The CIIE demonstrates China’s commitment to opening up and cooperation The eighth China International Import Expo(CIIE),held in Shanghai from 5 to 10 November,once again underscored China’s commitment to deepening i...The CIIE demonstrates China’s commitment to opening up and cooperation The eighth China International Import Expo(CIIE),held in Shanghai from 5 to 10 November,once again underscored China’s commitment to deepening institutional opening up.展开更多
The Regional Comprehensive Economic Partnership(RCEP)is a significant achievement in terms of Asia’s exploration and efforts toward regional economic integration.It integrates regional economic and trade cooperation ...The Regional Comprehensive Economic Partnership(RCEP)is a significant achievement in terms of Asia’s exploration and efforts toward regional economic integration.It integrates regional economic and trade cooperation mechanisms,demonstrates the determination and capabilities of Asian countries to promote open cooperation.展开更多
Addressing the pressing challenges of climate change and global warming requires a strong commitment to reducing carbon dioxide(CO_(2))emissions and achieving carbon neutrality.Efficient energy use and international t...Addressing the pressing challenges of climate change and global warming requires a strong commitment to reducing carbon dioxide(CO_(2))emissions and achieving carbon neutrality.Efficient energy use and international trade play crucial roles in promoting sustainable development and enhancing environmental quality.This study investigated the relationships of CO_(2) emissions with trade openness(export and import),energy consumption(renewable energy consumption and fossil fuel consumption),natural resource rents,and human capital across 20 developing countries(Brazil,Iran,Russia,China,Malaysia,Saudi Arabia,Colombia,Mexico,South Africa,Costa Rica,Morocco,Tunisia,Egypt,Pakistan,Türkiye,India,Peru,Viet Nam,Indonesia,and Philippines)from 1990 to 2022 using the augmented mean group(AMG)and common correlated effects mean group(CCEMG)methods.The findings revealed that export,renewable energy consumption,and human capital significantly reduce CO_(2) emissions,while import,fossil fuel consumption,and natural resource rents increase CO_(2) emissions,although the effect of natural resource rents is statistically insignificant.Causality tests indicated the bidirectional relationship of CO_(2) emissions with export,import,renewable energy consumption,and fossil fuel consumption,and the unidirectional causality from human capital to CO_(2) emissions.CO_(2) emissions drive the greenhouse effect,thereby raising global temperature and accelerating climate change.As reducing CO_(2) emissions becomes an urgent global priority,this study provides actionable insights by identifying key variables that mitigate emissions and enhance sustainability.By bridging research and policy,this study offers targeted recommendations to accelerate progress toward a low-carbon future.展开更多
Historically,geopolitical risk(GPR)has posed significant challenges to international economic,social,and political frameworks.This study investigated how internal GPR in the selected five Southeast Asian countries(Ind...Historically,geopolitical risk(GPR)has posed significant challenges to international economic,social,and political frameworks.This study investigated how internal GPR in the selected five Southeast Asian countries(Indonesia,South Korea,Malaysia,the Philippines,and Thailand)influences foreign direct investment(FDI)during 1996-2019.The stationarity of the data was assessed using the Augmented Dickey-Fuller(ADF)unit root test,which shows that the data became stationary after the first difference.The Kao,Pedroni,and Westerlund cointegration tests were employed to examine long-term cointegration among the selected variables(FDI,GPR index(GPRI),gross domestic product(GDP),inflation,interest rate,and trade openness(TOP)).The results indicated that these variables have a long-term cointegration.Consequently,regression analysis using the Pooled Ordinary Least Squares(OLS)regression,fixed effect,random effect,Arellano-Bond dynamic panel-data estimation,and system generalized moment method(GMM)revealed that GPRI and TOP negatively impacted FDI in the selected five Southeast Asian countries.At the same time,GDP,inflation,and interest rate positively influenced FDI in these countries.Because FDI is crucial to shaping a country’s macroeconomic structure,this study recommends that governments and central banks of the selected five Southeast Asian countries should implement policies and strategies to encourage foreign investments.展开更多
