vehicle(HDV)wi...With the development of intelligent and interconnected traffic system,a convergence of traffic stream is anticipated in the foreseeable future,where both connected automated vehicle(CAV)and human driven vehicle(HDV)will coexist.In order to examine the effect of CAV on the overall stability and energy consumption of such a heterogeneous traffic system,we first take into account the interrelated perception of distance and speed by CAV to establish a macroscopic dynamic model through utilizing the full velocity difference(FVD)model.Subsequently,adopting the linear stability theory,we propose the linear stability condition for the model through using the small perturbation method,and the validity of the heterogeneous model is verified by comparing with the FVD model.Through nonlinear theoretical analysis,we further derive the KdV-Burgers equation,which captures the propagation characteristics of traffic density waves.Finally,by numerical simulation experiments through utilizing a macroscopic model of heterogeneous traffic flow,the effect of CAV permeability on the stability of density wave in heterogeneous traffic flow and the energy consumption of the traffic system is investigated.Subsequent analysis reveals emergent traffic phenomena.The experimental findings demonstrate that as CAV permeability increases,the ability to dampen the propagation of fluctuations in heterogeneous traffic flow gradually intensifies when giving system perturbation,leading to enhanced stability of the traffic system.Furthermore,higher initial traffic density renders the traffic system more susceptible to congestion,resulting in local clustering effect and stop-and-go traffic phenomenon.Remarkably,the total energy consumption of the heterogeneous traffic system exhibits a gradual decline with CAV permeability increasing.Further evidence has demonstrated the positive influence of CAV on heterogeneous traffic flow.This research contributes to providing theoretical guidance for future CAV applications,aiming to enhance urban road traffic efficiency and alleviate congestion.展开更多
Understanding interactions between viruses and their hosts is conducive to enabling better application of viruses as biocontrol agents.Certain viruses carried by parasitic wasps enhance the parasitic efficiency of was...Understanding interactions between viruses and their hosts is conducive to enabling better application of viruses as biocontrol agents.Certain viruses carried by parasitic wasps enhance the parasitic efficiency of wasp-larvae by protecting them against the immune system of their Lepidopteran host.However,the relationship between prey pests and viruses found in predatory natural enemies remains unclear.Herein,we report the interaction between Arma chinensis virus-1(AcV-1),originally isolated from a predatory natural enemy,Arma chinensis(Hemiptera:Pentatomidae),and one of its prey species,Spodoptera frugiperda(Lepidoptera:Noctuidae).The results showed that the AcV-1 virus appeared harmful to the novel host S.frugiperda by inhibiting larval diet consumption and increasing pupal mortality.Meanwhile,sequencing data indicated that the virus altered the gene expression profiles of S.frugiperda.KEGG analysis showed that the proteasome and phagosome pathways related to protein degradation and immune response were significantly enriched.Although the expression levels of digestive enzyme genes did not change significantly,the total protease activity of AcV-1 virus-positive individuals was significantly decreased,suggesting that the virus inhibited diet consumption of S.frugiperda via the down-regulation of digestive enzyme activities.These results indicate that a virus initially isolated in a predatory natural enemy can decrease the fitness of its prey species.The virus was found to impact the host proteasome and phagosome pathways related to protein degradation and immunity,providing a potential mechanism to enhance controlling efficiency.展开更多
With the increasing penetration of wind and solar energies,the accompanying uncertainty that propagates in the system places higher requirements on the expansion planning of power systems.A source-grid-load-storage co...With the increasing penetration of wind and solar energies,the accompanying uncertainty that propagates in the system places higher requirements on the expansion planning of power systems.A source-grid-load-storage coordinated expansion planning model based on stochastic programming was proposed to suppress the impact of wind and solar energy fluctuations.Multiple types of system components,including demand response service entities,converter stations,DC transmission systems,cascade hydropower stations,and other traditional components,have been extensively modeled.Moreover,energy storage systems are considered to improve the accommodation level of renewable energy and alleviate the influence of intermittence.Demand-response service entities from the load side are used to reduce and move the demand during peak load periods.The uncertainties in wind,solar energy,and loads were simulated using stochastic programming.Finally,the effectiveness of the proposed model is verified through numerical simulations.展开更多
