A spectrum of physics-informed Gaussian processes for regression in engineering

Despite the growing availability of sensing and data in general, we remain unable to fully characterise many in-service engineering systems and structures from a purely data-driven approach. The vast data and resources available to capture human activity are unmatched in our engineered world, and, even in cases where data could be referred to as ``big,'' they will rarely hold information across operational windows or life spans. This paper pursues the combination of machine learning technology and physics-based reasoning to enhance our ability to make predictive models with limited data. By explicitly linking the physics-based view of stochastic processes with a data-based regression approach, a spectrum of possible Gaussian process models are introduced that enable the incorporation of different levels of expert knowledge of a system. Examples illustrate how these approaches can significantly reduce reliance on data collection whilst also increasing the interpretability of the model, another important consideration in this context

The electronic frailty index as an indicator of community healthcare service utilisation in the older population

Background: older people with frailty are particularly high users of healthcare services, however a lack of standardised recording of frailty in different healthcare electronic datasets has limited investigations into healthcare service usage and demand of the older frail population. Objectives: to investigate the community service demand of frail patients using the electronic frailty index (eFI) as a measure of frailty. Study design and setting a retrospective cohort study using anonymised linked healthcare patient data from primary care, community services and acute hospitals in Norfolk. Participants: patients aged 65 and over who had an eFI assessment score established in their primary care electronic patient record in Norwich based General Practices. Results: we include data from 22,859 patients with an eFI score. Frailty severity increased with age and was associated with increased acute hospital admission within a 6-month window. Patients with a frail eFI score were also more likely to have a community service referral within a 6-month window of frailty assessment, with a RR of 1.84 (1.76–1.93) for mild frailty, 1.96 (1.83–2.09) for moderate frailty and 2.95 (2.76–3.14) for severe frailty scores. We also found that frail patients had more community referrals per patient then those classified as fit and required more care plans per community referral. Conclusions: eFI score was an indicator of community service use, with increasing severity of frailty being associated with higher community healthcare requirements. The eFI may help planning of community services for the frail population

Training load and injury risk in elite Rugby Union:The largest investigation to date

AbstractTraining load monitoring has grown in recent years with the acute:chronic workload ratio (ACWR) widely used to aggregate data to inform decision-making on injury risk. Several methods have been described to calculate the ACWR and numerous methodological issues have been raised. Therefore, this study examined the relationship between the ACWR and injury in a sample of 696 players from 13 professional rugby clubs over two seasons for 1718 injuries of all types and a further analysis of 383 soft tissue injuries specifically. Of the 192 comparisons undertaken for both injury groups, 40% (all injury) and 31% (soft tissue injury) were significant. Furthermore, there appeared to be no calculation method that consistently demonstrated a relationship with injury. Some calculation methods supported previous work for a “sweet spot” in injury risk, while a substantial number of methods displayed no such relationship. This study is the largest to date to have investigated the relationship between the ACWR and injury risk and demonstrates that there appears to be no consistent association between the two. This suggests that alternative methods of training load aggregation may provide more useful information, but these should be considered in the wider context of other established risk factors.</jats:p

Women’s Endorsement of Heteronormative Dating Scripts is Predicted by Sexism, Feminist Identity, A Preference for Dominant Men, and A Preference Against Short-Term Relationships

Heteronormative dating scripts involve expectations for women and men to enact different behaviours in romantic contexts with one another, such as men paying on dates and making marriage proposals. While previous research has shown that sexism and feminist identity predicts the endorsement of these scripts, there is a lack of research on other potential predictors relevant to women’s personal preferences for partners and relationships. We examined these novel predictors in three online samples of single women in Australia (N₁ = 112, N₂ = 157, N₃ = 189). Hierarchical regressions and an integrative meta-analysis identified that women’s endorsement of heteronormative dating scripts was predicted by higher benevolent sexism, higher hostile sexism, and lower feminist identity, as well as a greater preference for dominant men as partners and a lower preference for short-term relationships. In addition, path modelling suggested that a greater preference for male partner dominance partially explained the association between women’s benevolent sexism and the endorsement of these scripts. Overall, women’s endorsement of heteronormative dating scripts was more strongly related to their sexist attitudes than their partner or relationship preferences, suggesting that traditional romantic prescriptions are interconnected with gender inequalities, despite the relevance of personal preferences beyond sexism

Training and match load in professional rugby union: Do contextual factors influence the training week?

Background: Rugby union demands a multifaceted approach to training, given the multiple physical and technical attributes required to play the sport. Objectives: The aim of this study is to describe the distribution of training throughout the week and investigate how this may be influenced by match-related factors. Methods: Training load data (session Rating of Perceived Exertion [sRPE], total distance and high-speed running [HSR]) were collected from six professional English rugby teams during the 2017/18 season. Five contextual factors were also recorded including: standard of opposition, competition type, result of previous fixture, surface type, and match venue. Results: The day prior to matches demonstrated the lowest training load (101 AU (95% CIs: 0-216 AU) , 1 047 m (95% CIs:1 128-1 686 m) and 59 m (95% CIs: 0-343 m), respectively), while four days prior to the match demonstrated the highest training load (464 AU (95% CIs: 350-578), 2 983 m (95% CIs: 2 704-3 262m) and 234m (95% CIs: 0-477m), respectively). Of the five contextual factors, competition type was the only variable that demonstrated greater than trivial findings, with training before European fixtures the lowest stimulus across the four different competition types. Standard of opposition, previous result, surface type and venue had only trivial effects on training load (effect sizes = -0.13 to 0.15). Conclusion: Future studies should outline the distribution of other training metrics, including contact and collision training. This study provides a multi-club evaluation that demonstrates the variety of loading strategies prior to competitive match play and highlights competition type as the most influential contextual factor impacting the average training load