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: As a result of the ambiguity and complexity
surrounding anaerobic digester foaming, efforts have been made by
various researchers to understand the process of anaerobic digester
foaming so as to proffer a solution that can be universally applied
rather than site specific. All attempts ranging from experimental
analysis to comparative review of other process has not fully
explained the conditions and process of foaming in anaerobic
digester. Studying the current available knowledge on foam
formation and relating it to anaerobic digester process and operating
condition, this piece of work presents a succinct and enhanced
understanding of foaming in anaerobic digesters as well as
introducing a simple method to identify the onset of anaerobic
digester foaming based on analysis of historical data from a field
scale system.
Abstract: A retrospective study conducted at Christian Medical
College (CMC) Teaching Hospital, Vellore, India on 14th August
2014 to assess the accuracy of clinically estimated foetal weight upon
labour admission. Estimating foetal weight is a crucial factor in
assessing maternal and foetal complications during and after labour.
Medical notes of ninety-eight postnatal women who fulfilled the
inclusion criteria were studied to evaluate the correlation between
their recorded Estimated Foetal Weight (EFW) on admission and
actual birth weight (ABW) of the newborn after delivery. Data
concerning maternal and foetal demographics was also noted.
Accuracy was determined by absolute percentage error and
proportion of estimates within 10% of ABW. Actual birth weights
ranged from 950-4080g. A strong positive correlation between EFW
and ABW (r=0.904) was noted. Term deliveries (≥40 weeks) in the
normal weight range (2500-4000g) had a 59.5% estimation accuracy
(n=74) compared to pre-term (4000g) were underestimated by 25% (n=3) and low birthweight
(LBW) babies were overestimated by 12.7% (n=9). Registrars who
estimated foetal weight were accurate in babies within normal weight
ranges. However, there needs to be an improvement in predicting
weight of macrosomic and LBW foetuses. We have suggested the use
of an amended version of the Johnson’s formula for the Indian
population for improvement and a need to re-audit once
implemented.
Abstract: Adoption of Information Systems (IS) is receiving
increasing attention such that its implications have been closely
monitored and studied by the IS management community, industry
and professional gatekeepers. Building on previous research
regarding the adoption of technology, this paper develops and
validates an integrated model of the adoption of mobile banking. The
model originates from the Technology Acceptance Model (TAM) and
the Theory of Planned Behaviour (TPB). This paper intends to offer a
preliminary scrutiny of the antecedents of the adoption of mobile
banking services in the context of a developing country. Data was
collected from Pakistan. The findings showed that an integrated TAM
and TPB model greatly explains the adoption intention of mobile
banking; and perceived behavioural control and its antecedents play a
significant role in predicting adoption Theoretical and managerial
implications of findings are presented and discussed.
Abstract: Prediction of maximum local scour is necessary for
the safety and economical design of the bridges. A number of
equations have been developed over the years to predict local scour
depth using laboratory data and a few pier equations have also been
proposed using field data. Most of these equations are empirical in
nature as indicated by the past publications. In this paper attempts
have been made to compute local depth of scour around bridge pier in
dimensional and non-dimensional form by using linear regression,
simple regression and SVM (Poly & Rbf) techniques along with few
conventional empirical equations. The outcome of this study suggests
that the SVM (Poly & Rbf) based modeling can be employed as an
alternate to linear regression, simple regression and the conventional
empirical equations in predicting scour depth of bridge piers. The
results of present study on the basis of non-dimensional form of
bridge pier scour indicate the improvement in the performance of
SVM (Poly & Rbf) in comparison to dimensional form of scour.
Abstract: The need to save time and cost of soil testing at the
planning stage of road work has necessitated developing predictive
models. This study proposes a model for predicting the dry density of
lateritic soils stabilized with corn cob ash (CCA) and blended cement
- CCA. Lateritic soil was first stabilized with CCA at 1.5, 3.0, 4.5 and
6% of the weight of soil and then stabilized with the same
proportions as replacement for cement. Dry density, specific gravity,
maximum degree of saturation and moisture content were determined
for each stabilized soil specimen, following standard procedure.
Polynomial equations containing alpha and beta parameters for CCA
and blended CCA-cement were developed. Experimental values were
correlated with the values predicted from the Matlab curve fitting
tool, and the Solver function of Microsoft Excel 2010. The correlation
coefficient (R2) of 0.86 was obtained indicating that the model could
be accepted in predicting the maximum dry density of CCA stabilized
soils to facilitate quick decision making in roadworks.
Abstract: The knitted fabric suffers a deformation in its
dimensions due to stretching and tension factors, transverse and
longitudinal respectively, during the process in rectilinear knitting
machines so it performs a dry relaxation shrinkage procedure and
thermal action of prefixed to obtain stable conditions in the knitting.
