Sensibility analysis of the pearl model for pesticide leaching in the State of Mato Grosso do Sul, Brazil

For an accurate use of pesticide leaching models it is necessary to assess the sensitivity of input parameters. The aim of this work was to carry out sensitivity analysis of the pesticide leaching model PEARL for contrasting soil types of Dourados river watershed in the state of Mato Grosso do Sul, Brazil. Sensitivity analysis was done by carrying out many simulations with different input parameters and calculating their influence on the output values. The approach used was called one-at-a-time sensitivity analysis, which consists in varying independently input parameters one at a time and keeping all others constant with the standard scenario. Sensitivity analysis was automated using SESAN tool that was linked to the PEARL model. Results have shown that only soil characteristics influenced the simulated water flux resulting in none variation of this variable for scenarios with different pesticides and same soil. All input parameters that showed the greatest sensitivity with regard to leached pesticide are related to soil and pesticide properties. Sensitivity of all input parameters was scenario dependent, confirming the need of using more than one standard scenario for sensitivity analysis of pesticide leaching models.

Parameter identification in time series models

Time series analysis can be categorized into three different approaches: classical, Box-Jenkins, and State space. Classical approach makes a basement for the analysis and Box-Jenkins approach is an improvement of the classical approach and deals with stationary time series. State space approach allows time variant factors and covers up a broader area of time series analysis. This thesis focuses on parameter identifiablity of different parameter estimation methods such as LSQ, Yule-Walker, MLE which are used in the above time series analysis approaches. Also the Kalman filter method and smoothing techniques are integrated with the state space approach and MLE method to estimate parameters allowing them to change over time. Parameter estimation is carried out by repeating estimation and integrating with MCMC and inspect how well different estimation methods can identify the optimal model parameters. Identification is performed in probabilistic and general senses and compare the results in order to study and represent identifiability more informative way.

Anaerobic performance in ice hockey : the effect of skate blade radius of hollow

The purpose of the study was to investigate the effect of skate blade radius of hollow (ROH) on anaerobic performance, specifically during the acceleration and stopping phases of an on-ice skating test. Fifteen, male Junior B hockey players (mean age 19 y ± 1.46) were recruited to participate. On-icc testing required each participant to complete an on-ice anaerobic performance test [Reed Repeat Skate (RRS)) on three separate days. During each on-ice test, the participant's skate blades were sharpened to one of three, randomly assigned, ROH values (0.63 cm, 1.27 cm, 1.90 cm). Performance times were recorded during each RRS and used to calculate anaerobic variables [anaerobic power (W), anaerobic capacity (W), and fatigue index (s, %)). Each RRS was video recorded for the purpose of motion analysis. Video footage was imported into Peak Motus™ to measure kinematic variables of the acceleration and stopping phases. The specific variables calculated from the acceleration phase were: average velocity over 6 m (m/s), average stride length (m), and mean stride rate (strides/s). The specific variables calculated from the stopping phase were: velocity at initiation of stopping (rn/s), stopping distance (m), stopping time (s). A repeated measures ANOV A was used to assess differences in mean performance and kinematic variables across the three selected hollows. Further analysis was conducted to assess differences in trial by trial performance and kinematic variables for all hollows. The primary findings of the study suggested that skate blade ROH can have a significant effect on kinematic variables, namely stride length and stride rate during the acceleration phase and stopping distance and stopping time during the stopping phase of an on-ice anaerobic performance test. During the acceleration phase, no significant difdifferences were found in SR and SL across the three selected hollows. Mean SR on the 1.27 cm hollow was significantly slower than both the 0.63 cm and 1.90 cm hollows and SL was significantly longer when skating on the 1.27 cm hollow in comparison to the 1.90 cm hollow. During the stopping phase, stopping distance on the 0.63 cm hollow (4.12 m ± 0.14) was significantly shorter than both the 1.27 cm hollow (4.43 m ± 0.08) (p < 0.05) and the 1.90 cm ho])ow (4.35 m ± 0.12) (p < 0.05). Mean ST was also significantly shorter when stopping on the 0.63 cm hollow then both the 1.27 cm and 1.90 cm hollows. Trial by trial results clearly illustrated the affect of fatigue on kinematic variables; AV, SR, IV decreased from trial 1 to 6. There was no significant effect on anaerobic performance variables during the RRS. Altering the skate blade ROH has a significant and practical affect on accelerating and stopping performance will be discussed in this paper.

