Abstract: In the last decades to supply the various and different
demands of clients, a lot of manufacturers trend to use the mixedmodel
assembly line (MMAL) in their production lines, since this
policy make possible to assemble various and different models of the
equivalent goods on the same line with the MTO approach.
In this article, we determine the sequence of (MMAL) line, with
applying the kitting approach and planning of rest time for general
workers to reduce the wastages, increase the workers effectiveness
and apply the sector of lean production approach.
This Multi-objective sequencing problem solved in small size with
GAMS22.2 and PSO meta heuristic in 10 test problems and compare
their results together and conclude that their results are very similar
together, next we determine the important factors in computing the
cost, which improving them cost reduced. Since this problem, is NPhard
in large size, we use the particle swarm optimization (PSO)
meta-heuristic for solving it. In large size we define some test
problems to survey it-s performance and determine the important
factors in calculating the cost, that by change or improved them
production in minimum cost will be possible.
Abstract: Noise level has critical effects on the diagnostic
performance of signal-averaged electrocardiogram (SAECG), because
the true starting and end points of QRS complex would be masked by
the residual noise and sensitive to the noise level. Several studies and
commercial machines have used a fixed number of heart beats
(typically between 200 to 600 beats) or set a predefined noise level
(typically between 0.3 to 1.0 μV) in each X, Y and Z lead to perform
SAECG analysis. However different criteria or methods used to
perform SAECG would cause the discrepancies of the noise levels
among study subjects. According to the recommendations of 1991
ESC, AHA and ACC Task Force Consensus Document for the use of
SAECG, the determinations of onset and offset are related closely to
the mean and standard deviation of noise sample. Hence this study
would try to perform SAECG using consistent root-mean-square
(RMS) noise levels among study subjects and analyze the noise level
effects on SAECG. This study would also evaluate the differences
between normal subjects and chronic renal failure (CRF) patients in
the time-domain SAECG parameters.
The study subjects were composed of 50 normal Taiwanese and 20
CRF patients. During the signal-averaged processing, different RMS
noise levels were adjusted to evaluate their effects on three time
domain parameters (1) filtered total QRS duration (fQRSD), (2) RMS
voltage of the last QRS 40 ms (RMS40), and (3) duration of the low
amplitude signals below 40 μV (LAS40). The study results
demonstrated that the reduction of RMS noise level can increase
fQRSD and LAS40 and decrease the RMS40, and can further increase
the differences of fQRSD and RMS40 between normal subjects and
CRF patients. The SAECG may also become abnormal due to the
reduction of RMS noise level. In conclusion, it is essential to establish
diagnostic criteria of SAECG using consistent RMS noise levels for
the reduction of the noise level effects.
Abstract: Canola is a specific edible type of rapeseed, developed
in the 1970s, which contains about 40 percent oil. This research was
carried out to determine the yield and some quality characteristics of
some winter canola cultivars during the 2010-2011 vegetation period
in Central Anatolia of Turkey. In this research; Oase, Dante,
Californium, Excalibur, Elvis, ES Hydromel, Licord, Orkan, Vectra,
Nelson, Champlain and NK Petrol winter canola varieties were used
as material. The field experiment was set up in a “Randomized
Complete Block Design” with three replications on 21 September
2010. In this research; seed yield, oil content, protein content, oil
yield and protein yield were examined.
As a result of this research; seed yield, oil content, oil yield and
protein yield (except protein content) were significant differences
between the cultivars. The highest seed yield (6348 kg ha-1) was
obtained from the NK Petrol, while the lowest seed yield (3949 kg
ha-1) was determined from the Champlain cultivar was obtained. The
highest oil content (46.73%) was observed from Oase and the lowest
value was obtained from Vectra (41.87%) cultivar. The highest oil
yield (2950 kg ha-1) was determined from NK Petrol while the least
value (1681 kg ha-1) was determined from Champlain cultivar. The
highest protein yield (1539.3 kg ha-1) was obtained from NK Petrol
and the lowest protein yield (976.5 kg ha-1) was obtained from
Champlain cultivar.
The main purpose of the cultivation of oil crops, to increase the
yield of oil per unit area. According the result of this research, NK
Petrol cultivar which ranks first with regard to both seed yield and oil
yield between cultivars as the most suitable winter canola cultivar of
local conditions.
Abstract: The new programming technologies allow for the
creation of components which can be automatically or manually
assembled to reach a new experience in knowledge understanding
and mastering or in getting skills for a specific knowledge area. The
project proposes an interactive framework that permits the creation,
combination and utilization of components that are specific to
mathematical training in high schools.
