The majority of multinational enterprises （MNEs） traditionally originate from developed countries. In the last ten years, however, there has been dramatic growth in foreign direct investment （FDI） from China. It i...The majority of multinational enterprises （MNEs） traditionally originate from developed countries. In the last ten years, however, there has been dramatic growth in foreign direct investment （FDI） from China. It is a comparatively new phenomenon that challenges the classic FDI theories. In this paper, we review the pros and cons of two important theories, known as the Owner- ship-Location-Internalization （0LI） model and Linkage-Leverage-Learning （LLL） model, and use the statistical data and company case studies from China to test the plausibility of these two models. We believe that neither of them suits totally： the OLI model is quite use- fill for understanding FDI from China to developing economies, while the LLL model is more powerful for explaining the FDI to de- veloped economies. We argue that the companies from China attain a very advantageous position as intermediates in the global economy They may catch up with the first movers if they integrate OLI-led and LLL-led FDI within one firm. This combination can bring to- gether the most advanced knowledge acquired in developed economies with the knowledge about adaptation needs and the needs for cost reduction in production as expressed in developing economies. It may also accelerate the knowledge transfer globally. We thus fill a gap in research into the geographical pattern of Chinese FDI and offer a deeper understanding of the internationalization of Chinese MNEs and revolving knowledge transfer.展开更多
This study traces the development of the Shanghai Typhoon Model(SHTM)from a traditional physics-based regional model toward a data-driven,machine-learning typhoon forecasting system.After upgrading its initial and bou...This study traces the development of the Shanghai Typhoon Model(SHTM)from a traditional physics-based regional model toward a data-driven,machine-learning typhoon forecasting system.After upgrading its initial and boundary conditions,SHTM now leverages large-scale constraints from machine-learning weather prediction(MLWP)models,resulting in an ML–physics hybrid framework.During Typhoon Danas(2025),the hybrid SHTM achieves substantially lower track errors than both the advanced ECMWF Integrated Forecasting System(IFS)and leading MLWP models such as PanGu and FuXi.Furthermore,the hybrid SHTM consistently maintains mean track errors below 200 km up to a forecast lead time of 108 hours,representing a significant advancement in forecast accuracy.In addition,this study highlights the technical roadmap for transitioning from a physics-based typhoon model to a fully data-driven ML typhoon forecast system.It also emphasizes that advances in the physical modeling framework provide a critical foundation for further improving the performance of future data-driven ML typhoon models.展开更多
Objectives:Colorectal cancer(CRC)remains a major contributor to global cancer mortality,ranking second worldwide for cancer-related deaths in 2022,and is characterized by marked heterogeneity in prognosis and therapeu...Objectives:Colorectal cancer(CRC)remains a major contributor to global cancer mortality,ranking second worldwide for cancer-related deaths in 2022,and is characterized by marked heterogeneity in prognosis and therapeutic response.We sought to construct a machine-learning prognosticmodel based on a complement-related risk signature(CRRS)and to situate this signature within the CRC immune microenvironment.Methods:Transcriptomic profiles with matched clinical annotations from TCGA and GEO CRC cohorts were analyzed.Prognostic CRRS genes were screened using Cox proportional hazards modeling alongside machine-learning procedures.A random survival forest(RSF)predictor was trained and externally validated.Comparisons of immune infiltration,mutational burden,pathway enrichment,and drug sensitivity were made between risk groups.The function of FAM84A,a key model gene,was examined in CRC cell lines.Results:The six-gene CRRS model accurately stratified patients by survival outcomes.Low-risk patients exhibited greater immune cell infiltration and higher predicted response to immunotherapy and chemotherapy,while high-risk patients showed enrichment of complement activation and matrix remodeling pathways.FAM84A was shown to promote CRC cell proliferation,migration,and epithelial–mesenchymal transition.Conclusion:CRRS is a critical modulator of the CRC immune microenvironment.The developed model enables precise risk prediction and supports individualized therapeutic decisions in CRC.展开更多
Accurate prediction of practical reduction electrode potentials(Ered)of electrolyte solvents of electrochemical energy storage devices relies on calculating the Gibbs free energy in their reduction reaction.However,th...Accurate prediction of practical reduction electrode potentials(Ered)of electrolyte solvents of electrochemical energy storage devices relies on calculating the Gibbs free energy in their reduction reaction.However,the emergence of new electrolyte solvents and additives leaves most of the reaction mechanisms unveiled.Here,we provide a machine-learning-assisted workflow of thermodynamically quantified Ered prediction for electrolyte solvents.A computational hydrogen electrode model based on density functional theory calculation is generalized for calculating the reaction free energy of electrochemical elementary steps.Machine-learning models are trained based on the organic and inorganic electrolyte solvents that possess experimentally identified reduction mechanisms.Validation of the best-scoring model is conducted by experimental validation of 6 additional solvents.Multiple thermodynamics features are found impactful on Ered through different chemical bonding with reaction intermediates.This workflow enables accurate Ered prediction for electrolyte solvents without identified reduction mechanisms,and is widely applicable in the electrochemical energy storage area.展开更多
