Adjustment of model parameters to estimate distribution transformers remaining lifespan

Currently, the electrical system in Argentina is working at its maximum capacity, decreasing the margin between the installed power and demanded consumption, and drastically reducing the service life of transformer substations due to overload (since the margin for summer peaks is small). The advent of the Smart Grids allows electricity distribution companies to apply data analysis techniques to manage resources more efficiently at different levels (avoiding damages, better contingency management, maintenance planning, etc.). The Smart Grids in Argentina progresses slowly due to the high costs involved. In this context, the estimation of the lifespan reduction of distribution transformers is a key tool to efficiently manage human and material resources, maximizing the lifetime of this equipment. Despite the current state of the smart grids, the electricity distribution companies can implement it using the available data. Thermal models provide guidelines for lifespan estimation, but the adjustment to particular conditions, brands, or material quality is done by adjusting parameters. In this work we propose a method to adjust the parameters of a thermal model using Genetic Algorithms, comparing the estimation values of top-oil temperature with measurements from 315 kVA distribution transformers, located in the province of Tucumán, Argentina. The results show that, despite limited data availability, the adjusted model is suitable to implement a transformer monitoring system.Fil: Jimenez, Victor Adrian. Universidad Tecnológica Nacional. Facultad Regional Tucumán. Centro de Investigación en Tecnologías Avanzadas de Tucumán; ArgentinaFil: Will, Adrian L. E.. Universidad Tecnológica Nacional. Facultad Regional Tucumán. Centro de Investigación en Tecnologías Avanzadas de Tucumán; ArgentinaFil: Gotay Sardiñas, Jorge. Universidad Tecnológica Nacional. Facultad Regional Tucumán. Centro de Investigación en Tecnologías Avanzadas de Tucumán; ArgentinaFil: Rodriguez, Sebastian Alberto. Universidad Tecnológica Nacional. Facultad Regional Tucumán. Centro de Investigación en Tecnologías Avanzadas de Tucumán; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Tucumán; Argentin

Estimación de radiación solar horaria utilizando modelos empíricos y redes neuronales artificiales.

La radiación solar es uno de los parámetros más importantes para el desarrollo de aplicaciones e investigaciones relacionadas a energías renovables. Sin embargo, la adquisición de mediciones de radiación solar no siempre es posible por diferentes motivos y es necesario contar con modelos que permitan estimarla. Estos modelos en su mayoría utilizan variables climáticas difíciles de medir y que no siempre están disponibles en todos los sitios. El objetivo de este trabajo es aplicar un método para estimar radiación solar horaria, basado en redes neuronales, utilizando como variables de entrada estimaciones de radiación solar obtenidas mediante un modelo matemático simple y variables climáticas de fácil adquisición: Temperatura, Presión y Humedad. Además, para comprobar que las redes neuronales son más adecuadas en estos casos se hizo una comparativa con estimaciones realizadas con regresión lineal. Los modelos generados fueron ajustados y validados con datos provenientes de cinco estaciones meteorológicas de la provincia de Tucumán, Argentina, logrando obtener en promedio un error de 11.0% (Root Mean Squared Error) con regresión lineal y 7.84% con redes neuronales.Palabras claves: radiación solar horaria, modelos empíricos, redes neuronales feedforward, regresión lineal, energía renovabl

Análisis Estadístico de Algoritmos Evolutivos para el problema de Selección de Variables

