Generalized collisional radiative model for light elements. Part B: Data for the Be isonuclear sequence

A first-stage collision database is assembled which contains electron-impact excitation, ionization, and recombination rate coefficients for Be, Be+, Be2+, and Be3+. The first-stage database is constructed using the R-matrix with pseudo-states, time-dependent close-coupling, and perturbative, distorted-wave methods. A second-stage collision database is then assembled which contains generalized collisional-radiative and radiated power loss coefficients. The second-stage database is constructed by solution of collisional-radiative equations in the quasi-static equilibrium approximation using the first-stage database. Both collision database stages reside in electronic form at the ORNL Controlled Fusion Atomic Data Center and in the ADAS database, and are easily accessed over the worldwide internet. © 2007 Elsevier Inc. All rights reserved.

Generalised collisional-radiative model for light elements. A: Data for the Li isonuclear sequence

A first stage collision database is assembled which contains electron-impact effective collision strengths, and ionization and recombination rate coefficients for Li, Li+, and Li2+. The first stage database is constructed using the R-matrix with pseudo-states, time-dependent close-coupling, converged close-coupling, and perturbative distorted-wave methods. A second stage collision database is then assembled which contains generalized collisional-radiative and radiated power loss coefficients. The second stage database is constructed by solution of collisional-radiative equations in the quasi-static equilibrium approximation using the first stage database. Both collision database stages reside in electronic form at the ORNL Controlled Fusion Atomic Data Center and in the ADAS database, and are easily accessed over the worldwide internet. ?? 2006 Elsevier Inc. All rights reserved.

Energy-efficient management of data center resources for cloud computing : a vision, architectural elements, and open challenges

Cloud computing is offering utility-oriented IT services to users worldwide. Based on a pay-as-you-go model, it enables hosting of pervasive applications from consumer, scientific, and business domains. However, data centers hosting Cloud applications consume huge amounts of energy, contributing to high operational costs and carbon footprints to the environment. Therefore, we need Green Cloud computing solutions that can not only save energy for the environment but also reduce operational costs. This paper presents vision, challenges, and architectural elements for energy-efficient management of Cloud computing environments. We focus on the development of dynamic resource provisioning and allocation algorithms that consider the synergy between various data center infrastructures (i.e., the hardware, power units, cooling and software), and holistically work to boost data center energy efficiency and performance. In particular, this paper proposes (a) architectural principles for energy-efficient management of Clouds; (b) energy-efficient resource allocation policies and scheduling algorithms considering quality-of-service expectations, and devices power usage characteristics; and (c) a novel software technology for energy-efficient management of Clouds. We have validated our approach by conducting a set of rigorous performance evaluation study using the CloudSim toolkit. The results demonstrate that Cloud computing model has immense potential as it offers significant performance gains as regards to response time and cost saving under dynamic workload scenarios.

Clinical pathological data and analyses of mineral elements of cattle affected by epizootic botulism in the State of São Paulo

In order to study laboratorial aspects of beef cow mortality, a syndrome popularly known as ''doenca da vaca caida'', examens were made of blood, cerebrospinal fluid, serum, bone and liver samples from 32 naturally affected 4 to 9 year old cows, 27 belonging to the Nellore breed and 5 were crossbred Nellore, all originating from farms located in municipalities near Botucatu, State of São Paulo. Laboratory determinations were analysed by descriptive statistics and included hematological values, total plasma protein, plasma fibrinogen, cerebrospinal fluid analysis, and concentration measurements of serum calcium, phosphorus, magnesium, sodium, potassium, chloride, total protein, albumin, globulin, alkaline phosphatase, aspartate aminotransferase, gama-glutamyltransferase and creatine kinase activities, included bone ash percentage and concentrations of calcium, phosphorus and magnesium, and also hepatic levels of copper, zinc, iron, manganese and cobalt. In addition, mouse bioassays and complement micro-fixation tests were performed to detect botulinum toxins in liver samples. The results indicated leukocytosis (13,3+/-3,9 x10(3)/mm(3)) with neutrophilia (8,9+/-3,2 x10(3)/mm(3)), hypocalcemia (7,8+/-1,7mg/dl), hypophosphatemia (3,6+/-1,6mg/dl), hypoalbuminemia (2,9+/-0,9g/dl), increased creatine kinase activity (691,0+/-829,7 UI/1), and reduced ash percentage (60,3+/-1,9%) and low phosphorus (17,2+/-0,4%) in bone. The other values were ail within normal limits. The diagnosis of botulism, involving type C and D toxins, was confirmed as the cause of the mortality in the region of study, what is strongly consistent with the other laboratorial findings.

Zirconia-based dental crown to support a removable partial denture: a three-dimensional finite element analysis using contact elements and micro-CT data

Veneer fracture is the most common complication in zirconia-based restorations. The aim of this study was to evaluate the mechanical behavior of a zirconia-based crown in a lower canine tooth supporting removable partial denture (RPD) prosthesis, varying the bond quality of the veneer/coping interface. Microtomography (μCT) data of an extracted left lower canine were used to build the finite element model (M) varying the core material (gold core - MAu; zirconia core - MZi) and the quality of the veneer/core interface (complete bonded - MZi; incomplete bonded - MZi-NL). The incomplete bonding condition was only applied for zirconia coping by using contact elements (Target/Contact) with 0.3 frictional coefficients. Stress fields were obtained using Ansys Workbench 10.0. The loading condition (L = 1 N) was vertically applied at the base of the RPD prosthesis metallic support towards the dental apex. Maximum principal (σmax) and von Mises equivalent (σvM) stresses were obtained. The σmax (MPa) for the bonded condition was similar between gold and zirconia cores (MAu, 0.42; MZi, 0.40). The incomplete bonded condition (MZi-NL) raised σmax in the veneer up to 800% (3.23 MPa) in contrast to the bonded condition. The peak of σvM increased up to 270% in the MZi-NL. The incomplete bond condition increasing the stress in the veneer/zirconia interface.

Determination of trace elements in bovine semen samples by inductively coupled plasma mass spectrometry and data mining techniques for identification of bovine class

The reproductive performance of cattle may be influenced by several factors, but mineral imbalances are crucial in terms of direct effects on reproduction. Several studies have shown that elements such as calcium, copper, iron, magnesium, selenium, and zinc are essential for reproduction and can prevent oxidative stress. However, toxic elements such as lead, nickel, and arsenic can have adverse effects on reproduction. In this paper, we applied a simple and fast method of multi-element analysis to bovine semen samples from Zebu and European classes used in reproduction programs and artificial insemination. Samples were analyzed by inductively coupled plasma spectrometry (ICP-MS) using aqueous medium calibration and the samples were diluted in a proportion of 1:50 in a solution containing 0.01% (vol/vol) Triton X-100 and 0.5% (vol/vol) nitric acid. Rhodium, iridium, and yttrium were used as the internal standards for ICP-MS analysis. To develop a reliable method of tracing the class of bovine semen, we used data mining techniques that make it possible to classify unknown samples after checking the differentiation of known-class samples. Based on the determination of 15 elements in 41 samples of bovine semen, 3 machine-learning tools for classification were applied to determine cattle class. Our results demonstrate the potential of support vector machine (SVM), multilayer perceptron (MLP), and random forest (RF) chemometric tools to identify cattle class. Moreover, the selection tools made it possible to reduce the number of chemical elements needed from 15 to just 8.