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
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
Online interactive learning plays a crucial role in improving online education quality.This grounded theory study examines:(1)what key factors shape EFL learners’online interactive learning,(2)how these factors form ...Online interactive learning plays a crucial role in improving online education quality.This grounded theory study examines:(1)what key factors shape EFL learners’online interactive learning,(2)how these factors form an empirically validated model,and(3)how they interact within this model,through systematic analysis of 9,207 discussion forum posts from a Chinese University MOOC platform.Results demonstrate that learning drive,course structure,teaching competence,interaction behavior,expected outcomes,and online learning context significantly influence EFL online interactive learning.The analysis reveals two key mechanisms:expected outcomes mediate the effects of learning drive(β=0.45),course structure,teaching competence,and interaction behavior(β=0.35)on learning outcomes,while online learning context moderates these relationships(β=0.25).Specifically,learning drive provides intrinsic/extrinsic motivation,whereas course structure,teaching competence,interaction behavior,and expected outcomes collectively enhance interaction quality and sustainability.These findings,derived through rigorous grounded theory methodology involving open,axial,and selective coding of large-scale interaction data,yield three key contributions:(1)a comprehensive theoretical model of EFL online learning dynamics,(2)empirical validation of mediation/moderation mechanisms,and(3)practical strategies for designing scaffolded interaction protocols and adaptive feedback systems.The study establishes that its theoretically saturated model(achieved after analyzing 7,366 posts with 1,841 verification cases)offers educators evidence-based approaches to optimize collaborative interaction in digital EFL environments.展开更多
Enhancing the mechanical properties is crucial for polyimide films,but the mechanical properties(Young's modulus,tensile strength,and elongation at break)mutually constrain each other,complicating simultaneous enh...Enhancing the mechanical properties is crucial for polyimide films,but the mechanical properties(Young's modulus,tensile strength,and elongation at break)mutually constrain each other,complicating simultaneous enhancement via traditional trial-and-error methods.In this work,we proposed a materials genome approach to design and screen phenylethynyl-terminated polyimides for films with enhanced mechani-cal properties.We first established machine learning models to predict Young's modulus,tensile strength,and elongation at break to explore the chemical space containing thousands of candidate structures.The accuracies of the machine learning models were verified by molecular dynamics simulations on screened polyimides and experimental testing on three representative polyimide films.The performance advantages of the best-selected polyimides were analyzed by comparing well-known polyimides based on molecular dynamics simulations,and the structural rationale was revealed by"gene"analysis and feature importance evaluation.This work provides a cost-effective strategy for designing polyimide films withenhancedmechanical properties.展开更多
In the rapidly evolving landscape of television advertising,optimizing ad schedules to maximize viewer engagement and revenue has become significant.Traditional methods often operate in silos,limiting the potential in...In the rapidly evolving landscape of television advertising,optimizing ad schedules to maximize viewer engagement and revenue has become significant.Traditional methods often operate in silos,limiting the potential insights gained from broader data analysis due to concerns over privacy and data sharing.This article introduces a novel approach that leverages Federated Learning(FL)to enhance TV ad schedule optimization,combining the strengths of local optimization techniques with the power of global Machine Learning(ML)models to uncover actionable insights without compromising data privacy.It combines linear programming for initial ads scheduling optimization with ML—specifically,a K-Nearest Neighbors(KNN)model—for predicting ad spot positions.Taking into account the diversity and the difficulty of the ad-scheduling problem,we propose a prescriptivepredictive approach in which first the position of the ads is optimized(using Google’s OR-Tools CP-SAT)and then the scheduled position of all ads will be the result of the optimization problem.Second,this output becomes the target of a predictive task that predicts the position of new entries based on their characteristics ensuring the implementation of the scheduling at large scale(using KNN,Light Gradient Boosting Machine and Random Forest).Furthermore,we explore the integration of FL to enhance predictive accuracy and strategic insight across different broadcasting networks while preserving data privacy.The FL approach resulted in 8750 ads being precisely matched to their optimal category placements,showcasing an alignment with the intended diversity objectives.Additionally,there was a minimal deviation observed,with 1133 ads positioned within a one-category variance from their ideal placement in the original dataset.展开更多
