Spacecraft launch depressurization loads

The pressure variation inside the launch vehicle fairing during climb through the atmosphere induces structural loads on the walls of closed-type spacecrafts or equipment boxes. If the evacuation of the air is not fast enough, excessive pressure loading can result in damage of elements exposed to the rising pressure jump, which depends mainly on the geometry of venting holes, the effective volume of air to be evacuated, and the characteristic time of pressure variation under the fairing. A theoretical study of the reservoir discharge forced by the fairing time-dependent pressure variation is presented. The basic mathematical model developed can yield both a numerical solution for the pressure jump and an asymptotic solution for the most relevant case, the small-prcssurc-jump limit, showing the dependence on a single nondimensional parameter: the ratio of the reservoir discharge to the fairing pressure profile characteristic times. The asymptotic solution validity range upper limit, obtained by comparison with the numerical solution, is determined by the starting of choked operation. Very high sensitivity of the maximum pressure jump to the ratio of characteristic times has been observed. Another relevant finding is that the pressure profiles for different launchers can be considered similar when rewritten in appropriate form and only their characteristic times are required for the analysis. The simple expressions of the asymptotic solution are a useful tool for preliminarily sizing the reservoir discharge geometry and estimating depressurization load

Identification Of Mitotically Competent SOX2+ Cells In White Matter Of Normal Human Adult Brain

SOX2 expression is linked to the undifferentiated state of stem cells in mammalian neurogenic niches. While its expression has been reported in the adult human subventricular zone (SVZ), to date it has not been detected in adult human white matter. Here we describe a population of SOX2+ cells from the white matter of the adult human temporal lobe, which proliferate and express glial markers in vitro

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An evolutionary algorithm for the surface structure problem

Many macroscopic properties: hardness, corrosion, catalytic activity, etc. are directly related to the surface structure, that is, to the position and chemical identity of the outermost atoms of the material. Current experimental techniques for its determination produce a “signature” from which the structure must be inferred by solving an inverse problem: a solution is proposed, its corresponding signature computed and then compared to the experiment. This is a challenging optimization problem where the search space and the number of local minima grows exponentially with the number of atoms, hence its solution cannot be achieved for arbitrarily large structures. Nowadays, it is solved by using a mixture of human knowledge and local search techniques: an expert proposes a solution that is refined using a local minimizer. If the outcome does not fit the experiment, a new solution must be proposed again. Solving a small surface can take from days to weeks of this trial and error method. Here we describe our ongoing work in its solution. We use an hybrid algorithm that mixes evolutionary techniques with trusted region methods and reuses knowledge gained during the execution to avoid repeated search of structures. Its parallelization produces good results even when not requiring the gathering of the full population, hence it can be used in loosely coupled environments such as grids. With this algorithm, the solution of test cases that previously took weeks of expert time can be automatically solved in a day or two of uniprocessor time

Level set implementation for the simulation of anisotropic etching: application to complex MEMS micromachining

