The Open University

Martial arts involve cutting, jumping and landing movements known to increase injury risk. Martial artists frequently sustain injuries. Recreational martial arts have yet to implement an evidence-informed prehabilitation programme to prevent these injuries. Here, we evaluate the Martial Arts Striking Sports prehabilitation programme (MASS-12) in a recreational setting. Three traditional martial arts warm-ups and two MASS-12 warm-ups were delivered over 2 weeks as a recreational Jiu Jitsu club hall. A coach and seven athletes (five male, two female), ranging in experience from white belt to brown belt, participated. Main Outcome Measures were the Frontal plane projection angle (FPPA), a measure of dynamic knee valgus and Single leg vertical hops (SLVH), a measure of performance. Over half of recreational athletes reported previous, serious lower limb injuries. Athlete FFPA and SLVH significantly improved (−11.9° to −1.93°, 25.2 vs 27.5 cm, respectively) after the MASS-12. Athletes appreciated the increased feedback and sense of preparedness. The coach found it easy to teach and perceived improvements in performance. The MASS-12 is easy for coaches to implement; improves lower limb alignment; and is accepted by athletes. A significant improvement on current practices, the MASS-12 should be widely implemented in recreational martial arts.

Many tools and interventions developed from research projects are not properly implemented in health and social care practice. Engaging with stakeholders is an important part of the implementation process when developing, sharing and embedding new tools. The Victoria & Stuart Project was a 2-year project that co-designed a toolkit of resources and approaches to support end-of-life care planning for people with intellectual disabilities. It had an inclusively delivered dissemination and engagement workstream to raise awareness of the toolkit throughout the project. In this paper, we describe and reflect on the project's public 'inspiring and informing' engagement activities. Engagement activities included a project website, weekly blog and newsletter. The project held webinars and was promoted on various social media platforms. One-to-one stakeholder consultations with all members of the project's Research Advisory Group were held. Presentations were given to both academic and non-academic audiences. Engagement activities were labour-intensive and time-consuming, particularly when creating regular content such as weekly blogs and social media posts. It became obvious that team members did not have expertise in marketing or communication skills and had to learn from experience. In addition, it was difficult to build and maintain a following with a changing social media landscape. There is evidence that the project is on its way to meet the project's engagement-related medium-term outcomes, such as being included in national palliative care reports and accreditation programmes as well as international guidelines. Public engagement is a complex, uncertain and non-linear undertaking, requiring continual reflection and refinement in response to changing circumstances within and beyond the research. We believe a key driver for successful engagement has been employing researchers with intellectual disabilities as well as working actively with core stakeholders throughout the project. Our engagement recommendations are: (1) Involve people with lived experience; (2) Plan and allocate time; (3) Include social media and/or marketing roles in research; (4) Tailor to the audience and platform; (5) Make it engaging and fun; (6) Learn how to make videos; (7) Keep presenting your work; and (8) Make time for stakeholder consultations. The research team included four researchers with intellectual disabilities (A.C., D.J., L.J. and R.K.-B.). Researchers with intellectual disabilities have been part of every step of the research process; from study design to data collection and analysis to dissemination of study findings, including engagement activities. Palliative care and intellectual disability service provider representatives (G.A., M.W., N.P., R.H. and S.S.) have been closely involved throughout the project. The research was supported by a Research Advisory Group comprising a variety of stakeholders, including people with intellectual disabilities, families, representatives from intellectual disability and palliative care organisations, and policymakers.

