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.

Safety-Constrained Multi-Agent Reinforcement Learning for Power Quality Control in Distributed Renewable Energy Networks

This paper examines the difficulties of managing distributed power systems,notably due to the increasing use of renewable energy sources,and focuses on voltage control challenges exacerbated by their variable nature in modern power grids.To tackle the unique challenges of voltage control in distributed renewable energy networks,researchers are increasingly turning towards multi-agent reinforcement learning(MARL).However,MARL raises safety concerns due to the unpredictability in agent actions during their exploration phase.This unpredictability can lead to unsafe control measures.To mitigate these safety concerns in MARL-based voltage control,our study introduces a novel approach:Safety-ConstrainedMulti-Agent Reinforcement Learning(SC-MARL).This approach incorporates a specialized safety constraint module specifically designed for voltage control within the MARL framework.This module ensures that the MARL agents carry out voltage control actions safely.The experiments demonstrate that,in the 33-buses,141-buses,and 322-buses power systems,employing SC-MARL for voltage control resulted in a reduction of the Voltage Out of Control Rate(%V.out)from0.43,0.24,and 2.95 to 0,0.01,and 0.03,respectively.Additionally,the Reactive Power Loss(Q loss)decreased from 0.095,0.547,and 0.017 to 0.062,0.452,and 0.016 in the corresponding systems.

Six-Dimensional Guidance: The Strategies of Thinking Quality Cultivation in Senior High School English Discourse Learning

Taking the discourse learning of the new senior high school English textbook published by the People’s Education Press as an example,combined with the“six-dimensional guidance”deep reading strategy,and through the six-skill training strategies of“memory skill training,understanding skill training,application skill training,analytical skill training,evaluation skill training,creative skill training,”this paper aims to cultivate students’thinking profundity,logic,flexibility,sensitivity,criticality,and originality.It also promotes the real implementation of senior high school English deep reading that points to the cultivation of thinking quality in classroom teaching,and realizes the transformation from“conventional reading”to“deep reading”that reflects the core literacy of the discipline.

A Survey into Teachers＇ Roles in Web-based College English Autonomous Learning

The paper, with the backdrop of web-based autonomous learning put forward by the recent college English teaching reform, aims to explore teachers＇ roles in this learning process in students＇ perception through the means of questionnaires and interviews. It further analyzes the possible reasons why students perceive their teachers＇ roles in such a way, in the hope of providing some implications for web-based college English autonomous learning.

Machine learning algorithms able to predict the prognosis of gastric cancer patients treated with immune checkpoint inhibitors

BACKGROUND Although immune checkpoint inhibitors(ICIs)have demonstrated significant survival benefits in some patients diagnosed with gastric cancer(GC),existing prognostic markers are not universally applicable to all patients with advanced GC.AIM To investigate biomarkers that predict prognosis in GC patients treated with ICIs and develop accurate predictive models.METHODS Data from 273 patients diagnosed with GC and distant metastasis,who un-derwent≥1 cycle(s)of ICIs therapy were included in this study.Patients were randomly divided into training and test sets at a ratio of 7:3.Training set data were used to develop the machine learning models,and the test set was used to validate their predictive ability.Shapley additive explanations were used to provide insights into the best model.RESULTS Among the 273 patients with GC treated with ICIs in this study,112 died within 1 year,and 129 progressed within the same timeframe.Five features related to overall survival and 4 related to progression-free survival were identified and used to construct eXtreme Gradient Boosting(XGBoost),logistic regression,and decision tree.After comprehensive evaluation,XGBoost demonstrated good accuracy in predicting overall survival and progression-free survival.CONCLUSION The XGBoost model aided in identifying patients with GC who were more likely to benefit from ICIs therapy.Patient nutritional status may,to some extent,reflect prognosis.

