Abstract: The garment manufacturing industry involves
sequential processes that are subjected to uncontrollable variations.
The industry depends on the skill of labour in handling the varieties
of fabrics and accessories, machines, as well as complicated sewing
operation. Due to these reasons, garment manufacturers have created
systems to monitor and to control the quality of the products on a
regular basis by conducting quality approaches to minimize variation.
With that, the aim of this research has been to ascertain the quality
approaches deployed by Malaysian garment manufacturers in three
key areas - quality systems and tools; quality control and types of
inspection; as well as sampling procedures chosen for garment
inspection. Besides, the focus of this research was to distinguish the
quality approaches adopted by companies that supplied finished
garments to both domestic and international markets. Feedback from
each company representative has been obtained via online survey,
which comprised of five sections and 44 questions on the
organizational profile and the quality approaches employed in the
garment industry. As a result, the response rate was 31%. The results
revealed that almost all companies have established their own
mechanism of process control by conducting a series of quality
inspections for daily production, either it was formally set up or
otherwise. In addition, quality inspection has been the predominant
quality control activity in the garment manufacturing, while the level
of complexity of these activities was substantially dictated by the
customers. Moreover, AQL-based sampling was utilized by
companies dealing with exports, whilst almost all the companies that
only concentrated on the domestic market were comfortable using
their own sampling procedures for garment inspection. Hence, this
research has provided insights into the implementation of a number
of quality approaches that were perceived as important and useful in
the garment manufacturing sector, which is truly labour-intensive.
Abstract: South Africa is in its post-industrial era moving from
the primary and secondary sector to the tertiary sector. The study
investigated the impact of the disaggregated energy consumption
(coal, oil, and electricity) on the primary, secondary and tertiary
sectors of the economy between 1980 and 2012 in South Africa.
Using vector error correction model, it was established that South
Africa is an energy dependent economy, and that energy (especially
electricity and oil) is a limiting factor of growth. This implies that
implementation of energy conservation policies may hamper
economic growth. Output growth is significantly outpacing energy
supply, which has necessitated load shedding. To meet up the excess
energy demand, there is a need to increase the generating capacity
which will necessitate increased investment in the electricity sector as
well as strategic steps to increase oil production. There is also need to
explore more renewable energy sources, in order to meet the growing
energy demand without compromising growth and environmental
sustainability. Policy makers should also pursue energy efficiency
policies especially at sectoral level of the economy.
Abstract: Presently various computational techniques are used
in modeling and analyzing environmental engineering data. In the
present study, an intra-comparison of polynomial and radial basis
kernel functions based on Support Vector Regression and, in turn, an
inter-comparison with Multi Linear Regression has been attempted in
modeling mass transfer capacity of vertical (θ = 90O) and inclined (θ
multiple plunging jets (varying from 1 to 16 numbers). The data set
used in this study consists of four input parameters with a total of
eighty eight cases, forty four each for vertical and inclined multiple
plunging jets. For testing, tenfold cross validation was used.
Correlation coefficient values of 0.971 and 0.981 along with
corresponding root mean square error values of 0.0025 and 0.0020
were achieved by using polynomial and radial basis kernel functions
based Support Vector Regression respectively. An intra-comparison
suggests improved performance by radial basis function in
comparison to polynomial kernel based Support Vector Regression.
Further, an inter-comparison with Multi Linear Regression
(correlation coefficient = 0.973 and root mean square error = 0.0024)
reveals that radial basis kernel functions based Support Vector
Regression performs better in modeling and estimating mass transfer
by multiple plunging jets.
Abstract: Annihilations, phase shifts, scattering lengths and
elastic cross sections of low energy positrons scattering from
magnesium atoms were studied using the least-squares variational
method (LSVM). The possibility of positron binding to the
magnesium atoms is investigated. A trial wave function is suggested
to represent e+-Mg elastic scattering and scattering parameters were
derived to estimate the binding energy and annihilation rates. The
trial function is taken to depend on several adjustable parameters, and
is improved iteratively by increasing the number of terms. The
present results have the same behavior as reported semi-empirical,
theoretical and experimental results. Especially, the estimated
positive scattering length supports the possibility of positronmagnesium
bound state system that was confirmed in previous
experimental and theoretical work.
