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.

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.

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.

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.

可生物降解塑料取代传统塑料--对碳排放的影响

In recent years,a great deal of attention has been focused on the environmental impact of plastics,includ-ing the carbon emissions related to plastics,which has promoted the application of biodegradable plas-tics.Countries worldwide have shown high interest in replacing traditional plastics with biodegradable plastics.However,no systematic comparison has been conducted on the carbon emissions of biodegrad-able versus traditional plastic products.This study evaluates the carbon emissions of traditional and biodegradable plastic products(BPPs)over four stages and briefly discusses environmental and economic perspectives.Four scenarios-namely,the traditional method,chemical recycling,industrial composting,and anaerobic digestion-are considered for the disposal of waste BPPs(WBPPs).The analysis takes China as a case study.The results show that the carbon emissions of 1000traditional plastic products(plastic bags,lunch boxes,cups,etc.)were52.09-150.36 carbon emissions equivalent of per kilogram(kg CO_(2)eq),with the stage of plastic production contributing 50.71%-50.77%.In comparison,1000 similar BPPs topped out at 21.06-56.86 kg CO_(2)eq,approximately 13.53%-62.19%lower than traditional plastic prod-ucts.The difference was mainly at the stages of plastic production and waste disposal,and the BPPs showed significant carbon reduction potential at the raw material acquisition stage.Waste disposal plays an important role in environmental impact,and composting and anaerobic digestion are considered to be preferable disposal methods for WBPPs.However,the high cost of biodegradable plastics is a challenge for their widespread use.This study has important reference significance for the sustainable development of the biodegradableplastics industry.

Hybrid model for BOF oxygen blowing time prediction based on oxygen balance mechanism and deep neural network

The amount of oxygen blown into the converter is one of the key parameters for the control of the converter blowing process,which directly affects the tap-to-tap time of converter. In this study, a hybrid model based on oxygen balance mechanism (OBM) and deep neural network (DNN) was established for predicting oxygen blowing time in converter. A three-step method was utilized in the hybrid model. First, the oxygen consumption volume was predicted by the OBM model and DNN model, respectively. Second, a more accurate oxygen consumption volume was obtained by integrating the OBM model and DNN model. Finally, the converter oxygen blowing time was calculated according to the oxygen consumption volume and the oxygen supply intensity of each heat. The proposed hybrid model was verified using the actual data collected from an integrated steel plant in China, and compared with multiple linear regression model, OBM model, and neural network model including extreme learning machine, back propagation neural network, and DNN. The test results indicate that the hybrid model with a network structure of 3 hidden layer layers, 32-16-8 neurons per hidden layer, and 0.1 learning rate has the best prediction accuracy and stronger generalization ability compared with other models. The predicted hit ratio of oxygen consumption volume within the error±300 m^(3)is 96.67%;determination coefficient (R^(2)) and root mean square error (RMSE) are0.6984 and 150.03 m^(3), respectively. The oxygen blow time prediction hit ratio within the error±0.6 min is 89.50%;R2and RMSE are0.9486 and 0.3592 min, respectively. As a result, the proposed model can effectively predict the oxygen consumption volume and oxygen blowing time in the converter.

Towards a new avenue for rapid synthesis of electrocatalytic electrodes via laser-induced hydrothermal reaction for water splitting

