The transducer model for contrast detection and discrimination: formal relations, implications, and an empirical test.

The transducer function mu for contrast perception describes the nonlinear mapping of stimulus contrast onto an internal response. Under a signal detection theory approach, the transducer model of contrast perception states that the internal response elicited by a stimulus of contrast c is a random variable with mean mu(c). Using this approach, we derive the formal relations between the transducer function, the threshold-versus-contrast (TvC) function, and the psychometric functions for contrast detection and discrimination in 2AFC tasks. We show that the mathematical form of the TvC function is determined only by mu, and that the psychometric functions for detection and discrimination have a common mathematical form with common parameters emanating from, and only from, the transducer function mu and the form of the distribution of the internal responses. We discuss the theoretical and practical implications of these relations, which have bearings on the tenability of certain mathematical forms for the psychometric function and on the suitability of empirical approaches to model validation. We also present the results of a comprehensive test of these relations using two alternative forms of the transducer model: a three-parameter version that renders logistic psychometric functions and a five-parameter version using Foley's variant of the Naka-Rushton equation as transducer function. Our results support the validity of the formal relations implied by the general transducer model, and the two versions that were contrasted account for our data equally well.

Efficient numerical methods for the random-field Ising model: Finite-size scaling, reweighting extrapolation, and computation of response functions

It was recently shown [Phys. Rev. Lett. 110, 227201 (2013)] that the critical behavior of the random-field Ising model in three dimensions is ruled by a single universality class. This conclusion was reached only after a proper taming of the large scaling corrections of the model by applying a combined approach of various techniques, coming from the zero-and positive-temperature toolboxes of statistical physics. In the present contribution we provide a detailed description of this combined scheme, explaining in detail the zero-temperature numerical scheme and developing the generalized fluctuation-dissipation formula that allowed us to compute connected and disconnected correlation functions of the model. We discuss the error evolution of our method and we illustrate the infinite limit-size extrapolation of several observables within phenomenological renormalization. We present an extension of the quotients method that allows us to obtain estimates of the critical exponent a of the specific heat of the model via the scaling of the bond energy and we discuss the self-averaging properties of the system and the algorithmic aspects of the maximum-flow algorithm used.

Mixtures of g-priors in Generalized Linear Models

Mixtures of Zellner's g-priors have been studied extensively in linear models and have been shown to have numerous desirable properties for Bayesian variable selection and model averaging. Several extensions of g-priors to Generalized Linear Models (GLMs) have been proposed in the literature; however, the choice of prior distribution of g and resulting properties for inference have received considerably less attention. In this paper, we extend mixtures of g-priors to GLMs by assigning the truncated Compound Confluent Hypergeometric (tCCH) distribution to 1/(1+g) and illustrate how this prior distribution encompasses several special cases of mixtures of g-priors in the literature, such as the Hyper-g, truncated Gamma, Beta-prime, and the Robust prior. Under an integrated Laplace approximation to the likelihood, the posterior distribution of 1/(1+g) is in turn a tCCH distribution, and approximate marginal likelihoods are thus available analytically. We discuss the local geometric properties of the g-prior in GLMs and show that specific choices of the hyper-parameters satisfy the various desiderata for model selection proposed by Bayarri et al, such as asymptotic model selection consistency, information consistency, intrinsic consistency, and measurement invariance. We also illustrate inference using these priors and contrast them to others in the literature via simulation and real examples.

