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This paper addresses the challenge of efficient detection of densely arranged unordered items under varying illumination. Specifically, a novel convolutional neural network-based method is proposed for item vector detection, recognition, and classification, termed Self-Attention and Concatenation-Based Detector (ACDet). In a benchmark pharmaceutical case study, rapid and accurate detection of pharmaceutical package contours is achieved, enabling the automatic and fast verification of both the quantity and types of pharmaceuticals during distribution. At the input stage, a combined image augmentation method is applied to improve the detection model’s ability to learn the appearance features of items from multiple angles. Based on YOLOv8 model, integrating computational module C2F with Attention (C2F-A), multidimensional self-attention reinforcement is applied to the outputs of multiple gradient streams. The designed Weighted Concatenation (WConcat) module self-learns to weight and concatenate multi-level feature maps, enhancing the model’s cognitive capability. Finally, simulation experiments are conducted to determine the optimal timing for utilizing each module. Simulation experiments compared the proposed ACDet with several state-of-the-art YOLO architecture models utilizing the benchmark Comprehensive Pharmaceutical Package Dataset (CPPD). ACDet achieved 81.0% mAP and 79.5% Smooth mAP on the CPPD dataset, outperforming other models by an average of 5.5% to 16.6%. On public datasets, the results were 52.2% and 51.0%, respectively. The impact of utilizing C2F-A at different stages on performance was also tested, concluding that the WConcat module does not necessitate spatial attention. Finally, in zero-shot testing, the verification success rate reached 99.91%. Our work shows that the proposed ACDet can overcome many challenges in complex object detection scenarios, enhancing robustness while maintaining a lightweight design. The proposed model can serve as a new benchmark. [ABSTRACT FROM AUTHOR]

The DECIDE project entitled "Career choices competencies for the post-pandemic future using multicriteria decision-making", aimed to empower young individuals in their career decision-making by providing them with personalised learning sources and tools to monitor the development of essential career competencies. This paper presents the findings from evaluating two key components of the DECIDE project: an e-guide for developing career competencies and a web-based application that monitors individuals' progress in competency development. These tools help young people identify the skills and knowledge they lack to meet the demands of employers for sustainable and innovative career paths. The e-guide was designed as a self-learning programme that guides users through interactive models focused on building the competencies required for sustainable and innovative career profiles. Pre-tests and post-tests were developed to assess the effectiveness of the e-guide and measure the participants' competency levels before and after engaging with the learning content. The application utilises advanced algorithms and visualisation techniques to analyse pre-test and post-test data, identify competency gaps, and provide users with a clear understanding of their competency development progress and areas for further improvements. The results of the testing and user feedback indicate that the developed tools positively impacted the development of career competencies. The study reveals that the e-guide provided educational value and effectively supported self-directed learning. At the same time, the web-based application offered a valuable tool for self-assessment and identifying competency gaps in career decision-making. [ABSTRACT FROM AUTHOR]

The present research aimed to determine the relationship between metacognitive strategies and critical thinking in English as a second language teaching, with the purpose of improving critical thinking. With a quantitative approach and through a quasi-experimental design, a correlational study was carried out using two instruments: the Metacognitive Awareness Inventory (mai) and the Cornell Critical Thinking Test Level Z, applied to 50 fourth-level students from the Language Center at Pontificia Universidad Católica, Ambato campus. To this end, an expert validation was conducted, where the instruments were evaluated. After their application, a positive correlation between metacognition and critical thinking was evidenced, using the Wilcoxon test. A p-value (Asymptotic Significance (two-tailed)) of less than 0.01 was obtained, leading to the conclusion that the more metacognitive strategies are used, the more students can enhance critical thinking areas, such as interpretation, analysis, evaluation, self-evaluation, inference, explanation, and, especially, self-regulation and self-learning of a second language. [ABSTRACT FROM AUTHOR]

Copyright of RIED: Revista Iberoamericana de Educación a Distancia is the property of Revista Iberoamericana de Educacion a Distancia and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Physical neural networks (PNN) using physical materials and devices to mimic synapses and neurons offer an energy-efficient way to implement artificial neural networks. Yet, training PNN is difficult and heavily relies on external computing resources. An emerging concept to solve this issue is called physical self-learning that uses intrinsic physical parameters as trainable weights. Under external inputs (i.e., training data), training is achieved by the natural evolution of physical parameters that intrinsically adapt modern learning rules via an autonomous physical process, eliminating the requirements on external computation resources. Here, we demonstrate a real spintronic system that mimics Hopfield neural networks (HNN), and unsupervised learning is intrinsically performed via the evolution of the physical process. Using magnetic texture-defined conductance matrix as trainable weights, we illustrate that under external voltage inputs, the conductance matrix naturally evolves and adapts Oja's learning algorithm in a gradient descent manner. The self-learning HNN is scalable and can achieve associative memories on patterns with high similarities. The fast spin dynamics and reconfigurability of magnetic textures offer an advantageous platform toward efficient autonomous training directly in materials. [ABSTRACT FROM AUTHOR]

