Dimensionamiento óptimo de generación distribuida en redes de distribución basado en la teoría de grafos

El objetivo principal del presente trabajo es encontrar una solución óptima para el dimensionamiento de generación distribuida (GD) en redes de distribución usando la teoría de grafos. Para ello se utiliza el algoritmo del árbol mínimo de expansión (MST), dentro del MST existen dos tipos de algoritmos altamente usados en sistemas eléctricos de distribución, los cuales son: Prim y Dijkstra. El algoritmo de Prim resuelve el árbol mínimo de expansión, en este caso el enrutamiento más corto entre nodos de distribución, mientras que, el algoritmo de Dijkstra encuentra la conexión más cercana de varios elementos que se dirigen a un solo objetivo. De esta manera se selecciona un mapa el cual contenga un escenario típico de viviendas urbanas, donde es posible diseñar un sistema eléctrico de distribución, en el cual se ubican geo-referenciadamente los siguientes elementos: transformadores de distribución, unidades de GD y usuarios finales. El algoritmo de Prim obtiene el enrutamiento óptimo de la red de media tensión de acuerdo a la ubicación de los usuarios finales, y el algoritmo de Dijkstra obtiene la conexión de baja tensión entre los trasformadores de distribución y los usuarios finales. Las unidades de GD van acopladas en los nodos de distribución de bajo voltaje, mejorando los perfiles de voltaje, reduciendo las perdidas eléctricas generadas por la distancia existente de los conductores.The main objective of this article is to show a solution for an optimal sizing of distributed generation (GD) in distribution networks using graph theory. For this purpose it uses the Minimal Spanning Tree (MST) algorithm, within the MST exist two types of algorithms highly used in electric distribution systems, those are: Prim and Dijkstra. Prim’s algorithm solves the minimal spanning tree, in this case the shortest path between distribution nodes, whereas that, Dijkstra’s algorithm find the nearest path of several elements which conduct to a single objective. In this manner a typical urban housing scenario was selected, where is possible sizing an electric distribution system, which locates geo-referenced the following elements: distributed transformers, GD units and final users. Prim’s algorithm obtains the optimal route of the médium voltaje and it depends on the location of end users, and Dijkstra’s algortihm obtains the low voltaje path between distributed transformers and end users. The GD units coupled with distribution low voltage node, providing to the electric distribution system, improving the voltaje profile, reducing power losses generated by the existent distance of the conductors

Treatment with tocilizumab or corticosteroids for COVID-19 patients with hyperinflammatory state: a multicentre cohort study (SAM-COVID-19)

Objectives: The objective of this study was to estimate the association between tocilizumab or corticosteroids and the risk of intubation or death in patients with coronavirus disease 19 (COVID-19) with a hyperinflammatory state according to clinical and laboratory parameters. Methods: A cohort study was performed in 60 Spanish hospitals including 778 patients with COVID-19 and clinical and laboratory data indicative of a hyperinflammatory state. Treatment was mainly with tocilizumab, an intermediate-high dose of corticosteroids (IHDC), a pulse dose of corticosteroids (PDC), combination therapy, or no treatment. Primary outcome was intubation or death; follow-up was 21 days. Propensity score-adjusted estimations using Cox regression (logistic regression if needed) were calculated. Propensity scores were used as confounders, matching variables and for the inverse probability of treatment weights (IPTWs). Results: In all, 88, 117, 78 and 151 patients treated with tocilizumab, IHDC, PDC, and combination therapy, respectively, were compared with 344 untreated patients. The primary endpoint occurred in 10 (11.4%), 27 (23.1%), 12 (15.4%), 40 (25.6%) and 69 (21.1%), respectively. The IPTW-based hazard ratios (odds ratio for combination therapy) for the primary endpoint were 0.32 (95%CI 0.22-0.47; p < 0.001) for tocilizumab, 0.82 (0.71-1.30; p 0.82) for IHDC, 0.61 (0.43-0.86; p 0.006) for PDC, and 1.17 (0.86-1.58; p 0.30) for combination therapy. Other applications of the propensity score provided similar results, but were not significant for PDC. Tocilizumab was also associated with lower hazard of death alone in IPTW analysis (0.07; 0.02-0.17; p < 0.001). Conclusions: Tocilizumab might be useful in COVID-19 patients with a hyperinflammatory state and should be prioritized for randomized trials in this situatio

