952 resultados para Non linear adaptive control


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Introduction: The SMILING project, a multicentric project fundedby the European Union, aims to develop a new gait and balance trainingprogram to prevent falls in older persons. The program includes the"SMILING shoe", an innovative device that generates mechanical perturbationwhile walking by changing the soles' inclination. Induced perturbationschallenge subjects' balance and force them to react to avoidfalls. By training specifically the complex motor reactions used to maintainbalance when walking on irregular ground, the program will improvesubjects' ability to react in situation of unsteadiness and reduce theirrisk of falling. Methods: The program will be evaluated in a multicentric,cross-over randomized controlled trial. Overall, 112 subjects (aged≥65 years, ≥1 falls, POMA score 22-26/28) will be enrolled. Subjectswill be randomised in 2 groups: group A begin the training with active"SMILING shoes", group B with inactive dummy shoes. After 4 weeksof training, group A and B will exchange the shoes. Supervised trainingsessions (30 minutes twice a week for 8 weeks) include walkingtasks of progressive difficulties.To avoid a learning effect, "SMILINGshoes" perturbations will be generated in a non-linear and chaotic way.Gait performance, fear of falling, and acceptability of the program willbe assessed. Conclusion: The SMILING program is an innovative interventionfor falls prevention in older persons based on gait and balancetraining using chaotic perturbations. Because of the easy use of the"SMILING shoes", this program could be used in various settings, suchas geriatric clinics or at home.

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BACKGROUND: The aim of this study was to assess the pharmacology, toxicity and activity of high-dose ifosfamide mesna +/- GM-CSF administered by a five-day continuous infusion at a total ifosfamide dose of 12-18 g/m2 in adult patients with advanced sarcomas. PATIENTS AND METHODS: Between January 1991 and October 1992 32 patients with advanced or metastatic sarcoma were entered the study. Twenty-seven patients were pretreated including twenty-three with prior ifosfamide at less than 8 g/m2 total dose/cycle. In 25 patients (27 cycles) extensive pharmacokinetic analyses were performed. RESULTS: The area under the plasma concentration-time curve (AUC) for ifosfamide increased linearly with dose while the AUC's of the metabolites measured in plasma by thin-layer chromatography did not increase with dose, particularly that of the active metabolite isophosphoramide mustard. Furthermore the AUC of the inactive carboxymetabolite did not increase with dose. Interpatient variability of pharmacokinetic parameters was high. Dose-limiting toxicity was myelosuppression at 18 g/m2 total dose with grade 4 neutropenia in five of six patients and grade 4 thrombocytopenia in four of six patients. Therefore the maximum tolerated dose was considered to be 18 g/m2 total dose. There was one CR and eleven PR in twenty-nine evaluable patients (overall response rate 41%). CONCLUSION: Both the activation and inactivation pathways of ifosfamide are non-linear and saturable at high-doses although the pharmacokinetics of the parent drug itself are dose linear. Ifosfamide doses greater than 14-16 g/m2 per cycle appear to result in a relative decrease of the active metabolite isophosphoramide mustard. These data suggest a dose-dependent saturation or even inhibition of ifosfamide metabolism by increasing high dose ifosfamide and suggest the need for further metabolic studies.

