Unipolar transport and shot noise in metal-semiconductor-metal structures

We carry out a self-consistent analytical theory of unipolar current and noise properties of metal-semiconductor-metal structures made of highly resistive semiconductors in the presence of an applied bias of arbitrary strength. By including the effects of the diffusion current we succeed in studying the whole range of carrier injection conditions going from low level injection, where the structure behaves as a linear resistor, to high level injection, where the structure behaves as a space charge limited diode. We show that these structures display shot noise at the highest voltages. Remarkably the crossover from Nyquist noise to shot noise exhibits a complicated behavior with increasing current where an initial square root dependence (double thermal noise) is followed by a cubic power law.

To select or to weigh: a comparative study of linear combination schemes for superparent-one-dependence estimators

We conduct a large-scale comparative study on linearly combining superparent-one-dependence estimators (SPODEs), a popular family of seminaive Bayesian classifiers. Altogether, 16 model selection and weighing schemes, 58 benchmark data sets, and various statistical tests are employed. This paper's main contributions are threefold. First, it formally presents each scheme's definition, rationale, and time complexity and hence can serve as a comprehensive reference for researchers interested in ensemble learning. Second, it offers bias-variance analysis for each scheme's classification error performance. Third, it identifies effective schemes that meet various needs in practice. This leads to accurate and fast classification algorithms which have an immediate and significant impact on real-world applications. Another important feature of our study is using a variety of statistical tests to evaluate multiple learning methods across multiple data sets.

Generation of dynamic structures in nonequilibrium reactive membranes

We present a nonequlibrium approach for the study of a flexible bilayer whose two components induce distinct curvatures. In turn, the two components are interconverted by an externally promoted reaction. Phase separation of the two species in the surface results in the growth of domains characterized by different local composition and curvature modulations. This domain growth is limited by the effective mixing due to the interconversion reaction, leading to a finite characteristic domain size. In addition to these effects, first introduced in our earlier work [ Phys. Rev. E 71 051906 (2005)], the important new feature is the assumption that the reactive process actively affects the local curvature of the bilayer. Specifically, we suggest that a force energetically activated by external sources causes a modification of the shape of the membrane at the reaction site. Our results show the appearance of a rich and robust dynamical phenomenology that includes the generation of traveling and/or oscillatory patterns. Linear stability analysis, amplitude equations, and numerical simulations of the model kinetic equations confirm the occurrence of these spatiotemporal behaviors in nonequilibrium reactive bilayers.

Structural investigations into the interaction of hemoglobin and part structures with bacterial endotoxins.

An understanding of details of the interaction mechanisms of bacterial endotoxins (lipopolysaccharide, LPS) with the oxygen transport protein hemoglobin is still lacking, despite its high biological relevance. Here, a biophysical investigation into the endotoxin:hemoglobin interaction is presented which comprises the use of various rough mutant LPS as well as free lipid A; in addition to the complete hemoglobin molecule from fetal sheep extract, also the partial structure alpha-chain and the heme-free sample are studied. The investigations comprise the determination of the gel-to-liquid crystalline phase behaviour of the acyl chains of LPS, the ultrastructure (type of aggregate structure and morphology) of the endotoxins, and the incorporation of the hemoglobins into artificial immune cell membranes and into LPS. Our data suggest a model for the interaction between Hb and LPS in which hemoglobins do not react strongly with the hydrophilic or with the hydrophobic moiety of LPS, but with the complete endotoxin aggregate. Hb is able to incorporate into LPS with the longitudinal direction parallel to the lipid A double-layer. Although this does not lead to a strong disturbance of the LPS acyl chain packing, the change of the curvature leads to a slightly conical molecular shape with a change of the three-dimensional arrangement from unilamellar into cubic LPS aggregates. Our previous results show that cubic LPS structures exhibit strong endotoxic activity. The property of Hb on the physical state of LPS described here may explain the observation of an increase in LPS-mediating endotoxicity due to the action of Hb.

A general linear relaxometry model of R1 using imaging data.

