Aiming at the problems of lagging curriculum,weak practice,and single evaluation in the cultivation of HarmonyOS Development talents,this study constructs a“teacher-machine-student”ternary interactive teaching model...Aiming at the problems of lagging curriculum,weak practice,and single evaluation in the cultivation of HarmonyOS Development talents,this study constructs a“teacher-machine-student”ternary interactive teaching model based on the Congyou platform.Through the building block curriculum system,the HarmonyOS technology stack is decoupled into dynamic capability units,and a multi-disciplinary cross-case library is jointly built with Huawei,which significantly improves the synchronization of teaching content and industrial technology.This paper innovatively designs an AI collaborative teaching system,which employs knowledge graphs to plan learning paths,utilizes virtual equipment clusters to simulate development environments,and establishes a“diagnosis-feedback-enhancement”closed loop through AI-based review,thereby effectively improving students’development efficiency and code reuse rate.A three-dimensional evaluation model integrating task outcomes,process performance,and innovation is constructed,incorporating indicators such as code standardization and an innovation index to strengthen the cultivation of engineering thinking and innovative ability.Furthermore,a data-driven support platform is built to generate student competency profiles,open up the“credit-competency-certification”pathway,promote the transformation of course achievements into contributions to the Huawei ecosystem,and significantly shorten the job adaptation cycle for graduates.The research results provide a replicable paradigm for the cultivation of domestic operating system talents.展开更多
Given the growing importance of social media in digital rural development, this study systematically investigated the influence pathways of social media use among rural women in China, drawing on the Technology Accept...Given the growing importance of social media in digital rural development, this study systematically investigated the influence pathways of social media use among rural women in China, drawing on the Technology Acceptance Model(TAM). Employing quantitative research methods, the study conducted empirical tests based on 367 valid questionnaires using Partial Least Squares Structural Equation Modeling(PLS-SEM) via SmartPLS 4.0 software. Results indicate that significant associations exist between perceived ease of use, perceived usefulness, attitudes toward use, behavioral intention, and actual usage behavior. Specifically, the study finds that rural women's perceived ease of use of social media has a significant and positive influence on both perceived usefulness and attitudes toward use. Perceived usefulness further significantly promotes attitudes toward use and behavioral intention. Moreover, positive attitudes toward usage and strong behavioral intentions were effectively converted into actual social media usage behaviors. This study not only validates the applicability and explanatory power of the TAM model in understanding the digital behavior of Chinese rural women but also provides quantitative evidence for how social media enhances their “digital visibility.” These findings offer practical insights for governments and platform providers to optimize user experiences and strengthen digital skills training. Despite its limitations, including a cross-sectional design and a regional sample, this research holds significant theoretical and practical implications.展开更多
In the context of global digital transformation and the rising prominence of maker education,this study explores the innovative integration of digital modeling technologies with traditional Nixing Pottery craftsmanshi...In the context of global digital transformation and the rising prominence of maker education,this study explores the innovative integration of digital modeling technologies with traditional Nixing Pottery craftsmanship.By constructing a teaching framework under maker education theory,the research investigates how 3D modeling,CAD design,and 3D printing technologies can empower learners to address challenges in cultural heritage preservation and artistic innovation.Through experimental teaching and case analysis,the study verifies that this integrated approach significantly enhances learners’digital literacy,creative thinking,and cultural identity while optimizing Nixing Pottery’s production processes and design possibilities.The findings contribute to theoretical models of technology-enhanced craft education and provide practical pathways for the digital transformation of intangible cultural heritage.展开更多
