Abstract: Heart-s electric field can be measured anywhere on
the surface of the body (ECG). When individuals touch, one person-s
ECG signal can be registered in other person-s EEG and elsewhere
on his body. Now, the aim of this study was to test the hypothesis
that physical contact (hand-holding) of two persons changes their
heart rate variability. Subjects were sixteen healthy female (age: 20-
26) which divided into eight sets. In each sets, we had two friends
that they passed intimacy test of J.sternberg. ECG of two subjects
(each set) acquired for 5 minutes before hand-holding (as control
group) and 5 minutes during they held their hands (as experimental
group). Then heart rate variability signals were extracted from
subjects' ECG and analyzed in linear feature space (time and
frequency domain) and nonlinear feature space. Considering the
results, we conclude that physical contact (hand-holding of two
friends) increases parasympathetic activity, as indicate by increase
SD1, SD1/SD2, HF and MF power (p
Abstract: Glomerular filtration rate (GFR) is a measure of
kidney function. It is usually estimated from serum concentrations of
cystatin C or creatinine although there has been considerable debate
in the literature about (i) the best equation to use and (ii) the
variability in the correlation between the concentrations of creatinine
and cystatin C. The equations for GFR can be written in a general
form and from these I calculate the error of the GFR estimates
associated with analyte measurement error. These show that the
error of the GFR estimates is such that it is not possible to distinguish
between the equations over much of the concentration range of either
analyte. The general forms of the equations are also used to derive
an expression for the concentration of cystatin C as a function of the
concentration of creatinine. This equation shows that these analyte
concentrations are not linearly related. Clinical reports of cystatin C
and creatinine concentration are consistent with the expression
derived.
Abstract: Matrix metalloproteinase-3 (MMP3) is key member
of the MMP family, and is known to be present in coronary
atherosclerotic. Several studies have demonstrated that MMP-3
5A/6A polymorphism modify each transcriptional activity in allele
specific manner. We hypothesized that this polymorphism may play
a role as risk factor for development of coronary stenosis. The aim of
our study was to estimate MMP-3 (5A/6A) gene polymorphism on
interindividual variability in risk for coronary stenosis in an Iranian
population.DNA was extracted from white blood cells and genotypes
were obtained from coronary stenosis cases (n=95) and controls
(n=100) by PCR (polymerase chain reaction) and restriction
fragment length polymorphism techniques. Significant differences
between cases and controls were observed for MMP3 genotype
frequencies (X2=199.305, p< 0.001); the 6A allele was less
frequently seen in the control group, compared to the disease group
(85.79 vs. 78%, 6A/6A+5A/6A vs. 5A/5A, P≤0.001). These data
imply the involvement of -1612 5A/6A polymorphism in coronary
stenosis, and suggest that probably the 6A/6A MMP-3 genotype is a
genetic susceptibility factor for coronary stenosis.
Abstract: Nanophotocatalysts such as titanium (TiO2), zinc (ZnO), and iron (Fe2O3) oxides can be used in organic pollutants oxidation, and in many other applications. But among the challenges for technological application (scale-up) of the nanotechnology scientific developments two aspects are still little explored: research on environmental risk of the nanomaterials preparation methods, and the study of nanomaterials properties and/or performance variability. The environmental analysis was performed for six different methods of ZnO nanoparticles synthesis, and showed that it is possible to identify the more environmentally compatible process even at laboratory scale research. The obtained ZnO nanoparticles were tested as photocatalysts, and increased the degradation rate of the Rhodamine B dye up to 30 times.
Abstract: This study assessed the effects of climate change on
Thai soybeans under simulation situations. Our study is focused on
temperature variability and effects on growth, yield, and genetic
changes in 2 generations of Chiang Mai 60 cultivars. In the
experiment, soybeans were exposed to 3 levels of air temperature for
8 h day-1 in an open top chamber for 2 cropping periods. Air
temperature levels in each treatment were controlled at 30-33°C (±
2.3) for LT-treatment, 33-36°C ( ± 2.4) for AT-treatment, and 36-40
°C ( ± 3.2) for HT-treatment, respectively. Positive effects of high
temperature became obvious at the maturing stage when yield
significantly increased in both cropping periods. Results in growth
indicated that shoot length at the pre-maturing stage
(V3-R3) was more positively affected by high temperature than at the
maturing stage. However, the positive effect on growth under high
temperature was not found in the 2nd cropping period. Finally, genetic
changes were examined in phenotype characteristics by the AFLPs
technique. The results showed that the high temperature factor clearly
caused genetic change in the soybeans and showed more alteration in
the 2nd cropping period.
