• Análisis bibliométrico de la producción científica latinoamericana sobre COVID-19

  Gregorio-Chaviano, Orlando; Limaymanta, Cesar H.; López-Mesa, Evony K. (Instituto Nacional de Salud (Colombia), 2020-05-28)

  Introducción. La propagación de la COVID-19, una enfermedad infecciosa causada por el nuevo coronavirus SARS-CoV-2, se ha convertido en una pandemia que, a la par de su rápida diseminación a nivel mundial, ha traído consigo un aumento exponencial de la cantidad de estudios relacionados con el tema, fenómeno en el que los investigadores de Latinoamérica han participado activamente. Objetivo. Llevar a cabo un estudio bibliométrico descriptivo para identificar las tendencias de la investigación sobre COVID-19 producida en Latinoamérica. Materiales y métodos. Se recurrió a las bases de datos Web of Science, Scopus y Pubmed para recuperar la producción científica latinoamericana sobre COVID-19. Se analizaron los indicadores bibliométricos de producción, visibilidad, impacto y colaboración para evaluar la participación regional en la investigación sobre el tema. Resultados. El análisis de 142 documentos evidenció un crecimiento exponencial de la producción científica en el corto periodo analizado, una significativa colaboración internacional (51,4 %), y el liderazgo de las instituciones regionales (71 %) en la investigación con aportes en revistas de alta visibilidad, especialmente de Colombia, Brasil y México. Conclusiones. El estudio evidenció resultados relevantes sobre la participación regional en la investigación sobre COVID-19, no solo en cuanto a la cantidad y el crecimiento exponencial, sino también a su calidad y excelencia, con una elevada tasa de colaboración internacional y de publicación en revistas de reconocido prestigio, lo que, además de ser clave para la visibilidad de los países, es un considerable aporte a las investigaciones que se realizan en otros contextos geográficos.

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  Method for Collecting Relevant Topics from Twitter supported by Big Data

  Silva, Jesús; Senior Naveda, Alexa; Gamboa Suarez, Ramiro; Hernández Palma, Hugo; Niebles Núẽz, William (Institute of Physics Publishing, 2020-01-07)

  There is a fast increase of information and data generation in virtual environments due to microblogging sites such as Twitter, a social network that produces an average of 8, 000 tweets per second, and up to 550 million tweets per day. That's why this and many other social networks are overloaded with content, making it difficult for users to identify information topics because of the large number of tweets related to different issues. Due to the uncertainty that harms users who created the content, this study proposes a method for inferring the most representative topics that occurred in a time period of 1 day through the selection of user profiles who are experts in sports and politics. It is calculated considering the number of times this topic was mentioned by experts in their timelines. This experiment included a dataset extracted from Twitter, which contains 10, 750 tweets related to sports and 8, 758 tweets related to politics. All tweets were obtained from user timelines selected by the researchers, who were considered experts in their respective subjects due to the content of their tweets. The results show that the effective selection of users, together with the index of relevance implemented for the topics, can help to more easily find important topics in both sport and politics.
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  Time Series Decomposition using Automatic Learning Techniques for Predictive Models

  Silva, Jesús; Hernández Palma, Hugo; Niebles Núẽz, William; Ovallos-Gazabon, David; Varela, Noel (Institute of Physics Publishing, 2020-01-07)

  This paper proposes an innovative way to address real cases of production prediction. This approach consists in the decomposition of original time series into time sub-series according to a group of factors in order to generate a predictive model from the partial predictive models of the sub-series. The adjustment of the models is carried out by means of a set of statistic techniques and Automatic Learning. This method was compared to an intuitive method consisting of a direct prediction of time series. The results show that this approach achieves better predictive performance than the direct way, so applying a decomposition method is more appropriate for this problem than non-decomposition.

