Interoperable Resource Brokering with Policy-based Provisioning and Job Allocation

The increasing needs for computational power in areas such as weather simulation, genomics or Internet applications have led to sharing of geographically distributed and heterogeneous resources from commercial data centers and scientific institutions. Research in the areas of utility, grid and cloud computing, together with improvements in network and hardware virtualization has resulted in methods to locate and use resources to rapidly provision virtual environments in a flexible manner, while lowering costs for consumers and providers. However, there is still a lack of methodologies to enable efficient and seamless sharing of resources among institutions. In this work, we concentrate in the problem of executing parallel scientific applications across distributed resources belonging to separate organizations. Our approach can be divided in three main points. First, we define and implement an interoperable grid protocol to distribute job workloads among partners with different middleware and execution resources. Second, we research and implement different policies for virtual resource provisioning and job-to-resource allocation, taking advantage of their cooperation to improve execution cost and performance. Third, we explore the consequences of on-demand provisioning and allocation in the problem of site-selection for the execution of parallel workloads, and propose new strategies to reduce job slowdown and overall cost.

Evolution of classifiers for pitch estimation of piano music using cartesian genetic programming

Pitch Estimation, also known as Fundamental Frequency (F0) estimation, has been a popular research topic for many years, and is still investigated nowadays. The goal of Pitch Estimation is to find the pitch or fundamental frequency of a digital recording of a speech or musical notes. It plays an important role, because it is the key to identify which notes are being played and at what time. Pitch Estimation of real instruments is a very hard task to address. Each instrument has its own physical characteristics, which reflects in different spectral characteristics. Furthermore, the recording conditions can vary from studio to studio and background noises must be considered. This dissertation presents a novel approach to the problem of Pitch Estimation, using Cartesian Genetic Programming (CGP).We take advantage of evolutionary algorithms, in particular CGP, to explore and evolve complex mathematical functions that act as classifiers. These classifiers are used to identify piano notes pitches in an audio signal. To help us with the codification of the problem, we built a highly flexible CGP Toolbox, generic enough to encode different kind of programs. The encoded evolutionary algorithm is the one known as 1 + , and we can choose the value for . The toolbox is very simple to use. Settings such as the mutation probability, number of runs and generations are configurable. The cartesian representation of CGP can take multiple forms and it is able to encode function parameters. It is prepared to handle with different type of fitness functions: minimization of f(x) and maximization of f(x) and has a useful system of callbacks. We trained 61 classifiers corresponding to 61 piano notes. A training set of audio signals was used for each of the classifiers: half were signals with the same pitch as the classifier (true positive signals) and the other half were signals with different pitches (true negative signals). F-measure was used for the fitness function. Signals with the same pitch of the classifier that were correctly identified by the classifier, count as a true positives. Signals with the same pitch of the classifier that were not correctly identified by the classifier, count as a false negatives. Signals with different pitch of the classifier that were not identified by the classifier, count as a true negatives. Signals with different pitch of the classifier that were identified by the classifier, count as a false positives. Our first approach was to evolve classifiers for identifying artifical signals, created by mathematical functions: sine, sawtooth and square waves. Our function set is basically composed by filtering operations on vectors and by arithmetic operations with constants and vectors. All the classifiers correctly identified true positive signals and did not identify true negative signals. We then moved to real audio recordings. For testing the classifiers, we picked different audio signals from the ones used during the training phase. For a first approach, the obtained results were very promising, but could be improved. We have made slight changes to our approach and the number of false positives reduced 33%, compared to the first approach. We then applied the evolved classifiers to polyphonic audio signals, and the results indicate that our approach is a good starting point for addressing the problem of Pitch Estimation.

Coordinated Scheduling of Wind-Thermal Gencos in Day-Ahead Markets

This paper presents a stochastic mixed-integer linear programming approach for solving the self-scheduling problem of a price-taker thermal and wind power producer taking part in a pool-based electricity market. Uncertainty on electricity price and wind power is considered through a set of scenarios. Thermal units are modeled by variable costs, start-up costs and technical operating constraints, such as: ramp up/down limits and minimum up/down time limits. An efficient mixed-integer linear program is presented to develop the offering strategies of the coordinated production of thermal and wind energy generation, aiming to maximize the expected profit. A case study with data from the Iberian Electricity Market is presented and results are discussed to show the effectiveness of the proposed approach.

Psychometric Properties Evaluation Of A New Ergonomics-related Job Factors Questionnaire Developed For Nursing Workers.

