Sistema de Información para Bienestar Laboral: Análisis y Diseño Software Administrativo para la Gestión de Mercados Virtuales

El presente trabajo está enfocado en investigar las mejores prácticas existentes actualmente de comercio electrónico y desarrollo de portales Web, para realizar las actividades de análisis y diseño de una aplicación que le permita a los empleados de las Empresas Públicas de Medellín y sus beneficiarios realizar la utilización del servicio de proveeduría de manera virtual

Sistema de Información para Bienestar Laboral: Análisis y Diseño Software Administrativo para la Gestión de Mercados Virtuales

El presente trabajo está enfocado en investigar las mejores prácticas existentes actualmente de comercio electrónico y desarrollo de portales Web, para realizar las actividades de análisis y diseño de una aplicación que le permita a los empleados de las Empresas Públicas de Medellín y sus beneficiarios realizar la utilización del servicio de proveeduría de manera virtual

The Research Journey as a Challenge Towards New Trends

The academic community of the department of Risaralda, in its permanent interest in evidencing the results of the research processes that are carried out from the Higher Education Institutions and as a product of the VI meeting of researchers of the department of Risaralda held in November 2021 presents its work: “The journey of research as a challenge towards new trends”, which reflects the result of the latest research and advances in different lines of knowledge in Agricultural Sciences, Health Sciences, Social Sciences and Technology and Information Sciences, which seek to solve and meet the demands of the different sectors. This work would not have been possible without the help of each of the teachers, researchers and authors who presented their articles that make up each of the chapters of the book, to them our gratitude for their commitment, dedication and commitment, since their sole purpose is to contribute from the academy and science to scientific and technological development in the search for the solution of problems and thus contribute to transform the reality of our society and communities. We also wish to extend our gratitude to the institutions of the Network that made this publication possible: UTP, UCP, UNAD, UNIREMINGTON; UNISARC, CIAF, Universidad Libre, Uniclaretiana, Fundación Universitaria Comfamiliar and UNIMINUTO, institutions that in one way or another allowed this work to become a reality, which we hope will be of interest to you.Preface............................................................................................................................7 Chapter 1. Technologies and Engineering Towards a humanization in Engineering using soft skills in training in Engineers.............................................................................................................11 Omar Iván Trejos Buriticá1, Luis Eduardo Muñoz Guerrero Innovative materials in construction: review from a bibliometric analysis....................................................................................................................27 Cristian Osorio Gómez, Daniel Aristizábal Torres, Alejandro Alzate Buitrago, Cristhian Camilo Amariles López Bibliometric review of disaster risk management: progress, trends, and challenges.........................................................................................................51 Alejandro Alzate Buitrago, Gloria Milena Molina Vinasco. Incidence of land coverage and geology, in the unstability of lands of the micro-basin of the Combia creek, Pereira, Risaralda....................................73 Alejandro Alzate Buitrago, Daniel Aristizábal Torres. Chapter 2. Arts, Humanities, and Social Sciences Training experience with teachers teaching mathematics using the inquiry methodology ...............................................................................................95 Vivian Libeth Uzuriaga López, Héctor Gerardo Sánchez Bedoya. Interpretation of the multiple representations of the fears associated to the boarding of limited visual patients in the elective I students’ written productions and low vision ...................................................................................113 Eliana Bermúdez Cardona, Ana María Agudelo Guevara, Caterine Villamarín Acosta. The relevance of local knowledge in social sciences............................................131 Alberto Antonio Berón Ospina, Isabel Cristina Castillo Quintero. Basic education students’ conceptions of conflict a view from the peace for the education....................................................................................................143 Astrid Milena Calderón Cárdenas,Carolina Aguirre Arias, Carolina Franco Ossa, Martha Cecilia Gutiérrez Giraldo, Orfa Buitrago. Comprehensive risk prevention in educational settings: an interdisciplinary and socio-educational approach ............................................................................163 Olga María Henao Trujillo, Claudia María López Ortiz. Chapter 