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

CSM-IXIM: A New Maize Simulation Model for DSSAT Version 4.5

Th e CERES-Maize model is the most widely used maize (Zea mays L.) model and is a recognized reference for comparing new developments in maize growth, development, and yield simulation. Th e objective of this study was to present and evaluate CSMIXIM, a new maize simulation model for DSSAT version 4.5. Code from CSM-CERES-Maize, the modular version of the model, was modifi ed to include a number of model improvements. Model enhancements included the simulation of leaf area, C assimilation and partitioning, ear growth, kernel number, grain yield, and plant N acquisition and distribution. Th e addition of two genetic coeffi cients to simulate per-leaf foliar surface produced 32% smaller root mean square error (RMSE) values estimating leaf area index than did CSM-CERES. Grain yield and total shoot biomass were correctly simulated by both models. Carbon partitioning, however, showed diff erences. Th e CSM-IXIM model simulated leaf mass more accurately, reducing the CSM-CERES error by 44%, but overestimated stem mass, especially aft er stress, resulting in similar average RMSE values as CSM-CERES. Excessive N uptake aft er fertilization events as simulated by CSM-CERES was also corrected, reducing the error by 16%. Th e accuracy of N distribution to stems was improved by 68%. Th ese improvements in CSM-IXIM provided a stable basis for more precise simulation of maize canopy growth and yield and a framework for continuing future model developments

Ground cover and leaf area index relationship in a grass, legume and crucifer crop

Canopy characterization is essential for describing the interaction of a crop with its environment. The goal of this work was to determine the relationship between leaf area index (LAI) and ground cover (GC) in a grass, a legume and a crucifer crop, and to assess the feasibility of using these relationships as well as LAI-2000 readings to estimate LAI. Twelve plots were sown with either barley (Hordeum vulgare L.), vetch (Vicia sativa L.), or rape (Brassica napus L.). On 10 sampling dates the LAI (both direct and LAI-2000 estimations), fraction intercepted of photosynthetically active radiation (FIPAR) and GC were measured. Linear and quadratic models fitted to the relationship between the GC and LAI for all of the crops, but they reached a plateau in the grass when the LAI mayor que 4. Before reaching full cover, the slope of the linear relationship between both variables was within the range of 0.025 to 0.030. The LAI-2000 readings were linearly correlated with the LAI but they tended to overestimation. Corrections based on the clumping effect reduced the root mean square error of the estimated LAI from the LAI-2000 readings from 1.2 to less than 0.50 for the crucifer and the legume, but were not effective for barley.

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.

Distributed target tracking based on belief propagation consensus

Distributed target tracking in wireless sensor networks (WSN) is an important problem, in which agreement on the target state can be achieved using conventional consensus methods, which take long to converge. We propose distributed particle filtering based on belief propagation (DPF-BP) consensus, a fast method for target tracking. According to our simulations, DPF-BP provides better performance than DPF based on standard belief consensus (DPF-SBC) in terms of disagreement in the network. However, in terms of root-mean square error, it can outperform DPF-SBC only for a specific number of consensus iterations.

Using a causal smoothing to improve the performance of an on-line neural network glucose prediction algorithm

This work evaluates a spline-based smoothing method applied to the output of a glucose predictor. Methods:Our on-line prediction algorithm is based on a neural network model (NNM). We trained/validated the NNM with a prediction horizon of 30 minutes using 39/54 profiles of patients monitored with the Guardian® Real-Time continuous glucose monitoring system The NNM output is smoothed by fitting a causal cubic spline. The assessment parameters are the error (RMSE), mean delay (MD) and the high-frequency noise (HFCrms). The HFCrms is the root-mean-square values of the high-frequency components isolated with a zero-delay non-causal filter. HFCrms is 2.90±1.37 (mg/dl) for the original profiles.

