Pathogenicity of Beauveria bassiana (Balsamo-Crivelli) and Metarhizium anisopliae (Metschnikof) isolates against life stages of Zeugodacus cucurbitae (Coquillett) (Diptera: Tephritidae)

Background Entomopathogenic fungi are primary pathogens that naturally afect insect pests by suppressing their populations and considered as an ecofriendly agents. The present study aimed to evaluate in vitro activity of diferent isolates of Beauveria bassiana and Metarhizium anisopliae against the development of larval stages of the Cucurbit fruit fy, Zeugodacus cucurbitae (Coquillett) (Diptera: Tephritidae). Results Larval mortality was signifcantly high with B. bassiana isolate Bb337 (5.82–21.70%) and with the lowest in M. anisopliae isolate MaD (1.49–6.33%). Pupal mortality rate was comparatively higher with more than 50%. The cadavers of all host instars produced conidia (sporulation). Sporulated dead larvae were signifcantly higher in Bb337 (61.10%) than at the least in MaD (18.60%) at 105 conidia/ml. At 108 conidia/ml, MaD induced the highest pupal cadavers with mycosis (32.42%). Regardless of applied fungal species, host instars mortality signifcantly increased with increasing concentration of B. bassiana isolates, suggesting a concentration-dependent response of Z. cucurbitae. Conclusion The tested isolates demonstrated their pathogenicity through vertical transmission of mycosis from one instar to another, regardless of the concentrations used

Algorithm Engineering in Robust Optimization

Robust optimization is a young and emerging field of research having received a considerable increase of interest over the last decade. In this paper, we argue that the the algorithm engineering methodology fits very well to the field of robust optimization and yields a rewarding new perspective on both the current state of research and open research directions. To this end we go through the algorithm engineering cycle of design and analysis of concepts, development and implementation of algorithms, and theoretical and experimental evaluation. We show that many ideas of algorithm engineering have already been applied in publications on robust optimization. Most work on robust optimization is devoted to analysis of the concepts and the development of algorithms, some papers deal with the evaluation of a particular concept in case studies, and work on comparison of concepts just starts. What is still a drawback in many papers on robustness is the missing link to include the results of the experiments again in the design

Large-scale optimization with the primal-dual column generation method

The primal-dual column generation method (PDCGM) is a general-purpose column generation technique that relies on the primal-dual interior point method to solve the restricted master problems. The use of this interior point method variant allows to obtain suboptimal and well-centered dual solutions which naturally stabilizes the column generation. As recently presented in the literature, reductions in the number of calls to the oracle and in the CPU times are typically observed when compared to the standard column generation, which relies on extreme optimal dual solutions. However, these results are based on relatively small problems obtained from linear relaxations of combinatorial applications. In this paper, we investigate the behaviour of the PDCGM in a broader context, namely when solving large-scale convex optimization problems. We have selected applications that arise in important real-life contexts such as data analysis (multiple kernel learning problem), decision-making under uncertainty (two-stage stochastic programming problems) and telecommunication and transportation networks (multicommodity network flow problem). In the numerical experiments, we use publicly available benchmark instances to compare the performance of the PDCGM against recent results for different methods presented in the literature, which were the best available results to date. The analysis of these results suggests that the PDCGM offers an attractive alternative over specialized methods since it remains competitive in terms of number of iterations and CPU times even for large-scale optimization problems.Comment: 28 pages, 1 figure, minor revision, scaled CPU time

A Primal-Dual Algorithm for Monotropic Programming and its Application to Network Optimization

This paper presents a new primal-dual algorithm for solving a class of monotropic programming problems. This class involves many problems arising in a number of important applications in telecommunications networks, transportation and water distribution. The proposed algorithm is inspired by Kallio and Ruszczynski approach for linear programming. The problem is replaced by a game using two different augmented Lagrangian functions defined for the primal and the dual problems. It is then possible to develop a block-wise Gauss-Siedel method to reach an equilibrium of the game with alternative steps made in each component of the primal and dual variables.OPTIMIZATION ; NETWORK ANALYSIS