Abstract: The increasing importance of data stream arising in a
wide range of advanced applications has led to the extensive study of
mining frequent patterns. Mining data streams poses many new
challenges amongst which are the one-scan nature, the unbounded
memory requirement and the high arrival rate of data streams. In this
paper, we propose a new approach for mining itemsets on data
stream. Our approach SFIDS has been developed based on FIDS
algorithm. The main attempts were to keep some advantages of the
previous approach and resolve some of its drawbacks, and
consequently to improve run time and memory consumption. Our
approach has the following advantages: using a data structure similar
to lattice for keeping frequent itemsets, separating regions from each
other with deleting common nodes that results in a decrease in search
space, memory consumption and run time; and Finally, considering
CPU constraint, with increasing arrival rate of data that result in
overloading system, SFIDS automatically detect this situation and
discard some of unprocessing data. We guarantee that error of results
is bounded to user pre-specified threshold, based on a probability
technique. Final results show that SFIDS algorithm could attain
about 50% run time improvement than FIDS approach.
Abstract: Avalanche velocity (from start to track zone) has been estimated in the present model for an avalanche which is triggered artificially by an explosive devise. The initial development of the model has been from the concept of micro-continuum theories [1], underwater explosions [2] and from fracture mechanics [3] with appropriate changes to the present model. The model has been computed for different slab depth R, slope angle θ, snow density ¤ü, viscosity μ, eddy viscosity η*and couple stress parameter η. The applicability of the present model in the avalanche forecasting has been highlighted.
Abstract: Studies were carried out to determine the in vitro
susceptibility of the typhoid pathogens to combined action of Euphorbia hirta, Euphorbia heterophylla and Phyllanthus niruri. Clinical isolates of the typhoid bacilli were subjected to susceptibility testing using agar diffusion technique and the minimum inhibitory
concentration (MIC) determined with tube dilution technique. These
isolates, when challenged with doses of the extracts from the three
medicinal plants showed zones of inhibition as wide as 26±0.2mm, 22±0.1mm and 18±0.0mm respectively. The minimum inhibitory concentration (MIC) revealed organisms inhibited at varying
concentrations of extracts: E. hirta (S. typhi 0.250mg/ml, S. paratyphi A 0.125mg/ml, S. paratyphi B 0.185mg/ml and S. paratyphi C 0.225mg/ml), E. heterophylla (S. typhi 0.280mg/ml, S. paratyphi A
0.150mg/ml, S. paratyphi B 0.200mg/ml and S. paratyphi C 0.250mg/ml) and P. niruri (S. typhi 0.150mg/ml, S. paratyphi A 0.100mg/ml, S. paratyphi B 0.115mg/ml and S. paratyphi C 0.125mg/ml). The results of the synergy between the three plants in
the ration of 1:1:1 showed very low MICs for the test pathogens as follows S. typhi 0.025mg/ml, S. paratyphi A 0.080mg/ml, S. paratyphi B 0.015mg/ml and S. paratyphi C 0.10mg/ml with the
diameter zone of inhibition (DZI) ranging from 35±0.2mm,
28±0.4mm, 20±0.1mm and 32±0.3mm respectively. The secondary
metabolites were identified using simple methods and HPLC. Organic components such as anthroquinones, different alkaloids,
tannins, 6-ethoxy-1,2,3,4-tetrahydro-2,2,4-trimethyl and steroids were identified. The prevalence of Salmonellae, a deadly infectious disease, is still very high in parts of Nigeria. The synergistic action of these three plants is very high. It is concluded that pharmaceutical companies should take advantage of these findings to develop new
anti-typhoid drugs from these plants.
Abstract: An accurate and proficient artificial neural network
(ANN) based genetic algorithm (GA) is developed for predicting of
nanofluids viscosity. A genetic algorithm (GA) is used to optimize
the neural network parameters for minimizing the error between the
predictive viscosity and the experimental one. The experimental
viscosity in two nanofluids Al2O3-H2O and CuO-H2O from 278.15
to 343.15 K and volume fraction up to 15% were used from
literature. The result of this study reveals that GA-NN model is
outperform to the conventional neural nets in predicting the viscosity
of nanofluids with mean absolute relative error of 1.22% and 1.77%
for Al2O3-H2O and CuO-H2O, respectively. Furthermore, the results
of this work have also been compared with others models. The
findings of this work demonstrate that the GA-NN model is an
effective method for prediction viscosity of nanofluids and have
better accuracy and simplicity compared with the others models.
