Deep learning algorithms have been rapidly incorporated into many different applications due to the increase in computational power and the availability of massive amounts of data.Recently,both deep learning and ensem...Deep learning algorithms have been rapidly incorporated into many different applications due to the increase in computational power and the availability of massive amounts of data.Recently,both deep learning and ensemble learning have been used to recognize underlying structures and patterns from high-level features to make predictions/decisions.With the growth in popularity of deep learning and ensemble learning algorithms,they have received significant attention from both scientists and the industrial community due to their superior ability to learn features from big data.Ensemble deep learning has exhibited significant performance in enhancing learning generalization through the use of multiple deep learning algorithms.Although ensemble deep learning has large quantities of training parameters,which results in time and space overheads,it performs much better than traditional ensemble learning.Ensemble deep learning has been successfully used in several areas,such as bioinformatics,finance,and health care.In this paper,we review and investigate recent ensemble deep learning algorithms and techniques in health care domains,medical imaging,health care data analytics,genomics,diagnosis,disease prevention,and drug discovery.We cover several widely used deep learning algorithms along with their architectures,including deep neural networks(DNNs),convolutional neural networks(CNNs),recurrent neural networks(RNNs),and generative adversarial networks(GANs).Common healthcare tasks,such as medical imaging,electronic health records,and genomics,are also demonstrated.Furthermore,in this review,the challenges inherent in reducing the burden on the healthcare system are discussed and explored.Finally,future directions and opportunities for enhancing healthcare model performance are discussed.展开更多
Diagnosis and treatment of breast cancer have been improved during the last decade; however, breast cancer is still a leading cause of death among women in the whole world. Early detection and accurate diagnosis of th...Diagnosis and treatment of breast cancer have been improved during the last decade; however, breast cancer is still a leading cause of death among women in the whole world. Early detection and accurate diagnosis of this disease has been demonstrated an approach to long survival of the patients. As an attempt to develop a reliable diagnosing method for breast cancer, we integrated support vector machine (SVM), k-nearest neighbor and probabilistic neural network into a complex machine learning approach to detect malignant breast tumour through a set of indicators consisting of age and ten cellular features of fine-needle aspiration of breast which were ranked according to signal-to-noise ratio to identify determinants distinguishing benign breast tumours from malignant ones. The method turned out to significantly improve the diagnosis, with a sensitivity of 94.04%, a specificity of 97.37%, and an overall accuracy up to 96.24% when SVM was adopted with the sigmoid kernel function under 5-fold cross validation. The results suggest that SVM is a promising methodology to be further developed into a practical adjunct implement to help discerning benign and malignant breast tumours and thus reduce the incidence of misdiagnosis.展开更多
The Approaches to Learning addresses how children learn-this includes children’s attitudes and interests in learning.This domain reflects behaviours and attitudes such as curiosity,problem-solving,maintaining attenti...The Approaches to Learning addresses how children learn-this includes children’s attitudes and interests in learning.This domain reflects behaviours and attitudes such as curiosity,problem-solving,maintaining attention and persistence.The research study focused on examining the fathers’parenting practices and the children’s approaches to learning from three through five years.The study used a cross sectional research design and data was generated using focal group discussions,interview guides and child behaviour rating scale on how fathers’parenting practices contribute to children’s approaches to learning.Results revealed that,Fathers’parenting practices and Children’s curiosity were found to have a very positive relationship(r=0.396,p<0.05).Fathers’parenting practices and children’s learning were found to have a significant positive relationship(r=0.420,p<0.05).Findings also indicated that fathers’parenting practices and children’s creativity were found to have an average positive relationship(r=0.379,p<0.05).Arising out of the findings,the study recommended that fathers’parenting programs be put in place to help them up bring the child in holistic manner.展开更多
The prediction of pregnancy-related hazards must be accurate and timely to safeguard mother and fetal health.This study aims to enhance risk prediction in pregnancywith a novel deep learningmodel based on a Long Short...The prediction of pregnancy-related hazards must be accurate and timely to safeguard mother and fetal health.This study aims to enhance risk prediction in pregnancywith a novel deep learningmodel based on a Long Short-Term Memory(LSTM)generator,designed to capture temporal relationships in cardiotocography(CTG)data.This methodology integrates CTG signals with demographic characteristics and utilizes preprocessing techniques such as noise reduction,normalization,and segmentation to create high-quality input for themodel.It uses convolutional layers to extract spatial information,followed by LSTM layers to model sequences for superior predictive performance.The overall results show that themodel is robust,with an accuracy of 91.5%,precision of 89.8%,recall of 90.4%,and F1-score of 90.1%that outperformed the corresponding baselinemodels,CNN(Convolutional Neural Network)and traditional RNN(Recurrent Neural Network),by 2.3%and 6.1%,respectively.Rather,the ability to detect pregnancy-related abnormalities has considerable therapeutic potential,with the possibility for focused treatments and individualized maternal healthcare approaches,the research team concluded.展开更多
