Toxicity of Bacillus thuringiensis to laboratory populations of the olive fruit fly (Dacus oleae)
A survey of Bacillus thuringiensis recovered from the environments of olive groves in Greece was carried out. Of 80 soil samples, 24 were found to contain B. thuringiensis with parasporal crystal inclusions; these were tested for toxicity against the olive fruit fly (Dacus oleae). Mortality levels of larvae caused by the different isolates varied from 7 to 87%. Higher levels of mortality were observed if a mixture of relatively pure crystals and spores was used compared with the mortality resulting from either fraction alone. We were able to show that the toxicity of the most active isolate is likely to be specific for D. oleae.
Larvicidal toxicity of Japanese Bacillus thuringiensis against the mosquito Anopheles stephensi
Japanese isolates of Bacillus thuringiensis were screened for larvicidal activity against the mosquito Anopheles stephensi, the urban malaria vector of the Indian subcontinent. Among more than 30 strains identified, larvicidal activity causing >80% mortality in 72 h was demonstrated for 41/1449 (2.8%) isolates. The majority of strains and isolates (97.2%) exhibited little or no larvicidal activity. Anopheles‐active strains belonged to more than 12 H serotypes, especially H3ade (serovar fukuokaensis) and H44 (serovar higo). SDS‐PAGE profiles of inclusion proteins showed 4 distinct types among 6 active strains examined. The most active Japanese isolates were H20 strain 89‐T‐34‐14 (LC50 4.4 μg/ml) and H44 serovar higo strain 74‐E‐45‐24 (LC50 7.6 μg/ml), respectively, 13‐fold and 23‐fold less active than the international standard H14 serovar israelensis (LC50 0.33 μg/ml).
A Short-Term and High-Resolution Distribution System Load Forecasting Approach Using Support Vector Regression With Hybrid Parameters Optimization
This paper proposes an approach for distribution system load forecasting, which aims to provide highly accurate short-term load forecasting with high resolution utilizing a support vector regression (SVR) based forecaster and a two-step hybrid parameters optimization method. Specifically, because the load profiles in distribution systems contain abrupt deviations, a data normalization is designed as the pretreatment for the collected historical load data. Then an SVR model is trained by the load data to forecast the future load. For better performance of SVR, a two-step hybrid optimization algorithm is proposed to determine the best parameters. In the first step of the hybrid optimization algorithm, a designed grid traverse algorithm (GTA) is used to narrow the parameters searching area from a global to local space. In the second step, based on the result of the GTA, particle swarm optimization is used to determine the best parameters in the local parameter space. After the best parameters are determined, the SVR model is used to forecast the short-term load deviation in the distribution system. The performance of the proposed approach is compared to some classic methods in later sections of this paper.