The main framework-s objectives are:
• authoring lessons by the teacher or the students; all they need
are simple operating skills for Equation Editor (or something
similar, or Latex); the rest are just drag & drop operations,
inserting data into a grid, or navigating through menus
• allowing sonorous presentations of mathematical texts and
solving hints (easier understood by the students)
• offering graphical representations of a mathematical function
edited in Equation
• storing of learning objects in a database
• storing of predefined lessons (efficient for expressions and
commands, the rest being calculations; allows a high
compression)
• viewing and/or modifying predefined lessons, according to the
curricula
The whole thing is focused on a mathematical expressions minicompiler,
storing the code that will be later used for different
purposes (tables, graphics, and optimisations).
Programming technologies used. A Visual C# .NET
implementation is proposed. New and innovative digital learning
objects for mathematics will be developed; they are capable to
interpret, contextualize and react depending on the architecture
where they are assembled.
Abstract: The purpose of this study was to determine the
influence of physical activity and dietary fat intake on Body Mass
Index (BMI) of lecturers within a higher learning institutionalized
setting. The study adopted a Cross-sectional Correlational Design
and included 120 lecturers selected proportionately by simple
random sampling techniques from a population of 600 lecturers. Data
was collected using questionnaires, which had sections including
physical activity checklist adopted from the international physical
activity questionnaire (IPAQ), 24-hour food recall, anthropometric
measurements mainly weight and height. Analysis involved the use
of bivariate correlations and linear regression. A significant inverse
association was registered between BMI and duration (in minutes)
spent doing moderate intense physical activity per day (r=-0.322,
p
Abstract: The stem cells have ability to differentiated
themselves through mitotic cell division and various range of
specialized cell types. Cellular differentiation is a way by which few
specialized cell develops into more specialized.This paper studies the
fundamental problem of computational schema for an artificial neural
network based on chemical, physical and biological variables of
state. By doing this type of study system could be model for a viable
propagation of various economically important stem cells
differentiation. This paper proposes various differentiation outcomes
of artificial neural network into variety of potential specialized cells
on implementing MATLAB version 2009. A feed-forward back
propagation kind of network was created to input vector (five input
elements) with single hidden layer and one output unit in output
layer. The efficiency of neural network was done by the assessment
of results achieved from this study with that of experimental data
input and chosen target data. The propose solution for the efficiency
of artificial neural network assessed by the comparatative analysis of
“Mean Square Error" at zero epochs. There are different variables of
data in order to test the targeted results.
Abstract: Different agricultural waste peels were assessed for
their suitability to be used as primary substrates for the
bioremediation of free cyanide (CN-) by a cyanide-degrading fungus
Aspergillus awamori isolated from cyanide containing wastewater.
The bioremediated CN- concentration were in the range of 36 to 110
mg CN-/L, with Orange (C. sinensis) > Carrot (D. carota) > Onion
(A. cepa) > Apple (M. pumila), being chosen as suitable substrates
for large scale CN- degradation processes due to: 1) the high
concentration of bioremediated CN-, 2) total reduced sugars released
into solution to sustain the biocatalyst, and 3) minimal residual NH4-
N concentration after fermentation. The bioremediation rate constants
(k) were 0.017h-1 (0h < t < 24h), with improved bioremediation rates
(0.02189h-1) observed after 24h. The averaged nitrilase activity was
~10 U/L.
Abstract: This study has investigated a vehicle Lumped
Parameter Model (LPM) in frontal crash. There are several ways for
determining spring and damper characteristics and type of problem
shall be considered as system identification. This study use Genetic
Algorithm (GA) procedure, being an effective procedure in case of
optimization issues, for optimizing errors, between target data
(experimental data) and calculated results (being obtained by
analytical solving). In this study analyzed model in 5-DOF then
compared our results with 5-DOF serial model. Finally, the response
of model due to external excitement is investigated.
Abstract: This paper proposes improved delay-dependent stability conditions of the linear time-delay systems of neutral type. The proposed methods employ a suitable Lyapunov-Krasovskii’s functional and a new form of the augmented system. New delay-dependent stability criteria for the systems are established in terms of Linear matrix inequalities (LMIs) which can be easily solved by various effective optimization algorithms. Numerical examples showed that the proposed method is effective and can provide less conservative results.
