Development and validation of the Limerence Questionnaire (LQ-11)

Limerence is an overwhelming and debilitating experience involving the intense and often obsessive attachment towards a person who becomes the limerent object, which when left unharnessed, typically results in negative outcomes. At present, there are no published measures to assess the construct of limerence. To address this gap, we developed a short self-report measure to measure limerence (The Limerence Questionnaire-11; LQ-11). This paper reports two studies with data from two different samples (Study 1, = 269; Study 2, = 401) of participants that had experienced or were currently experiencing limerence. Results from the exploratory factor analysis revealed a two-factor structure comprising of ' ' and ' (Study 1). Confirmatory Factor Analysis subsequently confirmed a two-factor structure with excellent internal reliability (Study 2). Results demonstrated that the LQ-11 had good concurrent, convergent and discriminant validity. The LQ-11 is an easily administrable questionnaire for potential use in both interpersonal research domains and in clinical and therapeutic settings

Putting on a disguise to fit in: A mixed methods study of experiences in autistic camouflaging

Background: A 3:1 male-to-female autism diagnosis ratio may be partly explained by more prevalent camouflaging behaviours among females, which can mask autistic traits. Methods: This mixed-methods study examined gender differences in camouflaging among 110 autistic adults (aged 18–64) using the Camouflaging Autistic Traits Questionnaire (CAT-Q) and explored lived experiences through interviews with eight participants (aged 18–37). Results: Quantitative results showed that females reported significantly higher total camouflaging scores than males, particularly in the compensation subtype, while differences in masking and assimilation were not significant. Thematic analysis of interviews identified four key overarching themes: camouflaging motivations and consequences, level of consciousness, affected identity, and experiences of support. Conclusions: Findings highlight the complex impact of camouflaging on daily life and its potential role in delayed diagnoses and mental health challenges. Further research should include diverse gender identities and a broader representation of the autism spectrum to inform better support and interventions

The role of interventions led by student teachers as autonomous approximations of practice for developing knowledge, skills and confidence in initial teacher education

This research paper outlines how a student-teacher-led intervention, where a student teacher designs and implements an instructional intervention that will support children to make progress, can be viewed as an approximation of practice. The research analysed student teachers’ self-evaluation documentation and utilised interviews with student teachers in one higher education institution in England and experienced teachers who have adopted mentorship roles in schools. The research finds that elements of core practices in teacher education can be seen through the student-teacher intervention, most notably the opportunity for student teachers to master skills with reduced complexity. In this way, the student-led interventions can be viewed as an approximation of practice. This approach to teacher education is enhanced by affording the student teacher with a degree of autonomy that they do not experience elsewhere in the typical English context. Despite the lack of immediate feedback, the student teacher is supported by receiving feedback in different forms. The paper concludes by suggesting that student-teacher-led interventions can be viewed as an autonomous approximation of practice that is beneficial in developing the confidence and skills of the student teacher

Investigating success in the transition to university: a systematic review of operationalisations of 'success'

The transition to university is a challenging period for students that has a range of negative consequences for them if it is not successful. Many stakeholders, not to mention the students themselves, want to ensure this transition is as successful as possible to ensure positive outcomes. As such, a body of research has attempted to explore this transition with the aim of identifying profiles of students, potential risk factors, and to tailor support and university induction activities. Though noble in intention, the literature body is highly disparate and contains methodological inconsistencies and flaws that make navigating the findings and moving to the development of practical applications for ensuring successful student transition difficult. The present paper consolidates part of a larger systematic review (preregistered on PROSPERO, CRD42022330515) to highlight the diverse range of operationalisations of said student transition ‘success’. From the 55 retained papers, 34 different measures are discussed in relation to 10 higher order domains. We propose a more parsimonious framework for a tripartite definition of success including a balance between academic outcomes, psychosocial development, and health and wellbeing

Psychology of Esports Special Issue: A Catalyst for Change

The special issue on "Psychology of Esports: Breakdown under Pressure" represents a significant advancement in understanding the psychological aspects of competitive gaming. The collected works explore diverse topics including mental health, cognitive processes, performance factors, and practical applications in esports. Key findings highlight the high prevalence of mental health issues among professional players, the importance of cognitive training and visual attention, and the impact of communication styles on team performance. Theoretical frameworks are proposed for integrating performance psychology theories and understanding decision-making processes in esports. The research also examines psychophysiological stress responses and motivational factors influencing performance. Challenges in the field are identified, including the need for more robust theoretical foundations, improved ecological validity, and larger sample sizes. Future directions for research are suggested, emphasizing longitudinal studies, cross-cultural perspectives, and interdisciplinary approaches. The special issue underscores the rapid growth of esports psychology as a field and the critical need for evidence-based practices to support the health, well-being, and performance of esports athletes in an evolving competitive landscape

Ways of seeing trees: in memory John Berger

Opening with a quote from a John Berger poem, and unfolding to the soundtrack of a Hindustani raag bhairav, this short lyrical filmpoem alludes to Berger's famous early work of art criticism Ways of Seeing - and pays tribute to his work as writer, artist and activist for global environmental justice. Audio Description notes and recordings are available via the link below