We consider an image semantic communication system in a time-varying fading Gaussian MIMO channel,with a finite number of channel states.A deep learning-aided broadcast approach scheme is proposed to benefit the adapt...We consider an image semantic communication system in a time-varying fading Gaussian MIMO channel,with a finite number of channel states.A deep learning-aided broadcast approach scheme is proposed to benefit the adaptive semantic transmission in terms of different channel states.We combine the classic broadcast approach with the image transformer to implement this adaptive joint source and channel coding(JSCC)scheme.Specifically,we utilize the neural network(NN)to jointly optimize the hierarchical image compression and superposition code mapping within this scheme.The learned transformers and codebooks allow recovering of the image with an adaptive quality and low error rate at the receiver side,in each channel state.The simulation results exhibit our proposed scheme can dynamically adapt the coding to the current channel state and outperform some existing intelligent schemes with the fixed coding block.展开更多
Low-voltage direct current(DC)microgrids have recently emerged as a promising and viable alternative to traditional alternating cur-rent(AC)microgrids,offering numerous advantages.Consequently,researchers are explorin...Low-voltage direct current(DC)microgrids have recently emerged as a promising and viable alternative to traditional alternating cur-rent(AC)microgrids,offering numerous advantages.Consequently,researchers are exploring the potential of DC microgrids across var-ious configurations.However,despite the sustainability and accuracy offered by DC microgrids,they pose various challenges when integrated into modern power distribution systems.Among these challenges,fault diagnosis holds significant importance.Rapid fault detection in DC microgrids is essential to maintain stability and ensure an uninterrupted power supply to critical loads.A primary chal-lenge is the lack of standards and guidelines for the protection and safety of DC microgrids,including fault detection,location,and clear-ing procedures for both grid-connected and islanded modes.In response,this study presents a brief overview of various approaches for protecting DC microgrids.展开更多
In response to the misconception that Communicative Language Teaching means no teaching of grammar,it is argued that grammar is as important as traffic rules for safe and smooth traffic on the road.To achieve appropri...In response to the misconception that Communicative Language Teaching means no teaching of grammar,it is argued that grammar is as important as traffic rules for safe and smooth traffic on the road.To achieve appropriate and effective communication,a communicative approach to college grammar teaching and learning is proposed.Both teachers and learners should change their attitudes toward and conceptions about grammar teaching and learning;additionally,teaching grammar in the company of reading and writing helps learners learn and acquire grammar in meaningful contexts.展开更多
Objectives： This study aimed to compare the learning curves of percutaneous endoscopic lumbar discectomy （PELD） in a transforaminal approach at the L4/5 and L5/S1 levels. Methods： We retrospectively reviewed the f...Objectives： This study aimed to compare the learning curves of percutaneous endoscopic lumbar discectomy （PELD） in a transforaminal approach at the L4/5 and L5/S1 levels. Methods： We retrospectively reviewed the first 60 cases at the L4/5 level （Group I） and the first 60 cases at the L5/S1 level （Group II） of PELD performed by one spine surgeon. The patients were divided into subgroups A, B, and C （Group I： A cases 1-20, B cases 21-40, C cases 41-60; Group I1： A cases 1-20, B cases 21-40, C cases 41-60）. Operation time was thoroughly analyzed. Results： Compared with the L4/5 level, the learning curve of transforaminal PELD at the L5/S1 level was flatter. The mean operation times of Groups IA, IB, and IC were （88.75±17.02）, （67.75±6.16）, and （64.85±7.82） min, respectively. There was a significant difference between Groups A and B （P〈0.05）, but no significant difference between Groups B and C （P=-0.20）. The mean operation times of Groups IIA, liB, and IIC were （117.25±13.62）, （109.50±11.20）, and （92.15±11.94） rain, respectively. There was no significant difference between Groups A and B （P=0.06）, but there was a significant difference between Groups B and C （P〈0.05）. There were 6 cases of postoperative dysesthesia （POD） in Group I and 2 cases in Group IIA （P=-0.27）. There were 2 cases of residual disc in Group I, and 4 cases in Group II （P=0.67）. There were 3 cases of recurrence in Group I, and 2 cases in Group II （P〉0.05）. Conclusions： Compared with the L5/S1 level, the learning curve of PELD in a transforaminal approach at the L4/5 level was steeper, suggesting that the L4/5 level might be easier to master after short-term professional training.展开更多
