A reference model as automated process for software adaptation at runtime

The development of self-adaptive software (SaS) has specific characteristics compared to traditional one, since it allows that changes to be incorporated at runtime. Automated processes have been used as a feasible solution to conduct the software adaptation at runtime. In parallel, reference model has been used to aggregate knowledge and architectural artifacts, since capture the systems essence of specific domains. However, there is currently no reference model based on reflection for the development of SaS. Thus, the main contribution of this paper is to present a reference model based on reflection for development of SaS that have a need to adapt at runtime. To present the applicability of this model, a case study was conducted and good perspective to efficiently contribute to the area of SaS has been obtained.

The optimal lysine and threonine intake for Cobb broiler breeder hens using Reading model

This study aimed to determine the optimal intake of lysine and threonine for broiler breeder hens. Two experiments were conducted to evaluate the responses of birds to digestible lysine (Lys) and threonine (Thr). Eight treatments were assessed in both experiments, with six replicates of eight birds in the Lys experiment and ten birds in the Thr experiment. The dietary levels of Lys and Thr were obtained by a dilution technique. The experimental period was ten weeks for each amino acid studied, which included six weeks of adaptation and four weeks of data collection. The amino acid intake, egg mass and body weight were adjusted using a Reading model. Based on the model coefficients, the cost of the synthetic amino acids sources and the price of fertile eggs determined the intake of each amino acid to maximize. The minimum intake of Lys and Thr reduced egg production by 40 and 30%, respectively, the weight of the eggs decreased by 12 and 9% with the same intake of Lys and Thr, respectively. The models generated by predicting Lys and Thr intake were as follows: Lys=11 x E+31 x W and Thr=9.5 x E+32 x W, where E=egg mass, g/bird per day, and W=body weight, kg/bird. Based on the models, 3 kg birds with an egg mass production of 50 g/day require 643 mg/bird per day of Lys and 569 mg/bird per day of Thr. The optimum economic intake was calculated at 954 and 834 mg/bird per day for Lys and Thr, respectively, reflecting a dietary concentration of 0.636% Lys and 0.556% Thr for a feed intake of 150 g/bird per day. (C) 2015 Elsevier B.V. All rights reserved.

Classification of intact açai (Euterpe oleracea Mart.) and jucara (Euterpe edulis Mart) fruits based on dry matter content by means of near infrared spectroscopy

Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)

Incorporating priorities for waiting customers in the hypercube queuing model with application to an emergency medical service system in Brazil

Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)

A unified solution to the small scale problems of the Lambda CDM model II: introducing parent-satellite interaction

Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)

Transcrição cooperativa de genes ribossomais em Escherichia coli usando um modelo estocástico e dependente de sequência

Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)

Perturbative super-Yang-Mills from the topological AdS 5 × S 5 sigma model

Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)

Bandit-Based Algorithms for Budgeted Learning

We explore the problem of budgeted machine learning, in which the learning algorithm has free access to the training examples’ labels but has to pay for each attribute that is specified. This learning model is appropriate in many areas, including medical applications. We present new algorithms for choosing which attributes to purchase of which examples in the budgeted learning model based on algorithms for the multi-armed bandit problem. All of our approaches outperformed the current state of the art. Furthermore, we present a new means for selecting an example to purchase after the attribute is selected, instead of selecting an example uniformly at random, which is typically done. Our new example selection method improved performance of all the algorithms we tested, both ours and those in the literature.

Landscape Genetics of Raccoons (Procyon lotor) Associated with Ridges and Valleys of Pennsylvania: Implications for Oral Rabies Vaccination Programs

Raccoons are the reservoir for the raccoon rabies virus variant in the United States. To combat this threat, oral rabies vaccination (ORV) programs are conducted in many eastern states. To aid in these efforts, the genetic structure of raccoons (Procyon lotor) was assessed in southwestern Pennsylvania to determine if select geographic features (i.e., ridges and valleys) serve as corridors or hindrances to raccoon gene flow (e.g., movement) and, therefore, rabies virus trafficking in this physiographic region. Raccoon DNA samples (n = 185) were collected from one ridge site and two adjacent valleys in southwestern Pennsylvania (Westmoreland, Cambria, Fayette, and Somerset counties). Raccoon genetic structure within and among these study sites was characterized at nine microsatellite loci. Results indicated that there was little population subdivision among any sites sampled. Furthermore, analyses using a model-based clustering approach indicated one essentially panmictic population was present among all the raccoons sampled over a reasonably broad geographic area (e.g., sites up to 36 km apart). However, a signature of isolation by distance was detected, suggesting that widths of ORV zones are critical for success. Combined, these data indicate that geographic features within this landscape influence raccoon gene flow only to a limited extent, suggesting that ridges of this physiographic system will not provide substantial long-term natural barriers to rabies virus trafficking. These results may be of value for future ORV efforts in Pennsylvania and other eastern states with similar landscapes.

A log-Birnbaum-Saunders regression model with asymmetric errors

This paper introduces a skewed log-Birnbaum-Saunders regression model based on the skewed sinh-normal distribution proposed by Leiva et al. [A skewed sinh-normal distribution and its properties and application to air pollution, Comm. Statist. Theory Methods 39 (2010), pp. 426-443]. Some influence methods, such as the local influence and generalized leverage, are presented. Additionally, we derived the normal curvatures of local influence under some perturbation schemes. An empirical application to a real data set is presented in order to illustrate the usefulness of the proposed model.