3. Natural and Agricultural Sciences Physicochemical characterization of three substrates used in the deep bedding system in swine .......................................................................................175 Juan Manuel Sánchez Rubio, Andrés Felipe Arias Roldan, Jesús Arturo Rincón Sanz, Jaime Andrés Betancourt Vásquez. Periodic solutions in AFM models........................................................................187 Daniel Cortés Zapata, Alexander Gutiérrez Gutiérrez. Phenology in flower and fruit of Rubus glaucus benth. Cv. Thornless in Risaralda: elements for phytosanitary management .........................................199 Shirley Palacios Castro, Andrés Alfonso Patiño Martínez, James Montoya Lerma, Ricardo Flórez, Harry Josué Pérez. Socio-economic and technical characterization of the cultivation of avocado (Persea americana) in Risaralda..............................................................217 Andrés Alfonso Patiño Martínez, Kelly Saudith Castañez Poveda, Eliana Gómez Correa. Biosecurity management in backyard systems in Santa Rosa de Cabal, Risaralda................................................................................................................227 Julia Victoria Arredondo Botero, Jaiver Estiben Ocampo Jaramillo, Juan Sebastián Mera Vallejo, Álvaro de Jesús Aranzazu Hernández. CONTENTS Physical-chemical diagnosis of soils in hillside areas with predominance of Lulo CV. La Selva production system in the department of Risaralda.............241 Adriana Patricia Restrepo Gallón, María Paula Landinez Montes, Jimena Tobón López. Digestibility of three concentrates used in canine feeding....................................271 María Fernanda Mejía Silva, Valentina Noreña Sánchez, Gastón Adolfo Castaño Jiménez. Chapter 4. Economic, Administrative, and Accounting Sciences Financial inclusion in households from socioeconomic strata 1 and 2 in the city of Pereira ..................................................................................................285 Lindy Neth Perea Mosquera, Marlen Isabel Redondo Ramírez, Angélica Viviana Morales. Internal marketing strategies as a competitive advantage for the company Mobilautos SAS de Dosquebradas........................................................................303 Inés Montoya Sánchez, Sandra Patricia Viana Bolaños, Ana María Barrera Rodríguez. Uses of tourist marketing in the tourist sector of the municipality of Belén de Umbría, Risaralda.............................................................................................319 Ana María Barrera Rodríguez, Paola Andrea Echeverri Gutiérrez, María Camila Parra Buitrago, Paola Andrea Martín Muñoz, Angy Paola Ángel Vélez, Luisa Natalia Trejos Ospina. Territorial prospective of Risaralda department (Colombia), based on the SDGS...............................................................................................................333 Juan Guillermo Gil García, Samanta Londoño Velásquez. Chapter 5. Health and Sports Sciences Performance evaluation in times of pandemic. What do medical students think?.......................................................................................................353 Samuel Eduardo Trujillo Henao, Rodolfo A. Cabrales Vega, Germán Alberto Moreno Gómez. The relevance of the therapist’s self and self-reference in the training of psychologists.....................................................................................................371 Maria Paula Marmolejo Lozano, Mireya Ospina Botero. Habits related to oral health which influence lifestyle of elder people in a wellness center for the elderly in Pereira 2020. .............................................387 Isadora Blanco Pérez, Olga Patricia Ramírez Rodríguez, Ángela María Rincón Hurtado. Analysis of the suicide trend in the Coffee Region in Colombia during the years 2012-2018 ..............................................................................................405 Germán Alberto Moreno Gómez, Jennifer Nessim Salazar, Jairo Franco Londoño, Juan Carlos Medina Osorio. Hind limb long bone fractures in canines and felines...........................................419 María Camila Cruz Vélez, Valentina Herrera Morales, Alba Nydia Restrepo Jiménez, Lina Marcela Palomino, Gabriel Rodolfo Izquierdo Bravo. Prevalence of overweight and obesity in children in the rural and urban area of Risaralda....................................................................................................439 Angela María Álvarez López, Angela Liceth Pérez Rendón, Alejandro Gómez Rodas, Luis Enrique Isaza Velásquez. Chapter 6. Architecture, Design and Advertising The artisan crafts of Risaralda, characteristics, importance, and risks within the Colombian Coffee Cultural Landscape, CCCL....................................457 Yaffa Nahir Ivette Gómez Barrera, Javier Alfonso López Morales

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

Effects of hospital facilities on patient outcomes after cancer surgery: an international, prospective, observational study

