Coordinated transportation of a large object by a team of three robots

Dynamical systems theory in this work is used as a theoretical language and tool to design a distributed control architecture for a team of three robots that must transport a large object and simultaneously avoid collisions with either static or dynamic obstacles. The robots have no prior knowledge of the environment. The dynamics of behavior is defined over a state space of behavior variables, heading direction and path velocity. Task constraints are modeled as attractors (i.e. asymptotic stable states) of the behavioral dynamics. For each robot, these attractors are combined into a vector field that governs the behavior. By design the parameters are tuned so that the behavioral variables are always very close to the corresponding attractors. Thus the behavior of each robot is controlled by a time series of asymptotical stable states. Computer simulations support the validity of the dynamical model architecture.

Coordinated transportation of a large object by a team of two robots

In this paper dynamical systems theory is used as a theoretical language and tool to design a distributed control architecture for a team of two robots that must transport a large object and simultaneously avoid collisions with obstacles (either static or dynamic). This work extends the previous work with two robots (see [1] and [5]). However here we demonstrate that it’s possible to simplify the architecture presented in [1] and [5] and reach an equally stable global behavior. The robots have no prior knowledge of the environment. The dynamics of behavior is defined over a state space of behavior variables, heading direction and path velocity. Task constrains are modeled as attractors (i.e. asymptotic stable states) of a behavioral dynamics. For each robot, these attractors are combined into a vector field that governs the behavior. By design the parameters are tuned so that the behavioral variables are always very close to the corresponding attractors. Thus the behavior of each robot is controlled by a time series of asymptotic stable states. Computer simulations support the validity of the dynamical model architecture.

Regional governance: strategies and disputes in health region management

OBJECTIVE To analyze the regional governance of the health systemin relation to management strategies and disputes.METHODOLOGICAL PROCEDURES A qualitative study with health managers from 19 municipalities in the health region of Bahia, Northeastern Brazil. Data were drawn from 17 semi-structured interviews of state, regional, and municipal health policymakers and managers; a focus group; observations of the regional interagency committee; and documents in 2012. The political-institutional and the organizational components were analyzed in the light of dialectical hermeneutics.RESULTS The regional interagency committee is the chief regional governance strategy/component and functions as a strategic tool for strengthening governance. It brings together a diversity of members responsible for decision making in the healthcare territories, who need to negotiate the allocation of funding and the distribution of facilities for common use in the region. The high turnover of health secretaries, their lack of autonomy from the local executive decisions, inadequate technical training to exercise their function, and the influence of party politics on decision making stand as obstacles to the regional interagency committee’s permeability to social demands. Funding is insufficient to enable the fulfillment of the officially integrated agreed-upon program or to boost public supply by the system, requiring that public managers procure services from the private market at values higher than the national health service price schedule (Brazilian Unified Health System Table). The study determined that “facilitators” under contract to health departments accelerated access to specialized (diagnostic, therapeutic and/or surgical) services in other municipalities by direct payment to physicians for procedure costs already covered by the Brazilian Unified Health System.CONCLUSIONS The characteristics identified a regionalized system with a conflictive pattern of governance and intermediate institutionalism. The regional interagency committee’s managerial routine needs to incorporate more democratic devices for connecting with educational institutions, devices that are more permeable to social demands relating to regional policy making.

Investment focused strategies in electricity markets using the ITEM-game simulator

This paper presents a methodology to establish investment and trading strategies of a power generation company. These strategies are integrated in the ITEM-Game simulator in order to test their results when played against defined strategies used by other players. The developed strategies are focused on investment decisions, although trading strategies are also implemented to obtain base case results. Two cases are studied considering three players with the same trading strategy. In case 1, all players also have the same investment strategy driven by a market target share. In case 2, player 1 has an improved investment strategy with a target share twice of the target of players 2 and 3. Results put in evidence the influence of the CO2 and fuel prices in the company investment decision. It is also observed the influence of the budget constraint which might prevent the player to take the desired investment decision.

Strategies for price reduction of HIV medicines under a monopoly situation in Brazil

ABSTRACT OBJECTIVE To analyze Government strategies for reducing prices of antiretroviral medicines for HIV in Brazil. METHODS Analysis of Ministry of Health purchases of antiretroviral medicines, from 2005 to 2013. Expenditures and costs of the treatment per year were analyzed and compared to international prices of atazanavir. Price reductions were estimated based on the terms of a voluntary license of patent rights and technology transfer in the Partnership for Productive Development Agreement for atazanavir. RESULTS Atazanavir, a patented medicine, represented a significant share of the expenditures on antiretrovirals purchased from the private sector. Prices in Brazil were higher than international references, and no evidence was found of a relationship between purchase volume and price paid by the Ministry of Health. Concerning the latest strategy to reduce prices, involving local production of the 200 mg capsule, the price reduction was greater than the estimated reduction. As for the 300 mg capsule, the amounts paid in the first two years after the Partnership for Productive Development Agreement were close to the estimated values. Prices in nominal values for both dosage forms remained virtually constant between 2011 (the signature of the Partnership for Productive Development Agreement), 2012 and 2013 (after the establishment of the Partnership). CONCLUSIONS Price reduction of medicines is complex in limited-competition environments. The use of a Partnership for Productive Development Agreement as a strategy to increase the capacity of local production and to reduce prices raises issues regarding its effectiveness in reducing prices and to overcome patent barriers. Investments in research and development that can stimulate technological accumulation should be considered by the Government to strengthen its bargaining power to negotiate medicines prices under a monopoly situation.