A Generic Model of Execution for Synthesizing Domain-Specific Models

Software engineering researchers are challenged to provide increasingly more pow- erful levels of abstractions to address the rising complexity inherent in software solu- tions. One new development paradigm that places models as abstraction at the fore- front of the development process is Model-Driven Software Development (MDSD). MDSD considers models as first class artifacts, extending the capability for engineers to use concepts from the problem domain of discourse to specify apropos solutions. A key component in MDSD is domain-specific modeling languages (DSMLs) which are languages with focused expressiveness, targeting a specific taxonomy of problems. The de facto approach used is to first transform DSML models to an intermediate artifact in a HLL e.g., Java or C++, then execute that resulting code. Our research group has developed a class of DSMLs, referred to as interpreted DSMLs (i-DSMLs), where models are directly interpreted by a specialized execution engine with semantics based on model changes at runtime. This execution engine uses a layered architecture and is referred to as a domain-specific virtual machine (DSVM). As the domain-specific model being executed descends the layers of the DSVM the semantic gap between the user-defined model and the services being provided by the underlying infrastructure is closed. The focus of this research is the synthesis engine, the layer in the DSVM which transforms i-DSML models into executable scripts for the next lower layer to process. The appeal of an i-DSML is constrained as it possesses unique semantics contained within the DSVM. Existing DSVMs for i-DSMLs exhibit tight coupling between the implicit model of execution and the semantics of the domain, making it difficult to develop DSVMs for new i-DSMLs without a significant investment in resources. At the onset of this research only one i-DSML had been created for the user- centric communication domain using the aforementioned approach. This i-DSML is the Communication Modeling Language (CML) and its DSVM is the Communication Virtual machine (CVM). A major problem with the CVM’s synthesis engine is that the domain-specific knowledge (DSK) and the model of execution (MoE) are tightly interwoven consequently subsequent DSVMs would need to be developed from inception with no reuse of expertise. This dissertation investigates how to decouple the DSK from the MoE and sub- sequently producing a generic model of execution (GMoE) from the remaining appli- cation logic. This GMoE can be reused to instantiate synthesis engines for DSVMs in other domains. The generalized approach to developing the model synthesis com- ponent of i-DSML interpreters utilizes a reusable framework loosely coupled to DSK as swappable framework extensions. This approach involves first creating an i-DSML and its DSVM for a second do- main, demand-side smartgrid, or microgrid energy management, and designing the synthesis engine so that the DSK and MoE are easily decoupled. To validate the utility of the approach, the SEs are instantiated using the GMoE and DSKs of the two aforementioned domains and an empirical study to support our claim of reduced developmental effort is performed.

Parallel nonconvex generalized Benders decomposition for natural gas production network planning under uncertainty

A scenario-based two-stage stochastic programming model for gas production network planning under uncertainty is usually a large-scale nonconvex mixed-integer nonlinear programme (MINLP), which can be efficiently solved to global optimality with nonconvex generalized Benders decomposition (NGBD). This paper is concerned with the parallelization of NGBD to exploit multiple available computing resources. Three parallelization strategies are proposed, namely, naive scenario parallelization, adaptive scenario parallelization, and adaptive scenario and bounding parallelization. Case study of two industrial natural gas production network planning problems shows that, while the NGBD without parallelization is already faster than a state-of-the-art global optimization solver by an order of magnitude, the parallelization can improve the efficiency by several times on computers with multicore processors. The adaptive scenario and bounding parallelization achieves the best overall performance among the three proposed parallelization strategies.

A Generalized Methodology to Characterize Composite Materials for Pyrolysis Models

The predictive capabilities of computational fire models have improved in recent years such that models have become an integral part of many research efforts. Models improve the understanding of the fire risk of materials and may decrease the number of expensive experiments required to assess the fire hazard of a specific material or designed space. A critical component of a predictive fire model is the pyrolysis sub-model that provides a mathematical representation of the rate of gaseous fuel production from condensed phase fuels given a heat flux incident to the material surface. The modern, comprehensive pyrolysis sub-models that are common today require the definition of many model parameters to accurately represent the physical description of materials that are ubiquitous in the built environment. Coupled with the increase in the number of parameters required to accurately represent the pyrolysis of materials is the increasing prevalence in the built environment of engineered composite materials that have never been measured or modeled. The motivation behind this project is to develop a systematic, generalized methodology to determine the requisite parameters to generate pyrolysis models with predictive capabilities for layered composite materials that are common in industrial and commercial applications. This methodology has been applied to four common composites in this work that exhibit a range of material structures and component materials. The methodology utilizes a multi-scale experimental approach in which each test is designed to isolate and determine a specific subset of the parameters required to define a material in the model. Data collected in simultaneous thermogravimetry and differential scanning calorimetry experiments were analyzed to determine the reaction kinetics, thermodynamic properties, and energetics of decomposition for each component of the composite. Data collected in microscale combustion calorimetry experiments were analyzed to determine the heats of complete combustion of the volatiles produced in each reaction. Inverse analyses were conducted on sample temperature data collected in bench-scale tests to determine the thermal transport parameters of each component through degradation. Simulations of quasi-one-dimensional bench-scale gasification tests generated from the resultant models using the ThermaKin modeling environment were compared to experimental data to independently validate the models.

