An ex vivo comparison of root canal length determination by three electronic apex locators at positions short of the apical foramen

Objective. The aim of this study was to evaluate the precision of working length determination of 3 electronic apex locators (EALs): Root ZX, RomiApex D-30, and Ipex at 0.0 mm, at the apical foramen (AF), and at 1.0 mm short of the AF. Methodology. Thirty-eight mandibular premolars had their real lengths previously determined. Electronic measurements were determined at 1.0 mm, followed by measurements at 0.0 mm, performed in triplicate. Results. Precision of devices at 1.0 mm and 0.0 mm were: 94.7% and 97.4%, respectively (Root ZX); 78.9% and 97.4% (RomiApex D-30); and 76.3% and 97.4% (Ipex). Although no statistical differences were observed between the EALs at 0.0, at 1.0 mm Root ZX performed significantly better than the others. Conclusion. The EALs had acceptable precision when measuring the working length at the AF. However, when used at levels short of the AF, only Root ZX did not suffer a significant negative effect on precision. (Oral Surg Oral Med Oral Pathol Oral Radiol Endod 2010;110:e57-e61)

The use of 16s rDNA restriction fragment length polymorphism analysis for the identification of non-fermenting gram negative bacilli in a cystic fibrosis microbiology referral service

The utility of 16s rDNA restriction fragment length polymorphism (RFLP) analysis for the partial genomovar differentiation of Burkholderia cepacia complex bacterium is well documented. We compared the 16s rDNA RFLP signatures for a number of non-fermenting gram negative bacilli (NF GNB) LMG control strains and clinical isolates pertaining to the genera Burkholderia, Pseudomonas, Achromobacter (Alcaligenes), Ralstonia, Stenotrophomonas and Pandoraea. A collection of 24 control strain (LMG) and 25 clinical isolates were included in the study. Using conventional PCR, a 1.2 kbp 16s rDNA fragment was generated for each organism. Following restriction digestion and electrophoresis, each clinical isolate RFLP signature was compared to those of the control strain panel. Nineteen different RFLP signatures were detected from the 28 control strains included in the study. TwentyoneyTwenty- five of the clinical isolates could be classified by RFLP analysis into a single genus and species when compared to the patterns produced by the control strain panel. Four clinical B. pseudomallei isolates produced RFLP signatures which were indistinguishable from B. cepacia genomovars I, III and VIII. The identity of these four isolates were confirmed using B. pseudomallei specific PCR. 16s rDNA RFLP analysis can be a useful identification strategy when applied to NF GNB, particularly for those which exhibit colistin sulfate resistance. The use of this molecular based methodology has proved very useful in the setting of a CF referral laboratory particularly when utilised in conjunction with B. cepacia complex and genomovar specific PCR techniques. Species specific PCR or sequence analysis should be considered for selected isolates; especially where discrepancies between epidemiology, phenotypic and genotypic characteristics occur.

Long-term price range forecast applied to risk management using regression models

Long-term contractual decisions are the basis of an efficient risk management. However those types of decisions have to be supported with a robust price forecast methodology. This paper reports a different approach for long-term price forecast which tries to give answers to that need. Making use of regression models, the proposed methodology has as main objective to find the maximum and a minimum Market Clearing Price (MCP) for a specific programming period, and with a desired confidence level α. Due to the problem complexity, the meta-heuristic Particle Swarm Optimization (PSO) was used to find the best regression parameters and the results compared with the obtained by using a Genetic Algorithm (GA). To validate these models, results from realistic data are presented and discussed in detail.

Genetic algorithm methodology applied to intelligent house control

In recent years the use of several new resources in power systems, such as distributed generation, demand response and more recently electric vehicles, has significantly increased. Power systems aim at lowering operational costs, requiring an adequate energy resources management. In this context, load consumption management plays an important role, being necessary to use optimization strategies to adjust the consumption to the supply profile. These optimization strategies can be integrated in demand response programs. The control of the energy consumption of an intelligent house has the objective of optimizing the load consumption. This paper presents a genetic algorithm approach to manage the consumption of a residential house making use of a SCADA system developed by the authors. Consumption management is done reducing or curtailing loads to keep the power consumption in, or below, a specified energy consumption limit. This limit is determined according to the consumer strategy and taking into account the renewable based micro generation, energy price, supplier solicitations, and consumers’ preferences. The proposed approach is compared with a mixed integer non-linear approach.

A data-mining-based methodology for transmission expansion planning

In recent decades, all over the world, competition in the electric power sector has deeply changed the way this sector’s agents play their roles. In most countries, electric process deregulation was conducted in stages, beginning with the clients of higher voltage levels and with larger electricity consumption, and later extended to all electrical consumers. The sector liberalization and the operation of competitive electricity markets were expected to lower prices and improve quality of service, leading to greater consumer satisfaction. Transmission and distribution remain noncompetitive business areas, due to the large infrastructure investments required. However, the industry has yet to clearly establish the best business model for transmission in a competitive environment. After generation, the electricity needs to be delivered to the electrical system nodes where demand requires it, taking into consideration transmission constraints and electrical losses. If the amount of power flowing through a certain line is close to or surpasses the safety limits, then cheap but distant generation might have to be replaced by more expensive closer generation to reduce the exceeded power flows. In a congested area, the optimal price of electricity rises to the marginal cost of the local generation or to the level needed to ration demand to the amount of available electricity. Even without congestion, some power will be lost in the transmission system through heat dissipation, so prices reflect that it is more expensive to supply electricity at the far end of a heavily loaded line than close to an electric power generation. Locational marginal pricing (LMP), resulting from bidding competition, represents electrical and economical values at nodes or in areas that may provide economical indicator signals to the market agents. This article proposes a data-mining-based methodology that helps characterize zonal prices in real power transmission networks. To test our methodology, we used an LMP database from the California Independent System Operator for 2009 to identify economical zones. (CAISO is a nonprofit public benefit corporation charged with operating the majority of California’s high-voltage wholesale power grid.) To group the buses into typical classes that represent a set of buses with the approximate LMP value, we used two-step and k-means clustering algorithms. By analyzing the various LMP components, our goal was to extract knowledge to support the ISO in investment and network-expansion planning.

