Bi-directional evolutionary structural optimization (BESO) for topology optimization of material’s microstructure

It is known that composite materials with improved properties can be achieved through modifications to the topology of their microstructures. Structural topology optimization approaches can be utilized as a systematic way for finding the best spatial distribution of constituent phases within the microstructures of materials/composites. This study presents a new approach for designing material’s microstructures based on the Bi-directional Evolutionary Structural Optimization (BESO) methodology. It is assumed that the materials/composites are composed of repeating microstructures known as periodic base cells (PBC). The goal is to find the best spatial distribution of constituent phases within the PBC, in such a way that materials with desired or improved functional properties are achieved. To this end, the Homogenization theory is applied to establish a relationship between properties of materials microstructure and their macroscopic characteristics. As the first step of this study, the optimization problem is formulated to find microstructures for materials with maximum stiffness, in the form of bulk or shear modulus, or thermal conductivity. Cellular materials, which are composed of one solid phase and one void phase, are considered at this stage. By conducting finite element analysis of the PBC, and applying the Homogenization theory, elemental sensitivity numbers are derived. By gradual removing and adding elements in an iterative process, the optimal topology of the solid phase within the PBC is found. In the next stage of this study, the aim is to combine additional performance constraint to the above procedure. Maximization of bulk or shear modulus is selected as the objective of the material design, subject to the constraint on the isotropy of material and volume constraint. The methodology is extended into topology optimization of microstructures for composites of two or more non-zero constituent phases. For design of material with maximum stiffness or thermal conductivity, the constituent phases are divided into groups and sensitivity analysis is performed between different groups. The developed methodology is also applied in designing functionally graded material (FGM), in which the mechanical property of material gradually changes. It is assumed that the microstructure of the FGM is composed of a series of cellular base cells in the direction of gradation and self-repeated in other directions. Finally, an approach is proposed for the topological design of FGMs with two non-zero constituent phases and multi graded properties. The objective of optimization is defined to find the stiffest materials with prescribed gradation of thermal conductivity. Similar to the approach used for cellular FGMs, the connectivity of base cells is maintained by considering three base cells at each stage. The effectiveness and computational efficiency of the proposed approaches are numerically tested, through designing a range of 2D and 3D microstructures for materials. A series of new and interesting microstructures of materials are presented. The results clearly indicate the advantages of BESO utilization in terms of computational costs and convergence speed, quality of generated microstructures, and ease of implementation as a post processing algorithm

Modeling and Analysis of Power Processing Systems (MAPPS). Volume 2: Appendices

The computer programs and derivations generated in support of the modeling and design optimization program are presented. Programs for the buck regulator, boost regulator, and buck-boost regulator are described. The computer program for the design optimization calculations is presented. Constraints for the boost and buck-boost converter were derived. Derivations of state-space equations and transfer functions are presented. Computer lists for the converters are presented, and the input parameters justified

A novel method for detecting optimal location and parameters of power system stabilizer (PSS) based on intelligent techniques

ABSTRACT: This paper presents a new technique to design a Power System Stabilizer (PSS) in multi-machine power system. The method is based on the Particle Swarm Optimization (PSO) algorithm for tuning PSS parameters including lead-lag compensator time constants as well as the controller gain. For evaluating the particles evolution throughout the searching process, an eigenvalue-based multi-objective function is used. The DIgSILENT is used as tool for modelling test system and programming PSO algorithm. Then by using a fuzzy approach implemented in Matlab/fuzzy toolbox the optimal number and location for PSSs specified. Two-area (four-machine 11bus) Power system is considered as the case study in this paper. Simulation results for various operating conditions prove the capability of the proposed algorithm in damping improvement of power system

Acute promyelocytic leukemia after whole brain irradiation of primary brain lymphomainan HIV-infected patient

The occurrence of acute promyelocytic leukemia (APL) in HIV-infected patients has been reported in only five cases. Due to a very small number of reported HIV/APL patients who have been treated with different therapies with the variable outcome, the prognosis of APL in the setting of the HIV-infection is unclear. Here, we report a case of an HIV-patient who developed APL and upon treatment entered a complete remission. A 25-years old male patient was diagnosed with HIV-infection in 1996, but remained untreated. In 2004, the patient was diagnosed with primary central nervous system lymphoma. We treated the patient with antiretroviral therapy and whole-brain irradiation, resulting in complete remission of the lymphoma. In 2006, prompted by a sudden neutropenia, we carried out a set of diagnostic procedures, revealing APL. Induction therapy consisted of standard treatment with all-trans-retinoic-acid (ATRA) and idarubicin. Subsequent cytological and molecular analysis of bone marrow demonstrated complete hematological and molecular remission. Due to the poor general condition, consolidation treatment with ATRA was given in March and April 2007. The last follow-up 14 months later, showed sustained molecular APL remission. In conclusion, we demonstrated that a complete molecular APL remission in an HIV-patient was achieved by using reduced-intensity treatment

