Concept Of Learning Research Articles

A review of multimodal deep learning methods for genomic-enabled prediction in plant breeding.

Deep learning methods have been applied when working to enhance the prediction accuracy of traditional statistical methods in the field of plant breeding. Although deep learning seems to be a promising approach for genomic prediction, it has proven to have some limitations, since its conventional methods fail to leverage all available information. Multimodal deep learning methods aim to improve the predictive power of their unimodal counterparts by introducing several modalities (sources) of input information. In this review, we introduce some theoretical basic concepts of multimodal deep learning and provide a list of the most widely used neural network architectures in deep learning, as well as the available strategies to fuse data from different modalities. We mention some of the available computational resources for the practical implementation of multimodal deep learning problems. We finally performed a review of applications of multimodal deep learning to genomic selection in plant breeding and other related fields. We present a meta-picture of the practical performance of multimodal deep learning methods to highlight how these tools can help address complex problems in the field of plant breeding. We discussed some relevant considerations that researchers should keep in mind when applying multimodal deep learning methods. Multimodal deep learning holds significant potential for various fields, including genomic selection. While multimodal deep learning displays enhanced prediction capabilities over unimodal deep learning and other machine learning methods, it demands more computational resources. Multimodal deep learning effectively captures intermodal interactions, especially when integrating data from different sources. To apply multimodal deep learning in genomic selection, suitable architectures and fusion strategies must be chosen. It is relevant to keep in mind that multimodal deep learning, like unimodal deep learning, is a powerful tool but should be carefully applied. Given its predictive edge over traditional methods, multimodal deep learning is valuable in addressing challenges in plant breeding and food security amid a growing global population.

Human Adaption to Climate Change: Marine Disaster Risk Reduction in the Era of Intelligence

With the intensification of global warming and sea level rise, extreme weather and climate events occur frequently, increasing the probability and destructive power of marine disasters. The purpose of this paper is to propose the specific application of artificial intelligence (AI) in marine disaster risk reduction. First, this paper uses computer vision to assess the vulnerability of the target and then uses CNN-LSTM to forecast tropical cyclones. Second, this paper proposes a social media communication mechanism based on deep learning and a psychological crisis intervention mechanism based on AIGC. In addition, the rescue response system based on an intelligent unmanned platform is also the focus of this research. Third, this paper also attempts to discuss disaster loss assessment and reconstruction based on machine learning and smart city concepts. After proposing specific application measures, this paper proposes three policy recommendations. The first one is improving legislation to break the technological trap of AI. The second one is promoting scientific and technological innovation to break through key technologies of AI. The third one is strengthening coordination and cooperation to build a disaster reduction system that integrates man and machine. The purpose of this paper is to reduce the risk of marine disasters by applying AI. Furthermore, we hope to provide scientific references for sustainability and human adaptation to climate change.

Virtual Digital Laboratory Vernier as a Tool of Implementing STEM-Education Principles in Physics Lessons

The article highlights the results of the theoretical and methodological analysis of the issue of the use of the Vernier digital virtual laboratory as an innovative tool for implementing the principles of STEM education in physics lessons in high school. The purpose of this research paper is to justify the feasibility of integrating digital laboratories into the physics curriculum, which allows for its modernization through the integration of science, technology, engineering and mathematics. The study's methodological foundation is based on the active learning concept developed by D. Sokoloff, R. Thornton and P. Laws. According to it, the active nature of learning physics using the Vernier digital laboratory is ensured by the ability to conduct real, high-precision, and multiple measurements of various physical quantities, analyze the obtained data, visualize them as graphs or charts, and interactively adjust and generalize the results of the experiment. The main advantages and disadvantages of using this innovative learning tool have been analyzed, and its compliance with safety, accessibility and adaptability requirements has been determined. The content of implementing the scientific, technological, engineering and mathematical aspects of STEM education through the methodology of using the Vernier digital laboratory in physics lessons has been explored. An example of a tentative lesson plan for a physics class using the Vernier digital laboratory is provided. Based on the conducted research and the practice of using the Vernier digital laboratory in middle school physics lessons, it has been concluded that it has significant and comprehensive educational potential as an innovative tool for implementing STEM education principles.

