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URI permanente para esta coleçãohttps://locus.ufv.br/handle/123456789/11798
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Item Sowing date reduces the incidence of wheat blast disease(Pesquisa Agropecuária Brasileira, 2016-05) Coelho, Maurício Antônio de Oliveira; Torres, Gisele Abigail Montan; Cecon, Paulo Roberto; Santana, Flávio MartinsThe objective of this work was to assess the effect of sowing date on the intensity of wheat blast disease, as well as the yield losses caused by this disease in different wheat (Triticum aestivum) genotypes. The experiments were conducted in 2013 at the Sertãozinho experimental station of Empresa de Pesquisa Agropecuária de Minas Gerais (Epamig), in the municipality of Patos de Minas, in the state of Minas Gerais, Brazil. Fourteen wheat genotypes and two sowing dates were evaluated. The experimental design was a randomized complete block with three replicates. The evaluated variables were: incidence, severity, thousand grain weight (TGW), grain yield, and yield losses. A disease index (DI) was calculated, based both on the incidence and the severity of the disease, to measure blast intensity in wheat. The sowing date significantly affected DI, TGW, and grain yield. Significant linear correlations were observed between DI and yield losses (0.89), between losses and TGW (-0.85), and between losses and grain yield (-0.93). For wheat blast, DIs greater than or equal to 0.5 indicate potential yield losses equal to or greater than 70%. The EP063030 line and the MGS Brilhante and BRS 264 cultivars are the most tolerant to blast, when exposed to high disease pressure.Item Design of a corporate SDI in power sector using a formal model(Infrastructures, 2017) Oliveira, Italo L.; Câmara, Jean H. S.; Torres, Rubens M.; Lisboa-Filho, JugurtaGeospatial data are essential for the decision-making process. However, obtaining and keeping such data up to date usually require much time and many financial resources. In order to minimize the production costs and incentivize sharing these data, countries are promoting the implementation of Spatial Data Infrastructures (SDI) at the different public administration levels. The International Cartographic Association (ICA) proposes a formal model that describes the main concepts of an SDI based on three of the five viewpoints of the Reference Model for Open Distributed Processing (RM-ODP). Afterwards, researchers extended ICA’s model to describe, more properly, the actors, hierarchical relationship and interactions related to the policies that drive an SDI. However, the proposed extensions are semantically inconsistent with the original proposal. Moreover, the use of ICA’s formal model and its extensions has not been assessed yet to specify a corporate-level SDI. This study describes the merger of actors and policies proposed by the ICA and its extensions in order to eliminate differences in the semantics or terminology among them. This unified model was applied to specify a corporate SDI for a large Brazilian corporation, the Minas Gerais Power Company (Companhia Energética de Minas Gerais (Cemig)), which is comprised of about 200 companies in the power sector. The case study presents part of the specification of the five RM-ODP viewpoints, i.e., the three viewpoints featured in ICA’s formal model (Enterprise, Information, and Computation) and the other two viewpoints that make up the RM-ODP (Engineering and Technology). The adapted ICA’s model proved adequate to describe SDI-Cemig. In addition, the case study may serve as an example of the specification and implementation of new SDIs, not only corporate ones, but also of public agencies at any hierarchical level.Item Erratum to: Mirnacle: machine learning with SMOTE and random forest for improving selectivity in pre-miRNA ab initio prediction(BMC Bioinformatics, 2017) Marques, Yuri Bento; Oliveira, Alcione de Paiva; Vasconcelos, Ana Tereza Ribeiro; Cerqueira, Fabio RibeiroMicroRNAs (miRNAs) are key gene expression regulators in plants and animals. Therefore, miRNAs are involved in several biological processes, making the study of these molecules one of the most relevant topics of molecular biology nowadays. However, characterizing miRNAs in vivo is still a complex task. As a consequence, in silico methods have been developed to predict miRNA loci. A common ab initio strategy to find miRNAs in genomic data is to search for sequences that can fold into the typical hairpin structure of miRNA precursors (pre-miRNAs). The current ab initio approaches, however, have selectivity issues, i.e., a high number of false positives is reported, which can lead to laborious and costly attempts to provide biological validation. This study presents an extension of the ab initio method miRNAFold, with the aim of improving selectivity through machine learning techniques, namely, random forest combined with the SMOTE procedure that copes with imbalance datasets. By comparing our method, termed Mirnacle, with other important approaches in the literature, we demonstrate that Mirnacle substantially improves selectivity without compromising sensitivity. For the three datasets used in our experiments, our method achieved at least 97% of sensitivity and could deliver a two-fold, 20-fold, and 6-fold increase in selectivity, respectively, compared with the best results of current computational tools. The extension of miRNAFold by the introduction of machine learning techniques, significantly increases selectivity in pre-miRNA ab initio prediction, which optimally contributes to