Abstract: Object: Review recent publications of patient safety
culture to investigate the relationship between leadership behavior,
safety culture, and safety performance in the healthcare industry.
Method: This study is a cross-sectional study, 350 questionnaires were
mailed to hospital workers with 195 valid responses obtained, and a
55.7% valid response rate. Confirmatory factor analysis (CFA) was
carried out to test the factor structure and determine if the composite
reliability was significant with a factor loading of >0.5, resulting in an
acceptable model fit. Results: Through the analysis of One-way
ANOVA, the results showed that physicians significantly have more
negative patient safety culture perceptions and safety performance
perceptions than non- physicians. Conclusions: The path analysis
results show that leadership behavior affects safety culture and safety
performance in the health care industry. Safety performance was
affected and improved with contingency leadership and a positive
patient safety organization culture. The study suggests improving
safety performance by providing a well-managed system that
includes: consideration of leadership, hospital worker training
courses, and a solid safety reporting system.
Abstract: In this paper, Zinc Oxide (ZnO) thin films are deposited on glass substrate by sol-gel method. The ZnO thin films with well defined orientation were acquired by spin coating of zinc acetate dehydrate monoethanolamine (MEA), de-ionized water and isopropanol alcohol. These films were pre-heated at 275°C for 10 min and then annealed at 350°C, 450°C and 550°C for 80 min. The effect of annealing temperature and different thickness on structure and surface morphology of the thin films were verified by Atomic Force Microscopy (AFM). It was found that there was a significant effect of annealing temperature on the structural parameters of the films such as roughness exponent, fractal dimension and interface width. Thin films also were characterizied by X-ray Diffractometery (XRD) method. XRD analysis revealed that the annealed ZnO thin films consist of single phase ZnO with wurtzite structure and show the c-axis grain orientation. Increasing annealing temperature increased the crystallite size and the c-axis orientation of the film after 450°C. Also In this study, ZnO thin films in different thickness have been prepared by sol-gel method on the glass substrate at room temperature. The thicknesses of films are 100, 150 and 250 nm. Using fractal analysis, morphological characteristics of surface films thickness in amorphous state were investigated. The results show that with increasing thickness, surface roughness (RMS) and lateral correlation length (ξ) are decreased. Also, the roughness exponent (α) and growth exponent (β) were determined to be 0.74±0.02 and 0.11±0.02, respectively.
Abstract: The advantage of solving the complex nonlinear
problems by utilizing fuzzy logic methodologies is that the
experience or expert-s knowledge described as a fuzzy rule base can
be directly embedded into the systems for dealing with the problems.
The current limitation of appropriate and automated designing of
fuzzy controllers are focused in this paper. The structure discovery
and parameter adjustment of the Branched T-S fuzzy model is
addressed by a hybrid technique of type constrained sparse tree
algorithms. The simulation result for different system model is
evaluated and the identification error is observed to be minimum.
Abstract: Loop detectors report traffic characteristics in real
time. They are at the core of traffic control process. Intuitively,
one would expect that as density of detection increases, so would
the quality of estimates derived from detector data. However, as
detector deployment increases, the associated operating and
maintenance cost increases. Thus, traffic agencies often need to
decide where to add new detectors and which detectors should
continue receiving maintenance, given their resource constraints.
This paper evaluates the effect of detector spacing on freeway
travel time estimation. A freeway section (Interstate-15) in Salt
Lake City metropolitan region is examined. The research reveals
that travel time accuracy does not necessarily deteriorate with
increased detector spacing. Rather, the actual location of detectors
has far greater influence on the quality of travel time estimates.
The study presents an innovative computational approach that
delivers optimal detector locations through a process that relies on
Genetic Algorithm formulation.
Abstract: This paper addresses modeling and optimization of process parameters in powder mixed electrical discharge machining (PMEDM). The process output characteristics include metal removal rate (MRR) and electrode wear rate (EWR). Grain size of Aluminum powder (S), concentration of the powder (C), discharge current (I) pulse on time (T) are chosen as control variables to study the process performance. The experimental results are used to develop the regression models based on second order polynomial equations for the different process characteristics. Then, a genetic algorithm (GA) has been employed to determine optimal process parameters for any desired output values of machining characteristics.
Abstract: The aim of this work is to study the elastic transfer
phenomenon which takes place in the elastic scattering of 16O on 12C
at energies near the Coulomb barrier. Where, the angular distribution
decrease steadily with increasing the scattering angle, then the cross
section will increase at backward angles due to the α-transfer process.