In this paper,we review and analyze intrusion detection systems for Agriculture 4.0 cyber security.Specifically,we present cyber security threats and evaluation metrics used in the performance evaluation of an intrusi...In this paper,we review and analyze intrusion detection systems for Agriculture 4.0 cyber security.Specifically,we present cyber security threats and evaluation metrics used in the performance evaluation of an intrusion detection system for Agriculture 4.0.Then,we evaluate intrusion detection systems according to emerging technologies,including,Cloud computing,Fog/Edge computing,Network virtualization,Autonomous tractors,Drones,Internet of Things,Industrial agriculture,and Smart Grids.Based on the machine learning technique used,we provide a comprehensive classification of intrusion detection systems in each emerging technology.Furthermore,we present public datasets,and the implementation frameworks applied in the performance evaluation of intrusion detection systems for Agriculture 4.0.Finally,we outline challenges and future research directions in cyber security intrusion detection for Agriculture 4.0.展开更多
Student-centered learning approach is focused on the students' demands and interests.Applying student-centered approach puts forward higher requirement to English teachers.This article first analyzes the theory of...Student-centered learning approach is focused on the students' demands and interests.Applying student-centered approach puts forward higher requirement to English teachers.This article first analyzes the theory of student-centered learning approach and compares teacher-centered approach with it.Based on the research information and teaching experience,the author summarizes four strategies about how to apply student-centered learning approach to English listening and speaking class in vocational schools.展开更多
DEAR EDITOR,Somatic mutations are a large category of genetic variations,which play an essential role in tumorigenesis. Detection of somatic single nucleotide variants(SNVs) could facilitate downstream analysis of tum...DEAR EDITOR,Somatic mutations are a large category of genetic variations,which play an essential role in tumorigenesis. Detection of somatic single nucleotide variants(SNVs) could facilitate downstream analysis of tumorigenesis. Many computational methods have been developed to detect SNVs, but most require normal matched samples to differentiate somatic SNVs from the normal state, which can be difficult to obtain.展开更多
L1 and L2 acquisition, in some respects, are similar. Language development in children goes hand in hand with physical and cognitive development. Children learn their first language by imitation, but not always and no...L1 and L2 acquisition, in some respects, are similar. Language development in children goes hand in hand with physical and cognitive development. Children learn their first language by imitation, but not always and not only by imitation. There seems to be some "innate capacities" that make children start to speak at the same time they do and in the way they do it. Adults learning a second language usually are controlled more by their motivation. But language input is important for both L1 and L2 acquisition. Though there are differences between CL1 and between CL2 and AL2, the way in which these learners acquire some of the grammatical morphemes is similar. This, together with some other evidence, shows that it is not only children who can acquire language. Adults can also acquire a language. But when adults acquire a language, they should also learn it. Some of the ways in which children acquire their language can be used as a model for L2 acquisition, even for Chinese students whose language is unrelated to English and whose culture is different. Learning the culture of the English-speaking countries will benefit the learning of the language. Like children, listening should also be well in advance of speaking in L2 acquisition. To train listening comprehension skills, Asher’s TPR approach proves more effective. TPR approach is at the moment limited to the beginning stage only. In order for students to gain all the five skills in a second language learning, namely, listening, speaking, reading, writing, and interpreting/translating, other methods should be used at the same time, or at later stages.展开更多
As the 21st century brings in a revolutionary change in the way students study at schools and universities, technology continues to play a crucial role in helping students achieve more conceptual and practical knowled...As the 21st century brings in a revolutionary change in the way students study at schools and universities, technology continues to play a crucial role in helping students achieve more conceptual and practical knowledge of topics taught in classrooms. Students with special needs too are now able to study in a general classroom setting, access relevant technologies and use them for higher cognitive development, helping them integrate with their surroundings. However, existing literature shows that though multiple learning tools exist that do enhance learning in special needs students, they either cater to specific areas of development such as Mathematics and English, or that are targeted towards a specified category of studentswith special needs such as autism and cerebral palsy. Furthermore, despite multiple laws and regulations supporting the right to education launched by the UAE （United Arab Emirates） government for special needs students, there seems to exist a need to provide classrooms across the country with educational applications that have a universal approach particularly in the UAE in order to include students with almost any special needs. This paper looks closely at the existing literature and highlights this gap, especially in the UAE and proposes to develop such a tool based on existing learning concepts.展开更多
The search for the chiral magnetic effect(CME) in relativistic heavy-ion collisions(HICs) is challenged by significant background contamination. We present a novel deep learning approach based on a U-Net architecture ...The search for the chiral magnetic effect(CME) in relativistic heavy-ion collisions(HICs) is challenged by significant background contamination. We present a novel deep learning approach based on a U-Net architecture to time-reversely unfold the dynamics of CME-related charge separation, enabling the reconstruction of the physics signal across the entire evolution of HICs. Trained on the events simulated by a multi-phase transport model with different cases of CME settings, our model learns to recover the charge separation based on final-state transverse momentum distributions at either the quark–gloun plasma freeze-out or hadronic freeze-out. This devises a methodological tool for the study of CME and underscores the promise of deep learning approaches in retrieving physics signals in HICs.展开更多
