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Showing 1,481 - 1,500 results of 2,583 for search '((( resourcessssss OR resources) selection functions ) OR ( sources detection functions ))', query time: 0.21s Refine Results
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    Demand-aware traffic cooperation for self-organizing cognitive small-cell networks by Changhua Yao, Lei Zhu, Yongxing Jia, Lei Wang

    Published 2019-01-01
    “…To solve the distributed self-organizing traffic cooperation coalition formation problem, we propose three coalition formation algorithms: the first one is the Binary Preferring Traffic Cooperation Coalition Formation Algorithm based on the traditional Binary Preferring order; the second one is the Best Selection Traffic Cooperation Coalition Formation Algorithm based on the functional Best Selection order to improve the converging speed; and the third one is the Probabilistic Decision Traffic Cooperation Coalition Formation Algorithm based on the functional Probabilistic Decision order to improve the performance of the formed coalition. …”
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  12. 1492

    Saline-alkali soil reclamation and utilization in China: progress and prospects by Guangzhou WANG, Gang NI, Gu FENG, Haley M. BURRILL, Jianfang LI, Junling ZHANG, Fusuo ZHANG

    Published 2024-06-01
    “…Following the soil management strategies, the concept of selecting crops for saline soil is proposed. This encompasses halophyte planting, salt-tolerant crop breeding and the application of saline-adapted functional microorganisms to improve the adaptation of crops. …”
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  13. 1493

    Mitochondrial genome assembly of the Chinese endemic species of Camellia luteoflora and revealing its repetitive sequence mediated recombination, codon preferences and MTPTs by Xu Xiao, Zhaohui Ran, Chao Yan, Weihao Gu, Zhi Li

    Published 2025-04-01
    “…Phylogenetic reconstruction using mitochondrial PCGs positioned C. luteoflora in closest evolutionary proximity to Camellia sinensis var. sinensis. Selection pressure analysis (Ka/Ks ratios < 1 for 11 PCGs) and nucleotide diversity assessment (Pi values: 0–0.00711) indicated strong purifying selection and low sequence divergence.This study provides the first comprehensive mitochondrial genomic resource for C. luteoflora, offering critical insights for germplasm conservation, comparative organelle genomics, phylogenetic resolution, and evolutionary adaptation studies in Camellia species.…”
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  14. 1494

    Hybrid deep learning optimization for smart agriculture: Dipper throated optimization and polar rose search applied to water quality prediction. by Amal H Alharbi, Faris H Rizk, Khaled Sh Gaber, Marwa M Eid, El-Sayed M El-Kenawy, Ehsan Khodadadi, Nima Khodadadi

    Published 2025-01-01
    “…We applied this hybrid strategy to a Radial Basis Function Network (RBFN), and validated its performance improvements through extensive experiments, including ANOVA and Wilcoxon tests for both feature selection and optimization phases. …”
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    Automated hypocenter determination of tectonic tremors in the Nankai subduction zone using convolutional neural networks combined with semblance analysis by Amane Sugii, Yoshihiro Hiramatsu, Takahiko Uchide, Kazutoshi Imanishi

    Published 2024-12-01
    “…The trained CNN model achieved high accuracy, with both precision and recall exceeding 97%, and correctly detected 96% of distant earthquakes. The probability of tectonic tremor as a function of the signal-to-noise ratio (SNR) increased steeply from 10 to 90% at an SNR of 4. …”
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  17. 1497

    The small RNA complement of adult Schistosoma haematobium. by Andreas J Stroehlein, Neil D Young, Pasi K Korhonen, Ross S Hall, Aaron R Jex, Bonnie L Webster, David Rollinson, Paul J Brindley, Robin B Gasser

    Published 2018-05-01
    “…Of the sncRNAs that were not miRNAs, some related to the spliceosome (n = 21), biogenesis of other RNAs (n = 3) or ribozyme functions (n = 16), whereas most others (n = 3798) were novel ('orphans') with unknown functions.…”
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    Allocating the Sample Size in Phase II and III Trials to Optimize Success Probability by Daniele De Martini

    Published 2014-11-01
    “…Being $r$ the rate of resources allocated to phase II, $OSP(r)$ is a concave function and there exists an optimal allocation $r_{opt}$ giving $\max\{OSP\}$. …”
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