Background Early death after cancer surgery is higher in low-income and middle-income countries (LMICs) compared with in high-income countries, yet the impact of facility characteristics on early postoperative outcomes is unknown. The aim of this study was to examine the association between hospital infrastructure, resource availability, and processes on early outcomes after cancer surgery worldwide.Methods A multimethods analysis was performed as part of the GlobalSurg 3 study-a multicentre, international, prospective cohort study of patients who had surgery for breast, colorectal, or gastric cancer. The primary outcomes were 30-day mortality and 30-day major complication rates. Potentially beneficial hospital facilities were identified by variable selection to select those associated with 30-day mortality. Adjusted outcomes were determined using generalised estimating equations to account for patient characteristics and country-income group, with population stratification by hospital.Findings Between April 1, 2018, and April 23, 2019, facility-level data were collected for 9685 patients across 238 hospitals in 66 countries (91 hospitals in 20 high-income countries; 57 hospitals in 19 upper-middle-income countries; and 90 hospitals in 27 low-income to lower-middle-income countries). The availability of five hospital facilities was inversely associated with mortality: ultrasound, CT scanner, critical care unit, opioid analgesia, and oncologist. After adjustment for case-mix and country income group, hospitals with three or fewer of these facilities (62 hospitals, 1294 patients) had higher mortality compared with those with four or five (adjusted odds ratio [OR] 3.85 [95% CI 2.58-5.75]; p<0.0001), with excess mortality predominantly explained by a limited capacity to rescue following the development of major complications (63.0% vs 82.7%; OR 0.35 [0.23-0.53]; p<0.0001). Across LMICs, improvements in hospital facilities would prevent one to three deaths for every 100 patients undergoing surgery for cancer.Interpretation Hospitals with higher levels of infrastructure and resources have better outcomes after cancer surgery, independent of country income. Without urgent strengthening of hospital infrastructure and resources, the reductions in cancer-associated mortality associated with improved access will not be realised

Pronóstico de accidentes de tráfico mediante el uso de técnicas de modelado predictivo

ilustraciones, diagramas, planosLos accidentes de tránsito son una gran preocupación a nivel mundial, ya que tienen un impacto significativo en la seguridad, la salud y el bienestar de las personas, por lo que constituyen un importante campo de investigación sobre el uso de técnicas y algoritmos de última generación para analizarlos y predecirlos. El estudio de los accidentes de tráfico se ha realizado a partir de la información publicada por las entidades de tráfico, pero gracias a la ubicuidad y disponibilidad de las redes sociales es posible disponer de información detallada y en tiempo real de los accidentes de tráfico, lo que permite realizar estudios detallados que incluyen eventos de accidentalidad vial no registrados. El objetivo de esta tesis es proponer un modelo predictivo para estimar la probabilidad de accidentes de tránsito en un área determinada mediante la integración de información proveniente de entidades oficiales y redes sociales relacionadas con accidentes viales y eventos de infraestructura vial. El modelo diseñado fue un modelo de aprendizaje profundo, compuesto por unidades recurrentes cerradas y redes neuronales convolucionales. Los resultados obtenidos se compararon con resultados publicados por otros investigadores y muestran resultados prometedores, lo que indica que, en el contexto del problema, el modelo de aprendizaje profundo propuesto supera a otros modelos de aprendizaje profundo disponibles en la literatura. La información proporcionada por el modelo puede ser valiosa para que las agencias de control de tráfico planifiquen actividades de prevención de accidentes de tráfico. (Texto tomado de la fuente)Traffic accidents are a major global concern as they have a significant impact on safety, health, and well-being. Therefore, it is an important area of research to analyze and predict accidents using state-of-the-art techniques and algorithms. Traditionally, the study of traffic accidents has been conducted using information from traffic entities and road police forces. However, with the rise of social media platforms, it's now possible to access detailed and real-time information about road accidents in a specific region, which allows for more comprehensive studies, even including unrecorded road accident events. This thesis aims to develop a predictive model that estimates the probability of road accidents in a specific area by combining information from official entities and social media related to road accidents and road infrastructure events. The proposed model is an ensemble deep learning model made up of Gated Recurrent Units and Convolutional Neural Networks. The results were compared with other published research and the outcomes are promising, indicating that the proposed ensemble deep learning model is more effective than other deep learning models reported in literature. The information provided by the model could be valuable for traffic control agencies to plan road accident prevention activities.DoctoradoPh.D. en Ingeniería – Sistemas y ComputaciónSistemas Inteligentes de Transporte IT

