The effects of feeding conjugated linoleic acid (CLA) on rat growth performance,serum lipoproteins and subsequent lipid composition of selected rat tissues

Four groups of 12 albino rats (six males and six females), at 4 weeks of age, were fed for 60 days a semi‐synthetic diet containing graded amounts of conjugated linoleic acid (CLA; 0.0, 0.5, 1.0 and 1.5%) to determine its effect on growth performance and serum lipoprotein profiles and its incorporation into abdominal and muscle lipids. Rats fed CLA responded by significantly improved body mass gains; however, this effect was observed only with the 1.0% CLA‐supplemented diet. It was also true for feed conversion efficiency. The changes in serum total cholesterol (TC) and HDL cholesterol concentrations were insignificant and showed an opposite pattern. However, the resulting HDL cholesterol/TC ratio was significantly (P ≤ 0.01) increased. At the same time, serum triacylglycerol (TAG) concentrations were significantly (P ≤ 0.01) elevated, thus counteracting the potential antiatherogenicity of the improved HDL cholesterol/TC ratio. Feeding CLA to rats resulted in its substantial incorporation into both abdominal and muscle lipids. Generally, tissue lipids were enriched with CLA at the expense of long‐chain polyunsaturated fatty acids, eg linoleate and arachidonate. This effect is likely to correspond with reduction of arachidonate‐derived eicosanoid synthesis. These data together suggest that CLA has a potent effect on lipid transport and metabolism in rats. Also, they confirm the potential of nutritional methods to provide CLA‐enriched functional animal products, especially meat, for human consumption. © 2000 Society of Chemical Industry     

Effect of apple cultivar and enzyme treatment on phenolic compounds content during clear apple juice production

The aim of the investigation was evaluation of phenolic compounds content, measured by HPLC method at selected stages of clear apple juice production with pectolytic mash enzymation from ‘Shampion’ and ‘Idared’ apples. Sum of phenolic compounds in ‘Shampion’ mash was significantly higher than in ‘Idared’ mash, 520 and 352 mg kg^?1, respectively. Enzymation of ‘Idared’ mash resulted in a higher phenolic compounds decrease compared to ‘Shampion’, 23% and 14% respectively. Sum of phenolics in juices from ‘Shampion’ for Panzym at 50 °C (366 mg kg^?1) was significantly higher than in juices from ‘Shampion’ for Rohapect at 20 °C and ‘Idared’ for Panzym at 50 °C (256 and 234 mg kg^?1, respectively). The lowest phenolics (95 mg kg^?1) was in juices from ‘Idared’ for Rohapect at 20 °C. There was a positive correlation (R² = 0.933) between phenolics content and antioxidant activity of juices (ABTS^?+). In general, enzymation at 50 °C with Panzym lead to a higher content of the phenolic compounds in juices compared to Rohapect at 20 °C.     

Processed wastewater sludge for improvement of mechanical properties of concretes

Two problems are addressed simultaneously. One is the utilisation of sludge from the treatment of wastewater. The other is the modification of the mechanical properties of concrete. The sludge was subjected to two series of treatments. In one series, coagulants were used, including ferrous sulphate, aluminium sulphate or aluminium polyhydroxychloride. In the other series, an electrochemical treatment was applied with several starting values of pH. Then, concretes consisting of a cement matrix, silica sand, marble and one of the sludges were developed. Specimens without sludge were prepared for comparison. Curing times and aggregate concentrations were varied. The compressive strength, compressive strain at yield point, and static and dynamic elastic moduli were determined. Diagrams of the compressive strength and compressive strain at the yield point as a function of time passed through the minima as a function of time for concretes containing sludge; therefore, the presence of sludge has beneficial effects on the long term properties. Some morphological changes caused by the presence of sludge are seen in scanning electron microscopy. A way of utilising sludge is thus provided together with a way to improve the compressive strain at yield point of concrete.     

Long-Term Simulation of Thermal Regime of Missouri River

Riverine water temperature extremes have typically been analyzed using event-based simulations, for example the 10-year 7-day low flows, combined with record-high air temperatures and other extreme conditions relevant to the location (e.g., maximum power generation). Using this combination of extreme conditions, one can estimate the maximum water temperature that may occur on the river. However, this method does not allow for calculation of, for example, the probability of a given temperature exceedence, or the average duration of such an excursion event. Alternatively, long-term continuous simulation using historical and physically representative reconstructed data records provides a large database of realistic events, which can be used to analyze the thermal regime of a river and its variability under current and changing conditions. This study applies such a procedure to the Missouri River between Gavins Point Dam at Yankton, S.D., and Rulo, Nebr. Along this reach, the thermal regime is influenced by six power installations, which release heated condenser cooling water to the main stem. Several scenarios were simulated numerically with the one-dimensional (1D) CHARIMA model to examine the effects of current power generation, as well as changing operational, hydrologic, and climatological conditions on the river thermal regime. Model simulations revealed that climate change and increased power demand may significantly affect the thermal regime of the Missouri River; however, the scenarios simulated in this study did not result in water temperatures that exceed the current temperature standards.     

