Simulation model using meta heuristic algorithms for achieving optimal arrangement of storage bins in a sawmill yard

Bin planning (arrangements) is a key factor in the timber industry. Improper planning of the storage bins may lead to inefficient transportation of resources, which threaten the overall efficiency and thereby limit the profit margins of sawmills. To address this challenge, a simulation model has been developed. However, as numerous alternatives are available for arranging bins, simulating all possibilities will take an enormous amount of time and it is computationally infeasible. A discrete-event simulation model incorporating meta-heuristic algorithms has therefore been investigated in this study. Preliminary investigations indicate that the results achieved by GA based simulation model are promising and better than the other meta-heuristic algorithm. Further, a sensitivity analysis has been done on the GA based optimal arrangement which contributes to gaining insights and knowledge about the real system that ultimately leads to improved and enhanced efficiency in sawmill yards. It is expected that the results achieved in the work will support timber industries in making optimal decisions with respect to arrangement of storage bins in a sawmill yard.

Phylogenetic structure of Guinea-Bissau ethnic groups for mitochondrial DNA and Y chromosome genetic systems

The maternal and paternal genetic profile of Guineans is markedly sub-Saharan West African, with the majority of lineages belonging to L0-L3 mtDNA sub-clusters and E3a-M2 and E1-M33 Y chromosome haplogroups. Despite the sociocultural differences among Guinea-Bissau ethnic groups,marked by the supposedly strict admixture barriers, their genetic pool remains largely common. Their extant variation coalesces at distinct timeframes, from the initial occupation of the area to later inputs of people. Signs of recent expansion in mtDNA haplogroups L2a-L2c and NRY E3a-M2 suggest population growth in the equatorial western fringe, possibly supported by an early local agricultural centre, and to which the Mandenka and the Balanta people may relate. Non-West African signatures are traceable in less frequent extant haplogroups, fitting well with the linguistic and historical evidence regarding particular ethnic groups: the Papel and Felupe-Djola people retain traces of their putative East African relatives; U6 and M1b among Guinea-Bissau Bak-speakers indicate partial diffusion to Sahel of North African lineages; U5b1b lineages in Fulbe and Papel represent a link to North African Berbers, emphasizing the great importance of post-glacial expansions; exact matches of R1b-P25 and E3b1-M78 with Europeans likely trace back to the times of the slave trade.

Genetic associations between age at first calving and heifer body weight and scrotal circumference in Nelore cattle

Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES)

Effect of energy intake on performance and carcass composition of broiler chickens from two different genetic groups

In order to evaluate the effect of energy intake and broiler genotype on performance, carcass yield, and fat deposition, 600 one-day-old male chicks from two different genetic groups (AgRoss 308 - commercial line and PCLC - Empresa Brasileira de Pesquisa Agropecuária (EMBRAPA) non-improved line) were fed diets with different metabolizable energy level (2950, 3200 and 3450 kcal/kg). A completely randomized experimental design in a 2X3 factorial arrangement with four replications of 25 birds per treatment was applied. In order to ensure different energy intake among treatments within each strain, feed intake was daily adjusted by pair-feeding schemes. AgRoss 308 broilers had better performance and carcass yield, and presented lower abdominal fat deposition rate. In both genetic groups, the highest dietary energy level increased weight gain, heart relative weight, and fat deposition. However, it reduced the difference between AgRoss 308 and PCLC for feed conversion ratio and carcass protein deposition. These findings allow concluding that genetic improvement had a significant effect on broiler energy metabolism, and that the highest performance differences between genetic groups are found when low-energy intake is imposed.

Genetic parameter estimates for buffalo milk yield, milk quality and mozzarella production and Bayesian inference analysis of their relationships

Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)

Identification of the endangered small red brocket deer (Mazama bororo) using noninvasive genetic techniques (Mammalia; Cervidae)

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

Algoritmo Q-learning como estratégia de exploração e/ou explotação para metaheurísticas GRASP e algoritmo genético