To fully leverage the advantages of mechanization and informatization in tunnel boring machine(TBM)operations,the authors aim to promote the advancement of tunnel construction technology toward intelligent development...To fully leverage the advantages of mechanization and informatization in tunnel boring machine(TBM)operations,the authors aim to promote the advancement of tunnel construction technology toward intelligent development.This involved exploring the deep integration of next-generation artificial intelligence technologies,such as sensing technology,automatic control technology,big data technology,deep learning,and machine vision,with key operational processes,including TBM excavation,direction adjustment,step changes,inverted arch block assembly,material transportation,and operation status assurance.The results of this integration are summarized as follows.(1)TBM key excavation parameter prediction algorithm was developed with an accuracy rate exceeding 90%.The TBM intelligent step-change control algorithm,based on machine vision,achieved an image segmentation accuracy rate of 95%and gripper shoe positioning error of±5 mm.(2)An automatic positioning system for inverted arch blocks was developed,enabling real-time perception of the spatial position and deviation during the assembly process.The system maintains an elevation positioning deviation within±3 mm and a horizontal positioning deviation within±10 mm,reducing the number of surveyors in each work team.(3)A TBM intelligent rail transportation system that achieves real-time human-machine positioning,automatic switch opening and closing,automatic obstacle avoidance,intelligent transportation planning,and integrated scheduling and command was designed.Each locomotive formation reduces one shunter and improves comprehensive transportation efficiency by more than 20%.(4)Intelligent analysis and prediction algorithms were developed to monitor and predict the trends of the hydraulic and gear oil parameters in real time,enhancing the proactive maintenance and system reliability.展开更多
基于GSMA Open Gateway倡议,电信运营商如何以全球统一的API标准科学而有效地开放网络原子能力成为一项具有挑战性的任务。分析了Open Gateway理念下的运营商网络能力开放体系建设方式,研究了典型应用实践,提出了运营商网络能力开放建议...基于GSMA Open Gateway倡议,电信运营商如何以全球统一的API标准科学而有效地开放网络原子能力成为一项具有挑战性的任务。分析了Open Gateway理念下的运营商网络能力开放体系建设方式,研究了典型应用实践,提出了运营商网络能力开放建议,进一步赋能运营商在企业内外部实现商业变现。展开更多
Purpose:For a set of 1,561 Open Access(OA)and non-OA journals in business and economics,this study evaluates the relationships between four citation metrics-five-year Impact Factor(5IF),CiteScore,Article Influence(AI)...Purpose:For a set of 1,561 Open Access(OA)and non-OA journals in business and economics,this study evaluates the relationships between four citation metrics-five-year Impact Factor(5IF),CiteScore,Article Influence(AI)score,and SCImago Journal Rank(SJR)-and the journal ratings assigned by expert reviewers.We expect that the OA journals will have especially high citation impact relative to their perceived quality(reputation).Design/methodology/approach:Regression is used to estimate the ratings assigned by expert reviewers for the 2021 CABS(Chartered Association of Business Schools)journal assessment exercise.The independent variables are the four citation metrics,evaluated separately,and a dummy variable representing the OA/non-OA status of each journal.Findings:Regardless of the citation metric used,OA journals in business and economics have especially high citation impact relative to their perceived quality(reputation).That is,they have especially low perceived quality(reputation)relative to their citation impact.Research limitations:These results are specific to the CABS journal ratings and the four citation metrics.However,there is strong