Showing 121 - 140 results of 21,342 for search '(( significant ((i decrease) OR (a decrease)) ) OR ( significant decrease decrease ))', query time: 0.61s Refine Results
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    HDECO: A method for Decreasing energy and cost by using virtual machine migration by considering hybrid parameters by Arash GhorbanniaDelavar (22563696)

    Published 2025
    “…<h2>Summary</h2><p dir="ltr">This research introduces <b>HDECO</b> (Hybrid Decreasing Energy and Cost Optimization) — a method designed to reduce both energy consumption and execution cost in cloud datacenters through intelligent virtual machine migration. …”
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    Evogliptin attenuates the phenotypic switch of VSMCs during CER treatment by decreasing the osteogenesis-associated genes <i>in-vitro.</i> by Razia Rashid Rahil (22772495)

    Published 2025
    “…(B, C) Summarized bar graph showed EVO significantly decreased calcium deposition and calcium content in CER treated P<sub>i</sub>-induced VSMCs. …”
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    Mechlorethamine gel causes epithelium thinning, epithelium-stroma separation, and decreased total stroma cell count. by Ana M. Sandoval-Castellanos (11611315)

    Published 2025
    “…<p>A) Epithelium thickness decreased, and B) the percentage of epithelium-stroma separation increased after NM exposure. …”
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    Decreased childhood asthma hospitalizations linked to hotter, drier climate with lower wind speed in drylands by Klézio Silva Monte (20579829)

    Published 2025
    “…Monthly trends in hospitalizations and climatic variables were calculated. A generalized additive model analyzed the association between these trends, and the Mann-Kendall test determined if trends were increasing, decreasing, or not significant. …”
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    Supplementary Material for: Longitudinal Decrease in Left Ventricular Size with Age: Impact on Mortality and Cardiovascular Hospitalization by figshare admin karger (2628495)

    Published 2025
    “…Sensitivity analysis using annual LVEDD change (>1mm/year) demonstrated a significant association with mortality (HR 1.45, 95% CI 1.26-1.66, p<0.001) and the combined endpoint of death/cardiovascular hospitalization (HR 1.26, 95% CI 1.12-1.41, p<0.001). …”
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    Presentation 1_Prehospital tranexamic acid decreases early mortality in trauma patients: a systematic review and meta-analysis.pdf by Yi Li (1144)

    Published 2025
    “…Compared to the control group, the prehospital TXA group exhibited a significant reduction in 24-h mortality with an OR of 0.72 and a 95% CI of 0.54–0.94 (p = 0.02), while no statistically significant difference in the incidence of venous thromboembolism (VTE; OR: 1.14, 95% CI: 0.98–1.33, p = 0.09). …”
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    Table_1_FMT intervention decreases urine 5-HIAA levels: a randomized double-blind controlled study.DOCX by Lihong Wang (14991)

    Published 2024
    “…However, in the placebo group, GSRS, CARS, and SRS scores showed no significant changes, while ABC scores decreased from 72 to 58.75 (p = 0.034). …”
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    Data from: Colony losses of stingless bees increase in agricultural areas, but decrease in forested areas by Malena Sibaja Leyton (18400983)

    Published 2025
    “…</p><p><br></p><p dir="ltr">#METADATA</p><p dir="ltr">#'data.frame': 472 obs. of 28 variables:</p><p dir="ltr"> #$ ID: Factor variable; a unique identity for the response to the survey</p><p dir="ltr"> #$ Year: Factor variable; six factors available (2016, 2017, 2018, 2019, 2020, 2021) representing the year for the response to the survey</p><p dir="ltr"> #$ N_dead_annual: Numeric variable; representing the number of colonies annually lost</p><p dir="ltr">#$ N_alive_annual: Numeric variable; representing the number of colonies annually alive</p><p dir="ltr"> #$ N_dead_dry: Numeric variable; representing the number of colonies lost during the dry season</p><p dir="ltr">#$ N_alive_dry: Numeric variable; representing the number of colonies alive during the dry season</p><p dir="ltr"> #$ N_dead_rainy: Numeric variable; representing the number of colonies lost during the rainy season</p><p dir="ltr">#$ N_alive_rainy: Numeric variable; representing the number of colonies alive during the rainy season</p><p dir="ltr"> #$ Education: Factor variable; four factors are available ("Self-taught","Learned from another melip","Intro training","Formal tech training"), representing the training level in meliponiculture</p><p dir="ltr"> #$ Operation_Size: Numeric variable; representing the number of colonies managed by the participant (in n)</p><p dir="ltr"> #$ propAgri: Numeric variable; representing the percentage of agricultural area surrounding the meliponary (in %)</p><p dir="ltr"> #$ propForest: Numeric variable; representing the percentage of forested area surrounding the meliponary (in %)</p><p dir="ltr">#$ temp.avg_annual: Numeric variable; representing the average annual temperature (in ºC)</p><p dir="ltr">#$ precip_annual_sum: Numeric variable; representing the total accumulated precipitation (in mm)</p><p dir="ltr">#$ precip_Oct_March_sum: Numeric variable; representing the total accumulated precipitation between October to March (in mm)</p><p dir="ltr">#$ precip_Apri_Sept_sum: Numeric variable; representing the total accumulated precipitation between April to September (in mm)</p><p dir="ltr">#$ temp.avg_Oct_March: Numeric variable; representing the total accumulated precipitation between October to March (in ºC)</p><p dir="ltr">#$ temp.avg_Apri_Sept: Numeric variable; representing the total accumulated precipitation between April to September (in ºC)</p><p dir="ltr"> #$ Importance_dead: Factor variable; three factors are available Normal","High","Very high"), representing the perception of the significance of annual colony losses</p><p dir="ltr"> #$ Climatic_environmental: Binary variable; representing if the participant considered climatic and environmental problems as a potential driver (1) or not (0) of their annual colony losses</p><p dir="ltr"> #$ Contamination: Binary variable; representing if the participant considered contamination problems as a potential driver (1) or not (0) of their annual colony losses</p><p dir="ltr"> #$ Nutritional: Binary variable; representing if the participant considered nutritional problems as a potential driver (1) or not (0) of their annual colony losses</p><p dir="ltr">#$ Sanitary: Binary variable; representing if the participant considered sanitary problems as a potential driver (1) or not (0) of their annual colony losses</p><p dir="ltr">#$ Queen: Binary variable; representing if the participant considered queen problems as a potential driver (1) or not (0) of their annual colony losses</p><p dir="ltr">#$ Time: Binary variable; representing if the participant considered time problems as a potential driver (1) or not (0) of their annual colony losses</p><p dir="ltr">#$ Economic: Binary variable; representing if the participant considered economic problems as a potential driver (1) or not (0) of their annual colony losses</p><p dir="ltr">#$ Attacks: Binary variable; representing if the participant considered time attacks as a potential driver (1) or not (0) of their annual colony losses</p><p dir="ltr">#$ Swarming: Binary variable; representing if the participant considered swarming problems as a potential driver (1) or not (0) of their annual colony losses</p><p><br></p>…”