Results 61 to 70 of about 207,512 (363)

Oviposition Decisions by Indianmeal Moth [PDF]

open access: yes, 2018
The Indianmeal moth, Plodia interpunctella,is a pest in the United States of stored grains. The larvae feed on grains, grain products, dried fruits, nuts, cereals and a variety of processed food products.
Campbell, James, Pierce, Josie
core  

The Genetic Basis of Pheromone Evolution in Moths.

open access: yesAnnual Review of Entomology, 2016
Moth sexual pheromones are widely studied as a fine-tuned system of intraspecific sexual communication that reinforces interspecific reproductive isolation.
A. Groot, T. Dekker, D. Heckel
semanticscholar   +1 more source

AInsectID Version 1.1: An Insect Species Identification Software Based on the Transfer Learning of Deep Convolutional Neural Networks

open access: yesAdvanced Intelligent Discovery, EarlyView.
This paper describes the basis for AInsectID Version 1, a GUI‐operable open‐source insect species identification, color processing, and image analysis software. This paper discusses our methods of algorithmic development, coupled to rigorous machine training used to enable high levels of validation accuracy.
Haleema Sadia, Parvez Alam
wiley   +1 more source

Oviposition by the Banded Sunflower Moth, \u3ci\u3eCochylis Hospes\u3c/i\u3e (Lepidoptera: Cochylidae) in Response to \u3ci\u3eHelianthus Annuus\u3c/i\u3e Pollen [PDF]

open access: yes, 2017
Oviposition on an artificial substrate by the banded sunflower moth Cochylis hospes Walsingham was examined in response to sunflower pollen (Helianthus annuus) and sunflower pollen extract.
Barker, John F, Grugel, Sharon
core   +2 more sources

Advances and Challenges of Using the Sterile Insect Technique for the Management of Pest Lepidoptera

open access: yesInsects, 2019
Over the past 30 years, the sterile insect technique (SIT) has become a regular component of area-wide integrated pest management (AW-IPM) programs against several major agricultural pests and vectors of severe diseases.
František Marec, Marc J. B. Vreysen
doaj   +1 more source

Competition between BCS-pairing and "moth-eaten effect" in BEC-BCS crossover [PDF]

open access: yes, 2011
We study the change in condensation energy from a single pair of fermionic atoms to a large number of pairs interacting via the reduced BCS potential. We find that the energy-saving due to correlations decreases when the pair number increases because the number of empty states available for pairing gets smaller ("moth-eaten effect").
arxiv   +1 more source

Delineating Species with DNA Barcodes: A Case of Taxon Dependent Method Performance in Moths

open access: yesPLoS ONE, 2015
The accelerating loss of biodiversity has created a need for more effective ways to discover species. Novel algorithmic approaches for analyzing sequence data combined with rapidly expanding DNA barcode libraries provide a potential solution.
Mari Kekkonen   +4 more
semanticscholar   +1 more source

Machine Learning‐Based Wind Classification by Wing Deformation in Biomimetic Flapping Robots: Biomimetic Flexible Structures Improve Wind Sensing

open access: yesAdvanced Intelligent Systems, EarlyView.
Strain gauges are attached to biomimetic flapping wings to investigate how wing strain sensors detect wind directions. A convolutional neural network model for wind direction classification is developed through experiments. The results reveal that wind classification is possible with strain data of only 0.2 flapping cycles, and the use of biomimetic ...
Kenta Kubota, Hiroto Tanaka
wiley   +1 more source

Check List of Identified Lepidoptera Collected at Mud Lake State Nature Preserve, Williams County, Ohio [PDF]

open access: yes, 2018
A total of 696 species ofLepidoptera is reported from the Mud Lake State Nature Preserve, Williams County, Ohio. This preserve is only a few miles from both the Indiana and Michigan state borders.
Rings, Roy W
core   +3 more sources

Evolving Deep Neural Network by Customized Moth Flame Optimization Algorithm for Underwater Targets Recognition [PDF]

open access: yesarXiv, 2023
This chapter proposes using the Moth Flame Optimization (MFO) algorithm for finetuning a Deep Neural Network to recognize different underwater sonar datasets. Same as other models evolved by metaheuristic algorithms, premature convergence, trapping in local minima, and failure to converge in a reasonable time are three defects MFO confronts in solving ...
arxiv  

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