Détection de chute à l'aide d'une caméra de profondeur

Les chutes chez les personnes âgées représentent un problème important de santé publique. Des études montrent qu’environ 30 % des personnes âgées de 65 ans et plus chutent chaque année au Canada, entraînant des conséquences néfastes sur les plans individuel, familiale et sociale. Face à une telle situation la vidéosurveillance est une solution efficace assurant la sécurité de ces personnes. À ce jour de nombreux systèmes d’assistance de services à la personne existent. Ces dispositifs permettent à la personne âgée de vivre chez elle tout en assurant sa sécurité par le port d'un capteur. Cependant le port du capteur en permanence par le sujet est peu confortable et contraignant. C'est pourquoi la recherche s’est récemment intéressée à l’utilisation de caméras au lieu de capteurs portables. Le but de ce projet est de démontrer que l'utilisation d'un dispositif de vidéosurveillance peut contribuer à la réduction de ce fléau. Dans ce document nous présentons une approche de détection automatique de chute, basée sur une méthode de suivi 3D du sujet en utilisant une caméra de profondeur (Kinect de Microsoft) positionnée à la verticale du sol. Ce suivi est réalisé en utilisant la silhouette extraite en temps réel avec une approche robuste d’extraction de fond 3D basée sur la variation de profondeur des pixels dans la scène. Cette méthode se fondera sur une initialisation par une capture de la scène sans aucun sujet. Une fois la silhouette extraite, les 10% de la silhouette correspondant à la zone la plus haute de la silhouette (la plus proche de l'objectif de la Kinect) sera analysée en temps réel selon la vitesse et la position de son centre de gravité. Ces critères permettront donc après analyse de détecter la chute, puis d'émettre un signal (courrier ou texto) vers l'individu ou à l’autorité en charge de la personne âgée. Cette méthode a été validée à l’aide de plusieurs vidéos de chutes simulées par un cascadeur. La position de la caméra et son information de profondeur réduisent de façon considérable les risques de fausses alarmes de chute. Positionnée verticalement au sol, la caméra permet donc d'analyser la scène et surtout de procéder au suivi de la silhouette sans occultation majeure, qui conduisent dans certains cas à des fausses alertes. En outre les différents critères de détection de chute, sont des caractéristiques fiables pour différencier la chute d'une personne, d'un accroupissement ou d'une position assise. Néanmoins l'angle de vue de la caméra demeure un problème car il n'est pas assez grand pour couvrir une surface conséquente. Une solution à ce dilemme serait de fixer une lentille sur l'objectif de la Kinect permettant l’élargissement de la zone surveillée.

Recurrence Quantification Analysis of System Signals for Detecting Tool and Chatter in Turning

In this thesis, the applications of the recurrence quantification analysis in metal cutting operation in a lathe, with specific objective to detect tool wear and chatter, are presented.This study is based on the discovery that process dynamics in a lathe is low dimensional chaotic. It implies that the machine dynamics is controllable using principles of chaos theory. This understanding is to revolutionize the feature extraction methodologies used in condition monitoring systems as conventional linear methods or models are incapable of capturing the critical and strange behaviors associated with the metal cutting process.As sensor based approaches provide an automated and cost effective way to monitor and control, an efficient feature extraction methodology based on nonlinear time series analysis is much more demanding. The task here is more complex when the information has to be deduced solely from sensor signals since traditional methods do not address the issue of how to treat noise present in real-world processes and its non-stationarity. In an effort to get over these two issues to the maximum possible, this thesis adopts the recurrence quantification analysis methodology in the study since this feature extraction technique is found to be robust against noise and stationarity in the signals.The work consists of two different sets of experiments in a lathe; set-I and set-2. The experiment, set-I, study the influence of tool wear on the RQA variables whereas the set-2 is carried out to identify the sensitive RQA variables to machine tool chatter followed by its validation in actual cutting. To obtain the bounds of the spectrum of the significant RQA variable values, in set-i, a fresh tool and a worn tool are used for cutting. The first part of the set-2 experiments uses a stepped shaft in order to create chatter at a known location. And the second part uses a conical section having a uniform taper along the axis for creating chatter to onset at some distance from the smaller end by gradually increasing the depth of cut while keeping the spindle speed and feed rate constant.The study concludes by revealing the dependence of certain RQA variables; percent determinism, percent recurrence and entropy, to tool wear and chatter unambiguously. The performances of the results establish this methodology to be viable for detection of tool wear and chatter in metal cutting operation in a lathe. The key reason is that the dynamics of the system under study have been nonlinear and the recurrence quantification analysis can characterize them adequately.This work establishes that principles and practice of machining can be considerably benefited and advanced from using nonlinear dynamics and chaos theory.

