Under the macro-background of “all-for-one” tourism,this study analyzes the related concepts and characteristics of inquiry-based learning tourism to understand the significance and role of developing inquiry-based ...Under the macro-background of “all-for-one” tourism,this study analyzes the related concepts and characteristics of inquiry-based learning tourism to understand the significance and role of developing inquiry-based learning tourism in China.With Binzhou as the object of study,this study also explores the development of inquiry-based learning tourism products from the aspects of the product development idea,level,framework and marketing,with a view to providing references for implementing inquiry-based learning tourism activities in other regions.展开更多
This study examined the potential effect of Inquiry-Based Learning Strategy(IBL)on the tenth-grade students’reading comprehension.Two groups and a quasi-experimental design were used.Two complete sections of grade 10...This study examined the potential effect of Inquiry-Based Learning Strategy(IBL)on the tenth-grade students’reading comprehension.Two groups and a quasi-experimental design were used.Two complete sections of grade 10 students from a public Secondary School for Girls in Irbid was randomly assigned by the researcher.The experimental group of 30 students was chosen first,and then the control group of 30 students.A pre-post reading comprehension test was designed before and after the study in order to fulfill its goals.Additionally,the experimental group was taught using the IBL strategy,whereas the control group was taught using the traditional teaching methods recommended in the tenth-grade Teacher’s Book.According to the findings,there were significant statistical differences favoring the experimental group over the control group.In light of the findings,the researcher recommended employing the IBL strategy to students with various levels and EFL skills.展开更多
Against the backdrop of China’s strong emphasis on cultivating top innovative talents,the high school physics curriculum,as a core component of scientific education,bears the crucial mission of fostering students’sc...Against the backdrop of China’s strong emphasis on cultivating top innovative talents,the high school physics curriculum,as a core component of scientific education,bears the crucial mission of fostering students’scientific thinking,creativity,and practical ability.However,current physics teaching still faces issues such as fragmented content systems,weak experimental and inquiry components,and single evaluation methods,which fail to align with the objectives of top-talent cultivation[1].Guided by the top-talent training model,this study systematically explores reform paths for the high school physics curriculum in terms of educational philosophy,course content,teaching methods,and evaluation mechanisms.The research proposes reconstructing the curriculum system around the main thread of“core concepts-inquiry practice-innovative application,”strengthening interdisciplinary integration and the inclusion of frontier modern physics topics.In teaching methodology,it advocates for a shift toward problem-driven,project-based,and self-directed learning models,leveraging information and intelligent technologies to enhance classroom effectiveness.In evaluation,it suggests building a comprehensive system centered on formative assessment and innovation capability evaluation.Based on the reform practice of a key high school’s top-talent experimental class,the findings show significant improvements in students’scientific inquiry skills and creative thinking,as well as optimization of teaching philosophy and classroom ecology.The results provide theoretical and practical references for high school physics curriculum reform in the new era and offer insights into the construction of top-talent cultivation systems.展开更多
Implementing the conventional total focus method(TFM)for visualizing internal damage in reinforced con-crete(RC)is beset with computational challenges and a high dependence on physical principles.To overcome these cha...Implementing the conventional total focus method(TFM)for visualizing internal damage in reinforced con-crete(RC)is beset with computational challenges and a high dependence on physical principles.To overcome these challenges,an efficient total focus imaging method based on deep learning is proposed.This method deals with array ultrasonic time-domain signals from cracked RC beams.A deep neural network(DNN)employing a feature extraction+multilevel feature fusion+matrix construction architec-ture was developed;this architecture enabled the DNN to learn the underlying physical principles of the TFM.The ar-chitecture effectively transformed ultrasonic time-domain signals into a B-scan matrix.Training,validation,and test-ing data were collected by measuring eight RC beams with preset artificial cracks using a low-frequency shear wave ar-ray ultrasonic system.The results demonstrated that the re-constructed B-scan matrices had a peak signal-to-noise ratio of 26.94 dB and a structural similarity index of 0.978.Fur-thermore,the proposed method required 42%fewer floating-point operations compared with physics-based cal-culations,achieving total focus imaging with lower compu-tational cost.The study facilitates the advancement of ultra-sonic total focus imaging of RC structures from physics-based methods to data-driven methods without re-quiring prior physical knowledge,thereby providing robust support for further nondestructive evaluation and quantita-tive analysis.展开更多
