Modeling hierarchical relationships in epidemiological studies: a Bayesian networks approach

Hierarchical relationships between risk factors are seldom taken into account in epidemiological studies though some authors stressed the importance of doing so, and proposed a conceptual framework in which each level of the hierarchy is modeled separately. The objective of this paper was to implement a simple version of their framework, and to propose an alternative procedure based on a Bayesian Network (BN). These approaches were illustrated in modeling the risk of diarrhea infection for 2740 children aged 0 to 59 months in Cameroon. The authors implemented a (naïve) logistic regression, a step-level logistic regression and also a BN. While the first approach is inadequate, the two others approaches both account for the hierarchical structure but to different estimates and interpretations. BN implementation showed that a child in a family in the poorest group has respectively 89%, 40% and 18% probabilities of having poor sanitation, being malnourished and having diarrhea. An advantage of the latter approach is that it enables one to determine the probability that a risk factor (and/or the outcome) is in a given state, given the states of the others. Although the BN considered here is very simple, the method can deal with more complicated models.Bayesian networks; hierarchical model; diarrhea infection; disease determinants; logistic regression

Multidimensional Nature of Undernutrition: A Statistical Approach

The statistical assessment of undernutrition is usually restricted to a pairwise analysis of anthropometric indicators. The main objective of this study was to model the associations between underweight, stunting and wasting and to check whether multidimensionality of undernutrition can be justified from a purely statistical point of view. 3742 children aged 0 to 59 months were enrolled in a cross-sectional household survey (2004 Cameroon Demographic and Health Surveys (DHS)). The saturated loglinear model and the multiple correspondence analysis (MCA) showed no interaction and a highly significant association between underweight and stunting (P=0), underweight and wasting (P=0); but not between stunting and wasting (P=0.430). Cronbach's alpha coefficient between weight-for-age, height-for-age and weight-for-height was 0.62 (95% CI 0.59, 0.64). Thus, the study of these associations is not straightforward as it would appear in a first instance. The lack of three-factor interaction and the value of the Cronbach's alpha coefficient indicate that undernutrition is indeed (statistically) multidimensional. The three indicators are not statistically redundant; thus for the case of Cameroon the choice of a particular anthropometric indicator should depend on the goal of the policy maker, as it comes out of this study that no single indicator is to be used for all situations.Stunting; Wasting; Underweight; anthropometric measures; Z-score; Loglinear models

Estimation of a multivariate mean under model selection uncertainty

<p>Model selection uncertainty would occur if we selected a model based on one data set and subsequently applied it for statistical inferences, because the "correct" model would not be selected with certainty. &nbsp;<br />When the selection and inference are based on the same dataset, some additional problems arise due to the correlation of the two stages (selection and inference). In this paper model selection uncertainty is considered and model averaging is proposed. The proposal is related to the theory of James and Stein of estimating more than three parameters from independent normal observations. We suggest that a model averaging scheme taking into account the selection procedure could be more appropriate than model selection alone. Some properties of this model averaging estimator are investigated; in particular we show using Stein's results that it is a minimax estimator and can outperform Stein-type estimators.</p

Using weight-for-age for predicting wasted children

Background: The equipments for taking body weights (scales) are more frequent in Cameroon health centres than measuring boards for heights. Even when the later exist there are some difficulties inherent in their qualities; thus the height measurement is not always available or accurate. Objective: To construct statistical models for predicting wasting from weight-for-age. Methods: 3742 children a ged 0 to 59 months were enrolled in a cross-sectional household survey (2004 Cameroon Demographic and Health Surveys (DHS)) covering the entire Cameroon national territory. Results: There were highly significant association between underweight and wasting. For all discriminant statistical methods used, the test error rates (using an independent testing sample) are less than 5%; the Area Under the Curve (AUC) using the Receiver Operating Characteristic (ROC) is 0.86. Conclusions: Weight-for-age can be used for accurately classifying a child whose wasting status is unknown. The result is useful in Cameroon as too often the height measurements may not be feasible, thus the need for estimating wasted children.Anthropometric measures, nutritional status, discriminant analysis, underweight, wasting

