Search Results (1,259)

Ocular artifacts, including blinks and saccades, pose significant challenges in the analysis of electroencephalographic (EEG) data, often obscuring crucial neural signals. This tutorial provides a comprehensive guide to the most effective methods for correcting these artifacts, with a focus on algorithms designed for both laboratory and real-world settings. We review traditional approaches, such as regression-based techniques and Independent Component Analysis (ICA), alongside more advanced methods like Artifact Subspace Reconstruction (ASR) and deep learning-based algorithms. Through detailed step-by-step instructions and comparative analysis, this tutorial equips researchers with the tools necessary to maintain the integrity of EEG data, ensuring accurate and reliable results in neurophysiological studies. The strategies discussed are particularly relevant for wearable EEG systems and real-time applications, reflecting the growing demand for robust and adaptable solutions in applied neuroscience. Full article

Game-based learning assessments rely on educational data mining approaches such as stealth assessments and quasi mixed methods that help gather data on student learning proficiency. Rarely do we see approaches where student proficiency in learning is woven into the game’s design. Educational burst games (EBGs) represent a new approach to improving learning proficiency by designing fast-paced, short, repetitive, and skill-based games. They have the potential to be effective learning interventions both during instruction in the classroom and during after-school activities such as assignments and homework. Over five years, we have developed two EBGs aimed at improving linear algebra concepts among undergraduate students. In this study, we provide the results of an in-depth evaluation of the two EBGs developed with 45 participants that represent our target population. We discuss the role of EBGs and their design constructs, such as pace and repetition, the effect of the format (2D vs. 3D), the complexity of the levels, and the influence of prior knowledge on the learning outcomes. Full article

With the increasing level of industrial informatization, massive industrial data require real-time and high-fidelity wireless transmission. Although some industrial wireless network protocols have been designed over the last few decades, most of them have limited coverage and narrow bandwidth. They cannot always ensure the certainty of information transmission, making it especially difficult to meet the requirements of low latency in industrial manufacturing fields. The 5G technology is characterized by a high transmission rate and low latency; therefore, it has good prospects in industrial applications. To apply 5G technology to factory environments with low latency requirements for data transmission, in this study, we analyze the statistical performance of the round-trip time (RTT) in a 5G-R15 communication system. The results indicate that the average value of 5G RTT is about 11 ms, which is less than the 25 ms of WIA-FA. We then consider 5G RTT data as a group of time series, utilizing the augmented Dickey–Fuller (ADF) test method to analyze the stability of the RTT data. We conclude that the RTT data are non-stationary. Therefore, firstly, the original 5G RTT series are subjected to first-order differencing to obtain differential sequences with stronger stationarity. Then, a time series analysis-based variational mode decomposition–long short-term memory (VMD-LSTM) method is proposed to separately predict each differential sequence. Finally, the predicted results are subjected to inverse difference to obtain the predicted value of 5G RTT, and a predictive error of 4.481% indicates that the method performs better than LSTM and other methods. The prediction results could be used to evaluate network performance based on business requirements, reduce the impact of instruction packet loss, and improve the robustness of control algorithms. The proposed early warning accuracy metrics for control issues can also be used to indicate when to retrain the model and to indicate the setting of the control cycle. The field of industrial control, especially in the manufacturing industry, which requires low latency, will benefit from this analysis. It should be noted that the above analysis and prediction methods are also applicable to the R16 and R17 versions. Full article

Social–emotional learning (SEL) is recognized as an important component of the educational system, significantly impacting student success. This study aims to examine how Arabic language textbooks used in Arab elementary schools in Israel serve as tools for developing social–emotional skills, beyond their role in language instruction. Using a content analysis and semiological analysis on nine ‘Arabic Our Language’ textbooks for Grades 1 to 6, the findings revealed that all categories of social–emotional skills defined by CASEL were represented, but with an imbalance in their prevalence. Interpersonal relationship skills were the most frequent, while social awareness was the least represented, despite its importance in the Israeli context, which is the context in which this study takes place. Additionally, lower grades (1–3) showed a lack of SEL content compared to higher grades (4–6), even though an early integration of these skills is more effective. Furthermore, the SEL content did not always align with the fundamental principles for optimal implementation. This study emphasizes the need for curricula and textbooks in Arabic language education that are adapted to each developmental stage and tailored to the Israeli socio-cultural context. The findings underscore the critical role of Arabic language textbooks in fostering a holistic educational experience, preparing students for both academic and social success, and serve as a call to action for curriculum designers and textbook authors to integrate social–emotional learning in linguistic educational materials. Full article

