FE-Analysis of end-notched beams and tenon joints – J-Integral versus cohesive zone modelling

Improved wood-based materials, on the one hand, and computer-operated machinery, on the other, enable new types of timber-to-timber joints. Furthermore, new materials, such as laminated veneer lumber with cross-layers made of soft- or hardwood offer new possibilities regarding timber-to-timber connections like tenon and mortise. Experimental investigations have shown comparable failure mechanisms in mode I and mode II for tenon and multiple tenon joints, dovetail connections as well as for end-notched beams. In the field of fracture mechanics different concepts exists to describe the fracture behaviour such as the strain energy release rate, the stress intensity factors and the J-integral method for timber-to-timber joints. In this study the J-integral was determined around the crack tip in a finite element (FE) model in timber connections. The failure criterion was defined when the J-integral equals the energy release rate GcI+II in mixed mode of the timber material (Fig. 1a). A sensitivity analysis showed that the failure load Vc was not depending significantly on the initial crack length, the element size, the distance from the crack tip of the chosen contour Γ1 and the size of the chosen contour. In the next step, the model was validated on experimental test results of end-notched beams, tenon connections and multiple tenon connections (Fig. 1b). The experimental and numerical results showed very good accordance for different connection size and up to seven possible crack layers (Fig. 1c). Furthermore, a comparison between different fracture mechanic models with strain energy release rates and cohesive zones showed good performance of the new approach. Afterwards, the FE model using the J-integral failure criterion was used to perform a comprehensive study of geometrical parameters for end-notched beams and tenon connections. Finally, the dataset was used as a basis to develop a engineering model for three types of timber connections. The evaluation of the J-integral allows to determine the failure load due to crack initiation under a mixed mode loading in a linear elastic FE model with pre-defined cracks. The advantage of the J-integral method is the significant decrease of the computing time for extensive parameter studies. The model for end-notched beams, tenon joints and multiple tenon connections is insensitive to common variations of material properties as well as for the choice of the initial crack length. The application on timber structures under pure shear failure or tension failure perpendicular to the grain will be part of future research

Opportunistic hip fracture risk prediction in Men from X-ray: Findings from the Osteoporosis in Men (MrOS) Study

Osteoporosis is a common disease that increases fracture risk. Hip fractures, especially in elderly people, lead to increased morbidity, decreased quality of life and increased mortality. Being a silent disease before fracture, osteoporosis often remains undiagnosed and untreated. Areal bone mineral density (aBMD) assessed by dual-energy X-ray absorptiometry (DXA) is the gold-standard method for osteoporosis diagnosis and hence also for future fracture prediction (prognostic). However, the required special equipment is not broadly available everywhere, in particular not to patients in developing countries. We propose a deep learning classification model (FORM) that can directly predict hip fracture risk from either plain radiographs (X-ray) or 2D projection images of computed tomography (CT) data. Our method is fully automated and therefore well suited for opportunistic screening settings, identifying high risk patients in a broader population without additional screening. FORM was trained and evaluated on X-rays and CT projections from the Osteoporosis in Men (MrOS) study. 3108 X-rays (89 incident hip fractures) or 2150 CTs (80 incident hip fractures) with a 80/20 split were used. We show that FORM can correctly predict the 10-year hip fracture risk with a validation AUC of 81.44 +- 3.11% / 81.04 +- 5.54% (mean +- STD) including additional information like age, BMI, fall history and health background across a 5-fold cross validation on the X-ray and CT cohort, respectively. Our approach significantly (p < 0.01) outperforms previous methods like Cox Proportional-Hazards Model and \frax with 70.19 +- 6.58 and 74.72 +- 7.21 respectively on the X-ray cohort. Our model outperform on both cohorts hip aBMD based predictions. We are confident that FORM can contribute on improving osteoporosis diagnosis at an early stage.Comment: Accepted at MICCAI 2022 Workshop (PRIME

Successive Cancellation Automorphism List Decoding of Polar Codes

The discovery of suitable automorphisms of polar codes gained a lot of attention by applying them in Automorphism Ensemble Decoding (AED) to improve the error-correction performance, especially for short block lengths. This paper introduces Successive Cancellation Automorphism List (SCAL) decoding of polar codes as a novel application of automorphisms in advanced Successive Cancellation List (SCL) decoding. Initialized with L permutations sampled from the automorphism group, a superposition of different noise realizations and path splitting takes place inside the decoder. In this way, the SCAL decoder automatically adapts to the channel conditions and outperforms the error-correction performance of conventional SCL decoding and AED. For a polar code of length 128, SCAL performs near Maximum Likelihood (ML) decoding with L=8, in contrast to M=16 needed decoder cores in AED. Application-Specific Integrated Circuit (ASIC) implementations in a 12 nm technology show that high-throughput, pipelined SCAL decoders outperform AED in terms of energy efficiency and power density, and SCL decoders additionally in area efficiency.Comment: 5 pages, 5 figures, submitted to IEEE for possible publicatio