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The aim of this study was to locate the breakpoints of cerebral and muscle oxygenation and muscle electrical activity during a ramp exercise in reference to the first and second ventilatory thresholds. Twenty-five cyclists completed a maximal ramp test on an electromagnetically braked cycle-ergometer with a rate of increment of 25 W/min. Expired gazes (breath-by-breath), prefrontal cortex and vastus lateralis (VL) oxygenation [Near-infrared spectroscopy (NIRS)] together with electromyographic (EMG) Root Mean Square (RMS) activity for the VL, rectus femoris (RF), and biceps femoris (BF) muscles were continuously assessed. There was a non-linear increase in both cerebral deoxyhemoglobin (at 56 ± 13% of the exercise) and oxyhemoglobin (56 ± 8% of exercise) concomitantly to the first ventilatory threshold (57 ± 6% of exercise, p > 0.86, Cohen's d < 0.1). Cerebral deoxyhemoglobin further increased (87 ± 10% of exercise) while oxyhemoglobin reached a plateau/decreased (86 ± 8% of exercise) after the second ventilatory threshold (81 ± 6% of exercise, p < 0.05, d > 0.8). We identified one threshold only for muscle parameters with a non-linear decrease in muscle oxyhemoglobin (78 ± 9% of exercise), attenuation in muscle deoxyhemoglobin (80 ± 8% of exercise), and increase in EMG activity of VL (89 ± 5% of exercise), RF (82 ± 14% of exercise), and BF (85 ± 9% of exercise). The thresholds in BF and VL EMG activity occurred after the second ventilatory threshold (p < 0.05, d > 0.6). Our results suggest that the metabolic and ventilatory events characterizing this latter cardiopulmonary threshold may affect both cerebral and muscle oxygenation levels, and in turn, muscle recruitment responses.

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In this paper we examine the effect of tax policy on the relationship between inequality and growth in a two-sector non-scale model. With non-scale models, the longrun equilibrium growth rate is determined by technological parameters and it is independent of macroeconomic policy instruments. However, this fact does not imply that fiscal policy is unimportant for long-run economic performance. It indeed has important effects on the different levels of key economic variables such as per capita stock of capital and output. Hence, although the economy grows at the same rate across steady states, the bases for economic growth may be different.The model has three essential features. First, we explicitly model skill accumulation, second, we introduce government finance into the production function, and we introduce an income tax to mirror the fiscal events of the 1980¿s and 1990¿s in the US. The fact that the non-scale model is associated with higher order dynamics enables it to replicate the distinctly non-linear nature of inequality in the US with relative ease. The results derived in this paper attract attention to the fact that the non-scale growth model does not only fit the US data well for the long-run (Jones, 1995b) but also that it possesses unique abilities in explaining short term fluctuations of the economy. It is shown that during transition the response of the relative simulated wage to changes in the tax code is rather non-monotonic, quite in accordance to the US inequality pattern in the 1980¿s and early 1990¿s.More specifically, we have analyzed in detail the dynamics following the simulation of an isolated tax decrease and an isolated tax increase. So, after a tax decrease the skill premium follows a lower trajectory than the one it would follow without a tax decrease. Hence we are able to reduce inequality for several periods after the fiscal shock. On the contrary, following a tax increase, the evolution of the skill premium remains above the trajectory carried on by the skill premium under a situation with no tax increase. Consequently, a tax increase would imply a higher level of inequality in the economy

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[cat] Una qüestió clau sobre la producció de salut relativament poc explorada es refereix a la influència dels factors socioeconòmics i mediambientals sobre el pes i l’obesitat. Aquesta problemàtica adquireix particular rellevància quan es comparen dos països Mediterranis com Itàlia i Espanya. És interessant adonar-se que l’obesitat a Espanya és 5 punts percentual més elevada al 2003 mentre que a l’any 1990 era aproximadament la mateixa en ambdós països. Aquesta article presenta una descomposició no lineal dels gaps o diferencials en taxes de sobrepès (índex de massa corporal – IMC- entre 25 i 29.9 9 kg/m2), obesitat classe 1 (IMC≥30 kg/m2) i classe 2 (IMC≥35 kg/m2) entre Espanya i Itàlia per gènere i grups d’edat. En explicar aquests gaps entre països aïllem les influències dels estils de vida, els efectes socioeconòmics i els mediambientals. Els nostres resultats indiquen que quan no es controla pels efectes mediambientals (efectes de grup o ‘peer effects’) els hàbits alimentaris i el nivell educatiu són els principals predictors del gaps totals entre països (36-52%), si bé aquests dos factors exerceixen un impacte diferenciat segons gènere i edat. Un tant paradoxalment, quan controlem pels efectes de grup aquests predictors perden la seva capacitat explicativa i els efectes de grup passen a explicar entre el 46-76% dels gaps en sobrepès i obesitat i mostren un patró creixent amb l’edat.