Assessment of data-driven modeling strategies for water delivery canals

Conferência: CONTROLO’2012 - 16-18 July 2012 - Funchal

Chaînes opératoires and resource-exploitation strategies in chimpanzee (Pan troglodytes) nut cracking

Journal of Human Evolution, V. 55, pp. 148-163

A stochastic programming approach for the development of offering strategies for a wind power producer

A stochastic programming approach is proposed in this paper for the development of offering strategies for a wind power producer. The optimization model is characterized by making the analysis of several scenarios and treating simultaneously two kinds of uncertainty: wind power and electricity market prices. The approach developed allows evaluating alternative production and offers strategies to submit to the electricity market with the ultimate goal of maximizing profits. An innovative comparative study is provided, where the imbalances are treated differently. Also, an application to two new realistic case studies is presented. Finally, conclusions are duly drawn.

Optimal offering strategies for wind power producers considering uncertainty and risk

This paper provides a two-stage stochastic programming approach for the development of optimal offering strategies for wind power producers. Uncertainty is related to electricity market prices and wind power production. A hybrid intelligent approach, combining wavelet transform, particle swarm optimization and adaptive-network-based fuzzy inference system, is used in this paper to generate plausible scenarios. Also, risk aversion is explicitly modeled using the conditional value-at-risk methodology. Results from a realistic case study, based on a wind farm in Portugal, are provided and analyzed. Finally, conclusions are duly drawn.

A new approach to assess defragmentation strategies in dynamically reconfigurable FPGAs

Fragmentation on dynamically reconfigurable FPGAs is a major obstacle to the efficient management of the logic space in reconfigurable systems. When resource allocation decisions have to be made at run-time a rearrangement may be necessary to release enough contiguous resources to implement incoming functions. The feasibility of run-time relocation depends on the processing time required to set up rearrangements. Moreover, the performance of the relocated functions should not be affected by this process or otherwise the whole system performance, and even its operation, may be at risk. Relocation should take into account not only specific functional issues, but also the FPGA architecture, since these two aspects are normally intertwined. A simple and fast method to assess performance degradation of a function during relocation and to speed up the defragmentation process, based on previous function labelling and on the application of the Euclidian distance concept, is proposed in this paper.

A visão e a voz do diretor: a construção do seu projeto de Intervenção

Dissertação apresentada para obtenção do grau de Mestre em Ciências da Educação - Área de especialização em Administração Escolar

Learning in higher education: strategies to overcome challenges faced by adult students – lessons drawn from two case studies in Portugal

This paper appears in International Journal of Information and Communication Technology Education edited by Lawrence A. Tomei (Ed.) Copyright 2007, IGI Global, www.igi-global.com. Posted by permission of the publisher. URL:http://www.idea-group.com/journals/details.asp?id=4287.

Adaptive learning in agents behaviour: A framework for electricity markets simulation

Electricity markets are complex environments, involving a large number of different entities, playing in a dynamic scene to obtain the best advantages and profits. MASCEM (Multi-Agent System for Competitive Electricity Markets) is a multi-agent electricity market simulator that models market players and simulates their operation in the market. Market players are entities with specific characteristics and objectives, making their decisions and interacting with other players. This paper presents a methodology to provide decision support to electricity market negotiating players. This model allows integrating different strategic approaches for electricity market negotiations, and choosing the most appropriate one at each time, for each different negotiation context. This methodology is integrated in ALBidS (Adaptive Learning strategic Bidding System) – a multiagent system that provides decision support to MASCEM's negotiating agents so that they can properly achieve their goals. ALBidS uses artificial intelligence methodologies and data analysis algorithms to provide effective adaptive learning capabilities to such negotiating entities. The main contribution is provided by a methodology that combines several distinct strategies to build actions proposals, so that the best can be chosen at each time, depending on the context and simulation circumstances. The choosing process includes reinforcement learning algorithms, a mechanism for negotiating contexts analysis, a mechanism for the management of the efficiency/effectiveness balance of the system, and a mechanism for competitor players' profiles definition.

Race-to-halt energy saving strategies

Energy consumption is one of the major issues for modern embedded systems. Early, power saving approaches mainly focused on dynamic power dissipation, while neglecting the static (leakage) energy consumption. However, technology improvements resulted in a case where static power dissipation increasingly dominates. Addressing this issue, hardware vendors have equipped modern processors with several sleep states. We propose a set of leakage-aware energy management approaches that reduce the energy consumption of embedded real-time systems while respecting the real-time constraints. Our algorithms are based on the race-to-halt strategy that tends to run the system at top speed with an aim to create long idle intervals, which are used to deploy a sleep state. The effectiveness of our algorithms is illustrated with an extensive set of simulations that show an improvement of up to 8% reduction in energy consumption over existing work at high utilization. The complexity of our algorithms is smaller when compared to state-of-the-art algorithms. We also eliminate assumptions made in the related work that restrict the practical application of the respective algorithms. Moreover, a novel study about the relation between the use of sleep intervals and the number of pre-emptions is also presented utilizing a large set of simulation results, where our algorithms reduce the experienced number of pre-emptions in all cases. Our results show that sleep states in general can save up to 30% of the overall number of pre-emptions when compared to the sleep-agnostic earliest-deadline-first algorithm.