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    36453 research outputs found

    The COVID-19 vaccines: recent development, challenges and prospects

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    The highly infectious coronavirus disease 2019 (COVID-19) associated with the pathogenic severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has spread to become a global pandemic. At present, the world is relying mainly on containment and hygiene-related measures, as well as repurposed drugs to control the outbreak. The development of COVID-19 vaccines is crucial for the world to return to pre-pandemic normalcy, and a collective global effort has been invested into protection against SARS-CoV-2. As of March 2021, thirteen vaccines have been approved for application whilst over 90 vaccine candidates are under clinical trials. This review focuses on the development of COVID-19 vaccines and highlights the efficacy and vaccination reactions of the authorised vaccines. The mechanisms, storage, and dosage specification of vaccine candidates at the advanced stage of development are also critically reviewed together with considerations for potential challenges. Whilst the development of a vaccine is, in general, in its infancy, current progress is promising. However, the world population will have to continue to adapt to the “new normal” and practice social distancing and hygienic measures, at least until effective vaccines are available to the general public

    Balancing performance and effort in deep learning via the fusion of real and synthetic cultural heritage photogrammetry training sets

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    Cultural heritage presents both challenges and opportunities for the adoption and use of deep learning in 3D digitisation and digitalisation endeavours. While unique features in terms of the identity of artefacts are important factors that can contribute to training performance in deep learning algorithms, challenges remain with regards to the laborious efforts in our ability to obtain adequate datasets that would both provide for the diversity of imageries, and across the range of multi-facet images for each object in use. One solution, and perhaps an important step towards the broader applicability of deep learning in the field of digital heritage is the fusion of both real and virtual datasets via the automated creation of diverse datasets that covers multiple views of individual objects over a range of diversified objects in the training pipeline, all facilitated by closerange photogrammetry generated 3D objects. The question is the ratio of the combination of real and synthetic imageries in which an inflection point occurs whereby performance is reduced. In this research, we attempt to reduce the need for manual labour by leveraging the flexibility provided for in automated data generation via close-range photogrammetry models with a view for future deep learning facilitated cultural heritage activities, such as digital identification, sorting, asset management and categorisation

    Challenging the commodification of human rights: the case of the right to housing

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    The profitability of commodified housing is driving extreme levels of corporate investment. To boost profits investors are exploiting “undervalued” low-income housing, evicting vulnerable individuals, hoarding land and charging exploitative fees. This is causing severe harm to individuals’ right to housing across the globe, including, inter alia, rapidly increasing prices and debt, increasing evictions, homelessness, and increased recourse to substandard accommodation. The harm is endemic, but the human rights response has been tepid. This paper argues that both state obligations and the content of the right to housing under the International Covenant on Economic, Social and Cultural Rights (ICESCR) can usefully address the problem. However, in communications with State Parties the Committee on Economic, Social and Cultural Rights (CESCR) addresses issues of commodification and affordability in vague terms that fail to generate meaningful obligations. The paper grounds the CESCR’s approach in theories of enforceability which argue that enforcement is more practicable when “clear violations” can be established. The CESCR offers clear statements of breach only when identifying explicitly wrongful practices, such as discriminatory laws. This approach, however, almost entirely occludes harm caused by the marketization of human rights. It skeletonizes the “protect” limb of state obligations, permits the long-term retrogression of affordability and enables the serious subsequent effects. The paper proposes that “clear violations” can be constructed from the results of, and laws constituting, harmful marketization. A three-stage process of identification of breach, standard-setting, and policy suggestions is recommended that can turn the long-term retrogression of access to housing into specific, measurable statements of violations and recommendations. This same approach is advocated for business responsibilities under the UN Guiding Principles on Business and Human Rights, with the content of these responsibilities also evaluated

    Classification of single cell types during leukemia therapy using artificial neural networks

