Evaluation of a minimum flexural reinforcement ratio using fracturebased modelling

Minimum reinforcement ratios are specified for reinforced concrete structures to provide enough ductility. The aims are to control cracking in the serviceability limit state and to prevent sudden failure by ensuring sufficient ductility after the loss of tensile stress in concrete due to cracking. This can provide a warning before collapse and time to take preventive or remedial measures. A review of past research reveals that there are large variations, and sometimes contradictions, in proposed minimum reinforcement requirements for flexural members. In this paper, a fracture mechanics-based model is used to study different local phenomena such as tensile and compressive concrete softening to more precisely describe the behaviour of reinforced concrete beams. The findings show a decrease in the minimum reinforcement ratio with increasing beam size. This contradicts the provisions of prevailing codes and standards which suggest no change in the minimum reinforcement ratio with size. Therefore, there is a need to review the minimum reinforcement provisions.The authors would like to thank the Yousef Jameel Foundation for their financial support of this research.This is the accepted manuscript. The final version is available at www.iabse.org/Geneva2015Report

Integrated Fracture-Based Model Formulation for RC Crack Analysis

There is an opportunity to revisit and generalise classical theories for concrete cracking in light of increased interest in the use of non-conventional reinforcing materials and material efficiency. Fracture-based models to describe concrete cracking have potential but a limitation has been that many variables and different phenomena have to be incorporated to produce realistic material models. In this paper, an integrated fracture-based model (IFBM) is developed to predict the behaviour of lightly reinforced concrete beams. The proposed model is a closed-form solution that integrates different local phenomena to more precisely describe the onset of cracking, crack propagation and crack rotation. The IFBM incorporates post-cracking tensile stresses in the concrete, the bond-slip behaviour between the reinforcement and concrete, and compression softening in the concrete compressive zone. The model can predict parameters such as the crack length development and crack mouth opening displacement in Mode I lightly reinforced concrete flexural specimens subjected to three-point bending. The predictions show a fairly good agreement with experimental results for small-scale reinforced concrete beams with low reinforcement ratios (0.15-0.5%). The ability of the IFBM to identify specific failure modes and to capture the crack propagation and crack rotation stages of behaviour in lightly reinforced concrete beams are particular advantages. Such an approach provides a powerful tool to study the problem of minimum reinforcement requirements

Experimental investigation of crack propagation and crack branching in lightly reinforced concrete beams using Digital Image Correlation

Relatively few fracture-oriented experimental studies have been conducted on concrete that is reinforced. An experimental investigation was therefore undertaken to explore the cracking process in lightly reinforced concrete (RC) beams and to observe the details of the localised fracture process zone development. More specifically, the aims were to investigate the relationships between beam height (120 mm, 220 mm and 320 mm), steel reinforcement ratio (0.1–0.5%), ductility and the onset of crack branching. RC beams were tested in three-point bending and experimental surface strains and crack openings were inferred using digital image correlation (DIC). It was found that the presence of the reinforcement prevented premature fracture and led to crack branching where a single crack bifurcated in the region of the compression zone. In the larger beams the branching developed at a lower relative height and a greater reinforcement ratio led to a shallower branching angle. These observations were associated with ductility measures for lightly reinforced concrete beams.The authors would like to thank the Yousef Jameel Foundation and Cambridge Overseas Trust (COT) for their financial support of this research

Using remote sensing to assess peatland resilience by estimating oil surface moisture and drought recovery

This is the author accepted manuscript. The final version is available from Elsevier via the DOI in this recordPeatland areas provide a range of ecosystem services, including biodiversity, carbon storage, clean water, and flood mitigation, but many areas of peatland in the UK have been degraded through human land use including drainage. Here, we explore whether remote sensing can be used to monitor peatland resilience to drought. We take resilience to mean the rate at which a system recovers from perturbation; here measured literally as a recovery timescale of a soil surface moisture proxy from drought lowering. Our objectives were (1) to assess the reliability of Sentinel-1 Synthetic Aperture Radar (SAR) backscatter as a proxy for water table depth (WTD); (2) to develop a method using SAR to estimate below-ground (hydrological) resilience of peatlands; (3) to apply the developed method to different sites and consider the links between resilience and land management. Our inferences of WTD from Sentinel-1 SAR data gave results with an average Pearson’s correlation of 0.77 when compared to measured WTD values. The 2018 summer drought was used to assess resilience across three different UK peatland areas (Dartmoor, the Peak District, and the Flow Country) by considering the timescale of the soil moisture proxy recovery. Results show clear areas of lower resilience within all three study sites, which often correspond to areas of high drainage and may be particularly vulnerable to increasing drought severity/events under climate change. This method is applicable to monitoring peatland resilience elsewhere over larger scales, and could be used to target restoration work towards the most vulnerable areas.Leverhulme Trus

Proximity extension assay testing reveals novel diagnostic biomarkers of atypical parkinsonian syndromes.

