Abstract: Elastic scattering of α-particles from 9Be and 11B
nuclei at different alpha energies have been analyzed. Optical model
parameters (OMPs) of α-particles elastic scattering by these nuclei at
different energies have been obtained. In the present calculations, the
real part of the optical potential are derived by folding of nucleonnucleon
(NN) interaction into nuclear matter density distribution of
the projectile and target nuclei using computer code FRESCO. A
density-dependent version of the M3Y interaction (CDM3Y6), which
is based on the G-matrix elements of the Paris NN potential, has been
used. Volumetric integrals of the real and imaginary potential depth
(JR, JW) have been calculated and found to be energy dependent.
Good agreement between the experimental data and the theoretical
predictions in the whole angular range. In double folding (DF)
calculations, the obtained normalization coefficient Nr is in the range
0.70–1.32.
Abstract: The railway transport is considered as a one of the
most environmentally friendly mode of transport. With future
prediction of increasing of freight transport there are lines facing
problems with demanded capacity. Increase of the track capacity
could be achieved by infrastructure constructive adjustments. The
contribution shows how the travel time can be minimized and the
track capacity increased by changing some of the basic infrastructure
and operation parameters, for example, the minimal curve radius of
the track, the number of tracks, or the usable track length at stations.
Calculation of the necessary parameter changes is based on the
fundamental physical laws applied to the train movement, and
calculation of the occupation time is dependent on the changes of
controlling the traffic between the stations.
Abstract: The recent instability in economy was found to be
influencing the situation in Malaysia whether directly or indirectly.
Taking that into consideration, the government needs to find the best
approach to balance its citizen’s socio-economic strata level urgently.
Through education platform is among the efforts planned and acted
upon for the purpose of balancing the effects of the influence,
through the exposure of social entrepreneurial activity towards youth
especially those in higher institution level. Armed with knowledge
and skills that they gained, with the support by entrepreneurial
culture and environment while in campus; indirectly, the students will
lean more on making social entrepreneurship as a career option when
they graduate. Following the issues of marketability and workability
of current graduates that are becoming dire, research involving how
far the willingness of student to create social innovation that
contribute to the society without focusing solely on personal gain is
relevant enough to be conducted. With that, this research is
conducted with the purpose of identifying the level of entrepreneurial
intention and social entrepreneurship among higher institution
students in Malaysia. Stratified random sampling involves 355
undergraduate students from five public universities had been made
as research respondents and data were collected through surveys. The
data was then analyzed descriptively using min score and standard
deviation. The study found that the entrepreneurial intention of higher
education students are on moderate level, however it is the contrary
for social entrepreneurship activities, where it was shown on a high
level. This means that while the students only have moderate level of
willingness to be a social entrepreneur, they are very committed to
created social innovation through the social entrepreneurship
activities conducted. The implication from this study can be
contributed towards the higher institution authorities in prediction the
tendency of student in becoming social entrepreneurs. Thus, the
opportunities and facilities for realizing the courses related to social
entrepreneurship must be created expansively so that the vision of
creating as many social entrepreneurs as possible can be achieved.
Abstract: The source of the jet noise is generated by rocket exhaust plume during rocket engine testing. A domain decomposition approach is applied to the jet noise prediction in this paper. The aerodynamic noise coupling is based on the splitting into acoustic sources generation and sound propagation in separate physical domains. Large Eddy Simulation (LES) is used to simulate the supersonic jet flow. Based on the simulation results of the flow-fields, the jet noise distribution of the sound pressure level is obtained by applying the Ffowcs Williams-Hawkings (FW-H) acoustics equation and Fourier transform. The calculation results show that the complex structures of expansion waves, compression waves and the turbulent boundary layer could occur due to the strong interaction between the gas jet and the ambient air. In addition, the jet core region, the shock cell and the sound pressure level of the gas jet increase with the nozzle size increasing. Importantly, the numerical simulation results of the far-field sound are in good agreement with the experimental measurements in directivity.
Abstract: The cities of Johannesburg and Pretoria both located in the Gauteng province are separated by a distance of 58 km. The traffic queues on the Ben Schoeman freeway which connects these two cities can stretch for almost 1.5 km. Vehicle traffic congestion impacts negatively on the business and the commuter’s quality of life. The goal of this paper is to identify variables that influence the flow of traffic and to design a vehicle traffic prediction model, which will predict the traffic flow pattern in advance. The model will unable motorist to be able to make appropriate travel decisions ahead of time. The data used was collected by Mikro’s Traffic Monitoring (MTM). Multi-Layer perceptron (MLP) was used individually to construct the model and the MLP was also combined with Bagging ensemble method to training the data. The cross—validation method was used for evaluating the models. The results obtained from the techniques were compared using predictive and prediction costs. The cost was computed using combination of the loss matrix and the confusion matrix. The predicted models designed shows that the status of the traffic flow on the freeway can be predicted using the following parameters travel time, average speed, traffic volume and day of month. The implications of this work is that commuters will be able to spend less time travelling on the route and spend time with their families. The logistics industry will save more than twice what they are currently spending.
