Abstract: Voltage level must be raised in order to deliver the
produced energy to the consumption zones with less loss and less
cost. Power transformers used to raise or lower voltage are important
parts of the energy transmission system. Power transformers used in
switchgear and power generation plants stay in human's intensive
habitat zones as a result of expanding cities. Accordingly, noise
levels produced by power transformers have begun more and more
important and they have established itself as one of the research field.
In this research, the noise cause on transformers has been
investigated, it's causes has been examined and noise measurement
techniques have been introduced. Examples of transformer noise test
results are submitted and precautions to be taken were discussed for
the purpose of decreasing of the noise which will occurred by
transformers.
Abstract: This research involved the use of word distributions
and morphological knowledge by speakers of Arabic learning English
connected different allomorphs in order to realize how the
morphology and syntax of English gives meaning through using
interactive crossword puzzles (ICP). Fifteen chapters covered with a
class of nine learners over an academic year of an intensive English
program were reviewed using the ICP. Learners were questioned
about how the use of this gaming element enhanced and motivated
their learning of English. The findings were positive indicating a
successful implementation of ICP both at creational and user levels.
This indicated a positive role technology had when learning and
teaching English through adopting an interactive gaming element for
learning English.
Abstract: Mumbai, being traditionally the epicenter of India's
trade and commerce, the existing major ports such as Mumbai and
Jawaharlal Nehru Ports (JN) situated in Thane estuary are also
developing its waterfront facilities. Various developments over the
passage of decades in this region have changed the tidal flux
entering/leaving the estuary. The intake at Pir-Pau is facing the
problem of shortage of water in view of advancement of shoreline,
while jetty near Ulwe faces the problem of ship scheduling due to
existence of shallower depths between JN Port and Ulwe Bunder. In
order to solve these problems, it is inevitable to have information
about tide levels over a long duration by field measurements.
However, field measurement is a tedious and costly affair;
application of artificial intelligence was used to predict water levels
by training the network for the measured tide data for one lunar tidal
cycle. The application of two layered feed forward Artificial Neural
Network (ANN) with back-propagation training algorithms such as
Gradient Descent (GD) and Levenberg-Marquardt (LM) was used to
predict the yearly tide levels at waterfront structures namely at Ulwe
Bunder and Pir-Pau. The tide data collected at Apollo Bunder, Ulwe,
and Vashi for a period of lunar tidal cycle (2013) was used to train,
validate and test the neural networks. These trained networks having
high co-relation coefficients (R= 0.998) were used to predict the tide
at Ulwe, and Vashi for its verification with the measured tide for the
year 2000 & 2013. The results indicate that the predicted tide levels
by ANN give reasonably accurate estimation of tide. Hence, the
trained network is used to predict the yearly tide data (2015) for
Ulwe. Subsequently, the yearly tide data (2015) at Pir-Pau was
predicted by using the neural network which was trained with the
help of measured tide data (2000) of Apollo and Pir-Pau. The analysis of measured data and study reveals that: The
measured tidal data at Pir-Pau, Vashi and Ulwe indicate that there is
maximum amplification of tide by about 10-20 cm with a phase lag
of 10-20 minutes with reference to the tide at Apollo Bunder
(Mumbai). LM training algorithm is faster than GD and with increase
in number of neurons in hidden layer and the performance of the
network increases. The predicted tide levels by ANN at Pir-Pau and
Ulwe provides valuable information about the occurrence of high and
low water levels to plan the operation of pumping at Pir-Pau and
improve ship schedule at Ulwe.
Abstract: In this paper, we investigate the low-lying energy
levels of the two-dimensional parabolic graphene quantum dots
(GQDs) in the presence of topological defects with long range
Coulomb impurity and subjected to an external uniform magnetic
field. The low-lying energy levels of the system are obtained within
the framework of the perturbation theory. We theoretically
demonstrate that a valley splitting can be controlled by geometrical
parameters of the graphene quantum dots and/or by tuning a uniform
magnetic field, as well as topological defects. It is found that, for
parabolic graphene dots, the valley splitting occurs due to the
introduction of spatial confinement. The corresponding splitting is
enhanced by the introduction of a uniform magnetic field and it
increases by increasing the angle of the cone in subcritical regime.
