An ordinary differential equation for velocity distribution and dip-phenomenon in open channel flows

An ordinary differential equation for velocity distribution in open channel flows is presented based on an analysis of the Reynolds-Averaged Navier-Stokes equations and a log-wake modified eddy viscosity distribution. This proposed equation allows to predict the velocity-dip-phenomenon, i.e. the maximum velocity below the free surface. Two different degrees of approximations are presented, a semi-analytical solution of the proposed ordinary differential equation, i.e. the full dip-modified-log-wake law and a simple dip-modified-log-wake law. Velocity profiles of the two laws and the numerical solution of the ordinary differential equation are compared with experimental data. This study shows that the dip correction is not efficient for a small Coles' parameter, accurate predictions require larger values. The simple dip-modified-log-wake law shows reasonable agreement and seems to be an interesting tool of intermediate accuracy. The full dip-modified-log-wake law, with a parameter for dip-correction obtained from an estimation of dip positions, provides accurate velocity profiles

Prediction of the shear strength of reinforced masonry walls using a large experimental database and artificial neural networks

This paper analyses the accuracy of a selection of expressions currently available to estimate the in-plane shear strength of reinforced masonry (RM) walls, including those presented in some international masonry codes. For this purpose, predictions of such expressions are compared with a set of xperimental results reported in the literature. The experimental database includes specimens built with ceramic bricks and concrete blocks tested in partially and fully grouted conditions, which typically present a shear failure mode. Based on the experimental data collected and using artificial neural networks (ANN), this paper presents alternative expressions to the different existing methods to predict the in-plane shear strength of RM walls. The wall aspect ratio, the axial pre-compression level on the wall, the compressive strength of masonry, as well as the amount and spacing of vertical and horizontal reinforcement throughout the wall are taken into consideration as the input parameters for the proposed expressions. The results obtained show that ANN-based proposals give good predictions and in general fit the experimental results better than other calculation methods.This work was supported by the Fondo Nacional de Ciencia y Tecnologia de Chile, (Fondecyt de Iniciacion) [grant number 11121161].Aguilar, V.; Sandoval, C.; Adam Martínez, JM.; Garzón-Roca, J.; Valdebenito, G. (2016). Prediction of the shear strength of reinforced masonry walls using a large experimental database and artificial neural networks. Structure and Infrastructure Engineering. 12(12):1661-1674. https://doi.org/10.1080/15732479.2016.1157824S16611674121

Bridging the gap between global models and full fluid models : a fast 1D semi-analytical fluid model for electronegative plasmas

Analytical and numerical models allow investigation of complicated discharge phenomena and the interplay that makes plasmas such a complex environment. Global models are quick to implement and can have almost negligible computation cost, but provide only bulk or spatially averaged values. Full fluid models take longer to develop, and can take days to solve, but provide accurate spatio-temporal profiles of the whole plasma. The work presented here details a different type of model, analytically similar to fluid models, but computationally closer to a global model, and able to give spatially resolved solutions for the challenging environment of electronegative plasmas. Included are non-isothermal electrons, gas heating, and coupled neutral dynamics. Solutions are reached in seconds to minutes, and spatial profiles are given for densities, fluxes, and temperatures. This allows the semi-analytical model to fill the gap that exists between global and full fluid models, extending the tools available to researchers. The semi-analytical model can perform broad parameter sweeps that are not practical with more computationally expensive models, as well as exposing non-trivial trends that global models cannot capture. Examples are given for a low pressure oxygen CCP. Excellent agreement is shown with a full fluid model, and comparisons are drawn with the corresponding global model

Psychological and social consequences among mothers suffering from perinatal loss: perspective from a low income country

<p>Abstract</p> <p>Background</p> <p>In developed countries, perinatal death is known to cause major emotional and social effects on mothers. However, little is known about these effects in low income countries which bear the brunt of perinatal mortality burden. This paper reports the impact of perinatal death on psychological status and social consequences among mothers in a rural area of Bangladesh.</p> <p>Methods</p> <p>A total of 476 women including 122 women with perinatal deaths were assessed with the Edinburgh Postnatal Depression Scale (EPDS-B) at 6 weeks and 6 months postpartum, and followed up for negative social consequences at 6 months postpartum. Trained female interviewers carried out structured interviews at women's home.</p> <p>Results</p> <p>Overall 43% (95% CI: 33.7-51.8%) of women with a perinatal loss at 6 weeks postpartum were depressed compared to 17% (95% CI: 13.7-21.9%) with healthy babies (p = < 0.001). Depression status were significantly associated with women reporting negative life changes such as worse relationships with their husband (adjusted OR = 3.89, 95% CI: 1.37-11.04) and feeling guilty (adjusted OR = 2.61, 95% CI: 1.22-5.63) following the results of their last pregnancy outcome after 6 months of childbirth.</p> <p>Conclusions</p> <p>This study highlights the greatly increased vulnerability of women with perinatal death to experience negative psychological and social consequences. There is an urgent need to develop appropriate mental health care services for mothers with perinatal deaths in Bangladesh, including interventions to develop positive family support.</p