A key role for chd1 in histone h3 dynamics at the 3\u27 ends of long genes in yeast

Chd proteins are ATP-dependent chromatin remodeling enzymes implicated in biological functions from transcriptional elongation to control of pluripotency. Previous studies of the Chd1 subclass of these proteins have implicated them in diverse roles in gene expression including functions during initiation, elongation, and termination. Furthermore, some evidence has suggested a role for Chd1 in replication-independent histone exchange or assembly. Here, we examine roles of Chd1 in replication-independent dynamics of histone H3 in both Drosophila and yeast. We find evidence of a role for Chd1 in H3 dynamics in both organisms. Using genome-wide ChIP-on-chip analysis, we find that Chd1 influences histone turnover at the 5\u27 and 3\u27 ends of genes, accelerating H3 replacement at the 5\u27 ends of genes while protecting the 3\u27 ends of genes from excessive H3 turnover. Although consistent with a direct role for Chd1 in exchange, these results may indicate that Chd1 stabilizes nucleosomes perturbed by transcription. Curiously, we observe a strong effect of gene length on Chd1\u27s effects on H3 turnover. Finally, we show that Chd1 also affects histone modification patterns over genes, likely as a consequence of its effects on histone replacement. Taken together, our results emphasize a role for Chd1 in histone replacement in both budding yeast and Drosophila melanogaster, and surprisingly they show that the major effects of Chd1 on turnover occur at the 3\u27 ends of genes

A hybrid method for accurate iris segmentation on at-a-distance visible-wavelength images

