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
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
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
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
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
In recent years,there has been an increasing need for climate information across diverse sectors of society.This demand has arisen from the necessity to adapt to and mitigate the impacts of climate variability and cha...In recent years,there has been an increasing need for climate information across diverse sectors of society.This demand has arisen from the necessity to adapt to and mitigate the impacts of climate variability and change.Likewise,this period has seen a significant increase in our understanding of the physical processes and mechanisms that drive precipitation and its variability across different regions of Africa.By leveraging a large volume of climate model outputs,numerous studies have investigated the model representation of African precipitation as well as underlying physical processes.These studies have assessed whether the physical processes are well depicted and whether the models are fit for informing mitigation and adaptation strategies.This paper provides a review of the progress in precipitation simulation overAfrica in state-of-the-science climate models and discusses the major issues and challenges that remain.展开更多
Utilizing finite element analysis,the ballistic protection provided by a combination of perforated D-shaped and base armor plates,collectively referred to as radiator armor,is evaluated.ANSYS Explicit Dynamics is empl...Utilizing finite element analysis,the ballistic protection provided by a combination of perforated D-shaped and base armor plates,collectively referred to as radiator armor,is evaluated.ANSYS Explicit Dynamics is employed to simulate the ballistic impact of 7.62 mm armor-piercing projectiles on Aluminum AA5083-H116 and Steel Secure 500 armors,focusing on the evaluation of material deformation and penetration resistance at varying impact points.While the D-shaped armor plate is penetrated by the armor-piercing projectiles,the combination of the perforated D-shaped and base armor plates successfully halts penetration.A numerical model based on the finite element method is developed using software such as SolidWorks and ANSYS to analyze the interaction between radiator armor and bullet.The perforated design of radiator armor is to maintain airflow for radiator function,with hole sizes smaller than the bullet core diameter to protect radiator assemblies.Predictions are made regarding the brittle fracture resulting from the projectile core′s bending due to asymmetric impact,and the resulting fragments failed to penetrate the perforated base armor plate.Craters are formed on the surface of the perforated D-shaped armor plate due to the impact of projectile fragments.The numerical model accurately predicts hole growth and projectile penetration upon impact with the armor,demonstrating effective protection of the radiator assemblies by the radiator armor.展开更多
The National Geophysical Data Center(NGDC)of the United States has collected aeromagnetic data for input into a series of geomagnetic models to improve model resolution;however,in the Tibetan Plateau region,ground-bas...The National Geophysical Data Center(NGDC)of the United States has collected aeromagnetic data for input into a series of geomagnetic models to improve model resolution;however,in the Tibetan Plateau region,ground-based observations remain insufficient to clearly reflect the characteristics of the region’s lithospheric magnetism.In this study,we evaluate the lithospheric magnetism of the Tibetan Plateau by using a 3D surface spline model based on observations from>200 newly constructed repeat stations(portable stations)to determine the spatial distribution of plateau geomagnetism,as well as its correlation with the tectonic features of the region.We analyze the relationships between M≥5 earthquakes and lithospheric magnetic field variations on the Tibetan Plateau and identify regions susceptible to strong earthquakes.We compare the geomagnetic results with those from an enhanced magnetic model(EMM2015)developed by the NGDC and provide insights into improving lithospheric magnetic field calculations in the Tibetan Plateau region.Further research reveals that these magnetic anomalies exhibit distinct differences from the magnetic-seismic correlation mechanisms observed in other tectonic settings;here,they are governed primarily by the combined effects of compressional magnetism,thermal magnetism,and deep thermal stress.This study provides new evidence of geomagnetic anomalies on the Tibetan Plateau,interprets them physically,and demonstrates their potential for identifying seismic hazard zones on the Plateau.展开更多
Automatic detection of student engagement levels from videos,which is a spatio-temporal classification problem is crucial for enhancing the quality of online education.This paper addresses this challenge by proposing ...Automatic detection of student engagement levels from videos,which is a spatio-temporal classification problem is crucial for enhancing the quality of online education.This paper addresses this challenge by proposing four novel hybrid end-to-end deep learning models designed for the automatic detection of student engagement levels in e-learning videos.The evaluation of these models utilizes the DAiSEE dataset,a public repository capturing student affective states in e-learning scenarios.The initial model integrates EfficientNetV2-L with Gated Recurrent Unit(GRU)and attains an accuracy of 61.45%.Subsequently,the second model combines EfficientNetV2-L with bidirectional GRU(Bi-GRU),yielding an accuracy of 61.56%.The third and fourth models leverage a fusion of EfficientNetV2-L with Long Short-Term Memory(LSTM)and bidirectional LSTM(Bi-LSTM),achieving accuracies of 62.11%and 61.67%,respectively.Our findings demonstrate the viability of these models in effectively discerning student engagement levels,with the EfficientNetV2-L+LSTM model emerging as the most proficient,reaching an accuracy of 62.11%.This study underscores the potential of hybrid spatio-temporal networks in automating the detection of student engagement,thereby contributing to advancements in online education quality.展开更多
Climate model prediction has been improved by enhancing model resolution as well as the implementation of sophisticated physical parameterization and refinement of data assimilation systems[section 6.1 in Wang et al.(...Climate model prediction has been improved by enhancing model resolution as well as the implementation of sophisticated physical parameterization and refinement of data assimilation systems[section 6.1 in Wang et al.(2025)].In relation to seasonal forecasting and climate projection in the East Asian summer monsoon season,proper simulation of the seasonal migration of rain bands by models is a challenging and limiting factor[section 7.1 in Wang et al.(2025)].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.
