Periodic dynamic systems for infected hosts and mosquitoes

A mathematical model for the purpose of analysing the dynamic of the populations of infected hosts anf infected mosquitoes when the populations of mosquitoes are periodic in time is here presented. By the computation of a parameter lambda (the spectral radius of a certain monodromy matrix) one can state that either the infection peters out naturally) (lambda <= 1) or if lambda > 1 the infection becomes endemic. The model generalizes previous models for malaria by considering the case of periodic coefficients; it is also a variation of that for gonorrhea. The main motivation for the consideration of this present model was the recent studies on mosquitoes at an experimental rice irrigation system, in the South-Eastern region of Brazil.

Effects of physical exercise training in DNA damage and repair - could the difference be in hOGG1 Ser326Cys polymorphism?

Acute physical exercise is associated with increased oxygen consumption, which could result in an increased formation of reactive oxygen species (ROS). ROS can react with several organic structures, namely DNA, causing strand breaks and a variety of modified bases in DNA. Physical exercise training seems to decrease the incidence of oxidative stress-associated diseases, and is considered as a key component of a healthy lifestyle. This is a result of exercise-induced adaptation, which has been associated with the possible increase in antioxidant activity and in oxidative damage repair enzymes, leading to an improved physiological function and enhanced resistance to oxidative stress (Radak et al. 2008). Human 8-oxoguanine DNA glycosylase 1 (hOGG1) is involved in the base excision repair (BER) pathway and encodes an enzyme responsible for removing the most common product of oxidative damage in DNA, 8-hydroxyguanine (8-OH-G). The genetic polymorphism of hOGG1 at codon 326 results in a serine (Ser) to cysteine (Cys) amino acid substitution (Ser326Cys). It has been suggested that the carriers of at least one hOGG1Cys variant allele exhibit lower 8-OH-G excision activity than the wild-type (Wilson et al. 2011). The aim of this study was to investigate the possible influence of hOGG1 Ser326Cys polymorphism on DNA damage and repair activity in response to 16 weeks of combined physical exercise training, in thirty healthy Caucasian men. Comet assay was carried out using peripheral blood lymphocytes and enabled the evaluation of DNA damage, both strand breaks and FPG-sensitive sites, and DNA repair activity. Genotypes were determined by PCR-RFLP analysis. The subjects with Ser/Ser genotype were considered as wild-type group (n=20), Ser/Cys and Cys/Cys genotype were analyzed together as mutant group (n=10). Regarding differences between pre and post-training in the wild-type group, the results showed a significant decrease in DNA strand breaks (DNA SBs) (p=0.002) and also in FPG-sensitive sites (p=0.017). No significant differences were observed in weight (p=0.389) and in lipid peroxidation (MDA) (p=0.102). A significant increase in total antioxidant capacity (evaluated by ABTS) was observed (p=0.010). Regarding mutant group, the results showed a significant decrease in DNA SBs (p=0.008) and in weight (p=0.028). No significant differences were observed in FPG-sensitive sites (p=0.916), in ABTS (p=0.074) and in MDA (p=0.086). No significant changes in DNA repair activity were observed in both genotype groups. This preliminary study suggests the possibility of different responses in DNA damage to physical exercise training, considering the hOGG1 Ser326Cys polymorphism.

Dynamic equilibrium through reinforcement learning

Reinforcement Learning is an area of Machine Learning that deals with how an agent should take actions in an environment such as to maximize the notion of accumulated reward. This type of learning is inspired by the way humans learn and has led to the creation of various algorithms for reinforcement learning. These algorithms focus on the way in which an agent’s behaviour can be improved, assuming independence as to their surroundings. The current work studies the application of reinforcement learning methods to solve the inverted pendulum problem. The importance of the variability of the environment (factors that are external to the agent) on the execution of reinforcement learning agents is studied by using a model that seeks to obtain equilibrium (stability) through dynamism – a Cart-Pole system or inverted pendulum. We sought to improve the behaviour of the autonomous agents by changing the information passed to them, while maintaining the agent’s internal parameters constant (learning rate, discount factors, decay rate, etc.), instead of the classical approach of tuning the agent’s internal parameters. The influence of changes on the state set and the action set on an agent’s capability to solve the Cart-pole problem was studied. We have studied typical behaviour of reinforcement learning agents applied to the classic BOXES model and a new form of characterizing the environment was proposed using the notion of convergence towards a reference value. We demonstrate the gain in performance of this new method applied to a Q-Learning agent.

