A New Speed Limit Recognition Methodology Based on Ensemble Learning:Hardware Validation

Advanced DriverAssistance Systems(ADAS)technologies can assist drivers or be part of automatic driving systems to support the driving process and improve the level of safety and comfort on the road.Traffic Sign Recognition System(TSRS)is one of themost important components ofADAS.Among the challengeswith TSRS is being able to recognize road signs with the highest accuracy and the shortest processing time.Accordingly,this paper introduces a new real time methodology recognizing Speed Limit Signs based on a trio of developed modules.Firstly,the Speed Limit Detection(SLD)module uses the Haar Cascade technique to generate a new SL detector in order to localize SL signs within captured frames.Secondly,the Speed Limit Classification(SLC)module,featuring machine learning classifiers alongside a newly developed model called DeepSL,harnesses the power of a CNN architecture to extract intricate features from speed limit sign images,ensuring efficient and precise recognition.In addition,a new Speed Limit Classifiers Fusion(SLCF)module has been developed by combining trained ML classifiers and the DeepSL model by using the Dempster-Shafer theory of belief functions and ensemble learning’s voting technique.Through rigorous software and hardware validation processes,the proposedmethodology has achieved highly significant F1 scores of 99.98%and 99.96%for DS theory and the votingmethod,respectively.Furthermore,a prototype encompassing all components demonstrates outstanding reliability and efficacy,with processing times of 150 ms for the Raspberry Pi board and 81.5 ms for the Nano Jetson board,marking a significant advancement in TSRS technology.

Research on Compatible Mode of Self-directed Learning and Cooperative Learning in Blended Teaching of Basic English Course

In the information age,blended teaching,no matter online or offline,has become the mainstream of college teaching reform.In this teaching model,self-directed learning and cooperative learning are the two main learning approaches.On the online teaching platform,students mainly learn knowledge-based content by self-directed learning,while practising their language skills by cooperative learning in flipped classroom activities.On one hand,it advocates student-centered strategy so as to improve students autonomous learning ability;on the other hand,teachers serve as a guide to organize the classroom activities;meanwhile,they give timely feedback to students in order to promote students’learning ability.In blended teaching model,this mutually compatible and reinforcing model of self-directed learning and cooperative learning is undoubtedly helpful to improve the teaching efficiency.

Vibration properties of Paulownia wood for Ruan sound quality using machine learning methods

As an important material for manufacturing resonant components of musical instruments,Paulownia has an important influence on the sound quality of Ruan.In this paper,a model for evaluating the sound quality of Ruan based on the vibration characteristics of wood is developed using machine learning methods.Generally,the selection of materials for Ruan manufacturing relies primarily on manually weighing,observing,striking,and listening by the instrument technician.Deficiencies in scientific theory have hindered the quality of the finished Ruan.In this study,nine Ruans were manufactured,and a prediction model of Ruan sound quality was proposed based on the raw material information of Ruans.Out of a total of 180 data sets,145 and 45 sets were chosen for training and validation,respec-tively.In this paper,typical correlation analysis was used to determine the correlation between two single indicators in two adjacent pairwise combinations of the measured objects in each stage of the production process in Ruan.The vibra-tion characteristics of the wood were tested,and a model for predicting the evaluation of Ruan’s acoustic qualities was developed by measuring the vibration characteristics of the resonating plate material.The acoustic quality of the Ruan sound board wood was evaluated and predicted using machine learning model generalized regression neural net-work.The results show that the prediction of Ruan sound quality can be achieved using Matlab simulation based on the vibration characteristics of the soundboard wood.When the model-predicted values were compared with the tradi-tional predicted results,it was found that the generalized regression neural network had good performance,achieving an accuracy of 93.8%which was highly consistent with the experimental results.It was concluded that the model can accurately predict the acoustic quality of the Ruan based on the vibration performance of the soundboards.

