Precision Management Of Soil Nutrients To Identify Significant Factors Influencing Potato Tuber Yield

2021 ◽  
Author(s):  
Humna Khan ◽  
Bishnu Acharya ◽  
Travis Esau ◽  
Aitazaz Farooque ◽  
Farhat Abbas ◽  
...  
Keyword(s):  
Potato Tuber ◽  
Soil Nutrients ◽  
Tuber Yield ◽  
Factors Influencing ◽  
Precision Management ◽  
Significant Factors

Identification of Significant Factors Affecting Potato Tuber Yield for Precision Management of Soil Nutrients

2021 ◽  
Vol 37 (3) ◽  
pp. 535-545
Author(s):  
Humna Khan ◽  
Travis Esau ◽  
Aitazaz A. Farooque ◽  
Farhat Abbas ◽  
Qamar U. Zaman ◽  
...  
Keyword(s):  
Potato Tuber ◽  
Soil Nutrients ◽  
Tuber Yield ◽  
Factors Affecting ◽  
Precision Management ◽  
Significant Factors

scholarly journals Identification of Significant Factors Affecting Potato Tuber Yield for Precision Management of Soil Nutrients

2020 ◽  
Author(s):  
Humna Khan ◽  
Bishnu Acharya ◽  
Travis Esau ◽  
Aitazaz Farooque ◽  
Farhat Abbas ◽  
...  
Keyword(s):  
Potato Tuber ◽  
Soil Nutrients ◽  
Tuber Yield ◽  
Factors Affecting ◽  
Precision Management ◽  
Significant Factors

Trust in Artificial Intelligence: Meta-Analytic Findings

2021 ◽  
pp. 001872082110139
Author(s):  
Alexandra D. Kaplan ◽  
Theresa T. Kessler ◽  
J. Christopher Brill ◽  
P. A. Hancock
Keyword(s):  
Artificial Intelligence ◽  
Empirical Research ◽  
Meta Analysis ◽  
Automated Vehicles ◽  
Factors Influencing ◽  
Moderating Factors ◽  
Shared Context ◽  
Build Systems ◽  
Significant Factors

Objective The present meta-analysis sought to determine significant factors that predict trust in artificial intelligence (AI). Such factors were divided into those relating to (a) the human trustor, (b) the AI trustee, and (c) the shared context of their interaction. Background There are many factors influencing trust in robots, automation, and technology in general, and there have been several meta-analytic attempts to understand the antecedents of trust in these areas. However, no targeted meta-analysis has been performed examining the antecedents of trust in AI. Method Data from 65 articles examined the three predicted categories, as well as the subcategories of human characteristics and abilities, AI performance and attributes, and contextual tasking. Lastly, four common uses for AI (i.e., chatbots, robots, automated vehicles, and nonembodied, plain algorithms) were examined as further potential moderating factors. Results Results showed that all of the examined categories were significant predictors of trust in AI as well as many individual antecedents such as AI reliability and anthropomorphism, among many others. Conclusion Overall, the results of this meta-analysis determined several factors that influence trust, including some that have no bearing on AI performance. Additionally, we highlight the areas where there is currently no empirical research. Application Findings from this analysis will allow designers to build systems that elicit higher or lower levels of trust, as they require.

scholarly journals Factors Affecting Secondary Traumatic Stress of Nurses Caring for COVID-19 Patients in South Korea

2021 ◽  
Vol 18 (13) ◽  
pp. 6843
Author(s):  
Mee Sun Lee ◽  
Sujin Shin ◽  
Eunmin Hong
Keyword(s):  
Social Support ◽  
Regression Analysis ◽  
Traumatic Stress ◽  
Secondary Traumatic Stress ◽  
Direct Care ◽  
Hierarchical Regression ◽  
Factors Affecting ◽  
Factors Influencing ◽  
Affect Patient Care ◽  
Significant Factors

The secondary traumatic stress (STS) of nurses caring for COVID-19 patients is expected to be high, and it can adversely affect patient care. The purpose of this study was to examine the degree of STS of nurses caring for COVID-19 patients, and we identified various factors that influence STS. This study followed a descriptive design. The data of 136 nurses who had provided direct care to COVID-19 patients from 5 September to 26 September 2020 were collected online. Hierarchical regression analysis was conducted to identify the factors influencing STS. Participants experienced moderate levels of STS. The regression model of Model 1 was statistically significant (F = 6.21, p < 0.001), and the significant factors influencing STS were the duration of care for patients with COVID-19 for more than 30 days (β = 0.28, p < 0.001) and working in an undesignated COVID-19 hospital (β = 0.21, p = 0.038). In Model 2, the factor influencing STS was the support of a friend in the category of social support (β = −0.21, p = 0.039). The nurses caring for COVID-19 patients are experiencing a persistent and moderate level of STS. This study can be used as basic data to treat and prevent STS.