The software technology field is facing new talent demands brought by the Information Technology Application Innovation(ITAI)industry.This paper takes Shanwei Institute of Technology as an example to deeply explore th...The software technology field is facing new talent demands brought by the Information Technology Application Innovation(ITAI)industry.This paper takes Shanwei Institute of Technology as an example to deeply explore the construction of a school-enterprise community education model driven by the ITAI industry.It establishes the Kirin Workshop training base to facilitate talent cultivation,integrates the ITAI Application Adaptation Center to enhance technical capabilities,cooperates with Liqi Technology to establish an industrial college for government talent training,adjusts the professional curriculum system,and arranges for students to participate in ITAI vocational skills competitions.The school-enterprise collaborative cultivation mechanism meets the talent needs of the ITAI field,with effective practical results.This paper also points out the shortcomings of the school-enterprise collaborative education model in the ITAI industry and provides optimization methods to explore new paths for industry-education integration and serve the development of regional and national ITAI industries^([1]).展开更多
With the advancement of digital technology,new technologies such as artificial intelligence,big data,and cloud computing have gradually permeated higher education,leading to fundamental changes in teaching and learnin...With the advancement of digital technology,new technologies such as artificial intelligence,big data,and cloud computing have gradually permeated higher education,leading to fundamental changes in teaching and learning methods.Therefore,in the process of reforming and developing higher education,it is essential to take digital technology empowering the optimization of the education industry as a breakthrough,focusing on five key areas:the construction of smart classrooms,the digital integration of teaching resources,the development of personalized learning support systems,the reform of online-offline hybrid teaching,and the intelligentization of educational management.This paper also examines the experiences,challenges,and shortcomings of typical universities in using digital technology to improve teaching quality,optimize resource allocation,and innovate teaching management models.Finally,corresponding countermeasures and suggestions are proposed to facilitate the smooth implementation of digital transformation in higher education institutions.展开更多
The integration of Green Artificial Intelligence(AI)technologies into educational systems offers a promising avenue to enhance operational efficiency while addressing sustainability challenges.Through a rigorous three...The integration of Green Artificial Intelligence(AI)technologies into educational systems offers a promising avenue to enhance operational efficiency while addressing sustainability challenges.Through a rigorous three-phase methodology combining literature review,AI agent development,and participatory workshop-based case analysis,this paper highlights the pivotal role of AI agents,as applications of Green AI technologies,in driving transformative outcomes within schools.By directly improving self-learning efficiency and reducing learning costs for students,enhancing management and service efficiency,reducing labor costs for schools,as well as minimizing resource dependence for both teachers and students,AI agents create a foundation for sustainable operations.These direct effects generate positive spillover effects,cascading into broader outcomes,including innovation performance,economic efficiency,and environmental sustainability,aligning with the United Nations Sustainable Development Goals(SDGs).By presenting a comprehensive conceptual model,this study demonstrates the pathways through which Green AI contributes to sustainable development in education and emphasizes its critical role in bridging technological innovation with sustainability.This framework provides significant theoretical insights for further empirical research while offering actionable strategies for policymakers and educators to harness Green AI for building sustainable schools with a student-centered approach.展开更多
The integration of Learning Management Systems(LMSs)into educational settings is becoming increasingly common,especially in the digital field.Understanding the factors influencing the acceptance and effective use of L...The integration of Learning Management Systems(LMSs)into educational settings is becoming increasingly common,especially in the digital field.Understanding the factors influencing the acceptance and effective use of LMS is essential to ensure successful implementation.The Technology Acceptance Model(TAM)has been widely used to check user acceptance of various technologies,including LMS.This study conducted a systematic literature review(SLR)to analyze existing research on the application of TAM in the context of LMS.A comprehensive search of the academic database was conducted to identify relevant studies published in 2010-2025.The review synthesizes findings related to the core constructs of TAM—Perceived Usability,Perceived Ease of Use,Behavioral Intent,and Actual Use—as well as extended factors such as system quality,self-efficacy,and social influence.The results reveal circumstantial evidence supporting the predictive power of TAM in LMS adoption,while also highlighting emerging trends and gaps in the literature.This review contributes to a deeper understanding of user acceptance in a digital learning environment and provides recommendations for future research and practical LMS implementation strategies.展开更多
