Climate change may have detrimental effects on different sectoral growth in global economy and according to the Intergovernmental Panel on Climate Change,the impacts of climate change will be more vigorous in the comi...Climate change may have detrimental effects on different sectoral growth in global economy and according to the Intergovernmental Panel on Climate Change,the impacts of climate change will be more vigorous in the coming years.The climatic and non-climatic driving forces behind the economic sectoral performances involve short-and long-run interconnections among variables.This study attempts to investigate the effect of climatic factors(temperature and precipitation)along with non-climatic factors,including foreign direct investment(FDI),human capital index(HCI),natural capital(NC),and information and communication technology(ICT)on three major sectors of the economy(agricultural sector,industrial sector,and service sector)through non-linear model framework by employing cross-sectionally augmented autoregressive distributed lag(CS-ARDL)estimation technique.It considers a panel of 56 selected countries from different income groups,including high-income countries,upper-middle-income countries,lower-middle-income countries,and low-income countries,covering the period 1985-2022.The confirmation of slope heterogeneity,cross-sectional dependence,stationarity,and cointegration among variables lends support to the robustness of results.The augmented mean group(AMG)robustness test was applied to check robustness and the results were found mostly consistent with estimation method.The results revealed that upper-middle-income countries are more vulnerable to extreme temperatures compared to high-income countries.The results also confirmed an inverted U-shaped relationship between each sector’s output and precipitation in upper-middle-income countries.In contrast,for upper-middle-income,lower-middle-income,and low-income countries,this relation exists in industrial sector only in long run.This indicates that precipitation is initially beneficial for production activities.However,beyond a certain threshold of precipitation,this trend reverses,i.e.,the output of the economic sectors tends to decline.Furthermore,there is no supporting evidence that confirms a short-run non-linear relation between precipitation and agricultural yields.In upper-middle-income countries,the results confirmed that FDI is a driving factor behind both agricultural sector and service sector in long run while short-run results indicated a negative association but insignificant.This study also showed that in long run,an increase in HCI contributes to improving the output of the three sectors for high-income countries.The empirical findings provide valuable insights for policy-makers and governments to formulate coherent adaptation and mitigation strategies,thereby accelerating the transition of sectoral productivity from low to high levels in the sample countries.展开更多
The automotive sector is crucial in modern society,facilitating essential transportation needs across personal,commercial,and logistical domains while significantly contributing to national economic development and em...The automotive sector is crucial in modern society,facilitating essential transportation needs across personal,commercial,and logistical domains while significantly contributing to national economic development and employment generation.The transformative impact of Artificial Intelligence(AI)has revolutionised multiple facets of the automotive industry,encompassing intelligent manufacturing processes,diagnostic systems,control mechanisms,supply chain operations,customer service platforms,and traffic management solutions.While extensive research exists on the above aspects of AI applications in automotive contexts,there is a compelling need to synthesise this knowledge comprehensively to guide and inspire future research.This review introduces a novel taxonomic framework that provides a holistic perspective on AI integration into the automotive sector,focusing on next-generation AI methods and their critical implementation aspects.Additionally,the proposed conceptual framework for real-time condition monitoring of electric vehicle subsystems delivers actionable maintenance recommendations to stakeholders,addressing a critical gap in the field.The review highlights that AI has significantly expedited the development of autonomous vehicles regarding navigation,decision-making,and safety features through the use of advanced algorithms and deep learning structures.Furthermore,it identifies advanced driver assistance systems,vehicle health monitoring,and predictive maintenance as the most impactful AI applications,transforming operational safety and maintenance efficiency in modern automotive technologies.The work is beneficial to understanding the various use cases of AI in the different automotive domains,where AI maintains a state-of-the-art for sector-specific applications,providing a strong foundation for meeting Industry 4.0 needs and encouraging AI use among more nascent industry segments.The current work is intended to consolidate previous works while shedding some light on future research directions in promoting further growth of AI-based innovations in the scope of automotive applications.展开更多
Some 20 years ago,10-yearold Wang Xingxing watched a documentary that featured a bipedal robot designed by Marc Raibert,now president of U.S.-based robot company Boston Dynamics.That moment planted the seed for his fu...Some 20 years ago,10-yearold Wang Xingxing watched a documentary that featured a bipedal robot designed by Marc Raibert,now president of U.S.-based robot company Boston Dynamics.That moment planted the seed for his future in innovation.Today,operating in China’s private sector,Wang’s company,Hangzhou-based Unitree Robotics in Zhejiang Province,is a globally recognized name in producing high-performance,general-purpose quadruped and humanoid robots.展开更多
This study analyzes the impact of profitability,firm size,and capital structure on firm value within the manufacturing sector.Utilizing a descriptive literature-based method,this research synthesizes various findings ...This study analyzes the impact of profitability,firm size,and capital structure on firm value within the manufacturing sector.Utilizing a descriptive literature-based method,this research synthesizes various findings from previous studies to provide a comprehensive overview of the relationships among these variables.Profitability,often measured by Return on Assets(ROA)or Return on Equity(ROE),is expected to have a positive relationship with firm value as it reflects a company’s ability to generate profits.Firm size,measured by total assets or the logarithm of sales,is also assumed to influence firm value,both through economies of scale and better access to capital markets.Meanwhile,capital structure,measured by Debt to Equity Ratio(DER)or Debt to Asset Ratio(DAR),has a complex impact;while debt can enhance firm value through tax shields,excessive debt levels can increase financial risk and potentially decrease firm value.The synthesis of literature indicates a general consensus on the importance of profitability and firm size in shaping firm value,though there is variation in the significance and direction of the relationship for capital structure variables.This study provides important implications for company managers and investors in making strategic decisions related to resource allocation and selecting an optimal capital structure to enhance firm value.展开更多
