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.

Physics-Based Active Learning for Design Space Exploration and Surrogate Construction for Multiparametric Optimization

The sampling of the training data is a bottleneck in the development of artificial intelligence(AI)models due to the processing of huge amounts of data or to the difficulty of access to the data in industrial practices.Active learning(AL)approaches are useful in such a context since they maximize the performance of the trained model while minimizing the number of training samples.Such smart sampling methodologies iteratively sample the points that should be labeled and added to the training set based on their informativeness and pertinence.To judge the relevance of a data instance,query rules are defined.In this paper,we propose an AL methodology based on a physics-based query rule.Given some industrial objectives from the physical process where the AI model is implied in,the physics-based AL approach iteratively converges to the data instances fulfilling those objectives while sampling training points.Therefore,the trained surrogate model is accurate where the potentially interesting data instances from the industrial point of view are,while coarse everywhere else where the data instances are of no interest in the industrial context studied.

Advances in Deep-Learning-based Precipitation Nowcasting Techniques

Precipitation nowcasting,as a crucial component of weather forecasting,focuses on predicting very short-range precipitation,typically within six hours.This approach relies heavily on real-time observations rather than numerical weather models.The core concept involves the spatio-temporal extrapolation of current precipitation fields derived from ground radar echoes and/or satellite images,which was generally actualized by employing computer image or vision techniques.Recently,with stirring breakthroughs in artificial intelligence(AI)techniques,deep learning(DL)methods have been used as the basis for developing novel approaches to precipitation nowcasting.Notable progress has been obtained in recent years,manifesting the strong potential of DL-based nowcasting models for their advantages in both prediction accuracy and computational cost.This paper provides an overview of these precipitation nowcasting approaches,from which two stages along the advancing in this field emerge.Classic models that were established on an elementary neural network dominated in the first stage,while large meteorological models that were based on complex network architectures prevailed in the second.In particular,the nowcasting accuracy of such data-driven models has been greatly increased by imposing suitable physical constraints.The integration of AI models and physical models seems to be a promising way to improve precipitation nowcasting techniques further.

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.

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.

Research on Application of Metacognitive Strategy in English Listening in the Web-based Self-access Learning Environment

Metacognitive strategies are regarded as advanced strategies in all the learning strategies.This study focuses on the application of metacognitive strategies in English listening in the web-based self-access learning environment(WSLE) and tries to provide some references for those students and teachers in the vocational colleges.

The Application of Problem Based Learning in Undergraduate Nursing Education: A Strategy for Curriculum Reform

Background: Problem based learning (PBL) is an innovative way of delivering instruction in which problems are used as the basis of learning. Problem based learning was developed in the 1960s by Harold Barrows at McMaster University Medical School in Canada. Since then, PBL had been im-plemented as a teaching method in other reputable education institutions internationally, includ-ing nursing education. Curriculum reform is proposed through PBL in conjunction with patient simulation in undergraduate nursing education. The first author, Tan Kan Ku, PhD Candidate, MHS (Transcultural Mental Health—by Research) worked as a Registered Nurse for more than two decades internationally in England, New Zealand, Saudi Arabia and Australia, where she worked as a Case Manager in Community Mental Health Rehabilitation Program. Since 2001, she focused on nurse education and research into the stigma of mental illness from a cross-cultural perspective. Currently, she teaches Mental Health, Cultural Diversity and Research in the Diploma of Nursing course at Victoria University in Melbourne, Australia, while completing her PhD thesis for examination at Charisma University. The second author, Dr. Michael Ha, FSA, MAAA, CFA, CPA (Australia) FRM, PRM, LLM, is the Founding Director of the MSc Financial Mathematics programme at Xian Jiaotong-Liverpool University. He was previously Vice President of Strategic Business In-itiatives Units at ING Life Insurance in its Taiwan operation. Ninety percent of his students are enrolled in the Financial Mathematics programme. They learn not only mathematics and statistics theories but also their applications in the Finance and Investment areas, especially Portfolio Con-struction and Financial Risk Management. Creating a real-world Finance work environment in university lecture-halls embracing theories and practice, Dr. Ha strongly believes the PBL method can be employed in the Financial Mathematics training agenda so students can be better-prepared for work. Students are no longer instructed-learners but active thinkers and problem-solvers. Conclusion: Educators in fields such as Medical, Nursing, Engineering, Financial Mathematics, Ac-counting, Computing, etc., need to be prepared to change their teaching philosophy from didactic to problem solving for PBL to be implemented. Constructive alignment is recommended for curri-culum reform.

