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Oropouche fever fatalities and vertical transmission in South America: implications of a potential new mode of transmission
Get PDFThe use and misuse of ‘everyday’ communication technologies in a family law setting: an exploratory study of post-separation experiences
No full textCheddar cheese production, structure and in-vitro semi-dynamic gastric digestion: The role of β-casein phenotype
No full textExploring Vietnamese student experiences of transitioning from high school into a transnational university
No full textFractal feature selection model for enhancing high-dimensional biological problems
Get PDFThe integration of biology, computer science, and statistics has given rise to the interdisciplinary field of bioinformatics, which aims to decode biological intricacies. It produces extensive and diverse features, presenting an enormous challenge in classifying bioinformatic problems. Therefore, an intelligent bioinformatics classification system must select the most relevant features to enhance machine learning performance. This paper proposes a feature selection model based on the fractal concept to improve the performance of intelligent systems in classifying high-dimensional biological problems. The proposed fractal feature selection (FFS) model divides features into blocks, measures the similarity between blocks using root mean square error (RMSE), and determines the importance of features based on low RMSE. The proposed FFS is tested and evaluated over ten high-dimensional bioinformatics datasets. The experiment results showed that the model significantly improved machine learning accuracy. The average accuracy rate was 79% with full features in machine learning algorithms, while FFS delivered promising results with an accuracy rate of 94%
A critical evaluation of adolescent resilience self-report scales: A scoping review
Get PDFValid quantitative measures of adolescent resilience are important for the development of knowledge and have implications for practice with adolescents. This scoping review followed Arksey and O'Malley's (2005) five step process and aimed to (1) identify the most used self-report scales that measure resilience of adolescents in studies published between 2000 and 2021, (2) describe the scales’ psychometric properties, (3) describe the scales’ conceptual and theoretical formulations, and (4) assess the scales’ relative strengths, weaknesses, and adequacy. A review of 118 papers revealed six commonly used scales. A construct validation approach adapted from Skinner (1981) and expanding on Pangallo et al., (2015), with evidence assessed in four stages (theoretical formulation, reliability, validity, and application) was utilised to critically evaluate the six scales. The results showed that the most adequate scale for measuring resilience in adolescent populations was the Child and Youth Resilience Measure, scoring 83% of points. The Connor-Davidson Resilience Scale (also scoring 83%) and The Resiliency Scales for Children and Adolescents (78%) were also found to be adequate. This review provides clinicians and researchers with a critical overview of common scales measuring resilience in adolescents, including their underlying theoretical basis. This is vital to ensure the measure chosen is valid and matches the theoretical aims of the research/ application. Our review also suggests that too often, researchers fail to look beyond the original validation study when selecting resilience scales, and often fail to analyse and report current psychometric data from the chosen scale