This study,using construction play as the context,investigates the role of high-quality teacher–child interaction in promoting young children’s approaches to learning.The study selected children and teachers from a ...This study,using construction play as the context,investigates the role of high-quality teacher–child interaction in promoting young children’s approaches to learning.The study selected children and teachers from a middle class in a kindergarten as participants.Employing a combination of questionnaires,interviews,and observational methods,a four-week systematic observation was conducted.The Approaches to Learning Observation Scale for Children Aged 3–6 and the CLASS Classroom Assessment Scoring System were used to quantitatively analyze changes in children’s core approaches to learning dimensions,including curiosity,perseverance,independence,and creativity,while comprehensively evaluating the impact of teacher–child interaction.The study further analyzed the behavioral characteristics of teacher–child interactions during construction play and identified areas for improvement.The findings indicate that when teachers implement high-quality interaction strategies—such as providing effective praise,enhancing emotional expression,and increasing exploratory questioning—during construction play,children’s performance across core approaches to learning dimensions improves significantly.Based on these findings,the study recommends optimizing teachers’core interactive behaviors,focusing on the connotation of approaches to learning in developmental assessment,and establishing a systematic training and research mechanism that integrates theory,reflection,and practice within professional learning communities,to ensure the continuous enhancement of teachers’professional capabilities.展开更多
Mixtures of chemical ingredients,such as formulations,are ubiquitous in materials science,but optimizing their properties remains challenging due to the vast design space.Computational approaches offer a promising sol...Mixtures of chemical ingredients,such as formulations,are ubiquitous in materials science,but optimizing their properties remains challenging due to the vast design space.Computational approaches offer a promising solution to traverse this space while minimizing trial-and-error experimentation.Using high-throughput classical molecular dynamics simulations,we generated a comprehensive dataset of over 30,000 solvent mixtures to evaluate three machine learning approaches that connect molecular structure and composition to property:formulation descriptor aggregation(FDA),formulation graph(FG),and Set2Set-based method(FDS2S).Our results demonstrate that our new FDS2S approach outperforms other approaches in predicting simulation-derived properties.Formulation-property relationships can reveal important substructures and identify promising formulations at least two to three times faster than random guessing.The models show robust transferability to experimental datasets,accurately predicting properties across energy,pharmaceutical,and petroleum applications.Our research demonstrates the utility of high-throughput simulations and machine learning tools to design formulations with promising properties.展开更多
Purpose:Catering for learner diversity is a key issue in the recent educational reforms in Hong Kong.The present study addresses this issue through an investigation of the relationships between students’learning styl...Purpose:Catering for learner diversity is a key issue in the recent educational reforms in Hong Kong.The present study addresses this issue through an investigation of the relationships between students’learning styles and approaches to learning in Hong Kong secondary schools.Design/Approach/Methods:A total of 6,054 junior secondary students in Hong Kong responded to a questionnaire consisting of two instruments.A series of confirmatory factor analysis,two-way analysis of variance,and structural equation modeling analysis were conducted.Findings:The results identified three types of learning style among the students which are characterized by a cognitive orientation,a social orientation,and a methodological orientation.Some significant gender-and achievement-level differences were revealed.Compared with the socially oriented learning style,the cognitively and methodologically oriented learning styles were more extensively and strongly related to students’approaches to learning,even though these students showed a greater preference for the socially oriented learning style.Originality/Value:It is unwise to blindly cater for students’learning styles in classroom teaching and curriculum design.Teachers should adopt a comprehensive and balanced approach toward the design of curriculum and teaching which not only highlights the congruence between students’learning styles and teacher’s pedagogy but also integrates the constructive frictions between them into classroom teaching.展开更多
Purpose:This study aims to explore Chilean students'digital technology usage patterns andapproaches to learningDesignlApproach/Methods:We conducted this study in two stages We worked with onesemester learning mana...Purpose:This study aims to explore Chilean students'digital technology usage patterns andapproaches to learningDesignlApproach/Methods:We conducted this study in two stages We worked with onesemester learning management systems(LMS),library,and students records data in the firstone.We performed a k-means cluster analysis to identify groups with similar usage patterns.Inthe second stage,we invited students from emerging dusters to participate in group interviews.Thematic analysis was employed to analyze them.Findings:Three groups were identified:ID digital library users/high performers,who adopteddeeper approaches to learning obtained higher marks,and used learning resources to integratematerials and expand understanding 2)LMS and physical library userslmid-performers,whoadopted mainly strategicapproaches obtained marks dlose to average,and used learning resources for studying in an organized manner toget good marks and 3)lower users of LMS andlibrarylmidlow performers,who adopted mainly a surface approach,obtained mid-to-lower-than-averagemarks,and used learning resources for minimum content understanding Originality/Value:We demonstrated the importance of combining learning analytics data withqualitative methods to make sense of digital technology usage patternss approaches to learningare associated with learning resources use.Practical recommendations are presented.展开更多