Abstract: In this investigation Salicylic acid, Sulfosalicylic acid
and Acetyl salicylic acid were chosen as a sample for thin layer
chromatography (TLC) on silica gel plates. Bicarbonate buffer at
different pH containing different amounts of boric acid was applied
as mobile phase. Specific interaction of these substances with boric
acid has effect on Rf in thin layer chromatography. Regular and
similar trend was observed in variations of Rf for mentioned
compounds in TLC by altering of percentages of boric acid in mobile
phase in pH range of 8-10. Also effect of organic solvent, mixture of
water/ organic solvent and organic solvent containing boric acid as
mobile phase was studied.
Abstract: This paper presents a new sensor-based online method for generating collision-free near-optimal paths for mobile robots pursuing a moving target amidst dynamic and static obstacles. At each iteration, first the set of all collision-free directions are calculated using velocity vectors of the robot relative to each obstacle and target, forming the Directive Circle (DC), which is a novel concept. Then, a direction close to the shortest path to the target is selected from feasible directions in DC. The DC prevents the robot from being trapped in deadlocks or local minima. It is assumed that the target's velocity is known, while the speeds of dynamic obstacles, as well as the locations of static obstacles, are to be calculated online. Extensive simulations and experimental results demonstrated the efficiency of the proposed method and its success in coping with complex environments and obstacles.
Abstract: This paper introduces new algorithms (Fuzzy relative
of the CLARANS algorithm FCLARANS and Fuzzy c Medoids
based on randomized search FCMRANS) for fuzzy clustering of
relational data. Unlike existing fuzzy c-medoids algorithm (FCMdd)
in which the within cluster dissimilarity of each cluster is minimized
in each iteration by recomputing new medoids given current
memberships, FCLARANS minimizes the same objective function
minimized by FCMdd by changing current medoids in such away
that that the sum of the within cluster dissimilarities is minimized.
Computing new medoids may be effected by noise because outliers
may join the computation of medoids while the choice of medoids in
FCLARANS is dictated by the location of a predominant fraction of
points inside a cluster and, therefore, it is less sensitive to the
presence of outliers. In FCMRANS the step of computing new
medoids in FCMdd is modified to be based on randomized search.
Furthermore, a new initialization procedure is developed that add
randomness to the initialization procedure used with FCMdd. Both
FCLARANS and FCMRANS are compared with the robust and
linearized version of fuzzy c-medoids (RFCMdd). Experimental
results with different samples of the Reuter-21578, Newsgroups
(20NG) and generated datasets with noise show that FCLARANS is
more robust than both RFCMdd and FCMRANS. Finally, both
FCMRANS and FCLARANS are more efficient and their outputs
are almost the same as that of RFCMdd in terms of classification
rate.
Abstract: This paper presents a new approach for image
segmentation by applying Pillar-Kmeans algorithm. This
segmentation process includes a new mechanism for clustering the
elements of high-resolution images in order to improve precision and
reduce computation time. The system applies K-means clustering to
the image segmentation after optimized by Pillar Algorithm. The
Pillar algorithm considers the pillars- placement which should be
located as far as possible from each other to withstand against the
pressure distribution of a roof, as identical to the number of centroids
amongst the data distribution. This algorithm is able to optimize the
K-means clustering for image segmentation in aspects of precision
and computation time. It designates the initial centroids- positions
by calculating the accumulated distance metric between each data
point and all previous centroids, and then selects data points which
have the maximum distance as new initial centroids. This algorithm
distributes all initial centroids according to the maximum
accumulated distance metric. This paper evaluates the proposed
approach for image segmentation by comparing with K-means and
Gaussian Mixture Model algorithm and involving RGB, HSV, HSL
and CIELAB color spaces. The experimental results clarify the
effectiveness of our approach to improve the segmentation quality in
aspects of precision and computational time.
Abstract: Phase-Contrast MR imaging methods are widely used
for measurement of blood flow velocity components. Also there are
some other tools such as CT and Ultrasound for velocity map
detection in intravascular studies. These data are used in deriving
flow characteristics. Some clinical applications are investigated
which use pressure distribution in diagnosis of intravascular disorders
such as vascular stenosis. In this paper an approach to the problem of
measurement of intravascular pressure field by using velocity field
obtained from flow images is proposed. The method presented in this
paper uses an algorithm to calculate nonlinear equations of Navier-
Stokes, assuming blood as an incompressible and Newtonian fluid.