Abstract We report a multi-epoch V-band campaign (2021–2023) with uniform processing and a common 550 km normalisation across Starlink generations. Median magnitudes (68% confidence intervals; N in brackets) are: v1.0: 5.365,[5.084,5.553] (34), Gen-2: 6.012,[5.801,6.173] (6), v1.5: 6.106,[6.065,6.154] (164), VisorSat: 6.618,[6.403,6.804] (54), DarkSat: 8.431,[5.916,10.947] (2). This yields the ordering v1.0 < Gen-2 ≲ v1.5 < VisorSat ≪ DarkSat, i.e. mitigation-era designs are typically fainter than the original v1.0. The Gen-2–v1.5 difference is small (0.094,mag) and Gen-2 has limited coverage (N = 6), so the trend is not strictly monotonic. We use an open, Python-based processing pipeline built on astropy for standard image calibration (bias, dark and flat-field correction), astrometric and photometric calibration against Gaia DR3, and derivation of the viewing geometry (solar phase angle, range, elongation and airmass). Satellite tracks are identified from TLE-based ephemerides, matched to the detections, and then used to measure a brightness value for each track in a reproducible way. At a common height, the medians are 5.37 (v1.0), 6.01 (Gen-2), 6.11 (v1.5), 6.62 (VisorSat) and 8.43 (DarkSat; N = 2), which are ∼0.4–1.6 mag brighter (numerically smaller) than the target of V ≈ 7. Thus, mitigation shows clear progress but does not yet meet the IAU CPS/SATCON goal of V ≥ 7 at ∼550 km. These benchmarks can guide future satellite designs, survey planning and avoidance strategies, with the main uncertainties arising from the very small samples for DarkSat and Gen-2.

This article presents two empirical studies investigating the extent to which general English proficiency (GEP) and selected learner dispositions in online learning maintain their predictive utility for academic success in English-medium instruction (EMI) contexts in Türkiye across the pre-pandemic, pandemic, and post-pandemic periods. Both studies sampled engineering students from a large public university, with Study 1 involving 474 participants and Study 2 including 460. Study 1 examined the predictive roles of GEP, online learning readiness, beliefs, and satisfaction during the pre-pandemic and pandemic periods. Findings showed that GEP was a significant predictor of academic success before the pandemic, but this predictive relationship was statistically disrupted during the pandemic period. Study 2 extended the analysis to the post-pandemic context, where EMI courses were delivered both face-to-face and online. Results revealed a reinstatement of GEP’s predictive power in face-to-face EMI settings, while readiness, beliefs, and satisfaction with online learning were stronger predictors in online courses. This fluctuation illustrates a ‘passing cloud effect’, in which the predictive weight of language proficiency temporarily wanes under extraordinary contextual conditions but resurfaces when structured environments return. Based on the findings, several pedagogical implications are provided, and some relevant suggestions are made, underscoring the fluctuating nature of academic success in EMI.

• High-precision mass-dependent Ni isotope data are presented for Apollo lunar basalts and dunite 72415. • Diffusion modelling accounts for the extreme Ni isotope fractionation in lunar dunite 72415 (δ⁶⁰/⁵⁸Ni = +1.80‰). • Lunar mare basalts exhibit significant δ⁶⁰/⁵⁸Ni variability not primarily controlled by magmatic differentiation. • PMELTS modelling indicates Ni isotope fractionation during lunar magma ocean crystallization. • The estimated bulk silicate Moon δ⁶⁰/⁵⁸Ni (0.18 ± 0.20‰) overlaps with published bulk silicate Earth values (∼0.11‰). Although the Moon is thought to have formed through a giant impact between proto-Earth and a Mars-sized body, the processes responsible for the chemical and mass-dependent isotopic differences between Earth and Moon remain debated. We report high-precision mass-dependent Ni isotope data for 19 Apollo samples, including one dunite (72415), fifteen low-Ti basalts, and three high-Ti basalts, analyzed by double-spike technique using a multi-collector plasma-sourced mass spectrometer. The dunite 72415 shows an extremely high δ 60/58 Ni value of +1.80 ± 0.01‰, which we attribute to kinetic isotope fractionation from Ni diffusion during re-equilibration between olivine and a later melt. Diffusion modeling of Ni–Fe–Mg systematics reproduces the observed heavy Ni enrichment. In contrast, low-Ti basalts display a mean δ 60/58 Ni of 0.23 ± 0.20‰ (2SD), unaffected by cosmic-ray exposure, while high-Ti basalts are slightly isotopically lighter (0.06 ± 0.22‰, 2SD). Petrological modeling using pMELTS with recently constrained silicate mineral-melt fractionation factors suggests limited Ni isotope fractionation (<0.05‰) during lunar magma ocean crystallization and partial melting, yielding an estimated bulk silicate Moon (BSM) δ 60/58 Ni = 0.18 ± 0.20‰ (2SD). This overlaps with the bulk silicate Earth (BSE: 0.11 ± 0.07‰), indicating that Ni depletion in the lunar mantle, by a factor of ∼4 relative to Earth, can be caused by core formation (that does not fractionate Ni isotopes). However, our modelling shows evaporative loss of Ni can elevate δ 60/58 Ni value of < 0.23‰, which remains consistent with those of BSM within uncertainty. Hence, the mechanism of Ni evaporation cannot be ruled out.