[EN] This work describes a new hybrid method for accurate iris segmentation from full-face images independently of the ethnicity of the subject. It is based on a combination of three methods: facial key-point detection, integro-differential operator (IDO) and mathematical morphology. First, facial landmarks are extracted by means of the Chehra algorithm in order to obtain the eye location. Then, the IDO is applied to the extracted sub-image containing only the eye in order to locate the iris. Once the iris is located, a series of mathematical morphological operations is performed in order to accurately segment it. Results are obtained and compared among four different ethnicities (Asian, Black, Latino and White) as well as with two other iris segmentation algorithms. In addition, robustness against rotation, blurring and noise is also assessed. Our method obtains state-of-the-art performance and shows itself robust with small amounts of blur, noise and/or rotation. Furthermore, it is fast, accurate, and its code is publicly available.Fuentes-Hurtado, FJ.; Naranjo Ornedo, V.; Diego-Mas, JA.; Alcañiz Raya, ML. (2019). A hybrid method for accurate iris segmentation on at-a-distance visible-wavelength images. EURASIP Journal on Image and Video Processing (Online). 2019(1):1-14. https://doi.org/10.1186/s13640-019-0473-0S11420191A. Radman, K. Jumari, N. Zainal, Fast and reliable iris segmentation algorithm. IET Image Process.7(1), 42–49 (2013).M. Erbilek, M. Fairhurst, M. C. D. C Abreu, in 5th International Conference on Imaging for Crime Detection and Prevention (ICDP 2013). Age prediction from iris biometrics (London, 2013), pp. 1–5. http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=6913712&isnumber=6867223 .A. Abbasi, M. Khan, Iris-pupil thickness based method for determining age group of a person. Int. Arab J. Inf. Technol. (IAJIT). 13(6) (2016).G. Mabuza-Hocquet, F. Nelwamondo, T. Marwala, in Intelligent Information and Database Systems. ed. by N. Nguyen, S. Tojo, L. Nguyen, B. Trawiński. Ethnicity Distinctiveness Through Iris Texture Features Using Gabor Filters. ACIIDS 2017. Lecture Notes in Computer Science, vol. 10192 (Springer, Cham, 2017).S. Lagree, K. W. Bowyer, in 2011 IEEE International Conference on Technologies for Homeland Security (HST). Predicting ethnicity and gender from iris texture (IEEEWaltham, 2011). p. 440–445. http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=6107909&isnumber=6107829 .J. G. Daugman, High confidence visual recognition of persons by a test of statistical independence. IEEE Trans. Pattern Anal. Mach. Intell.15(11), 1148–1161 (1993).N. Kourkoumelis, M. Tzaphlidou. Medical Safety Issues Concerning the Use of Incoherent Infrared Light in Biometrics, eds. A. Kumar, D. Zhang. Ethics and Policy of Biometrics. ICEB 2010. Lecture Notes in Computer Science, vol 6005 (Springer, Berlin, Heidelberg, 2010).R. P. Wildes, Iris recognition: an emerging biometric technology. Proc. IEEE. 85(9), 1348–1363 (1997).M. Kass, A. Witkin, D. Terzopoulos, Snakes: Active contour models. Int. J. Comput. Vision. 1(4), 321–331 (1988).S. J. Pundlik, D. L. Woodard, S. T. Birchfield, in 2008 IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops. Non-ideal iris segmentation using graph cuts (IEEEAnchorage, 2008). p. 1–6. http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=4563108&isnumber=4562948 .H. Proença, Iris recognition: On the segmentation of degraded images acquired in the visible wavelength. IEEE Trans. Pattern Anal. Mach. Intell.32(8), 1502–1516 (2010). http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=5156505&isnumber=5487331 .T. Tan, Z. He, Z. Sun, Efficient and robust segmentation of noisy iris images for non-cooperative iris recognition. Image Vision Comput.28(2), 223–230 (2010).C. -W. Tan, A. Kumar, in CVPR 2011 WORKSHOPS. Automated segmentation of iris images using visible wavelength face images (Colorado Springs, 2011). p. 9–14. http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=5981682&isnumber=5981671 .Y. -H. Li, M. Savvides, An automatic iris occlusion estimation method based on high-dimensional density estimation. IEEE Trans. Pattern Anal. Mach. Intell.35(4), 784–796 (2013).M. Yahiaoui, E. Monfrini, B. Dorizzi, Markov chains for unsupervised segmentation of degraded nir iris images for person recognition. Pattern Recogn. Lett.82:, 116–123 (2016).A. Radman, N. Zainal, S. A. Suandi, Automated segmentation of iris images acquired in an unconstrained environment using hog-svm and growcut. Digit. Signal Proc.64:, 60–70 (2017).N. Liu, H. Li, M. Zhang, J. Liu, Z. Sun, T. Tan, in 2016 International Conference on Biometrics (ICB). Accurate iris segmentation in non-cooperative environments using fully convolutional networks (Halmstad, 2016). p. 1–8. http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=7550055&isnumber=7550036 .Z. Zhao, A. Kumar, in 2017 IEEE International Conference on Computer Vision (ICCV). Towards more accurate iris recognition using deeply learned spatially corresponding features (Venice, 2017). p. 3829–3838. http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=8237673&isnumber=8237262 .P. Li, X. Liu, L. Xiao, Q. Song, Robust and accurate iris segmentation in very noisy iris images. Image Vision Comput.28(2), 246–253 (2010).D. S. Jeong, J. W. Hwang, B. J. Kang, K. R. Park, C. S. Won, D. -K. Park, J. Kim, A new iris segmentation method for non-ideal iris images. Image Vision Comput.28(2), 254–260 (2010).Y. Chen, M. Adjouadi, C. Han, J. Wang, A. Barreto, N. Rishe, J. Andrian, A highly accurate and computationally efficient approach for unconstrained iris segmentation. Image Vision Comput. 