Impact populationnel de la vaccination contre les virus du papillome humain : revue systématique et synthèse des prédictions de 16 modèles mathématiques dynamiques

Introduction: En 2015, 65 pays avaient des programmes de vaccination contre les VPH. La modélisation mathématique a joué un rôle crucial dans leur implantation. Objectifs: Nous avons réalisé une revue systématique et analysé les prédictions de modèles mathématiques de l’efficacité populationnelle de la vaccination sur la prévalence des VPH-16/18/6/11 chez les femmes et les hommes, afin d’évaluer la robustesse/variabilité des prédictions concernant l’immunité de groupe, le bénéfice ajouté par la vaccination des garçons et l’élimination potentielle des VPH-16/18/6/11. Méthodes: Nous avons cherché dans Medline/Embase afin d’identifier les modèles dynamiques simulant l’impact populationnel de la vaccination sur les infections par les VPH-16/18/6/11 chez les femmes et les hommes. Les équipes participantes ont réalisé des prédictions pour 19 simulations standardisées. Nous avons calculé la réduction relative de la prévalence (RRprev) 70 ans après l’introduction de la vaccination. Les résultats présentés correspondent à la médiane(10ème;90èmeperccentiles) des prédictions. Les cibles de la vaccination étaient les filles seulement ou les filles & garçons. Résultats: 16/19 équipes éligibles ont transmis leurs prédictions. Lorsque 40% des filles sont vaccinées, la RRprev du VPH-16 est 53%(46%;68%) chez les femmes et 36%(28%;61%) chez les hommes. Lorsque 80% des filles sont vaccinées, la RRprev est 93%(90%;100%) chez les femmes et 83%(75%;100%) chez les hommes. Vacciner aussi les garçons augmente la RRprev de 18%(13%;32%) chez les femmes et 35%(27%;39%) chez les hommes à 40% de couverture, et 7%(0%;10%) et 16%(1%;25%) à 80% de couverture. Les RRprev étaient plus élevées pour les VPH-18/6/11 (vs. VPH-16). Si 80% des filles & garçons sont vaccinés, les VPH-16/18/6/11 pourraient être éliminés. Interprétation: Même si les modèles diffèrent entre eux, les prédictions s’accordent sur: 1)immunité de groupe élevée même à basse couverture, 2)RRprev supérieures pour les VPH-18/6/11 (vs. VPH-16), 3)augmenter la couverture chez les filles a un meilleur impact qu’ajouter les garçons, 4)vacciner 80% des filles & garçons pourraient éliminer les VPH-16/18/6/11.

FOR-TSM: desarrollo de una herramienta de pronósticos con modelos de series de tiempo

Uno de los temas más complejos y necesarios en los cursos de Administración de Operaciones, es el uso de los pronósticos con modelos de series de tiempo (TSM por sus siglas en inglés) -- Para facilitar el entendimiento y ayudar a los estudiantes a comprender fácilmente los pronósticos de demanda, este proyecto presenta FOR TSM, una herramienta desarrollada en MS Excel VBA® -- La herramienta fue diseñada con una Interfaz gráfica de Usuario (GUI por sus siglas en inglés) para explicar conceptos fundamentales como la selección de los parámetros, los valores de inicialización, cálculo y análisis de medidas de desempeño y finalmente la selección de modelos

Análise da fluência em vigas de concreto armado simplesmente apoiadas usando o método dos elementos finitos

Reinforced concrete creep is a phenomenon of great importance. Despite being appointed as the main cause of several pathologies, its effects are yet considered in a simplified way by the structural designers. In addition to studying the phenomenon in reinforced concrete structures and its current account used in the structural analysis, this paper compares creep strains at simply supported reinforced concrete beams in analytical and in experimental forms with the finite element method (FEM) simulation results. The strains and deflections obtained through the analytical form were calculated with the Brazilian code NBR 6118 (2014) recommendations and the simplified method from CEB-FIP 90 and the experimental results were extracted from tests available in the literature. Finite element simulations are performed using ANSYS Workbench software, using its 3D SOLID 186 elements and the structure symmetry. Analyzes of convergence using 2D PLANE 183 elements are held as well. At the end, it is concluded that FEM analyses are quantitative and qualitative efficient for the estimation of this non-linearity and that the method utilized to obtain the creep coefficients values is sufficiently accurate.