Agriculture holds a pivotal position in the economic fabric of every nation,yet concerns about agricultural carbon emission intensity(ACI)have become a major hurdle to achieving global economic sustainability.Focusing...Agriculture holds a pivotal position in the economic fabric of every nation,yet concerns about agricultural carbon emission intensity(ACI)have become a major hurdle to achieving global economic sustainability.Focusing on 31 provincial-level regions in China,this study uses the Exploratory Spatio-temporal Data Analysis(ESTDA)and Panel Quantile Regression(PQR)model to analyze the spatio-temporal interaction characteristics and influencing factors of ACI in China from 2004 to 2023.The findings are as follows:(1)ACI showed an overall downward trend,and the spatial distribution pattern was characterized by“high in the western region and low along the southeastern coast”.Although the overall disparity tended to converge,some high-carbon-intensity regions exhibited extreme trends.ACI displayed clear spatial directionality,with the spatial center shifting steadily toward the northeast.(2)Regions in the northwest,northeast,and central-south parts exhibited strong local spatial structural dynamics,and the local spatial dependence of ACI in each region showed a nonlinear trend.Generally speaking,the spatial association pattern demonstrated a certain degree of inertia in spatial transfer,reflecting strong path dependence or spatial lock-in characteristics.(3)Optimization of industrial structure and improvement in agricultural mechanization will increase ACI,while economic development can effectively reduce it.The impact of urbanization on ACI exhibits a nonlinear pattern.The coordinated development of economic growth and urbanization significantly reduces ACI,with a stronger emission reduction observed in regions with low ACI.The optimization of industrial structure,when combined with urbanization and environmental regulation,contributes to significant emission reductions particularly in high-ACI areas.Similarly,the synergy between agricultural mechanization and urbanization effectively lowers emissions in low-ACI regions,though this effect diminishes in areas with higher ACI.展开更多
The thaw-freezing transition period is crucial to determine the initial sea ice status prior to the freezing season.The heat and mass balance at ice-ocean interface is the major driving process.In this study,we analyz...The thaw-freezing transition period is crucial to determine the initial sea ice status prior to the freezing season.The heat and mass balance at ice-ocean interface is the major driving process.In this study,we analyze heat fluxes profile through the ice from ice surface down to basal ice-ocean interface using the data measured by 11 thermistor stringbased ice mass balance buoys(IMBs)between September and December 2018 in the Pacific sector of Arctic Ocean.The conductive heat fluxes gradually decreased from surface downward through the lower ice layers due to the thermal inertia and energy storage in the brine pockets.At the ice bottom,the oceanic heat flux decreased from(5.9±1.3)W/m^(2)in mid-September to(1.8±0.8)W/m^(2)by the end of December in response to the decreasing of available absorbed solar radiation regulated by the latitude and sea ice concentration.The initial ice thicknesses can explain the onset of ice basal growth by 44.8%(R^(2)).From 15 September to the average onset of ice basal growth by 13 November,the accumulated heat fluxes released from the ice surface to the atmosphere,caused by the cooling of the ice layer,and from the ocean to the ice bottom were estimated as 25.73 MJ/m^(2),6.49 MJ/m^(2),and 20.30 MJ/m^(2),respectively.The latter two components mainly play the roles in buffering the onset of ice basal growth.展开更多
The optimal allocation of foreign resources requires a minimum level of domestic development,including financial development to benefit from the potential benefits of foreign direct investment.This study discusses the...The optimal allocation of foreign resources requires a minimum level of domestic development,including financial development to benefit from the potential benefits of foreign direct investment.This study discusses the mediating role of financial development in the effect of foreign direct investment on economic growth and establishes the banking sector threshold for the 18 least developed African countries over the period 2000 to 2020.We used the generalized method of moments(GMM)and the threshold regression(TR)as part of the dynamic panel data model.The results show the non-significant contribution of foreign direct investment and the banking sector to economic growth.After interaction,the effect of foreign direct investment becomes positive but not significant.However,the coefficient of the interaction variable is significantly negative.This implies that the financial system is unable to allocate foreign resources efficiently.For this reason,this paper resorted to applying the threshold regression to determine the minimum threshold of the banking sector and established a threshold of 74.58%.It therefore becomes necessary for the 18 least developed African countries to develop the financial system in order to get full benefits of foreign direct investment.展开更多
Germany aims to achieve a national climate-neutral energy system by 2045.The residential sector still accounts for 29%of end energy consumption,with 74%attributed to the direct use of fossil fuels for heating and hot ...Germany aims to achieve a national climate-neutral energy system by 2045.The residential sector still accounts for 29%of end energy consumption,with 74%attributed to the direct use of fossil fuels for heating and hot water.In order to reduce fossil energy use in the household sector,great efforts are being made to design new energy concepts that expand the use of renewable energies to supply electricity andheat.Onepossibility is toconvertparts of the natural gas grid to a hydrogen-based gas grid to deliver and store energy for urban quarters of buildings,especially with older building stock where electrification of heat via heat pumps is difficult due to technical,acoustical,and economic reasons.A comprehensive dataset was generated by a bottom-up analysis with open governmental and statistical data to determine regional building types regarding energy demand,solar potential,and existing grid infrastructure.The buildings’connections to the electricity,gas,and district heating networks are considered.From this,a representative sample dataset was chosen as input for a newly developed energy system model based on energy flow simulation.The model simulates the interaction of hydrogen generation(HG)(from excess solar energy by electrolysis),storage in a metal-hydride storage(MHS)tank,and hydrogen use in a connected fuel cell(FC),forming a local PVPtGtHP(Photovoltaic Power-to-Gas-to-Heat-and-Power)network.Next to the seasonal hydrogen storage path(HSP),a battery will complete the system to forma hybrid energy storage system(HESS).Paired with seasonal time series for PV power,electricity and heat demand,and a model for connection to grid infrastructure,the simulation of different hydrogen applications and MHS placements aims to analyze operating times and energy share of the systems’equipment and existing infrastructure.The method to obtain the data set together with the simulationmodel presented can be used by energy planners for cities,communities,and building developers to analyze the potentials of a quarter or region and plan a transition towards a more energy-efficient and sustainable energy system.展开更多
