Abstract: The flow field over a three dimensional pole barn
characterized by a cylindrical roof has been numerically investigated.
Wind pressure and viscous loads acting on the agricultural building
have been analyzed for several incoming wind directions, so as to
evaluate the most critical load condition on the structure. A constant
wind velocity profile, based on the maximum reference wind speed in
the building site (peak gust speed worked out for 50 years return
period) and on the local roughness coefficient, has been simulated.
In order to contemplate also the hazard due to potential air
wedging between the stored hay and the lower part of the ceiling, the
effect of a partial filling of the barn has been investigated.
The distribution of wind-induced loads on the structure have been
determined, allowing a numerical quantification of the effect of wind
direction on the induced stresses acting on a hemicylindrical roof.
Abstract: We propose a downlink multiple-input multipleoutput
(MIMO) multi-carrier code division multiple access (MCCDMA)
system with adaptive beamforming algorithm for smart
antennas. The algorithm used in this paper is based on the Least
Mean Square (LMS), with pilot channel estimation (PCE) and the
zero forcing equalizer (ZFE) in the receiver, requiring reference
signal and no knowledge channel. MC-CDMA is studied in a
multiple antenna context in order to efficiently exploit robustness
against multipath effects and multi-user flexibility of MC-CDMA and
channel diversity offered by MIMO systems for radio mobile
channels. Computer simulations, considering multi-path Rayleigh
Fading Channel, interference inter symbol and interference are
presented to verify the performance. Simulation results show that the
scheme achieves good performance in a multi-user system.
Abstract: Rapid advancement in computing technology brings
computers and humans to be seamlessly integrated in future. The
emergence of smartphone has driven computing era towards
ubiquitous and pervasive computing. Recognizing human activity has
garnered a lot of interest and has raised significant researches-
concerns in identifying contextual information useful to human
activity recognition. Not only unobtrusive to users in daily life,
smartphone has embedded built-in sensors that capable to sense
contextual information of its users supported with wide range
capability of network connections. In this paper, we will discuss the
classification algorithms used in smartphone-based human activity.
Existing technologies pertaining to smartphone-based researches in
human activity recognition will be highlighted and discussed. Our
paper will also present our findings and opinions to formulate
improvement ideas in current researches- trends. Understanding
research trends will enable researchers to have clearer research
direction and common vision on latest smartphone-based human
activity recognition area.
Abstract: This paper presents a generalization kernel for gravitational
potential determination by harmonic splines. It was shown
in [10] that the gravitational potential can be approximated using a
kernel represented as a Newton integral over the real Earth body. On
the other side, the theory of geopotential approximation by harmonic
splines uses spherically oriented kernels. The purpose of this paper
is to show that in the spherical case both kernels have the same type
of representation, which leads us to conclusion that it is possible
to consider the kernel represented as a Newton integral over the real
Earth body as a kind of generalization of spherically harmonic kernels
to real geometries.
Abstract: A concern that researchers usually face in different
applications of Artificial Neural Network (ANN) is determination of
the size of effective domain in time series. In this paper, trial and
error method was used on groundwater depth time series to determine
the size of effective domain in the series in an observation well in
Union County, New Jersey, U.S. different domains of 20, 40, 60, 80,
100, and 120 preceding day were examined and the 80 days was
considered as effective length of the domain. Data sets in different
domains were fed to a Feed Forward Back Propagation ANN with
one hidden layer and the groundwater depths were forecasted. Root
Mean Square Error (RMSE) and the correlation factor (R2) of
estimated and observed groundwater depths for all domains were
determined. In general, groundwater depth forecast improved, as
evidenced by lower RMSEs and higher R2s, when the domain length
increased from 20 to 120. However, 80 days was selected as the
effective domain because the improvement was less than 1% beyond
that. Forecasted ground water depths utilizing measured daily data
(set #1) and data averaged over the effective domain (set #2) were
compared. It was postulated that more accurate nature of measured
daily data was the reason for a better forecast with lower RMSE
(0.1027 m compared to 0.255 m) in set #1. However, the size of input
data in this set was 80 times the size of input data in set #2; a factor
that may increase the computational effort unpredictably. It was
concluded that 80 daily data may be successfully utilized to lower the
size of input data sets considerably, while maintaining the effective
information in the data set.
