Physics of heavy ions (1989-1990)

The results from studies on polar wind ion heating due to kinetic ion beam instabilities and the effects of such ion heating on the outflow of O(+) in the polar wind are presented and discussed. First, the linear instabilities associated with an O(+) and H(+) polar wind plasma in the presence of O(+) and H(+) beams for a range of O(+)/H(+) beam densities, T(sub e)/T(sub i), and ion beam speeds were examined. Then, nonlinear heating of the polar wind ions was studied, using numerical simulations. The O(+) and H(+) polar wind ions were modeled by isotropic Maxwellian distributions, and the electrons, O(+) beams, and H(+) beams were modeled by drifting Maxwellian distributions. The effects of the kinetic ion heating on the outflow of the polar wind ions were examined from the ionosphere, using a time-dependent hydrodynamic model. A numerical code to solve the O(+) and H(+) continuity and momentum equations in a flux tube from ionospheric to magnetospheric altitudes were developed. The effects of ion heating were included by allowing for the altitudinal variation of the ion temperatures in the momentum equation. The ion temperature profiles were specified based on the ion heating characteristics found from previous kinetic simulations. It was assumed that heating occurred above 1500 km and increased to a saturated value of temperature that was obtained directly from the kinetic simulation study. The characteristics of the dynamical polar wind without ion heating were studied, and a flux tube on closed field lines that suddenly became open at t = 0 was simulated. Then, the effects of ion heating were included. To gain some physical insight, two limiting cases were considered: preferential H(+) heating and preferential O(+) heating. How O(+) heating can lead to enhanced polar wind O(+) fluxes in the polar magnetosphere is shown

General Modified Friedmann Equations in Rainbow Flat Universe, by Thermodynamics

We investigate the derivation of Friedmann equations in Rainbow gravity following Jacobson thermodynamic approach. We do not restrict the rainbow functions to be constant as is customarily used, and show that the first law of thermodynamics with a corresponding `classical' proportionality between entropy and surface area, supplemented eventually by a `quantum' logarithmic correction, are not in general sufficient to obtain the equations in flat FRW metrics.Comment: 7 pages, to appear in EPJ

Reinforced concrete two-span continuous deep beams

Ye

Clustering large-scale data based on modified affinity propagation algorithm

Traditional clustering algorithms are no longer suitable for use in data mining applications that make use of large-scale data. There have been many large-scale data clustering algorithms proposed in recent years, but most of them do not achieve clustering with high quality. Despite that Affinity Propagation (AP) is effective and accurate in normal data clustering, but it is not effective for large-scale data. This paper proposes two methods for large-scale data clustering that depend on a modified version of AP algorithm. The proposed methods are set to ensure both low time complexity and good accuracy of the clustering method. Firstly, a data set is divided into several subsets using one of two methods random fragmentation or K-means. Secondly, subsets are clustered into K clusters using K-Affinity Propagation (KAP) algorithm to select local cluster exemplars in each subset. Thirdly, the inverse weighted clustering

Bond strength between corroded steel reinforcement and recycled aggregate concrete

YesThis paper investigates the bond performance of recycled coarse aggregate (RCA) concrete with un-corroded/corroded reinforcing steel bars, with the main parameters being RCA content, corrosion level, bar diameter and embedment length. For this purpose, 60 pull-out specimens containing different percentages of RCA (i.e. 0%, 25%, 50% and 100%) and steel bars of two diameters (12 and 20 mm) and different embedment lengths were tested. In order to establish various levels of corrosion, specimens were exposed to an electrochemical corrosion for 2, 5, 10 and 15 days. The bond strength between RAC concrete and un-corroded/corroded steel are compared to current codes and equations proposed by other researchers. Experimental results showed that larger corrosion rate of steel bars was observed with the increase of the replacement level of RCA due to its high porosity and water absorption. The use of RCA had a slight influence on bond strength for un-corroded specimens compared to that obtained from conventional concrete. Furthermore, the bond strength of RCA concrete was strongly affected by corrosion products; bond strength slightly enhanced for up to about 2% corrosion rate, and then significantly decreased as the corrosion time further increased, similar to that of conventional concrete. However, the rate of bond degradation between RCA concrete and corroded steel bars was much faster than that observed in corroded conventional concrete