China is undergoing an education reform that calls for a change from a rigid,fixed curriculum and didactic pedagogy to a more flexible,school-based curriculum and more inquiry-based pedagogy.This study investigated th...China is undergoing an education reform that calls for a change from a rigid,fixed curriculum and didactic pedagogy to a more flexible,school-based curriculum and more inquiry-based pedagogy.This study investigated the extent to which Chinese middle and high school teachers(a)endorse an inquiry-based approach and underlying learning principles,(b)practice this mode of teaching,and(c)believe that the approach is practically viable in the current educational contexts in China.A structured survey was developed to solicit Chinese teachers’responses to the above three issues.A total of 582 valid responses were collected,representing middle and high schools in different geographic locations.The results show that Chinese teachers are receptive to inquiry-based pedagogy but find practical constraints in fully implementing it.Several cultural and pragmatic reasons are explored.Policy implications are discussed with respect to teacher education/development,capacity building for the new pedagogy,and teaching/evaluation alignment.Finally cultural issues are discussed regarding using inquiry-based learning to enhance critical thinking and nurture independent thinkers.展开更多
Artificial intelligence has significantly altered many job workflows, hence expanding earlier notions of limitations, outcomes, size, and prices. GeoAI is a multidisciplinary field that encompasses computer science, e...Artificial intelligence has significantly altered many job workflows, hence expanding earlier notions of limitations, outcomes, size, and prices. GeoAI is a multidisciplinary field that encompasses computer science, engineering, statistics, and spatial science. Because this subject focuses on real-world issues, it has a significant impact on society and the economy. A broad context incorporating fundamental questions of theory, epistemology, and the scientific method is used to bring artificial intelligence (Al) and geography together. This connection has the potential to have far-reaching implications for the geographic study. GeoAI, or the combination of geography with artificial intelligence, offers unique solutions to a variety of smart city issues. This paper provides an overview of GeoAI technology, including the definition of GeoAI and the differences between GeoAI and traditional AI. Key steps to successful geographic data analysis include integrating AI with GIS and using GeoAI tools and technologies. Also shown are key areas of applications and models in GeoAI, likewise challenges to adopt GeoAI methods and technology as well as benefits. This article also included a case study on the use of GeoAI in Kuwait, as well as a number of recommendations.展开更多
Ahealth monitoring scheme is developed in this work by using hybrid machine learning strategies to iden-tify the fault severity and assess the health status of the aircraft gas turbine engine that is subject to compon...Ahealth monitoring scheme is developed in this work by using hybrid machine learning strategies to iden-tify the fault severity and assess the health status of the aircraft gas turbine engine that is subject to component degrada-tions that are caused by fouling and erosion.The proposed hybrid framework involves integrating both supervised recur-rent neural networks and unsupervised self-organizing maps methodologies,where the former is developed to extract ef-fective features that can be associated with the engine health condition and the latter is constructed for fault severity modeling and tracking of each considered degradation mode.Advantages of our proposed methodology are that it ac-complishes fault identification and health monitoring objectives by only discovering inherent health information that are available in the system I/O data at each operating point.The effectiveness of our approach is validated and justified with engine data under various degradation modes in compressors and turbines.展开更多
The unmanned combat aerial vehicle(UCAV)is a research hot issue in the world,and the situation assessment is an important part of it.To overcome shortcomings of the existing situation assessment methods,such as low ac...The unmanned combat aerial vehicle(UCAV)is a research hot issue in the world,and the situation assessment is an important part of it.To overcome shortcomings of the existing situation assessment methods,such as low accuracy and strong dependence on prior knowledge,a datadriven situation assessment method is proposed.The clustering and classification are combined,the former is used to mine situational knowledge,and the latter is used to realize rapid assessment.Angle evaluation factor and distance evaluation factor are proposed to transform multi-dimensional air combat information into two-dimensional features.A convolution success-history based adaptive differential evolution with linear population size reduc-tion-means(C-LSHADE-Means)algorithm is proposed.The convolutional pooling layer is used to compress the size of data and preserve the distribution characteristics.The LSHADE algorithm is used to initialize the center of the mean clustering,which over-comes the defect of initialization sensitivity.Comparing experi-ment with the seven clustering algorithms is done on the UCI data set,through four clustering indexes,and it proves that the method proposed in this paper has better clustering performance.A situation assessment model based on stacked autoen-coder and learning vector quantization(SAE-LVQ)network is constructed,and it uses SAE to reconstruct air combat data fea-tures,and uses the self-competition layer of the LVQ to achieve efficient classification.Compared with the five kinds of assess-ments models,the SAE-LVQ model has the highest accuracy.Finally,three kinds of confrontation processes from air combat maneuvering instrumentation(ACMI)are selected,and the model in this paper is used for situation assessment.The assessment results are in line with the actual situation.展开更多