Leakage current minimisation and power reduction techniques using sub-threshold design

Abstract: Low power IC solutions are in great demand with the rapid advancement of handheld devices, wearables, smart cards and radio frequency identification bringing a massive amount of new products to market that all have the same primary need: Powering the device as long as possible between the need to re- charge the batteries while at the same time dramatically decreasing the device leakage currents. The use of sub-threshold techniques can be a powerful way to create circuits that consume dramatically less energy than those built using standard design practices. In this research, a SOI device was built to compare their electrical characteristics using Silvaco software. The comparisons were focus! ed on three main electrical characteristics that are threshold voltage, sub-threshold voltage and leakage current. It was found that SOI devices are ideal candidates for low power operation

Estimating and Correcting the Effects of Model Selection Uncertainty

Die meisten statistischen Analysen werden in Unkenntnis des wahren Modells durchgeführt, d.h. dass das Modell, das die Daten erzeugte, unbekannt ist und die Daten zunächst dafür verwendet werden, mit Hilfe eines Modellauswahlkriteriums ein Modell aus einer Menge plausibler Modelle auszuwählen. Gewöhnlich werden die Daten dann verwendet, um Schlüsse über einige Variablen zu ziehen. Dabei wird die Modellunsicherheit, also die Tatsache, dass der Modellauswahlschritt mit den gleichen Daten durchgeführt wurde, ignoriert, obwohl man weiß, dass dies zu ungültigen Schlussfolgerungen führt. Die vorliegende Arbeit untersucht einige Aspekte des Problems sowohl aus bayesianischer als auch aus frequentistischer Sicht und macht neue Vorschläge, wie mit dem Problem umgegangen werden kann. Wir untersuchen bayesianische Modellmittelung (Bayesian model averaging =BMA) und zeigen, dass dessen frequentistisches Abschneiden nicht immer wohldefiniert ist, denn in einigen Fällen ist es unklar, ob BMA wirklich bayesianisch ist. Wir illustrieren diesen Punkt mit einer „vollständigen bayesianische Modellmittelung“, die anwendbar ist, wenn die interessierende Größe parametrisch ist. Wir stellen ein System vor, das die Komplexität von Schätzern nach der Modellauswahl aufdeckt („post-model-selection Schätzer“) und untersuchen ihre Eigenschaften im Kontext der linearen Regression für eine Vielzahl an Modellauswahlprozeduren. Wir zeigen, dass kein Modellauswahlkriterium gleichmäßig besser ist als alle anderen, im Sinne der Risikofunktion. Schlüsselzutaten des Problems werden identifiziert und verwendet, um zu zeigen, dass selbst konsistente Modellauswahlkriterien das Problem der Modellauswahlunsicherheit nicht lösen. Wir argumentieren außerdem, dass das Bedingen der Analyse auf die Teilmenge des Stichprobenraumes, die zu einem bestimmten Modell führte, unvollständig ist. Wir betrachten das Problem aus frequentistischer Sicht. Obwohl Modellmittelung und Modellauswahl normalerweise als zwei getrennte Herangehensweisen betrachtet werden, schlagen wir vor, das zweite als Spezialfall der Modellmittelung zu betrachten, in welcher die (zufälligen) Gewichte den Wert 1 für das ausgewählte Modell annehmen und 0 für alle anderen. Aus dieser Perspektive, und da die optimalen Gewichte in der Praxis nicht bestimmt werden können, kann nicht erwartet werden, dass eine der zwei Methoden die andere konsistent übertrifft. Es führt uns dazu, alternative Gewichte für die Mittelung vorzuschlagen, die dazu gedacht sind, die post-model-selection Schätzung zu verbessern. Die Innovation besteht darin, die Modellauswahlprozedur bei der Bestimmung der Gewichte zu berücksichtigen. Wir vergleichen die verschiedenen Methoden für einige einfache Fälle (lineare Regression und Häufigkeitsschätzung). Wir zeigen, dass Bootstrapverfahren keine guten Schätzer für die Eigenschaften der post-model-selection Schätzer liefern. Zurückkehrend zur bayesianischen Sicht zeigen wir auf, dass, solange die Analyse bedingt auf die Daten stattfindet, Modellauswahlunsicherheit kein Problem ist, nur die Unsicherheit des Modells an sich. Wenn jemand allerdings an den frequentistischen Eigenschaften der bayesianischen post-model-selection Schätzern interessiert ist, ist die Situation analog zu der in der frequentistischen Analyse. Hier schlagen wir wieder eine Alternative zur gewöhnlichen BMA vor, in der die Gewichte von den Auswahlkriterien des Modells abhängen und somit die Auswahlprozedur berücksichtigen. Wir zeigen außerdem, dass die Eigenschaften von Modellmittelung und post-model-selection Schätzern nur unter einem angenommenen wahren Modell hergeleitet werden können. Unter einer solchen Annahme würde man allerdings einfach das wahre Modell nehmen, ohne Modellwahl oder Modellmittelung anzuwenden. Dieser Zirkelschluss macht es so schwierig, mit dem Problem umzugehen. Traditionelle explorative frequentistische Datenanalyse und Aufstellung eines Modells kann als eine informelle Modellwahl betrachtet werden, in welcher die genaue Modellauswahlprozedur schwierig zu rekonstruieren ist, was es besonders schwierig macht, gültige Schlussfolgerungen zu ziehen. Ohne die Debatte über Vor- und Nachteile der bayesianischen und frequentistischen Methoden zu führen, möchten wir betonen, dass bayesianische Methoden vorzuziehen sind, um Modellauswahlunsicherheit zu vermeiden, solange die frequentistischen Eigenschaften des resultierenden Schätzers nicht von Interesse sind