Learning super-resolution and pyramidal convolution residual network for vehicle re-identification

Vehicle re-identification (Vehicle Re-ID) aims at retrieving and tracking the specified target vehicle with multiple other cameras, which can provide help in checking violations and catching fugitives, but there are still the following problems that need to be solved urgently. First, the existing collected Vehicle Re-ID data often have low resolution and blur in local regions, so that the Vehicle Re-ID algorithm cannot accurately extract subtle feature representations. In addition, small features are easy to cause the disappearance of features under the operation of a large convolution kernel, which makes the model unable to capture and learn subtle features, resulting in inaccurate judgment of vehicles. In this study, we propose a Vehicle Re-ID method based on super resolution and pyramidal convolution residual network. Firstly, a super-resolution image generation network leveraging generative adversarial networks (GANs) is proposed. This network employs both content loss and adversarial loss as optimization criteria, ensuring an efficient transformation from a low-resolution image into a super-resolution counterpart, while meticulously preserving intricate high-frequency details. Then, multi levels of pyramidal convolution operations are designed to generate multi-scale features, which can capture information on different scales. Moreover, the concept of residual learning is applied between the multi levels of pyramidal convolution operations to expedite model optimization and enhance recognition capabilities. Ultimately, the double pyramidal convolutions are meticulously employed on both the original image and the super-resolution image, yielding low-noise feature representations and intricate semantic information respectively. By seamlessly fusing these two diverse sources of information, the resultant combined features exhibit heightened discrimination capabilities and significantly bolster the robustness of image features. In order to verify the effectiveness of the proposed method, extensive experiments are carried out on VeRi-776 and VehicleID datasets. The experimental results show that the method proposed in this paper effectively captures the detail information of vehicle images, accurately distinguishes the subtle differences between different vehicles of the same type, and is superior to state-of-the-art methods.

Schneeball-Wırbelgruppe learning model: improving students’ concept mastery and critical thinking

Good concept mastery through critical thinking using various approaches, models, strategies, and methods of learning German is essential for the Education 4.0 era. Therefore, this study aimed to analyze the concept mastery and critical thinking skills of German language students at Pattimura University using collaborative learning that combines SchneeballWirbelgruppe learning. A one-group pretest-posttest design with a sample of 13 second-semester students in the academic year 2019/2020 was used. Furthermore, an essay test was used as an instrument while the data was analyzed using descriptive methods (N-gain test) and inferential methods (paired t-test). The descriptive analysis showed that the concept mastery and critical thinking skills of students were in the high and moderate categories. The paired t-test results showed a difference between the pretest and posttest using Schneeball-Wirbelgruppe learning model. Therefore, collaborative learning has a positive impact on improving students’ concept mastery and critical thinking skills in learning phonology and morphology courses. As for the implications of this research, the Schneeball-Wırbelgruppe learning model is recommended as a learning model that can be applied to other concepts in German language learning.

Evolutionary Stress Detection Framework through Machine Learning and IoT (MLIoT-ESD)

Background: Life nowadays is full of stress due to lifestyle changes and the modernera race. Almost everyone around us is suffering from stress and anxiety. Mostly, stress identification is done by medical practitioners in a very late stage in which suitable help measures cannot be provided and hence result in suicides or early age deaths due to cardiac arrest, etc. One major reason behind the delay is the time required in stress identification by traditional approaches, and above that, the amount of time and financial support expected is always not feasible to be available. Hence, in this paper, we proposed an evolutionary research framework for stress identification by the usage of both machine learning and IoT. Here, we also conducted a pilot study on 83 records available over the decade since 2014 using PRISMA guidelines, and a bibliographic network visualization was also performed using VOS viewer. Objectives: This study aimed to develop a stress detection framework using Machine Learning and the Internet of Things (IoT) as technology advanced over a decade. Methods: More than 80 research papers from honorable repositories like Scopus and Web of Science were gathered according to the guidelines of PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analysis) 2020, and the VOSviewer tool was further applied to construct the bibliographic depictions. Various datasets and methods used over ten years with their performance were also discussed. Results: This research was conducted to gather various types of stressors, the impact of various Machine Learning and IoT algorithms and concepts on various datasets and their respective results. Conclusion: Various available datasets and results with multiple algorithms were discussed in a crisp tabular form for better understanding. A methodology based on an amalgamation of Machine Learning and IoT was also proposed due to various research gaps available so that stress detection could be done in a cost-effective way.