The use of atomistic methods, such as the continuous cellular automaton (CCA), is currently regarded as an accurate and efficient approach for the simulation of anisotropic etching in the development of micro-electro-mechanical systems (MEMS). However, whenever the targeted etching condition is modified (e. g. by changing the substrate material, etchant type, concentration and/or temperature) this approach requires performing a time-consuming recalibration of the full set of internal atomistic rates defined within the method. Based on the level set (LS) approach as an alternative and using the experimental data directly as input, we present a fully operational simulator that exhibits similar accuracy to the latest CCA models. The proposed simulator is tested by describing a wide range of silicon and quartz MEMS structures obtained in different etchants through complex processes, including double-sided etching as well as different mask patterns during different etching steps and/or simultaneous masking materials on different regions of the substrate. The results demonstrate that the LS method is able to simulate anisotropic etching for complex MEMS processes with similar computational times and accuracy as the atomistic models.This work has been supported by the Spanish FPI-MICINN BES-2011-045940 grant and the Ramon y Cajal Fellowship Program by the Spanish Ministry of Science and Innovation. Also, we acknowledge support by the JAE-Doc grant from the Junta para la Ampliacion de Estudios program co-funded by FSE and the Professor Partnership Program by NVIDIA Corporation.Montoliu, C.; Ferrando Jódar, N.; Gosalvez Ayuso, MA.; Cerdá Boluda, J.; Colom Palero, RJ. (2013). Level set implementation for the simulation of anisotropic etching: application to complex MEMS micromachining. Journal of Micromechanics and Microengineering. 23(7). https://doi.org/10.1088/0960-1317/23/7/075017S237Weirauch, D. F. (1975). Correlation of the anisotropic etching of single−crystal silicon spheres and wafers. Journal of Applied Physics, 46(4), 1478-1483. doi:10.1063/1.321787Seidel, H. (1990). Anisotropic Etching of Crystalline Silicon in Alkaline Solutions. Journal of The Electrochemical Society, 137(11), 3612. doi:10.1149/1.2086277Zielke, D., & Frühauf, J. (1995). Determination of rates for orientation-dependent etching. Sensors and Actuators A: Physical, 48(2), 151-156. doi:10.1016/0924-4247(95)00993-0Wind, R. A., & Hines, M. A. (2000). Macroscopic etch anisotropies and microscopic reaction mechanisms: a micromachined structure for the rapid assay of etchant anisotropy. Surface Science, 460(1-3), 21-38. doi:10.1016/s0039-6028(00)00479-9Gosálvez, M. A., Sato, K., Foster, A. S., Nieminen, R. M., & Tanaka, H. (2007). An atomistic introduction to anisotropic etching. Journal of Micromechanics and Microengineering, 17(4), S1-S26. doi:10.1088/0960-1317/17/4/s01Sato, K., Shikida, M., Matsushima, Y., Yamashiro, T., Asaumi, K., Iriye, Y., & Yamamoto, M. (1998). Characterization of orientation-dependent etching properties of single-crystal silicon: effects of KOH concentration. Sensors and Actuators A: Physical, 64(1), 87-93. doi:10.1016/s0924-4247(97)01658-0Zubel, I., & Kramkowska, M. (2002). The effect of alcohol additives on etching characteristics in KOH solutions. Sensors and Actuators A: Physical, 101(3), 255-261. doi:10.1016/s0924-4247(02)00265-0Charbonnieras, A. R., & Tellier, C. R. (1999). Characterization of the anisotropic chemical attack of {hk0} silicon plates in a T.M.A.H. solution. Sensors and Actuators A: Physical, 77(2), 81-97. doi:10.1016/s0924-4247(99)00020-5Shikida, M., Sato, K., Tokoro, K., & Uchikawa, D. (2000). Differences in anisotropic etching properties of KOH and TMAH solutions. Sensors and Actuators A: Physical, 80(2), 179-188. doi:10.1016/s0924-4247(99)00264-2Gosálvez, M. A., Zubel, I., & Viinikka, E. (2010). Wet Etching of Silicon. Handbook of Silicon Based MEMS Materials and Technologies, 375-407. doi:10.1016/b978-0-8155-1594-4.00024-3Pal, P., Gosalvez, M. A., & Sato, K. (2010). Silicon Micromachining Based on Surfactant-Added Tetramethyl Ammonium Hydroxide: