Abstract: Formulation of a low oil-water ratio drilling mud with vegetable oil continuous phase without adversely affecting the mud rheology and stability has been a major challenge. A low oil-water ratio is beneficial in producing low fluid loss which is essential for wellbore stability. This study examined the possibility of 50/50 oil-water ratio invert emulsion drilling mud using a vegetable oil continuous phase. Jatropha oil was used as continuous phase. 12 ml of egg yolk which was separated from the albumen was added as the primary emulsifier additive. The rheological, stability and filtration properties were examined. The plastic viscosity and yield point were found to be 36cp and 17 Ib/100 ft2 respectively. The electrical stability at 48.9ºC was 353v and the 30 minutes fluid loss was 6ml. The results compared favourably with a similar formulation using 70/30 oil - water ratio giving plastic viscosity of 31cp, yield point of 17 Ib/100 ft2, electrical stability value of 480v and 12ml for the 30 minutes fluid loss. This study indicates that with a good mud composition using guided empiricism, 50/50 oil-water ratio invert emulsion drilling mud is feasible with a vegetable oil continuous phase. The choice of egg yolk as emulsifier additive is for compatibility with the vegetable oil and environmental concern. The high water content with no fluid loss additive will also minimise the cost of mud formulation.
Abstract: The abiotic elicitation is one of the methods for
increasing the secondary metabolites production in plant tissue
cultures and it seems to be more effective than traditional strategies.
This study verified the use of silver nitrate as elicitor to enhance
flavonolignans and flavonoid taxifolin production in suspension
culture of Sylibum marianum (L.) Gaertn. Silver nitrate in various
concentrations (5.887.10-3 mol/L, 5.887.10-4 mol/L, 5.887.10-5
mol/L) was used as elicitor. The content of secondary metabolites in
cell suspension cultures was determined by high performance liquid
chromatography. The samples were taken after 6, 12, 24, 48, 72 and
168 hours of treatment. The highest content of taxifolin production
(2.2 mg.g-1) in cell suspension culture of Silybum marianum (L.)
Gaertn. was detected after silver nitrate (5.887.10-4 mol/L) treatment
and 72 h application. Flavonolignans such as silybinA, silybin B,
silydianin, silychristin, isosilybin A, isosilybin B were not produced
by cell suspension culture of S. marianum after elicitor treatment.
Our results show that the secondarymetabolites could be released
from S. marianum cells into the nutrient medium by changed
permeability of cell wall.
Abstract: Background: Plantar pressure measurement is an effective method for assessing plantar loading and can be applied to evaluating movement performance of the foot. The purpose of this study is to explore the sprint athletes’ plantar loading characteristics and pain profiles in static standing. Methods: Experiments were undertaken on 80 first-division college sprint athletes and 85 healthy non-sprinters. ‘JC Mat’, the optical plantar pressure measurement was applied to examining the differences between both groups in the arch index (AI), three regional and six distinct sub-regional plantar pressure distributions (PPD), and footprint characteristics. Pain assessment and self-reported health status in sprint athletes were examined for evaluating their common pain areas. Results: Findings from the control group, the males’ AI fell into the normal range. Yet, the females’ AI was classified as the high-arch type. AI values of the sprint group were found to be significantly lower than the control group. PPD were higher at the medial metatarsal bone of both feet and the lateral heel of the right foot in the sprint group, the males in particular, whereas lower at the medial and lateral longitudinal arches of both feet. Footprint characteristics tended to support the results of the AI and PPD, and this reflected the corresponding pressure profiles. For the sprint athletes, the lateral knee joint and biceps femoris were the most common musculoskeletal pains. Conclusions: The sprint athletes’ AI were generally classified as high arches, and that their PPD were categorized between the features of runners and high-arched runners. These findings also correspond to the profiles of patellofemoral pain syndrome (PFPS)-related plantar pressure. The pain profiles appeared to correspond to the symptoms of high-arched runners and PFPS. The findings reflected upon the possible link between high arches and PFPS. The correlation between high-arched runners and PFPS development is worth further studies.
Abstract: The new methods as accelerated steam distillation
assisted by microwave (ASDAM) is a combination of microwave
heating and steam distillation, performed at atmospheric pressure at
very short extraction time. Isolation and concentration of volatile
compounds are performed by a single stage. (ASDAM) has been
compared with (ASDAM) with cryogrinding of seeds (CG) and a
conventional technique, hydrodistillation assisted by microwave
(HDAM), hydro-distillation (HD) for the extraction of essential oil
from aromatic herb as caraway and cumin seeds. The essential oils
extracted by (ASDAM) for 1 min were quantitatively (yield) and
qualitatively (aromatic profile) no similar to those obtained by
ASDAM-CG (1 min) and HD (for 3 h). The accelerated microwave
extraction with cryogrinding inhibits numerous enzymatic reactions
as hydrolysis of oils.