To develop a robust segmentation model, encoding the underlying features/structures of the input data is essential to discriminate the target structure from the background. To enrich the extracted feature maps, contrastive learning and self-learning techniques are employed, particularly when the size of the training dataset is limited. In this work, we set out to investigate the impact of contrastive learning and self-learning on the performance of the deep learning-based semantic segmentation. To this end, three different datasets were employed used for brain tumor and hippocampus delineation from MR images (BraTS and Decathlon datasets, respectively) and kidney segmentation from CT images (Decathlon dataset). Since data augmentation techniques are also aimed at enhancing the performance of deep learning methods, a deformable data augmentation technique was proposed and compared with contrastive learning and self-learning frameworks. The segmentation accuracy for the three datasets was assessed with and without applying data augmentation, contrastive learning, and self-learning to individually investigate the impact of these techniques. The self-learning and deformable data augmentation techniques exhibited comparable performance with Dice indices of 0.913 ± 0.030 and 0.920 ± 0.022 for kidney segmentation, 0.890 ± 0.035 and 0.898 ± 0.027 for hippocampus segmentation, and 0.891 ± 0.045 and 0.897 ± 0.040 for lesion segmentation, respectively. These two approaches significantly outperformed the contrastive learning and the original model with Dice indices of 0.871 ± 0.039 and 0.868 ± 0.042 for kidney segmentation, 0.872 ± 0.045 and 0.865 ± 0.048 for hippocampus segmentation, and 0.870 ± 0.049 and 0.860 ± 0.058 for lesion segmentation, respectively. The combination of self-learning with deformable data augmentation led to a robust segmentation model with no outliers in the outcomes. This work demonstrated the beneficial impact of self-learning and deformable data augmentation on organ and lesion segmentation, where no additional training datasets are needed. [ABSTRACT FROM AUTHOR]

Chatbots are emerging technologies with the potential to improve teaching and learning processes. This paper conducts a systematic review of research on chatbots in education, focusing on articles published in Online-Journals.org from 2011 to 2024. The aim is to examine the various aspects addressed by the authors, such as design principles, pedagogical roles, interaction styles, and evaluation methods for chatbots in educational contexts. The tools were classified according to the type of user they targeted, revealing that 42% were aimed at students, 11% at teachers, 29% at both types of users, and 18% at external users. The characteristics of the tools along the above dimensions were analyzed, highlighting trends, good practices, and observed limitations. The key findings, challenges, and implications of using chatbots to improve learning outcomes, and experiences were discussed. It was concluded that chatbots are an emerging technology that offers benefits such as teaching personalization, self-learning, and real-time feedback but also poses challenges, such as evaluation and research into their effectiveness for education. [ABSTRACT FROM AUTHOR]

Domain adaptation is a crucial factor in EEG emotion recognition as it allows the transfer of knowledge from labeled source domains to unlabeled target domains with different data distributions. However, traditional domain adaptation methods require labeled source domain data, which is often unavailable due to privacy concerns in EEG emotion recognition. Consequently, source-free unsupervised domain adaptation has emerged as a challenging problem. To address this issue, this study proposes a contrastive self-supervised learning (CSSL) framework for online calibration of EEG recognition without using source data. The CSSL framework comprises two steps. In the first step, shared and personalized generator and classifier models are trained using contrastive learning techniques to make shared and personalized features more suitable for clustering and generating pseudo-labels. In the second step, pseudo-labels generated by Gaussian mixture model are used to supervise the training of the target model. Meanwhile, to make the pseudo-labels more reliable, mutual information maximization is applied on enhanced adjacent samples. The experimental results indicate that the proposed method outperforms state-of-the-art methods, achieving an accuracy of 89.2% and 61.6% on the SEED and DEAP datasets, respectively. [ABSTRACT FROM AUTHOR]