Incorporation of Learning Strategies into Web-based Autonomous Listening

The thesis introduces a comparative study of students'autonomous listening practice in a web-based autonomous learning center and the traditional teacher-dominated listening practice in a traditional language lab.The purpose of the study is to find how students'listening strategies differ in these two approaches and thereby to find which one better facilitates students'listening proficiency.

QoE oriented intelligent online learning evaluation technology in B5G scenario

Students'demand for online learning has exploded during the post-COvID-19 pandemic era.However,due to their poor learning experience,students'dropout rate and learning performance of online learning are not always satisfactory.The technical advantages of Beyond Fifth Generation(B5G)can guarantee a good multimedia Quality of Experience(QoE).As a special case of multimedia services,online learning takes into account both the usability of the service and the cognitive development of the users.Factors that affect the Quality of Online Learning Experience(OL-QoE)become more complicated.To get over this dilemma,we propose a systematic scheme by integrating big data,Machine Learning(ML)technologies,and educational psychology theory.Specifically,we first formulate a general definition of OL-QoE by data analysis and experimental verification.This formula considers both the subjective and objective factors(i.e.,video watching ratio and test scores)that most affect OLQoE.Then,we induce an extended layer to the classic Broad Learning System(BLS)to construct an Extended Broad Learning System(EBLS)for the students'OL-QoE prediction.Since the extended layer can increase the width of the BLS model and reduce the redundant nodes of BLS,the proposed EBLS can achieve a trade-off between the prediction accuracy and computation complexity.Finally,we provide a series of early intervention suggestions for different types of students according to their predicted OL-QoE values.Through timely interventions,their OL-QoE and learning performance can be improved.Experimental results verify the effectiveness oftheproposed scheme.

Micro-Locational Fine Dust Prediction Utilizing Machine Learning and Deep Learning Models

Given the increasing number of countries reporting degraded air quality,effective air quality monitoring has become a critical issue in today’s world.However,the current air quality observatory systems are often prohibitively expensive,resulting in a lack of observatories in many regions within a country.Consequently,a significant problem arises where not every region receives the same level of air quality information.This disparity occurs because some locations have to rely on information from observatories located far away from their regions,even if they may be the closest available options.To address this challenge,a novel approach that leverages machine learning and deep learning techniques to forecast fine dust concentrations was proposed.Specifically,continuous location features in the form of latitude and longitude values were incorporated into our models.By utilizing a comprehensive dataset comprising weather conditions,air quality measurements,and location properties,various machine learning models,including Random Forest Regression,XGBoost Regression,AdaBoost Regression,and a deep learning model known as Long Short-Term Memory(LSTM)were trained.Our experimental results demonstrated that the LSTM model outperforms the other models,achieving the best score with a root mean squared error of 23.48 in predicting fine dust(PM10)concentrations on an hourly basis.Furthermore,the fact that incorporating location properties,such as longitude and latitude values,enhances the overall quality of the regression models was discovered.Additionally,the implications and contributions of our research were discussed.By implementing our approach,the cost associated with relying solely on existing observatories can be substantially reduced.This reduction in costs can pave the way for economically efficient fine dust observation systems,ensuring more widespread and accurate air quality monitoring across different regions.

Rock mass quality classification based on deep learning:A feasibility study for stacked autoencoders

Objective and accurate evaluation of rock mass quality classification is the prerequisite for reliable sta-bility assessment.To develop a tool that can deliver quick and accurate evaluation of rock mass quality,a deep learning approach is developed,which uses stacked autoencoders(SAEs)with several autoencoders and a softmax net layer.Ten rock parameters of rock mass rating(RMR)system are calibrated in this model.The model is trained using 75%of the total database for training sample data.The SAEs trained model achieves a nearly 100%prediction accuracy.For comparison,other different models are also trained with the same dataset,using artificial neural network(ANN)and radial basis function(RBF).The results show that the SAEs classify all test samples correctly while the rating accuracies of ANN and RBF are 97.5%and 98.7%,repectively,which are calculated from the confusion matrix.Moreover,this model is further employed to predict the slope risk level of an abandoned quarry.The proposed approach using SAEs,or deep learning in general,is more objective and more accurate and requires less human inter-vention.The findings presented here shall shed light for engineers/researchers interested in analyzing rock mass classification criteria or performing field investigation.