This paper deals with the iterative learning control （ILC） design for multiple-input multiple-output （MIMO）,time-delay systems （TDS）.Two feedback ILC schemes are considered using the so-called two-dimensional ...This paper deals with the iterative learning control （ILC） design for multiple-input multiple-output （MIMO）,time-delay systems （TDS）.Two feedback ILC schemes are considered using the so-called two-dimensional （2D） analysis approach.It shows that continuous-discrete 2D Roesser systems can be developed to describe the entire learning dynamics of both ILC schemes,based on which necessary and sufficient conditions for their stability can be provided.A numerical example is included to validate the theoretical analysis.展开更多
In today's modern electric vehicles,enhancing the safety-critical cyber-physical system(CPS)'s performance is necessary for the safe maneuverability of the vehicle.As a typical CPS,the braking system is crucia...In today's modern electric vehicles,enhancing the safety-critical cyber-physical system(CPS)'s performance is necessary for the safe maneuverability of the vehicle.As a typical CPS,the braking system is crucial for the vehicle design and safe control.However,precise state estimation of the brake pressure is desired to perform safe driving with a high degree of autonomy.In this paper,a sensorless state estimation technique of the vehicle's brake pressure is developed using a deep-learning approach.A deep neural network(DNN)is structured and trained using deep-learning training techniques,such as,dropout and rectified units.These techniques are utilized to obtain more accurate model for brake pressure state estimation applications.The proposed model is trained using real experimental training data which were collected via conducting real vehicle testing.The vehicle was attached to a chassis dynamometer while the brake pressure data were collected under random driving cycles.Based on these experimental data,the DNN is trained and the performance of the proposed state estimation approach is validated accordingly.The results demonstrate high-accuracy brake pressure state estimation with RMSE of 0.048 MPa.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
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
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
Cardiovascular disease is among the top five fatal diseases that affect lives worldwide.Therefore,its early prediction and detection are crucial,allowing one to take proper and necessary measures at earlier stages.Mac...Cardiovascular disease is among the top five fatal diseases that affect lives worldwide.Therefore,its early prediction and detection are crucial,allowing one to take proper and necessary measures at earlier stages.Machine learning(ML)techniques are used to assist healthcare providers in better diagnosing heart disease.This study employed three boosting algorithms,namely,gradient boost,XGBoost,and AdaBoost,to predict heart disease.The dataset contained heart disease-related clinical features and was sourced from the publicly available UCI ML repository.Exploratory data analysis is performed to find the characteristics of data samples about descriptive and inferential statistics.Specifically,it was carried out to identify and replace outliers using the interquartile range and detect and replace the missing values using the imputation method.Results were recorded before and after the data preprocessing techniques were applied.Out of all the algorithms,gradient boosting achieved the highest accuracy rate of 92.20%for the proposed model.The proposed model yielded better results with gradient boosting in terms of precision,recall,and f1-score.It attained better prediction performance than the existing works and can be used for other diseases that share common features using transfer learning.展开更多
Learner autonomy stands on the top priorities of scholars due to its pivotal role in fostering student-centered learning methods. It empowers the learners to be in charge of their learning process and be the center of...Learner autonomy stands on the top priorities of scholars due to its pivotal role in fostering student-centered learning methods. It empowers the learners to be in charge of their learning process and be the center of attention in language learning education. For this purpose, different AI tools were used and implemented in pedagogy to narrow the divide in promoting learning/teaching approaches. This study aims to gauge the impact of using LLM-ChatGPT to teach EFL learners the present simple tense autonomously via providing automated feedback, and chances for regular drillings without over reliance on teacher. It also aims to investigate the EFL learners’ perception of using LLM-ChatGPT as a reinforcement approach to learner autonomy. A cohort comprising 50 EFL learners would participate in the study and a between subject design method using control and experimental groups would be implemented. The findings of the study indicated that learners who were taught present simple tense’s rule through using LLM-ChatGPT application, with less teacher’s dominance, scored grades similar to those who were taught the same tense’s rule by the teacher (sage on the stage approach). This substantiates the idea that LLM-Chat GPT acts a role akin to teachers in teaching grammatical rules. Moreover, the learners felt that LLM-ChatGPT application had a positive impact on fostering autonomous learning.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