The Importance of Mathematical Models to Explore the Effects of Marijuana and Other Plant Based Products on Learning and Memory

This article describes the use of the first order system transfer function for learning and memory studies involving consumption of marijuana and other plant based products. We provide detailed instructions on how the model can be used to analyze the performance of individual participants using a memory test developed by the senior authors. The importance of identifying possible learning and memory deficits of marijuana is paramount due to the growing number of states in the U.S. legalizing marijuana use for medicinal and recreational purposes. The model can also be extended to other plant based products purported to improve memory. While this article does not study the effect of marijuana, we provide details on how it can be used by illustrating its application on individuals consuming an amphetamine-like psychostimulant drug using our own memory test.

Mathematical methods for main tenance and operation cost prediction based on transfer learning in State Grid

The electric power enterprise is an important basic energy industry for national development,and it is also the first basic industry of the national economy.With the continuous expansion of State Grid,the progressively complex operating conditions,and the increasing scope and frequency of data collection,how to make reasonable use of electrical big data,improve utilization,and provide a theoretical basis for the reliability of State Grid operation,has become a new research hot spot.Since electrical data has the characteristics of large volume,multiple types,low-value density,and fast processing speed,it is a challenge to mine and analyze it deeply,extract valuable information efficiently,and serve for the actual problem.According to the features of these data,this paper uses artificial intelligence methods such as time series and support vector regression to establish a data mining network model for standard cost prediction through transfer learning.The experimental results show that the model in this paper obtains better prediction results on a small sample data set,which verifies the feasibility of the deep transfer model.Compared with activity-based costing and the traditional prediction method,the average absolute error of the proposed method is reduced by 10%,which is effective and superior.

An innovative approach of using online problem‑based learning and case‑based learning in teaching disaster nursing during the COVID‑19 pandemic

Objective:The objective of this study is to evaluate the learning experience effect of online problem‑based learning(PBL)and case‑based learning(CBL)in teaching disaster nursing.Methods:According to the characteristics of online PBL and CBL,the revised curriculum experience questionnaire(CEQ)was used to evaluate the teaching quality.Cronbach’s coefficient and the reliability of the split‑half reliability questionnaire were calculated.The exploratory factor analysis of 26 items was carried out by principal component analysis and maximum variance rotation method.Kaiser‑Meyer‑Olkin(KMO)and Bartlett’s tests were used to test the validity of the questionnaire.The comparison between groups was performed by one‑way analysis of variance.Results:A total of 191 questionnaires were issued and 183 copies were recovered,with a recovery rate of 95.8%.The Cronbach’s alpha coefficient of CEQ is 0.929,and the Cronbach’s alpha coefficient of each dimension is between 0.713 and 0.924.After factor analysis,the KMO value was 0.817.The 26 items finally returned to 6 principal components,and all factor load values were above 0.7,indicating good factor analysis effect.This study found that students who learned disaster nursing had an ideal online learning experience,and the average value of CEQ was 3.74±0.42.In particular,male students,senior students or medical students had a higher curriculum experience score.In addition,compared with the national recruitment,the international students have higher curriculum experience score on the dimension of Appropriate Assessment Scale,indicating that the international students are more inclined to use online PBL and CBL.Conclusions:Using the revised CEQ is an innovative approach to evaluate the effect of online PBL and CBL in teaching disaster nursing,which can improve students’experience and curriculum quality.

Where Have Network-based Self-learning Classes Gone?——Reflections & Expectations on the Employment of Network-based Self-learning Classes

To respond to the further development of college English reforms,many universities employed network-based selflearning classes to aid the traditional classroom teaching,especially in teaching listening,but as time went by,some universities gradually gave them up.The paper intends to reflect on the employment of network-based self-learning listening classes,analyz ing the learning with and without its aid,and meanwhile introduce the need to re-employ it,and discuss how we can improve the network-based self-learning classes to help with students' listening.

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.

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.