Large area image sensing structures based on a-SiC : H: a dynamic characterization

In recent works large area hydrogenated amorphous silicon p-i-n structures with low conductivity doped layers were proposed as single element image sensors. The working principle of this type of sensor is based on the modulation, by the local illumination conditions, of the photocurrent generated by a light beam scanning the active area of the device. In order to evaluate the sensor capabilities is necessary to perform a response time characterization. This work focuses on the transient response of such sensor and on the influence of the carbon contents of the doped layers. In order to evaluate the response time a set of devices with different percentage of carbon incorporation in the doped layers is analyzed by measuring the scanner-induced photocurrent under different bias conditions.

Constructing good deals in discrete time arbitrage-free dynamic pricing models

Recent literature has proved that many classical pricing models (Black and Scholes, Heston, etc.) and risk measures (V aR, CV aR, etc.) may lead to “pathological meaningless situations”, since traders can build sequences of portfolios whose risk leveltends to −infinity and whose expected return tends to +infinity, i.e., (risk = −infinity, return = +infinity). Such a sequence of strategies may be called “good deal”. This paper focuses on the risk measures V aR and CV aR and analyzes this caveat in a discrete time complete pricing model. Under quite general conditions the explicit expression of a good deal is given, and its sensitivity with respect to some possible measurement errors is provided too. We point out that a critical property is the absence of short sales. In such a case we first construct a “shadow riskless asset” (SRA) without short sales and then the good deal is given by borrowing more and more money so as to invest in the SRA. It is also shown that the SRA is interested by itself, even if there are short selling restrictions.

Intermittent claudication and physical exercise: effectiveness of a home-based program

Peripheral arterial disease (PAD) as a high incidence in general population and 12% to 20% of population with more than 60 years has already clinical symptoms, such as intermittent claudication (IC), pain, loss of strength and functional incapacity. There are already some studies that refer the possible positive effects of physical exercise in functional consequences of PAD. The purpose of this study was to verify the results of a home-based (HB) weekly supervised physical exercise program in patients with IC in consequence of PAD in lower limbs, and observe the medium number of diary steps walked by the subjects of our study.

Ankle dynamic in stroke patients: agonist vs. antagonist muscle relations

O documento em anexo encontra-se na versão post-print (versão corrigida pelo editor).

Evidências de validade da versão brasileira do Exercise Motivation Inventory-2 em contexto de academia e personal training

Este estudo teve como objetivos verificar a validade fatorial e a validade interna da versão brasileira do Exercise Motivation Inventory-2 (EMI-2) e comparar os principais motivos para prática de exercício tendo em conta os contextos de academia e personal training. Um total de 588 praticantes de exercício da cidade de Pelotas/RS/Brasil (405 de academia e 183 de personal training) preencheram o EMI-2, o qual é constituído por 51 itens, agrupados em 14 motivos (fatores) para prática de exercício físico. A validade fatorial do EMI-2 foi testada através da realização de análises fatoriais confirmatórias e a validade interna através do alfa de Cronbach. Para a verificar o efeito do contexto nos motivos foi utilizada a MANOVA e calculado o tamanho do efeito. Os resultados obtidos dão suporte à estrutura original do EMI-2 com 14 fatores, nesta amostra. Verificou-se um efeito multivariado significativo do contexto sobre os motivos de prática [Wilks’ λ = 0.912, F (14, 573.000) = 3.9, p < 0.001, η² = 0.088]. Os motivos de “Prazer”, “Força e resistência”, “Desafio”, “Socialização”, “Competição” e “Reconhecimento Social” foram significativamente superiores no contexto de academia e os motivos de “Agilidade” e “Prevenção de Doenças” foram significativamente superiores no contexto de personal training.

