Learning of a Mimic Odor within Beehives Improves Pollination Service Efficiency in a Commercial Crop

2020 ◽  
Vol 30 (21) ◽  
pp. 4284-4290.e5 ◽  
Author(s):  
Walter M. Farina ◽  
Andrés Arenas ◽  
Paula C. Díaz ◽  
Cinthia Susic Martin ◽  
M. Cecilia Estravis Barcala
2021 ◽  
pp. 1-13
Author(s):  
Sun Jianzhu ◽  
Zhang Qingshan ◽  
Yu Yinyun

Multi-site selection is a hot research issue for equipment manufacturing enterprises. With the development of smart industry, equipment manufacturing enterprises have entered the era of personalized and small batch manufacturing. Enterprises want to better meet customer needs and win competition, they must carry out scientific factory planning and site selection, so as to ensure quick response to the market. Based on this, this paper proposes a two-stage location selection model. Firstly, the method uses fuzzy numbers to express the demand size of demand points. Secondly, the distance factor is used as a criterion to select the candidate manufacturing bases with sufficient available resources. Next, the location model of enterprise manufacturing base is established which the goal of maximizing service efficiency and the constraints of time, cost and demand. Finally, a random numerical example is used to simulate the model, and lingo is used to solve it.


1969 ◽  
Vol 14 (3) ◽  
pp. 484
Author(s):  
Robert B. Fetter ◽  
Martin S. Feldstein

2008 ◽  
Vol 48 (3) ◽  
pp. 296 ◽  
Author(s):  
C. J. Birch ◽  
G. McLean ◽  
A. Sawers

This paper reports on the use of APSIM – Maize for retrospective analysis of performance of a high input, high yielding maize crop and analysis of predicted performance of maize grown with high inputs over the long-term (>100 years) for specified scenarios of environmental conditions (temperature and radiation) and agronomic inputs (sowing date, plant population, nitrogen fertiliser and irrigation) at Boort, Victoria, Australia. It uses a high yielding (17 400 kg/ha dry grain, 20 500 kg/ha at 15% water) commercial crop grown in 2004–05 as the basis of the study. Yield for the agronomic and environmental conditions of 2004–05 was predicted accurately, giving confidence that the model could be used for the detailed analyses undertaken. The analysis showed that the yield achieved was close to that possible with the conditions and agronomic inputs of 2004–05. Sowing dates during 21 September to 26 October had little effect on predicted yield, except when combined with reduced temperature. Single year and long-term analyses concluded that a higher plant population (11 plants/m2) is needed to optimise yield, but that slightly lower N and irrigation inputs are appropriate for the plant population used commercially (8.4 plants/m2). Also, compared with changes in agronomic inputs increases in temperature and/or radiation had relatively minor effects, except that reduced temperature reduces predicted yield substantially. This study provides an approach for the use of models for both retrospective analysis of crop performance and assessment of long-term variability of crop yield under a wide range of agronomic and environmental conditions.


1965 ◽  
Vol 45 (4) ◽  
pp. 315-319
Author(s):  
W. A. Scott

A forced-warm-air system to prevent high humidity in Burley tobacco barns was built and tested. The system utilizes a small modified commercial crop dryer and an air-duct system constructed of light plywood and inflatable perforated polythene tubing. It effectively maintained the required humidity in wet weather. Building plans and operating instructions will be published separately.


2021 ◽  
Vol 17 (3) ◽  
pp. 50-62
Author(s):  
Ayodeji Samuel Makinde ◽  
Abayomi O. Agbeyangi ◽  
Wilson Nwankwo

Mobile number portability (MNP) across telecommunication networks entails the movement of a customer from one mobile service provider to another. This, often, is as a result of seeking better service delivery or personal choice. Churning prediction techniques seek to predict customers tending to churn and allow for improved customer sustenance campaigns and the cost therein through an improved service efficiency to customer. In this paper, MNP predicting model using integrated kernel logistic regression (integrated-KLR) is proposed. The Integrated-KLR is a combination of kernel logistic regression and expectation-maximization clustering which helps in proactively detecting potential customers before defection. The proposed approach was evaluated with five others, mostly used algorithms: SOM, MLP, Naïve Bayes, RF, J48. The proposed iKLR outperforms the other algorithms with ROC and PRC of 0.856 and 0.650, respectively.


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