Comparative Study of Evaluation Indicators for Learning Cities and the Implications

With the development of society,more and more cities are participating in the initiative to build learning cities.Constructing an evaluation indicator system for learning cities to monitor the progress and promote their growth has become increasingly important.This paper analyzes the preliminary framework of the UNESCO Global Learning City Index and R3L+Quality Framework.The comparison is made from the aspects of design philosophy,criteria of indicator,and the cycle of evaluation process.The findings suggest that the construction of an evaluation indicator system should be focused more on the diversity of learning city development,the construction of an evaluation process cycle,and the significance of building cooperative networks.

A Review of the Effectiveness of Web-based Course with College English Learners' Autonomous Learning

The paper is a literature review, aiming to examine the effectiveness of web-based college English learning which mainly focuses on learners' autonomous learning. Previous studies indicate that the web-based learning can improve learners' autonomous learning, as well as some problems found in their findings. Therefore, this paper first gives a summary and critique of research studies on the web-based autonomous learning and some factors influencing learners' autonomous learning ability;then, areas that deserve further study are also indicated.

Mediating effect of e-learning quality on learning outcomes through student satisfaction in nursing education

Objective:This study aimed to determine the relationships between e-learning quality,student satisfaction,and learning outcomes and the mediating effect of student satisfaction.Methods:A cross-sectional quantitative correlational study using a predictive design and multivariate analysis method was employed in this study.A sample of 241 nursing students were recruited through an online survey based on a stratified random sampling technique.The variance-based Par tial Least Squares Structural Modeling analysis method was used to test the possible relationship and mediating effect among the variables.Results:The findings revealed statistical y significant relationships between e-learning quality,student satisfaction,and learning outcomes.A mediating effect of 37.2%is predicted for e-learning quality on learning outcomes through student satisfaction.Conclusions:This study emphasizes the learning needs of working nurses and the impact on their learning outcomes in e-learning nursing undergraduate programs.Advances in e-learning education have assisted nurses to be more self-sufficient in their pursuit of lifelong learning.

A Stacked Ensemble Deep Learning Approach for Imbalanced Multi-Class Water Quality Index Prediction

A common difficulty in building prediction models with real-world environmental datasets is the skewed distribution of classes.There are significantly more samples for day-to-day classes,while rare events such as polluted classes are uncommon.Consequently,the limited availability of minority outcomes lowers the classifier’s overall reliability.This study assesses the capability of machine learning(ML)algorithms in tackling imbalanced water quality data based on the metrics of precision,recall,and F1 score.It intends to balance the misled accuracy towards the majority of data.Hence,10 ML algorithms of its performance are compared.The classifiers included are AdaBoost,SupportVector Machine,Linear Discriminant Analysis,k-Nearest Neighbors,Naive Bayes,Decision Trees,Random Forest,Extra Trees,Bagging,and the Multilayer Perceptron.This study also uses the Easy Ensemble Classifier,Balanced Bagging,andRUSBoost algorithm to evaluatemulti-class imbalanced learning methods.The comparison results revealed that a highaccuracy machine learning model is not always good in recall and sensitivity.This paper’s stacked ensemble deep learning(SE-DL)generalization model effectively classifies the water quality index(WQI)based on 23 input variables.The proposed algorithm achieved a remarkable average of 95.69%,94.96%,92.92%,and 93.88%for accuracy,precision,recall,and F1 score,respectively.In addition,the proposed model is compared against two state-of-the-art classifiers,the XGBoost(eXtreme Gradient Boosting)and Light Gradient Boosting Machine,where performance metrics of balanced accuracy and g-mean are included.The experimental setup concluded XGBoost with a higher balanced accuracy and G-mean.However,the SE-DL model has a better and more balanced performance in the F1 score.The SE-DL model aligns with the goal of this study to ensure the balance between accuracy and completeness for each water quality class.The proposed algorithm is also capable of higher efficiency at a lower computational time against using the standard SyntheticMinority Oversampling Technique(SMOTE)approach to imbalanced datasets.

