University of Stirling

This study explores how esports spectators' motivations lead to psychological benefits in two settings: official onsite events and unofficial online streams. The benefits examined are flow experience and subjective wellbeing. A professional research company conducted a cross-sectional survey of 400 South Korean esports consumers with 200 per viewing context. We used partial least squares structural equation modelling (PLS-SEM) to test the hypothesised relationships. The results showed that all three motivations predicted flow for onsite spectators, while only skill-based and relationship-based motivations influenced flow for online viewers. Entertainment-based motivations directly enhanced wellbeing in both contexts, while relationship-based motivations predicted wellbeing only for online viewers. Flow experience contributed significantly to wellbeing in both groups and fully mediated the skill-based motivation-wellbeing relationship. Despite these variations, multigroup analysis showed no significant differences between viewing contexts. These findings demonstrate that fundamental psychological mechanisms remain consistent across viewing contexts. • Flow experience fully mediates the relationship between skill-based motivations and wellbeing for both official onsite spectatorship and unofficial online viewership. • Entertainment-based motivations of esports consumption directly enhance wellbeing for both viewing contexts. • Permutation multigroup analysis confirms that the psychological pathway from motivation to wellbeing is consistent, showing no significant structural differences between onsite and online viewing contexts. • The study validates and extends Barney and Pennington's (2023) second order motivational framework in two different viewing contexts.

In contrast to chemical pest control, biological control (biocontrol) is generally considered evolutionarily stable, with pests rarely evolving resistance to agents such as parasitoid wasps. In 1997, Holt & Hochberg outlined five principles to explain this pattern. Here, we review post-1997 case studies where resistance has contributed to the breakdown of parasitoid-based biocontrol. We evaluate how these examples align with or challenge Holt & Hochberg's framework and propose updates to reflect new findings. While resistance remains rare, reported cases suggest that breakdowns are more likely when pests possess greater standing genetic variation than their parasitoid enemies. We argue that long-term stability depends not just on host constraints but also on the potential for parasitoid virulence to evolve. Finally, we offer practical recommendations for biocontrol practitioners and regulators to minimise the risk of resistance evolution in parasitoid-based systems.

Prior studies show significant increases in cardiorespiratory fitness (V̇O2max) and other health-related outcomes in response to reduced exertion high-intensity interval training (REHIT), yet these studies are characterized by small sample sizes which casts doubt on the true efficacy of this form of physical activity for enhancing fitness and health. We propose a definitive randomized controlled exercise training study in a large sample of inactive adults to examine the effects of REHIT on outcomes related to cardiometabolic health in comparison to a non-exercise control group. After baseline testing, 120 inactive men and women ages 18-64years will be randomized to a non-exercising control group or undergo 12 weeks of REHIT. Participants will be healthy, weight stable, and have BMI < 35kg/m2 and be required to maintain their habitual physical activity and dietary patterns during the study. Training will be supervised and performed 2days per week. Changes in V̇O2max, body composition, and substrate metabolism will be monitored to assess adaptations in cardiometabolic health in response to REHIT. To assess the feasibility of REHIT, various perceptual measures including RPE, affective valence, and enjoyment will be recorded, and quality of life will be measured. Data from the current study will advance physical activity research by bolstering the implementation of REHIT, which will contribute to reduced risk factors of cardiovascular disease and, in turn, improved public health. OSF (osf.io/wvcz4). Registered on November 3, 2025. gov NCT07328568. Registered on January 8, 2026.

Though Michael Burawoy had long abandoned any substantive interest in the politics of production, his work has continued to provide a conceptual language for important strands of contemporary research. This article seeks to critically evaluate this trajectory and legacy. The primary aim is to re-conceptualize the dimensions of consent making to create conditions for more-nuanced and less-pessimistic understandings of conflict and antagonism at work. We argue that Burawoy’s separation of the labour process from value extraction removes the source of resistance from inside the labour process and therefore diminishes the grounds for politics at work. The article recognizes that researchers in a Burawoyan tradition have applied its categories in creative and contingent way showing awareness of variation in regime dynamics, as well as political and ideological expression. However, we argue that the concept of consent still serves largely as a container category to describe the state of worker subjectivity amid workplace change. Shedding light on both the conceptual contradictions in Burawoy and the different dimensions of consent making and consent taking, we treat the potential effects on alignment of interests as an open and contingent matter and seek to offer conceptual clarifications that are a necessary precondition for putting a politics of production back in the picture.