With the advent of computing and communication technologies,it has become possible for a learner to expand his or her knowledge irrespective of the place and time.Web-based learning promotes active and independent lea...With the advent of computing and communication technologies,it has become possible for a learner to expand his or her knowledge irrespective of the place and time.Web-based learning promotes active and independent learning.Large scale e-learning platforms revolutionized the concept of studying and it also paved the way for innovative and effective teaching-learning process.This digital learning improves the quality of teaching and also promotes educational equity.However,the challenges in e-learning platforms include dissimilarities in learner’s ability and needs,lack of student motivation towards learning activities and provision for adaptive learning environment.The quality of learning can be enhanced by analyzing the online learner’s behavioral characteristics and their application of intelligent instructional strategy.It is not possible to identify the difficulties faced during the process through evaluation after the completion of e-learning course.It is thus essential for an e-learning system to include component offering adaptive control of learning and maintain user’s interest level.In this research work,a framework is proposed to analyze the behavior of online learners and motivate the students towards the learning process accordingly so as to increase the rate of learner’s objective attainment.Catering to the demands of e-learner,an intelligent model is presented in this study for e-learning system that apply supervised machine learning algorithm.An adaptive e-learning system suits every category of learner,improves the learner’s performance and paves way for offering personalized learning experiences.展开更多
The purposes of this research were to 1) develop of an e-learning benchmarking model for higher education institutions;2) analyze and synthesize e-learning indicators for e-learning benchmarking model. The research wa...The purposes of this research were to 1) develop of an e-learning benchmarking model for higher education institutions;2) analyze and synthesize e-learning indicators for e-learning benchmarking model. The research was conducted using the research and development methods. The result shows that there are eight elements of e-learning benchmarking model: 1) team/staffs 2) benchmarking’s title 3) comparative companies 4) benchmarking indicators 5) data collection method 6) analysis data and results 7) report of results and 8) action plan development. Moreover, four steps of benchmarking model will be used in this research. “Plan” is the step of setting team for benchmarking title and choosing the company to collect the benchmarking while “Do” is a field study in order to analyze and collect each indicator. The step “Check” presents the data to stakeholders and set the purposes of action plan. Finally, “Act” which is the development of action plan leads to the practice or implementation which related to auditing and evaluating.展开更多
Nowcasting and forecasting solar irradiance are vital for the optimal prediction of grid-connected solar photovoltaic(PV)power plants.These plants face operational challenges and scheduling dispatch difficulties due t...Nowcasting and forecasting solar irradiance are vital for the optimal prediction of grid-connected solar photovoltaic(PV)power plants.These plants face operational challenges and scheduling dispatch difficulties due to the fluctuating nature of their power output.As the generation capacity within the electric grid increases,accurately predicting this output becomes increasingly essential,especially given the random and non-linear characteristics of solar irradiance under variable weather conditions.This study presents a novel prediction method for solar irradiance,which is directly in correlation with PV power output,targeting both short-term and medium-term forecast horizons.Our proposed hybrid framework employs a fast trainable statistical learning technique based on the truncated-regularized kernel ridge regression model.The proposed method excels in forecasting solar irradiance,especially during highly intermittent weather periods.A key strength of our model is the incorporation of multiple historical weather parameters as inputs to generate accurate predictions of future solar irradiance values in its scalable framework.We evaluated the performance of our model using data sets from both cloudy and sunny days in Seattle and Medford,USA and compared it against three forecasting models:persistence,modified 24-hour persistence and least squares.Based on three widely accepted statistical performance metrics(root mean squared error,mean absolute error and coefficient of determination),our hybrid model demonstrated superior predictive accuracy in varying weather conditions and forecast horizons.展开更多
This paper presents a V-model of e-learning using the well-known Gagne nine steps for quality education. Our suggested model is based on our experience at the computer engineering departments at AL-Hussein Bin Talal U...This paper presents a V-model of e-learning using the well-known Gagne nine steps for quality education. Our suggested model is based on our experience at the computer engineering departments at AL-Hussein Bin Talal University, the University of Jordan and Albalqa Applied University. We applied the recommendations of the nine steps methodology to the e-learning environment. The V model suggested in this paper came up as a result of such application. Although this V model can be subject to some tuning and development in the future it proved to be highly efficient and easy to implement for the teacher and the student.展开更多