Decades of research in optimization problems have generated a considerable number of algorithms, both deterministic and heuristic. However, due to this wide range of possibilities, determine which one is the most appropriate for a specific problem is a complex task. In this paper, a comparison among different heuristic optimization algorithms using statistical test is proposed. Simulated Annealing (SA), Simple Genetic Algorithm (sGA), Genetic Algorithms Compact (cGA) and Deterministic Crowding (DC) were used, applied to the Variable Selection for estimation problem using Linear Regression. Three test cases were used: solar radiation at the province of Tucuman (Argentina), power consumption estimation in the same area, and estimation of the reappearance of cancer cells. We concluded that there is sufficient statistical evidence to affirm that the algorithms yield significantly different results. Also, we concluded that sGA and DC were the most suitable algorithms, obtaining similar fitness values, being sGA slightly better.Durante décadas de investigación en problemas de optimización, se han desarrollado innumerables algoritmos, tanto determinísticos como heurísticos. Aun así, debido a este amplio abanico de posibilidades resulta una tarea compleja determinar cuál de ellos es más adecuado para un problema específico. Se propone en este trabajo una comparativa entre diferentes algoritmos heurísticos de optimización haciendo uso de test estadísticos. Se consideró Simulated Annealing (SA), Algoritmo Genético Simple (sGA), Algoritmos Genético Compacto (cGA) y Deterministic Crowding (DC) aplicados al problema de Selección de Variables para Estimación utilizando Regresión Lineal. Se utilizaron tres casos de prueba, que consisten en la estimación de Radiación Solar, Consumo Eléctrico en la provincia de Tucumán (Argentina), y el tiempo de reaparición de células cancerígenas. Se comprobó que existe suficiente evidencia estadística para afirmar que los algoritmos arrojan resultados significativamente diferentes. Además se concluye que sGA y DC son los algoritmos más apropiados para estos problemas, permitiendo obtener valores de fitness similares siendo sGA ligeramente superior

Análisis de variables temporales para la predicción del consumo eléctrico

El problema de la predicción de consumo eléctrico a corto plazo o Short Term Load Forecasting (STLF), es un tema de capital importancia para las empresas de energía en la actualidad, ya que permite un manejo más eficiente, permitiendo un mejor aprovechamiento de los equipos y recursos. La predicción de la demanda es un problema complejo, ya que está relacionada a factores económicos, climáticos, temporales, y su comportamiento varía de una sociedad a otra. Cada uno de estos factores aporta determinadas variables que pueden ser representadas de diferentes maneras, en particular las temporales. Se plantea en este trabajo la hipótesis que el método utilizado para presentar las variables temporales a un sistema de predicción de consumo eléctrico afecta los resultados. Para verificar la hipótesis planteada, consideramos diferentes métodos de representación de estas variables, aplicados al problema de predicción de valores diarios de consumo eléctrico en la provincia de Tucumán, Argentina. La división de la variable temporal en variables día, día de la semana, mes y año en forma individual para cada periodo involucrado en el problema, resultó ser el método más conveniente, obteniendo una mejora de hasta el 10,56% respecto de otros métodos considerados.Fil: Lizondo, Diego Fernando. Universidad Tecnológica Nacional. Facultad Regional Tucumán. Centro de Investigación en Tecnologías Avanzadas de Tucumán; ArgentinaFil: Jimenez, Victor Adrian. Universidad Tecnológica Nacional. Facultad Regional Tucumán. Centro de Investigación en Tecnologías Avanzadas de Tucumán; ArgentinaFil: Villacís Postigo, Fernando. Universidad Tecnológica Nacional. Facultad Regional Tucumán. Centro de Investigación en Tecnologías Avanzadas de Tucumán; ArgentinaFil: Will, Adrian L. E.. Universidad Tecnológica Nacional. Facultad Regional Tucumán. Centro de Investigación en Tecnologías Avanzadas de Tucumán; Argentina. Universidad Nacional de Tucumán. Facultad de Ciencias Exactas y Tecnología; ArgentinaFil: Rodriguez, Sebastian Alberto. Universidad Tecnológica Nacional. Facultad Regional Tucumán. Centro de Investigación en Tecnologías Avanzadas de Tucumán; Argentin

Antimicrobial resistance among migrants in Europe: a systematic review and meta-analysis

