The incorporation of high precision vehicle positioning systems has been demanded by the autonomous electric vehicle (AEV) industry. For this reason, research on visual odometry (VO) and Artificial ...Intelligence (AI) to reduce positioning errors automatically has become essential in this field. In this work, a new method to reduce the error in the absolute location of AEV using fuzzy logic (FL) is presented. The cooperative data fusion of GPS, odometer, and stereo camera signals is then performed to improve the estimation of AEV localization. Although the most important challenge of this work focuses on the reduction in the odometry error in the vehicle, the defiance of synchrony and the information fusion of sources of different nature is solved. This research is integrated by three phases: data acquisition, data fusion, and statistical evaluation. The first one is data acquisition by using an odometer, a GPS, and a ZED camera in AVE’s trajectories. The second one is the data analysis and fuzzy fusion design using the MatLab 2019® fuzzy logic toolbox. The last is the statistical evaluation of the positioning error of the different sensors. According to the obtained results, the proposed model with the lowest error is that which uses all sensors as input (stereo camera, odometer, and GPS). It can be highlighted that the best proposed model manages to reduce the positioning mean absolute error (MAE) up to 25% with respect to the state of the art.
We report a single atom Rh1/CeO2 catalyst prepared by the high temperature (800 °C) atom trapping (AT) method which is stable under both oxidative and reductive conditions. Infrared spectroscopic and ...electron microscopy characterization revealed the presence of exclusively ionic Rh species. These ionic Rh species are stable even under reducing conditions (CO at 300 °C) due to the strong interaction between Rh and CeO2 achieved by the AT method, leading to high and reproducible CO oxidation activity regardless of whether the catalyst is reduced or oxidized. In contrast, ionic Rh species in catalysts synthesized by a conventional impregnation approach (e. g., calcined at 350 °C) can be readily reduced to form Rh nanoclusters/nanoparticles, which are easily oxidized under oxidative conditions, leading to loss of catalytic performance. The single atom Rh1/CeO2 catalysts synthesized by the AT method do not exhibit changes during redox cycling hence are promising catalysts for emission control where redox cycling is encountered, and severe oxidation (fuel cut) leads to loss of performance.
Active and Stable Catalysts for CO Oxidation: Rh1/CeO2 prepared by high temperature calcination (800 °C) atom trapping (AT) method is stable for CO Oxidation regardless being subjected to Oxidative/Reductive treatments relevant for emission control. Infrared spectroscopy, Electron Microscopy and Temperature‐Programmed Reactions revealed stronger interaction between Rh and CeO2 in AT catalyst than in conventionally synthesized Rh/CeO2 (Calcined at 350 °C).
Diabetes incidence has been a problem, because according with the World Health Organization and the International Diabetes Federation, the number of people with this disease is increasing very fast ...all over the world. Diabetic treatment is important to prevent the development of several complications, also lipid profile monitoring is important. For that reason the aim of this work is the implementation of machine learning algorithms that are able to classify cases, that corresponds to patients diagnosed with diabetes that have diabetes treatment, and controls that refers to subjects who do not have diabetes treatment but some of them have diabetes, bases on lipids profile levels. Logistic regression, K-nearest neighbor, decision trees and random forest were implemented, all of them were evaluated with accuracy, sensitivity, specificity and AUC-ROC curve metrics. Artificial neural network obtain an acurracy of 0.685 and an AUC value of 0.750, logistic regression achieve an accuracy of 0.729 and an AUC value of 0.795, K-nearest neighbor gets an accuracy of 0.669 and an AUC value of 0.709, on the other hand, decision tree reached an accuracy pg 0.691 and a AUC value of 0.683, finally random forest achieve an accuracy of 0.704 and an AUC curve of 0.776. The performance of all models was statistically significant, but the best performance model for this problem corresponds to logistic regression.
The coronavirus disease (COVID-19) is an infectious disease caused by the SARS-CoV-2 virus and is responsible for nearly 6 million deaths worldwide in the past 2 years. Machine learning (ML) models ...could help physicians in identifying high-risk individuals.
To study the use of ML models for COVID-19 prediction outcomes using clinical data and a combination of clinical and metabolic data, measured in a metabolomics facility from a public university.
A total of 154 patients were included in the study. "Basic profile" was considered with clinical and demographic variables (33 variables), whereas in the "extended profile," metabolomic and immunological variables were also considered (156 characteristics). A selection of features was carried out for each of the profiles with a genetic algorithm (GA) and random forest models were trained and tested to predict each of the stages of COVID-19.
The model based on extended profile was more useful in early stages of the disease. Models based on clinical data were preferred for predicting severe and critical illness and death. ML detected trimethylamine N-oxide, lipid mediators, and neutrophil/lymphocyte ratio as important variables.
ML and GAs provided adequate models to predict COVID-19 outcomes in patients with different severity grades.