Aiming at the personalized movie recommendation problem,a recommendation algorithm in-tegrating manifold learning and ensemble learning is studied.In this work,manifold learning is used to reduce the dimension of data...Aiming at the personalized movie recommendation problem,a recommendation algorithm in-tegrating manifold learning and ensemble learning is studied.In this work,manifold learning is used to reduce the dimension of data so that both time and space complexities of the model are mitigated.Meanwhile,gradient boosting decision tree(GBDT)is used to train the target user profile prediction model.Based on the recommendation results,Bayesian optimization algorithm is applied to optimize the recommendation model,which can effectively improve the prediction accuracy.The experimental results show that the proposed algorithm can improve the accuracy of movie recommendation.展开更多
The Gleason grade group(GG)is an important basis for assessing the malignancy of prostate can-cer,but it requires invasive biopsy to obtain pathology.To noninvasively evaluate GG,an automatic prediction method is prop...The Gleason grade group(GG)is an important basis for assessing the malignancy of prostate can-cer,but it requires invasive biopsy to obtain pathology.To noninvasively evaluate GG,an automatic prediction method is proposed based on multi-scale convolutional neural network of the ensemble attention module trained with curriculum learning.First,a lesion-attention map based on the image of the region of interest is proposed in combination with the bottleneck attention module to make the network more focus on the lesion area.Second,the feature pyramid network is combined to make the network better learn the multi-scale information of the lesion area.Finally,in the network training,a curriculum based on the consistency gap between the visual evaluation and the pathological grade is proposed,which further improves the prediction performance of the network.Ex-perimental results show that the proposed method is better than the traditional network model in predicting GG performance.The quadratic weighted Kappa is 0.4711 and the positive predictive value for predicting clinically significant cancer is 0.9369.展开更多
Open-set recognition(OSR)is a realistic problem in wireless signal recogni-tion,which means that during the inference phase there may appear unknown classes not seen in the training phase.The method of intra-class spl...Open-set recognition(OSR)is a realistic problem in wireless signal recogni-tion,which means that during the inference phase there may appear unknown classes not seen in the training phase.The method of intra-class splitting(ICS)that splits samples of known classes to imitate unknown classes has achieved great performance.However,this approach relies too much on the predefined splitting ratio and may face huge performance degradation in new environment.In this paper,we train a multi-task learning(MTL)net-work based on the characteristics of wireless signals to improve the performance in new scenes.Besides,we provide a dynamic method to decide the splitting ratio per class to get more precise outer samples.To be specific,we make perturbations to the sample from the center of one class toward its adversarial direction and the change point of confidence scores during this process is used as the splitting threshold.We conduct several experi-ments on one wireless signal dataset collected at 2.4 GHz ISM band by LimeSDR and one open modulation recognition dataset,and the analytical results demonstrate the effective-ness of the proposed method.展开更多
Introductory Statistics is a course taught in various community colleges, state colleges, and universities. Implementation of projects in this course has been shown to enhance students’ learning;in addition to increa...Introductory Statistics is a course taught in various community colleges, state colleges, and universities. Implementation of projects in this course has been shown to enhance students’ learning;in addition to increasing the ability of educators to assess students’ learning outcomes in detail. These projects are often inquiry-based and require a balance between flexibility and efficiency. Maintaining balance has provided the students’ opportunities through exploration and learner autonomy. In exploration, a student seeks new methods and options through experimenting. In this paper, we will discuss the benefits of exploration required by projects given in an introductory statistics course. The details of five different projects discussed in this paper—illustrate the practical influence that they could have on higher statistics courses.展开更多
The topographic information of a closed world is expressed as a graph. The plural mov- ingobjects which go and back in it according to a single moving strategy are supposed.The moving strategy is expressed by numerica...The topographic information of a closed world is expressed as a graph. The plural mov- ingobjects which go and back in it according to a single moving strategy are supposed.The moving strategy is expressed by numerical values as a decision table. Coding is performed with this table as chromosomes, and this is optimized by using genetic algorithm. These environments were realized on a computer, and the simulation was carried out. As the result, the learning of the method to act so that moving objects do not obstruct mutually was recognized, and it was confirmed that these methods are effective for optimizing moving strategy.展开更多
基金Sponsored by 2018 Dual Service Project of Binzhou University(BZXYSFW201808)
文摘Under the macro-background of “all-for-one” tourism,this study analyzes the related concepts and characteristics of inquiry-based learning tourism to understand the significance and role of developing inquiry-based learning tourism in China.With Binzhou as the object of study,this study also explores the development of inquiry-based learning tourism products from the aspects of the product development idea,level,framework and marketing,with a view to providing references for implementing inquiry-based learning tourism activities in other regions.
