Abstract: Estimation of a proportion has many applications in
economics and social studies. A common application is the estimation
of the low income proportion, which gives the proportion of people
classified as poor into a population. In this paper, we present this
poverty indicator and propose to use the logistic regression estimator
for the problem of estimating the low income proportion. Various
sampling designs are presented. Assuming a real data set obtained
from the European Survey on Income and Living Conditions, Monte
Carlo simulation studies are carried out to analyze the empirical
performance of the logistic regression estimator under the various
sampling designs considered in this paper. Results derived from
Monte Carlo simulation studies indicate that the logistic regression
estimator can be more accurate than the customary estimator under
the various sampling designs considered in this paper. The stratified
sampling design can also provide more accurate results.
Abstract: The measured data obtained from sensors in
continuous monitoring of civil structures are mainly used for modal
identification and damage detection. Therefore, when modal
identification analysis is carried out the quality in the identification of
the modes will highly influence the damage detection results. It is
also widely recognized that the usefulness of the measured data used
for modal identification and damage detection is significantly
influenced by the number and locations of sensors. The objective of
this study is the numerical implementation of two widely known
optimum sensor placement methods in beam-like structures.
Abstract: A total of 150 meat type chickens comprising 50 each
of Arbor Acre, Marshall and Ross were used for this study which
lasted for 10 weeks at the Federal University of Agriculture,
Abeokuta, Nigeria. Growth performance data were collected from the
third week through week 10 and data obtained were analysed using
the Generalized Linear Model Procedure. Heritability estimates (h2)
for body dimensions carried out on the chicken strains ranged from
low to high. Marshall broiler chicken strain had the highest h2 for
body weight 0.46±0.04, followed by Arbor Acre and Ross with h2
being 0.38±0.12 and 0.26±0.06, respectively. The repeatability
estimates for body weight in the three broiler strains were high, and it
ranged from 0.70 at week 4 to 0.88 at week 10. Relationships
between the body weight and linear body measurements in the broiler
chicken strains were positive and highly significant (p > 0.05).
Abstract: Opportunistic routing is used, where the network has
the features like dynamic topology changes and intermittent network
connectivity. In Delay tolerant network or Disruption tolerant
network opportunistic forwarding technique is widely used. The key
idea of opportunistic routing is selecting forwarding nodes to forward
data packets and coordination among these nodes to avoid duplicate
transmissions. This paper gives the analysis of pros and cons of
various opportunistic routing techniques used in MANET.
Abstract: This paper aims at finding a suitable neural network
for monitoring congestion level in electrical power systems. In this
paper, the input data has been framed properly to meet the target
objective through supervised learning mechanism by defining normal
and abnormal operating conditions for the system under study. The
congestion level, expressed as line congestion index (LCI), is
evaluated for each operating condition and is presented to the NN
along with the bus voltages to represent the input and target data.
Once, the training goes successful, the NN learns how to deal with a
set of newly presented data through validation and testing
mechanism. The crux of the results presented in this paper rests on
performance comparison of a multi-layered feed forward neural
network with eleven types of back propagation techniques so as to
evolve the best training criteria. The proposed methodology has been
tested on the standard IEEE-14 bus test system with the support of
MATLAB based NN toolbox. The results presented in this paper
signify that the Levenberg-Marquardt backpropagation algorithm
gives best training performance of all the eleven cases considered in
this paper, thus validating the proposed methodology.
Abstract: The European Union Survey on Income and Living
Conditions (EU-SILC) is a popular survey which provides
information on income, poverty, social exclusion and living
conditions of households and individuals in the European Union.
The EU-SILC contains variables which may contain outliers. The
presence of outliers can have an impact on the measures and
indicators used by the EU-SILC. In this paper, we used data sets
from various countries to analyze the presence of outliers. In addition,
we obtain some indicators after removing these outliers, and a
comparison between both situations can be observed. Finally, some
conclusions are obtained.
