Monitoreo e Instalación visual de señales a un motor eléctrico de inducción de instalación trifásica de forma jaula de ardilla, mediante la implementación de tecnología industria 4.0

Luis Ricardo Uribe Dávila (2023, [Tesis de maestría])

Vivimos la industria 4.0, misma que no es nueva, ya que sus orígenes se remontan a finales de la década de los 2000, en Alemania. Un pilar de la industria 4.0 es el análisis de datos, conocido como Big Data. El conocer los datos de un proceso, de un estudio, ayuda en gran medida a predecir el comportamiento que tendrá el proceso o la máquina a estudiar en un periodo a corto o mediano plazo. En el presente proyecto se analizan los datos arrojados por un motor eléctrico de corriente alterna, del tipo inducción, jaula de ardilla. El motor está diseñado para trabajar de manera continua, sin embargo, el uso que se le da, es meramente educativo; es decir, no sobre pasa las 15 horas por semana de uso. Mediante la toma de datos de las tres fases de corriente RMS o corriente de valor eficaz que posee el motor eléctrico que se realizará con el microcontrolador Arduino UNO, se analizarán los mismos mediante el software de cómputo numérico MATLAB, ordenando los datos, descartando valores que no aporten información relevante para lograr la predicción de datos. Por último, se llevará a conocer este proyecto a la carrera mecatrónica, área sistemas de manufactura flexible y área automatización, con el fin de que puedan observar de una mejor manera la aplicación y funcionamiento de uno de los pilares de la actual industria 4.0.

We live in industry 4.0, which is not new, since its origins date back to the late 2000s, in Germany. One pillar of industry 4.0 is data analysis, known as Big Data. Knowing the data of a process, of a study, helps greatly to predict the behavior that the process or machine will have to study in a short- or medium-term period. This project analyzes the data released by an electric motor of alternating current, of the type induction, squirrel cage. The engine is designed to work continuously, however, the use given to it is merely educational, that is; only not over spends 15 hours per week of use. By taking data from the three phases of RMS current or effective value current of the electric motor that will be made with the Arduino UNO micro controller, they will be analyzed using MATLAB numerical computing software, ordering the data, discarding values that do not provide relevant information to achieve data prediction. Finally, this project will be presented to the mechatronics career, flexible manufacturing systems area and automation area, so that they can observe in a better way the application and operation of one of the pillars of the current industry 4.0.

Mantenimiento predictivo Regresión lineal Industria 4.0 Big data Corriente RMS Predictive maintenance Linear regression Industry 4.0 Big data RMS Current INGENIERÍA Y TECNOLOGÍA CIENCIAS TECNOLÓGICAS OTRAS ESPECIALIDADES TECNOLÓGICAS OTRAS OTRAS

Natural disasters and economic growth: a synthesis of empirical evidence

Fernando Antonio Ignacio González (2023, [Artículo, Artículo])

Natural disasters pose a serious threat globally and, in the future, their frequency and severity are expected to increase due to climate change. Empirical evidence has reported conflicting results in terms of the impact of disasters on economic growth. In this context, the present work seeks to synthesize the recent empirical evidence related to this topic. More than 650 estimates, from studies published in the last five years (2015-2020), are used. Meta-analysis and meta-regression techniques are employed. The review includes three sources (Scopus, Science Direct, and Google Scholar). The results identified the existence of a negative and significant combined effect (-0.015). Developing countries are especially vulnerable to disasters. The negative impact is greater for disasters that occurred in the last decade -in relation to previous disasters-. These findings constitute a call for attention in favor of mitigation and adaptation policies.

Disasters GDP meta-analysis meta-regression desastres crecimiento PIB meta-análisis meta-regresión CIENCIAS SOCIALES CIENCIAS SOCIALES

Algorithmic differentiation of linear mixed models with variance-covariance structures

Fernando Henrique Toledo Jose Crossa Juan Burgueño Keith Gardner Rosa Angela Pacheco Gil (2023, [Objeto de congreso])

CIENCIAS AGROPECUARIAS Y BIOTECNOLOGÍA MATHEMATICAL MODELS ALGORITHMS LINEAR MODELS

Impact of adoption of heat-stress tolerant maize hybrid on yield and profitability: evidence from Terai region of Nepal

Atul Kulkarni Keshab Babu Koirala Pervez Zaidi (2023, [Artículo])

Inverse Probability Weighted Regression Heat Tolerant Maize Hybrid Partial Budget CIENCIAS AGROPECUARIAS Y BIOTECNOLOGÍA HEAT STRESS HEAT TOLERANCE MAIZE HYBRIDS BUDGETS YIELDS