Sustainably managing vehicles at their end-of-life stage(ELVs)presents significant potential forresource recovery,effectively addressing resource scarcity through the closure of the material loop.While ELVs in countri...Sustainably managing vehicles at their end-of-life stage(ELVs)presents significant potential forresource recovery,effectively addressing resource scarcity through the closure of the material loop.While ELVs in countries like Morocco have traditionally been treated as waste rather than secondaryresource material(SRM),they have the potential to reduce reliance on primary materials when usedjudiciously.Despite policymakers aiming for increased resource efficiency in the automobile sector,there is limited research exploring the role of the informal sector in recovering materials and parts fromELVs.This study investigates the ELV processing scenario at Salmia scrap market,recognized as one of Africa’s largest informal markets for ELVs.Using a mass-balance approach,the disposal of sedan cars isexamined,and a conceptual framework illustrating the process flow and interactions among multiplestakeholders is developed.From sampled sedan cars,approximately 7% of aluminum and 76%of iron,by weight,are recovered.These findings contribute to estimating the potential for recycling andrecovering materials from ELVs processed by the informal sector in Morocco.In a standard operationalcontext,estimations suggest that the sector holds substantial potential to recover aluminum and iron by2030.This underscores the importance of formalizing operations and integrating informal players intothe value chain to effectively address resource scarcity within a circular economy.展开更多
Given the unique challenges facing the railway industry, cybersecurity is a crucial issue that must be addressed proactively. This paper aims to provide a systematic review of cybersecurity threats that could impact t...Given the unique challenges facing the railway industry, cybersecurity is a crucial issue that must be addressed proactively. This paper aims to provide a systematic review of cybersecurity threats that could impact the safety and operations of rolling stock, the privacy and security of passengers and employees, and the public in general. The systematic literature review revealed that cyber threats to the railway industry can take many forms, including attacks on operational technology systems, data breaches, theft of sensitive information, and disruptions to train services. The consequences of these threats can be severe, leading to operational disruptions, financial losses, and loss of public trust in the railway system. To address these threats, railway organizations must adopt a proactive approach to security and implement robust cybersecurity measures tailored to the industry’s specific needs and challenges. This includes regular testing of systems for vulnerabilities, incident response plans, and employee training to identify and respond to cyber threats. Ensuring the system remains available, reliable, and maintainable is fundamental given the importance of railways as critical infrastructure and the potential harm that can be caused by cyber threats.展开更多
Recently,the transportation sector in China has gradually become the main source of urban air pollution and primary driver of carbon emissions growth.Considering air pollutants and greenhouse gases come from the same ...Recently,the transportation sector in China has gradually become the main source of urban air pollution and primary driver of carbon emissions growth.Considering air pollutants and greenhouse gases come from the same emission sources,it is necessary to establish an updated high-resolution emission inventory for the transportation sector in Central China,themost polluted region in China.The inventory includes on-road mobile,non-roadmobile,oil storage and transportation,and covers 9 types of air pollutants and 3 types of greenhouse gases.Based on the Long-range Energy Alternatives Planning System(LEAP)model,the emissions of pollutants were predicted for the period from2020 to 2035 in different scenarios.Results showed that in 2020,emissions of SO_(2),NO_(x),CO,PM_(10),PM_(2.5),VOCs,NH_(3),BC,OC,CO_(2),CH_(4),and N_(2)O in Henan Province were 27.5,503.2,878.6,20.1,17.4,222.1,21.5,9.4,2.9,92,077.9,6.0,and 10.4 kilotons,respectively.Energy demand and pollutant emissions in Henan Province are simulated under four scenarios(Baseline Scenario(BS),Pollution Abatement Scenario(PA),Green Transportation Scenario(GT),and Reinforcing Low Carbon Scenario(RLC)).The collaborative emission reduction effect is most significant in the RLC scenario,followed by the GT scenario.By 2035,under the RLC scenario,energy consumption and emissions of SO_(2),NO_(x),CO,PM_(10),PM_(2.5),VOCs,NH_(3),CO_(2),CH_(4),and N_(2)O are projected to decrease by 72.0%,30.0%,55.6%,56.0%,38.6%,39.7%,51.5%,66.1%,65.5%,55.4%,and 52.8%,respectively.This study provides fundamental data support for subsequent numerical simulations.展开更多
CO_(2) emissions(CEs)pose a growing threat to environmental changes and global warming,attracting extensive attention.Here,we leveraged near-real-time monitoring data spanning 2019 to 2022 to investigate spatiotempora...CO_(2) emissions(CEs)pose a growing threat to environmental changes and global warming,attracting extensive attention.Here,we leveraged near-real-time monitoring data spanning 2019 to 2022 to investigate spatiotemporal heterogeneity,sectoral contributions,provincial spatial correlation,and driving factors influencing CEs at the provincial level in China.Our analysis,integrating Moran’s Index analysis,Spearman correlation analysis,and the Geographically Weighted Regression model,unveiled China’s consistent world-leading CEs,surpassing 10,000 Mt over the study period.Spatially,CEs exhibited a heterogeneous distribution,with markedly higher emissions in eastern and northern regions compared to western and southern areas.Temporally,CEs displayed significant fluctuations,peaking in the fourth quarter before declining in subsequent quarters.Chinese NewYear and COVID-19 had the biggest effects on CEs,with average daily reductions of-20.8%and-18.9%,respectively,compared to the four-year average and the same period in 2019.Sectoral analysis highlighted the power and industry sectors as primary contributors to CEs in China,jointly accounting for 37.9%-40.2%and 43.5%-46.4%of total CEs,respectively.Spatial clustering analysis identified a distinct High-High agglomeration region,predominantly encompassing provinces such as Inner Mongolia,Shandong and Jiangsu.Furthermore,total energy consumption and electricity consumption emerged as significant drivers of CEs,exhibiting correlation coefficients exceeding 0.9,followed by exhaust emissions,population size,and gross domestic product.Moreover,the influence of drivers on provincial CEs exhibited notable spatial heterogeneity,with regression coefficients displaying a decreasing gradient from north to south.These findings provide scientific and technological support to realize the provincial dual-carbon goals in China.展开更多
