IonRock: software for solving strain gradients of ion-implanted semiconductors by X-ray diffraction measurements and evolutionary programming

We present a program that uses an optimization algorithm to fit rocking curves of ion-implanted semiconductors. This is an inverse problem that cannot be solved by simple methods. However, using recursion formulae for rocking curve calculations and a model of ion distribution after implantation, it is possible to fit experimental data with a general-purpose optimization method. In our case, we use a modified version of the genetic algorithm, which has been shown to be a good technique for this problem. The program also calculates rocking curves for a given ion profile, such as those generated by ion implantation simulation programs.

Program summary

Title of program: IonRock, version 1.0Catalogue identifier: ADTPProgram summary URL:http://cpc.cs.qub.ac.uk/summaries/ADTPProgram obtainable from: CPC Program Library, Queen's University of Belfast, N. IrelandComputer: any computer with Windows95 or later versionOperating systems: Windows95/98/2000/NT/XPProgramming language used: C++Memory required to execute with typical data: about 4 MBNo. of bytes in distributed program, including test data, etc.: 2 742 530No of lines in distributed program, including test data etc.: 49 988Distribution format: tar gzip fileNature of physical problem: strain determination on ion implanted zinc-blend structure semiconductorsMethod of solution: adapted version of the Genetic Algorithm meta-heuristicRestrictions on the complexity of the problem: strain determination is related to the strain generated by interstitial ions, which causes the left-side distortions on the rocking curveTypical running time: on an Athlon PC computer the computing time for solving the strain gradient using 16 layers takes from 10 to 30 minutes     

Molecular dynamics and circular dichroism studies of human and rat C-peptides

Proinsulin C-peptide has been recently described as an endogenous peptide hormone, responsible for important physiological functions others than its role in proinsulin processing. Accumulating evidences that C-peptide exerts beneficial effects in the treatment of long term complications of patients with type 1 diabetes mellitus indicate that this molecule may be administered together with insulin in future therapies. Despite its clear pharmacological interest, the secondary and three-dimensional (3D) structures of human C-peptide are still points of controversy. In the present work we report molecular dynamics (MD) simulations of human, rat I and rat II C-peptides. A common experimental strategy applied to all peptides consisted of homology building followed by multinanosecond MD simulations in vacuum and water. Circular dichroism (CD) experiments of each peptide in the absence and presence of 2,2,2-trifluoroethanol (TFE) were performed to support validation of the theoretical models. A multiple sequence alignment of 23 known mammalian C-peptides was constructed to identify significant conserved sites that would be important for the maintenance of secondary and tertiary structures. The analysis of the molecular dynamics trajectories for the human, rat I and rat II molecules have shown quite different general behavior, being the human C-peptide more flexible than the two others. Human and rat C-peptides exhibit very stable turn-like structures at the middle and C-terminal regions, which have been described as potential active sites of C-peptides. Human C-peptide also presented a short alpha-helix throughout the MD, which was not found in the rat molecules. CD data is in very good agreement with the MD results and both methods were able to identify a greater structural stability and potential in rat C-peptides when compared to the human C-peptide. The simulation results are discussed and validated in the light of multiple sequence alignment, recent experimental data from the literature and our own CD experiments.     

Cloud broker proposal based on multicriteria decision-making and virtual infrastructure migration

The adoption of infrastructure as a Service (IaaS) is a reality for academic, industrial, and governmental institutions. Cloud tenants request dynamically provisioned virtual infrastructures (VIs) tailored to their application requirements, detailing not only the virtual compute/storage resources but also the network components, topology, and services. The creation of a large number of cloud providers came along with the widespread use of VIs. The selection of an appropriate provider is a challenging task due to the diversity of the IaaS market and formally is a multicriteria analysis (NP-hard). Notwithstanding the provider selection complexity, the mobility of VI-hosted applications is limited due to the optimization anchors introduced by providers. Although the existing IaaS cloud brokers can indicate a hosting provider, they lack on conceptual and technical skills to migrate a VI and all its internal components between providers. This work enhances the state-of-the-art on IaaS cloud brokerage by proposing virtual infrastructure multicriteria allocation and migration–based broker (VIMAM), which performs a multicriteria analysis of providers and VI migration. VIMAM is driven by an analytic hierarchy process (AHP) to select an IaaS provider, offering a set of predefined weighting schemas to represent distinct tenant perspectives. Moreover, to migrate a VI, VIMAM takes into account the virtual machines, containers, switches, and other topology elements. In addition to discussing the AHP ranking weights and frequency of providers selection, the experimental analysis details the implementation of an OpenStack and Docker–based prototype for VI migration.     

Performance Analysis of Network Traffic Predictors in the Cloud

Predicting the inherent traffic behaviour of a network is an essential task, which can be used for various purposes, such as monitoring and managing the network’s infrastructure. However, the recent surge of dynamic environments, such as Internet of Things and Cloud Computing have hampered this task. This means that the traffic on these networks is even more complex, displaying a nonlinear behaviour with specific aperiodic characteristics during daily operation. Traditional network traffic predictors are usually based on large historical data bases which are used to train algorithms. This may not be suitable for these highly volatile environments, where the strength of the force exerted in the interaction between past and current values may change quickly with time. In light of this, a taxonomy for network traffic prediction models, including the review of state of the art, is presented here. In addition, an analysis mechanism, focused on providing a standardized approach for evaluating the best candidate predictor models for these environments, is proposed. These contributions favour the analysis of the efficacy and efficiency of network traffic prediction among several prediction models in terms of accuracy, historical dependency, running time and computational overhead. An evaluation of several prediction mechanisms is performed by assessing the Normalized Mean Square Error and Mean Absolute Percent Error of the values predicted by using traces taken from two real case studies in cloud computing.     

