Abstract: Near infrared (NIR) spectroscopy has always been of
great interest in the food and agriculture industries. The development
of prediction models has facilitated the estimation process in recent
years. In this study, 110 crude palm oil (CPO) samples were used to
build a free fatty acid (FFA) prediction model. 60% of the collected
data were used for training purposes and the remaining 40% used for
testing. The visible peaks on the NIR spectrum were at 1725 nm and
1760 nm, indicating the existence of the first overtone of C-H bands.
Principal component regression (PCR) was applied to the data in
order to build this mathematical prediction model. The optimal
number of principal components was 10. The results showed
R2=0.7147 for the training set and R2=0.6404 for the testing set.
Abstract: Self-compacting concrete (SCC) developed in Japan
in the late 80s has enabled the construction industry to reduce
demand on the resources, improve the work condition and also
reduce the impact of environment by elimination of the need for
compaction. Fuzzy logic (FL) approaches has recently been used to
model some of the human activities in many areas of civil
engineering applications. Especially from these systems in the model
experimental studies, very good results have been obtained. In the
present study, a model for predicting compressive strength of SCC
containing various proportions of fly ash, as partial replacement of
cement has been developed by using Fuzzy Inference System (FIS).
For the purpose of building this model, a database of experimental
data were gathered from the literature and used for training and
testing the model. The used data as the inputs of fuzzy logic models
are arranged in a format of five parameters that cover the total binder
content, fly ash replacement percentage, water content,
superplasticizer and age of specimens. The training and testing results
in the fuzzy logic model have shown a strong potential for predicting
the compressive strength of SCC containing fly ash in the considered
range.
Abstract: The study is devoted to define the optimal conditions
for the nitriding of pure iron at atmospheric pressure by using NH3-
Ar-C3H8 gas mixtures. After studying the mechanisms of phase
formation and mass transfer at the gas-solid interface, a mathematical
model is developed in order to predict the nitrogen transfer rate in the
solid, the ε-carbonitride layer growth rate and the nitrogen and
carbon concentration profiles. In order to validate the model and to
show its possibilities, it is compared with thermogravimetric
experiments, analyses and metallurgical observations (X-ray
diffraction, optical microscopy and electron microprobe analysis).
Results obtained allow us to demonstrate the sound correlation
between the experimental results and the theoretical predictions.
Abstract: The dramatic rise in the use of Social Media (SM)
platforms such as Facebook and Twitter provide access to an
unprecedented amount of user data. Users may post reviews on
products and services they bought, write about their interests, share
ideas or give their opinions and views on political issues. There is a
growing interest in the analysis of SM data from organisations for
detecting new trends, obtaining user opinions on their products and
services or finding out about their online reputations. A recent
research trend in SM analysis is making predictions based on
sentiment analysis of SM. Often indicators of historic SM data are
represented as time series and correlated with a variety of real world
phenomena like the outcome of elections, the development of
financial indicators, box office revenue and disease outbreaks. This
paper examines the current state of research in the area of SM mining
and predictive analysis and gives an overview of the analysis
methods using opinion mining and machine learning techniques.
Abstract: A key issue in seismic risk analysis within the context
of Performance-Based Earthquake Engineering is the evaluation of
the expected seismic damage of structures under a specific
earthquake ground motion. The assessment of the seismic
performance strongly depends on the choice of the seismic Intensity
Measure (IM), which quantifies the characteristics of a ground
motion that are important to the nonlinear structural response. Several
conventional IMs of ground motion have been used to estimate their
damage potential to structures. Yet, none of them has been proved to
be able to predict adequately the seismic damage. Therefore,
alternative, scalar intensity measures, which take into account not
only ground motion characteristics but also structural information
have been proposed. Some of these IMs are based on integration of
spectral values over a range of periods, in an attempt to account for
the information that the shape of the acceleration, velocity or
displacement spectrum provides. The adequacy of a number of these
IMs in predicting the structural damage of 3D R/C buildings is
investigated in the present paper. The investigated IMs, some of
which are structure specific and some are non structure-specific, are
defined via integration of spectral values. To achieve this purpose
three symmetric in plan R/C buildings are studied. The buildings are
subjected to 59 bidirectional earthquake ground motions. The two
horizontal accelerograms of each ground motion are applied along
the structural axes. The response is determined by nonlinear time
history analysis. The structural damage is expressed in terms of the
maximum interstory drift as well as the overall structural damage
index. The values of the aforementioned seismic damage measures
are correlated with seven scalar ground motion IMs. The comparative
assessment of the results revealed that the structure-specific IMs
present higher correlation with the seismic damage of the three
buildings. However, the adequacy of the IMs for estimation of the
structural damage depends on the response parameter adopted.
