Predicting structural models for silicon clusters

This article introduces an efficient method to generate structural models for medium-sized silicon clusters. Geometrical information obtained from previous investigations of small clusters is initially sorted and then introduced into our predictor algorithm in order to generate structural models for large clusters. The method predicts geometries whose binding energies are close (95%) to the corresponding value for the ground-state with very low computational cost. These predictions can be used as a very good initial guess for any global optimization algorithm. As a test case, information from clusters up to 14 atoms was used to predict good models for silicon clusters up to 20 atoms. We believe that the new algorithm may enhance the performance of most optimization methods whenever some previous information is available. (C) 2003 Wiley Periodicals, Inc.

Preliminary study of a methodology based on the degree-days concept for predicting the phenological phases of crops

This work develops a methodology (using the degree-days concept and linear regression), to forecast the duration of phenological phases in crops. An experiment was conducted in the greenhouse with three cultivars of cowpea (Vigna unguiculata (C.) Walp.), cv. California-781, Tvx 5058-09C and IT 81D-1032. The methodology was based on the relative thermal efficiency rate, determined for each species or cv. The results show that the proposed methodology may be a good alternative in works involving crops, especially because it does not require the repetition of the experiments.

Predicting beef carcass retail products of Mediterranean buffaloes by real-time ultrasound measures

Twenty eight Mediterranean buffaloes bulls were scanned with real-time ultrasound (RTU), slaughtered, and fabricated into retail cuts to determine the potential for ultrasound measures to predict carcass retail yield. Ultrasound measures of fat thickness, ribeye area and rump fat thickness were recorded three to five days prior to slaughter. Carcass measurements were taken, and one side of each carcass was fabricated into retail cuts. Stepwise regression analysis was used to compare possible models for prediction of either kilograms or percent retail product from carcass mesaurements and ultrasound measures. Results indicate that possible prediction models for percent or kilograms of retail products using RTU measures were similar in their predictive power and accuracy when compared to models derived from carcass measurements. Both fat thickness and ribeye area were over-predicted when measured ultrasonically compared to measurements taken on the carcass in the cooler. The mean absolute differences for both traits are larger than the mean differences, indicating that some images were interpreted to be larger and some smaller than actual carcass measurements. Ultrasound measurements of REA and FT had positive correlations with carcass measures of the same traits (r=.96 for REA and r=.99 for FT). Standard errors of prediction currently are being used as the standard to certify ultrasound technicians for accuracy. Regression equations using live weight (LW), rib eye area (REAU) and subcutaneous fat thickness (FTU) between 12(th) and 13 (th) ribs and also over the biceps femoris muscle (FTP8) by ultrasound explained 95% of the variation in the hot carcass weight when measure immediately before slaughter.

A QUANTITATIVE APPROACH TO SUBSIDIZE THE PRECAUTIONARY MANAGEMENT of THE SMALL-SCALE FISHERIES IN ITAIPU RESERVOIR, BRAZIL

Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)

Predicting body and carcass characteristics of 2 broiler chicken strains using support vector regression and neural network models

As a new modeling method, support vector regression (SVR) has been regarded as the state-of-the-art technique for regression and approximation. In this study, the SVR models had been introduced and developed to predict body and carcass-related characteristics of 2 strains of broiler chicken. To evaluate the prediction ability of SVR models, we compared their performance with that of neural network (NN) models. Evaluation of the prediction accuracy of models was based on the R-2, MS error, and bias. The variables of interest as model output were BW, empty BW, carcass, breast, drumstick, thigh, and wing weight in 2 strains of Ross and Cobb chickens based on intake dietary nutrients, including ME (kcal/bird per week), CP, TSAA, and Lys, all as grams per bird per week. A data set composed of 64 measurements taken from each strain were used for this analysis, where 44 data lines were used for model training, whereas the remaining 20 lines were used to test the created models. The results of this study revealed that it is possible to satisfactorily estimate the BW and carcass parts of the broiler chickens via their dietary nutrient intake. Through statistical criteria used to evaluate the performance of the SVR and NN models, the overall results demonstrate that the discussed models can be effective for accurate prediction of the body and carcass-related characteristics investigated here. However, the SVR method achieved better accuracy and generalization than the NN method. This indicates that the new data mining technique (SVR model) can be used as an alternative modeling tool for NN models. However, further reevaluation of this algorithm in the future is suggested.