• Propagation over grooved, convex surfaces is measured and modelled numerically. • Simulated and measured pulses show tails attributable to organ pipe resonances in the grooves. • Excess attenuation peaks correspond to higher pressure near the surface. • indicating a surface wave. • A high finite impedance on the grooves and curved surface is predicted to eliminate the surface wave. A numerical Boundary Element Method is used to investigate the propagation of cylindrical sound waves over rigid, grooved, convex surfaces at audio-frequencies. Laboratory measurements with broadband excitation and corresponding predictions reveal peaks in excess attenuation spectra. With short pulse excitation, measurements and predictions show delayed signals or “tails” following the main pulse arrivals. The spectral content of these tails corresponds to the peaks on the excess attenuation spectra and can be attributed to organ pipe (or quarter wavelength) resonance in the grooves. Predictions of the two-dimensional distribution of sound at the frequencies of these peaks show that the pressure field is restricted to a thin layer above the curved grooved surface, indicative of a surface wave which is eliminated by a very high finite impedance.

ABSTRACT This study examines how self‐efficacy, self‐regulation, and generative artificial intelligence (GenAI) ‐related factors interact to shape academic success and general English proficiency (GEP) within an EMI context. It draws on data from 754 EMI students at a major public university in Turkey, focusing on the social sciences and engineering disciplines. The findings reveal that for social science students, besides GEP, a positive perception towards GenAI, self‐efficacy, and self‐regulation are key to academic achievement—self‐belief and the ability to regulate one's learning are essential for mastering both language and content. For engineering students, however, success is primarily driven by GEP, GenAI competence, and a positive perception toward GenAI. When it comes to GEP, social science students benefit from GenAI competence to improve their language skills, while engineering students rely more on self‐efficacy and self‐regulation. Additionally, excessive time spent on GenAI platforms is correlated to poorer academic outcomes, highlighting the importance of quality over quantity in GenAI engagement. The findings are discussed, and implications for enhancing EMI strategies are presented.

Online learning platforms have expanded access to education but also raise concerns about biased content, particularly in text-based learning materials such as textbooks, lesson plans, and course excerpts. Such biases can perpetuate discrimination, can harm student outcomes, and can often be difficult to detect, as identification typically relies on time-consuming human review. Learning analytics (LA) can enhance this process by supporting human reviewers through automated detection, offering a scalable solution while retaining human judgment for nuanced evaluations. Accordingly, this LA study explores two research questions: RQ1: Which features might support the identification of ethnic bias in text-based online learning materials? and RQ2: Which classification approaches might be suitable for identifying ethnic bias in text-based online learning materials? First, we identified features signalling potential ethnic bias (presence or absence) in textual content using a dataset (N = 345) labelled by 193 students from diverse ethnic backgrounds. Then, we evaluated multiple machine learning (ML) models for their effectiveness in bias classification. The results suggest significant correlations between perceived bias and content from social sciences. Additionally, through bootstrap analysis, support vector machines and random forest classifiers showed consistent performance in bias identification (with F1-scores of 0.71 and 0.70 on the test set, respectively). In contrast, the naive Bayes (NB) model demonstrated the highest precision (0.75 on the test set). We discuss these findings and their implications for LA, emphasizing the importance of quality and inclusive educational tools. As an initial step toward automated bias classification in education, this study provides a foundation for spotting ethnic bias in learning content, supporting fairer technologies for more inclusive learning environments.