After the spread of COVID-19,e-learning systems have become crucial tools in educational systems worldwide,spanning all levels of education.This widespread use of e-learning platforms has resulted in the accumulation ...After the spread of COVID-19,e-learning systems have become crucial tools in educational systems worldwide,spanning all levels of education.This widespread use of e-learning platforms has resulted in the accumulation of vast amounts of valuable data,making it an attractive resource for predicting student performance.In this study,we aimed to predict student performance based on the analysis of data collected from the OULAD and Deeds datasets.The stacking method was employed for modeling in this research.The proposed model utilized weak learners,including nearest neighbor,decision tree,random forest,enhanced gradient,simple Bayes,and logistic regression algorithms.After a trial-and-error process,the logistic regression algorithm was selected as the final learner for the proposed model.The results of experiments with the above algorithms are reported separately for the pass and fail classes.The findings indicate that the accuracy of the proposed model on the OULAD dataset reached 98%.Overall,the proposed method improved accuracy by 4%on the OULAD dataset.展开更多
In recent times,technology has advanced significantly and is currently being integrated into educational environments to facilitate distance learning and interaction between learners.Integrating the Internet of Things...In recent times,technology has advanced significantly and is currently being integrated into educational environments to facilitate distance learning and interaction between learners.Integrating the Internet of Things(IoT)into education can facilitate the teaching and learning process and expand the context in which students learn.Nevertheless,learning data is very sensitive and must be protected when transmitted over the network or stored in data centers.Moreover,the identity and the authenticity of interacting students,instructors,and staff need to be verified to mitigate the impact of attacks.However,most of the current security and authentication schemes are centralized,relying on trusted third-party cloud servers,to facilitate continuous secure communication.In addition,most of these schemes are resourceintensive;thus,security and efficiency issues arise when heterogeneous and resource-limited IoT devices are being used.In this paper,we propose a blockchain-based architecture that accurately identifies and authenticates learners and their IoT devices in a decentralized manner and prevents the unauthorized modification of stored learning records in a distributed university network.It allows students and instructors to easily migrate to and join multiple universities within the network using their identity without the need for user re-authentication.The proposed architecture was tested using a simulation tool,and measured to evaluate its performance.The simulation results demonstrate the ability of the proposed architecture to significantly increase the throughput of learning transactions(40%),reduce the communication overhead and response time(26%),improve authentication efficiency(27%),and reduce the IoT power consumption(35%)compared to the centralized authentication mechanisms.In addition,the security analysis proves the effectiveness of the proposed architecture in resisting various attacks and ensuring the security requirements of learning data in the university network.展开更多
ePCMM (e-Learning Process Capability Maturity Model) is used for evaluating the capability and maturity of an institution engaged in e-Learning based on e-Learning key process areas. It is a stepwise process improveme...ePCMM (e-Learning Process Capability Maturity Model) is used for evaluating the capability and maturity of an institution engaged in e-Learning based on e-Learning key process areas. It is a stepwise process improvement which can be implemented by both staged model and continues model.展开更多
Rural domestic sewage treatment is critical for environmental protection.This study defines the spatial pattern of villages from the perspective of rural sewage treatment and develops an integrated decision-making sys...Rural domestic sewage treatment is critical for environmental protection.This study defines the spatial pattern of villages from the perspective of rural sewage treatment and develops an integrated decision-making system to propose a sewage treatment mode and scheme suitable for local conditions.By considering the village spatial layout and terrain factors,a decision tree model of residential density and terrain type was constructed with accuracies of 76.47%and 96.00%,respectively.Combined with binary classification probability unit regression,an appropriate sewage treatment mode for the village was determined with 87.00%accuracy.The Analytic Hierarchy Process(AHP),combined with the Technique for Order Preference(TOPSIS)by Similarity to an Ideal Solution model,formed the basis for optimal treatment process selection under different emission standards.Verification was conducted in 542 villages across three counties of the Inner Mongolia Autonomous Region,focusing on the standard effluent effect(0.3773),low investment cost(0.3196),and high standard effluent effect(0.5115)to determine the best treatment process for the same emission standard under different needs.The annual environmental and carbon emission benefits of sewage treatment in these villages were estimated.This model matches village density,geographic feature,and social development level,and provides scientific support and a theoretical basis for rural sewage treatment decision-making.展开更多