Spectral separability of riparian forests from small and medium-sized rivers across a latitudinal gradient using multispectral imagery

Spectral discrimination between riparian forests is a challenging issue due to the inherent complexity of species composition and the high spatial structural variability of these vegetation types. This study aimed to evaluate spectral separability among riparian forests, in small and medium-sized river catchment areas, in three bioclimatic zones of Portugal (temperate, transitional, and Mediterranean). We also assess the spectral differences using only the dominant riparian woody species in each riparian forest class, namely Alnus glutinosa, Salix salviifolia, and Nerium oleander.

Pixel values were extracted from high-resolution airborne multispectral imagery (red, green, blue, and near-infrared, 50 cm pixels) of 26 riparian forests located in the three bioclimatic zones. Spectral separability was calculated using the transformed divergence (TD) distance. Discriminant analysis (DA) was used to select the bands that contribute most to the spectral separability and for the classification accuracy assessment of the riparian forests. Species composition and percentage of canopy closure were collected for all the riparian forests in a field campaign and subjected to hierarchical clustering in order to validate the spectral separability analyses. Optical traits derived from field data were used to interpret the spectral differences between riparian forest classes.

The greatest spectral separability was observed between the temperate and the Mediterranean riparian forest classes. Global classification accuracy for the DA was 86.3% for riparian forest classes along medium-sized rivers and 70.1% in small-sized ones. The high floristic and spatial structure variability was responsible for the misclassification errors that occurred between the transitional and the other riparian forest classes. The spectral separability using only the dominant species was greater than that obtained using the overall species assemblages of the riparian forests. Alnus glutinosa had the highest level of classification accuracy, and this may be related to its peculiar yellowish-green tone. DA also revealed that all spectral bands were needed in order to distinguish the riparian forest classes.

This study provided evidence that the spectral discrimination of riparian forests can be explained on the basis of differences in species composition and cover, and by a convergence of optical traits, at both leaf and canopy levels. Spectral signatures of these riparian forests and related spectral signatures of key species are useful tools for evaluating the floristic deviations of actual riparian forests from their near-natural benchmarks.

     

Semantic E-Workflow Composition

Systems and infrastructures are currently being developed to support Web services. The main idea is to encapsulate an organization's functionality within an appropriate interface and advertise it as Web services. While in some cases Web services may be utilized in an isolated form, it is normal to expect Web services to be integrated as part of workflow processes. The composition of workflow processes that model e-service applications differs from the design of traditional workflows, in terms of the number of tasks (Web services) available to the composition process, in their heterogeneity, and in their autonomy. Therefore, two problems need to be solved: how to efficiently discover Web services—based on functional and operational requirements—and how to facilitate the interoperability of heterogeneous Web services. In this paper, we present a solution within the context of the emerging Semantic Web that includes use of ontologies to overcome some of the problem. We describe a prototype that has been implemented to illustrate how discovery and interoperability functions are achieved more efficiently.     

Design sensitivity analysis of nonlinear dynamic response of structural and mechanical systems

This paper describes a unified variational theory for design sensitivity analysis of nonlinear dynamic response of structural and mechanical systems for shape, nonshape, material and mechanical properties selection, as well as control problems. The concept of an adjoint system, the principle of virtual work and a Lagrangian-Eulerian formulation to describe the deformations and the design variations are used to develop a unified view point. A general formula for design sensitivity analysis is derived and interpreted for usual performance functionals. Analytical examples are utilized to demonstrate the use of the theory and give insights for application to more complex problems that must be treated numerically.Derivatives The comma notation for partial derivatives is used, i.e. G,_u = G/u. An upper dot represents material time derivative, i.e. ü = ²u/t². A prime implies derivative with respect to the time measured in the reference time-domain, i.e. u = du/d.     

Monolithic integration of Giant Magnetoresistance (GMR) devices onto standard processed CMOS dies

Giant Magnetoresistance (GMR) based technology is nowadays the preferred option for low magnetic fields sensing in disciplines such as biotechnology or microelectronics. Their compatibility with standard CMOS processes is currently investigated as a key point for the development of novel applications, requiring compact electronic readout. In this paper, such compatibility has been experimentally studied with two particular non-dedicated CMOS standards: 0.35 μm from AMS (Austria MicroSystems) and 2.5 μm from CNM (Centre Nacional de Microelectrònica, Barcelona) as representative examples. GMR test devices have been designed and fabricated onto processed chips from both technologies. In order to evaluate so obtained devices, an extended characterization has been carried out including DC magnetic measurements and noise analysis. Moreover, a 2D-FEM (Finite Element Method) model, including the dependence of the GMR device resistance with the magnetic field, has been also developed and simulated. Its potential use as electric current sensors at the integrated circuit level has also been demonstrated.