advanced studies on miRNAs, as the need of biological validations is diminished. Hopefully, new research, such as studies of severe diseases caused by miRNA malfunction, will benefit from the proposed computational tool.Item Scheduling unrelated parallel batch processing machines with non-identical job sizes and unequal ready times(Computers & Operations Research, 2017-02) Arroyo, José Elías Cláudio; Leung, Joseph Y. T.This research analyzes the problem of scheduling a set of n jobs with arbitrary job sizes and non-zero ready times on a set of m unrelated parallel batch processing machines so as to minimize the makespan. Unrelated parallel machine is a generalization of the identical parallel processing machines and is closer to real-world production systems. Each machine can accommodate and process several jobs simultaneously as a batch as long as the machine capacity is not exceeded. The batch processing time and the batch ready time are respectively equal to the largest processing time and the largest ready time among all the jobs in the batch. Motivated by the computational complexity and the practical relevance of the problem, we present several heuristics based on first-fit and best-fit earliest job ready time rules. We also present a mixed integer programming model for the problem and a lower bound to evaluate the quality of the heuristics. The small computational effort of deterministic heuristics, which is valuable in some practical applications, is also one of the reasons that motivates this study. The results show that the heuristic proposed in this paper has a superior performance compared to the heuristics based on ideas proposed in the literature.Item Predicting optimal solution costs with bidirectional stratified sampling in regular search spaces(Artificial Intelligence, 2016-01) Lelis, Levi H. S.; Stern, Roni; Arfaee, Shahab Jabbari; Zilles, Sandra; Felner, Ariel; Holte, Robert C.Optimal planning and heuristic search systems solve state-space search problems by finding a least-cost path from start to goal. As a byproduct of having an optimal path they also determine the optimal solution cost. In this paper we focus on the problem of determining the optimal solution cost for a state-space search problem directly, i.e., without actually finding a solution path of that cost. We present an algorithm, BiSS, which is a hybrid of bidirectional search and stratified sampling that produces accurate estimates of the optimal solution cost. BiSS is guaranteed to return the optimal solution cost in the limit as the sample size goes to infinity. We show empirically that BiSS produces accurate predictions in several domains. In addition, we show that BiSS scales to state spaces much larger than can be solved optimally. In particular, we estimate the average solution cost for the 6×6, 7×7, and 8×8 Sliding-Tile puzzle and provide indirect evidence that these estimates are accurate. As a practical application of BiSS, we show how to use its predictions to reduce the time required by another system to learn strong heuristic functions from days to minutes in the domains tested.Item Exploring potential implementations of PCE in IoT world(Optical Switching and Networking, 2017-11) Souza, Vitor Barbosa C.; Ramirez, Wilson; Marin-Tordera, Eva; Sanchez, SergioThe recently coined Internet of Things (IoT) paradigm leverages a large volume of heterogeneous Network Elements (NEs) demanding broad connectivity anywhere, anytime and anyhow, fueling the deployment of innovative Internet services, such as Cloud or Fog Computing, Data Center Networks (DCNs), Smart Cities or Smart Transportation. The proper deployment of these novel Internet services is imposing hard connectivity constraints, such as high transmission capacity, reliable communications, as well as an efficient control scheme capable of enabling an agile coordination of actions in large heterogeneous scenarios. In recent years, novel control schemes, such as the so-called Path Computation Element (PCE) has gained momentum in the network research community turning into real PCE implementations. Indeed, there is a wealth of studies assessing the PCE performance, clearly showing the potential benefits of decoupling routing control tasks from the forwarding nodes. Nevertheless, recognized the need for a control solution in IoT scenarios, there is not much published information analyzing PCE benefits in these IoT scenarios. In this paper, we distill how the PCE may gracefully provide for service composition in an agile manner, handling the specific constraints and requirements found in IoT scenarios. To this end, we propose a novel PCE strategy referred to as Service-Oriented PCE (SPCE), which enables network-aware service composition.Item Iterated greedy with random variable neighborhood descent for scheduling jobs on parallel machines with deterioration effect(Electronic Notes in Discrete Mathematics, 2017-04-14) Santos, Vívian L. Aguiar; Arroyo, José Elias C.In this paper, we study an unrelated parallel machine scheduling problem in which the jobs cause deterioration of the machines. This deterioration decreases the performance of the machines, and therefore, the processing times of the jobs are increased over time. The problem is to find the processing sequence of jobs on each machine in order to reduce the deterioration of the machines and consequently minimize the makespan. This problem is NP-hard when the number of machines is greater or equal than two, and hence we propose a heuristic based on the Iterated Greedy meta-heuristic coupled with a variant of the Variable Neighborhood