Agreement between numerical integration techniques during countermovement jumps with accentuated eccentric loading in youth athletes

This study evaluated agreement between a) force platform numerical integration techniques for calculating performance variables and b) three-dimensional (3D) motion capture and vertical ground reaction force (vGRF) methods for identifying the dumbbell release during countermovement jumps with accentuated eccentric loading (CMJAEL). Twenty adolescent participants (10 males, 10 females) performed CMJAEL with handheld dumbbells at 20%, 25% and 30% of body mass. Variables were compared across five integration methods using repeated measures Bland-Altman and two-way repeated measures ANOVA analyses (α = 0.05), with combined forward and backward integration serving as the criterion. Backward integration and after adjusting at the dumbbells release agreed with the criterion, while forward integration and adjusting at the bottom position did not. The dumbbell release point identified using 3D motion capture (criterion) was also compared to estimates derived from force platform data (vGRF method). The vGRF method identified the dumbbell release point in delay of 3D motion capture, with limits of agreement (LOA) between −0.17 and 0.03 s across conditions. These methods should not be used interchangeably; rather, we recommend that the vGRF method be used in situations whereby only force platforms are available, and that it is combined with forward and backward integration techniques

University of Chichester - Implementing a first destination social care, primary care and community degree at Masters level using a Blended learning Approach

This case study reflects on the antecedents and implementation of a new blended learning pre-registration M Level Adult Nursing cours

Predicting and controlling multiple transmissions of rotavirus using computational biomedical model in smart health infrastructures

Conventional laboratory investigation of rotavirus infection and its antigen in rectal swabs from infected persons uses Electron microscopy (EM) (i.e., non‐acute cases), genome, and antigen‐detecting assays. A recent update involves sorting, trapping, concentrating, and identifying infectious rotavirus particles in clinical samples leveraging activated magnetic microparticles with monoclonal antibodies. However, the routine detection of rotavirus in many specimens using the EM approach is laborious, costly, and requires highly skilled workers. A sustainable healthcare system should leverage the Internet of Things to operate Smart Health Infrastructures (SHI) for predictive control of contagious diseases such as the rotavirus. This paper proposes a biomedical model for predictive control of the virus spread based on Susceptible, Breastfeeding, Vaccinated, Infected, and Recovered (SBVIR) parameters. We introduce breastfeeding, vaccination, and saturated incidence rate variables to deconstruct the transmission dynamics. An efficiency test is conducted using RI control parameters B and V. Applying Lyapunov function analysis, we prove that the global stability of disease‐free and endemic equilibria exists under breastfeeding and vaccination conditions when the primary reproduction number is less than unity. Numerical simulation results show that breastfeeding and vaccination are optimal with SBVIR compared to SVIR, SBIR, and SIR parameters for rotavirus infection control by 99%, 26%, 19%, and 18%, respectively. On top of these, we show that the SBVIR model strongly agrees with real‐world data and can be used to forecast the infected population in a production health facility. Finally, we show multiple Internet of Things applications in SHI to control rotavirus transmission effectively

An adaptation of hybrid binary optimization algorithms for medical image feature selection in neural network for classification of breast cancer

The performance of neural network is largely dependent on their capability to extract very discriminant features supporting the characterization of abnormalities in the medical image. Several benchmark architectures have been proposed and the use of transfer learning has further made these architectures return good performances. Study has shown that the use of optimization algorithms for selection of relevant features has improved classifiers. However continuous optimization algorithms have mostly been used though it allows variables to take value within a range of values. The advantage of binary optimization algorithms is that it allows variables to be assigned only two states, and this have been sparsely applied to medical image feature optimization. This study therefore proposes hybrid binary optimization algorithms to efficiently identify optimal features subset in medical image feature sets. The binary dwarf mongoose optimizer (BDMO) and the particle swarm optimizer (PSO) were hybridized with the binary Ebola optimization search algorithm (BEOSA) on new nested transfer functions. Medical images passed through convolutional neural networks (CNN) returns extracted features into a continuous space which are piped through these new hybrid binary optimizers. Features in continuous space a mapped into binary space for optimization, and then mapped back into the continuous space for classification. Experimentation was conducted on medical image samples using the Curated Breast Imaging Subset of Digital Database for Screening Mammography (DDSM+CBIS). Results obtained from the evaluation of the hybrid binary optimization methods showed that they yielded outstanding classification accuracy, fitness, and cost function values of 0.965, 0.021 and 0.943. To investigate the statistical significance of the hybrid binary methods, the analysis of variance (ANOVA) test was conducted based on the two-factor analysis on the classification accuracy, fitness, and cost metrics. Furthermore, results returned from application of the binary hybrid methods medical image analysis showed classification accuracy of 0.8286, precision of 0.97, recall of 0.83, and F1-score of 0.99, AUC of 0.8291. Findings from the study showed that contrary to the popular approach of using continuous metaheuristic algorithms for feature selection problem, the binary metaheuristic algorithms are well suitable for handling the challenge. Complete source code can be accessed from: https://github.com/NathanielOy/hybridBinaryAlgorithm4FeatureSelectio