Abstract: The quality of a machined surface is becoming more and more important to justify the increasing demands of sophisticated component performance, longevity, and reliability. Usually, any machining operation leaves its own characteristic evidence on the machined surface in the form of finely spaced micro irregularities (surface roughness) left by the associated indeterministic characteristics of the different elements of the system: tool-machineworkpart- cutting parameters. However, one of the most influential sources in machining affecting surface roughness is the instantaneous state of tool edge. The main objective of the current work is to relate the in-process immeasurable cutting edge deformation and surface roughness to a more reliable easy-to-measure force signals using a robust non-linear time-dependent modeling regression techniques. Time-dependent modeling is beneficial when modern machining systems, such as adaptive control techniques are considered, where the state of the machined surface and the health of the cutting edge are monitored, assessed and controlled online using realtime information provided by the variability encountered in the measured force signals. Correlation between wear propagation and roughness variation is developed throughout the different edge lifetimes. The surface roughness is further evaluated in the light of the variation in both the static and the dynamic force signals. Consistent correlation is found between surface roughness variation and tool wear progress within its initial and constant regions. At the first few seconds of cutting, expected and well known trend of the effect of the cutting parameters is observed. Surface roughness is positively influenced by the level of the feed rate and negatively by the cutting speed. As cutting continues, roughness is affected, to different extents, by the rather localized wear modes either on the tool nose or on its flank areas. Moreover, it seems that roughness varies as wear attitude transfers from one mode to another and, in general, it is shown that it is improved as wear increases but with possible corresponding workpart dimensional inaccuracy. The dynamic force signals are found reasonably sensitive to simulate either the progressive or the random modes of tool edge deformation. While the frictional force components, feeding and radial, are found informative regarding progressive wear modes, the vertical (power) components is found more representative carrier to system instability resulting from the edge-s random deformation.
Abstract: The development of aid's systems for the medical
diagnosis is not easy thing because of presence of inhomogeneities in
the MRI, the variability of the data from a sequence to the other as
well as of other different source distortions that accentuate this
difficulty. A new automatic, contextual, adaptive and robust
segmentation procedure by MRI brain tissue classification is
described in this article. A first phase consists in estimating the
density of probability of the data by the Parzen-Rozenblatt method.
The classification procedure is completely automatic and doesn't
make any assumptions nor on the clusters number nor on the
prototypes of these clusters since these last are detected in an
automatic manner by an operator of mathematical morphology called
skeleton by influence zones detection (SKIZ). The problem of
initialization of the prototypes as well as their number is transformed
in an optimization problem; in more the procedure is adaptive since it
takes in consideration the contextual information presents in every
voxel by an adaptive and robust non parametric model by the
Markov fields (MF). The number of bad classifications is reduced by
the use of the criteria of MPM minimization (Maximum Posterior
Marginal).
Abstract: Wheat prediction was carried out using different meteorological variables together with agro meteorological indices in Ardebil district for the years 2004-2005 & 2005–2006. On the basis of correlation coefficients, standard error of estimate as well as relative deviation of predicted yield from actual yield using different statistical models, the best subset of agro meteorological indices were selected including daily minimum temperature (Tmin), accumulated difference of maximum & minimum temperatures (TD), growing degree days (GDD), accumulated water vapor pressure deficit (VPD), sunshine hours (SH) & potential evapotranspiration (PET). Yield prediction was done two months in advance before harvesting time which was coincide with commencement of reproductive stage of wheat (5th of June). It revealed that in the final statistical models, 83% of wheat yield variability was accounted for variation in above agro meteorological indices.
Abstract: Processes of plant breeding, testing and licensing of new varieties, patent protection in seed production, relations in trade and protection of copyright are dependent on identification, differentiation and characterization of plant genotypes. Therefore, we focused our research on utilization of wheat storage proteins as genetic markers suitable not only for differentiation of individual genotypes, but also for identification and characterization of their considerable properties. We analyzed a collection of 102 genotypes of bread wheat (Triticum aestivum L.), 41 genotypes of spelt wheat (Triticum spelta L.), and 35 genotypes of durum wheat (Triticum durum Desf.), in this study. Our results show, that genotypes of bread wheat and durum wheat were homogenous and single line, but spelt wheat genotypes were heterogenous. We observed variability of HMW-GS composition according to environmental factors and level of breeding and predict technological quality on the basis of Glu-score calculation.