Preparation for future conceptual learning: Content-specific long-term effects of early physics instruction.

Preparation for future conceptual learning: Content-specific long-term effects of early physics instruction.

Improving concept learning in education via category construction.

Improving concept learning in education via category construction.

Transforming the Future of Digital Health Education: Redesign of a Graduate Program Using Competency Mapping.

Digital transformation has disrupted many industries but is yet to revolutionize health care. Educational programs must be aligned with the reality that goes beyond developing individuals in their own professions, professionals wishing to make an impact in digital health will need a multidisciplinary understanding of how business models, organizational processes, stakeholder relationships, and workforce dynamics across the health care ecosystem may be disrupted by digital health technology. This paper describes the redesign of an existing postgraduate program, ensuring that core digital health content is relevant, pedagogically sound, and evidence-based, and that the program provides learning and practical application of concepts of the digital transformation of health. Existing subjects were mapped to the American Medical Informatics Association Clinical Informatics Core Competencies, followed by consultation with leadership to further identify gaps or opportunities to revise the course structure. New additions of core and elective subjects were proposed to align with the competencies. Suitable electives were chosen based on stakeholder feedback and a review of subjects in fields relevant to digital transformation of health. The program was revised with a new title, course overview, course intended learning outcomes, reorganizing of core subjects, and approval of new electives, adding to a suite of professional development offerings and forming a structured pathway to further qualification. Programs in digital health must move beyond purely informatics-based competencies toward enabling transformational change. Postgraduate program development in this field is possible within a short time frame with the use of established competency frameworks and expert and student consultation.

Praksis Pembelajaran Kewirausahaan Pada Unit Produksi Jasa Boga

The Praxis of Entrepreneurship Learning in the Food services Production Unit. This study aims to investigate teachers’ concepts of entrepreneurship learning, entrepreneurship competencies developed in the production unit, and entrepreneurship learning processes in the production unit of food services in SMK Negeri 6 Yogyakarta. This study employed the qualitative case study. The research subjects were the principal, head of the study program, head of the production unit, and students. Data were collected through interviews, observations, and documentation. The collected data were analyzed using Miles and Huberman model consisting of data reduction, data display, and conclusion. The result shows that: (1) Entrepreneurship learning needs hands-on-experiences-based; (2) the entrepreneurship competency developed through the production unit includes a) entrepreneurship knowledge, consisting of self-knowledge and practical knowledge, b) entrepreneurship attitudes, consisting of work ethos, autonomy, discipline, creativity, and innovativeness, and c) entrepreneurship skills, consisting of: technical skill, human relations skill, conceptual skill, decision making skill, time management skill, and leadership skill; (3) no instructional standard employed in to develop entrepreneurship in production unit. There are three entrepreneurship learning models employed in the production unit: inside production, outside production, and mobile canteen. The learning outcome is measured in two ways, i.e. performance and product monitoring. Meanwhile, evaluation of the students’ learning progress is conducted by using profit-based evaluation. Keywords: learning, entrepreneurship, production unit

EXPERIENCE OF IMPLEMENTING THE DISTANCE E-COURSE “STATISTICAL METHODS IN PHARMACY” IN THE MATHEMATICAL TRAINING OF FUTURE PHARMACEUTICAL SPECIALISTS

The article is dedicated to the issue of distance and online learning in higher pharmaceutical education. In the paper, the main principles and characteristic features of the concept of distance learning are outlined, the main advantages and disadvantages of using distance and e-learning in pharmaceutical and medical higher education are discussed. Considerable attention is paid to the use of distance learning technology in the process of math training for students pursuing higher education in the field of pharmacy. In addition to technical means and the structural-methodological component of the distance course development process, special attention is given to psycho-pedagogical aspects, which includes a focus on increasing the motivation of this group of education seekers to master disciplines related to exact sciences. It is demonstrated, using the example of the developed by the authors distance course “Statistical Methods in Pharmacy”, implemented with the LMS Moodle, that a successful combination of methodological approaches and innovative teaching methods, aimed at implementing a competency-based approach in education, along with modern information technology providing online access to educational content, including tools for feedback support, practical skills development, and automated self-assessment, ensures the effectiveness of achieving educational goals by learners and the formation of professional competencies defined by the educational program. The course was tested, certified, and implemented in the fundamental training of future pharmaceutical professionals at the National University of Pharmacy. It is concluded, that the implementation of distance learning technology in the practice of Ukrainian higher pharmaceutical and medical education is necessary not only due to the current conditions that make it impossible to organize full-fledged offline education but also because of the efficiency of these technologies, primarily for organizing independent students’ work, that ensures the development of learners’ autonomy, contributes to the development of information culture, general digital competence, and a range of soft skills, including communication, interaction, collaboration in a digital environment, and the rational use of study time.