evidence that CABS is closely related to several other expert ratings,and that 5IF,CiteScore,AI,and SJR are representative of the other citation metrics that might have been chosen.Practical implications:There are at least two possible explanations for these results:(1)expert evaluators are biased against OA journals,and(2)OA journals have especially high citation impact due to their increased accessibility.Although this study does not allow us to determine which of these explanations are supported,the results suggest that authors should consider publishing in OA journals whenever overall readership and citation impact are more important than journal reputation within a particular field.Moreover,the OA coefficients provide a useful indicator of the extent to which anti-OA bias(or the citation advantage of OA journals)is diminishing over time.Originality/value:This is apparently the first study to investigate the impact of OA status on the relationships between expert journal ratings and journal citation metrics.展开更多
本文根据企业公开信息,收集并整理了国内部分AI Agent应用相关企业。从Chatbots到AI Agents根据OpenAI的定义,通用人工智能(artificial general intelligence,AGI)的终极目标是创造一种能够像人类一样完成各种复杂任务,同时还能自然交...本文根据企业公开信息,收集并整理了国内部分AI Agent应用相关企业。从Chatbots到AI Agents根据OpenAI的定义,通用人工智能(artificial general intelligence,AGI)的终极目标是创造一种能够像人类一样完成各种复杂任务,同时还能自然交流的人工智能(AI)。OpenAI将AGI的发展分成了五个等级:第一级是“聊天机器人”(chatbots),具备语言对话能力的人工智能;第二级是“推理者”(reasoners),具备人类的推理水平,能解决多种复杂难题的人工智能;第三级是“代理人”(agents)。展开更多
Open caissons are widely used in foundation engineering because of their load-bearing efficiency and adaptability in diverse soil conditions.However,accurately predicting their undrained bearing capacity in layered so...Open caissons are widely used in foundation engineering because of their load-bearing efficiency and adaptability in diverse soil conditions.However,accurately predicting their undrained bearing capacity in layered soils remains a complex challenge.This study presents a novel application of five ensemble machine(ML)algorithms-random forest(RF),gradient boosting machine(GBM),extreme gradient boosting(XGBoost),adaptive boosting(AdaBoost),and categorical boosting(CatBoost)-to predict the undrained bearing capacity factor(Nc)of circular open caissons embedded in two-layered clay on the basis of results from finite element limit analysis(FELA).The input dataset consists of 1188 numerical simulations using the Tresca failure criterion,varying in geometrical and soil parameters.The FELA was performed via OptumG2 software with adaptive meshing techniques and verified against existing benchmark studies.The ML models were trained on 70% of the dataset and tested on the remaining 30%.Their performance was evaluated using six statistical metrics:coefficient of determination(R²),mean absolute error(MAE),root mean squared error(RMSE),index of scatter(IOS),RMSE-to-standard deviation ratio(RSR),and variance explained factor(VAF).The results indicate that all the models achieved high accuracy,with R²values exceeding 97.6%and RMSE values below 0.02.Among them,AdaBoost and CatBoost consistently outperformed the other methods across both the training and testing datasets,demonstrating superior generalizability and robustness.The proposed ML framework offers an efficient,accurate,and data-driven alternative to traditional methods for estimating caisson capacity in stratified soils.This approach can aid in reducing computational costs while improving reliability in the early stages of foundation design.展开更多
文摘The CIIE demonstrates China’s commitment to opening up and cooperation The eighth China International Import Expo(CIIE),held in Shanghai from 5 to 10 November,once again underscored China’s commitment to deepening institutional opening up.
文摘The Regional Comprehensive Economic Partnership(RCEP)is a significant achievement in terms of Asia’s exploration and efforts toward regional economic integration.It integrates regional economic and trade cooperation mechanisms,demonstrates the determination and capabilities of Asian countries to promote open cooperation.
基金support from the Philosophy and Social Sciences Excellent Innovation Team Construction Foundation of Jiangsu Province China(SJSZ2020-20).