Abstract Optimization problems are common across various fields, and one effective solution is the swarm intelligence algorithm.It is essential for the algorithm to deliver high-quality solutions for problems with varying characteristics. However, most existing swarm intelligence rely on fixed and monotonic search strategies, which limits their ability to handle the diverse and complex situations encountered when solving real-world optimization problems with unknown fitness landscapes. To extend the applicability of swarm intelligence and thus offer users an efficient black-box optimizer for various applications, a novel self-learning mechanism is proposed and applied to the Salp Swarm Algorithm (SSA) to develop the self-learning salp swarm algorithm (SLSSA) in this paper. In SLSSA, four distinct search strategies, including a novel multiple food sources search strategy, are adopted to strengthen the search agents’ abilities to conquer various difficulties in the search space. To improve the efficiency of the search process, the self-learning strategy dynamically determines the execution probability of each search strategy according to the quality of solutions it produced previously. Moreover, a parameter setting method is proposed in this paper, which eliminates the need for a trial-and-error approach and allows for straightforward configuration of the parameters that optimize the performance of SLSSA. In comparison with several highly regarded state-of-the-art peer algorithms, the performance of SLSSA in solving the CEC2014 benchmark functions was thoroughly examined. Subsequently, SLSSA was applied to train multi-layer perceptron classifiers and test on the UCI machine-learning datasets. The experimental results and analysis on benchmark functions and multi-layer perceptron classifier training problems demonstrate that SLSSA outperforms the competing algorithms in terms of solution accuracy, stability, and overall convergence speed. Moreover, computational time comparisons reveal that SLSSA achieves significant performance improvement with only a marginal increase in time cost compared to the original SSA.

In the field of aerospace, real-time and accurate monitoring of the milling state is of great significance for improving processing quality and processing efficiency. Relying on milling force signals to monitor tool wear state has problems such as high cost and limited size of machined parts. Aiming at the above problem, a milling cutter wear condition monitoring method based on spindle current is proposed. Firstly, based on the linear correlation between the milling force and the current increment theory, the milling experiment was carried out. The characteristic variation trend and spectral characteristics of the milling force signal and spindle current signal under the change of spindle speed, feed rate, and radial cutting width are analyzed. The cosine similarity demonstrates that the similarity of the root mean square values of the two is 0.9865, 0.9943, and 0.9421, respectively, and the changing trend of the spectral amplitude is consistent. In the iSESOL platform, a self-learning feature boundary model is built from learning data, signal interception, root mean square feature extraction, drawing boundary, and threshold judgment to monitor the wear state of the milling cutter online. Finally, the data in each wear stage are selected to verify the model, and the validity of the model is proved by setting the threshold beyond the boundary and the alarm trigger condition, in real-time, and accurately monitoring the wear state of the milling cutter online. [ABSTRACT FROM AUTHOR]

Optimization problems are common across various fields, and one effective solution is the swarm intelligence algorithm.It is essential for the algorithm to deliver high-quality solutions for problems with varying characteristics. However, most existing swarm intelligence rely on fixed and monotonic search strategies, which limits their ability to handle the diverse and complex situations encountered when solving real-world optimization problems with unknown fitness landscapes. To extend the applicability of swarm intelligence and thus offer users an efficient black-box optimizer for various applications, a novel self-learning mechanism is proposed and applied to the Salp Swarm Algorithm (SSA) to develop the self-learning salp swarm algorithm (SLSSA) in this paper. In SLSSA, four distinct search strategies, including a novel multiple food sources search strategy, are adopted to strengthen the search agents' abilities to conquer various difficulties in the search space. To improve the efficiency of the search process, the self-learning strategy dynamically determines the execution probability of each search strategy according to the quality of solutions it produced previously. Moreover, a parameter setting method is proposed in this paper, which eliminates the need for a trial-and-error approach and allows for straightforward configuration of the parameters that optimize the performance of SLSSA. In comparison with several highly regarded state-of-the-art peer algorithms, the performance of SLSSA in solving the CEC2014 benchmark functions was thoroughly examined. Subsequently, SLSSA was applied to train multi-layer perceptron classifiers and test on the UCI machine-learning datasets. The experimental results and analysis on benchmark functions and multi-layer perceptron classifier training problems demonstrate that SLSSA outperforms the competing algorithms in terms of solution accuracy, stability, and overall convergence speed. Moreover, computational time comparisons reveal that SLSSA achieves significant performance improvement with only a marginal increase in time cost compared to the original SSA. [ABSTRACT FROM AUTHOR]

