A generalized fuzzy Petri net model

The paper proposes a new model of Petri nets based on the use of logic based neurons. In contrast to the existing generalizations, this approach is aimed at neural-type modeling of the entire concept with a full exploitation of the learning capabilities of the processing units being used there. The places and transitions of the net are represented by OR and AND-type and DOMINANCE neurons, respectively. A correspondence between this model and the previous two-valued counterpart is also revealed. The learning aspects associated with the nets are investigated     

Synthesis and properties of peroxy derivatives of epoxy resins based on Bisphenol A: Effects of the presence of boron trifluoride ethereate

Oligomers containing peroxy groups have been synthesized on the basis of epoxides reacting with tert–butylhydroperoxide in the presence of boron trifluoride etherate. Dependence of reaction rates upon the quantity of catalyst, temperature and also nature of hydroperoxide has been investigated: A kinetic equation for the process has been formulated and also a kinetic scheme for the reaction taking into account the presence of boron trifluoride etherate is proposed. Structures of the synthesized peroxy oligomers have been confirmed by IR- and NMR-spectroscopy. The reaction initiating properties of the oligomers so synthesized were studied using as the example the styrene polymerization reaction. Electronic Publication     

Mechanical improvement of concrete by irradiated polypropylene fibers

Fiber reinforced concrete (FRC) contains fibers physically mixed with gravel, sand, cement, and water. So far, adequate mechanical performance of FRC has been obtained at high cost and using complex technologies; important here is the geometry and surface characteristics of the polymers. We have modified polymeric‐fiber surfaces by using gamma radiation. Irradiated polypropylene (PP) fibers were submitted to 0, 5, 10, 50, and 100 kGy of gamma irradiation dosages. First, tensile strength of PP fibers was evaluated, and then fibers blended at 0, 1.0, 1.5, and 2.0% in volume with Portland cement, gravel, sand, and water. The highest values of compressive strength were obtained with irradiated‐fibers at 10 kGy and 1.5% in volume of fiber. The result is 101 MPa, as compared to 35 MPa for simple concrete without fibers. POLYM. ENG. SCI., 45:1426–1431, 2005. © 2005 Society of Plastics Engineers     

Effects of fluoropolymer addition to an epoxy on scratch depth and recovery

Samples were prepared by addition of a fluorinated poly(aryl ether ketone) (12F-PEK) to a commercial epoxy resin and curing at either 24 °C or 70 °C. The concentrations of fluoropolymer in the samples were 0, 5, 10, 20, 30, 40, and 50%. Phase inversion facilitates the formation of more and more smooth surfaces for scratch testing with increasing fluoropolymer concentration. A progressively increasing load from 0.03 N to 12 N was administered with a diamond tip to the surfaces of the samples; depths were determined within ±7.5 nm. The original scratch depth is a function of the fluoropolymer concentration. Scratch recovery (healing) reaches values up to 95%. Plots of the residual depth versus concentration of the fluoropolymer at 4, 6, 8, and 10 N reveal minima for all forces and both temperatures. Addition of only 5% or 10% 12F-PEK improves the scratch recovery significantly. The results are explained by changes of material morphology with the fluoropolymer concentration. Electronic Publication     

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 study of uncertain state estimation

In this paper, we present results of uncertain state estimation of systems that are monitored with limited accuracy. For these systems, the representation of state uncertainty as confidence intervals offers significant advantages over the more traditional approaches with probabilistic representation of noise. While the filtered-white-Gaussian noise model can be defined on grounds of mathematical convenience, its use is necessarily coupled with a hope that an estimator with good properties in idealised noise will still perform well in real noise. In this study we propose a more realistic approach of matching the noise representation to the extent of prior knowledge. Both interval and ellipsoidal representation of noise illustrate the principle of keeping the noise model simple while allowing for iterative refinement of the noise as we proceed. We evaluate one nonlinear and three linear state estimation technique both in terms of computational efficiency and the cardinality of the state uncertainty sets. The techniques are illustrated on a synthetic and a real-life system.     