A Deep Learning Approach to Shape Optimization Problems for Flexoelectric Materials Using the Isogeometric Finite Element Method

A new approach for flexoelectricmaterial shape optimization is proposed in this study.In this work,a proxymodel based on artificial neural network(ANN)is used to solve the parameter optimization and shape optimization problems.To improve the fitting ability of the neural network,we use the idea of pre-training to determine the structure of the neural network and combine different optimizers for training.The isogeometric analysis-finite element method(IGA-FEM)is used to discretize the flexural theoretical formulas and obtain samples,which helps ANN to build a proxy model from the model shape to the target value.The effectiveness of the proposed method is verified through two numerical examples of parameter optimization and one numerical example of shape optimization.

Production Capacity Prediction Method of Shale Oil Based on Machine Learning Combination Model

The production capacity of shale oil reservoirs after hydraulic fracturing is influenced by a complex interplay involving geological characteristics,engineering quality,and well conditions.These relationships,nonlinear in nature,pose challenges for accurate description through physical models.While field data provides insights into real-world effects,its limited volume and quality restrict its utility.Complementing this,numerical simulation models offer effective support.To harness the strengths of both data-driven and model-driven approaches,this study established a shale oil production capacity prediction model based on a machine learning combination model.Leveraging fracturing development data from 236 wells in the field,a data-driven method employing the random forest algorithm is implemented to identify the main controlling factors for different types of shale oil reservoirs.Through the combination model integrating support vector machine(SVM)algorithm and back propagation neural network(BPNN),a model-driven shale oil production capacity prediction model is developed,capable of swiftly responding to shale oil development performance under varying geological,fluid,and well conditions.The results of numerical experiments show that the proposed method demonstrates a notable enhancement in R2 by 22.5%and 5.8%compared to singular machine learning models like SVM and BPNN,showcasing its superior precision in predicting shale oil production capacity across diverse datasets.

A Study on Effective Methods of English Vocabulary Learning

English is a key subject in high school that troubles many students,especially in the aspect of vocabulary learning.Only by laying a good vocabulary foundation can students better complete the learning tasks such as reading,writing,listening,and speaking training.This paper aims to explain the importance of improving the efficiency of English vocabulary learning and discuss the effective methods of English vocabulary learning in high school,in order to help more students find their own learning methods,improve vocabulary memory and application skills,and lay a solid foundation for follow-up learning,examination,and even work.

The Effectiveness of Group Cooperative Learning Method in Badminton Teaching in Colleges and Universities

In college badminton teaching,teachers utilize the group cooperative learning method,which not only helps to improve students’badminton skill level but also cultivates their teamwork spirit,communication skills,and self-management ability unconsciously.In view of this,this paper mainly describes the significance of applying the group cooperative learning method in college badminton teaching,analyzes the current problems in college badminton teaching,and aims to discover effective development strategies for group cooperative learning method in college badminton teaching in order to improve the effectiveness of college badminton teaching.

Relationship between self-directed learning readiness, learning attitude, and self-efficacy of nursing undergraduates

Objective: The purposes of this study were to analyze the influencing factors of self-directed learning readiness(SDLR) of nursing undergraduates and explore the impacts of learning attitude and self-efficacy on nursing undergraduates.Methods: A total of 500 nursing undergraduates were investigated in Tianjin, with the Chinese version of SDLR scale, learning attitude questionnaire of nursing college students, academic self-efficacy scale, and the general information questionnaire.Result: The score of SDLR was 149.99±15.73. Multiple stepwise regressions indicated that academic self-efficacy, learning attitude, attitudes to major of nursing, and level of learning difficulties were major influential factors and explained 48.1% of the variance in SDLR of nursing interns.Conclusions: The score of SDLR of nursing undergraduates is not promising. It is imperative to correct students' learning attitude, improve self-efficacy, and adopt appropriate teaching model to improve SDLR.

