ANN and Fuzzy c-Means applied to environmental pollution prediction

Salamanca, situated in center of Mexico is among the cities which suffer most from the air pollution in Mexico. The vehicular park and the industry, as well as orography and climatic characteristics have propitiated the increment in pollutant concentration of Sulphur Dioxide (SO2). In this work, a Multilayer Perceptron Neural Network has been used to make the prediction of an hour ahead of pollutant concentration. A database used to train the Neural Network corresponds to historical time series of meteorological variables and air pollutant concentrations of SO2. Before the prediction, Fuzzy c-Means and K-means clustering algorithms have been implemented in order to find relationship among pollutant and meteorological variables. Our experiments with the proposed system show the importance of this set of meteorological variables on the prediction of SO2 pollutant concentrations and the neural network efficiency. The performance estimation is determined using the Root Mean Square Error (RMSE) and Mean Absolute Error (MAE). The results showed that the information obtained in the clustering step allows a prediction of an hour ahead, with data from past 2 hours

Prediction of PM10 concentrations using Fuzzy c-Means and ANN

Salamanca has been considered among the most polluted cities in Mexico. The vehicular park, the industry and the emissions produced by agriculture, as well as orography and climatic characteristics have propitiated the increment in pollutant concentration of Particulate Matter less than 10 μg/m3 in diameter (PM10). In this work, a Multilayer Perceptron Neural Network has been used to make the prediction of an hour ahead of pollutant concentration. A database used to train the Neural Network corresponds to historical time series of meteorological variables (wind speed, wind direction, temperature and relative humidity) and air pollutant concentrations of PM10. Before the prediction, Fuzzy c-Means clustering algorithm have been implemented in order to find relationship among pollutant and meteorological variables. These relationship help us to get additional information that will be used for predicting. Our experiments with the proposed system show the importance of this set of meteorological variables on the prediction of PM10 pollutant concentrations and the neural network efficiency. The performance estimation is determined using the Root Mean Square Error (RMSE) and Mean Absolute Error (MAE). The results shown that the information obtained in the clustering step allows a prediction of an hour ahead, with data from past 2 hour

Enhanced fluid dynamics in 3D monolithic reactors to improve the chemical performance: experimental and numerical investigation

Three-dimensional (3D) Fe/SiC monoliths with parallel interconnected channels and different cell geometries (square, troncoconical, and triangular) were manufactured by robocasting and used as catalytic reactors in hydroxylation of phenol using hydrogen peroxide to produce dihydroxybenzenes; the reaction was performed at Cphenol,0 = 0.33 M, Cphenol,0:CH2O2,0 = 1:1 M, WR = 3.7 g, T = 80-90 °C, and τ = 0-254 gcat·h·L-1 with water as a solvent. The values of the apparent kinetic rate constants demonstrated the superior performance of the triangular cell monoliths for hydrogen peroxide decomposition, phenol hydroxylation, and dihydroxybenzene production reactions. A computational fluid dynamic model was validated with the experimental results. It demonstrated that the triangular cell monoliths, with a lower channel hydraulic diameter and not-facing interconnections, provided a higher internal macrotortuosity that induced an oscillating flow of the liquid phase inside the channels, leading to an additional transverse flow between adjacent parallel channels. This behavior, not observed in the other two geometries, resulted in a better overall performanceThe authors thank the financial support by the Community of Madrid through the project S2018/EMT-4341 and the Government of Spain through the projects: PGC2018- 095642-B-I00 and RTI2018-095052-B-I00 (MCIU/AEI/ FEDER, UE). Also, G. Vega acknowledges the Universidad Autónoma de Madrid for the predoctoral contrac

La situación de las personas mayores en Castilla y León

Producción CientíficaAnálisis del envejecimiento como rasgo demográfico fundamental en Castilla y León, características socioeconómicas de los mayores, atención a este grupo de población, problemática y perspectivas.GeografíaObra elaborada a partir del informe encargado por el Consejo Económico y Social de Castilla y León

