Intracorneal Ring Segment Implantation for the Management of Keratoconus in Children

The short-term safety and efficacy of intracorneal ring segment (ICRS) implantation in keratoconus eyes of children are investigated in this study. A retrospective interventional case series study including a total of 33 keratoconus eyes (age 8 to 17 years) that had undergone ICRS (Keraring segments, Mediphacos) implantation was conducted. Information about visual, refractive, pachymetric, corneal topographic and aberrometric, and corneal endothelial changes during a 3-month follow-up were extracted and analysed. A significant improvement was observed in logMAR corrected distance visual acuity (p = 0.005), combined with a statistically significant reduction in keratometric readings (p < 0.001). A reduction in the magnitude of corneal astigmatism of ≥1 D was observed in 52.8% of eyes. No significant changes were observed in corneal endothelial density (p = 0.317). Significant changes were found in the anterior vertical coma component (p = 0.002) as well as in the spherical aberration of the posterior corneal surface (p = 0.004). Only two relevant complications were described: one corneal microperforation with penetration of the ring segment into the anterior chamber (1 eye, 2.8%), and a case of ring extrusion (1 eye, 2.8%). ICRS implantation in children keratoconus eyes allows a reduction of corneal astigmatism, irregularity, and aberrations, leading to a significant visual improvement.The author David P Piñero has been supported by the Ministry of Economy, Industry and Competitiveness of Spain within the program Ramón y Cajal, RYC-2016-20471

Grundy domination and zero forcing in Kneser graphs

In this paper, we continue the investigation of different types of (Grundy) dominating sequences. We consider four different types of Grundy domination numbers and the related zero forcing numbers, focusing on these numbers in the well-known class of Kneser graphs Kn,r. In particular, we establish that the Grundy total domination number γ t gr(Kn,r) equals 2r r for any r ≥ 2 and n ≥ 2r + 1. For the Grundy domination number of Kneser graphs we get γgr(Kn,r) = α(Kn,r) whenever n is sufficiently larger than r. On the other hand, the zero forcing number Z(Kn,r) is proved to be n r − 2r r when n ≥ 3r + 1 and r ≥ 2, while lower and upper bounds are provided for Z(Kn,r) when 2r + 1 ≤ n ≤ 3r. Some lower bounds for different types of minimum ranks of Kneser graphs are also obtained along the way.Fil: Bresar, Bostjan. University of Maribor; Eslovenia. Institute Of Mathematics, Physics And Mechanics Ljubljana; EsloveniaFil: Kos, Tim. Institute Of Mathematics, Physics And Mechanics Ljubljana; EsloveniaFil: Torres, Pablo Daniel. Universidad Nacional de Rosario. Facultad de Ciencias Exactas Ingeniería y Agrimensura. Escuela de Ciencias Exactas y Naturales. Departamento de Matemática; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Rosario; Argentin

Los distintos lenguajes de circulación de la información, como instrumentos de recolección en las investigaciones didácticas

En este taller se muestran algunos de los desarrollos obtenidos en el seminario Configuraciones Didácticas dirigido por el Profesor Orlando Lurduy y en tres propuestas de investigación en el aula conducentes a optar el título de Licenciado en Educación Básica con Énfasis en Matemáticas. Por ultimo se pretende despertar un interés por identificar y mejorar el papel de los distintos tipos de lenguaje de la información en las investigacione

On the diameter of Schrijver graphs

For k ≥ 1 and n ≥ 2k, the well known Kneser graph KG(n, k) has all k-element subsets of an n-element set as vertices; two such subsets are adjacent if they are disjoint. Schrijver constructed a vertex-critical subgraph SG(n, k) of KG(n, k) with the same chromatic number. In this paper, we compute the diameter of the graph SG(2k + r,k) with r ≥ 1. We obtain that the diameter of SG(2k + r, k) is equal to 2 if r ≥ 2k - 2; 3 if k≥ - 2 ≤ r ≤ 2k - 3; k if r = 1; and for 2 ≤ r ≤ k - 3, we obtain that the diameter of SG(2k + r, k) is at most equal to k - r + 1.Fil: Pastine, Adrián Gabriel. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - San Luis. Instituto de Matemática Aplicada de San Luis "Prof. Ezio Marchi". Universidad Nacional de San Luis. Facultad de Ciencias Físico, Matemáticas y Naturales. Instituto de Matemática Aplicada de San Luis "Prof. Ezio Marchi"; ArgentinaFil: Torres, Pablo Daniel. Universidad Nacional de Rosario. Facultad de Ciencias Exactas, Ingeniería y Agrimensura; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Santa Fe; ArgentinaFil: Valencia Pabon, Mario. Universite Sorbonne Paris Nord; FranciaXI Latin and American Algorithms, Graphs and Optimization Symposium.Sao PauloBrasilUniversity of Sao Paul

¿Qué sabemos del saber que enseñamos? Elementos para mejorar su comprensión

Este taller presenta algunos aspectos a tener en cuenta al realizar una caracterización del saber puesto en juego en una secuencia de actividades, basándose en el modelo teórico del Enfoque Onto Semiótico (EOS) propuesto por Godino (2003) principalmente en las facetas Institucional y Personal del mismo. Se presenta algunos de los desarrollos tratados por el grupo investigador, con respecto a la representación de la función y la operatividad del número relativo

Synthesis of fluorescent dendrimeric antigen efficiently internalized by human dendritic cells

