The primary purpose of this study was to examine the differences in background characteristics and academic performance of students in distance learning and on-campus programs in allied healthcare education at one medical university in the Eastern United States. The study depended on data from 252 students, drawn from three disciplines, clinical laboratory science, health information administration, and nuclear medicine. The study employed the chi-square test and t-test for analyzing the data. The study's findings suggested no significant differences in terms of the background characteristics of gender and previous academic performance between distance and on-campus students. However, the two groups of students differed significantly in terms of their age composition such that, as expected, distance learning students comprised the majority of older students (25 years and older) relative to their on campus counterparts. The study further showed that, when assessed in terms of their final grade point averages as well as certification pass rates, distance and on campus students were indistinguishable from each other. Similar results were found when final GPA scores within the three separate disciplines were compared and in certification scores in two out of the three disciplines. However, the certification scores of nuclear medicine technology students were found to be significantly different between the two groups, in which on-campus students earned a significantly higher score than their counterparts in the distance learning program. Administrators and educators who are considering offering distance learning as a method of degree obtainment in allied healthcare education need data, such as reported in this study, when determining if distance learning can be as effective as on-campus learning in allied healthcare education.
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J Comput Neurosci
January 2025
Program in Neuroscience, Indiana University Bloomington, Bloomington, IN, USA.
Hippocampal representations of space and time seem to share a common coding scheme characterized by neurons with bell-shaped tuning curves called place and time cells. The properties of the tuning curves are consistent with Weber's law, such that, in the absence of visual inputs, width scales with the peak time for time cells and with distance for place cells. Building on earlier computational work, we examined how neurons with such properties can emerge through self-supervised learning.
View Article and Find Full Text PDFInfancy
January 2025
Donders Institute for Brain, Cognition and Behavior, Radboud University, Nijmegen, The Netherlands.
The ability to recognize and act on others' emotions is crucial for navigating social interactions successfully and learning about the world. One way in which others' emotions are observable is through their movement kinematics. Movement information is available even at a distance or when an individual's face is not visible.
View Article and Find Full Text PDFJ Chem Phys
January 2025
Machine Learning Group, Technische Universität Berlin, 10587 Berlin, Charlottenburg, Germany.
We introduce the alchemical harmonic approximation (AHA) of the absolute electronic energy for charge-neutral iso-electronic diatomics at fixed interatomic distance d0. To account for variations in distance, we combine AHA with this ansatz for the electronic binding potential, E(d)=(Eu-Es)Ec-EsEu-Esd/d0+Es, where Eu, Ec, Es correspond to the energies of the united atom, calibration at d0, and the sum of infinitely separated atoms, respectively. Our model covers the two-dimensional electronic potential energy surface spanned by distances of 0.
View Article and Find Full Text PDFActa Radiol
January 2025
Department of Otorhinolaryngology Head and Neck Surgery, The First Affiliated Hospital of Guangxi Medical University, Nanning, Guangxi, PR China.
Background: Segmentation of the cochlea in temporal bone computed tomography (CT) is the basis for image-guided otologic surgery. Manual segmentation is time-consuming and laborious.
Purpose: To assess the utility of deep learning analysis in automatic segmentation of the cochleae in temporal bone CT to differentiate abnormal images from normal images.
J Bone Oncol
February 2025
School of Mathematics and Computer Science, Quanzhou Normal University, Quanzhou, 362001, China.
Objective: Segmenting and reconstructing 3D models of bone tumors from 2D image data is of great significance for assisting disease diagnosis and treatment. However, due to the low distinguishability of tumors and surrounding tissues in images, existing methods lack accuracy and stability. This study proposes a U-Net model based on double dimensionality reduction and channel attention gating mechanism, namely the DCU-Net model for oncological image segmentation.
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