Abstract: This study examines perception of environmental
approach in small and medium-sized enterprises (SMEs) – the
process by which firms integrate environmental concern into
business. Based on a review of the literature, the paper synthesizes
focus on environmental issues with the reflection in a case study in
the Czech Republic. Two themes of corporate environmentalism are
discussed – corporate environmental orientation and corporate
stances toward environmental concerns. It provides theoretical
material on greening organizational culture that is helpful in
understanding the response of contemporary business to
environmental problems. We integrate theoretical predictions with
empirical findings confronted with reality. Scales to measure these
themes are tested in a survey of managers in 229 Czech firms. We
used the process of in-depth questioning. The research question was
derived and answered in the context of the corresponding literature
and conducted research. A case study showed us that environmental
approach is variety different (depending on the size of the firm) in
SMEs sector. The results of the empirical mapping demonstrate
Czech company’s approach to environment and define the problem
areas and pinpoint the main limitation in the expansion of
environmental aspects. We contribute to the debate for recognition of
the particular role of environmental issues in business reality.
Abstract: The presence of toxic heavy metals in industrial
effluents is one of the serious threats to the environment. Heavy
metals such as Cadmium, Chromium, Lead, Nickel, Zinc, Mercury,
Copper, Arsenic are found in the effluents of industries such as
foundries, electroplating, petrochemical, battery manufacturing,
tanneries, fertilizer, dying, textiles, metallurgical and metal finishing.
Tremendous increase of industrial copper usage and its presence in
industrial effluents has lead to a growing concern about the fate and
effects of Copper in the environment. Percolation of industrial
effluents through soils leads to contamination of ground water and
soils. The transport of heavy metals and their diffusion into the soils
has therefore, drawn the attention of the researchers.
In this study, an attempt has been made to delineate the
mechanisms of transport and fate of copper in terrestrial
environment. Column studies were conducted using perplex glass
square column of dimension side 15 cm and 1.35 m long. The soil
samples were collected from a natural drain near Mohali (India). The
soil was characterized to be poorly graded sandy loam. The soil was
compacted to the field dry density level of about 1.6 g/cm3. Break
through curves for different depths of the column were plotted. The
results of the column study indicated that the copper has high
tendency to flow in the soils and fewer tendencies to get absorbed on
the soil particles. The t1/2 estimates obtained from the studies can be
used for design copper laden wastewater disposal systems.
Abstract: Magnetic Resonance Imaging play a vital role in the decision-diagnosis process of brain MR images. For an accurate diagnosis of brain related problems, the experts mostly compares both T1 and T2 weighted images as the information presented in these two images are complementary. In this paper, rotational and translational invariant form of Local binary Pattern (LBP) with additional gray scale information is used to retrieve similar slices of T1 weighted images from T2 weighted images or vice versa. The incorporation of additional gray scale information on LBP can extract more local texture information. The accuracy of retrieval can be improved by extracting moment features of LBP and reweighting the features based on users feedback. Here retrieval is done in a single subject scenario where similar images of a particular subject at a particular level are retrieved, and multiple subjects scenario where relevant images at a particular level across the subjects are retrieved.
Abstract: Cloud Computing is a new technology that helps us to
use the Cloud for compliance our computation needs. Cloud refers to a scalable network of computers that work together like Internet. An
important element in Cloud Computing is that we shift processing, managing, storing and implementing our data from, locality into the
Cloud; So it helps us to improve the efficiency. Because of it is new
technology, it has both advantages and disadvantages that are
scrutinized in this article. Then some vanguards of this technology
are studied. Afterwards we find out that Cloud Computing will have
important roles in our tomorrow life!
Abstract: Although backpropagation ANNs generally predict
better than decision trees do for pattern classification problems, they
are often regarded as black boxes, i.e., their predictions cannot be
explained as those of decision trees. In many applications, it is
desirable to extract knowledge from trained ANNs for the users to
gain a better understanding of how the networks solve the problems.
