A benchmark study on identification of inelastic parameters based on deep drawing processes using pso – nelder mead hybrid approach

Optimization techniques have been increasingly used to identification of inelastic material parameters owing to their generality. Development of robust techniques to solving this class of inverse problems has been a challenge to researchers mainly due to the nonlinear character of the problem and behaviour of the objective function. Within this framework, this work discusses application of Particle Swarm Optimization (PSO) and a PSO – Nelder Mead hybrid approach to identification of inelastic parameters based on a benchmark solution of the deep drawing process

Sediment transport trend in an erosive sandy beach: the case of Matinhos Beach, south coast of Brazil

Sandy beaches have different shoreline change rates (i.e., erosion/accretion rates). An erosive process onbeaches poses risks for human-occupied areas. One example is Matinhos Beach (state of Paraná - Brazil),which has an average annual erosion rate of around 1.5 m yr-1 This study applied a methodology that combinesin situ measurements and numerical modeling to simulate the physical processes in the coastal area of Matinhosduring the 2018 Austral winter. Monthly DGPS surveys were carried out in the study area from June to September.The MOHID modeling system was applied to simulate hydrodynamics and sediment transport, consideringwaves and tidal forcing validated with in situ data. The WAVEWATCH III and SWAN models were applied in anesting approach to simulate the waves at Matinhos Beach. The GFS was used to assess the wind conditions.The study period showed a dynamic evolution of accretion and erosion between monthly measurements withno clear pattern in most profiles. Significant sand accumulation was observed near the headland. Morphologicalchanges were minor due to the predominance of low energy without significant storm events. The measuredmorphological changes are in line with the residual littoral drift obtained from the modeling results for the period.The residual current velocities were towards the southwest, with magnitudes ranging from 0.15 m s-1 to 0.2 m s-1.A slight variation in the angle of wave incidence (10°) may change the direction (southwest or northeast) andintensity of the littoral drift. The applied methodology can reduce uncertainty and support effective coastalmanagement. However, the seasonal scales of wave climate cannot be disregarded, nor can the need forcoastal oceanographic data

(Homo)glutathione Deficiency Impairs Root-knot Nematode Development in Medicago truncatula

Root-knot nematodes (RKN) are obligatory plant parasitic worms that establish and maintain an intimate relationship with their host plants. During a compatible interaction, RKN induce the redifferentiation of root cells into multinucleate and hypertrophied giant cells essential for nematode growth and reproduction. These metabolically active feeding cells constitute the exclusive source of nutrients for the nematode. Detailed analysis of glutathione (GSH) and homoglutathione (hGSH) metabolism demonstrated the importance of these compounds for the success of nematode infection in Medicago truncatula. We reported quantification of GSH and hGSH and gene expression analysis showing that (h)GSH metabolism in neoformed gall organs differs from that in uninfected roots. Depletion of (h)GSH content impaired nematode egg mass formation and modified the sex ratio. In addition, gene expression and metabolomic analyses showed a substantial modification of starch and γ-aminobutyrate metabolism and of malate and glucose content in (h)GSH-depleted galls. Interestingly, these modifications did not occur in (h)GSH-depleted roots. These various results suggest that (h)GSH have a key role in the regulation of giant cell metabolism. The discovery of these specific plant regulatory elements could lead to the development of new pest management strategies against nematodes

A benchmark study on identification of inelastic parameters based on deep drawing processes using pso – nelder mead hybrid approach

Optimization techniques have been increasingly used to identification of inelastic material parameters owing to their generality. Development of robust techniques to solving this class of inverse problems has been a challenge to researchers mainly due to the nonlinear character of the problem and behaviour of the objective function. Within this framework, this work discusses application of Particle Swarm Optimization (PSO) and a PSO – Nelder Mead hybrid approach to identification of inelastic parameters based on a benchmark solution of the deep drawing process