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Published By Editorial Pontificia Universidad Javeriana

2011-2769, 0123-2126

2021 ◽  
Vol 26 ◽  
Author(s):  
Diego Mendez-Chaves ◽  
Manuel Perez ◽  
Alejandro Farfan ◽  
Eduardo Gerlein

In order to properly monitor the health status of the hydrological resources of a region, in terms of water contamination, a scalable and low-cost system is necessary to map the water quality at different locations and allow the prioritization of more sophisticated and expensive monitoring campaigns on those areas where a suspicious behavior seems to be occurring. This paper presents the design and implementation process of such an IoT-based solution for low-cost and scalable water quality monitoring applications. To achieve that end, we propose the utilization of a low-cost inter-digital capacitance (IDC) sensor to characterize the conductivity of the water, a very telling parameter about the level of pollution in the water. Additionally, an embedded method to measure such sensor was designed and implemented, which considers the requirements of a portable platform: low computational capabilities, small memory and low power consumption. Our results show that an IDC sensor is capable of detecting the changes of the capacitance of the sample, and therefore mapping the changes in the conductivity of the water. Additionally, integrating an embedded measuring method is a valid option for in-situ characterization of water samples and the complete solution enables a new paradigm for water quality monitoring in large scale scenarios.


2021 ◽  
Vol 26 ◽  
Author(s):  
Eduard Alexander Gañán-Cárdenas ◽  
Jorge Isaac Pemberthy-Ruiz ◽  
Juan Carlos Rivera-Agudelo ◽  
Maria Clara Mendoza- Arango

Objective: The objective of this work is to build a prediction model for Operating Room Time (ORT) to be used in an intelligent scheduling system. This prediction is a complex exercise due to its high variability and multiple influential variables. Materials and methods: We assessed a new strategy using Latent Class Analysis (LCA) and clustering methods to identify subgroups of procedures and surgeries that are combined with prediction models to improve ORT estimates. Three tree-based models are assessed, Classification and Regression Trees (CART), Conditional Random Forest (CFOREST) and Gradient Boosting Machine (GBM), under two scenarios: (i) basic dataset of predictors and (ii) complete dataset with binary procedures. To evaluate the model, we use a test dataset and a training dataset to tune parameters. Results and discussion: The best results are obtained with GBM model using the complete dataset and the grouping variables, with an operational accuracy of 57.3% in the test set. Conclusion: The results indicate the GBM model outperforms other models and it improves with the inclusion of the procedures as binary variables and the addition of the grouping variables obtained with LCA and hierarchical clustering that perform the identification of homogeneous groups of procedures and surgeries.


2021 ◽  
Vol 26 ◽  
Author(s):  
Maria Camila Zapata Zúñiga ◽  
Miguel Angel Parra-Pérez ◽  
Johan Alexander Álvarez-Berrio ◽  
Nidia Isabel Molina-Gómez

This study aimed to evaluate the efficiency of technologies for removing antibiotics, antibiotic-resistant bacteria and their antibiotic resistance genes, and the countries where they have been developed. For this purpose, was conducted a systematic review to identify the tertiary treatments to remove the above-mentioned pollutants. The ScienceDirect and Scopus databases were used as sources of information, taking into account only experimental research from 2006 to 2019 and technologies with removal rates higher than 70% to the information analyses. From the analysis of 9 technologies evaluated, in a set of 47 investigations, photo-Fenton, and electrochemical treatments were found to be the most efficient in the removal of antibiotics; gamma radiation and photocatalysis with TiO2 and UV revealed better results in the removal of resistant microbial agents and their resistance genes, with efficiencies of 99.9%. As one of the largest producers and consumers of antibiotics, China appears to be the country with the most scientific research on the area. The importance of innovation in wastewater treatment processes to achieve better results in the remotion of antibiotics, antibiotic-resistant bacteria, and their resistance genes is highlighted, given the effects on the aquatic ecosystems and public health.