Abstract: Physiological control of a left ventricle assist device (LVAD) is generally a complicated task due to diverse operating environments and patient variability. In this work, a tracking control algorithm based on sliding mode and feed forward control for a class of discrete-time single input single output (SISO) nonlinear uncertain systems is presented. The controller was developed to track the reference trajectory to a set operating point without inducing suction in the ventricle. The controller regulates the estimated mean pulsatile flow Qp and mean pulsatility index of pump rotational speed PIω that was generated from a model of the assist device. We recall the principle of the sliding mode control theory then we combine the feed-forward control design with the sliding mode control technique to follow the reference trajectory. The uncertainty is replaced by its upper and lower boundary. The controller was tested in a computer simulation covering two scenarios (preload and ventricular contractility). The simulation results prove the effectiveness and the robustness of the proposed controller
Abstract: This study assesses the vulnerability of Bulgarian
agriculture to drought using the WINISAREG model and seasonal
standard precipitation index SPI(2) for the period 1951-2004. This
model was previously validated for maize on soils of different water
holding capacity (TAW) in various locations. Simulations are
performed for Plovdiv, Stara Zagora and Sofia. Results relative to
Plovdiv show that in soils of large TAW (180 mm m-1) net irrigation
requirements (NIRs) range 0-40 mm in wet years and 350-380 mm in
dry years. In soils of small TAW (116 mm m-1), NIRs reach 440 mm
in the very dry year. NIRs in Sofia are about 80 mm smaller. Rainfed
maize is associated with great yield variability (29%
Abstract: This paper employs a new approach to regulate the
blood glucose level of type I diabetic patient under an intensive
insulin treatment. The closed-loop control scheme incorporates
expert knowledge about treatment by using reinforcement learning
theory to maintain the normoglycemic average of 80 mg/dl and the
normal condition for free plasma insulin concentration in severe
initial state. The insulin delivery rate is obtained off-line by using Qlearning
algorithm, without requiring an explicit model of the
environment dynamics. The implementation of the insulin delivery
rate, therefore, requires simple function evaluation and minimal
online computations. Controller performance is assessed in terms of
its ability to reject the effect of meal disturbance and to overcome the
variability in the glucose-insulin dynamics from patient to patient.
Computer simulations are used to evaluate the effectiveness of the
proposed technique and to show its superiority in controlling
hyperglycemia over other existing algorithms
Abstract: The necessity of updating the numerical models inputs, because of geometrical and resistive variations in rivers subject to solid transport phenomena, requires detailed control and monitoring activities. The human employment and financial resources of these activities moves the research towards the development of expeditive methodologies, able to evaluate the outflows through the measurement of more easily acquirable sizes. Recent studies highlighted the dependence of the entropic parameter on the kinematical and geometrical flow conditions. They showed a meaningful variability according to the section shape, dimension and slope. Such dependences, even if not yet well defined, could reduce the difficulties during the field activities, and also the data elaboration time. On the basis of such evidences, the relationships between the entropic parameter and the geometrical and resistive sizes, obtained through a large and detailed laboratory experience on steady free surface flows in conditions of macro and intermediate homogeneous roughness, are analyzed and discussed.
Abstract: If price and quantity are the fundamental building
blocks of any theory of market interactions, the importance of trading
volume in understanding the behavior of financial markets is clear.
However, while many economic models of financial markets have
been developed to explain the behavior of prices -predictability,
variability, and information content- far less attention has been
devoted to explaining the behavior of trading volume. In this article,
we hope to expand our understanding of trading volume by
developing a new measure of herding behavior based on a cross
sectional dispersion of volumes betas. We apply our measure to the
Toronto stock exchange using monthly data from January 2000 to
December 2002. Our findings show that the herd phenomenon
consists of three essential components: stationary herding, intentional
herding and the feedback herding.
Abstract: Given the motivation of maps impact in enhancing the
perception of the quality of life in a region, this work examines the
use of spatial analytical techniques in exploring the role of space in
shaping human development patterns in Assiut governorate.
Variations of human development index (HDI) of the governorate-s
villages, districts and cities are mapped using geographic information
systems (GIS). Global and local spatial autocorrelation measures are
employed to assess the levels of spatial dependency in the data and to
map clusters of human development. Results show prominent
disparities in HDI between regions of Assiut. Strong patterns of
spatial association were found proving the presence of clusters on the
distribution of HDI. Finally, the study indicates several "hot-spots" in
the governorate to be area of more investigations to explore the
attributes of such levels of human development. This is very
important for accomplishing the development plan of poorest regions
currently adopted in Egypt.