Abstract: A clay soil classified as A-7-6 and CH soil according
to AASHTO and unified soil classification system respectively, was
stabilized using A-3 soil (AASHTO soil classification system). The
clay soil was replaced with 0%, 10%, 20%, to 100% A-3 soil,
compacted at both British Standard Light (BSL) and British Standard
Heavy (BSH) compaction energy levels and using Unconfined
Compressive Strength (UCS) as evaluation criteria. The Maximum
Dry Density (MDD) of the treated soils at both the BSL and BSH
compaction energy levels showed increase from 0% to 40% A-3 soil
replacement after which the values reduced to 100% replacement.
The trend of the Optimum Moisture Content (OMC) with varied A-3
soil replacement was similar to that of MDD but in a reversed order.
The OMC reduced from 0% to 40% A-3 soil replacement after which
the values increased to 100% replacement. This trend was attributed
to the observed reduction in void ratio from 0% to 40% replacement
after which the void ratio increased to 100% replacement. The
maximum UCS for the soil at varied A-3 soil replacement increased
from 272 and 770 kN/m2 for BSL and BSH compaction energy level
at 0% replacement to 295 and 795 kN/m2 for BSL and BSH
compaction energy level respectively at 10% replacement after which
the values reduced to 22 and 60 kN/m2 for BSL and BSH compaction
energy level respectively at 70% replacement. Beyond 70%
replacement, the mixtures could not be moulded for UCS test.
Abstract: The goal of this paper is proposing a supply chain
value dashboard in home appliance manufacturing firms to create
more value for all stakeholders via balanced scorecard approach.
Balanced scorecard is an effective approach that managers have used
to evaluate supply chain performance in many fields but there is a
lack of enough attention to all supply chain stakeholders, improving
value creation and, defining correlation between value indicators and
performance measuring quantitatively. In this research the key
stakeholders in home appliance supply chain, value indicators with
respect to create more value for stakeholders and the most important
metrics to evaluate supply chain value performance based on
balanced scorecard approach have been selected via literature review.
The most important indicators based on expert’s judgment acquired
by in survey focused on creating more value for. Structural equation
modelling has been used to disclose relations between value
indicators and balanced scorecard metrics. The important result of
this research is identifying effective value dashboard to create more
value for all stakeholders in supply chain via balanced scorecard
approach and based on an empirical study covering ten home
appliance manufacturing firms in Iran. Home appliance
manufacturing firms can increase their stakeholder's satisfaction by
using this value dashboard.
Abstract: In this paper, we describe an application for face
recognition. Many studies have used local descriptors to characterize
a face, the performance of these local descriptors remain low by
global descriptors (working on the entire image). The application of
local descriptors (cutting image into blocks) must be able to store
both the advantages of global and local methods in the Discrete
Cosine Transform (DCT) domain. This system uses neural network
techniques. The letter method provides a good compromise between
the two approaches in terms of simplifying of calculation and
classifying performance. Finally, we compare our results with those
obtained from other local and global conventional approaches.
Abstract: The relationship dependence between RSS and distance
in an enclosed environment is an important consideration because it is
a factor that can influence the reliability of any localization algorithm
founded on RSS. Several algorithms effectively reduce the variance of
RSS to improve localization or accuracy performance. Our proposed
algorithm essentially avoids this pitfall and consequently, its high
adaptability in the face of erratic radio signal. Using 3 anchors in
close proximity of each other, we are able to establish that RSS can be
used as reliable indicator for localization with an acceptable degree of
accuracy. Inherent in this concept, is the ability for each prospective
anchor to validate (guarantee) the position or the proximity of the
other 2 anchors involved in the localization and vice versa. This
procedure ensures that the uncertainties of radio signals due to
multipath effects in enclosed environments are minimized. A major
driver of this idea is the implicit topological relationship among
sensors due to raw radio signal strength. The algorithm is an area
based algorithm; however, it does not trade accuracy for precision
(i.e the size of the returned area).
