Abstract: Academics and researchers are interested in the effects of social media on college students, with a specific focus on the most popular social media website; Facebook. Previous studied have found contradictory result on the relationship between Facebook usage and the student engagement with positive, detrimental and no significant relationships. However, these studies were limited to western higher education system. This paper fills a gap in the literature by using a sample (300) of Sri Lankan management undergraduates to examine the relationship between Facebook usage and student engagement. Student engagement was measured 35 item scale based on the National Survey of Student Engagement and Facebook usage by Facebook intensity scale. Descriptive statistics, path analysis and structural equation modeling were applied as statistical tools and techniques. Results indicate that student engagement scale was significantly negatively related with the Facebook usage with the influence from student engagement on Facebook usage.
Abstract: Nowadays, precipitation prediction is required for proper planning and management of water resources. Prediction with neural network models has received increasing interest in various research and application domains. However, it is difficult to determine the best neural network architecture for prediction since it is not immediately obvious how many input or hidden nodes are used in the model. In this paper, neural network model is used as a forecasting tool. The major aim is to evaluate a suitable neural network model for monthly precipitation mapping of Myanmar. Using 3-layerd neural network models, 100 cases are tested by changing the number of input and hidden nodes from 1 to 10 nodes, respectively, and only one outputnode used. The optimum model with the suitable number of nodes is selected in accordance with the minimum forecast error. In measuring network performance using Root Mean Square Error (RMSE), experimental results significantly show that 3 inputs-10 hiddens-1 output architecture model gives the best prediction result for monthly precipitation in Myanmar.
Abstract: Our objectives were to evaluate the effects of sire
breed, type of protein supplement, level of supplementation and sex
on wool spinning fineness (SF), its correlations with other wool
characteristics and prediction accuracy in F1 Merino crossbred lambs.
Texel, Coopworth, White Suffolk, East Friesian and Dorset rams
were mated with 500 purebred Merino dams at a ratio of 1:100 in
separate paddocks within a single management system. The F1
progeny were raised on ryegrass pasture until weaning, before forty
lambs were randomly allocated to treatments in a 5 x 2 x 2 x 2
factorial experimental design representing 5 sire breeds, 2
supplementary feeds (canola or lupins), 2 levels of supplementation
(1% or 2% of liveweight) and sex (wethers or ewes). Lambs were
supplemented for six weeks after an initial three weeks of adjustment,
wool sampled at the commencement and conclusion of the feeding
trial and analyzed for SF, mean fibre diameter (FD), coefficient of
variation (CV), standard deviation, comfort factor (CF), fibre
curvature (CURV), and clean fleece yield. Data were analyzed using
mixed linear model procedures with sire fitted as a random effect,
and sire breed, sex, supplementary feed type, level of
supplementation and their second-order interactions as fixed effects.
Sire breed (P
Abstract: This paper proposes a novel approach to the question of lithofacies classification based on an assessment of the uncertainty in the classification results. The proposed approach has multiple neural networks (NN), and interval neutrosophic sets (INS) are used to classify the input well log data into outputs of multiple classes of lithofacies. A pair of n-class neural networks are used to predict n-degree of truth memberships and n-degree of false memberships. Indeterminacy memberships or uncertainties in the predictions are estimated using a multidimensional interpolation method. These three memberships form the INS used to support the confidence in results of multiclass classification. Based on the experimental data, our approach improves the classification performance as compared to an existing technique applied only to the truth membership. In addition, our approach has the capability to provide a measure of uncertainty in the problem of multiclass classification.
Abstract: The ability to predict an accurate temperature
distribution requires the knowledge of the losses, the thermal
characteristics of the materials, and the cooling conditions, all of
which are very difficult to quantify. In this paper, the impact of the
effects of iron and copper losses are investigated separately and
their effects on the heating in various points of the stator of an
induction motor, is highlighted by using two simple tests. In addition,
the effect of a defect, such as an open circuit in a phase of the stator,
on the heating is also obtained by a no-load test.
The squirrel cage induction motor is rated at 2.2 kW; 380 V; 5.2
A; Δ connected; 50 Hz; 1420 rpm and the class of insulation F, has
been thermally tested under several load conditions. Several
thermocouples were placed in strategic points of the stator.
