---

Wear phenomena in wheel-rail contact for railway vehicle is very important parameter. For this purpose, simulation and dynamic analyze of new and worn wheel profiles are done. Then wagon’s derailment factor along curved track is determined. Analytic solution is achieved by using Hertz contact theory and Kalker linear theory. Also, simulation and analysis is done in ADAMS/Rail software and for different wheel profiles and derailment factor is determining using derailment criteria. Results showing that derailment factor are low initially but vertical forces decreases and centrifugal force increases lateral forces and consequently derailment factor increases along wagon entering curved track and worn profiles have more tendencies exposed to derailment. However, permitted wear limit must be defined. In many cases, worn profiles havefewer tendencies to derailment. Using this method can determine wear limit of wheel profile to maintenance and re-profileoperation. It is revealed that the curve length does not affect on derailment factor. Also, damper coefficient does not affect on mean derailment factor But it is much more turbulence. Perturbation in curve beginning is considerable and at the end of the curve, it restores to initial value.

---

Twenty promising barley lines were evaluated at seven research stations in Iran, during two cropping seasons. The analysis of variance on grain yield data showed mean squares of environments, genotypes and Genotype×Environment Interaction (GEI) as significant, respectively accounting accounted for 60.38, 4.52 and 35.09% of treatment combination sum of squares. To find out the effects of GEI on grain yield, the data were subjected to Additive Main effects and Multiplicative Interaction (AMMI) and Sites Regression (SREG) GGE biplot analysis. Mega-environmental investigation is the most suitable way to utilize GEI. "Which-won-where" pattern was followed with three distinct mega-environments found in the barley assessment. Entries G5 and G6 showed general adaptability while G7 and G13 exhibited specific adaptation to Neishabour and Esfehan, respectively. Considering both techniques, genotype G1 revealed high grain yield along with yield stability. With regard to barley assessment, Esfehan was identified as a location with larger main effects interaction, making it a less predictable location for barley variety evaluation. The results finally indicated that AMMI and GGE biplot are informative methods to explore stability and adaptation pattern of genotypes in practical plant breeding and in subsequent variety recommendations. In addition, finding mega-environments help to identify the must suitable barley cultivars that can be recommended for areas within the mega-environment in either one or more test locations.