Comparison of three systems of classification in predicting the outcome of diabetic foot ulcers in a Brazilian population

Objective: The aim was to compare there ulcer classification systems as predictors of the outcome of diabetic foot ulcers; the Wagner, the University of Texas (UT) and the size (area, depth), sepsis, arteriopathy, denervation system (S(AD)SAD) systems in specialist clinic in Brazil.Methods: Ulcer area, depth, appearance, infection and associated ischaemia and neuropathy were recorded in a consecutive series of 94 subjects. A novel score, the S(AD)SAD score, was derived from the sum of individual items of the S(AD)SAD system, and was evaluated. Follow-up was for at least 6 months. The primary outcome measure was the incidence of healing.Results: Mean age was 57.6 years; 57 (60.6%) were made. Forty-eight ulcers (51.1%) healed without surgery; 11 (12.2%) subjects underwent minor amputation. Significant differences in terms of healing were observed for depth (P = 0.002), infection (P = 0.006) and denervation (P = 0.002) using the S(AD)SAD system, for UT grade (P = 0.002) and stage (P = 0.032) and for Wagner grades (P = 0.002). Ulcers with an S(AD)SAD score of <= 9 (total possible 15) were 7.6 times more likely to heal than scores >= 10 (P < 0.001).Conclusions: All three systems predicted ulcer outcome. The S(AD)SAD score of ulcer severity could represent a useful addition to routine clinical practice. The association between outcome and ulcer depth confirms earlier reports. The association with infection was stronger than that reported from the centres in Europe or North America. The very strong association with neuropathy has only previously been observed in Tanzania. Studies designed to compare the outcome in different countries should adopt systems of classification, which are valid for the populations studied.

Chemometric analysis of Hymenoptera toxins and defensins: A model for predicting the biological activity of novel peptides from venoms and hemolymph

When searching for prospective novel peptides, it is difficult to determine the biological activity of a peptide based only on its sequence. The trial and error approach is generally laborious, expensive and time consuming due to the large number of different experimental setups required to cover a reasonable number of biological assays. To simulate a virtual model for Hymenoptera insects, 166 peptides were selected from the venoms and hemolymphs of wasps, bees and ants and applied to a mathematical model of multivariate analysis, with nine different chemometric components: GRAVY, aliphaticity index, number of disulfide bonds, total residues, net charge, pI value, Boman index, percentage of alpha helix, and flexibility prediction. Principal component analysis (PCA) with non-linear iterative projections by alternating least-squares (NIPALS) algorithm was performed, without including any information about the biological activity of the peptides. This analysis permitted the grouping of peptides in a way that strongly correlated to the biological function of the peptides. Six different groupings were observed, which seemed to correspond to the following groups: chemotactic peptides, mastoparans, tachykinins, kinins, antibiotic peptides, and a group of long peptides with one or two disulfide bonds and with biological activities that are not yet clearly defined. The partial overlap between the mastoparans group and the chemotactic peptides, tachykinins, kinins and antibiotic peptides in the PCA score plot may be used to explain the frequent reports in the literature about the multifunctionality of some of these peptides. The mathematical model used in the present investigation can be used to predict the biological activities of novel peptides in this system, and it may also be easily applied to other biological systems. © 2011 Elsevier Inc.

Predicting academic success of engineering students in technical drawing from working memory

Tests on spatial aptitude, in particular Visualization, have been shown to be efficient predictors of the academic performance of Technical Drawing stu-dents. It has recently been found that Spatial Working Memory (a construct defined as the ability to perform tasks with a figurative content that require si-multaneous storage and transformation of information) is strongly associated with Visualization. In the present study we analyze the predictive efficiency of a bat-tery of tests that included tests on Visualization, SpatialWorking Memory, Spatial Short-term Memory and Executive Function on a sample of first year engineering students. The results show that Spatial Working Memory (SWM) is the most important predictor of academic success in Technical Drawing. In our view, SWM tests can be useful for detecting as early as possible those students who will require more attention and support in the teaching-learning process.

Validity of malnutrition scores for predicting mortality in chronic hemodialysis patients

Purpose: Malnutrition is a strong predictor of mortality in hemodialysis patients. Several scoring systems for evaluating nutritional status have been proposed. However, they rely on different sets of anthropometric and laboratory markers to make a diagnosis of malnutrition and assess its impact on prognosis. To validate them, nutritional scores should be compared with clinical outcomes. Thus, the purpose of this study was to assess malnutrition by three different nutrition scoring systems and determine which best predicts mortality in hemodialysis patients. Methods: This prospective study included 106 adult chronic hemodialysis patients. Their mean age was 56.3 ± 14.9 years and mean body mass index 24.8 (21.8-28.9); 52 % were men and they had been on dialysis for 24 (5-55) months. Nutritional status was classified according to the diagnostic systems proposed by Wolfson et al. (Am J Clin Nutr 39(4):547-555, 1984), International Society of Renal Nutrition and Metabolism (ISRNM) (Fouque et al. in Kidney Int 73(4):391-398, 2008), and Beberashvili et al. (Nephrol Dial Transplant 25(8):2662-2671, 2010). During about 2 years of follow-up, mortality was assessed by Kaplan-Meier curves, log-rank, and Cox's models adjusted for diabetes, sex, C-reactive protein, time on dialysis, age, and fractional urea clearance. Results: Twenty-three deaths (21.5 %) occurred during the study period. According to the systems of Wolfson, Beberashvili, and the ISRNM, 54, 32, and 20 % of patients, respectively, had malnutrition. Both univariate and multivariate analyses showed that the ISRNM system was the only one that predicted poorer survival (fourfold higher death risk) in malnourished patients. Conclusions: The scoring system proposed by the ISRNM most accurately identifies patients at higher risk of death. © 2013 Springer Science+Business Media Dordrecht.