Most of the machineries in small or large-scale industry have rotating elementsupported by bearings for rigid support and accurate movement. For proper functioning ofmachinery, condition monitoring of the bearing is v...Most of the machineries in small or large-scale industry have rotating elementsupported by bearings for rigid support and accurate movement. For proper functioning ofmachinery, condition monitoring of the bearing is very important. In present study soundsignal is used to continuously monitor bearing health as sound signals of rotatingmachineries carry dynamic information of components. There are numerous studies inliterature that are reporting superiority of vibration signal of bearing fault diagnosis.However, there are very few studies done using sound signal. The cost associated withcondition monitoring using sound signal (Microphone) is less than the cost of transducerused to acquire vibration signal (Accelerometer). This paper employs sound signal forcondition monitoring of roller bearing by K-star classifier and k-nearest neighborhoodclassifier. The statistical feature extraction is performed from acquired sound signals. Thentwo-layer feature selection is done using J48 decision tree algorithm and random treealgorithm. These selected features were classified using K-star classifier and k-nearestneighborhood classifier and parametric optimization is performed to achieve the maximumclassification accuracy. The classification results for both K-star classifier and k-nearestneighborhood classifier for condition monitoring of roller bearing using sound signals werecompared.展开更多
Transition structure calculations via quantum chemistry methods have become a staple in modern chemical reaction research.Yet,success rates in optimizing transition structures rely heavily on rational initial guesses ...Transition structure calculations via quantum chemistry methods have become a staple in modern chemical reaction research.Yet,success rates in optimizing transition structures rely heavily on rational initial guesses and expert supervision.We develop a machine learning approach that utilizes a bitmap representation of chemical structures to generate high-quality initial guesses for modeling transition states of chemical reactions.The core of the approach comprises a convolutional neural network methodology with a genetic algorithm.An extensive dataset derived from quantumchemistry computations is built,providing sufficient data on which the model can be trained,validated and tested.By applying the method to typical bi-molecular hydrogen abstraction reactions involving hydrofluorocarbons,hydrofluoroethers,and hydroxyl radicals—reactions critical in atmospheric fluoride degradation and global warming potential evaluation,yet extremely challenging to model,we achieve transition state optimizations with an impressive,verified success rate of 81.8%for hydrofluorocarbons and 80.9%for hydrofluoroethers.The reported work demonstrates the effectiveness of employing visual representation in chemical space exploration tasks and opens new avenues for the transition structure modeling.展开更多
We present a machine learning approach for the inverse design of organic semiconductor materials from benzene and thiophene-based polycyclic aromatic compounds(PACs).Inverse design is an efficient approach to material...We present a machine learning approach for the inverse design of organic semiconductor materials from benzene and thiophene-based polycyclic aromatic compounds(PACs).Inverse design is an efficient approach to materials discovery that aims to design materials with preset properties.However,it is complex due to the non-uniqueness and nonlinearity of property-to-structure relationships.We demonstrate the potential of this approach through the inverse design of PACs to achieve target HOMO-LUMO gaps,a key property for organic semiconductors,ranging from 1.36 eV to 4.37 eV with an error of 0.15 eV within Density Functional Theory uncertainty.The model uses goalconditioned reinforcement learning with chemical domain knowledge,allowing addressing design goals directly.To incorporate practical aspects such as chemical accessibility,the model can include soft constraints,such as minimizing ring count to favor smaller structures.Thus,our framework addresses key inverse design challenges while allowing prioritization of more optimal or diverse candidates.展开更多
The targeted design of functional materials often requires the concurrent optimization of multiple interdependent properties.For boron-doped graphene(BDG),both the band gap and work function critically influence perfo...The targeted design of functional materials often requires the concurrent optimization of multiple interdependent properties.For boron-doped graphene(BDG),both the band gap and work function critically influence performance in electronic and catalytic applications,yet existing machine learning(ML)approaches typically focus on single-property prediction and rely on hand-crafted features,limiting their generality.Here we present an adaptive edge-aware graph convolutional neural network with multi-task learning(AEGCNN-MTL)for simultaneous prediction of multiple material properties.On a DFT-computed BDG dataset of 2613 structures,AEGCNN-MTL achieved high accuracy(R2=0.9905 for band gap and 0.9778 for work function),and under identical training budgets,outperformed representative single-task GNN baselines.When transferred to the QM9 benchmark,the framework delivered competitive performance across 12 diverse quantum chemical properties,demonstrating strong generalization capability.These results highlight the potential of AEGCNN-MTL as a scalable and accurate tool for high-throughput,multi-property screening and the data-driven discovery of multifunctional materials.展开更多
基金funded by Taif University,Saudi Arabia,project No.(TU-DSPP-2024-263).