Adaptive neuro-fuzzy inference systems (ANFIS) for supervised classification of competence in MIS based on psychomotor skills

INTRODUCTION: Objective assessment of motor skills has become an important challenge in minimally invasive surgery (MIS) training.Currently, there is no gold standard defining and determining the residents' surgical competence.To aid in the decision process, we analyze the validity of a supervised classifier to determine the degree of MIS competence based on assessment of psychomotor skills METHODOLOGY: The ANFIS is trained to classify performance in a box trainer peg transfer task performed by two groups (expert/non expert). There were 42 participants included in the study: the non-expert group consisted of 16 medical students and 8 residents (< 10 MIS procedures performed), whereas the expert group consisted of 14 residents (> 10 MIS procedures performed) and 4 experienced surgeons. Instrument movements were captured by means of the Endoscopic Video Analysis (EVA) tracking system. Nine motion analysis parameters (MAPs) were analyzed, including time, path length, depth, average speed, average acceleration, economy of area, economy of volume, idle time and motion smoothness. Data reduction was performed by means of principal component analysis, and then used to train the ANFIS net. Performance was measured by leave one out cross validation. RESULTS: The ANFIS presented an accuracy of 80.95%, where 13 experts and 21 non-experts were correctly classified. Total root mean square error was 0.88, while the area under the classifiers' ROC curve (AUC) was measured at 0.81. DISCUSSION: We have shown the usefulness of ANFIS for classification of MIS competence in a simple box trainer exercise. The main advantage of using ANFIS resides in its continuous output, which allows fine discrimination of surgical competence. There are, however, challenges that must be taken into account when considering use of ANFIS (e.g. training time, architecture modeling). Despite this, we have shown discriminative power of ANFIS for a low-difficulty box trainer task, regardless of the individual significances between MAPs. Future studies are required to confirm the findings, inclusion of new tasks, conditions and sample population.

Estimating tremor in Vocal Fold Biomechanics for Neurological Disease characterisation

Neurological Diseases (ND) are affecting larger segments of aging population every year. Treatment is dependent on expensive accurate and frequent monitoring. It is well known that ND leave correlates in speech and phonation. The present work shows a method to detect alterations in vocal fold tension during phonation. These may appear either as hypertension or as cyclical tremor. Estimations of tremor may be produced by auto-regressive modeling of the vocal fold tension series in sustained phonation. The correlates obtained are a set of cyclicality coefficients, the frequency and the root mean square amplitude of the tremor. Statistical distributions of these correlates obtained from a set of male and female subjects are presented. Results from five study cases of female voice are also given.

Influence of gravity on heavy particle statistics in turbulent channel and pipe flow at matched Kàrmàn number

In this work, we use large eddy simulations (LES) and Lagrangian tracking to study the influence of gravity on particle statistics in a fully developed turbulent upward/downward flow in a vertical channel and pipe at matched Kàrmàn number. Only drag and gravity are considered in the equation of motion for solid particles, which are assumed to have no influence on the flow field. Particle interactions with the wall are fully elastic. Our findings obtained from the particle statistics confirm that: (i) the gravity seems to modify both the quantitative and qualitative behavior of the particle distribution and statistics of the particle velocity in wall normal direction; (ii) however, only the quantitative behavior of velocity particle in streamwise direction and the root mean square of velocity components is modified; (iii) the statistics of fluid and particles coincide very well near the wall in channel and pipe flow with equal Kàrmàn number; (iv) pipe curvature seems to have quantitative and qualitative influence on the particle velocity and on the particle concentration in wall normal direction.

Staphylococcal protein A: Unfolding pathways, unfolded states, and differences between the B and E domains

We present multiple native and denaturation simulations of the B and E domains of the three-helix bundle protein A, totaling 60 ns. The C-terminal helix (H3) consistently denatures later than either of the other two helices and contains residual helical structure in the denatured state. These results are consistent with experiments suggesting that the isolated H3 fragment is more stable than H1 and H2 and that H3 forms early in folding. Interestingly, the denatured state of the B domain is much more compact than that of the E domain. This sequence-dependent effect on the dimensions of the denatured state and the lack of correlation with structure suggest that the radius of gyration can be a misleading reaction coordinate for unfolding/folding. Various unfolding and refolding events are observed in the denaturation simulations. In some cases, the transitions are facilitated through interactions with other portions of the protein—contact-assisted helix formation. In the native simulations, the E domain is very stable: after 6 ns, the Cα root-mean-square deviation from the starting structure is less than 1.4 Å. In contrast, the native state of the B domain deviates more and its inter-helical angles fluctuate. In apparent contrast, we note that the B domain is thermodynamically more stable than the E domain. The simulations suggest that the increased stability of the B domain may be due to heightened mobility, and therefore entropy, in the native state and decreased mobility/entropy in the more compact denatured state.