Detection of pore space in CT soil images using artificial neural networks

Computed Tomography (CT) images provide a non-invasive alternative for observing soil structures, particularly pore space. Pore space in soil data indicates empty or free space in the sense that no material is present there except fluids such as air, water, and gas. Fluid transport depends on where pore spaces are located in the soil, and for this reason, it is important to identify pore zones. The low contrast between soil and pore space in CT images presents a problem with respect to pore quantification. In this paper, we present a methodology that integrates image processing, clustering techniques and artificial neural networks, in order to classify pore space in soil images. Image processing was used for the feature extraction of images. Three clustering algorithms were implemented (K-means, Fuzzy C-means, and Self Organising Maps) to segment images. The objective of clustering process is to find pixel groups of a similar grey level intensity and to organise them into more or less homogeneous groups. The segmented images are used for test a classifier. An Artificial Neural Network is characterised by a great degree of modularity and flexibility, and it is very efficient for large-scale and generic pattern recognition applications. For these reasons, an Artificial Neural Network was used to classify soil images into two classes (pore space and solid soil). Our methodology shows an alternative way to detect solid soil and pore space in CT images. The percentages of correct classifications of pore space of the total number of classifications among the tested images were 97.01%, 96.47% and 96.12%

Esofagitis eosinofílica: principal causa de disfagia en niños y adultos jóvenes

Eosinophilic esophagitis is a chronic inflammatory disease of the esophagus that occurs due to an immunoallergic response triggered in most cases by food antigens. Clinically it is characterized by signs and symptoms related to esophageal dysfunction and histologically by tissue inflammation in which eosinophils predominate. Eosinophilic eophagitis is diagnosed when there are symptoms related to esophageal dysfunction such as dysphagia and food impaction, the presence of ≥15 eosinophils per high-power field on esophageal mucosal biopsy, and after a thorough evaluation for disorders that may cause or contribute to esophageal eosinophilia. The treatment consists of dietary modifications, pharmacological therapies and endoscopic interventions. In 2022, dupilumab became the first Food and Drug Administration -approved treatment for eosinophilic esophagitis. The importance of health personnel dealing with this disease lies in the fact that although it is a chronic disease, its early and timely diagnosis improves the evolution of the disease and the quality of life of the patient's treatment.La esofagitis eosinofílica es una enfermedad inflamatoria del esófago de carácter crónico que ocurre por una respuesta inmunoalérgica desencadenada en la mayoría de los casos por antígenos alimentarios. Clínicamente, se caracteriza por signos y síntomas relacionados con disfunción esofágica, e histológicamente por inflamación tisular en la cual predominan los eosinófilos. La esofagitis eosinofílica se diagnostica cuando hay síntomas relacionados con disfunción esofágica, como disfagia e impactación alimentaria, presencia de ≥15 eosinófilos por campo de alto poder en la biopsia de la mucosa esofágica, y después de realizar una evaluación exhaustiva de trastornos que pueden causar o contribuir potencialmente a la eosinofilia esofágica. El tratamiento consiste en modificaciones dietéticas, terapias farmacológicas e intervenciones endoscópicas. En 2022, el dupilumab se convirtió en el primer tratamiento para la esofagitis eosinofílica aprobado por la “Food and Drug Administration”. La importancia de que el personal de salud se familiarice con en el manejo de esta enfermedad radica en que, si bien es una enfermedad crónica, su diagnóstico precoz y tratamiento oportuno mejoran la evolución de la enfermedad y la calidad de vida del paciente

Metabolic syndrome: overview and early approach to avoid cardiovascular risk and diabetes mellitus type 2