Techniques of optimization known as metaheuristics have achieved success in the resolution of many problems classified as NP-Hard. These methods use non deterministic approaches that reach very good solutions which, however, don t guarantee the determination of the global optimum. Beyond the inherent difficulties related to the complexity that characterizes the optimization problems, the metaheuristics still face the dilemma of xploration/exploitation, which consists of choosing between a greedy search and a wider exploration of the solution space. A way to guide such algorithms during the searching of better solutions is supplying them with more knowledge of the problem through the use of a intelligent agent, able to recognize promising regions and also identify when they should diversify the direction of the search. This way, this work proposes the use of Reinforcement Learning technique - Q-learning Algorithm - as exploration/exploitation strategy for the metaheuristics GRASP (Greedy Randomized Adaptive Search Procedure) and Genetic Algorithm. The GRASP metaheuristic uses Q-learning instead of the traditional greedy-random algorithm in the construction phase. This replacement has the purpose of improving the quality of the initial solutions that are used in the local search phase of the GRASP, and also provides for the metaheuristic an adaptive memory mechanism that allows the reuse of good previous decisions and also avoids the repetition of bad decisions. In the Genetic Algorithm, the Q-learning algorithm was used to generate an initial population of high fitness, and after a determined number of generations, where the rate of diversity of the population is less than a certain limit L, it also was applied to supply one of the parents to be used in the genetic crossover operator. Another significant change in the hybrid genetic algorithm is the proposal of a mutually interactive cooperation process between the genetic operators and the Q-learning algorithm. In this interactive/cooperative process, the Q-learning algorithm receives an additional update in the matrix of Q-values based on the current best solution of the Genetic Algorithm. The computational experiments presented in this thesis compares the results obtained with the implementation of traditional versions of GRASP metaheuristic and Genetic Algorithm, with those obtained using the proposed hybrid methods. Both algorithms had been applied successfully to the symmetrical Traveling Salesman Problem, which was modeled as a Markov decision process

Algoritmos genéticos: uso de lógica nebulosa e análise de convergência por cadeia de Markov

In this work, the Markov chain will be the tool used in the modeling and analysis of convergence of the genetic algorithm, both the standard version as for the other versions that allows the genetic algorithm. In addition, we intend to compare the performance of the standard version with the fuzzy version, believing that this version gives the genetic algorithm a great ability to find a global optimum, own the global optimization algorithms. The choice of this algorithm is due to the fact that it has become, over the past thirty yares, one of the more importan tool used to find a solution of de optimization problem. This choice is due to its effectiveness in finding a good quality solution to the problem, considering that the knowledge of a good quality solution becomes acceptable given that there may not be another algorithm able to get the optimal solution for many of these problems. However, this algorithm can be set, taking into account, that it is not only dependent on how the problem is represented as but also some of the operators are defined, to the standard version of this, when the parameters are kept fixed, to their versions with variables parameters. Therefore to achieve good performance with the aforementioned algorithm is necessary that it has an adequate criterion in the choice of its parameters, especially the rate of mutation and crossover rate or even the size of the population. It is important to remember that those implementations in which parameters are kept fixed throughout the execution, the modeling algorithm by Markov chain results in a homogeneous chain and when it allows the variation of parameters during the execution, the Markov chain that models becomes be non - homogeneous. Therefore, in an attempt to improve the algorithm performance, few studies have tried to make the setting of the parameters through strategies that capture the intrinsic characteristics of the problem. These characteristics are extracted from the present state of execution, in order to identify and preserve a pattern related to a solution of good quality and at the same time that standard discarding of low quality. Strategies for feature extraction can either use precise techniques as fuzzy techniques, in the latter case being made through a fuzzy controller. A Markov chain is used for modeling and convergence analysis of the algorithm, both in its standard version as for the other. In order to evaluate the performance of a non-homogeneous algorithm tests will be applied to compare the standard fuzzy algorithm with the genetic algorithm, and the rate of change adjusted by a fuzzy controller. To do so, pick up optimization problems whose number of solutions varies exponentially with the number of variables

Environmental and genetic factors affecting the weaning-estrus interval in sows

Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES)

Genetic and environmental effects on age at first farrowing in sows in southeastern Brazil

We evaluated genetic and environmental factors affecting age at first farrowing of sows in the Brazilian southeast. For this purpose, 466 observations regarding the age at first farrowing were made for Dalland-C40 (c) animals belonging to two herds. The effects of the environmental factors on this trait were assessed by means of a model that included, as random effects, the influence of the sow's father and mother and, as fixed effects, the influence the year of birth, the herd and the birth season, along with the covariable litter size at birth. The variance components were estimated using the derivative-free restricted maximum likelihood method. The estimated mean was 354.8 +/- 25.87 days, with a coefficient of variation of 7.29%. Significant effects on the trait were observed for the herd, the year and the season of birth; but a linear effect of litter size at birth on the age at first farrowing was not observed. The boar did not significantly contribute to the variation occurring among the sows, whereas the sow's mother caused significant variation. The heritability estimate for the age at first farrowing was 0.44 +/- 0.15, which is considered high. We concluded that herd effect and year and season of birth should be taken into consideration for an accurate genetic comparison; consequently, the animals should be joined into contemporary groups.

Genetic differences between strains of Biomphalaria glabrata (Planorbidae) that are susceptible and unsusceptible to schistosomiasis

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

Transmission system expansion planning by an extended genetic algorithm

The paper presents an extended genetic algorithm for solving the optimal transmission network expansion planning problem. Two main improvements have been introduced in the genetic algorithm: (a) initial population obtained by conventional optimisation based methods; (b) mutation approach inspired in the simulated annealing technique, the proposed method is general in the sense that it does not assume any particular property of the problem being solved, such as linearity or convexity. Excellent performance is reported in the test results section of the paper for a difficult large-scale real-life problem: a substantial reduction in investment costs has been obtained with regard to previous solutions obtained via conventional optimisation methods and simulated annealing algorithms; statistical comparison procedures have been employed in benchmarking different versions of the genetic algorithm and simulated annealing methods.