E-learning behavior data indicates several students’activities on the e-learning platform such as the number of accesses to a set of resources and number of participants in lectures.This article proposes a new analyt...E-learning behavior data indicates several students’activities on the e-learning platform such as the number of accesses to a set of resources and number of participants in lectures.This article proposes a new analytics systemto support academic evaluation for students via e-learning activities to overcome the challenges faced by traditional learning environments.The proposed e-learning analytics system includes a new deep forest model.It consists of multistage cascade random forests with minimal hyperparameters compared to traditional deep neural networks.The developed forest model can analyze each student’s activities during the use of an e-learning platform to give accurate expectations of the student’s performance before ending the semester and/or the final exam.Experiments have been conducted on the Open University Learning Analytics Dataset(OULAD)of 32,593 students.Our proposed deep model showed a competitive accuracy score of 98.0%compared to artificial intelligence-based models,such as ConvolutionalNeuralNetwork(CNN)and Long Short-TermMemory(LSTM)in previous studies.That allows academic advisors to support expected failed students significantly and improve their academic level at the right time.Consequently,the proposed analytics system can enhance the quality of educational services for students in an innovative e-learning framework.展开更多
We present a comprehensive description and benchmark evaluation of the global–regional chemical transport model called the Emission and Atmospheric Processes Integrated and Coupled Community(EPICC)model.The framework...We present a comprehensive description and benchmark evaluation of the global–regional chemical transport model called the Emission and Atmospheric Processes Integrated and Coupled Community(EPICC)model.The framework incorporates(1)grid configuration,(2)transport dynamics,(3)chemical mechanisms,(4)aerosol processes,(5)wet/dry deposition parameterizations,and(6)heterogeneous chemistry treatments associated with sulfate,nitrous acid(HONO)chemistry,and aerosol/cloud–photolysis interactions(APIs/CPIs).Openly shared with the atmospheric research community,the model facilitates integration of advanced physicochemical schemes to enhance simulation accuracy.Globally,the model demonstrates realistic representations of ozone(O_(3))and aerosol optical depth.The EPICC model generally demonstrates robust performance in simulating regional concentrations of O_(3) and PM_(2.5)(and its components)in China.It successfully captures vertical profiles of both global and regional O_(3).Notably,the model mitigates frequently reported sulfate underestimations in highly industrialized regions of China.The model accurately captures two regional severe pollution episodes observed in eastern China(January/June 2021).Sensitivity experiments highlight the critical roles of heterogeneous chemical mechanisms associated with sulfate,HONO chemistry,APIs,and CPIs in capturing PM_(2.5) and O_(3) concentrations in China.Improved sulfate mechanisms result in an increase of approximately 32.4%(2.8μg m^(−3))in simulated winter sulfate concentrations when observations exceed 10μg m^(−3).Enhanced HONO elevates winter O_(3) and PM_(2.5) by≤20 and≤10μg m^(−3),respectively.Overall,CPIs dominate over APIs in improving O_(3) and PM_(2.5) simulations across China.Locally,APIs mitigate PM_(2.5) and O_(3) discrepancies in the Sichuan Basin.Seasonal cloud–chemistry coupling explains the weaker impact of PM_(2.5) in summer.展开更多
Model evaluation using benchmark datasets is an important method to measure the capability of large language models(LLMs)in specific domains,and it is mainly used to assess the knowledge and reasoning abilities of LLM...Model evaluation using benchmark datasets is an important method to measure the capability of large language models(LLMs)in specific domains,and it is mainly used to assess the knowledge and reasoning abilities of LLMs.Therefore,in order to better assess the capability of LLMs in the agricultural domain,Agri-Eval was proposed as a benchmark for assessing the knowledge and reasoning ability of LLMs in agriculture.The assessment dataset used in Agri-Eval covered seven major disciplines in the agricultural domain:crop science,horticulture,plant protection,animal husbandry,forest science,aquaculture science,and grass science,and contained a total of 2283 questions.Among domestic general-purpose LLMs,DeepSeek R1 performed best with an accuracy rate of 75.49%.In the realm of international general-purpose LLMs,Gemini 2.0 pro exp 0205 standed out as the top performer,achieving an accuracy rate of 74.28%.As an LLMs in agriculture vertical,Shennong V2.0 outperformed all the LLMs in China,and the answer accuracy rate of agricultural knowledge exceeded that of all the existing general-purpose LLMs.The launch of Agri-Eval helped the LLM developers to comprehensively evaluate the model's capability in the field of agriculture through a variety of tasks and tests to promote the development of the LLMs in the field of agriculture.展开更多
In this paper,we establish and study a single-species logistic model with impulsive age-selective harvesting.First,we prove the ultimate boundedness of the solutions of the system.Then,we obtain conditions for the asy...In this paper,we establish and study a single-species logistic model with impulsive age-selective harvesting.First,we prove the ultimate boundedness of the solutions of the system.Then,we obtain conditions for the asymptotic stability of the trivial solution and the positive periodic solution.Finally,numerical simulations are presented to validate our results.Our results show that age-selective harvesting is more conducive to sustainable population survival than non-age-selective harvesting.展开更多
