The expression of homophobic violence in schools reveals the urgency of an analytical approach to debate the impact of this phenomenon on students' mental health. This article seeks to debate and better comprehend school memories from young gays, lesbians, and bisexuals, as well as to discuss how homophobic bullying affected their school trajectories. This study is based on cultural-historical psychology in intersection with gender and sexuality studies. In-depth online interviews were conducted with three young subjects who identified themselves as non-heterosexual. The interviews were recorded, transcribed, and analyzed through the analytical discourse tool defined as Nuclei of Meanings. The results were organized in two topics of discussion: (a) the problems associated with non-heterosexual identity in schools; (b) the search for other ways of experiencing sexual identity in school. Throughout the article, reflections were held about the challenges participants had to deal with in order to regularly attend school and be educated, as well as the obstacles they faced in building their own ways of recognizing their sexual identity. The unique ways in which these young subjects took a stand in the face of homophobic situations show new methods to create educational interventions in order to include sexual diversity and openness to different possibilities of being and acting.
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http://dx.doi.org/10.3390/ijerph20196810 | DOI Listing |
Brain Inform
January 2025
Department of Neurology, Massachusetts General Hospital, Harvard Medical School, Boston, MA, 02114, USA.
Cognitive resilience (CR) describes the phenomenon of individuals evading cognitive decline despite prominent Alzheimer's disease neuropathology. Operationalization and measurement of this latent construct is non-trivial as it cannot be directly observed. The residual approach has been widely applied to estimate CR, where the degree of resilience is estimated through a linear model's residuals.
View Article and Find Full Text PDFNat Mater
January 2025
Department of Electronic and Computer Engineering, The Hong Kong University of Science and Technology, Hong Kong, China.
Machine learning algorithms have proven to be effective for essential quantum computation tasks such as quantum error correction and quantum control. Efficient hardware implementation of these algorithms at cryogenic temperatures is essential. Here we utilize magnetic topological insulators as memristors (termed magnetic topological memristors) and introduce a cryogenic in-memory computing scheme based on the coexistence of a chiral edge state and a topological surface state.
View Article and Find Full Text PDFNPJ Digit Med
January 2025
School of Psychological Sciences, University of Haifa, Haifa, Israel.
Cognitive training is a promising intervention for psychological distress; however, its effectiveness has yielded inconsistent outcomes across studies. This research is a pre-registered individual-level meta-analysis to identify factors contributing to cognitive training efficacy for anxiety and depression symptoms. Machine learning methods, alongside traditional statistical approaches, were employed to analyze 22 datasets with 1544 participants who underwent working memory training, attention bias modification, interpretation bias modification, or inhibitory control training.
View Article and Find Full Text PDFSci Rep
January 2025
Key Laboratory of Ethnic Language Intelligent Analysis and Security Governance of MOE, Minzu University of China, Beijing, 100081, China.
Aspect Category Sentiment Analysis (ACSA) is a fine-grained sentiment analysis task aimed at predicting the sentiment polarity associated with aspect categories within a sentence.Most existing ACSA methods are based on a given aspect category to locate sentiment words related to it. When irrelevant sentiment words have semantic meaning for the given aspect category, it may cause the problem that sentiment words cannot be matched with aspect categories.
View Article and Find Full Text PDFACS Nano
January 2025
School of Science and Engineering, The Chinese University of Hong Kong, Shenzhen, Guangdong518172, China.
The hybrid magnetic heterostructures and superlattices, composed of organic and inorganic materials, have shown great potential for quantum computing and next-generation information technology. Organic materials generally possess designable structural motifs and versatile optical, electronic, and magnetic properties, but are too delicate for robust integration into solid-state devices. In contrast, inorganic systems provide robust solid-state interface and excellent electronic properties but with limited customization space.
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