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  Neural Networks for the Web Services Classification

  Silva, Jesús; Senior Naveda, Alexa; Solórzano Movilla, José; Niebles Núẽz, William; Hernández Palma, Hugo (Institute of Physics Publishing, 2020-01-07)

  This article introduces a n-gram-based approach to automatic classification of Web services using a multilayer perceptron-type artificial neural network. Web services contain information that is useful for achieving a classification based on its functionality. The approach relies on word n-grams extracted from the web service description to determine its membership in a category. The experimentation carried out shows promising results, achieving a classification with a measure F=0.995 using unigrams (2-grams) of words (characteristics composed of a lexical unit) and a TF-IDF weight.

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  Forecasting Electric Load Demand through Advanced Statistical Techniques

  Silva, Jesús; Senior Naveda, Alexa; García Guliany, Jesús; Niebles Núẽz, William; Hernández Palma, Hugo (Institute of Physics Publishing, 2020-01-07)

  Traditional forecasting models have been widely used for decision-making in production, finance and energy. Such is the case of the ARIMA models, developed in the 1970s by George Box and Gwilym Jenkins [1], which incorporate characteristics of the past models of the same series, according to their autocorrelation. This work compares advanced statistical methods for determining the demand for electricity in Colombia, including the SARIMA, econometric and Bayesian methods.

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  Parallel Algorithm for Reduction of Data Processing Time in Big Data

  Silva, Jesús; Hernández Palma, Hugo; Niebles Núẽz, William; Ovallos-Gazabon, David; Varela, Noel (Institute of Physics Publishing, 2020-01-07)

  Technological advances have allowed to collect and store large volumes of data over the years. Besides, it is significant that today's applications have high performance and can analyze these large datasets effectively. Today, it remains a challenge for data mining to make its algorithms and applications equally efficient in the need of increasing data size and dimensionality [1]. To achieve this goal, many applications rely on parallelism, because it is an area that allows the reduction of cost depending on the execution time of the algorithms because it takes advantage of the characteristics of current computer architectures to run several processes concurrently [2]. This paper proposes a parallel version of the FuzzyPred algorithm based on the amount of data that can be processed within each of the processing threads, synchronously and independently.

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  Temporary Variables for Predicting Electricity Consumption Through Data Mining

  Silva, Jesús; Senior Naveda, Alexa; Hernández Palma, Hugo; Niebles Núẽz, William; Niebles Núẽz, Leonardo (Institute of Physics Publishing, 2020-01-07)

  In the new global and local scenario, the advent of intelligent distribution networks or Smart Grids allows real-time collection of data on the operating status of the electricity grid. Based on this availability of data, it is feasible and convenient to predict consumption in the short term, from a few hours to a week. The hypothesis of the study is that the method used to present time variables to a prediction system of electricity consumption affects the results.

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  Natural Language Explanation Model for Decision Trees

  Silva, Jesús; Hernández Palma, Hugo; Niebles Núẽz, William; Ruiz-Lazaro, Alex; Varela, Noel (Institute of Physics Publishing, 2020-01-07)

  This study describes a model of explanations in natural language for classification decision trees. The explanations include global aspects of the classifier and local aspects of the classification of a particular instance. The proposal is implemented in the ExpliClas open source Web service [1], which in its current version operates on trees built with Weka and data sets with numerical attributes. The feasibility of the proposal is illustrated with two example cases, where the detailed explanation of the respective classification trees is shown.

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  Neural Networks for Tea Leaf Classification

  Silva, Jesús; Hernández Palma, Hugo; Niebles Núẽz, William; Ruiz-Lazaro, Alex; Varela, Noel (Institute of Physics Publishing, 2020-01-07)

  The process of classification of the raw material, is one of the most important procedures in any tea dryer, being responsible for ensuring a good quality of the final product. Currently, this process in most tea processing companies is usually handled by an expert, who performs the work manually and at his own discretion, which has a number of associated drawbacks. In this work, a solution is proposed that includes the planting, design, development and testing of a prototype that is able to correctly classify photographs corresponding to samples of raw material arrived at a dryer, using intelligence techniques (IA) type supervised for Classification by Artificial Neural Networks and not supervised with K-means Grouping for class preparation. The prototype performed well and is a reliable tool for classifying the raw material slammed into tea dryers.