Flow images usually suffer the lack of spatial resolution. Our
attempt is to consider the effect of spatial resolution on the pressure
distribution estimated from this method. In order to achieve this aim,
velocity map of a numerical phantom is derived at six different
spatial resolutions. To determine the effects of vascular stenoses on
pressure distribution, a stenotic phantom geometry is considered. A
comparison between the pressure distribution obtained from the
phantom and the pressure resulted from the algorithm is presented. In
this regard we also compared the effects of collocated and staggered
computational grids on the pressure distribution resulted from this
algorithm.
Abstract: The present study presents a new approach to automatic
data clustering and classification problems in large and complex
databases and, at the same time, derives specific types of explicit rules
describing each cluster. The method works well in both sparse and
dense multidimensional data spaces. The members of the data space
can be of the same nature or represent different classes. A number
of N-dimensional ellipsoids are used for enclosing the data clouds.
Due to the geometry of an ellipsoid and its free rotation in space
the detection of clusters becomes very efficient. The method is based
on genetic algorithms that are used for the optimization of location,
orientation and geometric characteristics of the hyper-ellipsoids. The
proposed approach can serve as a basis for the development of
general knowledge systems for discovering hidden knowledge and
unexpected patterns and rules in various large databases.
Abstract: Grid environments include aggregation of
geographical distributed resources. Grid is put forward in three types
of computational, data and storage. This paper presents a research on
data grid. Data grid is used for covering and securing accessibility to
data from among many heterogeneous sources. Users are not worry
on the place where data is located in it, provided that, they should get
access to the data. Metadata is used for getting access to data in data
grid. Presently, application metadata catalogue and SRB middle-ware
package are used in data grids for management of metadata. At this
paper, possibility of updating, streamlining and searching is provided
simultaneously and rapidly through classified table of preserving
metadata and conversion of each table to numerous tables.
Meanwhile, with regard to the specific application, the most
appropriate and best division is set and determined. Concurrency of
implementation of some of requests and execution of pipeline is
adaptability as a result of this technique.
Abstract: This paper presents a time control liquids mixing
system in the tanks as an application of fuzzy time control discrete
model. The system is designed for a wide range of industrial
applications. The simulation design of control system has three
inputs: volume, viscosity, and selection of product, along with the
three external control adjustments for the system calibration or to
take over the control of the system autonomously in local or
distributed environment. There are four controlling elements: rotatory
motor, grinding motor, heating and cooling units, and valves
selection, each with time frame limit. The system consists of three
controlled variables measurement through its sensing mechanism for
feed back control. This design also facilitates the liquids mixing
system to grind certain materials in tanks and mix with fluids under
required temperature controlled environment to achieve certain
viscous level. Design of: fuzzifier, inference engine, rule base,
deffuzifiers, and discrete event control system, is discussed. Time
control fuzzy rules are formulated, applied and tested using
MATLAB simulation for the system.
Abstract: Uterine and oviducal fluids are necessary for
capacitation of the spermatozoa and early embryonic development.
The aim of the present study was to determine the effects of estrous
cycle phases (follicular and luteal) on some biological parameters
(enzymes, electrolytes and total proteins) in uterine and oviducal
secretions of ewes. Oviducal and uterine fluids were collected,
diluted and centrifuged. According to our results, concentrations of
GPT, G6PDH, total proteins, K and Na were significantly (P
Abstract: Dynamic characteristics of a four-lobe journal bearing
of micropolar fluids are presented. Lubricating oil containing
additives and contaminants is modelled as micropolar fluid. The
modified Reynolds equation is obtained using the micropolar
lubrication theory and solving it by using finite difference technique.
The dynamic characteristics in terms of stiffness, damping
coefficients, the critical mass and whirl ratio are determined for
various values of size of material characteristic length and the
coupling number. The results show compared with Newtonian fluids,
that micropolar fluid exhibits better stability.