Effectiveness of the flipped classroom on the development of self-directed learning in nursing education:a meta-analysis

Objectives: To examine the best practice evidence of the effectiveness of the flipped classroom（FC） as a burgeoning teaching model on the development of self-directed learning in nursing education.Data sources: The relevant randomized controlled trial（RCT） and non-RCT comparative studies were searched from multiple electronic databases including PubMed, Embase, Web of Science, Cumulative Index to Nursing and Allied Health Literature（CINAHL）, Cochrane Central Register of Controlled Trials（CENTRAL）, Wanfang Data, China National Knowledge Infrastructure（CNKI）, and Chinese Science and Technology Periodical Database（VIP） from inception to June 2017.Review methods: The data were independently assessed and extracted for eligibility by two reviewers. The quality of included studies was assessed by another two reviewers using a standardized form and evaluated by using the Cochrane Collaboration’s risk of bias tool. The self-directed learning scores（continuous outcomes） were analyzed by using the 95% confidence intervals（Cls） with the standard deviation average（SMD） or weighted mean difference（WMD）. The heterogeneity was assessed using Cochran’s I;statistic.Results: A total of 12 studies, which encompassed 1440 nursing students（intervention group = 685, control group = 755）, were eligible for inclusion in this review. Of 12 included studies, the quality level of one included study was A and of the others was B. The pooled effect size showed that compared with traditional teaching models, the FC could improve nursing students’ selfdirected learning skill, as measured by the Self-Directed Learning Readiness Scale（SDLRS）, Self-Directed Learning Readiness Scale for Nursing Education（SDLRSNE）, Self-Regulated Learning Scale（SRL）, Autonomous Learning Competencies scale（ALC）, and Competencies of Autonomous Learning of Nursing Students（CALNS）. Overall scores and subgroup analyses with the SRL were all in favor of the FC.Conclusions: The result of this meta-analysis indicated that FCs could improve the effect of self-directed learning in nursing education.Future studies with more RCTs using the same measurement tools are needed to draw more authoritative conclusions.

Self-directed learning readiness and nursing competency among undergraduate nursing students in Fujian province of China

Aims:We examined the relationship between self-directed learning readiness(SDLR)and nursing competency among undergraduate nursing students.Background:There is little evidence-based data related to the relationship between selfdirected learning(SDL)and nursing competency.Methods:A descriptive correlational design was used.We conducted convenience sampling of 519 undergraduate nursing students from three universities during their final period of clinical practice.We investigated SDL according to the SDLR scale for nursing education(Chinese translation version),and used the Competency Inventory for Registered Nurses to evaluate nursing competency.Results:The mean SDLR score of the students was 148.55(standard deviation[SD]18.46),indicating intermediate and higher SDLR.The mean score for nursing competency was 142.31(SD30.39),indicating intermediate nursing competence.SDLR had a significant positive and strong relationship with nursing competency.Conclusion:SDLR is a predictor of nursing competency.

Impact of self-directed learning readiness and learning attitude on problem-solving ability among Chinese undergraduate nursing students

Objective: To explore the effects of self-directed learning readiness and learning attitude on problem-solving ability among Chinese undergraduate nursing students. Methods: A convenience sampling of 460 undergraduate nursing students was surveyed in Tianjin, China. Students who participated in the study completed a questionnaire that included social demographic questionnaire, Self-directed Learning Readiness Scale, Attitude to Learning Scale, and Social Problem-Solving Inventory. Pearson’s correlation analysis was performed to test the correlations among problem-solving ability, self-directed learning readiness, and learning attitude. Hierarchical linear regression analyses were performed to explore the mediating role of learning attitude. Results: The results showed that learning attitude (r=0.338, P<0.01) and self-directed learning readiness (r=0.493, P<0.01) were positively correlated with problem-solving ability. Learning attitude played a partial intermediary role between self-directed learning readiness and problem-solving ability (F=74.227, P<0.01). Conclusions: It is concluded that nursing educators should pay attention on students’ individual differences and take proper actions to inspire students’ self-directed learning readiness and learning attitude.