Microwave radiations constitute the adequate mean for the
extraction operations from the yields and high content in major
component majority point view, and allow to minimise considerably
the energy consumption, but especially heating time too, which is one
of essential parameters of artifacts formation.
The ASDAM and ASDAM-CG are green techniques and yields an
essential oil with higher amounts of more valuable oxygenated
compounds comparable to the biosynthesis compounds, and allows
substantial savings of costs, in terms of time, energy and plant
material.
Abstract: We present a new framework of the data-reusing (DR)
adaptive algorithms by incorporating a constraint on noise, referred
to as a noise constraint. The motivation behind this work is that the
use of the statistical knowledge of the channel noise can contribute
toward improving the convergence performance of an adaptive filter
in identifying a noisy linear finite impulse response (FIR) channel.
By incorporating the noise constraint into the cost function of the
DR adaptive algorithms, the noise constrained DR (NC-DR) adaptive
algorithms are derived. Experimental results clearly indicate their
superior performance over the conventional DR ones.
Abstract: We present a new subband adaptive filter (R-SAF)
which is robust against impulsive noise in system identification. To
address the vulnerability of adaptive filters based on the L2-norm
optimization criterion against impulsive noise, the R-SAF comes from
the L1-norm optimization criterion with a constraint on the energy
of the weight update. Minimizing L1-norm of the a posteriori error
in each subband with a constraint on minimum disturbance gives
rise to the robustness against the impulsive noise and the capable
convergence performance. Experimental results clearly demonstrate
that the proposed R-SAF outperforms the classical adaptive filtering
algorithms when impulsive noise as well as background noise exist.
Abstract: At present, application of the extension of soft set theory in decision making problems in day to day life is progressing rapidly. The concepts of fuzzy soft set and its properties have been evolved as an area of interest for the researchers. The generalization of the concepts recently got importance and a rapid growth in the research in this area witnessed its vital-ness. In this paper, an application of the concept of generalized fuzzy soft set to make decision in a social problem is presented. Further, this paper also highlights some of the key issues of the related areas.
Abstract: Hydrologic models are increasingly used as tools to
predict stormwater quantity and quality from urban catchments.
However, due to a range of practical issues, most models produce
gross errors in simulating complex hydraulic and hydrologic systems.
Difficulty in finding a robust approach for model calibration is one of
the main issues. Though automatic calibration techniques are
available, they are rarely used in common commercial hydraulic and
hydrologic modelling software e.g. MIKE URBAN. This is partly
due to the need for a large number of parameters and large datasets in
the calibration process. To overcome this practical issue, a
framework for automatic calibration of a hydrologic model was
developed in R platform and presented in this paper. The model was
developed based on the time-area conceptualization. Four calibration
parameters, including initial loss, reduction factor, time of
concentration and time-lag were considered as the primary set of
parameters. Using these parameters, automatic calibration was
performed using Approximate Bayesian Computation (ABC). ABC is
a simulation-based technique for performing Bayesian inference
when the likelihood is intractable or computationally expensive to
compute. To test the performance and usefulness, the technique was
used to simulate three small catchments in Gold Coast. For
comparison, simulation outcomes from the same three catchments
using commercial modelling software, MIKE URBAN were used.
The graphical comparison shows strong agreement of MIKE URBAN
result within the upper and lower 95% credible intervals of posterior
predictions as obtained via ABC. Statistical validation for posterior
predictions of runoff result using coefficient of determination (CD),
root mean square error (RMSE) and maximum error (ME) was found
reasonable for three study catchments. The main benefit of using
ABC over MIKE URBAN is that ABC provides a posterior
distribution for runoff flow prediction, and therefore associated
uncertainty in predictions can be obtained. In contrast, MIKE
URBAN just provides a point estimate. Based on the results of the
analysis, it appears as though ABC the developed framework
performs well for automatic calibration.
Abstract: This paper presents the strategic development plan of
winged rockets WIRES (WInged REusable Sounding rocket) aiming
at unmanned suborbital winged rocket for demonstrating future fully
reusable space transportation technologies, such as aerodynamics,
Navigation, Guidance and Control (NGC), composite structure,
propulsion system, and cryogenic tanks etc., by universities in
collaboration with government and industries, as well as the past and
current flight test results.
