Parallel Mapper

The construction of Mapper has emerged in the last decade as a powerful and effective topological data analysis tool that approximates and generalizes other topological summaries, such as the Reeb graph, the contour tree, split, and joint trees. In this paper, we study the parallel analysis of the construction of Mapper. We give a provably correct parallel algorithm to execute Mapper on multiple processors and discuss the performance results that compare our approach to a reference sequential Mapper implementation. We report the performance experiments that demonstrate the efficiency of our method

Predictive modeling of die filling of the pharmaceutical granules using the flexible neural tree

In this work, a computational intelligence (CI) technique named flexible neural tree (FNT) was developed to predict die filling performance of pharmaceutical granules and to identify significant die filling process variables. FNT resembles feedforward neural network, which creates a tree-like structure by using genetic programming. To improve accuracy, FNT parameters were optimized by using differential evolution algorithm. The performance of the FNT-based CI model was evaluated and compared with other CI techniques: multilayer perceptron, Gaussian process regression, and reduced error pruning tree. The accuracy of the CI model was evaluated experimentally using die filling as a case study. The die filling experiments were performed using a model shoe system and three different grades of microcrystalline cellulose (MCC) powders (MCC PH 101, MCC PH 102, and MCC DG). The feed powders were roll-compacted and milled into granules. The granules were then sieved into samples of various size classes. The mass of granules deposited into the die at different shoe speeds was measured. From these experiments, a dataset consisting true density, mean diameter (d50), granule size, and shoe speed as the inputs and the deposited mass as the output was generated. Cross-validation (CV) methods such as 10FCV and 5x2FCV were applied to develop and to validate the predictive models. It was found that the FNT-based CI model (for both CV methods) performed much better than other CI models. Additionally, it was observed that process variables such as the granule size and the shoe speed had a higher impact on the predictability than that of the powder property such as d50. Furthermore, validation of model prediction with experimental data showed that the die filling behavior of coarse granules could be better predicted than that of fine granules

Przewidywana moc wyjściowa elektrowni fotowoltaicznej określona przy użyciu zasad rozmytych

Photovoltaic Power Plants (PVPP) are classified as power energy sources with non-stabile supply of electric energy. It is necessary to back up power energy from PVPP for stabile electric network operation. We can set an optimal value of back up power energy with using a variety of prediction models and methods for PVPP Power output prediction. Fuzzy classi?ers and fuzzy rules can be informally defined as tools that use fuzzy sets or fuzzy logic for their operations. In this paper, we use genetic programming to evolve a fuzzy classi?er in the form of a fuzzy search expression to predict PVPP Power output.Elektrownie fotowoltaiczne (EF) są klasyfikowane jako źródła prądu elektrycznego o niestabilnej dostawie energii elektrycznej. Dla stabilnej pracy sieci elektrycznej konieczne jest wspieranie dostawy prądu z EF. Możemy ustalić optymalną wartość wspierającej dostawy prądu, stosując różne modele przewidywania i metody dla predykcji mocy wyjściowej z EF. Możliwe jest nieformalne określenie rozmytych klasyfikatorów i zasad jako narzędzi do ich działania, opartych na zbiorach rozmytych i logice rozmytej. W tej pracy stosujemy genetyczne programowanie do opracowania klasyfikatora rozmytego wyrażenia poszukiwania mocy wyjściowej EF

Cleavage of Vimentin by Different Retroviral Proteases

Proteases (PRs) of retroviruses cleave viral polyproteins into their mature structural proteins and replication enzymes. Besides this essential role in the replication cycle of retroviruses, PRs also cleave a variety of host cell proteins. We have analyzed the in vitro cleavage of mouse vimentin by proteases of human immunodeficiency virus type 1 (HIV-1) and type 2 (HIV-2), bovine leukemia virus (BLV), Mason-Pfizer monkey virus (M-PMV), myeloblastosis-associated virus (MAV), and two active-site mutants of MAV PR. Retroviral proteases display significant differences in specificity requirements. Here, we show a comparison of substrate specificities of several retroviral proteases on vimentin as a substrate. Vimentin was cleaved by all the proteases at different sites and with different rates. The results show that the physiologically important cellular protein vimentin can be degraded by different retroviral proteases

Quasigroup String Transformations and Hash Function Design

In this paper we propose two new types of compression functions, based on quasigroup string transformations. The first type uses known quasigroup string transformations, defined elsewhere, by changing alternately the transformation direction, going forward and backward through the string. Security of this design depends of the chosen quasigroup string transformation, the order of the quasigroup and the properties satisfied by the quasigroup operations. We illustrate how this type of compression function is applied in the design of the cryptographic hash function NaSHA. The second type of compression function uses new generic quasigroup string transformation, which combine two orthogonal quasigroup operations into a single one. This, in fact, is deployment of the concept of multipermutation for perfect generation of confusion and diffusion. One implementation of this transformation is by extended Feistel network F_{A,B,C} which has at least two orthogonal mates as orthomorphisms: its inverse F_{A,B,C}^{−1} and its square F_{A,B,C}^2