Self-directed learning readiness and social problem solving of nursing students in Macao

Objective The aims of this study were to describe nursing students′self-directed learning readiness and social problem solving and test their correlations in Macao.Methods This descriptive cross-sectional study was conducted on 140baccalaureate nursing students.A stratified random sampling was performed.The Self-directed Learning Readiness(SDLR)Scale and Chinese Social Problem-Solving Inventory-Revised(C-SPSI-R)were used.Results The response rate was 79.3%.Students possessed readiness for self-directed learning(mean 149.09±12.53,51.4%at high level,48.6%at low level).Regarding to social problem solving,the mean scores of each subscale were 9.35±3.25(Rational Problem Solving,RPS),10.26±3.23(Positive Problem Orientation,PPO),8.14±4.06(Negative Problem Orientation,NPO),5.67±4.44(Avoidance Style,AS),and 4.84±3.03(Impulsivity/Carelessness Style,ICS).SDLR was positively related to RPS and PPO,but was negatively related to AS.Conclusion Half of students possessed stronger readiness for self-directed learning.Students had a belief in the ability to solve problems,and adopted relevant strategies in solving problems.However,students still had negative and dysfunctional orientation and defective attempts in solving problems.Self-directed learning was positively related to positive and constructive orientation,but was negatively related to defective problem-solving pattern.Nurse educators should create educational climates for promoting student confidence and mutual responsibility for learning and their thinking process for problem solving.

Role of academic self-efficacy in the relationship between self-directed learning readiness and problem-solving ability among nursing students

Objective: Problem-solving should be a fundamental component of nursing education because It is a core ability for professional nurses. For more effective learning, nursing students must understand the relationship between self-directed learning readiness and problem-solving ability. The aim of this study was to investigate the relationships among self-directed learning readiness, problemsolving ability, and academic self-efficacy among undergraduate nursing students.Methods: From November to December 2016, research was conducted among 500 nursing undergraduate students in Tianjin, China,using a self-directed learning readiness scale, an academic self-efficacy scale, a questionnaire related to problem-solving, and selfdesigned demographics. The response rate was 85.8%.Results: For Chinese nursing students, self-directed learning readiness and academic self-efficacy reached a medium-to-high level,while problem-solving abilities were at a low level. There were significant positive correlations among the students' self-directed learning readiness, academic self-efficacy, and problem-solving ability. Furthermore, academic self-efficacy demonstrated a mediating effect on the relationship between the students' self-directed learning readiness and problem-solving ability.Conclusions: To enhance students' problem-solving ability, nursing educators should pay more attention to the positive impact of self-directed learning readiness and self-efficacy in nursing students' education.

Nursing Students’ Readiness for Self-Directed Learning and Its Effect on Learning Outcome in South-West Nigeria

Self-directed learning (SDL) uses diverse learning resources to solve identified problems in learning. Nursing is a lifelong learning profession and SDL is a valuable skill to remain relevant and productive professionals. Nursing students are expected to embrace SDL and develop these skills. However, there has been no evidence of this innovative process in South-West Nigeria. This study seeks to evaluate nursing students’ readiness for SDL and its effect on learning outcome. This quasi-experimental study purposively utilized 229 nursing students as participants. Baseline (P1) data was collected using Gugliemino’s SDL readiness scale (SDLRS) and a validated-structured questionnaire. Participants had a pre-test to assess knowledge at P1 followed by 6 weeks interaction using SDL on selected topics in Medical-surgical nursing and the same test at post-intervention (P2). Using a 50-point scale, knowledge was categorized as good ≥ 25 and poor < 25 and SDLRS on a 290-point scale was categorized as below average 5 - 201, average 202 - 226 and above average 227 - 290. Descriptive statistics, Chi-square test, t-test and linear regression analysis were used for analysis at p = 0.05. Nursing students’ SDLRS was average;mean = 203 ± 23.0. A significant difference exists between nursing students with good knowledge at P1 and P2. At P1, 39.2% had good knowledge, mean = 22.2 ± 6.3, and 90.1% at P2, mean = 30.6 ± 5.4, p < 0.05 also a significant relationship exist between SDLR and learning outcome at P2;p < 0.05. With the nursing students’ average SDL readiness level having a significant effect on learning outcome. Nursing training institutions should provide necessary resources to embrace SDL as a main-line teaching method to ensure competent life-long professionals.