Segmentation of Retinal Low-Cost Optical Coherence Tomography Images using Deep Learning

The treatment of age-related macular degeneration (AMD) requires continuous eye exams using optical coherence tomography (OCT). The need for treatment is determined by the presence or change of disease-specific OCT-based biomarkers. Therefore, the monitoring frequency has a significant influence on the success of AMD therapy. However, the monitoring frequency of current treatment schemes is not individually adapted to the patient and therefore often insufficient. While a higher monitoring frequency would have a positive effect on the success of treatment, in practice it can only be achieved with a home monitoring solution. One of the key requirements of a home monitoring OCT system is a computer-aided diagnosis to automatically detect and quantify pathological changes using specific OCT-based biomarkers. In this paper, for the first time, retinal scans of a novel self-examination low-cost full-field OCT (SELF-OCT) are segmented using a deep learning-based approach. A convolutional neural network (CNN) is utilized to segment the total retina as well as pigment epithelial detachments (PED). It is shown that the CNN-based approach can segment the retina with high accuracy, whereas the segmentation of the PED proves to be challenging. In addition, a convolutional denoising autoencoder (CDAE) refines the CNN prediction, which has previously learned retinal shape information. It is shown that the CDAE refinement can correct segmentation errors caused by artifacts in the OCT image.Comment: Accepted for SPIE Medical Imaging 2020: Computer-Aided Diagnosi

Laser-induced real-space topology control of spin wave resonances

Femtosecond laser excitation of materials that exhibit magnetic spin textures promises advanced magnetic control via the generation of ultrafast and non-equilibrium spin dynamics. We explore such possibilities in ferrimagnetic [Fe(0.35 nm)/Gd(0.40 nm)]$_{160}$ multilayers, which host a rich diversity of magnetic textures from stripe domains at low magnetic fields, a dense bubble/skyrmion lattice at intermediate fields, and a single domain state for high magnetic fields. Using femtosecond magneto-optics, we observe distinct coherent spin wave dynamics in response to a weak laser excitation allowing us to unambiguously identify the different magnetic spin textures. Moreover, employing strong laser excitation we show that we achieve versatile control of the coherent spin dynamics via non-equilibrium and ultrafast transformation of magnetic spin textures by both creating and annihilating bubbles/skyrmions. We corroborate our findings by micromagnetic simulations and by Lorentz transmission electron microscopy before and after laser exposure.Comment: 19 article pages, 12 supplementar

Bringing Anatomical Information into Neuronal Network Models

For constructing neuronal network models computational neuroscientists have access to wide-ranging anatomical data that nevertheless tend to cover only a fraction of the parameters to be determined. Finding and interpreting the most relevant data, estimating missing values, and combining the data and estimates from various sources into a coherent whole is a daunting task. With this chapter we aim to provide guidance to modelers by describing the main types of anatomical data that may be useful for informing neuronal network models. We further discuss aspects of the underlying experimental techniques relevant to the interpretation of the data, list particularly comprehensive data sets, and describe methods for filling in the gaps in the experimental data. Such methods of `predictive connectomics' estimate connectivity where the data are lacking based on statistical relationships with known quantities. It is instructive, and in certain cases necessary, to use organizational principles that link the plethora of data within a unifying framework where regularities of brain structure can be exploited to inform computational models. In addition, we touch upon the most prominent features of brain organization that are likely to influence predicted neuronal network dynamics, with a focus on the mammalian cerebral cortex. Given the still existing need for modelers to navigate a complex data landscape full of holes and stumbling blocks, it is vital that the field of neuroanatomy is moving toward increasingly systematic data collection, representation, and publication

CONTINUITY OF RESEARCH AND RESEARCH OF CONTINUITY: BASIC RESEARCH ON SETTLEMENT ARCHAEOLOGY OF THE IRON AGE IN THE BALTIC REGION. A NEW LONG-TERM RESEARCH PROJECT BY THE ACADEMY OF SCIENCE AND LITERATURE IN SCHLESWIG AND BERLIN

After several years of successful investigations in former East Prussia (today the Kaliningradskaya Oblast of the Russian Federation) and investigations in neighbouring countries around the Baltic Sea, the Centre for Baltic and Scandinavian Archaeology in Schleswig (ZBSA) in Germany has expanded its research plans for the next 18 years. The application for the project ‘Forschungskontinuität und Kontinuitätsforschung – Siedlungsarchäologische Grundlagenforschung zur Eisenzeit im Baltikum’ (Continuity of Research and Research of Continuity: Basic Research on Settlement Archaeology of the Iron Age in the Baltic Region), submitted in June 2010, was accepted by the Academy of Sciences and Literature in Mainz in December 2011.DOI: http://dx.doi.org/10.15181/ab.v17i0.4