Knowledge-reused transfer learning for molecular and materials science

Leveraging big data analytics and advanced algorithms to accelerate and optimize the process of molecular and materials design, synthesis, and application has revolutionized the field of molecular and materials science, allowing researchers to gain a deeper understanding of material properties and behaviors,leading to the development of new materials that are more efficient and reliable. However, the difficulty in constructing large-scale datasets of new molecules/materials due to the high cost of data acquisition and annotation limits the development of conventional machine learning(ML) approaches. Knowledgereused transfer learning(TL) methods are expected to break this dilemma. The application of TL lowers the data requirements for model training, which makes TL stand out in researches addressing data quality issues. In this review, we summarize recent progress in TL related to molecular and materials. We focus on the application of TL methods for the discovery of advanced molecules/materials, particularly, the construction of TL frameworks for different systems, and how TL can enhance the performance of models. In addition, the challenges of TL are also discussed.

The Influence of Academic Self-Efficacy on Learning Engagement in Online Education in China:The Mediating Role of Effort

The shift towards online intelligent learning has become the norm in education and is now a fundamental part of modern educational activities.However,this new model can influence students’learning behavior and lead to changes in their approach to learning.Based on online intelligent learning,we investigated how the academic self-efficacy of nursing students affects their engagement with learning and explored the role of academic attribution as a mediator.Five hundred fifty-three nursing college students from Hebei and Hunan provinces in China participated in the online questionnaire.The results revealed that effort plays a mediating role in the relationship between academic self-efficacy and learning engagement.

An Investigation on the Influence of Middle School Students'Grammar Learning Self-efficacy on Their Learning Performance

In middleschool English teaching,grammar instruction has always been an important and challenging aspect.Due to the strong influence of grammar explanation in Chinese,learning English grammar may be affected by negative transfer from their native language,which could pose certain obstacles to English acquisition.Self-efficacy,as a major emotional factor influencing learners'internal regulation strategies,has attracted wide attention from researchers both domestically and internationally for its impact on learning performance.High self-efficacy helps learners develop a more positive mindset towards learning,thereby contributing to improved learning performance.Therefore,this study focuses on investigating the relationship between grammatical self-efficacy and learning performance of English grammar among middle school students.This research contributes to a complementary understanding of grammatical self-efficacy among educators,enhancing their teaching skills,and effectively guiding students to learn English grammar with a more positive and healthy mindset,presenting a future grammar instruction in middle school English teaching.

Information and communication technology in science learning as a tool for “scientific thinking” in engineering education

New methodologies in science (also mathemat- ics) learning process and scientific thinking in the classroom activity of engineer students with ICT (information and communication technology, including also graphic calculator) are presented: visual modelling with ICT, action research with graphic calculator, insight in classroom, com- munications and reflection of integrative ac- tions. How can we show our students the beauty of science (and mathematics) with ICT and the way scientists think and try to find the truth? Is it possible to create the motivation in science learning for students using ICT or graphic cal- culator? How can we organize the engineer training on such professional activity in class- room? In this paper we try to answer the ques- tions using methodology of visual modelling and technology of resource lessons in high en- gineering school.

Deep Learning Enabled Microarray Gene Expression Classification for Data Science Applications

In bioinformatics applications,examination of microarray data has received significant interest to diagnose diseases.Microarray gene expression data can be defined by a massive searching space that poses a primary challenge in the appropriate selection of genes.Microarray data classification incorporates multiple disciplines such as bioinformatics,machine learning(ML),data science,and pattern classification.This paper designs an optimal deep neural network based microarray gene expression classification(ODNN-MGEC)model for bioinformatics applications.The proposed ODNN-MGEC technique performs data normalization process to normalize the data into a uniform scale.Besides,improved fruit fly optimization(IFFO)based feature selection technique is used to reduce the high dimensionality in the biomedical data.Moreover,deep neural network(DNN)model is applied for the classification of microarray gene expression data and the hyperparameter tuning of the DNN model is carried out using the Symbiotic Organisms Search(SOS)algorithm.The utilization of IFFO and SOS algorithms pave the way for accomplishing maximum gene expression classification outcomes.For examining the improved outcomes of the ODNN-MGEC technique,a wide ranging experimental analysis is made against benchmark datasets.The extensive comparison study with recent approaches demonstrates the enhanced outcomes of the ODNN-MGEC technique in terms of different measures.