28(2), 261–269 (2010).Z. Zhao, A. Kumar, in 2015 IEEE International Conference on Computer Vision (ICCV). An accurate iris segmentation framework under relaxed imaging constraints using total variation model (Santiago, 2015). p. 3828–3836. http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=7410793&isnumber=7410356 .Y. Hu, K. Sirlantzis, G. Howells, Improving colour iris segmentation using a model selection technique. Pattern Recogn. Lett.57:, 24–32 (2015).E. Ouabida, A. Essadique, A. Bouzid, Vander lugt correlator based active contours for iris segmentation and tracking. Expert Systems Appl.71:, 383–395 (2017).C. -W. Tan, A. Kumar, Unified framework for automated iris segmentation using distantly acquired face images. IEEE Trans. Image Proc.21(9), 4068–4079 (2012).C. -W. Tan, A. Kumar, in Proceedings of the 21st International Conference on Pattern Recognition (ICPR2012). Human identification from at-a-distance images by simultaneously exploiting iris and periocular features (Tsukuba, 2012). p. 553–556. http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=6460194&isnumber=6460043 .C. -W. Tan, A. Kumar, Towards online iris and periocular recognition under relaxed imaging constraints. IEEE Trans. Image Proc.22(10), 3751–3765 (2013).K. Y. Shin, Y. G. Kim, K. R. Park, Enhanced iris recognition method based on multi-unit iris images. Opt. Eng.52(4), 047201–047201 (2013).CASIA iris databases. http://biometrics.idealtest.org/ . Accessed 06 Sept 2017.WVU iris databases. hhttp://biic.wvu.edu/data-sets/synthetic-iris-dataset . Accessed 06 Sept 2017.UBIRIS iris database. http://iris.di.ubi.pt . Accessed 06 Sept 2017.MICHE iris database. http://biplab.unisa.it/MICHE/ . Accessed 06 Sept 2017.P. J. Phillips, et al, in 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR’05), 1. Overview of the face recognition grand challenge (San Diego, 2005). p. 947–954. http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=1467368&isnumber=31472 .D. S. Ma, J. Correll, B. Wittenbrink, The chicago face database: A free stimulus set of faces and norming data. Behav. Res. Methods. 47(4), 1122–1135 (2015).P. Soille, Morphological Image Analysis: Principles and Applications (Springer, 2013).A. K. Jain, Fundamentals of Digital Image Processing (Prentice-Hall, Inc., Englewood Cliffs, 1989).J. Daugman, How iris recognition works. IEEE Trans. Circ. Syst. Video Technol.14(1), 21–30 (2004).A. Asthana, S. Zafeiriou, S. Cheng, M. Pantic, in 2014 IEEE Conference on Computer Vision and Pattern Recognition. Incremental face alignment in the wild (Columbus, 2014). p. 1859–1866. http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=6909636&isnumber=6909393 .T. Baltrusaitis, P. Robinson, L. -P. Morency, in 2013 IEEE International Conference on Computer Vision Workshops. Constrained local neural fields for robust facial landmark detection in the wild (Sydney, 2013). p. 354–361. http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=6755919&isnumber=6755862 .X. Zhu, D. Ramanan, in Computer Vision and Pattern Recognition (CVPR), 2012 IEEE Conference On. Face detection, pose estimation, and landmark localization in the wild (IEEEBerlin Heidelberg, 2012), pp. 2879–2886.G. Tzimiropoulos, in 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR). Project-out cascaded regression with an application to face alignment (Boston, 2015). p. 3659–3667. http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=7298989&isnumber=7298593 .H. Hofbauer, F. Alonso-Fernandez, P. Wild, J. Bigun, A. Uhl, in 2014 22nd International Conference on Pattern Recognition. A ground truth for iris segmentation (Stockholm, 2014). p. 527–532. http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=6976811&isnumber=6976709 .H. Proença, L. A. Alexandre, in 2007 First IEEE International Conference on Biometrics: Theory, Applications, and Systems. The NICE.I: Noisy Iris Challenge Evaluation - Part I (Crystal City, 2007). p. 1–4. http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=4401910&isnumber=4401902 .J. Daugman, in European Convention on Security and Detection. High confidence recognition of persons by rapid video analysis of iris texture, (1995). p. 244–251. http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=491729&isnumber=10615 .Code of Matlab implementation of Daugman’s integro-differential operator (IDO). https://es.mathworks.com/matlabcentral/fileexchange/15652-iris-segmentation-using-daugman-s-integrodifferential-operator/ . Accessed 06 Sept 2017.Code of Matlab implementation of Zhao and Kumar’s iris segmentation framework under relaxed imaging constraints using total variation model. http://www4.comp.polyu.edu.hk/~csajaykr/tvmiris.htm/ . Accessed 06 Sept 2017.Code of Matlab implementation of presented work. https://gitlab.com/ffuentes/hybrid_iris_segmentation/ . Accessed 06 Sept 2017.Face and eye detection with OpenCV. https://docs.opencv.org/trunk/d7/d8b/tutorial_py_face_detection.html . Accessed 07 Sept 2018.A. K. Boyat, B. K. Joshi, 6. A review paper:noise models in digital image processing signal & image processing. An International Journal (SIPIJ), (2015), pp. 63–75. https://doi.org/10.5121/sipij.2015.6206 .A. Buades, Y. Lou, J. M. Morel, Z. Tang, Multi image noise estimation and denoising (2010). Available: https://hal.archives-ouvertes.fr/hal-00510866/

Toxocariosis en una población adulta

La toxocariosis es una zoonosis parasitaria de alta prevalencia especialmente en la población infantil. Sus manifestaciones clínicas son muy variadas y pueden asemejarse a las de otras etiologías. Los perros son los hospedadores definitivos alojando las formas adultas de T. canis. El hombre adquiere la infección oralmente al ingerir huevos larvados que eclosionan en el intestino delgado. Las larvas penetran la mucosa, llegan al hígado, pasan a circulación sistémica, desde donde pueden ir a cualquier órgano, incluidos pulmón, cerebro y corazón. Esto produce el Síndrome de Larva Migrans Visceral. Si la larva se aloja en el globo ocular se denomina Larva Migrans Ocular. El objetivo del presente trabajo fue determinar la prevalencia de la infección por Toxocara en una población adulta de la zona ribereña de Punta Lara, Partido de Ensenada.Facultad de Ciencias Veterinaria