文摘Addressing the pressing challenges of climate change and global warming requires a strong commitment to reducing carbon dioxide(CO_(2))emissions and achieving carbon neutrality.Efficient energy use and international trade play crucial roles in promoting sustainable development and enhancing environmental quality.This study investigated the relationships of CO_(2) emissions with trade openness(export and import),energy consumption(renewable energy consumption and fossil fuel consumption),natural resource rents,and human capital across 20 developing countries(Brazil,Iran,Russia,China,Malaysia,Saudi Arabia,Colombia,Mexico,South Africa,Costa Rica,Morocco,Tunisia,Egypt,Pakistan,Türkiye,India,Peru,Viet Nam,Indonesia,and Philippines)from 1990 to 2022 using the augmented mean group(AMG)and common correlated effects mean group(CCEMG)methods.The findings revealed that export,renewable energy consumption,and human capital significantly reduce CO_(2) emissions,while import,fossil fuel consumption,and natural resource rents increase CO_(2) emissions,although the effect of natural resource rents is statistically insignificant.Causality tests indicated the bidirectional relationship of CO_(2) emissions with export,import,renewable energy consumption,and fossil fuel consumption,and the unidirectional causality from human capital to CO_(2) emissions.CO_(2) emissions drive the greenhouse effect,thereby raising global temperature and accelerating climate change.As reducing CO_(2) emissions becomes an urgent global priority,this study provides actionable insights by identifying key variables that mitigate emissions and enhance sustainability.By bridging research and policy,this study offers targeted recommendations to accelerate progress toward a low-carbon future.
文摘Historically,geopolitical risk(GPR)has posed significant challenges to international economic,social,and political frameworks.This study investigated how internal GPR in the selected five Southeast Asian countries(Indonesia,South Korea,Malaysia,the Philippines,and Thailand)influences foreign direct investment(FDI)during 1996-2019.The stationarity of the data was assessed using the Augmented Dickey-Fuller(ADF)unit root test,which shows that the data became stationary after the first difference.The Kao,Pedroni,and Westerlund cointegration tests were employed to examine long-term cointegration among the selected variables(FDI,GPR index(GPRI),gross domestic product(GDP),inflation,interest rate,and trade openness(TOP)).The results indicated that these variables have a long-term cointegration.Consequently,regression analysis using the Pooled Ordinary Least Squares(OLS)regression,fixed effect,random effect,Arellano-Bond dynamic panel-data estimation,and system generalized moment method(GMM)revealed that GPRI and TOP negatively impacted FDI in the selected five Southeast Asian countries.At the same time,GDP,inflation,and interest rate positively influenced FDI in these countries.Because FDI is crucial to shaping a country’s macroeconomic structure,this study recommends that governments and central banks of the selected five Southeast Asian countries should implement policies and strategies to encourage foreign investments.
文摘To fully leverage the advantages of mechanization and informatization in tunnel boring machine(TBM)operations,the authors aim to promote the advancement of tunnel construction technology toward intelligent development.This involved exploring the deep integration of next-generation artificial intelligence technologies,such as sensing technology,automatic control technology,big data technology,deep learning,and machine vision,with key operational processes,including TBM excavation,direction adjustment,step changes,inverted arch block assembly,material transportation,and operation status assurance.The results of this integration are summarized as follows.(1)TBM key excavation parameter prediction algorithm was developed with an accuracy rate exceeding 90%.The TBM intelligent step-change control algorithm,based on machine vision,achieved an image segmentation accuracy rate of 95%and gripper shoe positioning error of±5 mm.(2)An automatic positioning system for inverted arch blocks was developed,enabling real-time perception of the spatial position and deviation during the assembly process.The system maintains an elevation positioning deviation within±3 mm and a horizontal positioning deviation within±10 mm,reducing the number of surveyors in each work team.(3)A TBM intelligent rail transportation system that achieves real-time human-machine positioning,automatic switch opening and closing,automatic obstacle avoidance,intelligent transportation planning,and integrated scheduling and command was designed.Each locomotive formation reduces one shunter and improves comprehensive transportation efficiency by more than 20%.(4)Intelligent analysis and prediction algorithms were developed to monitor and predict the trends of the hydraulic and gear oil parameters in real time,enhancing the proactive maintenance and system reliability.
