EXPLORING SPATIO-TEMPORAL HETEROGENEITY AND INTER-DOMAIN ECOLOGICAL NETWORKS OF BIOLOGICAL COMMUNITY IN A MARINE RANCHING HABITAT: IMPLICATIONS FOR FISHERY RESOURCES CONSERVATION

Exploring spatio-temporal heterogeneity and inter-domain ecological networks of biological community in a marine ranching habitat: Implications for fishery resources conservation

Habitat changes in marine ranching can cause variations in biological resources and community structure.However, the complex inter-domain ecological network (IDEN) in this particular habitat are not well understood.Thus, we employed field surveys and multivariate statistical analyses to explore the spatio-temporal heterogeneity and the IDEN of Snea

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The role of gallery forests in maintaining Phlebotominae populations: potential Leishmania spp. vectors in the Brazilian savanna

BACKGROUND Knowledge on synanthropic phlebotomines and their natural infection by lolasalinas.com Leishmania is necessary for the identification of potential areas for leishmaniasis occurrence.OBJECTIVE To analyse the occurrence of Phlebotominae in gallery forests and household units (HUs) in the city of Palmas and to determine the rate of natural

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Sliding Mode Controller with Disturbance Observer for Quadcopters; Experiments with Dynamic Disturbances and in Turbulent Indoor Space

moondrop quarks In this study, a sliding mode surface controller (SMC) designed for a quadcopter is experimentally tested.The SMC was combined with disturbance observers in six degrees of freedom of the quadcopter to effectively reject external disturbances.While respecting stability conditions all control parameters were automatically initialized

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A Collision Avoidance Method Based on Deep Reinforcement Learning

This paper 2006 nissan altima radio set out to investigate the usefulness of solving collision avoidance problems with the help of deep reinforcement learning in an unknown environment, especially in compact spaces, such as a narrow corridor.This research aims to determine whether a deep reinforcement learning-based collision avoidance method is su

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