Abstract: In this study, the locations and areas of commercial
accumulations were detected by using digital yellow page data. An
original buffering method that can accurately create polygons of
commercial accumulations is proposed in this paper.; by using this
method, distribution of commercial accumulations can be easily
created and monitored over a wide area. The locations, areas, and
time-series changes of commercial accumulations in the South Kanto
region can be monitored by integrating polygons of commercial
accumulations with the time-series data of digital yellow page data.
The circumstances of commercial accumulations were shown to vary
according to areas, that is, highly- urbanized regions such as the city
center of Tokyo and prefectural capitals, suburban areas near large
cities, and suburban and rural areas.
Abstract: The paper attempts a synthesis of problems relating to
municipal waste management in Nigeria and proposes a conceptual
knowledge management approach for tackling municipal waste
problems in cities across Nigeria. The application of knowledge
management approach and strategy is crucial for inculcating a change
of attitude towards improving the management of waste. The paper is
a review of existing literatures, information, policies and data on
municipal waste management in Nigeria. The inefficient management
of waste by individuals, households, consumers and waste
management companies can be attributed to inadequate information
on waste management benefits, lack of producers- involvement in
waste management as well as poor implementation of government
policies. The paper presents an alternative approach providing
solutions promoting efficient municipal waste management.
Abstract: We present analysis of spatial patterns of generic
disease spread simulated by a stochastic long-range correlation SIR
model, where individuals can be infected at long distance in a power
law distribution. We integrated various tools, namely perimeter,
circularity, fractal dimension, and aggregation index to characterize
and investigate spatial pattern formations. Our primary goal was to
understand for a given model of interest which tool has an advantage
over the other and to what extent. We found that perimeter and
circularity give information only for a case of strong correlation–
while the fractal dimension and aggregation index exhibit the growth
rule of pattern formation, depending on the degree of the correlation
exponent (β). The aggregation index method used as an alternative
method to describe the degree of pathogenic ratio (α). This study may
provide a useful approach to characterize and analyze the pattern
formation of epidemic spreading
Abstract: The present study is aim to prepare and evaluate the selfnanoemulsifying drug delivery (SNEDDS) system of a poorly water soluble drug valsartan in order to achieve a better dissolution rate which would further help in enhancing oral bioavailability. The present research work describes a SNEDDS of valsartan using labrafil M 1944 CS, Tween 80 and Transcutol HP. The pseudoternary phase diagrams with presence and absence of drug were plotted to check for the emulsification range and also to evaluate the effect of valsartan on the emulsification behavior of the phases. The mixtures consisting of oil (labrafil M 1944 CS) with surfactant (tween 80), co-surfactant (Transcutol HP) were found to be optimum formulations. Prepared formulations were evaluated for its particle size distribution, nanoemulsifying properties, robustness to dilution, self emulsication time, turbidity measurement, drug content and invitro dissolution. The optimized formulations are further evaluated for heating cooling cycle, centrifugation studies, freeze thaw cycling, particle size distribution and zeta potential were carried out to confirm the stability of the formed SNEDDS formulations. The prepared formulation revealed t a significant improvement in terms of the drug solubility as compared with marketed tablet and pure drug.
Abstract: The dynamics of Min proteins plays a center role in
accurate cell division. Although the nucleoids may presumably play
an important role in prokaryotic cell division, there is a lack of
models to account for its participation. In this work, we apply the
lattice Boltzmann method to investigate protein oscillation based on a
mesoscopic model that takes into account the nucleoid-s role. We
found that our numerical results are in reasonably good agreement
with the previous experimental results On comparing with the other
computational models without the presence of nucleoids, the
highlight of our finding is that the local densities of MinD and MinE
on the cytoplasmic membrane increases, especially along the cell
width, when the size of the obstacle increases, leading to a more
distinct cap-like structure at the poles. This feature indicated the
realistic pattern and reflected the combination of Min protein
dynamics and nucleoid-s role.
Abstract: This paper reports a distributed mutual exclusion
algorithm for mobile Ad-hoc networks. The network is clustered
hierarchically. The proposed algorithm considers the clustered
network as a logical tree and develops a token passing scheme
to get the mutual exclusion. The performance analysis and
simulation results show that its message requirement is optimal,
and thus the algorithm is energy efficient.
Abstract: We present a new method for the fully automatic 3D
reconstruction of the coronary artery centerlines, using two X-ray
angiogram projection images from a single rotating monoplane
acquisition system. During the first stage, the input images are
smoothed using curve evolution techniques. Next, a simple yet
efficient multiscale method, based on the information of the Hessian
matrix, for the enhancement of the vascular structure is introduced.
