A coupled compressible flow and geomechanics model for dynamic fracture aperture during carbon sequestration in coal

This paper presents the development of a discrete fracture model of fully coupled compressible fluid flow, adsorption and geomechanics to investigate the dynamic behaviour of fractures in coal. The model is applied in the study of geological carbon dioxide sequestration and differs from the dual porosity model developed in our previous work, with fractures now represented explicitly using lower‐dimensional interface elements. The model consists of the fracture‐matrix fluid transport model, the matrix deformation model and the stress‐strain model for fracture deformation. A sequential implicit numerical method based on Galerkin finite element is employed to numerically solve the coupled governing equations, and verification is completed using published solutions as benchmarks. To explore the dynamic behaviour of fractures for understanding the process of carbon sequestration in coal, the model is used to investigate the effects of gas injection pressure and composition, adsorption and matrix permeability on the dynamic behaviour of fractures. The numerical results indicate that injecting nonadsorbing gas causes a monotonic increase in fracture aperture; however, the evolution of fracture aperture due to gas adsorption is complex due to the swelling‐induced transition from local swelling to macro swelling. The change of fracture aperture is mainly controlled by the normal stress acting on the fracture surface. The fracture aperture initially increases for smaller matrix permeability and then declines after reaching a maximum value. When the local swelling becomes global, fracture aperture starts to rebound. However, when the matrix permeability is larger, the fracture aperture decreases before recovering to a higher value and remaining constant. Gas mixtures containing more carbon dioxide lead to larger closure of fracture aperture compared with those containing more nitrogen

Regional disparities and seasonal differences in climate risk to rice labour.

The 880 million agricultural workers of the world are especially vulnerable to increasing heat stress due to climate change, affecting the health of individuals and reducing labour productivity. In this study, we focus on rice harvests across Asia and estimate the future impact on labour productivity by considering changes in climate at the time of the annual harvest. During these specific times of the year, heat stress is often high compared to the rest of the year. Examining climate simulations of the Coupled Model Intercomparison Project 6 (CMIP6), we identified that labour productivity metrics for the rice harvest, based on local wet-bulb globe temperature, are strongly correlated with global mean near-surface air temperature in the long term (p ≪ 0.01, R 2 > 0.98 in all models). Limiting global warming to 1.5 °C rather than 2.0 °C prevents a clear reduction in labour capacity of 1% across all Asia and 2% across Southeast Asia, affecting the livelihoods of around 100 million people. Due to differences in mechanization between and within countries, we find that rice labour is especially vulnerable in Indonesia, the Philippines, Bangladesh, and the Indian states of West Bengal and Kerala. Our results highlight the regional disparities and importance in considering seasonal differences in the estimation of the effect of climate change on labour productivity and occupational heat-stress

The effect of voluntary fasting and dehydration on flicker-induced retinal vascular dilation in a healthy individual: a case report

INTRODUCTION: Dynamic retinal vessel analysis represents a well-established method for the assessment of vascular reactivity during both normal conditions and after various provocations. We present a case where the subject showed abnormal retinal vessel reactivity after fasting voluntarily for 20 hours. CASE PRESENTATION: A healthy, 21-year-old man who fasted voluntarily for 20 hours exhibited abnormal retinal vascular reactivity (dilation and constriction) after flicker provocation as measured using the Dynamic Retinal Vessel Analyser (Imedos, Jena, Germany). CONCLUSION: The abnormal vascular reactivity induced by fasting was significant; abnormal levels of important nutrients due to fasting and dehydration could play a role through altering the concentration of vasoactive substances such as nitric oxide. This hypothesis needs further investigation

Downscaling precipitation over High-mountain Asia using multi-fidelity Gaussian processes: improved estimates from ERA5

The rivers of High-mountain Asia provide freshwater to around 1.9 billion people. However, precipitation, the main driver of river flow, is still poorly understood due to limited in situ measurements in this area. Existing tools to interpolate these measurements or downscale and bias-correct precipitation models have several limitations. To overcome these challenges, this paper uses a probabilistic machine learning approach called multi-fidelity Gaussian processes (MFGPs) to downscale the fifth ECMWF climate reanalysis (ERA5). The method is first validated by downscaling ERA5 precipitation data over data-rich Europe and then data-sparse upper Beas and Sutlej river basins in the Himalayas. We find that MFGPs are simpler to implement and more applicable to smaller datasets than other state-of-the-art machine learning methods. MFGPs are also able to quantify and narrow the uncertainty associated with the precipitation estimates, which is especially needed over ungauged areas and can be used to estimate the likelihood of extreme events that lead to floods or droughts. Over the upper Beas and Sutlej river basins, the precipitation estimates from the MFGP model are similar to or more accurate than available gridded precipitation products (APHRODITE, TRMM, CRU TS, and bias-corrected WRF). The MFGP model and APHRODITE annual mean precipitation estimates generally agree with each other for this region, with the MFGP model predicting slightly higher average precipitation and variance. However, more significant spatial deviations between the MFGP model and APHRODITE over this region appear during the summer monsoon. The MFGP model also presents a more effective resolution, generating more structure at finer spatial scales than ERA5 and APHRODITE. MFGP precipitation estimates for the upper Beas and Sutlej basins between 1980 and 2012 at a 0.0625° resolution (approx. 7 km) are jointly published with this paper.</p