First principle leakage current reduction technique for CMOS devices

Abstract: This paper presents a comprehensive study of leakage reduction techniques applicable to CMOS based devices. In the process, mathematical equations that model the powerperformance trade-offs in CMOS logic circuits are presented. From those equations, suitable techniques for leakage reduction as pertaining to CMOS devices are deduced. Throughout this research it became evident that designing CMOS devices with high-κ dielectrics is a viable method for reducing leakages in cryptographic devices. To support our claim, a 22nm NMOS device was built and simulated in Athena software from Silvaco. The electrical characteristics of the fabricated device were extracted using the Atlas component of the simulator. From this research, it became evident that high-κ dielectric metal gate are capable of providing a reliable resistance to DPA and other form of attacks on cryptographic platforms such as smart card.The fabricated device showed a marked improvement on the I on/I off ratio, where the higher ratio means that the device is suitable for low power applications. Physical models used for simulation included Si3N4 and HfO2 as gate dielectric with TiSix as metal gate. From the simulation result, it was shown that HfO2 was the best dielectric material when TiSix is used as the metal gate

Treatment of capillarity rise on the underlying layer of a Roadway: case of the Bangangté – Foumbot – Bamendjing – Galim roadway

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Pan African Medical Journal (Pamj) – African Field Epidemiology Network (AFENET): A partnership for the future of medical publishing in Africa

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Simulation and parameter optimization of polysilicon gate biaxial strained silicon MOSFETs

Abstract: Although cryptography constitutes a considerable part of the overall security architecture for several use cases in embedded systems, cryptographic devices are still vulnerable to the diversity types of side channel attacks. Improvement in performance of Strained Silicon MOSFETs utilizing conventional device scaling has become more complex, because of the amount of physical limitations associated with the device miniaturization. Therefore, a great deal of attention has recently been paid to the mobility improvement technology through applying strain to CMOS channels. This paper reviews the characteristics of strained-Si CMOS with an emphasis on the mechanism of mobility enhancement due to strain. The device physics for improving the performance of MOSFETs is studied from the viewpoint of electronic states of carriers in inversion layers and, in particular, the sub-band structures. In addition, design and simulation of biaxial strained silicon NMOSFET (n-channel) is done using Silvaco’s Athena/Atlas simulator. From the results obtained, it became clear that biaxial strained silicon NMOS is one of the best alternatives to the current conventional MOSFET