The proliferation of high-dimensional data and the widespread use of complex models present central challenges in contemporary statistics and data science.Dimension reduction and model checking,as two foundational pil...The proliferation of high-dimensional data and the widespread use of complex models present central challenges in contemporary statistics and data science.Dimension reduction and model checking,as two foundational pillars supporting scientific inference and data-driven decisionmaking,have evolved through the collective wisdom of generations of statisticians.This special issue,titled"Recent Developments in Dimension Reduction and Model Checking for regressions",not only aims to showcase cutting-edge advances in the field but also carries a distinct sense of academic homage to honor the groundbreaking and enduring contributions of Professor Lixing Zhu,a leading scholar whose work has profoundly shaped both areas.展开更多
基金Under the auspices of National Natural Science Foundation of China(No.4097106941101120)+1 种基金State Scholarship Fund by China Scholaship CouncilMinistry of Education of the people's Republic of China(No.2009614028)
文摘The majority of multinational enterprises （MNEs） traditionally originate from developed countries. In the last ten years, however, there has been dramatic growth in foreign direct investment （FDI） from China. It is a comparatively new phenomenon that challenges the classic FDI theories. In this paper, we review the pros and cons of two important theories, known as the Owner- ship-Location-Internalization （0LI） model and Linkage-Leverage-Learning （LLL） model, and use the statistical data and company case studies from China to test the plausibility of these two models. We believe that neither of them suits totally： the OLI model is quite use- fill for understanding FDI from China to developing economies, while the LLL model is more powerful for explaining the FDI to de- veloped economies. We argue that the companies from China attain a very advantageous position as intermediates in the global economy They may catch up with the first movers if they integrate OLI-led and LLL-led FDI within one firm. This combination can bring to- gether the most advanced knowledge acquired in developed economies with the knowledge about adaptation needs and the needs for cost reduction in production as expressed in developing economies. It may also accelerate the knowledge transfer globally. We thus fill a gap in research into the geographical pattern of Chinese FDI and offer a deeper understanding of the internationalization of Chinese MNEs and revolving knowledge transfer.
基金supported by the Special Project-Original Exploration(Grant No.42450163)the National Youth Science Foundation of China Project(Grant No.4240050560)the Research and Development of Key Technologies for Artificial Intelligence Regional Typhoon Forecasting Model project.
文摘This study traces the development of the Shanghai Typhoon Model(SHTM)from a traditional physics-based regional model toward a data-driven,machine-learning typhoon forecasting system.After upgrading its initial and boundary conditions,SHTM now leverages large-scale constraints from machine-learning weather prediction(MLWP)models,resulting in an ML–physics hybrid framework.During Typhoon Danas(2025),the hybrid SHTM achieves substantially lower track errors than both the advanced ECMWF Integrated Forecasting System(IFS)and leading MLWP models such as PanGu and FuXi.Furthermore,the hybrid SHTM consistently maintains mean track errors below 200 km up to a forecast lead time of 108 hours,representing a significant advancement in forecast accuracy.In addition,this study highlights the technical roadmap for transitioning from a physics-based typhoon model to a fully data-driven ML typhoon forecast system.It also emphasizes that advances in the physical modeling framework provide a critical foundation for further improving the performance of future data-driven ML typhoon models.
文摘Objectives:Colorectal cancer(CRC)remains a major contributor to global cancer mortality,ranking second worldwide for cancer-related deaths in 2022,and is characterized by marked heterogeneity in prognosis and therapeutic response.We sought to construct a machine-learning prognosticmodel based on a complement-related risk signature(CRRS)and to situate this signature within the CRC immune microenvironment.Methods:Transcriptomic profiles with matched clinical annotations from TCGA and GEO CRC cohorts were analyzed.Prognostic CRRS genes were screened using Cox proportional hazards modeling alongside machine-learning procedures.A random survival forest(RSF)predictor was trained and externally validated.Comparisons of immune infiltration,mutational burden,pathway enrichment,and drug sensitivity were made between risk groups.The function of FAM84A,a key model gene,was examined in CRC cell lines.Results:The six-gene CRRS model accurately stratified patients by survival outcomes.Low-risk patients exhibited greater immune cell infiltration and higher predicted response to immunotherapy and chemotherapy,while high-risk patients showed enrichment of complement activation and matrix remodeling pathways.FAM84A was shown to promote CRC cell proliferation,migration,and epithelial–mesenchymal transition.Conclusion:CRRS is a critical modulator of the CRC immune microenvironment.The developed model enables precise risk prediction and supports individualized therapeutic decisions in CRC.
基金supported by National Key Research and Development(R&D)Program of China(2022YFB4101600)National Natural Science Foundation of China(22179139,22379157)+1 种基金Key Research and Development(R&D)Projects of Shanxi Province(202102040201003)Fundamental Research Program of Shanxi Province(202203021211203),The authors appreciate Beijing Paratera Technology Co.,Ltd,and the Supercomputer Center in Lvliang,China,for providing computational resources.
