Perceptions of distinctions between patient and healthcare zones among intensive care unit nurses at a Korean tertiary hospital: A cross-sectional study.

Background: Intensive care unit (ICU) patients face higher infection risks from invasive procedures, highlighting the critical role of ICU nurses in infection prevention. Clear differentiation between the patient and healthcare zones is essential for effective hand hygiene and disinfection, yet research on this topic is limited.

Aim: To assess ICU nurses' perception of the concept of patient and healthcare zones and evaluate their similarity and accuracy in identifying the zones.

Real-Time CNN Training and Compression for Neural-Enhanced Adaptive Live Streaming.

We propose a real-time convolutional neural network (CNN) training and compression method for delivering high-quality live video even in a poor network environment. The server delivers a low-resolution video segment along with the corresponding CNN for super resolution (SR), after which the client applies the CNN to the segment in order to recover high-resolution video frames. To generate a trained CNN corresponding to a video segment in real-time, our method rapidly increases the training accuracy by promoting the overfitting property of the CNN while also using curriculum-based training.

Predictions about reward outcomes in rhesus monkeys.

Human infants and nonhuman animals respond to surprising events by looking longer at unexpected than expected situations. These looking responses provide core cognitive evidence that nonverbal minds make predictions about possible outcomes and detect when these predictions fail to match reality. We propose that this phenomenon has crucial parallels with the processes of reward prediction error, indexing the difference between expected and actual reward outcomes.