Effects of Health Education with Problem-Based Learning Approaches on the Knowledge, Attitude, Practice and Coping Skills of Women with High-Risk Pregnancies in Plateau Areas

Objective: Given the unique cultural background, way of life, and physical environment of the Tibetan Plateau, this study aims to investigate the effects of health education using problem-based learning (PBL) approaches on the knowledge, attitude, practice, and coping skills of women with high-risk pregnancies in this region. Methods: 76 high-risk pregnancy cases were enrolled at Tibet’s Linzhi People’s Hospital between September 2023 and April 2024. 30 patients admitted between September 2023 and December 2023 were selected as the control group and were performed with regular patient education. 46 patients admitted between January 2024 and April 2024 were selected as the observation group and were performed regular patient education with problem-based learning approaches. Two groups’ performance on their health knowledge, attitude, practice and coping skills before and after interventions were evaluated, and patient satisfaction were measured at the end of the study. Results: There was no statistical significance (P P P Conclusions: Health education with problem-based learning approaches is worth promoting as it can help high-risk pregnant women in plateau areas develop better health knowledge, attitude and practice and healthier coping skills. Also, it can improve patient sanctification.

Prediction of Damping Capacity Demand in Seismic Base Isolators via Machine Learning

Base isolators used in buildings provide both a good acceleration reduction and structural vibration control structures.The base isolators may lose their damping capacity over time due to environmental or dynamic effects.This deterioration of them requires the determination of the maintenance and repair needs and is important for the long-termisolator life.In this study,an artificial intelligence prediction model has been developed to determine the damage and maintenance-repair requirements of isolators as a result of environmental effects and dynamic factors over time.With the developed model,the required damping capacity of the isolator structure was estimated and compared with the previously placed isolator capacity,and the decrease in the damping property was tried to be determined.For this purpose,a data set was created by collecting the behavior of structures with single degrees of freedom(SDOF),different stiffness,damping ratio and natural period isolated from the foundation under far fault earthquakes.The data is divided into 5 different damping classes varying between 10%and 50%.Machine learning model was trained in damping classes with the data on the structure’s response to random seismic vibrations.As a result of the isolator behavior under randomly selected earthquakes,the recorded motion and structural acceleration of the structure against any seismic vibration were examined,and the decrease in the damping capacity was estimated on a class basis.The performance loss of the isolators,which are separated according to their damping properties,has been tried to be determined,and the reductions in the amounts to be taken into account have been determined by class.In the developed prediction model,using various supervised machine learning classification algorithms,the classification algorithm providing the highest precision for the model has been decided.When the results are examined,it has been determined that the damping of the isolator structure with the machine learning method is predicted successfully at a level exceeding 96%,and it is an effective method in deciding whether there is a decrease in the damping capacity.

Triplet Label Based Image Retrieval Using Deep Learning in Large Database

Recent days,Image retrieval has become a tedious process as the image database has grown very larger.The introduction of Machine Learning(ML)and Deep Learning(DL)made this process more comfortable.In these,the pair-wise label similarity is used tofind the matching images from the database.But this method lacks of limited propose code and weak execution of misclassified images.In order to get-rid of the above problem,a novel triplet based label that incorporates context-spatial similarity measure is proposed.A Point Attention Based Triplet Network(PABTN)is introduced to study propose code that gives maximum discriminative ability.To improve the performance of ranking,a corre-lating resolutions for the classification,triplet labels based onfindings,a spatial-attention mechanism and Region Of Interest(ROI)and small trial information loss containing a new triplet cross-entropy loss are used.From the experimental results,it is shown that the proposed technique exhibits better results in terms of mean Reciprocal Rank(mRR)and mean Average Precision(mAP)in the CIFAR-10 and NUS-WIPE datasets.