PURPOSE: The longitudinal relaxation rate (R1 ) measured in vivo depends on the local microstructural properties of the tissue, such as macromolecular, iron, and water content. Here, we use whole brain multiparametric in vivo data and a general linear relaxometry model to describe the dependence of R1 on these components. We explore a) the validity of having a single fixed set of model coefficients for the whole brain and b) the stability of the model coefficients in a large cohort. METHODS: Maps of magnetization transfer (MT) and effective transverse relaxation rate (R2 *) were used as surrogates for macromolecular and iron content, respectively. Spatial variations in these parameters reflected variations in underlying tissue microstructure. A linear model was applied to the whole brain, including gray/white matter and deep brain structures, to determine the global model coefficients. Synthetic R1 values were then calculated using these coefficients and compared with the measured R1 maps. RESULTS: The model's validity was demonstrated by correspondence between the synthetic and measured R1 values and by high stability of the model coefficients across a large cohort. CONCLUSION: A single set of global coefficients can be used to relate R1 , MT, and R2 * across the whole brain. Our population study demonstrates the robustness and stability of the model. Magn Reson Med, 2014. © 2014 The Authors. Magnetic Resonance in Medicine published by Wiley Periodicals, Inc. Magn Reson Med 73:1309-1314, 2015. © 2014 Wiley Periodicals, Inc.

Profile Following for Inspection of Underwater Structures

We present a seabed profile estimation and following method for close proximity inspection of 3D underwater structures using autonomous underwater vehicles (AUVs). The presented method is used to determine a path allowing the AUV to pass its sensors over all points of the target structure, which is known as coverage path planning. Our profile following method goes beyond traditional seabed following at a safe altitude and exploits hovering capabilities of recent AUV developments. A range sonar is used to incrementally construct a local probabilistic map representation of the environment and estimates of the local profile are obtained via linear regression. Two behavior-based controllers use these estimates to perform horizontal and vertical profile following. We build upon these tools to address coverage path planning for 3D underwater structures using a (potentially inaccurate) prior map and following cross-section profiles of the target structure. The feasibility of the proposed method is demonstrated using the GIRONA 500 AUV both in simulation using synthetic and real-world bathymetric data and in pool trials

A modified structural stress method for fatigue assessment of welded structures

Fatigue life assessment of weldedstructures is commonly based on the nominal stress method, but more flexible and accurate methods have been introduced. In general, the assessment accuracy is improved as more localized information about the weld is incorporated. The structural hot spot stress method includes the influence of macro geometric effects and structural discontinuities on the design stress but excludes the local features of the weld. In this thesis, the limitations of the structural hot spot stress method are discussed and a modified structural stress method with improved accuracy is developed and verified for selected welded details. The fatigue life of structures in the as-welded state consists mainly of crack growth from pre-existing cracks or defects. Crack growth rate depends on crack geometry and the stress state on the crack face plane. This means that the stress level and shape of the stress distribution in the assumed crack path governs thetotal fatigue life. In many structural details the stress distribution is similar and adequate fatigue life estimates can be obtained just by adjusting the stress level based on a single stress value, i.e., the structural hot spot stress. There are, however, cases for which the structural stress approach is less appropriate because the stress distribution differs significantly from the more common cases. Plate edge attachments and plates on elastic foundations are some examples of structures with this type of stress distribution. The importance of fillet weld size and weld load variation on the stress distribution is another central topic in this thesis. Structural hot spot stress determination is generally based on a procedure that involves extrapolation of plate surface stresses. Other possibilities for determining the structural hot spot stress is to extrapolate stresses through the thickness at the weld toe or to use Dong's method which includes through-thickness extrapolation at some distance from the weld toe. Both of these latter methods are less sensitive to the FE mesh used. Structural stress based on surface extrapolation is sensitive to the extrapolation points selected and to the FE mesh used near these points. Rules for proper meshing, however, are well defined and not difficult to apply. To improve the accuracy of the traditional structural hot spot stress, a multi-linear stress distribution is introduced. The magnitude of the weld toe stress after linearization is dependent on the weld size, weld load and plate thickness. Simple equations have been derived by comparing assessment results based on the local linear stress distribution and LEFM based calculations. The proposed method is called the modified structural stress method (MSHS) since the structural hot spot stress (SHS) value is corrected using information on weld size andweld load. The correction procedure is verified using fatigue test results found in the literature. Also, a test case was conducted comparing the proposed method with other local fatigue assessment methods.

Is the relationship between major depressive disorder and self-reported alcohol use disorder an artificial one ?