Abstract: Digital cameras to reduce cost, use an image sensor to
capture color images. Color Filter Array (CFA) in digital cameras
permits only one of the three primary (red-green-blue) colors to be
sensed in a pixel and interpolates the two missing components
through a method named demosaicking. Captured data is interpolated
into a full color image and compressed in applications. Color
interpolation before compression leads to data redundancy. This
paper proposes a new Vector Quantization (VQ) technique to
construct a VQ codebook with Differential Evolution (DE)
Algorithm. The new technique is compared to conventional Linde-
Buzo-Gray (LBG) method.
Abstract: In this paper, we present an application of Riemannian
geometry for processing non-Euclidean image data. We consider the
image as residing in a Riemannian manifold, for developing a new
method to brain edge detection and brain extraction. Automating this
process is a challenge due to the high diversity in appearance brain
tissue, among different patients and sequences. The main contribution, in this paper, is the use of an edge-based
anisotropic diffusion tensor for the segmentation task by integrating
both image edge geometry and Riemannian manifold (geodesic,
metric tensor) to regularize the convergence contour and extract
complex anatomical structures. We check the accuracy of the
segmentation results on simulated brain MRI scans of single
T1-weighted, T2-weighted and Proton Density sequences. We
validate our approach using two different databases: BrainWeb
database, and MRI Multiple sclerosis Database (MRI MS DB). We
have compared, qualitatively and quantitatively, our approach with
the well-known brain extraction algorithms. We show that using
a Riemannian manifolds to medical image analysis improves the
efficient results to brain extraction, in real time, outperforming the
results of the standard techniques.
Abstract: The use of wireless technology in industrial networks
has gained vast attraction in recent years. In this paper, we have
thoroughly analyzed the effect of contention window (CW) size on
the performance of IEEE 802.11-based industrial wireless networks
(IWN), from delay and reliability perspective. Results show that the
default values of CWmin, CWmax, and retry limit (RL) are far from
the optimum performance due to the industrial application
characteristics, including short packet and noisy environment. In this
paper, an adaptive CW algorithm (payload-dependent) has been
proposed to minimize the average delay. Finally a simple, but
effective CW and RL setting has been proposed for industrial
applications which outperforms the minimum-average-delay solution
from maximum delay and jitter perspective, at the cost of a little
higher average delay. Simulation results show an improvement of up
to 20%, 25%, and 30% in average delay, maximum delay and jitter
respectively.
Abstract: This contribution is focused on the methodology for
identifying levels of quality and improving quality through new
logistics model in railway transport. It is oriented on the application
of dynamic quality models, which represent an innovative method of
evaluation quality services. Through this conception, time factor,
expected, and perceived quality in each moment of the transportation
process within logistics chain can be taken into account. Various
models describe the improvement of the quality which emphases the
time factor throughout the whole transportation logistics chain.
Quality of services in railway transport can be determined by the
existing level of service quality, by detecting the causes of
dissatisfaction employees but also customers, to uncover strengths
and weaknesses. This new logistics model is able to recognize critical
processes in logistic chain. It includes service quality rating that must
respect its specific properties, which are unrepeatability,
impalpability, their use right at the time they are provided and
particularly changeability, which is significant factor in the
conditions of rail transport as well. These peculiarities influence the
quality of service regarding the constantly increasing requirements
and that result in new ways of finding progressive attitudes towards
the service quality rating.