文摘Deep learning algorithms have been rapidly incorporated into many different applications due to the increase in computational power and the availability of massive amounts of data.Recently,both deep learning and ensemble learning have been used to recognize underlying structures and patterns from high-level features to make predictions/decisions.With the growth in popularity of deep learning and ensemble learning algorithms,they have received significant attention from both scientists and the industrial community due to their superior ability to learn features from big data.Ensemble deep learning has exhibited significant performance in enhancing learning generalization through the use of multiple deep learning algorithms.Although ensemble deep learning has large quantities of training parameters,which results in time and space overheads,it performs much better than traditional ensemble learning.Ensemble deep learning has been successfully used in several areas,such as bioinformatics,finance,and health care.In this paper,we review and investigate recent ensemble deep learning algorithms and techniques in health care domains,medical imaging,health care data analytics,genomics,diagnosis,disease prevention,and drug discovery.We cover several widely used deep learning algorithms along with their architectures,including deep neural networks(DNNs),convolutional neural networks(CNNs),recurrent neural networks(RNNs),and generative adversarial networks(GANs).Common healthcare tasks,such as medical imaging,electronic health records,and genomics,are also demonstrated.Furthermore,in this review,the challenges inherent in reducing the burden on the healthcare system are discussed and explored.Finally,future directions and opportunities for enhancing healthcare model performance are discussed.
基金Joint Research Project Between Chongqing University and National University of Singapore (No. ARF-151-000-014-112)the Basic Research & Applied Basic Research Program of Chongqing University (No.71341103)Natural Science Foundation of Chongqing S & T Committee(No. CSTC,2006BB5240)
文摘Diagnosis and treatment of breast cancer have been improved during the last decade; however, breast cancer is still a leading cause of death among women in the whole world. Early detection and accurate diagnosis of this disease has been demonstrated an approach to long survival of the patients. As an attempt to develop a reliable diagnosing method for breast cancer, we integrated support vector machine (SVM), k-nearest neighbor and probabilistic neural network into a complex machine learning approach to detect malignant breast tumour through a set of indicators consisting of age and ten cellular features of fine-needle aspiration of breast which were ranked according to signal-to-noise ratio to identify determinants distinguishing benign breast tumours from malignant ones. The method turned out to significantly improve the diagnosis, with a sensitivity of 94.04%, a specificity of 97.37%, and an overall accuracy up to 96.24% when SVM was adopted with the sigmoid kernel function under 5-fold cross validation. The results suggest that SVM is a promising methodology to be further developed into a practical adjunct implement to help discerning benign and malignant breast tumours and thus reduce the incidence of misdiagnosis.
文摘The Approaches to Learning addresses how children learn-this includes children’s attitudes and interests in learning.This domain reflects behaviours and attitudes such as curiosity,problem-solving,maintaining attention and persistence.The research study focused on examining the fathers’parenting practices and the children’s approaches to learning from three through five years.The study used a cross sectional research design and data was generated using focal group discussions,interview guides and child behaviour rating scale on how fathers’parenting practices contribute to children’s approaches to learning.Results revealed that,Fathers’parenting practices and Children’s curiosity were found to have a very positive relationship(r=0.396,p<0.05).Fathers’parenting practices and children’s learning were found to have a significant positive relationship(r=0.420,p<0.05).Findings also indicated that fathers’parenting practices and children’s creativity were found to have an average positive relationship(r=0.379,p<0.05).Arising out of the findings,the study recommended that fathers’parenting programs be put in place to help them up bring the child in holistic manner.
文摘The prediction of pregnancy-related hazards must be accurate and timely to safeguard mother and fetal health.This study aims to enhance risk prediction in pregnancywith a novel deep learningmodel based on a Long Short-Term Memory(LSTM)generator,designed to capture temporal relationships in cardiotocography(CTG)data.This methodology integrates CTG signals with demographic characteristics and utilizes preprocessing techniques such as noise reduction,normalization,and segmentation to create high-quality input for themodel.It uses convolutional layers to extract spatial information,followed by LSTM layers to model sequences for superior predictive performance.The overall results show that themodel is robust,with an accuracy of 91.5%,precision of 89.8%,recall of 90.4%,and F1-score of 90.1%that outperformed the corresponding baselinemodels,CNN(Convolutional Neural Network)and traditional RNN(Recurrent Neural Network),by 2.3%and 6.1%,respectively.Rather,the ability to detect pregnancy-related abnormalities has considerable therapeutic potential,with the possibility for focused treatments and individualized maternal healthcare approaches,the research team concluded.