Modeling of burden distribution in the blast furnace

The blast furnace is the main ironmaking production unit in the world which converts iron ore with coke and hot blast into liquid iron, hot metal, which is used for steelmaking. The furnace acts as a counter-current reactor charged with layers of raw material of very different gas permeability. The arrangement of these layers, or burden distribution, is the most important factor influencing the gas flow conditions inside the furnace, which dictate the efficiency of the heat transfer and reduction processes. For proper control the furnace operators should know the overall conditions in the furnace and be able to predict how control actions affect the state of the furnace. However, due to high temperatures and pressure, hostile atmosphere and mechanical wear it is very difficult to measure internal variables. Instead, the operators have to rely extensively on measurements obtained at the boundaries of the furnace and make their decisions on the basis of heuristic rules and results from mathematical models. It is particularly difficult to understand the distribution of the burden materials because of the complex behavior of the particulate materials during charging. The aim of this doctoral thesis is to clarify some aspects of burden distribution and to develop tools that can aid the decision-making process in the control of the burden and gas distribution in the blast furnace. A relatively simple mathematical model was created for simulation of the distribution of the burden material with a bell-less top charging system. The model developed is fast and it can therefore be used by the operators to gain understanding of the formation of layers for different charging programs. The results were verified by findings from charging experiments using a small-scale charging rig at the laboratory. A basic gas flow model was developed which utilized the results of the burden distribution model to estimate the gas permeability of the upper part of the blast furnace. This combined formulation for gas and burden distribution made it possible to implement a search for the best combination of charging parameters to achieve a target gas temperature distribution. As this mathematical task is discontinuous and non-differentiable, a genetic algorithm was applied to solve the optimization problem. It was demonstrated that the method was able to evolve optimal charging programs that fulfilled the target conditions. Even though the burden distribution model provides information about the layer structure, it neglects some effects which influence the results, such as mixed layer formation and coke collapse. A more accurate numerical method for studying particle mechanics, the Discrete Element Method (DEM), was used to study some aspects of the charging process more closely. Model charging programs were simulated using DEM and compared with the results from small-scale experiments. The mixed layer was defined and the voidage of mixed layers was estimated. The mixed layer was found to have about 12% less voidage than layers of the individual burden components. Finally, a model for predicting the extent of coke collapse when heavier pellets are charged over a layer of lighter coke particles was formulated based on slope stability theory, and was used to update the coke layer distribution after charging in the mathematical model. In designing this revision, results from DEM simulations and charging experiments for some charging programs were used. The findings from the coke collapse analysis can be used to design charging programs with more stable coke layers.

Improving risk stratification in non-ST-segment elevation myocardial infarction with combined assessment of GRACE and CRUSADE risk scores

BACKGROUND: Risk assessment is fundamental in the management of acute coronary syndromes (ACS), enabling estimation of prognosis. AIMS: To evaluate whether the combined use of GRACE and CRUSADE risk stratification schemes in patients with myocardial infarction outperforms each of the scores individually in terms of mortality and haemorrhagic risk prediction. METHODS: Observational retrospective single-centre cohort study including 566 consecutive patients admitted for non-ST-segment elevation myocardial infarction. The CRUSADE model increased GRACE discriminatory performance in predicting all-cause mortality, ascertained by Cox regression, demonstrating CRUSADE independent and additive predictive value, which was sustained throughout follow-up. The cohort was divided into four different subgroups: G1 (GRACE<141; CRUSADE<41); G2 (GRACE<141; CRUSADE≥41); G3 (GRACE≥141; CRUSADE<41); G4 (GRACE≥141; CRUSADE≥41). RESULTS: Outcomes and variables estimating clinical severity, such as admission Killip-Kimbal class and left ventricular systolic dysfunction, deteriorated progressively throughout the subgroups (G1 to G4). Survival analysis differentiated three risk strata (G1, lowest risk; G2 and G3, intermediate risk; G4, highest risk). The GRACE+CRUSADE model revealed higher prognostic performance (area under the curve [AUC] 0.76) than GRACE alone (AUC 0.70) for mortality prediction, further confirmed by the integrated discrimination improvement index. Moreover, GRACE+CRUSADE combined risk assessment seemed to be valuable in delineating bleeding risk in this setting, identifying G4 as a very high-risk subgroup (hazard ratio 3.5; P<0.001). CONCLUSIONS: Combined risk stratification with GRACE and CRUSADE scores can improve the individual discriminatory power of GRACE and CRUSADE models in the prediction of all-cause mortality and bleeding. This combined assessment is a practical approach that is potentially advantageous in treatment decision-making.

