Abstract: This study include the effect of strain and storage
period and their interaction on some quantitative and qualitative traits
and percentages of the egg components in the eggs collected at the
start of production (at age 24 weeks). Eggs were divided into three
storage periods (1, 7 and 14) days under refrigerator temperature (5-
7)0C. Fifty seven eggs obtained randomly from each strain including
Isa Brown and Lohman White. General Linear Model within
SAS programme was used to analyze the collected data
and correlations between the studied traits were calculated for each
strain.Average egg weight (EW), Haugh Unit (HU), yolk index (YI),
yolk % (HP), albumin % (AP) and yolk to albumin ratio (YAR) was
56.629 gm, 87.968 %, 0.493, 22.13%, 67.74% and 32.76
respectively. Egg produced from ISA Brown surpassed those
produced by Lohman White significantly (P
Abstract: The goal of Gene Expression Analysis is to understand the processes that underlie the regulatory networks and pathways controlling inter-cellular and intra-cellular activities. In recent times microarray datasets are extensively used for this purpose. The scope of such analysis has broadened in recent times towards reconstruction of gene networks and other holistic approaches of Systems Biology. Evolutionary methods are proving to be successful in such problems and a number of such methods have been proposed. However all these methods are based on processing of genotypic information. Towards this end, there is a need to develop evolutionary methods that address phenotypic interactions together with genotypic interactions. We present a novel evolutionary approach, called Phenomic algorithm, wherein the focus is on phenotypic interaction. We use the expression profiles of genes to model the interactions between them at the phenotypic level. We apply this algorithm to the yeast sporulation dataset and show that the algorithm can identify gene networks with relative ease.
Abstract: Fundamental sensor-motor couplings form the backbone
of most mobile robot control tasks, and often need to be implemented
fast, efficiently and nevertheless reliably. Machine learning
techniques are therefore often used to obtain the desired sensor-motor
competences.
In this paper we present an alternative to established machine
learning methods such as artificial neural networks, that is very fast,
easy to implement, and has the distinct advantage that it generates
transparent, analysable sensor-motor couplings: system identification
through nonlinear polynomial mapping.
This work, which is part of the RobotMODIC project at the
universities of Essex and Sheffield, aims to develop a theoretical understanding
of the interaction between the robot and its environment.
One of the purposes of this research is to enable the principled design
of robot control programs.
As a first step towards this aim we model the behaviour of the
robot, as this emerges from its interaction with the environment, with
the NARMAX modelling method (Nonlinear, Auto-Regressive, Moving
Average models with eXogenous inputs). This method produces
explicit polynomial functions that can be subsequently analysed using
established mathematical methods.
In this paper we demonstrate the fidelity of the obtained NARMAX
models in the challenging task of robot route learning; we present a
set of experiments in which a Magellan Pro mobile robot was taught
to follow four different routes, always using the same mechanism to
obtain the required control law.
Abstract: Nowadays, biometrical characterizations of Artemia
cysts are used as one of the most important factors in the study of
Artemia populations and intraspecific particularity; meanwhile these
characters can be used as economical indices. For example, typically
high hatching efficiency is possible due to the small diameter of
cysts (high number per gram); therefore small diameter of cysts
show someway high quality of cysts. This study was performed
during a ten year period, including two different ecological
conditions: rainy and drought. It is important from two different
aspects because it covers alteration of A. urmiana during ten years
also its variation in the best and worst environmental situations in
which salinity increased from 173.8 ppt in 1994 to 280.8 ppt in
2003/4. In this study the biometrical raw data of Artemia urmiana
cysts at seven stations from the Urmia Lake in 1994 and their seven
identical locations at 26 studied stations in 2003/4 were reanalyzed
again and compared together. Biometrical comparison of untreated
and decapsulated cysts in each of the seven similar stations showed a
highly significant variation between 1994 and 2003/4. Based on this
study, in whole stations the untreated and decapsulated cysts from
1994 were larger than cysts of 2003/4 without any exception. But
there was no logical relationship between salinity and chorion
thickness in the Urmia Lake. With regard to PCA analyses the
stations of two different studied years certainly have been separated
with factor 1 from each other. In conclusion, the interaction between
genetic and environmental factors can determine and explain
variation in the range of cysts diameter in Artemia.
