• Title/Summary/Keyword: 유전과 환경

Search Result 1,301, Processing Time 0.029 seconds

Quality Changes in Tomato Fruits Caused by Genotype and Environment Interactions (재배환경과 유전형의 상호작용에 따른 토마토 과실 품질 변화)

  • Park, Minwoo;Chung, Yong Suk;Lee, Sanghyeob
    • Horticultural Science & Technology
    • /
    • v.35 no.3
    • /
    • pp.361-372
    • /
    • 2017
  • Bred and grown around the world, tomato (Solanum spp.) has highly valuable fruits containings various anti-oxidants such as lycopene, flavonoids, glutamine, and ${\beta}-carotene$. Several studies have explored, way in which to enhance the growth, management and quality of tomato, we focus on the management of growth for yield rather than quality. The expression of superior agronomic traits depends on where cultivars are grown. We evaluated 10 cultivars grown in three environment for their lycopene. HTL3137 ($70.48mg{\cdot}kg^{-1}$), which was grown in Yoeju in spring/summer, contained the highest lycopene content, while HTL10256 ($20.9mg{\cdot}kg^{-1}$), which was grown in Suwon in spring/summer, contain the least lycopene.Correlations between color components and lycopene content varied according to growing location and season. In spring/summer-grown tomatoes from Suwon, no significant correlation was observed between any color component (redness [R], greenness [G], blueness [B], luminosity, $L^*$, $a^*$, $b^*$, hue and chroma) and lycopene content. A correlation was observed between B and lycopene content in tomatoes grown in Yeoju during the same season. In tomatoes grown in Yeoju in fall/winter, significant correlations were found between lycopene content and G, luminosity, $L^*$, and hue. Variance in interactions between genotype, environment, and genotype ${\times}$ environment (G ${\times}$ E) using Minimum Norm Quadratic Unbiased Estimate (MINQUE) analysis indicated that lycopene content depends on genotype (51.33%), environment (49.13%), and G ${\times}$ E (21.43%). However, when the Additive Main Effects and Multiplicative Interaction (AMMI) was used, the G ${\times}$ E value was highest.

파란발생에 관계하는 요인

  • 최진호
    • KOREAN POULTRY JOURNAL
    • /
    • v.19 no.12 s.218
    • /
    • pp.56-61
    • /
    • 1987
  • 파란을 일으키는 요인에는 영양, 생리, 유전, 환경온도, 사양관리, 질병 및 닭의 일령, 케이지 형태, 바닥구조, 집란횟수, 기계기구 등이 있다.

  • PDF

Hardware Implementation of Genetic Algorithm and Its Analysis (유전알고리즘의 하드웨어 구현 및 실험과 분석)

  • Dong, Sung-Soo;Lee, Chong-Ho
    • 전자공학회논문지 IE
    • /
    • v.46 no.2
    • /
    • pp.7-10
    • /
    • 2009
  • This paper presents the implementation of libraries of hardware modules for genetic algorithm using VHDL. Evolvable hardware refers to hardware that can change its architecture and behavior dynamically and autonomously by interacting with its environment. So, it is especially suited to applications where no hardware specifications can be given in advance. Evolvable hardware is based on the idea of combining reconfigurable hardware device with evolutionary computation, such as genetic algorithm. Because of parallel, no function call overhead and pipelining, a hardware genetic algorithm give speedup over a software genetic algorithm. This paper suggests the hardware genetic algorithm for evolvable embedded system chip. That includes simulation results and analysis for several fitness functions. It can be seen that our design works well for the three examples.

Shipyard Skid Sequence Optimization Using a Hybrid Genetic Algorithm

  • Min-Jae Choi;Yung-Keun Kwon
    • Journal of the Korea Society of Computer and Information
    • /
    • v.28 no.12
    • /
    • pp.79-87
    • /
    • 2023
  • In this paper, we propose a novel genetic algorithm to reduce the overall span time by optimizing the skid insertion sequence in the shipyard subassembly process. We represented a solution by a permutation of a set of skid ids and applied genetic operators suitable for such a representation. In addition, we combined the genetic algorithm and the existing heuristic algorithm called UniDev which is properly modified to improve the search performance. In particular, the slow skid search part in UniDev was changed to a greedy algorithm. Through extensive large-scaled simulations, it was observed that the span time of our method was stably minimized compared to Multi-Start search and a genetic algorithm combined with UniDev.