Extraction and Analysis of Social Networks Data to Detect Traffic Accidents

Traffic accident detection is an important strategy governments can use to implement policies intended to reduce accidents. They usually use techniques such as image processing, RFID devices, among others. Social network mining has emerged as a low-cost alternative. However, social networks come with several challenges such as informal language and misspellings. This paper proposes a method to extract traffic accident data from Twitter in Spanish. The method consists of four phases. The first phase establishes the data collection mechanisms. The second consists of vectorially representing the messages and classifying them as accidents or non-accidents. The third phase uses named entity recognition techniques to detect the location. In the fourth phase, locations pass through a geocoder that returns their geographic coordinates. This method was applied to Bogota city and the data on Twitter were compared with the official traffic information source; comparisons showed some influence of Twitter on the commercial and industrial area of the city. The results reveal how effective the information on accidents reported on Twitter can be. It should therefore be considered as a source of information that may complement existing detection methods

Extraction and Analysis of Social Networks Data to Detect Traffic Accidents

Traffic accident detection is an important strategy governments can use to implement policies intended to reduce accidents. They usually use techniques such as image processing, RFID devices, among others. Social network mining has emerged as a low-cost alternative. However, social networks come with several challenges such as informal language and misspellings. This paper proposes a method to extract traffic accident data from Twitter in Spanish. The method consists of four phases. The first phase establishes the data collection mechanisms. The second consists of vectorially representing the messages and classifying them as accidents or non-accidents. The third phase uses named entity recognition techniques to detect the location. In the fourth phase, locations pass through a geocoder that returns their geographic coordinates. This method was applied to Bogota city and the data on Twitter were compared with the official traffic information source; comparisons showed some influence of Twitter on the commercial and industrial area of the city. The results reveal how effective the information on accidents reported on Twitter can be. It should therefore be considered as a source of information that may complement existing detection methods

Deep Learning Ensemble Model for the Prediction of Traffic Accidents Using Social Media Data

Traffic accidents are a major concern worldwide, since they have a significant impact on people’s safety, health, and well-being, and thus, they constitute an important field of research on the use of state-of-the-art techniques and algorithms to analyze and predict them. The study of traffic accidents has been conducted using the information published by traffic entities and road police forces, but thanks to the ubiquity and availability of social media platforms, it is possible to have detailed and real-time information about road accidents in a given region, which allows for detailed studies that include unrecorded road accident events. The focus of this paper is to propose a model to predict traffic accidents using information gathered from social media and open data, applying an ensemble Deep Learning Model, composed of Gated Recurrent Units and Convolutional Neural Networks. The results obtained are compared with baseline algorithms and results published by other researchers. The results show promising outcomes, indicating that in the context of the problem, the proposed ensemble Deep Learning model outperforms the baseline algorithms and other Deep Learning models reported by literature. The information provided by the model can be valuable for traffic control agencies to plan road accident prevention activities

Temperature Prediction Using Multivariate Time Series Deep Learning in the Lining of an Electric Arc Furnace for Ferronickel Production

The analysis of data from sensors in structures subjected to extreme conditions such as the ones used in smelting processes is a great decision tool that allows knowing the behavior of the structure under different operational conditions. In this industry, the furnaces and the different elements are fully instrumented, including sensors to measure variables such as temperature, pressure, level, flow, power, electrode positions, among others. From the point of view of engineering and data analytics, this quantity of data presents an opportunity to understand the operation of the system under normal conditions or to explore new ways of operation by using information from models provided by using deep learning approaches. Although some approaches have been developed with application to this industry, it is still an open research area. As a contribution, this paper presents an applied deep learning temperature prediction model for a 75 MW electric arc furnace, which is used for ferronickel production. In general, the methodology proposed considers two steps: first, a data cleaning process to increase the quality of the data, eliminating both redundant information as well as atypical and unusual data, and second, a multivariate time series deep learning model to predict the temperatures in the furnace lining. The developed deep learning model is a sequential one based on GRU (gated recurrent unit) layer plus a dense layer. The GRU + Dense model achieved an average root mean square error (RMSE) of 1.19 °C in the test set of 16 different thermocouples radially distributed on the furnace