Copyright of Environmental & Social Management Journal / Revista de Gestão Social e Ambiental is the property of Environmental & Social Management Journal and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Objective: A comprehensive understanding of professional and technical terms is essential to achieving practical results in multidisciplinary projects dealing with health informatics and digital health. The medical informatics multilingual ontology (MIMO) initiative has been created through international cooperation. MIMO is continuously updated and comprises over 3700 concepts in 37 languages on the Health Terminology/Ontology Portal (HeTOP). Methods: We conducted case studies to assess the feasibility and impact of integrating MIMO into real-world healthcare projects. In HosmartAI, MIMO is used to index technological tools in a dedicated marketplace and improve partners' communication. Then, in SaNuRN, MIMO supports the development of a "Catalog and Index of Digital Health Teaching Resources" (CIDHR) backing digital health resources retrieval for health and allied health students. Results: In HosmartAI, MIMO facilitates the indexation of technological tools and smooths partners' interactions. In SaNuRN within CIDHR, MIMO ensures that students and practitioners access up-to-date, multilingual, and high-quality resources to enhance their learning endeavors. Conclusion: Integrating MIMO into training in smart hospital projects allows healthcare students and experts worldwide with different mother tongues and knowledge to tackle challenges facing the health informatics and digital health landscape to find innovative solutions improving initial and continuous education. [ABSTRACT FROM AUTHOR]

The adoption of chatbots in education is steadily increasing, offering a myriad of benefits to learners engaging in self-directed study. However, the reliability of information provided by chatbots poses certain obstacles, particularly in subjects with high academic content such as physics. This study proposes a five-step process for constructing chatbots, enabling educators to independently develop chatbots to support self-learning among students. This process focuses on enabling chatbots to autonomously respond to physics-related inquiries, a capability often lacking in prebuilt chatbots. A new chatbot for physics lessons on the conservation of energy law was developed by applying this methodology. To assess the feasibility and effectiveness of the developed chatbot, we conducted a pedagogical experimental study involving 100 tenth grade students divided into control and experimental groups. To ensure comparability between the groups, students were selected based on their physics grades and teacher feedback. Experimental data was collected through online feedback forms and a 45-minute physics test, processed using statistical techniques. The results indicate a high level of readiness among participants to utilize chatbots for self-learning and acknowledge their beneficial impact. Furthermore, students' knowledge quality improved with chatbot usage, affirming the feasibility and effectiveness of incorporating chatbots into physics education. These findings also validate the proposed five-step chatbot development process as rational and applicable. [ABSTRACT FROM AUTHOR]

Brain-computer interface (BCI) provides direct communication and control between the human brain and physical devices. It is achieved by converting EEG signals into control commands. Such interfaces have significantly improved the lives of disabled individuals suffering from neurological disorders—such as stroke, amyotrophic lateral sclerosis (ALS), and spinal cord injury—by extending their movement range and thereby promoting self-independence. Brain-controlled mobile robots, however, often face challenges in safety and control performance due to the inherent limitations of BCIs. This paper proposes a shared control scheme for brain-controlled mobile robots by utilizing fuzzy logic to enhance safety, control performance, and robustness. The proposed scheme is developed by combining a self-learning neuro-fuzzy (SLNF) controller with an obstacle avoidance controller (OAC). The SLNF controller robustly tracks the user's intentions, and the OAC ensures the safety of the mobile robot following the BCI commands. Furthermore, SLNF is a model-free controller that can learn as well as update its parameters online, diminishing the effect of disturbances. The experimental results prove the efficacy and robustness of the proposed SLNF controller including a higher task completion rate of 94.29% (compared to 79.29%, and 92.86% for Direct BCI and Fuzzy-PID, respectively), a shorter average task completion time of 85.31 s (compared to 92.01 s and 86.16 s for Direct BCI and Fuzzy-PID, respectively), and reduced settling time and overshoot. [ABSTRACT FROM AUTHOR]

The research purpose is to evaluate the effectiveness of the WeChat social networking platform for music education in China, considering the opportunities for communication and cognitive skills development of music students. The research underlines that the advantages of WeChat in learning are the updated educational information, learning flexibility, information selection to meet the student's interests, group work, and mobile learning. The development of recommendations for using WeChat in learning to play the piano consisted of a lecture platform with learning materials, automated knowledge testing, analysis of the musical repertoire, and self-learning of materials to acquire additional knowledge. The results revealed that group 1, which learned the program using the WeChat application developed a high level of communicative (82%) and cognitive (78%) skills. Group 2, which did not use modern technologies, had a high level of communication skills among 42% of students and cognitive skills among 31%. The development of communication skills depended on the group work used for the analysis of the repertoire (0.91), associated with the assessment of compositions, interpretation difficulties, and mechanisms of expressive performance. Cognitive skills depended on the self-learning of the topic (0.92) and the choice of the best material as well as the understanding of its key features. The practical research significance is to ensure the introduction of innovation in musical education using the WeChat platform. Future research is needed to determine the effectiveness of musical knowledge as a result of using the WeChat and Youku applications. [ABSTRACT FROM AUTHOR]