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  Análisis de las dificultades que presentan los estudiantes universitarios en matemática básica

  Soto Quiroz, Roger Iván; Yogui Takaesu, Daniel Noboru (Universidad Peruana Union, 2020-02-06)

  El presente estudio pretende analizar las diferentes dificultades—tanto académicas como actitudinales—que se les presenta a los estudiantes universitarios que llevan por segunda vez el curso de Matemática Básica. Esta es una investigación cualitativa, con diseño fenomenológico, con aplicación de la técnica de la entrevista no estructurada. El sustento teórico se basa en la comprensión lectora, competencias escolares, aprendizaje significativo, razonamiento cuantitativo y las actitudes hacia la matemática. La muestra la conformaron 10 estudiantes que llevan por segunda vez el curso de Matemática Básica: 03 son de la carrera de Diseño Profesional Gráfico, 03 de Diseño de interiores y 04 de Diseño y Gestión de Modas; 03 docentes que enseñan el curso y 05 tutores académicos que realizan actividades de tutoría académica y talleres de reforzamiento del curso. Los resultados permitieron afirmar que se descubrieron seis dificultades: 1) Dificultad en la comprensión lectora de los problemas matemáticos, 2) dificultad en comprender contenidos matemáticos básicos, 3) dificultad con la asistencia a clases, 4) dificultad en el interés y motivación por el curso, 5) dificultad en repasar los temas matemáticos, 6) dificultad en la asistencia a tutoría y talleres de reforzamiento.

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  "La literacidad para legislar”: Una ideología hegemónica reproducida por el diario peruano Correo

  Lovón Cueva, Marco Antonio (Universidad Catolica Silva Henriquez, 2020-05-25)

  Este estudio analiza la relación entre la ortografía de la excongresista Supa y las valoraciones expresadas por el diario Correo en torno a lo que se considera “la escritura” para legislar. Metodológicamente, se realizó un análisis ideológico del discurso periodístico. En el trabajo se identificaron dos representaciones acerca de la ortografía y la escritura en general. Considerando el modelo autónomo de la literacidad, se explica que Correo asume que la escritura crea y desarrolla habilidades cognitivas de nivel superior, y que conduce al progreso económico, por lo que cualquier persona no puede ser parlamentaria. Por otro lado, desde los nuevos estudios de la literacidad, se concluye que estas asunciones resultan ser creencias insostenibles, e incluso generadoras de racismo, porque la literacidad trasciende los aspectos técnicos y raciales.

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  Identification of Patterns of Fatal Injuries in Humans through Big Data

  Silva, Jesus; Romero, Ligia; Pineda, Omar Bonerge; Herazo-Beltran, Yaneth; Zilberman, Jack (Elsevier BV, 2020)

  External cause injuries are defined as intentionally or unintentionally harm or injury to a person, which may be caused by trauma, poisoning, assault, accidents, etc., being fatal (fatal injury) or not leading to death (non-fatal injury). External injuries have been considered a global health problem for two decades. This work aims to determine criminal patterns using data mining techniques to a sample of patients from Mumbai city in India.

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• A framework of quality control matrix in paprika chain value: An empirical investigation in Peru

  Garcia-Montero, D. (Springer, 2020-01-01)

  At present, in the agricultural industry, continuous improvement in products or crops and quality in processes is a challenging task. Therefore, several quality techniques were adopted in this industry and advantages were obtained. The main purpose of this document is to present the advantages offered by the use of quality control and the quality control matrix in order to reduce non-conformities in the production process. The similarities and differences between traditional quality control and modern control using QAM will be presented.