The State-of-the-Art Review on Applications of Intrusive Sensing,Image Processing Techniques,and Machine Learning Methods in Pavement Monitoring and Analysis

In modern transportation,pavement is one of the most important civil infrastructures for the movement of vehicles and pedestrians.Pavement service quality and service life are of great importance for civil engineers as they directly affect the regular service for the users.Therefore,monitoring the health status of pavement before irreversible damage occurs is essential for timely maintenance,which in turn ensures public transportation safety.Many pavement damages can be detected and analyzed by monitoring the structure dynamic responses and evaluating road surface conditions.Advanced technologies can be employed for the collection and analysis of such data,including various intrusive sensing techniques,image processing techniques,and machine learning methods.This review summarizes the state-ofthe-art of these three technologies in pavement engineering in recent years and suggests possible developments for future pavement monitoring and analysis based on these approaches.

A liquid loading prediction method of gas pipeline based on machine learning

The liquid loading is one of the most frequently encountered phenomena in the transportation of gas pipeline,reducing the transmission efficiency and threatening the flow assurance.However,most of the traditional mechanism models are semi-empirical models,and have to be resolved under different working conditions with complex calculation process.The development of big data technology and artificial intelligence provides the possibility to establish data-driven models.This paper aims to establish a liquid loading prediction model for natural gas pipeline with high generalization ability based on machine learning.First,according to the characteristics of actual gas pipeline,a variety of reasonable combinations of working conditions such as different gas velocity,pipe diameters,water contents and outlet pressures were set,and multiple undulating pipeline topography with different elevation differences was established.Then a large number of simulations were performed by simulator OLGA to obtain the data required for machine learning.After data preprocessing,six supervised learning algorithms,including support vector machine(SVM),decision tree(DT),random forest(RF),artificial neural network(ANN),plain Bayesian classification(NBC),and K nearest neighbor algorithm(KNN),were compared to evaluate the performance of liquid loading prediction.Finally,the RF and KNN with better performance were selected for parameter tuning and then used to the actual pipeline for liquid loading location prediction.Compared with OLGA simulation,the established data-driven model not only improves calculation efficiency and reduces workload,but also can provide technical support for gas pipeline flow assurance.

Soliton, breather, and rogue wave solutions for solving the nonlinear Schrodinger equation using a deep learning method with physical constraints

The nonlinear Schrodinger equation is a classical integrable equation which contains plenty of significant properties and occurs in many physical areas.However,due to the difficulty of solving this equation,in particular in high dimensions,lots of methods are proposed to effectively obtain different kinds of solutions,such as neural networks among others.Recently,a method where some underlying physical laws are embeded into a conventional neural network is proposed to uncover the equation’s dynamical behaviors from spatiotemporal data directly.Compared with traditional neural networks,this method can obtain remarkably accurate solution with extraordinarily less data.Meanwhile,this method also provides a better physical explanation and generalization.In this paper,based on the above method,we present an improved deep learning method to recover the soliton solutions,breather solution,and rogue wave solutions of the nonlinear Schrodinger equation.In particular,the dynamical behaviors and error analysis about the one-order and two-order rogue waves of nonlinear integrable equations are revealed by the deep neural network with physical constraints for the first time.Moreover,the effects of different numbers of initial points sampled,collocation points sampled,network layers,neurons per hidden layer on the one-order rogue wave dynamics of this equation have been considered with the help of the control variable way under the same initial and boundary conditions.Numerical experiments show that the dynamical behaviors of soliton solutions,breather solution,and rogue wave solutions of the integrable nonlinear Schrodinger equation can be well reconstructed by utilizing this physically-constrained deep learning method.