This reaction was also studied at different energies for tracking the
nuclear rainbow phenomenon. The experimental data of the angular
distribution at these energies were compared to the calculation
predictions. The optical potential codes such as SPIVAL and
Distorted Wave Born Approximation (DWUCK5) were used in
analysis.
Abstract: In this study, Friction Stir Processing (FSP) a recent grain refinement technique was employed to disperse micron-sized (2 *m) SiCp particles into aluminum alloy AA6063. The feasibility to fabricate bulk composites through FSP was analyzed and experiments were conducted at different traverse speeds and wider volumes of the specimens. Micro structural observation were carried out by employing optical microscopy test of the cross sections in both parallel and perpendicular to the tool traverse direction. Mechanical property including micro hardness was evaluated in detail at various regions on the specimen. The composites had an excellent bonding with aluminum alloy substrate and a significant increase of 30% in the micro hardness value of metal matrix composite (MMC) as to that of the base metal has observed. The observations clearly indicate that SiC particles were uniformly distributed within the aluminum matrix.
Abstract: Biological evolution has generated a rich variety of
successful solutions; from nature, optimized strategies can be
inspired. One interesting example is the ant colonies, which are able
to exhibit a collective intelligence, still that their dynamic is simple.
The emergence of different patterns depends on the pheromone trail,
leaved by the foragers. It serves as positive feedback mechanism for
sharing information.
In this paper, we use the dynamic of TASEP as a model of
interaction at a low level of the collective environment in the ant-s
traffic flow. This work consists of modifying the movement rules of
particles “ants" belonging to the TASEP model, so that it adopts with
the natural movement of ants. Therefore, as to respect the constraints
of having no more than one particle per a given site, and in order to
avoid collision within a bidirectional circulation, we suggested two
strategies: decease strategy and waiting strategy. As a third work
stage, this is devoted to the study of these two proposed strategies-
stability. As a final work stage, we applied the first strategy to the
whole environment, in order to get to the emergence of traffic flow,
which is a way of learning.
Abstract: Pentachlorophenol (PCP) is a polychlorinated
aromatic compound that is widespread in industrial effluents and is
considered to be a serious pollutant. Among the variety of industrial
effluents encountered, effluents from tanning industry are very
important and have a serious pollution potential. PCP is also formed
unintentionally in effluents of paper and pulp industries. It is highly
persistent in soils and is lethal to a wide variety of beneficial
microorganisms and insects, human beings and animals. The natural
processes that breakdown toxic chemicals in the environment have
become the focus of much attention to develop safe and environmentfriendly
deactivation technologies. Microbes and plants are among
the most important biological agents that remove and degrade waste
materials to enable their recycling in the environment. The present
investigation was carried out with the aim of developing a microbial
system for bioremediation of PCP polluted soils. A number of plant
species were evaluated for their ability to tolerate different
concentrations of pentachlorophenol (PCP) in the soil. The
experiment was conducted for 30 days under pot culture conditions.
The toxic effect of PCP on plants was studied by monitoring seed
germination, plant growth and biomass. As the concentration of PCP
was increased to 50 ppm, the inhibition of seed germination, plant
growth and biomass was also increased. Although PCP had a
negative effect on all plant species tested, maize and groundnut
showed the maximum tolerance to PCP. Other tolerating crops
included wheat, safflower, sunflower, and soybean. From the
rhizosphere soil of the tolerant seedlings, as many as twenty seven
PCP tolerant bacteria were isolated. From soybean, 8; sunflower, 3;
safflower 8; maize 2; groundnut and wheat, 3 each isolates were
made. They were screened for their PCP degradation potentials.
HPLC analyses of PCP degradation revealed that the isolate MAZ-2
degraded PCP completely. The isolate MAZ-1 was the next best
isolate with 90 per cent PCP degradation. These strains hold promise
to be used in the bioremediation of PCP polluted soils.
Abstract: This paper presents an approach which is based on the
use of supervised feed forward neural network, namely multilayer
perceptron (MLP) neural network and finite element method (FEM)
to solve the inverse problem of parameters identification. The
approach is used to identify unknown parameters of ferromagnetic
materials. The methodology used in this study consists in the
simulation of a large number of parameters in a material under test,
using the finite element method (FEM). Both variations in relative
magnetic permeability and electrical conductivity of the material
under test are considered. Then, the obtained results are used to
generate a set of vectors for the training of MLP neural network.
Finally, the obtained neural network is used to evaluate a group of
new materials, simulated by the FEM, but not belonging to the
original dataset. Noisy data, added to the probe measurements is used
to enhance the robustness of the method. The reached results
demonstrate the efficiency of the proposed approach, and encourage
future works on this subject.