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.
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.
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.
基金the World Climate Research Programme(WCRP),Climate Variability and Predictability(CLIVAR),and Global Energy and Water Exchanges(GEWEX)for facilitating the coordination of African monsoon researchsupport from the Center for Earth System Modeling,Analysis,and Data at the Pennsylvania State Universitythe support of the Office of Science of the U.S.Department of Energy Biological and Environmental Research as part of the Regional&Global Model Analysis(RGMA)program area。
文摘In recent years,there has been an increasing need for climate information across diverse sectors of society.This demand has arisen from the necessity to adapt to and mitigate the impacts of climate variability and change.Likewise,this period has seen a significant increase in our understanding of the physical processes and mechanisms that drive precipitation and its variability across different regions of Africa.By leveraging a large volume of climate model outputs,numerous studies have investigated the model representation of African precipitation as well as underlying physical processes.These studies have assessed whether the physical processes are well depicted and whether the models are fit for informing mitigation and adaptation strategies.This paper provides a review of the progress in precipitation simulation overAfrica in state-of-the-science climate models and discusses the major issues and challenges that remain.
文摘Utilizing finite element analysis,the ballistic protection provided by a combination of perforated D-shaped and base armor plates,collectively referred to as radiator armor,is evaluated.ANSYS Explicit Dynamics is employed to simulate the ballistic impact of 7.62 mm armor-piercing projectiles on Aluminum AA5083-H116 and Steel Secure 500 armors,focusing on the evaluation of material deformation and penetration resistance at varying impact points.While the D-shaped armor plate is penetrated by the armor-piercing projectiles,the combination of the perforated D-shaped and base armor plates successfully halts penetration.A numerical model based on the finite element method is developed using software such as SolidWorks and ANSYS to analyze the interaction between radiator armor and bullet.The perforated design of radiator armor is to maintain airflow for radiator function,with hole sizes smaller than the bullet core diameter to protect radiator assemblies.Predictions are made regarding the brittle fracture resulting from the projectile core′s bending due to asymmetric impact,and the resulting fragments failed to penetrate the perforated base armor plate.Craters are formed on the surface of the perforated D-shaped armor plate due to the impact of projectile fragments.The numerical model accurately predicts hole growth and projectile penetration upon impact with the armor,demonstrating effective protection of the radiator assemblies by the radiator armor.
基金supported by the CAS Pioneer Hundred Talents Program and Second Tibetan Plateau Scientific Expedition Research Program(2019QZKK0708)as well as the Basic Research Program of Qinghai Province:Lithospheric Geomagnetic Field of the Qinghai‒Tibet Plateau and the Relationship with Strong Earthquakes(2021-ZJ-969Q).
文摘The National Geophysical Data Center(NGDC)of the United States has collected aeromagnetic data for input into a series of geomagnetic models to improve model resolution;however,in the Tibetan Plateau region,ground-based observations remain insufficient to clearly reflect the characteristics of the region’s lithospheric magnetism.In this study,we evaluate the lithospheric magnetism of the Tibetan Plateau by using a 3D surface spline model based on observations from>200 newly constructed repeat stations(portable stations)to determine the spatial distribution of plateau geomagnetism,as well as its correlation with the tectonic features of the region.We analyze the relationships between M≥5 earthquakes and lithospheric magnetic field variations on the Tibetan Plateau and identify regions susceptible to strong earthquakes.We compare the geomagnetic results with those from an enhanced magnetic model(EMM2015)developed by the NGDC and provide insights into improving lithospheric magnetic field calculations in the Tibetan Plateau region.Further research reveals that these magnetic anomalies exhibit distinct differences from the magnetic-seismic correlation mechanisms observed in other tectonic settings;here,they are governed primarily by the combined effects of compressional magnetism,thermal magnetism,and deep thermal stress.This study provides new evidence of geomagnetic anomalies on the Tibetan Plateau,interprets them physically,and demonstrates their potential for identifying seismic hazard zones on the Plateau.
文摘Automatic detection of student engagement levels from videos,which is a spatio-temporal classification problem is crucial for enhancing the quality of online education.This paper addresses this challenge by proposing four novel hybrid end-to-end deep learning models designed for the automatic detection of student engagement levels in e-learning videos.The evaluation of these models utilizes the DAiSEE dataset,a public repository capturing student affective states in e-learning scenarios.The initial model integrates EfficientNetV2-L with Gated Recurrent Unit(GRU)and attains an accuracy of 61.45%.Subsequently,the second model combines EfficientNetV2-L with bidirectional GRU(Bi-GRU),yielding an accuracy of 61.56%.The third and fourth models leverage a fusion of EfficientNetV2-L with Long Short-Term Memory(LSTM)and bidirectional LSTM(Bi-LSTM),achieving accuracies of 62.11%and 61.67%,respectively.Our findings demonstrate the viability of these models in effectively discerning student engagement levels,with the EfficientNetV2-L+LSTM model emerging as the most proficient,reaching an accuracy of 62.11%.This study underscores the potential of hybrid spatio-temporal networks in automating the detection of student engagement,thereby contributing to advancements in online education quality.
文摘Climate model prediction has been improved by enhancing model resolution as well as the implementation of sophisticated physical parameterization and refinement of data assimilation systems[section 6.1 in Wang et al.(2025)].In relation to seasonal forecasting and climate projection in the East Asian summer monsoon season,proper simulation of the seasonal migration of rain bands by models is a challenging and limiting factor[section 7.1 in Wang et al.(2025)].