Abstract: Landfill leachates contain a number of persistent pollutants, including heavy metals. They have the ability to spread in ecosystems and accumulate in fish which most of them are classified as top-consumers of trophic chains. Fish are freely swimming organisms; but perhaps, due to their species-specific ecological and behavioral properties, they often prefer the most suitable biotopes and therefore, did not avoid harmful substances or environments. That is why it is necessary to evaluate the persistent pollutant dispersion in hydroecosystem using fish tissue metal concentration. In hydroecosystems of hybrid type (e.g. river-pond-river) the distance from the pollution source could be a perfect indicator of such a kind of metal distribution. The studies were carried out in the Kairiai landfill neighboring hybrid-type ecosystem which is located 5 km east of the Šiauliai City. Fish tissue (gills, liver, and muscle) metal concentration measurements were performed on two types of ecologically-different fishes according to their feeding characteristics: benthophagous (Gibel carp, roach) and predatory (Northern pike, perch). A number of mathematical models (linear, non-linear, using log and other transformations) have been applied in order to identify the most satisfactorily description of the interdependence between fish tissue metal concentration and the distance from the pollution source. However, the only one log-multiple regression model revealed the pattern that the distance from the pollution source is closely and positively correlated with metal concentration in all predatory fish tissues studied (gills, liver, and muscle).
Abstract: Magnetic Resonance Imaging Contrast Agents
(MRI-CM) are significant in the clinical and biological imaging as
they have the ability to alter the normal tissue contrast, thereby
affecting the signal intensity to enhance the visibility and detectability
of images. Superparamagnetic Iron Oxide (SPIO) nanoparticles,
coated with dextran or carboxydextran are currently available for
clinical MR imaging of the liver. Most SPIO contrast agents are
T2 shortening agents and Resovist (Ferucarbotran) is one of a
clinically tested, organ-specific, SPIO agent which has a low
molecular carboxydextran coating. The enhancement effect of
Resovist depends on its relaxivity which in turn depends on factors
like magnetic field strength, concentrations, nanoparticle properties,
pH and temperature. Therefore, this study was conducted to
investigate the impact of field strength and different contrast
concentrations on enhancement effects of Resovist. The study
explored the MRI signal intensity of Resovist in the physiological
range of plasma from T2-weighted spin echo sequence at three
magnetic field strengths: 0.47 T (r1=15, r2=101), 1.5 T (r1=7.4,
r2=95), and 3 T (r1=3.3, r2=160) and the range of contrast
concentrations by a mathematical simulation. Relaxivities of r1 and r2
(L mmol-1 Sec-1) were obtained from a previous study and the selected
concentrations were 0.05, 0.06, 0.07, 0.08, 0.09, 0.1, 0.2, 0.3, 0.4, 0.5,
0.6, 0.7, 0.8, 0.9, 1.0, 2.0, and 3.0 mmol/L. T2-weighted images were
simulated using TR/TE ratio as 2000 ms /100 ms. According to the
reference literature, with increasing magnetic field strengths, the
r1 relaxivity tends to decrease while the r2 did not show any
systematic relationship with the selected field strengths. In parallel,
this study results revealed that the signal intensity of Resovist at lower
concentrations tends to increase than the higher concentrations. The
highest reported signal intensity was observed in the low field strength
of 0.47 T. The maximum signal intensities for 0.47 T, 1.5 T and 3 T
were found at the concentration levels of 0.05, 0.06 and 0.05 mmol/L,
respectively. Furthermore, it was revealed that, the concentrations
higher than the above, the signal intensity was decreased
exponentially. An inverse relationship can be found between the field
strength and T2 relaxation time, whereas, the field strength was
increased, T2 relaxation time was decreased accordingly. However,
resulted T2 relaxation time was not significantly different between
0.47 T and 1.5 T in this study. Moreover, a linear correlation of
transverse relaxation rates (1/T2, s–1) with the concentrations of
Resovist can be observed. According to these results, it can conclude
that the concentration of SPIO nanoparticle contrast agents and the
field strengths of MRI are two important parameters which can affect the signal intensity of T2-weighted SE sequence. Therefore, when MR
imaging those two parameters should be considered prudently.