Abstract: Patient-specific models are instance-based learning
algorithms that take advantage of the particular features of the patient
case at hand to predict an outcome. We introduce two patient-specific
algorithms based on decision tree paradigm that use AUC as a
metric to select an attribute. We apply the patient specific algorithms
to predict outcomes in several datasets, including medical datasets.
Compared to the patient-specific decision path (PSDP) entropy-based
and CART methods, the AUC-based patient-specific decision path
models performed equivalently on area under the ROC curve (AUC).
Our results provide support for patient-specific methods being a
promising approach for making clinical predictions.
Abstract: This paper discusses the applicability of the numerical model for a damage prediction method of the accidental hydrogen explosion occurring in a hydrogen facility. The numerical model was based on an unstructured finite volume method (FVM) code “NuFD/FrontFlowRed”. For simulating unsteady turbulent combustion of leaked hydrogen gas, a combination of Large Eddy Simulation (LES) and a combustion model were used. The combustion model was based on a two scalar flamelet approach, where a G-equation model and a conserved scalar model expressed a propagation of premixed flame surface and a diffusion combustion process, respectively. For validation of this numerical model, we have simulated the previous two types of hydrogen explosion tests. One is open-space explosion test, and the source was a prismatic 5.27 m3 volume with 30% of hydrogen-air mixture. A reinforced concrete wall was set 4 m away from the front surface of the source. The source was ignited at the bottom center by a spark. The other is vented enclosure explosion test, and the chamber was 4.6 m × 4.6 m × 3.0 m with a vent opening on one side. Vent area of 5.4 m2 was used. Test was performed with ignition at the center of the wall opposite the vent. Hydrogen-air mixtures with hydrogen concentrations close to 18% vol. were used in the tests. The results from the numerical simulations are compared with the previous experimental data for the accuracy of the numerical model, and we have verified that the simulated overpressures and flame time-of-arrival data were in good agreement with the results of the previous two explosion tests.
Abstract: Data fusion technology can be the best way to extract
useful information from multiple sources of data. It has been widely
applied in various applications. This paper presents a data fusion
approach in multimedia data for event detection in twitter by using
Dempster-Shafer evidence theory. The methodology applies a mining
algorithm to detect the event. There are two types of data in the
fusion. The first is features extracted from text by using the bag-ofwords
method which is calculated using the term frequency-inverse
document frequency (TF-IDF). The second is the visual features
extracted by applying scale-invariant feature transform (SIFT). The
Dempster - Shafer theory of evidence is applied in order to fuse the
information from these two sources. Our experiments have indicated
that comparing to the approaches using individual data source, the
proposed data fusion approach can increase the prediction accuracy
for event detection. The experimental result showed that the proposed
method achieved a high accuracy of 0.97, comparing with 0.93 with
texts only, and 0.86 with images only.
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: Journal bearings used in IC engines are prone to premature
failures and are likely to fail earlier than the rated life due to
highly impulsive and unstable operating conditions and frequent
starts/stops. Vibration signature extraction and wear debris analysis
techniques are prevalent in industry for condition monitoring of
rotary machinery. However, both techniques involve a great deal of
technical expertise, time, and cost. Limited literature is available on
the application of these techniques for fault detection in reciprocating
machinery, due to the complex nature of impact forces that
confounds the extraction of fault signals for vibration-based analysis
and wear prediction. In present study, a simulation model was developed to investigate
the bearing wear behaviour, resulting because of different operating
conditions, to complement the vibration analysis. In current
simulation, the dynamics of the engine was established first, based on
which the hydrodynamic journal bearing forces were evaluated by
numerical solution of the Reynold’s equation. In addition, the
essential outputs of interest in this study, critical to determine wear
rates are the tangential velocity and oil film thickness between the
journals and bearing sleeve, which if not maintained appropriately,
have a detrimental effect on the bearing performance. Archard’s wear prediction model was used in the simulation to
calculate the wear rate of bearings with specific location information
as all determinative parameters were obtained with reference to crank
rotation. Oil film thickness obtained from the model was used as a
criterion to determine if the lubrication is sufficient to prevent contact
between the journal and bearing thus causing accelerated wear. A
limiting value of 1 μm was used as the minimum oil film thickness
needed to prevent contact. The increased wear rate with growing
severity of operating conditions is analogous and comparable to the
rise in amplitude of the squared envelope of the referenced vibration
signals. Thus on one hand, the developed model demonstrated its
capability to explain wear behaviour and on the other hand it also
helps to establish a co-relation between wear based and vibration
based analysis. Therefore, the model provides a cost effective and
quick approach to predict the impending wear in IC engine bearings
under various operating conditions.