BACKGROUND Robotic pancreaticoduodenectomy(RPD)can achieve similar surgical results to open and PD;however,RPD has a long learning curve and operation time(OT).To address this issue,we have summarized a surgical path ...BACKGROUND Robotic pancreaticoduodenectomy(RPD)can achieve similar surgical results to open and PD;however,RPD has a long learning curve and operation time(OT).To address this issue,we have summarized a surgical path to shorten the surgical learning curve and OT.AIM To investigate the effective learning curve of a“G”-shaped surgical approach in RPD for patients.METHODS A total of 60 patients,who received“G”-shaped RPD(GRPD)by a single surgeon in the First Hospital of Shanxi Medical University from May 2017 to April 2020,were included in this study.The OT,demographic data,intraoperative blood loss,complications,hospitalization time,and pathological results were recorded,and the cumulative sum(CUSUM)analysis was performed to evaluate the learning curve for GRPD.RESULTS According to the CUSUM analysis,the learning curve for GRPD was grouped into two phases:The early and late phases.The OT was 480±81.65 min vs 331±76.54 min,hospitalization time was 22±4.53 d vs 17±6.08 d,and blood loss was 308±54.78 mL vs 169.2±35.33 mL in the respective groups.Complications,including pancreatic fistula,bile leakage,reoperation rate,postoperative death,and delayed gastric emptying,were significantly decreased after this surgical technique.CONCLUSION GRPD can improve the learning curve and operative time,providing a new method for shortening the RPD learning curve.展开更多
The study was aimed at exploring the relationship between teaching approach and students'learning motivation.The participants were two college English teachers and their fixed group of students.The research lasted...The study was aimed at exploring the relationship between teaching approach and students'learning motivation.The participants were two college English teachers and their fixed group of students.The research lasted 16 weeks.The instruments used in the study were Attitude/Motivation Test Battery(AMTB) and classroom observation.Analyzing by the mean of the AM TB result,researchers got the popular tendencies of students'learning motivation level every four weeks.Comparing the change of students'learning motivation level and teacher participants'frequency of using the recommended teaching approach,this arti cle intends to achieve two purposes:1) Is students'learning motivation related to their teachers'teaching approach? 2) Among the approach recommended by literature,which works the best in the colleges in China?展开更多
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.
文摘Online interactive learning plays a crucial role in improving online education quality.This grounded theory study examines:(1)what key factors shape EFL learners’online interactive learning,(2)how these factors form an empirically validated model,and(3)how they interact within this model,through systematic analysis of 9,207 discussion forum posts from a Chinese University MOOC platform.Results demonstrate that learning drive,course structure,teaching competence,interaction behavior,expected outcomes,and online learning context significantly influence EFL online interactive learning.The analysis reveals two key mechanisms:expected outcomes mediate the effects of learning drive(β=0.45),course structure,teaching competence,and interaction behavior(β=0.35)on learning outcomes,while online learning context moderates these relationships(β=0.25).Specifically,learning drive provides intrinsic/extrinsic motivation,whereas course structure,teaching competence,interaction behavior,and expected outcomes collectively enhance interaction quality and sustainability.These findings,derived through rigorous grounded theory methodology involving open,axial,and selective coding of large-scale interaction data,yield three key contributions:(1)a comprehensive theoretical model of EFL online learning dynamics,(2)empirical validation of mediation/moderation mechanisms,and(3)practical strategies for designing scaffolded interaction protocols and adaptive feedback systems.The study establishes that its theoretically saturated model(achieved after analyzing 7,366 posts with 1,841 verification cases)offers educators evidence-based approaches to optimize collaborative interaction in digital EFL environments.