Abstract: Reverse engineering of full-genomic interaction networks based on compendia of expression data has been successfully applied for a number of model organisms. This study adapts these approaches for an important non-model organism: The major human fungal pathogen Candida albicans. During the infection process, the pathogen can adapt to a wide range of environmental niches and reversibly changes its growth form. Given the importance of these processes, it is important to know how they are regulated. This study presents a reverse engineering strategy able to infer fullgenomic interaction networks for C. albicans based on a linear regression, utilizing the sparseness criterion (LASSO). To overcome the limited amount of expression data and small number of known interactions, we utilize different prior-knowledge sources guiding the network inference to a knowledge driven solution. Since, no database of known interactions for C. albicans exists, we use a textmining system which utilizes full-text research papers to identify known regulatory interactions. By comparing with these known regulatory interactions, we find an optimal value for global modelling parameters weighting the influence of the sparseness criterion and the prior-knowledge. Furthermore, we show that soft integration of prior-knowledge additionally improves the performance. Finally, we compare the performance of our approach to state of the art network inference approaches.
Abstract: The response of growth and yield of rainfed-chickpea
to population density should be evaluated based on long-term
experiments to include the climate variability. This is achievable just
by simulation. In this simulation study, this evaluation was done by
running the CYRUS model for long-term daily weather data of five
locations in Iran. The tested population densities were 7 to 59 (with
interval of 2) stands per square meter. Various functions, including
quadratic, segmented, beta, broken linear, and dent-like functions,
were tested. Considering root mean square of deviations and linear
regression statistics [intercept (a), slope (b), and correlation
coefficient (r)] for predicted versus observed variables, the quadratic
and broken linear functions appeared to be appropriate for describing
the changes in biomass and grain yield, and in harvest index,
respectively. Results indicated that in all locations, grain yield tends
to show increasing trend with crowding the population, but
subsequently decreases. This was also true for biomass in five
locations. The harvest index appeared to have plateau state across
low population densities, but decreasing trend with more increasing
density. The turning point (optimum population density) for grain
yield was 30.68 stands per square meter in Isfahan, 30.54 in Shiraz,
31.47 in Kermanshah, 34.85 in Tabriz, and 32.00 in Mashhad. The
optimum population density for biomass ranged from 24.6 (in
Tabriz) to 35.3 stands per square meter (Mashhad). For harvest index
it varied between 35.87 and 40.12 stands per square meter.
Abstract: The International Classification of Primary Care (ICPC), which belongs to the WHO Family of International Classifications (WHO-FIC), has a low granularity, which is convenient for describing general medical practice. However, its lack of specificity makes it useful to be used along with an interface terminology. An international survey has been performed, using a questionnaire sent by email to experts from 25 countries, in order to describe the terminologies interfacing with ICPC. Eleven interface terminologies have been identified, developed in Argentina, Australia, Belgium (2), Canada, Denmark, France, Germany, Norway, South Africa, and The Netherlands. Globally, these systems have been poorly assessed until now.
Abstract: One of the determinants of a firm-s prosperity is the
customers- perceived service quality and satisfaction. While service
quality is wide in scope, and consists of various dimensions, there
may be differences in the relative importance of these dimensions in
affecting customers- overall satisfaction of service quality.
Identifying the relative rank of different dimensions of service quality
is very important in that it can help managers to find out which
service dimensions have a greater effect on customers- overall
satisfaction. Such an insight will consequently lead to more effective
resource allocation which will finally end in higher levels of
customer satisfaction. This issue – despite its criticality- has not
received enough attention so far. Therefore, using a sample of 240
bank customers in Iran, an artificial neural network is developed to
address this gap in the literature. As customers- evaluation of service
quality is a subjective process, artificial neural networks –as a brain
metaphor- may appear to have a potentiality to model such a
complicated process. Proposing a neural network which is able to
predict the customers- overall satisfaction of service quality with a
promising level of accuracy is the first contribution of this study. In
addition, prioritizing the service quality dimensions in affecting
customers- overall satisfaction –by using sensitivity analysis of
neural network- is the second important finding of this paper.
Abstract: A preliminary evaluation of the feasibility of installing small wind turbines on offshore oil and gas extraction platforms is presented. Some aerodynamic considerations are developed in order to determine the best rotor architecture to exploit the wind potential on such installations, assuming that wind conditions over the platforms are similar to those registered on the roofs of urban buildings. Economical considerations about both advantages and disadvantages of the exploitation of wind energy on offshore extraction platforms with respect to conventional offshore wind plants, is also presented. Finally, wind charts of European offshore winds are presented together with a map of the major offshore installations.
Abstract: The Brazilian legislation has only established
diagnostic reference levels (DRLs) in terms of Multiple Scan
Average Dose (MSAD) as a quality control parameter for computed
tomography (CT) scanners. Compliance with DRLs can be verified
by measuring the Computed Tomography Kerma Index (Ca,100) with
a pencil ionization chamber or by obtaining the kerma distribution in
CT scans with radiochromic films or rod shape lithium fluoride
termoluminescent dosimeters (TLD-100). TL dosimeters were used
to record kerma profiles and to determine MSAD values of a Bright
Speed model GE CT scanner. Measurements were done with
radiochromic films and TL dosimeters distributed in cylinders
positioned in the center and in four peripheral bores of a standard
polymethylmethacrylate (PMMA) body CT dosimetry phantom.
