Use of the Retarded Solution-Reprecipitation Process to Attain a Higher Initial Permeability in MnZn Ferrites

We investigated the effect of various amounts of liquid phase on microstructure development during sintering and the resulting magnetic permeability of MnZn ferrite (MZF) samples. Our results revealed that the microstructure and the final magnetic permeability depend on the thickness of the liquid-phase film during sintering. The solution-reprecipitation (S-R) process, which is associated with an intensive microstructure development in MZF, starts when a continuous liquid-phase film of critical thickness δ_o, which wets the MZF grains, is formed. The solid-state sintering that takes place before the formation of the continuous liquid-phase film is essential for the final microstructure of MZF.     

On minimum cost isolated failure immune networks

A telecommunications network is isolated failure immune (IFI) if and only if communication between operative sites can be completed as long as network failures are isolated. It is known that the class of minimal IFI networks is equivalent to the class of spanning 2-trees. To the best of our knowledge, this work is the first computational study dealing with the construction of a minimum cost IFI network. The problem is known to be NP-complete. We develop a tabu search based heuristic for solving the minimum cost spanning 2-tree (MCS2T) problem. The complex structure of 2-trees makes the tabu search heuristic highly dependent on the starting solution. We develop four heuristic algorithms to obtain diversified good starting solutions. They are: completion of a 2-tree from a spanning tree, two greedy approaches, and a method based on the recursive definition of a 2-tree. We also formulate an integer programming problem (IP) whose objective function value is a lower bound to the MCS2T problem. We solve the IP by developing a constraint generation scheme. The algorithms were tested on complete random graphs with Euclidean distances and on two real data sets (Civil Aeronautics Board) with instances of 10, 15, 20 and 25 nodes. As a result of this research for small problems (10 and 15 nodes), the heuristic solutions are on average within 0.8% from the optimal solution and for large problems (20 and 25 nodes), the average error is less than 2.8%.     

A quantitative analysis of the unit verification perspective on fault distributions in complex software systems: an operational replication

Unit verification, including software inspections and unit tests, is usually the first code verification phase in the software development process. However, principles of unit verification are weakly explored, mostly due to the lack of data, since unit verification data are rarely systematically collected and only a few studies have been published with such data from industry. Therefore, we explore the theory of fault distributions, originating in the quantitative analysis by Fenton and Ohlsson, in the weakly explored context of unit verification in large-scale software development. We conduct a quantitative case study on a sequence of four development projects on consecutive releases of the same complex software product line system for telecommunication exchanges. We replicate the operationalization from earlier studies, analyzed hypotheses related to the Pareto principle of fault distribution, persistence of faults, effects of module size, and quality in terms of fault densities, however, now from the perspective of unit verification. The patterns in unit verification results resemble those of later verification phases, e.g., regarding the Pareto principle, and may thus be used for prediction and planning purposes. Using unit verification results as predictors may improve the quality and efficiency of software verification.     

Simple and efficient ion imprinted polymer for recovery of uranium from environmental samples

Ion imprinted polymer material (IIP) was prepared by forming ternary complexes of uranyl imprint ion with 1-(prop-2-en-1-yl)-4-(pyridin-2-ylmethyl)piperazine and methacrylic acid followed by thermal copolymerization with ethylene glycol dimethacrylate as the cross-linking monomer in the presence of 1,1'-azobis(cyclohexanecarbonitrile) initiator and 2-methoxy ethanol porogenic solvent. HCl solution (5 mol/L) was used to leach out the uranyl template ion from the IIP particles. Similarly, the control polymer (CP) material was also prepared exactly under the same conditions as the IIP but without the uranyl ion template. Various parameters such as solution pH, initial concentration, aqueous phase volume, sorbent dosage, contact time and leaching solution volumes were investigated. SEM, IR and BET-surface area and pore size analysis were used for the characterization of IIP and CP materials. The extraction efficiency of the IIP and CP was compared using a batch and SPE mode of extraction. The optimal pH for quantitative removal is 4.0-8.0, sorbent amount is 20 mg, contact time is 20 min and the retention capacity is 120 mg of uranyl ion per g of IIP. The IIP prepared demonstrated superior selectivity towards coexisting cations and therefore it can be used for selective removal of uranium from complex matrices.     

A novel split-and-merge algorithm for hierarchical clustering of Gaussian mixture models

The paper presents a novel split-and-merge algorithm for hierarchical clustering of Gaussian mixture models, which tends to improve on the local optimal solution determined by the initial constellation. It is initialized by local optimal parameters obtained by using a baseline approach similar to k-means, and it tends to approach more closely to the global optimum of the target clustering function, by iteratively splitting and merging the clusters of Gaussian components obtained as the output of the baseline algorithm. The algorithm is further improved by introducing model selection in order to obtain the best possible trade-off between recognition accuracy and computational load in a Gaussian selection task applied within an actual recognition system. The proposed method is tested both on artificial data and in the framework of Gaussian selection performed within a real continuous speech recognition system, and in both cases an improvement over the baseline method has been observed.     