Dynamic artificial neural network for electricity market prices forecast

This paper presents an artificial neural network applied to the forecasting of electricity market prices, with the special feature of being dynamic. The dynamism is verified at two different levels. The first level is characterized as a re-training of the network in every iteration, so that the artificial neural network can able to consider the most recent data at all times, and constantly adapt itself to the most recent happenings. The second level considers the adaptation of the neural network’s execution time depending on the circumstances of its use. The execution time adaptation is performed through the automatic adjustment of the amount of data considered for training the network. This is an advantageous and indispensable feature for this neural network’s integration in ALBidS (Adaptive Learning strategic Bidding System), a multi-agent system that has the purpose of providing decision support to the market negotiating players of MASCEM (Multi-Agent Simulator of Competitive Electricity Markets).

Self-optimization for dynamic scheduling in manufacturing systems

Scheduling is a critical function that is present throughout many industries and applications. A great need exists for developing scheduling approaches that can be applied to a number of different scheduling problems with significant impact on performance of business organizations. A challenge is emerging in the design of scheduling support systems for manufacturing environments where dynamic adaptation and optimization become increasingly important. In this paper, we describe a Self-Optimizing Mechanism for Scheduling System through Nature Inspired Optimization Techniques (NIT).

A hybrid intelligent system for distributed dynamic scheduling

This chapter addresses the resolution of dynamic scheduling by means of meta-heuristic and multi-agent systems. Scheduling is an important aspect of automation in manufacturing systems. Several contributions have been proposed, but the problem is far from being solved satisfactorily, especially if scheduling concerns real world applications. The proposed multi-agent scheduling system assumes the existence of several resource agents (which are decision-making entities based on meta-heuristics) distributed inside the manufacturing system that interact with other agents in order to obtain optimal or near-optimal global performances.

MASDSheGATS – Scheduling System for Dynamic Manufacturing Environments

This chapter addresses the resolution of scheduling in manufacturing systems subject to perturbations. The planning of Manufacturing Systems involves frequently the resolution of a huge amount and variety of combinatorial optimisation problems with an important impact on the performance of manufacturing organisations. Examples of those problems are the sequencing and scheduling problems in manufacturing management, routing and transportation, layout design and timetabling problems.

A study on dynamic state information (DSI) around users for safe urban life

To select each node by devices and by contexts in urban computing, users have to put their plan information and their requests into a computing environment (ex. PDA, Smart Devices, Laptops, etc.) in advance and they will try to keep the optimized states between users and the computing environment. However, because of bad contexts, users may get the wrong decision, so, one of the users’ demands may be requesting the good server which has higher security. To take this issue, we define the structure of Dynamic State Information (DSI) which takes a process about security including the relevant factors in sending/receiving contexts, which select the best during user movement with server quality and security states from DSI. Finally, whenever some information changes, users and devices get the notices including security factors, then an automatic reaction can be possible; therefore all users can safely use all devices in urban computing.

MASDScheGATS: a prototype system for dynamic scheduling

A manufacturing system has a natural dynamic nature observed through several kinds of random occurrences and perturbations on working conditions and requirements over time. For this kind of environment it is important the ability to efficient and effectively adapt, on a continuous basis, existing schedules according to the referred disturbances, keeping performance levels. The application of Meta-Heuristics and Multi-Agent Systems to the resolution of this class of real world scheduling problems seems really promising. This paper presents a prototype for MASDScheGATS (Multi-Agent System for Distributed Manufacturing Scheduling with Genetic Algorithms and Tabu Search).

Multi-agent electricity market simulation with dynamic strategies & virtual power producers

Distributed energy resources will provide a significant amount of the electricity generation and will be a normal profitable business. In the new decentralized grid, customers will be among the many decentralized players and may even help to co-produce the required energy services such as demand-side management and load shedding. So, they will gain the opportunity to be more active market players. The aggregation of DG plants gives place to a new concept: the Virtual Power Producer (VPP). VPPs can reinforce the importance of these generation technologies making them valuable in electricity markets. In this paper we propose the improvement of MASCEM, a multi-agent simulation tool to study negotiations in electricity spot markets based on different market mechanisms and behavior strategies, in order to take account of decentralized players such as VPP.