Paediatric obesity, physical activity and the musculoskeletal system

The current epidemic of paediatric obesity is consistent with a myriad of health-related comorbid conditions. Despite the higher prevalence of orthopaedic conditions in overweight children, a paucity of published research has considered the influence of these conditions on the ability to undertake physical activity. As physical activity participation is directly related to improvements in physical fitness, skeletal health and metabolic conditions, higher levels of physical activity are encouraged, and exercise is commonly prescribed in the treatment and management of childhood obesity. However, research has not correlated orthopaedic conditions, including the increased joint pain and discomfort that is commonly reported by overweight children, with decreases in physical activity. Research has confirmed that overweight children typically display a slower, more tentative walking pattern with increased forces to the hip, knee and ankle during 'normal' gait. This research, combined with anthropometric data indicating a higher prevalence of musculoskeletal malalignment in overweight children, suggests that such individuals are poorly equipped to undertake certain forms of physical activity. Concomitant increases in obesity and decreases in physical activity level strongly support the need to better understand the musculoskeletal factors associated with the performance of motor tasks by overweight and obese children.

Power system parameters forecasting using Hilbert-Huang transform and machine learning

A novel hybrid data-driven approach is developed for forecasting power system parameters with the goal of increasing the efficiency of short-term forecasting studies for non-stationary time-series. The proposed approach is based on mode decomposition and a feature analysis of initial retrospective data using the Hilbert-Huang transform and machine learning algorithms. The random forests and gradient boosting trees learning techniques were examined. The decision tree techniques were used to rank the importance of variables employed in the forecasting models. The Mean Decrease Gini index is employed as an impurity function. The resulting hybrid forecasting models employ the radial basis function neural network and support vector regression. A part from introduction and references the paper is organized as follows. The second section presents the background and the review of several approaches for short-term forecasting of power system parameters. In the third section a hybrid machine learningbased algorithm using Hilbert-Huang transform is developed for short-term forecasting of power system parameters. Fourth section describes the decision tree learning algorithms used for the issue of variables importance. Finally in section six the experimental results in the following electric power problems are presented: active power flow forecasting, electricity price forecasting and for the wind speed and direction forecasting.

Experimental Active Vibration Control in Truss Structures Considering Uncertainties in System Parameters

Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)

Mating system parameters at hierarchical levels of fruits, individuals and populations in the Brazilian insect-pollinated tropical tree, Tabebuia roseo-alba (Bignoniaceae)

Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)

On the effects of system parameters on the response of a harmonically excited system consisting of weakly coupled nonlinear and linear oscillators

This paper discusses the dynamic behaviour of a nonlinear two degree-of-freedom system consisting of a harmonically excited linear oscillator weakly connected to a nonlinear attachment having linear and cubic restoring forces. The effects of the system parameters on the shape of the frequency-response curve are investigated, in particular those yielding the appearance and disappearance of outer and inner detached resonance curves. In contrast to the case when the linear stiffness of the attachment is zero, it is found that multivaluedness occurs at low frequencies as the resonant peak bends to the right. It is also found that as the coefficient of the linear term increases, the range of parameters yielding detached curves reduces. Compared to the case when the attached system has no linear stiffness term, this range of parameters corresponds to smaller values of the damping and nonlinear coefficients. Approximate analytical expressions for the jump-up and jump-down frequencies of the system under investigation are also derived. (C) 2011 Elsevier Ltd. All rights reserved.

Mating system parameters at hierarchical levels of fruits, individuals and populations in the Brazilian insect-pollinated tropical tree, Tabebuia roseo-alba (Bignoniaceae)

For many tree species, mating system analyses have indicated potential variations in the selfing rate and paternity correlation among fruits within individuals, among individuals within populations, among populations, and from one flowering event to another. In this study, we used eight microsatellite markers to investigate mating systems at two hierarchical levels (fruits within individuals and individuals within populations) for the insect pollinated Neotropical tree Tabebuia roseo-alba. We found that T. roseo-alba has a mixed mating system with predominantly outcrossed mating. The outcrossing rates at the population level were similar across two T. roseo-alba populations; however, the rates varied considerably among individuals within populations. The correlated paternity results at different hierarchical levels showed that there is a high probability of shared paternal parentage when comparing seeds within fruits and among fruits within plants and full-sibs occur in much higher proportion within fruits than among fruits. Significant levels of fixation index were found in both populations and biparental inbreeding is believed to be the main cause of the observed inbreeding. The number of pollen donors contributing to mating was low. Furthermore, open-pollinated seeds varied according to relatedness, including half-sibs, full-sibs, self-sibs and self- half-sibs. In both populations, the effective population size within a family (seed-tree and its offspring) was lower than expected for panmictic populations. Thus, seeds for ex situ conservation genetics, progeny tests and reforestation must be collected from a large number of seed-trees to guarantee an adequate effective population in the sample.