Diabetes is a chronic and noncommunicable but preventable disease that is affecting the Mexican population at worrying levels, being the first place in prevalence worldwide. Early diabetes detection ...has become important to prevent other health conditions that involve low organ yield until the patient death. Based on this problem, this work proposes the architecture of an Artificial Neural Network (ANN) for the automated classification of healthy patients from diabetics patients. The analysis was performed used a set of 19 para-clinical features to determine the health status of the patients. The developed model was evaluated through a statistical analysis based on the calculation of the loss function, accuracy, area under the curve (AUC) and receiving operating characteristics (ROC) curve. The results obtained present statistically significant values, with accuracy of 0.94 and AUC values of 0.98. Based on these results, it is possible to conclude that the ANN implemented in this work can classify patients with presence of diabetes from controls with significant accuracy, presenting preliminary results for the development of a diagnostic tool that can be supportive for health specialists.
Los criterios diagnósticos, los tratamientos en el momento de la admisión y los fármacos utilizados en pacientes con síndrome coronario agudo están bien definidos en innumerables guías. Sin embargo, ...existe incertidumbre acerca de las medidas para recomendar durante la planificación del egreso de los pacientes. Este documento reúne las evidencias más recientes y el tratamiento estandarizado y óptimo para los pacientes al momento del egreso de una hospitalización por un síndrome coronario agudo, para un cuidado integral y seguro en la transición del paciente entre la atención del evento agudo y el cuidado ambulatorio, con el objetivo de optimizar la recuperación de miocardio viable, garantizar la prevención secundaria más adecuada, reducir el riesgo de un nuevo evento coronario y la mortalidad, así como la adecuada reinserción de los pacientes en la vida cotidiana.
Abstract
We report a single atom Rh
1
/CeO
2
catalyst prepared by the high temperature (800 °C) atom trapping (AT) method which is stable under both oxidative and reductive conditions. Infrared ...spectroscopic and electron microscopy characterization revealed the presence of exclusively ionic Rh species. These ionic Rh species are stable even under reducing conditions (CO at 300 °C) due to the strong interaction between Rh and CeO
2
achieved by the AT method, leading to high and reproducible CO oxidation activity regardless of whether the catalyst is reduced or oxidized. In contrast, ionic Rh species in catalysts synthesized by a conventional impregnation approach (e. g., calcined at 350 °C) can be readily reduced to form Rh nanoclusters/nanoparticles, which are easily oxidized under oxidative conditions, leading to loss of catalytic performance. The single atom Rh
1
/CeO
2
catalysts synthesized by the AT method do not exhibit changes during redox cycling hence are promising catalysts for emission control where redox cycling is encountered, and severe oxidation (fuel cut) leads to loss of performance.
Resumen Los vinos especiales tipo “Fino” se caracterizan por largos periodos de crianza biológica durante los cuales se desarrolla una bio-película denominada “velo de flor” formada principalmente ...por cepas de Saccharomyces cerevisiae y otras levaduras no- Saccharomyces . La legislación establece para estos vinos un contenido en etanol del 15- 17 % v/v y sin embargo, el mercado actual demanda vinos con menor contenido. Esta comunicación presenta los resultados de análisis realizados en velos y vinos de barriles de la solera con 14 % v/v en alcohol, seleccionados en una bodega de la DOP Montilla-Moriles y en otra de la DOP Jerez. El estudio microbiológico de vinos de la solera con diferente contenido alcohólico a los 10 meses de crianza no presentó grandes diferencias entre ellos, siendo Torulaspora delbrueckii , la especie aislada con mayor frecuencia entre las no- Saccharomyces . Se observaron cambios en el contenido de ciertos alcoholes superiores, compuestos carbonílicos, ésteres etílicos y polioles en las dos bodegas. La evaluación sensorial realizada por los expertos catadores de ambos Consejos reguladores solo mostró diferencias significativas entre las muestras de vinos con el contenido habitual y los vinos con menor alcohol en la limpidez en los vinos de Jerez y en el carácter frutal en vinos de Montilla-Moriles.
The main characteristic of the production of special “Fino” type wines is the long biological aging period, under a biofilm called “flor velum” developed in their surface, which is formed by Saccharomyces cerevisiae strains and other non- Saccharomyces yeasts. The current legislation establishes an ethanol content about 15-17% v/v for these wines. Nevertheless, current market demands wines with a lower ethanol content. This communication shows the results obtained for the velum and wine samples from selected solera barrels with near to 14% v/v ethanol contents from one winery in the PDO Montilla-Moriles and another in PDO Jerez. The microbiological study of solera wines with different alcoholic contents at 10 months of aging does not present great differences between them, being Torulaspora delbrueckii the most frequently isolated species from no- Saccharomyces . Changes in the content of certain higher alcohols, carbonyl compounds, ethyl esters and polyols were observed in the two wineries. The sensory evaluation made by the expert judges from both Regulatory Councils only showed significant differences between the wine samples with the usual content and the wines with less alcohol in the clarity visual attribute in Jerez winery and in fruit attribute only in wines from Montilla-Moriles winery.
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