Background: As large language models continue to expand in size and diversity, their substantial potential and the relevance of their applications are increasingly being acknowledged. The rapid advancement of these models also holds profound implications for the long-term design of stimulus-responsive materials used in drug delivery. Methods: The large model used Hugging Face’s Transformers package with BigBird, Gemma, and GPT NeoX architectures. Pre-training used the PubChem dataset, and fine-tuning used QM7b. Chemist instruction training was based on Direct Preference Optimization. Drug Likeness, Synthetic Accessibility, and PageRank Scores were used to filter molecules. All computational chemistry simulations were performed using ORCA and Time-Dependent Density-Functional Theory. Results: To optimize large models for extensive dataset processing and comprehensive learning akin to a chemist’s intuition, the integration of deeper chemical insights is imperative. Our study initially compared the performance of BigBird, Gemma, GPT NeoX, and others, specifically focusing on the design of photoresponsive drug delivery molecules. We gathered excitation energy data through computational chemistry tools and further investigated light-driven isomerization reactions as a critical mechanism in drug delivery. Additionally, we explored the effectiveness of incorporating human feedback into reinforcement learning to imbue large models with chemical intuition, enhancing their understanding of relationships involving -N=N- groups in the photoisomerization transitions of photoresponsive molecules. Conclusions: We implemented an efficient design process based on structural knowledge and data, driven by large language model technology, to obtain a candidate dataset of specific photoswitchable molecules. However, the lack of specialized domain datasets remains a challenge for maximizing model performance. Full article

As global populations age, there is an urgent need to address the unique learning requirements of older adults in the context of e-learning. This study builds upon prior work to investigate the connections between older adults’ cognitive profiles, learning preferences, and attitudes toward technology in the United States. Through a survey of 203 U.S. adults aged 55 and above, data were collected on participant demographics, learning preferences, and attitudes towards technology. The results reveal a tech-savvy sample that is most comfortable with everyday applications and favors practical, visual learning approaches. Key findings include high levels of internet and smartphone adoption, varying confidence levels across different mobile applications, and strong preferences for step-by-step instructions, examples, and graphics in e-learning modules. This mixed-method study serves as a foundation for future research aimed at increasing the adoption and effectiveness of e-learning among older adults in the U.S. and globally, ultimately contributing to the overall quality of life and support for active-aging initiatives. Full article

The emergence of large language models (LLMs) has provided robust support for application tasks across various domains, such as name entity recognition (NER) in the general domain. However, due to the particularity of the medical domain, the research on understanding and improving the effectiveness of LLMs on biomedical named entity recognition (BNER) tasks remains relatively limited, especially in the context of Chinese text. In this study, we extensively evaluate several typical LLMs, including ChatGLM2-6B, GLM-130B, GPT-3.5, and GPT-4, on the Chinese BNER task by leveraging a real-world Chinese electronic medical record (EMR) dataset and a public dataset. The experimental results demonstrate the promising yet limited performance of LLMs with zero-shot and few-shot prompt designs for Chinese BNER tasks. More importantly, instruction fine-tuning significantly enhances the performance of LLMs. The fine-tuned offline ChatGLM2-6B surpassed the performance of the task-specific model BiLSTM+CRF (BC) on the real-world dataset. The best fine-tuned model, GPT-3.5, outperforms all other LLMs on the publicly available CCKS2017 dataset, even surpassing half of the baselines; however, it still remains challenging for it to surpass the state-of-the-art task-specific models, i.e., Dictionary-guided Attention Network (DGAN). To our knowledge, this study is the first attempt to evaluate the performance of LLMs on Chinese BNER tasks, which emphasizes the prospective and transformative implications of utilizing LLMs on Chinese BNER tasks. Furthermore, we summarize our findings into a set of actionable guidelines for future researchers on how to effectively leverage LLMs to become experts in specific tasks. Full article

Background/Objectives: Telepractice interventions have been found to alleviate barriers families face when seeking communication interventions. This study is a multiple-baseline single-subject design that measures parent communication opportunities and parent responsiveness to determine if parent training through online modules created for parents of children with communication support needs can be effective for training parents of autistic children with communication support needs. Methods: This study replicates work by utilizing online training used as well as the same variables and definitions. This study expands the original study by providing the children with speech-generating devices (SGDs). SGDs are an assistive technology tool to increase language production and give access to language to minimally verbal autistic people. A central difference between this study and study is that the only training parents received was the online modules and written instructions to set up the SGD. Results: Overall, the POWR modules appear to positively impact the communication opportunities provided by the parent during play and activities, increase child communication, and improve parent proficiency in implementing the POWR strategy. Conclusions: There is a need for a larger single-case study or a randomized control trial to replicate these findings. Additional instruction may be needed for parents of children with autism around responsive interactions. This study adds to innovative ways of providing family-centered training and access to AAC for those with barriers to service. Full article