Pembentukan Karakter di SDN Kertaungaran Melalui Konsep Pembelajaran Ki Hajar Dewantara

This research aims to find out the formation of character in SDN Kertaungaran through the learning concept of Ki Hajar Dewantara. This study uses a qualitative approach with a case study method of 13 research subjects who were selected using the purposive sampling technique. The data analysis technique uses the miles and huberman model. Data analysis is carried out with the stages of data reduction, data presentation, and conclusion drawn. Data collection is carried out through observation, interviews, and documentation. Interviews were conducted with school principals, lower grade teachers and higher-grade teachers and lower grade students and high-grade students at SD Negeri Kertaungaran. The results of the analysis show that the character at SDN Kertungaran through the learning concept of Ki Hajar Dewantara has been well embedded. Just as the teacher has set a good example for the students, it also gives encouragement to the students. The roles of family, school, and community all synergize to shape the character of students. Other activities such as religious activities, learning evaluations and integration of local culture can also shape students' character. Learning sports, sports, and sports play a role in improving the character of discipline, cooperation, and responsibility. Schools must stick to the learning concept of Ki Hajar Dewantara and practice it gradually and consistently.

Predicting the success of startups using a machine learning approach

Successful investment in early-stage companies has high uncertainty. More specifically, the tools available to investors need to be more robust to reduce the risk and manage the uncertainty of startups. This research aims to use machine learning methods to design a prediction solution to identify successful startups for investors. In order to design the predicting solution and provide policies, classification, and clustering algorithms have been utilized to predict the success of startups and perform feature importance analysis based on the SHAP and permutation methods. Subsequently, the performance of four classification algorithms, such as Random Forest, Gradient Boost, Multilayer Perceptron, Logistic Regression and Support Vector Machine, are compared to predict business success. Meanwhile, Random Forest and Gradient Boosting algorithms showed the best accuracy, which was equal to 82% and 80%, respectively. Based on the feature importance of Random Forest and Gradient Boosting, which is obtained from the SHAP method, indicated that the higher values of “Number of followers on LinkedIn”, “Number of employees on LinkedIn”, “Number of followers on Twitter”, and “Last raised amount” have higher SHAP values and a more significant impact on the model output. Three clustering algorithms including hierarchy, K-means, and DBSCAN were also compared. Among them, the K-means algorithm performs best with 72% silhouette, and K-means was employed to explain each cluster’s characteristics. Finally, an effective artificial intelligence-based prediction solution has been proposed to show the way for investors to apply machine learning concepts to predict the success of startups.

Parents' use of sustained shared thinking during joint mathematics activities with young children: Aninvestigation of its measurement, antecedents, and outcomes.

There is currently a dearth of tools to assess parents' use of effective interactive strategies for supporting early mathematics learning. One potential such strategy is sustained shared thinking. This study therefore constructed and validated a scale for measuring parents' use of sustained shared thinking during joint mathematics activities with young children, and examined its antecedents and outcomes. Four hundred and sixty-six parents completed a questionnaire about their conceptions of mathematics teaching and learning, home practices, as well as their kindergarten children's approach and avoidance motivation to learn mathematics. Additionally, the children were tested on numeration skills. Results showed that our new scale has three factors: exchanging ideas with children about mathematical problem-solving processes, creating a child-centred atmosphere for mathematics learning, and engaging children in mathematical thinking. Parents' uses of these three strategies were predicted by their constructivist conception of mathematics teaching and learning, and were differentially associated with the children's numeration skills, approach, and avoidance motivation to learn mathematics. Potential uses of our new scale in future home mathematics environment research are discussed.

