58622 research outputs found
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An agent-based modelling approach to investigate the impact of gender on tuberculosis transmission in Uganda
Get PDFTuberculosis (TB) is an airborne disease caused by the pathogen Mycobacterium tuberculosis. In 2023, it returned to being the leading cause of death from an infectious agent globally, replacing COVID-19; in the nineteenth century, one in seven of all humans died of tuberculosis. More than 10 million people are diagnosed with TB every year. The majority of cases in adults occur in males (62.5% of all global adult cases in 2023, compared to 37.5% in females). The main reasons for males suffering from a higher burden of global TB cases, compared to females, may be in large part due to population-scale factors, such as employment type, the quantity and type of social contacts they make, and their health-seeking behaviours (e.g. differences in diagnostic and treatment delays between genders). To investigate which population-scale factors are most important in determining this higher TB burden in males, we have developed an age- and gender-stratified, spatially heterogeneous epidemiological agent-based model. We have focused specifically on Kampala, the capital of Uganda, which is a high-burden TB country. We considered counterfactual scenarios to elucidate the impact of gender on the epidemiology of TB. Setting disease progression parameters equal between the genders leads to a reduction in both male-to-female case ratio and total case numbers
Research methods and generative artificial intelligence in applied linguistics
Get PDFSince the release of ChatGPT, there has been an explosion of interest in Generative Artificial Intelligence (GenAI) and a desire to understand how these tools can be utilised in almost every domain of human activity. Never before in human history have we had a tool that could simulate so many human capabilities. Yet, just as with humans, these tools have been found to have substantial limitations. These limitations have led us to question the roles they can play in high-stakes tasks, such as research. At the same time, they have challenged our current conventions and norms of authorship, transparency, and accountability, thus forcing us to consider:•What tasks should be left to humans exclusively?•What tasks can AI do alone?•What tasks can/should AI and humans do together?For some scholars, clear red lines have been drawn. For example, 416 experienced qualitative researchers from 38 countries wrote a commentary rejecting the use of GenAI for reflexive qualitative research (Jowsey, Braun, Clark et al., 2025). They provided three legitimate reasons for their rejection: (1) GenAI as simulated intelligence is incapable of meaning-making; (2) Qualitative research should remain a distinctly human practice; (3) the established harms of GenAI, especially to the environment and workers in the Global South. Nguyen and Welch (2025) have similar concerns and arguments against the use of GenAI in qualitative research. For other scholars, GenAI offers significant potential to reshape “the academic research lifecycle—from ideation and literature discovery to hypothesis formation, methodological planning, and data acquisition” (Haber et al., 2025, p. 27). Meanwhile, other scholars, including us, take a pragmatic stance, trying to balance the potential of these tools with the explicit threats they pose to our epistemologies, methodological rigor, integrity, and ethics (Roe, 2025; Moorhouse, Nejadghanbar & Yeo, 2025; Moorhouse, Consoli & Curle, 2025)
A theory of change approach to enhance the post-2030 sustainable development agenda
Get PDFAs the 2030 deadline for the Sustainable Development Goals(SDGs) nears and progress remains limited, researchers are proposing measuresto enhance the next, post-2030, agenda to improve implementation (1–3). Withmore proposals expected in future, we argue for a systematic approach to helpresearchers and policy-makers design and assess them. This requires a theory ofchange that explains how and why proposals will improve implementation of thenext agenda, while also considering their political feasibility. We start byconstructing an implicit theory of change underpinning the current 2030 Agenda(4) to revisit how the SDGs were intended to work and identify key successesand failures. We then propose an approach for assessing proposals put forwardto improve the post-2030 agenda on the basis of their impact and feasibility. A better approach is needed to assess potential impact and feasibility of proposals.<p/
Irreverent Pessimism (A Planetary Life Without Appeal)
Get PDFPerhaps the most disorienting feature of the long socioecological disaster that makes ever sprawling environmental disasters amplify the effects of ongoing and uneven conditions of poverty, violence, and dispossession, is not its urgency, but its permanence: that there is no foreseeable future in which redemption would prevail. What might it take to refuse the cruel hope for a redemptive future without seeking consolation in the despair that such forlorn hope precipitates? At a time when everyone is extorted to save the world or be damned, this article explores the irreverent pessimism of what, after Camus, one might call a planetary “life without appeal.” Refusing to be content with what is now deemed proper to damned of the earth, irreverent pessimism affirms the insubordinate disposition of a life lived in the most radical immanence of a freedom born not of hope but of the ongoing improvisation of a revolt without future
Dataset for "Fast structural analysis of concrete thin-shells using deep learning"
No full textThis dataset contains scripts and data supporting the following research article: Pollet, M., Shepherd, P., Hawkins, W., and Costa, E., 2026. Fast structural analysis of concrete thin-shells using deep learning. Computers & Structures, 320, 108042. Concrete thin-shells are materially efficient structures, which can be used to reduce the environmental impact of concrete structures. Their shape is typically determined iteratively and evaluated through Finite Element Analysis (FEA). This research proposes the use of surrogate models as faster alternatives to FEA, thus enabling wider design space exploration. This dataset contains deep learning models – Multilayer Perceptrons, Convolutional Neural Networks, and Graph Neural Networks – that have been trained to predict the buckling factor and stress fields of concrete thin-shells of various shapes under design loads. It also contains the Python scripts that were used to train these models and assess their performance. Running these scripts necessitates the associated ConcreteShellFEA dataset to be downloaded. Further details about this data can be found in the related research article
Dataset for "Co-creation of an airflow and COVID-19 transmission risk model for shelter design"
No full textThis dataset underpins a journal article titled "Co-creation of an Airflow and COVID-19 Transmission Risk Model for Shelter Design." The paper introduces the first collaboratively developed tool designed to guide shelter design by ensuring adequate natural ventilation, optimal indoor air quality, and minimized airborne transmission risks. This study explores the development and application of this tool to promote healthier shelters and enhance the shelter design process. Data was collected using the JISC online tool across two phases: the first before the tool's creation and the second after its implementation by participants. The dataset includes responses from online surveys conducted with participants from various global locations. It encompasses information on shelter designers' experience in shelter construction, their background knowledge of natural airflow and indoor air quality, and feedback on the usability of the co-created tool
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