The impact of demographic dynamics on food consumption and its environmental outcomes:Evidence from China

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.

Women’s empowerment and food consumption:Evidence from female-headed households in Tanzania

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.

Biodegradable and flexible i-carrageenan based RRAM with ultralow power consumption

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.

Research on Interpolation Method for Missing Electricity Consumption Data

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.

Effects of connected automated vehicle on stability and energy consumption of heterogeneous traffic flow system

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.

Joint Optimization of Energy Consumption and Network Latency in Blockchain-Enabled Fog Computing Networks

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.

Effect of bubble morphology and behavior on power consumption in non-Newtonian fluids’aeration process

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.

Expand New Consumption and Unleash the Potential of Domestic Demand

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.

Research on Cultural Consumption and Urban Integration of Floating Population

This paper examined the relationship between cultural consumption and the floating population’s integration into host cities based on data from China’s Seventh National Population Census and the 2018 China Migrants Dynamic Survey(CMDS).The findings indicate that improving the floating population’s consumption level and quality,especially the quality of development-oriented cultural consumption,can significantly improve the level of their integration.Moreover,development-oriented cultural consumption has a positive effect on the floating population’s integration and social participation,while entertainment-oriented cultural consumption facilitates their integration mainly by improving their sense of well-being.These findings could guide policymakers in developing targeted cultural consumption policies,implementing specific regional industry adjustments,and expanding domestic consumption demand.

Exploring the Element Integration of Urban and Rural“Space of Flows”in the Context of Consumption Upgrading:A Case Study of Changsi Village in Anhui Province

The mobility and interaction between urban and rural areas are becoming more and more intensive,and their links and exchanges are increasingly closer due to constant flow of factors such as information,capital,personnel and technology.In this context,the element integration of urban and rural“space of flows”can promote the integrated development of urban and rural areas,improve the consumption environment and experience,and promote the industrial upgrading and technological progress.To realize the element integration of urban and rural“space of flows”,it is necessary to explore and innovate in infrastructure construction,information technology application,industrial cooperation and cultural exchanges.Government departments,enterprises and social organizations also need to work together to give play to their respective advantages and jointly promote the process of element integration of urban and rural“space of flows”.

Does economic growth stimulate energy consumption?New evidence from national and regional levels in China

A detailed investigation of the nexus between economic growth and energy use is imperative for formulating sustainable development policies.In this study,we examine panel cointegration and causality relations among economic growth,energy use,capital stock,and labor in 30 Chinese provinces between 2000-2019.We conduct a comprehensive empirical analysis based on panel modeling and a neoclassical production function.The findings of the second-generation panel unit root and co-integration tests reveal that these variables have long term co-integration linkages.We then perform a panel cointegration estimation using the fully modified ordinary least squares technique and find that total energy consumption,electricity consumption,capital stock,and labor significantly influence economic growth at the national and regional levels in China.Moreover,the outcomes of the Dumitrescu-Hurlin causality test indicate the existence of a two-way causal nexus between economic output and total energy consumption at the national level,but only a causal link from GDP to total energy use in the eastern and central regions.Conversely,a causality from total energy use to economic output is identified in the western region.Finally,we provide policy implications for the sustainable development of both energy and the economy at the national and regional levels.

Research on the optimization strategy of customers’electricity consumption based on big data

Current power systems face significant challenges in supporting large-scale access to new energy sources,and the potential of existing flexible resources needs to be fully explored from the power supply,grid,and customer perspectives.This paper proposes a multi-objective electricity consumption optimization strategy considering the correlation between equipment and electricity consumption.It constructs a multi-objective electricity consumption optimization model that considers the correlation between equipment and electricity consumption to maximize economy and comfort.The results show that the proposed method can accurately assess the potential for electricity consumption optimization and obtain an optimal multi-objective electricity consumption strategy based on customers’actual electricity consumption demand.

Survey of Cereal Consumption Habits in the Community of Djougou, Benin

The cereal group occupies a prominent place in the dietary habits of people in northern Benin and there is little recent information on cereal consumption. This study aims to assess the consumption, acquisition and supply of cereals to households in the community of Djougou. A semi-directive survey with KoBoCollect was conducted among 369 households to collect individual cereal food consumption data. The survey data processed by statistical tools showed that the most consumed cereals are maize (95%, p = 0.887), millet (58%, p = 0.755), rice (55%, p = 0.753), sorghum (15%, p = 0.635), wheat (5%, p = 0.920) and fonio barely 5%. The most common mode of acquisition in Djougou is purchase (50%, p = 0.947) but donation is also observed (25%, p = 0.988) as well as production observed in 20.6% of households. Purchases are made from retailers in local markets (45%, p = 0.920) but also in streets and alleys (30%, p = 0.765). The most widely used preservation technique is drying at room temperature (70%, p = 0.995). Households most often dry in the areas provided in the field (50%, p = 0.783) and at home (40%, p = 0.643). The preferred storage location is the kitchen (60%, p = 0.790). The bedroom (20%, 0.669) and the store (15%, 0.522) are the alternative places for storing cereals. In addition, the supply costs of cereals increased between 2020 and 2021. This vertiginous rise in prices is due, among other things, to the covid19 pandemic. The various data generated not only make it possible to have fresh data but also to invest them in the assessment of health risks for the achievement of a high level of protection of the health and life of consumers.