Analysis of System Signals Based on Cross Recurrence Method for System Dynamics Characterization

Natural systems are inherently non linear. Recurrent behaviours are typical of natural systems. Recurrence is a fundamental property of non linear dynamical systems which can be exploited to characterize the system behaviour effectively. Cross recurrence based analysis of sensor signals from non linear dynamical system is presented in this thesis. The mutual dependency among relatively independent components of a system is referred as coupling. The analysis is done for a mechanically coupled system specifically designed for conducting experiment. Further, cross recurrence method is extended to the actual machining process in a lathe to characterize the chatter during turning. The result is verified by permutation entropy method. Conventional linear methods or models are incapable of capturing the critical and strange behaviours associated with the dynamical process. Hence any effective feature extraction methodologies should invariably gather information thorough nonlinear time series analysis. The sensor signals from the dynamical system normally contain noise and non stationarity. In an effort to get over these two issues to the maximum possible extent, this work adopts the cross recurrence quantification analysis (CRQA) methodology since it is found to be robust against noise and stationarity in the signals. The study reveals that the CRQA is capable of characterizing even weak coupling among system signals. It also divulges the dependence of certain CRQA variables like percent determinism, percent recurrence and entropy to chatter unambiguously. The surrogate data test shows that the results obtained by CRQA are the true properties of the temporal evolution of the dynamics and contain a degree of deterministic structure. The results are verified using permutation entropy (PE) to detect the onset of chatter from the time series. The present study ascertains that this CRP based methodology is capable of recognizing the transition from regular cutting to the chatter cutting irrespective of the machining parameters or work piece material. The results establish this methodology to be feasible for detection of chatter in metal cutting operation in a lathe.

Compositional Data Analysis with R

R from http://www.r-project.org/ is ‘GNU S’ – a language and environment for statistical computing and graphics. The environment in which many classical and modern statistical techniques have been implemented, but many are supplied as packages. There are 8 standard packages and many more are available through the cran family of Internet sites http://cran.r-project.org . We started to develop a library of functions in R to support the analysis of mixtures and our goal is a MixeR package for compositional data analysis that provides support for operations on compositions: perturbation and power multiplication, subcomposition with or without residuals, centering of the data, computing Aitchison’s, Euclidean, Bhattacharyya distances, compositional Kullback-Leibler divergence etc. graphical presentation of compositions in ternary diagrams and tetrahedrons with additional features: barycenter, geometric mean of the data set, the percentiles lines, marking and coloring of subsets of the data set, theirs geometric means, notation of individual data in the set . . . dealing with zeros and missing values in compositional data sets with R procedures for simple and multiplicative replacement strategy, the time series analysis of compositional data. We’ll present the current status of MixeR development and illustrate its use on selected data sets

Impacto de las cirugías múltiples, en un solo tiempo quirúrgico, sobre el patrón de marcha en niños con parálisis cerebral