Abstract: This research paper aims to identify, analyze and rank
factors affecting labor productivity in Spain with respect to their
relative importance. Using a selected set of 35 factors, a structured
questionnaire survey was utilized as the method to collect data from
companies. Target population is comprised by a random
representative sample of practitioners related with the Spanish
construction industry. Findings reveal the top five ranked factors are
as follows: (1) shortage or late supply of materials; (2) clarity of the
drawings and project documents; (3) clear and daily task assignment;
(4) tools or equipment shortages; (5) level of skill and experience of
laborers. Additionally, this research also pretends to provide simple
and comprehensive recommendations so that they could be
implemented by construction managers for an effective management
of construction labor forces.
Abstract: Currently, biological control programs in greenhouse
crops involve the use, at the same time, several natural enemies
during the crop cycle. Also, large number of plant species grown in
greenhouses, among them, the used cultivars are also wide. However,
the cultivar effects on entomophagous species efficacy (predators and
parasitoids) have been scarcely studied. A new method had been
developed, using the factitious prey or host Ephestia kuehniella. It
allow us to evaluate, under greenhouse or controlled conditions
(semi-field), the cultivar effects on the entomophagous species
effectiveness. The work was carried out in greenhouse tomato crop. It
has been found the biological and ecological activities of predatory
species (Nesidiocoris tenuis) and egg-parasitoid (Trichogramma
achaeae) can be well represented with the use of the factitious prey
or host; being better in the former than the latter. The data found in
the trial are shown and discussed. The developed method could be
applied to evaluate new plant materials before making available to
farmers as commercial varieties, at low costs and easy use.
Abstract: The paper describes the experiments and the kinetic
parameters calculus of the gasoil hydrofining. They are presented
experimental results of gasoil hidrofining using Mo and promoted
with Ni on aluminum support catalyst. The authors have adapted a
kinetic model gasoil hydrofining. Using this proposed kinetic model
and the experimental data they have calculated the parameters of the
model. The numerical calculus is based on minimizing the difference
between the experimental sulf concentration and kinetic model
estimation.
Abstract: In this study, a comparative analysis of the approaches
associated with the use of neural network algorithms for effective
solution of a complex inverse problem – the problem of identifying
and determining the individual concentrations of inorganic salts in
multicomponent aqueous solutions by the spectra of Raman
scattering of light – is performed. It is shown that application of
artificial neural networks provides the average accuracy of
determination of concentration of each salt no worse than 0.025 M.
The results of comparative analysis of input data compression
methods are presented. It is demonstrated that use of uniform
aggregation of input features allows decreasing the error of
determination of individual concentrations of components by 16-18%
on the average.
Abstract: The article presents the results of the application of
artificial neural networks to separate the fluorescent contribution of
nanodiamonds used as biomarkers, adsorbents and carriers of drugs
in biomedicine, from a fluorescent background of own biological
fluorophores. The principal possibility of solving this problem is
shown. Use of neural network architecture let to detect fluorescence
of nanodiamonds against the background autofluorescence of egg
white with high accuracy - better than 3 ug/ml.
Abstract: Frequent pattern mining is the process of finding a
pattern (a set of items, subsequences, substructures, etc.) that occurs
frequently in a data set. It was proposed in the context of frequent
itemsets and association rule mining. Frequent pattern mining is used
to find inherent regularities in data. What products were often
purchased together? Its applications include basket data analysis,
cross-marketing, catalog design, sale campaign analysis, Web log
(click stream) analysis, and DNA sequence analysis. However, one of
the bottlenecks of frequent itemset mining is that as the data increase
the amount of time and resources required to mining the data
increases at an exponential rate. In this investigation a new algorithm
is proposed which can be uses as a pre-processor for frequent itemset
mining. FASTER (FeAture SelecTion using Entropy and Rough sets)
is a hybrid pre-processor algorithm which utilizes entropy and roughsets
to carry out record reduction and feature (attribute) selection
respectively. FASTER for frequent itemset mining can produce a
speed up of 3.1 times when compared to original algorithm while
maintaining an accuracy of 71%.
Abstract: Frequent, continuous speech training has proven to be
a necessary part of a successful speech therapy process, but
constraints of traveling time and employment dispensation become
key obstacles especially for individuals living in remote areas or for
dependent children who have working parents. In order to ameliorate
speech difficulties with ample guidance from speech therapists, a
website has been developed that supports speech therapy and training
for people with articulation disorders in the standard Thai language.