To facilitate the coordinated and large-scale participation of residential flexible loads in demand response(DR),a load aggregator(LA)can integrate these loads for scheduling.In this study,a residential DR optimizatio...To facilitate the coordinated and large-scale participation of residential flexible loads in demand response(DR),a load aggregator(LA)can integrate these loads for scheduling.In this study,a residential DR optimization scheduling strategy was formulated considering the participation of flexible loads in DR.First,based on the operational characteristics of flexible loads such as electric vehicles,air conditioners,and dishwashers,their DR participation,the base to calculate the compensation price to users,was determined by considering these loads as virtual energy storage.It was quantified based on the state of virtual energy storage during each time slot.Second,flexible loads were clustered using the K-means algorithm,considering the typical operational and behavioral characteristics as the cluster centroid.Finally,the LA scheduling strategy was implemented by introducing a DR mechanism based on the directrix load.The simulation results demonstrate that the proposed DR approach can effectively reduce peak loads and fill valleys,thereby improving the load management performance.展开更多
Demand-responsive transportation(DRT)is a flexible passenger service designed to enhance road efficiency,reduce peak-hour traffic,and boost passenger satisfaction.However,existing optimization methods for initial pass...Demand-responsive transportation(DRT)is a flexible passenger service designed to enhance road efficiency,reduce peak-hour traffic,and boost passenger satisfaction.However,existing optimization methods for initial passenger requests fall short in addressing real-time passenger needs.Consequently,there is a need to develop realtime DRT route optimization methods that integrate both initial and real-time requests.This paper presents a twostage,multi-objective optimization model for DRT vehicle scheduling.The first stage involves an initial scheduling model aimed at minimizing vehicle configuration,and operational,and CO_(2)emission costs while ensuring passenger satisfaction.The second stage develops a real-time scheduling model to minimize additional operational costs,penalties for time window violations,and costs due to rejected passengers,thereby addressing real-time demands.Additionally,an enhanced genetic algorithm based on Non-dominated Sorting Genetic Algorithm-II(NSGA-II)is designed,incorporating multiple crossover points to accelerate convergence and improve solution efficiency.The proposed scheduling model is validated using a real network in Shanghai.Results indicate that realtime scheduling can serve more passengers,and improve vehicle utilization and occupancy rates,with only a minor increase in total operational costs.Compared to the traditional NSGA-II algorithm,the improved version enhances convergence speed by 31.7%and solution speed by 4.8%.The proposed model and algorithm offer both theoretical and practical guidance for real-world DRT scheduling.展开更多
Fog computing is considered as a solution to accommodate the emergence of booming requirements from a large variety of resource-limited Internet of Things(IoT)devices.To ensure the security of private data,in this pap...Fog computing is considered as a solution to accommodate the emergence of booming requirements from a large variety of resource-limited Internet of Things(IoT)devices.To ensure the security of private data,in this paper,we introduce a blockchain-enabled three-layer device-fog-cloud heterogeneous network.A reputation model is proposed to update the credibility of the fog nodes(FN),which is used to select blockchain nodes(BN)from FNs to participate in the consensus process.According to the Rivest-Shamir-Adleman(RSA)encryption algorithm applied to the blockchain system,FNs could verify the identity of the node through its public key to avoid malicious attacks.Additionally,to reduce the computation complexity of the consensus algorithms and the network overhead,we propose a dynamic offloading and resource allocation(DORA)algorithm and a reputation-based democratic byzantine fault tolerant(R-DBFT)algorithm to optimize the offloading decisions and decrease the number of BNs in the consensus algorithm while ensuring the network security.Simulation results demonstrate that the proposed algorithm could efficiently reduce the network overhead,and obtain a considerable performance improvement compared to the related algorithms in the previous literature.展开更多
This study aims to analysis the influence of economic growth(EG)and energy consumption(EC)on sulfur dioxide emissions(SE)in China.Accordingly,this study explores the link between EG,EC,and SE for 30 provinces in China...This study aims to analysis the influence of economic growth(EG)and energy consumption(EC)on sulfur dioxide emissions(SE)in China.Accordingly,this study explores the link between EG,EC,and SE for 30 provinces in China over the span of 2000-2019.This study also analyzes cross-sectional dependence tests,panel unit root tests,Westerlund panel cointegration tests,Dumitrescu-Hurlin(D-H)causality tests.According to the test results,there is an inverted U-shaped association between EG and SE,and the assumption of the Environmental Kuznets Curve(EKC)is verified.The signs of EG and EC in the fixed effect(FE)and random effect(RE)methods are in line with those in the dynamic ordinary least squares(DOLS),fully modified ordinary least squares(FMOLS)and autoregressive distributed lag(ARDL)estimators.Moreover,the results verified that EC can obviously positive impact the SE.To reduce SE in China,government and policymakers can improve air quality by developing cleaner energy sources and improving energy efficiency.This requires the comprehensive use of policies,regulations,economic incentives,and public participation to promote sustainable development.展开更多