文摘This study examined the potential effect of Inquiry-Based Learning Strategy(IBL)on the tenth-grade students’reading comprehension.Two groups and a quasi-experimental design were used.Two complete sections of grade 10 students from a public Secondary School for Girls in Irbid was randomly assigned by the researcher.The experimental group of 30 students was chosen first,and then the control group of 30 students.A pre-post reading comprehension test was designed before and after the study in order to fulfill its goals.Additionally,the experimental group was taught using the IBL strategy,whereas the control group was taught using the traditional teaching methods recommended in the tenth-grade Teacher’s Book.According to the findings,there were significant statistical differences favoring the experimental group over the control group.In light of the findings,the researcher recommended employing the IBL strategy to students with various levels and EFL skills.
文摘Against the backdrop of China’s strong emphasis on cultivating top innovative talents,the high school physics curriculum,as a core component of scientific education,bears the crucial mission of fostering students’scientific thinking,creativity,and practical ability.However,current physics teaching still faces issues such as fragmented content systems,weak experimental and inquiry components,and single evaluation methods,which fail to align with the objectives of top-talent cultivation[1].Guided by the top-talent training model,this study systematically explores reform paths for the high school physics curriculum in terms of educational philosophy,course content,teaching methods,and evaluation mechanisms.The research proposes reconstructing the curriculum system around the main thread of“core concepts-inquiry practice-innovative application,”strengthening interdisciplinary integration and the inclusion of frontier modern physics topics.In teaching methodology,it advocates for a shift toward problem-driven,project-based,and self-directed learning models,leveraging information and intelligent technologies to enhance classroom effectiveness.In evaluation,it suggests building a comprehensive system centered on formative assessment and innovation capability evaluation.Based on the reform practice of a key high school’s top-talent experimental class,the findings show significant improvements in students’scientific inquiry skills and creative thinking,as well as optimization of teaching philosophy and classroom ecology.The results provide theoretical and practical references for high school physics curriculum reform in the new era and offer insights into the construction of top-talent cultivation systems.
基金Science & Technology Specific Project of Jiangsu Province (No. BZ2024047)Key R&D Program of Ningbo (No. 2024H013)the National Natural Science Foundation of China (No. W2412092)。
文摘Implementing the conventional total focus method(TFM)for visualizing internal damage in reinforced con-crete(RC)is beset with computational challenges and a high dependence on physical principles.To overcome these challenges,an efficient total focus imaging method based on deep learning is proposed.This method deals with array ultrasonic time-domain signals from cracked RC beams.A deep neural network(DNN)employing a feature extraction+multilevel feature fusion+matrix construction architec-ture was developed;this architecture enabled the DNN to learn the underlying physical principles of the TFM.The ar-chitecture effectively transformed ultrasonic time-domain signals into a B-scan matrix.Training,validation,and test-ing data were collected by measuring eight RC beams with preset artificial cracks using a low-frequency shear wave ar-ray ultrasonic system.The results demonstrated that the re-constructed B-scan matrices had a peak signal-to-noise ratio of 26.94 dB and a structural similarity index of 0.978.Fur-thermore,the proposed method required 42%fewer floating-point operations compared with physics-based cal-culations,achieving total focus imaging with lower compu-tational cost.The study facilitates the advancement of ultra-sonic total focus imaging of RC structures from physics-based methods to data-driven methods without re-quiring prior physical knowledge,thereby providing robust support for further nondestructive evaluation and quantita-tive analysis.