Deep Transfer Learning Driven Automated Fall Detection for Quality of Living of Disabled Persons

Mobile communication and the Internet of Things(IoT)technologies have recently been established to collect data from human beings and the environment.The data collected can be leveraged to provide intelligent services through different applications.It is an extreme challenge to monitor disabled people from remote locations.It is because day-to-day events like falls heavily result in accidents.For a person with disabilities,a fall event is an important cause of mortality and post-traumatic complications.Therefore,detecting the fall events of disabled persons in smart homes at early stages is essential to provide the necessary support and increase their survival rate.The current study introduces a Whale Optimization Algorithm Deep Transfer Learning-DrivenAutomated Fall Detection(WOADTL-AFD)technique to improve the Quality of Life for persons with disabilities.The primary aim of the presented WOADTL-AFD technique is to identify and classify the fall events to help disabled individuals.To attain this,the proposed WOADTL-AFDmodel initially uses amodified SqueezeNet feature extractor which proficiently extracts the feature vectors.In addition,the WOADTLAFD technique classifies the fall events using an extreme Gradient Boosting(XGBoost)classifier.In the presented WOADTL-AFD technique,the WOA approach is used to fine-tune the hyperparameters involved in the modified SqueezeNet model.The proposedWOADTL-AFD technique was experimentally validated using the benchmark datasets,and the results confirmed the superior performance of the proposedWOADTL-AFD method compared to other recent approaches.

Improved Soil Quality Prediction Model Using Deep Learning for Smart Agriculture Systems

Soil is the major source of infinite lives on Earth and the quality of soil plays significant role on Agriculture practices all around.Hence,the evaluation of soil quality is very important for determining the amount of nutrients that the soil require for proper yield.In present decade,the application of deep learning models in many fields of research has created greater impact.The increasing soil data availability of soil data there is a greater demand for the remotely avail open source model,leads to the incorporation of deep learning method to predict the soil quality.With that concern,this paper proposes a novel model called Improved Soil Quality Prediction Model using Deep Learning(ISQP-DL).The work considers the chemical,physical and biological factors of soil in particular area to estimate the soil quality.Firstly,pH rating of soil samples has been collected from the soil testing laboratory from which the acidic range has been categorized through soil test and the same data has been taken as input to the Deep Neural Network Regression(DNNR)model.Secondly,soil nutrient data has been given as second input to the DNNR model.By utilizing this data set,the DNNR method is used to evaluate the fertility rate by which the soil quality has been estimated.For training and testing,the model uses Deep Neural Network Regression(DNNR),by utilizing the dataset.The results show that the proposed model is effective for SQP(Soil Quality Prediction Model)with efficient good fitting and generality is enhanced with input features with higher rate of classification accuracy.The results show that the proposed model achieves 96.7%of accuracy rate compared with existing models.

Deep Learning Model for News Quality Evaluation Based on Explicit and Implicit Information