With the development of unmanned aircraft and artificial intelligence technology, the future of air combat is moving towards unmanned and autonomous direction. In this paper, we introduce a new layered decision framework designed to address the six-degrees-of-freedom (6-DOF) aircraft within-visual-range (WVR) air-combat challenge. The decision-making process is divided into two layers, each of which is addressed separately using reinforcement learning (RL). The upper layer is the combat policy, which determines maneuvering instructions based on the current combat situation (such as altitude, speed, and attitude). The lower layer control policy then uses these commands to calculate the input signals from various parts of the aircraft (aileron, elevator, rudder, and throttle). Among them, the control policy is modeled as a Markov decision framework, and the combat policy is modeled as a partially observable Markov decision framework. We describe the two-layer training method in detail. For the control policy, we designed rewards based on expert knowledge to accurately and stably complete autonomous driving tasks. At the same time, for combat policy, we introduce a self-game-based course learning, allowing the agent to play against historical policies during training to improve performance. The experimental results show that the operational success rate of the proposed method against the game theory baseline reaches 85.7%. Efficiency was also outstanding, with an average 13.6% reduction in training time compared to the RL baseline. Full article

This study investigated the impact of compassion-based programs on gifted students’ emotional style, self-compassion, empathy, and well-being. The study conducted a quasi-experimental study of 30 academically gifted junior high school students in the eighth grade in Taiwan. Seventeen students were in the experimental group, and 13 were in the control group. The study adopted an emotional style scale, a self-compassion scale, an empathy scale, and a well-being scale. Covariance analysis was used to analyze the data. The results found that the students in the experimental group scored significantly higher than the students in the control group on some subscales and total scores of four tests in part. Students also learned more about themselves, identified and adjusted cognitive patterns, communicative skills, and so on, based on their feedback. The results provide some suggestions about future curriculum design, instruction, and counseling based on positive psychology. Full article

This study explores the development and evaluation of a game aimed at fostering sustainable self-help and mutual help education for disaster prevention. The game, developed using Unity and Blender, addresses the critical need for effective disaster preparedness, emphasizing the importance of community cooperation, as evidenced by the Great Hanshin-Awaji Earthquake, in which most rescues were performed by neighbors. Additionally, it features realistic disaster scenarios, and the game’s design incorporates gamification and simulation elements to enhance learning and engagement. An experiment involving 20 participants aged 20–21 years was conducted to evaluate the game’s effectiveness. Participants played the game on desktop personal computers for at least 10 min, and their performance and awareness were measured through pre- and post-gameplay questionnaires. The results indicated no statistically significant improvement in the ability to cooperate with strangers, provide correct instructions, or overall consciousness of helping others. However, a slight increase in the average scores was observed. Participant feedback highlighted the game’s realistic approach and suggested improvements in operability and platform compatibility. The study concludes that while the game shows promise, further development and research are needed to enhance its educational impact and effectiveness in disaster preparedness. Full article

This article summarizes the literature on the design of a conceptual framework for secondary translanguaging classrooms. As school districts move to expand dual language programs beyond elementary schools, they are obligated to ensure that these programs are grounded in sound educational theory. Rooted in culturally sustaining perspectives, we propose a conceptual framework for dynamic dual language programming that is inclusive of translanguaging pedagogies. This conceptual framework includes key scholarship on language planning from sociocultural perspectives and offers examples of instructional approaches aligned to these perspectives. The purpose of this manuscript is to inform practice and suggest areas of potential research for secondary dual language education. Full article

In this paper, we present the design and fabrication of a novel chip-on-tip catheter, which uses a microcamera and optical fibres to capture in vivo images in a beating porcine heart thanks to a saline flush to clear the blood field. Here, we demonstrate the medical utility and mechanical robustness of this catheter platform system, which could be used for other optical diagnostic techniques, surgical guidance, and clinical navigation. We also discuss some of the challenges and system requirements associated with developing a miniature prototype for such a study and present assembly instructions. Methods of clearing the blood field are discussed, including an integrated flush channel at the distal end. This permits the capture of images of the endocardial walls. The device was navigated under fluoroscopic guiding, through a guiding catheter to various locations of the heart, where images were successfully acquired. Images were captured at the intra-atrial septum, in the left atrium after a trans-septal cross procedure, and in the left ventricle, which are, to the best of our knowledge, the first images captured in an in vivo beating heart using endoscopic techniques. Full article

Teaching physical education (PE) involves adopting contemporary instructional models and teaching methods. Especially at an undergraduate level, the teachers’ participation in professional communities can support their self-efficacy in adopting context-specific instructional models, leading to an improvement in student learning. The aim of the present study was to examine the effects of preservice PE teachers’ participation in a professional development (PD) program designed according to the principles of the community of inquiry (CoI) on their self-efficacy in teaching physical education using instructional models. Twenty-three preservice PE teachers (male = 11, female = 12) at the University of Athens, Greece, participated during the 2022–2023 spring semester in a PD program specifically designed according to the CoI principles to support them in the use of PE curriculum models in a secondary school practicum. A pre-post convergent mixed methodology was used, with quantitative (Ohio State Teacher Efficacy Scale, OSTES) and qualitative (semi-structured interviews) data evaluating the changes in the participants’ self-efficacy in the use of instructional models. Results showed that even though the program was evaluated as effective in terms of supporting the participants’ knowledge and skills on the use of the models, there were no statistically significant changes in their OSTES self-efficacy indices. Given the complexity of PE teaching and the latent structure of the self-efficacy trait, a longer duration of similar PD programs is suggested. Full article