Electrochemical production of hydrogen from water requires the development ofelectrocatalysts that are active,stable,and low-cost for water splitting.To address these challenges,researchers are increasingly exploring binder-free electrocatalytic integratedelectrodes (IEs) as an alternative to conventional powder-based electrode preparation methods,for the former is highly desirable to improve the catalytic activity and long-term stability for large-scale applications of electrocatalysts.Herein,we demonstrate a laser-inducedhydrothermal reaction (LIHR) technique to grow NiMoO4nanosheets on nickel foam,which is then calcined under H2/Ar mixed gases to prepare the IE IE-NiMo-LR.This electrode exhibits superior hydrogen evolution reaction performance,requiring overpotentials of 59,116 and143 mV to achieve current densities of 100,500 and 1000 mA·cm-2.During the 350 h chronopotentiometry test at current densities of 100 and 500 m A·cm-2,the overpotentialremains essentially unchanged.In addition,NiFe-layered double hydroxide grown on Ni foam is also fabricated with the same LIHR method and coupled with IE-NiMo-IR to achieve water splitting.This combination exhibits excellent durability under industrial current density.The energy consumption and production efficiency of the LIHR method are systematicallycompared with the conventional hydrothermal method.The LIHR method significantly improves the production rate by over 19 times,while consuming only 27.78%of the total energy required by conventional hydrothermal methods to achieve the same production.

Summer Atmospheric Water Cycle under the Transition Influence of the Westerly and Summer Monsoon over the Yarlung Zangbo River Basin in the Southern Tibetan Plateau

This study compares the summer atmospheric water cycle,including moisture sources and consumption,in the upstream,midstream,and downstream regions of the Yarlung Zangbo River Basin in the southern Tibetan Plateau.The evolutions of moisture properties under the influence of the westerly and summer southerly monsoon are examined using 5-yr multi-source measurements and ERA5 reanalysis data.Note that moisture consumption in this study is associated with clouds,precipitation,and diabatic heating.Compared to the midstream and downstream regions,the upstream region has less moisture,clouds,and precipitation,where the moisture is brought by the westerly.In early August,the vertical wet advection over this region becomes enhanced and generates more high clouds and precipitation.The midstream region has moisture carried by the westerly in June and by the southerly monsoon from July to August.The higher vertical wet advection maximum here forms more high clouds,with a precipitation peak in early July.The downstream region is mainly affected by the southerly-driven wet advection.The rich moisture and strong vertical wet advection here produce the most clouds and precipitation among the three regions,with a precipitation peak in late June.The height of the maximum moisture condensation is different between the midstream region(325 hPa)and the other two regions(375 hPa),due to the higher upward motion maximum in the midstream region.The diabatic heating structures show that stratiform clouds dominate the upstream region,stratiform clouds and deep convection co-exist in the midstream region,and deep convection systems characterize the downstream region.

Research on a TOPSIS energy efficiency evaluation system for crude oil gathering and transportation systems based on a GA-BP neural network

As the main link of ground engineering,crude oil gathering and transportation systems require huge energy consumption and complex structures.It is necessary to establish an energy efficiency evaluation system for crude oil gathering and transportation systems and identify the energy efficiency gaps.In this paper,the energy efficiency evaluation system of the crude oil gathering and transportation system in an oilfield in western China is established.Combined with the big data analysis method,the GA-BP neural network is used to establish the energy efficiency index prediction model for crude oil gathering and transportation systems.The comprehensive energy consumption,gas consumption,power consumption,energy utilization rate,heat utilization rate,and power utilization rate of crude oil gathering and transportation systems are predicted.Considering the efficiency and unit consumption index of the crude oil gathering and transportation system,the energy efficiency evaluation system of the crude oil gathering and transportation system is established based on a game theory combined weighting method and TOPSIS evaluation method,and the subjective weight is determined by the triangular fuzzy analytic hierarchy process.The entropy weight method determines the objective weight,and the combined weight of game theory combines subjectivity with objectivity to comprehensively evaluate the comprehensive energy efficiency of crude oil gathering and transportation systems and their subsystems.Finally,the weak links in energy utilization are identified,and energy conservation and consumption reduction are improved.The above research provides technical support for the green,efficient and intelligent development of crude oil gathering and transportation systems.

