• 제목/요약/키워드: seminole

검색결과 8건 처리시간 0.032초

고랭지에서 화이트 클로버의 품종별 수량성과 생육특성 (Growth Characteristics and Productivities of White Clover(Trifolium repens) Varieties at the Alpine Areas)

  • 이종경;정종원;김종근;윤세형;백봉현;나기준;이성철;이주삼
    • 한국초지조사료학회지
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    • 제23권2호
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    • pp.115-120
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    • 2003
  • 본 시험은 화이트클로버 California Ladino(대조품종), Seminole, Sonja, Milo, Rinendel, Alberta 및 Sona 7품종을 축산기술연구소 대관령지소(표고 800m)와 남원지소(표고 450m)에서 각각 난괴법 3반복으로 수행하였다. 화이트 클로버의 엽색은 대조품종 Ladino와 Seminole을 제외하고 모두 연녹색이었고 엽폭도 대형이었다. 내한성은 대조품종인 Ladino 보다 좋은 품종은 Seminole로서 85.2%였다. 화이트 클로버 품종의 건물수량은 대관령에서 대조품종 Ladino보다 많은 Milo와 Rinendel이 남원에서는 대조품종인 Ladino 품종이 우수하였다. 대관령에서 화이트클로버의 품종별 사료가치중 ADF 함량은 대관령과 남원에서 Ladino가 각각 24.3과 23.6%로 가장 낮았고 NDF 함량은 품종간에 큰 차이를 보이지는 않았지만 Sonja, Ladino 및 Rinendel이 낮은 편에 속하였다. 화이트클로버의 조단백질 함량은 대관령지역에서는 Seminole이 가장 높았으며 남원지역에서는 Rinendel이 가장 높았다. 화이트클로버의 조단백질 생산량을 보면 대관령지역에서는 Milo가 가장 높았고 남원지역에서는 Ladino가 가장 높았다. 이상으로 화이트클로버의 건물수량과 사료가치로 미루어 볼때 대관령지역에서는 Milo와 Rinendel이, 남원지역에서는 Ladino와 Seminole이 유망한 품종으로 여겨진다.

Research on Patchwork's Origin and Development

  • Wang, Jianping;Li, Xiujie;Mi, Jianuan
    • The International Journal of Costume Culture
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    • 제12권1호
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    • pp.97-100
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    • 2009
  • Patchwork is a handicraft to put some certain shapes of small cloths together. Historical records of patchwork was discovered in Ancient Egypt as far back as BC 1000. Patchwork has been popular in the England and in around 13-$14^{th}$ century, European spliced little pieces of cloths for cold necessities, which made the handle of patchwork techniques gradually tend to decorative other than utility. Patchwork designs and techniques were taken across the Atlantic to North America with the early settlers in the mid-eighteenth century. In the early years of $20^{th}$ century, owing to the continuous technological advances, woman got more job opportunities that made patchwork technologies withered. Patchwork art continually evolved on the basis of historical and cultural factors to new styles, the famous Hawaiian, Stained Glass, Mola, Celtic, Victoria, Seminole and many other patchwork styles like that perfect embodied different art and cultures of different nations in different times.

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GENERAL FRAMEWORK FOR PROXIMAL POINT ALGORITHMS ON (A, η)-MAXIMAL MONOTONICIT FOR NONLINEAR VARIATIONAL INCLUSIONS

  • Verma, Ram U.
    • 대한수학회논문집
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    • 제26권4호
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    • pp.685-693
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    • 2011
  • General framework for proximal point algorithms based on the notion of (A, ${\eta}$)-maximal monotonicity (also referred to as (A, ${\eta}$)-monotonicity in literature) is developed. Linear convergence analysis for this class of algorithms to the context of solving a general class of nonlinear variational inclusion problems is successfully achieved along with some results on the generalized resolvent corresponding to (A, ${\eta}$)-monotonicity. The obtained results generalize and unify a wide range of investigations readily available in literature.

만다린 잡종에서 자연발생 배수체의 발생 빈도와 생장 특성 (Frequency and Growth Characteristics of Polyploids Occurred Spontaneously in Some Mandarin Hybrids)