A new fluorescent dendrimeric antigen (DeAn) based on a dendron with amoxicilloyl terminal groups has been synthetized. The synthesis implies a novel class of all-aliphatic polyamide dendrimer (BisAminoalkylPolyAmide Dendrimers, or BAPAD).[1] The introduction of a cystamine core allows the incorporation of this dendrons into a 1,8-naphthalimide fluorofore functionalized with a maleimide group. The fluorescence properties of this DeAn has been studied and compared with the properties of an equivalent dendron possessing amino-terminal groups. This DeAn has been used as a synthetic antigen in a biomedical assay that tests the amoxicillin sensitivity of dendritic cells (DC) from tolerant and allergic patients.Universidad de Málaga. Campus de Excelencia Internacional Andalucía Tech

Total dominating sequences in trees, split graphs, and under modular decomposition

A sequence of vertices in a graph G with no isolated vertices is called a total dominating sequence if every vertex in the sequence totally dominates at least one vertex that was not totally dominated by preceding vertices in the sequence, and, at the end all vertices of G are totally dominated (by definition a vertex totally dominates its neighbors). The maximum length of a total dominating sequence is called the Grundy total domination number, γgr t(G), of G, as introduced in Brešar et al. (2016). In this paper we continue the investigation of this concept, mainly from the algorithmic point of view. While it was known that the decision version of the problem is NP-complete in bipartite graphs, we show that this is also true if we restrict to split graphs. A linear time algorithm for determining the Grundy total domination number of an arbitrary forest T is presented, based on the formula γgr t(T)=2τ(T), where τ(T) is the vertex cover number of T. A similar efficient algorithm is presented for bipartite distance-hereditary graphs. Using the modular decomposition of a graph, we present a frame for obtaining polynomial algorithms for this problem in classes of graphs having relatively simple modular subgraphs. In particular, a linear algorithm for determining the Grundy total domination number of P4-tidy graphs is presented. In addition, we prove a realization result by exhibiting a family of graphs Gk such that γgr t(Gk)=k, for any k∈Z+∖{1,3}, and showing that there are no graphs G with γgr t(G)∈{1,3}. We also present such a family, which has minimum possible order and size among all graphs with Grundy total domination number equal to k.Fil: Brešar, Boštjan. University of Maribor; Eslovenia. Institute of Mathematics, Physics and Mechanics; EsloveniaFil: Kos, Tim. Institute of Mathematics, Physics and Mechanics; EsloveniaFil: Nasini, Graciela Leonor. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Rosario; Argentina. Universidad Nacional de Rosario. Facultad de Ciencias Exactas Ingeniería y Agrimensura. Escuela de Ciencias Exactas y Naturales. Departamento de Matemática; ArgentinaFil: Torres, Pablo Daniel. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Rosario; Argentina. Universidad Nacional de Rosario. Facultad de Ciencias Exactas Ingeniería y Agrimensura. Escuela de Ciencias Exactas y Naturales. Departamento de Matemática; Argentin

Intelligent energy storage management trade-off system applied to Deep Learning predictions

The control of the electrical power supply is one of the key bases to reach the sustainable development goals set by United Nations. The achievement of these objectives encourages a dual strategy of creation and diffusion of renewable energies and other technologies of zero emission. Thus, meet the emerging necessities require, inevitably, a significant transformation of the building sector to improve the design of the electrical infrastructure. This improvement should be linked to advanced techniques that allows the identification of complex patterns in large amount of data, such as Deep Learning ones, in order to mitigate potential uncertainties. Accurate electricity and energy supply prediction models, in combination with storage systems will be reflected directly in efficiency improvements in buildings. In this paper, a branch of Deep Learning models, known as Standard Neural Networks, are used to predict electricity consumption and photovoltaic generation with the purpose of reduce the energy wasted, by managing the storage system using Reinforcement Learning technique. Specifically, Deep Reinforcement Learning is applied using the Deep Q-Learning agent. Furthermore, the accuracy of the predicted variables is measured by means of normalized Mean Bias Error (nMBE), and normalized Root Mean Squared Error (nRMSE). The methodologies developed are validated in an existing building, the School of Mining and Energy Engineering located on the Campus of the University of Vigo.Agencia Estatal de Investigación | Ref. TED2021-130677B-I00Financiado para publicación en acceso aberto: Universidade de Vigo/CISU

Machine learning and deep learning models applied to photovoltaic production forecasting

The increasing trend in energy demand is higher than the one from renewable generation, in the coming years. One of the greatest sources of consumption are buildings. The energy management of a building by means of the production of photovoltaic energy in situ is a common alternative to improve sustainability in this sector. An efficient trade-off of the photovoltaic source in the fields of Zero Energy Buildings (ZEB), nearly Zero Energy Buildings (nZEB) or MicroGrids (MG) requires an accurate forecast of photovoltaic production. These systems constantly generate data that are not used. Artificial Intelligence methods can take advantage of this missing information and provide accurate forecasts in real time. Thus, in this manuscript a comparative analysis is carried out to determine the most appropriate Artificial Intelligence methods to forecast photovoltaic production in buildings. On the one hand, the Machine Learning methods considered are Random Forest (RF), Extreme Gradient Boost (XGBoost), and Support Vector Regressor (SVR). On the other hand, Deep Learning techniques used are Standard Neural Network (SNN), Recurrent Neural Network (RNN), and Convolutional Neural Network (CNN). The models are checked with data from a real building. The models are validated using normalized Mean Bias Error (nMBE), normalized Root Mean Squared Error (nRMSE), and the coefficient of variation (R2). Standard deviation is also used in conjunction with these metrics. The results show that the models forecast the test set with errors of less than 2.00% (nMBE) and 7.50% (nRMSE) in the case of considering nights, and 4.00% (nMBE) and 11.50% (nRMSE) if nights are not considered. In both situations, the R2 is greater than 0.85 in all models.Universidade de Vigo | Ref. 00VI 131H 641021