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    We trained artificial neural network (ANN) models to classify peripheral blood mononuclear cells (PBMC) in chronic lymphoid leukemia (CLL) patients. The classification task was to determine differences in gene expression profiles in PBMC pre-treatment (with ibrutinib) and on days 30, 120, 150, and 280 after the start of treatment. Twelve datasets represented clinical samples containing a total 48,016 single cell profiles were used to train and test ANN models to classify the progress of therapy by gene expression changes. The accuracy of ANN classification was >92% in internal cross-validation. External cross-validation, using independent data sets for training and testing, showed the accuracy of classification of post-treatment PBMCs to more than 80%. To the best of our knowledge, this is the first study that has demonstrated the potential of ANNs with 10x single cell gene expression data for detecting the changes during treatment of CLL

    Impact of stoichiometry and strain on Ge1−x Sn x alloys from first principles calculations

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    We calculate the electronic structure of germanium-tin (Ge1-x Sn x ) binary alloys for 0 ≤ x ≤ 1 using density functional theory (DFT). Relaxed alloys with semiconducting or semimetallic behaviour as a function of Sn composition x are identified, and the impact of epitaxial strain is investigated by constraining supercell lattice constants perpendicular to the [001] growth direction to the lattice constants of Ge, zinc telluride, or cadmium telluride substrates. It is found that application of 1% tensile strain reduces the Sn composition required to bring the (positive) direct band gap to zero by approximately 5% compared to a relaxed Ge1-x Sn x alloy having the same gap at Γ. On the other hand, compressive strain has comparatively less impact on the alloy band gap at Γ. Using DFT calculated alloy lattice and elastic constants, the critical thickness for Ge1-x Sn x thin films as a function of x and substrate lattice constant is estimated, and validated against supercell DFT calculations and experiment. The analysis correctly predicts the Sn composition range at which it becomes energetically favourable for Ge1-x Sn x /Ge to become amorphous. The influence of stoichiometry and strain is examined in relation to reducing the magnitude of the inverted ('negative') Γ7-Γ8+ band gap, which is characteristic of semimetallic alloy electronic structure. Based on our findings, strategies for engineering the semimetal-to-semiconductor transition via strain and quantum confinement in Ge1-x Sn x nanostructures are proposed. © 2021 IOP Publishing Ltd

    “No future for Libya with Gaddafi”: classical realism, status and revenge in the UK intervention in Libya

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    Why did Britain intervene in Libya in 2011? Several explanations suggest themselves: security, R2P and status. The article shows that status was a significant motivating factor, and this demonstrates a dynamic that helps to refine a classical realist theory of intervention. The article calls for status to be seen intrinsically and instrumentally, and for more attention to be paid to the related motive of revenge. The findings suggest (though do not prove from a causal standpoint) that status may be a stronger motive than security for state decision-makers. The article’s central empirical argument is that regime change in Libya was not the last stage of Britain’s foreign policy of intervention. Rather, intervention was the last stage in Britain’s status and revenge-driven foreign policy of regime change. Britain saw the Libya crisis as a chance to preserve its great power status and revenge Muammar Gaddafi for past wrongs

    Experimental data on water vapour adsorption on silica gel in fully packed and Z-annulus packed beds

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    Experimental data on water vapour adsorption on silica gel in four packed bed configurations namely fully packed bed (FPB), large annulus packed bed (LAPB), medium annulus packed bed (MAPB) and small annulus packed bed (SAPB) are presented. Raw temperature data from designated mass transfer zones (MTZ) in the packed beds and on their corresponding walls are presented along with data of the inlet and outlet moist air conditions. Pressure transducers installed at the inlet and outlet provided pressure data. The presented data also covers the material properties of the silica gel for adsorption obtained through material testing and analysis in the laboratory. With detailed experimental methodology and comprehensive material and water vapour adsorption data, this article can help other researchers to validate and verify the performance of their adsorption systems. The material property data presented can also help investigators to use appropriate experimentally determined property values of silica gel in their theoretical studies. Furthermore, this data can serve as a basis of comparison for heat and mass transfer in other experimental adsorption systems

    Correlation-aided method for identification and gradation of periodicities in hydrologic time series