基金supported by a Fulbright research grant from the US State Department to the first author during 2008-2009The opinions expressed in the article do not necessarily reflect the position of the US government.
文摘China is undergoing an education reform that calls for a change from a rigid,fixed curriculum and didactic pedagogy to a more flexible,school-based curriculum and more inquiry-based pedagogy.This study investigated the extent to which Chinese middle and high school teachers(a)endorse an inquiry-based approach and underlying learning principles,(b)practice this mode of teaching,and(c)believe that the approach is practically viable in the current educational contexts in China.A structured survey was developed to solicit Chinese teachers’responses to the above three issues.A total of 582 valid responses were collected,representing middle and high schools in different geographic locations.The results show that Chinese teachers are receptive to inquiry-based pedagogy but find practical constraints in fully implementing it.Several cultural and pragmatic reasons are explored.Policy implications are discussed with respect to teacher education/development,capacity building for the new pedagogy,and teaching/evaluation alignment.Finally cultural issues are discussed regarding using inquiry-based learning to enhance critical thinking and nurture independent thinkers.
文摘Artificial intelligence has significantly altered many job workflows, hence expanding earlier notions of limitations, outcomes, size, and prices. GeoAI is a multidisciplinary field that encompasses computer science, engineering, statistics, and spatial science. Because this subject focuses on real-world issues, it has a significant impact on society and the economy. A broad context incorporating fundamental questions of theory, epistemology, and the scientific method is used to bring artificial intelligence (Al) and geography together. This connection has the potential to have far-reaching implications for the geographic study. GeoAI, or the combination of geography with artificial intelligence, offers unique solutions to a variety of smart city issues. This paper provides an overview of GeoAI technology, including the definition of GeoAI and the differences between GeoAI and traditional AI. Key steps to successful geographic data analysis include integrating AI with GIS and using GeoAI tools and technologies. Also shown are key areas of applications and models in GeoAI, likewise challenges to adopt GeoAI methods and technology as well as benefits. This article also included a case study on the use of GeoAI in Kuwait, as well as a number of recommendations.
基金The Natural Sciences and Engineering Research Council of Canada(NSERC)the Department of National Defence(DND)under the Discovery Grant and DND Supplemental Programs。
文摘Ahealth monitoring scheme is developed in this work by using hybrid machine learning strategies to iden-tify the fault severity and assess the health status of the aircraft gas turbine engine that is subject to component degrada-tions that are caused by fouling and erosion.The proposed hybrid framework involves integrating both supervised recur-rent neural networks and unsupervised self-organizing maps methodologies,where the former is developed to extract ef-fective features that can be associated with the engine health condition and the latter is constructed for fault severity modeling and tracking of each considered degradation mode.Advantages of our proposed methodology are that it ac-complishes fault identification and health monitoring objectives by only discovering inherent health information that are available in the system I/O data at each operating point.The effectiveness of our approach is validated and justified with engine data under various degradation modes in compressors and turbines.
基金supported by the Natural Science Foundation of Shaanxi Province(2020JQ-481,2021JM-224)the Aeronautical Science Foundation of China(201951096002).
文摘The unmanned combat aerial vehicle(UCAV)is a research hot issue in the world,and the situation assessment is an important part of it.To overcome shortcomings of the existing situation assessment methods,such as low accuracy and strong dependence on prior knowledge,a datadriven situation assessment method is proposed.The clustering and classification are combined,the former is used to mine situational knowledge,and the latter is used to realize rapid assessment.Angle evaluation factor and distance evaluation factor are proposed to transform multi-dimensional air combat information into two-dimensional features.A convolution success-history based adaptive differential evolution with linear population size reduc-tion-means(C-LSHADE-Means)algorithm is proposed.The convolutional pooling layer is used to compress the size of data and preserve the distribution characteristics.The LSHADE algorithm is used to initialize the center of the mean clustering,which over-comes the defect of initialization sensitivity.Comparing experi-ment with the seven clustering algorithms is done on the UCI data set,through four clustering indexes,and it proves that the method proposed in this paper has better clustering performance.A situation assessment model based on stacked autoen-coder and learning vector quantization(SAE-LVQ)network is constructed,and it uses SAE to reconstruct air combat data fea-tures,and uses the self-competition layer of the LVQ to achieve efficient classification.Compared with the five kinds of assess-ments models,the SAE-LVQ model has the highest accuracy.Finally,three kinds of confrontation processes from air combat maneuvering instrumentation(ACMI)are selected,and the model in this paper is used for situation assessment.The assessment results are in line with the actual situation.
