Situation awareness in outage work – A study of events occurring in U.S. nuclear power plants between 2016 and 2020

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On the Relations between Lithium-Ion Battery Reaction Entropy, Surface Temperatures and Degradation

Understanding and mitigating the degradation of batteries is important for financial as well as environmental reasons. Many studies look at cell degradation in terms of capacity losses and the mechanisms causing them. However, in this study, we take a closer look at how degradation affects heat sources in batteries, thereby requiring dynamic cooling strategies for battery systems throughout the battery life. In this work, we have studied and compared reversible (entropy-related) and non-reversible heat sources in a commercial LCO-graphite lithium-ion battery (LIB) alongside measuring the surface temperature as a function of the State of Health (SoH). In addition, we studied the effect of different thermal management strategies on both degradation and cooling efficiency. We found that entropic heating plays a major role in overall heat generation. This causes large variations in heat generation and battery temperature over both State of Charge (SoC) and charge versus discharge. The maximum battery temperature increases when the cell degrades as irreversible heat generation increases. Temperature variations over the cell thickness are substantial and increase drastically when the cell degrades. In addition, significant increases in thickness were observed as a result of cell degradation. Furthermore, cycling at elevated temperatures resulted in a larger thickness increase with significant gas production

On the Relations between Lithium-Ion Battery Reaction Entropy, Surface Temperatures and Degradation

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Increased Incidence of Loco-Regional Recurrences Among African American Women with Terminal Stage Breast Cancer

A prospective analysis of women with terminal breast cancer admitted to CHNE from November 2006–August 2007 evaluated anecdotal observations that African American (AA) women are likelier than Caucasian women to evidence loco-regional recurrences (LRR). Women with terminal breast cancer who were admitted to CHNE, a not-for-profit hospice serving over 90% of Northeast Florida hospice patients, were eligible for participation. 134 terminal breast cancer patients were assessed by hospice nurses for LRR presence via chest wall examination. 80% of them (107) were Caucasian, 17% (23) were AA and 3% (4) were of other ethnicities. Evidence of LRR were noted in 13% of the women (17/134). The proportion of patients with LRR was higher in AA women than Caucasian women (26% vs. 10%, 6/23 vs. 11/107, respectively), although this difference was not statistically significant (p = 0.08). The majority of Caucasian women with LRR consented to a medical record review, but a minority of AA women consented (8/11 vs. 2/6, respectively, p = 0.16)

Barriers to Systematic Model Transformation Testing

International audienceModel Driven Engineering (MDE) techniques support extensive use of models in order to manage the increasing complexity of software systems. Approp riate abstractions of software system elements can ease reasoning and understand ing and thus limit the risk of errors in large systems. Automatic model transfor mations play a critical role in MDE since they automate complex, tedious, error- prone, and recurrent software development tasks. A fault in a transformation can introduce a fault in the transformed model, which if undetected and not removed , can propagate to other models in successive development steps. As a fault prop agates further, it becomes more difficult to detect and isolate. Since model tra nsformations are meant to be reused, faults present in them may result in many f aulty models. Model transformations constitute a class of programs with unique c haracteristics that make testing them challenging. The complexity of input and o utput data, lack of model management tools, and the heterogeneity of transformat ion languages pose special problems to testers of transformations. In this paper we identify current model transformation characteristics that contribute to the difficulty of systematically testing transformations. We present promising solu tions and propose possible ways to overcome these barriers

A multi-model comparison of meteorological drivers of surface ozone over Europe

The implementation of European emission abatement strategies has led to a significant reduction in the emissions of ozone precursors during the last decade. Ground-level ozone is also influenced by meteorological factors such as temperature, which exhibit interannual variability and are expected to change in the future. The impacts of climate change on air quality are usually investigated through air-quality models that simulate interactions between emissions, meteorology and chemistry. Within a multi-model assessment, this study aims to better understand how air-quality models represent the relationship between meteorological variables and surface ozone concentrations over Europe. A multiple linear regression (MLR) approach is applied to observed and modelled time series across 10 European regions in springtime and summertime for the period of 2000–2010 for both models and observations. Overall, the air-quality models are in better agreement with observations in summertime than in springtime and particularly in certain regions, such as France, central Europe or eastern Europe, where local meteorological variables show a strong influence on surface ozone concentrations. Larger discrepancies are found for the southern regions, such as the Balkans, the Iberian Peninsula and the Mediterranean basin, especially in springtime. We show that the air-quality models do not properly reproduce the sensitivity of surface ozone to some of the main meteorological drivers, such as maximum temperature, relative humidity and surface solar radiation. Specifically, all air-quality models show more limitations in capturing the strength of the ozone–relative-humidity relationship detected in the observed time series in most of the regions, for both seasons. Here, we speculate that dry-deposition schemes in the air-quality models might play an essential role in capturing this relationship. We further quantify the relationship between ozone and maximum temperature (mo3 − T, climate penalty) in observations and air-quality models. In summertime, most of the air-quality models are able to reproduce the observed climate penalty reasonably well in certain regions such as France, central Europe and northern Italy. However, larger discrepancies are found in springtime, where air-quality models tend to overestimate the magnitude of the observed climate penalty

Plasticity in growth of farmed and wild Atlantic salmon:Is the increased growth rate of farmed salmon caused by evolutionary adaptations to the commercial diet?