Aspect based sentiment analysis using multi-criteria decision-making and deep learning under COVID-19 pandemic in India

The COVID-19 pandemic has a significant impact on the global economy and health.While the pandemic continues to cause casualties in millions,many countries have gone under lockdown.During this period,people have to stay within walls and become more addicted towards social networks.They express their emotions and sympathy via these online platforms.Thus,popular social media(Twitter and Facebook)have become rich sources of information for Opinion Mining and Sentiment Analysis on COVID-19-related issues.We have used Aspect Based Sentiment Analysis to anticipate the polarity of public opinion underlying different aspects from Twitter during lockdown and stepwise unlock phases.The goal of this study is to find the feelings of Indians about the lockdown initiative taken by the Government of India to stop the spread of Coronavirus.India-specific COVID-19 tweets have been annotated,for analysing the sentiment of common public.To classify the Twitter data set a deep learning model has been proposed which has achieved accuracies of 82.35%for Lockdown and 83.33%for Unlock data set.The suggested method outperforms many of the contemporary approaches(long shortterm memory,Bi-directional long short-term memory,Gated Recurrent Unit etc.).This study highlights the public sentiment on lockdown and stepwise unlocks,imposed by the Indian Government on various aspects during the Corona outburst.

Ensemble Based Learning with Accurate Motion Contrast Detection

Recent developments in computer vision applications have enabled detection of significant visual objects in video streams.Studies quoted in literature have detected objects from video streams using Spatiotemporal Particle Swarm Optimization(SPSOM)and Incremental Deep Convolution Neural Networks(IDCNN)for detecting multiple objects.However,the study considered opticalflows resulting in assessing motion contrasts.Existing methods have issue with accuracy and error rates in motion contrast detection.Hence,the overall object detection performance is reduced significantly.Thus,consideration of object motions in videos efficiently is a critical issue to be solved.To overcome the above mentioned problems,this research work proposes a method involving ensemble approaches to and detect objects efficiently from video streams.This work uses a system modeled on swarm optimization and ensemble learning called Spatiotemporal Glowworm Swarm Optimization Model(SGSOM)for detecting multiple significant objects.A steady quality in motion contrasts is maintained in this work by using Chebyshev distance matrix.The proposed system achieves global optimization in its multiple object detection by exploiting spatial/temporal cues and local constraints.Its experimental results show that the proposed system scores 4.8%in Mean Absolute Error(MAE)while achieving 86%in accuracy,81.5%in precision,85%in recall and 81.6%in F-measure and thus proving its utility in detecting multiple objects.

A hybrid agent⁃based machine learning method for human⁃centred energy consumption prediction

Occupant behaviour has significant impacts on the performance of machine learning algorithms when predicting building energy consumption.Due to a variety of reasons(e.g.,underperforming building energy management systems or restrictions due to privacy policies),the availability of occupational data has long been an obstacle that hinders the performance of machine learning algorithms in predicting building energy consumption.Therefore,this study proposed an agent⁃based machine learning model whereby agent⁃based modelling was employed to generate simulated occupational data as input features for machine learning algorithms for building energy consumption prediction.Boruta feature selection was also introduced in this study to select all relevant features.The results indicated that the performances of machine learning algorithms in predicting building energy consumption were significantly improved when using simulated occupational data,with even greater improvements after conducting Boruta feature selection.

Exploring the Application Effect of Flipped Classroom Combined with Problem-Based Learning Teaching Method in Clinical Skills Teaching of Standardized Training for Resident Doctors of Traditional Chinese Medicine

Objective: To explore the application effect of flipped classroom combined with problem-based learning teaching method in clinical skills teaching of standardized training for resident doctors of traditional Chinese Medicine. Methods: The study used the experimental control method. The study lasted from September to November 2022. The subjects of this study were 49 students of standardized training for resident doctors of traditional Chinese Medicine from grades 2020, 2021 and 2022 of Dazhou integrated TCM & Western Medicine Hospital. They were randomly divided into experiment group (25) and control group (24). The experiment group adopted flipped classroom combined with problem-based learning teaching method, and the control group adopted traditional teaching method. The teaching content was 4 basic clinical skill projects, including four diagnoses of traditional Chinese Medicine, cardiopulmonary resuscitation, dressing change procedure, acupuncture and massage. The evaluation method was carried out by comparing the students’ performance and a self-designed questionnaire was used to investigate the students’ evaluation of the teaching method. Results: The test scores of total scores in the experimental group (90.12 ± 5.89) were all higher than those in the control group (81.47 ± 7.96) (t = 4.53, P P Conclusions: The teaching process of the flipped classroom combined with problem-based learning teaching method is conducive to improving the efficiency of classroom teaching, cultivating students’ self-learning ability, and enhancing students’ willingness to learn.