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    Identification of periodicities in hydrological time series and evaluation of their statistical significance are not only important for water-related studies, but also challenging issues due to the complex variability of hydrological processes. In this article, we develop a “Moving Correlation Coefficient Analysis” (MCCA) method for identifying periodicities of a time series. In the method, the correlation between the original time series and the periodic fluctuation is used as a criterion, aiming to seek out the periodic fluctuation that fits the original time series best, and to evaluate its statistical significance. Consequently, we take periodic components consisting of simple sinusoidal variation as an example, and do statistical experiments to verify the applicability and reliability of the developed method by considering various parameters changing. Three other methods commonly used, harmonic analysis method (HAM), power spectrum method (PSM) and maximum entropy method (MEM) are also applied for comparison. The results indicate that the efficiency of each method is positively connected to the length and amplitude of samples, but negatively correlated with the mean value, variation coefficient and length of periodicity, without relationship with the initial phase of periodicity. For those time series with higher noise component, the developed MCCA method performs best among the four methods. Results from the hydrological case studies in the Yangtze River basin further verify the better performances of the MCCA method compared to other three methods for the identification of periodicities in hydrologic time series

    A target-driven decision-making multi-layered approach for optimal building retrofits via agglomerative hierarchical clustering: a case study in China

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    The optimisation of energy, environmental and economic (3E) outcomes is the principal approach to identifying retrofit solutions for a sustainable built environment. By applying this approach and defining a set performance target, this study proposes a makeshift decision framework that integrates a data mining procedure (agglomerative hierarchical clustering (AHC)) into the multi-objective decision-making process to provide a simplified 3E assessment of building retrofits on a macro-scale. The framework comprises of three methodological models: (1) a building stock aggregation model, (2) an individualistic 3E model that provides the sensitivity analysis for (3) a life cycle cost-environmental assessment model. The framework is demonstrated and validated with a case study aimed at achieving the set EUI targets for low-rise office buildings (LOB) in Shanghai. The model defines 4 prototypical buildings for the existing LOB blocks, which are used for the individual evaluation of 12 commonly applied retrofit measures. Subsequently, a simplified LCC-environmental assessment was performed to evaluate the 3E prospects of 2048 possible retrofit combinations. The results uniquely identify retrofit solutions to attain set performance targets and optimal building performance. Furthermore, the decision criteria for different investment scenarios are discussed. Overall, this study provides building investors an innovative framework for a facile and holistic assessment of a broader range of retrofit alternatives based on set performance targets

    An investigation into the impact of variations of ambient air pollution and meteorological factors on lung cancer mortality in Yangtze River Delta

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    Lung cancer (LC) mortality, as one of the top cancer deaths in China, has been associated with increased levels of exposure to ambient air pollutants. In this study, different lag times on weekly basis were applied to study the association of air pollutants (PM2.5, PM10, and NO2) and LC mortality in Ningbo, and in subpopulations at different age groups and genders. Furthermore, seasonal variations of pollutant concentrations and meteorological variables (temperature, relative humidity, and wind speed) were analysed. A generalised additive model (GAM) using Poisson regression was employed to estimate the effect of single pollutant model on LC mortality in Yangtze River Delta using Ningbo as a case study. It was reported that there were statistically significant relationships between lung cancer mortality and air pollutants. Increases of 6.2% (95% confidence interval [CI]: 0.2% to 12.6%) and 4.3% (95% CI: 0.1% to 8.5%) weekly total LC mortality with a 3-week lag time were linked to each 10 μg/m3 increase of weekly average PM2.5 and PM10 respectively. The association of air pollutants (PM2.5, PM10 and NO2) and LC mortality with a 3-week lag time was also found statistically significant during periods of low temperature (T < 18 °C), low relative humidity (H < 73.7%) and low wind speed (u < 2.8 m/s), respectively. The female population was found to be more susceptible to the exposure to air pollution than the male population. In addition, the population with an age of 50 years or above was shown to be more sensitive to ambient air pollutant. These outcomes indicated that increased risk of lung cancer mortality was evidently linked to exposure to ambient air pollutant on a weekly basis. The impact of weekly variation on the LC mortality and air pollutant levels should be considered in air pollution-related health burden analysis

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