Abstract: The increasing availability of information about earth
surface elevation (Digital Elevation Models DEM) generated from
different sources (remote sensing, Aerial Images, Lidar) poses the
question about how to integrate and make available to the most than
possible audience this huge amount of data. In order to exploit the potential of 3D elevation representation the
quality of data management plays a fundamental role. Due to the high
acquisition costs and the huge amount of generated data, highresolution
terrain surveys tend to be small or medium sized and
available on limited portion of earth. Here comes the need to merge
large-scale height maps that typically are made available for free at
worldwide level, with very specific high resolute datasets. One the
other hand, the third dimension increases the user experience and the
data representation quality, unlocking new possibilities in data
analysis for civil protection, real estate, urban planning, environment
monitoring, etc. The open-source 3D virtual globes, which are
trending topics in Geovisual Analytics, aim at improving the
visualization of geographical data provided by standard web services
or with proprietary formats. Typically, 3D Virtual globes like do not
offer an open-source tool that allows the generation of a terrain
elevation data structure starting from heterogeneous-resolution terrain
datasets. This paper describes a technological solution aimed to set
up a so-called “Terrain Builder”. This tool is able to merge
heterogeneous-resolution datasets, and to provide a multi-resolution
worldwide terrain services fully compatible with CesiumJS and
therefore accessible via web using traditional browser without any
additional plug-in.
Abstract: With 40% of total world energy consumption,
building systems are developing into technically complex large
energy consumers suitable for application of sophisticated power
management approaches to largely increase the energy efficiency
and even make them active energy market participants. Centralized
control system of building heating and cooling managed by
economically-optimal model predictive control shows promising
results with estimated 30% of energy efficiency increase. The research
is focused on implementation of such a method on a case study
performed on two floors of our faculty building with corresponding
sensors wireless data acquisition, remote heating/cooling units and
central climate controller. Building walls are mathematically modeled
with corresponding material types, surface shapes and sizes. Models
are then exploited to predict thermal characteristics and changes in
different building zones. Exterior influences such as environmental
conditions and weather forecast, people behavior and comfort
demands are all taken into account for deriving price-optimal climate
control. Finally, a DC microgrid with photovoltaics, wind turbine,
supercapacitor, batteries and fuel cell stacks is added to make the
building a unit capable of active participation in a price-varying
energy market. Computational burden of applying model predictive
control on such a complex system is relaxed through a hierarchical
decomposition of the microgrid and climate control, where the
former is designed as higher hierarchical level with pre-calculated
price-optimal power flows control, and latter is designed as lower
level control responsible to ensure thermal comfort and exploit
the optimal supply conditions enabled by microgrid energy flows
management. Such an approach is expected to enable the inclusion
of more complex building subsystems into consideration in order to
further increase the energy efficiency.