文摘Accurate prediction of practical reduction electrode potentials(Ered)of electrolyte solvents of electrochemical energy storage devices relies on calculating the Gibbs free energy in their reduction reaction.However,the emergence of new electrolyte solvents and additives leaves most of the reaction mechanisms unveiled.Here,we provide a machine-learning-assisted workflow of thermodynamically quantified Ered prediction for electrolyte solvents.A computational hydrogen electrode model based on density functional theory calculation is generalized for calculating the reaction free energy of electrochemical elementary steps.Machine-learning models are trained based on the organic and inorganic electrolyte solvents that possess experimentally identified reduction mechanisms.Validation of the best-scoring model is conducted by experimental validation of 6 additional solvents.Multiple thermodynamics features are found impactful on Ered through different chemical bonding with reaction intermediates.This workflow enables accurate Ered prediction for electrolyte solvents without identified reduction mechanisms,and is widely applicable in the electrochemical energy storage area.
文摘With the advent of computing and communication technologies,it has become possible for a learner to expand his or her knowledge irrespective of the place and time.Web-based learning promotes active and independent learning.Large scale e-learning platforms revolutionized the concept of studying and it also paved the way for innovative and effective teaching-learning process.This digital learning improves the quality of teaching and also promotes educational equity.However,the challenges in e-learning platforms include dissimilarities in learner’s ability and needs,lack of student motivation towards learning activities and provision for adaptive learning environment.The quality of learning can be enhanced by analyzing the online learner’s behavioral characteristics and their application of intelligent instructional strategy.It is not possible to identify the difficulties faced during the process through evaluation after the completion of e-learning course.It is thus essential for an e-learning system to include component offering adaptive control of learning and maintain user’s interest level.In this research work,a framework is proposed to analyze the behavior of online learners and motivate the students towards the learning process accordingly so as to increase the rate of learner’s objective attainment.Catering to the demands of e-learner,an intelligent model is presented in this study for e-learning system that apply supervised machine learning algorithm.An adaptive e-learning system suits every category of learner,improves the learner’s performance and paves way for offering personalized learning experiences.
文摘The purposes of this research were to 1) develop of an e-learning benchmarking model for higher education institutions;2) analyze and synthesize e-learning indicators for e-learning benchmarking model. The research was conducted using the research and development methods. The result shows that there are eight elements of e-learning benchmarking model: 1) team/staffs 2) benchmarking’s title 3) comparative companies 4) benchmarking indicators 5) data collection method 6) analysis data and results 7) report of results and 8) action plan development. Moreover, four steps of benchmarking model will be used in this research. “Plan” is the step of setting team for benchmarking title and choosing the company to collect the benchmarking while “Do” is a field study in order to analyze and collect each indicator. The step “Check” presents the data to stakeholders and set the purposes of action plan. Finally, “Act” which is the development of action plan leads to the practice or implementation which related to auditing and evaluating.
基金supported by the Khalifa University of Science and Technology under Award No.RC2 DSO and the Advanced Power and Energy Center.
文摘Nowcasting and forecasting solar irradiance are vital for the optimal prediction of grid-connected solar photovoltaic(PV)power plants.These plants face operational challenges and scheduling dispatch difficulties due to the fluctuating nature of their power output.As the generation capacity within the electric grid increases,accurately predicting this output becomes increasingly essential,especially given the random and non-linear characteristics of solar irradiance under variable weather conditions.This study presents a novel prediction method for solar irradiance,which is directly in correlation with PV power output,targeting both short-term and medium-term forecast horizons.Our proposed hybrid framework employs a fast trainable statistical learning technique based on the truncated-regularized kernel ridge regression model.The proposed method excels in forecasting solar irradiance,especially during highly intermittent weather periods.A key strength of our model is the incorporation of multiple historical weather parameters as inputs to generate accurate predictions of future solar irradiance values in its scalable framework.We evaluated the performance of our model using data sets from both cloudy and sunny days in Seattle and Medford,USA and compared it against three forecasting models:persistence,modified 24-hour persistence and least squares.Based on three widely accepted statistical performance metrics(root mean squared error,mean absolute error and coefficient of determination),our hybrid model demonstrated superior predictive accuracy in varying weather conditions and forecast horizons.
文摘This paper presents a V-model of e-learning using the well-known Gagne nine steps for quality education. Our suggested model is based on our experience at the computer engineering departments at AL-Hussein Bin Talal University, the University of Jordan and Albalqa Applied University. We applied the recommendations of the nine steps methodology to the e-learning environment. The V model suggested in this paper came up as a result of such application. Although this V model can be subject to some tuning and development in the future it proved to be highly efficient and easy to implement for the teacher and the student.
