Fatty acid composition of egg yolk phospholipid fractions following feed supplementation of Lohmann Brown hens with humic-fat preparations

The effect of feed supplementation with humic-fat preparations on the fatty acid composition of egg yolk phospholipid fractions was studied. The Lohmann Brown was the experimental hen strain and two feeding mixtures were applied – the standard feed and the mixture supplemented with humic-fat preparations. The fatty acid profile of yolk lipids was significantly affected (P = .05) by the supplementation of feed and total increase of n-3 polyunsaturated fatty acids (PUFA) was established. A different distribution of n-3 PUFA in egg yolk lipid fractions was observed: α-linolenic acid (18:3) was found in the triacylglycerol fraction, whereas docosahexaenoic acid (22:6) was detected in the phospholipid fractions. Supplementation of the hen’s diet with humic-fat preparations resulted in the production of n-3 enriched eggs with decreased n-6/n-3 ratio in all phospholipid fractions in comparison to the eggs from the control group.     

A normal wiggly hesitant fuzzy linguistic projection-based multiattributive border approximation area comparison method

As a useful information representation tool, hesitant fuzzy linguistic term set (HFLTS) allows decision makers (DMs) to express their cognitive preferences in terms of several ordered and continuous linguistic terms. Considering the fact that much valuable information related to the cognitive behavior of DMs is hidden in the original evaluation information, this paper studies how to comprehensively mine uncertain information from original hesitant fuzzy linguistic evaluation information given by DMs. To address this objective, we present a new representation tool, normal wiggly hesitant fuzzy linguistic term set (NWHFLTS), which not only retains the original evaluation information, but also delivers and quantifies potential uncertain information, and can also help DMs express their evaluation information in a more complete manner. First, we develop the basic operations, score function, and comparison rule of NWHFLTS based on linguistic scale functions (LSFs), and propose the projection measure, the normal projection measure, and the normalized projection-based distance measure to describe the degree of deviation between two NWHFLTSs. Furthermore, for the case when the attribute weight is completely unknown, we combine the multiattributive border approximation area comparison (MABAC) method and develop a new method called as normal wiggly hesitant fuzzy linguistic projection-based MABAC to solve the multiattribute decision-making problems where attribute values are expressed in the form of NWHFLTS. Finally, through a practical example of marine ecological security situation, the specific calculation steps of this method are exemplified, the feasibility and advancement of the proposed method are demonstrated via a comprehensive comparative study.     

Surface modification of polyethylene by photochemical grafting with 2-hydroxyethylmethacrylate

The photochemical grafting of 2-hydroxyethylmethacrylate onto low-density polyethylene film is described. The grafting technique employed involved irradiating a solution of 2-hydroxyethylmethacrylate and benzophenone in acetone spread between films of poly(ethylene terephthalate) or glass and low-density polyethylene. After irradiation for 2 min, the contact angle of the polyethylene films with water fell from 97° to about 50°. The contact angle of the poly(ethylene terephthalate) substrate also fell during grafting. X-ray photoelectron spectroscopy was consistent with the presence of poly(2-hydroxethylmethacrylate) at the surface of the polyethylene. The effect of solvent on the photochemical grafting of 2-hydroxyethylmethacrylate onto low-density polyethylene is discussed. © 1993 John Wiley & Sons, Inc.     

Synthesis and properties of porous ion-exchange membranes,I. Polymer-polymer-filler system

A method of preparation of porous ion-exchange membranes (PIEM's) with sulfonic groups is presented. One of the four studied polymer blends is recommended for PIEM's preparation. It is the ternary blend of polyethylene with poly(styrene-co-divinylbenzene), isotactic polypropylene, and calcium carbonate. The effect of blend composition on membrane properties is also discussed. Both blend components, PP and CaCO₃, affected the membrane porosity while only PP governed the pore diameter. A model of PIEM's creation is suggested. The antifouling behaviour of PIEM's was evaluated by means of ultrafiltration of bovine serum albumine or poly(ethylene glycol) solutions as well as skimmed-milk and an active sludge designated for bioconversion of aldehydes. It was found that membranes with ion-exchange capacity higher than 1 mmol/g offer antifouling effect. PIEM's have also higher solute rejection parameter and are easier to regenerate than membranes without sulfonic groups.     

