Effect of Different Ecological Conditions on Content of Phytonutrients in Industrial Tomatoes

Tomatoes (Solanum lycopersicon L.) are one of the most important and most widely consumed vegetables in the world. The fruit contains considerable amount of different phytonutrients such as carotenoids, tocopherols, and vitamin C. In the present work, effects of some abiotic factors on the concentration of phytonutrients were investigated in tomato cultivated in two different types of soil. It was found that the type of soil had slight effect on the most important vital nutrients, while the ecological factors, particularly precipitation and average temperature 3 weeks before harvest, were of significant influence on such nutrients. It was found that low temperature and high precipitation before harvest caused the levels of carotenoids, tocopherol, and vitamin C to significantly increase by 65%, 46%, and 28%, respectively

Towards efficient loading of change-based models

This paper proposes and evaluates an efficient approach for loading models stored in a change-based format. The work builds on language-independent change-based persistence (CBP) of models conforming to object-oriented metamodelling architectures such as MOF and EMF, an approach which persists a model’s editing history rather than its current state. We evaluate the performance of the proposed loading approach and assess its impact on saving change-based models. Our results show that the proposed approach significantly improves loading times compared to the baseline CBP loading approach, and has a negligible impact on saving

An equation-free approach to coarse-graining the dynamics of networks

© American Institute of Mathematical Sciences. We propose and illustrate an approach to coarse-graining the dynamics of evolving networks, i.e., networks whose connectivity changes dynamically. The approach is based on the equation-free framework: short bursts of detailed network evolution simulations are coupled with lifting and restriction operators that translate between actual network realizations and their appropriately chosen coarse observables. This framework is used here to accelerate temporal simulations through coarse projective integration, and to implement coarse-grained fixed point algorithms through matrix-free Newton-Krylov. The approach is illustrated through a very simple network evolution example, for which analytical approximations to the coarse-grained dynamics can be independently obtained, so as to validate the computational results. The scope and applicability of the approach, as well as the issue of selection of good coarse observables are discussed