This study analyzes the evolution of China's green technology innovation cooperation network from 2011 to 2020,utilizing green patent application data.Employing a Spatial Durbin Model(SDM),we scrutinized the netwo...This study analyzes the evolution of China's green technology innovation cooperation network from 2011 to 2020,utilizing green patent application data.Employing a Spatial Durbin Model(SDM),we scrutinized the network's influence on urban carbon emissions,utilizing panel data encompassing 323 city nodes.Results show network expansion and a shift in central nodes from eastern coastal areas to interior cities,with Beijing,Shenzhen,Nanjing,and Shanghai consistently acting as key innovation hubs.A core-periphery structure emerged,clustering cities into high-and low-cooperation clusters.Core cities,particularly Beijing,which gain informational advantages by bridging non-overlapping nodes and exhibit distinct characteristics in terms of the structural hole indexes,reflecting their multifaceted roles within the network.SDM analysis indicates that the green technology innovation cooperation network has a significant positive impact on urban carbon reduction efforts.Specifically,degree centrality,closeness centrality,effective size,efficiency,and hierarchy of node cities exhibit a negative correlation with carbon emissions,suggesting that higher centrality and efficiency within the network correlate with lower emissions.Conversely,betweenness centrality and constraint have a positive impact on emissions,indicating that cities that act as bridges in the network may paradoxically contribute to higher emissions.Moreover,the network's influence on carbon emissions is nuanced across different green technology sectors.Cooperation in areas such as waste management,alternative energy production,energy conservation,agriculture and forestry,and transportation is found to have a more substantial impact on carbon reduction than cooperation in nuclear power,and administrative,regulatory,and design fields.展开更多
Accurate parameter extraction of photovoltaic(PV)models plays a critical role in enabling precise performance prediction,optimal system sizing,and effective operational control under diverse environmental conditions.W...Accurate parameter extraction of photovoltaic(PV)models plays a critical role in enabling precise performance prediction,optimal system sizing,and effective operational control under diverse environmental conditions.While a wide range of metaheuristic optimisation techniques have been applied to this problem,many existing methods are hindered by slow convergence rates,susceptibility to premature stagnation,and reduced accuracy when applied to complex multi-diode PV configurations.These limitations can lead to suboptimal modelling,reducing the efficiency of PV system design and operation.In this work,we propose an enhanced hybrid optimisation approach,the modified Spider Wasp Optimization(mSWO)with Opposition-Based Learning algorithm,which integrates the exploration and exploitation capabilities of the Spider Wasp Optimization(SWO)metaheuristic with the diversityenhancing mechanism of Opposition-Based Learning(OBL).The hybridisation is designed to dynamically expand the search space coverage,avoid premature convergence,and improve both convergence speed and precision in highdimensional optimisation tasks.The mSWO algorithm is applied to three well-established PV configurations:the single diode model(SDM),the double diode model(DDM),and the triple diode model(TDM).Real experimental current-voltage(I-V)datasets from a commercial PV module under standard test conditions(STC)are used for evaluation.Comparative analysis is conducted against eighteen advanced metaheuristic algorithms,including BSDE,RLGBO,GWOCS,MFO,EO,TSA,and SCA.Performance metrics include minimum,mean,and maximum root mean square error(RMSE),standard deviation(SD),and convergence behaviour over 30 independent runs.The results reveal that mSWO consistently delivers superior accuracy and robustness across all PV models,achieving the lowest RMSE values of 0.000986022(SDM),0.000982884(DDM),and 0.000982529(TDM),with minimal SD values,indicating remarkable repeatability.Convergence analyses further show that mSWO reaches optimal solutions more rapidly and with fewer oscillations than all competing methods,with the performance gap widening as model complexity increases.These findings demonstrate that mSWO provides a scalable,computationally efficient,and highly reliable framework for PV parameter extraction.Its adaptability to models of growing complexity suggests strong potential for broader applications in renewable energy systems,including performance monitoring,fault detection,and intelligent control,thereby contributing to the optimisation of next-generation solar energy solutions.展开更多
