Abstract: Travel Demands influence micro-level traffic behavior,
furthermore traffic states. In order to evaluate the effect of travel
demands on traffic states, this paper introduces the Demand-
Motivation-Behaviors (DMB) micro traffic behavior analysis model
which denotes that vehicles behaviors are determines by motivations
that relies on traffic demands from the perspective of behavior
science. For vehicles, there are two kinds of travel demands: reaching
travel destinations from orientations and meeting expectations of
travel speed. To satisfy travel demands, the micro traffic behaviors are
delivered such as car following behavior, optional and mandatory lane
changing behaviors. Especially, mandatory lane changing behaviors
depending on travel demands take strong impact on traffic states.
In this paper, we define the DMB-based cellular automate traffic
simulation model to evaluate the effect of travel demands on traffic
states under the different δ values that reflect the ratio of mandatory
lane-change vehicles.
Abstract: The purpose of this paper is to examine co-creation of non-economic values in Islamic banking services and their significance for service science by comparing Islamic and conventional banking services. Although many scholars have discussed co-creation of values in services, most of them have focused on only economic values.
Following Sharia (Islamic principles that are based on Qur’an and Sunnah) traditions, Islamic banking is more concerned with such non-economic values as well-being, partnership, fairness, trust, and justice, than such economic values as money in terms of interest. Therefore, it may be more sustainable and suitable for today’s unpredictable socio-economic environments.
We also argue that Islamic banking is essentially a value co-creation business model that fits better with the so-called Service-Dominant Logic (SDL) than conventional banking. This paper explores a new frontier of value co-creation in services, thereby contributing to further development of service science.
Abstract: At present the process of formation of corporate
values in Kazakh universities is under the influence of a whole range
of socio-economic and cultural changes: on the one hand universities
must maintain and transmit traditional cultural values of education,
on the other, to improve quality of service and to involve young
people to science, providing thus own competitiveness. Thus, this
article presents some results of two cycles of sociological research
conducted in 2012 and aimed at identifying possible ways to
popularize science and readiness to participate of youth in given
activities, expectations of young scientists and the prospects of future
development of the Kazakh science.
Abstract: Soft set theory was initiated by Molodtsov in 1999. In the past years, this theory had been applied to many branches of mathematics, information science and computer science. In 2003, Maji et al. introduced some operations of soft sets and gave some operational rules. Recently, some of these operational rules are pointed out to be not true. Furthermore, Ali et al., in their paper, introduced and discussed some new operations of soft sets. In this paper, we further investigate these operational rules given by Maji et al. and Ali et al.. We obtain some sufficient-necessary conditions such that corresponding operational rules hold and give correct forms for some operational rules. These results will be help for us to use rightly operational rules of soft sets in research and application of soft set theory.
Abstract: This qualitative, quantitative mixed-method study explores how students- motivation and interest in creative hands-on activities affected their conceptual understanding of science. The objectives of this research include developing a greater understanding about how creative activities, incorporated into the classroom as instructional strategies, increase student motivation and their learning or mastery of science concepts. The creative activities are viewed as a motivational tool, a specific type of task, which have an impact on student goals. Pre-and-post tests, pre-and-post interviews, and student responses measure motivational-goal theory variables, interest in the activity, and conceptual change. Implications for education and future research will be discussed.
Abstract: the data of Taiwanese 8th grader in the 4th cycle of
Trends in International Mathematics and Science Study (TIMSS) are
analyzed to examine the influence of the science teachers- preference
in experimental teaching on the relationships between the affective
variables ( the perceived usefulness of science, ease of using science
and science learning interest) and the academic achievement in science.
After dealing with the missing data, 3711 students and 145 science
teacher-s data were analyzed through a Hierarchical Linear Modeling
technique. The major objective of this study was to determine the role
of the experimental teaching moderates the relationship between
perceived usefulness and achievement.
Abstract: As research performance in academia is treated as one of indices for national competency, many countries devote much attention and resources to increasing their research performance. Understand the research trend is the basic step to improve the research performance. The goal of this research is to design an analysis system to evaluate research trends from analyzing data from different countries. In this paper, information system researches in Taiwan and other countries, including Asian countries and prominent countries represented by the Group of Eight (G8) is used as example. Our research found the trends are varied in different countries. Our research suggested that Taiwan-s scholars can pay more attention to interdisciplinary applications and try to increase their collaboration with other countries, in order to increase Taiwan's competency in the area of information science.
Abstract: To investigate some relations between higher mathe¬matics scores in Chinese graduate student entrance examination and calculus (resp. linear algebra, probability statistics) scores in subject's completion examination of Chinese university, we select 20 students as a sample, take higher mathematics score as a decision attribute and take calculus score, linear algebra score, probability statistics score as condition attributes. In this paper, we are based on rough-set theory (Rough-set theory is a logic-mathematical method proposed by Z. Pawlak. In recent years, this theory has been widely implemented in the many fields of natural science and societal science.) to investigate importance of condition attributes with respective to decision attribute and strength of condition attributes supporting decision attribute. Results of this investigation will be helpful for university students to raise higher mathematics scores in Chinese graduate student entrance examination.