Type-2 fuzzy neural networks with fuzzy clustering and differential evolution optimization

In many real-world problems involving pattern recognition, system identification and modeling, control, decision making, and forecasting of time-series, available data are quite often of uncertain nature. An interesting alternative is to employ type-2 fuzzy sets, which augment fuzzy models with expressive power to develop models, which efficiently capture the factor of uncertainty. The three-dimensional membership functions of type-2 fuzzy sets offer additional degrees of freedom that make it possible to directly and more effectively account for model’s uncertainties. Type-2 fuzzy logic systems developed with the aid of evolutionary optimization forms a useful modeling tool subsequently resulting in a collection of efficient “If-Then” rules.The type-2 fuzzy neural networks take advantage of capabilities of fuzzy clustering by generating type-2 fuzzy rule base, resulting in a small number of rules and then optimizing membership functions of type-2 fuzzy sets present in the antecedent and consequent parts of the rules. The clustering itself is realized with the aid of differential evolution.Several examples, including a benchmark problem of identification of nonlinear system, are considered. The reported comparative analysis of experimental results is used to quantify the performance of the developed networks.     

Rough subspace-based clustering ensemble for categorical data

Clustering categorical data arising as an important problem of data mining has recently attracted much attention. In this paper, the problem of unsupervised dimensionality reduction for categorical data is first studied. Based on the theory of rough sets, the attributes of categorical data are decomposed into a number of rough subspaces. A novel clustering ensemble algorithm based on rough subspaces is then proposed to deal with categorical data. The algorithm employs some of rough subspaces with high quality to cluster the data and yields a robust and stable solution by exploiting the resulting partitions. We also introduce a cluster index to evaluate the solution of clustering algorithm for categorical data. Experimental results for selected UCI data sets show that the proposed method produces better results than those obtained by other methods when being evaluated in terms of cluster validity indexes.     

Subspace learning for unsupervised feature selection via matrix factorization

Dimensionality reduction is an important and challenging task in machine learning and data mining. Feature selection and feature extraction are two commonly used techniques for decreasing dimensionality of the data and increasing efficiency of learning algorithms. Specifically, feature selection realized in the absence of class labels, namely unsupervised feature selection, is challenging and interesting. In this paper, we propose a new unsupervised feature selection criterion developed from the viewpoint of subspace learning, which is treated as a matrix factorization problem. The advantages of this work are four-fold. First, dwelling on the technique of matrix factorization, a unified framework is established for feature selection, feature extraction and clustering. Second, an iterative update algorithm is provided via matrix factorization, which is an efficient technique to deal with high-dimensional data. Third, an effective method for feature selection with numeric data is put forward, instead of drawing support from the discretization process. Fourth, this new criterion provides a sound foundation for embedding kernel tricks into feature selection. With this regard, an algorithm based on kernel methods is also proposed. The algorithms are compared with four state-of-the-art feature selection methods using six publicly available datasets. Experimental results demonstrate that in terms of clustering results, the proposed two algorithms come with better performance than the others for almost all datasets we experimented with here.     

A comparative experimental study of type-1/type-2 fuzzy cascade controller based on genetic algorithms and particle swarm optimization

In this study, we introduce the design methodology of an optimized fuzzy controller with the aid of particle swarm optimization (PSO) for ball and beam system.The ball and beam system is a well-known control engineering experimental setup which consists of servo motor, beam and ball. This system exhibits a number of interesting and challenging properties when being considered from the control perspective. The ball and beam system determines the position of ball through the control of a servo motor. The displacement change of the position of ball leads to the change of the angle of the beam which determines the position angle of a servo motor.The fixed membership function design of type-1 based fuzzy logic controller (FLC) leads to the difficulty of rule-based control design when representing linguistic nature of knowledge. In type-2 FLC as the expanded type of type-1 FL, we can effectively improve the control characteristic by using the footprint of uncertainty (FOU) of the membership functions. Type-2 FLC exhibits some robustness when compared with type-1 FLC.Through computer simulation as well as real-world experiment, we apply optimized type-2 fuzzy cascade controllers based on PSO to ball and beam system. To evaluate performance of each controller, we consider controller characteristic parameters such as maximum overshoot, delay time, rise time, settling time, and a steady-state error. In the sequel, the optimized fuzzy cascade controller is realized and also experimented with through running two detailed comparative studies including type-1/type-2 fuzzy controller and genetic algorithms/particle swarm optimization.