Pretraining geospatial foundation models (FMs) is expensive, and architectural choices control inductive bias for multiscale context, cross-resolution behavior, and band/sensor variation. Therefore, benchmarking reduces the risk of scaling the “wrong” base. A model benchmarking framework for geospatial image segmentation is a critical prerequisite for developing robust and scalable geospatial FMs. In the emerging era of Earth observation FMs, success hinges on strong, well-characterized base architectures that can generalize across sensors, modalities, and geographies. The extreme heterogeneity of Earth observation vision data (different spectral bands, resolutions, and regions) makes such generalization especially challenging, underscoring the need for systematic, controlled benchmarking across diverse model families to identify viable architectures for different scenarios.Our work rigorously evaluates a broad spectrum of segmentation architectures and backbones under consistent conditions. We benchmark classical convolutional architectures (U-Net, DeepLab, UPerNet, FPN, PAN, and LinkNet) alongside modern transformer-based models (Dense Prediction Transformer (DPT) and SegFormer). For this comparison, we use representative backbones from both CNNs (ResNet and MobileNet) and Mix Vision Transformer (MiT). By comparing these heterogeneous models on equal footing, we determine which architectural patterns and hybrid combinations yield representations most conducive to generalization. This diversity in evaluation identifies well-founded architectural bases for geospatial FMs.To guide architecture selection and pipeline design, we deploy a comprehensive suite of metrics covering both accuracy and efficiency. We evaluate segmentation accuracy via IoU, Dice, and boundary F1-score, and also measure efficiency (convergence speed and inference latency). These holistic benchmarks reveal critical trade-offs. For instance, some lightweight CNN models excel in speed, while transformer models boost boundary F-1 score. By capturing such nuances, our benchmark informs which architectures are best suited as general-purpose base models. It highlights how certain encoder–decoder combinations optimally balance performance and efficiency, and flags architectures with high transfer-readiness for new tasks and domains.The result is a reproducible, transferable model landscape that serves as a blueprint for FM development. Our benchmark framework effectively preconditions the FM pipeline, enabling researchers to enter the scaling phase with proven architecture candidates that have demonstrated cross-task and cross-sensor robustness. This “model landscape” allows subsequent large-scale pretraining to confidently build on architectures that ensure broad downstream generalization even in agentic (autonomous) deployment scenarios.Finally, we situate this work within the broader trend toward sensor-agnostic, self-supervised FMs in Earth observation. We argue that intelligent architecture search must precede any massive self-supervised pretraining effort. Early vetting of architectures under diverse conditions ensures that large-scale training resources are invested in the most promising designs. In summary, we frame this hybrid benchmarking framework as a strategic new layer in the geospatial FM ecosystem. The insights extend beyond segmentation, providing a reference point for building fine-tunable, sensor-agnostic foundation models that can be readily adapted to various downstream tasks and even deployed onboard satellites or other edge platforms. By solidifying architecture evaluation as an essential step, this work makes a serious scientific and strategic contribution toward the next generation of Earth observation AI.

Pharmaceuticals are increasingly recognised as a class of emerging contaminants of concern in rivers. Their continuous release from human use and variable removal in sewage treatment works (STWs) can produce ecologically relevant concentrations and contribute to antimicrobial resistance. We developed a probabilistic catchment-scale model based on a Bayesian Belief Network (BN) to quantify pharmaceutical concentrations and the probability of exceeding predicted no-effect concentrations (PNECs) at a monthly time step. The BN embeds a stochastic mass-balance linking monthly prescribing rates, excretion fractions, STW removal efficiencies and river discharge to produce posterior distributions of concentrations for 16 pharmaceuticals at 20 monitoring points in a medium size Scottish catchment. Model inputs were derived from Scotland’s National Health Service (NHS) prescribing records, a literature compilation of excretion and removal data, and a calibrated SWAT hydrological model. Simulated posterior concentration distributions generally agreed with observations and were typically within one order of magnitude for most compounds, indicating satisfactory performance. Highest exceedance probabilities were predicted for azithromycin, diclofenac, ibuprofen and clarithromycin, particularly at heavily impacted sites and during low-flow summer months. Scenario analyses show that future drier summers (UKCP18 RCP8.5) increase exceedance probabilities, and that substantial reductions in prescribing or markedly improved STW removal efficiencies are needed to reduce risks for high-impact compounds. The BN framework transparently captures uncertainty, supports diagnostic inference to prioritise interventions and is readily extensible to include additional sources (e.g. combined storm overflow and septic tanks) and pollutant mixture risk assessment.

We present the first tree-ring based summer surface temperature reconstruction (1382-2017) for the Southern Hemisphere that expresses strong volcanically forced cooling. The Northern Patagonia (NPAT) reconstruction is based on RW and Blue Intensity (BI) parameters measured from Araucaria araucana trees from 6 locations across the middle to southern end of the species’ range in Argentina. This multi-TR-parameter reconstruction explains 53% of the summer surface temperature variance (1903-2017), which is on par with the fidelity of TR based reconstructions from the Northern Hemisphere. The reconstruction coheres strongly with surface mean air temperatures for a large region in South America including sea surface temperatures well into the southeastern Pacific for these latitudes. The warmest 10-year period is 2008-2017, corresponding to the last ten years of the reconstruction, while the coldest period is 1455-1464. The coldest reconstructed year is 1971, followed by 1460. For both the NPAT reconstruction and a range of model simulations, superposed epoch analysis, using major tropical eruptions since the 1400s, indicates a significant post-eruption mean surface cooling of ca. 0.4 - 1.0 oC, depending on which volcanic events are used. The degree of relative cooling is on par, or even stronger, with the cooling represented by individual TR records used in the Northern Hemisphere N-TREND database suggesting that the volcanic response in northern Patagonia over the last 6 centuries is equivalent, or even more extreme, to what is observed in many Northern Hemisphere locations. Our results indicate that the use of ring-density parameters is of paramount importance for assessing past volcanically forced cooling in the Southern Hemisphere, but the dating and seasonality of the eruptions as well as the continentality and mid-latitude location of the woodland sites may also be important factors for capturing the signal of volcanic cooling.