Performance and Wellbeing Research Priorities in Premiership Women's Rugby: A Delphi Study Including Players and Staff

Women's sport has seen substantial growth in recent years, with increased attention to athlete performance and welfare. To support the ongoing professionalisation of women's rugby, performance and wellbeing must be prioritised. This study used a three‐round Delphi‐process to establish performance and wellbeing research priorities for Premiership Women's Rugby (PWR) in England. In Round 1, players and staff provided research priorities, which were grouped into higher‐order categories and themes via content analysis. In Rounds 2 and 3, participants ranked higher‐order categories on a 1–5 Likert scale. Consensus was defined as ≥ 70% agreement. Seventy‐seven participants responded in Round 1 (47 and 43 in Rounds 2 and 3). Player and staff experience of playing or working in PWR was 5.0 (2.0–7.0) and 2.5 (2.0–4.0) years. Following Round 1321 research priorities were provided, 32 higher‐order research priorities and 14 categories were identified, within three themes: performance, wellbeing and injury. Following Round 3, nine research priorities reached consensus within performance ( n  = 1), wellbeing ( n  = 4) and injury ( n  = 4). The highest rated priority was ‘ Investigate the impact of being a dual‐career athlete on wellbeing, and any support mechanisms required ’ (79%). Future research should prioritise studies which are feasible and currently lack a comprehensive evidence‐base. This will enable researchers and governing bodies to address relevant knowledge gaps and inform ongoing performance and player safety initiatives. The research priorities identified in this study, by PWR players and staff, could be investigated to support the development of women's rugby domestically. These findings may also be applicable to other women's sports and leagues globally

The systems evaluation network: building capability and capacity in the use of systems science across public health

Background: The Systems Evaluation Network (SEN) aims to build capability and capacity regarding the use of systems science in public health evaluation. The SEN was established in June 2021 and 3 years from its inception, we undertook a member survey to understand the engagement with, and impact of, the SEN. Methods: An 18-item cross-sectional survey captured quantitative and qualitative responses regarding SEN member perspectives, centring around their experience of the SEN, associated impacts, and future requirements. We analysed quantitative data descriptively and qualitative data through content analysis. Sub-group analyses explored differences between those working in academia vs practice/policy. Results: Seventy-three participants completed the survey, with 60% working in academia and 40% in practice/policy. Considering experiences of the SEN, participants felt the SEN has shared information about innovative methods and evaluation approaches (94.0% agreed), has provided the opportunity to share and learn with other members (86.0% agreed), and has improved knowledge of systems evaluation methods (86.2% agreed). Regarding impacts of the SEN, participants stated that the SEN has increased their capability to apply systems-oriented methods and evaluation of systems approaches (76% agreed) and has facilitated relationships with others (56.9% agreed). Participants shared future capability requirements for evaluation, which focused on methods (e.g. systems dynamics modelling and ripple effects mapping), approaches (e.g. developmental evaluation and embedded researchers), and other ways in which capability could be increased (e.g. by using case studies). Conclusion: This paper illustrates the experiences and impacts of the SEN, identifying its strengths such as the wide range of topics/content and the flexible and accessible delivery format, but contrast against the difficulties of fostering new relationships in an online setting. These findings can help inform the future direction of the SEN and provide insight to other online communities of practice

Bypass Tank Integration for Quick Start-Up and Peak Load Support in Thermal Storage Heating Systems

Supercooled phase change materials are highly promising for space heating applications due to their ability to release latent heat upon crystallisation initiation, even at ambient temperatures. This property enables more effective solar energy utilisation and significant reductions in carbon emissions for short- to medium-term thermal storage. However, the widely used sodium acetate trihydrate has a melting point of 56–58 °C, which often necessitates auxiliary heating in cold seasons or when sudden short-term demand arises during morning warm-up and evening peaks. To address this limitation, this study proposes a bypass tank configuration incorporating a coil-integrated latent heat storage unit filled with erythritol, enabling rapid high-temperature boosting (10–20 °C) during morning warm-up and evening peak periods for a short time. The erythritol tank is charged via PV-powered electric heaters and engaged only when sodium acetate trihydrate storage cannot maintain the required supply temperature and heating. A dynamic model of the coil-integrated tank was developed, experimentally validated, and further examined through CFD simulations to capture discharge behaviour under varying inlet temperatures and flow rates. Real weather data and building heating profiles were used to evaluate the system’s practical boosting capability. Results show that a 35 L erythritol tank can sustain outlet temperatures above 50 °C at moderate flow rates, deliver boosting durations of up to 22 min, and reliably support dual-peak operation within a single day. The findings highlight the effectiveness of erythritol as a high-temperature bypass storage medium for improving quick start-up performance, reducing reliance on auxiliary electric heaters, and enhancing operational flexibility in solar-assisted heating systems