Análise e modelagem do escoamento transitório de fluidos de perfuração

Drilling fluids present a thixotropic behavior and they usually gel when at rest. The sol-gel transition is fundamental to prevent the deposit of rock fragments, generated by drilling the well, over the drill bit during eventual stops. Under those conditions, high pressures are then required in order to break-up the gel when circulation is resumed. Moreover, very high pressures can damage the rock formation at the bottom of the well. Thus, a better understanding of thixotropy and the behavior of thixotropic materials becomes increasingly important for process control. The mechanisms that control thixotropy are not yet well defined and modeling is still a challenge. The objective of this work is to develop a mathematical model to study the pressure transmission in drilling fluids. This work presents a review of thixotropy and of different mathematical models found in the literature that are used to predict such characteristic. It also shows a review of transient flows of compressible fluids. The problem is modeled as the flow between the drillpipe and the annular region (space between the wall and the external part of the drillpipe). The equations that describe the problem (mass conservation, momentum balance, constitutive and state) are then discretized and numerically solved by using a computational algorithm in Fortran. The model is validated with experimental and numerical data obtained from the literature. Comparisons between experimental data obtained from Petrobras and calculated by three viscoplastic and one pseudoplastic models are conducted. The viscoplastic fluids, due to the yield stress, do not fully transmit the pressure to the outlet of the annular space. Sensibility analyses are then conducted in order to evaluate the thixotropic effect in pressure transmission.

Comparação do controle do inversor trifásico conectado à rede com filtro LCL considerando o amortecimento passivo e ativo

Distributed generation systems must fulfill standards specifications of current harmonics injected to the grid. In order to satisfy these grid requirements, passive filters are connected between inverter and grid. This work compares the characteristic response of the traditional inductive (L) filter with the inductive-capacitive-inductive (LCL) filter. It is shown that increasing the inductance L leads to a good ripple current suppression around the inverter switching frequency. The LCL filter provides better harmonic attenuation and reduces the filter size. The main drawback is the LCL filter impedance, which is characterized by a typical resonance peak, which must be damped to avoid instability. Passive or active techniques can be used to damp the LCL resonance. To address this issue, this dissertation presents a comparison of current control for PV grid-tied inverters with L filter and LCL filter and also discuss the use of active and passive damping for different regions of resonance frequency. From the mathematical models, a design methodology of the controllers was developed and the dynamic behavior of the system operating in closed loop was investigated. To validate the studies developed during this work, experimental results are presented using a three-phase 5kW experimental platform. The main components and their functions are discussed in this work. Experimental results are given to support the theoretical analysis and to illustrate the performance of grid-connected PV inverter system. It is shown that the resonant frequency of the system, and sampling frequency can be associated in order to calculate a critical frequency, below which is essential to perform the damping of the LCL filter. Also, the experimental results show that the active buffer per virtual resistor, although with a simple development, is effective to damp the resonance of the LCL filter and allow the system to operate stable within predetermined parameters.

Class-D audio amplifiers with negative feedback

There are many different designs for audio amplifiers. Class-D, or switching, amplifiers generate their output signal in the form of a high-frequency square wave of variable duty cycle (ratio of on time to off time). The square-wave nature of the output allows a particularly efficient output stage, with minimal losses. The output is ultimately filtered to remove components of the spectrum above the audio range. Mathematical models are derived here for a variety of related class-D amplifier designs that use negative feedback. These models use an asymptotic expansion in powers of a small parameter related to the ratio of typical audio frequencies to the switching frequency to develop a power series for the output component in the audio spectrum. These models confirm that there is a form of distortion intrinsic to such amplifier designs. The models also explain why two approaches used commercially succeed in largely eliminating this distortion; a new means of overcoming the intrinsic distortion is revealed by the analysis. Copyright (2006) Society for Industrial and Applied Mathematics