Hysteresis thresholding using different image quantiles, is used to
threshold the arteries. This stage is followed by a thinning procedure
to extract the centerlines. The resulting skeleton image is then pruned
using morphological and pattern recognition techniques to remove
non-vessel like structures. Finally, edge-based stereo correspondence
is solved using a parallel evolutionary optimization method based on
f symbiosis. The detected 2D centerlines combined with disparity
map information allow the reconstruction of the 3D vessel
centerlines. The proposed method has been evaluated on patient data
sets for evaluation purposes.
Abstract: The PAX6, a transcription factor, is essential for the morphogenesis of the eyes, brain, pituitary and pancreatic islets. In rodents, the loss of Pax6 function leads to central nervous system defects, anophthalmia, and nasal hypoplasia. The haplo-insufficiency of Pax6 causes microphthalmia, aggression and other behavioral abnormalities. It is also required in brain patterning and neuronal plasticity. In human, heterozygous mutation of Pax6 causes loss of iris [aniridia], mental retardation and glucose intolerance. The 3- deletion in Pax6 leads to autism and aniridia. The phenotypes are variable in peneterance and expressivity. However, mechanism of function and interaction of PAX6 with other proteins during development and associated disease are not clear. It is intended to explore interactors of PAX6 to elucidated biology of PAX6 function in the tissues where it is expressed and also in the central regulatory pathway. This report describes In-silico approaches to explore interacting proteins of PAX6. The models show several possible proteins interacting with PAX6 like MITF, SIX3, SOX2, SOX3, IPO13, TRIM, and OGT. Since the Pax6 is a critical transcriptional regulator and master control gene of eye and brain development it might be interacting with other protein involved in morphogenesis [TGIF, TGF, Ras etc]. It is also presumed that matricelluar proteins [SPARC, thrombospondin-1 and osteonectin etc] are likely to interact during transport and processing of PAX6 and are somewhere its cascade. The proteins involved in cell survival and cell proliferation can also not be ignored.
Abstract: Due to the complex network architecture, the mobile
adhoc network-s multihop feature gives additional problems to the
users. When the traffic load at each node gets increased, the
additional contention due its traffic pattern might cause the nodes
which are close to destination to starve the nodes more away from the
destination and also the capacity of network is unable to satisfy the
total user-s demand which results in an unfairness problem. In this
paper, we propose to create an algorithm to compute the optimal
MAC-layer bandwidth assigned to each flow in the network. The
bottleneck links contention area determines the fair time share which
is necessary to calculate the maximum allowed transmission rate used
by each flow. To completely utilize the network resources, we
compute two optimal rates namely, the maximum fair share and
minimum fair share. We use the maximum fair share achieved in
order to limit the input rate of those flows which crosses the
bottleneck links contention area when the flows that are not allocated
to the optimal transmission rate and calculate the following highest
fair share. Through simulation results, we show that the proposed
protocol achieves improved fair share and throughput with reduced
delay.
Abstract: This research details a Computational Fluid Dynamics (CFD) approach to model fluid flow in a journal bearing with 8 equispaced semi-circular axial grooves. Water is used as the lubricant and is fed from one end of the bearing to the other, under pressure. The geometry of the bearing is modeled using a commercially available modeling software GAMBIT and the flow analysis is performed using a dedicated CFD analysis software FLUENT. The pressure distribution in the bearing clearance is obtained from FLUENT for various whirl ratios and is used to calculate the hydrodynamic force components in the radial and tangential direction of the bearing. These values along with the various whirl speeds can be used to do a regression analysis to determine the stiffness and damping coefficients. The values obtained are then compared with the stiffness and damping coefficients of a 3 Axial groove water lubricated journal bearing and those obtained from a FORTRAN code for a similar bearing.
Abstract: Grid computing is a high performance computing
environment to solve larger scale computational applications. Grid
computing contains resource management, job scheduling, security
problems, information management and so on. Job scheduling is a
fundamental and important issue in achieving high performance in
grid computing systems. However, it is a big challenge to design an
efficient scheduler and its implementation. In Grid Computing, there
is a need of further improvement in Job Scheduling algorithm to
schedule the light-weight or small jobs into a coarse-grained or
group of jobs, which will reduce the communication time,
processing time and enhance resource utilization. This Grouping
strategy considers the processing power, memory-size and
bandwidth requirements of each job to realize the real grid system.
The experimental results demonstrate that the proposed scheduling
algorithm efficiently reduces the processing time of jobs in
comparison to others.
Abstract: A seismic isolation pad produced by utilizing the scrap
tire rubber which contains interleaved steel reinforcing cords has been
proposed. The steel cords are expected to function similar to the steel
plates used in conventional laminated rubber bearings. The scrap tire
rubber pad (STRP) isolator is intended to be used in low rise
residential buildings of highly seismic areas of the developing
countries. Experimental investigation was conducted on unbonded
STRP isolators, and test results provided useful information including
stiffness, damping values and an eventual instability of the isolation
unit. Finite element analysis (FE analysis) of STRP isolator was
carried out on properly bonded samples. These types of isolators
provide positive incremental force resisting capacity up to shear strain
level of 155%. This paper briefly discusses the force deformation
behavior of bonded STRP isolators including stability of the isolation
unit.
