Fattening performance, carcass traits and meat quality characteristics of calves sired by Charolais, Simmental and Eastern Anatolian Red sires mated to Eastern Anatolian Red dams

Comparisons were made among calves sired by Charolais (C), Simmental (S) and Eastern Anatolian Red (EAR) breeds of bulls for fattening, carcass and meat quality traits when mated to EAR dams. C- and S-sired calves had 43.1% and 36.4% higher daily weight gain, 44.5% and 43.9% heavier final weight in fattening, respectively. Calves produced by C sires had best feed efficiency value (6.51 vs. 7.44 and 7.22) compared to the S and EAR sire breed groups. Carcasses of C- and S-sired calves had heavier weight, higher dressing percentage and greater Longissimus dorsi (LD) muscle area than those of EAR-sired calves. USDA yield grades were lower (P<0.01) for carcasses from C and S sires, and highest for carcasses from EAR calves. C-sired calves received higher (P<0.01) ratings for panel tenderness score, lower shear force value and number of chews before swallow than S- and EAR-sired progeny. Overall results of the study suggested that fattening performance, carcass and meat quality characteristics might be considerably improved by using C sires in the crossbreeding program as sire breed.     

Learning of fuzzy cognitive maps using density estimate

Fuzzy cognitive maps (FCMs) are convenient and widely used architectures for modeling dynamic systems, which are characterized by a great deal of flexibility and adaptability. Several recent works in this area concern strategies for the development of FCMs. Although a few fully automated algorithms to learn these models from data have been introduced, the resulting FCMs are structurally considerably different than those developed by human experts. In particular, maps that were learned from data are much denser (with the density over 90% versus about 40% density of maps developed by humans). The sparseness of the maps is associated with their interpretability: the smaller the number of connections is, the higher is the transparency of the map. To this end, a novel learning approach, sparse real-coded genetic algorithms (SRCGAs), to learn FCMs is proposed. The method utilizes a density parameter to guide the learning toward a formation of maps of a certain predefined density. Comparative tests carried out for both synthetic and real-world data demonstrate that, given a suitable density estimate, the SRCGA method significantly outperforms other state-of-the-art learning methods. When the density estimate is unknown, the new method can be used in an automated fashion using a default value, and it is still able to produce models whose performance exceeds or is equal to the performance of the models generated by other methods.     

A Novel Framework for Imputation of Missing Values in Databases

Many of the industrial and research databases are plagued by the problem of missing values. Some evident examples include databases associated with instrument maintenance, medical applications, and surveys. One of the common ways to cope with missing values is to complete their imputation (filling in). Given the rapid growth of sizes of databases, it becomes imperative to come up with a new imputation methodology along with efficient algorithms. The main objective of this paper is to develop a unified framework supporting a host of imputation methods. In the development of this framework, we require that its usage should (on average) lead to the significant improvement of accuracy of imputation while maintaining the same asymptotic computational complexity of the individual methods. Our intent is to provide a comprehensive review of the representative imputation techniques. It is noticeable that the use of the framework in the case of a low-quality single-imputation method has resulted in the imputation accuracy that is comparable to the one achieved when dealing with some other advanced imputation techniques. We also demonstrate, both theoretically and experimentally, that the application of the proposed framework leads to a linear computational complexity and, therefore, does not affect the asymptotic complexity of the associated imputation method.     

Prediction of structural classes for protein sequences and domains—Impact of prediction algorithms, sequence representation and homology, and test procedures on accuracy

This paper addresses computational prediction of protein structural classes. Although in recent years progress in this field was made, the main drawback of the published prediction methods is a limited scope of comparison procedures, which in same cases were also improperly performed. Two examples include using protein datasets of varying homology, which has significant impact on the prediction accuracy, and comparing methods in pairs using different datasets. Based on extensive experimental work, the main aim of this paper is to revisit and reevaluate state of the art in this field. To this end, this paper performs a first-of-its-kind comprehensive and multi-goal study, which includes investigation of eight prediction algorithms, three protein sequence representations, three datasets with different homologies and finally three test procedures. Quality of several previously unused prediction algorithms, newly proposed sequence representation, and a new-to-the-field testing procedure is evaluated. Several important conclusions and findings are made. First, the logistic regression classifier, which was not previously used, is shown to perform better than other prediction algorithms, and high quality of previously used support vector machines is confirmed. The results also show that the proposed new sequence representation improves accuracy of the high quality prediction algorithms, while it does not improve results of the lower quality classifiers. The study shows that commonly used jackknife test is computationally expensive, and therefore computationally less demanding 10-fold cross-validation procedure is proposed. The results show that there is no statistically significant difference between these two procedures. The experiments show that sequence homology has very significant impact on the prediction accuracy, i.e. using highly homologous datasets results in higher accuracies. Thus, results of several past studies that use homologous datasets should not be perceived as reliable. The best achieved prediction accuracy for low homology datasets is about 57% and confirms results reported by Wang and Yuan [How good is the prediction of protein structural class by the component-coupled method?. Proteins 2000;38:165–175]. For a highly homologous dataset instance based classification is shown to be better than the previously reported results. It achieved 97% prediction accuracy demonstrating that homology is a major factor that can result in the overestimated prediction accuracy.     

