Influence of psychosocial stress on changes of hypothalamo-pituitary-adrenocortical hormones and sleep dependent on CRHR1 genotype

In dieser Studie wurde der Einfluss psychosozialen Stresses auf den Schlaf und die Hormone der Hypothalamus-Hypophysen-Nebennierenrinden-Achse in Abhängigkeit vom CHRHR1 Genotyp (rs110402) untersucht. Dabei zeigte sich, dass die Cortisol-Ausschüttung nach Stress bei den homozygoten T-Trägern signifikant höher war, als bei den homozygoten C Trägern. Im Schlaf konnte lediglich ein genetisch unabhängiger Stress-Effekt in Form einer verringerten Schlaf-Effizienz, verringerten Zeit im REM Schlaf, sowie erhöhte Latenzen für N1, N2 und N3, sowie eine längere Zeit wach festgestellt werden

DO RISK PERCEPTIONS INFLUENCE PHYSICIAN\u27S RESISTANCE TO USE ELECTRONIC MEDICAL RECORDS? AN EXPLORATORY RESEARCH IN GERMAN HOSPITALS

IT in health care can lower the cost of health care delivery, improve the quality of care for patients and reduce medical errors.Given these strong advantages, it is interesting that technology diffusion for process support in hospitals is somewhat slow.The major process of a hospital -delivering patient care- is still supported by traditional paper files in the vast majority ofGerman hospitals.In this paper, we ask what the barriers towards implementing and using an electronic medical record (EMR) -the electronicpatient file- might be. Technology resistance theories indicate that perceived risks are a major inhibitor towards systemsacceptance. In the absence of thorough empirical studies, we start our investigation by conducting exploratory research intothe risks hospital-based physicians associate with using an EMR. A list of possible risks was derived from the literature and10 physicians were interviewed to gather their assessment. Our findings show that, indeed, physicians associate several riskswith adopting EMRs, thereby suggesting these risks will need to be mitigated to enable proper user acceptance

Parametrised polyconvex hyperelasticity with physics-augmented neural networks

In the present work, neural networks are applied to formulate parametrised hyperelastic constitutive models. The models fulfill all common mechanical conditions of hyperelasticity by construction. In particular, partially input-convex neural network (pICNN) architectures are applied based on feed-forward neural networks. Receiving two different sets of input arguments, pICNNs are convex in one of them, while for the other, they represent arbitrary relationships which are not necessarily convex. In this way, the model can fulfill convexity conditions stemming from mechanical considerations without being too restrictive on the functional relationship in additional parameters, which may not necessarily be convex. Two different models are introduced, where one can represent arbitrary functional relationships in the additional parameters, while the other is monotonic in the additional parameters. As a first proof of concept, the model is calibrated to data generated with two differently parametrised analytical potentials, whereby three different pICNN architectures are investigated. In all cases, the proposed model shows excellent performance

Polyconvex anisotropic hyperelasticity with neural networks

In the present work, two machine learning based constitutive models for finite deformations are proposed. Using input convex neural networks, the models are hyperelastic, anisotropic and fulfill the polyconvexity condition, which implies ellipticity and thus ensures material stability. The first constitutive model is based on a set of polyconvex, anisotropic and objective invariants. The second approach is formulated in terms of the deformation gradient, its cofactor and determinant, uses group symmetrization to fulfill the material symmetry condition, and data augmentation to fulfill objectivity approximately. The extension of the dataset for the data augmentation approach is based on mechanical considerations and does not require additional experimental or simulation data. The models are calibrated with highly challenging simulation data of cubic lattice metamaterials, including finite deformations and lattice instabilities. A moderate amount of calibration data is used, based on deformations which are commonly applied in experimental investigations. While the invariant-based model shows drawbacks for several deformation modes, the model based on the deformation gradient alone is able to reproduce and predict the effective material behavior very well and exhibits excellent generalization capabilities. In addition, the models are calibrated with transversely isotropic data, generated with an analytical polyconvex potential. For this case, both models show excellent results, demonstrating the straightforward applicability of the polyconvex neural network constitutive models to other symmetry groups

Combined Level-Set-XFEM-Density Topology Optimization of Four-Dimensional Printed Structures Undergoing Large Deformation

Advancement of additive manufacturing is driving a need for design tools that exploit the increasing fabrication freedom. Multimaterial, three-dimensional (3D) printing allows for the fabrication of components from multiple materials with different thermal, mechanical, and “active” behavior that can be spatially arranged in 3D with a resolution on the order of tens of microns. This can be exploited to incorporate shape changing features into additively manufactured structures. 3D printing with a downstream shape change in response to an external stimulus such as temperature, humidity, or light is referred to as four-dimensional (4D) printing. In this paper, a design methodology to determine the material layout of 4D printed materials with internal, programmable strains is introduced to create active structures that undergo large deformation and assume a desired target displacement upon heat activation. A level set (LS) approach together with the extended finite element method (XFEM) is combined with density-based topology optimization to describe the evolving multimaterial design problem in the optimization process. A finite deformation hyperelastic thermomechanical model is used together with an higher-order XFEM scheme to accurately predict the behavior of nonlinear slender structures during the design evolution. Examples are presented to demonstrate the unique capabilities of the proposed framework. Numerical predictions of optimized shape-changing structures are compared to 4D printed physical specimen and good agreement is achieved. Overall, a systematic design approach for creating 4D printed active structures with geometrically nonlinear behavior is presented which yields nonintuitive material layouts and geometries to achieve target deformations of various complexities

Parametrized polyconvex hyperelasticity with physics-augmented neural networks

In the present work, neural networks are applied to formulate parametrized hyperelastic constitutive models. The models fulfill all common mechanical conditions of hyperelasticity by construction. In particular, partially input convex neural network (pICNN) architectures are applied based on feed-forward neural networks. Receiving two different sets of input arguments, pICNNs are convex in one of them, while for the other, they represent arbitrary relationships which are not necessarily convex. In this way, the model can fulfill convexity conditions stemming from mechanical considerations without being too restrictive on the functional relationship in additional parameters, which may not necessarily be convex. Two different models are introduced, where one can represent arbitrary functional relationships in the additional parameters, while the other is monotonic in the additional parameters. As a first proof of concept, the model is calibrated to data generated with two differently parametrized analytical potentials, whereby three different pICNN architectures are investigated. In all cases, the proposed model shows excellent performance

Particle shape characterisation and its application to discrete element modelling

Increasing importance has been placed on particle shape implementation within discrete element modelling (DEM) in order to more accurately reflect the non-spherical behaviour of the bulk material being handled. As computational resources grow, complex particle shapes are increasingly being modelled as the associated simulation times become more realistic to provide timely solutions. The objective of this research is to assess particle shape descriptors through a digital image segmentation technique, and to further implement particle shape parameters into generation of corresponding irregular shaped DEM particles. Separated and lumped particle images were analysed and reconstructed through the development of two distinct methodologies. Subsequently, various particle shape descriptors were obtained using combinations of image segmentation algorithms, including mathematical morphology processing, thresholding, edge detection, region growing, region splitting and region merging. DEM particles were subsequently created using particle shape results obtained above. Shape parameters of DEM particles were then examined and validated against the real particle shape parameters