Price forecasting in the day-ahead Iberian electricity market using a conjectural variations Arima model

Price forecast is a matter of concern for all participants in electricity markets, from suppliers to consumers through policy makers, which are interested in the accurate forecast of day-ahead electricity prices either for better decisions making or for an improved evaluation of the effectiveness of market rules and structure. This paper describes a methodology to forecast market prices in an electricity market using an ARIMA model applied to the conjectural variations of the firms acting in an electricity market. This methodology is applied to the Iberian electricity market to forecast market prices in the 24 hours of a working day. The methodology was then compared with two other methodologies, one called naive and the other a direct forecast of market prices using also an ARIMA model. Results show that the conjectural variations price forecast performs better than the naive and that it performs slightly better than the direct price forecast.

Demand Response Programs Definition Using Demand Price Elasticity to Define Consumers Aggregation for an Improved Remuneration Structure

The positioning of the consumers in the power systems operation has been changed in the recent years, namely due to the implementation of competitive electricity markets. Demand response is an opportunity for the consumers’ participation in electricity markets. Smart grids can give an important support for the integration of demand response. The methodology proposed in the present paper aims to create an improved demand response program definition and remuneration scheme for aggregated resources. The consumers are aggregated in a certain number of clusters, each one corresponding to a distinct demand response program, according to the economic impact of the resulting remuneration tariff. The knowledge about the consumers is obtained from its demand price elasticity values. The illustrative case study included in the paper is based on a 218 consumers’ scenario.

Modelling HIV/AIDS length of stay in Portugal

Tese apresentada como requisito parcial para obtenção do grau de Doutor em Estatística e Gestão de Informação pelo Instituto Superior de Estatística e Gestão de Informação da Universidade Nova de Lisboa

Oil price shocks and the Portuguese economy since the 1970s

This paper assesses empirically the effect of oil price shocks on Portuguese aggregate economic activity, industrial production and price level. We take the usual multivariate VAR methodology to investigate the magnitude and stability of this relationship. In doing so, we follow the approach presented in the recent literature and adopt different oil price specifications. We conclude that, as for most industrialized countries, the nature of this relationship changed in the mid-1980s. Furthermore, we show that the main Portuguese macroeconomic variables have become progressively less responsive to oil shocks and the adjustment towards equilibrium has become increasingly faster.

The challenges of establishing the electric vehicle in Ireland

This thesis investigates the challenges of establishing the electric vehicle (EV) in Ireland and how the Irish government and industry are trying to meet them. It further seeks to provide information on Irish consumers’ attitudes towards the electric vehicle and their willingness to purchase it. The review of the literature showed that the Irish government is investing significant funds in trying to establish the market for the electric vehicle and position itself as a world leader in adopting the electric vehicle. The EV will also have an important role to play in how Ireland meets its targets for CO2 reductions towards 2020. Climate change and use of fossil fuels are driving the need for increased use of renewable energy and increased energy independence while reducing the greenhouse gas emissions that are the leading cause of climate change. The transport sector is almost completely dependent on the use of fossil fuel and resultantly is one of the largest sources of these GHG emissions. These issues are leading to the design and production of more energy efficient and environmentally friendly vehicles. The ultimate goal is to achieve a zero emissions vehicle. The electric vehicle is presently the only vehicle being mass produced that has the potential to be zero emissions. There are however issues that customers may not be willing to overlook such as the lower range of the vehicle and the length of time it takes to recharge. Vehicle cost is also an important issue that customers may not overlook. Knowing what the consumer’s attitudes are towards the EV and their willingness to purchase them is important as these new vehicles begin to appear in the showrooms. The consumers will be vital to how successful this market becomes. Using an online questionnaire methodology, in a sample of 118 consumers, the major conclusion to be drawn from the research is that the vehicle price, the convenience to recharge and vehicle range were the three most essential issues for the consumers if they were purchasing an EV. The success of the electric vehicle market may depend on what measures are taken to overcome them.

Bidding at sequential first-price auctions with(out) supply uncertainty: a laboratory analysis

We report on a series of experiments that test the effects of an uncertain supply on the formation of bids and prices in sequential first-price auctions with private-independent values and unit-demands. Supply is assumed uncertain when buyers do not know the exact number of units to be sold (i.e., the length of the sequence). Although we observe a non-monotone behavior when supply is certain and an important overbidding, the data qualitatively support our price trend predictions and the risk neutral Nash equilibrium model of bidding for the last stage of a sequence, whether supply is certain or not. Our study shows that behavior in these markets changes significantly with the presence of an uncertain supply, and that it can be explained by assuming that bidders formulate pessimistic beliefs about the occurrence of another stage.

Reverse transcription-polymerase chain reaction construction of plasmid-based, full-length cDNA libraries from Leishmania infantum for in vitro expression screening

We describe a streamlined reverse transcription-polymerase chain reaction methodology for constructing full-length cDNA libraries of trypanosomatids on the basis of conserved sequences located at the 5' and 3'ends of trans-spliced mRNAs. The amplified cDNA corresponded to full-length messengers and was amenable to in vitro expression. Fractionated libraries could be rapidly constructed in a plasmid vector by the TA cloning method (Invitrogen). We believe this is useful when there are concerns over the use of restriction enzymes and phage technology as well as in cases where expression of proteins in their native conformation is desired.