Coordinated Scheduling of Wind-Thermal Gencos in Day-Ahead Markets

This paper presents a stochastic mixed-integer linear programming approach for solving the self-scheduling problem of a price-taker thermal and wind power producer taking part in a pool-based electricity market. Uncertainty on electricity price and wind power is considered through a set of scenarios. Thermal units are modeled by variable costs, start-up costs and technical operating constraints, such as: ramp up/down limits and minimum up/down time limits. An efficient mixed-integer linear program is presented to develop the offering strategies of the coordinated production of thermal and wind energy generation, aiming to maximize the expected profit. A case study with data from the Iberian Electricity Market is presented and results are discussed to show the effectiveness of the proposed approach.

Classi cation of new electricity customers based on surveys and smart metering data

This paper proposes a process for the classifi cation of new residential electricity customers. The current state of the art is extended by using a combination of smart metering and survey data and by using model-based feature selection for the classifi cation task. Firstly, the normalized representative consumption profi les of the population are derived through the clustering of data from households. Secondly, new customers are classifi ed using survey data and a limited amount of smart metering data. Thirdly, regression analysis and model-based feature selection results explain the importance of the variables and which are the drivers of diff erent consumption profi les, enabling the extraction of appropriate models. The results of a case study show that the use of survey data signi ficantly increases accuracy of the classifi cation task (up to 20%). Considering four consumption groups, more than half of the customers are correctly classifi ed with only one week of metering data, with more weeks the accuracy is signifi cantly improved. The use of model-based feature selection resulted in the use of a signifi cantly lower number of features allowing an easy interpretation of the derived models.

Optimal Wind Bidding Strategies in Day-Ahead Markets

This paper presents a computer application for wind energy bidding in a day-ahead electricity market to better accommodate the variability of the energy source. The computer application is based in a stochastic linear mathematical programming problem. The goal is to obtain the optimal bidding strategy in order to maximize the revenue. Electricity prices and financial penalties for shortfall or surplus energy deliver are modeled. Finally, conclusions are drawn from an illustrative case study, using data from the day-ahead electricity market of the Iberian Peninsula.

Data, Competition, and Consumer Privacy in Digital Markets

In digital markets personal information is pervasively collected by firms. In the first chapter I study data ownership and product customization when there is exclusive access to non rival but excludable data about consumer preferences. I show that an incumbent firm does not have an incentive to sell an exclusively held dataset with a rival firm, but instead it has an incentive to trade a customizing technology with the other firm. In the second chapter I investigate the effects of consumer information on the intensity of competition. In a two dimensional model of product differentiation, firms use information on preferences to practice price discrimination. I contrast a full privacy and a no privacy benchmark with a regime in which firms are able to target consumers only partially. When data is partially informative, firms are always better-off with price discrimination and an exclusive access to user data is not necessarily a competition policy concern. From a consumer protection perspective, the policy recommendation is that the regulator should promote either no privacy or full privacy. In the third chapter I introduce a data broker that observes either only one or both dimensions of consumer information and sells this data to competing firms for price discrimination purposes. When the seller exogenously holds a partially informative dataset, an exclusive allocation arises. Instead, when the dataset held is fully informative, the data broker trades information non exclusively but each competitor acquires consumer data on a different dimension. When data collection is made endogenous, non exclusivity is robust if collection costs are not too high. The competition policy suggestion is that exclusivity should not be banned per se, but it is data differentiation in equilibrium that rises market power in competitive markets. Upstream competition is sufficient to ensure that both firms get access to consumer information.

The blue side of diabetes: assessing the impact of depression in people with type 2 diabetes using real-world data

The aim of the thesis is to assess the impact of depression in people with type 2 diabetes. Using Healthcare Utilization Databases, I estimated in a large population-based cohort with type 2 diabetes the incidence of depression over 10 year-period, identified the demographic and clinical predictors of depression, and determined the extent to which depression is a risk factor for acute and long-term complications and mortality. In the context of COVID-19 pandemic, I evaluated whether the presence of a history of depression in type 2 diabetes increased the Emergency Department (ED) access rate for diabetes-related complications, and I investigated changes in the incidence of depression during the first year of the pandemic. Findings from the first study indicated that developing depression was associated with being a woman, being over 65 years, living in rural areas, having insulin as initial diabetes medication and having comorbid conditions; the study also confirmed that depression was associated with an increased risk for acute and long-term diabetes complications and all-cause mortality. The second observational study showed a higher rate of ED access for diabetes-related complications during the pandemic in people with type 2 diabetes and a history of depression than in those without a history of depression, similar to what was observed in a pre-pandemic period. As shown in the third population-based study, the incidence of depression decreased in 2020 compared to 2019, mainly during the first and the second waves of the COVID-19 pandemic, when people probably had difficulty reaching healthcare services. This new real-world evidence will help healthcare professionals identify timely patients at high risk of developing depression. Lastly, policymakers and physicians will benefit from new evidence of the effects of the COVID-19 pandemic on depression in people with type 2 diabetes to ensure a high level of care during crisis periods.