Introducción: La Parálisis Cerebral (PC) es la enfermedad neurológica más incapacitante en niños, su historia natural tiende al deterioro motor y funcional. Con este estudio se busca establecer sí las cirugías múltiples de miembros inferiores, en un tiempo quirúrgico, mantienen el nivel motor y funcional. Material y Método: Estudio analítico de cohortes. Se compara un grupo de pacientes sometidos a cirugías múltiples contra un grupo de pacientes no operados, en el Instituto de Ortopedia Infantil Roosevelt. Se evaluaron los pacientes con dos Laboratorios para el Análisis del Movimiento (LAM) y se midieron los desenlaces mediante el cambio en la puntuación del perfil de marcha (GPS) y el nivel funcional motor grueso (GMFCS). Resultados: 109 pacientes cumplieron con los criterios de selección, 67 pacientes fueron sometidos a cirugía y 42 pacientes no. Los pacientes operados mejoraron el GPS promedio (diferencia -1,94; p=0,002) comparado con los pacientes no operados (diferencia 1,74; p=0,001), indicando una mejoría significativa de la cinemática de la marcha. En un modelo de regresión logística predictivo, el paciente que es operado tiene una probabilidad del 78% de mantener su patrón de marcha, mientras que sí no se opera su probabilidad disminuye al 37%. El nivel funcional motor GMFCS no mostró cambios significativos entre los grupos. Discusión: Las cirugías múltiples de miembros inferiores mantienen de manera significativa el patrón de marcha en pacientes con PC. Se destaca el seguimiento de los pacientes mediante el LAM y se sugiere el uso del GPS para valorar resultados en este tipo de pacientes.

Spatio-temporal analysis of an invasive plant pathogen (Phytophthora ramorum) in England and Wales

Phytophthora ramorum is a damaging invasive plant pathogen and was first discovered in the UK in 2002. Spatial point analyses were applied to the occurrence of this disease in England and Wales during the period of 2003-2006 in order to assess its spatio-temporal spread. Out of the 4301 garden centres and nurseries (GCN) surveyed, there were 164, 105, 123 and 41 sites with P. ramorum in 2003, 2004, 2005 and 2006, respectively. Spatial analysis of the observed point patterns of GCN outbreaks suggested that these sites were significantly clumped within a radius of ca 60 km in 2003, but not in later years. Further analyses were conducted to determine the relationship of GCN outbreak sites over two consecutive years and thus to infer possible disease spread over time. This analysis suggested that disease spread among GCN sites was most likely to have occurred within a distance of 60 km for 2003-2004, but not for the later years. There were 35, 63, 81 and 58 sites with P. ramorum in the semi-natural environment (SNE). Analyses were carried out to assess whether infected GCN sites could act as an inoculum source of infected SNE plants or vice versa. In all years, there was a significant spatial closeness among GCN and SNE outbreak sites within a distance of 1 km. But a significant relationship over a longer distance (within 60 km) was only observed between cases in 2003 and 2004. These analyses suggest that statutory actions taken so far appear to have reduced the extent of long-distance spread of P. ramorum among garden centres and nurseries, but not the disease spread at a shorter distance between GCN and SNE sites.

Early identification of stimulant treatment responders, partial responders and non-responders using objective measures in children and adolescents with hyperkinetic disorder

Background: The aim of this study was to evaluate stimulant medication response following a single dose of methylphenidate (MPH) in children and young people with hyperkinetic disorder using infrared motion analysis combined with a continuous performance task (QbTest system) as objective measures. The hypothesis was put forward that a moderate testdose of stimulant medication could determine a robust treatment response, partial response and non-response in relation to activity, attention and impulse control measures. Methods: The study included 44 children and young people between the ages of 7-18 years with a diagnosis of hyperkinetic disorder (F90 & F90.1). A single dose-protocol incorporated the time course effects of both immediate release MPH and extended release MPH (Concerta XL, Equasym XL) to determine comparable peak efficacy periods post intake. Results: A robust treatment response with objective measures reverting to the population mean was found in 37 participants (84%). Three participants (7%) demonstrated a partial response to MPH and four participants (9%) were determined as non-responders due to deteriorating activity measures together with no improvements in attention and impulse control measures. Conclusion: Objective measures provide early into prescribing the opportunity to measure treatment response and monitor adverse reactions to stimulant medication. Most treatment responders demonstrated an effective response to MPH on a moderate testdose facilitating a swift and more optimal titration process.