A Research of the Course “Taishan Cultural Communication with the World” under Blended Learning Model and Outcome-Based Education Concept

The course “Taishan Cultural Communication with the World” has been online and offline teaching and learning for two terms based on the theoretical ideas: Blended Learning and Outcome-Based Education. This paper uses the data from one semester to state how to carry out the program and the good results. At the same time disadvantages are also the points that should be taken into consideration. From the teaching and learning practice, students have benefited from the online videos, complementary materials and discussions;they need to be guided as well, especially the guidance offline to make up. Furthermore, the balance of time online and offline is a great challenge.

TBL(Team-based learning)教学法在局解教学中的设计与评价

为适应现代医学发展的要求,在日益增多的医学教学改革尝试中,TBL教学法引起人们的关注。本文通过在局部解剖学教学中开展TBL教学,并且和传统教学方法做了对比研究。结果提示在局部解剖学教学中采用TBL教学法有利于提高学生学习兴趣及解决问题的能力,有利于动态评价学生的学习状态。

PBL(Problem-based Learning)教学法道路规划与几何设计教学中的应用与探索

立足于道路规划与几何设计信息量大,涉及专业基础知识广、实践性强等特点,将PBL教学法在教学中进行了应用与探索,从教师设计问题、组建学习小组、问题探索与交流、教师总结评价等方面进行了教学设计和应用研究,实践证明PBL教学有助于提高学生的自主创新学习能力及学习的积极性,显著提升了教学效果。

长学制传染病教学中TBL(Team-Based Learning)模式的应用和改进

目的:研究TBL教学在八年制学生传染病教学中的应用成效及存在的问题,为改进和推广该教学方法提供参考依据。方法:对2006级八年制学生部分理论课采用TBL教学,进行闭卷考试及问卷调查。结论:与传统教学模式相比,TBL教学对提高学生学习兴趣,培养学分分析问题、解决问题、沟通能力和团队协作精神以及提高考试成绩均有帮助。

Prediction of Lubricant Physicochemical Properties Based on Gaussian Copula Data Expansion

The composition of base oils affects the performance of lubricants made from them.This paper proposes a hybrid model based on gradient-boosted decision tree(GBDT)to analyze the effect of different ratios of KN4010,PAO40,and PriEco3000 component in a composite base oil system on the performance of lubricants.The study was conducted under small laboratory sample conditions,and a data expansion method using the Gaussian Copula function was proposed to improve the prediction ability of the hybrid model.The study also compared four optimization algorithms,sticky mushroom algorithm(SMA),genetic algorithm(GA),whale optimization algorithm(WOA),and seagull optimization algorithm(SOA),to predict the kinematic viscosity at 40℃,kinematic viscosity at 100℃,viscosity index,and oxidation induction time performance of the lubricant.The results showed that the Gaussian Copula function data expansion method improved the prediction ability of the hybrid model in the case of small samples.The SOA-GBDT hybrid model had the fastest convergence speed for the samples and the best prediction effect,with determination coefficients(R^(2))for the four indicators of lubricants reaching 0.98,0.99,0.96 and 0.96,respectively.Thus,this model can significantly reduce the model’s prediction error and has good prediction ability.

A Study on the Explainability of Thyroid Cancer Prediction:SHAP Values and Association-Rule Based Feature Integration Framework