Quantitative analysis of the impact factors in energy-related CO2 emissions serves as an important guide for reducing carbon emissions and building an environmentally-friendly society. This paper aims to use LMDI meth...Quantitative analysis of the impact factors in energy-related CO2 emissions serves as an important guide for reducing carbon emissions and building an environmentally-friendly society. This paper aims to use LMDI method and a modified STIRPAT model to research the conventional energy-related CO_2 emissions in Kazakhstan after the collapse of the Soviet Union. The results show that the trajectory of CO2 emissions displayed U-shaped curve from 1992 to 2013. Based on the extended Kaya identity and additive LMDI method, we decomposed total CO2 emissions into four influencing factors. Of those, the economic active effect is the most influential factor driving CO2 emissions, which produced 110.86 Mt CO2 emissions, with a contribution rate of 43.92%. The second driving factor is the population effect, which led to 11.87 Mt CO2 emissions with a contribution rate of 4.7%. On the contrary, the energy intensity effect is the most inhibiting factor, which caused –110.90 Mt CO2 emissions with a contribution rate of –43.94%, followed by the energy carbon structure effect resulting in –18.76 Mt CO2 emissions with a contribution rate of –7.43%. In order to provide an in-depth examination of the change response between energy-related CO2 emissions and each impact factor, we construct a modified STIRPAT model based on ridge regression estimation. The results indicate that for every 1% increase in population size, economic activity, energy intensity and energy carbon structure, there is a subsequent increase in CO_2 emissions of 3.13%, 0.41%, 0.30% and 0.63%, respectively.展开更多
This study aims to develop a system dynamic(SD)forecasting model based on the STIRPAT model to forecast the effect of an IDR 30 per kg CO_(2)e carbon tax on carbon emissions,estimate future carbon emissions under ten ...This study aims to develop a system dynamic(SD)forecasting model based on the STIRPAT model to forecast the effect of an IDR 30 per kg CO_(2)e carbon tax on carbon emissions,estimate future carbon emissions under ten scenarios,without and with the carbon tax,and estimate the environmental Kuznets curve(EKC)to predict Indonesia’s carbon emission peak.Carbon emission drivers in this study are decomposed into several factors,namely energy structure,energy intensity,industrial structure,GDP per capita,population,and fixed-asset investment.This study included nuclear power utilization starting in 2038.The research gaps addressed by this study compared to previous research are(1)use of the ex-ante approach,(2)inclusion of nuclear power plants,(3)testing the EKC hypothesis,and(4)contribution to government policy.The simulation results show that under the carbon tax,carbon emissions can be reduced by improving renewable energy structures,adjusting industrial structures to green businesses,and emphasizing fixed asset investment more environmentally friendly.Moreover,the result approved the EKC hypothesis.It shows an inverse U-shaped curve between GDP per capita and CO_(2)emissions in Indonesia.Indonesia’s fastest carbon emission peak is under scenario seven and is expected in 2040.Although an IDR 30 per kg CO_(2)e carbon tax and nuclear power will take decades to reduce carbon emissions,the carbon tax can still be a reference and has advantages to implement.This result can be a good beginning step for Indonesia,which has yet to gain experience with a carbon tax that can be implemented immediately and is helpful to decision-makers in putting into practice sensible measures to attain Indonesia’s carbon emission peaking.This research provides actionable insights internationally on carbon tax policies,nuclear energy adoption,EKC dynamics,global policy implications,and fostering international cooperation for carbon emission reductions.展开更多
文摘Aiming at the problems of lagging curriculum,weak practice,and single evaluation in the cultivation of HarmonyOS Development talents,this study constructs a“teacher-machine-student”ternary interactive teaching model based on the Congyou platform.Through the building block curriculum system,the HarmonyOS technology stack is decoupled into dynamic capability units,and a multi-disciplinary cross-case library is jointly built with Huawei,which significantly improves the synchronization of teaching content and industrial technology.This paper innovatively designs an AI collaborative teaching system,which employs knowledge graphs to plan learning paths,utilizes virtual equipment clusters to simulate development environments,and establishes a“diagnosis-feedback-enhancement”closed loop through AI-based review,thereby effectively improving students’development efficiency and code reuse rate.A three-dimensional evaluation model integrating task outcomes,process performance,and innovation is constructed,incorporating indicators such as code standardization and an innovation index to strengthen the cultivation of engineering thinking and innovative ability.Furthermore,a data-driven support platform is built to generate student competency profiles,open up the“credit-competency-certification”pathway,promote the transformation of course achievements into contributions to the Huawei ecosystem,and significantly shorten the job adaptation cycle for graduates.The research results provide a replicable paradigm for the cultivation of domestic operating system talents.