A new rule extraction algorithm, called rule extraction from artificial
neural networks (REANN) is proposed and implemented to extract
symbolic rules from ANNs. A standard three-layer feedforward ANN
is the basis of the algorithm. A four-phase training algorithm is
proposed for backpropagation learning. Explicitness of the extracted
rules is supported by comparing them to the symbolic rules generated
by other methods. Extracted rules are comparable with other methods
in terms of number of rules, average number of conditions for a rule,
and predictive accuracy. Extensive experimental studies on several
benchmarks classification problems, such as breast cancer, iris,
diabetes, and season classification problems, demonstrate the
effectiveness of the proposed approach with good generalization
ability.
Abstract: During the last couple of years, the degree of dependence on IT systems has reached a dimension nobody imagined to be possible 10 years ago. The increased usage of mobile devices (e.g., smart phones), wireless sensor networks and embedded devices (Internet of Things) are only some examples of the dependency of modern societies on cyber space. At the same time, the complexity of IT applications, e.g., because of the increasing use of cloud computing, is rising continuously. Along with this, the threats to IT security have increased both quantitatively and qualitatively, as recent examples like STUXNET or the supposed cyber attack on Illinois water system are proofing impressively. Once isolated control systems are nowadays often publicly available - a fact that has never been intended by the developers. Threats to IT systems don’t care about areas of responsibility. Especially with regard to Cyber Warfare, IT threats are no longer limited to company or industry boundaries, administrative jurisdictions or state boundaries. One of the important countermeasures is increased cooperation among the participants especially in the field of Cyber Defence. Besides political and legal challenges, there are technical ones as well. A better, at least partially automated exchange of information is essential to (i) enable sophisticated situational awareness and to (ii) counter the attacker in a coordinated way. Therefore, this publication performs an evaluation of state of the art Intrusion Detection Message Exchange protocols in order to guarantee a secure information exchange between different entities.
Abstract: Achievement motivation is believed to promote
giftedness attracting people to invest in many programs to adopt
gifted students providing them with challenging activities.
Intellectual giftedness is founded on the fluid intelligence and
extends to more specific abilities through the growth and inputs from
the achievement motivation. Acknowledging the roles played by the
motivation in the development of giftedness leads to an effective
nurturing of gifted individuals. However, no study has investigated
the direct and indirect effects of the achievement motivation and
fluid intelligence on intellectual giftedness. Thus, this study
investigated the contribution of motivation factors to giftedness
development by conducting tests of fluid intelligence using Cattell
Culture Fair Test (CCFT) and analytical abilities using culture
reduced test items covering problem solving, pattern recognition,
audio-logic, audio-matrices, and artificial language, and self report
questionnaire for the motivational factors. A number of 180 highscoring
students were selected using CCFT from a leading university
in Malaysia. Structural equation modeling was employed using Amos
V.16 to determine the direct and indirect effects of achievement
motivation factors (self confidence, success, perseverance,
competition, autonomy, responsibility, ambition, and locus of
control) on the intellectual giftedness. The findings showed that the
hypothesized model fitted the data, supporting the model postulates
and showed significant and strong direct and indirect effects of the
motivation and fluid intelligence on the intellectual giftedness.
Abstract: In this paper, a PSO-based approach is proposed to
derive a digital controller for redesigned digital systems having an interval plant based on resemblance of the extremal gain/phase
margins. By combining the interval plant and a controller as an interval system, extremal GM/PM associated with the loop transfer function
can be obtained. The design problem is then formulated as an optimization problem of an aggregated error function revealing the deviation on the extremal GM/PM between the redesigned digital
system and its continuous counterpart, and subsequently optimized by
a proposed PSO to obtain an optimal set of parameters for the digital controller. Computer simulations have shown that frequency
responses of the redesigned digital system having an interval plant bare a better resemblance to its continuous-time counter part by the incorporation of a PSO-derived digital controller in comparison to those obtained using existing open-loop discretization methods.
Abstract: In this paper, the application of neural networks to study the design of short-term temperature forecasting (STTF) Systems for Kermanshah city, west of Iran was explored. One important architecture of neural networks named Multi-Layer Perceptron (MLP) to model STTF systems is used. Our study based on MLP was trained and tested using ten years (1996-2006) meteorological data. The results show that MLP network has the minimum forecasting error and can be considered as a good method to model the STTF systems.