＂Learning on Gaming＂ Improves Science Learning in a STEAM Interdisciplinary Approach

The ＂Gaming to Learn＂ consists in playing a game and consequently getting a learning goal： play a game, even not specifically didactic to derive a learning outcome. The ＂Game-Based Learning＂ consists of tips, techniques and tools that apply the principles of game design to the learning process-a dynamic way to engage learners and help educators assess learning. The authors＇ approach ＂Learning on Gaming＂ is different： you learn ＂while＂ you play. To apply this concept, the authors realize GeoQuest, a Computer Class Role Playing Game to teach Earth Science in an STEAM （Science, Technology, Engineering, Art and Math） educational approach. The authors have realised a role playing computer game called GeoQuest creating at the same time a Role Playing Engine which involves all students to the game through their personal mobiles or tablets, giving a total interaction of the whole class to the game. Players can also discover where they are from the story of some historical and mythological figures they meet on their path. They can interact to solve several quests appearing during the game related to mineralogy, volcanology, geodynamics, history, myths.

The 3E learning cycle in the television show Sid the Science Kid

This article reports on a research study on Sid the Science Kid PBS television show including meth-ods for preschool teachers to promote the inclusion of the 3E Learning Cycle and the tents of the nature of sci-ence in their preschool science education curriculum. We discussed： （a）the value of the Sid the Science Kidmedia tool and its relationship to the nature of science; （b）how to identify the 3E＇s Learning Cycle in the Sidthe Science Kid media tool. The goal of this study is to analyze if the 3E＇s （Explain, Explore, Engage） are pre-sent in a television promoting inquiry for young learners. We are suggesting the Sid media tool be a model forthe explicit teaching of the 3E＇s and the nature of science, not behavior management.

Metacognitive Ability and its Relationship with CET4 Score——A Study on Science Students under Online College English Autonomous Learning

By using 162 third-year science students from the Independent College in Shandong University of Science and Tech nology,this paper investigated the relationship between their metacognitive ability and their CET4 score.The results indicated that their metacognitive ability,and the three subcategories have positive significant correlations with the students'CET4 score.

AI for Science:科研应用及其带来的革新与挑战

人工智能驱动的科学研究(AI for Science)被视为科学发现的第五范式的曙光。依循演绎主义的科学研究逻辑,梳理了人工智能在科学假设生成、数据收集以及分析挖掘中的应用。人工智能“数据算法算力”三原则,对科学数据的质量、算法的复杂性以及计算能力提出了更高的要求。AI for Science时代预计会出现科技巨头、AI专家、软硬件工程师、政府以及教育机构等紧密协同的新型科研模式。然而,AI算法的黑箱特性对科学研究的可解释性和可重复性构成潜在威胁。因此,在推进人工智能驱动的科学研究的发展过程中,必须坚持伦理优先的原则,注重科学数据的安全性管理,防范化解大模型分布外泛化带来的解释性弱等问题。

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.

Adaptive Music Recommendation: Applying Machine Learning Algorithms Using Low Computing Device

In the digital music landscape, the accuracy and response speed of music recommendation systems (MRS) are crucial for user experience optimization. Traditional MRS often relies on the use of high-performance servers for large-scale training to produce recommendation results, which may result in the inability to achieve music recommendation in some areas due to substandard hardware conditions. This study evaluates the adaptability of four popular machine learning algorithms (K-means clustering, fuzzy C-means (FCM) clustering, hierarchical clustering, and self-organizing map (SOM)) on low-computing servers. Our comparative analysis highlights that while K-means and FCM are robust in high-performance settings, they underperform in low-power scenarios where SOM excels, delivering fast and reliable recommendations with minimal computational overhead. This research addresses a gap in the literature by providing a detailed comparative analysis of MRS algorithms, offering practical insights for implementing adaptive MRS in technologically diverse environments. We conclude with strategic recommendations for emerging streaming services in resource-constrained settings, emphasizing the need for scalable solutions that balance cost and performance. This study advocates an adaptive selection of recommendation algorithms to manage operational costs effectively and accommodate growth.

The Role of Machine Learning and Deep Learning Approaches to Improve Optical Communication Systems

In recent years, there has been a revolution in the way that we transmit information through optical communication systems, allowing for fast and high-capacity data transmission using optical communication systems. Due to the growing demand for higher-capacity and faster networks, traditional optical communication systems are reaching their limits due to the increasing demand for faster and higher-capacity networks. The advent of machine learning and deep learning approaches has led to the emergence of powerful tools that can dramatically enhance the performance of optical communication systems with significant efficiency improvements. In this paper, we provide an overview of the role that machine learning (ML) and deep learning can play in enhancing the performance of various aspects of optical communication systems, including modulation techniques, channel modelling, equalization, and system optimization methods. The paper discusses the advantages of these approaches, such as improved spectral efficiency, reduced latency, and improved robustness to impairments in the channel, such as spectrum degradation. Additionally, a discussion is made regarding the potential challenges and limitations associated with using machine learning and deep learning in optical communication systems as well as their potential benefits. The purpose of this paper is to provide insight and highlight the potential of these approaches to improve optical communication in the future.

