This study was aimed at exploring links between adolescents' deep and surface approaches to learning, Fear of Missing Out (FoMO), and Problematic Internet Use (PIU) by using Partial Least Squares Structural Equation Modeling (PLS-SEM). The analysis corroborated the postulated positive links between surface learning, FoMO, and PIU. Moreover, the FoMO construct represented a complimentary mediation between the surface learning approach and PIU constructs. This study may lead to a plausible inference according to which both FoMO and surface learning share a common core characteristic of decreased levels of self-regulation that might lead to PIU. Having students acquire and practice skills of self-regulation might help them control their levels of FoMO, and consequently their PIU at schools or out-of-school learning environments.
Download full-text PDF |
Source |
|---|---|
| http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6112085 | PMC |
| http://dx.doi.org/10.1016/j.invent.2018.05.002 | DOI Listing |
Publication Analysis
Top Keywords
Similar Publications
Integrating machine learning and structure-based approaches for repurposing potent tyrosine protein kinase Src inhibitors to treat inflammatory disorders.
Sci Rep
January 2025
State Key Laboratory of Chemical Resources Engineering, Beijing University of Chemical Technology, Beijing, 100029, People's Republic of China.
Tyrosine-protein kinase Src plays a key role in cell proliferation and growth under favorable conditions, but its overexpression and genetic mutations can lead to the progression of various inflammatory diseases. Due to the specificity and selectivity problems of previously discovered inhibitors like dasatinib and bosutinib, we employed an integrated machine learning and structure-based drug repurposing strategy to find novel, targeted, and non-toxic Src kinase inhibitors. Different machine learning models including random forest (RF), k-nearest neighbors (K-NN), decision tree, and support vector machine (SVM), were trained using already available bioactivity data of Src kinase targeting compounds.
View Article and Find Full Text PDFAI Interventions to Alleviate Healthcare Shortages and Enhance Work Conditions in Critical Care: Qualitative Analysis.
J Med Internet Res
January 2025
ETH Zurich, Zurich, Switzerland.
Background: The escalating global scarcity of skilled health care professionals is a critical concern, further exacerbated by rising stress levels and clinician burnout rates. Artificial intelligence (AI) has surfaced as a potential resource to alleviate these challenges. Nevertheless, it is not taken for granted that AI will inevitably augment human performance, as ill-designed systems may inadvertently impose new burdens on health care workers, and implementation may be challenging.
View Article and Find Full Text PDFApplication of artificial intelligence for nutrient estimation in surface water bodies of basins with intensive agriculture.
Integr Environ Assess Manag
January 2025
División de Estudios de Posgrado e Investigación, Tecnológico Nacional de México/IT de Culiacán, Culiacán, Sinaloa, México.
Eutrophication is one of the most relevant concerns due to the risk to water supply and food security. Nitrogen and phosphorus chemical species concentrations determined the risk and magnitude of eutrophication. These analyses are even more relevant in basins with intensive agriculture due to agrochemical discharges.
View Article and Find Full Text PDFAutomated and Efficient Sampling of Chemical Reaction Space.
Adv Sci (Weinh)
January 2025
Department of Chemistry, Yonsei University, 50 Yonsei-ro, Seodaemun-gu, Seoul, 03722, Republic of Korea.
Machine learning interatomic potentials (MLIPs) promise quantum-level accuracy at classical force field speeds, but their performance hinges on the quality and diversity of training data. An efficient and fully automated approach to sample chemical reaction space without relying on human intuition, addressing a critical gap in MLIP development is presented. The method combines the speed of tight-binding calculations with selective high-level refinement, generating diverse datasets that capture both equilibrium and reactive regions of potential energy surfaces.
View Article and Find Full Text PDFPhysics-Based Synthetic Data Model for Automated Segmentation in Catalysis Microscopy.
Microsc Microanal
January 2025
Fritz-Haber-Institut der Max-Planck-Gesellschaft, Berlin 14195, Germany.
In catalysis research, the amount of microscopy data acquired when imaging dynamic processes is often too much for nonautomated quantitative analysis. Developing machine learned segmentation models is challenged by the requirement of high-quality annotated training data. We thus substitute expert-annotated data with a physics-based sequential synthetic data model.
View Article and Find Full Text PDFWant AI Summaries of new PubMed Abstracts delivered to your In-box?
Enter search terms and have AI summaries delivered each week - change queries or unsubscribe any time!