EI Salvador is gradually consolidating its position as a strategic destination for foreign direct investment,especially for countries with extensive experience in industrial,technological,and logistics development,suc...EI Salvador is gradually consolidating its position as a strategic destination for foreign direct investment,especially for countries with extensive experience in industrial,technological,and logistics development,such as the People’s Republic of China.Thanks to ambitious economic policies based on sustainability,inclusivity,and global competitiveness,the country has clearly defined a development agenda focused on priority sectors with high potential for international cooperation.展开更多
In the workshop of Guangshan White Shark Card Clothing Co.,Ltd.,precision machines are meticulously crafting the tips of card clothing with millimeter-level accuracy.These seemingly tiny textile accessories play a cru...In the workshop of Guangshan White Shark Card Clothing Co.,Ltd.,precision machines are meticulously crafting the tips of card clothing with millimeter-level accuracy.These seemingly tiny textile accessories play a crucial role in the global cotton and chemical fiber industry chains.As China's textile industry encounters trade barriers in the process of globalization,this company specializing in card clothing manufacturing is striving to explore a differentiated path in overseas markets.展开更多
The increasing data pool in finance sectors forces machine learning(ML)to step into new complications.Banking data has significant financial implications and is confidential.Combining users data from several organizat...The increasing data pool in finance sectors forces machine learning(ML)to step into new complications.Banking data has significant financial implications and is confidential.Combining users data from several organizations for various banking services may result in various intrusions and privacy leakages.As a result,this study employs federated learning(FL)using a flower paradigm to preserve each organization’s privacy while collaborating to build a robust shared global model.However,diverse data distributions in the collaborative training process might result in inadequate model learning and a lack of privacy.To address this issue,the present paper proposes the imple-mentation of Federated Averaging(FedAvg)and Federated Proximal(FedProx)methods in the flower framework,which take advantage of the data locality while training and guaranteeing global convergence.Resultantly improves the privacy of the local models.This analysis used the credit card and Canadian Institute for Cybersecurity Intrusion Detection Evaluation(CICIDS)datasets.Precision,recall,and accuracy as performance indicators to show the efficacy of the proposed strategy using FedAvg and FedProx.The experimental findings suggest that the proposed approach helps to safely use banking data from diverse sources to enhance customer banking services by obtaining accuracy of 99.55%and 83.72%for FedAvg and 99.57%,and 84.63%for FedProx.展开更多
In this paper,we propose a Multi-token Sector Antenna Neighbor Discovery(M-SAND)protocol to enhance the efficiency of neighbor discovery in asynchronous directional ad hoc networks.The central concept of our work invo...In this paper,we propose a Multi-token Sector Antenna Neighbor Discovery(M-SAND)protocol to enhance the efficiency of neighbor discovery in asynchronous directional ad hoc networks.The central concept of our work involves maintaining multiple tokens across the network.To prevent mutual interference among multi-token holders,we introduce the time and space non-interference theorems.Furthermore,we propose a master-slave strategy between tokens.When the master token holder(MTH)performs the neighbor discovery,it decides which 1-hop neighbor is the next MTH and which 2-hop neighbors can be the new slave token holders(STHs).Using this approach,the MTH and multiple STHs can simultaneously discover their neighbors without causing interference with each other.Building on this foundation,we provide a comprehensive procedure for the M-SAND protocol.We also conduct theoretical analyses on the maximum number of STHs and the lower bound of multi-token generation probability.Finally,simulation results demonstrate the time efficiency of the M-SAND protocol.When compared to the QSAND protocol,which uses only one token,the total neighbor discovery time is reduced by 28% when 6beams and 112 nodes are employed.展开更多
Investing in projects that support environmental benefits,such as tree harvesting,has the potential to reduce air pollution levels in the atmosphere in the future.However,this kind of investment may increase the curre...Investing in projects that support environmental benefits,such as tree harvesting,has the potential to reduce air pollution levels in the atmosphere in the future.However,this kind of investment may increase the current level of emissions.Therefore,it is necessary to estimate how much the policy affects the current level of CO_(2) emissions.This makes sure the policy doesn’t increase the level of CO_(2) emis-sions.This study aims to analyze the effect of the One Bil-lion Trees program on CO_(2) emissions in New Zealand by employing the 2020 input–output table analysis.This inves-tigation examines the direct and indirect effects of policy on both the demand and supply sides across six regions of New Zealand.The results of this study for the first year of plantation suggest that the policy increases the level of CO_(2) emissions in all regions,especially in the Waikato region.The direct and indirect impact of the policy leads to 64 kt of CO_(2) emissions on the demand side and 270 kt of CO_(2) emis-sions on the supply side.These lead to 0.19 and 0.74%of total CO_(2) emissions being attributed to investment shocks.Continuing the policy is recommended,as it has a low effect on CO_(2) emissions.However,it is crucial to prioritize the use of low-carbon machinery that uses fossil fuels during the plantation process.展开更多
This paper uses an SBM-GML index model to assess Green Total Factor Productivity(GTFP)in China's carbon-intensive sectors and conducts an empirical investigation into which factors influence GTFP in these sectors....This paper uses an SBM-GML index model to assess Green Total Factor Productivity(GTFP)in China's carbon-intensive sectors and conducts an empirical investigation into which factors influence GTFP in these sectors.The GTFP in the carbon-intensive sectors experienced a decline between 2006 and 2011,followed by an upward trend beginning in 2012.Technological progress was the primary driver of GTFP growth,while business size was also a notable contributor.Irrational energy structures negatively influenced the high-quality development of the carbon-intensive sectors,and environmental regulation and foreign direct investment(FDI)have not yet significantly impacted GTFP.Based on these findings,this paper suggests that the carbon-intensive sectors should expedite their green transitions by focusing on system improvement,technological innovations,energy revolutions,and high-level opening up.展开更多
