Abstract: Appropriate ventilation in a classroom is helpful for
enhancing air exchange rate and student concentration. This study
focuses on the effects of fenestration in a four-story school building by
performing numerical simulation of a building when considering
indoor and outdoor environments simultaneously. The wind profile
function embedded in PHOENICS code was set as the inlet boundary
condition in a suburban environment. Sixteen fenestration
combinations were compared in a classroom containing thirty seats.
This study evaluates mean age of air (AGE) and airflow pattern of a
classroom on different floors. Considering both wind profile and
fenestration effects, the airflow on higher floors is channeled toward
the area near ceiling in a room and causes older mean age of air in the
breathing zone. The results in this study serve as a useful guide for
enhancing natural ventilation in a typical school building.
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: 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: The distinction among urban, periurban and rural areas represents a classical example of uncertainty in land classification. Satellite images, geostatistical analysis and all kinds of spatial data are very useful in urban sprawl studies, but it is important to define precise rules in combining great amounts of data to build complex knowledge about territory. Rough Set theory may be a useful method to employ in this field. It represents a different mathematical approach to uncertainty by capturing the indiscernibility. Two different phenomena can be indiscernible in some contexts and classified in the same way when combining available information about them. This approach has been applied in a case of study, comparing the results achieved with both Map Algebra technique and Spatial Rough Set. The study case area, Potenza Province, is particularly suitable for the application of this theory, because it includes 100 municipalities with different number of inhabitants and morphologic features.
Abstract: In this paper we propose a robust environmental sound classification approach, based on spectrograms features driven from log-Gabor filters. This approach includes two methods. In the first methods, the spectrograms are passed through an appropriate log-Gabor filter banks and the outputs are averaged and underwent an optimal feature selection procedure based on a mutual information criteria. The second method uses the same steps but applied only to three patches extracted from each spectrogram.
To investigate the accuracy of the proposed methods, we conduct experiments using a large database containing 10 environmental sound classes. The classification results based on Multiclass Support Vector Machines show that the second method is the most efficient with an average classification accuracy of 89.62 %.
Abstract: Monitoring lightning electromagnetic pulses (sferics)
and other terrestrial as well as extraterrestrial transient radiation signals
is of considerable interest for practical and theoretical purposes
in astro- and geophysics as well as meteorology. Managing a continuous
flow of data, automisation of the detection and classification
process is important. Features based on a combination of wavelet and
statistical methods proved efficient for analysis and characterisation
of transients and as input into a radial basis function network that is
trained to discriminate transients from pulse like to wave like.
Abstract: Classifier fusion may generate more accurate
classification than each of the basic classifiers. Fusion is often based
on fixed combination rules like the product, average etc. This paper
presents decision templates as classifier fusion method for the
recognition of the handwritten English and Farsi numerals (1-9).
The process involves extracting a feature vector on well-known
image databases. The extracted feature vector is fed to multiple
classifier fusion. A set of experiments were conducted to compare
decision templates (DTs) with some combination rules. Results from
decision templates conclude 97.99% and 97.28% for Farsi and
English handwritten digits.
Abstract: The task of face recognition has been actively
researched in recent years. This paper provides an up-to-date review of major human face recognition research. We first present an
overview of face recognition and its applications. Then, a literature review of the most recent face recognition techniques is presented.
Description and limitations of face databases which are used to test
the performance of these face recognition algorithms are given. A
brief summary of the face recognition vendor test (FRVT) 2002, a
large scale evaluation of automatic face recognition technology, and
its conclusions are also given. Finally, we give a summary of the research results.
Abstract: The standard investigational method for obstructive
sleep apnea syndrome (OSAS) diagnosis is polysomnography (PSG),
which consists of a simultaneous, usually overnight recording of
multiple electro-physiological signals related to sleep and
wakefulness. This is an expensive, encumbering and not a readily
repeated protocol, and therefore there is need for simpler and easily
implemented screening and detection techniques. Identification of
apnea/hypopnea events in the screening recordings is the key factor
for the diagnosis of OSAS. The analysis of a solely single-lead
electrocardiographic (ECG) signal for OSAS diagnosis, which may
be done with portable devices, at patient-s home, is the challenge of
the last years. A novel artificial neural network (ANN) based
approach for feature extraction and automatic identification of
respiratory events in ECG signals is presented in this paper. A
nonlinear principal component analysis (NLPCA) method was
considered for feature extraction and support vector machine for
classification/recognition. An alternative representation of the
respiratory events by means of Kohonen type neural network is
discussed. Our prospective study was based on OSAS patients of the
Clinical Hospital of Pneumology from Iaşi, Romania, males and
females, as well as on non-OSAS investigated human subjects. Our
computed analysis includes a learning phase based on cross signal
PSG annotation.
