The inherent dynamical in grid computing has made it extremely difficult to come up with near-optimal solutions to efficiently schedule tasks in grids. Task Scheduling plays crucial role in Grid computing. It is a challenge-able issue among scientists to achieve better results especially in makespan based on various AI methods. Nowadays, non deterministic algorithms provide better results for these tasks. In this study the task scheduling problem in Grid computing environments has been addressed. In this paper, Queen Bee Algorithm is used for resolving scheduling problem and the obtained results are compared with several Meta–heuristic Algorithms which are developed to solve the problem. As it illustrated, queen bee algorithm is declined considerably makespan and execution time parameters rather than others in different states.

"Networks are mathematically directed (in practical applications also undirected) graphs and a graph is a one-dimensional abstract complex, i.e., a topological space. Network theory focuses on various topological structures and properties, dynamic properties, and functionality-topology relationship, etc. There are some common mathematical foundations, theories and methodology for network analysis, in which graph theory, statistics, and operational research, etc., are the fundamental sciences of network analysis. Various emerging biological networks, at both micro- and macro- levels, will provide numerous sources for the development of general network theory and methodology and also facilitate the development of theory and methodology of biological networks.

Biological network analysis is a fast moving science. Many core scientific issues, for example, ecological structure, co-evolution, co-extinction and biodiversity conservation in ecology, and cancer development and metabolic regulation in health science, etc., are expected to be addressed by network approaches and network analysis. Network analysis is becoming the core methodology to treat complex biological systems. As the fast development of this area, more and more papers on biological networks are published.

At present explosive numbers of quality papers on biological networks are being published each year. The initiation of the journal, Network Biology, will provide a public and unified platform for the publication of these studies. From this integrated and unique journal, researchers, university teachers and students will thoroughly have an in-depth and complete insight on knowledge, methodology and recent advances of biological networks.

In the view of system dynamics, biological networks are always self-organized systems with emergent, autonomous and adaptive properties. Their dynamics can be represented by agent-based modeling, individual-based modeling and some other methodologies like neural network modeling. Therefore, agent-based modeling, individual-based modeling, self-organization of biological systems, and neural network modeling, etc., fall into the aims and scope of the journal, Network Biology.

The NETWORK BIOLOGY (ISSN 2220-8879) is an open access, peer-reviewed online journal that considers scientific articles in all different areas of network biology. It is the transactions of the International Society of Network Biology.It dedicates to the latest advances in network biology. The goal of this journal is to keep a record of the state-of-the-art research and promote the research work in these fast moving areas. The topics to be covered by Network Biology include, but are not limited to:

•Theories, algorithms and programs of network analysis
•Innovations and applications of biological networks
•Dynamics, optimization and control of biological networks
•Ecological networks, food webs and natural equilibrium
•Co-evolution, co-extinction, biodiversity conservation
•Metabolic networks, protein-protein interaction networks, biochemical reaction networks, gene networks, transcriptional regulatory networks, cell cycle networks, phylogenetic networks, network motifs
•Physiological networks
•Network regulation of metabolic processes, human diseases and ecological systems
•Social networks, epidemiological networks
•System complexity, self-organized systems, emergence of biological systems, agent-based modeling, individual-based modeling, neural network modeling, and other network-based modeling, etc.
We are also interested in short communications that clearly address a specific issue or completely present a new ecological network, food web, or metabolic or gene network, etc.

Authors can submit their works to the email box of this journal, networkbiology@iaees.org. All manuscripts submitted to this journal must be previously unpublished and may not be considered for publication elsewhere at any time during review period of this journal. Authors are asked to read Author Guidelines before submitting manuscripts.

In addition to free submissions from authors around the world, special issues are also accepted. The organizer of a special issue can collect submissions (yielded from a research project, a research group, etc.) on a specific research topic, or submissions of a scientific conference for publication of special issue.

