Self-harm, in-person bullying and cyberbullying in secondary school-aged children: a data linkage study in Wales

Introduction Although the evidence base on bullying victimization and self-harm in young people has been growing, most studies were cross-sectional, relied on self-reported non-validated measures of self-harm, and did not separate effects of in-person and cyberbullying. This study aimed to assess associations of self-harm following in-person bullying at school and cyberbullying victimization controlling for covariates. Methods School survey data from 11 to 16 years pupils collected in 2017 from 39 Welsh secondary schools were linked to routinely collected data. Inverse probability weighting was performed to circumvent selection bias. Survival analyses for recurrent events were conducted to evaluate relative risks (adjusted hazard ratios [AHR]) of self-harm among bullying groups within 2 years following survey completion. Results A total of 35.0% (weighted N = 6813) of pupils reported being bullied, with 18.1%, 6.4% and 10.5% being victims of in-person bullying at school only, cyberbullying only and both in-person bullying at school and cyberbullying respectively. Adjusting for covariates, effect sizes for self-harm were significant after being in-person bullied at school only (AHR = 2.2 [1.1–4.3]) and being both in-person bullied at school and cyberbullied (AHR = 2.2 [1.0–4.7]) but not being cyberbullied only (AHR = 1.2 [0.4–3.3]). Feeling lonely during recent summer holidays was also a robust predictor (AHR = 2.2 [1.2–4.0]). Conclusions We reaffirm the role of in-person bullying victimization on self-harm. Pupils were twice as likely to self-harm following in-person bullying as their nonvictimised peers. Interventions for young people that minimize the potential impacts of bullying on self-harm should also include strategies to prevent loneliness

Pollen classification based on geometrical, descriptors and colour features using decorrelation stretching method

Abstract. Saving earth's biodiversity for future generations is an important global task, where automatic recognition of pollen species by means of computer vision represents a highly prioritized issue. This work focuses on analysis and classification stages. A combination of geometrical measures, Fourier descriptors of morphological details using Discrete Cosine Transform (DCT) in order to select their most significant values, and colour information over decorrelated stretched images are proposed as pollen grains discriminative features. A MultiLayer neural network was used as classifier applying scores fusion techniques. 17 tropical honey plant species have been classified achieving a mean of 96.49% 1.16 of success