A path-based framework for analyzing large markov models

Reliability and dependability modeling can be employed during many stages of analysis of a computing system to gain insights into its critical behaviors. To provide useful results, realistic models of systems are often necessarily large and complex. Numerical analysis of these models presents a formidable challenge because the sizes of their state-space descriptions grow exponentially in proportion to the sizes of the models. On the other hand, simulation of the models requires analysis of many trajectories in order to compute statistically correct solutions. This dissertation presents a novel framework for performing both numerical analysis and simulation. The new numerical approach computes bounds on the solutions of transient measures in large continuous-time Markov chains (CTMCs). It extends existing path-based and uniformization-based methods by identifying sets of paths that are equivalent with respect to a reward measure and related to one another via a simple structural relationship. This relationship makes it possible for the approach to explore multiple paths at the same time,· thus significantly increasing the number of paths that can be explored in a given amount of time. Furthermore, the use of a structured representation for the state space and the direct computation of the desired reward measure (without ever storing the solution vector) allow it to analyze very large models using a very small amount of storage. Often, path-based techniques must compute many paths to obtain tight bounds. In addition to presenting the basic path-based approach, we also present algorithms for computing more paths and tighter bounds quickly. One resulting approach is based on the concept of path composition whereby precomputed subpaths are composed to compute the whole paths efficiently. Another approach is based on selecting important paths (among a set of many paths) for evaluation. Many path-based techniques suffer from having to evaluate many (unimportant) paths. Evaluating the important ones helps to compute tight bounds efficiently and quickly.

Estudo experimental do escoamento bifásico líquido gás em golfadas com leve mudança de direção

Gas-liquid two-phase flow is very common in industrial applications, especially in the oil and gas, chemical, and nuclear industries. As operating conditions change such as the flow rates of the phases, the pipe diameter and physical properties of the fluids, different configurations called flow patterns take place. In the case of oil production, the most frequent pattern found is slug flow, in which continuous liquid plugs (liquid slugs) and gas-dominated regions (elongated bubbles) alternate. Offshore scenarios where the pipe lies onto the seabed with slight changes of direction are extremely common. With those scenarios and issues in mind, this work presents an experimental study of two-phase gas-liquid slug flows in a duct with a slight change of direction, represented by a horizontal section followed by a downward sloping pipe stretch. The experiments were carried out at NUEM (Núcleo de Escoamentos Multifásicos UTFPR). The flow initiated and developed under controlled conditions and their characteristic parameters were measured with resistive sensors installed at four pipe sections. Two high-speed cameras were also used. With the measured results, it was evaluated the influence of a slight direction change on the slug flow structures and on the transition between slug flow and stratified flow in the downward section.

Simulação numérica do escoamento monofásico em uma bomba centrífuga radial

Centrifugal pumps are vastly used in many industrial applications. Knowledge of how these components behave in several circumstances is crucial for the development of more efficient and, therefore, less expensive pumping installations. The combination of multiple impellers, vaned diffusers and a volute might introduce several complex flow characteristics that largely deviate from regular inviscid pump flow theory. Computational Fluid Dynamics can be very helpful to extract information about which physical phenomena are involved in such flows. In this sense, this work performs a numerical study of the flow in a two-stage centrifugal pump (Imbil ITAP 65-330/2) with a vaned diffuser and a volute. The flow in the pump is modeled using the software Ansys CFX, by means of a multi-block, transient rotor-stator technique, with structured grids for all pump parts. The simulations were performed using water and a mixture of water and glycerin as work fluids. Several viscosities were considered, in a range between 87 and 720 cP. Comparisons between experimental data obtained by Amaral (2007) and numerical head curves showed a good agreement, with an average deviation of 6.8% for water. The behavior of velocity, pressure and turbulence kinetic energy fields was evaluated for several operational conditions. In general, the results obtained by this work achieved the proposed goals and are a significant contribution to the understanding of the flow studied.