Repacking protein cores with backbone freedom: structure prediction for coiled coils.

Progress in homology modeling and protein design has generated considerable interest in methods for predicting side-chain packing in the hydrophobic cores of proteins. Present techniques are not practically useful, however, because they are unable to model protein main-chain flexibility. Parameterization of backbone motions may represent a general and efficient method to incorporate backbone relaxation into such fixed main-chain models. To test this notion, we introduce a method for treating explicitly the backbone motions of alpha-helical bundles based on an algebraic parameterization proposed by Francis Crick in 1953 [Crick, F. H. C. (1953) Acta Crystallogr. 6, 685-689]. Given only the core amino acid sequence, a simple calculation can rapidly reproduce the crystallographic main-chain and core side-chain structures of three coiled coils (one dimer, one trimer, and one tetramer) to within 0.6-A root-mean-square deviations. The speed of the predictive method [approximately 3 min per rotamer choice on a Silicon Graphics (Mountain View, CA) 4D/35 computer] permits it to be used as a design tool.

Impact of artifact correction methods on R-R interbeat signals to quantifying heart rate variability (HRV) according to linear and nonlinear methods.

In the analysis of heart rate variability (HRV) are used temporal series that contains the distances between successive heartbeats in order to assess autonomic regulation of the cardiovascular system. These series are obtained from the electrocardiogram (ECG) signal analysis, which can be affected by different types of artifacts leading to incorrect interpretations in the analysis of the HRV signals. Classic approach to deal with these artifacts implies the use of correction methods, some of them based on interpolation, substitution or statistical techniques. However, there are few studies that shows the accuracy and performance of these correction methods on real HRV signals. This study aims to determine the performance of some linear and non-linear correction methods on HRV signals with induced artefacts by quantification of its linear and nonlinear HRV parameters. As part of the methodology, ECG signals of rats measured using the technique of telemetry were used to generate real heart rate variability signals without any error. In these series were simulated missing points (beats) in different quantities in order to emulate a real experimental situation as accurately as possible. In order to compare recovering efficiency, deletion (DEL), linear interpolation (LI), cubic spline interpolation (CI), moving average window (MAW) and nonlinear predictive interpolation (NPI) were used as correction methods for the series with induced artifacts. The accuracy of each correction method was known through the results obtained after the measurement of the mean value of the series (AVNN), standard deviation (SDNN), root mean square error of the differences between successive heartbeats (RMSSD), Lomb\'s periodogram (LSP), Detrended Fluctuation Analysis (DFA), multiscale entropy (MSE) and symbolic dynamics (SD) on each HRV signal with and without artifacts. The results show that, at low levels of missing points the performance of all correction techniques are very similar with very close values for each HRV parameter. However, at higher levels of losses only the NPI method allows to obtain HRV parameters with low error values and low quantity of significant differences in comparison to the values calculated for the same signals without the presence of missing points.

Simulação e modelagem computacional de dados de espalhamento à baixos ângulos - enfoque em estruturas de alta simetria