Modeling the differentiation of A- and C-type baroreceptor firing patterns

The baroreceptor neurons serve as the primary transducers of blood pressure for the autonomic nervous system and are thus critical in enabling the body to respond effectively to changes in blood pressure. These neurons can be separated into two types (A and C) based on the myelination of their axons and their distinct firing patterns elicited in response to specific pressure stimuli. This study has developed a comprehensive model of the afferent baroreceptor discharge built on physiological knowledge of arterial wall mechanics, firing rate responses to controlled pressure stimuli, and ion channel dynamics within the baroreceptor neurons. With this model, we were able to predict firing rates observed in previously published experiments in both A- and C-type neurons. These results were obtained by adjusting model parameters determining the maximal ion-channel conductances. The observed variation in the model parameters are hypothesized to correspond to physiological differences between A- and C-type neurons. In agreement with published experimental observations, our simulations suggest that a twofold lower potassium conductance in C-type neurons is responsible for the observed sustained basal firing, whereas a tenfold higher mechanosensitive conductance is responsible for the greater firing rate observed in A-type neurons. A better understanding of the difference between the two neuron types can potentially be used to gain more insight into the underlying pathophysiology facilitating development of targeted interventions improving baroreflex function in diseased individuals, e.g. in patients with autonomic failure, a syndrome that is difficult to diagnose in terms of its pathophysiology.Comment: Keywords: Baroreflex model, mechanosensitivity, A- and C-type afferent baroreceptors, biophysical model, computational mode

Structural shape optimization using Cartesian grids and automatic h-adaptive mesh projection

[EN] We present a novel approach to 3D structural shape optimization that leans on an Immersed Boundary Method. A boundary tracking strategy based on evaluating the intersections between a fixed Cartesian grid and the evolving geometry sorts elements as internal, external and intersected. The integration procedure used by the NURBS-Enhanced Finite Element Method accurately accounts for the nonconformity between the fixed embedding discretization and the evolving structural shape, avoiding the creation of a boundary-fitted mesh for each design iteration, yielding in very efficient mesh generation process. A Cartesian hierarchical data structure improves the efficiency of the analyzes, allowing for trivial data sharing between similar entities or for an optimal reordering of thematrices for the solution of the system of equations, among other benefits. Shape optimization requires the sufficiently accurate structural analysis of a large number of different designs, presenting the computational cost for each design as a critical issue. The information required to create 3D Cartesian h- adapted mesh for new geometries is projected from previously analyzed geometries using shape sensitivity results. Then, the refinement criterion permits one to directly build h-adapted mesh on the new designs with a specified and controlled error level. Several examples are presented to show how the techniques here proposed considerably improve the computational efficiency of the optimization process.The authors wish to thank the Spanish Ministerio de Economia y Competitividad for the financial support received through the project DPI2013-46317-R and the FPI program (BES-2011-044080), and the Generalitat Valenciana through the project PROMETEO/2016/007.Marco, O.; Ródenas, J.; Albelda Vitoria, J.; Nadal, E.; Tur Valiente, M. (2017). 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Visual engagement with urban street edges: insights using mobile eye-tracking

This study provides empirical insight into the extent to which pedestrians visually engage with urban street edges and how social and spatial factors impact such engagement. This was achieved using mobile eye-tracking. The gaze distribution of 24 study participants was systematically recorded as they carried out everyday tasks on differing streets. The findings demonstrated that street edges are the most visually engaged component of streets; that street edge visual engagement is impacted by everyday social tasks as well as the spatial and physical materiality of edges on differing streets; and that street edges, which attract a lot of visual engagement while undertaking optional tasks, also attract greater amounts of visual engagement while undertaking necessary tasks. These findings offer new insight into urban street edge engagement from the direct perspective of street inhabitants and in doing so provide greater understanding of how street edges are experienced