Radiation effects on a GE:GA photoconductive detector

We have investigated radiation effects on a Ge:Ga photoconductive infrared detector at low photon background level of 4×10⁸ ph cm^–2s^–1, using Cobalt 60 as a gamma ray source. The irradiation immediately induced spike noises which degrded NEP (short term effect), while it gradually increased responsivity (long term effect). After the removal of the gamma ray source, the spikes disappeared while the responsivity still stayed in a higher level and gradually decreased with a time scale of several hours. The responsivity-change-rate before and after the irradiation is smaller for a higher bias voltage. Finally we have made the first trial to cure the long term effect, using a flashing procedure and have found out its effectiveness.     

Bayesian testing of nonparametric hypotheses and its application to global optimization

Random distribution functions are the basic tool for solving nonparametric decision-theoretic problems. In 1974, Doksum introduced the family of distributions neutral to the right, that is, distributions such thatF(t ₁),[F(t ₂)–F(t ₁)]/[1 –F(t ₁)],...,[F(t _k)–F(t _{k – 1})]/[1 –F(t _{k – 1})] are independent whenevert ₁ < ... <t _kIn practice, application of distributions neutral to the right has been prevented by the lack of a manageable analytical expression for probabilities of the typeP(F(t)<q) for fixedt andq. A subclass of such distributions can be provided which allows for a close expression of the characteristic function of log[1–F(t)], given the sample. Then, thea posteriori distribution ofF(t) is obtained by numerical evaluation of a Fourier integral. As an application, the global optimization problem is formulated as a problem of inference about the quantiles of the distributionF(y) of the random variableY=f(X), wheref is the objective function andX is a random point in the search domain.The author thanks J. Koronacki and R. Zielinski of the Polish Academy of Sciences for their valuable criticism during the final draft of the paper.     

Modeling of radiative heat transfer in 3D complex boiler with non-gray sooting media

The radiative heat transfer problem is solved for 3D complex industrial boiler with five baffles containing a mixture of carbon dioxide and water vapor for non-uniform temperature fields. A numerical formulation using the FTn finite volume method coupled with the bounded high-order resolution CLAM scheme, the blocked-off-region procedure and the narrow-band based weighted-sum-of-gray-gases (WSGG) [Kim OJ, Song T-H. Data base of WSGGM-based spectral model for radiation properties of combustion products, JQSRT 2000; 64: 379-94] model is adapted. The effect of soot volumetric fraction, particle temperature and uniform particle concentration on the radiative heat flux and radiative heat source is investigated and discussed. Also the advantages, in non-gray media, of the FTnFVM compared to the classical FVM are highlighted.     

General bounds on the isovector coupling constants of the weak neutral current

We show that experimental data on inclusive neutrino reactions can be used to obtain general bounds on the coupling constants of the isovector part of the hadronic weak neutral current provided this isovector current is related to the charged current by isospin rotation. These bounds are free from the assumption of a specific model for the neutral current as well as any dynamical assumption on the hadronic structure functions. We derive upper bounds on the coupling constants which involve only the cross sections for isospin-averaged nucleon target as well as lower bounds which require a knowledge of the cross sections for proton and neutron separately.     

Statistical Inference of Truncated Normal Distribution Based on the Generalized Progressive Hybrid Censoring

In this paper, the parameter estimation problem of a truncated normal distribution is discussed based on the generalized progressive hybrid censored data. The desired maximum likelihood estimates of unknown quantities are firstly derived through the Newton–Raphson algorithm and the expectation maximization algorithm. Based on the asymptotic normality of the maximum likelihood estimators, we develop the asymptotic confidence intervals. The percentile bootstrap method is also employed in the case of the small sample size. Further, the Bayes estimates are evaluated under various loss functions like squared error, general entropy, and linex loss functions. Tierney and Kadane approximation, as well as the importance sampling approach, is applied to obtain the Bayesian estimates under proper prior distributions. The associated Bayesian credible intervals are constructed in the meantime. Extensive numerical simulations are implemented to compare the performance of different estimation methods. Finally, an authentic example is analyzed to illustrate the inference approaches.     