In the restructured electricity market,microgrid(MG),with the incorporation of smart grid technologies,distributed energy resources(DERs),a pumped-storage-hydraulic(PSH)unit,and a demand response program(DRP),is a sma...In the restructured electricity market,microgrid(MG),with the incorporation of smart grid technologies,distributed energy resources(DERs),a pumped-storage-hydraulic(PSH)unit,and a demand response program(DRP),is a smarter and more reliable electricity provider.DER consists of gas turbines and renewable energy sources such as photovoltaic systems and wind turbines.Better bidding strategies,prepared by MG operators,decrease the electricity cost and emissions from upstream grid and conventional and renewable energy sources(RES).But it is inefficient due to the very high sporadic characteristics of RES and the very high outage rate.To solve these issues,this study suggests non-dominated sorting genetic algorithm Ⅱ(NSGA-Ⅱ)for an optimal bidding strategy considering pumped hydroelectric energy storage and DRP based on outage conditions and uncertainties of renewable energy sources.The uncertainty related to solar and wind units is modeled using lognormal and Weibull probability distributions.TOU-based DRP is used,especially considering the time of outages along with the time of peak loads and prices,to enhance the reliability of MG and reduce costs and emissions.展开更多
To improve the resilience of a distribution system against extreme weather,a fuel-based distributed generator(DG)allocation model is proposed in this study.In this model,the DGs are placed at the planning stage.When a...To improve the resilience of a distribution system against extreme weather,a fuel-based distributed generator(DG)allocation model is proposed in this study.In this model,the DGs are placed at the planning stage.When an extreme event occurs,the controllable generators form temporary microgrids(MGs)to restore the load maximally.Simultaneously,a demand response program(DRP)mitigates the imbalance between the power supply and demand during extreme events.To cope with the fault uncertainty,a robust optimization(RO)method is applied to reduce the long-term investment and short-term operation costs.The optimization is formulated as a tri-level defenderattacker-defender(DAD)framework.At the first level,decision-makers work out the DG allocation scheme;at the second level,the attacker finds the optimal attack strategy with maximum damage;and at the third level,restoration measures,namely distribution network reconfiguration(DNR)and demand response are performed.The problem is solved by the nested column and constraint generation(NC&CG)method and the model is validated using an IEEE 33-node system.Case studies validate the effectiveness and superiority of the proposed model according to the enhanced resilience and reduced cost.展开更多
文摘This year's government work report proposes to promote the stable growth of consumption,stimulate consumption potential,and promote new types of consumption such as digital consumption,green consumption,and healthy consumption.
基金supported by the Program for Innovative Research Team(in Science and Technology)in University of Henan Province(No.22IRTSTHN016)the Hubei Natural Science Foundation(No.2021CFB156)the Japan Society for the Promotion of Science(JSPS)Grants-in-Aid for Scientific Research(KAKENHI)(No.JP21K17737).
文摘Bio-inspired computer modelling brings solutions fromthe living phenomena or biological systems to engineering domains.To overcome the obstruction problem of large-scale wind power consumption in Northwest China,this paper constructs a bio-inspired computer model.It is an optimal wind power consumption dispatching model of multi-time scale demand response that takes into account the involved high-energy load.First,the principle of wind power obstruction with the involvement of a high-energy load is examined in this work.In this step,highenergy load model with different regulation characteristics is established.Then,considering the multi-time scale characteristics of high-energy load and other demand-side resources response speed,a multi-time scale model of coordination optimization is built.An improved bio-inspired model incorporating particle swarm optimization is applied to minimize system operation and wind curtailment costs,as well as to find the most optimal energy configurationwithin the system.Lastly,we take an example of regional power grid in Gansu Province for simulation analysis.Results demonstrate that the suggested scheduling strategy can significantly enhance the wind power consumption level and minimize the system’s operational cost.
基金This work was supported by the Qinchuangyuan Project of Shaanxi Province,China(QCYRCXM-2022-145)the Major Project of the Key Research Base of Humanities and Social Sciences of the Ministry of Education,China(22JJD790052)+1 种基金the Chinese Universities Scientific Fund(Z1010422003)the National Natural Science Foundation of China(72373117).