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• Replenishment system using inventory models with continuous review and quantitative forecasting to reduce stock-outs in a commercial company

  Malca-Ramirez, C. (Springer, 2020-01-01)

  In recent years, stock-outs significantly affects commercial enterprises with deficit equivalent to a large percentage of the total sales, due to its impact on current and future demand, regardless of their financial success. Carried out a qualitative and quantitative analysis of the current situation to identify the problem. In addition, the root causes were identified with the help of quality tools such as Pareto chart and Ishikawa and a questionnaire based on the SCOR model and management indicators. In this way why it is proposed a replenishment system applying models of inventories with continuous review and quantitative forecasts. The proposal consists of five phases: (i) identify the key products; (ii) Do quantitative forecasting; (iii) Determine inventory levels. (iv) Establish the new flow of information; (v) Establish new policies and procedures. We evaluate the models of inventories and forecasts through the program arrobaRisk.

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  Metabolomic markers of antepartum depression and suicidal ideation

  Mitro, S.D. (Elsevier B.V., 2020-02-01)

  Background: Recent analyses have described metabolomic markers for depression and suicidal ideation in non-pregnant adults. We examined the metabolomic profile of antepartum depression and suicidal ideation during mid-pregnancy, a time of high susceptibility to mood disorders. Methods: We collected fasting blood from 100 pregnant Peruvian women and profiled 307 plasma metabolites using liquid chromatography-mass spectrometry. We used the Patient Health Questionnaire 9 to define antepartum depression (score ≥ 10) and suicidal ideation (having thoughts that you would be better off dead, or of hurting yourself). Logistic regression was used to calculate odds ratios (ORs). Results: Three triacylglycerol metabolites (C48:5 triacylglycerol [OR = =1.89; 95% confidence interval (CI): 1.14–3.14], C50:6 triacylglycerol [OR = =1.88; 95%CI: 1.13–3.14], C46:4 triacylglycerol [OR = =1.89; 95%CI: 1.11–3.21]) were associated with higher odds of antepartum depression and 4 metabolites (betaine [OR = =0.56; 95%CI:0.33–0.95], citrulline [OR = =0.58; 95%CI: 0.34–0.98], C5 carnitine [OR = =0.59; 95%CI: 0.36–0.99], C5:1 carnitine [OR = =0.59; 95%CI: 0.35–1.00]) with lower odds of antepartum depression. Twenty-six metabolites, including 5-hydroxytryptophan (OR = =0.52; 95%CI: 0.30–0.92), phenylalanine (OR = =0.41; 95%CI: 0.19–0.91), and betaine (OR = =0.53; 95%CI: 0.28–0.99) were associated with lower odds of suicidal ideation. Limitations: Our cross-sectional study could not determine whether metabolites prospectively predict outcomes. No metabolites remained significant after multiple testing correction; these novel findings should be replicated in a larger sample. Conclusions: Antepartum suicidal ideation metabolomic markers are similar to markers of depression among non-pregnant adults, and distinct from markers of antepartum depression. Findings suggest that mood disorder in pregnancy shares metabolomic similarities to mood disorder at other times and may further understanding of these conditions’ pathophysiology.

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  Analyzing investments in flood protection structures: A real options approach

  Gomez-Cunya, L. (Elsevier Ltd, 2020-02-01)

  The soaring number of natural hazards in recent years due largely to climate change has resulted in an even higher level of investment in flood protection structures. However, such investments tend to be made in the aftermath of disasters. Very little is known about the proactive planning of flood protection investments that account for uncertainties associated with flooding events. Understanding the uncertainties such as “when” to invest on these structures to achieve the most optimal cost-saving amount is outmost important. This study fills this large knowledge gap by developing an investment decision-making assessment framework that determines an optimal timing of flood protection investment options. It combines real options with a net present value analysis to examine managerial flexibility in various investment timing options. Historical data that contain information about river water discharges were leveraged as a random variable in the modeling framework because it may help investors better understand the probability of extreme events, and particularly, flooding uncertainties. A lattice model was then used to investigate potential alternatives of investment timing and to evaluate the benefits of delaying investments in each case. The efficacy of the proposed framework was demonstrated by an illustrative example of flood protection investment. The framework will be used to help better inform decision makers.