Irradiations were done using a protocol for adult chest. The
maximum values were found at the midpoint of the longitudinal axis.
The MSAD values obtained with three dosimetric techniques were
compared.
Abstract: In the current economy of increasing global
competition, many organizations are attempting to use knowledge as
one of the means to gain sustainable competitive advantage. Besides
large organizations, the success of SMEs can be linked to how well
they manage their knowledge. Despite the profusion of research
about knowledge management within large organizations, fewer
studies tried to analyze KM in SMEs.
This research proposes a new framework showing the determinant
role of organizational dimensions onto KM approaches. The paper
and its propositions are based on a literature review and analysis.
In this research, personalization versus codification,
individualization versus institutionalization and IT-based versus non
IT-based are highlighted as three distinct dimensions of knowledge
management approaches.
The study contributes to research by providing a more nuanced
classification of KM approaches and provides guidance to managers
about the types of KM approaches that should be adopted based on
the size, geographical dispersion and task nature of SMEs.
To the author-s knowledge, the paper is the first of its kind to
examine if there are suitable configurations of KM approaches for
SMEs with different dimensions. It gives valuable information, which
hopefully will help SME sector to accomplish KM.
Abstract: An autonomous environmental monitoring system
(Smart Landfill) has been constructed for the quantitative
measurement of the components of landfill gas found at borehole
wells at the perimeter of landfill sites. The main components of
landfill gas are the greenhouse gases, methane and carbon dioxide
and have been monitored in the range 0-5 % volume. This monitoring
system has not only been tested in the laboratory but has been
deployed in multiple field trials and the data collected successfully
compared with on-site monitors. This success shows the potential of
this system for application in environments where reliable gas
monitoring is crucial.
Abstract: This article proposes a new methodology to be used by SMEs (Small and Medium enterprises) to characterize their performance in quality, highlighting weaknesses and area for improvement. The methodology aims to identify the principal causes of quality problems and help to prioritize improvement initiatives. This is a self-assessment methodology that intends to be easy to implement by companies with low maturity level in quality. The methodology is organized in six different steps which includes gathering information about predetermined processes and subprocesses of quality management, defined based on the well-known Juran-s trilogy for quality management (Quality planning, quality control and quality improvement) and, predetermined results categories, defined based on quality concept. A set of tools for data collecting and analysis, such as interviews, flowcharts, process analysis diagrams and Failure Mode and effects Analysis (FMEA) are used. The article also presents the conclusions obtained in the application of the methodology in two cases studies.
Abstract: While financial institutions have faced difficulties
over the years for a multitude of reasons, the major cause of serious
banking problems continues to be directly related to lax credit
standards for borrowers and counterparties, poor portfolio risk
management, or a lack of attention to changes in economic or other
circumstances that can lead to a deterioration in the credit standing of
a bank's counterparties. Credit risk is most simply defined as the
potential that a bank borrower or counterparty will fail to meet its
obligations in accordance with agreed terms. The goal of credit risk
management is to maximize a bank's risk-adjusted rate of return by
maintaining credit risk exposure within acceptable parameters. Banks
need to manage the credit risk inherent in the entire portfolio as well
as the risk in individual credits or transactions. Banks should also
consider the relationships between credit risk and other risks. The
effective management of credit risk is a critical component of a
comprehensive approach to risk management and essential to the
long-term success of any banking organization. In this research we
also study the relationship between credit risk indices and borrower-s
timely payback in Karafarin bank.
Abstract: There is lot of work done in prediction of the fault proneness of the software systems. But, it is the severity of the faults that is more important than number of faults existing in the developed system as the major faults matters most for a developer and those major faults needs immediate attention. In this paper, we tried to predict the level of impact of the existing faults in software systems. Neuro-Fuzzy based predictor models is applied NASA-s public domain defect dataset coded in C programming language. As Correlation-based Feature Selection (CFS) evaluates the worth of a subset of attributes by considering the individual predictive ability of each feature along with the degree of redundancy between them. So, CFS is used for the selecting the best metrics that have highly correlated with level of severity of faults. The results are compared with the prediction results of Logistic Models (LMT) that was earlier quoted as the best technique in [17]. The results are recorded in terms of Accuracy, Mean Absolute Error (MAE) and Root Mean Squared Error (RMSE). The results show that Neuro-fuzzy based model provide a relatively better prediction accuracy as compared to other models and hence, can be used for the modeling of the level of impact of faults in function based systems.