Furthermore, it was confirmed that the widely used spectral
acceleration at the fundamental period of the structure is a good
indicator of the expected earthquake damage level.
Abstract: Predicting earthquakes is an important issue in the
study of geography. Accurate prediction of earthquakes can help
people to take effective measures to minimize the loss of personal
and economic damage, such as large casualties, destruction of
buildings and broken of traffic, occurred within a few seconds.
United States Geological Survey (USGS) science organization
provides reliable scientific information about Earthquake Existed
throughout history & the Preliminary database from the National
Center Earthquake Information (NEIC) show some useful factors to
predict an earthquake in a seismic area like Aleutian Arc in the U.S.
state of Alaska. The main advantage of this prediction method that it
does not require any assumption, it makes prediction according to the
future evolution of the object's time series. The article compares
between simulation data result from trained BP and RBF neural
network versus actual output result from the system calculations.
Therefore, this article focuses on analysis of data relating to real
earthquakes. Evaluation results show better accuracy and higher
speed by using radial basis functions (RBF) neural network.
Abstract: The knowledge of biodiesel density over large ranges
of temperature and pressure is important for predicting the behavior
of fuel injection and combustion systems in diesel engines, and for
the optimization of such systems. In this study, cottonseed oil was
transesterified into biodiesel and its density was measured at
temperatures between 288 K and 358 K and pressures between 0.1
MPa and 30 MPa, with expanded uncertainty estimated as ±1.6 kg⋅m-
3. Experimental pressure-volume-temperature (pVT) cottonseed data
was used along with literature data relative to other 18 biodiesels, in
order to build a database used to test the correlation of density with
temperarure and pressure using the Goharshadi–Morsali–Abbaspour
equation of state (GMA EoS). To our knowledge, this is the first that
density measurements are presented for cottonseed biodiesel under
such high pressures, and the GMA EoS used to model biodiesel
density. The new tested EoS allowed correlations within 0.2 kg·m-3
corresponding to average relative deviations within 0.02%. The built
database was used to develop and test a new full predictive model
derived from the observed linear relation between density and degree
of unsaturation (DU), which depended from biodiesel FAMEs
profile. The average density deviation of this method was only about
3 kg.m-3 within the temperature and pressure limits of application.
These results represent appreciable improvements in the context of
density prediction at high pressure when compared with other
equations of state.
Abstract: Series of laboratory tests were carried out to study the
extent of scour caused by a three-dimensional wall jets exiting from a
square cross-section nozzle and into a non-cohesive sand beds.
Previous observations have indicated that the effect of the tail water
depth was significant for densimetric Froude number greater than ten.
However, the present results indicate that the cut off value could be
lower depending on the value of grain size-to-nozzle width ratio.
Numbers of equations are drawn out for a better scaling of numerous
scour parameters. Also suggested the empirical prediction of scour to
predict the scour centre line profile and plan view of scour profile at
any particular time.
Abstract: Pavement surface unevenness plays a pivotal role on
roughness index of road which affects on riding comfort ability.
Comfort ability refers to the degree of protection offered to vehicle
occupants from uneven elements in the road surface. So, it is
preferable to have a lower roughness index value for a better riding
quality of road users. Roughness is generally defined as an
expression of irregularities in the pavement surface which can be
measured using different equipments like MERLIN, Bump integrator,
Profilometer etc. Among them Bump Integrator is quite simple and
less time consuming in case of long road sections. A case study is
conducted on low volume roads in West District in Tripura to
determine roughness index (RI) using Bump Integrator at the
standard speed of 32 km/h. But it becomes too tough to maintain the
requisite standard speed throughout the road section. The speed of
Bump Integrator (BI) has to lower or higher in some distinctive
situations. So, it becomes necessary to convert these roughness index
values of other speeds to the standard speed of 32 km/h. This paper
highlights on that roughness index conversional model. Using SPSS
(Statistical Package of Social Sciences) software a generalized
equation is derived among the RI value at standard speed of 32 km/h
and RI value at other speed conditions.