A Microleakage Study Of Gutta-percha/ah Plus And Resilon/real Self-etch Systems After Different Irrigation Protocols.

The development and maintenance of the sealing of the root canal system is the key to the success of root canal treatment. The resin-based adhesive material has the potential to reduce the microleakage of the root canal because of its adhesive properties and penetration into dentinal walls. Moreover, the irrigation protocols may have an influence on the adhesiveness of resin-based sealers to root dentin. The objective of the present study was to evaluate the effect of different irrigant protocols on coronal bacterial microleakage of gutta-percha/AH Plus and Resilon/Real Seal Self-etch systems. One hundred ninety pre-molars were used. The teeth were divided into 18 experimental groups according to the irrigation protocols and filling materials used. The protocols used were: distilled water; sodium hypochlorite (NaOCl)+eDTA; NaOCl+H3PO4; NaOCl+eDTA+chlorhexidine (CHX); NaOCl+H3PO4+CHX; CHX+eDTA; CHX+ H3PO4; CHX+eDTA+CHX and CHX+H3PO4+CHX. Gutta-percha/AH Plus or Resilon/Real Seal Se were used as root-filling materials. The coronal microleakage was evaluated for 90 days against Enterococcus faecalis. Data were statistically analyzed using Kaplan-Meier survival test, Kruskal-Wallis and Mann-Whitney tests. No significant difference was verified in the groups using chlorhexidine or sodium hypochlorite during the chemo-mechanical preparation followed by eDTA or phosphoric acid for smear layer removal. The same results were found for filling materials. However, the statistical analyses revealed that a final flush with 2% chlorhexidine reduced significantly the coronal microleakage. A final flush with 2% chlorhexidine after smear layer removal reduces coronal microleakage of teeth filled with gutta-percha/AH Plus or Resilon/Real Seal SE.

The Real-life Experience With Cardiovascular Complications In The First Dose Of Fingolimod For Multiple Sclerosis.

Fingolimod is a new and efficient treatment for multiple sclerosis (MS). The drug administration requires special attention to the first dose, since cardiovascular adverse events can be observed during the initial six hours of fingolimod ingestion. The present study consisted of a review of cardiovascular data on 180 patients with MS receiving the first dose of fingolimod. The rate of bradycardia in these patients was higher than that observed in clinical trials with very strict inclusion criteria for patients. There were less than 10% of cases requiring special attention, but no fatal cases. All but one patient continued the treatment after this initial dose. This is the first report on real-life administration of fingolimod to Brazilian patients with MS, and one of the few studies with these characteristics in the world.

Augmented Mixed Models For Clustered Proportion Data.

Often in biomedical research, we deal with continuous (clustered) proportion responses ranging between zero and one quantifying the disease status of the cluster units. Interestingly, the study population might also consist of relatively disease-free as well as highly diseased subjects, contributing to proportion values in the interval [0, 1]. Regression on a variety of parametric densities with support lying in (0, 1), such as beta regression, can assess important covariate effects. However, they are deemed inappropriate due to the presence of zeros and/or ones. To evade this, we introduce a class of general proportion density, and further augment the probabilities of zero and one to this general proportion density, controlling for the clustering. Our approach is Bayesian and presents a computationally convenient framework amenable to available freeware. Bayesian case-deletion influence diagnostics based on q-divergence measures are automatic from the Markov chain Monte Carlo output. The methodology is illustrated using both simulation studies and application to a real dataset from a clinical periodontology study.

