Ionizing Radiation: Effective Physical Agents for Economic Crop Seed Priming and the Underlying Physiological Mechanisms.

To overcome various factors that limit crop production and to meet the growing demand for food by the increasing world population. Seed priming technology has been proposed, and it is considered to be a promising strategy for agricultural sciences and food technology. This technology helps to curtail the germination time, increase the seed vigor, improve the seedling establishment, and enhance the stress tolerance, all of which are conducive to improving the crop yield. Meanwhile, it can be used to reduce seed infection for better physiological or phytosanitary quality. Compared to conventional methods, such as the use of water or chemical-based agents, X-rays, gamma rays, electron beams, proton beams, and heavy ion beams have emerged as promising physics strategies for seed priming as they are time-saving, more effective, environmentally friendly, and there is a greater certainty for yield improvement. Ionizing radiation (IR) has certain biological advantages over other seed priming methods since it generates charged ions while penetrating through the target organisms, and it has enough energy to cause biological effects. However, before the wide utilization of ionizing priming methods in agriculture, extensive research is needed to explore their effects on seed priming and to focus on the underlying mechanism of them. Overall, this review aims to highlight the current understanding of ionizing priming methods and their applicability for promoting agroecological resilience and meeting the challenges of food crises nowadays.

Evolutionary squeaky wheel optimization: a new framework for analysis.

Squeaky wheel optimization (SWO) is a relatively new metaheuristic that has been shown to be effective for many real-world problems. At each iteration SWO does a complete construction of a solution starting from the empty assignment. Although the construction uses information from previous iterations, the complete rebuilding does mean that SWO is generally effective at diversification but can suffer from a relatively weak intensification. Evolutionary SWO (ESWO) is a recent extension to SWO that is designed to improve the intensification by keeping the good components of solutions and only using SWO to reconstruct other poorer components of the solution. In such algorithms a standard challenge is to understand how the various parameters affect the search process. In order to support the future study of such issues, we propose a formal framework for the analysis of ESWO. The framework is based on Markov chains, and the main novelty arises because ESWO moves through the space of partial assignments. This makes it significantly different from the analyses used in local search (such as simulated annealing) which only move through complete assignments. Generally, the exact details of ESWO will depend on various heuristics; so we focus our approach on a case of ESWO that we call ESWO-II and that has probabilistic as opposed to heuristic selection and construction operators. For ESWO-II, we study a simple problem instance and explicitly compute the stationary distribution probability over the states of the search space. We find interesting properties of the distribution. In particular, we find that the probabilities of states generally, but not always, increase with their fitness. This nonmonotonocity is quite different from the monotonicity expected in algorithms such as simulated annealing.