Health beliefs affect the correct replacement of daily disposable contact lenses:predicting compliance with the Health Belief Model and the Theory of Planned Behaviour

Purpose: To assess the compliance of Daily Disposable Contact Lenses (DDCLs) wearers with replacing lenses at a manufacturer-recommended replacement frequency. To evaluate the ability of two different Health Behavioural Theories (HBT), The Health Belief Model (HBM) and The Theory of Planned Behaviour (TPB), in predicting compliance. Method: A multi-centre survey was conducted using a questionnaire completed anonymously by contact lens wearers during the purchase of DDCLs. Results: Three hundred and fifty-four questionnaires were returned. The survey comprised 58.5% females and 41.5% males (mean age 34. ±. 12. years). Twenty-three percent of respondents were non-compliant with manufacturer-recommended replacement frequency (re-using DDCLs at least once). The main reason for re-using DDCLs was "to save money" (35%). Predictions of compliance behaviour (past behaviour or future intentions) on the basis of the two HBT was investigated through logistic regression analysis: both TPB factors (subjective norms and perceived behavioural control) were significant (p. <. 0.01); HBM was less predictive with only the severity (past behaviour and future intentions) and perceived benefit (only for past behaviour) as significant factors (p. <. 0.05). Conclusions: Non-compliance with DDCLs replacement is widespread, affecting 1 out of 4 Italian wearers. Results from the TPB model show that the involvement of persons socially close to the wearers (subjective norms) and the improvement of the procedure of behavioural control of daily replacement (behavioural control) are of paramount importance in improving compliance. With reference to the HBM, it is important to warn DDCLs wearers of the severity of a contact-lens-related eye infection, and to underline the possibility of its prevention.

Obtaining environmental favourability functions from logistic regression

Logistic regression is a statistical tool widely used for predicting species’ potential distributions starting from presence/absence data and a set of independent variables. However, logistic regression equations compute probability values based not only on the values of the predictor variables but also on the relative proportion of presences and absences in the dataset, which does not adequately describe the environmental favourability for or against species presence. A few strategies have been used to circumvent this, but they usually imply an alteration of the original data or the discarding of potentially valuable information. We propose a way to obtain from logistic regression an environmental favourability function whose results are not affected by an uneven proportion of presences and absences. We tested the method on the distribution of virtual species in an imaginary territory. The favourability models yielded similar values regardless of the variation in the presence/absence ratio. We also illustrate with the example of the Pyrenean desman’s (Galemys pyrenaicus) distribution in Spain. The favourability model yielded more realistic potential distribution maps than the logistic regression model. Favourability values can be regarded as the degree of membership of the fuzzy set of sites whose environmental conditions are favourable to the species, which enables applying the rules of fuzzy logic to distribution modelling. They also allow for direct comparisons between models for species with different presence/absence ratios in the study area. This makes themmore useful to estimate the conservation value of areas, to design ecological corridors, or to select appropriate areas for species reintroductions.

Distinguishing the distributions of two cryptic frogs (Anura: Discoglossidae) using molecular data and environmental modeling

Currently, the identification of two cryptic Iberian amphibians, Discoglossus galganoi Capula, Nascetti, Lanza, Bullini and Crespo, 1985 and Discoglossus jeanneae Busack, 1986, relies on molecular characterization. To provide a means to discern the distributions of these species, we used 385-base-pair sequences of the cytochrome b gene to identify 54 Spanish populations of Discoglossus. These data and a series of environmental variables were used to build up a logistic regression model capable of probabilistically designating a specimen of Discoglossus found in any Universal Transverse Mercator (UTM) grid cell of 10 km × 10 km to one of the two species. Western longitudes, wide river basins, and semipermeable (mainly siliceous) and sandstone substrates favored the presence of D. galganoi, while eastern longitudes, mountainous areas, severe floodings, and impermeable (mainly clay) or basic (limestone and gypsum) substrates favored D. jeanneae. Fifteen percent of the UTM cells were predicted to be shared by both species, whereas 51% were clearly in favor of D. galganoi and 34% were in favor of D. jeanneae, considering odds of 4:1. These results suggest that these two species have parapatric distributions and allow for preliminary identification of potential secondary contact areas. The method applied here can be generalized and used for other geographic problems posed by cryptic species.