文摘With increasing population and changing demographics,food consumption has experienced a significant transition in quantity and quality.However,a dearth of knowledge remains regarding its environmental impacts and how it responds to demographic dynamics,particularly in emerging economies like China.Using the two-stage Quadratic Almost Demand System(QUAIDS)model,this study empirically examines the impact of demographic dynamics on food consumption and its environmental outcomes based on the provincial data from 2000 to 2020 in China.Under various scenarios,according to changes in demographics,we extend our analysis to project the long-term trend of food consumption and its environmental impacts,including greenhouse gas(GHG)emissions,water footprint(WF),and land appropriation(LA).The results reveal that an increase in the proportion of senior people significantly decreases the consumption of grain and livestock meat and increases the consumption of poultry,egg,and aquatic products,particularly for urban residents.Moreover,an increase in the proportion of males in the population leads to higher consumption of poultry and aquatic products.Correspondingly,in the current scenario of an increased aging population and sex ratio,it is anticipated that GHG emissions,WF,and LA are likely to decrease by 1.37,2.52,and 3.56%,respectively.More importantly,in the scenario adhering to the standards of nutritional intake according to the Dietary Guidelines for Chinese Residents in 2022,GHG emissions,WF,and LA in urban areas would increase by 12.78,20.94,and 18.32%,respectively.Our findings suggest that changing demographics should be considered when designing policies to mitigate the diet-environment-health trilemma and achieve sustainable food consumption.
基金This study was supported by the Chinese University Scientific Fund(2023TC105)the National Nature Science Foundation of China(72361147521&72061147002).
文摘Despite the growing recognition of women’s increasing role in the household and corresponding empowerment programs in sub-Saharan Africa,intensive research on the relationship between women’s influence and household food consumption is minimal.Using the most recent(2017-2018)national household survey data from Tanzania,this study examined the influence of women’s empowerment on household food consumption.First,we compared the monthly consumption of eight food categories between female-headed households(FHHs)and male-headed households(MHHs)using both descriptive statistics and the propensity score matching(PSM)method.Furthermore,we adopted the two-stage Linear Expenditure System and Almost Ideal Demand System model(LES-AIDS)to estimate income and price elasticities for the two household types.The results show that FHHs consume bread and cereals,fish,oils and fats,vegetables,and confectionery(sugar,jam,honey,chocolate,etc.)more than MHHs.Moreover,FHHs have a significantly higher income elasticity of demand for all food groups than MHHs.They are also more price elastic than MHHs in meat,fish,oils,fats,sugar,jam,honey,chocolate,etc.
基金financial support of the National Natural Science Foundation of China(21776122).
文摘Due to a prolonged operation time and low mass transfer efficiency, the primary challenge in the aeration process of non-Newtonian fluids is the high energy consumption, which is closely related to the form and rate of impeller, ventilation, rheological properties and bubble morphology in the reactor. In this perspective, through optimal computational fluid dynamics models and experiments, the relationship between power consumption, volumetric mass transfer rate(kLa) and initial bubble size(d0) was constructed to establish an efficient operation mode for the aeration process of non-Newtonian fluids. It was found that reducing the d0could significantly increase the oxygen mass transfer rate, resulting in an obvious decrease in the ventilation volume and impeller speed. When d0was regulated within 2-5 mm,an optimal kLa could be achieved, and 21% of power consumption could be saved, compared to the case of bubbles with a diameter of 10 mm.
基金supported by the Science and Technology Project of State Grid Jiangxi Electric Power Corporation Limited‘Research on Key Technologies for Non-Intrusive Load Identification for Typical Power Industry Users in Jiangxi Province’(521852220004)。
文摘Studying user electricity consumption behavior is crucial for understanding their power usage patterns.However,the traditional clustering methods fail to identify emerging types of electricity consumption behavior.To address this issue,this paper introduces a statistical analysis of clusters and evaluates the set of indicators for power usage patterns.The fuzzy C-means clustering algorithm is then used to analyze 6 months of electricity consumption data in 2017 from energy storage equipment,agricultural drainage irrigation,port shore power,and electric vehicles.Finally,the proposed method is validated through experiments,where the Davies-Bouldin index and profile coefficient are calculated and compared.Experiments showed that the optimal number of clusters is 4.This study demonstrates the potential of using a fuzzy C-means clustering algorithmin identifying emerging types of electricity consumption behavior,which can help power system operators and policymakers to make informed decisions and improve energy efficiency.