Chaotic vibrations of spherical and conical axially symmetric shells

Summary Chaotic vibrations of deterministic, geometrically nonlinear, elastic, spherical and conical axially summetric shells, subject to sign-changing transversal load using the variational principle, are analysed. The paper is motivated by an observation that variational equations of the hybrid type are suitableto solve many dynamical problems of the shells theory. It is assumed that the shell material is isotropic, and the Hook's principle holds. Intertial forces in directions tangent to mean shell surface and rotation inertia of a normal shell cross section are neglected. A transition form PDEs to ODEs (the Cauchy problem) is realized through the Ritz procedure. Next, the Cauchy problem is solved using the fourth-order Runge-Kutta method. Qualitative and quantitative analysis is carried out in the frame of both nonlinear dynamics and quantitative theory of differential equations. New scenarios from harmonic to chaotic dynamics are detected. Various vibration forms development versus control parameters (rise of arc; amplitude and frequency of the exciting force and number of vibrational modes accounted) are illustrated and discussed.     

Environmental decision‐making using Bayesian networks: creating an environmental report card

Environmental report cards are popular mechanisms for summarising the overall status of an environmental system of interest. This paper describes the development of such a report card in the context of a study for Gladstone Harbour in Queensland, Australia. The harbour is within the World Heritage‐protected Great Barrier Reef and is the location of major industrial development, hence the interest in developing a way of reporting its health in a statistically valid, transparent and sustainable manner. A Bayesian network (BN) approach was used because of its ability to aggregate and integrate different sources of information, provide probabilistic estimates of interest and update these estimates in a natural manner as new information becomes available. BN modelling is an iterative process, and in the context of environmental reporting, this is appealing as model development can be initiated while quantitative knowledge is still under development, and subsequently refined as more knowledge becomes available. Moreover, the BN model helps build the maturity of the quantitative information needed and helps target investment in monitoring and/or process modelling activities to inform the approach taken. The model is able to incorporate spatial and temporal information and may be structured in such a way that new indicators of relevance to the underlying environmental gradient being monitored may replace less informative indicators or be added to the model with minimal effort. The model described here focuses on the environmental component, but has the capacity to also incorporate social, cultural and economic components of the Gladstone Harbour Report Card. Copyright © 2016 John Wiley & Sons, Ltd.     

Dynamic Bayesian estimation of displacement parameters of continuous curve box based on Novozhilov theory

The finite strip controlling equation of pinned curve box was deduced on basis of Novozhilov theory and with flexibility method, and the problem of continuous curve box was resolved. Dynamic Bayesian error function of displacement parameters of continuous curve box was found. The corresponding formulas of dynamic Bayesian expectation and variance were derived. After the method of solving the automatic search of step length was put forward, the optimization estimation computing formulas were also obtained by adapting conjugate gradient method. Then the steps of dynamic Bayesian estimation were given in detail. Through analysis of a classic example, the criterion of judging the precision of the known information is gained as well as some other important conclusions about dynamic Bayesian stochastic estimation of displacement parameters of continuous curve box.     

Markov jump random c.d.f.’s and their posterior distributions

In this article we introduce the class of Markov jump random c.d.f.’s as a sub-class of the Q$Q$-Markov prior distributions studied in R.M. Balan [Q$Q$-Markov random probability measures and their posterior distributions, Stochastic Process. Appl. 109 (2004) 296–316]. Our main result states that if the prior distribution of a sample is a Markov jump process, then the posterior distribution can also be viewed as the distribution of a Markov jump process, whose transition mechanism and infinitesimal behavior have been updated in the light of the new data.     

Biases and Variability from Costly Bayesian Inference

When humans infer underlying probabilities from stochastic observations, they exhibit biases and variability that cannot be explained on the basis of sound, Bayesian manipulations of probability. This is especially salient when beliefs are updated as a function of sequential observations. We introduce a theoretical framework in which biases and variability emerge from a trade-off between Bayesian inference and the cognitive cost of carrying out probabilistic computations. We consider two forms of the cost: a precision cost and an unpredictability cost; these penalize beliefs that are less entropic and less deterministic, respectively. We apply our framework to the case of a Bernoulli variable: the bias of a coin is inferred from a sequence of coin flips. Theoretical predictions are qualitatively different depending on the form of the cost. A precision cost induces overestimation of small probabilities, on average, and a limited memory of past observations, and, consequently, a fluctuating bias. An unpredictability cost induces underestimation of small probabilities and a fixed bias that remains appreciable even for nearly unbiased observations. The case of a fair (equiprobable) coin, however, is singular, with non-trivial and slow fluctuations in the inferred bias. The proposed framework of costly Bayesian inference illustrates the richness of a ‘resource-rational’ (or ‘bounded-rational’) picture of seemingly irrational human cognition.