文摘Climate change may have detrimental effects on different sectoral growth in global economy and according to the Intergovernmental Panel on Climate Change,the impacts of climate change will be more vigorous in the coming years.The climatic and non-climatic driving forces behind the economic sectoral performances involve short-and long-run interconnections among variables.This study attempts to investigate the effect of climatic factors(temperature and precipitation)along with non-climatic factors,including foreign direct investment(FDI),human capital index(HCI),natural capital(NC),and information and communication technology(ICT)on three major sectors of the economy(agricultural sector,industrial sector,and service sector)through non-linear model framework by employing cross-sectionally augmented autoregressive distributed lag(CS-ARDL)estimation technique.It considers a panel of 56 selected countries from different income groups,including high-income countries,upper-middle-income countries,lower-middle-income countries,and low-income countries,covering the period 1985-2022.The confirmation of slope heterogeneity,cross-sectional dependence,stationarity,and cointegration among variables lends support to the robustness of results.The augmented mean group(AMG)robustness test was applied to check robustness and the results were found mostly consistent with estimation method.The results revealed that upper-middle-income countries are more vulnerable to extreme temperatures compared to high-income countries.The results also confirmed an inverted U-shaped relationship between each sector’s output and precipitation in upper-middle-income countries.In contrast,for upper-middle-income,lower-middle-income,and low-income countries,this relation exists in industrial sector only in long run.This indicates that precipitation is initially beneficial for production activities.However,beyond a certain threshold of precipitation,this trend reverses,i.e.,the output of the economic sectors tends to decline.Furthermore,there is no supporting evidence that confirms a short-run non-linear relation between precipitation and agricultural yields.In upper-middle-income countries,the results confirmed that FDI is a driving factor behind both agricultural sector and service sector in long run while short-run results indicated a negative association but insignificant.This study also showed that in long run,an increase in HCI contributes to improving the output of the three sectors for high-income countries.The empirical findings provide valuable insights for policy-makers and governments to formulate coherent adaptation and mitigation strategies,thereby accelerating the transition of sectoral productivity from low to high levels in the sample countries.
基金The authors are grateful to the Universiti Malaysia Pahang Al-Sultan Abdullah and the Malaysian Ministry of Higher Education for their generous support and funding provided through University Distinguished Research Grants(Project No.RDU223016)as well as financial assistance provided through the Fundamental Research Grant Scheme(No.FRGS/1/2022/TK10/UMP/02/35).
文摘The automotive sector is crucial in modern society,facilitating essential transportation needs across personal,commercial,and logistical domains while significantly contributing to national economic development and employment generation.The transformative impact of Artificial Intelligence(AI)has revolutionised multiple facets of the automotive industry,encompassing intelligent manufacturing processes,diagnostic systems,control mechanisms,supply chain operations,customer service platforms,and traffic management solutions.While extensive research exists on the above aspects of AI applications in automotive contexts,there is a compelling need to synthesise this knowledge comprehensively to guide and inspire future research.This review introduces a novel taxonomic framework that provides a holistic perspective on AI integration into the automotive sector,focusing on next-generation AI methods and their critical implementation aspects.Additionally,the proposed conceptual framework for real-time condition monitoring of electric vehicle subsystems delivers actionable maintenance recommendations to stakeholders,addressing a critical gap in the field.The review highlights that AI has significantly expedited the development of autonomous vehicles regarding navigation,decision-making,and safety features through the use of advanced algorithms and deep learning structures.Furthermore,it identifies advanced driver assistance systems,vehicle health monitoring,and predictive maintenance as the most impactful AI applications,transforming operational safety and maintenance efficiency in modern automotive technologies.The work is beneficial to understanding the various use cases of AI in the different automotive domains,where AI maintains a state-of-the-art for sector-specific applications,providing a strong foundation for meeting Industry 4.0 needs and encouraging AI use among more nascent industry segments.The current work is intended to consolidate previous works while shedding some light on future research directions in promoting further growth of AI-based innovations in the scope of automotive applications.
文摘Some 20 years ago,10-yearold Wang Xingxing watched a documentary that featured a bipedal robot designed by Marc Raibert,now president of U.S.-based robot company Boston Dynamics.That moment planted the seed for his future in innovation.Today,operating in China’s private sector,Wang’s company,Hangzhou-based Unitree Robotics in Zhejiang Province,is a globally recognized name in producing high-performance,general-purpose quadruped and humanoid robots.
文摘This study analyzes the impact of profitability,firm size,and capital structure on firm value within the manufacturing sector.Utilizing a descriptive literature-based method,this research synthesizes various findings from previous studies to provide a comprehensive overview of the relationships among these variables.Profitability,often measured by Return on Assets(ROA)or Return on Equity(ROE),is expected to have a positive relationship with firm value as it reflects a company’s ability to generate profits.Firm size,measured by total assets or the logarithm of sales,is also assumed to influence firm value,both through economies of scale and better access to capital markets.Meanwhile,capital structure,measured by Debt to Equity Ratio(DER)or Debt to Asset Ratio(DAR),has a complex impact;while debt can enhance firm value through tax shields,excessive debt levels can increase financial risk and potentially decrease firm value.The synthesis of literature indicates a general consensus on the importance of profitability and firm size in shaping firm value,though there is variation in the significance and direction of the relationship for capital structure variables.This study provides important implications for company managers and investors in making strategic decisions related to resource allocation and selecting an optimal capital structure to enhance firm value.