Metabolic syndrome is considered a cluster of risk factors that include abdominal obesity, insulin resistance, elevated blood pressure, and atherogenic dyslipidemia. The origin of metabolic syndrome is due to a combination of genetic and epigenetic factors, lifestyle and environmental factors. Cardiovascular disease and type 2 diabetes mellitus are the main coexisting conditions. Diagnosis is made when three or more of the five criteria are present, including: 1) systolic blood pressure ≥130 mm/Hg and/or diastolic blood pressure ≥85 mm/Hg, 2) triglyceride levels ≥150 mg/dL, 3) HDL levels <40 mg/dL in men and <50 mg/dl in woman, 4) glucose levels ≥100 mg/dL and 5) abdominal circumference ≥ 88 cm for women and ≥ 102 cm in men. Treatment is based on both pharmacological and non-pharmacological measures. Metabolic syndrome is a condition that should always be kept in mind and should be excluded in the risk population. Anthropometric measurements are a good method of early detection to be able to start with complementary laboratory tests and thus diagnose the pathology in a timely manner and thus avoid long-term repercussions.El síndrome metabólico se considera un conjunto de factores de riesgo que incluyen obesidad abdominal, resistencia a la insulina, presión arterial elevada y dislipidemia aterogénica. El origen del síndrome metabólico se debe a la combinación de los factores genéticos y epigenéticos, estilo de vida y factores ambientales. La enfermedad cardiovascular y la diabetes mellitus tipo 2 son las principales condiciones coexistentes. El diagnóstico se realiza cuando hay presencia de tres o más de los cinco criterios que incluyen: 1) presión arterial sistólica ≥130 mm/Hg y/o presión arterial diastólica ≥85 mm/Hg, 2) niveles de triglicéridos ≥150 mg/dL, 3) niveles de HDL <40 mg/dl en hombres y <50 md/dl en mujeres, 4) niveles de glucosa ≥100 mg/dL y 5) circunferencia abdominal ≥88 cm para las mujeres y ≥ 102 cm en hombres. El tratamiento se basa en medidas tanto farmacológicas como no farmacológicas. El síndrome metabólico es una condición que siempre debe tenerse presente y que debe de ser excluida en la población de riesgo. Las medidas antropométricas constituyen un buen método de detección temprana para poder iniciar con las pruebas complementarias de laboratorio y así diagnosticar la patología de manera oportuna y así evitar las repercusiones a largo plazo

Microcalcification Detection Applying Artificial Neural Networks and Mathematical Morphology in Digital Mammograms

Breast cancer is one of the leading causes to women mortality in the world and early detection is an important means to reduce the mortality rate. The presence of microcalcifications clusters has been considered as a very important indicator of malignant types of breast cancer and its detection is important to prevent and treat the disease. This paper presents an alternative and effective approach in order to detect microcalcifications clusters in digitized mammograms based on the synergy of the image processing, pattern recognition and artificial intelligence. The mathematical morphology is an image processing technique used for the purpose of image enhancement. A k-means algorithm is used to cluster the data based on the features vectors and finally an artificial neural network-based classifier is applied and the classification performance is evaluated by a ROC curve. Experimental results indicate that the percentage of correct classification was 99.72%, obtaining 100% true positive (sensitivity) and 99.67% false positive (specificity), with the best classifier proposed. In case of the best classifier, we obtained a performance evaluation of classification of Az = 0.987

Unsupervised method to classify PM10 pollutant concentrations

In this paper a method based mainly on Data Fusion and Artificial Neural Networks to classify one of the most important pollutants such as Particulate Matter less than 10 micrometer in diameter (PM10) concentrations is proposed. The main objective is to classify in two pollution levels (Non-Contingency and Contingency) the pollutant concentration. Pollutant concentrations and meteorological variables have been considered in order to build a Representative Vector (RV) of pollution. RV is used to train an Artificial Neural Network in order to classify pollutant events determined by meteorological variables. In the experiments, real time series gathered from the Automatic Environmental Monitoring Network (AEMN) in Salamanca Guanajuato Mexico have been used. The method can help to establish a better air quality monitoring methodology that is essential for assessing the effectiveness of imposed pollution controls, strategies, and facilitate the pollutants reduction