文摘Purpose:For a set of 1,561 Open Access(OA)and non-OA journals in business and economics,this study evaluates the relationships between four citation metrics-five-year Impact Factor(5IF),CiteScore,Article Influence(AI)score,and SCImago Journal Rank(SJR)-and the journal ratings assigned by expert reviewers.We expect that the OA journals will have especially high citation impact relative to their perceived quality(reputation).Design/methodology/approach:Regression is used to estimate the ratings assigned by expert reviewers for the 2021 CABS(Chartered Association of Business Schools)journal assessment exercise.The independent variables are the four citation metrics,evaluated separately,and a dummy variable representing the OA/non-OA status of each journal.Findings:Regardless of the citation metric used,OA journals in business and economics have especially high citation impact relative to their perceived quality(reputation).That is,they have especially low perceived quality(reputation)relative to their citation impact.Research limitations:These results are specific to the CABS journal ratings and the four citation metrics.However,there is strong evidence that CABS is closely related to several other expert ratings,and that 5IF,CiteScore,AI,and SJR are representative of the other citation metrics that might have been chosen.Practical implications:There are at least two possible explanations for these results:(1)expert evaluators are biased against OA journals,and(2)OA journals have especially high citation impact due to their increased accessibility.Although this study does not allow us to determine which of these explanations are supported,the results suggest that authors should consider publishing in OA journals whenever overall readership and citation impact are more important than journal reputation within a particular field.Moreover,the OA coefficients provide a useful indicator of the extent to which anti-OA bias(or the citation advantage of OA journals)is diminishing over time.Originality/value:This is apparently the first study to investigate the impact of OA status on the relationships between expert journal ratings and journal citation metrics.
文摘本文根据企业公开信息,收集并整理了国内部分AI Agent应用相关企业。从Chatbots到AI Agents根据OpenAI的定义,通用人工智能(artificial general intelligence,AGI)的终极目标是创造一种能够像人类一样完成各种复杂任务,同时还能自然交流的人工智能(AI)。OpenAI将AGI的发展分成了五个等级:第一级是“聊天机器人”(chatbots),具备语言对话能力的人工智能;第二级是“推理者”(reasoners),具备人类的推理水平,能解决多种复杂难题的人工智能;第三级是“代理人”(agents)。
文摘Open caissons are widely used in foundation engineering because of their load-bearing efficiency and adaptability in diverse soil conditions.However,accurately predicting their undrained bearing capacity in layered soils remains a complex challenge.This study presents a novel application of five ensemble machine(ML)algorithms-random forest(RF),gradient boosting machine(GBM),extreme gradient boosting(XGBoost),adaptive boosting(AdaBoost),and categorical boosting(CatBoost)-to predict the undrained bearing capacity factor(Nc)of circular open caissons embedded in two-layered clay on the basis of results from finite element limit analysis(FELA).The input dataset consists of 1188 numerical simulations using the Tresca failure criterion,varying in geometrical and soil parameters.The FELA was performed via OptumG2 software with adaptive meshing techniques and verified against existing benchmark studies.The ML models were trained on 70% of the dataset and tested on the remaining 30%.Their performance was evaluated using six statistical metrics:coefficient of determination(R²),mean absolute error(MAE),root mean squared error(RMSE),index of scatter(IOS),RMSE-to-standard deviation ratio(RSR),and variance explained factor(VAF).The results indicate that all the models achieved high accuracy,with R²values exceeding 97.6%and RMSE values below 0.02.Among them,AdaBoost and CatBoost consistently outperformed the other methods across both the training and testing datasets,demonstrating superior generalizability and robustness.The proposed ML framework offers an efficient,accurate,and data-driven alternative to traditional methods for estimating caisson capacity in stratified soils.This approach can aid in reducing computational costs while improving reliability in the early stages of foundation design.