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• Missing asthma patients in epidemiologic survey?

  Rommel Diaz, F. (Taylor and Francis Ltd., 2020-02-01)

  Cartas al editor.

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• High-Risk, but Hidden: Binge Drinking among Men Who Have Sex with Men and Transgender Women in Lima, Peru, 2012-2014

  Passaro, R.C. (Taylor and Francis Ltd., 2020-02-03)

  Background: Binge drinking (BD) is common in Peru, but may not be routinely detected by standard assessments of hazardous drinking. Objectives: We describe prevalence and risk behaviors of men who have sex with men (MSM) and transgender women (TW) in Peru who met criteria for BD as compared with those who met criteria for hazardous drinking. Methods: In a cross-sectional sample of MSM and TW from Lima (2012-2014), we calculated prevalence of BD (consuming ≥6 alcoholic drinks per occasion by AUDIT-3 criteria), conducted bivariate analyses of associations of BD with demographic and behavioral characteristics, and compared prevalence and behaviors of BD to those of hazardous drinkers (identified by AUDIT-10 criteria). Results: Of 1,520 MSM (n = 1,384) and TW (n = 137) with median age 27 years, 74.4% of MSM and 86.9% of TW met criteria for BD. Among MSM, BD was associated with a greater likelihood of using alcohol (41.6% vs. 13.8%; p <.01) or drugs (7.8% vs. 2.8%; p <.01) prior to a recent sexual contact. Among TW, BD was associated with greater frequency of alcohol use (44.9% vs. 11.1%; p <.01) or unprotected anal intercourse (58.8% vs. 33.3%; p =.04) during ≥1 of their three most recent sexual contacts. There was a higher prevalence of BD (75.5%) than hazardous drinking (53.2%) in our sample, with binge drinkers exhibiting similar sexual risk behaviors to hazardous drinkers. Conclusions: Binge drinking is common among MSM and TW in Lima, associated with risky sexual behavior, and may not be adequately captured by AUDIT-10 criteria.

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  Evaluation of the Evacuation of Essential Buildings: Interaction of Structural and Human Behaviour through Nonlinear Time-History Analysis and Agent-Based Modelling

  Delgado, M. (Institute of Physics Publishing, 2020-02-07)

  In this article, a performance assessment of the evacuation system is established for educational buildings. Structural and geotechnical information of the building is collected and introduced into a database. A similar procedure was realized for the information related to the occupants. Using this information, a) the structural fragility and localized collapse were determined and b) the interaction of the person with the partial collapse was established. For the first aspect, nonlinear time history was used, and for the second, the agent-based modeling was applied to recreate the reaction of people that face the micro collapse. Therefore, the important results of this evaluation are: 1) To localize collapsed beans and columns that make inoperable evacuation routes, 2) to localize bottleneck areas that people concentration during evacuation, and 3) quantification of affected people, in terms of persons caught up in the building that cannot evacuate.

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  Mechanical Splices for Seismic Retrofitting of Concrete Structures

  Huaco, G. (Institute of Physics Publishing, 2020-02-07)

  As an alternative to lap splicing, mechanical splices can be used for retrofit purposes. They are generally most economical than traditional lap splices when available spacing or length makes laps difficult to utilize. Mechanical splices are frequently used in new construction. However, their use is limited and not practical for use in retrofitted structures. However, if the bars to be joined do not need to be threaded in order to be connected with a special mechanical splice, such mechanical splices can be useful. It is presented a proposal of using two types of mechanical splices for retrofit purposes. Cycle Tension and cycle tension-compression tests are presented and discussed. It was found that mechanical splices are suitable and have acceptable response under seismic loads.

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