基金supported in part by the Research Start-Up Fund for Talent Researcher of Nanjing Agricultural University(77H0603)in part by the National Natural Science Foundation of China(62072248)。
文摘In this paper,we review and analyze intrusion detection systems for Agriculture 4.0 cyber security.Specifically,we present cyber security threats and evaluation metrics used in the performance evaluation of an intrusion detection system for Agriculture 4.0.Then,we evaluate intrusion detection systems according to emerging technologies,including,Cloud computing,Fog/Edge computing,Network virtualization,Autonomous tractors,Drones,Internet of Things,Industrial agriculture,and Smart Grids.Based on the machine learning technique used,we provide a comprehensive classification of intrusion detection systems in each emerging technology.Furthermore,we present public datasets,and the implementation frameworks applied in the performance evaluation of intrusion detection systems for Agriculture 4.0.Finally,we outline challenges and future research directions in cyber security intrusion detection for Agriculture 4.0.
文摘Student-centered learning approach is focused on the students' demands and interests.Applying student-centered approach puts forward higher requirement to English teachers.This article first analyzes the theory of student-centered learning approach and compares teacher-centered approach with it.Based on the research information and teaching experience,the author summarizes four strategies about how to apply student-centered learning approach to English listening and speaking class in vocational schools.
基金supported by the CAS Pioneer Hundred Talents Program and National Natural Science Foundation of China (32070683) to Y.P.C。
文摘DEAR EDITOR,Somatic mutations are a large category of genetic variations,which play an essential role in tumorigenesis. Detection of somatic single nucleotide variants(SNVs) could facilitate downstream analysis of tumorigenesis. Many computational methods have been developed to detect SNVs, but most require normal matched samples to differentiate somatic SNVs from the normal state, which can be difficult to obtain.
文摘L1 and L2 acquisition, in some respects, are similar. Language development in children goes hand in hand with physical and cognitive development. Children learn their first language by imitation, but not always and not only by imitation. There seems to be some "innate capacities" that make children start to speak at the same time they do and in the way they do it. Adults learning a second language usually are controlled more by their motivation. But language input is important for both L1 and L2 acquisition. Though there are differences between CL1 and between CL2 and AL2, the way in which these learners acquire some of the grammatical morphemes is similar. This, together with some other evidence, shows that it is not only children who can acquire language. Adults can also acquire a language. But when adults acquire a language, they should also learn it. Some of the ways in which children acquire their language can be used as a model for L2 acquisition, even for Chinese students whose language is unrelated to English and whose culture is different. Learning the culture of the English-speaking countries will benefit the learning of the language. Like children, listening should also be well in advance of speaking in L2 acquisition. To train listening comprehension skills, Asher’s TPR approach proves more effective. TPR approach is at the moment limited to the beginning stage only. In order for students to gain all the five skills in a second language learning, namely, listening, speaking, reading, writing, and interpreting/translating, other methods should be used at the same time, or at later stages.
文摘As the 21st century brings in a revolutionary change in the way students study at schools and universities, technology continues to play a crucial role in helping students achieve more conceptual and practical knowledge of topics taught in classrooms. Students with special needs too are now able to study in a general classroom setting, access relevant technologies and use them for higher cognitive development, helping them integrate with their surroundings. However, existing literature shows that though multiple learning tools exist that do enhance learning in special needs students, they either cater to specific areas of development such as Mathematics and English, or that are targeted towards a specified category of studentswith special needs such as autism and cerebral palsy. Furthermore, despite multiple laws and regulations supporting the right to education launched by the UAE （United Arab Emirates） government for special needs students, there seems to exist a need to provide classrooms across the country with educational applications that have a universal approach particularly in the UAE in order to include students with almost any special needs. This paper looks closely at the existing literature and highlights this gap, especially in the UAE and proposes to develop such a tool based on existing learning concepts.
基金supported by the National Natural Science Foundation of China (Grant Nos.12147101 and 12325507)the National Key Research and Development Program of China (Grant No.2022YFA1604900)+4 种基金the Guangdong Major Project of Basic and Applied Basic Research (Grant No.2020B0301030008 for S.G.and G.M.)the CUHK-Shenzhen university development fund (Grant Nos.UDF01003041 and UDF03003041)Shenzhen Peacock Fund (Grant No.2023TC0179 for K.Z.)the RIKEN TRIP initiative (RIKEN Quantum),JSPS KAKENHI (Grant No.25H01560)JST-BOOST (Grant No.JPMJBY24H9 for L.W.)。
文摘The search for the chiral magnetic effect(CME) in relativistic heavy-ion collisions(HICs) is challenged by significant background contamination. We present a novel deep learning approach based on a U-Net architecture to time-reversely unfold the dynamics of CME-related charge separation, enabling the reconstruction of the physics signal across the entire evolution of HICs. Trained on the events simulated by a multi-phase transport model with different cases of CME settings, our model learns to recover the charge separation based on final-state transverse momentum distributions at either the quark–gloun plasma freeze-out or hadronic freeze-out. This devises a methodological tool for the study of CME and underscores the promise of deep learning approaches in retrieving physics signals in HICs.