An optimization of allocation of information granularity in the interpretation of data structures: toward granular fuzzy clustering

Clustering forms one of the most visible conceptual and algorithmic framework of developing information granules. In spite of the algorithm being used, the representation of information granules-clusters is predominantly numeric (coming in the form of prototypes, partition matrices, dendrograms, etc.). In this paper, we consider a concept of granular prototypes that generalizes the numeric representation of the clusters and, in this way, helps capture more details about the data structure. By invoking the granulation-degranulation scheme, we design granular prototypes being reflective of the structure of data to a higher extent than the representation that is provided by their numeric counterparts (prototypes). The design is formulated as an optimization problem, which is guided by the coverage criterion, meaning that we maximize the number of data for which their granular realization includes the original data. The granularity of the prototypes themselves is treated as an important design asset; hence, its allocation to the individual prototypes is optimized so that the coverage criterion becomes maximized. With this regard, several schemes of optimal allocation of information granularity are investigated, where interval-valued prototypes are formed around the already produced numeric representatives. Experimental studies are provided in which the design of granular prototypes of interval format is discussed and characterized.     

Single amino acid substitutions can further increase the stability of a thermophilic L-lactate dehydrogenase

Lactate dehydrogenases are of considerable interest as stereospecificcatalysts in the chemical preparation of enantiomerically pure-hydroxyacid synthons. For such applications in synthetic organicchemistry it would be desirable to have enzymes which tolerateelevated temperatures for prolonged reaction times, to increaseproductivity and to extend then applicability to poor substrates.Here, two examples are reported of significant thermostabilizations,induced by sitedirected mutagenesis, of an already thermostableprotein, the L-lactate dehydrogenase (EC 1.1.1.27 [EC] , 35 kDa permonomer subunit) from Bacillus stearothermophilus. Thermal inactivationof this enzyme is accompanied by irreversible unfolding of thenative protein structure. The replacement of Argl71 by Tyr stabilizesthe enzyme against thermal inactivation and unfolding. Thisstabilizing effect appears to be based on improved interactionsbetween the subunits in the core of the active dimeric or tetramericforms of the enzyme. The thermal stability of L-lactate dehydrogenasevariants with an active site Arg residue, either in the 171(wild-type) or in the 102 position, is further increased bysulfate ions. The two stabilizing effects are additive, as foundfor the Argl71Tyr/ Gln1O2Arg double mutant, for which the stabilityof the protein in 100 mM sulfate solution reaches that of L-lactatedehydrogenases from extreme thermophiles. All mutant proteinsretain significant catalytic activity, both in the presenceand absence of stnhilfoing salts, and are viable catalysts inpreparative scale reactions.     

Role of HMOX1 Promoter Genetic Variants in Chemoresistance and Chemotherapy Induced Neutropenia in Children with Acute Lymphoblastic Leukemia

Whilst the survival rates of childhood acute lymphoblastic leukemia (ALL) have increased remarkably over the last decades, the therapy resistance and toxicity are still the major causes of treatment failure. It was shown that overexpression of heme oxygenase-1 (HO-1) promotes proliferation and chemoresistance of cancer cells. In humans, the HO-1 gene (HMOX1) expression is modulated by two polymorphisms in the promoter region: (GT)n-length polymorphism and single-nucleotide polymorphism (SNP) A(−413)T, with short GT repeat sequences and 413-A variants linked to an increased HO-1 inducibility. We found that the short alleles are significantly more frequent in ALL patients in comparison to the control group, and that their presence may be associated with a higher risk of treatment failure, reflecting the role of HO-1 in chemoresistance. We also observed that the presence of short alleles may predispose to develop chemotherapy-induced neutropenia. In case of SNP, the 413-T variant co-segregated with short or long alleles, while 413-A almost selectively co-segregated with long alleles, hence it is not possible to determine if SNPs are actually of phenotypic significance. Our results suggest that HO-1 can be a potential target to overcome the treatment failure in ALL patients.