  • 송관정;김샛별;박재현;오은의;이경욱;김동욱;강종훈;김정순;오정환
    • 원예과학기술지
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    • 제29권6호
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    • pp.617-622
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    • 2011
  • 자연발생 배수체를 이용한 다양한 육종 소재를 개발함에 있어 그 효율성을 평가하고자 종자의배형성이 다른 일부 만다린 잡종에 대해 종자 형성 정도, 자연발생 배수체의 발생 빈도, 배수체의 생장 특성을 분석하였다. 다배성의 'Amakusa', 'Haruka', 'Hayaka' 및 'Seminole' 4품종과 단배성의 'Benibae'와 'Harehime' 2품종에 대해 방임수분된 과실로부터 종자를 채취하였다. 과실당 종자수는 10.0개이었고, 이 중 소형의 발육종자 형성 빈도는 25.1%이었다. 이들 소형 종자의 기내 발아 식물체에 대해 배수체 분석기 및 염색체 분석으로 배수성을 분석하고 배수체를 선발하였다. 'Harehime' 3배체 1개, 'Amakusa' 4배체 1개, 그리고 'Benibae' 4배체 1개가 각각 획득되었다. 이들 4배체와 3배체의 잎의 형태, 두께, 엽병 길이 및 절간장을 2배체와 비교하였는데, 큰 차이는 나타나지 않았다. 그러나 기공의 크기와 분포에 있어서는 분명한 차이를 나타내어 3 또는 4배수체 식물에서 기공의 크기가 커지고 분포 밀도는 감소하였다. 엽록소 함량을 나타내는 SPAD 값과 광합성 정도에 있어서는 큰 차이를 나타내지 않았다. 본 연구결과로 감귤 만다린 잡종에서 방임수분으로도 자연발생 배수체 생산이 가능하며, 다배성보다는 단배성에서 발생 빈도가 높은 것을 확인할 수 있었다.

Impact of Bridge Construction on County Population in Georgia

  • Jeong, M. Myung;Kang, Mingon;Jung, Younghan E.
    • 국제학술발표논문집
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    • The 9th International Conference on Construction Engineering and Project Management
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    • pp.1017-1023
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    • 2022
  • Past research shows that the construction of new infrastructure accelerates economic growth in the region by attracting more people and commodities. However, the previous studies only considered large-scale infrastructures such as sea-cross bridges and channel tunnels. There is a paucity of literature on regional infrastructure and its impact on socio-economic indicators. This paper explores the impact of new bridge construction on the human population, particularly focusing on regional bridges constructed during the 2000s in the state of Georgia. The human population at a county level was selected as a single socio-economic factor to be evaluated. A total of 124 cases were investigated as to whether the emergence of a new bridge affected the population change. The interrupted time series analysis was used to statistically examine the significance of population change due to the construction by treating each new bridge as an intervention event. The results show that, out of the 124 cases, the population of 67 cases significantly increased after the bridge construction, while the population of 57 cases was not affected by the construction at a significance level of 0.05. The 124 cases were also analyzed by route type, functional class, and traffic volume, but the results revealed, unlike large-scale infrastructure, that no clear evidence was found that a new bridge would bring an increase in the human population at a county level.

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Safety Education in the Curriculum of Construction Programs

  • Awolusi, Ibukun;Sulbaran, Tulio;Song, Siyuan;Nnaji, Chukwuma;Ostadalimakhmalbaf, Mohammadreza
    • 국제학술발표논문집
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    • The 9th International Conference on Construction Engineering and Project Management
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    • pp.508-515
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    • 2022
  • Construction safety education will continue to attract the interests of construction educators, researchers, and industry professionals due to its immense influence on accident reduction and prevention. A well-educated workforce with a thorough understanding of safety requirements and procedures is needed to develop and apply effective safety and health programs as well as devise strategic means of preventing injuries, illnesses, and fatalities on construction projects. The objective of this research is to evaluate construction safety education in the curriculum of construction programs in the United States. An analysis of construction safety courses across accredited construction programs in the U.S. is conducted to synthesize important details and common themes. A nationwide characterization of the safety courses presented followed by an assessment selected a few programs as a pilot study. Critical elements of the courses such as course titles, course year, credit hours, topics covered, and alignment with professional certification or outreach training courses are characterized. Findings from the study reveal the similarities and variations that exist among safety courses taught in different construction programs in the U.S. These findings could result from several influencing factors, which could be the subject of further investigations geared toward improving safety education in construction programs.

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Blind Drift Calibration using Deep Learning Approach to Conventional Sensors on Structural Model

  • Kutchi, Jacob;Robbins, Kendall;De Leon, David;Seek, Michael;Jung, Younghan;Qian, Lei;Mu, Richard;Hong, Liang;Li, Yaohang
    • 국제학술발표논문집
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    • The 9th International Conference on Construction Engineering and Project Management
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    • pp.814-822
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    • 2022
  • The deployment of sensors for Structural Health Monitoring requires a complicated network arrangement, ground truthing, and calibration for validating sensor performance periodically. Any conventional sensor on a structural element is also subjected to static and dynamic vertical loadings in conjunction with other environmental factors, such as brightness, noise, temperature, and humidity. A structural model with strain gauges was built and tested to get realistic sensory information. This paper investigates different deep learning architectures and algorithms, including unsupervised, autoencoder, and supervised methods, to benchmark blind drift calibration methods using deep learning. It involves a fully connected neural network (FCNN), a long short-term memory (LSTM), and a gated recurrent unit (GRU) to address the blind drift calibration problem (i.e., performing calibrations of installed sensors when ground truth is not available). The results show that the supervised methods perform much better than unsupervised methods, such as an autoencoder, when ground truths are available. Furthermore, taking advantage of time-series information, the GRU model generates the most precise predictions to remove the drift overall.

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