基金Beijing Education Science"14th Five-Year Plan"General Project of the 2022,Cultivation Strategies for Children's Approaches to Learning during the Transition from Early Childhood to Primary School(SDDB22207)。
文摘This study,using construction play as the context,investigates the role of high-quality teacher–child interaction in promoting young children’s approaches to learning.The study selected children and teachers from a middle class in a kindergarten as participants.Employing a combination of questionnaires,interviews,and observational methods,a four-week systematic observation was conducted.The Approaches to Learning Observation Scale for Children Aged 3–6 and the CLASS Classroom Assessment Scoring System were used to quantitatively analyze changes in children’s core approaches to learning dimensions,including curiosity,perseverance,independence,and creativity,while comprehensively evaluating the impact of teacher–child interaction.The study further analyzed the behavioral characteristics of teacher–child interactions during construction play and identified areas for improvement.The findings indicate that when teachers implement high-quality interaction strategies—such as providing effective praise,enhancing emotional expression,and increasing exploratory questioning—during construction play,children’s performance across core approaches to learning dimensions improves significantly.Based on these findings,the study recommends optimizing teachers’core interactive behaviors,focusing on the connotation of approaches to learning in developmental assessment,and establishing a systematic training and research mechanism that integrates theory,reflection,and practice within professional learning communities,to ensure the continuous enhancement of teachers’professional capabilities.
文摘Mixtures of chemical ingredients,such as formulations,are ubiquitous in materials science,but optimizing their properties remains challenging due to the vast design space.Computational approaches offer a promising solution to traverse this space while minimizing trial-and-error experimentation.Using high-throughput classical molecular dynamics simulations,we generated a comprehensive dataset of over 30,000 solvent mixtures to evaluate three machine learning approaches that connect molecular structure and composition to property:formulation descriptor aggregation(FDA),formulation graph(FG),and Set2Set-based method(FDS2S).Our results demonstrate that our new FDS2S approach outperforms other approaches in predicting simulation-derived properties.Formulation-property relationships can reveal important substructures and identify promising formulations at least two to three times faster than random guessing.The models show robust transferability to experimental datasets,accurately predicting properties across energy,pharmaceutical,and petroleum applications.Our research demonstrates the utility of high-throughput simulations and machine learning tools to design formulations with promising properties.
文摘Purpose:Catering for learner diversity is a key issue in the recent educational reforms in Hong Kong.The present study addresses this issue through an investigation of the relationships between students’learning styles and approaches to learning in Hong Kong secondary schools.Design/Approach/Methods:A total of 6,054 junior secondary students in Hong Kong responded to a questionnaire consisting of two instruments.A series of confirmatory factor analysis,two-way analysis of variance,and structural equation modeling analysis were conducted.Findings:The results identified three types of learning style among the students which are characterized by a cognitive orientation,a social orientation,and a methodological orientation.Some significant gender-and achievement-level differences were revealed.Compared with the socially oriented learning style,the cognitively and methodologically oriented learning styles were more extensively and strongly related to students’approaches to learning,even though these students showed a greater preference for the socially oriented learning style.Originality/Value:It is unwise to blindly cater for students’learning styles in classroom teaching and curriculum design.Teachers should adopt a comprehensive and balanced approach toward the design of curriculum and teaching which not only highlights the congruence between students’learning styles and teacher’s pedagogy but also integrates the constructive frictions between them into classroom teaching.
基金supported by the Iniciativa Milenio,Agencia Nacional de Investigacion yDesairollo(ANID)(grant Millennium Nucleus,NMEdSup)and Fondecyt Regular,Agencia Nacional deInvestigacion y Desairollo(grant number 1161413)。
文摘Purpose:This study aims to explore Chilean students'digital technology usage patterns andapproaches to learningDesignlApproach/Methods:We conducted this study in two stages We worked with onesemester learning management systems(LMS),library,and students records data in the firstone.We performed a k-means cluster analysis to identify groups with similar usage patterns.Inthe second stage,we invited students from emerging dusters to participate in group interviews.Thematic analysis was employed to analyze them.Findings:Three groups were identified:ID digital library users/high performers,who adopteddeeper approaches to learning obtained higher marks,and used learning resources to integratematerials and expand understanding 2)LMS and physical library userslmid-performers,whoadopted mainly strategicapproaches obtained marks dlose to average,and used learning resources for studying in an organized manner toget good marks and 3)lower users of LMS andlibrarylmidlow performers,who adopted mainly a surface approach,obtained mid-to-lower-than-averagemarks,and used learning resources for minimum content understanding Originality/Value:We demonstrated the importance of combining learning analytics data withqualitative methods to make sense of digital technology usage patternss approaches to learningare associated with learning resources use.Practical recommendations are presented.
