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 hours

Microcalcifications Detection using PFCM and ANN

This work presents a method to detect Microcalcifications in Regions of Interest from digitized mammograms. The method is based mainly on the combination of Image Processing, Pattern Recognition and Artificial Intelligence. The Top-Hat transform is a technique based on mathematical morphology operations that, in this work is used to perform contrast enhancement of microcalcifications in the region of interest. In order to find more or less homogeneous regions in the image, we apply a novel image sub-segmentation technique based on Possibilistic Fuzzy c-Means clustering algorithm. From the original region of interest we extract two window-based features, Mean and Deviation Standard, which will be used in a classifier based on a Artificial Neural Network in order to identify microcalcifications. Our results show that the proposed method is a good alternative in the stage of microcalcifications detection, because this stage is an important part of the early Breast Cancer detection

Inverse kinematics of a 6 DoF human upper limb using ANFIS and ANN for anticipatory actuation in ADL-based physical Neurorehabilitation

Objective: This research is focused in the creation and validation of a solution to the inverse kinematics problem for a 6 degrees of freedom human upper limb. This system is intended to work within a realtime dysfunctional motion prediction system that allows anticipatory actuation in physical Neurorehabilitation under the assisted-as-needed paradigm. For this purpose, a multilayer perceptron-based and an ANFIS-based solution to the inverse kinematics problem are evaluated. Materials and methods: Both the multilayer perceptron-based and the ANFIS-based inverse kinematics methods have been trained with three-dimensional Cartesian positions corresponding to the end-effector of healthy human upper limbs that execute two different activities of the daily life: "serving water from a jar" and "picking up a bottle". Validation of the proposed methodologies has been performed by a 10 fold cross-validation procedure. Results: Once trained, the systems are able to map 3D positions of the end-effector to the corresponding healthy biomechanical configurations. A high mean correlation coefficient and a low root mean squared error have been found for both the multilayer perceptron and ANFIS-based methods. Conclusions: The obtained results indicate that both systems effectively solve the inverse kinematics problem, but, due to its low computational load, crucial in real-time applications, along with its high performance, a multilayer perceptron-based solution, consisting in 3 input neurons, 1 hidden layer with 3 neurons and 6 output neurons has been considered the most appropriated for the target application.

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.

Association of breakfast consumption with objectively measured and self-reported physical activity, sedentary time and physical fitness in European adolescents: the HELENA (Healthy Lifestyle in Europe by Nutrition in Adolescence) Study

Objective: To examine the association of breakfast consumption with objectively measured and self-reported physical activity, sedentary time and physical fitness. Design: The HELENA (Healthy Lifestyle in Europe by Nutrition in Adolescence) Cross-Sectional Study. Breakfast consumption was assessed by two non-consecutive 24 h recalls and by a ‘Food Choices and Preferences’ questionnaire. Physical activity, sedentary time and physical fitness components (cardiorespiratory fitness, muscular fitness and speed/agility) were measured and self-reported. Socio-economic status was assessed by questionnaire. Setting: Ten European cities. Subjects: Adolescents (n 2148; aged 12?5–17?5 years). Results: Breakfast consumption was not associated with measured or self-reported physical activity. However, 24 h recall breakfast consumption was related to measured sedentary time in males and females; although results were not confirmed when using other methods to assess breakfast patterns or sedentary time. Breakfast consumption was not related to muscular fitness and speed/agility in males and females. However, male breakfast consumers had higher cardiorespiratory fitness compared with occasional breakfast consumers and breakfast skippers, while no differences were observed in females. Overall, results were consistent using different methods to assess breakfast consumption or cardiorespiratory fitness (all P#0?005). In addition, both male and female breakfast skippers (assessed by 24 h recall) were less likely to have high measured cardiorespiratory fitness compared with breakfast consumers (OR50?33; 95% CI 0?18, 0?59 and OR50?56; 95 %CI 0?32, 0?98,respectively). Results persisted across methods. Conclusions: Skipping breakfast does not seem to be related to physical activity,sedentary time or muscular fitness and speed/agility as physical fitness components in European adolescents; yet it is associated with both measured and self-reported cardiorespiratory fitness, which extends previous findings.

Analysis of the discharge capacity of radial-gated spillways using CFD and ANN - Oliana Dam case study.

The paper focuses on the analysis of radial-gated spillways, which is carried out by the solution of a numerical model based on the finite element method (FEM). The Oliana Dam is considered as a case study and the discharge capacity is predicted both by the application of a level-set-based free-surface solver and by the use of traditional empirical formulations. The results of the analysis are then used for training an artificial neural network to allow real-time predictions of the discharge in any situation of energy head and gate opening within the operation range of the reservoir. The comparison of the results obtained with the different methods shows that numerical models such as the FEM can be useful as a predictive tool for the analysis of the hydraulic performance of radial-gated spillways.

Dietary and lifestyle quality indices with/without physical activity and markers of insulin resistance in European adolescents: the HELENA study

Resistencia a la insulina en adolescentes

Associations between macronutrient intake and serum lipid profile depend on body fat in European adolescents: the Healthy Lifestyle in Europe by Nutrition in Adolescence (HELENA) study

The present study aimed to investigate the relationships between macronutrient intake and serum lipid profile in adolescents from eight European cities participating in the HELENA (Healthy Lifestyle in Europe by Nutrition in Adolescence) cross-sectional study (2006–7), and to assess the role of body fat-related variables in these associations. Weight, height, waist circumference, skinfold thicknesses, total choles- terol, HDL-cholesterol (HDL-C), LDL-cholesterol, TAG, apoB and apoA1 were measured in 454 adolescents (44 % boys) aged 12·5–17·5 years. Macronutrient intake (g/4180 kJ per d (1000 kcal per d)) was assessed using two non-consecutive 24 h dietary recalls. Associations were evaluated by multi-level analysis and adjusted for sex, age, maternal education, centre, sum of four skinfolds, moderate-to-vigorous.