基金supported by the National Key R&D Program of China(No.2022YFB3707302)the National Natural Science Foundation of China(Nos.52394271 , 52394270).
文摘Enhancing the mechanical properties is crucial for polyimide films,but the mechanical properties(Young's modulus,tensile strength,and elongation at break)mutually constrain each other,complicating simultaneous enhancement via traditional trial-and-error methods.In this work,we proposed a materials genome approach to design and screen phenylethynyl-terminated polyimides for films with enhanced mechani-cal properties.We first established machine learning models to predict Young's modulus,tensile strength,and elongation at break to explore the chemical space containing thousands of candidate structures.The accuracies of the machine learning models were verified by molecular dynamics simulations on screened polyimides and experimental testing on three representative polyimide films.The performance advantages of the best-selected polyimides were analyzed by comparing well-known polyimides based on molecular dynamics simulations,and the structural rationale was revealed by"gene"analysis and feature importance evaluation.This work provides a cost-effective strategy for designing polyimide films withenhancedmechanical properties.
基金supported by a grant of the Ministry of Research,Innovation and Digitization,CNCS/CCCDI-UEFISCDI,project number COFUND-DUT-OPEN4CEC-1,within PNCDI IV.
文摘In the rapidly evolving landscape of television advertising,optimizing ad schedules to maximize viewer engagement and revenue has become significant.Traditional methods often operate in silos,limiting the potential insights gained from broader data analysis due to concerns over privacy and data sharing.This article introduces a novel approach that leverages Federated Learning(FL)to enhance TV ad schedule optimization,combining the strengths of local optimization techniques with the power of global Machine Learning(ML)models to uncover actionable insights without compromising data privacy.It combines linear programming for initial ads scheduling optimization with ML—specifically,a K-Nearest Neighbors(KNN)model—for predicting ad spot positions.Taking into account the diversity and the difficulty of the ad-scheduling problem,we propose a prescriptivepredictive approach in which first the position of the ads is optimized(using Google’s OR-Tools CP-SAT)and then the scheduled position of all ads will be the result of the optimization problem.Second,this output becomes the target of a predictive task that predicts the position of new entries based on their characteristics ensuring the implementation of the scheduling at large scale(using KNN,Light Gradient Boosting Machine and Random Forest).Furthermore,we explore the integration of FL to enhance predictive accuracy and strategic insight across different broadcasting networks while preserving data privacy.The FL approach resulted in 8750 ads being precisely matched to their optimal category placements,showcasing an alignment with the intended diversity objectives.Additionally,there was a minimal deviation observed,with 1133 ads positioned within a one-category variance from their ideal placement in the original dataset.
基金supported in part by the National Key R&D Project of China under Grant 2020YFA0712300National Natural Science Foundation of China under Grant NSFC-62231022,12031011supported in part by the NSF of China under Grant 62125108。
文摘We consider an image semantic communication system in a time-varying fading Gaussian MIMO channel,with a finite number of channel states.A deep learning-aided broadcast approach scheme is proposed to benefit the adaptive semantic transmission in terms of different channel states.We combine the classic broadcast approach with the image transformer to implement this adaptive joint source and channel coding(JSCC)scheme.Specifically,we utilize the neural network(NN)to jointly optimize the hierarchical image compression and superposition code mapping within this scheme.The learned transformers and codebooks allow recovering of the image with an adaptive quality and low error rate at the receiver side,in each channel state.The simulation results exhibit our proposed scheme can dynamically adapt the coding to the current channel state and outperform some existing intelligent schemes with the fixed coding block.
文摘Low-voltage direct current(DC)microgrids have recently emerged as a promising and viable alternative to traditional alternating cur-rent(AC)microgrids,offering numerous advantages.Consequently,researchers are exploring the potential of DC microgrids across var-ious configurations.However,despite the sustainability and accuracy offered by DC microgrids,they pose various challenges when integrated into modern power distribution systems.Among these challenges,fault diagnosis holds significant importance.Rapid fault detection in DC microgrids is essential to maintain stability and ensure an uninterrupted power supply to critical loads.A primary chal-lenge is the lack of standards and guidelines for the protection and safety of DC microgrids,including fault detection,location,and clear-ing procedures for both grid-connected and islanded modes.In response,this study presents a brief overview of various approaches for protecting DC microgrids.