文摘Given the growing importance of social media in digital rural development, this study systematically investigated the influence pathways of social media use among rural women in China, drawing on the Technology Acceptance Model(TAM). Employing quantitative research methods, the study conducted empirical tests based on 367 valid questionnaires using Partial Least Squares Structural Equation Modeling(PLS-SEM) via SmartPLS 4.0 software. Results indicate that significant associations exist between perceived ease of use, perceived usefulness, attitudes toward use, behavioral intention, and actual usage behavior. Specifically, the study finds that rural women's perceived ease of use of social media has a significant and positive influence on both perceived usefulness and attitudes toward use. Perceived usefulness further significantly promotes attitudes toward use and behavioral intention. Moreover, positive attitudes toward usage and strong behavioral intentions were effectively converted into actual social media usage behaviors. This study not only validates the applicability and explanatory power of the TAM model in understanding the digital behavior of Chinese rural women but also provides quantitative evidence for how social media enhances their “digital visibility.” These findings offer practical insights for governments and platform providers to optimize user experiences and strengthen digital skills training. Despite its limitations, including a cross-sectional design and a regional sample, this research holds significant theoretical and practical implications.
文摘In the context of global digital transformation and the rising prominence of maker education,this study explores the innovative integration of digital modeling technologies with traditional Nixing Pottery craftsmanship.By constructing a teaching framework under maker education theory,the research investigates how 3D modeling,CAD design,and 3D printing technologies can empower learners to address challenges in cultural heritage preservation and artistic innovation.Through experimental teaching and case analysis,the study verifies that this integrated approach significantly enhances learners’digital literacy,creative thinking,and cultural identity while optimizing Nixing Pottery’s production processes and design possibilities.The findings contribute to theoretical models of technology-enhanced craft education and provide practical pathways for the digital transformation of intangible cultural heritage.
基金supported by the Foundation of Shanwei Institute of Technology(swjy23-008).
文摘The software technology field is facing new talent demands brought by the Information Technology Application Innovation(ITAI)industry.This paper takes Shanwei Institute of Technology as an example to deeply explore the construction of a school-enterprise community education model driven by the ITAI industry.It establishes the Kirin Workshop training base to facilitate talent cultivation,integrates the ITAI Application Adaptation Center to enhance technical capabilities,cooperates with Liqi Technology to establish an industrial college for government talent training,adjusts the professional curriculum system,and arranges for students to participate in ITAI vocational skills competitions.The school-enterprise collaborative cultivation mechanism meets the talent needs of the ITAI field,with effective practical results.This paper also points out the shortcomings of the school-enterprise collaborative education model in the ITAI industry and provides optimization methods to explore new paths for industry-education integration and serve the development of regional and national ITAI industries^([1]).
文摘With the advancement of digital technology,new technologies such as artificial intelligence,big data,and cloud computing have gradually permeated higher education,leading to fundamental changes in teaching and learning methods.Therefore,in the process of reforming and developing higher education,it is essential to take digital technology empowering the optimization of the education industry as a breakthrough,focusing on five key areas:the construction of smart classrooms,the digital integration of teaching resources,the development of personalized learning support systems,the reform of online-offline hybrid teaching,and the intelligentization of educational management.This paper also examines the experiences,challenges,and shortcomings of typical universities in using digital technology to improve teaching quality,optimize resource allocation,and innovate teaching management models.Finally,corresponding countermeasures and suggestions are proposed to facilitate the smooth implementation of digital transformation in higher education institutions.
基金2024 Academic Research of Zhejiang Technical Institute of Economics:“Spillover Effects of Multimodal AI Agents on Green School Development”(Project No.:X2024038)2024-2025 Research and Creative Project,Department of Culture and Tourism:“The Application of Digital Information Technology in Safety Early Warning and Supervision of Cultural Relics in Zhejiang,China”(Project No.:2024KYY045)2024 General Research Project of Zhejiang Provincial Department of Education:“Empirical Research on Low-Carbon Economy Driving the Development of New Quality Productivity:A Case Study of Zhejiang Province”(Project No.:Y202456145)。
文摘The integration of Green Artificial Intelligence(AI)technologies into educational systems offers a promising avenue to enhance operational efficiency while addressing sustainability challenges.Through a rigorous three-phase methodology combining literature review,AI agent development,and participatory workshop-based case analysis,this paper highlights the pivotal role of AI agents,as applications of Green AI technologies,in driving transformative outcomes within schools.By directly improving self-learning efficiency and reducing learning costs for students,enhancing management and service efficiency,reducing labor costs for schools,as well as minimizing resource dependence for both teachers and students,AI agents create a foundation for sustainable operations.These direct effects generate positive spillover effects,cascading into broader outcomes,including innovation performance,economic efficiency,and environmental sustainability,aligning with the United Nations Sustainable Development Goals(SDGs).By presenting a comprehensive conceptual model,this study demonstrates the pathways through which Green AI contributes to sustainable development in education and emphasizes its critical role in bridging technological innovation with sustainability.This framework provides significant theoretical insights for further empirical research while offering actionable strategies for policymakers and educators to harness Green AI for building sustainable schools with a student-centered approach.