Abstract: Green roof system is considered a relatively new
concept in Malaysia even though it has been implemented widely in
the developed countries. Generally, green roofs provide many
benefits such as enhancing aesthetical quality of the built
environment, reduce urban heat island effect, reduce energy
consumption, improve stormwater attenuation, and reduce noise
pollution. A better understanding on the implementation of green roof
system in Malaysia is crucial, as Malaysia’s climate is different if
compared with the climate in temperate countries where most of the
green roof studies have been conducted. This study has concentrated
on the technical aspect of green roof system which focuses on i) types
of plants and method of planting; ii) engineering design for green
roof system; iii) its hydrological performance on reducing stormwater
runoff; and iv) benefits of green roofs with respect to energy.
Literature review has been conducted to identify the development and
obstacles associated with green roofs systems in Malaysia. The study
had identified the challenges and potentials of green roofs
development in Malaysia. This study also provided the
recommendations on standard design and strategies on the
implementation of green roofs in Malaysia in the near future.
Abstract: For several hundred years, the design of railway tracks
has practically remained unchanged. Traditionally, rail tracks are
placed on a ballast layer due to several reasons, including economy,
rapid drainage, and high load bearing capacity. The primary function
of ballast is to distributing dynamic track loads to sub-ballast and
subgrade layers, while also providing lateral resistance and allowing
for rapid drainage. Upon repeated trainloads, the ballast becomes
fouled due to ballast degradation and the intrusion of fines which
adversely affects the strength and deformation behaviour of ballast.
This paper presents the use of three-dimensional discrete element
method (DEM) in studying the shear behaviour of the fouled ballast
subjected to direct shear loading. Irregularly shaped particles of
ballast were modelled by grouping many spherical balls together in
appropriate sizes to simulate representative ballast aggregates. Fouled
ballast was modelled by injecting a specified number of miniature
spherical particles into the void spaces. The DEM simulation
highlights that the peak shear stress of the ballast assembly decreases
and the dilation of fouled ballast increases with an increase level of
fouling. Additionally, the distributions of contact force chain and
particle displacement vectors were captured during shearing progress,
explaining the formation of shear band and the evolutions of
volumetric change of fouled ballast.
Abstract: Energy consumption data, in particular those involving
public buildings, are impacted by many factors: the building structure,
climate/environmental parameters, construction, system operating
condition, and user behavior patterns. Traditional methods for data
analysis are insufficient. This paper delves into the data mining
technology to determine its application in the analysis of building
energy consumption data including energy consumption prediction,
fault diagnosis, and optimal operation. Recent literature are reviewed
and summarized, the problems faced by data mining technology in the
area of energy consumption data analysis are enumerated, and research
points for future studies are given.
Abstract: This paper aims to analysis the behavior of DC corona
discharge in wire-to-plate electrostatic precipitators (ESP). Currentvoltage
curves are particularly analyzed. Experimental results show
that discharge current is strongly affected by the applied voltage. The proposed method of current identification is to use the method
of least squares. Least squares problems that of into two categories:
linear or ordinary least squares and non-linear least squares,
depending on whether or not the residuals are linear in all unknowns.
The linear least-squares problem occurs in statistical regression
analysis; it has a closed-form solution. A closed-form solution (or
closed form expression) is any formula that can be evaluated in a
finite number of standard operations. The non-linear problem has no
closed-form solution and is usually solved by iterative.
Abstract: Segmentation of left ventricle (LV) from cardiac
ultrasound images provides a quantitative functional analysis of the
heart to diagnose disease. Active Shape Model (ASM) is widely used
for LV segmentation, but it suffers from the drawback that
initialization of the shape model is not sufficiently close to the target,
especially when dealing with abnormal shapes in disease. In this work,
a two-step framework is improved to achieve a fast and efficient LV
segmentation. First, a robust and efficient detection based on Hough
forest localizes cardiac feature points. Such feature points are used to
predict the initial fitting of the LV shape model. Second, ASM is
applied to further fit the LV shape model to the cardiac ultrasound
image. With the robust initialization, ASM is able to achieve more
accurate segmentation. The performance of the proposed method is
evaluated on a dataset of 810 cardiac ultrasound images that are mostly
abnormal shapes. This proposed method is compared with several
combinations of ASM and existing initialization methods. Our
experiment results demonstrate that accuracy of the proposed method
for feature point detection for initialization was 40% higher than the
existing methods. Moreover, the proposed method significantly
reduces the number of necessary ASM fitting loops and thus speeds up
the whole segmentation process. Therefore, the proposed method is
able to achieve more accurate and efficient segmentation results and is
applicable to unusual shapes of heart with cardiac diseases, such as left
atrial enlargement.
