The need for environmental education, which incorporates the life cycle concept into the learning program, will become increasingly greater all over the world. In the present study, an e-learning system, which is made...The need for environmental education, which incorporates the life cycle concept into the learning program, will become increasingly greater all over the world. In the present study, an e-learning system, which is made up of 3 parts including text-based learning materials, quizzes to review the content of the learning materials and CO<sub>2</sub> emission simulation, was designed and developed with the purpose of supporting environmental learning. Targeting a wide range of people, the operation period of this system was 1 month. Based on the results of questionnaire survey for users, it was evident that the quiz function and the simulation function of CO<sub>2</sub> emission contributed to the efficiency in environmental learning, and the format of the e-learning system was effective and helpful for environmental learning. Additionally, with the users’ awareness related to environmental conservation before and after using the system, significant changes in awareness were seen in areas such as behavioral intention, sense of urgency and sense of connection. Furthermore, as it was revealed that 62% of the total access numbers were from mobile devices, it was effective to prepare an interface optimized for mobile devices enabling users to use the system from their smartphones and tablet PCs.展开更多
This article reports on a research study on Sid the Science Kid PBS television show including meth-ods for preschool teachers to promote the inclusion of the 3E Learning Cycle and the tents of the nature of sci-ence i...This article reports on a research study on Sid the Science Kid PBS television show including meth-ods for preschool teachers to promote the inclusion of the 3E Learning Cycle and the tents of the nature of sci-ence in their preschool science education curriculum. We discussed： （a）the value of the Sid the Science Kidmedia tool and its relationship to the nature of science; （b）how to identify the 3E＇s Learning Cycle in the Sidthe Science Kid media tool. The goal of this study is to analyze if the 3E＇s （Explain, Explore, Engage） are pre-sent in a television promoting inquiry for young learners. We are suggesting the Sid media tool be a model forthe explicit teaching of the 3E＇s and the nature of science, not behavior management.展开更多
The reasonable quantification of the concrete freezing environment on the Qinghai–Tibet Plateau(QTP) is the primary issue in frost resistant concrete design, which is one of the challenges that the QTP engineering ma...The reasonable quantification of the concrete freezing environment on the Qinghai–Tibet Plateau(QTP) is the primary issue in frost resistant concrete design, which is one of the challenges that the QTP engineering managers should take into account. In this paper, we propose a more realistic method to calculate the number of concrete freeze–thaw cycles(NFTCs) on the QTP. The calculated results show that the NFTCs increase as the altitude of the meteorological station increases with the average NFTCs being 208.7. Four machine learning methods, i.e., the random forest(RF) model, generalized boosting method(GBM), generalized linear model(GLM), and generalized additive model(GAM), are used to fit the NFTCs. The root mean square error(RMSE) values of the RF, GBM, GLM, and GAM are 32.3, 4.3, 247.9, and 161.3, respectively. The R^(2) values of the RF, GBM, GLM, and GAM are 0.93, 0.99, 0.48, and 0.66, respectively. The GBM method performs the best compared to the other three methods, which was shown by the results of RMSE and R^(2) values. The quantitative results from the GBM method indicate that the lowest, medium, and highest NFTC values are distributed in the northern, central, and southern parts of the QTP, respectively. The annual NFTCs in the QTP region are mainly concentrated at 160 and above, and the average NFTCs is 200 across the QTP. Our results can provide scientific guidance and a theoretical basis for the freezing resistance design of concrete in various projects on the QTP.展开更多
Deep learning has achieved great success in a variety of research fields and industrial applications.However,when applied to seismic inversion,the shortage of labeled data severely influences the performance of deep l...Deep learning has achieved great success in a variety of research fields and industrial applications.However,when applied to seismic inversion,the shortage of labeled data severely influences the performance of deep learning-based methods.In order to tackle this problem,we propose a novel seismic impedance inversion method based on a cycle-consistent generative adversarial network(Cycle-GAN).The proposed Cycle-GAN model includes two generative subnets and two discriminative subnets.Three kinds of loss,including cycle-consistent loss,adversarial loss,and estimation loss,are adopted to guide the training process.Benefit from the proposed structure,the information contained in unlabeled data can be extracted,and adversarial learning further guarantees that the prediction results share similar distributions with the real data.Moreover,a neural network visualization method is adopted to show that the proposed CNN model can learn more distinguishable features than the conventional CNN model.The robustness experiments on synthetic data sets show that the proposed method can achieve better performances than other methods in most cases.And the blind-well experiments on real seismic profiles show that the predicted impedance curve of the proposed method maintains a better correlation with the true impedance curve.展开更多
The P-type update law has been the mainstream technique used in iterative learning control(ILC)systems,which resembles linear feedback control with asymptotical convergence.In recent years,finite-time control strategi...The P-type update law has been the mainstream technique used in iterative learning control(ILC)systems,which resembles linear feedback control with asymptotical convergence.In recent years,finite-time control strategies such as terminal sliding mode control have been shown to be effective in ramping up convergence speed by introducing fractional power with feedback.In this paper,we show that such mechanism can equally ramp up the learning speed in ILC systems.We first propose a fractional power update rule for ILC of single-input-single-output linear systems.A nonlinear error dynamics is constructed along the iteration axis to illustrate the evolutionary converging process.Using the nonlinear mapping approach,fast convergence towards the limit cycles of tracking errors inherently existing in ILC systems is proven.The limit cycles are shown to be tunable to determine the steady states.Numerical simulations are provided to verify the theoretical results.展开更多
