Showing 1,981 - 2,000 results of 186,324 for search 'by~', query time: 1.02s Refine Results
  1. 1981

    Crystal structure of 1,10-phenanthrolinium bromide dihydrate, C12H9N2Br by Adeloye Adewale O., Clayton Hadley S.

    Published 2023-10-01
    “…C12H9N2Br, triclinic, P1‾$\overline{1}$ (no. 2), a = 7.4005(13) Å, b = 9.4122(16) Å, c = 9.7916(17) Å, α = 98.235(9)°, β = 101.744(8)°, γ = 111.005(7)°, V = 605.69(18) Å3, Z = 2, Rgt(F) = 0.0209, wRref(F2) = 0.0514, T = 100(2) K.…”
    Get full text
    Article
  2. 1982
  3. 1983
  4. 1984
  5. 1985

    Comparative Antifungal Activity of Cilofungin (LY121019) against Candida Species, Including Evaluation of Susceptibility Testing Method by Abdul H Chagla, John H Hii, Daryl J Hoban, Andrew E Simor, Santiago Ferro, Evelyn Witwicki, Ruby Poon, Donald E Low

    Published 1992-01-01
    “…Candida tropicalis and Candida glabrata (90% minimal inhibitory concentration [MIC] 3.2 μg/mL) but was inactive against other Candida species. …”
    Get full text
    Article
  6. 1986

    A SA-BP neural network algorithm based channel quality evaluation method in optical access network by Bingqing CAI, Siya XU, Feng QI, Weichun GE, Guiping ZHOU, Botao YU, Yueyue LI

    Published 2018-04-01
    “…So far,existing works related to quality evaluation of channel mainly focused on equipment layer and network layer in optical access network.Most of researches lack synthesis evaluation methods covering physical layer,network layer and business layer.As a result,it is hard for administrators to judge the actual quality of channel by network monitoring completely and accurately.To solve this problem,key influence factors of channel quality in optical access network was analyzed firstly,and a comprehensive multi-layer-multi-index evaluation model of channel quality was proposed.Then a SA-BP neural network algorithm was designed to train the parameters of the proposed evaluation model to make the result more accurate.The simulation results show that the proposed method has higher accuracy and stability,and can improve the quality and efficiency of network operation and maintenance.…”
    Get full text
    Article
  7. 1987
  8. 1988
  9. 1989
  10. 1990

    A Risk Assessment of Underwater Cultural Heritage for Wave-Induced Hazards: The Impact of Climate Change on Cadiz Bay by C. Ferrero-Martín, A. Izquierdo, M. Bethencourt, T. Fernández-Montblanc

    Published 2025-01-01
    “…The approach uses hybrid downscaling of bias-corrected wave fields from the RCP4.5 and RCP8.5 CMIP5 scenarios. The methodology was applied in the Bay of Cadiz, where an overall reduction in wave energy flux was observed. …”
    Get full text
    Article
  11. 1991
  12. 1992
  13. 1993
  14. 1994

    Relationship Model Analysis of Personal Factors, Celebrity Endorsement, Buying Behavior, and Word of Mouth on Tourists on Komodo Island by Ramdhan Kurniawan, Usep Suhud, Fauzy Rahman Kosasih, Zulmi Ramdani

    Published 2023-04-01
    “…The study results show that personal factors and celebrity endorsements have a partial effect on buying behavior, as well as buying behavior which significantly affects the word of mouth of research respondents. …”
    Get full text
    Article
  15. 1995
  16. 1996
  17. 1997

    An Improved Bi-Switch Flyback Converter with Loss Analysis for Active Cell Balancing of the Lithium-Ion Battery String by Sugumaran G., Amutha Prabha N.

    Published 2024-01-01
    “…The proposed topology can decrease the number of switching devices as well as the size and cost of the system. The bi-switch flyback converter eliminates the need for a separate buffer circuit to minimise leakage and electromagnetic inductance. …”
    Get full text
    Article
  18. 1998
  19. 1999
  20. 2000

    Understanding the spread of agriculture in the Western Mediterranean (6th-3rd millennia BC) with Machine Learning tools by Maria Elena Castiello, Emmanuele Russo, Héctor Martínez-Grau, Ana Jesus, Georgina Prats, Ferran Antolín

    Published 2025-01-01
    “…While farming practices and settlements in the Western Mediterranean differ greatly from those known in the Eastern Mediterranean and central Europe, the extent to which these differences are connected to the local environment and climate is unclear. Here, we tackle this question by compiling data and proxies at a superregional and multi-scale level, including archaeobotanical information, radiocarbon dates and paleoclimatic models, then applying a machine learning approach to investigate the impact of ecological and climatic constraints on the first Neolithic humans and crops. …”
    Get full text
    Article