This paper presents a dry relaxation shrinkage prediction of Bordeaux
fiber using a feed forward neural network and linear regression
models. Six operational alternatives of shrinkage were predicted. A
comparison of the results was performed finding neural network
models with higher levels of explanation of the variability and
prediction. The presence of different reposes is included. The models
were obtained through a neural toolbox of Matlab and Minitab
software with real data in a knitting company of Southern
Guanajuato. The results allow predicting dry relaxation shrinkage of
each alternative operation.
Abstract: Predicting earnings management is vital for the capital
market participants, financial analysts and managers. The aim of this
research is attempting to respond to this query: Is there a significant
difference between the regression model and neural networks’
models in predicting earnings management, and which one leads to a
superior prediction of it? In approaching this question, a Linear
Regression (LR) model was compared with two neural networks
including Multi-Layer Perceptron (MLP), and Generalized
Regression Neural Network (GRNN). The population of this study
includes 94 listed companies in Tehran Stock Exchange (TSE)
market from 2003 to 2011. After the results of all models were
acquired, ANOVA was exerted to test the hypotheses. In general, the
summary of statistical results showed that the precision of GRNN did
not exhibit a significant difference in comparison with MLP. In
addition, the mean square error of the MLP and GRNN showed a
significant difference with the multi variable LR model. These
findings support the notion of nonlinear behavior of the earnings
management. Therefore, it is more appropriate for capital market
participants to analyze earnings management based upon neural
networks techniques, and not to adopt linear regression models.
Abstract: This work explores the inter-region investment
behaviors of Integrated Circuit (IC) design industry from Taiwan to
China using the amount of foreign direct investment (FDI). According
to the mutual dependence among different IC design industrial
locations, Lotka-Volterra model is utilized to explore the FDI
interactions between South and East China. Effects of inter-regional
collaborations on FDI flows into China are considered. The analysis
results show that FDIs into South China for IC design industry
significantly inspire the subsequent FDIs into East China, while FDIs
into East China for Taiwan’s IC design industry significantly hinder
the subsequent FDIs into South China. Because the supply chain along
IC industry includes upstream IC design, midstream manufacturing, as
well as downstream packing and testing enterprises, IC design industry
has to cooperate with IC manufacturing, packaging and testing
industries in the same area to form a strong IC industrial cluster.
Taiwan’s IC design industry implement the largest FDI amount into
East China and the second largest FDI amount into South China
among the four regions: North, East, Mid-West and South China. If IC
design houses undertake more FDIs in South China, those in East
China are urged to incrementally implement more FDIs into East
China to maintain the competitive advantages of the IC supply chain in
East China. On the other hand, as the FDIs in East China rise, the FDIs
in South China will successively decline since capitals have
concentrated in East China. In addition, this investigation proves that
the prediction of Lotka-Volterra model in FDI trends is accurate
because the industrial interactions between the two regions are
included. Finally, this work confirms that the FDI flows cannot reach a
stable equilibrium point, so the FDI inflows into East and South China
will expand in the future.
Abstract: The current study aims to highlight the loading
characteristics impact on the time evolution (focusing particularly on
long term effects) of the deformation of realized reinforced concrete
beams. Namely the tension stiffening code provisions (i.e. within
Eurocode 2) are reviewed with a clear intention to reassess their
operational value and predicting capacity. In what follows the
experimental programme adopted along with some preliminary
findings and numerical modeling attempts are presented. For a range of long slender reinforced concrete simply supported
beams (4200 mm) constant static sustained and repeated cyclic
loadings were applied mapping the time evolution of deformation.
All experiments were carried out at the Heavy Structures Lab of the
University of Leeds. During tests the mid-span deflection, creep
coefficient and shrinkage strains were monitored for duration of 90
days. The obtained results are set against the values predicted by
Eurocode 2 and the tools within an FE commercial package (i.e.
Midas FEA) to yield that existing knowledge and practise is at times
over-conservative.
Abstract: This paper presents a methodology for probabilistic
assessment of bearing capacity and prediction of failure mechanism
of masonry vaults at the ultimate state with consideration of the
natural variability of Young’s modulus of stones. First, the
computation model is explained. The failure mode corresponds to the
four-hinge mechanism. Based on this consideration, the study of a
vault composed of 16 segments is presented. The Young’s modulus of
the segments is considered as random variable defined by a mean
value and a coefficient of variation. A relationship linking the vault
bearing capacity to the voussoirs modulus variation is proposed. The
most probable failure mechanisms, in addition to that observed in the
deterministic case, are identified for each variability level as well as
their probability of occurrence. The results show that the mechanism
observed in the deterministic case has decreasing probability of
occurrence in terms of variability, while the number of other
mechanisms and their probability of occurrence increases with the
coefficient of variation of Young’s modulus. This means that if a
significant change in the Young’s modulus of the segments is proven,
taking it into account in computations becomes mandatory, both for
determining the vault bearing capacity and for predicting its failure
mechanism.