文摘The integration of Learning Management Systems(LMSs)into educational settings is becoming increasingly common,especially in the digital field.Understanding the factors influencing the acceptance and effective use of LMS is essential to ensure successful implementation.The Technology Acceptance Model(TAM)has been widely used to check user acceptance of various technologies,including LMS.This study conducted a systematic literature review(SLR)to analyze existing research on the application of TAM in the context of LMS.A comprehensive search of the academic database was conducted to identify relevant studies published in 2010-2025.The review synthesizes findings related to the core constructs of TAM—Perceived Usability,Perceived Ease of Use,Behavioral Intent,and Actual Use—as well as extended factors such as system quality,self-efficacy,and social influence.The results reveal circumstantial evidence supporting the predictive power of TAM in LMS adoption,while also highlighting emerging trends and gaps in the literature.This review contributes to a deeper understanding of user acceptance in a digital learning environment and provides recommendations for future research and practical LMS implementation strategies.
基金supported by the National Natural Science Foundation of China(72573020,72103022).
文摘This study analyzes the evolution of China's green technology innovation cooperation network from 2011 to 2020,utilizing green patent application data.Employing a Spatial Durbin Model(SDM),we scrutinized the network's influence on urban carbon emissions,utilizing panel data encompassing 323 city nodes.Results show network expansion and a shift in central nodes from eastern coastal areas to interior cities,with Beijing,Shenzhen,Nanjing,and Shanghai consistently acting as key innovation hubs.A core-periphery structure emerged,clustering cities into high-and low-cooperation clusters.Core cities,particularly Beijing,which gain informational advantages by bridging non-overlapping nodes and exhibit distinct characteristics in terms of the structural hole indexes,reflecting their multifaceted roles within the network.SDM analysis indicates that the green technology innovation cooperation network has a significant positive impact on urban carbon reduction efforts.Specifically,degree centrality,closeness centrality,effective size,efficiency,and hierarchy of node cities exhibit a negative correlation with carbon emissions,suggesting that higher centrality and efficiency within the network correlate with lower emissions.Conversely,betweenness centrality and constraint have a positive impact on emissions,indicating that cities that act as bridges in the network may paradoxically contribute to higher emissions.Moreover,the network's influence on carbon emissions is nuanced across different green technology sectors.Cooperation in areas such as waste management,alternative energy production,energy conservation,agriculture and forestry,and transportation is found to have a more substantial impact on carbon reduction than cooperation in nuclear power,and administrative,regulatory,and design fields.
基金funded by Princess Nourah bint Abdulrahman University Researchers Supporting Project number(PNURSP2025R442)Princess Nourah bint Abdulrahman University,Riyadh,Saudi Arabia.
文摘Accurate parameter extraction of photovoltaic(PV)models plays a critical role in enabling precise performance prediction,optimal system sizing,and effective operational control under diverse environmental conditions.While a wide range of metaheuristic optimisation techniques have been applied to this problem,many existing methods are hindered by slow convergence rates,susceptibility to premature stagnation,and reduced accuracy when applied to complex multi-diode PV configurations.These limitations can lead to suboptimal modelling,reducing the efficiency of PV system design and operation.In this work,we propose an enhanced hybrid optimisation approach,the modified Spider Wasp Optimization(mSWO)with Opposition-Based Learning algorithm,which integrates the exploration and exploitation capabilities of the Spider Wasp Optimization(SWO)metaheuristic with the diversityenhancing mechanism of Opposition-Based Learning(OBL).The hybridisation is designed to dynamically expand the search space coverage,avoid premature convergence,and improve both convergence speed and precision in highdimensional optimisation tasks.The mSWO algorithm is applied to three well-established PV configurations:the single diode model(SDM),the double diode model(DDM),and the triple diode model(TDM).Real experimental current-voltage(I-V)datasets from a commercial PV module under standard test conditions(STC)are used for evaluation.Comparative analysis is conducted against eighteen advanced metaheuristic algorithms,including BSDE,RLGBO,GWOCS,MFO,EO,TSA,and SCA.Performance metrics include minimum,mean,and maximum root mean square error(RMSE),standard deviation(SD),and convergence behaviour over 30 independent runs.The results reveal that mSWO consistently delivers superior accuracy and robustness across all PV models,achieving the lowest RMSE values of 0.000986022(SDM),0.000982884(DDM),and 0.000982529(TDM),with minimal SD values,indicating remarkable repeatability.Convergence analyses further show that mSWO reaches optimal solutions more rapidly and with fewer oscillations than all competing methods,with the performance gap widening as model complexity increases.These findings demonstrate that mSWO provides a scalable,computationally efficient,and highly reliable framework for PV parameter extraction.Its adaptability to models of growing complexity suggests strong potential for broader applications in renewable energy systems,including performance monitoring,fault detection,and intelligent control,thereby contributing to the optimisation of next-generation solar energy solutions.