文摘Most of the machineries in small or large-scale industry have rotating elementsupported by bearings for rigid support and accurate movement. For proper functioning ofmachinery, condition monitoring of the bearing is very important. In present study soundsignal is used to continuously monitor bearing health as sound signals of rotatingmachineries carry dynamic information of components. There are numerous studies inliterature that are reporting superiority of vibration signal of bearing fault diagnosis.However, there are very few studies done using sound signal. The cost associated withcondition monitoring using sound signal (Microphone) is less than the cost of transducerused to acquire vibration signal (Accelerometer). This paper employs sound signal forcondition monitoring of roller bearing by K-star classifier and k-nearest neighborhoodclassifier. The statistical feature extraction is performed from acquired sound signals. Thentwo-layer feature selection is done using J48 decision tree algorithm and random treealgorithm. These selected features were classified using K-star classifier and k-nearestneighborhood classifier and parametric optimization is performed to achieve the maximumclassification accuracy. The classification results for both K-star classifier and k-nearestneighborhood classifier for condition monitoring of roller bearing using sound signals werecompared.
基金support from the National Natural Science Foundation of China(No.52488201,No.52276212)National Key Research and Development Program of China(No.2022YFB3803600)+3 种基金the Suzhou Science and Technology Program(SYG202101)the Natural Science Foundation of Jiangsu Province(No.BK20231211)the Key Research and Development Program in Shaanxi Province of China(No.2023-YBGY-300)the China Fundamental Research Funds for the Central Universities.O.V.P.acknowledges support of the USA National Science Foundation(CHE-2154367).
文摘Transition structure calculations via quantum chemistry methods have become a staple in modern chemical reaction research.Yet,success rates in optimizing transition structures rely heavily on rational initial guesses and expert supervision.We develop a machine learning approach that utilizes a bitmap representation of chemical structures to generate high-quality initial guesses for modeling transition states of chemical reactions.The core of the approach comprises a convolutional neural network methodology with a genetic algorithm.An extensive dataset derived from quantumchemistry computations is built,providing sufficient data on which the model can be trained,validated and tested.By applying the method to typical bi-molecular hydrogen abstraction reactions involving hydrofluorocarbons,hydrofluoroethers,and hydroxyl radicals—reactions critical in atmospheric fluoride degradation and global warming potential evaluation,yet extremely challenging to model,we achieve transition state optimizations with an impressive,verified success rate of 81.8%for hydrofluorocarbons and 80.9%for hydrofluoroethers.The reported work demonstrates the effectiveness of employing visual representation in chemical space exploration tasks and opens new avenues for the transition structure modeling.
基金Tri M. Nguyen acknowledges Ho-Chi-Minh City University of Technology (HCMUT), VNU-HCM, for supporting this study.
文摘We present a machine learning approach for the inverse design of organic semiconductor materials from benzene and thiophene-based polycyclic aromatic compounds(PACs).Inverse design is an efficient approach to materials discovery that aims to design materials with preset properties.However,it is complex due to the non-uniqueness and nonlinearity of property-to-structure relationships.We demonstrate the potential of this approach through the inverse design of PACs to achieve target HOMO-LUMO gaps,a key property for organic semiconductors,ranging from 1.36 eV to 4.37 eV with an error of 0.15 eV within Density Functional Theory uncertainty.The model uses goalconditioned reinforcement learning with chemical domain knowledge,allowing addressing design goals directly.To incorporate practical aspects such as chemical accessibility,the model can include soft constraints,such as minimizing ring count to favor smaller structures.Thus,our framework addresses key inverse design challenges while allowing prioritization of more optimal or diverse candidates.
基金supported by the key project of science and technology research program of Chongqing Education Commission of China(KJZD-K202501109)the National Natural Science Foundation of China(U22A20434)Scientific research foundation of Ministry of Industry and Information Technology of the People's Republic of China(TC220A04A-43).
文摘The targeted design of functional materials often requires the concurrent optimization of multiple interdependent properties.For boron-doped graphene(BDG),both the band gap and work function critically influence performance in electronic and catalytic applications,yet existing machine learning(ML)approaches typically focus on single-property prediction and rely on hand-crafted features,limiting their generality.Here we present an adaptive edge-aware graph convolutional neural network with multi-task learning(AEGCNN-MTL)for simultaneous prediction of multiple material properties.On a DFT-computed BDG dataset of 2613 structures,AEGCNN-MTL achieved high accuracy(R2=0.9905 for band gap and 0.9778 for work function),and under identical training budgets,outperformed representative single-task GNN baselines.When transferred to the QM9 benchmark,the framework delivered competitive performance across 12 diverse quantum chemical properties,demonstrating strong generalization capability.These results highlight the potential of AEGCNN-MTL as a scalable and accurate tool for high-throughput,multi-property screening and the data-driven discovery of multifunctional materials.
