Estudo clínico prospectivo e paralelo de implantes dentários instalados em pacientes com histórico de doença periodontal agressiva e crônica : aspectos clínicos, microbiológicos e imunológicos

Orientador: Márcio Zaffalon CasatiTese (doutorado) - Universidade Estadual de Campinas, Faculdade de Odontologia de PiracicabaResumo: O objetivo deste estudo foi avaliar os parâmetros clínicos, imunoenzimáticos e microbiológicos de implantes dentais de estágio único instalados em pacientes com histórico de periodontite agressiva, periodontite crônica e saúde, considerando a hipótese de nulidade de que não existe diferença entre os grupos. Foram selecionados pacientes que apresentaram histórico de periodontite agressiva generalizada (PAG) e periodontite crônica generalizada (PCG) com indicação de reabilitação protética implanto suportada. Os pacientes com necessidade de reabilitação unitária foram divididos em 3 grupos: Grupo PAG (n=13): pacientes apresentando histórico de periodontite agressiva generalizada; Grupo PCG (n=18): pacientes com histórico de PCG ; e Grupo Controle (n=14): pacientes sem histórico de periodontite. Todos os implantes foram instalados em estágio único e, após 3 meses, receberam reabilitação com próteses metalocerâmicas unitárias aparafusadas. Profundidade de sondagem, nível clínico de inserção relativo e posição da margem gengival relativo foram avaliados nos implantes no momento da instalação da prótese e 1, 3 e 6 meses após o carregamento. Avaliação radiográfica foi feita no momento 7 dias após a cirurgia, na instalação da prótese e 6 meses após o carregamento protético. Avaliação microbiológica foi realizada imediatamente após a instalação da prótese, 1, 3 e 6 meses após, por meio de PCR real time, determinando a quantidade dos microrganismos A. actinomycetemcomitans, P. gingivalis e, T. forsythia Avaliação imunológica foi realizada utilizando o sistema LUMINEX/MAGPIX® com amostras de fluido periimplantar coletado aos 15 dias após a cirurgia, imediatamente após a instalação da prótese e 6 meses após o carregamento protético, avaliando as concentrações de IL1?, IL4, IL6, IL8, IL10, TNF?, INF? e GM-CSF, além de marcadores de osteogênese e osteoclasia (OPG e RANKL). Não foram observadas diferenças entre os perfis de pacientes quanto aos parâmetros clínicos e radiográficos ao redor dos implantes em nenhum dos períodos de avaliação. No primeiro mês após a instalação das próteses verificou-se maior concentração de Aa (p 0.05) were considered at any evaluation period. At the first month after implant loading, those in the HGAgP group presented a higher level of Aa (p < 0.05). Six months after implant loading, those in the HH group presented a lower level of Pg (p < 0.05). The immunologic evaluation showed higher values of OPG in those in the HH group at implant loading, and a higher IL-4 level in those in the HH group six months after implant loading. It can be concluded that, after six months of implant loading, despite some micro- and immunological differences, there are no clinical differences or additional RMBR around implants placed in patients with a history of periodontal diseaseDoutoradoPeriodontiaDoutor em Clínica Odontológic

Simulated Annealing Approach Applied to the Energy Resource Management Considering Demand Response for Electric Vehicles

The aggregation and management of Distributed Energy Resources (DERs) by an Virtual Power Players (VPP) is an important task in a smart grid context. The Energy Resource Management (ERM) of theses DERs can become a hard and complex optimization problem. The large integration of several DERs, including Electric Vehicles (EVs), may lead to a scenario in which the VPP needs several hours to have a solution for the ERM problem. This is the reason why it is necessary to use metaheuristic methodologies to come up with a good solution with a reasonable amount of time. The presented paper proposes a Simulated Annealing (SA) approach to determine the ERM considering an intensive use of DERs, mainly EVs. In this paper, the possibility to apply Demand Response (DR) programs to the EVs is considered. Moreover, a trip reduce DR program is implemented. The SA methodology is tested on a 32-bus distribution network with 2000 EVs, and the SA results are compared with a deterministic technique and particle swarm optimization results

Maximizing the Social Welfare of Virtual Power Players Operation in Case of Excessive Wind Power

The integration of growing amounts of distributed generation in power systems, namely at distribution networks level, has been fostered by energy policies in several countries around the world, including in Europe. This intensive integration of distributed, non-dispatchable, and natural sources based generation (including wind power) has caused several changes in the operation and planning of power systems and of electricity markets. Sometimes the available non-dispatchable generation is higher than the demand. This generation must be used; otherwise it is wasted if not stored or used to supply additional demand. New policies and market rules, as well as new players, are needed in order to competitively integrate all the resources. The methodology proposed in this paper aims at the maximization of the social welfare in a distribution network operated by a virtual power player that aggregates and manages the available energy resources. When facing a situation of excessive non-dispatchable generation, including wind power, real time pricing is applied in order to induce the increase of consumption so that wind curtailment is minimized. This method is especially useful when actual and day-ahead resources forecast differ significantly. The distribution network characteristics and concerns are addressed by including the network constraints in the optimization model. The proposed methodology has been implemented in GAMS optimization tool and its application is illustrated in this paper using a real 937-bus distribution network with 20.310 consumers and 548 distributed generators, some of them non-dispatchable and with must take contracts. The implemented scenario corresponds to a real day in Portuguese power system