基金National Natural Science Foundation of China,No.42230106,No.42171250State Key Laboratory of Earth Surface Processes and Resource Ecology,No.2022-ZD-04。
文摘Agriculture holds a pivotal position in the economic fabric of every nation,yet concerns about agricultural carbon emission intensity(ACI)have become a major hurdle to achieving global economic sustainability.Focusing on 31 provincial-level regions in China,this study uses the Exploratory Spatio-temporal Data Analysis(ESTDA)and Panel Quantile Regression(PQR)model to analyze the spatio-temporal interaction characteristics and influencing factors of ACI in China from 2004 to 2023.The findings are as follows:(1)ACI showed an overall downward trend,and the spatial distribution pattern was characterized by“high in the western region and low along the southeastern coast”.Although the overall disparity tended to converge,some high-carbon-intensity regions exhibited extreme trends.ACI displayed clear spatial directionality,with the spatial center shifting steadily toward the northeast.(2)Regions in the northwest,northeast,and central-south parts exhibited strong local spatial structural dynamics,and the local spatial dependence of ACI in each region showed a nonlinear trend.Generally speaking,the spatial association pattern demonstrated a certain degree of inertia in spatial transfer,reflecting strong path dependence or spatial lock-in characteristics.(3)Optimization of industrial structure and improvement in agricultural mechanization will increase ACI,while economic development can effectively reduce it.The impact of urbanization on ACI exhibits a nonlinear pattern.The coordinated development of economic growth and urbanization significantly reduces ACI,with a stronger emission reduction observed in regions with low ACI.The optimization of industrial structure,when combined with urbanization and environmental regulation,contributes to significant emission reductions particularly in high-ACI areas.Similarly,the synergy between agricultural mechanization and urbanization effectively lowers emissions in low-ACI regions,though this effect diminishes in areas with higher ACI.
基金The National Key Research and Development Program of China under contract No.2021YFC2803300the National Natural Science Foundation of China under contract No.42325604+1 种基金the Ministry of Industry and Information Technology of China under contract No.CBG2N21-2-1Program of Shanghai Academic/Technology Research Leader under contract No.22XD1403600.
文摘The thaw-freezing transition period is crucial to determine the initial sea ice status prior to the freezing season.The heat and mass balance at ice-ocean interface is the major driving process.In this study,we analyze heat fluxes profile through the ice from ice surface down to basal ice-ocean interface using the data measured by 11 thermistor stringbased ice mass balance buoys(IMBs)between September and December 2018 in the Pacific sector of Arctic Ocean.The conductive heat fluxes gradually decreased from surface downward through the lower ice layers due to the thermal inertia and energy storage in the brine pockets.At the ice bottom,the oceanic heat flux decreased from(5.9±1.3)W/m^(2)in mid-September to(1.8±0.8)W/m^(2)by the end of December in response to the decreasing of available absorbed solar radiation regulated by the latitude and sea ice concentration.The initial ice thicknesses can explain the onset of ice basal growth by 44.8%(R^(2)).From 15 September to the average onset of ice basal growth by 13 November,the accumulated heat fluxes released from the ice surface to the atmosphere,caused by the cooling of the ice layer,and from the ocean to the ice bottom were estimated as 25.73 MJ/m^(2),6.49 MJ/m^(2),and 20.30 MJ/m^(2),respectively.The latter two components mainly play the roles in buffering the onset of ice basal growth.
文摘The optimal allocation of foreign resources requires a minimum level of domestic development,including financial development to benefit from the potential benefits of foreign direct investment.This study discusses the mediating role of financial development in the effect of foreign direct investment on economic growth and establishes the banking sector threshold for the 18 least developed African countries over the period 2000 to 2020.We used the generalized method of moments(GMM)and the threshold regression(TR)as part of the dynamic panel data model.The results show the non-significant contribution of foreign direct investment and the banking sector to economic growth.After interaction,the effect of foreign direct investment becomes positive but not significant.However,the coefficient of the interaction variable is significantly negative.This implies that the financial system is unable to allocate foreign resources efficiently.For this reason,this paper resorted to applying the threshold regression to determine the minimum threshold of the banking sector and established a threshold of 74.58%.It therefore becomes necessary for the 18 least developed African countries to develop the financial system in order to get full benefits of foreign direct investment.
基金supported by the German Federal Ministry for Economic Affairs and Climate Action[BMWK SimBench-Sektor project,grant number 03EI1058C].
文摘Germany aims to achieve a national climate-neutral energy system by 2045.The residential sector still accounts for 29%of end energy consumption,with 74%attributed to the direct use of fossil fuels for heating and hot water.In order to reduce fossil energy use in the household sector,great efforts are being made to design new energy concepts that expand the use of renewable energies to supply electricity andheat.Onepossibility is toconvertparts of the natural gas grid to a hydrogen-based gas grid to deliver and store energy for urban quarters of buildings,especially with older building stock where electrification of heat via heat pumps is difficult due to technical,acoustical,and economic reasons.A comprehensive dataset was generated by a bottom-up analysis with open governmental and statistical data to determine regional building types regarding energy demand,solar potential,and existing grid infrastructure.The buildings’connections to the electricity,gas,and district heating networks are considered.From this,a representative sample dataset was chosen as input for a newly developed energy system model based on energy flow simulation.The model simulates the interaction of hydrogen generation(HG)(from excess solar energy by electrolysis),storage in a metal-hydride storage(MHS)tank,and hydrogen use in a connected fuel cell(FC),forming a local PVPtGtHP(Photovoltaic Power-to-Gas-to-Heat-and-Power)network.Next to the seasonal hydrogen storage path(HSP),a battery will complete the system to forma hybrid energy storage system(HESS).Paired with seasonal time series for PV power,electricity and heat demand,and a model for connection to grid infrastructure,the simulation of different hydrogen applications and MHS placements aims to analyze operating times and energy share of the systems’equipment and existing infrastructure.The method to obtain the data set together with the simulationmodel presented can be used by energy planners for cities,communities,and building developers to analyze the potentials of a quarter or region and plan a transition towards a more energy-efficient and sustainable energy system.