Abstract: Advances in computing applications in recent years
have prompted the demand for more flexible scheduling models for
QoS demand. Moreover, in practical applications, partly violated
temporal constraints can be tolerated if the violation meets certain
distribution. So we need extend the traditional Liu and Lanland model
to adapt to these circumstances. There are two extensions, which are
the (m, k)-firm model and Window-Constrained model. This paper
researches on weakly hard real-time constraints and their combination
to support QoS. The fact that a practical application can tolerate some
violations of temporal constraint under certain distribution is
employed to support adaptive QoS on the open real-time system. The
experiment results show these approaches are effective compared to
traditional scheduling algorithms.
Abstract: Multicast Network Technology has pervaded our
lives-a few examples of the Networking Techniques and also for the
improvement of various routing devices we use. As we know the
Multicast Data is a technology offers many applications to the user
such as high speed voice, high speed data services, which is presently
dominated by the Normal networking and the cable system and
digital subscriber line (DSL) technologies. Advantages of Multi cast
Broadcast such as over other routing techniques. Usually QoS
(Quality of Service) Guarantees are required in most of Multicast
applications. The bandwidth-delay constrained optimization and we
use a multi objective model and routing approach based on genetic
algorithm that optimizes multiple QoS parameters simultaneously.
The proposed approach is non-dominated routes and the performance
with high efficiency of GA. Its betterment and high optimization has
been verified. We have also introduced and correlate the result of
multicast GA with the Broadband wireless to minimize the delay in
the path.
Abstract: Competitive relationships among Bradyrhizobium
japonicum USDA serogroup 123, 122 and 138 were screened versus
the standard commercial soybean variety Williams and two
introductions P1 377578 "671" in a field trial. Displacement of strain
123 by an effective strain should improved N2 fixation. Root nodules
were collected and strain occupancy percentage was determined
using strain specific fluorescent antibodies technique. As anticipated
the strain USDA 123 dominated 92% of nodules due to the high
affinity between the host and the symbiont. This dominance was
consistent and not changed materially either by inoculation practice
or by introducing new strainan. The interrelationship between the
genotype Williams and serogroup 122 & 138 was found very weak
although the cell density of the strain in the rhizosphere area was
equal. On the other hand, the nodule occupancy of genotypes 671 and
166 with rhizobia serogroup 123 was almost diminished to zero. .
The data further exhibited that the genotypes P1 671 and P1 166 have
high affinity to colonize with strains 122 and 138 whereas Williams
was highly promiscuous to strain 123.
Abstract: In this paper is shown that the probability-statistic methods application, especially at the early stage of the aviation gas turbine engine (GTE) technical condition diagnosing, when the flight information has property of the fuzzy, limitation and uncertainty is unfounded. Hence is considered the efficiency of application of new technology Soft Computing at these diagnosing stages with the using of the Fuzzy Logic and Neural Networks methods. Training with high accuracy of fuzzy multiple linear and non-linear models (fuzzy regression equations) which received on the statistical fuzzy data basis is made. Thus for GTE technical condition more adequate model making are analysed dynamics of skewness and kurtosis coefficients' changes. Researches of skewness and kurtosis coefficients values- changes show that, distributions of GTE work parameters have fuzzy character. Hence consideration of fuzzy skewness and kurtosis coefficients is expedient. Investigation of the basic characteristics changes- dynamics of GTE work parameters allows to draw conclusion on necessity of the Fuzzy Statistical Analysis at preliminary identification of the engines' technical condition. Researches of correlation coefficients values- changes shows also on their fuzzy character. Therefore for models choice the application of the Fuzzy Correlation Analysis results is offered. For checking of models adequacy is considered the Fuzzy Multiple Correlation Coefficient of Fuzzy Multiple Regression. At the information sufficiency is offered to use recurrent algorithm of aviation GTE technical condition identification (Hard Computing technology is used) on measurements of input and output parameters of the multiple linear and non-linear generalised models at presence of noise measured (the new recursive Least Squares Method (LSM)). The developed GTE condition monitoring system provides stage-bystage estimation of engine technical conditions. As application of the given technique the estimation of the new operating aviation engine temperature condition was made.
Abstract: Environmental awareness and the recent
environmental policies have forced many electric utilities to
restructure their operational practices to account for their emission
impacts. One way to accomplish this is by reformulating the
traditional economic dispatch problem such that emission effects are
included in the mathematical model. This paper presents a Particle
Swarm Optimization (PSO) algorithm to solve the Economic-
Emission Dispatch problem (EED) which gained recent attention due
to the deregulation of the power industry and strict environmental
regulations. The problem is formulated as a multi-objective one with
two competing functions, namely economic cost and emission
functions, subject to different constraints. The inequality constraints
considered are the generating unit capacity limits while the equality
constraint is generation-demand balance. A novel equality constraint
handling mechanism is proposed in this paper. PSO algorithm is
tested on a 30-bus standard test system. Results obtained show that
PSO algorithm has a great potential in handling multi-objective
optimization problems and is capable of capturing Pareto optimal
solution set under different loading conditions.