Mobile robot navigation has been a very popular topic of practice among researchers since a while. With the goal of enhancing the autonomy in mobile robot navigation, numerous algorithms (traditional AI-based, swarm intelligence-based, self-learning-based) have been built and implemented independently, and also in blended manners. Nevertheless, the problem of efficient autonomous robot navigation persists in multiple degrees due to the limitation of these algorithms. The lack of knowledge on the implemented techniques and their shortcomings act as a hindrance to further development on this topic. This is why an extensive study on the previously implemented algorithms, their applicability, their weaknesses as well as their potential needs to be conducted in order to assess how to improve mobile robot navigation performance. In this review paper, a comprehensive review of mobile robot navigation algorithms has been conducted. The findings suggest that, even though the self-learning algorithms require huge amounts of training data and have the possibility of learning erroneous behavior, they possess huge potential to overcome challenges rarely addressed by the other traditional algorithms. The findings also insinuate that in the domain of machine learning-based algorithms, integration of knowledge representation with a neuro-symbolic approach has the capacity to improve the accuracy and performance of self-robot navigation training by a significant margin. [ABSTRACT FROM AUTHOR]

Abstract Artificial Intelligence (AI) chatbots are increasingly becoming integral components of the digital learning ecosystem. As AI technologies continue to evolve, it is crucial to understand the factors influencing their adoption and use among students in higher education. This study is undertaken against this backdrop to explore the behavioral determinants associated with the use of the AI Chatbot, ChatGPT, among university students. The investigation delves into the role of ChatGPT’s self-learning capabilities and their influence on students’ knowledge acquisition and application, subsequently affecting the individual impact. It further elucidates the correlation of chatbot personalization with novelty value and benefits, underscoring their importance in shaping students’ behavioral intentions. Notably, individual impact is revealed to have a positive association with perceived benefits and behavioral intention. The study also brings to light potential barriers to AI chatbot adoption, identifying privacy concerns, technophobia, and guilt feelings as significant detractors from behavioral intention. However, despite these impediments, innovativeness emerges as a positive influencer, enhancing behavioral intention and actual behavior. This comprehensive exploration of the multifaceted influences on student behavior in the context of AI chatbot utilization provides a robust foundation for future research. It also offers invaluable insights for AI chatbot developers and educators, aiding them in crafting more effective strategies for AI integration in educational settings.

Copyright of Revista Daena: International Journal of Good Conscience is the property of Spenta University Mexico and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Journal of Arts & Social Sciences (JASS) is the property of Sultan Qaboos University and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Physical neural networks are artificial neural networks that mimic synapses and neurons using physical systems or materials. These networks harness the distinctive characteristics of physical systems to carry out computations effectively, potentially surpassing the constraints of conventional digital neural networks. A recent advancement known as “physical self-learning” aims to achieve learning through intrinsic physical processes rather than relying on external computations. This article offers a comprehensive review of the progress made in implementing physical self-learning across various physical systems. Prevailing learning strategies that contribute to the realization of physical self-learning are discussed. Despite challenges in understanding the fundamental mechanism of learning, this work highlights the progress towards constructing intelligent hardware from the ground up, incorporating embedded self-organizing and self-adaptive dynamics in physical systems. [ABSTRACT FROM AUTHOR]

GIScience is essential to geography education. Different curriculums and textbooks have been developed to teach GIScience in classrooms through lectures and exercises corresponding to the educational situation of each country. We developed e-learning materials based on existing research outcomes for GIScience education in Japan, such as the Japanese GIS core curriculum for university education. Using the materials, we held a three-day intensive GIScience class at the University of Tokyo from 2018 to 2022 with questionnaire surveys to investigate the educational effects of the materials and factors influencing GIScience learning. The attending students browsed the materials and studied GIScience by themselves but asked questions of teachers if necessary. The questionnaire survey results indicate that most students felt geospatial data processing is somewhat complex, but they were satisfied with the opportunity of GIScience learning. Whether the students thought the exercise was simple and easy depended on their confidence in computer knowledge and operation and their preferred learning style at their own pace. The students' satisfaction level and ease in using the material correlate with their motivation level, the number of questions they asked the others, and their preferred style for learning at their own pace. [ABSTRACT FROM AUTHOR]