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: Nowadays, education cannot be imagined without digital technologies. It broadens the horizons of teaching learning processes. Several universities are offering online courses. For evaluation purpose, e-examination systems are being widely adopted in academic environments. Multiple-choice tests are extremely popular. Moving away from traditional examinations to e-examination, Moodle as Learning Management Systems (LMS) is being used. Moodle logs every click that students make for attempting and navigational purposes in e-examination. Data mining has been applied in various domains including retail sales, bioinformatics. In recent years, there has been increasing interest in the use of data mining in e-learning environment. It has been applied to discover, extract, and evaluate parameters related to student’s learning performance. The combination of data mining and e-learning is still in its babyhood. Log data generated by the students during online examination can be used to discover knowledge with the help of data mining techniques. In web based applications, number of right and wrong answers of the test result is not sufficient to assess and evaluate the student’s performance. So, assessment techniques must be intelligent enough. If student cannot answer the question asked by the instructor then some easier question can be asked. Otherwise, more difficult question can be post on similar topic. To do so, it is necessary to identify difficulty level of the questions. Proposed work concentrate on the same issue. Data mining techniques in specific clustering is used in this work. This method decide difficulty levels of the question and categories them as tough, easy or moderate and later this will be served to the desire students based on their performance. Proposed experiment categories the question set and also group the students based on their performance in examination. This will help the instructor to guide the students more specifically. In short mined knowledge helps to support, guide, facilitate and enhance learning as a whole.
Abstract: With the strengthened regulation on the mandatory use
of recycled aggregate, development of construction materials using
recycled aggregate has recently increased. This study aimed to secure
the performance of asphalt concrete mixture by developing
recycled-modified asphalt using recycled basalt aggregate from the
Jeju area. The strength of the basalt aggregate from the Jeju area used
in this study was similar to that of general aggregate, while the specific
surface area was larger due to the development of pores. Modified
asphalt was developed using a general aggregate-recycled aggregate
ratio of 7:3, and the results indicated that the Marshall stability
increased by 27% compared to that of asphalt concrete mixture using
only general aggregate, and the flow values showed similar levels.
Also, the indirect tensile strength increased by 79%, and the toughness
increased by more than 100%. In addition, the TSR for examining
moisture resistance was 0.95 indicating that the reduction in the
indirect tensile strength due to moisture was very low (5% level), and
the developed recycled-modified asphalt could satisfy all the quality
standards of asphalt concrete mixture.
Abstract: This paper aims to link together the concepts of job
satisfaction, work engagement, trust, job meaningfulness and loyalty
to the organisation focusing on specific type of employment –
academic jobs. The research investigates the relationships between
job satisfaction, work engagement and loyalty as well as the impact
of trust and job meaningfulness on the work engagement and loyalty.
The survey was conducted in one of the largest Latvian higher
education institutions and the sample was drawn from academic staff
(n=326). Structured questionnaire with 44 reflective type questions
was developed to measure the constructs. Data was analysed using
SPSS and Smart-PLS software. Variance based structural equation
modelling (PLS-SEM) technique was used to test the model and to
predict the most important factors relevant to employee engagement
and loyalty. The first order model included two endogenous
constructs (loyalty and intention to stay and recommend to work in
this organisation, and employee engagement), as well as six
exogenous constructs (feeling of fair treatment and trust in
management; career growth opportunities; compensation, pay and
benefits; management; colleagues and teamwork; and finally job
meaningfulness). Job satisfaction was developed as second order
construct and both: first and second order models were designed for
data analysis. It was found that academics are more engaged than
satisfied with their work and main reason for that was found to be job
meaningfulness, which is significant predictor for work engagement,
but not for job satisfaction. Compensation is not significantly related
to work engagement, but only to job satisfaction. Trust was not
significantly related neither to engagement, nor to satisfaction,
however, it appeared to be significant predictor of loyalty and
intentions to stay with the University. Paper revealed academic jobs
as specific kind of employment where employees can be more
engaged than satisfied and highlighted the specific role of job
meaningfulness in the University settings.