Espectroscopia no infravermelho próximo e métodos de calibração multivariada aplicados à determinação simultânea de parâmetros bioquímicos em plasma sanguíneo

In this work, the quantitative analysis of glucose, triglycerides and cholesterol (total and HDL) in both rat and human blood plasma was performed without any kind of pretreatment of samples, by using near infrared spectroscopy (NIR) combined with multivariate methods. For this purpose, different techniques and algorithms used to pre-process data, to select variables and to build multivariate regression models were compared between each other, such as partial least squares regression (PLS), non linear regression by artificial neural networks, interval partial least squares regression (iPLS), genetic algorithm (GA), successive projections algorithm (SPA), amongst others. Related to the determinations of rat blood plasma samples, the variables selection algorithms showed satisfactory results both for the correlation coefficients (R²) and for the values of root mean square error of prediction (RMSEP) for the three analytes, especially for triglycerides and cholesterol-HDL. The RMSEP values for glucose, triglycerides and cholesterol-HDL obtained through the best PLS model were 6.08, 16.07 e 2.03 mg dL-1, respectively. In the other case, for the determinations in human blood plasma, the predictions obtained by the PLS models provided unsatisfactory results with non linear tendency and presence of bias. Then, the ANN regression was applied as an alternative to PLS, considering its ability of modeling data from non linear systems. The root mean square error of monitoring (RMSEM) for glucose, triglycerides and total cholesterol, for the best ANN models, were 13.20, 10.31 e 12.35 mg dL-1, respectively. Statistical tests (F and t) suggest that NIR spectroscopy combined with multivariate regression methods (PLS and ANN) are capable to quantify the analytes (glucose, triglycerides and cholesterol) even when they are present in highly complex biological fluids, such as blood plasma

Determinação de parâmetros (sólidos solúveis, pH e acidez titulável) em ameixas intactas usando espectroscopia no infravermelho próximo e seleção de comprimento de onda

The aim of this study was to evaluate the potential of near-infrared reflectance spectroscopy (NIRS) as a rapid and non-destructive method to determine the soluble solid content (SSC), pH and titratable acidity of intact plums. Samples of plum with a total solids content ranging from 5.7 to 15%, pH from 2.72 to 3.84 and titratable acidity from 0.88 a 3.6% were collected from supermarkets in Natal-Brazil, and NIR spectra were acquired in the 714 2500 nm range. A comparison of several multivariate calibration techniques with respect to several pre-processing data and variable selection algorithms, such as interval Partial Least Squares (iPLS), genetic algorithm (GA), successive projections algorithm (SPA) and ordered predictors selection (OPS), was performed. Validation models for SSC, pH and titratable acidity had a coefficient of correlation (R) of 0.95 0.90 and 0.80, as well as a root mean square error of prediction (RMSEP) of 0.45ºBrix, 0.07 and 0.40%, respectively. From these results, it can be concluded that NIR spectroscopy can be used as a non-destructive alternative for measuring the SSC, pH and titratable acidity in plums

Evaluation of level of DNA damage in blood leukocytes of non-diabetic and diabetic rat exposed to cigarette smoke

The objective of the present study was to use the comet assay to evaluate the steady-state level of DNA damage in peripheral blood leukocytes from diabetic and non-diabetic female Wistar rats exposed to air or to cigarette smoke. A total of 20 rats were distributed into four experimental groups (n= 5 rats/group): non-diabetic (control) and diabetic exposed to filtered air; non-diabetic and diabetic exposed to cigarette smoke. A pancreatic beta (beta)-cytotoxic agent, streptozotocin (40 mg/kg b.w.) was used to induce experimental diabetes in rats. Rats placed into whole-body exposure chambers were exposed for 30 min to filtered air (control) or to tobacco smoke generated from 10 cigarettes, twice a day, for 2 months. At the end of the 2-month exposure period, each rat was anesthetized and humanely killed to obtain blood samples for genotoxicity analysis using the alkaline comet assay. Blood wleukocytes sampled from diabetic rats presented higher DNA damage values (tail moment =0.57 +/- 0.05; tail length =19.92 +/- 0.41, p < 0.05) compared to control rats (tail moment =0.34 +/- 0.02; tail length= 17.42 +/- 0.33). Non-diabetic (tail moment =0.43 +/- 0.04, p > 0.05) and diabetic rats (tail moment= 0.41 +/- 0.03, p > 0.05) exposed to cigarette smoke presented non-significant increases in DNA damage levels compared to control group. In conclusion, our data show that the exposure of diabetic rats to cigarette smoke produced no additional genotoxicity in peripheral blood cells of female Wistar rats. (c) 2007 Elsevier B.V. All rights reserved.