Abstract: Studying on the response of vegetation phenology to
climate change at different temporal and spatial scales is important for
understanding and predicting future terrestrial ecosystem dynamics
and the adaptation of ecosystems to global change. In this study, the
Moderate Resolution Imaging Spectroradiometer (MODIS)
Normalized Difference Vegetation Index (NDVI) dataset and climate
data were used to analyze the dynamics of grassland phenology as well
as their correlation with climatic factors in different eco-geographic
regions and elevation units across the Tibetan Plateau. The results
showed that during 2003–2012, the start of the grassland greening
season (SOS) appeared later while the end of the growing season
(EOS) appeared earlier following the plateau’s precipitation and heat
gradients from southeast to northwest. The multi-year mean value of
SOS showed differences between various eco-geographic regions and
was significantly impacted by average elevation and regional average
precipitation during spring. Regional mean differences for EOS were
mainly regulated by mean temperature during autumn. Changes in
trends of SOS in the central and eastern eco-geographic regions were
coupled to the mean temperature during spring, advancing by about
7d/°C. However, in the two southwestern eco-geographic regions,
SOS was delayed significantly due to the impact of spring
precipitation. The results also showed that the SOS occurred later with
increasing elevation, as expected, with a delay rate of 0.66 d/100m.
For 2003–2012, SOS showed an advancing trend in low-elevation
areas, but a delayed trend in high-elevation areas, while EOS was
delayed in low-elevation areas, but advanced in high-elevation areas.
Grassland SOS and EOS changes may be influenced by a variety of
other environmental factors in each eco-geographic region.
Abstract: Tannase (tannin acyl hydrolase, E.C.3.1.1.20) is an
important hydrolysable enzyme with innumerable applications and
industrial potential. In the present study, a kinetic model has been
developed for the batch fermentation used for the production of
tannase by A.flavus MTCC 3783. Maximum tannase activity of
143.30 U/ml was obtained at 96 hours under optimum operating
conditions at 35oC, an initial pH of 5.5 and with an inducer tannic
acid concentration of 3% (w/v) for a fermentation period of 120
hours. The biomass concentration reaches a maximum of 6.62 g/l at
96 hours and further there was no increase in biomass concentration
till the end of the fermentation. Various unstructured kinetic models
were analyzed to simulate the experimental values of microbial
growth, tannase activity and substrate concentration. The Logistic
model for microbial growth , Luedeking - Piret model for production
of tannase and Substrate utilization kinetic model for utilization of
substrate were capable of predicting the fermentation profile with
high coefficient of determination (R2) values of 0.980, 0.942 and
0.983 respectively. The results indicated that the unstructured models
were able to describe the fermentation kinetics more effectively.
Abstract: Human beings have the ability to make logical
decisions. Although human decision - making is often optimal, it is
insufficient when huge amount of data is to be classified. Medical
dataset is a vital ingredient used in predicting patient’s health
condition. In other to have the best prediction, there calls for most
suitable machine learning algorithms. This work compared the
performance of Artificial Neural Network (ANN) and Decision Tree
Algorithms (DTA) as regards to some performance metrics using
diabetes data. WEKA software was used for the implementation of
the algorithms. Multilayer Perceptron (MLP) and Radial Basis
Function (RBF) were the two algorithms used for ANN, while
RegTree and LADTree algorithms were the DTA models used. From
the results obtained, DTA performed better than ANN. The Root
Mean Squared Error (RMSE) of MLP is 0.3913 that of RBF is
0.3625, that of RepTree is 0.3174 and that of LADTree is 0.3206
respectively.
Abstract: This study investigates the effects of the lead angle
and chip thickness variation on surface roughness during the
machining of compacted graphite iron using ceramic cutting tools
under dry cutting conditions. Analytical models were developed for
predicting the surface roughness values of the specimens after the
face milling process. Experimental data was collected and imported
to the artificial neural network model. A multilayer perceptron model
was used with the back propagation algorithm employing the input
parameters of lead angle, cutting speed and feed rate in connection
with chip thickness. Furthermore, analysis of variance was employed
to determine the effects of the cutting parameters on surface
roughness. Artificial neural network and regression analysis were
used to predict surface roughness. The values thus predicted were
compared with the collected experimental data, and the
corresponding percentage error was computed. Analysis results
revealed that the lead angle is the dominant factor affecting surface
roughness. Experimental results indicated an improvement in the
surface roughness value with decreasing lead angle value from 88° to
45°.