scholarly journals Individual Condensation Trails in Aircraft Trajectory Optimization

2019 ◽  
Vol 11 (21) ◽  
pp. 6082 ◽  
Author(s):  
Judith Rosenow ◽  
Hartmut Fricke
Keyword(s):  
Global Warming ◽  
Trajectory Optimization ◽  
Wind Shear ◽  
Impact Factors ◽  
Factors Influencing ◽  
Order Of Magnitude ◽  
Aircraft Trajectory ◽  
The Impact ◽  
Arbitrary Trajectory ◽  
Significant Factors

Contrails are one of the driving contributors to global warming, induced by aviation. The quantification of the impact of contrails on global warming is nontrivial and requires further in-depth investigation. In detail, condensation trails might even change the algebraic sign between a cooling and a warming effect in an order of magnitude, which is comparable to the impact of aviation-emitted carbon dioxides and nitrogen oxides. This implies the necessity to granularly consider the environmental impact of condensation trails in single-trajectory optimization tools. The intent of this study is the elaboration of all significant factors influencing on the net effect of single condensation trails. Possible simplifications will be proposed for a consideration in single-trajectory optimization tools. Finally, the effects of the most important impact factors, such as latitude, time of the year, and time of the day, wind shear, and atmospheric turbulence as well as their consideration in a multi-criteria trajectory optimization tool are exemplified. The results can be used for an arbitrary trajectory optimization tool with environmental optimization intents.

The effects of the growth-regulating compounds methyl decanoate and 2, 3, 5-triiodobenzoic acid on potato tuber size

1976 ◽  
Vol 86 (2) ◽  
pp. 251-255
Author(s):  
D. C. E. Wurr
Keyword(s):  
Potato Tuber ◽  
Size Distribution ◽  
Tuber Yield ◽  
Potato Crop ◽  
Total Yield ◽  
Tuber Size ◽  
Tuber Initiation ◽  
Triiodobenzoic Acid ◽  
Tuber Size Distribution ◽  
Methyl Decanoate

SummaryApplication of methyl decanoate to a potato crop about the time of tuber initiation reduced the total yield and the yield of tubers in the grade 2·5–5·5 cm though neither of these reductions were significant. However, application of 2,3,5-triiodobenzoic acid increased the yield of tubers 2·5–5·5 cm by up to 20% while having no significant effect on total tuber yield. This change in the tuber size distribution was due to a more even partition of photosynthate between tubers and not to an increase in the total number of tubers.

scholarly journals Significant factors influencing the accuracy of sputum cytology for primary lung cancers.

1984 ◽  
Vol 23 (4) ◽  
pp. 598-603 ◽  
Author(s):  
Toshiro KAWAI ◽  
Kou KANEKO ◽  
Seiichi KOBAYASHI ◽  
Sachiko KUBONO ◽  
Eriko KATSUKAWA ◽  
...  
Keyword(s):  
Sputum Cytology ◽  
Lung Cancers ◽  
Factors Influencing ◽  
Significant Factors

scholarly journals Impact of Income and Different Generation Cohorts on Nursery Products and Landscaping Project Spending

2013 ◽  
Vol 45 (1) ◽  
pp. 65-77 ◽  
Author(s):  
Lu Jin ◽  
Michael K. Wohlgenant ◽  
Charles D. Safley
Keyword(s):  
Household Income ◽  
The Elderly ◽  
Bedding Plants ◽  
Income Elasticities ◽  
Factors Influencing ◽  
Generation Cohorts ◽  
Dependent Variables ◽  
Consumer Survey Data ◽  
Significant Factors ◽  
Budget Expenditure

Socioeconomic factors influencing consumer demand for nursery products and landscape projects were investigated using consumer survey data collected from North Carolina in 2008. Tobit models were estimated for censored dependent variables, budget expenditure shares on nursery products, and landscape spending. The most significant factors influencing the share of income spent on nursery products were age and household income. The elderly and baby boomers tend to spend less on bedding plants, perennials, and outdoor hardscapes than Generations X and Y. The income elasticities suggest that the amount spent on outdoor living projects is sensitive to changes in household income, whereas spending in vegetable plants and chemicals is less responsive to income.

Sign in / Sign up

Export Citation Format

Share Document