2021 ◽  
Vol 26 ◽  
Author(s):  
Laura Moreno-Escandón ◽  
Daniela Perea-Soto ◽  
Jonathan Soto-Paz ◽  
Patricia Torres-Lozada ◽  
Luis Fernando Marmolejo-Rebellón

Objetivo: Evaluar cómo la modificación de cachaza (CA) como material de enmienda, afectación de la eficiencia del proceso y la calidad del producto final del compostaje de biorresiduos de origen municipal (BOM). Materiales y métodos: En este estudio se evaluó una escala piloto y en términos de estabilidad, madurez y valor agrícola, el efecto de la modificación de CA sobre el compuesto de BOM en cuatro proporciones de mezcla BOM: CA (100: 00 - control; 90:10; 80:20 y 70:30). Resultados: Los resultados que afectan en general, la modificación de CA alcanzan temperaturas termofílicas en menor tiempo que el tratamiento control y alcanzan mejores condiciones de estabilidad (consumo de oxígeno <1,0 mgO 2/ gSVh), Índice de Germinación (IG> 80%: indicador de un producto maduro) e Índice de calidad de compost (IF> 3,5: indicador de alto potencial de fertilización de suelos), siendo la proporción 80:20 la mejor calidad del producto (mayor contenido de N total: 2,32%, PT: 1,42%, CIC: 65,5 meq / 100g), menor valor de CE (0,38 dS / m) y de coliformes totales y fecales (15,3 y 4,0 NMP / g respectivamente), cumpliendo con normas técnicas como la colombiana y la chilena para productos orgánicos utilizados como fertilizantes y acondicionadores del suelo, además de los mejores resultados de IG e IF (123,40% y 4,67 respectivamente).  


2021 ◽  
Vol 26 ◽  
Author(s):  
Sepideh Abolghasem ◽  
Nicolás Mancilla-Cubides

Modern production process is accompanied with new challenges in reducing the environmental impacts related to machining processes. The turning process is a manufacturing process widely used with numerous applications for creating engineering components. Accordingly, many studies have been conducted in order to optimize the machining parameters and facilitate the decision-making process. This work aims to optimize the quality of the machined products (surface finish) and the productivity rate of the turning manufacturing process. To do so, we use Aluminum as the material test to perform the turning process with cutting speed, feed rate, depth of cut, and nose radius of the cutting tool as our design factors. Product quality is quantified using surface roughness (R_a) and the productivity rate based on material removal rate (MRR). We develop a predictive and optimization model by coupling Artificial Neural Networks (ANN) and the Particle Swarm Optimization (PSO) multi-function optimization technique, as an alternative to predict the model response (R_a) first and then search for the optimal value of turning parameters to minimize the surface roughness (R_a) and maximize the material removal rate (MRR). The results obtained by the proposed models indicate good match between the predicted and experimental values proving that the proposed ANN model is capable to predict the surface roughness accurately. The optimization model PSO has provided a Pareto Front for the optimal solution determining the best machining parameters for minimum R_a and maximum MRR. The results from this study offer application in the real industry where the selection of optimal machining parameters helps to manage two conflicting objectives, which eventually facilitate the decision-making process of machined products.


2021 ◽  
Vol 25 ◽  
Author(s):  
Ciro Alberto Amaya-Guio ◽  
Lina Patricia Navas ◽  
Cesar Humberto Torres-Gonzalez

Objective: Propose a methodology to determine the number of medical students who can rotate, for the practice of medicine, in a university hospital, so that the quality of training processes and in-patient care are assured. Materials and Methods: A three-step procedure is presented, in order to find the number of students that the institution can accept simultaneously. Results: The method is based on an integer linear model and it was implemented to assess installed capacity of General Surgery service at Hospital Universitario Clínica San Rafael, increasing in two students (33 %) the training capacity. Conclusions: The proposed methodology not only guaranties the quality of training processes and in-patient care, but also generates other intangible results such as having a more agile way of planning, reducing the planification time. The methodology is easily extended to other services within hospitals.


2021 ◽  
Vol 25 ◽  
Author(s):  
Horacio Sánchez ◽  
Wilmer Ponce ◽  
Beatriz Brito ◽  
William Viera ◽  
Ricardo Baquerizo ◽  
...  

Objective: To obtain biofilms from starch and cellulose present in the avocado (Persea americana) peel and seed. Materials and methods: The starch characterization included humidity, gelatinization temperature, paste clarity, absorption index, solubility index, swelling power, amylose, amylopectin, amount, and starch yield. Five mixtures were made with 3 g of starch, 5 mL of 30 % NaOH (w/v), 3 g of cellulose, and different proportions for glycerin: 2 g; 2.5 g; 3 g; 3.5 g; 4 g, and PVA: 2 g, 3 g, 4 g, 5 g, and 6 g. Films were formed on acrylic plates, using the casting method. The bioplastic was characterized in terms of moisture, solubility in water, density, thickness, biodegradability, stress, deformation, and modulus of elasticity. Results and discusión: The addition of cellulose to the mixture does not contribute to film formation, unlike PVA which did. The film had the best physical appearance with a mixture of 2 g of glycerin and 6 g of PVA. The bioplastic characterization was 23.43 % humidity, 39.39 % for water solubility, 1.52 g/cm3 density, 0.58 mm thickness, 21.03 % weight loss for the biodegradability test, 1.53 MPa for tension, 21.25 % deformation, and 10,04 MPa for the modulus of elasticity. Conclusions: The bioplastic obtained did not show the resistance of traditional plastic. However, the results obtained serve as a starting point for the realization of other formulations, aimed at producing a bioplastic capable of competing with its synthetic relatives.