Abstract: Investigating language acquisition is one of the most
challenging problems in the area of studying language. Syllable
learning as a level of language acquisition has a considerable
significance since it plays an important role in language acquisition.
Because of impossibility of studying language acquisition directly
with children, especially in its developmental phases, computer
models will be useful in examining language acquisition. In this
paper a computer model of early language learning for syllable
learning is proposed. It is guided by a conceptual model of syllable
learning which is named Directions Into Velocities of Articulators
model (DIVA). The computer model uses simple associational and
reinforcement learning rules within neural network architecture
which are inspired by neuroscience. Our simulation results verify the
ability of the proposed computer model in producing phonemes
during babbling and early speech. Also, it provides a framework for
examining the neural basis of language learning and communication
disorders.
Abstract: Adopting Zakowski-s upper approximation operator
C and lower approximation operator C, this paper investigates
granularity-wise separations in covering approximation spaces. Some
characterizations of granularity-wise separations are obtained by
means of Pawlak rough sets and some relations among granularitywise
separations are established, which makes it possible to research
covering approximation spaces by logical methods and mathematical
methods in computer science. Results of this paper give further
applications of Pawlak rough set theory in pattern recognition and
artificial intelligence.
Abstract: A direct connection between ElectroEncephaloGram
(EEG) and the genetic information of individuals has been
investigated by neurophysiologists and psychiatrists since 1960-s;
and it opens a new research area in the science. This paper focuses on
the person identification based on feature extracted from the EEG
which can show a direct connection between EEG and the genetic
information of subjects. In this work the full EO EEG signal of
healthy individuals are estimated by an autoregressive (AR) model
and the AR parameters are extracted as features. Here for feature
vector constitution, two methods have been proposed; in the first
method the extracted parameters of each channel are used as a
feature vector in the classification step which employs a competitive
neural network and in the second method a combination of different
channel parameters are used as a feature vector. Correct classification
scores at the range of 80% to 100% reveal the potential of our
approach for person classification/identification and are in agreement
to the previous researches showing evidence that the EEG signal
carries genetic information. The novelty of this work is in the
combination of AR parameters and the network type (competitive
network) that we have used. A comparison between the first and the
second approach imply preference of the second one.
Abstract: The theory of Groebner Bases, which has recently been
honored with the ACM Paris Kanellakis Theory and Practice Award,
has become a crucial building block to computer algebra, and is
widely used in science, engineering, and computer science. It is wellknown
that Groebner bases computation is EXP-SPACE in a general
polynomial ring setting.
However, for many important applications in computer science
such as satisfiability and automated verification of hardware and
software, computations are performed in a Boolean ring. In this paper,
we give an algorithm to show that Groebner bases computation is PSPACE
in Boolean rings. We also show that with this discovery,
the Groebner bases method can theoretically be as efficient as
other methods for automated verification of hardware and software.
Additionally, many useful and interesting properties of Groebner
bases including the ability to efficiently convert the bases for different
orders of variables making Groebner bases a promising method in
automated verification.
Abstract: Conceptualization strengthens intelligent systems in generalization skill, effective knowledge representation, real-time inference, and managing uncertain and indefinite situations in addition to facilitating knowledge communication for learning agents situated in real world. Concept learning introduces a way of abstraction by which the continuous state is formed as entities called concepts which are connected to the action space and thus, they illustrate somehow the complex action space. Of computational concept learning approaches, action-based conceptualization is favored because of its simplicity and mirror neuron foundations in neuroscience. In this paper, a new biologically inspired concept learning approach based on the probabilistic framework is proposed. This approach exploits and extends the mirror neuron-s role in conceptualization for a reinforcement learning agent in nondeterministic environments. In the proposed method, instead of building a huge numerical knowledge, the concepts are learnt gradually from rewards through interaction with the environment. Moreover the probabilistic formation of the concepts is employed to deal with uncertain and dynamic nature of real problems in addition to the ability of generalization. These characteristics as a whole distinguish the proposed learning algorithm from both a pure classification algorithm and typical reinforcement learning. Simulation results show advantages of the proposed framework in terms of convergence speed as well as generalization and asymptotic behavior because of utilizing both success and failures attempts through received rewards. Experimental results, on the other hand, show the applicability and effectiveness of the proposed method in continuous and noisy environments for a real robotic task such as maze as well as the benefits of implementing an incremental learning scenario in artificial agents.
Abstract: Proteomics is one of the largest areas of research for
bioinformatics and medical science. An ambitious goal of proteomics
is to elucidate the structure, interactions and functions of all proteins
within cells and organisms. Predicting Protein-Protein Interaction
(PPI) is one of the crucial and decisive problems in current research.