Allergological evaluation of a dog population in a veterinary immuno-allergology consultation: What correlates in a canine model

Introduction: Allergic dermatitis (AD) is the most common canine pruritic condition in veterinary dermatology. Allergic dermatitis to flea bites presents the highest prevalence, followed by atopic dermatitis and food AD. This study aimed to identify possible correlation between data from clinical signs, intradermal tests (IDT) and specific IgE levels, which are used in dog AD assessment. Methods: Fifty five dogs from the Veterinary Hospital of the University of Évora (Portugal) and Rof Codina University Hospital (Lugo, Spain) outpatient consultations were studied by means of clinical inquiry, IDT and specific IgE determination. Thirty five of the patients belonged to predisposed breeds, 30 were females and 25 males. Forty one (74%) were indoor. Results: In 82% of cases first clinical signs appeared before the age of 3 years and 24% even before 1 year old. In 70% of the individuals clinical signs included itching, which was generalized in 66%, with 78% of paw licking and chewing. Clinical profile showed seasonal worsening in 64% of cases. From the 69.1% of dogs already presenting with dermatitis, 50% also presented external otitis and 28.9% self-inflicted alopecia. "Intense itching" was found in 10.5%, "medium itching" in 81.6% and “mild itching” in 5.26% of the patients. Prevalence of positive IDT was 37.3 % to Lep d, 29.41% to Der f, 27.5% to Der p, 25.5% to Dac g and 21.6% to Malassezia sp. From the 37 dogs submitted to food IDT, 40.5% revealed positive to beef, 27% to chicken, 27% to porc and 5.4% to lamb. Specific IgE > 150 EAU was found in 84% of dogs to indoor allergen sources and in 68% to pollens. A negative correlation was found between an outdoor life and the intensity (p = 0.033) and precocity (p = 0.026) of clinical signs. Sensitization to pollens was found positively correlated with the seasonality of clinical signs (p = 0.001) and the positivity for Dac g (p = 0.007). The prevalence of chronic otitis correlated positively with alopecia and reactivity to Lep d (p = 0.008), Plantago lanceolata (p = 0.026) and Platanus acerifolia (p = 0.017). There was no correlation between the results of ITD and specific IgE. Conclusion: We can conclude that correlation between different clinical signs and positive testing for some allergenic sources may occur, as well as between sensitization to pollens and the beginning, the intensity and the seasonality of dog patient clinical signs.