Abstract: In this study, three robust predicting methods, namely artificial neural network (ANN), adaptive neuro fuzzy inference system (ANFIS) and support vector machine (SVM) were used for computing the resonant frequency of A-shaped compact microstrip antennas (ACMAs) operating at UHF band. Firstly, the resonant frequencies of 144 ACMAs with various dimensions and electrical parameters were simulated with the help of IE3D™ based on method of moment (MoM). The ANN, ANFIS and SVM models for computing the resonant frequency were then built by considering the simulation data. 124 simulated ACMAs were utilized for training and the remaining 20 ACMAs were used for testing the ANN, ANFIS and SVM models. The performance of the ANN, ANFIS and SVM models are compared in the training and test process. The average percentage errors (APE) regarding the computed resonant frequencies for training of the ANN, ANFIS and SVM were obtained as 0.457%, 0.399% and 0.600%, respectively. The constructed models were then tested and APE values as 0.601% for ANN, 0.744% for ANFIS and 0.623% for SVM were achieved. The results obtained here show that ANN, ANFIS and SVM methods can be successfully applied to compute the resonant frequency of ACMAs, since they are useful and versatile methods that yield accurate results.
Abstract: Fast speed drives for Permanent Magnet Synchronous
Motor (PMSM) is a crucial performance for the electric traction
systems. In this paper, PMSM is derived with a Model-based
Predictive Control (MPC) technique. Fast speed tracking is achieved
through optimization of the DC source utilization using MPC. The
technique is based on predicting the optimum voltage vector applied
to the driver. Control technique is investigated by comparing to the
cascaded PI control based on Space Vector Pulse Width Modulation
(SVPWM). MPC and SVPWM-based FOC are implemented with the
TMS320F2812 DSP and its power driver circuits. The designed MPC
for a PMSM drive is experimentally validated on a laboratory test
bench. The performances are compared with those obtained by a
conventional PI-based system in order to highlight the improvements,
especially regarding speed tracking response.
Abstract: The fatigue life of tubular joints commonly found in
offshore industry is not only dependent on the value of hot-spot stress
(HSS), but is also significantly influenced by the through-thethickness
stress distribution characterized by the degree of bending
(DoB). The determination of DoB values in a tubular joint is essential
for improving the accuracy of fatigue life estimation using the stresslife
(S–N) method and particularly for predicting the fatigue crack
growth based on the fracture mechanics (FM) approach. In the
present paper, data extracted from finite element (FE) analyses of
tubular KT-joints, verified against experimental data and parametric
equations, was used to investigate the effects of geometrical
parameters on DoB values at the crown 0°, saddle, and crown 180°
positions along the weld toe of central brace in tubular KT-joints
subjected to axial loading. Parametric study was followed by a set of
nonlinear regression analyses to derive DoB parametric formulas for
the fatigue analysis of KT-joints under axial loads. The tubular KTjoint
is a quite common joint type found in steel offshore structures.
However, despite the crucial role of the DoB in evaluating the fatigue
performance of tubular joints, this paper is the first attempt to study
and formulate the DoB values in KT-joints.
Abstract: In the present study, RBF neural networks were used
for predicting the performance and emission parameters of a
biodiesel engine. Engine experiments were carried out in a 4 stroke
diesel engine using blends of diesel and Honge methyl ester as the
fuel. Performance parameters like BTE, BSEC, Tex and emissions
from the engine were measured. These experimental results were
used for ANN modeling.
RBF center initialization was done by random selection and by
using Clustered techniques. Network was trained by using fixed and
varying widths for the RBF units. It was observed that RBF results
were having a good agreement with the experimental results.
Networks trained by using clustering technique gave better results
than using random selection of centers in terms of reduced MRE and
increased prediction accuracy. The average MRE for the performance
parameters was 3.25% with the prediction accuracy of 98% and for
emissions it was 10.4% with a prediction accuracy of 80%.
Abstract: This paper analyzes the conceptual framework of three
statistical methods, multiple regression, path analysis, and structural
equation models. When establishing research model of the statistical
modeling of complex social phenomenon, it is important to know the
strengths and limitations of three statistical models. This study
explored the character, strength, and limitation of each modeling and
suggested some strategies for accurate explaining or predicting the
causal relationships among variables. Especially, on the studying of
depression or mental health, the common mistakes of research
modeling were discussed.