Substructuring approache in state space models for dynamic system parameters identification

In the recent decade, the request for structural health monitoring expertise increased exponentially in the United States. The aging issues that most of the transportation structures are experiencing can put in serious jeopardy the economic system of a region as well as of a country. At the same time, the monitoring of structures is a central topic of discussion in Europe, where the preservation of historical buildings has been addressed over the last four centuries. More recently, various concerns arose about security performance of civil structures after tragic events such the 9/11 or the 2011 Japan earthquake: engineers looks for a design able to resist exceptional loadings due to earthquakes, hurricanes and terrorist attacks. After events of such a kind, the assessment of the remaining life of the structure is at least as important as the initial performance design. Consequently, it appears very clear that the introduction of reliable and accessible damage assessment techniques is crucial for the localization of issues and for a correct and immediate rehabilitation. The System Identification is a branch of the more general Control Theory. In Civil Engineering, this field addresses the techniques needed to find mechanical characteristics as the stiffness or the mass starting from the signals captured by sensors. The objective of the Dynamic Structural Identification (DSI) is to define, starting from experimental measurements, the modal fundamental parameters of a generic structure in order to characterize, via a mathematical model, the dynamic behavior. The knowledge of these parameters is helpful in the Model Updating procedure, that permits to define corrected theoretical models through experimental validation. The main aim of this technique is to minimize the differences between the theoretical model results and in situ measurements of dynamic data. Therefore, the new model becomes a very effective control practice when it comes to rehabilitation of structures or damage assessment. The instrumentation of a whole structure is an unfeasible procedure sometimes because of the high cost involved or, sometimes, because it’s not possible to physically reach each point of the structure. Therefore, numerous scholars have been trying to address this problem. In general two are the main involved methods. Since the limited number of sensors, in a first case, it’s possible to gather time histories only for some locations, then to move the instruments to another location and replay the procedure. Otherwise, if the number of sensors is enough and the structure does not present a complicate geometry, it’s usually sufficient to detect only the principal first modes. This two problems are well presented in the works of Balsamo [1] for the application to a simple system and Jun [2] for the analysis of system with a limited number of sensors. Once the system identification has been carried, it is possible to access the actual system characteristics. A frequent practice is to create an updated FEM model and assess whether the structure fulfills or not the requested functions. Once again the objective of this work is to present a general methodology to analyze big structure using a limited number of instrumentation and at the same time, obtaining the most information about an identified structure without recalling methodologies of difficult interpretation. A general framework of the state space identification procedure via OKID/ERA algorithm is developed and implemented in Matlab. Then, some simple examples are proposed to highlight the principal characteristics and advantage of this methodology. A new algebraic manipulation for a prolific use of substructuring results is developed and implemented.

Sweet's syndrome involoving the musculoskeletal system during treatment of promyelocytic leukemia with all-trans retinoic acid

Induction therapy of promyelocytic leukemia with all-trans retinoic acid is a standard therapy despite significant side-effects. The most important, the "retinoic acid syndrome", consists of a hyperinflammatory reaction with capillary leakage (edema, pleural, and pericardial effusion), infiltration of myeloid cells into internal organs and systemic signs of inflammation. We describe here two cases of another hyperinflammatory reaction during all-trans retinoic acid therapy, the Sweet's syndrome, consisting of infiltrates of the skin and internal organs by neutrophilic granulocytes. Fever, painful erythematous cutaneous plaques, prominent musculoskeletal involvement (myositis, fasciitis), a sterile pulmonary infiltration and intercurrent proteinuria characterized the clinical course of all-trans retinoic acid-associated Sweet's syndrome. Treatment with glucocorticoids led to resolution of the syndrome within 48 h. Three other cases of all-trans retinoic acid-associated Sweet's syndrome without involvement of internal organs, prominent on our cases, were published previously. Recognition of ATRA-associated Sweet's syndrome is of practical importance.