Determination of economic injury level of Sitobion avenae (Hom.: Aphididae) on wheat of Chameran variety in Ahwaz

The drought phenomenon in recent years has increased the oat aphid (Sitobion avenae Fab.) population in khuzestan wheat fields. Considering the existence of different natural enemies of this pest in cereal fields, the best control method would be based on selective pesticide application which is less harmful to environment and natural enemies. Thus determination of pest Economic injury level (EIL) became a necessity to prevent immethodical usage of pesticides in wheat vulnerable agroecosystem. Hence, an experiment was conducted in complete randomized block design based on split plot with two factors and three replications including growing stages and aphid density in Chamran cultivar in agricultural research station of Ahwaz during 2010 to 2011. Growing stages (Early, middle and late of milky stage) and aphid density (0, 5-10, 11-15, 16-20, 21-25, 26-30 and 31-35 aphid on spike) were considered as mainplot and subplot, respectively. All treatments applied in a net cage. Averages of yield in different aphid density treatments were grouped by Duncan- multiple range test. Regression equations and curves were obtained between aphid density and seed weight in each spike. Grain threshold method was used to EIL measurement. The results showed that EIL was 17, 22, 42 aphids/spike in early, middle and late of milky stage of wheat with no calculating of natural enemies but ET was 12, 17, 31 aphids/spike in the first year and EIL was 9, 18, 49 aphids/spike and ET was 7, 14, 37 aphids/spike in the second year. Also, EIL was 8, 16, 44 aphids/spike and ET was 6, 12, 33 aphids/spike in 2014-2015, respectively

Determination of economic injury level of Lipaphis erysimi (Hemiptera: Aphididae) on canola var. Hayola 401 in Khuzestan

Canola, an oil seed with high contents of oil, is a major farming in Khuzestan province, where there is an increase over its cultivation year by year. One of the canola key pests is mustard aphid (Lipaphis erysimi Kalt.) in this province. However, there was not available information on the economic injury level (EIL) of the pest that is much vital for correct decision making on pest control. Therefore, a study on EIL was conducted through complete randomized block design with 5 replications and 11 treatments (0, 5, 10, 15, 20, 25, 30, 35, 40, 45, and 50 aphid per central stem of Hayola 401 variety of canola) inside a netted cage. This research was conducted in Behbahan Agricultural Research Station during 2004-2006. The average of seed yield and also the yeild components was analyzed with Duncanâs multiple range tests. The damage of the aphid was estimated by regression equation. The injury level was estimated by Grain threshold method. The results indicated that EIL was 7.53 and 2.49 cm aphid per central stem of canola in Behbahan region in 2004-2005 and 2005-2006, respectively. Economic threshold (ET) was 5.65 and 1.87 cm2 aphid per central stem of canola

Metaheuristic optimization of reinforced concrete footings

The primary goal of an engineer is to find the best possible economical design and this goal can be achieved by considering multiple trials. A methodology with fast computing ability must be proposed for the optimum design. Optimum design of Reinforced Concrete (RC) structural members is the one of the complex engineering problems since two different materials which have extremely different prices and behaviors in tension are involved. Structural state limits are considered in the optimum design and differently from the superstructure members, RC footings contain geotechnical limit states. This study proposes a metaheuristic based methodology for the cost optimization of RC footings by employing several classical and newly developed algorithms which are powerful to deal with non-linear optimization problems. The methodology covers the optimization of dimensions of the footing, the orientation of the supported columns and applicable reinforcement design. The employed relatively new metaheuristic algorithms are Harmony Search (HS), Teaching-Learning Based Optimization algorithm (TLBO) and Flower Pollination Algorithm (FPA) are competitive for the optimum design of RC footings

Optimum design of reinforced concrete retaining walls with the flower pollination algorithm

The flower pollination algorithm (FPA) is anefficient metaheuristicoptimizationalgorithm mimickingthe pollinationprocessof flowering species. In this study, FPA is applied, for first time, to the optimum design of reinforced concrete (RC) cantilever retaining walls. It is foundthat FPA offers important savings with respect to conventional design approachesand that it outperformsgenetic algorithm (GA)andthe particle swarm optimization (PSO) algorithm in this designproblem.Furthermore, parameter tuning reveals that the best FPA performance is achieved for switch probability values ranging between 0.4 and 0.7, a population size of 20 individualsand aLévy flightstep sizescale factor of 0.5. Finally, parametric optimum designs show that theoptimumcost of RC retaining walls increases rapidly with the wallheight and smoothly with the magnitude of surcharge loadin

Fitness Varying Gravitational Constant in GSA

Gravitational Search Algorithm (GSA) is a recent metaheuristic algorithm inspired by Newton's law of gravity and law of motion. In this search process, position change is based on the calculation of step size which depends upon a constant namely, Gravitational Constant (G). G is an exponentially decreasing function throughout the search process. Further, inspite of having different masses, the value of G remains same for each agent, which may cause inappropriate step size of agents for the next move, and thus leads the swarm towards stagnation or sometimes skipping the true optima. To overcome stagnation, we first propose a gravitational constant having different scaling characteristics for different phase of the search process. Secondly, a dynamic behavior is introduced in this proposed gravitational constant which varies according to the fitness of the agents. Due to this behavior, the gravitational constant will be different for every agent based on its fitness and thus will help in controlling the acceleration and step sizes of the agents which further improve exploration and exploitation of the solution search space. The proposed strategy is tested over 23 well-known classical benchmark functions and 11 shifted and biased benchmark functions. Various statistical analyses and a comparative study with original GSA, Chaos-based GSA (CGSA), Bio-geography Based Optimization (BBO) and DBBO has been carried out

living and learning a new identity for student housing in city suburbs

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Solubility in various solvents, synthesis, characterization and spectroscopic studies (FT–IR, UV–vis and NMR), X–ray crystallography, NBO, NLO and FMO analysis of Pd(II) complexe

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Solubility in various solvents, synthesis, characterization and spectroscopic studies (FT–IR, UV–vis and NMR), X–ray crystallography, NBO, NLO and FMO analysis of Pd(II) complexe

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Data for: Molecular structure, the effect of solvent on UV–vis and NMR, FT–IR and FT–Raman spectra, NBO, Frontier molecular orbital analysis of Mitomycin anticancer drug

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