A multiobjective design of a patient and anaesthetist-friendly neuromuscular blockade controller

During surgeries (especially in long ones), patients are subject to a substantial amount of drug dosage necessary to achieve the required neuromuscular blockade level. This paper aims at the development of a fuzzy controller that satisfies two important goals: 1) an optimization of the amount of drug (atracurium) required to induce an adequate level of relaxation and 2) a concomitant ability to explain the undertaken control decision at the level of natural language. For instance, statements of the form "Since the difference between the target and the current blockade level is near zero, a small quantity of drug infusion is currently being applied", where "near zero" and "small" are linguistic terms that are represented as fuzzy sets. In this sense, we can regard this controller as a construct that is human friendly and highly interpretable (transparent). To address the two objectives outlined above, we consider the use of a multiobjective evolutionary optimization. How the quality of the control action and the controller interpretability are formalized and captured in this optimization framework is presented. The effectiveness of the approach is demonstrated through a comprehensive suite of experiments involving 100 simulated patients (used for training) and 500 patients (forming the test set), validating the approach for application in the operating theater.     

Experience-consistent modeling: Regression and classification problems

In this study, we are concerned with system modeling which involves limited data and reconciles the developed model with some previously acquired domain knowledge being captured in the format of already constructed models. Each of these previously available models was formed on a basis of extensive data sets which are not available for the current identification pursuits. To emphasize the nature of modeling being guided by the reconciliation mechanisms, we refer to this mode of identification as experience-consistent modeling. The paper presents the conceptual and algorithmic framework by focusing on regression models. By forming a certain extended form of the performance index, it is shown that the domain knowledge captured by regression models can play a similar role as a regularization component used quite commonly in system identification. Experimental results involve both synthetic low-dimensional data and selected data coming from Machine Learning repository. The data used in the experiments tackle regression models as well as classification problems (two-class classifiers).     

Genetically optimized fuzzy polynomial neural networks with fuzzy set-based polynomial neurons

In this paper, we propose and investigate a new category of neurofuzzy networks—fuzzy polynomial neural networks (FPNN) endowed with fuzzy set-based polynomial neurons (FSPNs) We develop a comprehensive design methodology involving mechanisms of genetic optimization, and genetic algorithms (GAs) in particular. The conventional FPNNs developed so far are based on the mechanisms of self-organization, fuzzy neurocomputing, and evolutionary optimization. The design of the network exploits the FSPNs as well as the extended group method of data handling (GMDH). Let us stress that in the previous development strategies some essential parameters of the networks (such as the number of input variables, the order of the polynomial, the number of membership functions, and a collection of the specific subset of input variables) being available within the network are provided by the designer in advance and kept fixed throughout the overall development process. This restriction may hamper a possibility of developing an optimal architecture of the model. The design proposed in this study addresses this issue. The augmented and genetically developed FPNN (gFPNN) results in a structurally optimized structure and comes with a higher level of flexibility in comparison to the one we encounter in the conventional FPNNs. The GA-based design procedure being applied at each layer of the FPNN leads to the selection of the most suitable nodes (or FSPNs) available within the FPNN. In the sequel, two general optimization mechanisms are explored. First, the structural optimization is realized via GAs whereas the ensuing detailed parametric optimization is carried out in the setting of a standard least square method-based learning. The performance of the gFPNN is quantified through experimentation in which we use a number of modeling benchmarks—synthetic and experimental data being commonly used in fuzzy or neurofuzzy modeling. The obtained results demonstrate the superiority of the proposed networks over the models existing in the references.     

A Human–Computer Cooperation Fuzzy c‐Means Clustering with Interval‐Valued Weights

In this paper, a fuzzy c‐means clustering algorithm based on interval‐valued weights is proposed for improving clustering performance. In the proposed algorithm, the interval‐valued weights are first constructed by synergy of the ReliefF algorithm and the analytic hierarchy process (AHP) method, and then they are transformed into a constraint condition associating with each weight variable in the weighted clustering objective function. In the sequence, the weighted clustering objective function is solved by combining the Lagrange multiplier method with the gradient‐based iteration computation. In the whole process of algorithm iteration, a compulsion strategy with human–computer cooperation is adopted to ensure each weight variable satisfies interval constraint itself. Three well‐known data set are used to perform profound experiments. Experimental results clearly show that the proposed algorithm has better clustering performance than other the weighted fuzzy c‐means clustering algorithm.