In here a novel method is described to improve student success rates in a first-year basic chemistry theoretical/practical hybrid course (n = 31 students) by implementing simple ways of formative assessment. This to reduce student dropout rates following the philosophy of encouraging students' self-control. Essential is to train first-year bachelor students in their self-learning skills and to enhance their evaluative judgment. As a result, students are able to provide better quality of the assessment products at the end of the course. In practice the course is redesigned and intervention tools are integrated at multiple levels throughout the course. The lecturers' role was adapted to a coaching role, thereby introducing low-effort personalized micro-interventions to meet the personalized needs of students. To clarify these learning needs for students, awareness of the quality desired for the final assessment products is important. Awareness was improved by providing examples of varying quality and introducing multiple peer- and self-assessment moments during the course. The final evaluation of the course examination products showed that the quality of the laboratory notebook was substantially higher after following this approach. Additionally students learned other important skills such as self-learning skills, collaborating in practical work and giving and receiving feedback. Unexpectedly, the high perceived lecturers' workload decreased. The work presented here provides a novel approach in the form of a model and a practical blueprint with tools for a practical chemistry course design which develops students' self-learning skills thereby substantially improving student success rates. In our example course, the ultimate student success rate increased form 83 % to 95 % after using this novel approach. • A context-rich environment enables Chemical Engineering students' learning. • Examples help students to get a clear understanding of Intended Learning Outcomes. • Comparison of various examples leads to a set of distinctive criteria for students. • Combining a feedback process with formative assessment enables deep learning. • Continuously keeping track of learning progress provides improved student success. [ABSTRACT FROM AUTHOR]

Purpose: Ultrasound is becoming an essential tool for hand surgeons, but most of them are trained on the job, without any diploma or dedicated training. The aim of this study was to assess the ability of hand surgeons new to ultrasound to identify hand and wrist anatomical structures. Methods: A monocentric study was conducted from January 2022 to April 2022. Ten residents and five attending hand surgeons, ultrasound novices, were involved in this study. The participants underwent two tests, wherein they were required to identify 17 anatomical structures using ultrasound, on the same subject. The second test was similar and carried out 2 to 6 weeks later by all participants. The number of structures successfully identified and if it was the case, the detection time per structure, were recorded. The correlations between participants age, years of surgical experience, surgical background (orthopedic or plastic) and the ability to perform immediately during the first test or to progress between the two tests were also assessed. Results: The average number of structures identified during the first test (T1) was 14.1+/-2.1 (82.9%), versus 16.2+/-0.8 (95.3%) structures during the second test (T2) (p = 0.001). The mean detection time per structure was 53.4 +/- 18.9 s during T1 versus 27.7 +/- 7.2 s during T2 (p < 0.0001). A moderate negative correlation between the progression in the number of anatomical structures identified between the two tests and the years of surgical experience (ρ=-0.56; p = 0.029) was found. The other parameters were neither correlated with the ability to perform at the first test nor with the progression between the two tests. Conclusion: Hand surgeons new to ultrasound are most of the time able to identify hand and wrist anatomical structures. Comparison of their first and second tests showed significant potential for improvement in anatomical structure identification and detection time of those, especially in surgeons with limited surgical experience. [ABSTRACT FROM AUTHOR]

Artificial Intelligence (AI) chatbots are increasingly becoming integral components of the digital learning ecosystem. As AI technologies continue to evolve, it is crucial to understand the factors influencing their adoption and use among students in higher education. This study is undertaken against this backdrop to explore the behavioral determinants associated with the use of the AI Chatbot, ChatGPT, among university students. The investigation delves into the role of ChatGPT's self-learning capabilities and their influence on students' knowledge acquisition and application, subsequently affecting the individual impact. It further elucidates the correlation of chatbot personalization with novelty value and benefits, underscoring their importance in shaping students' behavioral intentions. Notably, individual impact is revealed to have a positive association with perceived benefits and behavioral intention. The study also brings to light potential barriers to AI chatbot adoption, identifying privacy concerns, technophobia, and guilt feelings as significant detractors from behavioral intention. However, despite these impediments, innovativeness emerges as a positive influencer, enhancing behavioral intention and actual behavior. This comprehensive exploration of the multifaceted influences on student behavior in the context of AI chatbot utilization provides a robust foundation for future research. It also offers invaluable insights for AI chatbot developers and educators, aiding them in crafting more effective strategies for AI integration in educational settings. [ABSTRACT FROM AUTHOR]

