The effect of weld residual stresses and their re-distribution with crack growth during fatigue under constant amplitude loading

In this work the evolution of the residual stresses in a MIG-welded 2024-T3 aluminium alloy M(T) specimen during in situ fatigue crack growth at constant load amplitude has been measured with neutron diffraction. The plastic relaxation and plasticity-induced residual stresses associated with the fatigue loading were found to be small compared with the stresses arising due to elastic re-distribution of the initial residual stress field. The elastic re-distribution was modelled with a finite element simulation and a good correlation between the experimentally-determined and the modelled stresses was found. A significant mean stress effect on the fatigue crack growth rate was seen and this was also accurately predicted using the measured initial residual stresses

Teaching as a system: COVID-19 as a lens into teacher change

In the spring of 2020, schools and universities around the world were closed because of the COVID-19 pandemic. The relative lockdown affected more than 1.5 billion learners as teachers and students sheltered at home for several weeks. As schooling moved online, teachers were forced to change how they taught. In the research presented here, we focus on university mathematics professors, and we analyze how their practice, knowledge, and beliefs intertwine and change under these circumstances. More specifically, the context of the pandemic and the relative lockdown provides us with the experimental basis to argue that the new practice affected both knowledge and beliefs of mathematics teachers and that practice, knowledge, and beliefs form a system. Being part of a system, the reactions to change in practice can be of two types, namely, the system as a whole tries to resist change, or the system as a whole changes - and it changes significantly. The research presented here proposes a model for describing and analyzing what we called a teaching system and examines three cases that help to better depict the systemic nature of teaching

Frequencies of some B blood group alleles in laying hens from a selection and crossbreeding experiment

<p/

The Static Failure of Adhesively Bonded Metal Laminate Structures: A Cohesive Zone Approach

Data on distribution, ecology, biomass, recruitment, growth, mortality and productivity of the West African bloody cockle Anadara senilis were collected at the Banc d'Aguuin, Mauritania, in early 1985 and 1986. Ash-free dry weight appeared to be correlated best with shell height. A. senilis was abundant on the tidal flats of landlocked coastal bays, but nearly absent on the tidal flats bordering the open sea. The average biomass for the entire area of tidal flats was estimated at 5.5 g·m−2 ash-free dry weight. The A. senilis population appeared to consist mainly of 10 to 20-year-old individuals, showing a very slow growth and a production: biomass ratio of about 0.02 y−1. Recruitment appeared negligible and mortality was estimated to be about 10% per year. Oystercatchers (Haematopus ostralegus), the gastropod Cymbium cymbium and unknown fish species were responsible for a large share of this. The distinction of annual growth marks permitted the assessment of year-class strength, which appeared to be correlated with the average discharge of the river Senegal. This may be explained by assuming that year-class strength and river discharge both are correlated with rainfall at the Banc d'Arguin.

The evolution of ice-wedge polygon networks in tundra fire scars

Abstract In response to increasing temperatures and precipitation in the Arctic, ice-rich permafrost landscapes are undergoing rapid changes. In permafrost lowland landscapes, polygonal ice wedges are especially vulnerable, and their melting induces widespread subsidence triggering the transition from low-centered (LCP) to high-centered polygons (HCP) by forming degrading troughs. This process has an important impact on surface hydrology, as the connectivity of such trough networks determines the rate of drainage of an entire landscape (Liljedahl et al., 2016). While scientists have observed this degradation trend throughout large domains in the polygonal patterned Arctic landscape over timescales of multiple decades, it is especially evident in disturbed areas such as fire scars (Jones et al., 2015). Here, wildfires removed the insulating organic soil layer. We can therefore observe the LCP-to-HCP transition within only several years. Until now, studies on quantifying trough connectivity have been limited to local field studies and sparse time series only. With high-resolution Earth observation data, a more comprehensive analysis is possible. However, when considering the vast and ever-growing volumes of data generated, highly automated and scalable methods are needed that allow scientists to extract information on the geomorphic state and on changes over time of ice-wedge trough networks. In this study, we combine very-high-resolution (VHR) aerial imagery and comprehensive databases of segmented polygons derived from VHR optical satellite imagery (Witharana et al., 2018) to investigate the changing polygonal ground landscapes and their environmental implications in fire scars in Northern and Western Alaska. Leveraging the automated and scalable nature of our recently introduced approach (Rettelbach et al., 2021), we represent the polygon networks as graphs (a concept from computer science to describe complex networks) and use graph metrics to describe the state of these (hydrological) trough networks. Due to a lack of historical data, we cannot investigate a dense time series of a single representative study area on the evolution of the network, but rather leverage the possibilities of a space-for-time substitution. Thus, we focus on data from multiple fire scars of different ages (up to 120 years between date of disturbance and date of acquisition). With our approach, we might infer past and future states of degradation from the currently prevailing spatial patterns showing how this type of disturbed landscape evolves over space and time. It further allows scientists to gain insights into the complex geomorphology, hydrology, and ecology of landscapes, thus helping to quantify how they interact with climate change

