An attempt is made in the current research to obtain the fundamental buckling torque and the associated buckled shape of an annular plate. The plate is subjected to a torque on its outer edge. An isotropic homogeneous plate is considered. The governing equations of the plate in polar coordinates are established with the aid of the Mindlin plate theory. Deformations and stresses of the plate prior to buckling are determined using the axisymmetric flatness conditions. Small perturbations are then applied to construct the linearised stability equations which govern the onset of buckling. To solve the highly coupled equations in terms of displacements and rotations, periodic auxiliary functions and the generalised differential quadrature method are applied. The coupled linear algebraic equations are a set of homogeneous equations dealing with the buckling state of the plate subjected to a unique torque. Benchmark results are given in tabular presentations for combinations of free, simply-supported, and clamped types of boundary conditions. It is shown that the critical buckling torque and its associated shape highly depend upon the combination of boundary conditions, radius ratio, and the thickness ratio.

This study was conducted to predict the yield and biomass of lentil (Lens culinaris L.) affected by weeds using artificial neural network and multiple regression models. Systematic sampling was done at 184 sampling points at the 8-leaf to early-flowering and at lentil maturity. The weed density and height as well as canopy cover of the weeds and lentil were measured in the first sampling stage. In addition, weed species richness, diversity and evenness were calculated. The measured variables in the first sampling stage were considered as predictive variables. In the second sampling stage, lentil yield and biomass dry weight were recorded at the same sampling points as the first sampling stage. The lentil yield and biomass were considered as dependent variables. The model input data included the total raw and standardized variables of the first sampling stage, as well as the raw and standardized variables with a significant relationship to the lentil yield and biomass extracted from stepwise regression and correlation methods. The results showed that neural network prediction accuracy was significantly more than multiple regression. The best network in predicting yield of lentil was the principal component analysis network (PCA), made from total standardized data, with a correlation coefficient of 80% and normalized root mean square error of 5.85%. These values in the best network (a PCA neural network made from standardized data with significant relationship to lentil biomass) were 79% and 11.36% for lentil biomass prediction, respectively. Our results generally showed that the neural network approach could be used effectively in lentil yield prediction under weed interference conditions.

In order to evaluate morphological and physiological traits related to drought tolerance and to determine the best criteria for screening and identification of drought-tolerant genotypes, we grew two tolerant genotypes (MCC392, MCC877) and two sensitive genotypes (MCC68, MCC448) of chickpea under drought stress (25% field capacity) and control (100% field capacity) conditions and assessed the effect of drought stress on growth, water relations, photosynthesis, chlorophyll fluorescence and chlorophyll content in the seedling, early flowering and podding stages. Drought stress significantly decreased shoot dry weight, CO2 assimilation rate (A), transpiration rate (E), and Psii photochemical efficiency (Fv/Fm) in all genotypes. In the seedling and podding stages, Psii photochemical efficiency was higher in tolerant genotypes than in sensitive genotypes under drought stress. Water use efficiency (WUE) and CO2 assimilation rate were also higher in tolerant than in sensitive genotypes in all investigated stages under drought stress. Our results indicated that water use efficiency, A and Fv/Fm can be useful markers in studies of tolerance to drought stress and in screening adapted cultivars of chickpea under drought stress.