Review of Deep Learning in High-Energy Heavy-Ion Collisions

The hot deconfined matter called quark–gluon plasma (QGP) can be generated in relativistic heavy-ion collisions (HICs). Its properties under high temperatures have been widely studied. Since the short-lived QGP is not directly observable, data-driven methods, including deep learning, are often used to infer the initial-state properties from the final distributions of hadrons. This paper reviews various applications of machine learning in relativistic heavy-ion collisions, explains the fundamental concepts of deep learning, and discusses how the properties of HIC data can be interpreted using efficient machine learning models.

The correlation between medical students’ clinical dishonesty, psychological distress, and moral intelligence

IntroductionClinical dishonesty is one of the components of academic dishonesty that deals with the unprofessional behavior of students in hospital and clinic environments (medical students, nursing students, etc.). Psychological distress and low moral intelligence among students can be known as predisposing factors in performing dishonest clinical behaviors. The present research addresses a gap in the scientific literature by investigating dishonest behavior among medical students.MethodsThis cross-sectional study examined medical students’ clinical dishonesty, psychological distress, and moral intelligence. Rafati et al.‘s questionnaire was used to investigate clinical dishonesty, Kessler’s Psychological Distress Questionnaire (K6) was used for psychological distress, and Lenik and Keil’s (2005) questionnaire was used to determine moral intelligence. Cochran’s formula was used to calculate the sample size and the simple random sample (SRS) method was used for sampling. Data were statistically analyzed in SPSS version 27 (SPSS Inc., Chicago, IL, United States). a P-value less than 0.05 was considered significant.Results317 medical students were included in this study, of which 176 (55.5%) were male and 141 (44.5%) were female. We found a direct and significant statistical correlation between clinical dishonesty and students’ distress (Correlation Coefficient: 0.162, P-value < 0.001). In addition, there was a statistically significant inverse correlation between clinical dishonesty and moral intelligence (Correlation Coefficient: -0.241, P-value: 0.004). Moreover, there was a higher rate of clinical dishonesty among senior medical students (P-value < 0.001). Moreover, the most dishonest clinical behaviors are as follows: [1] Disclosure of patient information in public or with non-medical personnel (76%), Incorrect examination of vital signs and physical examinations (69.4%), Not reporting incidents or errors of others involving patients (41.6%).ConclusionFinally, most students have experienced engaging in at least one clinically dishonest behavior. Such actions increase with the progress of the educational level so that it reaches its peak at the internship stage. Moral intelligence is a learnable concept, and mental distress also has its own treatments. Therefore, improving these two factors can reduce clinical dishonesty among medical students.

Exploration of Blended Teaching Mode for Ideological and Political Courses in Universities

In the era of “Internet plus,” the hybrid teaching model has injected new vitality into the reform of ideological and political education in colleges and universities, promoted the connection between online and offline teaching, built an air cloud classroom, promoted the sharing of high-quality educational resources, and enabled the reform of ideological and political education, which is conducive to stimulating students’ interest in learning ideological and political courses and improving the quality of ideological and political education. College ideological and political education teachers should actively learn the concept of blended learning, create micro lessons based on teaching content, introduce current political news, and use micro lessons to guide students’ pre-class preparation. Universities can carry out online live teaching, conduct interactive sessions, group discussions, and online testing to improve the quality of online teaching, collect online teaching data, conduct precise offline teaching, and optimize the connection between in-class and out-of-class teaching, utilizing big data to conduct teaching evaluation, collect student teaching suggestions, and comprehensively improve the quality of blended learning in ideological and political courses.

The Concept of Flexible Learning for Teaching Art Disciplines in Elementary Schools for National Minorities in modern PR China