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In this paper we examine the effect of tax policy on the relationship between inequality and growth in a two-sector non-scale model. With non-scale models, the longrun equilibrium growth rate is determined by technological parameters and it is independent of macroeconomic policy instruments. However, this fact does not imply that fiscal policy is unimportant for long-run economic performance. It indeed has important effects on the different levels of key economic variables such as per capita stock of capital and output. Hence, although the economy grows at the same rate across steady states, the bases for economic growth may be different.The model has three essential features. First, we explicitly model skill accumulation, second, we introduce government finance into the production function, and we introduce an income tax to mirror the fiscal events of the 1980¿s and 1990¿s in the US. The fact that the non-scale model is associated with higher order dynamics enables it to replicate the distinctly non-linear nature of inequality in the US with relative ease. The results derived in this paper attract attention to the fact that the non-scale growth model does not only fit the US data well for the long-run (Jones, 1995b) but also that it possesses unique abilities in explaining short term fluctuations of the economy. It is shown that during transition the response of the relative simulated wage to changes in the tax code is rather non-monotonic, quite in accordance to the US inequality pattern in the 1980¿s and early 1990¿s.More specifically, we have analyzed in detail the dynamics following the simulation of an isolated tax decrease and an isolated tax increase. So, after a tax decrease the skill premium follows a lower trajectory than the one it would follow without a tax decrease. Hence we are able to reduce inequality for several periods after the fiscal shock. On the contrary, following a tax increase, the evolution of the skill premium remains above the trajectory carried on by the skill premium under a situation with no tax increase. Consequently, a tax increase would imply a higher level of inequality in the economy

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Soil porosity, especially pore size distribution, is an important controlling factor for soil infiltration, hydraulic conductivity, and water retention. This study aimed to verify the effect of secondary-treated domestic wastewater (STW) on the porosity of a sandy loam Oxisol in the city of Lins, state of São Paulo, Brazil. The two-year experiment was divided into three plots: soil cultivated with corn and sunflower and irrigated with STW, soil cultivated and irrigated with sodic groundwater, and non-irrigated and non-cultivated soil (control). At the end of the experiment, undisturbed core samples were sampled from 0 to 2.0 m (8 depths). The water retention curves were obtained by tension plates and Richard's pressure plate apparatus, and the pore size distribution inferred from the retention curves. It was found that irrigation with treated wastewater and treated groundwater led to a decrease in microporosity (V MI), defined as the pore class ranging from 0.2 to 50 μm diameter. On the other hand, a significant increase in cryptoporosity (V CRI) (< 0.2 μm) was identified throughout the soil profile. The presence of Na+ in both waters confirmed the role of this ion on pore size distribution and soil moisture (higher water retention).

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Soil penetration resistance is an important property that affects root growth and elongation and water movement in the soil. Since no-till systems tend to increase organic matter in the soil, the purpose of this study was to evaluate the efficiency with which soil penetration resistance is estimated using a proposed model based on moisture content, density and organic matter content in an Oxisol containing 665, 221 and 114 g kg-1 of clay, silt and sand respectively under annual no-till cropping, located in Londrina, Paraná State, Brazil. Penetration resistance was evaluated at random locations continually from May 2008 to February 2011, using an impact penetrometer to obtain a total of 960 replications. For the measurements, soil was sampled at depths of 0 to 20 cm to determine gravimetric moisture (G), bulk density (D) and organic matter content (M). The penetration resistance curve (PR) was adjusted using two non-linear models (PR = a Db Gc and PR' = a Db Gc Md), where a, b, c and d are coefficients of the adjusted model. It was found that the model that included M was the most efficient for estimating PR, explaining 91 % of PR variability, compared to 82 % of the other model.