Abstract: In this paper, computational fluid dynamics (CFD) is utilized to characterize a prototype biolistic delivery system, the biomedical device based on the contoured-shock-tube design (CST), with the aim at investigating shocks induced flow instabilities within the contoured shock tube. The shock/interface interactions, the growth of perturbation at an interface between two fluids of different density are interrogated. The key features of the gas dynamics and gas-particle interaction are discussed
Abstract: Considering non-ideal behavior of fluids and its effects on hydrodynamic and mass transfer in multiphase flow is very essential. Simulations were performed that takes into account the effects of mass transfer and mixture non-ideality on hydrodynamics reported by Irani et al. In this paper, by assuming the density of phases to be constant and Raullt-s law instead of using EOS and fugacity coefficient definition, respectively for both the liquid and gas phases, the importance of non-ideality effects on mass transfer and hydrodynamic behavior was studied. The results for a system of octane/propane (T=323 K, P =445 kpa) also indicated that the assumption of constant density in simulation had major role to diverse from experimental data. Furthermore, comparison between obtained results and the previous report indicated significant differences between experimental data and simulation results with more ideal assumptions.
Abstract: Today, cancer remains one of the major diseases that
lead to death. The main obstacle in chemotherapy as a main cancer
treatment is the toxicity to normal cells due to Multidrug Resistance
(MDR) after the use of anticancer drugs. Proposed solution to
overcome this problem is the use of MDR efflux inhibitor of cinchona
alkaloids which is delivered together with anticancer drugs
encapsulated in the form of polymeric nanoparticles. The particles
were prepared by the hydration method. The characterization of
nanoparticles was particle size, zeta potential, entrapment efficiency
and in vitro drug release. Combination nanoparticle size ranged 29-45
nm with a neutral surface charge. Entrapment efficiency was above
87% for the use quinine, quinidine or cinchonidine in combination
with etoposide. The release test results exhibited that the cinchona
alkaloids release released faster than that of etoposide. Collectively,
cinchona alkaloids can be packaged along with etoposide in
nanomicelles for better cancer therapy.
Abstract: Some meta-schedulers query the information system of individual supercomputers in order to submit jobs to the least busy supercomputer on a computational Grid. However, this information can become outdated by the time a job starts due to changes in scheduling priorities. The MSR scheme is based on Multiple Simultaneous Requests and can take advantage of opportunities resulting from these priorities changes. This paper presents the SWARM meta-scheduler, which can speed up the execution of large sets of tasks by minimizing the job queuing time through the submission of multiple requests. Performance tests have shown that this new meta-scheduler is faster than an implementation of the MSR scheme and the gLite meta-scheduler. SWARM has been used through the GridQTL project beta-testing portal during the past year. Statistics are provided for this usage and demonstrate its capacity to achieve reliably a substantial reduction of the execution time in production conditions.
Abstract: The conceptually construction of axially chiral indolostilbenesi.eN-(2-{(E)-2-[2'-(1-Acetyl-1H-indol-2-yl)-3'chloro-4,4',6,6'-tetramethoxy[1,1'-biphenyl]-2yl]ethenyl}phenyl)acetamide and N-(2-{(E)-2-[2'-(1-Acetyl-1H-indol-2-yl)-3'-chloro-2,4',6,6'-tetramethoxy[1,1'-biphenyl]-4-yl]ethenyl}phenyl) acetamide are described in this paper. These structure, were obtained by the tactical combination of palladium-catalyzed coupling which produced 10-acetamido-3,5-dimethoxystilbene, follow by FeCl3-induced oxidative cyclization/dimerisation. All structures were unambiguously confirmed by 1D (1H, 13C) and 2D NMR experiment, (COSY, HMQC, HMBC) and mass spectrometry.
Abstract: In this study a clustering technique has been implemented which is K-Means like with hierarchical initial set (HKM). The goal of this study is to prove that clustering document sets do enhancement precision on information retrieval systems, since it was proved by Bellot & El-Beze on French language. A comparison is made between the traditional information retrieval system and the clustered one. Also the effect of increasing number of clusters on precision is studied. The indexing technique is Term Frequency * Inverse Document Frequency (TF * IDF). It has been found that the effect of Hierarchical K-Means Like clustering (HKM) with 3 clusters over 242 Arabic abstract documents from the Saudi Arabian National Computer Conference has significant results compared with traditional information retrieval system without clustering. Additionally it has been found that it is not necessary to increase the number of clusters to improve precision more.