Etching Mechanism and Advanced Applications. Japanese Journal of Applied Physics, 49(5), 056702. doi:10.1143/jjap.49.056702Zubel, I., & Kramkowska, M. (2004). Etch rates and morphology of silicon (h k l) surfaces etched in KOH and KOH saturated with isopropanol solutions. Sensors and Actuators A: Physical, 115(2-3), 549-556. doi:10.1016/j.sna.2003.11.010Fruhauf, J., Trautmann, K., Wittig, J., & Zielke, D. (1993). A simulation tool for orientation dependent etching. Journal of Micromechanics and Microengineering, 3(3), 113-115. doi:10.1088/0960-1317/3/3/004Than, O., & Büttgenbach, S. (1994). Simulation of anisotropic chemical etching of crystalline silicon using a cellular automata model. Sensors and Actuators A: Physical, 45(1), 85-89. doi:10.1016/0924-4247(94)00820-5Camon, H., Gue, A. M., Danel, J. S., & Djafari-Rouhani, M. (1992). Modelling of anisotropic etching in silicon-based sensor application. Sensors and Actuators A: Physical, 33(1-2), 103-105. doi:10.1016/0924-4247(92)80237-wGosalvez, M. ., Nieminen, R. ., Kilpinen, P., Haimi, E., & Lindroos, V. (2001). Anisotropic wet chemical etching of crystalline silicon: atomistic Monte-Carlo simulations and experiments. Applied Surface Science, 178(1-4), 7-26. doi:10.1016/s0169-4332(01)00233-1Zhenjun Zhu, & Chang Liu. (2000). Micromachining process simulation using a continuous cellular automata method. Journal of Microelectromechanical Systems, 9(2), 252-261. doi:10.1109/84.846706Gosalvez, M. A., Yan Xing, & Sato, K. (2008). Analytical Solution of the Continuous Cellular Automaton for Anisotropic Etching. Journal of Microelectromechanical Systems, 17(2), 410-431. doi:10.1109/jmems.2008.916339Ferrando, N., Gosálvez, M. A., Cerdá, J., Gadea, R., & Sato, K. (2011). Faster and exact implementation of the continuous cellular automaton for anisotropic etching simulations. Journal of Micromechanics and Microengineering, 21(2), 025021. doi:10.1088/0960-1317/21/2/025021Ferrando, N., Gosálvez, M. A., Cerdá, J., Gadea, R., & Sato, K. (2011). Octree-based, GPU implementation of a continuous cellular automaton for the simulation of complex, evolving surfaces. Computer Physics Communications, 182(3), 628-640. doi:10.1016/j.cpc.2010.11.004Moktadir, Z., & Camon, H. (1997). Monte Carlo simulation of anisotropic etching of silicon: investigation of surface properties. Modelling and Simulation in Materials Science and Engineering, 5(5), 481-488. doi:10.1088/0965-0393/5/5/004Flidr, J., Huang, Y.-C., & Hines, M. A. (1999). An atomistic mechanism for the production of two- and three-dimensional etch hillocks on Si(111) surfaces. The Journal of Chemical Physics, 111(15), 6970-6981. doi:10.1063/1.479990Gos lvez, M. A., & Nieminen, R. M. (2003). Surface morphology during anisotropic wet chemical etching of crystalline silicon. New Journal of Physics, 5, 100-100. doi:10.1088/1367-2630/5/1/400Xing, Y., Gosálvez, M. A., Sato, K., Tian, M., & Yi, H. (2012). Evolutionary determination of kinetic Monte Carlo rates for the simulation of evolving surfaces in anisotropic etching of silicon. Journal of Micromechanics and Microengineering, 22(8), 085020. doi:10.1088/0960-1317/22/8/085020Xing, Y., Gosálvez, M. A., Sato, K., & Yi, H. (2009). Orientation-dependent surface morphology of crystalline silicon during anisotropic etching using a continuous cellular automaton. Journal of Micromechanics and Microengineering, 20(1), 015019. doi:10.1088/0960-1317/20/1/015019Zhou, Z., Huang, Q., Li, W., & Deng, W. (2007). A cellular automaton-based simulator for silicon anisotropic etching processes considering high index planes. Journal of Micromechanics and Microengineering, 17(4), S38-S49. doi:10.1088/0960-1317/17/4/s03Gosálvez, M. A., Xing, Y., Sato, K., & Nieminen, R. M. (2009). Discrete and continuous cellular automata for the simulation of propagating surfaces. Sensors and Actuators A: Physical, 155(1), 98-112. doi:10.1016/j.sna.2009.08.012Ferrando, N., Gosálvez, M. A., & Colóm, R. J. (2012). Evolutionary continuous cellular automaton for the simulation of wet etching of quartz. Journal of Micromechanics and Microengineering, 22(2), 