Spatiotemporal Characteristics of the Largest HIV-1 CRF02_AG Outbreak in Spain: Evidence for Onward Transmissions

Background and Aim: The circulating recombinant form 02_AG (CRF02_AG) is the predominant clade among the human immunodeficiency virus type-1 (HIV-1) non-Bs with a prevalence of 5.97% (95% Confidence Interval-CI: 5.41–6.57%) across Spain. Our aim was to estimate the levels of regional clustering for CRF02_AG and the spatiotemporal characteristics of the largest CRF02_AG subepidemic in Spain.Methods: We studied 396 CRF02_AG sequences obtained from HIV-1 diagnosed patients during 2000–2014 from 10 autonomous communities of Spain. Phylogenetic analysis was performed on the 391 CRF02_AG sequences along with all globally sampled CRF02_AG sequences (N = 3,302) as references. Phylodynamic and phylogeographic analysis was performed to the largest CRF02_AG monophyletic cluster by a Bayesian method in BEAST v1.8.0 and by reconstructing ancestral states using the criterion of parsimony in Mesquite v3.4, respectively.Results: The HIV-1 CRF02_AG prevalence differed across Spanish autonomous communities we sampled from (p &lt; 0.001). Phylogenetic analysis revealed that 52.7% of the CRF02_AG sequences formed 56 monophyletic clusters, with a range of 2–79 sequences. The CRF02_AG regional dispersal differed across Spain (p = 0.003), as suggested by monophyletic clustering. For the largest monophyletic cluster (subepidemic) (N = 79), 49.4% of the clustered sequences originated from Madrid, while most sequences (51.9%) had been obtained from men having sex with men (MSM). Molecular clock analysis suggested that the origin (tMRCA) of the CRF02_AG subepidemic was in 2002 (median estimate; 95% Highest Posterior Density-HPD interval: 1999–2004). Additionally, we found significant clustering within the CRF02_AG subepidemic according to the ethnic origin.Conclusion: CRF02_AG has been introduced as a result of multiple introductions in Spain, following regional dispersal in several cases. We showed that CRF02_AG transmissions were mostly due to regional dispersal in Spain. The hot-spot for the largest CRF02_AG regional subepidemic in Spain was in Madrid associated with MSM transmission risk group. The existence of subepidemics suggest that several spillovers occurred from Madrid to other areas. CRF02_AG sequences from Hispanics were clustered in a separate subclade suggesting no linkage between the local and Hispanic subepidemics

Discovering HIV related information by means of association rules and machine learning

Acquired immunodeficiency syndrome (AIDS) is still one of the main health problems worldwide. It is therefore essential to keep making progress in improving the prognosis and quality of life of affected patients. One way to advance along this pathway is to uncover connections between other disorders associated with HIV/AIDS-so that they can be anticipated and possibly mitigated. We propose to achieve this by using Association Rules (ARs). They allow us to represent the dependencies between a number of diseases and other specific diseases. However, classical techniques systematically generate every AR meeting some minimal conditions on data frequency, hence generating a vast amount of uninteresting ARs, which need to be filtered out. The lack of manually annotated ARs has favored unsupervised filtering, even though they produce limited results. In this paper, we propose a semi-supervised system, able to identify relevant ARs among HIV-related diseases with a minimal amount of annotated training data. Our system has been able to extract a good number of relationships between HIV-related diseases that have been previously detected in the literature but are scattered and are often little known. Furthermore, a number of plausible new relationships have shown up which deserve further investigation by qualified medical experts