This paper proposes an offline gradient method with smoothing L1/2 regularization for learning and pruning of the pi-sigma neural networks (PSNNs). The original L1/2 regularization term is not smooth at the origin, since it involves the absolute value function. This causes oscillation in the computation and difficulty in the convergence analysis. In this paper, we propose to use a smooth function to replace and approximate the absolute value function, ending up with a smoothing L1/2 regularization method for PSNN. Numerical simulations show that the smoothing L1/2 regularization method eliminates the oscillation in computation and achieves better learning accuracy. We are also able to prove a convergence theorem for the proposed learning method.
Chlorophyll index and leaf nitrogen status (SPAD value) was incorporated into the nonrectangular hyperbola (NRH) equation for photosynthetic light-response (PLR) curve to establish a modified NRH equation to overcome the parameter variation. Ten PLR curves measured on rice leaves with different SPAD values were collected from pot experiments with different nitrogen (N) dosages. The coefficients of initial slope of the PLR curve and the maximum net photosynthetic rate in NRH equation increased linearly with the increase of leaf SPAD. The modified NRH equation was established by multiplying a linear SPAD-based adjustment factor with the NRH equation. It was sufficient in describing the PLR curves with unified coefficients for rice leaf with different SPAD values. SPAD value, as the indicator of leaf N status, could be used for modification of NRH equation to overcome the shortcoming of large coefficient variations between individual leaves with different N status. The performance of the SPAD-modified NRH equation should be further validated by data collected from different kinds of plants growing under different environments., J. Z. Xu, Y. M. Yu, S. Z. Peng, S. H. Yang, L. X. Liao., and Obsahuje bibliografii
In this paper, Runge-Kutta methods are discussed for numerical solutions of conservative systems. For the energy of conservative systems being as close to the initial energy as possible, a modified version of explicit Runge-Kutta methods is presented. The order of the modified Runge-Kutta method is the same as the standard Runge-Kutta method, but it is superior in energy-preserving to the standard one. Comparing the modified Runge-Kutta method with the standard Runge-Kutta method, numerical experiments are provided to illustrate the effectiveness of the modified Runge-Kutta method.
Uric acid is the final product of human purine metabolism. It was pointed out that this compound acts as an antioxidant and is able to react with reactive oxygen species forming allantoin. Therefore, the measurement of allantoin levels may be used for the determination of oxidative stress in humans. The aim of the study was to clarify the antioxidant effect of uric acid during intense exercise. Whole blood samples were obtained from a group of healthy subjects. Allantoin, uric acid, and malondialdehyde levels in plasma and erythrocytes were measured using a HPLC with UV/Vis detection. Statistical significant differences in allantoin and uric acid levels during short-term intense exercise were found. Immediately after intense exercise, the plasma allantoin levels increased on the average of 200 % in comparison to baseline. Plasma uric acid levels increased slowly, at an average of 20 %. On the other hand, there were no significant changes in plasma malondialdehyde. The results suggest that uric acid, important antioxidant, is probably oxidized by reactive oxygen species to allantoin. Therefore allantoin may be suitable candidate for a marker of acute oxidative stress., R. Kanďár, X. Štramová, P. Drábková, J. Křenková., and Obsahuje bibliografii
We consider the quotient categories of two categories of modules relative to the Serre classes of modules which are bounded as abelian groups and we prove a Morita type theorem for some equivalences between these quotient categories.