This web-based program has the ability to record speech training
exercises for each speech trainee. The records will be stored in a
database for the speech therapist to investigate, evaluate, compare
and keep track of all trainees’ progress in detail. Speech trainees can
request live discussions via video conference call when needed.
Communication through this web-based program facilitates and
reduces training time in comparison to walk-in training or
appointments. This type of training also allows people with
articulation disorders to practice speech lessons whenever or
wherever is convenient for them, which can lead to a more regular
training processes.
Abstract: From an organizational perspective, leaders are a
variation of the same talent pool in that they all score a larger than
average value on the bell curve that maps leadership behaviors and
characteristics, namely competence, vision, communication,
confidence, cultural sensibility, stewardship, empowerment,
authenticity, reinforcement, and creativity. The question that remains
unanswered and essentially unresolved is how to explain the irony
that leaders are so much alike yet their organizations diverge so
noticeably in their ability to innovate. Leadership intersects with
innovation at the point where human interactions get exceedingly
complex and where certain paradoxical forces cohabit: conflict with
conciliation, sovereignty with interdependence, and imagination with
realism. Rather than accepting that leadership is without context, we
argue that leaders are specialists of their domain and that those
effective at leading for innovation are distinct within the broader pool
of leaders. Keeping in view the extensive literature on leadership and
innovation, we carried out a quantitative study with data collected
over a five-year period involving 240 participants from across five
dissimilar companies based in the United States. We found that while
innovation and leadership are, in general, strongly interrelated (r =
.89, p = 0.0), there are five qualities that set leaders apart on
innovation. These qualities include a large radius of trust, a restless
curiosity with a low need for acceptance, an honest sense of self and
other, a sense for knowledge and creativity as the yin and yang of
innovation, and an ability to use multiple senses in the engagement
with followers. When these particular behaviors and characteristics
are present in leaders, organizations out-innovate their rivals by a
margin of 29.3 per cent to gain an unassailable edge in a business
environment that is regularly disruptive. A strategic outcome of this
study is a psychometric scale named iLeadership, proposed with the
underlying evidence, limitations, and potential for leadership and
innovation in organizations.c
Abstract: Web search engines are designed to retrieve and
extract the information in the web databases and to return dynamic
web pages. The Semantic Web is an extension of the current web in
which it includes semantic content in web pages. The main goal of
semantic web is to promote the quality of the current web by
changing its contents into machine understandable form. Therefore,
the milestone of semantic web is to have semantic level information
in the web. Nowadays, people use different keyword- based search
engines to find the relevant information they need from the web.
But many of the words are polysemous. When these words are
used to query a search engine, it displays the Search Result Records
(SRRs) with different meanings. The SRRs with similar meanings are
grouped together based on Word Sense Disambiguation (WSD). In
addition to that semantic annotation is also performed to improve the
efficiency of search result records. Semantic Annotation is the
process of adding the semantic metadata to web resources. Thus the
grouped SRRs are annotated and generate a summary which
describes the information in SRRs. But the automatic semantic
annotation is a significant challenge in the semantic web. Here
ontology and knowledge based representation are used to annotate
the web pages.
Abstract: With the rapid progress of modern cities, the railway
construction must be developing quickly in China.As a typical
high-density country, shopping center on the subway should be one
important factor during the process of urban development. The paper
discusses the influence of the layout of shopping center on the subway,
and put it in the time and space’s axis of Shanghai urban development.
We usethe digital technology to establish the database of relevant
information. And then get the change role about shopping center on
subway in Shanghaiby the Kernel density estimate.The result shows
the development of shopping center on subway has a relationship with
local economic strength, population size, policysupport, and city
construction. And the suburbanization trend of shopping center would
be increasingly significant.By this case research, we could see the
Kernel density estimate is an efficient analysis method on the spatial
layout. It could reveal the characters of layout form of shopping center
on subway in essence. And it can also be applied to the other research
of space form.
