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The development, implementation and validation of an objective match scoring system for elite netball
(2025) Butterworth, Andrew; Hamblen, Andrew; O’Donoghue, Peter; Department of Sport Science
This study created and empirically validated an objective match scoring system for the dynamic and tactically rich sport of elite netball. Vast data was captured from all matches (488) played over five-seasons in the UK Netball Superleague. This was ordered into evenly weighted segments for each performance indicator, with each segment assigned a 0 to 10 value on a sliding scale, with the lowest segmented performances awarded a 0, and the highest a 10. Next, individual performance indicators for both teams in all 308 matches from seasons 2021, 2022 and 2023 were calculated an out of ten score, based on which segment they fell within. A resultant out of ten score for the overall match performance of each team was then computed, showing its objective strength. Use of this system was then implemented within elite coaches pre- and post-match analysis processes, with mixed methods utilised to ascertain the empirical validity in practice. Quantitative analysis concludes that the system accurately scores the outcome of matches with statistical significance, whilst qualitative data confirms the usefulness for elite coaching practice, specifically as an aid to previewing and reviewing performances. We conclude that the tool is an objective, trustworthy addition to the coaching process.
Verk
Pre-trained deep learning space mapping with intermittent model enhancement for fast multi-fidelity microwave design optimization
(2026-09-14) Koziel, Slawomir; Pietrenko-Dabrowska, Anna; Department of Engineering
Parameter tuning is essential in the development of microwave devices. In recent years, a growing interest in formal optimization methods has emerged, driven by their ability to simultaneously adjust multiple decision variables, even under constraints. Their disadvantage is their high computational cost, which is a serious obstacle to the optimization of electromagnetic (EM) models. This difficulty can be alleviated using multi-fidelity simulations. Problem-independent approaches rely on low-fidelity models constructed through coarse-discretization EM analysis (in contrast to problem-specific equivalent network representations), where a critical factor is the appropriate model correction strategy. This paper introduces a versatile deep-learning space mapping (DLSM) strategy that leverages a reusable pre-trained neural network surrogate. The non-parametric DLSM model implements multi-point response correction, applied independently to the real and imaginary components of relevant S-parameter responses. Furthermore, it is trained as a function of the problem's decision variables and response interrelations. It is embedded in a gradient-based optimization loop, where it is locally retrained using intermittent high-fidelity simulations and sample weighting to place greater emphasis on the neighborhood of the current solution. Comprehensive verification of the procedure involving three planar circuits underscores its competitive efficacy, with a mean running cost of 16 high-fidelity simulations and relative savings over the baseline algorithm up to 87%. Meanwhile, consistent results obtained for multiple scenarios targeting diverse performance specifications corroborate DLSM reusability and applicability across broad ranges of operating conditions.
Verk
What Static Connectivity Misses : Dynamic Alpha-Band Brain Network States in First-Episode Psychosis
(2026-09-11) Aubonnet, Romain; Hassan, Mahmoud; Gargiulo, Paolo; Seri, Stefano; Di Lorenzo, Giorgio; Department of Engineering
This study investigates resting-state EEG alpha-band connectivity in first-episode psychosis (FEP) using a novel dynamic connectivity pipeline and examines its relationship with cognitive functioning and psychopathological scores. Data from 78 individuals with FEP and 60 healthy controls (CTR) were analyzed. Source estimation was performed using eLORETA, and connectivity was quantified with the weighted phase-lag index. Static connectivity matrices were assessed using graph theory and edge-wise metrics. Dynamic connectivity matrices were clustered into five distinct brain network states (BNS) using a modified k-means algorithm, from which temporal and graph theory metrics were extracted. Static connectivity metrics revealed lower alpha connectivity in FEP than in controls. The dynamic approach identified reduced variability in the characteristic path length within the default mode network–associated BNS 1 in FEP, suggesting diminished adaptive modulation of functional integration. Subgroup analysis by medication status uncovered distinct BNS signatures for medicated and unmedicated FEP. BNS metrics were correlated with social cognition measures in CTR and with positive formal thought disorder in FEP, whereas static metrics showed no such associations. These findings suggest that relative to static connectivity metrics, dynamic connectivity provides non-redundant information and underscores the impact of medication on neural dynamics in psychosis.
Verk
Public perceptions of gambling in the UK Armed Forces : understanding stigma via qualitative inquiry
(2026-09-09) Smith, Jessica; Treacy, Samantha; Dymond, Simon; Torrance, Jamie; Department of Psychology
Background: Existing research has identified elevated public stigma toward Armed Forces personnel experiencing gambling-related harm. However, the reasoning underlying these perceptions remains poorly understood. Qualitative research can explore the drivers of such stigma and inform potential approaches to reduce it. Methods: A qualitative online survey was conducted with UK-based civilian respondents (N = 52) recruited via Prolific. Participants were screened to ensure they could meaningfully discuss gambling and military life. Open-ended questions invited participants to reflect on perceived risks and discuss attitudes toward gambling in both military and civilian contexts. Responses were analyzed using thematic analysis. Results: Four overarching themes with eleven sub-themes were identified: (1) Constructing the Unknown, (2) Perceived Drivers of Gambling within Military Culture, (3) Locating Responsibility and (4) Responding to Military Gambling. Conclusion: Findings suggest that public interpretations of military gambling-related harm were shaped by tensions between gambling and an idealized military identity, alongside negotiated understandings of responsibility across individual, institutional, and societal levels.
Verk
Motivators of Sexual Violence Disclosures and Mental Health : A Mixed Methods Investigation
(2026-09-04) Sigurvinsdottir, Rannveig; Thorvaldsdóttir, Karen Birna; Jónsdóttir, Erla Katrín; Ásgeirsdóttir, Bryndís Björk; Department of Psychology
The aim of this mixed methods study was to examine motivators and characteristics of sexual violence disclosures and relationships with mental health. A representative national sample of male and female Icelandic adult survivors (N = 473) took part in a survey. Disclosure was common, especially among women, and psychological symptoms were greatest among late disclosers. Motivators of disclosure included violence itself, trauma and recovery, and social context and movements. Social context and movements disclosures related to lower psychological symptoms. Further research is needed to understand how disclosing related to social movements, such as #MeToo, could potentially confer mental health benefits.

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