Performed in a Slovenian higher education institution, the presented research was designed to help investigate which factors influence the ways a student perceives an e-course's usefulness in a blended learning environment. The study is based on an online questionnaire completed by 539 students whose participation in the survey was voluntary. Using structural equation modelling, the students' perceptions of different aspects were investigated, including their attitudes to course topics and technology, learning preferences, teachers' role in course design and managing the teaching process. The empirical results show e-learning is positively perceived to be usefulness when: (1) the teacher is engaged and their activities in an e-course, with the (2) a student's attitude to the subject matter and the lecturer's classroom performance having a direct impact, and (3) technology acceptance having an indirect impact. No major differences were revealed when the model was tested on student subgroups sorted by gender, year of study, and students' weekly spare-time activities.
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http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6872162 | PMC |
http://journals.plos.org/plosone/article?id=10.1371/journal.pone.0223767 | PLOS |
Int J Comput Assist Radiol Surg
January 2025
Faculty of Computer Science and Research Campus STIMULATE, Otto-von-Guericke University of Magdeburg, Magdeburg, Germany.
Purpose: Structured abdominal examination is an essential part of the medical curriculum and surgical training, requiring a blend of theory and practice from trainees. Current training methods, however, often do not provide adequate engagement, fail to address individual learning needs or do not cover rare diseases.
Methods: In this work, an application for structured Abdominal Examination Training using Augmented Reality (AETAR) is presented.
J Environ Manage
January 2025
GAIKER Technology Centre, Basque Research and Technology Alliance (BRTA), Parque Tecnológico, Edificio 202, 48170, Zamudio, Spain.
Current industrial separation and sorting technologies struggle to efficiently identify and classify a large part of Waste of Electric and Electronic Equipment (WEEE) plastics due to their high content of certain additives. In this study, Raman spectroscopy in combination with machine learning methods was assessed to develop classification models that could improve the identification and separation of Polystyrene (PS), Acrylonitrile Butadiene Styrene (ABS), Polycarbonate (PC) and the blend PC/ABS contained in WEEE streams, including black plastics, to increase their recycling rate, and to enhance plastics circularity. Raman spectral analysis was carried out with two lasers of different excitation wavelengths (785 nm and 1064 nm) and varying setting parameters (laser power, integration time, focus distance) with the aim at reducing the fluorescence.
View Article and Find Full Text PDFBMC Public Health
January 2025
Department of Women's and Children's Health, Karolinska Institutet, Tomtebodavägen 18A, Stockholm, Solna, 171 77, Sweden.
Background: Globally, the quality of maternal and newborn care remains inadequate, as seen through indicators like perineal injuries and low Apgar scores. While midwifery practices have the potential to improve care quality and health outcomes, there is a lack of evidence on how midwife-led initiatives, particularly those aimed at improving the use of dynamic birth positions, intrapartum support, and perineal protection, affect these outcomes.
Objective: To explore how the use of dynamic birth positions, intrapartum support, and perineal protection impact the incidence of perineal injuries and the 5-min Apgar score within the context of a midwife-led quality improvement intervention.
Med Biol Eng Comput
January 2025
Department of Computer Science and Engineering, Shri Shankaracharya Institute of Professional Management and Technology, Raipur, (C.G.), India.
This study presents an advanced methodology for 3D heart reconstruction using a combination of deep learning models and computational techniques, addressing critical challenges in cardiac modeling and segmentation. A multi-dataset approach was employed, including data from the UK Biobank, MICCAI Multi-Modality Whole Heart Segmentation (MM-WHS) challenge, and clinical datasets of congenital heart disease. Preprocessing steps involved segmentation, intensity normalization, and mesh generation, while the reconstruction was performed using a blend of statistical shape modeling (SSM), graph convolutional networks (GCNs), and progressive GANs.
View Article and Find Full Text PDFSci Rep
January 2025
Physics Department, Science College, Princess Nourah Bint Abdulrahman University, Riyadh, Saudi Arabia.
Semantic segmentation of high-resolution images from remote sensing is crucial across various sectors. However, due to limitations in computational resources and the complexity of network architectures, many sophisticated semantic segmentation models struggle with efficiency in real-world applications, leading to an interest in developing lightweight model like borders. These models often employ a dual-branch structure, which balances processing speed and performance effectively.
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