A finite element approach for predicting the ultimate rotation capacity of RC beams

This paper presents a numerical approach to model the complex failure mechanisms that define the ultimate rotational capacity of reinforced concrete beams. The behavior in tension and compression is described by a constitutive damage model derived from a combination of two specific damage models [1]. The nonlinear behavior of the compressed region is treated by the compressive damage model based on the Drucker-Prager criterion written in terms of the effective stresses. The tensile damage model employs a failure criterion based on the strain energy associated with the positive part the effective stress tensor. This model is used to describe the behavior of very thin bands of strain localization, which are embedded in finite elements to represent multiple cracks that occur in the tensioned region [2]. The softening law establishes dissipation energy compatible with the fracture energy of the concrete. The reinforcing steel bars are modeled by truss elements with elastic-perfect plastic behavior. It is shown that the resulting approach is able to predict the different stages of the collapse mechanism of beams with distinct sizes and reinforcement ratios. The tensile damage model and the finite element embedded crack approach are able to describe the stiffness reduction due to concrete cracking in the tensile zone. The truss elements are able to reproduce the effects of steel yielding and, finally, the compressive damage model is able to describe the non-linear behavior of the compressive zone until the complete collapse of the beam due to crushing of concrete. The proposed approach is able to predict well the plastic rotation capacity of tested beams [3], including size-scale effects.

Carbohydrate use and reduction in number of balance beam falls: Implications for mental and physical fatigue

Background: Artistic Gymnastics is a sport where athletes are frequently fatigued. One element that might influence this aspect is carbohydrate, an important energy substrate for the muscles and the CNS. Our goal was to investigate the influence of fatigue over artistic gymnastics athlete's performance and the effects of a carbohydrate supplementation on their performance. Methods: We evaluated 15 athletes divided in 2 groups (control and fatigue) from 12 to 14 years old in two different experimental days. On the first day (water day), they did 5 sets of exercises on the balance beam (experimental protocol) ingesting only water, CG (control group) warmed up before the experimental protocol and FG (fatigue group) did a fatigue circuit, warm up exercises and then the experimental protocol. On the second day (carbohydrate day), we used the same protocol but CG ingested a sugar free flavored juice and FG ingested a 20% concentration maltodextrin solution before the protocol on the balance beam. Results: We observed a greater number of falls from the balance beam from the FG on the first day (5.40 ± 1.14 FG vs 3.33 ± 1.37 CG; p = 0.024) and a decrease in the number of falls on the second day (2.29 ± 1.25 FG water day vs 5.40 ± 1.14 FG carbohydrate day; p = 0.0013). Carbohydrate solution was able to supply muscle demands and improve the athlete's focus showed by the reduced number of falls. © 2013 Batatinha et al.; licensee BioMed Central Ltd.

Predicting biological parameters of estuarine benthic communities using models based on environmental data

Este trabalho objetivou predizer parâmetros da estrutura de associações macrobentônicas (composição específica, abundância, riqueza, diversidade e equitatividade) em estuários do Sul do Brasil, utilizando modelos baseados em dados ambientais (características dos sedimentos, salinidade, temperaturas do ar e da água, e profundidade). As amostragens foram realizadas sazonalmente em cinco estuários entre o inverno de 1996 e o verão de 1998. Em cada estuário as amostras foram coletadas em áreas não poluídas, com características semelhantes quanto a presença ou ausência de vegetação, profundidade e distância da desenbocadura. Para a obtenção dos modelos de predição, foram utilizados dois métodos: o primeiro baseado em Análise Discriminante Múltipla (ADM) e o segundo em Regressão Linear Múltipla (RLM). Os modelos baseados em ADM apresentaram resultados melhores do que os baseados em regressão linear. Os melhores resultados usando RLM foram obtidos para diversidade e riqueza. É possível então, concluir que modelos como aqui derivados podem representar ferramentas muito úteis em estudos de monitoramento ambiental em estuários.