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关键词： eHealth   general: behaviour change   lifestyle: education   lifestyle: rehabilitation   lifestyle: pulmonary   medicine: personalised medicine  

摘要： Introduction A pulmonary rehabilitation (PR) programme, including exercise training, education, and behaviour change, is highly recommended in treatment guidelines for chronic obstructive pulmonary disease (COPD). A new PR educational material for PR using an interactive three-dimensional (3D) visualisation technique was developed. There is little known regarding using 3D in this setting. The aim was to evaluate, within a PR programme setting, differences between outcomes of education through interactive 3D compared with education by means of 2D visualisation in patients with COPD regarding health-related quality of life (HRQL), physical capacity, exercise self-efficacy, compliance to exercise training, compliance to medication, and the use of health care and to describe learning styles. Methods Patients were cluster randomised to PR at the University Hospital clinic, including exercise training, for 10 weeks with education by 3D (n = 27) or education by traditional 2D technique (n = 17). At follow-ups, HRQL, physical capacity, self-efficacy, handgrip strength, compliance to exercise training, compliance to medication and health care utilisation were assessed. Results No significant differences were observed in HRQL in relation to COPD between the groups after 10 weeks. Differences were found in handgrip strength at baseline and at 10 weeks (p < 0.01). The 3D group had improved scores in HRQL assessed with the Leicester Cough Questionnaire (LCQ-S) psychological domain (p = 0.022). In the 2D group, the LCQ-S social domain (p = 0.028), psychological domain (p = 0.021) and the St George's Respiratory Questionnaire total (p = 0.050) were increased. The most common learning mode and learning style in both groups was reflective observation and divergent style. Conclusion An interactive education in 3D technique used in a PR programme for patients with COPD yielded no significant differences compared to conventional techniques. Three-dimensional techniques can be used in P

关键词： Robots   Three-dimensional displays   Programming   Visualization   Programming profession   Mobile applications   Education   Usability   Python   Operating systems   Computational thinking   educational robotics (ER)   pre-university education   programming  

摘要： RoblockLLy is an educational robotics simulator designed for primary and secondary school students, whose goal is to increase their interest in science, technology, engineering, and mathematics. In the particular case of computer science, it allows developing computational thinking skills. It has been designed with ease of use in mind. This free tool is available through a web browser and does not need a complex installation or specific hardware requirements, allowing educational robotics to be introduced to a wide range of users by working on practical projects that will help them understand key concepts of robotics and programming. The effectiveness of RoblockLLy has been validated based on motivation, usability, and user experience criteria. The tool was validated with 212 secondary school students (12-16 years old). Specifically, motivation was measured with the Intrinsic Motivation Inventory, usability with the System Usability Scale, and user experience with the User Experience Questionnaire. Generally speaking, the results demonstrate that students perceived RoblockLLy as a novel and interesting tool. The ratings for usability were predominantly positive, although a few students indicated a preference for expert assistance. The overall rating of the user experience was positive as well, yet notable differences in attitudes toward motivation and usability were observed between genders.

关键词： Computer science   Scientific data   Sleep disorders  

摘要： Poor quality and poor duration of sleep have been associated with cognitive decline, diseases, and disorders. Therefore, sleep studies are imperative to recapitulate phenotypes associated with poor sleep quality and uncover mechanisms contributing to psychopathology. Classification of sleep stages, vigilance state bout durations, and number of transitions amongst vigilance states serves as a proxy for evaluating sleep quality in preclinical studies. Currently, the gold standard for sleep staging is expert human inspection of polysomnography (PSG) obtained from preclinical rodent models and this approach is immensely time consuming. To accelerate the analysis, we developed a deep-learning-based software tool for automated sleep stage classification in rats. This study aimed to develop an automated method for classifying three sleep stages in rats (REM/paradoxical sleep, NREM/slow-wave sleep, and wakefulness) using a deep learning approach based on single-channel EEG data. Single-channel EEG data were acquired from 16 rats, each undergoing two 24 h recording sessions. The data were labeled by human experts in 10 s epochs corresponding to three stages: REM/paradoxical sleep, NREM/slow-wave sleep, and wakefulness. A deep neural network (DNN) model was designed and trained to classify these stages using the raw temporal data from the EEG. The DNN achieved strong performance in predicting the three sleep stages, with an average F1 score of 87.6% over a cross-validated test set. The algorithm was able to predict key parameters of sleep architecture, including total bout duration, average bout duration, and number of bouts, with significant accuracy. Our deep learning model effectively automates the classification of sleep stages using single-channel EEG data in rats, reducing the need for labor-intensive manual annotation. This tool enables high-throughput sleep studies and may accelerate research into sleep-related pathologies. Furthermore, we provide over 700 h of expert