Abstract: This paper focuses on the mathematical modeling for
solidification of Al alloy in a cube mold cavity to study the
solidification behavior of casting process. The parametric
investigation of solidification process inside the cavity was
performed by using computational solidification/melting model
coupled with Volume of fluid (VOF) model. The implicit filling
algorithm is used in this study to understand the overall process from
the filling stage to solidification in a model metal casting process.
The model is validated with past studied at same conditions. The
solidification process is analyzed by including the effect of pouring
velocity as well as natural convection from the wall and geometry of
the cavity. These studies show the possibility of various defects
during solidification process.
Abstract: Response Surface Methods (RSM) provide
statistically validated predictive models that can then be manipulated
for finding optimal process configurations. Variation transmitted to
responses from poorly controlled process factors can be accounted
for by the mathematical technique of propagation of error (POE),
which facilitates ‘finding the flats’ on the surfaces generated by
RSM. The dual response approach to RSM captures the standard
deviation of the output as well as the average. It accounts for
unknown sources of variation. Dual response plus propagation of
error (POE) provides a more useful model of overall response
variation. In our case, we implemented this technique in predicting
compressive strength of concrete of 28 days in age. Since 28 days is
quite time consuming, while it is important to ensure the quality
control process. This paper investigates the potential of using design
of experiments (DOE-RSM) to predict the compressive strength of
concrete at 28th day. Data used for this study was carried out from
experiment schemes at university of Benghazi, civil engineering
department. A total of 114 sets of data were implemented. ACI mix
design method was utilized for the mix design. No admixtures were
used, only the main concrete mix constituents such as cement, coarseaggregate,
fine aggregate and water were utilized in all mixes.
Different mix proportions of the ingredients and different water
cement ratio were used. The proposed mathematical models are
capable of predicting the required concrete compressive strength of
concrete from early ages.
Abstract: A seizure prediction method is proposed by extracting
global features using phase correlation between adjacent epochs for
detecting relative changes and local features using fluctuation/
deviation within an epoch for determining fine changes of different
EEG signals. A classifier and a regularization technique are applied
for the reduction of false alarms and improvement of the overall
prediction accuracy. The experiments show that the proposed method
outperforms the state-of-the-art methods and provides high prediction
accuracy (i.e., 97.70%) with low false alarm using EEG signals in
different brain locations from a benchmark data set.
Abstract: In this paper, a robust fault detection and isolation
(FDI) scheme is developed to monitor a multivariable nonlinear
chemical process called the Chylla-Haase polymerization reactor,
when it is under the cascade PI control. The scheme employs a radial
basis function neural network (RBFNN) in an independent mode to
model the process dynamics, and using the weighted sum-squared
prediction error as the residual. The Recursive Orthogonal Least
Squares algorithm (ROLS) is employed to train the model to
overcome the training difficulty of the independent mode of the
network. Then, another RBFNN is used as a fault classifier to isolate
faults from different features involved in the residual vector. Several
actuator and sensor faults are simulated in a nonlinear simulation of
the reactor in Simulink. The scheme is used to detect and isolate the
faults on-line. The simulation results show the effectiveness of the
scheme even the process is subjected to disturbances and
uncertainties including significant changes in the monomer feed rate,
fouling factor, impurity factor, ambient temperature, and
measurement noise. The simulation results are presented to illustrate
the effectiveness and robustness of the proposed method.
Abstract: Production fluids are transported from the platform to
tankers or process facilities through transfer pipelines. Water being
one of the heavier phases tends to settle at the bottom of pipelines
especially at low flow velocities and this has adverse consequences
for pipeline integrity. On restart after a shutdown, this could result in
corrosion and issues for process equipment, thus the need to have the
heavier liquid dispersed into the flowing lighter fluid. This study
looked at the flow regime of low water cut and low flow velocity oil
and water flow using conductive film thickness probes in a large
diameter 4-inch pipe to obtain oil and water interface height and the
interface structural velocity. A wide range of 0.1–1.0 m/s oil and
water mixture velocities was investigated for 0.5–5% water cut. Two
fluid model predictions were used to compare with the experimental
results.