DETECTION OF CLOUDS IN MEDIUM-RESOLUTION SATELLITE IMAGERY USING DEEP CONVOLUTIONAL NEURAL NETS

Cloud detection is an inextricable pre-processing step in remote sensing image analysis workflows. Most of the traditional rule-based and machine-learning-based algorithms utilize low-level features of the clouds and classify individual cloud pixels based on their spectral signatures. Cloud detection using such approaches can be challenging due to a multitude of factors including harsh lighting conditions, the presence of thin clouds, the context of surrounding pixels, and complex spatial patterns. In recent studies, deep convolutional neural networks (CNNs) have shown outstanding results in the computer vision domain. These methods are practiced for better capturing the texture, shape as well as context of images. In this study, we propose a deep learning CNN approach to detect cloud pixels from medium-resolution satellite imagery. The proposed CNN accounts for both the low-level features, such as color and texture information as well as high-level features extracted from successive convolutions of the input image. We prepared a cloud-pixel dataset of approximately 7273 randomly sampled 320 by 320 pixels image patches taken from a total of 121 Landsat-8 (30m) and Sentinel-2 (20m) image scenes. These satellite images come with cloud masks. From the available data channels, only blue, green, red, and NIR bands are fed into the model. The CNN model was trained on 5300 image patches and validated on 1973 independent image patches. As the final output from our model, we extract a binary mask of cloud pixels and non-cloud pixels. The results are benchmarked against established cloud detection methods using standard accuracy metrics

Identification of a serum biomarker signature associated with metastatic prostate cancer

Purpose: Improved early diagnosis and determination of aggressiveness of prostate cancer (PC) is important to select suitable treatment options and to decrease over-treatment. The conventional marker is total prostate specific antigen (PSA) levels in blood, but lacks specificity and ability to accurately discriminate indolent from aggressive disease. Experimental design: In this study, we sought to identify a serum biomarker signature associated with metastatic PC. We measured 157 analytes in 363 serum samples from healthy subjects, patients with non-metastatic PC and patients with metastatic PC, using a recombinant antibody microarray. Results: A signature consisting of 69 proteins differentiating metastatic PC patients from healthy controls was identified. Conclusions and clinical relevance: The clinical value of this biomarker signature requires validation in larger independent patient cohorts before providing a new prospect for detection of metastatic PC

Mapping snow depth within a tundra ecosystem using multiscale observations and Bayesian methods

This paper compares and integrates different strategies to characterize the variability of end-of-winter snow depth and its relationship to topography in ice-wedge polygon tundra of Arctic Alaska. Snow depth was measured using in situ snow depth probes and estimated using ground-penetrating radar (GPR) surveys and the photogrammetric detection and ranging (phodar) technique with an unmanned aerial system (UAS). We found that GPR data provided high-precision estimates of snow depth (RMSE  =  2.9 cm), with a spatial sampling of 10 cm along transects. Phodar-based approaches provided snow depth estimates in a less laborious manner compared to GPR and probing, while yielding a high precision (RMSE  =  6.0 cm) and a fine spatial sampling (4 cm × 4 cm). We then investigated the spatial variability of snow depth and its correlation to micro- and macrotopography using the snow-free lidar digital elevation map (DEM) and the wavelet approach. We found that the end-of-winter snow depth was highly variable over short (several meter) distances, and the variability was correlated with microtopography. Microtopographic lows (i.e., troughs and centers of low-centered polygons) were filled in with snow, which resulted in a smooth and even snow surface following macrotopography. We developed and implemented a Bayesian approach to integrate the snow-free lidar DEM and multiscale measurements (probe and GPR) as well as the topographic correlation for estimating snow depth over the landscape. Our approach led to high-precision estimates of snow depth (RMSE  =  6.0 cm), at 0.5 m resolution and over the lidar domain (750 m × 700 m)