025021. doi:10.1088/0960-1317/22/2/025021Osher, S., & Sethian, J. A. (1988). Fronts propagating with curvature-dependent speed: Algorithms based on Hamilton-Jacobi formulations. Journal of Computational Physics, 79(1), 12-49. doi:10.1016/0021-9991(88)90002-2Adalsteinsson, D., & Sethian, J. A. (1995). A Level Set Approach to a Unified Model for Etching, Deposition, and Lithography I: Algorithms and Two-Dimensional Simulations. Journal of Computational Physics, 120(1), 128-144. doi:10.1006/jcph.1995.1153Adalsteinsson, D., & Sethian, J. A. (1995). A Level Set Approach to a Unified Model for Etching, Deposition, and Lithography II: Three-Dimensional Simulations. Journal of Computational Physics, 122(2), 348-366. doi:10.1006/jcph.1995.1221Adalsteinsson, D., & Sethian, J. A. (1997). A Level Set Approach to a Unified Model for Etching, Deposition, and Lithography. Journal of Computational Physics, 138(1), 193-223. doi:10.1006/jcph.1997.5817Ertl, O., & Selberherr, S. (2009). A fast level set framework for large three-dimensional topography simulations. Computer Physics Communications, 180(8), 1242-1250. doi:10.1016/j.cpc.2009.02.002Ertl, O., & Selberherr, S. (2010). Three-dimensional level set based Bosch process simulations using ray tracing for flux calculation. Microelectronic Engineering, 87(1), 20-29. doi:10.1016/j.mee.2009.05.011Burzynski, T., & Papini, M. (2010). Level set methods for the modelling of surface evolution in the abrasive jet micromachining of features used in MEMS and microfluidic devices. Journal of Micromechanics and Microengineering, 20(8), 085004. doi:10.1088/0960-1317/20/8/085004Radjenović, B., Lee, J. K., & Radmilović-Radjenović, M. (2006). Sparse field level set method for non-convex Hamiltonians in 3D plasma etching profile simulations. Computer Physics Communications, 174(2), 127-132. doi:10.1016/j.cpc.2005.09.010Radjenović, B., Radmilović-Radjenović, M., & Mitrić, M. (2006). Nonconvex Hamiltonians in three dimensional level set simulations of the wet etching of silicon. Applied Physics Letters, 89(21), 213102. doi:10.1063/1.2388860Branislav, R., & Marija, R.-R. (2010). Level set simulations of the anisotropic wet etching process for device fabrication in nanotechnologies. Hemijska industrija, 64(2), 93-97. doi:10.2298/hemind100205008rRadjenović, B., Radmilović-Radjenović, M., & Mitrić, M. (2010). Level Set Approach to Anisotropic Wet Etching of Silicon. Sensors, 10(5), 4950-4967. doi:10.3390/s100504950Radjenović, B., & Radmilović-Radjenović, M. (2011). Three-Dimensional Simulations of the Anisotropic Etching Profile Evolution for Producing Nanoscale Devices. Acta Physica Polonica A, 119(3), 447-450. doi:10.12693/aphyspola.119.447Crandall, M. G., & Lions, P.-L. (1984). Two approximations of solutions of Hamilton-Jacobi equations. Mathematics of Computation, 43(167), 1-1. doi:10.1090/s0025-5718-1984-0744921-8Whitaker, R. T. (1998). International Journal of Computer Vision, 29(3), 203-231. doi:10.1023/a:1008036829907Gomes, J., & Faugeras, O. (2000). Reconciling Distance Functions and Level Sets. Journal of Visual Communication and Image Representation, 11(2), 209-223. doi:10.1006/jvci.1999.0439Fukuzawa, K., Terada, S., Shikida, M., Amakawa, H., Zhang, H., & Mitsuya, Y. (2007). Mechanical design and force calibration of dual-axis micromechanical probe for friction force microscopy. Journal of Applied Physics, 101(3), 034308. doi:10.1063/1.2434825Schröpfer, G., Labachelerie, M. de, Ballandras, S., & Blind, P. (1998). Collective wet etching of a 3D monolithic silicon seismic mass system. Journal of Micromechanics and Microengineering, 8(2), 77-79. doi:10.1088/0960-1317/8/2/008Wilke, N., Reed, M. L., & Morrissey, A. (2006). The evolution from convex corner undercut towards microneedle formation: theory and experimental verification. Journal of Micromechanics and Microengineering, 16(4), 808-814. doi:10.1088/0960-1317/16/4/018Liang, J., Kohsaka, F., Matsuo, T., & Ueda, T. (2007). 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Bio-inspired computational memory model of the Hippocampus: an approach to a neuromorphic spike-based Content-Addressable Memory