Abstract: This article describes the results of research focused
on quality of railway freight transport services. Improvement of these
services has a crucial importance in customer considering on the
future use of railway transport. Processes filling the customer
demands and output quality assessment were defined as a part of the
research. In this contribution is introduced the map of quality
planning and the algorithm of applied methodology. It characterizes a
model which takes into account characters of transportation with
linking a perception services quality in ordinary and extraordinary
operation. Despite the fact that rail freight transport has its solid
position in the transport market, lots of carriers worldwide have been
experiencing a stagnation for a couple of years. Therefore, specific
results of the research have a significant importance and belong to
numerous initiatives aimed to develop and support railway transport
not only by creating a single railway area or reducing noise but also
by promoting railway services. This contribution is focused also on
the application of dynamic quality models which represent an
innovative method of evaluation quality services. Through this
conception, time factor, expected, and perceived quality in each
moment of the transportation process can be taken into account.
Abstract: Scheduled waste management is very important in
environmental and health aspects. In delivering services, highway
industry has been indirectly involved in producing scheduled wastes.
This paper aims to define the scheduled waste, to provide a
conceptual framework of the scheduled waste management in
highway industry, to highlight the effect of improper management of
scheduled waste and to encourage future researchers to identify and
share the present practice of scheduled waste management in their
country. The understanding on effective management of scheduled
waste will help the operators of highway industry, the academicians,
future researchers, and encourage a friendly environment around the
world. The study on scheduled waste management in highway
industry is very crucial as highway transverse and run along
kilometers crossing the various type of environment, residential and
schools. Using Environmental Quality (Scheduled Waste)
Regulations 2005 as a guide, this conceptual paper highlight several
scheduled wastes produced by highway industry in Malaysia and
provide a conceptual framework of scheduled waste management that
focused on the highway industry. Understanding on scheduled waste
management is vital in order to preserve the environment. Besides
that, the waste substances are hazardous to human being. Many
diseases have been associated with the improper management of
schedule waste such as cancer, throat irritation and respiration
problem.
Abstract: The growth in the demand of electrical energy is
leading to load on the Power system which increases the occurrence
of frequent oscillations in the system. The reason for the oscillations
is due to the lack of damping torque which is required to dominate
the disturbances of Power system. By using FACT devices, such as
Unified Power Flow Controller (UPFC) can control power flow,
reduce sub-synchronous resonances and increase transient stability.
Hence, UPFC is used to damp the oscillations occurred in Power
system. This research focuses on adapting the neuro fuzzy controller
for the UPFC design by connecting the infinite bus (SMIB - Single
machine Infinite Bus) to a linearized model of synchronous machine
(Heffron-Phillips) in the power system. This model gains the
capability to improve the transient stability and to damp the
oscillations of the system.
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: In this article, the radial displacement error correction
capability of a high precision spindle grinding caused by unbalance
force was investigated. The spindle shaft is considered as a flexible
rotor mounted on two sets of angular contact ball bearing. Finite
element methods (FEM) have been adopted for obtaining the
equation of motion of the spindle. In this paper, firstly, natural
frequencies, critical frequencies, and amplitude of the unbalance
response caused by residual unbalance are determined in order to
investigate the spindle behaviors. Furthermore, an optimization
design algorithm is employed to minimize radial displacement of the
spindle which considers dimension of the spindle shaft, the dynamic
characteristics of the bearings, critical frequencies and amplitude of
the unbalance response, and computes optimum spindle diameters
and stiffness and damping of the bearings. Numerical simulation
results show that by optimizing the spindle diameters, and stiffness
and damping in the bearings, radial displacement of the spindle can
be reduced. A spindle about 4 μm radial displacement error can be
compensated with 2 μm accuracy. This certainly can improve the
accuracy of the product of machining.
Abstract: The aim of the study is to compare behavioral and
EEG reactions in Turkic-speaking inhabitants of Siberia (Tuvinians
and Yakuts) and Russians during the recognition of syntax errors in
native and foreign languages. Sixty-three healthy aboriginals of the
Tyva Republic, 29 inhabitants of the Sakha (Yakutia) Republic, and
55 Russians from Novosibirsk participated in the study. EEG were
recorded during execution of error-recognition task in Russian and
English language (in all participants) and in native languages
(Tuvinian or Yakut Turkic-speaking inhabitants). Reaction time (RT)
and quality of task execution were chosen as behavioral measures.