Color based image segmentation using different versions of k-means in two spaces

In this paper color based image segmentation is done in two spaces. First in LAB color space and second in RGB space all that done using three versions of K-Means: K-Means, Weighted K-Means and Inverse Weighted K-Means clustering algorithms for different types of images: biological images (tissues and blood cells) and ordinary full colored images. Comparison and analysis are done between these three algorithms in order to differentiate between them

Species distribution and antimicrobial susceptibility of gram-negative aerobic bacteria in hospitalized cancer patients

<p>Abstract</p> <p>Background</p> <p>Nosocomial infections pose significant threats to hospitalized patients, especially the immunocompromised ones, such as cancer patients.</p> <p>Methods</p> <p>This study examined the microbial spectrum of gram-negative bacteria in various infection sites in patients with leukemia and solid tumors. The antimicrobial resistance patterns of the isolated bacteria were studied.</p> <p>Results</p> <p>The most frequently isolated gram-negative bacteria were <it>Klebsiella pneumonia </it>(31.2%) followed by <it>Escherichia coli </it>(22.2%). We report the isolation and identification of a number of less-frequent gram negative bacteria (<it>Chromobacterium violacum</it>, <it>Burkholderia cepacia, Kluyvera ascorbata, Stenotrophomonas maltophilia, Yersinia pseudotuberculosis</it>, and <it>Salmonella arizona</it>). Most of the gram-negative isolates from Respiratory Tract Infections (RTI), Gastro-intestinal Tract Infections (GITI), Urinary Tract Infections (UTI), and Bloodstream Infections (BSI) were obtained from leukemic patients. All gram-negative isolates from Skin Infections (SI) were obtained from solid-tumor patients. In both leukemic and solid-tumor patients, gram-negative bacteria causing UTI were mainly <it>Escherichia coli </it>and <it>Klebsiella pneumoniae</it>, while gram-negative bacteria causing RTI were mainly <it>Klebsiella pneumoniae</it>. <it>Escherichia coli </it>was the main gram-negative pathogen causing BSI in solid-tumor patients and GITI in leukemic patients. Isolates of <it>Escherichia coli, Klebsiella, Enterobacter</it>, <it>Pseudomonas, and Acinetobacter </it>species were resistant to most antibiotics tested. There was significant imipenem -resistance in <it>Acinetobacter </it>(40.9%), <it>Pseudomonas </it>(40%), and <it>Enterobacter </it>(22.2%) species, and noticeable imipinem-resistance in <it>Klebsiell</it>a (13.9%) and <it>Escherichia coli </it>(8%).</p> <p>Conclusion</p> <p>This is the first study to report the evolution of imipenem-resistant gram-negative strains in Egypt. Mortality rates were higher in cancer patients with nosocomial <it>Pseudomonas </it>infections than any other bacterial infections. Policies restricting antibiotic consumption should be implemented to avoid the evolution of newer generations of antibiotic resistant-pathogens.</p

Avoiding objects with few neighbors in the K-Means process and adding ROCK Links to its distance

K-means is considered as one of the most common and powerful algorithms in data clustering, in this paper we're going to present new techniques to solve two problems in the K-means traditional clustering algorithm, the 1st problem is its sensitivity for outliers, in this part we are going to depend on a function that will help us to decide if this object is an outlier or not, if it was an outlier it will be expelled from our calculations, that will help the K-means to make good results even if we added more outlier points; in the second part we are going to make K-means depend on Rock links in addition to its traditional distance, Rock links takes into account the number of common neighbors between two objects, that will make the K-means able to detect shapes that can't be detected by the traditional K-means

Arabic morphological tools for text mining

Arabic Language has complex morphology; this led to unavailability to standard Arabic morphological analysis tools until now. In this paper, we present and evaluate existing common Arabic stemming/light stemming algorithms, we also implement and integrate Arabic morphological analysis tools into the leading open source machine learning and data mining tools, Weka and RapidMiner