Machine learning(ML)has emerged as a significant tool in the field of biorefinery,offering the capability to analyze and predict complex processes with efficiency.This article reviews the current state of biorefinery ...Machine learning(ML)has emerged as a significant tool in the field of biorefinery,offering the capability to analyze and predict complex processes with efficiency.This article reviews the current state of biorefinery and its classification,highlighting various commercially successful biorefineries.Further,we delve into different categories of ML models,including their algorithms and applications in various stages of biorefinery lifecycle,such as biomass characterization,pretreatment,lignin valorization,chemical,thermochemical and biochemical conversion processes,supply chain analysis,and life cycle assessment.The benefits and limitations of each of these algorithms are discussed in detail.Finally,the article concludes with a discussion of the limitations and future prospects of ML in the field of biorefineries.展开更多
Deep learning(DL)techniques,which do not need complex preprocessing and feature analysis,are used in many areas of medicine and achieve promising results.On the other hand,in medical studies,a limited dataset decrease...Deep learning(DL)techniques,which do not need complex preprocessing and feature analysis,are used in many areas of medicine and achieve promising results.On the other hand,in medical studies,a limited dataset decreases the abstraction ability of the DL model.In this context,we aimed to produce synthetic brain images including three tumor types(glioma,meningioma,and pituitary),unlike traditional data augmentation methods,and classify them with DL.This study proposes a tumor classification model consisting of a Dense Convolutional Network(DenseNet121)-based DL model to prevent forgetting problems in deep networks and delay information flow between layers.By comparing models trained on two different datasets,we demonstrated the effect of synthetic images generated by Cycle Generative Adversarial Network(CycleGAN)on the generalization of DL.One model is trained only on the original dataset,while the other is trained on the combined dataset of synthetic and original images.Synthetic data generated by CycleGAN improved the best accuracy values for glioma,meningioma,and pituitary tumor classes from 0.9633,0.9569,and 0.9904 to 0.9968,0.9920,and 0.9952,respectively.The developed model using synthetic data obtained a higher accuracy value than the related studies in the literature.Additionally,except for pixel-level and affine transform data augmentation,synthetic data has been generated in the figshare brain dataset for the first time.展开更多
Technological innovations have revolutionized the educational technology into various dimensions. Educational processes without educational technology have no value in this modern world. In education domain, the educa...Technological innovations have revolutionized the educational technology into various dimensions. Educational processes without educational technology have no value in this modern world. In education domain, the educational software has simplified the processes in greater extend. A implemented while developing such educational software. In particu proper lar, the development methodology has to be software developed to enrich these education processes should follow a development strategy to motivate the end users to utilize the hypermedia potentials. The software development life cycle （SDLC） has different phases in designing such educationa technology and assists the end users to benefit from the modern technology. This study identifies the various factors to be considered at each phase of the SDLC while developing educational software. Also, this study proposes some suggestions to be followed in ESDLC with respect to educational processes perspectives. The core idea of this study is to identify the various issues in implementing such educational software in day to day teaching and learning processes.展开更多
Cultivating environmental literacy is one of the most important tasks in the face of climate change.The purpose is to construct the general curriculum content of improving climate change adaptation to environmental li...Cultivating environmental literacy is one of the most important tasks in the face of climate change.The purpose is to construct the general curriculum content of improving climate change adaptation to environmental literacy,and to plan the evaluation mechanism of learning effectiveness.The use of learning theory,Problem-Based Learning(PBL)theory and Plan-Do-Check-Act(PDCA)cycle theory to improve the curriculum content and teaching continued to improve.This study focuses on the design coxirses from the three cognitive aspects of"conceptual cognition,""practical exercise" and "hands-on experience."Teach students how to cope with and respond to climate change to establish environmental literacy to mitigate the impact of natural reactions,and enhance awareness of environmental literacy by learning the science of climate adaptation and mitigation.The results of the actual implementation of the effectiveness assessment shows that,through studenfs feedback learning results,the courses presented gains for more,to know the appropriateness and necessity of curriculum planning,can be provided to the basic research of environmental literacy teaching curriculum planning.展开更多
文摘The need for environmental education, which incorporates the life cycle concept into the learning program, will become increasingly greater all over the world. In the present study, an e-learning system, which is made up of 3 parts including text-based learning materials, quizzes to review the content of the learning materials and CO<sub>2</sub> emission simulation, was designed and developed with the purpose of supporting environmental learning. Targeting a wide range of people, the operation period of this system was 1 month. Based on the results of questionnaire survey for users, it was evident that the quiz function and the simulation function of CO<sub>2</sub> emission contributed to the efficiency in environmental learning, and the format of the e-learning system was effective and helpful for environmental learning. Additionally, with the users’ awareness related to environmental conservation before and after using the system, significant changes in awareness were seen in areas such as behavioral intention, sense of urgency and sense of connection. Furthermore, as it was revealed that 62% of the total access numbers were from mobile devices, it was effective to prepare an interface optimized for mobile devices enabling users to use the system from their smartphones and tablet PCs.