The objectives of this study were to develop a questionnaire that evaluates the perception of nursing workers to job factors that may contribute to musculoskeletal symptoms, and to evaluate its psychometric properties. Internationally recommended methodology was followed: construction of domains, items and the instrument as a whole, content validity, and pre-test. Psychometric properties were evaluated among 370 nursing workers. Construct validity was analyzed by the factorial analysis, known-groups technique, and convergent validity. Reliability was assessed through internal consistency and stability. Results indicated satisfactory fit indices during confirmatory factor analysis, significant difference (p < 0.01) between the responses of nursing and office workers, and moderate correlations between the new questionnaire and Numeric Pain Scale, SF-36 and WRFQ. Cronbach's alpha was close to 0.90 and ICC values ranged from 0.64 to 0.76. Therefore, results indicated that the new questionnaire had good psychometric properties for use in studies involving nursing workers.

Polímeros biomiméticos em química analítica. Parte 1: preparo e aplicações de MIP (Molecularly Imprinted Polymers) em técnicas de extração e separação

MIPs are synthetic polymers that are used as biomimetic materials simulating the mechanism verified in natural entities such as antibodies and enzymes. Although MIPs have been successfully used as an outstanding tool for enhancing the selectivity or different analytical approaches, such as separation science and electrochemical and optical sensors, several parameters must be optimized during their synthesis. Therefore, the state-of-the-art of MIP production as well as the different polymerization methods are discussed. The potential selectivity of MIPs in the extraction and separation techniques focusing mainly on environmental, clinical and pharmaceutical samples as applications for analytical purposes is presented.

Polímeros biomiméticos em química analítica. Parte 2: aplicações de MIP (Molecularly Imprinted Polymers) no desenvolvimento de sensores químicos

The aim of this paper is the description of the strategies and advances in the use of MIP in the development of chemical sensors. MIP has been considered an emerging technology, which allows the synthesis of materials that can mimic some highly specific natural receptors such as antibodies and enzymes. In recent years a great number of publications have demonstrated a growth in their use as sensing phases in the construction of sensors . Thus, the MIP technology became very attractive as a promising analytical tool for the development of sensors.

Projeto de um cortador de base para colhedora de cana-de-açúcar utilizando otimização matemática

A base-cutter represented for a mechanism of four bars, was developed using the Autocad program. The normal force of reaction of the profile in the contact point was determined through the dynamic analysis. The equations of dynamic balance were based on the laws of Newton-Euler. The linkage was subject to an optimization technique that considered the peak value of soil reaction force as the objective function to be minimized while the link lengths and the spring constant varied through a specified range. The Algorithm of Sequential Quadratic Programming-SQP was implemented of the program computational Matlab. Results were very encouraging; the maximum value of the normal reaction force was reduced from 4,250.33 to 237.13 N, making the floating process much less disturbing to the soil and the sugarcane rate. Later, others variables had been incorporated the mechanism optimized and new otimization process was implemented .

Using sentinels to detect intersections of convex and nonconvex polygons

We describe finite sets of points, called sentinels, which allow us to decide if isometric copies of polygons, convex or not, intersect. As an example of the applicability of the concept of sentinel, we explain how they can be used to formulate an algorithm based on the optimization of differentiable models to pack polygons in convex sets. Mathematical subject classification: 90C53, 65K05.

Simplified Mathematical Model for an Anaerobic Sequencing Batch Biofilm Reactor Treating Lipid-Rich Wastewater Subject to Rising Organic Loading Rates

This study proposes a simplified mathematical model to describe the processes occurring in an anaerobic sequencing batch biofilm reactor (ASBBR) treating lipid-rich wastewater. The reactor, subjected to rising organic loading rates, contained biomass immobilized cubic polyurethane foam matrices, and was operated at 32 degrees C +/- 2 degrees C, using 24-h batch cycles. In the adaptation period, the reactor was fed with synthetic substrate for 46 days and was operated without agitation. Whereas agitation was raised to 500 rpm, the organic loading rate (OLR) rose from 0.3 g chemical oxygen demand (COD) . L(-1) . day(-1) to 1.2 g COD . L(-1) . day(-1). The ASBBR was fed fat-rich wastewater (dairy wastewater), in an operation period lasting for 116 days, during which four operational conditions (OCs) were tested: 1.1 +/- 0.2 g COD . L(-1) . day(-1) (OC1), 4.5 +/- 0.4 g COD . L(-1) . day(-1) (OC2), 8.0 +/- 0.8 g COD . L(-1) . day(-1) (OC3), and 12.1 +/- 2.4 g COD . L(-1) . day(-1) (OC4). The bicarbonate alkalinity (BA)/COD supplementation ratio was 1:1 at OC1, 1:2 at OC2, and 1:3 at OC3 and OC4. Total COD removal efficiencies were higher than 90%, with a constant production of bicarbonate alkalinity, in all OCs tested. After the process reached stability, temporal profiles of substrate consumption were obtained. Based on these experimental data a simplified first-order model was fit, making possible the inference of kinetic parameters. A simplified mathematical model correlating soluble COD with volatile fatty acids (VFA) was also proposed, and through it the consumption rates of intermediate products as propionic and acetic acid were inferred. Results showed that the microbial consortium worked properly and high efficiencies were obtained, even with high initial substrate concentrations, which led to the accumulation of intermediate metabolites and caused low specific consumption rates.