文摘Sustainably managing vehicles at their end-of-life stage(ELVs)presents significant potential forresource recovery,effectively addressing resource scarcity through the closure of the material loop.While ELVs in countries like Morocco have traditionally been treated as waste rather than secondaryresource material(SRM),they have the potential to reduce reliance on primary materials when usedjudiciously.Despite policymakers aiming for increased resource efficiency in the automobile sector,there is limited research exploring the role of the informal sector in recovering materials and parts fromELVs.This study investigates the ELV processing scenario at Salmia scrap market,recognized as one of Africa’s largest informal markets for ELVs.Using a mass-balance approach,the disposal of sedan cars isexamined,and a conceptual framework illustrating the process flow and interactions among multiplestakeholders is developed.From sampled sedan cars,approximately 7% of aluminum and 76%of iron,by weight,are recovered.These findings contribute to estimating the potential for recycling andrecovering materials from ELVs processed by the informal sector in Morocco.In a standard operationalcontext,estimations suggest that the sector holds substantial potential to recover aluminum and iron by2030.This underscores the importance of formalizing operations and integrating informal players intothe value chain to effectively address resource scarcity within a circular economy.
文摘Given the unique challenges facing the railway industry, cybersecurity is a crucial issue that must be addressed proactively. This paper aims to provide a systematic review of cybersecurity threats that could impact the safety and operations of rolling stock, the privacy and security of passengers and employees, and the public in general. The systematic literature review revealed that cyber threats to the railway industry can take many forms, including attacks on operational technology systems, data breaches, theft of sensitive information, and disruptions to train services. The consequences of these threats can be severe, leading to operational disruptions, financial losses, and loss of public trust in the railway system. To address these threats, railway organizations must adopt a proactive approach to security and implement robust cybersecurity measures tailored to the industry’s specific needs and challenges. This includes regular testing of systems for vulnerabilities, incident response plans, and employee training to identify and respond to cyber threats. Ensuring the system remains available, reliable, and maintainable is fundamental given the importance of railways as critical infrastructure and the potential harm that can be caused by cyber threats.
基金supported by the 2020 National Supercomputing Zhengzhou Center Innovation Ecosystem Construction Technology Project(No.201400210700)Zhengzhou PM2.5 and O3 Collaborative Control and Monitoring Project(No.20220347A).
文摘Recently,the transportation sector in China has gradually become the main source of urban air pollution and primary driver of carbon emissions growth.Considering air pollutants and greenhouse gases come from the same emission sources,it is necessary to establish an updated high-resolution emission inventory for the transportation sector in Central China,themost polluted region in China.The inventory includes on-road mobile,non-roadmobile,oil storage and transportation,and covers 9 types of air pollutants and 3 types of greenhouse gases.Based on the Long-range Energy Alternatives Planning System(LEAP)model,the emissions of pollutants were predicted for the period from2020 to 2035 in different scenarios.Results showed that in 2020,emissions of SO_(2),NO_(x),CO,PM_(10),PM_(2.5),VOCs,NH_(3),BC,OC,CO_(2),CH_(4),and N_(2)O in Henan Province were 27.5,503.2,878.6,20.1,17.4,222.1,21.5,9.4,2.9,92,077.9,6.0,and 10.4 kilotons,respectively.Energy demand and pollutant emissions in Henan Province are simulated under four scenarios(Baseline Scenario(BS),Pollution Abatement Scenario(PA),Green Transportation Scenario(GT),and Reinforcing Low Carbon Scenario(RLC)).The collaborative emission reduction effect is most significant in the RLC scenario,followed by the GT scenario.By 2035,under the RLC scenario,energy consumption and emissions of SO_(2),NO_(x),CO,PM_(10),PM_(2.5),VOCs,NH_(3),CO_(2),CH_(4),and N_(2)O are projected to decrease by 72.0%,30.0%,55.6%,56.0%,38.6%,39.7%,51.5%,66.1%,65.5%,55.4%,and 52.8%,respectively.This study provides fundamental data support for subsequent numerical simulations.
基金supported by the National Natural Science Foundation of China(No.52200120)the R&D Program of Beijing Municipal Education Commission(No.KM202310011003).
文摘CO_(2) emissions(CEs)pose a growing threat to environmental changes and global warming,attracting extensive attention.Here,we leveraged near-real-time monitoring data spanning 2019 to 2022 to investigate spatiotemporal heterogeneity,sectoral contributions,provincial spatial correlation,and driving factors influencing CEs at the provincial level in China.Our analysis,integrating Moran’s Index analysis,Spearman correlation analysis,and the Geographically Weighted Regression model,unveiled China’s consistent world-leading CEs,surpassing 10,000 Mt over the study period.Spatially,CEs exhibited a heterogeneous distribution,with markedly higher emissions in eastern and northern regions compared to western and southern areas.Temporally,CEs displayed significant fluctuations,peaking in the fourth quarter before declining in subsequent quarters.Chinese NewYear and COVID-19 had the biggest effects on CEs,with average daily reductions of-20.8%and-18.9%,respectively,compared to the four-year average and the same period in 2019.Sectoral analysis highlighted the power and industry sectors as primary contributors to CEs in China,jointly accounting for 37.9%-40.2%and 43.5%-46.4%of total CEs,respectively.Spatial clustering analysis identified a distinct High-High agglomeration region,predominantly encompassing provinces such as Inner Mongolia,Shandong and Jiangsu.Furthermore,total energy consumption and electricity consumption emerged as significant drivers of CEs,exhibiting correlation coefficients exceeding 0.9,followed by exhaust emissions,population size,and gross domestic product.Moreover,the influence of drivers on provincial CEs exhibited notable spatial heterogeneity,with regression coefficients displaying a decreasing gradient from north to south.These findings provide scientific and technological support to realize the provincial dual-carbon goals in China.