Water resource forecasting with machine learning and deep learning:A scientometric analysis

Water prediction plays a crucial role in modern-day water resource management,encompassing both logical hydro-patterns and demand forecasts.To gain insights into its current focus,status,and emerging themes,this study analyzed 876 articles published between 2015 and 2022,retrieved from the Web of Science database.Leveraging CiteSpace visualization software,bibliometric techniques,and literature review methodologies,the investigation identified essential literature related to water prediction using machine learning and deep learning approaches.Through a comprehensive analysis,the study identified significant countries,institutions,authors,journals,and keywords in this field.By exploring this data,the research mapped out prevailing trends and cutting-edge areas,providing valuable insights for researchers and practitioners involved in water prediction through machine learning and deep learning.The study aims to guide future inquiries by highlighting key research domains and emerging areas of interest.

An Empirical Study on Self-Efficacy of Online Learning of Overseas Chinese Children in Europe

In the post-Covid-19 pandemic era,it is more difficult for some Chinese schools in Europe to provide online extra classes for overseas Chinese children after school hours,as they did previously.To meet students'multifaceted learning needs,online extra classes teaching,including online Chinese language classes and some online art classes,is increasingly being offered as a supplement to the diversity of teaching activities in Chinese schools in Europe,with the ultimate goal of improving the learning abilities of overseas Chinese children while relieving pressure on teaching resources in schools.Children’s learning self-efficacy in online extracurricular courses has its own uniqueness,which can be considered from three dimensions,including learning confidence,learning ability,and self-assessment ability.This study aims to examine the factors influencing the self-efficacy of overseas Chinese children and to make optimization suggestions for better teaching methods.In search of that,an online questionnaire survey with 127 participants from overseas Chinese children aged􀀘to􀀔􀀘was collected.The findings indicate that the role of learning confidence in overseas Chinese children outweighs their learning ability and self-assessment ability.Gender and age have a negligible effect on self-efficacy but have an impact on learning confidence.Chinese schools in Europe do not need to show gender differences when conducting classroom activities in online teaching to improve the online self-efficacy of Chinese children,and efforts should also be made to keep the courage of older students to trial and error.Teachers are expected to investigate more aspects of their students'personalities in future classrooms rather than sticking to a consistent and unchanging teaching model.

AI for Science时代下的电池平台化智能研发

在AI for Science时代,电池设计自动化智能研发(battery design automation,BDA)平台通过整合先进的人工智能技术,为电池研发领域带来了革命性进展。BDA平台覆盖了文献调研、实验设计、合成制备、表征测试和分析优化这五个电池研发的关键环节,利用机器学习、多尺度建模、预训练模型等先进算法,结合软件工程开发用户交互友好的工具,加速从理论设计到实验验证的整个电池研发周期。通过自动化的实验设计、合成制备、表征测试和性能优化,BDA平台不仅提升了研发效率,还提高了电池设计的精确度和可靠性,推动了电池技术向更高能量密度、更长循环寿命和更低成本的方向发展。

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.

近代中国科学观发展轨迹探析——以清末民初science概念内涵的演化为中心

中国古代的"科学"一词意味着"科举之学"及"分科之学",明治维新前后日本学者率先用"科学"翻译science并赋予其近代意蕴。清末民初伴随着"西学东渐"的潮流,国人或取道日本,或留学欧美,在传播西方文化和科学知识的过程中借鉴日本的翻译方式以"科学"指称近代science,并使其内涵不断演化和发展,其基本轨迹可归结为由侧重阐述"学术分科"观念发展到重视揭示"公理公例"之学,而科学方法论的导入及探索成为贯穿其中的一条主线,从而推动了近代中国科学观渐趋丰富、全面,使之具备"学术分科"的形态、探求"公理公例"的主旨以及注重观察、实验和归纳的方法这三个主要方面。

e-Learning情感计算模型设计研究

目前,绝大多数e-Learning系统是在传统智力观指导下设计开发而成,严重忽视了学习者情感智能的发展。脑科学研究表明,情感在注意、记忆、推理和决策等方面发挥重要作用。以情感计算学说、人工心理学、脑科学等相关理论为指导,赋予计算机和网络以情感智能,以设计出支持适应性学习的e-Learning情感计算模型。