Abstract: Taxation as a potent fiscal policy instrument through which infrastructures and social services that drive the development process of any society has been ineffective in Nigeria. The adoption of appropriate measures is, however, a requirement for the generation of adequate tax revenue. This study set out to investigates efficiency and effectiveness in the administration of tax in Nigeria, using Cross River State as a case-study. The methodology to achieve this objective is a qualitative technique using structured questionnaires to survey the three senatorial districts in the state; the central limit theory is adopted as our analytical technique. Result showed a significant degree of inefficiency in the administration of taxes. It is recommended that periodic review and update of tax policy will bring innovation and effectiveness in the administration of taxes. Also proper appropriation of tax revenue will drive development in needed infrastructural and social services.
Abstract: Lateral expansion is a factor defining the level of
confinement in reinforced concrete columns. Therefore, predicting
the lateral strain relationship with axial strain becomes an important
issue. Measuring lateral strains in experiments is difficult and only
few report experimental lateral strains. Among the existing analytical
formulations, two recent models are compared with available test
results in this paper with shortcomings highlighted. A new analytical
model is proposed here for lateral strain axial strain relationship and
is based on the supposition that the concrete behaves linear elastic in
the early stages of loading and then nonlinear hardening up to the
peak stress and then volumetric expansion. The proposal for the
lateral strain axial strain relationship after the peak stress is mainly
based on the hypothesis that the plastic lateral strain varies linearly
with the plastic axial strain and it is shown that this is related to the
lateral confinement level.
Abstract: Predicting protein-protein interactions represent a key step in understanding proteins functions. This is due to the fact that proteins usually work in context of other proteins and rarely function alone. Machine learning techniques have been applied to predict protein-protein interactions. However, most of these techniques address this problem as a binary classification problem. Although it is easy to get a dataset of interacting proteins as positive examples, there are no experimentally confirmed non-interacting proteins to be considered as negative examples. Therefore, in this paper we solve this problem as a one-class classification problem using one-class support vector machines (SVM). Using only positive examples (interacting protein pairs) in training phase, the one-class SVM achieves accuracy of about 80%. These results imply that protein-protein interaction can be predicted using one-class classifier with comparable accuracy to the binary classifiers that use artificially constructed negative examples.
Abstract: Knowledge management (KM) is generally
considered to be a positive process in an organisation, facilitating
opportunities to achieve competitive advantage via better quality
information handling, compilation of expert know-how and rapid
response to fluctuations in the business environment. The KM
paradigm as portrayed in the literature informs the processes that can
increase intangible assets so that corporate knowledge is preserved.
However, in some instances, knowledge management exists in a
universe of dynamic tension among the conflicting needs to respect
privacy and intellectual property (IP), to guard against data theft, to
protect national security and to stay within the laws. While the
Knowledge Management literature focuses on the bright side of the
paradigm, there is also a different side in which knowledge is
distorted, suppressed or misappropriated due to personal or
organisational motives (the paradox). This paper describes the ethical
paradoxes that occur within the taxonomy and deontology of
knowledge management and suggests that recognising both the
promises and pitfalls of KM requires wisdom.
Abstract: Stable bacterial polymorphism on a single limiting resource may appear if between the evolved strains metabolic interactions take place that allow the exchange of essential nutrients [8]. Towards an attempt to predict the possible outcome of longrunning evolution experiments, a network based on the metabolic capabilities of homogeneous populations of every single gene knockout strain (nodes) of the bacterium E. coli is reconstructed. Potential metabolic interactions (edges) are allowed only between strains of different metabolic capabilities. Bacterial communities are determined by finding cliques in this network. Growth of the emerged hypothetical bacterial communities is simulated by extending the metabolic flux balance analysis model of Varma et al [2] to embody heterogeneous cell population growth in a mutual environment. Results from aerobic growth on 10 different carbon sources are presented. The upper bounds of the diversity that can emerge from single-cloned populations of E. coli such as the number of strains that appears to metabolically differ from most strains (highly connected nodes), the maximum clique size as well as the number of all the possible communities are determined. Certain single gene deletions are identified to consistently participate in our hypothetical bacterial communities under most environmental conditions implying a pattern of growth-condition- invariant strains with similar metabolic effects. Moreover, evaluation of all the hypothetical bacterial communities under growth on pyruvate reveals heterogeneous populations that can exhibit superior growth performance when compared to the performance of the homogeneous wild-type population.