Learning Dynamics, Pattern Recognition Capability and Interpretability of the Tsetlin Machine

The inability to trace an AI’s reasoning process and understand why it makes each decision is known as the black box problem. This remains one of the major barriers to the trusted and widespread use of machine learning in many application domains. The paper explores pattern recognition performance and learning dynamics of the Tsetlin Machine – a new explainable logic-based machine-learning approach. Tsetlin Machine uses a collection of finite-state automata with a unique logic-based learning mechanism and provides a promising alternative to Artificial Neural Networks with several advantages, such as interpretability, low complexity, suitability for hardware implementation and high performance. This work investigates Tsetlin Machine’s mechanism for constructing conjunctive clauses from data and their interpretation for pattern recognition on several datasets. We demonstrate that during training the logical clauses learn persistent sub-patterns within the class. Each clause creates a class template by clustering a certain number of similar class samples, combining them through literal-wise logical conjunction (i.e., AND-ing). The number of class samples that each clause combines depends on Tsetlin Machine’s hyperparameters. The more class samples that are combined, the more general the clauses become. The paper aims at uncovering how Tsetlin Machine’s hyperparameters influence the balance between clause generalization and specialization and how this affects the accuracy of pattern recognition. It also studies the evolution of the machine’s internal state, its convergence and training completion

Opportunities and costs for shared ground loops

Shared ground loops (SGLs) combine shared ground heat exchangers with distributed heat pumps across multiple properties and may offer a route to decarbonise heating where individual heat pumps or heat networks are not feasible. SGLs can be installed in homes and buildings with limited outside space for a heat pump or insufficient demand density to support a heat network. To make the most of potential opportunities, greater awareness of factors shaping UK deployment is needed. Through a mixed-methods approach combining rapid evidence assessment, case studies and policy mapping, this study finds SGLs mostly limited to deployment by social landlords and in new build settings, with wider use impacted by high capital costs, policy gaps around mid-scale solutions, market concentration around a single supplier, and the need for business models applicable to mixed-tenure settings. SGLs are particularly suitable for dwellings in higher density areas outside of government-designated Heat Network Zones, where it is expected that large heat networks will deliver the lowest-cost route to decarbonising heat. We suggest policy and practice recommendations intended to create conditions for wider deployment. At a national policymaker level, SGL suitability for mid-scale, medium-density settings and support for a flexible energy system should be more clearly recognised, especially in areas outside Heat Network Zones. At the individual company level, deployment would be supported through development of utility-style business models and installation approaches by infrastructure developers which can offer SGLs to households of a range of tenure types

Market integration across green, energy and carbon markets in emerging economies

The connection between green investments, carbon markets, and traditional energy markets in emerging economies is still not well understood. Yet understanding how risk moves between these markets is important for helping investors manage volatility more effectively. Thus, this study examines how volatility is transmitted among green bond markets, energy sector indices, and carbon price indices in the MSCI Emerging Markets (MSCI EM). Using the Time-Varying Parameter Vector Autoregression (TVP-VAR) model, it captures the strength and direction of risk transmission across these sectors. The analysis is based on daily data covering green finance indices, carbon markets, energy indices, and oil prices. The findings show that the relationships between green finance, carbon pricing, and traditional energy markets are non-static, as they change over time. These evolving connections have important implications for investors and policymakers, particularly in terms of building diversified portfolios, managing risk, and shaping sustainable financial systems in emerging economies

A deep learning model to enhance lung cancer detection using ‘Dual-Branch’ model classification approach