The Application of Solid Waste in Thermal Insulation Materials: A Review

As socioeconomic development continues,the issue of building energy consumption has attracted significant attention,and improving the thermal insulation performance of buildings has become a crucial strategic measure.Simultaneously,the application of solid waste in insulation materials has also become a hot topic.This paper reviews the sources and classifications of solid waste,focusing on research progress in its application as insulation materials in the domains of daily life,agriculture,and industry.The research shows that incorporating household solid waste materials,such as waste glass,paper,and clothing scraps into cementitious thermal insulation can significantly reduce the thermal conductivity of the materials,leading to excellent thermal insulation properties.Insulation materials prepared from agricultural solid waste,such as barley straw,corn stalk,chicken feather,and date palm fibers,possess characteristics of lightweight and strong thermal insulation.Industrial solid waste,including waste tires,iron tailings,and coal bottom ash,can also be utilized in the preparation of insulation materials.These innovative applications not only have positive environmental significance by reducing waste emissions and resource consumption,but also provide efficient and sustainable insulation solutions for the construction industry.However,to further optimize the mix design and enhance the durability of insulation materials,continuous research is required to investigate the mechanisms through which solid waste impacts the performance of insulation materials.

The Effect of Lateral Offset Distance on the Aerodynamics and Fuel Economy of Vehicle Queues

The vehicle industry is always in search of breakthrough energy-saving and emission-reduction technologies.In recent years,vehicle intelligence has progressed considerably,and researchers are currently trying to take advantage of these developments.Here we consider the case of many vehicles forming a queue,i.e.,vehicles traveling at a predetermined speed and distance apart.While the majority of existing studies on this subject have focused on the influence of the longitudinal vehicle spacing,vehicle speed,and the number of vehicles on aerodynamic drag and fuel economy,this study considers the lateral offset distance of the vehicle queue.The group fuel consumption savings rate is calculated and analyzed.As also demonstrated by experimental results,some aerodynamic benefits exist.Moreover,the fuel consumption saving rate of the vehicle queue decreases as the lateral offset distance increases.

Intelligent Energy Utilization Analysis Using IUA-SMD Model Based Optimization Technique for Smart Metering Data

Smart metering has gained considerable attention as a research focus due to its reliability and energy-efficient nature compared to traditional electromechanical metering systems. Existing methods primarily focus on data management,rather than emphasizing efficiency. Accurate prediction of electricity consumption is crucial for enabling intelligent grid operations,including resource planning and demandsupply balancing. Smart metering solutions offer users the benefits of effectively interpreting their energy utilization and optimizing costs. Motivated by this,this paper presents an Intelligent Energy Utilization Analysis using Smart Metering Data(IUA-SMD)model to determine energy consumption patterns. The proposed IUA-SMD model comprises three major processes:data Pre-processing,feature extraction,and classification,with parameter optimization. We employ the extreme learning machine(ELM)based classification approach within the IUA-SMD model to derive optimal energy utilization labels. Additionally,we apply the shell game optimization(SGO)algorithm to enhance the classification efficiency of the ELM by optimizing its parameters. The effectiveness of the IUA-SMD model is evaluated using an extensive dataset of smart metering data,and the results are analyzed in terms of accuracy and mean square error(MSE). The proposed model demonstrates superior performance,achieving a maximum accuracy of65.917% and a minimum MSE of0.096. These results highlight the potential of the IUA-SMD model for enabling efficient energy utilization through intelligent analysis of smart metering data.

Multi-Time Scale Optimal Scheduling of a Photovoltaic Energy Storage Building System Based on Model Predictive Control

Building emission reduction is an important way to achieve China’s carbon peaking and carbon neutrality goals.Aiming at the problem of low carbon economic operation of a photovoltaic energy storage building system,a multi-time scale optimal scheduling strategy based on model predictive control(MPC)is proposed under the consideration of load optimization.First,load optimization is achieved by controlling the charging time of electric vehicles as well as adjusting the air conditioning operation temperature,and the photovoltaic energy storage building system model is constructed to propose a day-ahead scheduling strategy with the lowest daily operation cost.Second,considering inter-day to intra-day source-load prediction error,an intraday rolling optimal scheduling strategy based on MPC is proposed that dynamically corrects the day-ahead dispatch results to stabilize system power fluctuations and promote photovoltaic consumption.Finally,taking an office building on a summer work day as an example,the effectiveness of the proposed scheduling strategy is verified.The results of the example show that the strategy reduces the total operating cost of the photovoltaic energy storage building system by 17.11%,improves the carbon emission reduction by 7.99%,and the photovoltaic consumption rate reaches 98.57%,improving the system’s low-carbon and economic performance.

