Broad Perspectives of the Experience of Romantic Relationships and Sexual Education in Neurodivergent Adolescents and Young Adults

Neurodivergent young people tend to struggle with building and maintaining their romantic relationships. Despite this, there appears to be a lack of appropriate sexuality education delivered to them. This review aims to present and discuss the most current literature (conducted between 2015 and current) on romantic relationships and sexuality education in young people with Autism Spectrum Disorder (ASD), Attention-Deficit/Hyperactivity Disorder (ADHD), and ASD co-occurring with ADHD. Six internet-based bibliographic databases were used for the present review that followed the Preferred Reporting Items for Systematic reviews and Meta-Analyses guidelines. Thirty-one studies were identified in this review. Twenty-six studies investigated the topic in the autistic young population. Four studies explored qualitatively and 11 quantitatively young people’s perspectives of their romantic relationship experiences. One study investigated qualitatively and three quantitatively young people’s perspectives on sexuality education. One study explored qualitatively and five quantitatively young people’s romantic relationship experiences and two explored qualitatively and three quantitatively sexuality education from caregivers’ perspectives. Five studies (all quantitative, self-reports) investigated romantic relationship experiences in the young population with ADHD. The studies conducted on the topic from the educational professionals’ perspectives were absent in the literature. The literature was also non-existent on the topic in the population with ASD co-occurring with ADHD. To the researchers’ knowledge, this is the first review exploring romantic relationships and sexuality education in three groups of neurodivergent young people (with ASD, ADHD, and ASD co-occurring with ADHD)

SPARC 2021 - Against all odds : Salford postgraduate annual research conference book of abstracts

Welcome to the Book of Abstracts for the 2021 SPARC conference. Our conference is called “Against All Odds” as we celebrate the achievements of our PGRs and their supervisors, who have continued to excel despite the most challenging circumstances. For this reason, we showcase the work of our PGRs alongside the outstanding supervision that they receive, with our Doctoral School Best Supervisor awards. We also focus on developing resilience and maintaining good mental health in the research environment, supported by exceptional keynote speakers, including our very own Dr Michelle Howarth and Ruby Wax OBE, which makes this year’s conference extra special

A Dynamic Early Stopping Criterion for Random Search in SVM Hyperparameter Optimization

We introduce a dynamic early stopping condition for Random Search optimization algorithms. We test our algorithm for SVM hyperparameter optimization for classification tasks, on six commonly used datasets. According to the experimental results, we reduce significantly the number of trials used. Since each trial requires a re-training of the SVM model, our method accelerates the RS optimization. The code runs on a multi-core system and we analyze the achieved scalability for an increasing number of cores

Prediction of new bioactive molecules of chemical compound using boosting ensemble methods

Virtual screening (VS) methods can be categorized into structure-based virtual screening (SBVS) that involves knowledge about the target’s 3D structure and ligand-based virtual screening (LBVS) approaches that utilize information from at least one identified ligand. However, the activity prediction of new bioactive molecules in highly diverse data set is still less accurate and the result is not comprehensive enough since only one approach is applied at one time. This paper aims to recommend the boosting ensemble method, MultiBoost, into LBVS using the well-known chemoinformatics database, the MDL Drug Data Report (MDDR). The experimental results were compared with Support Vector Machines (SVM). The final outcomes showed that MultiBoost ensemble classifiers had improved the effectiveness of the prediction of new bioactive molecules in high diverse data