基金CAS Strategic Priority Research Program,No.XDA19030204CAS Western Light Program,No.2015-XBQN-B-17
文摘Quantitative analysis of the impact factors in energy-related CO2 emissions serves as an important guide for reducing carbon emissions and building an environmentally-friendly society. This paper aims to use LMDI method and a modified STIRPAT model to research the conventional energy-related CO_2 emissions in Kazakhstan after the collapse of the Soviet Union. The results show that the trajectory of CO2 emissions displayed U-shaped curve from 1992 to 2013. Based on the extended Kaya identity and additive LMDI method, we decomposed total CO2 emissions into four influencing factors. Of those, the economic active effect is the most influential factor driving CO2 emissions, which produced 110.86 Mt CO2 emissions, with a contribution rate of 43.92%. The second driving factor is the population effect, which led to 11.87 Mt CO2 emissions with a contribution rate of 4.7%. On the contrary, the energy intensity effect is the most inhibiting factor, which caused –110.90 Mt CO2 emissions with a contribution rate of –43.94%, followed by the energy carbon structure effect resulting in –18.76 Mt CO2 emissions with a contribution rate of –7.43%. In order to provide an in-depth examination of the change response between energy-related CO2 emissions and each impact factor, we construct a modified STIRPAT model based on ridge regression estimation. The results indicate that for every 1% increase in population size, economic activity, energy intensity and energy carbon structure, there is a subsequent increase in CO_2 emissions of 3.13%, 0.41%, 0.30% and 0.63%, respectively.
基金funded by the DRTPM of the Indonesian Ministry of Education and Culture with contract number 15455/UN19.5.1.3/AL04.2023.
文摘This study aims to develop a system dynamic(SD)forecasting model based on the STIRPAT model to forecast the effect of an IDR 30 per kg CO_(2)e carbon tax on carbon emissions,estimate future carbon emissions under ten scenarios,without and with the carbon tax,and estimate the environmental Kuznets curve(EKC)to predict Indonesia’s carbon emission peak.Carbon emission drivers in this study are decomposed into several factors,namely energy structure,energy intensity,industrial structure,GDP per capita,population,and fixed-asset investment.This study included nuclear power utilization starting in 2038.The research gaps addressed by this study compared to previous research are(1)use of the ex-ante approach,(2)inclusion of nuclear power plants,(3)testing the EKC hypothesis,and(4)contribution to government policy.The simulation results show that under the carbon tax,carbon emissions can be reduced by improving renewable energy structures,adjusting industrial structures to green businesses,and emphasizing fixed asset investment more environmentally friendly.Moreover,the result approved the EKC hypothesis.It shows an inverse U-shaped curve between GDP per capita and CO_(2)emissions in Indonesia.Indonesia’s fastest carbon emission peak is under scenario seven and is expected in 2040.Although an IDR 30 per kg CO_(2)e carbon tax and nuclear power will take decades to reduce carbon emissions,the carbon tax can still be a reference and has advantages to implement.This result can be a good beginning step for Indonesia,which has yet to gain experience with a carbon tax that can be implemented immediately and is helpful to decision-makers in putting into practice sensible measures to attain Indonesia’s carbon emission peaking.This research provides actionable insights internationally on carbon tax policies,nuclear energy adoption,EKC dynamics,global policy implications,and fostering international cooperation for carbon emission reductions.
