Showing 1,001 - 1,020 results of 63,112 for search 'ZEE5~', query time: 2.12s Refine Results
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    Modeling Lane-Keeping Behavior of Bicyclists Using Survival Analysis Approach by Hongwei Guo, Wuhong Wang, Weiwei Guo, Facheng Zhao

    Published 2013-01-01
    “…After the model estimation, the effects of the selected variables on the lane-keeping behavior are discussed. The results are expected to give a better understanding of the bicyclist behavior.…”
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    Deep Interest-Shifting Network with Meta-Embeddings for Fresh Item Recommendation by Zhao Li, Haobo Wang, Donghui Ding, Shichang Hu, Zhen Zhang, Weiwei Liu, Jianliang Gao, Zhiqiang Zhang, Ji Zhang

    Published 2020-01-01
    “…Second, since the items are fresh, many of them have never been exposed to users, leading to a severe cold-start problem. …”
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  6. 1006

    Automatic Monitoring System for Seed Germination Test Based on Deep Learning by Qi Peng, Lifen Tu, Yunyun Wu, Zhenyu Yu, Gerui Tang, Wei Song

    Published 2022-01-01
    “…In order to improve breeding efficiency, an automatic monitoring system for seed germination tests based on deep learning was designed. …”
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  7. 1007

    Establishment of Food Plots for White-tailed Deer in Central and South Florida by Emma V. Willcox, Brandon J. Schad, William M. Giuliano, James F. Selph

    Published 2007-07-01
    “…WEC 223/UW262: Establishment of Food Plots for White-Tailed Deer in Central and South Florida (ufl.edu) …”
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    RYE BREEDING FOR LOW-PENTOSANS AND POSSIBILITY OF ITS USE IN ANIMAL FEEDING by V. D. Kobyliansky, O. V. Solodukhina, I. V. Lunegova, S. P. Novikova, M. S. Hlopyuk, V. I. Makarov

    Published 2018-06-01
    “…Together with breeders from other institutions we developed the first six low-pentosan varieties of winter rye suitable for grain forage use. Seeds of low-pentosan forage rye do not form viscous mucus in animal stomachs, thus eliminating the problem that exists when feeding classic baking rye. …”
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    DWNet: Dual-Window Deep Neural Network for Time Series Prediction by Jin Fan, Yipan Huang, Ke Zhang, Sen Wang, Jinhua Chen, Baiping Chen

    Published 2021-01-01
    “…In this paper, we propose a dual-window deep neural network (DWNet) to predict time series. …”
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    Monitoring Population Phenology of Asian Citrus Psyllid Using Deep Learning by Maria Bibi, Muhammad Kashif Hanif, Muhammad Umer Sarwar, Muhammad Irfan Khan, Shouket Zaman Khan, Casper Shikali Shivachi, Asad Anees

    Published 2021-01-01
    “…In the current study, several prediction models were developed based on regression algorithms of machine learning to monitor different phenological stages of Asian citrus psyllid to predict its population about different abiotic variables (average maximum temperature, average minimum temperature, average weekly temperature, average weekly relative humidity, and average weekly rainfall) and biotic variable (host plant phenological patterns) in citrus-growing regions of Pakistan. …”
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  14. 1014

    Mutagenicity and Acute Oral Toxicity Test for Herbal Poultry Feed Supplements by Boddapati Srinivasa Rao, C. V. Chandrasekaran, H. S. Srikanth, Murugan Sasikumar, R. Edwin Jothie, Begum Haseena, Bethapudi Bharathi, Ramasamy Selvam, D’Souza Prashanth

    Published 2018-01-01
    “…The present study was designed to investigate five poultry feed supplements (PFS), EGMAX® (to revitalize ovarian activity), FEED-X™ (feed efficiency enhancer), KOLIN PLUS™ (natural replacer of synthetic choline chloride), PHYTOCEE® (natural defence enhancer), and STODI® (to prevent and control loose droppings), for their possible mutagenicity and toxicity. …”
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    Gross and Histopathology of Goats Feeding on Opuntia stricta in Laikipia County, Kenya by Jackson M. Ncebere, Paul G. Mbuthia, Robert M. Waruiru, Peter K. Gathumbi

    Published 2021-01-01
    “…This could affect the socioeconomics and livelihoods of communities in the study area, and therefore, the spread of this plant needs to be controlled.…”
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    Conditional Analysis for Mixed Covariates, with Application to Feed Intake of Lactating Sows by S. Y. Park, C. Li, S. M. Mendoza Benavides, E. van Heugten, A. M. Staicu

    Published 2019-01-01
    “…The methodology is motivated by and applied to a lactating sow study, where the primary interest is to understand how the dynamic change of minute-by-minute temperature in the farrowing rooms within a day (functional covariate) is associated with low quantiles of feed intake of lactating sows, while accounting for other sow-specific information (vector covariate).…”
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