Recommending high-quality news to users is vital in improving user stickiness and news platforms’reputation.However,existing news quality evaluation methods,such as clickbait detection and popularity prediction,are challenging to reflect news quality comprehensively and concisely.This paper defines news quality as the ability of news articles to elicit clicks and comments from users,which represents whether the news article can attract widespread attention and discussion.Based on the above definition,this paper first presents a straightforward method to measure news quality based on the comments and clicks of news and defines four news quality indicators.Then,the dataset can be labeled automatically by the method.Next,this paper proposes a deep learning model that integrates explicit and implicit news information for news quality evaluation(EINQ).The explicit information includes the headline,source,and publishing time of the news,which attracts users to click.The implicit information refers to the news article’s content which attracts users to comment.The implicit and explicit information affect users’click and comment behavior differently.For modeling explicit information,the typical convolution neural network(CNN)is used to get news headline semantic representation.For modeling implicit information,a hierarchical attention network(HAN)is exploited to extract news content semantic representation while using the latent Dirichlet allocation(LDA)model to get the subject distribution of news as a semantic supplement.Considering the different roles of explicit and implicit information for quality evaluation,the EINQ exploits an attention layer to fuse them dynamically.The proposed model yields the Accuracy of 82.31%and the F-Score of 80.51%on the real-world dataset from Toutiao,which shows the effectiveness of explicit and implicit information dynamic fusion and demonstrates performance improvements over a variety of baseline models in news quality evaluation.This work provides empirical evidence for explicit and implicit factors in news quality evaluation and a new idea for news quality evaluation.

Unveiling the Predictive Capabilities of Machine Learning in Air Quality Data Analysis: A Comparative Evaluation of Different Regression Models

Air quality is a critical concern for public health and environmental regulation. The Air Quality Index (AQI), a widely adopted index by the US Environmental Protection Agency (EPA), serves as a crucial metric for reporting site-specific air pollution levels. Accurately predicting air quality, as measured by the AQI, is essential for effective air pollution management. In this study, we aim to identify the most reliable regression model among linear discriminant analysis (LDA), quadratic discriminant analysis (QDA), logistic regression, and K-nearest neighbors (KNN). We conducted four different regression analyses using a machine learning approach to determine the model with the best performance. By employing the confusion matrix and error percentages, we selected the best-performing model, which yielded prediction error rates of 22%, 23%, 20%, and 27%, respectively, for LDA, QDA, logistic regression, and KNN models. The logistic regression model outperformed the other three statistical models in predicting AQI. Understanding these models' performance can help address an existing gap in air quality research and contribute to the integration of regression techniques in AQI studies, ultimately benefiting stakeholders like environmental regulators, healthcare professionals, urban planners, and researchers.

A machine learning approach to quality-control Argo temperature data

本文提出了一种基于机器学习的Argo浮标温度异常值检测方法.该方法采用机器学习无监督算法高斯混合模型对Argo浮标数据进行聚类分析,并构建包围所有数据点的最小多边形的凸包.基于射线投影算法实现点在多边形内分析,通过自动识别数据点位于凸包内外来判断该数据点数据质量的好坏.本文采用南海区域Argo浮标数据对该方法进行测试,结果表明该方法可以识别70%以上的包含异常值的温度剖面,同时自动标记出各异常值点.

On Cognitive Style in English Web-based Autonomous Learning

Since Henry Holec first put forward the term‘Autonomy'in 1980's, autonomous learning has been drawing the universal attention of scholars both at home and abroad. Promoting learners' ability of self-regulated learning has been taken as one of the important goals of modern education. College English autonomous learning based on network environment does not mean free study without any restraints or monitoring, but rather involves the self-monitoring and external monitoring. Meanwhile, different learners may have different cognitive styles in their learning processes, which may have an influence on the improvement of the learners' efficiency in the autonomous language learning. Proper monitoring models coordinating with the students' different field cognitive styles.

An Investigation into the Web-Based Learning in Jiangsu Open University(JSOU)——A case Study of College English

College English is a compulsory course for all registered online learners in Jiangsu Open University and students have been practicing web-based learning instead of face-to-face classes ever since 2014.Questionnaires and interviews are adopted to look into the 4-year-long practice of web-based learning in College English in JSOU.By analyzing the data obtained from both teachers and students,the findings show:(1)web-based learning caters to online learners in that the online learning materials,particularly micro-lessons,are well-designed and easily accessible.(2)web-based learning helps teachers monitor the learning process of online learners and therefore assures the quality of online learning.(3)web-based learning enhances effective learning since students and teachers can communicate conveniently and instantly via online chat rooms and instant messaging software.