This study proposes a push notification system that combines digital real-time learning, roll-call, and feedback collection functions. With the gradually flourishing online real-time learning systems, this research further builds roll-call and feedback functions for students to enhance concentration and provide opinions. Additionally, the lecturers can do a roll call irregularly and randomly through the push notification function, avoiding students logging in but away from the keyboard. Lecturers can also send questions to a specific student or invite all students to answer; the replies can show students' learning performance. The system will store each notification in a database and analyse messages automatically to record roll calls. Moreover, the system can record the time and intervals of student feedback, enabling lecturers to check students' attention and learning conditions. Currently, most online digital systems depend on the lecturer to be responsible for the entire system; taking a roll call and asking questions will consume the lecturer's teaching time and strength. The system developed in this article can do roll calls and feedback by push notifications, reducing lecturers' workload. Furthermore, the roll-call and automatic record functions can save the time of paperwork after a course.
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http://dx.doi.org/10.3389/fpsyg.2022.767389 | DOI Listing |
Res Pract Thromb Haemost
January 2025
Oxford Haemophilia and Thrombosis Centre, Department of Haematology, Oxford University Hospitals National Health Service Foundation Trust, Nuffield Orthopaedic Centre, Oxford, UK.
A state of the art lecture titled "Transfusion therapy in trauma-what to give? Empiric vs guided" was presented at the International Society on Thrombosis and Haemostasis Congress in 2024. Uncontrolled bleeding is the commonest preventable cause of death after traumatic injury. Hemostatic resuscitation is the foundation of contemporary transfusion practice for traumatic bleeding and has 2 main aims: to immediately support the circulating blood volume and to treat/prevent the associated trauma-induced coagulopathy.
View Article and Find Full Text PDFFront Bioeng Biotechnol
January 2025
Center for Orthopaedic Biomechanics, University of Denver, Denver, CO, United States.
Introduction: Accurate prediction of knee biomechanics during total knee replacement (TKR) surgery is crucial for optimal outcomes. This study investigates the application of machine learning (ML) techniques for real-time prediction of knee joint mechanics.
Methods: A validated finite element (FE) model of the lower limb was used to generate a dataset of knee joint kinematics, kinetics, and contact mechanics.
Front Vet Sci
January 2025
Divison of Small Animal Surgery, Department of Clinical Veterinary Medicine, Vetsuisse-Faculty, University of Bern, Bern, Switzerland.
Introduction: Sacroiliac luxation is a common traumatic feline injury, with the small size of the sacral body being a challenge for surgical stabilization. This study compared an innovative computer-guided drilling method with the conventional fluoroscopy-controlled freehand technique. Neuronavigation, using CT-based planning and real-time tracking, was evaluated against the freehand method for accuracy and time efficiency.
View Article and Find Full Text PDFSci Rep
January 2025
The Alan Turing Institute, London, UK.
Air pollution in cities, especially NO, is linked to numerous health problems, ranging from mortality to mental health challenges and attention deficits in children. While cities globally have initiated policies to curtail emissions, real-time monitoring remains challenging due to limited environmental sensors and their inconsistent distribution. This gap hinders the creation of adaptive urban policies that respond to the sequence of events and daily activities affecting pollution in cities.
View Article and Find Full Text PDFAnal Chim Acta
March 2025
Artificial Intelligence Research Center, Chang Gung University, Taoyuan, 333323, Taiwan; Department of Artificial Intelligence, College of Intelligent Computing, Chang Gung University, Taoyuan, 333323, Taiwan. Electronic address:
Background: In recent years, employing deep learning methods in the biosensing area has significantly reduced data analysis time and enhanced data interpretation and prediction accuracy. In some SPR fields, research teams have further enhanced detection capabilities using deep learning techniques. However, the application of deep learning to spectroscopic surface plasmon resonance (SPR) biosensors has not been reported.
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