'Keeping our Faith Alive': Investigating Buddhism Practice during COVID-19 to Inform Design for the Online Community Practice of Faith

Supportive digital technologies for the community practice of Faith remain relatively under-explored in Human Computer Interaction (HCI). We report on interviews with 12 members of a Buddhist community in the UK who self-organized and used video-conferencing tools to remain connected to their faith community during the COVID-19 pandemic, aiming to understand how they adopted online tools for their practice while shaping new collective experiences. Findings from Reflexive Thematic Analysis were combined with autoethnographic insights from the first author, also a community member. We evidence qualities of the practice that were valued by participants before and during the pandemic, and the limitations of existing tools and screen-based interactions. We contribute empirical insights on mediated religious and spiritual practice, advancing HCI discourses on Techno-Spirituality, Tangible Embodied Interaction, Soma Design and More-than-Human Worlds. We further develop design considerations for enriching spiritual experiences that are meaningful to practitioners in communities of faith. © 2023 Owner/Author.

SELF-ORGANIZED TEACHERS LEARNING TO OVERCOME THE CHALLENGES OF REMOTE STUDIES

The time of Covid-19 required appropriate infrastructure, effective planning, digitally competent adult teachers, high quality training content, user-friendly tools, as well as digitally competent learners, to make education and training systems in the digital area. The readiness of the teacher to implement the remote studies, digital skills and ability to learn, as well as mutual cooperation were the determining factors in ensuring quality education during the Pandemic. This article provides a brief insight into a survey of university teachers about the challenges and disadvantages of distance learning, and describes how teachers developed their skills by collaborating and teaching each other. © 2022 Proceedings of the 19th International Conference on Cognition and Exploratory Learning in the Digital Age, CELDA 2022. All rights reserved.

Viruses as biomaterials

Viruses lacking the capacity to infect mammals exhibit minimal toxicity, good biocompatibility, and well-defined structures. As self-organized biomolecular assemblies, they can be produced from standard biological techniques on a large scale at a low cost. Genetic, chemical, self-assembly, and mineralization techniques have been applied to allow them to display functional peptides or proteins, encapsulate therapeutic drugs and genes, assemble with other materials, and be conjugated with bioactive molecules, enabling them to bear different biochemical properties. So far, a variety of viruses (infecting bacteria, plants, or animals), as well as their particle variants, have been used as biomaterials to advance human disease prevention, diagnosis, and treatment. Specifically, the virus-based biomaterials can serve as multifunctional nanocarriers for targeted therapy, antimicrobial agents for infectious disease treatment, hierarchically structured scaffolds for guiding cellular differentiation and promoting tissue regeneration, versatile platforms for ultrasensitive disease detection, tissue-targeting probes for precision bioimaging, and effective vaccines and immunotherapeutic agents for tackling challenging diseases. This review provides an in-depth discussion of these exciting applications. It also gives an overview of the viruses from materials science perspectives and attempts to correlate the structures, properties, processing, and performance of virus-based biomaterials. It describes the use of virus-based biomaterials for preventing and treating COVID-19 and discusses the challenges and future directions of virus-based biomaterials research. It summarizes the progressive clinical trials of using viruses in humans. With the impressive progress made in the exciting field of virus-based biomaterials, it is clear that viruses are playing key roles in advancing important areas in biomedicine such as early detection and prevention, drug delivery, infectious disease treatment, cancer therapy, nanomedicine, and regenerative medicine. © 2023 Elsevier B.V.

Construction and Effect Analysis of MOOC-Based Self-organized Teaching and Research Model for Primary and Secondary School Teachers

The outbreak of the COVID-19 epidemic has promoted the development of online teaching and research for primary and secondary school teachers, and has forced them to improve their informatized teaching abilities rapidly. This research is guided by the basic principles of online teaching and learning, integrated into the 'Internet +' thinking, and adopts literature research and theoretical deduction to determine self-organized teaching and research steps, which includes 'Clear needs and starting evolution-Spontaneous learning, triggering ups and downs- Interactive communication and cause mutations- Generating results and triggering transitions-Migration practices, gathering and bifurcating'. This research gives full play to the supporting role of MOOC (Massive Open Online Course) platforms, resources and tools. Finally, the MOOC-based self-organized teaching and research model for primary and secondary school teachers is constructed. On this basis, this research relies on the 'Smart Classroom Teaching' on the Chinese University MOOCs Platform, and the quasiexperimental research method is used to carry out the practical application of the self-organized teaching and research model. Effect analysis shows that this model can improve the teaching and research performance of primary and secondary school teachers in a short period, prompting teachers to achieve a deep understanding of smart classroom teaching, and effectively solve real-world education and teaching problems. © 2021 IEEE.

Hybrid Classifiers Based on CNN, LSOF, GMDH in COVID-19 Pneumonic Lesions Types Classification Task

The paper proposes new network tools for solving classification problems based on the hybridization of convolutional networks, self-organized forests, and Group Method of Data Handling classifiers. To determine the types of lung lesions in COVID-19 on computed tomography slices the new solutions have been applied. The features of the texture obtained from the matrices of GLCM, GLRLM, GLSZM, GLDM, NGTDM statistics are used here as classification features. The mechanism of transformation of texture matrices into class-oriented features based on hybrid architectures of neural networks is developed for this. The concept development of self-organized forest according to the principles of GMDH with the use of logistical mechanisms and optimization of voting functions (LSOF) is proposed. Classifiers based on CNN, LSOF, and GMDH are used to determine the type of lung lesions 'ground-glass', 'crazy-paving', 'consolidation'on CT images of patients. The classification results obtained by the proposed algorithms have been compared with other modern methods. Data for the research were provided by the State Institution 'F.G. Y anovsky National Institute of Phthisiology and Pulmonology of the NAMS of Ukraine'. © 2021 IEEE.