Impact of muscle fatigue on mechanics and motor control of walking

Pós-graduação em Ciências da Motricidade - IBRC

Revisão sistemática do efeito do envelhecimento no andar livre e adaptativo

Aging seems to impair the walking. However, it is not clear the effects of aging on walking. The aim of this study was to determine changes in kinematic, kinetic and electromyographic parameters of the free and adaptive gait, in preferred velocity, caused by aging. The initial search strategy was performed to identify all articles that examined the free and adaptive gait. The electronic databases analyzed were: MEDLINE, PubMed, EMBASE, CINAHL, Sports Discus, DARE, PsychInfo, ERIC, AusportMed, AMI, Cochrane and PEDro. Twenty-three articles were reviewed in full. Elderly are slower, with shorter step length and longer double support duration than young adults during free and adaptive gait. Even, they showed higher muscular demands, with redistribution of joint power and torque and decreased force in the propulsion and absorption phases. It was concluded that elderly present altered kinematic, kinetic and electromyographic parameters of free and adaptive gait compared to young adults.

Dengue Virus Type 3 Adaptive Changes during Epidemics in Sao Jose de Rio Preto, Brazil, 2006-2007

Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)

Interactions of age and leg muscle fatigue on unobstructed walking and obstacle crossing

Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES)

Modulation of cell proliferation, survival and gene expression by RAGE and TLR signaling in cells of the innate and adaptive immune response: role of p38 MAPK and NF-KB

Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES)

Monitoring muscle damage markers during a four-week downhill walking exercise program

Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES)

Adaptive morphology of the heart of Southern-Fur-Seal (Arctocephalus australis - Zimmermamm, 1783)

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

A Reference Architecture based on Reflection for Self-adaptive Software

Self-adaptive Software (SaS) presents specific characteristics compared to traditional ones, as it makes possible adaptations to be incorporated at runtime. These adaptations, when manually performed, normally become an onerous, error-prone activity. In this scenario, automated approaches have been proposed to support such adaptations; however, the development of SaS is not a trivial task. In parallel, reference architectures are reusable artifacts that aggregate the knowledge of architectures of software systems in specific domains. They have facilitated the development, standardization, and evolution of systems of those domains. In spite of their relevance, in the SaS domain, reference architectures that could support a more systematic development of SaS are not found yet. Considering this context, the main contribution of this paper is to present a reference architecture based on reflection for SaS, named RA4SaS (Reference Architecture for SaS). Its main purpose is to support the development of SaS that presents adaptations at runtime. To show the viability of this reference architecture, a case study is presented. As result, it has been observed that RA4SaS has presented good perspective to efficiently contribute to the area of SaS.

Effects of potassium and sodium supply on drought-adaptive mechanisms in Eucalyptus grandis plantations

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

Adaptive Visual Re-Weighting in Children's Postural Control

Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)

Fast fault section estimation in distribution control centers using adaptive genetic algorithm

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

Combined active and semi-supervised learning using particle walking temporal dynamicscombined active and semi-supervised learning using particle walking temporal dynamics

Both Semi-Supervised Leaning and Active Learning are techniques used when unlabeled data is abundant, but the process of labeling them is expensive and/or time consuming. In this paper, those two machine learning techniques are combined into a single nature-inspired method. It features particles walking on a network built from the data set, using a unique random-greedy rule to select neighbors to visit. The particles, which have both competitive and cooperative behavior, are created on the network as the result of label queries. They may be created as the algorithm executes and only nodes affected by the new particles have to be updated. Therefore, it saves execution time compared to traditional active learning frameworks, in which the learning algorithm has to be executed several times. The data items to be queried are select based on information extracted from the nodes and particles temporal dynamics. Two different rules for queries are explored in this paper, one of them is based on querying by uncertainty approaches and the other is based on data and labeled nodes distribution. Each of them may perform better than the other according to some data sets peculiarities. Experimental results on some real-world data sets are provided, and the proposed method outperforms the semi-supervised learning method, from which it is derived, in all of them.