58622 research outputs found
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China's National Climate Policy Database (V1)
No full textChina's National Climate Policy Database (V1) collects all national-level policies addressing climate change issued by the Chinese government between 2016 and 2022. In addition to tracking the policies issued across different sectors, the database also maps policy instruments used and measures the intensity of each policy
Physico-Chemical Characterisation of Particulate Matter and Ash from Biomass Combustion in Rural Indian Kitchens
Get PDFIn developing countries, indoor air pollution in rural areas is often attributed to the use of solid biomass fuels for cooking. Such fuels generate particulate matter (PM), carbon monoxide (CO), carbon dioxide (CO2), polyaromatic hydrocarbons (PAHs), and volatile organic compounds (VOCs). PM created from biomass combustion is a pollutant particularly damaging to health. This rigorous study employed a personal sampling device and multi stage cascade impactor to collect airborne PM (including PM2.5) and deposited ash from 20 real-world kitchen microenvironments. A robust analysis of the PM was undertaken using a range of morphological, physical, and chemical techniques, the results of which were then compared to a controlled burn experiment. Results revealed that airborne PM was predominantly carbon (~85%), with the OC/EC ratio varying between 1.17 and 11.5. Particles were primarily spherical nanoparticles (50–100 nm) capable of deep penetration into the human respiratory tract (HRT). This is the first systematic characterisation of biomass cooking emissions in authentic rural kitchen settings, linking particle morphology, chemistry and toxicology at health-relevant scales. Toxic heavy metals like Cr, Pb, Cd, Zn, and Hg were detected in PM, while ash was dominated by crustal elements such as Ca, Mg and P. VOCs comprised benzene derivatives, esters, ethers, ketones, tetramethysilanes(TMS), and nitrogen-, phosphorus- and sulphur-containing compounds. This researchshowcases a unique collection technique that gathered particles indicative of their potential for penetration and deposition in the HRT. Impact stems from the close link between the physico-chemical properties of particle emissions and their environmental and epidemiological effects. By providing a critical evidence base for exposure modelling, risk assessment and clean cooking interventions, this study delivers internationally significant insights. Our methodological innovation, capturing respirable nanoparticles under real-world conditions, offers a transferable framework for indoor air quality research across low- and middle-income countries. The findings therefore advance both fundamental understanding of combustion-derived nanoparticle behaviour and practical knowledge to inform public health, environmental policy, and the UN Sustainable Development Goals
Dataset for "Effects of a combined energy restriction and vigorous-intensity exercise intervention on the human gut microbiome: A randomised controlled trial"
No full textThis CSV file contains the raw data for all the anthropological, physiological, and biochemical parameters for each human participant, pre and post intervention or control reported in the associated article in the Journal of Physiology
Dataset for "The Catalytic Enantioselective [1,2]-Wittig Rearrangement Cascade of Allylic Ethers"
No full textThis data set includes output files from the quantum chemical calculations run with Gaussian16 (Revision C.01) that support our computational mechanistic study of the enantioselective [1,2]-Wittig rearrangement of allylic ethers. It also contains three sets of in situ reaction monitoring data (collected by University of St Andrews contributors) and a Python script that fits the rate constants of a first-order kinetics model to the experimental data
Dataset for Kurhan et al. "The development of a silage based biorefinery to deliver the maximum nutritional benefit for human consumption from UK grasslands"
No full textThis data set contains the underlying data and information presented in the paper, "The development of a silage based biorefinery to deliver the maximum nutritional benefit for human consumption from UK grasslands". Water-soluble protein and vitamins were extracted from silage using a twin-screw extruder at room temperature. The solids from the extruder, which contained further insoluble protein and the carbohydrates from the silage, were depolymerised and used to culture the oleaginous yeast Metschnikowia pulcherrima, producing further mycoprotein and lipid from the system. Included in the dataset are: - an analysis of the silage feedstock, specifically the quantities of dry matter/ash, carbon and nitrogen, key essential amino acids, and Vitamin B1, B2, B3 (nicotinamide and nicotinic acid) and B6 (pyridoxine, pyridoxal and pyridoxamine); - a comparison of amino acid profiles in the silage (before and after extraction) and a comparison to other common protein sources; - a comparison of the performance of the extruder at different power levels, liquid feeding quantities, temperatures, and screw speeds, with regard to nitrogen content, protein content and yield; - the fatty acid profile of the M. pulcherrima yeast grown on the resulting hydrolysate produced from the residues of the process
Deconstructing and reconstructing African development studies through student engagement and cross-cultural collaboration at a UK university
Get PDFThis paper reflects on the authors’ experience of seeking student feedback and peer recommendations to reconstruct learning content for the delivery of a module on African development studies at a university in the United Kingdom (UK). This restructuring process relied on a form of Appreciative Inquiry that covered the four stages of discovery, dreaming, delivery, and destiny. The paper finds that most students enter university with a limited understanding of Africa framed by overwhelmingly negative associations and have difficulty accessing positive narratives about African countries in their programmes of study. Consequently, problematizing generalised perceptions of Africa must form an obligatory part of transformative change in UK universities, so that students can gain an education that provides them with a more rounded understanding of and appreciation for the continent and its people. The paper also highlights that for sustained, transformative change to occur, efforts to decolonise teaching must go beyond the fragility of siloed initiatives to institution-wide efforts that include establishing training programs and the targeted recruitment of staff with specialised knowledge. Through a discussion of its key findings, the paper seeks to contribute to a growing body of literature providing practical examples of how universities in the UK (and beyond) can decolonise, diversify, and update their social science curricula
Comic Relief:Sport for Change Research
Get PDFThis report presents the findings of a piece of independent research which sought to examine Comic Relief’s ‘Sport for Change’ funding approach. It charts the operationalisation and impact of this approach which, since 2002, has invested £80 million into projects in the UK and internationally. During that time over 500 projects have been funded which have used a variety of sports, including skateboarding, surfing, football, martial arts, yoga and boxing, to promote an array of social issues such as: education, employment, mental health, gender equality, social inclusion
AI and Inverse Methods for Building Digital Twins in Neuroscience
Get PDFDeep learning is revolutionizing Neuroscience and Healthcare. One driver for this change is the exponential growth in the volume of biomedical data generated by modern medical imaging technology and automated data acquisition systems, such as automated patch clamps. Another driver is the increasing reliance of clinical diagnosis on deep learning algorithms to detect abnormalities in MRI scans. By automating the analysis of MRI images, algorithms free clinical staff from repetitive time and consuming tasks while removing subjectivity in the diagnosis and classification of brain tumors. Deep learning algorithms routinely assist neurosurgeons during critical operations, for example by stimulating surrounding brain tissue during tumor resection. Deep learning algorithms are also increasingly capable of forecasting epileptic seizures and assisting researchers in understanding how language is coded in the brain. Digital twins of brain activity trained on electroencephalographic time series have predicted epileptic seizures and connectivity changes in the brain during language comprehension. Machine learning is also progressing neuroscience by modelling biocircuits at the single neuron level. Recursive neural networks trained on electrophysiological data are making accurate predictions of the voltage oscillations of central pattern generators. The dynamics of individual ionic currents is also inferred to a good degree of accuracy when additional information in the form of surrogate model is provided. This importantly suggests that the dynamics of the membrane voltage which is observed and the ionic current waveforms which cannot be directly measured may be reconstructed from the analysis of electrophysiological time series
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