Abstract: In this paper, de Laval rotor system has been
characterized by a hinge model and its transient response numerically
treated for a dynamic solution. The effect of the ensuing non-linear
disturbances namely rub and breathing crack is numerically
simulated. Subsequently, three analysis methods: Orbit Analysis, Fast
Fourier Transform (FFT), and Wavelet Transform (WT) are
employed to extract features of the vibration signal of the faulty
system. An analysis of the system response orbits clearly indicates
the perturbations due to the rotor-to-stator contact. The sensitivities
of WT to the variation in system speed have been investigated by
Continuous Wavelet Transform (CWT). The analysis reveals that
features of crack, rubs and unbalance in vibration response can be
useful for condition monitoring. WT reveals its ability to detect nonlinear
signal, and obtained results provide a useful tool method for
detecting machinery faults.
Abstract: Increase of emergency incidents and crisis situations
requires proactive crisis management of authorities and for its
solution. Application Business Continuity Management helps the
crisis management authorities to quickly and responsibly respond to
threats. It also helps effectively and efficiently planning powers and
resources. The main goal of this article is describing Military
Continuity Management System (MCMS) based on the principles of
Business Continuity Management System (BCMS) for dealing with
floods in the territory of the selected municipalities. There are
explained steps of loading, running and evaluating activities in the
software application MCMS. Software MCMS provides complete
control over the tasks, contribute a comprehensive and responsible
approach solutions to solution floods in the municipality.
Abstract: Ensuring of continuity of business is basic strategy of
every company. Continuity of organization activities includes
comprehensive procedures that help in solving unexpected situations
of natural and anthropogenic character (for example flood, blaze,
economic situations). Planning of continuity operations is a process
that helps identify critical processes and implement plans for the
security and recovery of key processes. The aim of this article is to
demonstrate application of system approach to managing business
continuity called business continuity management systems in military
issues. This article describes the life cycle of business continuity
management which is based on the established cycle PDCA (Plan-
Do-Check-Act). After this is carried out by activities which are
making by University of Defence during activation of forces and
means of the integrated rescue system in case of emergencies -
accidents at a nuclear power plant in Czech Republic. Activities of
various stages of deployment earmarked forces and resources are
managed and evaluated by using MCMS application (Military
Continuity Management System).
Abstract: Sentiment analysis means to classify a given review
document into positive or negative polar document. Sentiment
analysis research has been increased tremendously in recent times
due to its large number of applications in the industry and academia.
Sentiment analysis models can be used to determine the opinion of
the user towards any entity or product. E-commerce companies can
use sentiment analysis model to improve their products on the basis
of users’ opinion. In this paper, we propose a new One-class Support
Vector Machine (One-class SVM) based sentiment analysis model
for movie review documents. In the proposed approach, we initially
extract features from one class of documents, and further test the
given documents with the one-class SVM model if a given new test
document lies in the model or it is an outlier. Experimental results
show the effectiveness of the proposed sentiment analysis model.
Abstract: We present probabilistic multinomial Dirichlet
classification model for multidimensional data and Gaussian process
priors. Here, we have considered efficient computational method that
can be used to obtain the approximate posteriors for latent variables
and parameters needed to define the multiclass Gaussian process
classification model. We first investigated the process of inducing a
posterior distribution for various parameters and latent function by
using the variational Bayesian approximations and important sampling
method, and next we derived a predictive distribution of latent
function needed to classify new samples. The proposed model is
applied to classify the synthetic multivariate dataset in order to verify
the performance of our model. Experiment result shows that our model
is more accurate than the other approximation methods.
Abstract: In this paper, we propose the variational EM inference
algorithm for the multi-class Gaussian process classification model
that can be used in the field of human behavior recognition. This
algorithm can drive simultaneously both a posterior distribution of a
latent function and estimators of hyper-parameters in a Gaussian
process classification model with multiclass. Our algorithm is based
on the Laplace approximation (LA) technique and variational EM
framework. This is performed in two steps: called expectation and
maximization steps. First, in the expectation step, using the Bayesian
formula and LA technique, we derive approximately the posterior
distribution of the latent function indicating the possibility that each
observation belongs to a certain class in the Gaussian process
classification model. Second, in the maximization step, using a derived
posterior distribution of latent function, we compute the maximum
likelihood estimator for hyper-parameters of a covariance matrix
necessary to define prior distribution for latent function. These two
steps iteratively repeat until a convergence condition satisfies.
Moreover, we apply the proposed algorithm with human action
classification problem using a public database, namely, the KTH
human action data set. Experimental results reveal that the proposed
algorithm shows good performance on this data set.