Abstract: The load frequency control problem of power systems has attracted a lot of attention from engineers and researchers over the years. Increasing and quickly changing load demand, coupled with the inclusion of more generators with high variability (solar and wind power generators) on the network are making power systems more difficult to regulate. Frequency changes are unavoidable but regulatory authorities require that these changes remain within a certain bound. Engineers are required to perform the tricky task of adjusting the control system to maintain the frequency within tolerated bounds. It is well known that to minimize frequency variations, a large proportional feedback gain (speed regulation constant) is desirable. However, this improvement in performance using proportional feedback comes about at the expense of a reduced stability margin and also allows some steady-state error. A conventional PI controller is then included as a secondary control loop to drive the steadystate error to zero. In this paper, we propose a robust controller to replace the conventional PI controller which guarantees performance and stability of the power system over the range of variation of the speed regulation constant. Simulation results are shown to validate the superiority of the proposed approach on a simple single-area power system model.
Abstract: Embedded systems need to respect stringent real
time constraints. Various hardware components included in such
systems such as cache memories exhibit variability and therefore
affect execution time. Indeed, a cache memory access from an
embedded microprocessor might result in a cache hit where the
data is available or a cache miss and the data need to be fetched
with an additional delay from an external memory. It is therefore
highly desirable to predict future memory accesses during
execution in order to appropriately prefetch data without incurring
delays. In this paper, we evaluate the potential of several artificial
neural networks for the prediction of instruction memory
addresses. Neural network have the potential to tackle the nonlinear
behavior observed in memory accesses during program
execution and their demonstrated numerous hardware
implementation emphasize this choice over traditional forecasting
techniques for their inclusion in embedded systems. However,
embedded applications execute millions of instructions and
therefore millions of addresses to be predicted. This very
challenging problem of neural network based prediction of large
time series is approached in this paper by evaluating various neural
network architectures based on the recurrent neural network
paradigm with pre-processing based on the Self Organizing Map
(SOM) classification technique.
Abstract: Diabetes mellitus (DM) is frequently characterized by
autonomic nervous dysfunction. Analysis of heart rate variability
(HRV) has become a popular noninvasive tool for assessing the
activities of autonomic nervous system (ANS). In this paper, changes
in ANS activity are quantified by means of frequency and time
domain analysis of R-R interval variability. Electrocardiograms
(ECG) of 16 patients suffering from DM and of 16 healthy volunteers
were recorded. Frequency domain analysis of extracted normal to
normal interval (NN interval) data indicates significant difference in
very low frequency (VLF) power, low frequency (LF) power and
high frequency (HF) power, between the DM patients and control
group. Time domain measures, standard deviation of NN interval
(SDNN), root mean square of successive NN interval differences
(RMSSD), successive NN intervals differing more than 50 ms (NN50
Count), percentage value of NN50 count (pNN50), HRV triangular
index and triangular interpolation of NN intervals (TINN) also show
significant difference between the DM patients and control group.
Abstract: Zero inflated strict arcsine model is a newly developed
model which is found to be appropriate in modeling overdispersed
count data. In this study, we extend zero inflated strict arcsine model
to zero inflated strict arcsine regression model by taking into
consideration the extra variability caused by extra zeros and
covariates in count data. Maximum likelihood estimation method is
used in estimating the parameters for this zero inflated strict arcsine
regression model.
Abstract: In this paper spatial variability of some chemical and
physical soil properties were investigated in mountain rangelands of
Nesho, Mazandaran province, Iran. 110 soil samples from 0-30 cm
depth were taken with systematic method on grid 30×30 m2 in
regions with different vegetation cover and transported to laboratory.
Then soil chemical and physical parameters including Acidity (pH),
Electrical conductivity, Caco3, Bulk density, Particle density, total
phosphorus, total Nitrogen, available potassium, Organic matter,
Saturation moisture, Soil texture (percentage of sand, silt and clay),
Sodium, Calcium, magnesium were measured in laboratory. Data
normalization was performed then was done statistical analysis for
description of soil properties and geostatistical analysis for indication
spatial correlation between these properties and were perpetrated
maps of spatial distribution of soil properties using Kriging method.
Results indicated that in the study area Saturation moisture and
percentage of Sand had highest and lowest spatial correlation
respectively.