文摘In response to the misconception that Communicative Language Teaching means no teaching of grammar,it is argued that grammar is as important as traffic rules for safe and smooth traffic on the road.To achieve appropriate and effective communication,a communicative approach to college grammar teaching and learning is proposed.Both teachers and learners should change their attitudes toward and conceptions about grammar teaching and learning;additionally,teaching grammar in the company of reading and writing helps learners learn and acquire grammar in meaningful contexts.
文摘Objectives： This study aimed to compare the learning curves of percutaneous endoscopic lumbar discectomy （PELD） in a transforaminal approach at the L4/5 and L5/S1 levels. Methods： We retrospectively reviewed the first 60 cases at the L4/5 level （Group I） and the first 60 cases at the L5/S1 level （Group II） of PELD performed by one spine surgeon. The patients were divided into subgroups A, B, and C （Group I： A cases 1-20, B cases 21-40, C cases 41-60; Group I1： A cases 1-20, B cases 21-40, C cases 41-60）. Operation time was thoroughly analyzed. Results： Compared with the L4/5 level, the learning curve of transforaminal PELD at the L5/S1 level was flatter. The mean operation times of Groups IA, IB, and IC were （88.75±17.02）, （67.75±6.16）, and （64.85±7.82） min, respectively. There was a significant difference between Groups A and B （P〈0.05）, but no significant difference between Groups B and C （P=-0.20）. The mean operation times of Groups IIA, liB, and IIC were （117.25±13.62）, （109.50±11.20）, and （92.15±11.94） rain, respectively. There was no significant difference between Groups A and B （P=0.06）, but there was a significant difference between Groups B and C （P〈0.05）. There were 6 cases of postoperative dysesthesia （POD） in Group I and 2 cases in Group IIA （P=-0.27）. There were 2 cases of residual disc in Group I, and 4 cases in Group II （P=0.67）. There were 3 cases of recurrence in Group I, and 2 cases in Group II （P〉0.05）. Conclusions： Compared with the L5/S1 level, the learning curve of PELD in a transforaminal approach at the L4/5 level was steeper, suggesting that the L4/5 level might be easier to master after short-term professional training.
基金supported by the National Natural Science Foundation of China(No.60727002,60774003,60921001,90916024)the COSTIND(No.A2120061303)the National 973 Program(No.2005CB321902)
文摘This paper deals with the iterative learning control （ILC） design for multiple-input multiple-output （MIMO）,time-delay systems （TDS）.Two feedback ILC schemes are considered using the so-called two-dimensional （2D） analysis approach.It shows that continuous-discrete 2D Roesser systems can be developed to describe the entire learning dynamics of both ILC schemes,based on which necessary and sufficient conditions for their stability can be provided.A numerical example is included to validate the theoretical analysis.
文摘In today's modern electric vehicles,enhancing the safety-critical cyber-physical system(CPS)'s performance is necessary for the safe maneuverability of the vehicle.As a typical CPS,the braking system is crucial for the vehicle design and safe control.However,precise state estimation of the brake pressure is desired to perform safe driving with a high degree of autonomy.In this paper,a sensorless state estimation technique of the vehicle's brake pressure is developed using a deep-learning approach.A deep neural network(DNN)is structured and trained using deep-learning training techniques,such as,dropout and rectified units.These techniques are utilized to obtain more accurate model for brake pressure state estimation applications.The proposed model is trained using real experimental training data which were collected via conducting real vehicle testing.The vehicle was attached to a chassis dynamometer while the brake pressure data were collected under random driving cycles.Based on these experimental data,the DNN is trained and the performance of the proposed state estimation approach is validated accordingly.The results demonstrate high-accuracy brake pressure state estimation with RMSE of 0.048 MPa.
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.
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.
基金This work was supported by National Research Foundation of Korea-Grant funded by the Korean Government(MSIT)-NRF-2020R1A2B5B02002478.