Comparative Study on Energy Consumption Structure of Ordinary Rural Households and Rural Tourism Households in Zhejiang Province, China

With the rapid development of rural tourism in China, more and more rural households operate a rural tourism business. The purpose of this study is to understand the energy consumption characteristic of ordinary rural households (ORHs) and rural tourism households (RTHs) in the mountainous area and islands area in Zhejiang province. 225 households were surveyed, including 185 ORHs and 40 RTHs, based on a field survey in Quzhou (mountainous area) and Zhoushan (islands area). Results reveal that energy consumption of ORHs is low, but energy comsumption of RTHs is high, about 3 to 5 times higher than that of ORHs. Given the results, the government and RTHs should pay more attention to take measures to reduce energy comsumption. Meanwhile, the factors affecting households’ energy consumption are also analyzed. Energy consumption of ORHs is affected by frequently used area, family income level and permanent population. Then energy consumption of RTHs is mainly related to the total building area, number of air conditioner (AC), number of guestrooms and family income level.

Causal Association Between Tea Consumption and Gout:A Mendelian Randomization Study

Objective Evidence from prospective studies on the consumption of tea and risk of gout is conflicting and limited.We aimed to investigate the potential causal effects of tea intake on gout using Mendelian randomization(MR).Methods Genome-wide association studies in UK Biobank included 349376 individuals and successfully discovered single-nucleotide polymorphisms linked to consumption of one cup of tea per day.Summary statistics from the Chronic Kidney Disease Genetics consortium included 13179 cases and 750634 controls for gout.Two-sample MR analyses were used to evaluate the relationship between tea consumption and gout risk.The inverse-variance weighted(IVW)method was used for primary analysis,and sensitivity analyses were also conducted to validate the potential causal effect.Results In this study,the genetically predicted increase in tea consumption per cup was associated with a lower risk of gout in the IVW method(OR:0.90;95%CI:0.82–0.98).Similar results were found in weighted median methods(OR:0.88;95%CI:0.78–1.00),while no significant associations were found in MR-Egger(OR:0.89;95%CI:0.71–1.11),weighted mode(OR:0.80;95%CI:0.65–0.99),and simple mode(OR:1.01;95%CI:0.75–1.36).In addition,no evidence of pleiotropy was detected by MR-Egger regression(P=0.95)or MR-PRESSO analysis(P=0.07).Conclusion This study provides evidence for the daily consumption of an extra cup of tea to reduce the risk of gout.

Power Consumption Characteristics Research on Mobile System of Electrically Driven Large-Load-Ratio Six-Legged Robot

The electrically driven large-load-ratio six-legged robot with engineering capability can be widely used in outdoor and planetary exploration.However,due to the particularity of its parallel structure,the effective utilization rate of energy is not high,which has become an important obstacle to its practical application.To research the power consumption characteristics of robot mobile system is beneficial to speed up it toward practicability.Based on the configuration and walking modes of robot,the mathematical model of the power consumption of mobile system is set up.In view of the tripod gait is often selected for the six-legged robots,the simplified power consumption model of mobile system under the tripod gait is established by means of reducing the dimension of the robot’s statically indeterminate problem and constructing the equal force distribution.Then,the power consumption of robot mobile system is solved under different working conditions.The variable tendencies of the power consumption of robot mobile system are respectively obtained with changes in the rotational angles of hip joint and knee joint,body height,and span.The articulated rotational zones and the ranges of body height and span are determined under the lowest power consumption.According to the walking experiments of prototype,the variable tendencies of the average power consumption of robot mobile system are respectively acquired with changes in duty ratio,body height,and span.Then,the feasibility and correctness of theory analysis are verified in the power consumption of robot mobile system.The proposed analysis method in this paper can provide a reference on the lower power research of the large-load-ratio multi-legged robots.