Cancer remains a life-threatening global challenge, with lung cancer ranking among the most devastating forms, impacting millions annually. Early detection and accurate classification are essential for improving patient survival rates, and computed tomography (CT) has become a critical tool in lung cancer diagnosis. Despite advancements, previous studies have faced notable challenges, particularly a shortage of available samples and limitations in input modalities, both of which hinder model performance. Addressing these issues, this research introduces the Dual-Branch Model Classification Approach (DbMCA), a two-stage strategy that integrates image and mask data to enhance detection accuracy and scalability. Two comparative experiments were conducted using the LIDC-IDRI dataset with varying data sizes to evaluate the impact of sample size and dual-input modalities. The DbMCA achieved remarkable results, as it performed higher accuracy results a 91.21% accuracy and 91.18% F1-score in the smaller dataset and an exceptional 98.04% accuracy and 98.01% F1-score in the larger dataset. CNN performance on sparse mask data declines with scale, while DNN and SVM consistently outperform it, highlighting architecture sensitivity to sparsity. This demonstrates the model’s improved discriminative power and potential for detecting subtle lung cancer patterns, however, based on statistical evidence DbMCA significantly outperforms weaker baselines and successfully integrates multi-modal information. Nonetheless, certain limitations were observed, such as the high computational requirements stemming from large sample sizes, the constrained information provided by segmentation masks, and the presence of potential biases in the dataset. These challenges hinder the model’s ability to generalize effectively. Future research should aim to enhance image quality, broaden the scope of datasets, and overcome segmentation-related constraints to make further progress in lung cancer detection. The DbMCA represents a significant step forward in improving the performance and scalability of diagnostic tools, offering the potential for more effective and lifesaving interventions in lung cancer care

How AI-induced existential threats affect consumer skepticism toward corporate marketing communications in the hospitality and tourism industry

While artificial intelligence (AI) technology offers a variety of potential advantages, the general public has voiced serious concerns that the development of AI could pose threats to human existence. This work explores how AI-induced existential threats shape consumers’ cognitive beliefs about corporate marketing communications in the hospitality and tourism industry (e.g., hotel corporate social responsibility activities and travel package advertisements). Through one survey and four experiments, this research uncovers an important unforeseen dark side of AI. That is, AI-induced existential threats lead to consumer skepticism toward corporate marketing communications. In addition, we find that consumers’ zero-sum mindset is the psychological mechanism explaining the negative AI effect. Moreover, this paper identifies regulatory focus as a critical boundary condition. Specifically, the unintended effect of AI-induced existential threats only occurs among promotion-focused individuals but disappears among prevention-focused individuals

Coach‐Perpetrated Interpersonal Violence: Witnessing, Perceived Harmfulness and the Role of Coaching Motivational Climate

Coach‐perpetrated interpersonal violence can pose significant risks to athletes' development as well as psychological, physical and social well‐being worldwide. This study examined the perceived harmfulness of witnessed coach‐perpetrated interpersonal violence behaviours in the North Mediterranean region, alongside any associations with coaching climates (empowering and disempowering). Data were collected from 494 active coaches across Cyprus, Greece, Italy, Malta, Spain and Portugal through an online questionnaire where they reported witnessing and perceived harm of psychological, physical, instrumental and sexual violence, as well as their coaching climates. The analysis showed psychological violence as the most frequently witnessed form and physical violence being perceived as the most harmful one. An empowering coaching climate, characterised by autonomy support and positive reinforcement, correlated positively with higher perceived harm, especially for psychological and instrumental violence. Conversely, a disempowering climate, marked by control and punitive behaviours, correlated with lower perceived harm. Gender, coach education and professional status were found to influence coaches' perceptions, highlighting that cultural and structural complexities have a role towards interpersonal violence tolerance. The study underscores the critical need for culturally tailored safe sport initiatives, mandatory training of coaches in safe coaching behaviours and practices and proactive safeguarding measures to mitigate interpersonal violence across diverse sporting contexts. Culturally informed interventions need to challenge the normalisation of violence in coaching and encourage empowering climates that place athletes in the centre and prioritise their welfare

What do teachers do?

Applying for and obtaining your first post is an important undertaking. Knowing the stages involved in the application and interview process will allow you to consider careful where and when you apply. This chapter provides clear guidance to support you in applying for your first post as well as what happens when you are offered a post. It encourages you to think about what type of post you want to apply for and provides insight into what is expected from applications from employers. It considers the interview process as a whole looking not only what take place during an interview, but also what might be expected of you once you have accepted your new role