Abstract: This paper describes the design concepts and
implementation of a 5-Joint mechanical arm for a rescue robot named
CEO Mission II. The multi-joint arm is a five degree of freedom
mechanical arm with a four bar linkage, which can be stretched to
125 cm. long. It is controlled by a teleoperator via the user-friendly
control and monitoring GUI program. With Inverse Kinematics
principle, we developed the method to control the servo angles of all
arm joints to get the desired tip position. By clicking the determined
tip position or dragging the tip of the mechanical arm on the
computer screen to the desired target point, the robot will compute
and move its multi-joint arm to the pose as seen on the GUI screen.
The angles of each joint are calculated and sent to all joint servos
simultaneously in order to move the mechanical arm to the desired
pose at once. The operator can also use a joystick to control the
movement of this mechanical arm and the locomotion of the robot.
Many sensors are installed at the tip of this mechanical arm for
surveillance from the high level and getting the vital signs of victims
easier and faster in the urban search and rescue tasks. It works very
effectively and easy to control. This mechanical arm and its software
were developed as a part of the CEO Mission II Rescue Robot that
won the First Runner Up award and the Best Technique award from
the Thailand Rescue Robot Championship 2006. It is a low cost,
simple, but functioning 5-Jiont mechanical arm which is built from
scratch, and controlled via wireless LAN 802.11b/g. This 5-Jiont
mechanical arm hardware concept and its software can also be used
as the basic mechatronics to many real applications.
Abstract: Virtual touch screen using camera is an ordinary screen which uses a camera to imitate the touch screen by taking a picture of an indicator, e.g., finger, which is laid on the screen, converting the indicator tip position on the picture to the position on the screen, and moving the cursor on the screen to that position. In fact, the indicator is not laid on the screen directly, but it is intervened by the cover at some intervals. In spite of this gap, if the eye-indicator-camera angle is not large, the mapping from the indicator tip positions on the image to the corresponding cursor positions on the screen is not difficult and could be done with a little error. However, the larger the angle is, the bigger the error in the mapping occurs. This paper proposes cursor position estimation model for virtual touch screen using camera which could eliminate this kind of error. The proposed model (i) moves the on-screen pilot cursor to the screen position which locates on the screen at the position just behind the indicator tip when the indicator tip has been looked from the camera position, and then (ii) converts that pilot cursor position to the desirable cursor position (the position on the screen when it has been looked from the user-s eye through the indicator tip) by using the bilinear transformation. Simulation results show the correctness of the estimated cursor position by using the proposed model.
Abstract: The significant effects of the interactions between the
system boundaries and the near wall molecules in miniaturized
gaseous devices lead to the formation of the Knudsen layer in which
the Navier-Stokes-Fourier (NSF) equations fail to predict the correct
associated phenomena. In this paper, the well-known lattice
Boltzmann method (LBM) is employed to simulate the fluid flow and
heat transfer processes in rarefied gaseous micro media. Persuaded
by the problematic deficiency of the LBM in capturing the Knudsen
layer phenomena, present study tends to concentrate on the effective
molecular mean free path concept the main essence of which is to
compensate the incapability of this mesoscopic method in dealing
with the momentum and energy transport within the above mentioned
kinetic boundary layer. The results show qualitative and quantitative
accuracy comparable to the solutions of the linearized Boltzmann
equation or the DSMC data for the Knudsen numbers of O (1) .
Abstract: This study investigated the relationship between urban
and rural ozone concentrations and quantified the extent to which
ambient rural conditions and the concentrations of other pollutants
can be used to predict urban ozone concentrations. The study
describes the variations of ozone in weekday and weekends as well as
the daily maximum recorded at selected monitoring stations. The
results showed that Putrajaya station had the highest concentrations
of O3 on weekend due the titration of NO during the weekday.