Descent method that uses a random ordering of neighborhoods in local search phase. The performance of our heuristic, named IG-RVND, is compared with the state-of-the-art meta-heuristic proposed in the literature for the problem under study. The results show that the our heuristic outperform the existing algorithm by a significant margin.Item ILS heuristics for the single-machine scheduling problem with sequence-dependent family setup times to minimize total Tardiness(Journal of Applied Mathematics, 2016-09-19) Jacob, Vinícius Vilar; Arroyo, José Elias C.This paper addresses a single-machine scheduling problem with sequence dependent family setup times. In this problem the jobs are classified into families according to their similarity characteristics. Setup times are required on each occasion when the machine switches from processing jobs in one family to jobs in another family. The performance measure to be minimized is the total tardiness with respect to the given due dates of the jobs. The problem is classified as NP-hard in the ordinary sense. Since the computational complexity associated with the mathematical formulation of the problem makes it difficult for optimization solvers to deal with large-sized instances in reasonable solution time, efficient heuristic algorithms are needed to obtain near-optimal solutions. In this work we propose three heuristics based on the Iterated Local Search (ILS) metaheuristic. The first heuristic is a basic ILS, the second uses a dynamic perturbation size, and the third uses a Path Relinking (PR) technique as an intensification strategy. We carry out comprehensive computational and statistical experiments in order to analyze the performance of the proposed heuristics. The computational experiments show that the ILS heuristics outperform a genetic algorithm proposed in the literature. The ILS heuristic with dynamic perturbation size and PR intensification has a superior performance compared to other heuristics.Item Rama: a machine learning approach for ribosomal protein prediction in plants(Scientific Reports, 2017-11-24) Carvalho, Thales Francisco Mota; Silva, José Cleydson F.; Calil, Iara Pinheiro; Fontes, Elizabeth Pacheco Batista; Cerqueira, Fabio RibeiroRibosomal proteins (RPs) play a fundamental role within all type of cells, as they are major components of ribosomes, which are essential for translation of mRNAs. Furthermore, these proteins are involved in various physiological and pathological processes. The intrinsic biological relevance of RPs motivated advanced studies for the identification of unrevealed RPs. In this work, we propose a new computational method, termed Rama, for the prediction of RPs, based on machine learning techniques, with a particular interest in plants. To perform an effective classification, Rama uses a set of fundamental attributes of the amino acid side chains and applies a two-step procedure to classify proteins with unknown function as RPs. The evaluation of the resultant predictive models showed that Rama could achieve mean sensitivity, precision, and specificity of 0.91, 0.91, and 0.82, respectively. Furthermore, a list of proteins that have no annotation in Phytozome v.10, and are annotated as RPs in Phytozome v.12, were correctly classified by our models. Additional computational experiments have also shown that Rama presents high accuracy to differentiate ribosomal proteins from RNA-binding proteins. Finally, two novel proteins of Arabidopsis thaliana were validated in biological experiments.Item Mirnacle: machine learning with SMOTE and random forest for improving selectivity in pre-miRNA ab initio prediction(BMC Bioinformatics, 2016-12-15) Marques, Yuri Bento; Oliveira, Alcione de Paiva; Vasconcelos, Ana Tereza Ribeiro; Cerqueira, Fabio RibeiroMicroRNAs (miRNAs) are key gene expression regulators in plants and animals. Therefore, miRNAs are involved in several biological processes, making the study of these molecules one of the most relevant topics of molecular biology nowadays. However, characterizing miRNAs in vivo is still a complex task. As a consequence, in silico methods have been developed to predict miRNA loci. A common ab initio strategy to find miRNAs in genomic data is to search for sequences that can fold into the typical hairpin structure of miRNA precursors (pre-miRNAs). The current ab initio approaches, however, have selectivity issues, i.e., a high number of false positives is reported, which can lead to laborious and costly attempts to provide biological validation. This study presents an extension of the ab initio method miRNAFold, with the aim of improving selectivity through machine learning techniques, namely, random forest combined with the SMOTE procedure that copes with imbalance datasets. By comparing our method, termed Mirnacle, with other important approaches in the literature, we demonstrate that Mirnacle substantially improves selectivity without compromising sensitivity. For the three datasets used in our experiments, our method achieved at least 97% of sensitivity and could deliver a two-fold, 20-fold, and 6-fold increase in selectivity, respectively, compared with the best results of current computational tools. The extension of miRNAFold by the introduction of machine learning techniques, significantly increases selectivity in pre-miRNA ab initio prediction, which optimally contributes to advanced studies on miRNAs, as the need of biological validations is diminished. Hopefully, new research, such as studies of severe diseases caused by miRNA malfunction, will benefit from the proposed computational tool.