Genomic data offer a great opportunity and at the same time a lot of
challenges for the identification of these interactions. Many methods
have already been proposed in this regard. In case of in-silico
identification, most of the methods require both positive and negative
examples of protein interaction and the perfection of these examples
are very much crucial for the final prediction accuracy. Positive
examples are relatively easy to obtain from well known databases. But
the generation of negative examples is not a trivial task. Current PPI
identification methods generate negative examples based on some
assumptions, which are likely to affect their prediction accuracy.
Hence, if more reliable negative examples are used, the PPI prediction
methods may achieve even more accuracy. Focusing on this issue, a
graph based negative example generation method is proposed, which
is simple and more accurate than the existing approaches. An
interaction graph of the protein sequences is created. The basic
assumption is that the longer the shortest path between two
protein-sequences in the interaction graph, the less is the possibility of
their interaction. A well established PPI detection algorithm is
employed with our negative examples and in most cases it increases
the accuracy more than 10% in comparison with the negative pair
selection method in that paper.
Abstract: Cluster analysis divides data into groups that are
meaningful, useful, or both. Analysis of biological data is creating a
new generation of epidemiologic, prognostic, diagnostic and
treatment modalities. Clustering of protein sequences is one of the
current research topics in the field of computer science. Linear
relation is valuable in rule discovery for a given data, such as if value
X goes up 1, value Y will go down 3", etc. The classical linear
regression models the linear relation of two sequences perfectly.
However, if we need to cluster a large repository of protein sequences
into groups where sequences have strong linear relationship with
each other, it is prohibitively expensive to compare sequences one by
one. In this paper, we propose a new technique named General
Regression Model Technique Clustering Algorithm (GRMTCA) to
benignly handle the problem of linear sequences clustering. GRMT
gives a measure, GR*, to tell the degree of linearity of multiple
sequences without having to compare each pair of them.
Abstract: The simulation of external aerodynamics is one of the most challenging and important automotive CFD applications. With the rapid developments of digital computers, CFD is used as a practical tool in modern fluid dynamics research. It integrates fluid mechanics disciplines, mathematics and computer science. In this study, two different types of simulations were made, one for the flow around a simplified high speed passenger car with a rear-spoiler and the other for the flow without a rear-spoiler. The standard k-ε model is selected to numerically simulate the external flow field of the simplified Camry model with or without a rear-spoiler. Through an analysis of the simulation results, a new rear spoiler is designed and it shows a mild reduction of the vehicle aerodynamics drag. This leads to less vehicle fuel consumption on the road.
Abstract: Impact is one of very important subjects which always have been considered in mechanical science. Nature of impact is such that which makes its control a hard task. Therefore it is required to present the transfer of impact to other vulnerable part of a structure, when it is necessary, one of the best method of absorbing energy of impact, is by using Thin-walled tubes these tubes collapses under impact and with absorption of energy, it prevents the damage to other parts.Purpose of recent study is to survey the deformation and energy absorption of tubes with different type of cross section (rectangular or square) and with similar volumes, height, mean cross section thickness, and material under loading with different speeds. Lateral loading of tubes are quasi-static type and beside as numerical analysis, also experimental experiences has been performed to evaluate the accuracy of the results. Results from the surveys is indicates that in a same conditions which mentioned above, samples with square cross section ,absorb more energy compare to rectangular cross section, and also by increscent in speed of loading, energy absorption would be more.
Abstract: Pattern matching based on regular tree grammars have been widely used in many areas of computer science. In this paper, we propose a pattern matcher within the framework of code generation, based on a generic and a formalized approach. According to this approach, parsers for regular tree grammars are adapted to a general pattern matching solution, rather than adapting the pattern matching according to their parsing behavior. Hence, we first formalize the construction of the pattern matches respective to input trees drawn from a regular tree grammar in a form of the so-called match trees. Then, we adopt a recently developed generic parser and tightly couple its parsing behavior with such construction. In addition to its generality, the resulting pattern matcher is characterized by its soundness and efficient implementation. This is demonstrated by the proposed theory and by the derived algorithms for its implementation. A comparison with similar and well-known approaches, such as the ones based on tree automata and LR parsers, has shown that our pattern matcher can be applied to a broader class of grammars, and achieves better approximation of pattern matches in one pass. Furthermore, its use as a machine code selector is characterized by a minimized overhead, due to the balanced distribution of the cost computations into static ones, during parser generation time, and into dynamic ones, during parsing time.
Abstract: An upwind difference approximation is used for a singularly perturbed problem in material science. Based on the discrete Green-s function theory, the error estimate in maximum norm is achieved, which is first-order uniformly convergent with respect to the perturbation parameter. The numerical experimental result is verified the valid of the theoretical analysis.
Abstract: Regarding to the fast growth of computer, internet, and virtual learning in our country (Iran) and need computer-based learning systems and multimedia tools as an essential part of such education, designing and implementing such systems would help teach different field such as science. This paper describes the basic principle of multimedia. At the end, with a description of learning science to the infant students, the method of this system will be explained.