基金supported financially by the National Key Research and Development Program of China(Grant No.2023YFB4402301)the National Science Fund for Distinguished Young Scholars(Grant No.52025022)+3 种基金the National Natural Science Foundation of China(Grant Nos.U19A2091,62004016,51732003,52072065,11974072,52372137,and 52272140)the“111”Project(Grant No.B13013)the Fundamental Research Funds for the Central Universities(Grant Nos.2412022QD036 and 2412023YQ004)the funding from Jilin Province(Grant Nos.20210201062GX,20220502002GH,20230402072GH,20230101017JC,and 20210509045RQ)。
文摘Transient memories,which can physically disappear without leaving traceable remains over a period of normal operation,are attracting increasing attention for potential applications in the fields of data security and green electronics.Resistive random access memory(RRAM)is a promising candidate for next-generation memory.In this context,biocompatible l-carrageenan(l-car),extracted from natural seaweed,is introduced for the fabrication of RRAM devices(Ag/l-car/Pt).Taking advantage of the complexation processes between the functional groups(C–O–C,C–O–H,et al.)and Ag metal ions,a lower migration barrier of Ag ions and a high-speed switching(22.2 ns for SET operation/26 ns for RESET operation)were achieved,resulting in an ultralow power consumption of 56 fJ.And the prepared Ag/l-car/Pt RRAM devices also revealed the capacities of multilevel storage and flexibility.In addition,thanks to the hydrophilic groups of l-car molecule,the RRAM devices can be rapidly dissolved in deionized(DI)water within 13 minutes,showing excellent transient characteristics.This work demonstrates that l-car based RRAM devices have great potential for applications in secure storage applications,flexible electronics and transient electronics.
文摘In light of the rapid expansion of China s economy,there has been a notable shift in the consumption patterns of urban and rural residents.This is evident in the increased consumption of agricultural products,with fruit consumption representing a particularly pronounced trend.The 2023 Beijing Consumption Statistics indicate that demand for fruit is increasing,accompanied by heightened competition for quality and brands.The paper is based on research that examines the current situation of fruit consumption among urban and rural residents in Beijing.The results indicate that when urban and rural residents in Beijing purchase fruits,price is the most significant factor influencing fruit consumption.Taste,appearance,and variety are also considered to be important influencing factors,while the influence of factors such as fruit type,consumption pattern,and fruit brand is relatively minor.
基金the National Natural Science Foundation of China Youth Fund,Research on Security Low Carbon Collaborative Situation Awareness of Comprehensive Energy System from the Perspective of Dynamic Security Domain(52307130).
文摘To address the issues of limited demand response data,low generalization of demand response potential evaluation,and poor demand response effect,the article proposes a demand response potential feature extraction and prediction model based on data mining and a demand response potential assessment model for adjustable loads in demand response scenarios based on subjective and objective weight analysis.Firstly,based on the demand response process and demand response behavior,obtain demand response characteristics that characterize the process and behavior.Secondly,establish a feature extraction and prediction model based on data mining,including similar day clustering,time series decomposition,redundancy processing,and data prediction.The predicted values of each demand response feature on the response day are obtained.Thirdly,the predicted data of various characteristics on the response day are used as demand response potential evaluation indicators to represent different demand response scenarios and adjustable loads,and a demand response potential evaluation model based on subjective and objective weight allocation is established to calculate the demand response potential of different adjustable loads in different demand response scenarios.Finally,the effectiveness of the method proposed in the article is verified through examples,providing a reference for load aggregators to formulate demand response schemes.
基金This research was funded by the National Nature Sciences Foundation of China(Grant No.42250410321).
文摘Missing value is one of the main factors that cause dirty data.Without high-quality data,there will be no reliable analysis results and precise decision-making.Therefore,the data warehouse needs to integrate high-quality data consistently.In the power system,the electricity consumption data of some large users cannot be normally collected resulting in missing data,which affects the calculation of power supply and eventually leads to a large error in the daily power line loss rate.For the problem of missing electricity consumption data,this study proposes a group method of data handling(GMDH)based data interpolation method in distribution power networks and applies it in the analysis of actually collected electricity data.First,the dependent and independent variables are defined from the original data,and the upper and lower limits of missing values are determined according to prior knowledge or existing data information.All missing data are randomly interpolated within the upper and lower limits.Then,the GMDH network is established to obtain the optimal complexity model,which is used to predict the missing data to replace the last imputed electricity consumption data.At last,this process is implemented iteratively until the missing values do not change.Under a relatively small noise level(α=0.25),the proposed approach achieves a maximum error of no more than 0.605%.Experimental findings demonstrate the efficacy and feasibility of the proposed approach,which realizes the transformation from incomplete data to complete data.Also,this proposed data interpolation approach provides a strong basis for the electricity theft diagnosis and metering fault analysis of electricity enterprises.