Método para reconhecimento de vogais e extração de parâmetros acústicos para analises forenses

Forensic speaker comparison exams have complex characteristics, demanding a long time for manual analysis. A method for automatic recognition of vowels, providing feature extraction for acoustic analysis is proposed, aiming to contribute as a support tool in these exams. The proposal is based in formant measurements by LPC (Linear Predictive Coding), selectively by fundamental frequency detection, zero crossing rate, bandwidth and continuity, with the clustering being done by the k-means method. Experiments using samples from three different databases have shown promising results, in which the regions corresponding to five of the Brasilian Portuguese vowels were successfully located, providing visualization of a speaker’s vocal tract behavior, as well as the detection of segments corresponding to target vowels.

The study of the impact of demand controlled ventilation in energy consumption of offices in Portuguese commercial buildings

Indoor Air 2016 - The 14th International Conference of Indoor Air Quality and Climate

Exploring the potential role of allostatic load biomarkers in risk assessment of patients presenting with depressive symptoms

Background: Depression is a major health problem worldwide and the majority of patients presenting with depressive symptoms are managed in primary care. Current approaches for assessing depressive symptoms in primary care are not accurate in predicting future clinical outcomes, which may potentially lead to over or under treatment. The Allostatic Load (AL) theory suggests that by measuring multi-system biomarker levels as a proxy of measuring multi-system physiological dysregulation, it is possible to identify individuals at risk of having adverse health outcomes at a prodromal stage. Allostatic Index (AI) score, calculated by applying statistical formulations to different multi-system biomarkers, have been associated with depressive symptoms. Aims and Objectives: To test the hypothesis, that a combination of allostatic load (AL) biomarkers will form a predictive algorithm in defining clinically meaningful outcomes in a population of patients presenting with depressive symptoms. The key objectives were: 1. To explore the relationship between various allostatic load biomarkers and prevalence of depressive symptoms in patients, especially in patients diagnosed with three common cardiometabolic diseases (Coronary Heart Disease (CHD), Diabetes and Stroke). 2 To explore whether allostatic load biomarkers predict clinical outcomes in patients with depressive symptoms, especially in patients with three common cardiometabolic diseases (CHD, Diabetes and Stroke). 3 To develop a predictive tool to identify individuals with depressive symptoms at highest risk of adverse clinical outcomes. Methods: Datasets used: ‘DepChron’ was a dataset of 35,537 patients with existing cardiometabolic disease collected as a part of routine clinical practice. ‘Psobid’ was a research data source containing health related information from 666 participants recruited from the general population. The clinical outcomes for 3 both datasets were studied using electronic data linkage to hospital and mortality health records, undertaken by Information Services Division, Scotland. Cross-sectional associations between allostatic load biomarkers calculated at baseline, with clinical severity of depression assessed by a symptom score, were assessed using logistic and linear regression models in both datasets. Cox’s proportional hazards survival analysis models were used to assess the relationship of allostatic load biomarkers at baseline and the risk of adverse physical health outcomes at follow-up, in patients with depressive symptoms. The possibility of interaction between depressive symptoms and allostatic load biomarkers in risk prediction of adverse clinical outcomes was studied using the analysis of variance (ANOVA) test. Finally, the value of constructing a risk scoring scale using patient demographics and allostatic load biomarkers for predicting adverse outcomes in depressed patients was investigated using clinical risk prediction modelling and Area Under Curve (AUC) statistics. Key Results: Literature Review Findings. The literature review showed that twelve blood based peripheral biomarkers were statistically significant in predicting six different clinical outcomes in participants with depressive symptoms. Outcomes related to both mental health (depressive symptoms) and physical health were statistically associated with pre-treatment levels of peripheral biomarkers; however only two studies investigated outcomes related to physical health. Cross-sectional Analysis Findings: In DepChron, dysregulation of individual allostatic biomarkers (mainly cardiometabolic) were found to have a non-linear association with increased probability of co-morbid depressive symptoms (as assessed by Hospital Anxiety and Depression Score HADS-D≥8). A composite AI score constructed using five biomarkers did not lead to any improvement in