Processing welding slag

Magnitogorsk Metallurgical Combine. Translated from Metallurg, No. 12, pp. 39–40, December, 1990     

Numerical and Linguistic Prediction of Time Series With the Use of Fuzzy Cognitive Maps

In this paper, we introduce a novel approach to time-series prediction realized both at the linguistic and numerical level. It exploits fuzzy cognitive maps (FCMs) along with a recently proposed learning method that takes advantage of real-coded genetic algorithms. FCMs are used for modeling and qualitative analysis of dynamic systems. Within the framework of FCMs, the systems are described by means of concepts and their mutual relationships. The proposed prediction method combines FCMs with granular, fuzzy-set-based model of inputs. One of their main advantages is an ability to carry out modeling and prediction at both numerical and linguistic levels. A comprehensive set of experiments has been carried out with two major goals in mind. One is to assess quality of the proposed architecture, the other to examine the influence of its parameters of the prediction technique on the quality of prediction. The obtained results, which are compared with other prediction techniques using fuzzy sets, demonstrate that the proposed architecture offers substantial accuracy expressed at both linguistic and numerical levels.     

Forecasting based on neural network approach of solar potential in Turkey

As Turkey lies near the sunny belt between 36 and 42°N latitudes, most of the locations in Turkey receive abundant solar energy. Average annual temperature is 18–20 °C on the south coast, falls down to 14–16 °C on the west coast, and fluctuates 4–18 °C in the central parts. The yearly average solar radiation is 3.6 kW h/m² day, and the total yearly radiation period is 2610 h. The main focus of this study is put forward to solar energy potential in Turkey using artificial neural networks (ANNs). Scaled conjugate gradient (SCG), Pola-Ribiere conjugate gradient (CGP), and Levenberg–Marquardt (LM) learning algorithms and logistic sigmoid transfer function were used in the network. In order to train the neural network, meteorological data for last 4 years (2000–2003) from 12 cities (Çanakkale, Kars, Hakkari, Sakarya, Erzurum, Zonguldak, Balıkesir, Artvin, Çorum, Konya, Siirt, Tekirdağ) spread over Turkey were used as training (nine stations) and testing (three stations) data. Meteorological and geographical data (latitude, longitude, altitude, month, mean sunshine duration, and mean temperature) is used as input to the network. Solar radiation is the output. The maximum mean absolute percentage error was found to be less than 6.78% and R² values to be about 99.7768% for the testing stations. These values were found to be 5.283 and 99.897% for the training stations. The trained and tested ANN models show greater accuracy for evaluating solar resource posibilities in regions where a network of monitoring stations have not been established in Turkey. The predictions from ANN models could enable scientists to locate and design solar energy systems in Turkey and determine the best solar technology.     

Availability of membrane technology for purifying and concentrating oligosaccharides

The effectiveness of combined membrane processing with ultrafiltration (UF) and nanofiltration (NF) for purifying and concentrating oligosaccharides from chicory rootstock was examined. Commercially available powdered chicory rootstock was adopted as the model material for the process. First, a crude extract solution was prepared by dissolving and stewing the powder in hot water, the solution was prefiltered using soy cloth and then the solution was clarified by UF and purified and concentrated by NF. The resulting NF retentate, in which mono- and disaccharide content was reduced from 9.0% of the initial solution to 2.6%, was obtained as a 20-fold concentrated product. High-performance liquid chromatography analysis showed that the NF membrane had specificity of rejection for saccharides of particular degrees of polymerization and rejection performance remained constant throughout the processing. On the whole, the results indicate that the combined membrane-processing system is quite promising for value-added products using purified and concentrated oligosaccharides.