tt-Covering Arrays Generated by a Tiling Probability Model

A t-\a covering array is an $m\times n$ matrix, with entries from an alphabet of size $\alpha$, such that for any choice of $t$ rows, and any ordered string of $t$ letters of the alphabet, there exists a column such that the "values" of the rows in that column match those of the string of letters. We use the Lov\'asz Local Lemma in conjunction with a new tiling-based probability model to improve the upper bound on the smallest number of columns $N=N(m,t,\alpha)$ of a t-\a covering array.Comment: 7 page

Nickel in Non-ferrous General Engineering Alloys

ALTHOUGH nickel in its wrought forms has important applications as an engineering material, particularly in the chemical and electronic industries, the major usage of the metal, in both ferrous and non-ferrous metallurgy, is as an alloying element. Nickel-base alloys can be broadly divided into the nickel-chromium -base " Superalloys" for high-temperature service , and materials , such as nickel-copper and nickel-chromium- iron alloys, which are used for more general engineering purposes, particularly where resist-ance to corrosion is involved. The technology of the former type of material tends to be rather specialized, since it is concerned very largely with high-temperature properties, and it is not proposed to discuss these high-temperature alloys in this paper but to describe some recent developments in the nickel-base alloys of general engineering interest and in the nickel-containing copper-base and aluminiumbase alloys

Nickel in Non-ferrous General Engineering Alloys

Some recent developments in the nickel-containing copper and aluminium alloys and in nickel alloys of general engineering interest, i.e. excluding the high-temperature alloys, are described in details in the paper. Alloy 625:- The nickel-chromium-molybdenum alloy has recently been developed in the U.S.A. as a high-temperature material. It is now finding application in this field, but other uses which depend on the wet corrosion-resistance are under active investigation

Effect of Microwaves on the pH and °Brix value of Cranberry, Grape, Blackberry and Lemon

The microwaves find the potential capability in variety of applications, however the effects ofmicrowaves on the materials plays an important role. It is often discussed about the adverse effects ofmicrowaves on the fruits however this can be verified by considering two characteristics of fruits such as °Brixand pH value.This paper focuses on the effect of microwaves exposure on some fruits. It aims to explain theeffect of microwave heating on °Brix and pH value of the Cranberry, Grape, Blackberry and Lemon fruits.Two basic sections are considered here, one without exposure and other with exposure to microwaves.°Brixand pH values of Cranberry, Grape, Blackberry and Lemon are measured before exposure to microwaves.Brix measurement is done by the Aichose Refractometer &pH value is measured by AMT28F pH meter.Thechange in pH and °Brix value was noted for Cranberry, Grape, Blackberry and Lemon after exposure tomicrowaves for given time intervals of 5, 10, 15 and 20 seconds of heating at 700 W power. With graphicalanalysis, USDAstandards(United States Department of Agriculture) are utilized to validate the results.The°Brix and pH values for all fruits have shown the variation when exposed to microwaveshowever the °Brixand pH value were lying in the permissible limits referred by USDA standards after exposure to themicrowaves

Feasibility of generating synthetic CT from T1-weighted MRI using a linear mixed-effects regression model

Generation of synthetic computed tomography (sCT) for magnetic resonance imaging (MRI)-only radiotherapy is emerging as a promising direction because it can eliminate the registration error and simplify clinical workflow. The goal of this study was to generate accurate sCT from standard T1-weighted MRI for brain patients. CT and MRI data of twelve patients with brain tumors were retrospectively collected. Linear mixed-effects regression models were fitted between CT and T1-weighted MRI intensities for different segments in the brain. The whole brain sCTs were generated by combining predicted segments together. Mean absolute error (MAE) between real CTs and sCTs across all patients was 71.1 ±5.5 Hounsfield Unit (HU). Average differences in the HU values were 1.7 ±7.1 HU (gray matter), 0.9 ±5.1 HU (white matter), -24.7 ±8.0 HU (cerebrospinal fluid), 76.4 ±17.8 HU (bone), 20.9 ±20.4 HU (fat), -69.4 ±28.3 HU (air). A simple regression technique has been devised that is capable of producing accurate HU maps from standard T1-weighted MRI, and exceptionally low MAE values indicate accurate prediction of sCTs. Improvement is needed in segmenting MRI using a more automatic approach