A combined study of morphology, stem anatomy and isozyme patterns was used to reveal the identity of sterile plants from two rivers on the Germany/France border. A detailed morphological examination proved that the putative hybrid is clearly intermediate between Potamogeton natans and P. nodosus. The stem anatomy had characteristics of both species. The most compelling evidence came from the isozyme analysis. The additive “hybrid” banding patterns of the six enzyme systems studied indicate inheritance from P. natans and P. nodosus. In contrast, other morphologically similar hybrids were excluded: P. ×gessnacensis (= P. natans × P. polygonifolius) by all the enzyme systems, P. ×fluitans (= P. lucens × P. natans) by AAT, EST and 6PGDH, and P. ×sparganiifolius (= P. gramineus × P. natans) by AAT and EST. All samples of P. ×schreberi are of a single multi-enzyme phenotype, suggesting that they resulted from a single hybridization event and that the present-day distribution of P. ×schreberi along the Saarland/Moselle border was achieved by means of vegetative propagation and long-distance dispersal. Neither of its parental species occur with P. ×schreberi or are present upstream, which suggests that this hybrid has persisted vegetatively for a long time in the absence of its parents. The total distribution of this hybrid is reviewed and a detailed account of the records from Germany is given. P. ×schreberi appears to be a rare hybrid. The risk of incorrect determination resulting from the identification of insufficiently developed or inadequately preserved plant material is discussed.
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 present paper we assess the climate change impact on mean runoff between the periods 1961-1990 (control period) and 2070-2099 (scenario period) in the Czech Republic. Hydrological balance is modelled with a conceptual hydrological model BILAN at 250 catchments of different sizes and climatic conditions. Climate change scenarios are derived using simple delta approach, i.e. observed series of precipitation, temperature and relative air humidity are perturbed in order to give the same changes between the control and scenario period as in the ensemble of 15 transient regional climate model (RCM) simulations. The parameters of the hydrological model are for each catchment estimated using observed data. These parameters are subsequently used to derive discharge series under climate change conditions for each RCM simulation. Although the differences in the absolute values of the changes in runoff are considerable, robust patterns of changes can be identified. The majority of the scenarios project an increase in winter runoff in the northern part of the Czech Republic, especially at catchments with high elevation. The scenarios also agree on a decrease in spring and summer runoff in most of the catchments. and V článku předkládáme výsledky modelování změn hydrologického režimu v důsledku změn klimatu mezi časovými obdobími 1961-1990 a 2070-2099 podle souboru patnácti regionálních klimatických modelů pro 250 povodí v České republice. Hydrologická bilance byla modelována pomocí konceptuálního hydrologického modelu BILAN. Časové řady ovlivněné změnou klimatu byly získány jednoduchou přírůstkovou metodou, tj. pozorované časové řady srážek, teplot a vlhkostí vzduchu (vstupy do modelu BILAN) byly opraveny pro každou simulaci pomocí přírůstkových faktorů tak, aby měsíční změny těchto veličin byly stejné jako podle uvažované simulace klimatického modelu. Hydrologický model je nakalibrován s využitím pozorovaných dat, identifikované parametry jsou následně využity pro simulaci hydrologické bilance pro řady ovlivněné klimatickou změnou. Základní podstata možných změn hydrologické bilance na území České republiky vyplývá z projekcí srážek a teplot pro Evropu, tj. postupné zvyšování teplot během celého roku a pokles letních, růst zimních a stagnace ročních srážek. V období od začátku podzimu do začátku léta dochází k růstu srážek, jenž je doprovázen řádově stejným růstem územního výparu způsobeným růstem teplot. V letním období dochází k poklesu srážek a v důsledku úbytku zásob vody v povodí nemůže docházet k výraznému zvyšování územního výparu. Důležitým faktorem ovlivňující změny odtoku je posun doby tání v důsledku vyšší teploty přibližně z dubna na leden-únor. Změny odtoku v období leden-květen jsou tedy dominantně určeny právě odlišnou dynamikou sněhové zásoby, změny v letním období zejména úbytkem srážek. Výsledné odhady změn odtoku jsou zatíženy značnou nejistotou, nicméně lze identifikovat robustní jevy společné pro řadu simulací. Jak ukazují výsledky, na většině modelovaných povodí je pokles odtoků v období od dubna do října společný valné většině modelů. Na druhé straně, růst odtoku v zimních měsících je značně nejistý. S tím souvisí i nejistota spojená se změnami roční bilance odtoků.
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.