The use of wind energy for electric power generation provides a clean and renewable source. Therefore there is an increasing interest in developing and exploiting natural energy generation system. Switched reluctance generators (SRGs) have the potential to be a robust and highly efficient electrical conversion system for variable-speed wind applications. This study presents a new approach for optimising performance of a SRG intended for variable-speed direct drive wind turbine applications. DC bus voltage level and phase voltage switching angles have been identified as control variables affecting power generation. Owing to highly non-linear characteristics of SRG, iterative simulation of the generator model on the range of control variables can be used for finding output power profile. Since it is a multidimensional search space, the number of iterations is very big. Differential evolution (DE) strategy has been introduced to find optimal firing angles and DC bus voltage level under multiple operating conditions. Optimisation of the control variables is performed using a machine model based on the measured characteristics. Selected operating points are experimentally tested using a 4 kW 1500 rpm SRG prototype. DE algorithm is a viable alternative for generating optimal control in multidimensional optimisation of SRG wind energy generation.

The simple structure, low manufacturing cost, rugged behavior, high torque per unit volume, and wide torque-speed range make a switched reluctance motor (SRM) very attractive for industrial applications. However, these advantages are overshadowed by its inherent high torque ripple, acoustic noise, and difficulty to control. The controlled parameters in SRM drives can be selected as the turn-on angle, the turn-off angle, and the current reference. This paper investigates the problem of optimal control parameters considering the maximum average torque, minimum copper losses, and minimum torque ripple as the main objectives in SRM drives. The use of evolutionary algorithms (EAs) to solve problems with multiple objectives has attracted much attention recently. Differential evolution (DE) is an EA that was developed to handle optimization problems over continuous domains. A multiobjective DE (MODE) technique is introduced here to find the optimal firing angles under multiple operating conditions. The simulation results carried out on a 4-phase 8/6 pole SRM show that the proposed MODE can be a reliable alternative for generating optimal control in the multiobjective optimization of SRM drive systems.

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This work belongs to a breeding program held to improve and develop the genetic resources of tossa jute cropped in Tunisian arid regions. The main objectives of this study were (i) the comparison of the nutritional composition (minerals, phenolic compounds) and the antioxidant activities of fifteen tossa jute populations and (ii) the identification of the superior performing ones. The results of the analysis of variances (ANOVA) revealed significant differences between the assessed populations in terms of mineral and phenolic compositions as well as in terms of antioxidant activity (DPPH). The univariate comparison of means tests and the hierarchical cluster analysis identified two interesting groups of tossa jute populations; The first group was presented by P11 and P15; which were characterized by the highest mineral composition; and the second group brings together the populations P4 and P12; which were characterized by the highest phenolic composition. This germplasm recommended to be the subject of further studies to achieve the breeding program to develop more performing variety jute for the Tunisian arid regions.

Pearl millet (Pennisetum glaucum (L.) R. Br., 2n = 2x = 14, Poaceae), is a cross-pollinated, warm-season crop grown worldwide. To select genotypes for breeding pearl millet cultivars that adapt to drought condition in southern Tunisia, we evaluated the grain yield (GY) and yield-related traits using a set of 27 landraces at two locations in southern Tunisia for two grown seasons (2019 and 2020). The genetic variability, phenotypic and genotypic association, and path coefficient (PC), based on grain yield (GY) and different yield-related agronomic traits, were evaluated. Analysis of variance and BLUPs value revealed a wide range of variability and the possibility of genetic selection for traits that are advantageous. Broad sense heritability (H) for all the traits ranged from 24.10% for grain yield (GY) to 57.11% for spike length (SL), indicating low to moderate inheritability. Genetic advance as a percentage of the mean (GAM) ranged from high (29.56%) for principal panicle weight (PPW) to moderate for all the traits except from plant high (PH) (7.31%). For all the traits, the phenotypic coefficient of variation (PCV) was higher than genotypic coefficient of variation (GCV), indicating the magnitude of environmental conditions. GY was significantly correlated with all the traits at the genotypic and phenotypic level. According to the path coefficient, the traits PPW and SL displayed the highest direct effects on GY. Heatmap analysis demonstrated a clear segregation between the early and late genotypes based on their geographic origin. Based on the cluster analysis and FAI-BLUPS analysis, genotypes G11, G13, G12, G17 and G18 were selected as the best-performing genotypes with the shortest cycle.

Alfalfa breeding in areas with high abiotic constraints has made little progress in improving productivity and tolerance. The difficulty lies in identifying and characterizing the parameters related to resistance to different climate pressures. Various phenological, morphological, physiological and chemical processes are the origin of avoidance and tolerance strategies. The purpose of this work was to study the morphophysiological and chemical divergences between eight perennial alfalfa genotypes from different origins: 3 native genotypes (IRA, Gabès, Chenini) and five exotic ones (Ameristand, Bami, Prosementi, Tamentit and Tata) exhibiting a high tolerance to the arid bioclimate outside oasis. The obtained results allowed to classify the genotypes into two sufficiently homogeneous groups which could be considered as an interesting breeding genetic pool for the chosen selection criteria. This pool is the starting genetic material for the creation of synthetic varieties adapted to arid conditions outside oases characterized by higher fresh and dry yields (Ameristand, Bami, and Tamentit), with excellent tolerance to salinity (Gabès and IRA).

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