The brain has computational capabilities that surpass those of modern systems, being able to solve complex problems efficiently in a simple way. Neuromorphic engineering aims to mimic biology in order to develop new systems capable of incorporating such capabilities. Bio-inspired learning systems continue to be a challenge that must be solved, and much work needs to be done in this regard. Among all brain regions, the hippocampus stands out as an autoassociative short-term memory with the capacity to learn and recall memories from any fragment of them. These characteristics make the hippocampus an ideal candidate for developing bio-inspired learning systems that, in addition, resemble content-addressable memories. Therefore, in this work we propose a bio-inspired spiking content-addressable memory model based on the CA3 region of the hippocampus with the ability to learn, forget and recall memories, both orthogonal and non-orthogonal, from any fragment of them. The model was implemented on the SpiNNaker hardware platform using Spiking Neural Networks. A set of experiments based on functional, stress and applicability tests were performed to demonstrate its correct functioning. This work presents the first hardware implementation of a fully-functional bio-inspired spiking hippocampal content-addressable memory model, paving the way for the development of future more complex neuromorphic systems.Comment: 15 pages, 5 figures, journal, Spiking Neural Networ

Short Course in Extracellular Vesicles – The Transition from Tissue to Liquid Biopsies

Extracellular vesicles (EVs), including exosomes and microvesicles, carry a variety of bio-macromolecules, including mRNA, microRNA, other non-coding RNAs, proteins and lipids. EVs have emerged as a promising, minimally invasive (liquid biopsies) and novel source of material for molecular diagnostics, and may provide a surrogate to tissue biopsy-based biomarkers for a variety of diseases. Although EVs can be easily identified and collected from biological fluids using commercial kits, further research and proper validation is needed in order for them to be useful in the clinical setting. Currently, several EV-based research and diagnostic companies have developed research-based kits and are in the process of working with clinical laboratories to develop and validate EV-based assays for a variety of diseases. The successful clinical application of EV-based diagnostic assays will require close collaboration between industry, academia, regulatory agencies and access to patient samples. We expect that international, integrative and interdisciplinary translational research teams, along with the emergence of FDA-approved platforms, will set the framework for EV-based diagnostics. We recognize that the EV field offers new promise for personalized/precision medicine and targeted treatment in a variety of diseases. A short course was held as a four-session webinar series in September and October 2014, presented by pioneers and experts in the EV domain, covering a broad range of topics from an overview of the field to its applications, and the current state and challenges of the commercialization of EVs for research and an introduction to the clinic. It was concluded with a panel discussion on the regulatory aspects and funding opportunities in this field. A summary of the short course is presented as a meeting dispatch

SVis: A computational steering visualization environment for surface structure determination

The arrangement of atoms at the surface of a solid accounts for many of its properties: Hardness, chemical activity, corrosion, etc. are dictated by the precise surface structure. Hence, finding it, has a broad range of technical and industrial applications. The ability to solve this problem opens the possibility of designing by computer materials with properties tailored to specific applications. Since the search space grows exponentially with the number of atoms, its solution cannot be achieved for arbitrarily large structures. Presently, a trial and error procedure is used: an expert proposes an structure as a candidate solution and tries a local optimization procedure on it. The solution relaxes to the local minimum in the attractor basin corresponding to the initial point, that might be the one corresponding to the global minimum or not. This procedure is very time consuming and, for reasonably sized surfaces, can take many iterations and much effort from the expert. Here we report on a visualization environment designed to steer this process in an attempt to solve bigger structures and reduce the time needed. The idea is to use an immersive environment to interact with the computation. It has immediate feedback to assess the quality of the proposed structure in order to let the expert explore the space of candidate solutions. The visualization environment is also able to communicate with the de facto local solver used for this problem. The user is then able to send trial structures to the local minimizer and track its progress as they approach the minimum. This allows for simultaneous testing of candidate structures. The system has also proved very useful as an educational tool for the field