文摘This article reports on a research study on Sid the Science Kid PBS television show including meth-ods for preschool teachers to promote the inclusion of the 3E Learning Cycle and the tents of the nature of sci-ence in their preschool science education curriculum. We discussed： （a）the value of the Sid the Science Kidmedia tool and its relationship to the nature of science; （b）how to identify the 3E＇s Learning Cycle in the Sidthe Science Kid media tool. The goal of this study is to analyze if the 3E＇s （Explain, Explore, Engage） are pre-sent in a television promoting inquiry for young learners. We are suggesting the Sid media tool be a model forthe explicit teaching of the 3E＇s and the nature of science, not behavior management.
基金supported by Shandong Provincial Natural Science Foundation (grant number: ZR2023MD036)Key Research and Development Project in Shandong Province (grant number: 2019GGX101064)project for excellent youth foundation of the innovation teacher team, Shandong (grant number: 2022KJ310)。
文摘The reasonable quantification of the concrete freezing environment on the Qinghai–Tibet Plateau(QTP) is the primary issue in frost resistant concrete design, which is one of the challenges that the QTP engineering managers should take into account. In this paper, we propose a more realistic method to calculate the number of concrete freeze–thaw cycles(NFTCs) on the QTP. The calculated results show that the NFTCs increase as the altitude of the meteorological station increases with the average NFTCs being 208.7. Four machine learning methods, i.e., the random forest(RF) model, generalized boosting method(GBM), generalized linear model(GLM), and generalized additive model(GAM), are used to fit the NFTCs. The root mean square error(RMSE) values of the RF, GBM, GLM, and GAM are 32.3, 4.3, 247.9, and 161.3, respectively. The R^(2) values of the RF, GBM, GLM, and GAM are 0.93, 0.99, 0.48, and 0.66, respectively. The GBM method performs the best compared to the other three methods, which was shown by the results of RMSE and R^(2) values. The quantitative results from the GBM method indicate that the lowest, medium, and highest NFTC values are distributed in the northern, central, and southern parts of the QTP, respectively. The annual NFTCs in the QTP region are mainly concentrated at 160 and above, and the average NFTCs is 200 across the QTP. Our results can provide scientific guidance and a theoretical basis for the freezing resistance design of concrete in various projects on the QTP.
基金financially supported by the NSFC(Grant No.41974126 and 41674116)the National Key Research and Development Program of China(Grant No.2018YFA0702501)the 13th 5-Year Basic Research Program of China National Petroleum Corporation(CNPC)(2018A-3306)。
文摘Deep learning has achieved great success in a variety of research fields and industrial applications.However,when applied to seismic inversion,the shortage of labeled data severely influences the performance of deep learning-based methods.In order to tackle this problem,we propose a novel seismic impedance inversion method based on a cycle-consistent generative adversarial network(Cycle-GAN).The proposed Cycle-GAN model includes two generative subnets and two discriminative subnets.Three kinds of loss,including cycle-consistent loss,adversarial loss,and estimation loss,are adopted to guide the training process.Benefit from the proposed structure,the information contained in unlabeled data can be extracted,and adversarial learning further guarantees that the prediction results share similar distributions with the real data.Moreover,a neural network visualization method is adopted to show that the proposed CNN model can learn more distinguishable features than the conventional CNN model.The robustness experiments on synthetic data sets show that the proposed method can achieve better performances than other methods in most cases.And the blind-well experiments on real seismic profiles show that the predicted impedance curve of the proposed method maintains a better correlation with the true impedance curve.