Abstract: Landfill waste is a common problem as it has an
economic and environmental impact even if it is closed. Landfill
waste contains a high density of various persistent compounds such
as heavy metals, organic and inorganic materials. As persistent
compounds are slowly-degradable or even non-degradable in the
environment, they often produce sublethal or even lethal effects on
aquatic organisms. The aims of the present study were to estimate
sublethal effects of the Kairiai landfill (WGS: 55°55‘46.74“,
23°23‘28.4“) leachate on the locomotor activity of rainbow trout
Oncorhynchus mykiss juveniles using the original system package
developed in our laboratory for automated monitoring, recording and
analysis of aquatic organisms’ activity, and to determine patterns of
fish behavioral response to sublethal effects of leachate. Four
different concentrations of leachate were chosen: 0.125; 0.25; 0.5 and
1.0 mL/L (0.0025; 0.005; 0.01 and 0.002 as part of 96-hour LC50,
respectively). Locomotor activity was measured after 5, 10 and 30
minutes of exposure during 1-minute test-periods of each fish (7 fish
per treatment). The threshold-effect-concentration amounted to 0.18
mL/L (0.0036 parts of 96-hour LC50). This concentration was found
to be even 2.8-fold lower than the concentration generally assumed to
be “safe” for fish. At higher concentrations, the landfill leachate
solution elicited behavioral response of test fish to sublethal levels of
pollutants. The ability of the rainbow trout to detect and avoid
contaminants occurred after 5 minutes of exposure. The intensity of
locomotor activity reached a peak within 10 minutes, evidently
decreasing after 30 minutes. This could be explained by the
physiological and biochemical adaptation of fish to altered
environmental conditions. It has been established that the locomotor
activity of juvenile trout depends on leachate concentration and
exposure duration. Modeling of these parameters showed that the
activity of juveniles increased at higher leachate concentrations, but
slightly decreased with the increasing exposure duration. Experiment
results confirm that the behavior of rainbow trout juveniles is a
sensitive and rapid biomarker that can be used in combination with
the system for fish behavior monitoring, registration and analysis to
determine sublethal concentrations of pollutants in ambient water.
Further research should be focused on software improvement aimed
to include more parameters of aquatic organisms’ behavior and to
investigate the most rapid and appropriate behavioral responses in
different species. In practice, this study could be the basis for the
development and creation of biological early-warning systems
(BEWS).
Abstract: Myoelectric control system is the fundamental
component of modern prostheses, which uses the myoelectric signals
from an individual’s muscles to control the prosthesis movements.
The surface electromyogram signal (sEMG) being noninvasive has
been used as an input to prostheses controllers for many years.
Recent technological advances has led to the development of
implantable myoelectric sensors which enable the internal
myoelectric signal (MES) to be used as input to these prostheses
controllers. The intramuscular measurement can provide focal
recordings from deep muscles of the forearm and independent signals
relatively free of crosstalk thus allowing for more independent
control sites. However, little work has been done to compare the two
inputs. In this paper we have compared the classification accuracy of
six pattern recognition based myoelectric controllers which use
surface myoelectric signals recorded using untargeted (symmetric)
surface electrode arrays to the same controllers with multichannel
intramuscular myolectric signals from targeted intramuscular
electrodes as inputs. There was no significant enhancement in the
classification accuracy as a result of using the intramuscular EMG
measurement technique when compared to the results acquired using
the surface EMG measurement technique. Impressive classification
accuracy (99%) could be achieved by optimally selecting only five
channels of surface EMG.
Abstract: A 15-storey RC building, studied in this paper, is
representative of modern building type constructed in Madina City in
Saudi Arabia before 10 years ago. These buildings are almost
consisting of reinforced concrete skeleton i.e. columns, beams and
flat slab as well as shear walls in the stairs and elevator areas
arranged in the way to have a resistance system for lateral loads
(wind – earthquake loads). In this study, the dynamic properties of
the 15-storey RC building were identified using ambient motions
recorded at several, spatially-distributed locations within each
building. Three dimensional pushover analysis (Nonlinear static
analysis) was carried out using SAP2000 software incorporating
inelastic material properties for concrete, infill and steel. The effect
of modeling the building with and without infill walls, on the
performance point as well as capacity and demand spectra due to EQ
design spectrum function in Madina area has been investigated. ATC-
40 capacity and demand spectra are utilized to get the modification
factor (R) for the studied building. The purpose of this analysis is to
evaluate the expected performance of structural systems by
estimating, strength and deformation demands in design, and
comparing these demands to available capacities at the performance
levels of interest. The results are summarized and discussed.