AIMS: Many studies have suggested a close relationship between alcohol use disorder (AUD) and major depressive disorder (MDD). This study aimed to test whether the relationship between self-reported AUD and MDD was artificially strengthened by the diagnosis of MDD. This association was tested comparing relationships between alcohol use and AUD for depressive people and non-depressive people. METHODS: As part of the Cohort Study on Substance Use Risk Factors, 4352 male Swiss alcohol users in their early twenties answered questions concerning their alcohol use, AUD and MDD at two time points. Generalized linear models for cross-sectional and longitudinal associations were calculated. RESULTS: For cross-sectional associations, depressive participants reported a higher number of AUD symptoms (β = 0.743, P < 0.001) than non-depressive participants. Moreover, there was an interaction (β = -0.204, P = 0.001): the relationship between alcohol use and AUD was weaker for depressive participants rather than non-depressive participants. For longitudinal associations, there were almost no significant relationships between MDD at baseline and AUD at follow-up, but the interaction was still significant (β = -0.249, P < 0.001). CONCLUSION: MDD thus appeared to be a confounding variable in the relationship between alcohol use and AUD, and self-reported measures of AUD seemed to be overestimated by depressive people. This result brings into question the accuracy of self-reported measures of substance use disorders. Furthermore, it adds to the emerging debate about the usefulness of substance use disorder as a concept, when heavy substance use itself appears to be a sensitive and reliable indicator.

HETEROTIC GROUP FORMATION IN PSIDIUM GUAJAVA L. BY ARTIFICIAL NEURAL NETWORK AND DISCRIMINANT ANALYSIS

ABSTRACT The present study aimed at evaluating the heterotic group formation in guava based on quantitative descriptors and using artificial neural network (ANN). For such, we evaluated eight quantitative descriptors. Large genetic variability was found for the eight quantitative traits in the 138 genotypes of guava. The artificial neural network technique determined that the optimal number of groups was three. The grouping consistency was determined by linear discriminant analysis, which obtained classification percentage of the groups, with a value of 86 %. It was concluded that the artificial neural network method is effective to detect genetic divergence and heterotic group formation.

Rainfall erosivity for the State of Rio de Janeiro estimated by artificial neural network

The Artificial Neural Networks (ANNs) are mathematical models method capable of estimating non-linear response plans. The advantage of these models is to present different responses of the statistical models. Thus, the objective of this study was to develop and to test ANNs for estimating rainfall erosivity index (EI30) as a function of the geographical location for the state of Rio de Janeiro, Brazil and generating a thematic visualization map. The characteristics of latitude, longitude e altitude using ANNs were acceptable to estimating EI30 and allowing visualization of the space variability of EI30. Thus, ANN is a potential option for the estimate of climatic variables in substitution to the traditional methods of interpolation.

Determination of the influence of the variation of reducing and non-reducing sugars on coffee quality with use of artificial neural network

The present study aimed at evaluating the use of Artificial Neural Network to correlate the values resulting from chemical analyses of samples of coffee with the values of their sensory analyses. The coffee samples used were from the Coffea arabica L., cultivars Acaiá do Cerrado, Topázio, Acaiá 474-19 and Bourbon, collected in the southern region of the state of Minas Gerais. The chemical analyses were carried out for reducing and non-reducing sugars. The quality of the beverage was evaluated by sensory analysis. The Artificial Neural Network method used values from chemical analyses as input variables and values from sensory analysis as output values. The multiple linear regression of sensory analysis values, according to the values from chemical analyses, presented a determination coefficient of 0.3106, while the Artificial Neural Network achieved a level of 80.00% of success in the classification of values from the sensory analysis.

An Inspection System to Identify Fatigue Damage on Steel-Bridge Structures

Crack formation and growth in steel bridge structural elements may be due to loading oscillations. The welded elements are liable to internal discontinuities along welded joints and sensible to stress variations. The evaluation of the remaining life of a bridge is needed to make cost-effective decisions regarding inspection, repair, rehabilitation, and replacement. A steel beam model has been proposed to simulate crack openings due to cyclic loads. Two possible alternatives have been considered to model crack propagation, which the initial phase is based on the linear fracture mechanics. Then, the model is extended to take into account the elastoplastic fracture mechanic concepts. The natural frequency changes are directly related to moment of inertia variation and consequently to a reduction in the flexural stiffness of a steel beam. Thus, it is possible to adopt a nondestructive technique during steel bridge inspection to quantify the structure eigenvalue variation that will be used to localize the grown fracture. A damage detection algorithm is developed for the proposed model and the numerical results are compared with the solutions achieved by using another well know computer code.