Esta tese apresenta uma abordagem para a criação rápida de modelos em diferentes geometrias (complexas ou de alta simetria) com objetivo de calcular a correspondente intensidade espalhada, podendo esta ser utilizada na descrição de experimentos de es- palhamento à baixos ângulos. A modelagem pode ser realizada com mais de 100 geome- trias catalogadas em um Banco de Dados, além da possibilidade de construir estruturas a partir de posições aleatórias distribuídas na superfície de uma esfera. Em todos os casos os modelos são gerados por meio do método de elementos finitos compondo uma única geometria, ou ainda, compondo diferentes geometrias, combinadas entre si a partir de um número baixo de parâmetros. Para realizar essa tarefa foi desenvolvido um programa em Fortran, chamado de Polygen, que permite modelar geometrias convexas em diferentes formas, como sólidos, cascas, ou ainda com esferas ou estruturas do tipo DNA nas arestas, além de usar esses modelos para simular a curva de intensidade espalhada para sistemas orientados e aleatoriamente orientados. A curva de intensidade de espalhamento é calculada por meio da equação de Debye e os parâmetros que compõe cada um dos modelos, podem ser otimizados pelo ajuste contra dados experimentais, por meio de métodos de minimização baseados em simulated annealing, Levenberg-Marquardt e algorítmicos genéticos. A minimização permite ajustar os parâmetros do modelo (ou composição de modelos) como tamanho, densidade eletrônica, raio das subunidades, entre outros, contribuindo para fornecer uma nova ferramenta para modelagem e análise de dados de espalhamento. Em outra etapa desta tese, é apresentado o design de modelos atomísticos e a sua respectiva simulação por Dinâmica Molecular. A geometria de dois sistemas auto-organizado de DNA na forma de octaedro truncado, um com linkers de 7 Adeninas e outro com linkers de ATATATA, foram escolhidas para realizar a modelagem atomística e a simulação por Dinâmica Molecular. Para este sistema são apresentados os resultados de Root Mean Square Deviations (RMSD), Root Mean Square Fluctuations (RMSF), raio de giro, torção das hélices duplas de DNA além da avaliação das ligações de Hidrogênio, todos obtidos por meio da análise de uma trajetória de 50 ns.

Validación local de la temperatura superficial del mar del sensor MODIS en aguas someras del Mediterráneo occidental

La temperatura superficial del mar (SST) estimada a partir de los productos 11 μm diurnos y nocturnos y 4 μm nocturnos del sensor MODIS (Moderate Resolution Imaging Spectroradiometer) a bordo de la plataforma Aqua, han sido comparados con datos medidos in situ a tres profundidades diferentes (15, 50 y 100 cm) en una zona costera del Mediterráneo Occidental. Esta comparación ha permitido analizar la incertidumbre que existe en la estimación de este parámetro en aguas someras y próximas a la costa mediante imágenes de satélite de baja resolución espacial. Los resultados obtenidos demuestran que el producto diurno SST_11 μm, obtiene los estadísticos RMSE (error cuadrático medio) y r2 (coeficiente de correlación de Pearson) más ajustados con valores de 1°C y 0,96, respectivamente, para la profundidad 50 cm.

Visual and optical performance and quality of life after implantation of posterior chamber phakic intraocular lens

Purpose To evaluate visual, optical, and quality of life (QoL) outcomes and intercorrelations after bilateral implantation of posterior chamber phakic intraocular lenses. Methods Twenty eyes with high to moderate myopia of 10 patients that underwent PRL implantation (Phakic Refractive Lens, Carl Zeiss Meditec AG) were examined. Refraction, visual acuity, photopic and low mesopic contrast sensitivity (CS) with and without glare, ocular aberrations, as well as QoL outcomes (National Eye Institute Refractive Error Quality of Life Instrument-42, NEI RQL-42) were evaluated at 12 months postoperatively. Results Significant improvement in uncorrected (UDVA) and best-corrected distance (CDVA) visual acuities were found postoperatively (p < 0.01), with significant reduction in spherical equivalent (p < 0.01). Low mesopic CS without glare was significantly better than measurements with glare for 1.5, 3, and 6 cycles/degree (p < 0.01). No significant correlations between higher order root mean square (RMS) with CDVA (r = −0.26, p = 0.27) and CS (r ≤ 0.45, p ≥ 0.05) were found. Postoperative binocular photopic CS for 12 cycles/degree and 18 cycles/degree correlated significantly with several RQL-42 scales. Glare index correlated significantly with CS measures and scotopic pupil size (r = −0.551, p = 0.04), but not with higher order RMS (r = −0.02, p = 0.94). Postoperative higher order RMS, postoperative primary coma and postoperative spherical aberration was significant higher for 5-mm pupil diameter (p < 0.01) compared with controls. Conclusions Correction of moderate to high myopia by means of PRL implantation had a positive impact on CS and QoL. The aberrometric increase induced by the surgery does not seem to limit CS and QoL. However, perception of glare is still a relevant disturbance in some cases possibly related to the limitation of the optical zone of the PRL.