Deep Learning Applied to Computational Mechanics:A Comprehensive Review,State of the Art,and the Classics

Three recent breakthroughs due to AI in arts and science serve as motivation:An award winning digital image,protein folding,fast matrix multiplication.Many recent developments in artificial neural networks,particularly deep learning(DL),applied and relevant to computational mechanics(solid,fluids,finite-element technology)are reviewed in detail.Both hybrid and pure machine learning(ML)methods are discussed.Hybrid methods combine traditional PDE discretizations with ML methods either(1)to help model complex nonlinear constitutive relations,(2)to nonlinearly reduce the model order for efficient simulation(turbulence),or(3)to accelerate the simulation by predicting certain components in the traditional integration methods.Here,methods(1)and(2)relied on Long-Short-Term Memory(LSTM)architecture,with method(3)relying on convolutional neural networks.Pure ML methods to solve(nonlinear)PDEs are represented by Physics-Informed Neural network(PINN)methods,which could be combined with attention mechanism to address discontinuous solutions.Both LSTM and attention architectures,together with modern and generalized classic optimizers to include stochasticity for DL networks,are extensively reviewed.Kernel machines,including Gaussian processes,are provided to sufficient depth for more advanced works such as shallow networks with infinite width.Not only addressing experts,readers are assumed familiar with computational mechanics,but not with DL,whose concepts and applications are built up from the basics,aiming at bringing first-time learners quickly to the forefront of research.History and limitations of AI are recounted and discussed,with particular attention at pointing out misstatements or misconceptions of the classics,even in well-known references.Positioning and pointing control of a large-deformable beam is given as an example.

The Effectiveness of Strategies-based Instruction on Postgraduates' English Vocabulary Learning

This ten-week quasi-experimental study was undertaken to explore the effectiveness of strategies-based vocabulary instruction on English vocabulary learning of postgraduate learners.By the questionnaires and vocabulary tests administered before and after the instruction,the experimental group and the control group were compared to find out whether reading comprehension plus SBI method was more effective than reading only method in postgraduates' English vocabulary learning.

The problem-based learning integrated with simulation to improve nursing students’ self-efficacy

Introduction: The purposes of this study were to describe the simulation integrated with problem-based learning (SIM-PBL) module to educate the nursing process for clients with hypertension and to evaluate its effectiveness on nursing students’ self-efficacy (SE). Methods: This study was a one group pre- and post-test design. Twenty five students received a 5-hour SIM-PBL program focused on nursing care of clients with hypertension. A newly developed self-report questionnaire was used to assess SE in four areas of the nursing process with a scale of 0 (not at all confident) to 10 (totally confident). The four areas were subjective data assessment, physical examination, prioritizing nursing care and health promotion advices. Results: At baseline, students’ SE ranged from 5.5 ± 1.4 (prioritizing nursing care) to 7.6 ± 1.4 (subjective data assessment). After SIM-PBL education, all areas of nursing process presented statistically significant improvements of SE. The improvements were most noticeable in prioritizing nursing care. Conclusion: The SIM-PBL module was effective in improving the students’ self-efficacy in the nursing process for patients with hypertension. Further studies are recommended in developing SIM-PBL modules for diverse nursing topics and evaluating their effectiveness in various aspects of students’ competency.

Do the Application of Problem-Based Learning on Undergraduate Nursing Students’ Is Effective? A Case Study Review

Objectives: The study’s aims to determine and assess the application of problem-based learning to undergraduate nursing students. Background: Nursing students are the upcoming health care delivery system;according to their standard of learning, it will affect their clinical training. Method: The study design is a case study review, the data was collected using many articles related to problem-based learning collected from E-books and E-journals websites like CINAHEL, Google Scholar, etc. After that, the data was analyzed and evaluated related to the application of problem-based learning on undergraduate nursing students. Result: The result appeared that most of the research proved and supported that the application of problem-based learning is effective for undergraduate nursing students, and students can solve patients’ problems in a better way. Conclusion: In conclusion, problem-based learning is an essential part of the nursing diagnosis process that will increase knowledge, and performance, and merge it with the nursing concepts.

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.

Analysis on Output Teaching Mode of College English Based on Implicit Learning Theory

The application of language,to a great extant,requires learners to understand the inputted information quickly as well as automatically,and combine verbal fragments into meaningful outputted language. This type of spontaneous mechanism depends on the effective input of language and long-rang internalization of language structure,which helps to form the implicit knowledge in students' conceptual system,thus to realize the automatic use of language. Therefore,the article intends to combine implicit learning theory with the output teaching mode with a purpose of working out a practical teaching mode to enhance the teaching effect and college students' applied abilities to use English.

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.