Modelo de análise técnico-tática do jogo de handebol : necessidades, perspectivas e implicações de um modelo de interpretação das situações de jogo em tempo real

Universidade Estadual de Campinas . Faculdade de Educação Física

Estudo da imagem corporal da mulher : corpo (ir)real x corpo ideal

Universidade Estadual de Campinas. Faculdade de Educação Física

Assessing influence in survival data with a cure fraction and covariates

Diagnostic methods have been an important tool in regression analysis to detect anomalies, such as departures from error assumptions and the presence of outliers and influential observations with the fitted models. Assuming censored data, we considered a classical analysis and Bayesian analysis assuming no informative priors for the parameters of the model with a cure fraction. A Bayesian approach was considered by using Markov Chain Monte Carlo Methods with Metropolis-Hasting algorithms steps to obtain the posterior summaries of interest. Some influence methods, such as the local influence, total local influence of an individual, local influence on predictions and generalized leverage were derived, analyzed and discussed in survival data with a cure fraction and covariates. The relevance of the approach was illustrated with a real data set, where it is shown that, by removing the most influential observations, the decision about which model best fits the data is changed.

Validation of reference genes for gene expression studies in the honey bee, Apis mellifera, by quantitative real-time RT-PCR

For obtaining accurate and reliable gene expression results it is essential that quantitative real-time RT-PCR (qRT-PCR) data are normalized with appropriate reference genes. The current exponential increase in postgenomic studies on the honey bee, Apis mellifera, makes the standardization of qRT-PCR results an important task for ongoing community efforts. For this aim we selected four candidate reference genes (actin, ribosomal protein 49, elongation factor 1-alpha, tbp-association factor) and used three software-based approaches (geNorm, BestKeeper and NormFinder) to evaluate the suitability of these genes as endogenous controls. Their expression was examined during honey bee development, in different tissues, and after juvenile hormone exposure. Furthermore, the importance of choosing an appropriate reference gene was investigated for two developmentally regulated target genes. The results led us to consider all four candidate genes as suitable genes for normalization in A. mellifera. However, each condition evaluated in this study revealed a specific set of genes as the most appropriated ones.

Detection of dengue virus in saliva and urine by real time RT-PCR

Early diagnosis of dengue virus (DENV) infection is important for patient management and control of dengue outbreaks. The objective of this study was to analyze the usefulness of urine and saliva samples for early diagnosis of DENV infection by real time RT-PCR. Two febrile patients, who have been attended at the General Hospital of the School of Medicine of Ribeirao Preto, Sao Paulo University were included in the study. Serum, urine and saliva samples collected from both patients were subjected to real time RT-PCR for DENV detection and quantification. Dengue RNA was detected in serum, urine and saliva samples of both patients. Patient 1 was infected with DENV-2 and patient 2 with DENV-3. Data presented in this study suggest that urine and saliva could be used as alternative samples for early diagnosis of dengue virus infection when blood samples are difficult to obtain, e.g.,in newborns and patients with hemorrhagic syndromes.

Real-Time Voltage Regulation in Power Distribution System Using Fuzzy Control

Two different fuzzy approaches to voltage control in electric power distribution systems are introduced in this paper. The real-time controller in each case would act on power transformers equipped with under-load tap changers. Learning systems are employed to turn the voltage-control relays into adaptive devices. The scope of this study has been limited to the power distribution substation, and the voltage measurements and control actions are carried out on the secondary bus. The capacity of fuzzy systems to handle approximate data, together with their unique ability to interpret qualitative information, make it possible to design voltage-control strategies that satisfy the requirements of the Brazilian regulatory bodies and the real concerns of the electric power distribution companies. Fuzzy control systems based on these two strategies have been implemented and the test results were highly satisfactory.

Data Mining Applied to Harmonic Current Sources Identification in Residential Consumers

This work proposes a method based on both preprocessing and data mining with the objective of identify harmonic current sources in residential consumers. In addition, this methodology can also be applied to identify linear and nonlinear loads. It should be emphasized that the entire database was obtained through laboratory essays, i.e., real data were acquired from residential loads. Thus, the residential system created in laboratory was fed by a configurable power source and in its output were placed the loads and the power quality analyzers (all measurements were stored in a microcomputer). So, the data were submitted to pre-processing, which was based on attribute selection techniques in order to minimize the complexity in identifying the loads. A newer database was generated maintaining only the attributes selected, thus, Artificial Neural Networks were trained to realized the identification of loads. In order to validate the methodology proposed, the loads were fed both under ideal conditions (without harmonics), but also by harmonic voltages within limits pre-established. These limits are in accordance with IEEE Std. 519-1992 and PRODIST (procedures to delivery energy employed by Brazilian`s utilities). The results obtained seek to validate the methodology proposed and furnish a method that can serve as alternative to conventional methods.