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Wyświetlanie 1-2 z 2
Tytuł:
Case study of developing an integrated water and nitrogen scheme for agricultural system on the North China Plain
Autorzy:
Liu, Y.
Tao, F.
Lao, Y.
Ma, J.
Powiązania:
https://bibliotekanauki.pl/articles/25700.pdf
Data publikacji:
2013
Wydawca:
Polska Akademia Nauk. Instytut Agrofizyki PAN
Opis:
Appropriate irrigation and nitrogen fertilization, along with suitable crop management strategies, are essential prerequisites for optimum yields in agricultural systems. This research attempts to provide a scientific basis for sustainable agricultural production management for the North China Plain and other semi-arid regions. Based on a series of 72 treatments over 2003-2008, an optimized water and nitrogen scheme for winter wheat/summer maize cropping system was developed. Integrated systems incorporating 120 mm of water with 80 kg N ha-1 N fertilizer were used to simulate winter wheat yields in Hebei and 120 mm of water with 120 kg N ha-1 were used to simulate winter wheat yields in Shandong and Henan provinces in 2000-2007. Similarly, integrated treatments of 40 kg N ha-1 N fertilizer were used to simulate summer maize yields in Hebei, and 80 kg N ha-1 was used to simulate summer maize yields in Shandong and Henan provinces in 2000-2007. Under the optimized scheme, 341.74 107 mm ha-1 of water and 575.79 104 Mg of urea fertilizer could be saved per year under the wheat/maize rotation system. Despite slight drops in the yields of wheat and maize in some areas, water and fertilizer saving has tremendous long-term eco-environmental benefits.
Źródło:
International Agrophysics; 2013, 27, 4
0236-8722
Pojawia się w:
International Agrophysics
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
Using a vision cognitive algorithm to schedule virtual machines
Autorzy:
Zhao, J.
Mhedheb, Y.
Tao, J.
Jrad, F.
Liu, Q.
Streit, A.
Powiązania:
https://bibliotekanauki.pl/articles/330838.pdf
Data publikacji:
2014
Wydawca:
Uniwersytet Zielonogórski. Oficyna Wydawnicza
Tematy:
cloud computing
vision cognitive algorithm
VM scheduling
simulation
chmura obliczeniowa
algorytm poznawczy
szeregowanie
symulacja
Opis:
Scheduling virtual machines is a major research topic for cloud computing, because it directly influences the performance, the operation cost and the quality of services. A large cloud center is normally equipped with several hundred thousand physical machines. The mission of the scheduler is to select the best one to host a virtual machine. This is an NP-hard global optimization problem with grand challenges for researchers. This work studies the Virtual Machine (VM) scheduling problem on the cloud. Our primary concern with VM scheduling is the energy consumption, because the largest part of a cloud center operation cost goes to the kilowatts used. We designed a scheduling algorithm that allocates an incoming virtual machine instance on the host machine, which results in the lowest energy consumption of the entire system. More specifically, we developed a new algorithm, called vision cognition, to solve the global optimization problem. This algorithm is inspired by the observation of how human eyes see directly the smallest/largest item without comparing them pairwisely. We theoretically proved that the algorithm works correctly and converges fast. Practically, we validated the novel algorithm, together with the scheduling concept, using a simulation approach. The adopted cloud simulator models different cloud infrastructures with various properties and detailed runtime information that can usually not be acquired from real clouds. The experimental results demonstrate the benefit of our approach in terms of reducing the cloud center energy consumption.
Źródło:
International Journal of Applied Mathematics and Computer Science; 2014, 24, 3; 535-550
1641-876X
2083-8492
Pojawia się w:
International Journal of Applied Mathematics and Computer Science
Dostawca treści:
Biblioteka Nauki
Artykuł
    Wyświetlanie 1-2 z 2

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