Abstract: Human activities are increasingly based on the use of remote resources and services, and on the interaction between
remotely located parties that may know little about each other. Mobile agents must be prepared to execute on different hosts with
various environmental security conditions. The aim of this paper is to
propose a trust based mechanism to improve the security of mobile
agents and allow their execution in various environments. Thus, an
adaptive trust mechanism is proposed. It is based on the dynamic interaction between the agent and the environment. Information
collected during the interaction enables generation of an environment
key. This key informs on the host-s trust degree and permits the mobile agent to adapt its execution. Trust estimation is based on
concrete parameters values. Thus, in case of distrust, the source of problem can be located and a mobile agent appropriate behavior can
be selected.
Abstract: According to the interaction of inflation and
unemployment, expectation of the rate of inflation in Croatia is
estimated. The interaction between inflation and unemployment is
shown by model based on three first-order differential i.e. difference
equations: Phillips relation, adaptive expectations equation and
monetary-policy equation. The resulting equation is second order
differential i.e. difference equation which describes the time path of
inflation. The data of the rate of inflation and the rate of
unemployment are used for parameters estimation. On the basis of
the estimated time paths, the stability and convergence analysis is
done for the rate of inflation.
Abstract: Interaction Model plays an important role in Modelbased
Intelligent Interface Agent Architecture for developing
Intelligent User Interface. In this paper we are presenting some
improvements in the algorithms for development interaction model of
interface agent including: the action segmentation algorithm, the
action pair selection algorithm, the final action pair selection
algorithm, the interaction graph construction algorithm and the
probability calculation algorithm. The analysis of the algorithms also
presented. At the end of this paper, we introduce an experimental
program called “Personal Transfer System".
Abstract: Interaction effects of xanthan gum (XG), carboxymethyl
cellulose (CMC), and locust bean gum (LBG) on the flow properties
of oil-in-water emulsions were investigated by a mixture design
experiment. Blends of XG, CMC and LBG were prepared according
to an augmented simplex-centroid mixture design (10 points) and used
at 0.5% (wt/wt) in the emulsion formulations. An appropriate
mathematical model was fitted to express each response as a function
of the proportions of the blend components that are able to
empirically predict the response to any blend of combination of the
components. The synergistic interaction effect of the ternary
XG:CMC:LBG blends at approximately 33-67% XG levels was
shown to be much stronger than that of the binary XG:LBG blend at
50% XG level (p < 0.05). Nevertheless, an antagonistic interaction
effect became significant as CMC level in blends was more than 33%
(p < 0.05). Yield stress and apparent viscosity (at 10 s-1) responses
were successfully fitted with a special quartic model while flow
behaviour index and consistency coefficient were fitted with a full
quartic model (R2
adjusted ≥ 0.90). This study found that a mixture
design approach could serve as a valuable tool in better elucidating
and predicting the interaction effects beyond the conventional twocomponent
blends.
Abstract: Motion capturing technology has been used for quite a
while and several research has been done within this area. Nevertheless,
we discovered open issues within current motion capturing
environments. In this paper we provide a state-of-the-art overview of
the addressed research areas and show issues with current motion
capturing environments. Observations, interviews and questionnaires
have been used to reveal the challenges actors are currently facing in
a motion capturing environment. Furthermore, the idea to create a
more immersive motion capturing environment to improve the acting
performances and motion capturing outcomes as a potential solution
is introduced. It is hereby the goal to explain the found open issues
and the developed ideas which shall serve for further research as a
basis. Moreover, a methodology to address the interaction and
systems design issues is proposed. A future outcome could be that
motion capture actors are able to perform more naturally, especially
if using a non-body-worn solution.
Abstract: In this work, the autoregressive vectors are used to
know dynamics of the Agricultural export and import, and the real
effective exchange rate (REER). In order to analyze the interactions,
the impulse- response function is used in decomposition of variance,
causality of Granger as well as the methodology of Johansen to know
the relations co integration. The REER causes agricultural export and
import in the sense of Granger. The influence displays the
innovations of the REER on the agricultural export and import is not
very great and the duration of the effects is short. It displays that
REER has an immediate positive effect, after the tenth year it
displays smooth results on the agricultural export. Evidence of a
vector exists co integration, In short run, REER has smaller effects
on export and import, compared to the long-run effects.
Abstract: Heavy metal pollution is an environmental concern.