文摘Cardiovascular disease is among the top five fatal diseases that affect lives worldwide.Therefore,its early prediction and detection are crucial,allowing one to take proper and necessary measures at earlier stages.Machine learning(ML)techniques are used to assist healthcare providers in better diagnosing heart disease.This study employed three boosting algorithms,namely,gradient boost,XGBoost,and AdaBoost,to predict heart disease.The dataset contained heart disease-related clinical features and was sourced from the publicly available UCI ML repository.Exploratory data analysis is performed to find the characteristics of data samples about descriptive and inferential statistics.Specifically,it was carried out to identify and replace outliers using the interquartile range and detect and replace the missing values using the imputation method.Results were recorded before and after the data preprocessing techniques were applied.Out of all the algorithms,gradient boosting achieved the highest accuracy rate of 92.20%for the proposed model.The proposed model yielded better results with gradient boosting in terms of precision,recall,and f1-score.It attained better prediction performance than the existing works and can be used for other diseases that share common features using transfer learning.
文摘Learner autonomy stands on the top priorities of scholars due to its pivotal role in fostering student-centered learning methods. It empowers the learners to be in charge of their learning process and be the center of attention in language learning education. For this purpose, different AI tools were used and implemented in pedagogy to narrow the divide in promoting learning/teaching approaches. This study aims to gauge the impact of using LLM-ChatGPT to teach EFL learners the present simple tense autonomously via providing automated feedback, and chances for regular drillings without over reliance on teacher. It also aims to investigate the EFL learners’ perception of using LLM-ChatGPT as a reinforcement approach to learner autonomy. A cohort comprising 50 EFL learners would participate in the study and a between subject design method using control and experimental groups would be implemented. The findings of the study indicated that learners who were taught present simple tense’s rule through using LLM-ChatGPT application, with less teacher’s dominance, scored grades similar to those who were taught the same tense’s rule by the teacher (sage on the stage approach). This substantiates the idea that LLM-Chat GPT acts a role akin to teachers in teaching grammatical rules. Moreover, the learners felt that LLM-ChatGPT application had a positive impact on fostering autonomous learning.
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.
基金Supported by Shanxi Provincial Science and Technology Department Social Development Fund,No.201903D321144.
文摘BACKGROUND Robotic pancreaticoduodenectomy(RPD)can achieve similar surgical results to open and PD;however,RPD has a long learning curve and operation time(OT).To address this issue,we have summarized a surgical path to shorten the surgical learning curve and OT.AIM To investigate the effective learning curve of a“G”-shaped surgical approach in RPD for patients.METHODS A total of 60 patients,who received“G”-shaped RPD(GRPD)by a single surgeon in the First Hospital of Shanxi Medical University from May 2017 to April 2020,were included in this study.The OT,demographic data,intraoperative blood loss,complications,hospitalization time,and pathological results were recorded,and the cumulative sum(CUSUM)analysis was performed to evaluate the learning curve for GRPD.RESULTS According to the CUSUM analysis,the learning curve for GRPD was grouped into two phases:The early and late phases.The OT was 480±81.65 min vs 331±76.54 min,hospitalization time was 22±4.53 d vs 17±6.08 d,and blood loss was 308±54.78 mL vs 169.2±35.33 mL in the respective groups.Complications,including pancreatic fistula,bile leakage,reoperation rate,postoperative death,and delayed gastric emptying,were significantly decreased after this surgical technique.CONCLUSION GRPD can improve the learning curve and operative time,providing a new method for shortening the RPD learning curve.
文摘The study was aimed at exploring the relationship between teaching approach and students'learning motivation.The participants were two college English teachers and their fixed group of students.The research lasted 16 weeks.The instruments used in the study were Attitude/Motivation Test Battery(AMTB) and classroom observation.Analyzing by the mean of the AM TB result,researchers got the popular tendencies of students'learning motivation level every four weeks.Comparing the change of students'learning motivation level and teacher participants'frequency of using the recommended teaching approach,this arti cle intends to achieve two purposes:1) Is students'learning motivation related to their teachers'teaching approach? 2) Among the approach recommended by literature,which works the best in the colleges in China?