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Microstructure imaging from diffusion magnetic resonance (MR) data represents an invaluable tool to study non-invasively the morphology of tissues and to provide a biological insight into their microstructural organization. In recent years, a variety of biophysical models have been proposed to associate particular patterns observed in the measured signal with specific microstructural properties of the neuronal tissue, such as axon diameter and fiber density. Despite very appealing results showing that the estimated microstructure indices agree very well with histological examinations, existing techniques require computationally very expensive non-linear procedures to fit the models to the data which, in practice, demand the use of powerful computer clusters for large-scale applications. In this work, we present a general framework for Accelerated Microstructure Imaging via Convex Optimization (AMICO) and show how to re-formulate this class of techniques as convenient linear systems which, then, can be efficiently solved using very fast algorithms. We demonstrate this linearization of the fitting problem for two specific models, i.e. ActiveAx and NODDI, providing a very attractive alternative for parameter estimation in those techniques; however, the AMICO framework is general and flexible enough to work also for the wider space of microstructure imaging methods. Results demonstrate that AMICO represents an effective means to accelerate the fit of existing techniques drastically (up to four orders of magnitude faster) while preserving accuracy and precision in the estimated model parameters (correlation above 0.9). We believe that the availability of such ultrafast algorithms will help to accelerate the spread of microstructure imaging to larger cohorts of patients and to study a wider spectrum of neurological disorders.

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The nutritional state of the pineapple plant has a large effect on plant growth, on fruit production, and fruit quality. The aim of this study was to assess the uptake, accumulation, and export of nutrients by the irrigated 'Vitória' pineapple plant during and at the end of its development. A randomized block statistical design with four replications was used. The treatments were defined by different times of plant collection: at 270, 330, 390, 450, 510, 570, 690, 750, and 810 days after planting (DAP). The collected plants were separated into the following components: leaves, stem, roots, fruit, and slips for determination of fresh and dry matter weight at 65 ºC. After drying, the plant components were ground for characterization of the composition and content of nutrients taken up and exported by the pineapple plant. The results were subjected to analysis of variance, and non-linear regression models were fitted for the significant differences identified by the F test (p<0.01). The leaves and the stem were the plant components that showed the greatest accumulation of nutrients. For production of 72 t ha-1 of fruit, the macronutrient accumulation in the 'Vitória' pineapple exhibited the following decreasing order: K > N > S > Ca > Mg > P, which corresponded to 898, 452, 134, 129, 126, and 107 kg ha-1, respectively, of total accumulation. The export of macronutrients by the pineapple fruit was in the following decreasing order: K > N > S > Ca > P > Mg, which was equivalent to 18, 17, 11, 8, 8, and 5 %, respectively, of the total accumulated by the pineapple. The 'Vitória' pineapple plant exported 78 kg ha-1 of N, 8 kg ha-1 of P, 164 kg ha-1 of K, 14 kg ha-1 of S, 10 kg ha-1 of Ca, and 6 kg ha-1 of Mg by the fruit. The nutrient content exported by the fruits represent important components of nutrient extraction from the soil, which need to be restored, while the nutrients contained in the leaves, stems and roots can be incorporated in the soil within a program of recycling of crop residues.

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Estimating the time since discharge of a spent cartridge or a firearm can be useful in criminal situa-tions involving firearms. The analysis of volatile gunshot residue remaining after shooting using solid-phase microextraction (SPME) followed by gas chromatography (GC) was proposed to meet this objective. However, current interpretative models suffer from several conceptual drawbacks which render them inadequate to assess the evidential value of a given measurement. This paper aims to fill this gap by proposing a logical approach based on the assessment of likelihood ratios. A probabilistic model was thus developed and applied to a hypothetical scenario where alternative hy-potheses about the discharge time of a spent cartridge found on a crime scene were forwarded. In order to estimate the parameters required to implement this solution, a non-linear regression model was proposed and applied to real published data. The proposed approach proved to be a valuable method for interpreting aging-related data.