Abstract: The substantial similarity of fatigue mechanism in a
new test rig for rolling contact fatigue (RCF) has been investigated. A
new reduced-scale test rig is designed to perform controlled RCF
tests in wheel-rail materials. The fatigue mechanism of the rig is
evaluated in this study using a combined finite element-fatigue
prediction approach. The influences of loading conditions on fatigue
crack initiation have been studied. Furthermore, the effects of some
artificial defects (squat-shape) on fatigue lives are examined. To
simulate the vehicle-track interaction by means of the test rig, a threedimensional
finite element (FE) model is built up. The nonlinear
material behaviour of the rail steel is modelled in the contact
interface. The results of FE simulations are combined with the critical
plane concept to determine the material points with the greatest
possibility of fatigue failure. Based on the stress-strain responses, by
employing of previously postulated criteria for fatigue crack initiation
(plastic shakedown and ratchetting), fatigue life analysis is carried
out. The results are reported for various loading conditions and
different defect sizes. Afterward, the cyclic mechanism of the test rig
is evaluated from the operational viewpoint. The results of fatigue
life predictions are compared with the expected number of cycles of
the test rig by its cyclic nature. Finally, the estimative duration of the
experiments until fatigue crack initiation is roughly determined.
Abstract: Different services based on different switching
techniques in wireless networks leads to drastic changes in the
properties of network traffic. Because of these diversities in services,
network traffic is expected to undergo qualitative and quantitative
variations. Hence, assumption of traffic characteristics and the
prediction of network events become more complex for the wireless
networks. In this paper, the traffic characteristics have been studied
by collecting traces from the mobile switching centre (MSC). The
traces include initiation and termination time, originating node, home
station id, foreign station id. Traffic parameters namely, call interarrival
and holding times were estimated statistically. The results
show that call inter-arrival and distribution time in this wireless
network is heavy-tailed and follow gamma distributions. They are
asymptotically long-range dependent. It is also found that the call
holding times are best fitted with lognormal distribution. Based on
these observations, an analytical model for performance estimation is
also proposed.
Abstract: Characterization of the engineering behavior of
unsaturated soil is dependent on the soil-water characteristic curve
(SWCC), a graphical representation of the relationship between water
content or degree of saturation and soil suction. A reasonable
description of the SWCC is thus important for the accurate prediction
of unsaturated soil parameters. The measurement procedures for
determining the SWCC, however, are difficult, expensive, and timeconsuming.
During the past few decades, researchers have laid a
major focus on developing empirical equations for predicting the
SWCC, with a large number of empirical models suggested. One of
the most crucial questions is how precisely existing equations can
represent the SWCC. As different models have different ranges of
capability, it is essential to evaluate the precision of the SWCC
models used for each particular soil type for better SWCC estimation.
It is expected that better estimation of SWCC would be achieved via
a thorough statistical analysis of its distribution within a particular
soil class. With this in view, a statistical analysis was conducted in
order to evaluate the reliability of the SWCC prediction models
against laboratory measurement. Optimization techniques were used
to obtain the best-fit of the model parameters in four forms of SWCC
equation, using laboratory data for relatively coarse-textured (i.e.,
sandy) soil. The four most prominent SWCCs were evaluated and
computed for each sample. The result shows that the Brooks and
Corey model is the most consistent in describing the SWCC for sand
soil type. The Brooks and Corey model prediction also exhibit
compatibility with samples ranging from low to high soil water
content in which subjected to the samples that evaluated in this study.
Abstract: This study aimed at investigating whether the
functional brain networks constructed using the initial EEG (obtained
when patients first visited hospital) can be correlated with the
progression of cognitive decline calculated as the changes of
mini-mental state examination (MMSE) scores between the latest and
initial examinations. We integrated the time–frequency cross mutual
information (TFCMI) method to estimate the EEG functional
connectivity between cortical regions, and the network analysis based
on graph theory to investigate the organization of functional networks
in aMCI. Our finding suggested that higher integrated functional
network with sufficient connection strengths, dense connection
between local regions, and high network efficiency in processing
information at the initial stage may result in a better prognosis of the
subsequent cognitive functions for aMCI. In conclusion, the functional
connectivity can be a useful biomarker to assist in prediction of
cognitive declines in aMCI.
Abstract: Load Forecasting plays a key role in making today's
and future's Smart Energy Grids sustainable and reliable. Accurate
power consumption prediction allows utilities to organize in advance
their resources or to execute Demand Response strategies more
effectively, which enables several features such as higher
sustainability, better quality of service, and affordable electricity
tariffs. It is easy yet effective to apply Load Forecasting at larger
geographic scale, i.e. Smart Micro Grids, wherein the lower available
grid flexibility makes accurate prediction more critical in Demand
Response applications. This paper analyses the application of
short-term load forecasting in a concrete scenario, proposed within the
EU-funded GreenCom project, which collect load data from single
loads and households belonging to a Smart Micro Grid. Three
short-term load forecasting techniques, i.e. linear regression, artificial
neural networks, and radial basis function network, are considered,
compared, and evaluated through absolute forecast errors and training
time. The influence of weather conditions in Load Forecasting is also
evaluated. A new definition of Gain is introduced in this paper, which
innovatively serves as an indicator of short-term prediction
capabilities of time spam consistency. Two models, 24- and
1-hour-ahead forecasting, are built to comprehensively compare these
three techniques.