Abstract: Orthogonal Frequency Division Multiplexing
(OFDM) has been used in many advanced wireless communication
systems due to its high spectral efficiency and robustness to
frequency selective fading channels. However, the major concern
with OFDM system is the high peak-to-average power ratio (PAPR)
of the transmitted signal. Some of the popular techniques used for
PAPR reduction in OFDM system are conventional partial transmit
sequences (CPTS) and clipping. In this paper, a parallel
combination/hybrid scheme of PAPR reduction using clipping and
CPTS algorithms is proposed. The proposed method intelligently
applies both the algorithms in order to reduce both PAPR as well as
computational complexity. The proposed scheme slightly degrades
bit error rate (BER) performance due to clipping operation and it can
be reduced by selecting an appropriate value of the clipping ratio
(CR). The simulation results show that the proposed algorithm
achieves significant PAPR reduction with much reduced
computational complexity.
Abstract: Teaching of mathematics to engineering students is an
open ended problem in education. The main goal of mathematics
learning for engineering students is the ability of applying a wide
range of mathematical techniques and skills in their engineering
classes and later in their professional work. Most of the
undergraduate engineering students and faculties feels that no efforts
and attempts are made to demonstrate the applicability of various
topics of mathematics that are taught thus making mathematics
unavoidable for some engineering faculty and their students. The lack
of understanding of concepts in engineering mathematics may hinder
the understanding of other concepts or even subjects. However, for
most undergraduate engineering students, mathematics is one of the
most difficult courses in their field of study. Most of the engineering students never understood mathematics or
they never liked it because it was too abstract for them and they could
never relate to it. A right balance of application and concept based
teaching can only fulfill the objectives of teaching mathematics to
engineering students. It will surely improve and enhance their
problem solving and creative thinking skills. In this paper, some practical (informal) ways of making
mathematics-teaching application based for the engineering students
is discussed. An attempt is made to understand the present state of
teaching mathematics in engineering colleges. The weaknesses and
strengths of the current teaching approach are elaborated. Some of
the causes of unpopularity of mathematics subject are analyzed and a
few pragmatic suggestions have been made. Faculty in mathematics
courses should spend more time discussing the applications as well as
the conceptual underpinnings rather than focus solely on strategies
and techniques to solve problems. They should also introduce more
‘word’ problems as these problems are commonly encountered in
engineering courses. Overspecialization in engineering education
should not occur at the expense of (or by diluting) mathematics and
basic sciences. The role of engineering education is to provide the
fundamental (basic) knowledge and to teach the students simple
methodology of self-learning and self-development. All these issues
would be better addressed if mathematics and engineering faculty
join hands together to plan and design the learning experiences for
the students who take their classes. When faculties stop competing
against each other and start competing against the situation, they will
perform better. Without creating any administrative hassles these
suggestions can be used by any young inexperienced faculty of
mathematics to inspire engineering students to learn engineering
mathematics effectively.
Abstract: The use of optical technologies in the
telecommunications has been increasing due to its ability to transmit
large amounts of data over long distances. However, as in all systems
of data transmission, optical communication channels suffer from
undesirable and non-deterministic effects, being essential to know the
same. Thus, this research allows the assessment of these effects, as
well as their characterization and beneficial uses of these effects.
Abstract: In this research, we propose to conduct diagnostic and
predictive analysis about the key factors and consequences of urban
population relocation. To achieve this goal, urban simulation models
extract the urban development trends as land use change patterns from
a variety of data sources. The results are treated as part of urban big
data with other information such as population change and economic
conditions. Multiple data mining methods are deployed on this data to
analyze nonlinear relationships between parameters. The result
determines the driving force of population relocation with respect to
urban sprawl and urban sustainability and their related parameters.
This work sets the stage for developing a comprehensive urban
simulation model for catering to specific questions by targeted users. It
contributes towards achieving sustainability as a whole.