58622 research outputs found
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Hope and Hardship: Children’s Perspectives on their Work and Life in Artisanal and Small-Scale Mining:Terre des Hommes Child Labour Report 2025
Get PDFThis report is part of a series published by Terre des Hommes (TDH). Its purpose is to shed light on global child work and labour, its content, context, and consequences. This year’s report focuses on children’s work in the extractive industries, particularly ASM. It begins with a brief overview of debates over how to understand and respond to children’s work in this sector. The next section discusses the approach to reviewing the literature and the methods used to research with children and their families, before contextualising the three case study mining communities studied, in Bolivia, India, and Zimbabwe.The main section of the report presents empirical answers to key questions around working children in ASM, weaving together the views of children and families and evidence from the global research literature. Finally, tying together these grassroots perspectives with the literature reviewed, the conclusion points in the direction of recommended actions for different stakeholders
Multiscale Analysis on Three-Dimensional Heat Transfer of Needle-Punched Composite Preforms:Virtual Fiber Models and Layered Homogenization-Reintegration Method
Get PDFAccurate virtual fiber component models of needle-punched quartz fiber felt/fabric composite preforms (NQFFCP) are constructed, including needle-punched fiber bundles along thickness direction (NFBATD), fiber felt and fabric models. Micro-CT technology is used to quantify geometric structure of NQFFCP and its component materials. Based on this, a comprehensive “layered homogenization-reintegration method” is proposed to construct 3D finite element (FE) heat transfer models of NQFFCP at different needle-punched densities (NDs). A Hot-Disk thermal analyzer is utilized to measure the anisotropic thermal conductivity (ATC) of materials. Additionally, 3D temperature and heat flux distributions of NQFFCP at different NDs are simulated, and their ATC are predicted. Furthermore, temperature and heat distribution characteristics are simulated and studied at multiscale. At fiber-scale, effects of yarn interlacing and fiber orientation on heat flux distribution are analyzed, while the influence of NFBATD on heat transfer in felt and fabric layer fields is demonstrated. Finally, the correlation among NDs, ATC and NQFFCP structure variety is analyzed and revealed through FE and experimental methods.</p
The Use of Large Language Models for Qualitative Research:The Deep Computational Text Analyser (DECOTA)
Get PDFMachine-assisted approaches for free-text analysis are rising in popularity, owing to a growing need to rapidly analyze large volumes of qualitative data. In both research and policy settings, these approaches have promise in providing timely insights into public perceptions and enabling policymakers to understand their community’s needs. However, current approaches still require expert human interpretation—posing a financial and practical barrier for those outside of academia. For the first time, we propose and validate the Deep Computational Text Analyser (DECOTA)—a novel machine learning methodology that automatically analyzes large free-text data sets and outputs concise themes. Building on structural topic modeling approaches, we used two fine-tuned large language models and sentence transformers to automatically derive “codes” and their corresponding “themes”, as in inductive thematic analysis. To fully automate the process, we designed and validated a novel algorithm to choose the optimal number of “topics” for the structural topic modeling. DECOTA outputs key codes and themes, their prevalence, and how prevalence varies across covariates such as age and gender. Each code is accompanied by three representative quotes. Four data sets previously analyzed using thematic analysis were triangulated with DECOTA’s codes and themes. We found that DECOTA is approximately 378 times faster and 1,920 times cheaper than human coding and consistently yields codes in agreement with or complementary to human coding (averaging 91.6% for codes and 90% for themes). The implications for evidence-based policy development, public engagement with policymaking, and psychometric measure development are discussed. Computational approaches are increasingly being used to quickly process large volumes of free-text data. These approaches hold promise in helping academics study public perceptions, and policymakers understand their community’s needs. However, current methods still require expert human interpretation, which can be costly and impractical. In this article, we developed the Deep Computational Text Analyser (DECOTA), a novel machine learning methodology designed to automatically analyze large free-text data sets to produce concise “themes” within the data. DECOTA uses several custom-trained models to detect themes and subthemes within the data, as a human may do when categorizing free-text responses. Our approach gives information about how common each subtheme and theme is, how common they are among different demographic groups, and offers example quotes. We compared how similar DECOTA’s analysis was to human coders, using four example free-text data sets. DECOTA’s outputs were highly consistent with human analyses, detecting 91.6% of all human subthemes and 90% of the humans’ themes. We noted that DECOTA was approximately 378 times faster and 1,920 times cheaper than human analysis. The potential uses of this methodology for policymakers and academics are discussed.</p
Angular Combining of Forecasts of Probability Distributions