文摘EI Salvador is gradually consolidating its position as a strategic destination for foreign direct investment,especially for countries with extensive experience in industrial,technological,and logistics development,such as the People’s Republic of China.Thanks to ambitious economic policies based on sustainability,inclusivity,and global competitiveness,the country has clearly defined a development agenda focused on priority sectors with high potential for international cooperation.
文摘In the workshop of Guangshan White Shark Card Clothing Co.,Ltd.,precision machines are meticulously crafting the tips of card clothing with millimeter-level accuracy.These seemingly tiny textile accessories play a crucial role in the global cotton and chemical fiber industry chains.As China's textile industry encounters trade barriers in the process of globalization,this company specializing in card clothing manufacturing is striving to explore a differentiated path in overseas markets.
文摘The increasing data pool in finance sectors forces machine learning(ML)to step into new complications.Banking data has significant financial implications and is confidential.Combining users data from several organizations for various banking services may result in various intrusions and privacy leakages.As a result,this study employs federated learning(FL)using a flower paradigm to preserve each organization’s privacy while collaborating to build a robust shared global model.However,diverse data distributions in the collaborative training process might result in inadequate model learning and a lack of privacy.To address this issue,the present paper proposes the imple-mentation of Federated Averaging(FedAvg)and Federated Proximal(FedProx)methods in the flower framework,which take advantage of the data locality while training and guaranteeing global convergence.Resultantly improves the privacy of the local models.This analysis used the credit card and Canadian Institute for Cybersecurity Intrusion Detection Evaluation(CICIDS)datasets.Precision,recall,and accuracy as performance indicators to show the efficacy of the proposed strategy using FedAvg and FedProx.The experimental findings suggest that the proposed approach helps to safely use banking data from diverse sources to enhance customer banking services by obtaining accuracy of 99.55%and 83.72%for FedAvg and 99.57%,and 84.63%for FedProx.
基金supported in part by the National Natural Science Foundations of CHINA(Grant No.61771392,No.61771390,No.61871322 and No.61501373)Science and Technology on Avionics Integration Laboratory and the Aeronautical Science Foundation of China(Grant No.201955053002 and No.20185553035)。
文摘In this paper,we propose a Multi-token Sector Antenna Neighbor Discovery(M-SAND)protocol to enhance the efficiency of neighbor discovery in asynchronous directional ad hoc networks.The central concept of our work involves maintaining multiple tokens across the network.To prevent mutual interference among multi-token holders,we introduce the time and space non-interference theorems.Furthermore,we propose a master-slave strategy between tokens.When the master token holder(MTH)performs the neighbor discovery,it decides which 1-hop neighbor is the next MTH and which 2-hop neighbors can be the new slave token holders(STHs).Using this approach,the MTH and multiple STHs can simultaneously discover their neighbors without causing interference with each other.Building on this foundation,we provide a comprehensive procedure for the M-SAND protocol.We also conduct theoretical analyses on the maximum number of STHs and the lower bound of multi-token generation probability.Finally,simulation results demonstrate the time efficiency of the M-SAND protocol.When compared to the QSAND protocol,which uses only one token,the total neighbor discovery time is reduced by 28% when 6beams and 112 nodes are employed.
基金Open Access funding enabled and organized by CAUL and its Member Institutions
文摘Investing in projects that support environmental benefits,such as tree harvesting,has the potential to reduce air pollution levels in the atmosphere in the future.However,this kind of investment may increase the current level of emissions.Therefore,it is necessary to estimate how much the policy affects the current level of CO_(2) emissions.This makes sure the policy doesn’t increase the level of CO_(2) emis-sions.This study aims to analyze the effect of the One Bil-lion Trees program on CO_(2) emissions in New Zealand by employing the 2020 input–output table analysis.This inves-tigation examines the direct and indirect effects of policy on both the demand and supply sides across six regions of New Zealand.The results of this study for the first year of plantation suggest that the policy increases the level of CO_(2) emissions in all regions,especially in the Waikato region.The direct and indirect impact of the policy leads to 64 kt of CO_(2) emissions on the demand side and 270 kt of CO_(2) emis-sions on the supply side.These lead to 0.19 and 0.74%of total CO_(2) emissions being attributed to investment shocks.Continuing the policy is recommended,as it has a low effect on CO_(2) emissions.However,it is crucial to prioritize the use of low-carbon machinery that uses fossil fuels during the plantation process.
基金part of the project“Research on the Investment Game and Market Improvement of Social Capital in Supporting Poverty Reduction and Development in Ethnic Regions in Western China” (16XMZ094)funded by the National Social Science Fund of China。
文摘This paper uses an SBM-GML index model to assess Green Total Factor Productivity(GTFP)in China's carbon-intensive sectors and conducts an empirical investigation into which factors influence GTFP in these sectors.The GTFP in the carbon-intensive sectors experienced a decline between 2006 and 2011,followed by an upward trend beginning in 2012.Technological progress was the primary driver of GTFP growth,while business size was also a notable contributor.Irrational energy structures negatively influenced the high-quality development of the carbon-intensive sectors,and environmental regulation and foreign direct investment(FDI)have not yet significantly impacted GTFP.Based on these findings,this paper suggests that the carbon-intensive sectors should expedite their green transitions by focusing on system improvement,technological innovations,energy revolutions,and high-level opening up.