The concept of flexible learning is increasingly important in contemporary education systems, particularly for teaching art and aesthetic disciplines. In China, this approach is especially relevant in schools serving national minorities, where diverse cultural and linguistic backgrounds necessitate a more adaptable and inclusive educational framework. This article aims to explore the implementation of flexible learning for teaching art and aesthetic disciplines in modern elementary schools in China, focusing on the experiences and strategies used to organise the educational process for representatives of national minorities. When writing the article, the methods typical for researching papers in the field of general education and pedagogical comparative studies were used, in particular: literature review, case studies, document analysis, comparative analysis, observation. The article results. Flexible learning refers to educational approaches that provide students with choices regarding how, what, when, and where they learn. It emphasises adaptability to individual learning needs, preferences, and cultural contexts. Implementing flexible learning in art and aesthetic disciplines involves several strategies to accommodate the unique needs of national minority students (such as cultural and artistic integration, use of technology, community involvement and flexible learning environments) and is widely used in PR China in regions with significant populations of national minorities, providing valuable results. However, implementing flexible learning for art and aesthetic disciplines in schools for national minorities comes with challenges, including resource limitations and the need for special programs of teacher training activities including cultural sensitivity, which are the point of constant attention of the Chinese government. The study concludes that the implementation of flexible learning for teaching art and aesthetic disciplines in modern elementary schools in China, particularly for national minorities, demonstrates a commitment to inclusivity and adaptability in education. By integrating bilingual education, technology, community involvement, and flexible learning environments, these schools can provide a more personalised and culturally relevant education for their students. Despite the challenges, the benefits of this approach are significant and include promoting cultural preservation, artistic expression, and social cohesion.

Organizational Strategies for Providing Foreign Language Education Services to Adults Over 45: International Practices and Approaches

Organizing educational services for people over the age of 45 is essential for their personal development, economic participation, social inclusion, and mental well-being. International and local legislation supports these initiatives, aiming to provide equitable and inclusive educational opportunities for all ages and to use the benefits of these people being fruitfully involved in the economic and social life of a country. The article aims to examine the strategies for providing educational services to adult learners (citizens over the age of 45) in different countries (European countries, the USA, Australia, China, Ukraine, Brazil, and Japan), with a focus on whether this process is integrated into social services and formal and informal education systems within the framework of the concept of lifelong learning. The methods included literature review on databases such as Google Scholar, JSTOR, and ERIC; comparative analysis of educational policy documents; case studies and reports from different nations; meta-analysis; field observations; and others typical to the field of comparative education and general education. They were utilized to provide a comprehensive and well-rounded analysis of how different countries organize and implement foreign language education services for adults over the age of 45. The findings reveal that the approaches to providing foreign language education for adults over 45 varies widely across different countries, reflecting each nation's unique historical traditions, cultural values, and current societal needs. This diversity in practice can be categorized into four main groups: services provided by social services; formal education systems; informal education systems; and self-organized services to present the prevailing approach to providing foreign language education for individuals over 45 in each country. The study concludes that the provision of foreign language learning services for individuals over 45 varies significantly across countries, reflecting diverse historical traditions, cultural attitudes, and current needs. Whether through social services, formal education systems, informal educational channels, or self-organization, each country’s approach highlights the importance of adapting educational services to meet the needs of an adult population seeking continued personal and professional growth.

Comparison Between Gans and Diffusion Models in the Generation of Synthetic Images for Enhancing Tree Species Recognition

Objective: This study investigates the application of Generative Adversarial Networks (GANs) and Diffusion Models in data augmentation to improve the classification of tree species images, which is essential for sustainable forest management. Theoretical Framework: Fundamental concepts of machine learning, generative and classifier networks, as well as data augmentation techniques through synthetic image generation, are presented, establishing a solid foundation for the research. Method: The research utilized 2,178 images of cross-sections of wood from 18 species, applying Deep Convolutional GAN (DCGAN) and U-Net with diffusion on a rare species, evaluated using FID and IS metrics. The generated images were used to train and validate classification models, assessed by F1-score. Results and Discussion: The results revealed that Diffusion Models generated more realistic images and performed better in the classification of the rare species. The discussion contextualizes these results in light of the theoretical framework, exploring their implications for environmental management. Limitations, such as the impact of indiscriminate addition of synthetic data, are also addressed. Research Implications: The practical and theoretical implications of this research are discussed, providing insights into how the results can be applied or influence practices in the field of forest management and environmental management. These implications may cover areas such as sustainable innovation and biodiversity conservation. Originality/Value: This study contributes to the literature by demonstrating the potential of Diffusion Models to outperform GANs in generating synthetic images for sustainable forest management purposes. The relevance and value of this research are evidenced by its practical application in promoting innovative and sustainable practices in environmental management.