关键词： Deepfakes   Feature extraction   Streaming media   Accuracy   Computer science   Visualization   Petroleum   Systematic literature review   Robustness   Real-time systems   Deepfake   multimedia   audio-video analysis   temporal analysis   media authenticity  

摘要： The rise of deepfake technology poses significant threats to the authenticity of content on social media. This research introduces Sach-AI, a pioneering framework for detecting various deepfakes in video, audio, and image data. Leveraging the power of deep neural networks, Sach-AI utilizes Eulerian Video Magnification combined with the ResNext architecture for enhanced detection. For video deepfakes, Long Short-Term Memory (LSTM) networks are integrated to improve classification tasks. This combination allows Sach-AI to effectively address the evolving, multimodal nature of deepfakes. The framework has been rigorously evaluated using diverse datasets, such as Celeb-DF and FaceForensics++, demonstrating its robustness and accuracy. Sach-AI achieved 97.76% accuracy in video deepfake detection, surpassing Intel's FakeCatcher, 99.13% accuracy in audio deepfake detection, and 93.64% accuracy in image deepfake detection. These results underscore Sach-AI's reliability in safeguarding digital media integrity against deceptive synthetic content in an era increasingly dominated by artificial technologies.

关键词： online STEM education   student engagement   qualitative research   thematic analysis   learning strategies   course design   peer interaction   digital learning environments   educational resources  

摘要： Student engagement is widely recognized as a critical factor influencing the success of engineering students in both traditional and online learning environments. Research indicates that engaged students develop a deeper understanding of course content, acquire practical skills, and maintain motivation in their field. Despite the theoretical importance of engagement, applying these concepts to specific online STEM courses presents challenges, particularly in mitigating student disengagement. This study addresses the need to better understand the characteristics of online STEM courses and student experiences that influence engagement. The study aims to identify the key factors influencing student engagement in online STEM education. By examining students' experiences and perceptions, the research seeks to provide insights into the strategies and activities that promote active learning and foster a sense of community in online courses. In-depth interviews were conducted with 33 undergraduate students enrolled in online computer science courses at Oregon State University. The interviews were transcribed and analyzed using thematic analysis to identify emerging themes and patterns related to student engagement. The findings reveal two main themes influencing student engagement in STEM courses: course characteristics & structure, and human interactions in learning. Course characteristics & structure, including resources, content delivery format, and course difficulty, impacted student engagement. Human interactions, both student-student and student-instructor/TA, played a crucial role in fostering engagement and a sense of community. The insights gained contribute to the understanding of effective teaching practices in online computer science education and provide valuable recommendations for designing engaging and inclusive online learning experiences that promote student success. The study highlights the importance of well-structured courses, diverse learning resources

关键词： Computer Science   Programming Languages,F.3.2  

摘要： We propose a new step-wise approach to proving observational equivalence, and in particular reasoning about fragility of observational equivalence. Our approach is based on what we call local reasoning. The local reasoning exploits the graphical concept of neighbourhood, and it extracts a new, formal, concept of robustness as a key sufficient condition of observational equivalence. Moreover, our proof methodology is capable of proving a generalised notion of observational equivalence. The generalised notion can be quantified over syntactically restricted contexts instead of all contexts, and also quantitatively constrained in terms of the number of reduction steps. The operational machinery we use is given by a hypergraph-rewriting abstract machine inspired by Girard's Geometry of Interaction. The behaviour of language features, including function abstraction and application, is provided by hypergraph-rewriting rules. We demonstrate our proof methodology using the call-by-value lambda-calculus equipped with (higher-order) state.