A systematic machine learning method for reservoir identification and production prediction

Reservoir identification and production prediction are two of the most important tasks in petroleum exploration and development.Machine learning(ML)methods are used for petroleum-related studies,but have not been applied to reservoir identification and production prediction based on reservoir identification.Production forecasting studies are typically based on overall reservoir thickness and lack accuracy when reservoirs contain a water or dry layer without oil production.In this paper,a systematic ML method was developed using classification models for reservoir identification,and regression models for production prediction.The production models are based on the reservoir identification results.To realize the reservoir identification,seven optimized ML methods were used:four typical single ML methods and three ensemble ML methods.These methods classify the reservoir into five types of layers:water,dry and three levels of oil(I oil layer,II oil layer,III oil layer).The validation and test results of these seven optimized ML methods suggest the three ensemble methods perform better than the four single ML methods in reservoir identification.The XGBoost produced the model with the highest accuracy;up to 99%.The effective thickness of I and II oil layers determined during the reservoir identification was fed into the models for predicting production.Effective thickness considers the distribution of the water and the oil resulting in a more reasonable production prediction compared to predictions based on the overall reservoir thickness.To validate the superiority of the ML methods,reference models using overall reservoir thickness were built for comparison.The models based on effective thickness outperformed the reference models in every evaluation metric.The prediction accuracy of the ML models using effective thickness were 10%higher than that of reference model.Without the personal error or data distortion existing in traditional methods,this novel system realizes rapid analysis of data while reducing the time required to resolve reservoir classification and production prediction challenges.The ML models using the effective thickness obtained from reservoir identification were more accurate when predicting oil production compared to previous studies which use overall reservoir thickness.

Model Change Active Learning in Graph-Based Semi-supervised Learning

Active learning in semi-supervised classification involves introducing additional labels for unlabelled data to improve the accuracy of the underlying classifier.A challenge is to identify which points to label to best improve performance while limiting the number of new labels."Model Change"active learning quantifies the resulting change incurred in the classifier by introducing the additional label(s).We pair this idea with graph-based semi-supervised learning(SSL)methods,that use the spectrum of the graph Laplacian matrix,which can be truncated to avoid prohibitively large computational and storage costs.We consider a family of convex loss functions for which the acquisition function can be efficiently approximated using the Laplace approximation of the posterior distribution.We show a variety of multiclass examples that illustrate improved performance over prior state-of-art.

Machine learning models for the density and heat capacity of ionic liquid-water binary mixtures

Ionic liquids(ILs),because of the advantages of low volatility,good thermal stability,high gas solubility and easy recovery,can be regarded as the green substitute for traditional solvent.However,the high viscosity and synthesis cost limits their application,the hybrid solvent which combining ILs together with others especially water can solve this problem.Compared with the pure IL systems,the study of the ILs-H_(2)O binary system is rare,and the experimental data of corresponding thermodynamic properties(such as density,heat capacity,etc.)are less.Moreover,it is also difficult to obtain all the data through experiments.Therefore,this work establishes a predicted model on ILs-water binary systems based on the group contribution(GC)method.Three different machine learning algorithms(ANN,XGBoost,LightBGM)are applied to fit the density and heat capacity of ILs-water binary systems.And then the three models are compared by two index of MAE and R^(2).The results show that the ANN-GC model has the best prediction effect on the density and heat capacity of ionic liquid-water mixed system.Furthermore,the Shapley additive explanations(SHAP)method is harnessed to scrutinize the significance of each structure and parameter within the ANN-GC model in relation to prediction outcomes.The results reveal that system components(XIL)within the ILs-H_(2)O binary system exert the most substantial influence on density,while for the heat capacity,the substituents on the cation exhibit the greatest impact.This study not only introduces a robust prediction model for the density and heat capacity properties of IL-H_(2)O binary mixtures but also provides insight into the influence of mixture features on its density and heat capacity.