A thermodynamic perspective on the medium dependence of propagation coefficients and reactivity ratios in radical polymerization

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Energy resource management under the influence of the weekend transition considering an intensive use of electric vehicles

Energy resource scheduling is becoming increasingly important, as the use of distributed resources is intensified and of massive electric vehicle is envisaged. The present paper proposes a methodology for day-ahead energy resource scheduling for smart grids considering the intensive use of distributed generation and Vehicle-to-Grid (V2G). This method considers that the energy resources are managed by a Virtual Power Player (VPP) which established contracts with their owners. It takes into account these contracts, the users' requirements subjected to the VPP, and several discharge price steps. The full AC power flow calculation included in the model takes into account network constraints. The influence of the successive day requirements on the day-ahead optimal solution is discussed and considered in the proposed model. A case study with a 33-bus distribution network and V2G is used to illustrate the good performance of the proposed method

Energy resources management in three distinct time horizons considering a large variation in wind power

The intensive use of distributed generation based on renewable resources increases the complexity of power systems management, particularly the short-term scheduling. Demand response, storage units and electric and plug-in hybrid vehicles also pose new challenges to the short-term scheduling. However, these distributed energy resources can contribute significantly to turn the shortterm scheduling more efficient and effective improving the power system reliability. This paper proposes a short-term scheduling methodology based on two distinct time horizons: hour-ahead scheduling, and real-time scheduling considering the point of view of one aggregator agent. In each scheduling process, it is necessary to update the generation and consumption operation, and the storage and electric vehicles status. Besides the new operation condition, more accurate forecast values of wind generation and consumption are available, for the resulting of short-term and very short-term methods. In this paper, the aggregator has the main goal of maximizing his profits while, fulfilling the established contracts with the aggregated and external players

Defining Electricity Tariffs Using the Knowledge About the Consumers Profiles in ELECON Project

The increasing importance of the integration of distributed generation and demand response in the power systems operation and planning, namely at lower voltage levels of distribution networks and in the competitive environment of electricity markets, leads us to the concept of smart grids. In both traditional and smart grid operation, non-technical losses are a great economic concern, which can be addressed. In this context, the ELECON project addresses the use of demand response contributions to the identification of non-technical losses. The present paper proposes a methodology to be used by Virtual Power Players (VPPs), which are entities able to aggregate distributed small-size resources, aiming to define the best electricity tariffs for several, clusters of consumers. A case study based on real consumption data demonstrates the application of the proposed methodology

Strategic bidding in electricity markets: An agent-based simulator with game theory for scenario analysis

Electricity markets are complex environments, involving a large number of different entities, with specific characteristics and objectives, making their decisions and interacting in a dynamic scene. Game-theory has been widely used to support decisions in competitive environments; therefore its application in electricity markets can prove to be a high potential tool. This paper proposes a new scenario analysis algorithm, which includes the application of game-theory, to evaluate and preview different scenarios and provide players with the ability to strategically react in order to exhibit the behavior that better fits their objectives. This model includes forecasts of competitor players’ actions, to build models of their behavior, in order to define the most probable expected scenarios. Once the scenarios are defined, game theory is applied to support the choice of the action to be performed. Our use of game theory is intended for supporting one specific agent and not for achieving the equilibrium in the market. 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. The scenario analysis algorithm has been tested within MASCEM and our experimental findings with a case study based on real data from the Iberian Electricity Market are presented and discussed

Day-ahead Resource Scheduling Including Demand Response for Electric Vehicles

The energy resource scheduling is becoming increasingly important, as the use of distributed resources is intensified and massive gridable vehicle (V2G) use is envisaged. This paper presents a methodology for day-ahead energy resource scheduling for smart grids considering the intensive use of distributed generation and V2G. The main focus is the comparison of different EV management approaches in the day-ahead energy resources management, namely uncontrolled charging, smart charging, V2G and Demand Response (DR) programs i n the V2G approach. Three different DR programs are designed and tested (trip reduce, shifting reduce and reduce+shifting). Othe r important contribution of the paper is the comparison between deterministic and computational intelligence techniques to reduce the execution time. The proposed scheduling is solved with a modified particle swarm optimization. Mixed integer non-linear programming is also used for comparison purposes. Full ac power flow calculation is included to allow taking into account the network constraints. A case study with a 33-bus distribution network and 2000 V2G resources is used to illustrate the performance of the proposed method

Distributed, Agent-Based Intelligent System for Demand Response Program Simulation in Smart Grids

A distributed, agent-based intelligent system models and simulates a smart grid using physical players and computationally simulated agents. The proposed system can assess the impact of demand response programs