Abstract: Wind energy is rapidly emerging as the primary
source of electricity in the Philippines, although developing an
accurate wind resource model is difficult. In this study, Weather
Research and Forecasting (WRF) Model, an open source mesoscale
Numerical Weather Prediction (NWP) model, was used to produce a
1-year atmospheric simulation with 4 km resolution on the Ilocos
Region of the Philippines. The WRF output (netCDF) extracts the
annual mean wind speed data using a Python-based Graphical User
Interface. Lastly, wind resource assessment was produced using a
GIS software. Results of the study showed that it is more flexible to
use Python scripts than using other post-processing tools in dealing
with netCDF files. Using WRF Model, Python, and Geographic
Information Systems, a reliable wind resource map is produced.
Abstract: The fundamental issue in understanding the origin and
growth mechanism of nanomaterials, from a fundamental unit is a big
challenging problem to the scientists. Recently, an immense attention
is generated to the researchers for prediction of exceptionally stable
atomic cluster units as the building units for future smart materials.
The present study is a systematic investigation on the stability and
electronic properties of a series of bimetallic (semiconductor-alkaline
earth) clusters, viz., BxMg3 (x=1-5) is performed, in search for
exceptional and/ or unusual stable motifs. A very popular hybrid
exchange-correlation functional, B3LYP along with a higher basis
set, viz., 6-31+G[d,p] is employed for this purpose under the density
functional formalism. The magic stability among the concerned
clusters is explained using the jellium model. It is evident from the
present study that the magic stability of B4Mg3
cluster arises due to
the jellium shell closure.
Abstract: In this study, the three-dimensional cavitating
turbulent flow in a complete Francis turbine is simulated using
mixture model for cavity/liquid two-phase flows. Numerical analysis
is carried out using ANSYS CFX software release 12, and standard k-ε
turbulence model is adopted for this analysis. The computational
fluid domain consist of spiral casing, stay vanes, guide vanes, runner
and draft tube. The computational domain is discretized with a threedimensional
mesh system of unstructured tetrahedron mesh. The
finite volume method (FVM) is used to solve the governing equations
of the mixture model. Results of cavitation on the runner’s blades
under three different boundary conditions are presented and
discussed. From the numerical results it has been found that the
numerical method was successfully applied to simulate the cavitating
two-phase turbulent flow through a Francis turbine, and also
cavitation is clearly predicted in the form of water vapor formation
inside the turbine. By comparison the numerical prediction results
with a real runner; it’s shown that the region of higher volume
fraction obtained by simulation is consistent with the region of runner
cavitation damage.
Abstract: The wider growing Finite Element Method (FEM)
application is caused by its benefits of cost saving and environment
friendly. Also, by using FEM a deep understanding of certain
phenomenon can be achieved. This paper observed the role of
material properties and volumetric change when Solid State Phase
Transformation (SSPT) takes place in residual stress formation due to
a welding process of ferritic steels through coupled Thermo-
Metallurgy-Mechanical (TMM) analysis. The correctness of FEM residual stress prediction was validated by
experiment. From parametric study of the FEM model, it can be
concluded that the material properties change tend to over-predicts
residual stress in the weld center whilst volumetric change tend to
underestimates it. The best final result is the compromise of both by
incorporates them in the model which has a better result compared to
a model without SSPT.
Abstract: Recent investigations have demonstrated the global
sea level rise due to climate change impacts. In this study, climate
changes study the effects of increasing water level in the strait of
Hormuz. The probable changes of sea level rise should be
investigated to employ the adaption strategies. The climatic output
data of a GCM (General Circulation Model) named CGCM3 under
climate change scenario of A1b and A2 were used. Among different
variables simulated by this model, those of maximum correlation
with sea level changes in the study region and least redundancy
among themselves were selected for sea level rise prediction by using
stepwise regression. One of models (Discrete Wavelet artificial
Neural Network) was developed to explore the relationship between
climatic variables and sea level changes. In these models, wavelet
was used to disaggregate the time series of input and output data into
different components and then ANN was used to relate the
disaggregated components of predictors and input parameters to each
other. The results showed in the Shahid Rajae Station for scenario
A1B sea level rise is among 64 to 75 cm and for the A2 Scenario sea
level rise is among 90 t0 105 cm. Furthermore, the result showed a
significant increase of sea level at the study region under climate
change impacts, which should be incorporated in coastal areas
management.