Bee Colony
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PLoS ONE ◽  
2021 ◽  
Vol 16 (10) ◽  
pp. e0258801
Rosanna N. Punko ◽  
Robert W. Currie ◽  
Medhat E. Nasr ◽  
Shelley E. Hoover

The epidemiology of Nosema spp. in honey bees, Apis mellifera, may be affected by winter conditions as cold temperatures and differing wintering methods (indoor and outdoor) provide varying levels of temperature stress and defecation flight opportunities. Across the Canadian Prairies, including Alberta, the length and severity of winter vary among geographic locations. This study investigates the seasonal pattern of Nosema abundance in two Alberta locations using indoor and outdoor wintering methods and its impact on bee population, survival, and commercial viability. This study found that N. ceranae had a distinct seasonal pattern in Alberta, with high spore abundance in spring, declining to low levels in the summer and fall. The results showed that fall Nosema monitoring might not be the best indicator of treatment needs or future colony health outcomes. There was no clear pattern for differences in N. ceranae abundance by location or wintering method. However, wintering method affected survival with colonies wintered indoors having lower mortality and more rapid spring population build-up than outdoor-wintered colonies. The results suggest that the existing Nosema threshold should be reinvestigated with wintering method in mind to provide more favorable outcomes for beekeepers. Average Nosema abundance in the spring was a significant predictor of end-of-study winter colony mortality, highlighting the importance of spring Nosema monitoring and treatments.

Materials ◽  
2021 ◽  
Vol 14 (21) ◽  
pp. 6373
Mahalingam Siva Kumar ◽  
Devaraj Rajamani ◽  
Emad Abouel Nasr ◽  
Esakki Balasubramanian ◽  
Hussein Mohamed ◽  

This paper focusses on a hybrid approach based on genetic algorithm (GA) and an adaptive neuro fuzzy inference system (ANFIS) for modeling the correlation between plasma arc cutting (PAC) parameters and the response characteristics of machined Monel 400 alloy sheets. PAC experiments are performed based on box-behnken design methodology by considering cutting speed, gas pressure, arc current, and stand-off distance as input parameters, and surface roughness (Ra), kerf width (kw), and micro hardness (mh) as response characteristics. GA is efficaciously utilized as the training algorithm to optimize the ANFIS parameters. The training, testing errors, and statistical validation parameter results indicated that the ANFIS learned by GA outperforms in the forecasting of PAC responses compared with the results of multiple linear regression models. Besides that, to obtain the optimal combination PAC parameters, multi-response optimization was performed using a trained ANFIS network coupled with an artificial bee colony algorithm (ABC). The superlative responses, such as Ra of 1.5387 µm, kw of 1.2034 mm, and mh of 176.08, are used to forecast the optimum cutting conditions, such as a cutting speed of 2330.39 mm/min, gas pressure of 3.84 bar, arc current of 45 A, and stand-off distance of 2.01 mm, respectively. Furthermore, the ABC predicted results are validated by conducting confirmatory experiments, and it was found that the error between the predicted and the actual results are lower than 6.38%, indicating the adoptability of the proposed ABC in optimizing real-world complex machining processes.

2021 ◽  
Mohamed Shams

Abstract This paper provides the field application of the bee colony optimization algorithm in assisting the history match of a real reservoir simulation model. Bee colony optimization algorithm is an optimization technique inspired by the natural optimization behavior shown by honeybees during searching for food. The way that honeybees search for food sources in the vicinity of their nest inspired computer science researchers to utilize and apply same principles to create optimization models and techniques. In this work the bee colony optimization mechanism is used as the optimization algorithm in the assisted the history matching workflow applied to a reservoir simulation model of WD-X field producing since 2004. The resultant history matched model is compared with with those obtained using one the most widely applied commercial AHM software tool. The results of this work indicate that using the bee colony algorithm as the optimization technique in the assisted history matching workflow provides noticeable enhancement in terms of match quality and time required to achieve a reasonable match.

Guohui Huang ◽  

The stock market is very volatile, so the change of the stock price is also widely concerned by investors. In this paper, a new stock price forecasting model based on Quantum Particle Swarm Optimization(QPSO) , Quantum Bee Colony Optimization Algorithm(QABC) and Quantum Fruit Fly Optimization Algorithm (QFOA) is proposed. The three methods all use BP neural network to adjust the parameters of particle swarm, bee colony and Drosophila to reach the optimal parameters. Taking the daily closing price of CITIC Securities and Tianfeng Securities, a large-scale and a small-scale securities company, as the object of empirical analysis, comparing the accuracy of the three methods in predicting stocks, it also analyzes whether the size of the company has an effect on the accuracy of the model. The results show that the prediction effect of qpso is the best, and the size of the company has some influence on the prediction effect.

Ecology ◽  
2021 ◽  
Rosemary L. Malfi ◽  
Elizabeth Crone ◽  
Maj Rundlöf ◽  
Neal M. Williams

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