BACKGROUND: Rates of antimicrobial resistance (AMR) are rising globally and there is concern that increased migration is contributing to the burden of antibiotic resistance in Europe. However, the effect of migration on the burden of AMR in Europe has not yet been comprehensively examined. Therefore, we did a systematic review and meta-analysis to identify and synthesise data for AMR carriage or infection in migrants to Europe to examine differences in patterns of AMR across migrant groups and in different settings. METHODS: For this systematic review and meta-analysis, we searched MEDLINE, Embase, PubMed, and Scopus with no language restrictions from Jan 1, 2000, to Jan 18, 2017, for primary data from observational studies reporting antibacterial resistance in common bacterial pathogens among migrants to 21 European Union-15 and European Economic Area countries. To be eligible for inclusion, studies had to report data on carriage or infection with laboratory-confirmed antibiotic-resistant organisms in migrant populations. We extracted data from eligible studies and assessed quality using piloted, standardised forms. We did not examine drug resistance in tuberculosis and excluded articles solely reporting on this parameter. We also excluded articles in which migrant status was determined by ethnicity, country of birth of participants' parents, or was not defined, and articles in which data were not disaggregated by migrant status. Outcomes were carriage of or infection with antibiotic-resistant organisms. We used random-effects models to calculate the pooled prevalence of each outcome. The study protocol is registered with PROSPERO, number CRD42016043681. FINDINGS: We identified 2274 articles, of which 23 observational studies reporting on antibiotic resistance in 2319 migrants were included. The pooled prevalence of any AMR carriage or AMR infection in migrants was 25·4% (95% CI 19·1-31·8; I2 =98%), including meticillin-resistant Staphylococcus aureus (7·8%, 4·8-10·7; I2 =92%) and antibiotic-resistant Gram-negative bacteria (27·2%, 17·6-36·8; I2 =94%). The pooled prevalence of any AMR carriage or infection was higher in refugees and asylum seekers (33·0%, 18·3-47·6; I2 =98%) than in other migrant groups (6·6%, 1·8-11·3; I2 =92%). The pooled prevalence of antibiotic-resistant organisms was slightly higher in high-migrant community settings (33·1%, 11·1-55·1; I2 =96%) than in migrants in hospitals (24·3%, 16·1-32·6; I2 =98%). We did not find evidence of high rates of transmission of AMR from migrant to host populations. INTERPRETATION: Migrants are exposed to conditions favouring the emergence of drug resistance during transit and in host countries in Europe. Increased antibiotic resistance among refugees and asylum seekers and in high-migrant community settings (such as refugee camps and detention facilities) highlights the need for improved living conditions, access to health care, and initiatives to facilitate detection of and appropriate high-quality treatment for antibiotic-resistant infections during transit and in host countries. Protocols for the prevention and control of infection and for antibiotic surveillance need to be integrated in all aspects of health care, which should be accessible for all migrant groups, and should target determinants of AMR before, during, and after migration. FUNDING: UK National Institute for Health Research Imperial Biomedical Research Centre, Imperial College Healthcare Charity, the Wellcome Trust, and UK National Institute for Health Research Health Protection Research Unit in Healthcare-associated Infections and Antimictobial Resistance at Imperial College London

The 16th Data Release of the Sloan Digital Sky Surveys : First Release from the APOGEE-2 Southern Survey and Full Release of eBOSS Spectra

This paper documents the 16th data release (DR16) from the Sloan Digital Sky Surveys (SDSS), the fourth and penultimate from the fourth phase (SDSS-IV). This is the first release of data from the Southern Hemisphere survey of the Apache Point Observatory Galactic Evolution Experiment 2 (APOGEE-2); new data from APOGEE-2 North are also included. DR16 is also notable as the final data release for the main cosmological program of the Extended Baryon Oscillation Spectroscopic Survey (eBOSS), and all raw and reduced spectra from that project are released here. DR16 also includes all the data from the Time Domain Spectroscopic Survey and new data from the SPectroscopic IDentification of ERosita Survey programs, both of which were co-observed on eBOSS plates. DR16 has no new data from the Mapping Nearby Galaxies at Apache Point Observatory (MaNGA) survey (or the MaNGA Stellar Library "MaStar"). We also preview future SDSS-V operations (due to start in 2020), and summarize plans for the final SDSS-IV data release (DR17).Peer reviewe

Search for dark matter produced in association with bottom or top quarks in √s = 13 TeV pp collisions with the ATLAS detector

A search for weakly interacting massive particle dark matter produced in association with bottom or top quarks is presented. Final states containing third-generation quarks and miss- ing transverse momentum are considered. The analysis uses 36.1 fb−1 of proton–proton collision data recorded by the ATLAS experiment at √s = 13 TeV in 2015 and 2016. No significant excess of events above the estimated backgrounds is observed. The results are in- terpreted in the framework of simplified models of spin-0 dark-matter mediators. For colour- neutral spin-0 mediators produced in association with top quarks and decaying into a pair of dark-matter particles, mediator masses below 50 GeV are excluded assuming a dark-matter candidate mass of 1 GeV and unitary couplings. For scalar and pseudoscalar mediators produced in association with bottom quarks, the search sets limits on the production cross- section of 300 times the predicted rate for mediators with masses between 10 and 50 GeV and assuming a dark-matter mass of 1 GeV and unitary coupling. Constraints on colour- charged scalar simplified models are also presented. Assuming a dark-matter particle mass of 35 GeV, mediator particles with mass below 1.1 TeV are excluded for couplings yielding a dark-matter relic density consistent with measurements