Amplitude and cortical distribution of P300 and P600 peaks of ERP
were used as a measure of speech-related brain activity. In Tuvinians,
there were no differences in the P300 and P600 amplitudes as well as
in cortical topology for Russian and Tuvinian languages, but there
was a difference for English. In Yakuts, the P300 and P600
amplitudes and topology of ERP for Russian language were the same
as Russians had for native language. In Yakuts, brain reactions during
Yakut and English language comprehension had no difference, while
the Russian language comprehension was differed from both Yakut
and English. We found out that the Tuvinians recognized both Russian and
Tuvinian as native languages, and English as a foreign language. The
Yakuts recognized both English and Yakut as foreign languages, but
Russian as a native language. According to the inquirer, both
Tuvinians and Yakuts use the national language as a spoken
language, whereas they do not use it for writing. It can well be a
reason that Yakuts perceive the Yakut writing language as a foreign
language while writing Russian as their native.
Abstract: Parboiled rice was developed to produce rice, which
has a low glycemic index for diabetics. However, diabetics also have
a chromium (Cr) deficiency. Thus, it is important to fortify rice with
Cr to increase the Cr content. Moreover, parboiled rice becomes
rancid easily and has a musty odor, rendering the rice unfavorable.
Natural herbs such as pandan leaves (Pandanus amaryllifolius
Roxb.), bay leaves (Syzygium polyanthum [Wigh] Walp) and
cinnamon bark powder (Cinnamomon cassia) are commonly added to
food as aroma enhancers. Previous research has shown that these
herbs could improve insulin sensitivity. The purpose of this study
was to evaluate the effect of herbal extract coatings on the cooking
quality and the preference level of chromium fortified - parboiled rice
(CFPR). The rice grain variety used for this experiment was Ciherang
and the fortificant was CrCl3. The three herbal extracts used for
coating the CFPR were cinnamon, pandan and bay leaf, with
concentration variations of 3%, 6%, and 9% (w/w) for each of the
extracts. The samples were analyzed for their alkali spreading value,
cooking time, elongation, water uptake ratio, solid loss, colour and
lightness; and their sensory properties were determined by means of
an organoleptic test. The research showed that coating the CFPR with
pandan and cinnamon extracts at a concentration of 3% each
produced a preferred CFPR. When coated with those herbal extracts
the CFPR had the following cooking quality properties: alkali
spreading value 5 (intermediate gelatinization temperature), cooking
time, 26-27 min, color value, 14.95-15.00, lightness, 42.30 – 44.06,
elongation, 1.53 – 1.54, water uptake ratio , 4.05-4.06, and solid loss,
0.09/100 g – 0.13 g/100 g.
Abstract: People, throughout the history, have made estimates
and inferences about the future by using their past experiences.
Developing information technologies and the improvements in the
database management systems make it possible to extract useful
information from knowledge in hand for the strategic decisions.
Therefore, different methods have been developed. Data mining by
association rules learning is one of such methods. Apriori algorithm,
one of the well-known association rules learning algorithms, is not
commonly used in spatio-temporal data sets. However, it is possible
to embed time and space features into the data sets and make Apriori
algorithm a suitable data mining technique for learning spatiotemporal
association rules. Lake Van, the largest lake of Turkey, is a
closed basin. This feature causes the volume of the lake to increase or
decrease as a result of change in water amount it holds. In this study,
evaporation, humidity, lake altitude, amount of rainfall and
temperature parameters recorded in Lake Van region throughout the
years are used by the Apriori algorithm and a spatio-temporal data
mining application is developed to identify overflows and newlyformed
soil regions (underflows) occurring in the coastal parts of
Lake Van. Identifying possible reasons of overflows and underflows
may be used to alert the experts to take precautions and make the
necessary investments.