An analysis of long-term trends, seasonality and short-term dynamics in water quality data from Plynlimon, Wales

This paper examines two hydrochemical time-series derived from stream samples taken in the Upper Hafren catchment, Plynlimon, Wales. One time-series comprises data collected at 7-hour intervals over 22 months (Neal et al., submitted, this issue), while the other is based on weekly sampling over 20 years. A subset of determinands: aluminium, calcium, chloride, conductivity, dissolved organic carbon, iron, nitrate, pH, silicon and sulphate are examined within a framework of non-stationary time-series analysis to identify determinand trends, seasonality and short-term dynamics. The results demonstrate that both long-term and high-frequency monitoring provide valuable and unique insights into the hydrochemistry of a catchment. The long-term data allowed analysis of long-termtrends, demonstrating continued increases in DOC concentrations accompanied by declining SO4 concentrations within the stream, and provided new insights into the changing amplitude and phase of the seasonality of the determinands such as DOC and Al. Additionally, these data proved invaluable for placing the short-term variability demonstrated within the high-frequency data within context. The 7-hour data highlighted complex diurnal cycles for NO3, Ca and Fe with cycles displaying changes in phase and amplitude on a seasonal basis. The high-frequency data also demonstrated the need to consider the impact that the time of sample collection can have on the summary statistics of the data and also that sampling during the hours of darkness provides additional hydrochemical information for determinands which exhibit pronounced diurnal variability. Moving forward, this research demonstrates the need for both long-term and high-frequency monitoring to facilitate a full and accurate understanding of catchment hydrochemical dynamics.

Kinematical analysis of redundant drive wire mechanisms with velocity constraint

In this paper, we propose a new velocity constraint type for Redundant Drive Wire Mechanisms. The purpose of this paper is to demonstrate that the proposed velocity constraint module can fix the orientation of the movable part and to use the kinematical analysis method to obtain the moving direction of the movable part. First, we discuss the necessity of using this velocity constraint type and the possible applications of the proposed mechanism. Second, we derive the basic equations of a wire mechanism with this constraint type. Next, we present a method of motion analysis on active and passive constraint spaces, which is used to find the moving direction of a movable part. Finally, we apply the above analysis method on a wire mechanism with a velocity constraint module and on a wire mechanism with four double actuator modules. By evaluating the results, we prove the validity of the proposed constraint type.

The time-course of morphological processing in a second language

We report findings from psycholinguistic experiments investigating the detailed timing of processing morphologically complex words by proficient adult second (L2) language learners of English in comparison to adult native (L1) speakers of English. The first study employed the masked priming technique to investigate -ed forms with a group of advanced Arabic-speaking learners of English. The results replicate previously found L1/L2 differences in morphological priming, even though in the present experiment an extra temporal delay was offered after the presentation of the prime words. The second study examined the timing of constraints against inflected forms inside derived words in English using the eye-movement monitoring technique and an additional acceptability judgment task with highly advanced Dutch L2 learners of English in comparison to adult L1 English controls. Whilst offline the L2 learners performed native-like, the eye-movement data showed that their online processing was not affected by the morphological constraint against regular plurals inside derived words in the same way as in native speakers. Taken together, these findings indicate that L2 learners are not just slower than native speakers in processing morphologically complex words, but that the L2 comprehension system employs real-time grammatical analysis (in this case, morphological information) less than the L1 system.

Experimental characterization of nonlinear systems: a real-time evaluation of the analogous Chua`s circuit behavior

This paper presents an experimental characterization of the behavior of an analogous version of the Chua`s circuit. The electronic circuit signals are captured using a data acquisition board (DAQ) and processed using LabVIEW environment. The following aspects of the time series analysis are analyzed: time waveforms, phase portraits, frequency spectra, Poincar, sections, and bifurcation diagram. The circuit behavior is experimentally mapped with the parameter variations, where are identified equilibrium points, periodic and chaotic attractors, and bifurcations. These analysis techniques are performed in real-time and can be applied to characterize, with precision, several nonlinear systems.

Measuring time series predictability using support vector regression

Most studies involving statistical time series analysis rely on assumptions of linearity, which by its simplicity facilitates parameter interpretation and estimation. However, the linearity assumption may be too restrictive for many practical applications. The implementation of nonlinear models in time series analysis involves the estimation of a large set of parameters, frequently leading to overfitting problems. In this article, a predictability coefficient is estimated using a combination of nonlinear autoregressive models and the use of support vector regression in this model is explored. We illustrate the usefulness and interpretability of results by using electroencephalographic records of an epileptic patient.