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Abstract

Development and demography of Adalia decempunctata L. were studied under laboratory conditions at seven constant temperatures (12, 16, 20, 24, 28, 32 and 36°C). First instar larvae failed to develop to second instar at 12°С and no development occurred at 36°C. The total developmental time varied from 47.92 days at 16°C to 15.94 days at 28°C and increased at 32°C. The lower temperature thresholds of 11.05 and 9.90°C, and thermal constants of 290.84 day-degree and 326.34 day-degree were estimated by traditional and Ikemoto-Takai linear models, respectively. The lower temperature threshold (Tmin) values estimated by Analytis, Briere-1, Briere-2 and Lactin-2 for total immature stages were 11.99, 12.24, 10.30 and 10.8°C, respectively. The estimated fastest developmental temperatures (Tfast) by the Analytis, Briere-1, Briere-2 and Lactin-2 for overall immature stages development of A. decempunctata were 31.5, 31.1, 30.7 and 31.7°C, respectively. Analytis, Briere-1, Briere-2 and Lactin-2 measured the upper temperature threshold (Tmax) at 33.14, 36.65, 32.75 and 32.61°C. The age-stage specific survival rate (sxj) curves clearly depicted the highest and lowest survival rates at 16 and 32°C for males and females. The age-specific fecundity (mx) curves revealed higher fecundity rate when fed A. gossypii at 24 and 28°C. The highest and lowest values of intrinsic rate of increase (r) were observed at 28 and 16°C (0.1945 d–1 and 0.0592 d–1, respectively). Also, the trend of changes in the finite rate of increase (λ) was analogous with intrinsic rate of increase. The longest and shortest mean generation time (T) was observed at 16 and 28°C, respectively and the highest net reproductive rates (R0) was estimated at 24 and 28°C. According to the results, the most suitable temperature seems to be 28°C due to the shortest developmental time, highest survival rate, and highest intrinsic rate of increase.
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Abstract

An original fuzzy team control model is presented in this article. The model is based on a non-traditional combination of classical and contemporary achievements of management and mathematical theories of fuzzy logic and fuzzy sets. In methodological terms, the article also offers a set of tools for measuring and evaluating both team performance and the effectiveness of the team control system in the organization. Fuzzy tools and techniques for decision-making, studying of hidden effects and joint influences, and quantification of evaluations are employed in this set of tools. The suggested fuzzy model contributes to overcoming theoretical deficits on the issues of team control, and the methodology of team control fills a gap in the toolkit of team management. The results from verification of the fuzzy team control model at a small-sized Bulgarian enterprise are also discussed in this article. They indicate that it is possible to develop a fuzzy model for team control, increasing the effectiveness of the team control system in the enterprise.
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