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The two-dimensional (2D) delamination growth in composite laminates under the Mode-II fracture condition was experimentally investigated using a novel set-up. Circular plate specimens with a centrally embedded pre-crack of two sizes were semi-clamped along the edge allowing sliding movement and loaded transversally at the center. In addition, one-dimensional (1D) experiments were conducted on specimens of the same material in an endloaded split set-up for comparison. For 2D experiments, the whole delamination process could be divided into three stages, i.e., crack initiation, slow propagation and subsequent rapid propagation. The corresponding load-deflection response, crack-length changes, stiffness and compliance variations in each stage were discussed. In contrast to 1D experiments, the load continued to increase in 2D cases even after rapid crack propagation due to the increasing crack-front length. Under post-inspection with a digital microscope, a much longer fracture process zone, including large-scale fiber bridging, was observed in 2D compared to 1D specimens.

Machine learning covers a large set of algorithms that can be trained to identify patterns in data. Thanks to the increase in the amount of data and computing power available, it has become pervasive across scientific disciplines. We first highlight why machine learning is needed in marine ecology. Then we provide a quick primer on machine learning techniques and vocabulary. We built a database of ∼1000 publications that implement such techniques to analyse marine ecology data. For various data types (images, optical spectra, acoustics, omics, geolocations, biogeochemical profiles, and satellite imagery), we present a historical perspective on applications that proved influential, can serve as templates for new work, or represent the diversity of approaches. Then, we illustrate how machine learning can be used to better understand ecological systems, by combining various sources of marine data. Through this coverage of the literature, we demonstrate an increase in the proportion of marine ecology studies that use machine learning, the pervasiveness of images as a data source, the dominance of machine learning for classification-type problems, and a shift towards deep learning for all data types. This overview is meant to guide researchers who wish to apply machine learning methods to their marine datasets. Machine learning in marine ecology: an overview of techniques and applications

Understanding the variations in past ocean carbonate chemistry is critical to elucidating the role of the oceans in balancing the global carbon cycle. The fossil shells from marine calcifiers present in the sedimentary record are widely applied as past ocean carbon cycle proxies. However, the interpretation of these records can be challenging due to the complex physiological and ecological response to the carbonate system during an organisms' life cycle and the potential for preservation at the seafloor. Here we present a new dissolution proxy based on the morphological attributes of coccolithophores from the Noëlaerhabdaceae family (Emiliania huxleyi > 2 μm, and small Gephyrocapsa spp.). To evaluate the influences of coccolithophore calcification and coccolith preservation on fossil morphology, we measured morphological attributes, mass, length, thickness, and shape factor (ks) of coccoliths in a laboratory dissolution experiment and surface sediment samples from the South China Sea. The coccolith morphological data in surface sediments were also analyzed with environment settings, namely surface temperature, nutrients, pH, chlorophyll a concentration, and carbonate saturation of bottom water by a redundancy analysis. Statistical analysis indicates that carbonate saturation of the deep ocean explains the highest proportion of variation in the morphological data instead of the environmental variables of the surface ocean. Moreover, the dissolution trajectory in the ks vs. length of coccoliths is comparable between natural samples and laboratory dissolution experiments, emphasizing the importance of carbonate saturation on fossil coccolith morphology. However, the mean ks alone cannot fully explain the main variations observed in our work. We propose that the normalized ks variation (σ/ks), which is the ratio between the standard deviation of ks (σ) and the mean ks, could reflect different degrees of dissolution and size-selective dissolution, influenced by the assemblage composition. Applied together with the σ/ks ratio, the ks factor of fossil coccoliths in deep ocean sediments could be a potential proxy for a quantitative reconstruction of past carbonate dissolution dynamics. Biogeosciences, 20 (9) ISSN:1726-4170

The data shows the experimental results for Mode-II fracture properties of glass-fiber/epoxy composite laminates, which consist of 14 layers of continuous filament mats and 6 layers of quadriaxial sewed fabrics. The experiments were conducted using an end-loaded split testing apparatus according to the standard ISO 15114:2014, and the results were processed using the experimental compliance method with correction factors considered. Note the crack length measurements include microcracking-zone length. Due to the transverse shear effects from the large thickness and the asymmetric fiber layup of the sub-laminates, the R-curves derived contain certain errors. THIS DATASET IS ARCHIVED AT DANS/EASY, BUT NOT ACCESSIBLE HERE. TO VIEW A LIST OF FILES AND ACCESS THE FILES IN THIS DATASET CLICK ON THE DOI-LINK ABOVE