Relation extraction is an important task in information extraction, which aims to identify the relation between two given entities. The algorithm based on distant supervision can automatically generate a large amount of annotated data, which becomes the main method to deal with the task of relation extraction. However, previous studies rely too much on the precision of supervision information and ignore the effective supervision information hidden in the case of mislabeling, which leads to the loss of supervision information. To solve this problem, we propose the distantly supervised relation extraction model based on residual attention and self-learning. The model uses residual attention to extract features, and then uses self-learning idea to generate corrected labels for training data, which are added into the training process as supervisory signals to prevent parameter error updates caused by noisy labels. The model can not only reduce the problem of mislabeling caused by distant supervision, but also makes full use of the available supervisory information in the data to improve data utilization. Experiments show that compared with the existing mainstream baseline methods, the proposed model has higher precision and recall. [ABSTRACT FROM AUTHOR]

Copyright of Jordanian Educational Journal is the property of Association of Arab Universities and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

We are currently experiencing a period of rapid advancement in various areas of science and technology. The integration of high throughput 'omics' techniques with advanced biostatistics, and the help of artificial intelligence, is significantly impacting our understanding of sperm biology. These advances will have an appreciable impact on the practice of reproductive medicine in horses. This article provides a brief overview of recent advances in the field of spermatology and how they are changing assessment of sperm quality. This article is written from the authors' perspective, using the stallion as a model. We aim to portray a brief overview of the changes occurring in the assessment of sperm motility and kinematics, advances in flow cytometry, implementation of 'omics' technologies, and the use of artificial intelligence/self-learning in data analysis. We also briefly discuss how some of the advances can be readily available to the practitioner, through the implementation of 'on-farm' devices and telemedicine. Sperm analysis will evolve through the 'omics', telemedicine, advanced flow cytometry, and the artificial intelligence.Image by Biorender.com. This article belongs to the Collection Dedication to Jim Cummins. [ABSTRACT FROM AUTHOR]

Image super-resolution (SR) is a classic problem of image processing. This paper proposes a self-learning interpolation method (SLIM) based on a single image by combining grid feature mapping with binary decision tree, which is not only transparent as the interpolation-based methods, but also achieves comparable performance as the learning-based methods. Firstly, it downsamples the given image I LR to obtain its low–low-resolution image I LLR , which is used to obtain sample data for the self-learning interpolation algorithm for enlarging I LLR to get I LR . Secondly, it provides a structural feature classification method to divide all of the samples into several groups, such that each class of I LLR is mapped to a matrix of coefficients for calculating the values of the pixels of I LR . The image I LR is approximated by executing the decision tree to refine the corresponding mapping matrix. Finally, the resulting high-resolution image I HR is obtained from the given image I LR by using the mapping matrixes. Experimental results show that SLIM achieves more smooth edges and better details on subjective vision than prevailing SR methods, and it is a transparent one but achieves comparable performances on PSNR and SSIM with the learning-based methods, while it outperforms the interpolation-based methods. It means that SLIM is both transparent and efficient and has much better subjective vision than other SR methods. [ABSTRACT FROM AUTHOR]

Academic motivation is a pivotal factor in shaping students' educational trajectories. Mathematics education holds a unique position as a school subject, due to its influence on students' grade transitions and cognitive development at both basic and advanced levels. From March to May 2023, the AI Education (AIEDN) research project investigated how an AI-based learning assistant can enhance students' understanding of the subject matter through video-based learning. For this study, 275 students were selected in the age range of 14-20 from two secondary (N=137) and two grammar schools (N=138) in Baden-Württemberg, Germany. The quantitative experiment tested the extent to which learners solve more tasks, build broader (transfer) knowledge, and retain it. Students were given a set of mathematical problems on an unknown topic to solve in 90 minutes. The test group used the AI assistant to ask questions, while the control group used only keyword searches. The results of the t-test indicated that the test group who used the AI Learning assistant in the advanced course achieved a significantly strong (Cohen's d=.63) increase in performance results (T(19)=-2.82; p<.01). Conversely, the advanced course control group as well as both the test and control groups of the basic course(s) showed no significant improvements. [ABSTRACT FROM AUTHOR]