Abstract: In this study, aerobic digestion of tannery industry
wastewater was carried out using mixed culture obtained from
common effluent treatment plant treating tannery wastewater. The
effect of pH, temperature, inoculum concentration, agitation speed
and initial substrate concentration on the reduction of organic matters
were found. The optimum conditions for COD reduction was found
to be pH - 7 (60%), temperature - 30ÔùªC (61%), inoculum
concentration - 2% (61%), agitation speed - 150rpm (65%) and initial
substrate concentration - 1560 mg COD/L (74%). Kinetics studies
were carried by using Monod model, First order, Diffusional model
and Singh model. From the results it was found that the Monod
model suits well for the degradation of tannery wastewater using
mixed microbial consortium.
Abstract: The UML modeling of complex distributed systems often is a great challenge due to the large amount of parallel real-time operating components. In this paper the problems of verification of such systems are discussed. ECPN, an Extended Colored Petri Net is defined to formally describe state transitions of components and interactions among components. The relationship between sequence diagrams and Free Choice Petri Nets is investigated. Free Choice Petri Net theory helps verifying the liveness of sequence diagrams. By converting sequence diagrams to ECPNs and then comparing behaviors of sequence diagram ECPNs and statecharts, the consistency among models is analyzed. Finally, a verification process for an example model is demonstrated.
Abstract: The purpose of the study is to determine the primary mathematics student teachers- views related to use instructional technology tools in course of the learning process and to reveal how the sample presentations towards different mathematical concepts affect their views. This is a qualitative study involving twelve mathematics students from a public university. The data gathered from two semi-structural interviews. The first one was realized in the beginning of the study. After that the representations prepared by the researchers were showed to the participants. These representations contain animations, Geometer-s Sketchpad activities, video-clips, spreadsheets, and power-point presentations. The last interview was realized at the end of these representations. The data from the interviews and content analyses were transcribed and read and reread to explore the major themes. Findings revealed that the views of the students changed in this process and they believed that the instructional technology tools should be used in their classroom.
Abstract: In this paper, we analyzed the pattern of urban spatial structure of Siheung City that had been divided into two parts and presented alternative plans in order to get rid of these phenomena. Concerning patterns of urban spatial structure, we examined it through means of analyzing status of land use, population density and distribution of residence, status of distribution of main facilities, medical facilities, status of distribution of cultural facilities, distribution of land prices and traffic volume trends. The results of study revealed that status of facilities distribution and distribution of land prices, etc. were bisected by the surrounding area of former municipal office and the district of Sihwa, which were both regarded as one apex of the city divide, forming a duo-centric city. In order to get rid of this problem concerned with urban spatial structure that has been bisected, it is required that measures in order to expand facilities in Siheung City should be taken.
Abstract: Not with standing the importance of foreign highly
skilled professionals for host economies, there is a paucity of
research studies investigating the role of the corporate social context
during the integration process. This research aims to address this
paucity by exploring the role of social capital in the integration of
foreign health professionals. It does so by using a qualitative research
approach. In this pilot study the hospital sector forms this study-s
sample and interviews were conducted with HR managers, foreign
health professionals and external HR consultants. It was found that
most of the participating hospitals had not established specific HR
practices and had only partly linked the development of
organisational social capital with a successful integration process.
This research contributes, for example, to the HR literature on the
integration of self-initiated expatriates by analysing the role of HRM
in generating organisational social capital needed for a successful
integration process.
Abstract: Ensemble learning algorithms such as AdaBoost and
Bagging have been in active research and shown improvements in
classification results for several benchmarking data sets with mainly
decision trees as their base classifiers. In this paper we experiment to
apply these Meta learning techniques with classifiers such as random
forests, neural networks and support vector machines. The data sets
are from MAGIC, a Cherenkov telescope experiment. The task is to
classify gamma signals from overwhelmingly hadron and muon
signals representing a rare class classification problem. We compare
the individual classifiers with their ensemble counterparts and
discuss the results. WEKA a wonderful tool for machine learning has
been used for making the experiments.