Kernel Learning for Explainable Climate Science

The Upper Indus Basin, Himalayas provides water for 270 million people and countless ecosystems. However, precipitation, a key component to hydrological modelling, is poorly understood in this area. A key challenge surrounding this uncertainty comes from the complex spatial-temporal distribution of precipitation across the basin. In this work we propose Gaussian processes with structured non-stationary kernels to model precipitation patterns in the UIB. Previous attempts to quantify or model precipitation in the Hindu Kush Karakoram Himalayan region have often been qualitative or include crude assumptions and simplifications which cannot be resolved at lower resolutions. This body of research also provides little to no error propagation. We account for the spatial variation in precipitation with a non-stationary Gibbs kernel parameterised with an input dependent lengthscale. This allows the posterior function samples to adapt to the varying precipitation patterns inherent in the distinct underlying topography of the Indus region. The input dependent lengthscale is governed by a latent Gaussian process with a stationary squared-exponential kernel to allow the function level hyperparameters to vary smoothly. In ablation experiments we motivate each component of the proposed kernel by demonstrating its ability to model the spatial covariance, temporal structure and joint spatio-temporal reconstruction. We benchmark our model with a stationary Gaussian process and a Deep Gaussian processes.Comment: 16th Bayesian Modelling Applications Workshop at UAI, 2022 (Eindhoven, Netherlands

‘Is the library open?’: Correlating unaffiliated access to academic libraries with open access support

© 2019, Igitur, Utrecht Publishing and Archiving Services. All rights reserved. In the context of a growing international focus on open access publishing options and mandates, this paper explores the extent to which the ideals of ‘openness’ are also being applied to physical knowledge resources and research spaces. This study, which forms part of the larger Curtin Open Knowledge Initiative project, investigates the relationship between academic library access policies and institutional positions on open access or open science publishing. Analysis of library access policies and related documents from twenty academic institutions in Asia, Australia, Europe, North America, Africa and the United Kingdom shows that physical access to libraries for members of the public who are not affiliated with a university is often the most restricted category of access. Many libraries impose financial and sometimes security barriers on entry to buildings, limiting access to collections in print and other non-digital formats. The limits placed on physical access to libraries contrast strongly with the central role that these institutions play in facilitating open access in digital form for research outputs through institutional repositories and open access publishing policies. We compared library access policies and practices with open access publishing and research sharing policies for the same institutions and found limited correlation between both sets of policies. Comparing the two assessments using Spearman’s rank correlation coefficient confirmed open access policies have a direct association with the narrow aspects of public access provided through online availability of formal publications, but are not necessarily associated (in the universities in this study) with delivering on a broader commitment to public access to knowledge. The results suggest that while institutional mission statements and academic library policies may refer to sharing of knowledge and research and community collaboration, multiple layers of library user categories, levels of privilege and fees charged can inhibit the realisation of these goals. As open access publishing options and mandates expand, physical entry to academic libraries and access to print and electronic resources has contracted. This varies within and across countries, but it conflicts with global library and information commitments to open access to knowledge

Convolutional conditional neural processes for local climate downscaling

A new model is presented for multisite statistical downscaling of temperature and precipitation using convolutional conditional neural processes (convCNPs). ConvCNPs are a recently developed class of models that allow deep-learning techniques to be applied to off-the-grid spatio-temporal data. In contrast to existing methods that map from low-resolution model output to high-resolution predictions at a discrete set of locations, this model outputs a stochastic process that can be queried at an arbitrary latitude–longitude coordinate. The convCNP model is shown to outperform an ensemble of existing downscaling techniques over Europe for both temperature and precipitation taken from the VALUE intercomparison project. The model also outperforms an approach that uses Gaussian processes to interpolate single-site downscaling models at unseen locations. Importantly, substantial improvement is seen in the representation of extreme precipitation events. These results indicate that the convCNP is a robust downscaling model suitable for generating localised projections for use in climate impact studies

Universities and Knowledge Sharing: Evaluating progress to openness at the institutional level

Universities are key sites of knowledge creation. Governments and research funders are increasingly interested in ensuring that their investments in the production of new knowledge deliver a quantifiable return on investment, including in the form of ‘impact’. Ensuring that research outputs are not locked behind paywalls, and that research data can be interrogated and built upon are increasingly central to efforts to improve the effectiveness of global research landscapes. We argue that mandating and promoting open access (OA) for published research outputs, as well as the sharing of research data are important elements of building a vibrant open knowledge system, but they are not enough. Supporting diversity within knowledge-making institutions; enabling collaboration across boundaries between universities and wider communities; and addressing inequalities in access to knowledge resources and in opportunities to contribute to knowledge making processes are also important. New tools are needed to help universities, funders, and communities to understand the extent to which a university is operating as an effective open knowledge institution; as well as the steps that might be taken to improve open knowledge performance. This paper discusses our team’s efforts to develop a model of Open Knowledge that is not confined to measures of OA and open data. The Curtin Open Knowledge Initiative is a project of the Centre for Culture and Technology at Curtin University. With funding from the university, we are exploring the extent to which universities are functioning as effective open knowledge institutions; as well as the types of information that universities, funders, and communities might need to understand an institution’s open knowledge performance and how it might be improved. The challenges of data collection on open knowledge practices at scale, and across national, cultural and linguistic boundaries are also discussed