Phytoremediation is a low-cost, environmental-friendly approach to
solve this problem. Mustard has the potential in reducing heavy metal
contents in soils. Among mustard (Brassica juncea (L.) Czern &
Coss) genotypes in Sri Lanka, accessions 7788, 8831 and 5088 give
significantly a high yield. Therefore, present study was conducted to
quantify the phytoextractive potential among these local mustard
accessions and to assess the interaction of heavy metals, Pb, Co, Mn
on phytoextraction. A pot experiment was designed with acid washed
sand (quartz) and a series of heavy metal solutions of 0, 25, 50, 75
and 100 μg/g. Experiment was carried out with factorial
experimental design. Mustard accessions were tolerant to heavy
metals and could be successfully used in removal of Pb, Co and Mn
and they are capable of accumulating significant quantities of heavy
metals in vegetative and reproductive organs. The order of the
accumulative potential of Pb, Co and Mn in mustard accessions is,
root > shoot >seed.
Abstract: Understanding the cell's large-scale organization is an interesting task in computational biology. Thus, protein-protein interactions can reveal important organization and function of the cell. Here, we investigated the correspondence between protein interactions and function for the yeast. We obtained the correlations among the set of proteins. Then these correlations are clustered using both the hierarchical and biclustering methods. The detailed analyses of proteins in each cluster were carried out by making use of their functional annotations. As a result, we found that some functional classes appear together in almost all biclusters. On the other hand, in hierarchical clustering, the dominancy of one functional class is observed. In the light of the clustering data, we have verified some interactions which were not identified as core interactions in DIP and also, we have characterized some functionally unknown proteins according to the interaction data and functional correlation. In brief, from interaction data to function, some correlated results are noticed about the relationship between interaction and function which might give clues about the organization of the proteins, also to predict new interactions and to characterize functions of unknown proteins.
Abstract: The quantum mechanics simulation was applied for
calculating the interaction force between 2 molecules based on atomic level. For the simple extractive distillation system, it is ternary
components consisting of 2 closed boiling point components (A,lower boiling point and B, higher boiling point) and solvent (S). The
quantum mechanics simulation was used to calculate the intermolecular force (interaction force) between the closed boiling
point components and solvents consisting of intermolecular between
A-S and B-S.
The requirement of the promising solvent for extractive distillation
is that solvent (S) has to form stronger intermolecular force with only
one component than the other component (A or B). In this study, the
systems of aromatic-aromatic, aromatic-cycloparaffin, and paraffindiolefin
systems were selected as the demonstration for solvent
selection. This study defined new term using for screening the solvents called relative interaction force which is calculated from the
quantum mechanics simulation. The results showed that relative
interaction force gave the good agreement with the literature data
(relative volatilities from the experiment). The reasons are discussed. Finally, this study suggests that quantum mechanics results can improve the relative volatility estimation for screening the solvents leading to reduce time and money consuming
Abstract: Simultaneous Saccharification and Fermentation (SSF) of sugarcane bagasse by cellulase and Pachysolen tannophilus MTCC *1077 were investigated in the present study. Important process variables for ethanol production form pretreated bagasse were optimized using Response Surface Methodology (RSM) based on central composite design (CCD) experiments. A 23 five level CCD experiments with central and axial points was used to develop a statistical model for the optimization of process variables such as incubation temperature (25–45°) X1, pH (5.0–7.0) X2 and fermentation time (24–120 h) X3. Data obtained from RSM on ethanol production were subjected to the analysis of variance (ANOVA) and analyzed using a second order polynomial equation and contour plots were used to study the interactions among three relevant variables of the fermentation process. The fermentation experiments were carried out using an online monitored modular fermenter 2L capacity. The processing parameters setup for reaching a maximum response for ethanol production was obtained when applying the optimum values for temperature (32°C), pH (5.6) and fermentation time (110 h). Maximum ethanol concentration (3.36 g/l) was obtained from 50 g/l pretreated sugarcane bagasse at the optimized process conditions in aerobic batch fermentation. Kinetic models such as Monod, Modified Logistic model, Modified Logistic incorporated Leudeking – Piret model and Modified Logistic incorporated Modified Leudeking – Piret model have been evaluated and the constants were predicted.
Abstract: The study of the interaction between humans and
computers has been emerging during the last few years. This
interaction will be more powerful if computers are able to perceive
and respond to human nonverbal communication such as emotions. In
this study, we present the image-based approach to emotion
classification through lower facial expression. We employ a set of
feature points in the lower face image according to the particular face
model used and consider their motion across each emotive expression
of images. The vector of displacements of all feature points input to
the Adaptive Support Vector Machines (A-SVMs) classifier that
classify it into seven basic emotions scheme, namely neutral, angry,
disgust, fear, happy, sad and surprise. The system was tested on the
Japanese Female Facial Expression (JAFFE) dataset of frontal view
facial expressions [7]. Our experiments on emotion classification
through lower facial expressions demonstrate the robustness of
Adaptive SVM classifier and verify the high efficiency of our
approach.