Abstract: The purposes of this research were to study concepts
and strategies of human resource development in the automotive
manufacturers and to articulate the proposals against the government
about the human resource development for automotive industry. In
the present study, qualitative study was an in-depth interview in
which the qualitative data were collected from the executive or the
executive of human resource division from five automotive
companies - Toyota Motor (Thailand) Co., Ltd., Nissan Motor
(Thailand) Co., Ltd., Mitsubishi Motors (Thailand) Co., Ltd., Honda
Automobile (Thailand) Co., Ltd., and Suzuki Motor (Thailand) Co.,
Ltd. Qualitative data analysis was performed by using inter-coder
agreement technique. The research findings were as follows:
The external factors included the current conditions of the
automotive industry, government’s policy related to the automotive
industry, technology, labor market and human resource development
systems of the country. The internal factors included management,
productive management, organizational strategies, leadership,
organizational culture and philosophy of human resource
development. These factors were affected to the different concept of
human resources development -the traditional human resource
development and the strategies of human resource development. The
organization focuses on human resources as intellectual capital and
uses the strategies of human resource development in all
development processes. The strategies of human resource
development will enhance the ability of human resources in the
organization and the country.
Abstract: Despite four years of study in the tourism industry, the
Bachelor’s graduates cannot perform their jobs as experienced tour
guides. This research aimed to develop French teaching and studying
for Tourism with two main purposes: to analyze ‘Moves’ used in oral
presentations at tourist attraction; and to study content in guiding
presentations or 'Guide Speak'. The study employed audio recording
of these presentations as an interview method in authentic situations,
having four tour guides as respondents and information providers.
The data was analyzed via moves and content analysis. The results
found that there were eight Moves used; namely, Welcoming,
Introducing oneself, Drawing someone’s attention, Giving
information, Explaining, Highlighting, Persuading and Saying
goodbye. In terms of content, the information being presented
covered the outstanding characteristics of the places and wellintegrated
with other related content. The findings were used as
guidelines for curriculum development; in particular, the core content
and the presentation forming the basis for students to meet the
standard requirements of the labor-market and professional schemes.
Abstract: This paper presents the voltage problem location
classification using performance of Least Squares Support Vector
Machine (LS-SVM) and Learning Vector Quantization (LVQ) in
electrical power system for proper voltage problem location
implemented by IEEE 39 bus New- England. The data was collected
from the time domain simulation by using Power System Analysis
Toolbox (PSAT). Outputs from simulation data such as voltage, phase
angle, real power and reactive power were taken as input to estimate
voltage stability at particular buses based on Power Transfer Stability
Index (PTSI).The simulation data was carried out on the IEEE 39 bus
test system by considering load bus increased on the system. To verify
of the proposed LS-SVM its performance was compared to Learning
Vector Quantization (LVQ). The results showed that LS-SVM is faster
and better as compared to LVQ. The results also demonstrated that the
LS-SVM was estimated by 0% misclassification whereas LVQ had
7.69% misclassification.
Abstract: Consumer-to-Consumer (C2C) E-commerce has been
growing at a very high speed in recent years. Since identical or
nearly-same kinds of products compete one another by relying on
keyword search in C2C E-commerce, some sellers describe their
products with spam keywords that are popular but are not related to
their products. Though such products get more chances to be retrieved
and selected by consumers than those without spam keywords,
the spam keywords mislead the consumers and waste their time.
This problem has been reported in many commercial services like
ebay and taobao, but there have been little research to solve this
problem. As a solution to this problem, this paper proposes a method
to classify whether keywords of a product are spam or not. The
proposed method assumes that a keyword for a given product is
more reliable if the keyword is observed commonly in specifications
of products which are the same or the same kind as the given
product. This is because that a hierarchical category of a product
in general determined precisely by a seller of the product and so is
the specification of the product. Since higher layers of the hierarchical
category represent more general kinds of products, a reliable degree
is differently determined according to the layers. Hence, reliable
degrees from different layers of a hierarchical category become
features for keywords and they are used together with features only
from specifications for classification of the keywords. Support Vector
Machines are adopted as a basic classifier using the features, since
it is powerful, and widely used in many classification tasks. In
the experiments, the proposed method is evaluated with a golden
standard dataset from Yi-han-wang, a Chinese C2C E-commerce,
and is compared with a baseline method that does not consider
the hierarchical category. The experimental results show that the
proposed method outperforms the baseline in F1-measure, which
proves that spam keywords are effectively identified by a hierarchical
category in C2C E-commerce.