AEC set-up optimisation with computed radiography imaging

Phototimer set-up is a critical procedure for dose and image quality optimisation in computed radiography (CR) systems. While a conventional radiography automatic exposure control device (AEC) can be calibrated in order to gain a constant optical density on the film independent of beam quality and patient size, CR detectors present a high dynamic range which allows a much larger dose interval, but with different image quality levels. CR leads to a less frequent exam repetition, but may produce quite noisy images if the exposure level on the plate is not correct. The aim of this work is to evaluate the performance of a CR plate (Agfa MD40) in order to optimally calibrate an AEC device. The plate response has been characterised in terms of digital signal, exposure on the plate and signal-to-noise ratio for different beam qualities, in a patient of standard size.     

Investigation on thermal radiation spectra of coal ash deposits

This paper deals with thermal radiation properties of ash deposits on a pulverized coal boiler of an electric power plant. Normal emittance spectra in the 2.5–25 μm interval, and total normal emittance, were measured on 4 kinds of ash layers of a mm magnitude order thickness, at 560 → 1460 → 560 K in heating and cooling. It was found that ash powder layers are opaque for infrared radiation. The emittance increases with ash radiation wavelength and temperature. Ash powder is sintered and fused above 1200 K. The emittance of the sintered layer is above that of the unsintered layer. The authors propose, and explain by an example, correlating the experimentally obtained emittance spectra of ash deposits with a continuous curve, the formula of which defines the dependence of emittance on wavelength and temperature, i.e. ε = ε(λ,Т). Use of this formula, with parameter values determined by the proposed methodology, may greatly simplify the practical application of the experimentally determined emittances in the thermal design of existing and new steam boiler furnaces.     

A three dimensional exact equation for the turbulent dissipation rate of Generalised Newtonian Fluids

The flow of non-Newtonian fluids is of interest in many biological and industrial applications, including nanofluids. Most of the papers of the literature on turbulent non-Newtonian fluids focused the attention on viscoelastic fluids. In order to make accurate and low cost prediction of turbulent inelastic non-Newtonian fluids, a RANS Generalised Newtonian Fluid (GNF) turbulence model, based on the exact equations for the turbulent variables, is required. In a previous paper of the same authors the exact equations for the turbulent kinetic energy and the dissipation rate have been derived in a two-dimensional (2D) domain, through the introduction of an apparent viscosity equation. The aim of the present paper is to extend the approach to a three-dimensional (3D) domain, giving the full mathematical demonstration of the exact equations.     

Ponceau 2R staining on semi-thin sections of tissues fixed in glutaraldehyde-osmium tetroxide and embedded in epoxy resins

Proteins were stained (brilliant red) by using 0·5% Ponceau 2R (C.I. 16150) in sulphuric acid (pH 2) at 40°C on sections 1–2 μm thick of tissues fixed in glutaraldehyde-osmium tetroxide and embedded in epoxy resins. The staining is free from precipitates and does not require the removal of the embedding media. Section bleaching with 1–2% periodic acid at 40°C for 30–90 min was also carried out in order to obtain a more brilliant colour of the proteins which may be photographed using black and white films.     

Code obfuscation against abstraction refinement attacks

Code protection technologies require anti reverse engineering transformations to obfuscate programs in such a way that tools and methods for program analysis become ineffective. We introduce the concept of model deformation inducing an effective code obfuscation against attacks performed by abstract model checking. This means complicating the model in such a way a high number of spurious traces are generated in any formal verification of the property to disclose about the system under attack.We transform the program model in order to make the removal of spurious counterexamples by abstraction refinement maximally inefficient. Because our approach is intended to defeat the fundamental abstraction refinement strategy, we are independent from the specific attack carried out by abstract model checking. A measure of the quality of the obfuscation obtained by model deformation is given together with a corresponding best obfuscation strategy for abstract model checking based on partition refinement.     

Using Physical and Logical Constraints for Invoice Understanding

This work presents a methodology for invoice understanding. The invoices of our domain can be grouped into classes according to their logo. The understanding phase is based on two knowledge levels: a specific knowledge for each class, called a document model; and knowledge on the whole domain of interest, called a domain model. The invoices of a known class are understood by its document model, while the invoices of an unknown class are understood by using the domain model. The main contribution of this work is related to the use of the physical and logical constraints of the domain of interest for document understanding, without using an OCR system. Our approach has been tested by some experiments that are intended to identify some regions within invoices of unknown classes. In most cases, the results have shown the reliability of the approach.     

On the implementation of frontier-to-root tree automata inrecursive neural networks

We explore the node complexity of recursive neural network implementations of frontier-to-root tree automata (FRA). Specifically, we show that an FRAO (Mealy version) with m states, l input-output labels, and maximum rank N can be implemented by a recursive neural network with O( radical(log l+log m)lm(N)/log l+N log m) units and four computational layers, i.e., without counting the input layer. A lower bound is derived which is tight when no restrictions are placed on the number of layers. Moreover, we present a construction with three computational layers having node complexity of O((log l+log m) radicallm (N)) and O((log l+log m)lm(N)) connections. A construction with two computational layers is given that implements any given FRAO with a node complexity of O(lm(N)) and O((log l+log m)lm(N)) connections. As a corollary we also get a new upper bound for the implementation of finite-state automata into recurrent neural networks with three computational layers.     

Optimization of wide-band linear arrays

An optimization method is proposed for linear arrays to be used in ultrasound systems under wide-band operation. A fast algorithm, the threshold accepting, has been utilized to determine the element positions and weight coefficients of a linear array that generates a desired beam pattern. To reduce the computational burden in the optimization procedure, an efficient numerical routine for the beam pattern evaluation has been implemented. We address the optimization problem of both dense and sparse wideband arrays. In the first case, the goal is to minimize the side-lobe energy by varying the element weights; we compare the optimized beam pattern with that obtained with classical shading functions, showing that better results can be achieved with a wide-band optimization. We also consider the optimization of the layout (positions and weights) of a sparse linear array to achieve a desired beam pattern with a fixed or minimum number of array elements. The comparison of the proposed method with a narrow-band optimization algorithm is presented, showing that better performances (about -7 dB further reduction of the side-lobe level) can be achieved with a wide-band sparse array optimization. Further numerical simulations are given, showing that the proposed method yields better results than wideband sparse random arrays and periodic arrays with the same aperture width     

Extension of soil thermal conductivity models to frozen meats with low and high fat content

Thermal conductivity models of frozen soils were analyzed and compared with similar models developed for frozen foods. In total, eight thermal conductivity models and 54 model versions were tested against experimental data of 13 meat products in the temperature range from 0 to −40 °C. The model by deVries, with water+ice (wi) as the continuous phase, showed overall the best predictions. The use of wi leads generally to improved predictions in comparison to ice; water as the continuous phase is beneficial only to deVries model, mostly from −1 to −20 °C; fat is advantageous only to meats with high fat content. The results of this work suggest that the more sophisticated way of estimating the thermal conductivity for a disperse phase in the deVries model might be more appropriate than the use of basic multi-phase models (geometric mean, parallel, and series). Overall, relatively small differences in predictions were observed between the best model versions by deVries, Levy, Mascheroni, Maxwell or Gori as applied to frozen meats with low content of fat. These differences could also be generated by uncertainty in meat composition, temperature dependence of thermal conductivity of ice, measurement errors, and limitation of predictive models.