文摘After the spread of COVID-19,e-learning systems have become crucial tools in educational systems worldwide,spanning all levels of education.This widespread use of e-learning platforms has resulted in the accumulation of vast amounts of valuable data,making it an attractive resource for predicting student performance.In this study,we aimed to predict student performance based on the analysis of data collected from the OULAD and Deeds datasets.The stacking method was employed for modeling in this research.The proposed model utilized weak learners,including nearest neighbor,decision tree,random forest,enhanced gradient,simple Bayes,and logistic regression algorithms.After a trial-and-error process,the logistic regression algorithm was selected as the final learner for the proposed model.The results of experiments with the above algorithms are reported separately for the pass and fail classes.The findings indicate that the accuracy of the proposed model on the OULAD dataset reached 98%.Overall,the proposed method improved accuracy by 4%on the OULAD dataset.
文摘In recent times,technology has advanced significantly and is currently being integrated into educational environments to facilitate distance learning and interaction between learners.Integrating the Internet of Things(IoT)into education can facilitate the teaching and learning process and expand the context in which students learn.Nevertheless,learning data is very sensitive and must be protected when transmitted over the network or stored in data centers.Moreover,the identity and the authenticity of interacting students,instructors,and staff need to be verified to mitigate the impact of attacks.However,most of the current security and authentication schemes are centralized,relying on trusted third-party cloud servers,to facilitate continuous secure communication.In addition,most of these schemes are resourceintensive;thus,security and efficiency issues arise when heterogeneous and resource-limited IoT devices are being used.In this paper,we propose a blockchain-based architecture that accurately identifies and authenticates learners and their IoT devices in a decentralized manner and prevents the unauthorized modification of stored learning records in a distributed university network.It allows students and instructors to easily migrate to and join multiple universities within the network using their identity without the need for user re-authentication.The proposed architecture was tested using a simulation tool,and measured to evaluate its performance.The simulation results demonstrate the ability of the proposed architecture to significantly increase the throughput of learning transactions(40%),reduce the communication overhead and response time(26%),improve authentication efficiency(27%),and reduce the IoT power consumption(35%)compared to the centralized authentication mechanisms.In addition,the security analysis proves the effectiveness of the proposed architecture in resisting various attacks and ensuring the security requirements of learning data in the university network.
文摘ePCMM (e-Learning Process Capability Maturity Model) is used for evaluating the capability and maturity of an institution engaged in e-Learning based on e-Learning key process areas. It is a stepwise process improvement which can be implemented by both staged model and continues model.
基金supported by the Central Government Guiding Local Science and Technology Development Fund Project(No.2024SZY0343)the Joint Research Program for Ecological Conservation and High Quality Development of the Yellow River Basin(No.2022-YRUC-01-050205)+2 种基金the Higher Education Scientific Research Project of Inner Mongolia Autonomous Region(No.NJZZ23078)the project of Inner Mongolia"Prairie Talents"Engineering Innovation Entrepreneurship Talent Team,the Major Projects of Erdos Science and Technology(No.2022EEDSKJZDZX015)the Innovation Team of the Inner Mongolia Academy of Science and Technology(No.CXTD2023-01-016).
文摘Rural domestic sewage treatment is critical for environmental protection.This study defines the spatial pattern of villages from the perspective of rural sewage treatment and develops an integrated decision-making system to propose a sewage treatment mode and scheme suitable for local conditions.By considering the village spatial layout and terrain factors,a decision tree model of residential density and terrain type was constructed with accuracies of 76.47%and 96.00%,respectively.Combined with binary classification probability unit regression,an appropriate sewage treatment mode for the village was determined with 87.00%accuracy.The Analytic Hierarchy Process(AHP),combined with the Technique for Order Preference(TOPSIS)by Similarity to an Ideal Solution model,formed the basis for optimal treatment process selection under different emission standards.Verification was conducted in 542 villages across three counties of the Inner Mongolia Autonomous Region,focusing on the standard effluent effect(0.3773),low investment cost(0.3196),and high standard effluent effect(0.5115)to determine the best treatment process for the same emission standard under different needs.The annual environmental and carbon emission benefits of sewage treatment in these villages were estimated.This model matches village density,geographic feature,and social development level,and provides scientific support and a theoretical basis for rural sewage treatment decision-making.
基金The authors thank to the deanship of scientific research at Shaqra University for funding this research work through the Project Number(SU-ANN-2023017).
文摘E-learning behavior data indicates several students’activities on the e-learning platform such as the number of accesses to a set of resources and number of participants in lectures.This article proposes a new analytics systemto support academic evaluation for students via e-learning activities to overcome the challenges faced by traditional learning environments.The proposed e-learning analytics system includes a new deep forest model.It consists of multistage cascade random forests with minimal hyperparameters compared to traditional deep neural networks.The developed forest model can analyze each student’s activities during the use of an e-learning platform to give accurate expectations of the student’s performance before ending the semester and/or the final exam.Experiments have been conducted on the Open University Learning Analytics Dataset(OULAD)of 32,593 students.Our proposed deep model showed a competitive accuracy score of 98.0%compared to artificial intelligence-based models,such as ConvolutionalNeuralNetwork(CNN)and Long Short-TermMemory(LSTM)in previous studies.That allows academic advisors to support expected failed students significantly and improve their academic level at the right time.Consequently,the proposed analytics system can enhance the quality of educational services for students in an innovative e-learning framework.