Abstract: Using Dynamic Bayesian Networks (DBN) to model genetic regulatory networks from gene expression data is one of the major paradigms for inferring the interactions among genes. Averaging a collection of models for predicting network is desired, rather than relying on a single high scoring model. In this paper, two kinds of model searching approaches are compared, which are Greedy hill-climbing Search with Restarts (GSR) and Markov Chain Monte Carlo (MCMC) methods. The GSR is preferred in many papers, but there is no such comparison study about which one is better for DBN models. Different types of experiments have been carried out to try to give a benchmark test to these approaches. Our experimental results demonstrated that on average the MCMC methods outperform the GSR in accuracy of predicted network, and having the comparable performance in time efficiency. By proposing the different variations of MCMC and employing simulated annealing strategy, the MCMC methods become more efficient and stable. Apart from comparisons between these approaches, another objective of this study is to investigate the feasibility of using DBN modeling approaches for inferring gene networks from few snapshots of high dimensional gene profiles. Through synthetic data experiments as well as systematic data experiments, the experimental results revealed how the performances of these approaches can be influenced as the target gene network varies in the network size, data size, as well as system complexity.
Abstract: Web-based cooperative learning focuses on (1) the interaction and the collaboration of community members, and (2) the sharing and the distribution of knowledge and expertise by network technology to enhance learning performance. Numerous research literatures related to web-based cooperative learning have demonstrated that cooperative scripts have a positive impact to specify, sequence, and assign cooperative learning activities. Besides, literatures have indicated that role-play in web-based cooperative learning environments enhances two or more students to work together toward the completion of a common goal. Since students generally do not know each other and they lack the face-to-face contact that is necessary for the negotiation of assigning group roles in web-based cooperative learning environments, this paper intends to further extend the application of genetic algorithm (GA) and propose a GA-based algorithm to tackle the problem of role assignment in web-based cooperative learning environments, which not only saves communication costs but also reduces conflict between group members in negotiating role assignments.
Abstract: The purposes of this research are: 1) to study the media
literacy of early teenagers, and 2) to study the interaction between
gender and timing of media exposure that affects the media literacy
of teenagers. The sample of the study included 400 young people
aged between 11 to 17 and who were living in Bangkok. The data
was collected using questionnaires. Two-way ANOVA was used in
analyzing the collected data. The result revealed that gender and
timing of media exposure affected the media literacy of early
teenagers with statistical significance at the level of 0.05.
Abstract: This paper presents the design and implements the prototype of an intelligent data processing framework in ubiquitous sensor networks. Much focus is put on how to handle the sensor data stream as well as the interoperability between the low-level sensor data and application clients. Our framework first addresses systematic middleware which mitigates the interaction between the application layer and low-level sensors, for the sake of analyzing a great volume of sensor data by filtering and integrating to create value-added context information. Then, an agent-based architecture is proposed for real-time data distribution to efficiently forward a specific event to the appropriate application registered in the directory service via the open interface. The prototype implementation demonstrates that our framework can host a sophisticated application on the ubiquitous sensor network and it can autonomously evolve to new middleware, taking advantages of promising technologies such as software agents, XML, cloud computing, and the like.
Abstract: Cognitive models allow predicting some aspects of utility
and usability of human machine interfaces (HMI), and simulating
the interaction with these interfaces. The action of predicting is based
on a task analysis, which investigates what a user is required to do
in terms of actions and cognitive processes to achieve a task. Task
analysis facilitates the understanding of the system-s functionalities.
Cognitive models are part of the analytical approaches, that do not
associate the users during the development process of the interface.
This article presents a study about the evaluation of a human
machine interaction with a contextual assistant-s interface using ACTR
and GOMS cognitive models. The present work shows how these
techniques may be applied in the evaluation of HMI, design and
research by emphasizing firstly the task analysis and secondly the
time execution of the task. In order to validate and support our
results, an experimental study of user performance is conducted at
the DOMUS laboratory, during the interaction with the contextual
assistant-s interface. The results of our models show that the GOMS
and ACT-R models give good and excellent predictions respectively
of users performance at the task level, as well as the object level.
Therefore, the simulated results are very close to the results obtained
in the experimental study.