OBJECTIVE: The high degree of clinical overlap between atypical parkinsonian syndromes (APS) and Parkinson's disease (PD) makes diagnosis challenging. We aimed to identify novel diagnostic protein biomarkers of APS using multiplex proximity extension assay (PEA) testing. METHODS: Cerebrospinal fluid (CSF) samples from two independent cohorts, each consisting of APS and PD cases, and controls, were analysed for neurofilament light chain (NF-L) and Olink Neurology and Inflammation PEA biomarker panels. Whole-cohort comparisons of biomarker concentrations were made between APS (n=114), PD (n=37) and control (n=34) groups using logistic regression analyses that included gender, age and disease duration as covariates. RESULTS: APS versus controls analyses revealed 11 CSF markers with significantly different levels in cases and controls (p<0.002). Four of these markers also reached significance (p<0.05) in APS versus PD analyses. Disease-specific analyses revealed lower group levels of FGF-5, FGF-19 and SPOCK1 in multiple system atrophy compared with progressive supranuclear palsy and corticobasal syndrome. Receiver operating characteristic curve analyses suggested that the diagnostic accuracy of NF-L was superior to the significant PEA biomarkers in distinguishing APS, PD and controls. The biological processes regulated by the significant proteins include cell differentiation and immune cell migration. Delta and notch-like epidermal growth factor-related receptor (DNER) had the strongest effect size in APS versus controls and APS versus PD analyses. DNER is highly expressed in substantia nigra and is an activator of the NOTCH1 pathway which has been implicated in the aetiology of other neurodegenerative disorders including Alzheimer's disease. CONCLUSIONS: PEA testing has identified potential novel diagnostic biomarkers of APS

Quantitatively monitoring the resilience of patterned vegetation in the Sahel

This is the final version. Available on open access from Wiley via the DOI in this recordData availability statement: The data that support the findings of this study are openly available in Zenodo. Processed images can be found at https://doi.org/10.5281/zenodo.5536861. Analysis results can be found at https://doi.org/10.5281/zenodo.4050362.Patterning of vegetation in drylands is a consequence of localised feedback mechanisms. Such feedbacks also determine ecosystem resilience - i.e. the ability to recover from perturbation. Hence the patterning of vegetation has been hypothesised to be an indicator of resilience, i.e. spots are less resilient than labyrinths. Previous studies have made this qualitative link and used models to quantitatively explore it, but few have quantitatively analysed available data to test the hypothesis. Here we provide methods for quantitatively monitoring the resilience of patterned vegetation, applied to 40 sites in the Sahel (a mix of previously identified and new ones). We show that an existing quantification of vegetation patterns in terms of a feature vector metric can effectively distinguish gaps, labyrinths, spots, and a novel category of spot-labyrinths at their maximum extent, whereas NDVI does not. The feature vector pattern metric correlates with mean precipitation. We then explored two approaches to measuring resilience. First we treated the rainy season as a perturbation and examined the subsequent rate of decay of patterns and NDVI as possible measures of resilience. This showed faster decay rates - conventionally interpreted as greater resilience - associated with wetter, more vegetated sites. Second we detrended the seasonal cycle and examined temporal autocorrelation and variance of the residuals as possible measures of resilience. Autocorrelation and variance of our pattern metric increase with declining mean precipitation, consistent with loss of resilience. Thus, drier sites appear less resilient, but we find no significant correlation between the mean or maximum value of the pattern metric (and associated morphological pattern types) and either of our measures of resilience.Leverhulme TrustAlan Turing InstituteScience and Technology Facilities Council (STFC

Community-driven tree planting greens the neighbouring landscape

This is the final version. Available on open access from Nature Research via the DOI in this recordNature-based solutions to climate change are growing policy priorities yet remain hard to quantify. Here we use remote sensing to quantify direct and indirect benefits from community-led agroforestry by The International Small group and Tree planting program (TIST) in Kenya. Since 2005, TIST-Kenya has incentivised smallholder farmers to plant trees for agricultural benefit and to sequester CO2. We use Landsat-7 satellite imagery to examine the effect on the historically deforested landscape around Mount Kenya. We identify positive greening trends in TIST groves during 2000-2019 relative to the wider landscape. These groves cover 27,198 ha, and a further 27,750 ha of neighbouring agricultural land is also positively influenced by TIST. This positive 'spill-over' impact of TIST activity occurs at up to 360 m distance. TIST also benefits local forests, e.g. through reducing fuelwood and fodder extraction. Our results show that community-led initiatives can lead to successful landscape-scale regreening on decadal timescales.Leverhulme TrustA. G. Leventis FoundationUKRIEngineering and Physical Sciences Research Council (EPSRC)Alan Turing Institut

Acute kidney injury in patients hospitalized with COVID-19 from the ISARIC WHO CCP-UK Study: a prospective, multicentre cohort study.