Tick saliva inhibits the chemotactic function of MIP-1 alpha and selectively impairs chemotaxis of immature dendritic cells by down-regulating cell-surface CCR5

Ticks are blood-feeding arthropods that secrete immunomodulatory molecules through their saliva to antagonize host inflammatory and immune responses. As dendritic cells (DCs) play a major role in host immune responses, we studied the effects of Rhipicephalus sanguineus tick saliva on DC migration and function. Bone marrow-derived immature DCs pre-exposed to tick saliva showed reduced migration towards macrophage inflammatory protein (MIP)-1 alpha, MIP-1 beta and regulated upon activation, normal T cell expressed and secreted (RANTES) chemokines in a Boyden microchamber assay. This inhibition was mediated by saliva which significantly reduced the percentage and the average cell-surface expression of CC chemokine receptor CCR5. In contrast, saliva did not alter migration of DCs towards MIP-3 beta, not even if the cells were induced for maturation. Next, we evaluated the effect of tick saliva on the activity of chemokines related to DC migration and showed that tick saliva per se inhibits the chemotactic function of MIP-1 alpha, while it did not affect RANTES, MIP-1 beta and MIP-3 beta. These data suggest that saliva possibly reduces immature DC migration, while mature DC chemotaxis remains unaffected. In support of this, we have analyzed the percentage of DCs on mice 48 h after intradermal inoculation with saliva and found that the DC turnover in the skin was reduced compared with controls. Finally, to test the biological activity of the saliva-exposed DCs, we transferred DCs pre-cultured with saliva and loaded with the keyhole limpet haemocyanin (KLH) antigen to mice and measured their capacity to induce specific T cell cytokines. Data showed that saliva reduced the synthesis of both T helper (Th)1 and Th2 cytokines, suggesting the induction of a non-polarised T cell response. These findings propose that the inhibition of DCs migratory ability and function may be a relevant mechanism used by ticks to subvert the immune response of the host. (c) 2007 Australian Society for Parasitology Inc. Published by Elsevier Ltd. All rights reserved.

A STUDY OF PENALTY FORMULATIONS USED IN THE NUMERICAL APPROXIMATION OF A RADIALLY SYMMETRIC ELASTICITY PROBLEM

We consider a class of two-dimensional problems in classical linear elasticity for which material overlapping occurs in the absence of singularities. Of course, material overlapping is not physically realistic, and one possible way to prevent it uses a constrained minimization theory. In this theory, a minimization problem consists of minimizing the total potential energy of a linear elastic body subject to the constraint that the deformation field must be locally invertible. Here, we use an interior and an exterior penalty formulation of the minimization problem together with both a standard finite element method and classical nonlinear programming techniques to compute the minimizers. We compare both formulations by solving a plane problem numerically in the context of the constrained minimization theory. The problem has a closed-form solution, which is used to validate the numerical results. This solution is regular everywhere, including the boundary. In particular, we show numerical results which indicate that, for a fixed finite element mesh, the sequences of numerical solutions obtained with both the interior and the exterior penalty formulations converge to the same limit function as the penalization is enforced. This limit function yields an approximate deformation field to the plane problem that is locally invertible at all points in the domain. As the mesh is refined, this field converges to the exact solution of the plane problem.

Fast experimental identification of suspension models for control design

This paper presents both the theoretical and the experimental approaches of the development of a mathematical model to be used in multi-variable control system designs of an active suspension for a sport utility vehicle (SUV), in this case a light pickup truck. A complete seven-degree-of-freedom model is successfully quickly identified, with very satisfactory results in simulations and in real experiments conducted with the pickup truth. The novelty of the proposed methodology is the use of commercial software in the early stages of the identification to speed up the process and to minimize the need for a large number of costly experiments. The paper also presents major contributions to the identification of uncertainties in vehicle suspension models and in the development of identification methods using the sequential quadratic programming, where an innovation regarding the calculation of the objective function is proposed and implemented. Results from simulations of and practical experiments with the real SUV are presented, analysed, and compared, showing the potential of the method.