基金supported by the Surface Project of the National Natural Science Foundation of China(No.71273024)the Fundamental Research Funds for the Central Universities of China(2021YJS080).
文摘This study proposes a prediction model considering external weather and holiday factors to address the issue of accurately predicting urban taxi travel demand caused by complex data and numerous influencing factors.The model integrates the Complete Ensemble Empirical Mode Decomposition with Adaptive Noise(CEEMDAN)and Convolutional Long Short Term Memory Neural Network(ConvLSTM)to predict short-term taxi travel demand.The CEEMDAN decomposition method effectively decomposes time series data into a set of modal components,capturing sequence characteristics at different time scales and frequencies.Based on the sample entropy value of components,secondary processing of more complex sequence components after decomposition is employed to reduce the cumulative prediction error of component sequences and improve prediction efficiency.On this basis,considering the correlation between the spatiotemporal trends of short-term taxi traffic,a ConvLSTM neural network model with Long Short Term Memory(LSTM)time series processing ability and Convolutional Neural Networks(CNN)spatial feature processing ability is constructed to predict the travel demand for urban taxis.The combined prediction model is tested on a taxi travel demand dataset in a certain area of Beijing.The results show that the CEEMDAN-ConvLSTM prediction model outperforms the LSTM,Autoregressive Integrated Moving Average model(ARIMA),CNN,and ConvLSTM benchmark models in terms of Symmetric Mean Absolute Percentage Error(SMAPE),Root Mean Square Error(RMSE),Mean Absolute Error(MAE),and R2 metrics.Notably,the SMAPE metric exhibits a remarkable decline of 21.03%with the utilization of our proposed model.These results confirm that our study provides a highly accurate and valid model for taxi travel demand forecasting.
基金Project supported by the Fundamental Research Funds for Central Universities,China(Grant No.2022YJS065)the National Natural Science Foundation of China(Grant Nos.72288101 and 72371019).
文摘With the development of intelligent and interconnected traffic system,a convergence of traffic stream is anticipated in the foreseeable future,where both connected automated vehicle(CAV)and human driven vehicle(HDV)will coexist.In order to examine the effect of CAV on the overall stability and energy consumption of such a heterogeneous traffic system,we first take into account the interrelated perception of distance and speed by CAV to establish a macroscopic dynamic model through utilizing the full velocity difference(FVD)model.Subsequently,adopting the linear stability theory,we propose the linear stability condition for the model through using the small perturbation method,and the validity of the heterogeneous model is verified by comparing with the FVD model.Through nonlinear theoretical analysis,we further derive the KdV-Burgers equation,which captures the propagation characteristics of traffic density waves.Finally,by numerical simulation experiments through utilizing a macroscopic model of heterogeneous traffic flow,the effect of CAV permeability on the stability of density wave in heterogeneous traffic flow and the energy consumption of the traffic system is investigated.Subsequent analysis reveals emergent traffic phenomena.The experimental findings demonstrate that as CAV permeability increases,the ability to dampen the propagation of fluctuations in heterogeneous traffic flow gradually intensifies when giving system perturbation,leading to enhanced stability of the traffic system.Furthermore,higher initial traffic density renders the traffic system more susceptible to congestion,resulting in local clustering effect and stop-and-go traffic phenomenon.Remarkably,the total energy consumption of the heterogeneous traffic system exhibits a gradual decline with CAV permeability increasing.Further evidence has demonstrated the positive influence of CAV on heterogeneous traffic flow.This research contributes to providing theoretical guidance for future CAV applications,aiming to enhance urban road traffic efficiency and alleviate congestion.