Control of Structures with Cubic and Quadratic Non-Linearities with Time Delay Consideration

This paper studies the effect of time delay on the active non-linear control of dynamically loaded flexible structures. The behavior of non-linear systems under state feedback control, considering a fixed time delay for the control force, is investigated. A control method based on non-linear optimal control, using a tensorial formulation and state feedback control is used. The state equations and the control forces are expressed in polynomial form and a performance index, quadratic in both state vector and control forces, is used. General polynomial representations of the non-linear control law are obtained and implemented for control algorithms up to the fifth order. This methodology is applied to systems with quadratic and cubic non-linearities. Strongly non-linear systems are tested and the effectiveness of the control system including a delay for the application of control forces is discussed. Numerical results indicate that the adopted control algorithm can be efficient for non-linear systems, chiefly in the presence of strong non-linearities but increasing time delay reduces the efficiency of the control system. Numerical results emphasize the importance of considering time delay in the project of active structural control systems.

Intima-media thickness evaluation by B-mode ultrasound: Correlation with blood pressure levels and cardiac structures

The aim of this study was to analyze the thickness of the intima-media complex (IMC) using a noninvasive method. The carotid and femoral common arteries were evaluated by noninvasive B-mode ultrasound in 63 normotensive and in 52 hypertensive subjects and the thickness of the IMC was tested for correlation with blood pressure, cardiac structures and several clinical and biological parameters. The IMC was thicker in hypertensive than in normotensive subjects (0.67 ± 0.13 and 0.62 ± 0.16 vs 0.54 ± 0.09 and 0.52 ± 0.11 mm, respectively, P<0.0001). In normotensive patients, the simple linear regression showed significant correlations between IMC and age, body mass index and 24-h systolic blood pressure for both the carotid and femoral arteries. In hypertensives the carotid IMC was correlated with age and 24-h systolic blood pressure while femoral IMC was correlated only with 24-h diastolic blood pressure. Forward stepwise regression showed that age, body mass index and 24-h systolic blood pressure influenced the carotid IMC relationship (r2 = 0.39) in normotensives. On the other hand, the femoral IMC relationship was influenced by 24-h systolic blood pressure and age (r2 = 0.40). In hypertensives, age and 24-h systolic blood pressure were the most important determinants of carotid IMC (r2 = 0.37), while femoral IMC was influenced only by 24-h diastolic blood pressure (r2 = 0.10). There was an association between carotid IMC and echocardiographic findings in normotensives, while in hypertensives only the left posterior wall and interventricular septum were associated with femoral IMC. We conclude that age and blood pressure influence the intima-media thickness, while echocardiographic changes are associated with the IMC.

Previsão de estabilidade de óleos vegetais através da rede de inteligência artificial

Redes Neurais Artificiais são técnicas computacionais que se utilizam de um modelo matemático capaz de adquirir conhecimentos pela experiência; esse comportamento inteligente da rede provém das interações entre unidades de processamento, denominadas de neurônios artificiais. O objetivo deste trabalho foi criar uma rede neural capaz de prever a estabilidade de óleos vegetais, a partir de dados de suas composições químicas, visando um modelo para a previsão da shelf-life de óleos vegetais, tendo como parâmetros apenas dados de suas composições químicas. Os primeiros passos do processo de desenvolvimento da rede consistiram na coleta de dados relativos ao problema e sua separação em um conjunto de treinamento e outro de testes. Estes conjuntos apresentaram como variáveis dados de composição química, que incluíram os valores totais em ácidos graxos, fenóis, tocoferóis e a composição individual em ácidos graxos. O passo seguinte foi a execução do treinamento, onde o padrão de entrada apresentado à rede como parâmetro de estabilidade foi o índice de peróxido, determinado experimentalmente por um período de 16 dias de armazenagem na ausência de luz, a 65ºC. Após o treinamento foi testada a capacidade de previsão adquirida pela rede, em função do parâmetro de estabilidade adotado, mas com um novo grupo de óleos. Seguindo o teste, foi determinada a correlação linear entre os valores de estabilidade previstos pela rede e aqueles determinados experimentalmente. Com os resultados obtidos, pode-se confirmar a viabilidade de previsão da estabilidade de óleos vegetais pela rede neural, a partir de dados de sua composição química, utilizando como parâmetro de estabilidade o índice de peróxido.