Network Biology 
ISSN 2220-8879
URL: http://www.iaees.org/publications/journals/nb/online-version.asp
RSS feed: http://www.iaees.org/publications/journals/nb/rss.xml
E-mail: networkbiology@iaees.org
Editor-in-Chief: WenJun Zhang"

Side weirs are diversion structures extensively used in irrigation, flood protection and combined sewer systems. Accurate estimation of the discharge coefficient (C d) of side weirs is essential to compute the water surface profile over the weirs and to determine the lateral outflow rate from the system. In this paper, we have utilized a linear genetic programming (LGP) technique to develop new empirical formulas for the estimation of C d of sharp-edged rectangular side weirs located in circular channels. For this aim, we have employed a total of 1,686 laboratory experimental observations in both sub-and supercritical flow regimes in order to train and validate the proposed models. The performance of the LGP-based models was also compared with those of different multilinear and nonlinear regression models in terms of root mean squared errors, mean absolute errors, and determination coefficient. The results indicated that an explicit LGP-based model using only mathematical functions could be employed successfully in C d estimation in both sub-and supercritical flow conditions. Genetic-based sensitivity analysis among the input parameters demonstrated that Froude number at upstream of the weir has the most impact on the C d

In this paper, we propose a robust genetic programming (RGP) model for a dynamic strategy of stock portfolio insurance. With portfolio insurance strategy, we divide the money in a risky asset and a risk-free asset. Our applied strategy is based on a constant proportion portfolio insurance strategy. For determining the amount for investing in the risky asset, a critical parameter is a constant risk multiplier that is calculated in our proposed model using RGP to reflect market dynamics. Our model includes four main steps: (1) Selecting the best stocks for constructing a portfolio using a density-based clustering strategy. (2) Enhancing the robustness of our proposed model with an application of the Adaptive Neuro-Fuzzy Inference Systems (ANFIS) for forecasting the future prices of the selected stocks. The findings show that using ANFIS, instead of a regular multi-layer artificial neural network improves the prediction accuracy and our model's robustness. (3) Implementing the RGP model for calculating the risk multiplier. Risk variables are used to generate equation trees for calculating the risk multiplier. (4) Determining the optimal portfolio weights of the assets using the well-known Markowitz portfolio optimization model. Experimental results show that our proposed strategy outperforms our previous model. ARTICLE HISTORY

A new method was proposed to predict the compressive bearing capacity of driven piles based on the number of hammer strikes in the last one meter of pile penetration (known here as Flap number). To collect the data, a literature review was done on technical publications and pile driving record reports that were accessible to the authors at the time of publication. The data of a hundred driven piles including Flap number, basic properties of the surrounding soil, pile geometry, and pile-soil friction angle was collected. These data were initially used in the artificial neural network to establish a relation for predicting pile capacity. Subsequently, by using genetic programing and linear regression, equations for determining pile bearing capacity with respect to the Flap number, soil parameters, and pile geometries were proposed. Finally, the performance of all applied methods in predicting the pile bearing capacity were compared. The utmost importance was given to the comparison of the accuracy of the three models as well as the error estimation.

We present an architecture for data semantics discovery capable of extracting semantically-rich content from human-readable files without prior specification of the file format. The architecture, based on work at the intersection of knowledge representation and machine learning , includes machine learning modules for automatic file format identification , tokenization, and entity identification. The process is driven by an ontology of domain-specific concepts. The ontology also provides an abstraction layer for querying the extracted data. We provide a general description of the architecture as well as details of the current implementation. Although the architecture can be applied in a variety of domains, we focus on cyber-forensics applications, aiming to allow one to parse data sources, such as log files, for which there are no readily-available parsing and analysis tools, and to aggregate and query data from multiple , diverse systems across large networks. The key contributions of our work are: the development of an architecture that constitutes a substantial step toward solving a highly-practical open problem; the creation of one of the first comprehensive ontologies of cyber assets; the development and demonstration of an innovative, non-trivial combination of declarative knowledge specification and machine learning.