基金supported by the Major Special Projects for Green Pest Control,China(110202101028(LS-03),201938,110202201017(LS-01)and 110202001035(LS04))the National Natural Science Foundation of China(31901893)the Agricultural Science and Technology Innovation Program of Chinese Academy of Agricultural Sciences(ASTIP-TRIC04)。
文摘Understanding interactions between viruses and their hosts is conducive to enabling better application of viruses as biocontrol agents.Certain viruses carried by parasitic wasps enhance the parasitic efficiency of wasp-larvae by protecting them against the immune system of their Lepidopteran host.However,the relationship between prey pests and viruses found in predatory natural enemies remains unclear.Herein,we report the interaction between Arma chinensis virus-1(AcV-1),originally isolated from a predatory natural enemy,Arma chinensis(Hemiptera:Pentatomidae),and one of its prey species,Spodoptera frugiperda(Lepidoptera:Noctuidae).The results showed that the AcV-1 virus appeared harmful to the novel host S.frugiperda by inhibiting larval diet consumption and increasing pupal mortality.Meanwhile,sequencing data indicated that the virus altered the gene expression profiles of S.frugiperda.KEGG analysis showed that the proteasome and phagosome pathways related to protein degradation and immune response were significantly enriched.Although the expression levels of digestive enzyme genes did not change significantly,the total protease activity of AcV-1 virus-positive individuals was significantly decreased,suggesting that the virus inhibited diet consumption of S.frugiperda via the down-regulation of digestive enzyme activities.These results indicate that a virus initially isolated in a predatory natural enemy can decrease the fitness of its prey species.The virus was found to impact the host proteasome and phagosome pathways related to protein degradation and immunity,providing a potential mechanism to enhance controlling efficiency.
基金supported by Science and Technology Project of SGCC(SGSW0000FZGHBJS2200070)。
文摘With the increasing penetration of wind and solar energies,the accompanying uncertainty that propagates in the system places higher requirements on the expansion planning of power systems.A source-grid-load-storage coordinated expansion planning model based on stochastic programming was proposed to suppress the impact of wind and solar energy fluctuations.Multiple types of system components,including demand response service entities,converter stations,DC transmission systems,cascade hydropower stations,and other traditional components,have been extensively modeled.Moreover,energy storage systems are considered to improve the accommodation level of renewable energy and alleviate the influence of intermittence.Demand-response service entities from the load side are used to reduce and move the demand during peak load periods.The uncertainties in wind,solar energy,and loads were simulated using stochastic programming.Finally,the effectiveness of the proposed model is verified through numerical simulations.
基金supported by the Basic Science(Natural Science)Research Project of Jiangsu Higher Education Institutions(No.23KJB470020)the Natural Science Foundation of Jiangsu Province(Youth Fund)(No.BK20230384)。
文摘To facilitate the coordinated and large-scale participation of residential flexible loads in demand response(DR),a load aggregator(LA)can integrate these loads for scheduling.In this study,a residential DR optimization scheduling strategy was formulated considering the participation of flexible loads in DR.First,based on the operational characteristics of flexible loads such as electric vehicles,air conditioners,and dishwashers,their DR participation,the base to calculate the compensation price to users,was determined by considering these loads as virtual energy storage.It was quantified based on the state of virtual energy storage during each time slot.Second,flexible loads were clustered using the K-means algorithm,considering the typical operational and behavioral characteristics as the cluster centroid.Finally,the LA scheduling strategy was implemented by introducing a DR mechanism based on the directrix load.The simulation results demonstrate that the proposed DR approach can effectively reduce peak loads and fill valleys,thereby improving the load management performance.
文摘Demand-responsive transportation(DRT)is a flexible passenger service designed to enhance road efficiency,reduce peak-hour traffic,and boost passenger satisfaction.However,existing optimization methods for initial passenger requests fall short in addressing real-time passenger needs.Consequently,there is a need to develop realtime DRT route optimization methods that integrate both initial and real-time requests.This paper presents a twostage,multi-objective optimization model for DRT vehicle scheduling.The first stage involves an initial scheduling model aimed at minimizing vehicle configuration,and operational,and CO_(2)emission costs while ensuring passenger satisfaction.The second stage develops a real-time scheduling model to minimize additional operational costs,penalties for time window violations,and costs due to rejected passengers,thereby addressing real-time demands.Additionally,an enhanced genetic algorithm based on Non-dominated Sorting Genetic Algorithm-II(NSGA-II)is designed,incorporating multiple crossover points to accelerate convergence and improve solution efficiency.The proposed scheduling model is validated using a real network in Shanghai.Results indicate that realtime scheduling can serve more passengers,and improve vehicle utilization and occupancy rates,with only a minor increase in total operational costs.Compared to the traditional NSGA-II algorithm,the improved version enhances convergence speed by 31.7%and solution speed by 4.8%.The proposed model and algorithm offer both theoretical and practical guidance for real-world DRT scheduling.