2021 ◽  
Vol 25 ◽  
Author(s):  
Hernan Dario Bolaños ◽  
Francisco Botero

Objective: Identify and characterize subsynchronous hydrodynamics phenomena in a low specific speed centrifugal pump based on its four-quadrant characteristic curve. Materials: A 1.5 HP ITT Goulds pump instrumented with pressure transductors, an accelerometer, a torque sensor and a tachometer. Flow rate measurement was done with an ultrasonic transit time clamp-on flow meter. Methods: Time and frequency domain analysis with phase analysis were used to identify spectral components linked to hydrodynamic phenomena such as rotating stall and surge. Results and discussion: This work approaches an alternative method to calculate the phase angle using pressure signals without filtering. Related with hydrodynamic phenomena, the evidence collected suggests the presence of rotating stall in some operation points of the four-quadrant characteristic curve. Furthermore, in the third quadrant, rotating stall coexist with surge. Conclusions: The instrumentation and methods regarded in this work allow to collect evidence to identify in-phase and out of phase subsynchronous hydrodynamic phenomena. The classic cross-correlation-based method was improved to ease the diagnosis of subsynchronous phenomena by visual inspection. A new quantitative approach was introduced to detect subsynchronous phenomena, based on the Fourier analysis; it was validated with a case study for which the classical method was not suitable.


2021 ◽  
Vol 25 ◽  
Author(s):  
Luis Felipe Ariza-Vesga ◽  
Johan Sebastian Eslava-Garzon

The objective of this paper is to extend into the OpenAirInterface platform the Coordinated Scheduling (CS) technique to allocate resource blocks among User Equipment (UE) in a wisely way and to control the energy efficiency, the throughput, and the inter-cell interference for Cloud Radio Access Networks (C-RANs). It is achieved by modifying the OpenAirInterface scheduler code, increasing the Remote Radio Unit (RRU) scalability, and employing some component carriers of the Radio Cloud Center (RCC), each one them with one or more UEs. The hardware utilized is composed of general-purpose processors and fast Ethernet transport ports, and the software is recent frequency-domain methodologies in a software-only environment where the use of radio units are not required. However, the USRP B200 mini-i radio unit and the UE (Samsung Galaxy S8) were considered only for validation purposes. The emulations using frequency-domain methodologies, compatible with fourth and fifth-generation cellular systems, allowed real-time emulations and reduced 10-fold the multipath channel’s signal processing complexity compared to time-domain methodologies. The results show we can emulate a real-time static coordinated scheduling proof-of-concept for one C-RAN composed of one RCC, three RRUs, and three UEs. In the end, it is evaluated the reproducibility and the scalability of synthetic networks composed of one RRU and at least one UE, without using software-defined radio units, reducing prototyping uncertainties of the physical hardware and the total price of the experiment.


2021 ◽  
Vol 25 ◽  
Author(s):  
Andrea Carolina Pabón-Beltrán ◽  
Felipe Sanabria-Martínez ◽  
Custodio Vásquez-Quintero ◽  
José José Barba-Ortega ◽  
Ely Dannier Valbuena-Niño

In this research, the concentration-depth profiles reached by titanium and nitrogen particles, on the surface of AISI/SAE 1020 carbon steel substrates, by using of ion implantation technique, are studied. The ions are surface deposited by means of high voltage pulsed discharges and electric arc discharge under high vacuum conditions. The concentration and position distribution of the metallic and non-metallic species are obtained by simulation of the interaction of ions with the matter, stopping and ranges of ions in the matter, by the computer program transport of ions in matter. The implantation dose is calculated from the discharge data and the previously established study parameters in this work. From the simulation results, the depth profiles demonstrated that titanium and nitrogen ions may reach up to 300 Å and 600 Å and concentrations of 1.478 x 1016 ions⁄cm2 and 2.127 x 1016 ions⁄cm2, respectively. The formation of titanium microdroplets upon the surface of the substrates is identified from the micrographs obtained by the scanning electron microscopy technique; furthermore, the presence of titanium and nitrogen implanted on the surface of the substrate is verified through the elemental composition analysis by the energy dispersive spectroscopy, validating the effect of ion implantation on ferrous alloys.


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