Closed-loop stable model predictive control of integrating systems with dead time

Model predictive control (MPC) applications in the process industry usually deal with process systems that show time delays (dead times) between the system inputs and outputs. Also, in many industrial applications of MPC, integrating outputs resulting from liquid level control or recycle streams need to be considered as controlled outputs. Conventional MPC packages can be applied to time-delay systems but stability of the closed loop system will depend on the tuning parameters of the controller and cannot be guaranteed even in the nominal case. In this work, a state space model based on the analytical step response model is extended to the case of integrating time systems with time delays. This model is applied to the development of two versions of a nominally stable MPC, which is designed to the practical scenario in which one has targets for some of the inputs and/or outputs that may be unreachable and zone control (or interval tracking) for the remaining outputs. The controller is tested through simulation of a multivariable industrial reactor system. (C) 2012 Elsevier Ltd. All rights reserved.

OXYGEN ABUNDANCES IN LOW- AND HIGH-alpha FIELD HALO STARS AND THE DISCOVERY OF TWO FIELD STARS BORN IN GLOBULAR CLUSTERS

Oxygen abundances of 67 dwarf stars in the metallicity range -1.6 < [Fe/H] < -0.4 are derived from a non-LTE analysis of the 777 nm O I triplet lines. These stars have precise atmospheric parameters measured by Nissen and Schuster, who find that they separate into three groups based on their kinematics and alpha-element (Mg, Si, Ca, Ti) abundances: thick disk, high-alpha halo, and low-alpha halo. We find the oxygen abundance trends of thick-disk and high-alpha halo stars very similar. The low-alpha stars show a larger star-to-star scatter in [O/Fe] at a given [Fe/H] and have systematically lower oxygen abundances compared to the other two groups. Thus, we find the behavior of oxygen abundances in these groups of stars similar to that of the a elements. We use previously published oxygen abundance data of disk and very metal-poor halo stars to present an overall view (-2.3 < [Fe/H] < +0.3) of oxygen abundance trends of stars in the solar neighborhood. Two field halo dwarf stars stand out in their O and Na abundances. Both G53-41 and G150-40 have very low oxygen and very high sodium abundances, which are key signatures of the abundance anomalies observed in globular cluster (GC) stars. Therefore, they are likely field halo stars born in GCs. If true, we estimate that at least 3% +/- 2% of the local field metal-poor star population was born in GCs.

Network-based stochastic semisupervised learning

Semisupervised learning is a machine learning approach that is able to employ both labeled and unlabeled samples in the training process. In this paper, we propose a semisupervised data classification model based on a combined random-preferential walk of particles in a network (graph) constructed from the input dataset. The particles of the same class cooperate among themselves, while the particles of different classes compete with each other to propagate class labels to the whole network. A rigorous model definition is provided via a nonlinear stochastic dynamical system and a mathematical analysis of its behavior is carried out. A numerical validation presented in this paper confirms the theoretical predictions. An interesting feature brought by the competitive-cooperative mechanism is that the proposed model can achieve good classification rates while exhibiting low computational complexity order in comparison to other network-based semisupervised algorithms. Computer simulations conducted on synthetic and real-world datasets reveal the effectiveness of the model.

Decreasing the number of false positives in sequence classification

Abstract Background A large number of probabilistic models used in sequence analysis assign non-zero probability values to most input sequences. To decide when a given probability is sufficient the most common way is bayesian binary classification, where the probability of the model characterizing the sequence family of interest is compared to that of an alternative probability model. We can use as alternative model a null model. This is the scoring technique used by sequence analysis tools such as HMMER, SAM and INFERNAL. The most prevalent null models are position-independent residue distributions that include: the uniform distribution, genomic distribution, family-specific distribution and the target sequence distribution. This paper presents a study to evaluate the impact of the choice of a null model in the final result of classifications. In particular, we are interested in minimizing the number of false predictions in a classification. This is a crucial issue to reduce costs of biological validation. Results For all the tests, the target null model presented the lowest number of false positives, when using random sequences as a test. The study was performed in DNA sequences using GC content as the measure of content bias, but the results should be valid also for protein sequences. To broaden the application of the results, the study was performed using randomly generated sequences. Previous studies were performed on aminoacid sequences, using only one probabilistic model (HMM) and on a specific benchmark, and lack more general conclusions about the performance of null models. Finally, a benchmark test with P. falciparum confirmed these results. Conclusions Of the evaluated models the best suited for classification are the uniform model and the target model. However, the use of the uniform model presents a GC bias that can cause more false positives for candidate sequences with extreme compositional bias, a characteristic not described in previous studies. In these cases the target model is more dependable for biological validation due to its higher specificity.

Shear force and torsion in reinforced concrete beam elements: theoretical analysis based on Brazilian Standard Code ABNT NBR 6118:2007

Reinforced concrete beam elements are submitted to applicable loads along their life cycle that cause shear and torsion. These elements may be subject to only shear, pure torsion or both, torsion and shear combined. The Brazilian Standard Code ABNT NBR 6118:2007 [1] fixes conditions to calculate the transverse reinforcement area in beam reinforced concrete elements, using two design models, based on the strut and tie analogy model, first studied by Mörsch [2]. The strut angle θ (theta) can be considered constant and equal to 45º (Model I), or varying between 30º and 45º (Model II). In the case of transversal ties (stirrups), the variation of angle α (alpha) is between 45º and 90º. When the equilibrium torsion is required, a resistant model based on space truss with hollow section is considered. The space truss admits an inclination angle θ between 30º and 45º, in accordance with beam elements subjected to shear. This paper presents a theoretical study of models I and II for combined shear and torsion, in which ranges the geometry and intensity of action in reinforced concrete beams, aimed to verify the consumption of transverse reinforcement in accordance with the calculation model adopted As the strut angle on model II ranges from 30º to 45º, transverse reinforcement area (Asw) decreases, and total reinforcement area, which includes longitudinal torsion reinforcement (Asℓ), increases. It appears that, when considering model II with strut angle above 40º, under shear only, transverse reinforcement area increases 22% compared to values obtained using model I.