Abstract: As the global climate changes, the threat from
landslides and debris flows increases. Learning how a watershed
initiates landslides under abnormal rainfall conditions and predicting
landslide magnitude and frequency distribution is thus important.
Landslides show a power-law distribution in the frequency-area
distribution. The distribution curve shows an exponent gradient 1.0 in
the Sandpile model test. Will the landslide frequency-area statistics
show a distribution similar to the Sandpile model under extreme
rainfall conditions? The purpose of the study is to identify the extreme
rainfall-induced landslide frequency-area distribution in the Laonong
River Basin in southern Taiwan. Results of the analysis show that a
lower gradient of landslide frequency-area distribution could be
attributed to the transportation and deposition of debris flow areas that
are included in the landslide area.
Abstract: The measurement of anesthetic depth is necessary in
anesthesiology. NN10 is very simple method among the RR intervals
analysis methods. NN10 parameter means the numbers of above the 10
ms intervals of the normal to normal RR intervals.
Bispectrum analysis is defined as 2D FFT. EEG signal reflected the
non-linear peristalsis phenomena according to the change brain
function. After analyzing the bispectrum of the 2 dimension, the most
significant power spectrum density peaks appeared abundantly at the
specific area in awakening and anesthesia state. These points are
utilized to create the new index since many peaks appeared at the
specific area in the frequency coordinate. The measured range of an
index was 0-100. An index is 20-50 at an anesthesia, while the index is
90-60 at the awake.
In this paper, the relation between NN10 parameter using ECG and
bisepctrum index using EEG is observed to estimate the depth of
anesthesia during anesthesia and then we estimated the utility of the
anesthetic.
Abstract: A semi-active control strategy for suspension
systems of passenger cars is presented employing
Magnetorheological (MR) dampers. The vehicle is modeled with
seven DOFs including the, roll pitch and bounce of car body, and
the vertical motion of the four tires. In order to design an optimal
controller based on the actuator constraints, a Linear-Quadratic
Regulator (LQR) is designed. The design procedure of the LQR
consists of selecting two weighting matrices to minimize the energy
of the control system. This paper presents a hybrid optimization
procedure which is a combination of gradient-based and
evolutionary algorithms to choose the weighting matrices with
regards to the actuator constraint. The optimization algorithm is
defined based on maximum comfort and actuator constraints. It is
noted that utilizing the present control algorithm may significantly
reduce the vibration response of the passenger car, thus, providing
a comfortable ride.
Abstract: Support vector machines (SVMs) are considered to be
the best machine learning algorithms for minimizing the predictive
probability of misclassification. However, their drawback is that for
large data sets the computation of the optimal decision boundary is a
time consuming function of the size of the training set. Hence several
methods have been proposed to speed up the SVM algorithm. Here
three methods used to speed up the computation of the SVM
classifiers are compared experimentally using a musical genre
classification problem. The simplest method pre-selects a random
sample of the data before the application of the SVM algorithm. Two
additional methods use proximity graphs to pre-select data that are
near the decision boundary. One uses k-Nearest Neighbor graphs and
the other Relative Neighborhood Graphs to accomplish the task.
Abstract: The complex structure of lignocellulose leads to great
difficulties in converting it to fermentable sugars for the ethanol
production. The major hydrolysis impediments are the crystallinity of
cellulose and the lignin content. To improve the efficiency of
enzymatic hydrolysis, microbial pretreatment of corncob was
investigated using two bacterial strains of Bacillus subtilis A 002 and
Cellulomonas sp. TISTR 784 (expected to break open the crystalline
part of cellulose) and lignin-degrading fungus, Phanerochaete
sordida SK7 (expected to remove lignin from lignocellulose). The
microbial pretreatment was carried out with each strain under its
optimum conditions. The pretreated corncob samples were further
hydrolyzed to produce reducing glucose with low amounts of
commercial cellulase (25 U·g-1 corncob) from Aspergillus niger. The
corncob samples were determined for composition change by X-ray
diffraction (XRD), Fourier transform infrared spectroscopy (FTIR),
and scanning electron microscope (SEM). According to the results,
the microbial pretreatment with fungus, P. sordida SK7 was the most
effective for enhancing enzymatic hydrolysis, approximately, 40%
improvement.