In the era of advanced machine learning techniques,the development of accurate predictive models for complex medical conditions,such as thyroid cancer,has shown remarkable progress.Accurate predictivemodels for thyroid cancer enhance early detection,improve resource allocation,and reduce overtreatment.However,the widespread adoption of these models in clinical practice demands predictive performance along with interpretability and transparency.This paper proposes a novel association-rule based feature-integratedmachine learning model which shows better classification and prediction accuracy than present state-of-the-artmodels.Our study also focuses on the application of SHapley Additive exPlanations(SHAP)values as a powerful tool for explaining thyroid cancer prediction models.In the proposed method,the association-rule based feature integration framework identifies frequently occurring attribute combinations in the dataset.The original dataset is used in trainingmachine learning models,and further used in generating SHAP values fromthesemodels.In the next phase,the dataset is integrated with the dominant feature sets identified through association-rule based analysis.This new integrated dataset is used in re-training the machine learning models.The new SHAP values generated from these models help in validating the contributions of feature sets in predicting malignancy.The conventional machine learning models lack interpretability,which can hinder their integration into clinical decision-making systems.In this study,the SHAP values are introduced along with association-rule based feature integration as a comprehensive framework for understanding the contributions of feature sets inmodelling the predictions.The study discusses the importance of reliable predictive models for early diagnosis of thyroid cancer,and a validation framework of explainability.The proposed model shows an accuracy of 93.48%.Performance metrics such as precision,recall,F1-score,and the area under the receiver operating characteristic(AUROC)are also higher than the baseline models.The results of the proposed model help us identify the dominant feature sets that impact thyroid cancer classification and prediction.The features{calcification}and{shape}consistently emerged as the top-ranked features associated with thyroid malignancy,in both association-rule based interestingnessmetric values and SHAPmethods.The paper highlights the potential of the rule-based integrated models with SHAP in bridging the gap between the machine learning predictions and the interpretability of this prediction which is required for real-world medical applications.

PBL(Project-based Learning)教学模式在高职英语自主学习课程中的应用研究

本文采用行动研究的方法,以武汉职业技术学院的英语听说过级自主学习课程为例,探讨如何在自主学习课程中应用PBL的教学模式改进自主学习课程的考核方式,提高学生的自主学习能力和意识。文章首先分析了自主学习课程在实施过程中遇到的问题,详细描述了PBL教学模式的总体设计构想和具体的实施步骤,并对实施的结果进行了讨论。经过统计和问卷调查结果发现,应用PBL模式后,实验班的学生的学习兴趣有了很大的提高,有利于培养他们的自主学习能力和合作精神。同时,试验前后的听力测试表明,实验班听力成绩较对照班也有了显著的提高。可见,PBL模式的应用能在一定程度上改进目前在自主学习课程中所遇到的一些问题。

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.

基于PBL(Project-based Learning)的商务英语混合式教学模式研究

商务英语课程是融英语语言知识、商务知识及商务实践为一体的课程,其知识性和实践性并重的特点决定了传统的以教师讲授为主的教学模式无法满足培养社会需要的商务英语人才的需求,教学模式亟待改革。项目学习法是学生在实践中构建知识的一种教学模式,而线上线下资源有机融合的混合式教学模式能够拓展课堂容量,使学习不受时空地域的限制,二者相结合是商务英语教学改革的重要方向,教学评价体系的改革是确保新教学模式有效实施的重要保证。

A Teaching Case of Subject Clause Based on Implicit Learning

The New English curriculum criteria suggest teaching English grammar based on the students’cognitive characteristics and emotional needs,helping them discover the rules and encouraging them to master the grammar by using it.But due to the limited time in a lesson,many English teachers adopt a simple approach to teach grammar,in which students are required to memorize the rules first and then practice a lot.This approach is effec-

Properties of Ti-Based Hydrogen Storage Alloy

An efficient and safe hydrogen storage method is one of the important links for the large-scale development of hydrogen in the future. Because of its low price and simple design, Ti-based hydrogen storage alloys are considered to be suitable for practical applications. In this paper, we review the latest research on Ti-based hydrogen storage alloys. Firstly, the machine learning and density functional theory are introduced to provide theoretical guidance for the optimization of Ti-based hydrogen storage alloys. Then, in order to improve the hydrogen storage performance, we briefly introduce the research of AB type and AB2 type Ti-based alloys, focusing on doping elements and adaptive after treatment. Finally, suggestions for the future research and development of Ti-based hydrogen storage alloys are proposed. .

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.

Project-based Language Learning: an Activity Theory Analysis in SOE Language Learning

This study focuses on the effectiveness of the project-based language learning(PBLL) in a college Secretarial Oral English(SOE) Module. Student reflections of the language project work have been analyzed through Activity Theory. Moreover,Data has been collected and categorized based on the components of complex human activity: the subject, object, tools(signs,symbols, and language), the community in which the activity take place, division of labor, and rules. The findings theoretically support the outcome of project-based language learning which align with the object of the activity.