Abstract: In this study, aeroelastic response and performance
analyses have been conducted for a 5MW-Class composite wind
turbine blade model. Advanced coupled numerical method based on
computational fluid dynamics (CFD) and computational flexible
multi-body dynamics (CFMBD) has been developed in order to
investigate aeroelastic responses and performance characteristics of
the rotating composite blade. Reynolds-Averaged Navier-Stokes
(RANS) equations with k-ω SST turbulence model were solved for
unsteady flow problems on the rotating turbine blade model. Also,
structural analyses considering rotating effect have been conducted
using the general nonlinear finite element method. A fully implicit
time marching scheme based on the Newmark direct integration
method is applied to solve the coupled aeroelastic governing equations
of the 3D turbine blade for fluid-structure interaction (FSI) problems.
Detailed dynamic responses and instantaneous velocity contour on the
blade surfaces which considering flow-separation effects were
presented to show the multi-physical phenomenon of the huge rotating
wind- turbine blade model.
Abstract: Discovering new biological knowledge from the highthroughput biological data is a major challenge to bioinformatics today. To address this challenge, we developed a new approach for protein classification. Proteins that are evolutionarily- and thereby functionally- related are said to belong to the same classification. Identifying protein classification is of fundamental importance to document the diversity of the known protein universe. It also provides a means to determine the functional roles of newly discovered protein sequences. Our goal is to predict the functional classification of novel protein sequences based on a set of features extracted from each protein sequence. The proposed technique used datasets extracted from the Structural Classification of Proteins (SCOP) database. A set of spectral domain features based on Fast Fourier Transform (FFT) is used. The proposed classifier uses multilayer back propagation (MLBP) neural network for protein classification. The maximum classification accuracy is about 91% when applying the classifier to the full four levels of the SCOP database. However, it reaches a maximum of 96% when limiting the classification to the family level. The classification results reveal that spectral domain contains information that can be used for classification with high accuracy. In addition, the results emphasize that sequence similarity measures are of great importance especially at the family level.
Abstract: Covering approximation spaces is a class of important
generalization of approximation spaces. For a subset X of a covering
approximation space (U, C), is X definable or rough? The
answer of this question is uncertain, which depends on covering
approximation operators endowed on (U, C). Note that there are many
various covering approximation operators, which can be endowed
on covering approximation spaces. This paper investigates covering
approximation spaces endowed ten covering approximation operators
respectively, and establishes some relations among definable subsets,
inner definable subsets and outer definable subsets in covering approximation
spaces, which deepens some results on definable subsets
in approximation spaces.
Abstract: Studies on residential satisfaction have been actively
discussed under family house setting. However, limited studies have
been conducted on student residential satisfaction. This study is an
attempt to fill the research gap. It focuses on the influence of socioeconomic
on students- satisfaction with the universities- student
housing facilities. The students who stayed at the on-campus student
housing were the respondents. This study employed two-stage cluster
sampling method in classifying the respondents. Self-administered
questionnaires were distributed face-to-face to the students. In
general, it is confirmed that students- socio-economic backgrounds
have influence on the students- satisfaction with their housing
facilities. The main influential factors were the students- economic
status, sense of sharing, and ethnicity of their roommates.
Furthermore, this study could also provide a useful feedback for the
universities in order to improve their student housing facilities.
Abstract: Resource-constrained project scheduling is an NPhard
optimisation problem. There are many different heuristic
strategies how to shift activities in time when resource requirements
exceed their available amounts. These strategies are frequently based
on priorities of activities. In this paper, we assume that a suitable
heuristic has been chosen to decide which activities should be
performed immediately and which should be postponed and
investigate the resource-constrained project scheduling problem
(RCPSP) from the implementation point of view. We propose an
efficient routine that, instead of shifting the activities, extends their
duration. It makes it possible to break down their duration into active
and sleeping subintervals. Then we can apply the classical Critical
Path Method that needs only polynomial running time. This
algorithm can simply be adapted for multiproject scheduling with
limited resources.
