Power components estimation using two-stage Newton type algorithm

In this paper an efficient two-stage Newton type algorithm for power components estimation is presented. In the first algorithm stage the spectra and fundamental frequency of input currents and voltages are estimated. In the second stage the power components are calculated based on the results obtained in the first stage. By this, power components definitions according to the IEEE Standard 1459-2000 are used. In the algorithm the signal frequency was considered as an unknown parameter simultaneously estimated with signal spectrum. By this, the algorithm becomes insensitive to frequency changes. On the other hand, the signal model became non-linear, so the strategies of non-linear estimation were used. The algorithm performances are tested using computer simulated and laboratory tests.     

Morphological and structural features of individual and composite nanooxides with alumina, silica, and titania in powders and aqueous suspensions

Morphological, structural, and adsorptive characteristics of such fumed oxides as alumina, silica, composite silica/alumina and alumina/silica/titania were studied using a variety of experimental methods. Heating of nanooxides caused several processes resulting in the temperature dependent structural changes of the materials because of changes in the amounts of intact and dissociatively adsorbed water not only at the surface of the nanoparticles but also in their volume that lead to changes in the oxide matrix structure. These processes dependent on the composition of nanooxides are much stronger for nanosilica than for alumina or composite oxides. The morphology of primary nanoparticles is more stable on different treatments than the structure of secondary and ternary particles. The amounts of water adsorbed onto the nanooxides from air correspond to the amounts of water strongly bound (unfrozen at T < 273 K) to the oxide surfaces in aqueous suspensions.     

Influence of the aqueous-enzymatic method on the oxidative stability of plum kernel oil

This study demonstrates the usefulness of an aqueous-enzymatic process for preparing plum kernel oil. The plum kernel oil was obtained by the aqueous-enzymatic method and by extraction with an organic solvent according to the common method, respectively. Oxidative stability of these oils was determined at moderate temperature by the Schaal Oven test, at elevated temperature by the Rancimat method and with exposure to artificial daylight. Oil prepared with the aqueous-enzymatic method showed a major oxidative stability at moderate temperature, below 60°C, in comparison with oils extracted with the common method. At the elevated temperature and under the conditions of photooxidation the oil extracted with the aqueous-enzymatic method exhibited a lower oxidative stability than the corresponding oil extracted by the common method.     

Mycobacterium chelonae Infection Identified by Metagenomic Next-Generation Sequencing as the Probable Cause of Acute Contained Rupture of a Biological Composite Graft—A Case Report

We present the case of a 72-year-old female patient with acute contained rupture of a biological composite graft, 21 months after replacement of the aortic valve and the ascending aorta due to an aortic dissection. Auramine-rhodamine staining of intraoperative biopsies showed acid-fast bacilli, but classical culture and molecular methods failed to identify any organism. Metagenomic analysis indicated infection with Mycobacterium chelonae, which was confirmed by target-specific qPCR. The complexity of the sample required a customized bioinformatics pipeline, including cleaning steps to remove sequences of human, bovine ad pig origin. Our study underlines the importance of multiple testing to increase the likelihood of pathogen identification in highly complex samples.     

Boosting Algorithms for Parallel and Distributed Learning

The growing amount of available information and its distributed and heterogeneous nature has a major impact on the field of data mining. In this paper, we propose a framework for parallel and distributed boosting algorithms intended for efficient integrating specialized classifiers learned over very large, distributed and possibly heterogeneous databases that cannot fit into main computer memory. Boosting is a popular technique for constructing highly accurate classifier ensembles, where the classifiers are trained serially, with the weights on the training instances adaptively set according to the performance of previous classifiers. Our parallel boosting algorithm is designed for tightly coupled shared memory systems with a small number of processors, with an objective of achieving the maximal prediction accuracy in fewer iterations than boosting on a single processor. After all processors learn classifiers in parallel at each boosting round, they are combined according to the confidence of their prediction. Our distributed boosting algorithm is proposed primarily for learning from several disjoint data sites when the data cannot be merged together, although it can also be used for parallel learning where a massive data set is partitioned into several disjoint subsets for a more efficient analysis. At each boosting round, the proposed method combines classifiers from all sites and creates a classifier ensemble on each site. The final classifier is constructed as an ensemble of all classifier ensembles built on disjoint data sets. The new proposed methods applied to several data sets have shown that parallel boosting can achieve the same or even better prediction accuracy considerably faster than the standard sequential boosting. Results from the experiments also indicate that distributed boosting has comparable or slightly improved classification accuracy over standard boosting, while requiring much less memory and computational time since it uses smaller data sets.     

Software quality improvement: a model based on managing factors impacting software quality

Software quality is recognized as being very significant for achieving competitiveness in the software industry, so improvements in this area are gaining increasing importance. Software quality improvements can only be achieved by managing all of the factors that influence it. However, in a real business system, there are a great number of factors impacting software quality, while the processes are stochastic and resources are limited, so economic data should also be taken into consideration. This paper uses a Markov chain and proposes a systematic framework for modelling the stochastic processes of a quality management system and selection of the optimum set of factors impacting software quality. A methodology is presented for managing the factors that affect software quality with an illustrative hypothetical example for convenience of application of the proposed methodology.     

Aqueous-enzymatic extraction of plum kernel oil

An aqueous-enzymatic extraction process of plum kernel oil was investigated on a laboratory scale, varying several processing parameters, with main emphasis on the oil yield. Efficient recovery of oil was related to three operations: pretreatment, enzymatic reaction and separation of oil. Maximum oil yield of about 70% (estimated by the Soxhlet method) was obtained at an enzyme concentration of 0.5%, extraction temperature of 45°C, pH 4.5, treatment time of 1 h and dilution ratio of 1:4. The aqueous-enzymatic extraction did not have any determining effect on the fatty acid composition, tocopherol composition, iodine value and saponification value. The free fatty acid content was higher, while the phosphatide content and peroxide value were lower in the oil extracted by the aqueous-enzymatic process as compared to the Soxhlet extracted samples.     

Tolerating permanent and transient value faults

Transmission faults allow us to reason about permanent and transient value faults in a uniform way. However, all existing solutions to consensus in this model are either in the synchronous system, or require strong conditions for termination, that exclude the case where all messages of a process can be corrupted. In this paper we introduce eventual consistency in order to overcome this limitation. Eventual consistency denotes the existence of rounds in which processes receive the same set of messages. We show how eventually consistent rounds can be simulated from eventually synchronous rounds, and how eventually consistent rounds can be used to solve consensus. Depending on the nature and number of permanent and transient transmission faults, we obtain different conditions on $n$ , the number of processes, in order to solve consensus in our weak model.     

Charges and Fractal Properties of Nanoparticles — Combustion Products of Aluminum Agglomerates

The disperse, structural, and electrophysical characteristics of fine alumina produced by combustion of metal droplet agglomerates were studied experimentally. Data were obtained by transmission electron microscopy and video recording of aerosol particles moving in a homogeneous electric field. The aerosol particles are aggregates with sizes ranging from a fraction of a micrometer to a few micrometers and a fractal dimension of 1.60± 0.04 which consist of primary particles with sizes of a few to hundred nanometers. Most of the aggregates have electric charges, both positive and negative. The characteristic charge of the aggregates is equal to a few units of elementary charge. Some large aggregates rotate when the electric field polarity changes, i.e., they are dipoles.