Additionally, Jerantut had the lowest average concentration with a
reading value high on Wednesdays. The comparisons of average and
maximum concentrations of ozone for the three stations showed that
the strongest significant correlation is recorded in Jerantut station
with the value R2= 0.769. Ozone concentrations originating from a
neighbouring urban site form a better predictor to the urban ozone
concentrations than widespread rural ozone at some levels of
temporal averaging. It is found that in urban and rural of Malaysian
peninsular, the concentration of ozone depends on the concentration
of NOx and seasonal meteorological factors. The HYSPLIT Model
(the northeast monsoon) showed that the wind direction can also
influence the concentration of ozone in the atmosphere in the studied
areas.
Abstract: The main objective of this paper is to provide an efficient tool for delineating brain tumors in three-dimensional magnetic resonance images. To achieve this goal, we use basically a level-sets approach to delineating three-dimensional brain tumors. Then we introduce a compression plan of 3D brain structures based for the meshes simplification, adapted for time to the specific needs of the telemedicine and to the capacities restricted by network communication. We present here the main stages of our system, and preliminary results which are very encouraging for clinical practice.
Abstract: It is believed that major account on language diversity must be taken in learning, and especially in learning using ICT. This paper-s objective is to exhibit language and communication barriers in learning, to approach the topic from socioculture and cognitivist perspectives, and to give exploratory solutions of handling such barriers. The review is mainly conducted by approaching the journal Computers & Education, but also an initially broad search was conducted. The results show that not much attention is paid on language and communication barriers in an immediate relation to learning using ICT. The results shows, inter alia, that language and communication barriers are caused because of not enough account is taken on both the individual-s background and the technology.
Abstract: Route bus system is the fundamental public transportation
system and has an important role in every province. To improve
the usability of it greatly, we develop an AR application for "Bus-
Net". The Bus-Net system is the shortest path planning system.
Bus-Net supports bus users to make a plan to change buses by
providing them with information about the direction. However, with
Bus-Net, these information are provided in text-base. It is difficult
to understand them for the person who does not know the place. We
developed the AR application for Bus-Net. It supports the action of
a bus user in an innovative way by putting information on a camera
picture and leading the way to a bus stop. The application also inform
the user the correct bus to get, the direction the bus takes and the
fare, which ease many anxieties and worries people tend to feel when
they take buses.
Abstract: An accident is an unexpected and unplanned situation
that happens and affects human in a negative outcome. The accident
can cause an injury to a human biological organism. Thus, the
provision of initial care for an illness or injury is very important
move to prepare the patients/victims before sending to the doctor. In
this paper, a First Aid Application is developed to give some
directions for preliminary taking care of patient/victim via Android
mobile device. Also, the navigation function using Google Maps API
is implemented in this paper for searching a suitable path to the
nearest hospital. Therefore, in the emergency case, this function can
be activated and navigate patients/victims to the hospital with the
shortest path.
Abstract: Iodine radionuclides in accident releases under severe
accident conditions at NPP with VVER are the most radiationimportant
with a view to population dose generation at the beginning
of the accident. To decrease radiation consequences of severe
accidents the technical solutions for severe accidents management
have been proposed in MIR.1200 project, with consideration of the
measures for suppression of volatile iodine forms generation in the
containment. Behavior dynamics of different iodine forms in the
containment under severe accident conditions has been analyzed for
the purpose of these technical solutions justification.
Abstract: Classification is an interesting problem in functional
data analysis (FDA), because many science and application problems
end up with classification problems, such as recognition, prediction,
control, decision making, management, etc. As the high dimension
and high correlation in functional data (FD), it is a key problem to
extract features from FD whereas keeping its global characters, which
relates to the classification efficiency and precision to heavens. In this
paper, a novel automatic method which combined Genetic Algorithm
(GA) and classification algorithm to extract classification features is
proposed. In this method, the optimal features and classification model
are approached via evolutional study step by step. It is proved by
theory analysis and experiment test that this method has advantages in
improving classification efficiency, precision and robustness whereas
using less features and the dimension of extracted classification
features can be controlled.