Recently, there has been a surge in interest in language teaching and learning. This has coincided with a growing demand for language teaching applications, particularly among students and self-learner's eager to acquire a new language quickly. Consequently, a diverse array of language teaching applications has emerged, each varying in its goals, methods, and outcomes. However, despite the abundance of language teaching applications, many lack a proper scientific methodology in their development. Consequently, this research aims to examine the construction of scientific curricula within applications designed for teaching Arabic to non-native speakers. It particularly focuses on the application 'AlifBee' as a model. This research focuses on identifying the scientific foundations underpinning the development of curricula in language teaching applications. Additionally, it aims to analyze the content of the 'AlifBee' application, examining its components, objectives, and methodology, including the method of preparation. This research comprises an introduction, three chapters, and a conclusion. The introduction addresses the topic's importance, its problem, questions, and approach. The first chapter presents the research's most important concepts. The second chapter focuses on the scientific foundations for building curricula in applications designed for teaching Arabic to non-native speakers. The third chapter offers a general analysis of the content and methodology of the 'AlifBee' application. Finally, the conclusion summarizes the research results and recommendations. The research yielded several significant findings. Firstly, it highlighted that the process of developing language teaching applications is both crucial and delicate, necessitating clear scientific principles and guidelines. Secondly, it was found that most applications for teaching Arabic to speakers of other languages lack a foundation in scientific curriculum development, rendering them largely ineffective. However, the 'AlifBee' application stands out for its use of a clear scientific approach in content creation, grounded in second language acquisition theory. [ABSTRACT FROM AUTHOR]

Teaching–learning-based optimization (TLBO) algorithm is an excellent human-inspired optimization technique. This paper proposes an innovative improved version of TLBO—collectivism teaching–learning-based optimization (CTLBO) algorithm. This algorithm imitates group and individual behaviours in the reality of teaching and learning, applies group-individual multi-mode cooperation strategies to form new search mechanism. The CTLBO contains three phases, i.e. preparation phase, teaching and learning phases. In the preparation phase, there are two operators, i.e. teacher self-learning and teacher-learner interaction operators. In the teaching phase, class teaching and performance-based group teaching operators are implied. In the learning phase, neighbour learning, student self-learning and team-learning strategies are mixed together to form three operators. Two sets of experiments are conducted to test the performance of CTLBO. The first set of experiments validates the improvement effect of CTLBO by comparing it with the original TLBO and other authors' improved versions of TLBO. The second set of experiments illustrates the advantage of CTLBO by comparing it with other 17 meta-heuristic algorithms in solving 30 general benchmark functions and 15 CEC2015 test suit functions. The results of experiments show that CTLBO algorithm has significant improvement effect compared with TLBO. It is the most effective one amongst the improved versions of TLBO selected for comparison, and outperforms all other 17 meta-heuristic algorithms. The algorithm can significantly improve the convergence ability and the accuracy in solving different-scale complex optimization models. [ABSTRACT FROM AUTHOR]

A moving object is often excited by time-dependent factors. If the equation of motion and time-dependent factor can be distilled from data, a foundation for further analysis can be established. This paper proposes a method to discover the equation of motion and time-dependent factors without prior information and customized preparation. We first construct statistics to determine whether the system is subject to time-dependent factors. For the motivated system, we introduce the time-dependent Fourier series to approximate the excitation. Finally, we achieve the identification by simple steps. The new method does not require the prior knowledge of the system. Since the new method determines whether the time-dependent factor is present and which state variable is motivated, it avoids the confusion caused by inappropriately adding time-dependent terms to the identification. As the designed Fourier series can approximate the unknown model accurately, the new method does not require developing customized identification for each problem. We apply the new method to discover dynamical systems from data collected from Liénard equation and SD oscillator driven by various time-dependent factors. The new method can accurately discover dynamical systems from the collected short-time data and predict many dynamical behaviors, including the long-time evolution of the system, the multi-stable dynamics, and qualitative changes in the dynamics as the time-dependent factor varies. The results show that the new identification method can discover nonautonomous dynamical systems effectively. [ABSTRACT FROM AUTHOR]

Introduction: Self-directed Learning (SDL) has been suggested as a principle of adult learning to promote lifelong learning abilities among students. Though SDL is not a new concept, there is a lack of uniform implementation across the institutions in India. The Competency-based Medical Education (CBME) proposed by the National Medical Commission in 2019 has emphasised SDL for medical students. Aim: To evaluate the effectiveness of an online, case-based SDL activity in Biochemistry for the first-year MBBS students. Materials and Methods: This was a quasi-experimental study conducted at Father Muller Medical College, Mangaluru, Karnataka, India from May to July 2021. A purposive sampling technique was used, and 138 first-year MBBS students were enrolled for the research. The topic of lipid metabolism was chosen for SDL. Parallel to the online didactic lectures, a case-based, team-based, online SDL was implemented. Students were provided with case scenarios and were instructed to discuss the case scenarios and find answers to the accompanying questions in allotted groups for 15 days. A three-hour session was held for the presentation of the SDL, followed by a post-test and reflections of students. The data were analysed using Statistical Package for Social Sciences (SPSS) version 25.0. The significance of the difference in pretest and post-test scores was assessed by paired t-test, and the level of significance was set at a p-value