基金Supported by the Educational Commission of Liaoning Province of China(No.LQGD2017027).
文摘Aiming at the personalized movie recommendation problem,a recommendation algorithm in-tegrating manifold learning and ensemble learning is studied.In this work,manifold learning is used to reduce the dimension of data so that both time and space complexities of the model are mitigated.Meanwhile,gradient boosting decision tree(GBDT)is used to train the target user profile prediction model.Based on the recommendation results,Bayesian optimization algorithm is applied to optimize the recommendation model,which can effectively improve the prediction accuracy.The experimental results show that the proposed algorithm can improve the accuracy of movie recommendation.
基金Foundation item:the Suzhou Municipal Health and Family Planning Commission's Key Diseases Diagnosis and Treatment Program(No.LCzX202001)the Science and Technology Development Project ofSuzhou(Nos.SS2019012andSKY2021031)+1 种基金the Youth Innovation Promotion Association CAS(No.2021324)the Medical Research Project of Jiangsu Provincial Health and Family Planning Commission(No.M2020068)。
文摘The Gleason grade group(GG)is an important basis for assessing the malignancy of prostate can-cer,but it requires invasive biopsy to obtain pathology.To noninvasively evaluate GG,an automatic prediction method is proposed based on multi-scale convolutional neural network of the ensemble attention module trained with curriculum learning.First,a lesion-attention map based on the image of the region of interest is proposed in combination with the bottleneck attention module to make the network more focus on the lesion area.Second,the feature pyramid network is combined to make the network better learn the multi-scale information of the lesion area.Finally,in the network training,a curriculum based on the consistency gap between the visual evaluation and the pathological grade is proposed,which further improves the prediction performance of the network.Ex-perimental results show that the proposed method is better than the traditional network model in predicting GG performance.The quadratic weighted Kappa is 0.4711 and the positive predictive value for predicting clinically significant cancer is 0.9369.
文摘Open-set recognition(OSR)is a realistic problem in wireless signal recogni-tion,which means that during the inference phase there may appear unknown classes not seen in the training phase.The method of intra-class splitting(ICS)that splits samples of known classes to imitate unknown classes has achieved great performance.However,this approach relies too much on the predefined splitting ratio and may face huge performance degradation in new environment.In this paper,we train a multi-task learning(MTL)net-work based on the characteristics of wireless signals to improve the performance in new scenes.Besides,we provide a dynamic method to decide the splitting ratio per class to get more precise outer samples.To be specific,we make perturbations to the sample from the center of one class toward its adversarial direction and the change point of confidence scores during this process is used as the splitting threshold.We conduct several experi-ments on one wireless signal dataset collected at 2.4 GHz ISM band by LimeSDR and one open modulation recognition dataset,and the analytical results demonstrate the effective-ness of the proposed method.
文摘Introductory Statistics is a course taught in various community colleges, state colleges, and universities. Implementation of projects in this course has been shown to enhance students’ learning;in addition to increasing the ability of educators to assess students’ learning outcomes in detail. These projects are often inquiry-based and require a balance between flexibility and efficiency. Maintaining balance has provided the students’ opportunities through exploration and learner autonomy. In exploration, a student seeks new methods and options through experimenting. In this paper, we will discuss the benefits of exploration required by projects given in an introductory statistics course. The details of five different projects discussed in this paper—illustrate the practical influence that they could have on higher statistics courses.
文摘The topographic information of a closed world is expressed as a graph. The plural mov- ingobjects which go and back in it according to a single moving strategy are supposed.The moving strategy is expressed by numerical values as a decision table. Coding is performed with this table as chromosomes, and this is optimized by using genetic algorithm. These environments were realized on a computer, and the simulation was carried out. As the result, the learning of the method to act so that moving objects do not obstruct mutually was recognized, and it was confirmed that these methods are effective for optimizing moving strategy.