Unsupervised segmentation and clustering time series approach to Southern Africa rainfall regime changes

Lovemore  Chipindu Walter Mupangwa Isaiah Nyagumbo Mainassara Zaman-Allah (2023, [Artículo])

Autoregressive Integrated Moving Average Facebook Prophet Hidden Markov Model Regression Regression with Hidden Logistic Process CIENCIAS AGROPECUARIAS Y BIOTECNOLOGÍA COASTAL AREAS SEMIARID ZONES SUBHUMID ZONES RAINFALL CLIMATE CHANGE

Big data, small explanatory and predictive power: Lessons from random forest modeling of on-farm yield variability and implications for data-driven agronomy

Martin van Ittersum (2023, [Artículo])

Context: Collection and analysis of large volumes of on-farm production data are widely seen as key to understanding yield variability among farmers and improving resource-use efficiency. Objective: The aim of this study was to assess the performance of statistical and machine learning methods to explain and predict crop yield across thousands of farmers’ fields in contrasting farming systems worldwide. Methods: A large database of 10,940 field-year combinations from three countries in different stages of agricultural intensification was analyzed. Random effects models were used to partition crop yield variability and random forest models were used to explain and predict crop yield within a cross-validation scheme with data re-sampling over space and time. Results: Yield variability in relative terms was smallest for wheat and barley in the Netherlands and for wheat in Ethiopia, intermediate for rice in the Philippines, and greatest for maize in Ethiopia. Random forest models comprising a total of 87 variables explained a maximum of 65 % of cereal yield variability in the Netherlands and less than 45 % of cereal yield variability in Ethiopia and in the Philippines. Crop management related variables were important to explain and predict cereal yields in Ethiopia, while predictive (i.e., known before the growing season) climatic variables and explanatory (i.e., known during or after the growing season) climatic variables were most important to explain and predict cereal yield variability in the Philippines and in the Netherlands, respectively. Finally, model cross-validation for regions or years not seen during model training reduced the R2 considerably for most crop x country combinations, while for wheat in the Netherlands this was model dependent. Conclusion: Big data from farmers’ fields is useful to explain on-farm yield variability to some extent, but not to predict it across time and space. Significance: The results call for moderate expectations towards big data and machine learning in agronomic studies, particularly for smallholder farms in the tropics where model performance was poorest independently of the variables considered and the cross-validation scheme used.

Model Accuracy Model Precision Linear Mixed Models CIENCIAS AGROPECUARIAS Y BIOTECNOLOGÍA MACHINE LEARNING SUSTAINABLE INTENSIFICATION BIG DATA YIELDS MODELS AGRONOMY

Point placement of late vegetative stage nitrogen splits increase the productivity, N-use efficiency and profitability of tropical maize under decade long conservation agriculture

Hari Sankar Nayak C.M. Parihar Shankar Lal Jat ML JAT Ahmed Abdallah (2022, [Artículo])

Non-Linear Growth Model Nitrogen Remobilization Right Placement Precision Nitrogen Management CIENCIAS AGROPECUARIAS Y BIOTECNOLOGÍA GROWTH MODELS NITROGEN NUTRIENT MANAGEMENT

Response of African sorghum genotypes for drought tolerance under variable environments

Hussein Shimelis Baloua Nébié (2023, [Artículo])

Additive Main Effect and Multiplicative Interaction Best Linear Unbiased Estimates Drought Tolerance Indices CIENCIAS AGROPECUARIAS Y BIOTECNOLOGÍA ABIOTIC STRESS DROUGHT TOLERANCE SORGHUM GENOTYPES

Complex traits and candidate genes: estimation of genetic variance components across multiple genetic architectures

GIOVANNY COVARRUBIAS-PAZARAN Hans-Peter Piepho (2023, [Artículo])

Average Semivariance Linear Mixed Model Variance Component Estimation Polygenic Inheritance Oligogenic Inheritance Mendelian Inheritance CIENCIAS AGROPECUARIAS Y BIOTECNOLOGÍA MENDELISM GENETIC VARIANCE GENOME-WIDE ASSOCIATION STUDIES PHENOTYPES CHROMOSOME MAPPING

Enviromic prediction enables the characterization and mapping of Eucalyptus globulus Labill breeding zones

Germano Costa Neto Jose Crossa (2024, [Artículo])

Forest Tree Breeding Genomic Relationship Matrix Genomic Selection Best Linear Unbiased Prediction CIENCIAS AGROPECUARIAS Y BIOTECNOLOGÍA FOREST TREES BREEDING MARKER-ASSISTED SELECTION MYRTACEAE EUCALYPTUS GLOBULUS