Surgical site infection after gastrointestinal surgery in high-income, middle-income, and low-income countries: a prospective, international, multicentre cohort study

Background: Surgical site infection (SSI) is one of the most common infections associated with health care, but its importance as a global health priority is not fully understood. We quantified the burden of SSI after gastrointestinal surgery in countries in all parts of the world. Methods: This international, prospective, multicentre cohort study included consecutive patients undergoing elective or emergency gastrointestinal resection within 2-week time periods at any health-care facility in any country. Countries with participating centres were stratified into high-income, middle-income, and low-income groups according to the UN's Human Development Index (HDI). Data variables from the GlobalSurg 1 study and other studies that have been found to affect the likelihood of SSI were entered into risk adjustment models. The primary outcome measure was the 30-day SSI incidence (defined by US Centers for Disease Control and Prevention criteria for superficial and deep incisional SSI). Relationships with explanatory variables were examined using Bayesian multilevel logistic regression models. This trial is registered with ClinicalTrials.gov, number NCT02662231. Findings: Between Jan 4, 2016, and July 31, 2016, 13 265 records were submitted for analysis. 12 539 patients from 343 hospitals in 66 countries were included. 7339 (58·5%) patient were from high-HDI countries (193 hospitals in 30 countries), 3918 (31·2%) patients were from middle-HDI countries (82 hospitals in 18 countries), and 1282 (10·2%) patients were from low-HDI countries (68 hospitals in 18 countries). In total, 1538 (12·3%) patients had SSI within 30 days of surgery. The incidence of SSI varied between countries with high (691 [9·4%] of 7339 patients), middle (549 [14·0%] of 3918 patients), and low (298 [23·2%] of 1282) HDI (p < 0·001). The highest SSI incidence in each HDI group was after dirty surgery (102 [17·8%] of 574 patients in high-HDI countries; 74 [31·4%] of 236 patients in middle-HDI countries; 72 [39·8%] of 181 patients in low-HDI countries). Following risk factor adjustment, patients in low-HDI countries were at greatest risk of SSI (adjusted odds ratio 1·60, 95% credible interval 1·05–2·37; p=0·030). 132 (21·6%) of 610 patients with an SSI and a microbiology culture result had an infection that was resistant to the prophylactic antibiotic used. Resistant infections were detected in 49 (16·6%) of 295 patients in high-HDI countries, in 37 (19·8%) of 187 patients in middle-HDI countries, and in 46 (35·9%) of 128 patients in low-HDI countries (p < 0·001). Interpretation: Countries with a low HDI carry a disproportionately greater burden of SSI than countries with a middle or high HDI and might have higher rates of antibiotic resistance. In view of WHO recommendations on SSI prevention that highlight the absence of high-quality interventional research, urgent, pragmatic, randomised trials based in LMICs are needed to assess measures aiming to reduce this preventable complication

Azimuthal anisotropy of charged jet production in root s(NN)=2.76 TeV Pb-Pb collisions

We present measurements of the azimuthal dependence of charged jet production in central and semi-central root s(NN) = 2.76 TeV Pb-Pb collisions with respect to the second harmonic event plane, quantified as nu(ch)(2) (jet). Jet finding is performed employing the anti-k(T) algorithm with a resolution parameter R = 0.2 using charged tracks from the ALICE tracking system. The contribution of the azimuthal anisotropy of the underlying event is taken into account event-by-event. The remaining (statistical) region-to-region fluctuations are removed on an ensemble basis by unfolding the jet spectra for different event plane orientations independently. Significant non-zero nu(ch)(2) (jet) is observed in semi-central collisions (30-50% centrality) for 20 <p(T)(ch) (jet) <90 GeV/c. The azimuthal dependence of the charged jet production is similar to the dependence observed for jets comprising both charged and neutral fragments, and compatible with measurements of the nu(2) of single charged particles at high p(T). Good agreement between the data and predictions from JEWEL, an event generator simulating parton shower evolution in the presence of a dense QCD medium, is found in semi-central collisions. (C) 2015 CERN for the benefit of the ALICE Collaboration. Published by Elsevier B.V. This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/).Peer reviewe