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When dealing with multi-angular image sequences, problems of reflectance changes due either to illumination and acquisition geometry, or to interactions with the atmosphere, naturally arise. These phenomena interplay with the scene and lead to a modification of the measured radiance: for example, according to the angle of acquisition, tall objects may be seen from top or from the side and different light scatterings may affect the surfaces. This results in shifts in the acquired radiance, that make the problem of multi-angular classification harder and might lead to catastrophic results, since surfaces with the same reflectance return significantly different signals. In this paper, rather than performing atmospheric or bi-directional reflection distribution function (BRDF) correction, a non-linear manifold learning approach is used to align data structures. This method maximizes the similarity between the different acquisitions by deforming their manifold, thus enhancing the transferability of classification models among the images of the sequence.

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Background: MLPA method is a potentially useful semi-quantitative method to detect copy number alterations in targeted regions. In this paper, we propose a method for the normalization procedure based on a non-linear mixed-model, as well as a new approach for determining the statistical significance of altered probes based on linear mixed-model. This method establishes a threshold by using different tolerance intervals that accommodates the specific random error variability observed in each test sample.Results: Through simulation studies we have shown that our proposed method outperforms two existing methods that are based on simple threshold rules or iterative regression. We have illustrated the method using a controlled MLPA assay in which targeted regions are variable in copy number in individuals suffering from different disorders such as Prader-Willi, DiGeorge or Autism showing the best performace.Conclusion: Using the proposed mixed-model, we are able to determine thresholds to decide whether a region is altered. These threholds are specific for each individual, incorporating experimental variability, resulting in improved sensitivity and specificity as the examples with real data have revealed.

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[cat] Una qüestió clau sobre la producció de salut relativament poc explorada es refereix a la influència dels factors socioeconòmics i mediambientals sobre el pes i l’obesitat. Aquesta problemàtica adquireix particular rellevància quan es comparen dos països Mediterranis com Itàlia i Espanya. És interessant adonar-se que l’obesitat a Espanya és 5 punts percentual més elevada al 2003 mentre que a l’any 1990 era aproximadament la mateixa en ambdós països. Aquesta article presenta una descomposició no lineal dels gaps o diferencials en taxes de sobrepès (índex de massa corporal – IMC- entre 25 i 29.9 9 kg/m2), obesitat classe 1 (IMC≥30 kg/m2) i classe 2 (IMC≥35 kg/m2) entre Espanya i Itàlia per gènere i grups d’edat. En explicar aquests gaps entre països aïllem les influències dels estils de vida, els efectes socioeconòmics i els mediambientals. Els nostres resultats indiquen que quan no es controla pels efectes mediambientals (efectes de grup o ‘peer effects’) els hàbits alimentaris i el nivell educatiu són els principals predictors del gaps totals entre països (36-52%), si bé aquests dos factors exerceixen un impacte diferenciat segons gènere i edat. Un tant paradoxalment, quan controlem pels efectes de grup aquests predictors perden la seva capacitat explicativa i els efectes de grup passen a explicar entre el 46-76% dels gaps en sobrepès i obesitat i mostren un patró creixent amb l’edat.

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This paper presents multiple kernel learning (MKL) regression as an exploratory spatial data analysis and modelling tool. The MKL approach is introduced as an extension of support vector regression, where MKL uses dedicated kernels to divide a given task into sub-problems and to treat them separately in an effective way. It provides better interpretability to non-linear robust kernel regression at the cost of a more complex numerical optimization. In particular, we investigate the use of MKL as a tool that allows us to avoid using ad-hoc topographic indices as covariables in statistical models in complex terrains. Instead, MKL learns these relationships from the data in a non-parametric fashion. A study on data simulated from real terrain features confirms the ability of MKL to enhance the interpretability of data-driven models and to aid feature selection without degrading predictive performances. Here we examine the stability of the MKL algorithm with respect to the number of training data samples and to the presence of noise. The results of a real case study are also presented, where MKL is able to exploit a large set of terrain features computed at multiple spatial scales, when predicting mean wind speed in an Alpine region.