Modelação do cancro da mama na região do Alentejo

Estudos epidemiológicos são estudos estatísticos onde se procura relacionar ocorrências de eventos de saúde com uma ou várias causas específicas. A importância que os modelos epidemiológicos assumem hoje no estudo de doenças de foro oncológico, em particular no estabelecimento das suas etiologias, é incontornável. Segundo Ogden, J. (1999) o cancro é "um crescimento incontrolável de células anormais que produzem tumores chamados neoplasias". Estes tumores podem ter origem benigna (não se espalham pelo corpo) ou maligna (apresentam metastização de outros órgãos). Sendo uma doença actual, com uma elevada taxa de incidência em Portugal quando comparada com outras doenças (Instituto Nacional de Estatística- INE, 2009), aumentando esta taxa com a idade tal como refere Marques, L. (2003), podendo ocorrer o diagnóstico desta doença em qualquer idade. De acordo com INE (2000) pode dizer-se que o cancro está entre as três principais causas de morte em Portugal, registando-se um aumento progressivo do seu peso proporcional, sendo o cancro da mama o tipo de cancro mais comum entre as mulheres e uma das doenças com maior impacto na nossa sociedade. O objectivo principal deste trabalho é a estimação e modelação do risco de contrair uma doença de natureza não contagiosa e rara (neste caso, cancro da mama), usando dados da região do Alentejo. Pretende-se fazer um apanhado das metodologias mais empregues nesta área e aplicá-las na prática, com ênfase nos estudos caso-controlo e nos modelos lineares generalizados (GLM) - mais concretamente regressão logística. Os estudos caso-controlo são usados para identificar os factores que podem contribuir para uma condição médica, comparando indivíduos que têm essa condição (casos) com pacientes que não têm a condição, mas que de resto são semelhantes (controlos). Neste trabalho utilizou-se essa metodologia para estudar a associação entre o viver em ambiente rural/urbano e o cancro da mama. Tendo em conta que o objectivo principal deste estudo se prende com o estudo da relação entre variáveis, mais propriamente, análise de influência que uma ou mais variáveis (explicativas) têm sobre uma variável de interesse (resposta), para esse efeito são estudados os modelos lineares generalizados - GLM - unificados na mesma moldura teórica pela primeira vez por Nelder & Wedderburn (1972) - e, posteriormente aplicados ao conjunto de dados sobre cancro da mama na Região do Alentejo. O presente trabalho pretende assim, ser um contributo na identificação de factores de risco do cancro da mama na região do Alentejo. ABSTRACT: Epidemiological studies are statistical studies where attempts to relate occurrences of health events with one or more specific causes. The importance of epidemiological models that are far in the study of diseases of cancer forum, particularly in establishing their etiology, is inescapable. According to Ogden, J. (1999) cancer is "an incontrollable growth of abnormal cells that produce tumors called cancer". These tumors may be benign (not spread throughout the body) or malignant (show metastasis to other organs). Being a current illness with a high incidence rate in Portugal compared with the same respect to other diseases (National Statistics 1nstitute -1NE, 2009) having an increasing rate with age as mentioned Marques, L. (2003), and can possibly be diagnosed at any age. According to 1NE (2000) the cancer is among the top three causes of death in Portugal and there is a progressive increase of its proportional weight. Breast cancer is the most common form of cancer among women and the diseases with major impact in our society. The main objective of this work is to model and estimate the risk of contracting a non-contagious and rare disease (in this case, breast cancer), using data from the Alentejo region. It is intended to summarize some of the methodologies employed in this area and apply them in practice, with emphasis on case-control studies and generalized linear models (GLM) - more specifically the logistic regression. The case-control studies are used to identify factors that may contribute to a medical condition, comparing individuals who have this condition (cases) with patients who have not the condition but that are otherwise similar (controls). ln this work we used this methodology to study the association between living in a rural/urban and breast cancer. Given that the main objective of this study rather relates to the study of the relationship between variables to analyze the influence that one or more variables (explanatory) have on a variable (response), for this purpose we study the generalized linear models - GLM - first unified in the same theoretical framework by Nelder and Wedderburn (1972) and subsequently applied to the data set on breast cancer in the Alentejo region. This work intends to be a contribution in identifying risk factors for breast cancer in the Alentejo region.

Single diode model parameters analysis of photovoltaic cell

In this paper it is proposed to obtain enhanced and more efficient parameters model from generalized five parameters (single diode) model of PV cells. The paper also introduces, describes and implements a seven parameter model for photovoltaic cell (PV cell) which includes two internal parameters and five external parameters. To obtain the model the mathematical equations and an equivalent circuit consisting of a photo generated current source, a series resistor, a shunt resistor and a diode is used. The fundamental equation of PV cell is used to analyse and best fit the observation data. Especially bisection iteration method is used to obtain the expected result and to understand the deviation of changes in different parameters situation at various conditions respectively. The produced model can be used of measuring and understanding the actions of photovoltaic cells for certain changes and parameters extraction. The effect is also studied with I-V and P-V characteristics of PV cells though it is a challenge to optimize the output with real time simulation. The working procedure is also discussed and an experiment presented to get the closure and insight about the produced model and to decide upon the model validity. At the end, we observed that the result of the simulation is very close to the produced model.

An approximate method for the solution of an influence function foil rolling model

Fleck and Johnson (Int. J. Mech. Sci. 29 (1987) 507) and Fleck et al. (Proc. Inst. Mech. Eng. 206 (1992) 119) have developed foil rolling models which allow for large deformations in the roll profile, including the possibility that the rolls flatten completely. However, these models require computationally expensive iterative solution techniques. A new approach to the approximate solution of the Fleck et al. (1992) Influence Function Model has been developed using both analytic and approximation techniques. The numerical difficulties arising from solving an integral equation in the flattened region have been reduced by applying an Inverse Hilbert Transform to get an analytic expression for the pressure. The method described in this paper is applicable to cases where there is or there is not a flat region.