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Muutokset persoonallisuus-, temperamentti- ja luonne-indikaattoreissa lääkehoidon vaikutuksen seurauksena
No full textPrisoners' Perceptions and Their Agency on Sustainability Transformation in Finland
No full textSustainability transformation is essential for our time, requiring the involvement of all citizens. Several prisons worldwide have developed various sustainable development (SD) programs for prisoners. However, it remains unclear how prisoners perceive SD, which can be a significant obstacle to their agency. This study explores the perceptions of individuals imprisoned for more than 2 years regarding the environmental, economic, cultural, and social aspects of SD and their potential to influence and implement sustainability activities. Qualitative interview data (N = 8) were collected from three Finnish prisons in October 2024 and February 2025. The data were analyzed using inductive systematic thematic analysis, resulting in five themes. Prisoners' perceptions of sustainability vary widely, from personal sustainability actions to larger global perspectives. While prison structures were seen as significant barriers to participation, small-scale activities were still valued. The findings emphasize the need to promote inclusive practices and critical engagement within prisons to aid rehabilitation and to recognize prisoners as moral and political agents
Metamaterials and carbon-based thin films for terahertz absorption and bolometric detection
No full textJoint Use of Time Series and Graph Data for Fake Comment Detection in CafeBazaar Dataset
No full textFake comments pose a significant challenge for e-commerce, as they manipulate customer perception and harm legitimate businesses. We propose a novel approach using recurrent neural networks to detect anomalies in user behavior patterns. We model user activity on Cafebazaar e-commerce as a multivariate time series and employ a long short-term memory autoencoder to determine periods of abnormal activity. By incorporating our model with existing methods, we observe significant improvement in the detection accuracy of fraudulent users, reaching 99 percent precision with the dataset. Our proposed model can enhance other fake comment detection methods, particularly those that struggle with the cold start problem, which often lacks information about the majority of users. The proposed system has been successfully implemented in a real-life application, removing 1.9 million fake comments from the Cafebazaar platform. We also provide a new dataset for fake comment detection that researchers can use to evaluate existing methods or develop new approaches
Aivojen hemodynaamiset muutokset fNIRS-kuvantamismenetelmällä tarkasteltuna epäsanojen tuottamisen ja kuuntelemisen aikana
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Barriers and Facilitators of Implementing Clinical Practice Guidelines and De-implementing Low-value Care in Prescribing – a Systematic Review
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