Abstract. As peatlands are a major terrestrial sink in the global carbon cycle, gaining understanding of their development and changes throughout time is essential to predict their future carbon budget and potentially mitigate negative influences of climate change. With this aim to understand peat development, many studies have investigated the paleoecological dynamics through the analysis of various proxies, including pollen, macrofossil, elemental, and biomarker analyses. However, as each of these proxies are known to have their own benefits and limitations, examining them in parallel potentially allows for a deeper understanding of these paleoecological dynamics at the peatland and for a systematic comparison of the power of these individual proxies. In this study, we therefore analyzed soil cores from a peatland in Germany (Beerberg, Thuringia) to a) characterize the vegetation dynamics over the course of the peatland development during the late Holocene and b) evaluate to what extent the inclusion of multiple proxies, specifically pollen, macrofossil, and biomarkers, contributes to a deeper understanding of those dynamics and interaction among factors. We found that, despite a major shift in regional forest composition from primarily beech to spruce as well as many indicators of human impact in the region, the local plant population in the Beerberg area remained stable over time following the initial phase of peatland development up until the last couple of centuries. Therefore, little variation could be derived from the paleobotanical data alone. The combination of pollen and macrofossil analyses with the elemental and biomarker analyses enabled further understanding of the site development as these proxies added valuable additional information including the occurrence of climatic variations, such as the Little Ice Age, and more recent disturbances such as drainage and dust deposition.

The start-up, build-up and demise of cold-water coral mounds are governed by environmental changes at global, regional and local scales. Whilst the formation of cold-water coral mounds across the globe is widely documented to follow interglacial-glacial cycles, less is known about their response to local environmental fluctuations during short time intervals. This study investigates the local variations in coral mound build-up along Brittlestar Ridge I (East Melilla Coral Province, Southeast Alboran Sea) by comparing three on-mound gravity cores collected ∼1 km apart, together with five previously described on-mound records, along a longitudinal transect on the ridge crest. Radiocarbon foraminiferal dating associated to U-series coral dating allowed to correlate the different records and to estimate mound aggradation rates, whilst grain-size analysis provided information on bottom current velocities. Prior to a rapid period of coral mound build-up initiated at ∼14.75 ka BP, the three cores present an occurrence of cm-thick bryozoan-dominated intervals nearly entirely consisting of the erect cheleistome Buskea dichotoma. Offsets between benthic foraminiferal and coral ages suggest that older dead allochtonous benthic foraminifera are possibly eroded from neighbouring settings, transported and deposited within the coral framework. In contrast, younger benthic foraminifera would develop on dead coral framework during periods of reef stagnation. The comparison of all cores indicates that mound build-up along the ridge did not follow the same timing during the last ∼16 kyr and that mound aggradation was marked by a staggered dynamic. Both local differences in bottom current velocities and patchiness of other key environmental actors (e.g. substrate availability) are suspected to drive the observed staggered mound build-up. Cold-water coral mound build-up shows important differences during Greenland Interstadial 1 (i.e. the Bølling-Allerød) and the Holocene, hence examplifying how local environmental variability may overprint global and regional climate variability over short time periods. Marine Geology, 457 ISSN:0025-3227

Abstract. Quantifying the link between microstructure and effective elastic properties of snow, firn, and bubbly ice is essential for many applications in cryospheric sciences. The microstructure of snow and ice can be characterized by different types of fabrics (crystallographic, geometrical) that gives rise to macroscopically anisotropic elastic behavior. While the impact of the crystallographic fabric has been extensively studied in deep firn, the present work investigates the influence of the geometrical fabric over the entire range of possible volume fractions. To this end we have computed the effective elasticity tensor of snow, firn, and ice by finite element simulations based on 395 X-ray tomography images comprising samples from the laboratory, Alps, Greenland, and Antarctica. We employed a variant of the Eshelby tensor that has been previously utilized for the parametrization of thermal and dielectric properties of snow and utilized Hashin-Shtrikman bounds to capture the nonlinear interplay between density and geometrical anisotropy. From that we derive a closed-form parametrization for all components of the (transverse isotropic) elastic tensor for all volume fractions using 2 fit parameters per tensor component. Finally we used the Thomsen parameter to compare the geometrical anisotropy to the crystallographic anisotropy in bubbly ice. While the geometrical anisotropy is clearly dominating up to ice volume fractions of Ø ≈ 0.7, a thorough understanding of elasticity in bubbly ice may require a coupled elastic theory that includes geometrical and crystallographic anisotropy.