Get PDFWhen multiple forecasts are available for a probability distribution, forecast combining enables a pragmatic synthesis of the information to extract the wisdom of the crowd. The linear opinion pool has been widely used, whereby the combining is applied to the probabilities of the distributional forecasts. However, it has been argued that this will tend to deliver overdispersed distributions, prompting the combination to be applied, instead, to the quantiles of the distributional forecasts. Results from different applications are mixed, leaving it as an empirical question whether to combine probabilities or quantiles. In this paper, we present an alternative approach. Looking at the distributional forecasts, combining the probabilities can be viewed as vertical combining, with quantile combining seen as horizontal combining. Our proposal is to allow combining to take place on an angle between the extreme cases of vertical and horizontal combining. We term this angular combining. The angle is a parameter that can be optimized using a proper scoring rule. For implementation, we provide a pragmatic numerical approach and a simulation algorithm. Among our theoretical results, we show that, as with vertical and horizontal averaging, angular averaging results in a distribution with mean equal to the average of the means of the distributions that are being combined. We also show that angular averaging produces a distribution with lower variance than vertical averaging, and, under certain assumptions, greater variance than horizontal averaging. We provide empirical results for distributional forecasts of Covid mortality, macroeconomic survey data, and electricity prices
Generative AI and the future of writing for publication:Insights from applied linguistics journal editors
Get PDFThe emergence of Generative Artificial Intelligence (GenAI) is reshaping academic writing and publishing practices. As knowledge curators, applied linguistics journal editors need to respond to GenAI developments. Yet, little is known about their perspectives on GenAI in academic writing and publishing. These perspectives could influence their editorial decisions and journal policies - potentially defining how scholars write for publication. Through in-depth semi-structured interviews, this study explored the perceptions of ten applied linguistics journal editors towards GenAI in academic writing for publication. Analysis shows that the development of GenAI is putting additional strain on the editorial process, which is already struggling. It highlights that current publisher and journal policies on GenAI are ambiguous, leading to confusing and questionable research practices. Editors are cautious about the use of GenAI in applied linguistics research and writing, with only the use of these tools to improve writing quality universally acceptable. Transparency is seen as essential. The findings highlight a pressing need for discipline-specific guidance on the acceptable uses of GenAI in academic publishing and the development of methodological models that detail ways GenAI can be integrated into the field's rich and diverse research traditions.</p
Substantial oxygen loss and chemical expansion in lithium-rich layered oxides at moderate delithiation
Get PDFDelithiation of layered oxide electrodes triggers irreversible oxygen loss, one of the primary degradation modes in lithium-ion batteries. However, the delithiation-dependent mechanisms of oxygen loss remain poorly understood. Here we investigate the oxygen non-stoichiometry in Li1.18–xNi0.21Mn0.53Co0.08O2–δ electrodes as a function of Li content by using cycling protocols with long open-circuit voltage steps at varying states of charge. Surprisingly, we observe substantial oxygen loss even at moderate delithiation, corresponding to 2.5, 4.0 and 7.6 ml O2 per gram of Li1.18–xNi0.21Mn0.53Co0.08O2–δ after resting at upper capacity cut-offs of 135, 200 and 265 mAh g−1 for 100 h. Our observations suggest an intrinsic oxygen instability consistent with predictions of high oxygen activity at intermediate potentials versus Li/Li+. In addition, we observe a large chemical expansion coefficient with respect to oxygen non-stoichiometry, which is about three times greater than those of classical oxygen-deficient materials such as fluorite and perovskite oxides. Our work challenges the conventional wisdom that deep delithiation is a necessary condition for oxygen loss in layered oxide electrodes and highlights the importance of calendar ageing for investigating oxygen stability.</p
Quasi-Monte Carlo methods for uncertainty quantification of wave propagation and scattering problems modelled by the Helmholtz equation
Get PDFWe analyse and implement a quasi-Monte Carlo (QMC) finite element method (FEM) for the forward problem of uncertainty quantification (UQ) for the Helmholtz equation with random coefficients, both in the second-order and zero-order terms of the equation, thus modelling wave scattering in random media. The problem is formulated on the infinite propagation domain, after scattering by the heterogeneity, and also (possibly) a bounded impenetrable scatterer. The spatial discretization scheme includes truncation to a bounded domain via a perfectly matched layer (PML) technique and then FEM approximation. A special case is the problem of an incident plane wave being scattered by a bounded sound-soft impenetrable obstacle surrounded by a random heterogeneous medium, or more simply, just scattering by the random medium. The random coefficients are assumed to be affine separable expansions with infinitely many independent uniformly distributed and bounded random parameters. As quantities of interest for the UQ, we consider the expectation of general linear functionals of the solution, with a special case being the far-field pattern of the scattered field. The numerical method consists of (a) dimension truncation in parameter space, (b) application of an adapted QMC method to compute expected values, and (c) computation of samples of the PDE solution via PML truncation and FEM approximation. Our error estimates are explicit in (the dimension truncation parameter), (the number of QMC points), (the FEM grid size) and (most importantly), (the Helmholtz wavenumber). The method is also exponentially accurate with respect to the PML truncation radius. Illustrative numerical experiments are given
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