The anadromous Rutilus kutum Kamenskii, 1901is the most important fish in north of Iran with the highest economic value. Ignoring the natural process of reproduction due artificial propagation, catching some migrating broodstocks from the sea (not from the estuaries) and mixing the produced fingerlings in culture ponds within restocking programs in the last 30 years have caused an uncertainty about homing success and population differentiation in the fish. A 13-landmark morphometric truss network system was used to investigate the hypothesis of homeward migration success and population differentiation, while the previous studies revealed low genetic variability among the fish populations. A total of 504 adult specimens were caught from four localities along the southern Caspian Sea in the reproductive period. Univariate analysis of variance showed significant differences among the four groups for 78 standardized morphometric measurements. In linear discriminant function analysis (DFA), the overall assignments of individuals into their original groups were 73.3 % and 80.3 % in males and females, respectively. The principal component analysis (PCA) and clustering analysis based on Euclidean square distances showed that the 504 examined specimens grouped into distinct areas. The results showed that each sampling site represents independent population which confirmed the success of homeward migration. The high grouping ranges suggests that almost all populations return to their birthplace river to breed, which results in high inbreeding. The results can be interesting for management and conservation programs of this valuable species in the Caspian Sea.
Modern organizations tend to constitute of communities of practice to cover the side effect of standardization and centralization of knowledge. The distributed nature of knowledge in groups, teams and other departments of organization and complexity of this tacit knowledge lead us to use community of practice as an environment to share knowledge. In this paper we propose an agent mediated community of a practice system using MAS-CommonKADS methodology. We support the principle of autonomy since every single agent, even those in the same community, needs its own autonomy in order to model an organization and its individuals correctly, using this approach, the natural model for an agent based on knowledge sharing system has been resulted. We presented all models of MAS-CommonKADS methodology required for developing the multi-agent system. We found MAS-CommonKADS useful to design Knowledge Management applications. Because of detailed description of agents, a resulted design model could be simply implemented. We modeled our system using Rebeca and verified it to show that by use of our system, knowledge sharing can be satisfied.
In this paper, we propose a new economic dispatch model with random wind power, demand response and carbon tax. The specific feature of the demand response model is that the consumer's electricity demand is divided into two parts: necessary part and non-essential part. The part of the consumer's participation in the demand response is the non-essential part of the electricity consumption. The optimal dispatch objective is to obtain the minimum total cost (fuel cost, random wind power cost and emission cost) and the maximum consumer's non-essential demand response benefit while satisfying some given constraints. In order to solve the optimal dispatch objective, a multi-subpopulation bat optimization algorithm (MSPBA) is proposed by using different search strategies. Finally, a case of an economic dispatch model is given to verify the feasibility and effectiveness of the established mathematical model and proposed algorithm. The economic dispatch model includes three thermal generators, two wind turbines and two consumers. The simulation results show that the proposed model can reduce the consumer's electricity demand, reduce fuel cost and reduce the impact on the environment while considering random wind energy, non-essential demand response and carbon tax. In addition, the superiority of the proposed algorithm is verified by comparing with the optimization results of CPLEX+YALMIP toolbox for MATLAB, BA, DBA and ILSSIWBA.
Finding reducts is one of the key problems in the increasing applications of rough set theory, which is also one of the bottlenecks of the rough set methodology. The population-based reduction approaches are attractive to find multiple reducts in the decision systems, which could be applied to generate multi-knowledge and to improve decision accuracy. In this paper, we design a multi-swarm synergetic optimization algorithm (MSSO) for rough set reduction and multi-knowledge extraction. It is a multi-swarm based search approach, in which different individual trends to be encoded to different reduct. The approach discovers the best feature combinations in an efficient way to observe the change of positive region as the particles proceed throughout the search space. The performance of our approach is evaluated and compared with Standard Particle Swarm Optimization (SPSO) and Genetic Algorithms (GA). Empirical results illustrate that the approach can be applied for multiple reduct problems and multi-knowledge extraction effectively.
It is shown that if g is of bounded variation in the sense of Hardy-Krause on ∏m i=1 [ai , bi ], then gχ ∏m i=1 (ai ,bi ) is of bounded variation there. As a result, we obtain a simple proof of Kurzweil’s multidimensional integration by parts formula.
Based on the randomness and fuzziness of the cloud model during the transformation from the qualitative concept to the quantitative numerical value, with the theory that any data distribution can be decompounded into several normal distributions, this paper puts forward a method of multi-classification based on the cloud model. By this method, multiple classification is transformed to a superposed cloud model with training samples as the cloud expectation, while the test samples are regarded as the `cloud droplets', and their classifications of membership degree in a cloud model can be calculated. Considering the effect of the number of training samples on the membership degree, the cloud model is weighted by the ratio of the total number of training samples to the number of training samples in a single class so that the data distribution of the samples can be balanced. The formula of multiple classification based on the cloud model has the structure identical to that of Support Vector Machines, and the hyper entropy in cloud models exerts similar punishment on the noise samples just like the loose coefficients in Support Vector Machines; therefore, the reasonability of the method is theoretically proved. Compared with Support Vector Machine, the method discussed in this paper does not require any large-scale quadratic programming, thus the algorithm of the method is simpler. Last but not the least, five types of data distribution samples are selected for the comparative experiment, and comparison is made with four other classification methods; the result shows that the accuracy and stability of the algorithm is high, and its implementations on the high dimensional multiple classifications are especially satisfactory.