Abstract: Carbon tetrachloride (CCl4) is a well-known
hepatotoxin and exposure to this chemical is known to induce
oxidative stress and causes liver injury by the formation of free
radicals. Flacourtia indica commonly known as 'Baichi' has been
reported as an effective remedy for the treatment of a variety of
diseases. The objective of this study was to investigate the
hepatoprotective activity of aqueous extract of leaves of Flacourtia
indica against CCl4 induced hepatotoxicity. Animals were pretreated
with the aqueous extract of Flacourtia indica (250 & 500 mg/kg
body weight) for one week and then challenged with CCl4 (1.5 ml/kg
bw) in olive oil (1:1, v/v) on 7th day. Serum marker enzymes (ALP,
AST, ALT, Total Protein & Total Bilirubin) and TBARS level
(Marker for oxidative stress) were estimated in all the study groups.
Alteration in the levels of biochemical markers of hepatic damage
like AST, ALT, ALP, Total Protein, Total Bilirubin and lipid
peroxides (TBARS) were tested in both CCl4 treated and extract
treated groups. CCl4 has enhanced the AST, ALT, ALP and the
Lipid peroxides (TBARS) in liver. Treatment of aqueous extract of
Flacourtia indica leaves (250 & 500 mg/kg) exhibited a significant
protective effect by altering the serum levels of AST, ALT, ALP,
Total Protein, Total Bilirubin and liver TBARS. These biochemical
observations were supported by histopathological study of liver
sections. From this preliminary study it has been concluded that the
aqueous extract of the leaves of Flacourtia indica protects liver
against oxidative damages and could be used as an effective protector
against CCl4 induced hepatic damage. Our findings suggested that
Flacourtia indica possessed good hepatoprotective activity
Abstract: The present research was designed to investigate the
anti-microbial activity of aristolochic acid from the root of
Aristolochia bracteata. From the methanolic & ethyl extract extracts
of Aristolochia bracteata aristolochic acid I was isolated and
conformed through IR, NMR & MS. The percentage purity of
aristolochic acid I was determined by UV & HPLC method. Antibacterial
activity of extracts of Aristolochia bracteata and the
isolated compound was determined by disc diffusion method. The
results reveled that the isolated aristolochic acid from methanolic
extract was more pure than the compound from ethyl acetate extract.
The various extracts (500μg/disc) of Aristolochia bracteata showed
moderate antibacterial activity with the average zone of inhibition of
7-18 mm by disc diffusion method. Among the extracts, ethyl acetate
& methanol extracts were shown good anti-microbial activity and the
growth of E.coli (18 mm) was strongly inhibited. Microbial assay of
isolated compound (Aristolochic acid I) from ethyl acetate &
methanol extracts were shown good antimicrobial activity and the
zone of inhibition of both at higher concentration 50 μg/ml was
similar with the standard aristolochic acid. It may be concluded that
the isolated compound of aristolochic acid I has good anti-bacterial
activity.
Abstract: Predicting short term wind speed is essential in order
to prevent systems in-action from the effects of strong winds. It also
helps in using wind energy as an alternative source of energy, mainly
for Electrical power generation. Wind speed prediction has
applications in Military and civilian fields for air traffic control,
rocket launch, ship navigation etc. The wind speed in near future
depends on the values of other meteorological variables, such as
atmospheric pressure, moisture content, humidity, rainfall etc. The
values of these parameters are obtained from a nearest weather
station and are used to train various forms of neural networks. The
trained model of neural networks is validated using a similar set of
data. The model is then used to predict the wind speed, using the
same meteorological information. This paper reports an Artificial
Neural Network model for short term wind speed prediction, which
uses back propagation algorithm.
Abstract: Availability of high dimensional biological datasets such as from gene expression, proteomic, and metabolic experiments can be leveraged for the diagnosis and prognosis of diseases. Many classification methods in this area have been studied to predict disease states and separate between predefined classes such as patients with a special disease versus healthy controls. However, most of the existing research only focuses on a specific dataset. There is a lack of generic comparison between classifiers, which might provide a guideline for biologists or bioinformaticians to select the proper algorithm for new datasets. In this study, we compare the performance of popular classifiers, which are Support Vector Machine (SVM), Logistic Regression, k-Nearest Neighbor (k-NN), Naive Bayes, Decision Tree, and Random Forest based on mock datasets. We mimic common biological scenarios simulating various proportions of real discriminating biomarkers and different effect sizes thereof. The result shows that SVM performs quite stable and reaches a higher AUC compared to other methods. This may be explained due to the ability of SVM to minimize the probability of error. Moreover, Decision Tree with its good applicability for diagnosis and prognosis shows good performance in our experimental setup. Logistic Regression and Random Forest, however, strongly depend on the ratio of discriminators and perform better when having a higher number of discriminators.