关键词： Internet of Things   Probabilistic logic   Performance evaluation   Energy efficiency   Energy consumption   Delays   Computer science   Bandwidth   Wireless sensor networks   Security   name data networking   efficient pre-caching technique   network architecture  

摘要： The current Internet of Things (IoT) networks experience considerable challenges due to the limitation of device capacities, energy constraints, and data accessibility problems. The existing IP-based Internet architecture is inadequate to handle the huge volume of interconnected devices and data because of restricted address capacity and ineffective data retrieval processes. To address these challenges, Named Data Networking (NDN) appears as a promising solution, offering content-centric approaches and proficient in-network caching. This study presents a novel caching technique, named the Efficient Pre-Caching Technique (EPCT), which depends on two mechanisms, such as content selection using similarity functions and content placement. The proposed technique aims to improve cache hit ratios, latency, and hop count ratios. A full performance test in a simulated environment shows that EPCT is better than other NDN-based IoT caching techniques by highlighting its ability to make NDN-based IoT networks more efficient overall. Our simulation results represent that EPCT considerably improves the performance through key metrics. In particular, it increases the cache hit ratio up to 38%, reduce the average hop count by 26%, and decreases the content retrieval latency around 31% when it is compared with existing caching techniques such as CEE, MPC, and CFPC. These considerable improvements highlight the EPCT potential to increase data availability and ease the network traffic in dynamic IoT settings.

关键词： 数据驱动   初中英语   教学评”一体化   教学模式  

摘要： 在深化新时代教育评价改革背景下，为更好地适应个性化和精准化教学的需求，南宁市龙堤路初级中学创造性地构建了“1+5”数据驱动的“教学评”一体化应用模式，即以数据驱动为内核，分别对应学、教、考、管、评五个方面。以外研版八年级英语上册Unit 6 “I’m going to study computer science”为例，“1+5”数据驱动的“教学评”一体化应用模式具体实践如下：学方面，构建个性化的自主学习环境；教方面，创新教学模式，由经验教学向精准教学转变；考方面，建设优质校本资源库，创新资源应用服务体系；管方面，改变教学管理方式，提升教学管理能力；评方面，构建数字化多维评价方式。

关键词： Education   Data mining   Training   Computer science   Planning   Market research   Learning management systems   Interviews   Focusing   Conferences   Higher education   evaluation tool   computer science   data mining  

摘要： This work introduces an innovative teaching methodology based on microcompetences applied in a higher education context. The intervention involved creating a repository of practical case studies in the form of quizzes and integrating microcompetences into each course activity. The digital tool Sapiens was used to identify learning deficiencies and provide both collective and individualized feedback. The results indicate a significant increase in student participation and academic performance compared to previous years. Furthermore, students voluntarily used virtual teaching modalities to reinforce their knowledge, particularly in more complex areas. Data mining techniques identified performance patterns among students, highlighting the methodology's effectiveness in improving both transversal and specific competences. The study's findings underscore the importance of implementing microcompetency-based methodologies in higher education to enhance the quality of learning and continuous assessment. This approach not only facilitated a deeper understanding of course content but also promoted critical thinking, abstract reasoning, and interpersonal skills, preparing students for future academic and professional challenges. Additionally, the flexibility and adaptability of the digital tools used provided a seamless transition across different teaching modalities, such as in-person, hybrid, and online formats. Thus, the implementation of this innovative methodology has demonstrated its potential to significantly improve student engagement, participation, and academic success, thereby contributing to a more effective and comprehensive educational experience in higher education.

摘要： There has been growing advocacy for a nuanced understanding of health practices and challenges in diverse Black contexts in recent years. This symposium aims to show the multifaceted intersection of health, culture, and well-being within Black communities, offering a comprehensive exploration of chronic disease management, family caregiving, and mental well-being. Ado-Mensah scrutinizes the nuanced roles and characteristics of family caregivers in African American and Black immigrant communities, shedding light on the unique challenges they face in the context of diabetes management. Groves introduces a novel approach to hypertension management by combining the DASH diet with culturally tailored recipes, recognizing the importance of culturally sensitive interventions for improving health outcomes among Black women. Kum explores the cultural aspects influencing family caregiving practices among African and Afro-Caribbean immigrants, contributing valuable insights into the broader context of eldercare within these communities. Lastly, Hutton sheds light on mental health challenges, emphasizing the need for a nuanced understanding of depression and loneliness symptoms within the diverse diasporic experiences of African Americans and Black communities. Together, these contributions weave a rich tapestry of knowledge, providing valuable insights into disease management, caregiving dynamics, and mental well-being in the intricate intersection of health and culture within Black communities.