Background: Domestication of Atlantic salmon for commercial aquaculture has resulted in farmed salmon displaying substantially higher growth rates than wild salmon under farming conditions. In contrast, growth differences between farmed and wild salmon are much smaller when compared in the wild. The mechanisms underlying this contrast between environments remain largely unknown. It is possible that farmed salmon have adapted to the high-energy pellets developed specifically for aquaculture, contributing to inflated growth differences when fed on this diet. We studied growth and survival of 15 families of farmed, wild and F1 hybrid salmon fed three contrasting diets under hatchery conditions; a commercial salmon pellet diet, a commercial carp pellet diet, and a mixed natural diet consisting of preserved invertebrates commonly found in Norwegian rivers.  Results: For all groups, despite equal numbers of calories presented by all diets, overall growth reductions as high 68 and 83%, relative to the salmon diet was observed in the carp and natural diet treatments, respectively. Farmed salmon outgrew hybrid (intermediate) and wild salmon in all treatments. The relative growth difference between wild and farmed fish was highest in the carp diet (1: 2.1), intermediate in the salmon diet (1:1.9) and lowest in the natural diet (1:1.6). However, this trend was non-significant, and all groups displayed similar growth reaction norms and plasticity towards differing diets across the treatments.  Conclusions: No indication of genetic-based adaptation to the form or nutritional content of commercial salmon diets was detected in the farmed salmon. Therefore, we conclude that diet alone, at least in the absence of other environmental stressors, is not the primary cause for the large contrast in growth differences between farmed and wild salmon in the hatchery and wild. Additionally, we conclude that genetically-increased appetite is likely to be the primary reason why farmed salmon display higher growth rates than wild salmon when fed ad lib rations under hatchery conditions. Our results contribute towards an understanding of the potential genetic changes that have occurred in farmed salmon in response to domestication, and the potential mechanisms underpinning genetic and ecological interactions between farmed escapees and wild salmonids

Predation of Atlantic salmon across ontogenetic stages and impacts on populations

Managers and stakeholders increasingly ask whether predation is a driving force behind the poor status of many species, and whether predator control is likely to be a successful management action to intervene. We review existing literature on Atlantic salmon Salmo salar predation and predator control, as well as general ecological theory on the role of predation in the life cycle of this iconic fish. Many bird, mammal, and fish predators target salmon at different life stages. In healthy salmon populations, predation is likely compensated for by reduced intra-specific competition during the freshwater stage. There is little evidence that predation alone has been an underlying mechanism for driving salmon populations below conservation limits. However, depending on the predator’s response to salmon abundance, predation may keep decimated populations from recovering, even when the actual causes of decline have been removed. Under such a scenario, predation control may contribute to recovery, but there are no strong examples that clearly demonstrate the efficacy of managing predators to recover threatened salmon populations, challenging further applications.publishedVersio

Utility of routine viral load, CD4 cell count, and clinical monitoring among adults with HIV receiving antiretroviral therapy in Uganda: randomised trial

Objective To evaluate the use of routine laboratory monitoring in terms of clinical outcomes among patients receiving antiretroviral therapy (ART) in Uganda

Validation of Canopy Height Profile methodology for small-footprint full-waveform airborne LiDAR data in a discontinuous canopy environment

A Canopy Height Profile (CHP) procedure presented in Harding et al. (2001) for large footprint LiDAR data was tested in a closed canopy environment as a way of extracting vertical foliage profiles from LiDAR raw-waveform. In this study, an adaptation of this method to small-footprint data has been shown, tested and validated in an Australian sparse canopy forest at plot- and site-level. Further, the methodology itself has been enhanced by implementing a dataset-adjusted reflectance ratio calculation according to Armston et al. (2013) in the processing chain, and tested against a fixed ratio of 0.5 estimated for the laser wavelength of 1550nm. As a by-product of the methodology, effective leaf area index (LAIe) estimates were derived and compared to hemispherical photography-derived values. To assess the influence of LiDAR aggregation area size on the estimates in a sparse canopy environment, LiDAR CHPs and LAIes were generated by aggregating waveforms to plot- and site-level footprints (plot/site-aggregated) as well as in 5m grids (grid-processed). LiDAR profiles were then compared to leaf biomass field profiles generated based on field tree measurements. The correlation between field and LiDAR profiles was very high, with a mean R2 of 0.75 at plot-level and 0.86 at site-level for 55 plots and the corresponding 11 sites. Gridding had almost no impact on the correlation between LiDAR and field profiles (only marginally improvement), nor did the dataset-adjusted reflectance ratio. However, gridding and the dataset-adjusted reflectance ratio were found to improve the correlation between raw-waveform LiDAR and hemispherical photography LAIe estimates, yielding the highest correlations of 0.61 at plot-level and of 0.83 at site-level. This proved the validity of the approach and superiority of dataset-adjusted reflectance ratio of Armston et al. (2013) over a fixed ratio of 0.5 for LAIe estimation, as well as showed the adequacy of small-footprint LiDAR data for LAIe estimation in discontinuous canopy forests