the observed strength of the association. In Psobid, BMI was found to have a significant cross-sectional association with the probability of depressive symptoms (assessed by General Health Questionnaire GHQ-28≥5). BMI, triglycerides, highly sensitive C - reactive 4 protein (CRP) and High Density Lipoprotein-HDL cholesterol were found to have a significant cross-sectional relationship with the continuous measure of GHQ-28. A composite AI score constructed using 12 biomarkers did not show a significant association with depressive symptoms among Psobid participants. Longitudinal Analysis Findings: In DepChron, three clinical outcomes were studied over four years: all-cause death, all-cause hospital admissions and composite major adverse cardiovascular outcome-MACE (cardiovascular death or admission due to MI/stroke/HF). Presence of depressive symptoms and composite AI score calculated using mainly peripheral cardiometabolic biomarkers was found to have a significant association with all three clinical outcomes over the following four years in DepChron patients. There was no evidence of an interaction between AI score and presence of depressive symptoms in risk prediction of any of the three clinical outcomes. There was a statistically significant interaction noted between SBP and depressive symptoms in risk prediction of major adverse cardiovascular outcome, and also between HbA1c and depressive symptoms in risk prediction of all-cause mortality for patients with diabetes. In Psobid, depressive symptoms (assessed by GHQ-28≥5) did not have a statistically significant association with any of the four outcomes under study at seven years: all cause death, all cause hospital admission, MACE and incidence of new cancer. A composite AI score at baseline had a significant association with the risk of MACE at seven years, after adjusting for confounders. A continuous measure of IL-6 observed at baseline had a significant association with the risk of three clinical outcomes- all-cause mortality, all-cause hospital admissions and major adverse cardiovascular event. Raised total cholesterol at baseline was associated with lower risk of all-cause death at seven years while raised waist hip ratio- WHR at baseline was associated with higher risk of MACE at seven years among Psobid participants. There was no significant interaction between depressive symptoms and peripheral biomarkers (individual or combined) in risk prediction of any of the four clinical outcomes under consideration. Risk Scoring System Development: In the DepChron cohort, a scoring system was constructed based on eight baseline demographic and clinical variables to predict the risk of MACE over four years. The AUC value for the risk scoring system was modest at 56.7% (95% CI 55.6 to 57.5%). In Psobid, it was not possible to perform this analysis due to the low event rate observed for the clinical outcomes. Conclusion: Individual peripheral biomarkers were found to have a cross-sectional association with depressive symptoms both in patients with cardiometabolic disease and middle-aged participants recruited from the general population. AI score calculated with different statistical formulations was of no greater benefit in predicting concurrent depressive symptoms or clinical outcomes at follow-up, over and above its individual constituent biomarkers, in either patient cohort. SBP had a significant interaction with depressive symptoms in predicting cardiovascular events in patients with cardiometabolic disease; HbA1c had a significant interaction with depressive symptoms in predicting all-cause mortality in patients with diabetes. Peripheral biomarkers may have a role in predicting clinical outcomes in patients with depressive symptoms, especially for those with existing cardiometabolic disease, and this merits further investigation.

Análise comportamental : um estudo por análise de sobrevivência, aplicada a cartões de crédito

A capacidade de adaptação e rapidez de decisão, distinguem as empresas que melhor conseguem competir e crescer no mercado global. Para atuar rapidamente, as organizações precisam de sistemas de informação cada vez mais eficazes, surgindo recentemente uma nova função considerada fundamental para as empresas, que é a de Cientista de Dados. É neste contexto e para responder aos desafios atuais e futuros, que surgem sistemas de informação cada vez mais avançados, suportados por modelos de análise e visualização estatística. Este trabalho consiste em criar uma metodologia de desenvolvimento de modelos de previsão de incumprimento e perfil do consumidor, aplicado a cartões de crédito, com base numa exposição de análise comportamental, utilizando técnicas de análise de sobrevivência. São definidas técnicas de tratamento dos dados recolhidos, estimado modelo não-paramétrico de Kaplan-Meier e vários modelos de Cox de riscos proporcionais. Com recurso à curva ROC, dependente do tempo, à AUC e ao índice de Gini, conclui-se que o modelo final apresenta um desempenho positivo para identificar os clientes em situação de incumprimento ou com propensão a incumprir.