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.

Investigating the Creation of Cultural Atmospheres in Physical Bookstores Within the Context of Scene-Based Consumption:A Perspective Based on the Ways of Seeing

In an era of scene-based consumption,the essence of operating a bookstore lies in accepting and enabling the bookstore to play its role as a cultural medium for browsing and appreciating books,which in turn fosters the development of a distinctive bookstore culture.This new type of consumption context has led to an evolution in the book-reading culture in physical bookstores that is characterized by a shift from a paradigm of passive reading to one where the emphasis is on interactive viewing.This transition has laid the foundation for the creation of cultural atmospheres in bookstores,and it highlights the visual interactions that now exist between readers and books as well as with other related cultural industries.The dominant and fundamental logic behind this process is symbiosis,experience,aesthetics,immersion,and creativity.However,when a form of culture that focuses on consumption-oriented browsing begins to overshadow knowledge acquisition during the process of book reading,the cultural essence of bookstores is likely to be diminished.Therefore,the cultural essence within the scene-based consumption context should be enhanced by creating innovative viewing activities that showcase the cultural and emotional implications inherent in the scene itself,and thus help to align the identity of the bookstore with its in-situ cultural space.

Research on ECMS Based on Segmented Path Braking Energy Recovery in a Fuel Cell Vehicle

Proton exchange membrane fuel cells are widely regarded as having the potential to replace internal combustion engines in vehicles.Since fuel cells cannot recover energy and have a slow dynamic response,they need to be used with different power sources.Developing efficient energy management strategies to achieve excellent fuel economy is the goal of research.This paper proposes an adaptive equivalent fuel minimum consumption strategy(AECMS)to solve the problem of the poor economy of the whole vehicle caused by the wrong selection of equivalent factors(EF)in traditional ECMS.In this method,the kinematics interval is used to update the equivalent factor by considering the penalty term of energy recovery on SOC changes.Finally,the optimized equivalent factor is substituted into the optimization objective function to achieve efficient energy regulation.Simulation results under the New European Driving Cycle show that compared with the traditional ECMS based on fixed SOC benchmarks,the proposed method improves fuel economy by 1.7%while ensuring vehicle power and increases SOC by 30%.

A Predictive Energy Management Strategies for Mining Dump Trucks

The plug-in hybrid vehicles(PHEV)technology can effectively address the issues of poor dynamics and higher energy consumption commonly found in traditional mining dump trucks.Meanwhile,plug-in hybrid electric trucks can achieve excellent fuel economy through efficient energy management strategies(EMS).Therefore,a series hybrid system is constructed based on a 100-ton mining dump truck in this paper.And inspired by the dynamic programming(DP)algorithm,a predictive equivalent consumption minimization strategy(P-ECMS)based on the DP optimization result is proposed.Based on the optimal control manifold and the SOC reference trajectory obtained by the DP algorithm,the P-ECMS strategy performs real-time stage parameter optimization to obtain the optimal equivalent factor(EF).Finally,applying the equivalent consumption minimization strategy(ECMS)realizes real-time control.The simulation results show that the equivalent fuel consumption of the P-ECMS strategy under the experimentally collected mining cycle conditions is 150.8 L/100 km,which is 10.9%less than that of the common CDCS strategy(169.3 L/100 km),and achieves 99.47%of the fuel saving effect of the DP strategy(150 L/100 km).