基金National Key Scientific and Technological Infrastructure project “Earth System Science Numerical Simulator Facility” (EarthLab)supported by the National Natural Science Foundation of China (Grant No. 92044302)the National Key Research Development Program of China (Grant No. 2022YFC3700703)
文摘We present a comprehensive description and benchmark evaluation of the global–regional chemical transport model called the Emission and Atmospheric Processes Integrated and Coupled Community(EPICC)model.The framework incorporates(1)grid configuration,(2)transport dynamics,(3)chemical mechanisms,(4)aerosol processes,(5)wet/dry deposition parameterizations,and(6)heterogeneous chemistry treatments associated with sulfate,nitrous acid(HONO)chemistry,and aerosol/cloud–photolysis interactions(APIs/CPIs).Openly shared with the atmospheric research community,the model facilitates integration of advanced physicochemical schemes to enhance simulation accuracy.Globally,the model demonstrates realistic representations of ozone(O_(3))and aerosol optical depth.The EPICC model generally demonstrates robust performance in simulating regional concentrations of O_(3) and PM_(2.5)(and its components)in China.It successfully captures vertical profiles of both global and regional O_(3).Notably,the model mitigates frequently reported sulfate underestimations in highly industrialized regions of China.The model accurately captures two regional severe pollution episodes observed in eastern China(January/June 2021).Sensitivity experiments highlight the critical roles of heterogeneous chemical mechanisms associated with sulfate,HONO chemistry,APIs,and CPIs in capturing PM_(2.5) and O_(3) concentrations in China.Improved sulfate mechanisms result in an increase of approximately 32.4%(2.8μg m^(−3))in simulated winter sulfate concentrations when observations exceed 10μg m^(−3).Enhanced HONO elevates winter O_(3) and PM_(2.5) by≤20 and≤10μg m^(−3),respectively.Overall,CPIs dominate over APIs in improving O_(3) and PM_(2.5) simulations across China.Locally,APIs mitigate PM_(2.5) and O_(3) discrepancies in the Sichuan Basin.Seasonal cloud–chemistry coupling explains the weaker impact of PM_(2.5) in summer.
文摘Model evaluation using benchmark datasets is an important method to measure the capability of large language models(LLMs)in specific domains,and it is mainly used to assess the knowledge and reasoning abilities of LLMs.Therefore,in order to better assess the capability of LLMs in the agricultural domain,Agri-Eval was proposed as a benchmark for assessing the knowledge and reasoning ability of LLMs in agriculture.The assessment dataset used in Agri-Eval covered seven major disciplines in the agricultural domain:crop science,horticulture,plant protection,animal husbandry,forest science,aquaculture science,and grass science,and contained a total of 2283 questions.Among domestic general-purpose LLMs,DeepSeek R1 performed best with an accuracy rate of 75.49%.In the realm of international general-purpose LLMs,Gemini 2.0 pro exp 0205 standed out as the top performer,achieving an accuracy rate of 74.28%.As an LLMs in agriculture vertical,Shennong V2.0 outperformed all the LLMs in China,and the answer accuracy rate of agricultural knowledge exceeded that of all the existing general-purpose LLMs.The launch of Agri-Eval helped the LLM developers to comprehensively evaluate the model's capability in the field of agriculture through a variety of tasks and tests to promote the development of the LLMs in the field of agriculture.
基金Supported by the National Natural Science Foundation of China(12261018)Universities Key Laboratory of Mathematical Modeling and Data Mining in Guizhou Province(2023013)。
文摘In this paper,we establish and study a single-species logistic model with impulsive age-selective harvesting.First,we prove the ultimate boundedness of the solutions of the system.Then,we obtain conditions for the asymptotic stability of the trivial solution and the positive periodic solution.Finally,numerical simulations are presented to validate our results.Our results show that age-selective harvesting is more conducive to sustainable population survival than non-age-selective harvesting.
文摘The proliferation of high-dimensional data and the widespread use of complex models present central challenges in contemporary statistics and data science.Dimension reduction and model checking,as two foundational pillars supporting scientific inference and data-driven decisionmaking,have evolved through the collective wisdom of generations of statisticians.This special issue,titled"Recent Developments in Dimension Reduction and Model Checking for regressions",not only aims to showcase cutting-edge advances in the field but also carries a distinct sense of academic homage to honor the groundbreaking and enduring contributions of Professor Lixing Zhu,a leading scholar whose work has profoundly shaped both areas.
