Behavior of some Hungarian wheat varieties to seed soaking in gibberellic acid under salt stress

Various abiotic stresses including high salinity strongly affect seed germination. Three Hungarian wheat landraces Gamási, Fóti and Kartali have been tested for seed germination, seedling growth and chemicals parameters when seeds were soaked in gibberellic acid (GA3) at the concentrations of 0, 75 and 150 ppm and at four levels of salinity stress 0, 5, 10 and 15 dSm-1. A laboratory experiment took place at Research Institute of Nyíregyháza. Factorial Experiment based on Randomized Complete Block Design (RCBD) in four replications has been conducted. Seed soaking in GA3 before sowing significantly affected germination characteristics, seedling properties and the results of chemical analysis. The highest germination as well as K+ content of seeds were observed at seed soaking into 150 ppm GA3. However, Na+ and proline contents in seedling were decreased. The behavior of the tested Hungarian wheat varieties was genotype-dependent. Increasing salinity stress up to 15 dSm-1 significantly affected germination characteristics, seedlings parameters and chemical analysis. Fóti variety turned out to be the best at 150 ppm GA3 compared to the other two varieties. Moreover, it had the lowest Na+ and proline contents and highest K+ content

Modelling, Simulation and Fuzzy Self-Tuning Control of D-STATCOM in a Single Machine Infinite Bus Power System

© 2019 Bentham Science Publishers. This is the accepted manuscript version of an article which has been published in final form at https://doi.org/10.2174/2352096511666180314141205In recent years, demand for electricity has increased considerably, while the expansion of generation and transmission has been very slow due to limited investment in resources and environmental restrictions. Methods: As a result, the power system becomes vulnerable to disturbances and instability. FACTS (Flexible AC Transmission Systems) technology has now been accepted as a potential solution to this problem. This paper deals with the modelling, simulation and fuzzy self-tuning control of a D-STATCOM to enhance the stability and improve the critical fault clearing time(CCT) in a single machine infinite bus (SMIB).A detailed modelling of the D-STATCOM and comprehensive derivation of the fuzzy logic self-tuning control is presented. Results: The dynamic performance of the power system with the proposed control scheme is validated through in a simulation study carried out under Matlab/Simulink and SimPowerSystems toolbox. Conclusion: The results demonstrate a significant enhancement of the power system stability under the simulated fault conditions considered.Peer reviewe

Evolutionary Computation, Optimization and Learning Algorithms for Data Science

A large number of engineering, science and computational problems have yet to be solved in a computationally efficient way. One of the emerging challenges is how evolving technologies grow towards autonomy and intelligent decision making. This leads to collection of large amounts of data from various sensing and measurement technologies, e.g., cameras, smart phones, health sensors, smart electricity meters, and environment sensors. Hence, it is imperative to develop efficient algorithms for generation, analysis, classification, and illustration of data. Meanwhile, data is structured purposefully through different representations, such as large-scale networks and graphs. We focus on data science as a crucial area, specifically focusing on a curse of dimensionality (CoD) which is due to the large amount of generated/sensed/collected data. This motivates researchers to think about optimization and to apply nature-inspired algorithms, such as evolutionary algorithms (EAs) to solve optimization problems. Although these algorithms look un-deterministic, they are robust enough to reach an optimal solution. Researchers do not adopt evolutionary algorithms unless they face a problem which is suffering from placement in local optimal solution, rather than global optimal solution. In this chapter, we first develop a clear and formal definition of the CoD problem, next we focus on feature extraction techniques and categories, then we provide a general overview of meta-heuristic algorithms, its terminology, and desirable properties of evolutionary algorithms

Research trends in combinatorial optimization

Acknowledgments This work has been partially funded by the Spanish Ministry of Science, Innovation, and Universities through the project COGDRIVE (DPI2017-86915-C3-3-R). In this context, we would also like to thank the Karlsruhe Institute of Technology. Open access funding enabled and organized by Projekt DEAL.Peer reviewedPublisher PD

SUGAR BEET PRODUCTIVITY AS AFFECTED BY FOLIAR SPRAYING WITH METHANOL AND BORON International Journal of Agriculture Sciences

Abstract-Two field experiments were carried out at Kafr El-Garayda Village, Bialla Center, Kafr El-Sheikh Governorate, Egypt during 2010/2011 and 2011/2012 seasons to study the effect of methanol and boron foliar spraying on growth, yield and quality of sugar beet. This aimed to study four aqueous methanol solutions control without 0, 15, 30 and 45% (v/v) sprayed three times during growth stages which occupied the vertical plots and four boron concentrations control without 0, 40, 80 and 120 ppm as boric acid applied as foliar spraying at two times during growth season and assigned in the horizontal plots. Results indicated that foliar application of 30% methanol solution significantly increased all studied characters i.e. total chlorophyll, leaf area/plant, foliage and root fresh weight, foliage and root length and root diameter, total soluble solids, sucrose and apparent purity percentages, root, top and sugar yields/ha, whereas harvest index was decreased. Moreover, increasing methanol concentration to 45% tended to decrease all above mentioned characters. Application of 80 ppm boron significantly improved root yield and its attributes and root quality, on contrarily harvest index was decreased. Maximum top, root, sugar yields/ha and root quality produced by foliar spraying of 30% methanol and 80 ppm boron

Virtual interpolation of discrete multi-objective programming solutions with probabilistic operation

This work presents a novel framework to address the long term operation of a class of multi-objective programming problems. The proposed approach considers a stochastic operation and evaluates the long term average operating costs/profits. To illustrate the approach, a two-phase method is proposed which solves a prescribed number of K monoobjective problems to identify a set of K points in the Paretooptimal region. In the second phase, one searches for a set of non-dominated probability distributions that define the probability that the system operates at each point selected in the first phase, at any given operation period. Each probability distribution generates a vector of average long-term objectives and one solves for the Pareto-optimal set with respect to the average objectives. The proposed approach can generate virtual operating points with average objectives that need not have a feasible solution with an equal vector of objectives. A few numerical examples are presented to illustrate the proposed method