基金supported in part by the National Natural Science Foundation of China(NSFC)under Grant 62371082 and 62001076in part by the National Key R&D Program of China under Grant 2021YFB1714100in part by the Natural Science Foundation of Chongqing under Grant CSTB2023NSCQ-MSX0726 and cstc2020jcyjmsxmX0878.
文摘Fog computing is considered as a solution to accommodate the emergence of booming requirements from a large variety of resource-limited Internet of Things(IoT)devices.To ensure the security of private data,in this paper,we introduce a blockchain-enabled three-layer device-fog-cloud heterogeneous network.A reputation model is proposed to update the credibility of the fog nodes(FN),which is used to select blockchain nodes(BN)from FNs to participate in the consensus process.According to the Rivest-Shamir-Adleman(RSA)encryption algorithm applied to the blockchain system,FNs could verify the identity of the node through its public key to avoid malicious attacks.Additionally,to reduce the computation complexity of the consensus algorithms and the network overhead,we propose a dynamic offloading and resource allocation(DORA)algorithm and a reputation-based democratic byzantine fault tolerant(R-DBFT)algorithm to optimize the offloading decisions and decrease the number of BNs in the consensus algorithm while ensuring the network security.Simulation results demonstrate that the proposed algorithm could efficiently reduce the network overhead,and obtain a considerable performance improvement compared to the related algorithms in the previous literature.
文摘This study aims to analysis the influence of economic growth(EG)and energy consumption(EC)on sulfur dioxide emissions(SE)in China.Accordingly,this study explores the link between EG,EC,and SE for 30 provinces in China over the span of 2000-2019.This study also analyzes cross-sectional dependence tests,panel unit root tests,Westerlund panel cointegration tests,Dumitrescu-Hurlin(D-H)causality tests.According to the test results,there is an inverted U-shaped association between EG and SE,and the assumption of the Environmental Kuznets Curve(EKC)is verified.The signs of EG and EC in the fixed effect(FE)and random effect(RE)methods are in line with those in the dynamic ordinary least squares(DOLS),fully modified ordinary least squares(FMOLS)and autoregressive distributed lag(ARDL)estimators.Moreover,the results verified that EC can obviously positive impact the SE.To reduce SE in China,government and policymakers can improve air quality by developing cleaner energy sources and improving energy efficiency.This requires the comprehensive use of policies,regulations,economic incentives,and public participation to promote sustainable development.
文摘In the restructured electricity market,microgrid(MG),with the incorporation of smart grid technologies,distributed energy resources(DERs),a pumped-storage-hydraulic(PSH)unit,and a demand response program(DRP),is a smarter and more reliable electricity provider.DER consists of gas turbines and renewable energy sources such as photovoltaic systems and wind turbines.Better bidding strategies,prepared by MG operators,decrease the electricity cost and emissions from upstream grid and conventional and renewable energy sources(RES).But it is inefficient due to the very high sporadic characteristics of RES and the very high outage rate.To solve these issues,this study suggests non-dominated sorting genetic algorithm Ⅱ(NSGA-Ⅱ)for an optimal bidding strategy considering pumped hydroelectric energy storage and DRP based on outage conditions and uncertainties of renewable energy sources.The uncertainty related to solar and wind units is modeled using lognormal and Weibull probability distributions.TOU-based DRP is used,especially considering the time of outages along with the time of peak loads and prices,to enhance the reliability of MG and reduce costs and emissions.
基金supported by the Technology Project of State Grid Jiangsu Electric Power Co.,Ltd.,China (J2022160,Research on Key Technologies of Distributed Power Dispatching Control for Resilience Improvement of Distribution Networks).
文摘To improve the resilience of a distribution system against extreme weather,a fuel-based distributed generator(DG)allocation model is proposed in this study.In this model,the DGs are placed at the planning stage.When an extreme event occurs,the controllable generators form temporary microgrids(MGs)to restore the load maximally.Simultaneously,a demand response program(DRP)mitigates the imbalance between the power supply and demand during extreme events.To cope with the fault uncertainty,a robust optimization(RO)method is applied to reduce the long-term investment and short-term operation costs.The optimization is formulated as a tri-level defenderattacker-defender(DAD)framework.At the first level,decision-makers work out the DG allocation scheme;at the second level,the attacker finds the optimal attack strategy with maximum damage;and at the third level,restoration measures,namely distribution network reconfiguration(DNR)and demand response are performed.The problem is solved by the nested column and constraint generation(NC&CG)method and the model is validated using an IEEE 33-node system.Case studies validate the effectiveness and superiority of the proposed model according to the enhanced resilience and reduced cost.
