In this study, network analysis was conducted using an exploratory approach on the variables of self-efficacy, academic resilience (AR), cognitive test anxiety and academic achievement (ACH), which are frequently examined in educational research. Data were collected from a total of 828 Turkish secondary school adolescents (51.9% female), using three different self-reported scales for self-efficacy, AR and cognitive test anxiety, as well as an ACH scale. The data were analyzed using regularized partial correlation network analysis (EBICglasso). The results show that academic self-efficacy (ASE) stands out among the variables of the study and that there is a positive relationship between ASE and all other variables except cognitive test anxiety. Besides, increasing students' ASE and AR levels plays a notable role in increasing their ACH levels. By providing new evidence on the relationships among these variables, this study offers insights that may inspire educational policy interventions.
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http://dx.doi.org/10.1017/gmh.2025.17 | DOI Listing |
Pediatr Infect Dis J
March 2025
Department of Pediatrics, University of Alabama at Birmingham, Birmingham, Alabama.
Background: Congenital cytomegalovirus is the leading cause of nongenetic sensorineural hearing loss. Treatment with (val)ganciclovir improves audiologic outcomes. Neutropenia is a common adverse event, but correlates that predict who will develop neutropenia have not been identified.
View Article and Find Full Text PDFJ Am Chem Soc
March 2025
Institute for Decarbonization Materials, University of California, Berkeley, California 94720, United States.
The efficient removal of CO from exhaust streams and even directly from air is necessary to forestall climate change, lending urgency to the search for new materials that can rapidly capture CO at high capacity. The recent discovery that diamine-appended metal-organic frameworks can exhibit cooperative CO uptake via the formation of ammonium carbamate chains begs the question of whether simple organic polyamine molecules could be designed to achieve a similar switch-like behavior with even higher separation capacities. Here, we present a solid molecular triamine, 1,3,5-tris(aminomethyl)benzene (TriH), that rapidly captures large quantities of CO upon exposure to humid air to form the porous, crystalline, ammonium carbamate network solid TriH(CO)·HO (TriHCO).
View Article and Find Full Text PDFMol Inform
March 2025
Faculty of Information Technology, HUTECH University, Ho Chi Minh City, Vietnam.
Within a recent decade, graph neural network (GNN) has emerged as a powerful neural architecture for various graph-structured data modelling and task-driven representation learning problems. Recent studies have highlighted the remarkable capabilities of GNNs in handling complex graph representation learning tasks, achieving state-of-the-art results in node/graph classification, regression, and generation. However, most traditional GNN-based architectures like GCN and GraphSAGE still faced several challenges related to the capability of preserving the multi-scaled topological structures.
View Article and Find Full Text PDFHealth Promot Chronic Dis Prev Can
March 2025
Evidence Synthesis and Knowledge Translation Unit, Centre for Surveillance and Applied Research, Health Promotion and Chronic Disease Prevention Branch, Public Health Agency of Canada, Ottawa, Ontario, Canada.
Introduction: We investigated the prevalence of new or persistent manifestations experienced by COVID-19 survivors at 3 or more months after their initial infection, collectively known as post-COVID-19 condition (PCC).
Methods: We searched four electronic databases and major grey literature resources for prospective studies, systematic reviews, authoritative reports and population surveys. A random-effects meta-analysis pooled the prevalence data of 22 symptoms and outcomes.
Sci Adv
March 2025
Department of Neurology, Johns Hopkins University, Baltimore, MD 21205, USA.
There is great interest in using genetically tractable organisms such as to gain insights into the regulation and function of sleep. However, sleep phenotyping in has largely relied on simple measures of locomotor inactivity. Here, we present FlyVISTA, a machine learning platform to perform deep phenotyping of sleep in flies.
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