The transaction behavior of cryptocurrency and electricity consumption

Rapidly increasing cryptocurrency prices have encouraged cryptocurrency miners to participate in cryptocurrency production,increasing network hashrates and electricity consumption.Growth in network hashrates has further crowded out small cryptocurrency investors owing to the heightened costs of mining hardware and electricity.These changes prompt cryptocurrency miners to become new investors,leading to cryptocurrency price increases.The potential bidirectional relationship between cryptocurrency price and electricity consumption remains unidentified.Hence,this research thus utilizes July 312015–July 122019 data from 13 cryptocurrencies to investigate the short-and long-run causal effects between cryptocurrency transaction and electricity consumption.Particularly,we consider structural breaks induced by external shocks through stationary analysis and comovement relationships.Over the examined time period,we found that the series of cryptocurrency transaction and electricity consumption gradually returns to mean convergence after undergoing daily shocks,with prices trending together with hashrates.Transaction fluctuations exert both a temporary effect and permanent influence on electricity consumption.Therefore,owing to the computational power deployed to wherever high profit is found,transactions are vital determinants of electricity consumption.

Residential Energy Consumption Forecasting Based on Federated Reinforcement Learning with Data Privacy Protection

Most studies have conducted experiments on predicting energy consumption by integrating data formodel training.However, the process of centralizing data can cause problems of data leakage.Meanwhile,many laws and regulationson data security and privacy have been enacted, making it difficult to centralize data, which can lead to a datasilo problem. Thus, to train the model while maintaining user privacy, we adopt a federated learning framework.However, in all classical federated learning frameworks secure aggregation, the Federated Averaging (FedAvg)method is used to directly weight the model parameters on average, which may have an adverse effect on te model.Therefore, we propose the Federated Reinforcement Learning (FedRL) model, which consists of multiple userscollaboratively training the model. Each household trains a local model on local data. These local data neverleave the local area, and only the encrypted parameters are uploaded to the central server to participate in thesecure aggregation of the global model. We improve FedAvg by incorporating a Q-learning algorithm to assignweights to each locally uploaded local model. And the model has improved predictive performance. We validatethe performance of the FedRL model by testing it on a real-world dataset and compare the experimental results withother models. The performance of our proposed method in most of the evaluation metrics is improved comparedto both the centralized and distributed models.

Intelligent Energy Consumption For Smart Homes Using Fused Machine-Learning Technique

Energy is essential to practically all exercises and is imperative for the development of personal satisfaction.So,valuable energy has been in great demand for many years,especially for using smart homes and structures,as individuals quickly improve their way of life depending on current innovations.However,there is a shortage of energy,as the energy required is higher than that produced.Many new plans are being designed to meet the consumer’s energy requirements.In many regions,energy utilization in the housing area is 30%–40%.The growth of smart homes has raised the requirement for intelligence in applications such as asset management,energy-efficient automation,security,and healthcare monitoring to learn about residents’actions and forecast their future demands.To overcome the challenges of energy consumption optimization,in this study,we apply an energy management technique.Data fusion has recently attracted much energy efficiency in buildings,where numerous types of information are processed.The proposed research developed a data fusion model to predict energy consumption for accuracy and miss rate.The results of the proposed approach are compared with those of the previously published techniques and found that the prediction accuracy of the proposed method is 92%,which is higher than the previously published approaches.

Low-noise,low-power-consumption seafloor vector magnetometer

The seafloor vector magnetometer is an effective tool for marine geomagnetic surveys and seafloor magnetotelluric(MT)detection.However,the noise,power consumption,cost,and volume characteristics of existing seafloor vector magnetometers are insufficient for practical use.Therefore,a low-noise,low-power-consumption seafloor vector magnetometer that can be used for data acquisition of deep-ocean geomagnetic vector components is developed and presented.A seafloor vector magnetometer mainly consists of a fluxgate sensor,data acquisition module,acoustic release module,glass sphere,frame,burn-wire release,and anchor.A new low-noise data acquisition module and a fluxgate sensor greatly reduce power consumption.Furthermore,compact size is achieved by integrating an acoustic telemetry module and replacing the acoustic release with an external burn-wire release.The new design and magnetometer characteristics reduce the volume of the instrument and the cost of hardware considerably,thereby improving the integrity and deployment efficiency of the equipment.Theoretically,it can operate for 90 days underwater at a maximum depth of 6000 m.The seafloor vector magnetometer was tested in the South China Sea and the Philippine Sea and obtained high-quality geomagnetic data.The deep-water environment facilitates magnetic field data measurements,and the magnetometer has an approximate noise level of 10 pT/rt(Hz)@1 Hz,a peak-to-peak value error of 0.2 nT,and approximate power consumption of 200 mW.The fluxgate sensor can measure the magnetic field in the lower frequency band and realize geomagnetic field measurements over prolonged periods.