BACKGROUND: Acute kidney injury (AKI) is common in coronavirus disease 2019 (COVID-19). This study investigated adults hospitalized with COVID-19 and hypothesized that risk factors for AKI would include comorbidities and non-White race. METHODS: A prospective multicentre cohort study was performed using patients admitted to 254 UK hospitals with COVID-19 between 17 January 2020 and 5 December 2020. RESULTS: Of 85 687 patients, 2198 (2.6%) received acute kidney replacement therapy (KRT). Of 41 294 patients with biochemistry data, 13 000 (31.5%) had biochemical AKI: 8562 stage 1 (65.9%), 2609 stage 2 (20.1%) and 1829 stage 3 (14.1%). The main risk factors for KRT were chronic kidney disease (CKD) [adjusted odds ratio (aOR) 3.41: 95% confidence interval 3.06-3.81], male sex (aOR 2.43: 2.18-2.71) and Black race (aOR 2.17: 1.79-2.63). The main risk factors for biochemical AKI were admission respiratory rate >30 breaths per minute (aOR 1.68: 1.56-1.81), CKD (aOR 1.66: 1.57-1.76) and Black race (aOR 1.44: 1.28-1.61). There was a gradated rise in the risk of 28-day mortality by increasing severity of AKI: stage 1 aOR 1.58 (1.49-1.67), stage 2 aOR 2.41 (2.20-2.64), stage 3 aOR 3.50 (3.14-3.91) and KRT aOR 3.06 (2.75-3.39). AKI rates peaked in April 2020 and the subsequent fall in rates could not be explained by the use of dexamethasone or remdesivir. CONCLUSIONS: AKI is common in adults hospitalized with COVID-19 and it is associated with a heightened risk of mortality. Although the rates of AKI have fallen from the early months of the pandemic, high-risk patients should have their kidney function and fluid status monitored closely

Anthropogenic disturbance in tropical forests can double biodiversity loss from deforestation

Concerted political attention has focused on reducing deforestation1,2,3, and this remains the cornerstone of most biodiversity conservation strategies4,5,6. However, maintaining forest cover may not reduce anthropogenic forest disturbances, which are rarely considered in conservation programmes6. These disturbances occur both within forests, including selective logging and wildfires7,8, and at the landscape level, through edge, area and isolation effects9. Until now, the combined effect of anthropogenic disturbance on the conservation value of remnant primary forests has remained unknown, making it impossible to assess the relative importance of forest disturbance and forest loss. Here we address these knowledge gaps using a large data set of plants, birds and dung beetles (1,538, 460 and 156 species, respectively) sampled in 36 catchments in the Brazilian state of Pará. Catchments retaining more than 69–80% forest cover lost more conservation value from disturbance than from forest loss. For example, a 20% loss of primary forest, the maximum level of deforestation allowed on Amazonian properties under Brazil’s Forest Code5, resulted in a 39–54% loss of conservation value: 96–171% more than expected without considering disturbance effects. We extrapolated the disturbance-mediated loss of conservation value throughout Pará, which covers 25% of the Brazilian Amazon. Although disturbed forests retained considerable conservation value compared with deforested areas, the toll of disturbance outside Pará’s strictly protected areas is equivalent to the loss of 92,000–139,000 km2 of primary forest. Even this lowest estimate is greater than the area deforested across the entire Brazilian Amazon between 2006 and 2015 (ref. 10). Species distribution models showed that both landscape and within-forest disturbances contributed to biodiversity loss, with the greatest negative effects on species of high conservation and functional value. These results demonstrate an urgent need for policy interventions that go beyond the maintenance of forest cover to safeguard the hyper-diversity of tropical forest ecosystems

Integrating BDI agents with Agent-based simulation platforms

Agent-Based Models (ABMs) is increasingly being used for exploring and supporting decision making about social science scenarios involving modelling of human agents. However existing agent-based simulation platforms (e.g., SWARM, Repast) provide limited support for the simulation of more complex cognitive agents required by such scenarios. We present a framework that allows Belief-Desire Intention (BDI) cognitive agents to be embedded in an ABM system. Architecturally, this means that the "brains" of an agent can be modelled in the BDI system in the usual way, while the "body" exists in the ABM system. The architecture is exible in that the ABM can still have non-BDI agents in the simulation, and the BDI-side can have agents that do not have a physical counterpart (such as an organisation). The framework addresses a key integration challenge of coupling event-based BDI systems, with time-stepped ABM systems. Our framework is modular and supports integration off-the-shelf BDI systems with off-the-shelf ABM systems. The framework is Open Source, and all integrations and applications are available for use by the modelling community