基金supported by the National Natural Science Foundation of China(62173333)Australian Research Council Discovery Program(DP200101199)。
文摘The P-type update law has been the mainstream technique used in iterative learning control(ILC)systems,which resembles linear feedback control with asymptotical convergence.In recent years,finite-time control strategies such as terminal sliding mode control have been shown to be effective in ramping up convergence speed by introducing fractional power with feedback.In this paper,we show that such mechanism can equally ramp up the learning speed in ILC systems.We first propose a fractional power update rule for ILC of single-input-single-output linear systems.A nonlinear error dynamics is constructed along the iteration axis to illustrate the evolutionary converging process.Using the nonlinear mapping approach,fast convergence towards the limit cycles of tracking errors inherently existing in ILC systems is proven.The limit cycles are shown to be tunable to determine the steady states.Numerical simulations are provided to verify the theoretical results.
基金the institutional research funding supported by SRUC,UK。
文摘Machine learning(ML)has emerged as a significant tool in the field of biorefinery,offering the capability to analyze and predict complex processes with efficiency.This article reviews the current state of biorefinery and its classification,highlighting various commercially successful biorefineries.Further,we delve into different categories of ML models,including their algorithms and applications in various stages of biorefinery lifecycle,such as biomass characterization,pretreatment,lignin valorization,chemical,thermochemical and biochemical conversion processes,supply chain analysis,and life cycle assessment.The benefits and limitations of each of these algorithms are discussed in detail.Finally,the article concludes with a discussion of the limitations and future prospects of ML in the field of biorefineries.
文摘Deep learning(DL)techniques,which do not need complex preprocessing and feature analysis,are used in many areas of medicine and achieve promising results.On the other hand,in medical studies,a limited dataset decreases the abstraction ability of the DL model.In this context,we aimed to produce synthetic brain images including three tumor types(glioma,meningioma,and pituitary),unlike traditional data augmentation methods,and classify them with DL.This study proposes a tumor classification model consisting of a Dense Convolutional Network(DenseNet121)-based DL model to prevent forgetting problems in deep networks and delay information flow between layers.By comparing models trained on two different datasets,we demonstrated the effect of synthetic images generated by Cycle Generative Adversarial Network(CycleGAN)on the generalization of DL.One model is trained only on the original dataset,while the other is trained on the combined dataset of synthetic and original images.Synthetic data generated by CycleGAN improved the best accuracy values for glioma,meningioma,and pituitary tumor classes from 0.9633,0.9569,and 0.9904 to 0.9968,0.9920,and 0.9952,respectively.The developed model using synthetic data obtained a higher accuracy value than the related studies in the literature.Additionally,except for pixel-level and affine transform data augmentation,synthetic data has been generated in the figshare brain dataset for the first time.
文摘Technological innovations have revolutionized the educational technology into various dimensions. Educational processes without educational technology have no value in this modern world. In education domain, the educational software has simplified the processes in greater extend. A implemented while developing such educational software. In particu proper lar, the development methodology has to be software developed to enrich these education processes should follow a development strategy to motivate the end users to utilize the hypermedia potentials. The software development life cycle （SDLC） has different phases in designing such educationa technology and assists the end users to benefit from the modern technology. This study identifies the various factors to be considered at each phase of the SDLC while developing educational software. Also, this study proposes some suggestions to be followed in ESDLC with respect to educational processes perspectives. The core idea of this study is to identify the various issues in implementing such educational software in day to day teaching and learning processes.
文摘Cultivating environmental literacy is one of the most important tasks in the face of climate change.The purpose is to construct the general curriculum content of improving climate change adaptation to environmental literacy,and to plan the evaluation mechanism of learning effectiveness.The use of learning theory,Problem-Based Learning(PBL)theory and Plan-Do-Check-Act(PDCA)cycle theory to improve the curriculum content and teaching continued to improve.This study focuses on the design coxirses from the three cognitive aspects of"conceptual cognition,""practical exercise" and "hands-on experience."Teach students how to cope with and respond to climate change to establish environmental literacy to mitigate the impact of natural reactions,and enhance awareness of environmental literacy by learning the science of climate adaptation and mitigation.The results of the actual implementation of the effectiveness assessment shows that,through studenfs feedback learning results,the courses presented gains for more,to know the appropriateness and necessity of curriculum planning,can be provided to the basic research of environmental literacy teaching curriculum planning.