Reorganization of the Brain Extracellular Matrix in Hippocampal Sclerosis

The extracellular matrix (ECM) is an important regulator of excitability and synaptic plasticity, especially in its highly condensed form, the perineuronal nets (PNN). In patients with drug-resistant mesial temporal lobe epilepsy (MTLE), hippocampal sclerosis type 1 (HS1) is the most common histopathological finding. This study aimed to evaluate the ECM profile of HS1 in surgically treated drug-resistant patients with MTLE in correlation to clinical findings. Hippocampal sections were immunohistochemically stained for aggrecan, neurocan, versican, chondroitin-sulfate (CS56), fibronectin, Wisteria floribunda agglutinin (WFA), a nuclear neuronal marker (NeuN), parvalbumin (PV), and glial-fibrillary-acidic-protein (GFAP). In HS1, besides the reduced number of neurons and astrogliosis, we found a significantly changed expression pattern of versican, neurocan, aggrecan, WFA-specific glycosylation, and a reduced number of PNNs. Patients with a lower number of epileptic episodes had a less intense diffuse WFA staining in Cornu Ammonis (CA) fields. Our findings suggest that PNN reduction, changed ECM protein, and glycosylation expression pattern in HS1 might be involved in the pathogenesis and persistence of drug-resistant MTLE by contributing to the increase of CA pyramidal neurons’ excitability. This research corroborates the validity of ECM molecules and their modulators as a potential target for the development of new therapeutic approaches to drug-resistant epilepsy.     

Distributed Computation Multi-agent System

This article addresses a formal model of a distributed computation multi-agent system. This model has evolved from the experimental research on using multi-agent systems as a ground for developing fuzzy cognitive maps. The main paper contribution is a distributed computation multi-agent system definition and mathematical formalization based on automata theory. This mathematical formalization is tested by developing distributed computation multi-agent systems for fuzzy cognitive maps and artificial neural networks – two typical distributed computation systems. Fuzzy cognitive maps are distributed computation systems used for qualitative modeling and behavior simulation, while artificial neural networks are used for modeling and simulating complex systems by creating a non-linear statistical data model. An artificial neural network encapsulates in its structure data patterns that are hidden in the data used to create the network. Both of these systems are well suited for formal model testing. We have used evolutionary incremental development as an agent design method which has shown to be a good approach to develop multi-agent systems according to the formal model of a distributed computation multi-agent system.     

Regulation of S100As Expression by Inflammatory Cytokines in Chronic Lymphocytic Leukemia

The calcium-binding proteins S100A4, S100A8, and S100A9 are upregulated in chronic lymphocytic leukemia (CLL), while the S100A9 promotes NF-κB activity during disease progression. The S100-protein family has been involved in several malignancies as mediators of inflammation and proliferation. The hypothesis of our study is that S100A proteins are mediators in signaling pathways associated with inflammation-induced proliferation, such as NF-κB, PI3K/AKT, and JAK/STAT. The mononuclear cells (MNCs) of CLL were treated with proinflammatory IL-6, anti-inflammatory IL-10 cytokines, inhibitors of JAK1/2, NF-κB, and PI3K signaling pathways, to evaluate S100A4, S100A8, S100A9, and S100A12 expression as well as NF-κB activation by qRT-PCR, immunocytochemistry, and immunoblotting. The quantity of S100A4, S100A8, and S100A9 positive cells (p < 0.05) and their protein expression (p < 0.01) were significantly decreased in MNCs of CLL patients compared to healthy controls. The S100A levels were generally increased in CD19⁺ cells compared to MNCs of CLL. The S100A4 gene expression was significantly stimulated (p < 0.05) by the inhibition of the PI3K/AKT signaling pathway in MNCs. IL-6 stimulated S100A4 and S100A8 protein expression, prevented by the NF-κB and JAK1/2 inhibitors. In contrast, IL-10 reduced S100A8, S100A9, and S100A12 protein expressions in MNCs of CLL. Moreover, IL-10 inhibited activation of NF-κB signaling (4-fold, p < 0.05). In conclusion, inflammation stimulated the S100A protein expression mediated via the proliferation-related signaling and balanced by the cytokines in CLL.     

Cooperative linear cargo transport with molecular spiders

Molecular spiders are nanoscale walkers made with DNA enzyme legs attached to a common body. They move over a surface of DNA substrates, cleaving them and leaving behind product DNA strands, which they are able to revisit. Simple one-dimensional models of spider motion show significant superdiffusive motion when the leg-substrate bindings are longer-lived than the leg-product bindings. This gives the spiders potential as a faster-than-diffusion transport mechanism. However, analysis shows that single-spider motion eventually decays into an ordinary diffusive motion, owing to the ever increasing size of the region of cleaved products. Inspired by cooperative behavior of natural molecular walkers, we propose a symmetric exclusion process model for multiple walkers interacting as they move over a one-dimensional lattice. We show that when walkers are sequentially released from the origin, the collective effect is to prevent the leading walkers from moving too far backwards. Hence, there is an effective outward pressure on the leading walkers that keeps them moving superdiffusively for longer times, despite the growth of the product region. Multi-spider systems move faster and farther than single spiders or systems with multiple simple random walkers.