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.

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

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)

Towards the minimal amount of exercise for improving metabolic health: beneficial effects of reduced-exertion high-intensity interval training

High-intensity interval training (HIT) has been proposed as a time-efficient alternative to traditional cardiorespiratory exercise training, but is very fatiguing. In this study, we investigated the effects of a reduced-exertion HIT (REHIT) exercise intervention on insulin sensitivity and aerobic capacity. Twenty-nine healthy but sedentary young men and women were randomly assigned to the REHIT intervention (men, n = 7; women, n = 8) or a control group (men, n = 6; women, n = 8). Subjects assigned to the control groups maintained their normal sedentary lifestyle, whilst subjects in the training groups completed three exercise sessions per week for 6 weeks. The 10-min exercise sessions consisted of low-intensity cycling (60 W) and one (first session) or two (all other sessions) brief ‘all-out’ sprints (10 s in week 1, 15 s in weeks 2–3 and 20 s in the final 3 weeks). Aerobic capacity ( V˙O2peakV˙O2peak ) and the glucose and insulin response to a 75-g glucose load (OGTT) were determined before and 3 days after the exercise program. Despite relatively low ratings of perceived exertion (RPE 13 ± 1), insulin sensitivity significantly increased by 28% in the male training group following the REHIT intervention (P < 0.05). V˙O2peakV˙O2peak increased in the male training (+15%) and female training (+12%) groups (P < 0.01). In conclusion we show that a novel, feasible exercise intervention can improve metabolic health and aerobic capacity. REHIT may offer a genuinely time-efficient alternative to HIT and conventional cardiorespiratory exercise training for improving risk factors of T2D

Affect in mathematics education

There are two different uses for the word “affect” in behavioral sciences. Often it is used as an overarching umbrella concept that covers attitudes, beliefs, motivation, emotions, and all other noncognitive aspects of human mind. In this article, however, the word affect is used in a more narrow sense, referring to emotional states and traits. A more technical definition of emotions, states, and traits will follow later.Peer reviewe

Cold season emissions dominate the Arctic tundra methane budget

Arctic terrestrial ecosystems are major global sources of methane (CH4); hence, it is important to understand the seasonal and climatic controls on CH4 emissions from these systems. Here, we report year-round CH4 emissions from Alaskan Arctic tundra eddy flux sites and regional fluxes derived from aircraft data. We find that emissions during the cold season (September to May) account for >= 50% of the annual CH4 flux, with the highest emissions from noninundated upland tundra. A major fraction of cold season emissions occur during the "zero curtain" period, when subsurface soil temperatures are poised near 0 degrees C. The zero curtain may persist longer than the growing season, and CH4 emissions are enhanced when the duration is extended by a deep thawed layer as can occur with thick snow cover. Regional scale fluxes of CH4 derived from aircraft data demonstrate the large spatial extent of late season CH4 emissions. Scaled to the circumpolar Arctic, cold season fluxes from tundra total 12 +/- 5 (95% confidence interval) Tg CH4 y(-1), similar to 25% of global emissions from extratropical wetlands, or similar to 6% of total global wetland methane emissions. The dominance of late-season emissions, sensitivity to soil environmental conditions, and importance of dry tundra are not currently simulated in most global climate models. Because Arctic warming disproportionally impacts the cold season, our results suggest that higher cold-season CH4 emissions will result from observed and predicted increases in snow thickness, active layer depth, and soil temperature, representing important positive feedbacks on climate warming.Peer reviewe

The Residual Stress Relaxation Behavior of Weldments During Cyclic Loading

Accurate measurement of residual stress is necessary to obtain reliable predictions of fatigue lifetime and enable estimation of time-to-facture for any given stress level. In this article, relaxation of welding residual stresses as a function of cyclic loading was documented on three common steels: AISI 1008, ASTM A572, and AISI 4142. Welded specimens were subjected to cyclic bending (R = 0.1) at different applied stresses, and the residual stress relaxation existing near the welds was measured as a function of cycles. The steels exhibited very different stress relaxation behaviors during cyclic loadings, which can be related to the differences in the microstructures of the specimens. A phenomenological model, which treats dislocation motion during cyclic loading as being analogous to creep of dislocations, is proposed for estimation of the residual stress relaxation

Sphingomyelin Synthases Regulate Protein Trafficking and Secretion

Sphingomyelin synthases (SMS1 and 2) represent a class of enzymes that transfer a phosphocholine moiety from phosphatidylcholine onto ceramide thus producing sphingomyelin and diacylglycerol (DAG). SMS1 localizes at the Golgi while SMS2 localizes both at the Golgi and the plasma membrane. Previous studies from our laboratory showed that modulation of SMS1 and, to a lesser extent, of SMS2 affected the formation of DAG at the Golgi apparatus. As a consequence, down-regulation of SMS1 and SMS2 reduced the localization of the DAG-binding protein, protein kinase D (PKD), to the Golgi. Since PKD recruitment to the Golgi has been implicated in cellular secretion through the trans golgi network (TGN), the effect of down-regulation of SMSs on TGN-to-plasma membrane trafficking was studied. Down regulation of either SMS1 or SMS2 significantly retarded trafficking of the reporter protein vesicular stomatitis virus G protein tagged with GFP (VSVG-GFP) from the TGN to the cell surface. Inhibition of SMSs also induced tubular protrusions from the trans Golgi network reminiscent of inhibited TGN membrane fission. Since a recent study demonstrated the requirement of PKD activity for insulin secretion in beta cells, we tested the function of SMS in this model. Inhibition of SMS significantly reduced insulin secretion in rat INS-1 cells. Taken together these results provide the first direct evidence that both enzymes (SMS1 and 2) are capable of regulating TGN-mediated protein trafficking and secretion, functions that are compatible with PKD being a down-stream target for SMSs in the Golgi