Abstract: The mathematical analysis on radiation obtained and
the development of the solar photovoltaic (PV) array groundwater
pumping is needed in the rural areas of Thohoyandou for sizing and
power performance subject to the climate conditions within the area.
A simple methodology approach is developed for the directed
coupled solar, controller and submersible ground water pump system.
The system consists of a PV array, pump controller and submerged
pump, battery backup and charger controller. For this reason, the
theoretical solar radiation is obtained for optimal predictions and
system performance in order to achieve different design and
operating parameters. Here the examination of the PV schematic
module in a Direct Current (DC) application is used for obtainable
maximum solar power energy for water pumping. In this paper, a
simple efficient photovoltaic water pumping system is presented with
its theoretical studies and mathematical modeling of photovoltaics
(PV) system.
Abstract: Mass flow measurement is the basis of most technoeconomic
formulations in the chemical industry. This calls for
reliable and accurate detection of mass flow. Flow measurement
laboratory experiments were conducted using various instruments.
These consisted of orifice plates, various sized rotameters, wet gas
meter and soap bubble meter. This work was aimed at evaluating
appropriate operating conditions and accuracy of the aforementioned
devices. The experimental data collected were compared to
theoretical predictions from Bernoulli’s equation and calibration
curves supplied by the instrument’s manufacturers. The results
obtained showed that rotameters were more reliable for measuring
high and low flow rates; while soap-bubble meters and wet-gas
meters were found to be suitable for measuring low flow rates. The
laboratory procedures and findings of the actual work can assist
engineering students and professionals in conducting their flow
measurement laboratory test work.
Abstract: An analysis of the Australian Diabetes Screening
Study estimated undiagnosed diabetes mellitus [DM] prevalence in a
high risk general practice based cohort. DM prevalence varied from
9.4% to 18.1% depending upon the diagnostic criteria utilised with
age being a highly significant risk factor. Utilising the gold standard
oral glucose tolerance test, the prevalence of DM was 22-23% in
those aged >= 70 years and
Abstract: The construction of a new airport or the extension of
an existing one requires massive investments and many times public
private partnerships were considered in order to make feasible such
projects. One characteristic of these projects is uncertainty with
respect to financial and environmental impacts on the medium to long
term. Another one is the multistage nature of these types of projects.
While many airport development projects have been a success, some
others have turned into a nightmare for their promoters.
This communication puts forward a new approach for airport
investment risk assessment. The approach takes explicitly into
account the degree of uncertainty in activity levels prediction and
proposes milestones for the different stages of the project for
minimizing risk. Uncertainty is represented through fuzzy dual theory
and risk management is performed using dynamic programming. An
illustration of the proposed approach is provided.
Abstract: Recently GPS data is used in a lot of studies to
automatically reconstruct travel patterns for trip survey. The aim is to
minimize the use of questionnaire surveys and travel diaries so as to
reduce their negative effects. In this paper data acquired from GPS and
accelerometer embedded in smart phones is utilized to predict the
mode of transportation used by the phone carrier. For prediction,
Support Vector Machine (SVM) and Adaptive boosting (AdaBoost)
are employed. Moreover a unique method to improve the prediction
results from these algorithms is also proposed. Results suggest that the
prediction accuracy of AdaBoost after improvement is relatively better
than the rest.
Abstract: Artificial Neural Networks (ANN) trained using backpropagation
(BP) algorithm are commonly used for modeling
material behavior associated with non-linear, complex or unknown
interactions among the material constituents. Despite multidisciplinary
applications of back-propagation neural networks
(BPNN), the BP algorithm possesses the inherent drawback of
getting trapped in local minima and slowly converging to a global
optimum. The paper present a hybrid artificial neural networks and
genetic algorithm approach for modeling slump of ready mix
concrete based on its design mix constituents. Genetic algorithms
(GA) global search is employed for evolving the initial weights and
biases for training of neural networks, which are further fine tuned
using the BP algorithm. The study showed that, hybrid ANN-GA
model provided consistent predictions in comparison to commonly
used BPNN model. In comparison to BPNN model, the hybrid ANNGA
model was able to reach the desired performance goal quickly.
Apart from the modeling slump of ready mix concrete, the synaptic
weights of neural networks were harnessed for analyzing the relative
importance of concrete design mix constituents on the slump value.
The sand and water constituents of the concrete design mix were
found to exhibit maximum importance on the concrete slump value.