Abstract: The goal of this paper is to develop a model to
integrate “pricing" and “advertisement" for short life cycle products,
such as branded fashion clothing products. To achieve this goal, we
apply the concept of “Dynamic Pricing". There are two classes of
advertisements, for the brand (regardless of product) and for a
particular product. Advertising the brand affects the demand and
price of all the products. Thus, the model considers all these products
in relation with each other. We develop two different methods to
integrate both types of advertisement and pricing. The first model is
developed within the framework of dynamic programming. However,
due to the complexity of the model, this method cannot be applicable
for large size problems. Therefore, we develop another method,
called hieratical approach, which is capable of handling the real
world problems. Finally, we show the accuracy of this method, both
theoretically and also by simulation.
Abstract: In hydrocyclones, the particle separation efficiency is
limited by the suspended fine particles, which are discharged with the
coarse product in the underflow. It is well known that injecting water
in the conical part of the cyclone reduces the fine particle fraction in
the underflow. This paper presents a mathematical model that
simulates the water injection in the conical component. The model
accounts for the fluid flow and the particle motion. Particle
interaction, due to hindered settling caused by increased density and
viscosity of the suspension, and fine particle entrainment by settling
coarse particles are included in the model. Water injection in the
conical part of the hydrocyclone is performed to reduce fine particle
discharge in the underflow. The model demonstrates the impact of
the injection rate, injection velocity, and injection location on the
shape of the partition curve. The simulations are compared with
experimental data of a 50-mm cyclone.
Abstract: This paper applies fuzzy clustering algorithm in classifying real estate companies in China according to some general financial indexes, such as income per share, share accumulation fund, net profit margins, weighted net assets yield and shareholders' equity. By constructing and normalizing initial partition matrix, getting fuzzy similar matrix with Minkowski metric and gaining the transitive closure, the dynamic fuzzy clustering analysis for real estate companies is shown clearly that different clustered result change gradually with the threshold reducing, and then, it-s shown there is the similar relationship with the prices of those companies in stock market. In this way, it-s great valuable in contrasting the real estate companies- financial condition in order to grasp some good chances of investment, and so on.
Abstract: Retinal vascularity assessment plays an important role in diagnosis of ophthalmic pathologies. The employment of digital images for this purpose makes possible a computerized approach and has motivated development of many methods for automated vascular tree segmentation. Metrics based on contingency tables for binary classification have been widely used for evaluating performance of these algorithms and, concretely, the accuracy has been mostly used as measure of global performance in this topic. However, this metric shows very poor matching with human perception as well as other notable deficiencies. Here, a new similarity function for measuring quality of retinal vessel segmentations is proposed. This similarity function is based on characterizing the vascular tree as a connected structure with a measurable area and length. Tests made indicate that this new approach shows better behaviour than the current one does. Generalizing, this concept of measuring descriptive properties may be used for designing functions for measuring more successfully segmentation quality of other complex structures.
Abstract: In text categorization problem the most used method
for documents representation is based on words frequency vectors
called VSM (Vector Space Model). This representation is based only
on words from documents and in this case loses any “word context"
information found in the document. In this article we make a
comparison between the classical method of document representation
and a method called Suffix Tree Document Model (STDM) that is
based on representing documents in the Suffix Tree format. For the
STDM model we proposed a new approach for documents
representation and a new formula for computing the similarity
between two documents. Thus we propose to build the suffix tree
only for any two documents at a time. This approach is faster, it has
lower memory consumption and use entire document representation
without using methods for disposing nodes. Also for this method is
proposed a formula for computing the similarity between documents,
which improves substantially the clustering quality. This
representation method was validated using HAC - Hierarchical
Agglomerative Clustering. In this context we experiment also the
stemming influence in the document preprocessing step and highlight
the difference between similarity or dissimilarity measures to find
“closer" documents.
Abstract: The fast growing accessibility and capability of emerging technologies have fashioned enormous possibilities of designing, developing and implementing innovative teaching methods in the classroom. The global technological scenario has paved the way to new pedagogies in teaching-learning process focusing on technology based learning environment and its impact on student achievement. The present experimental study was conducted to determine the effectiveness of technology based learning environment on student achievement in English as a foreign language. The sample of the study was 90 students of 10th grade of a public school located in Islamabad. A pretest- posttest equivalent group design was used to compare the achievement of the two groups. A Pretest and A posttest containing 50 items each from English textbook were developed and administered. The collected data were statistically analyzed. The results showed that there was a significant difference between the mean scores of Experimental group and the Control group. The performance of Experimental group was better on posttest scores that indicted that teaching through technology based learning environment enhanced the achievement level of the students. On the basis of the results, it was recommended that teaching and learning through information and communication technologies may be adopted to enhance the language learning capability of the students.