Ecologists trying to understand the value of habitat to animals must first describe the value of resources contained in the habitat to animals and, second, they must describe spatial variation in resource quality at a resolution relevant to individual animal foraging. We addressed these issues in a study of koalas (Phascolarctos cinereus) in a Eucalyptus woodland. We measured beneficial and deterrent chemical characteristics as well as the palatability of trees using a near-infrared spectroscopic model based on direct feeding experiments. Tree use by koalas was influenced by tree size and foliar quality but was also context-dependent: trees were more likely to be visited if they were surrounded by small, unpalatable trees or by large, palatable trees. Spatial autocorrelation analysis and several mapping approaches demonstrated that foliar quality is spatially structured in the woodland at a scale relevant to foraging decisions by koalas and that the spatial structure is an important component of habitat quality.
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http://dx.doi.org/10.1890/09-1714.1 | DOI Listing |
Sensors (Basel)
January 2025
Department of Computer Science, King AbdulAziz University, Jeddah 21589, Saudi Arabia.
Traffic flow prediction is a pivotal element in Intelligent Transportation Systems (ITSs) that provides significant opportunities for real-world applications. Capturing complex and dynamic spatio-temporal patterns within traffic data remains a significant challenge for traffic flow prediction. Different approaches to effectively modeling complex spatio-temporal correlations within traffic data have been proposed.
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Department of Mechanical Engineering, Politecnico di Milano, Via Giuseppe La Masa 1, 20156 Milan, Italy.
Radiofrequency ablation (RFA) is a minimally invasive procedure that utilizes localized heat to treat tumors by inducing localized tissue thermal damage. The present study aimed to evaluate the temperature evolution and spatial distribution, ablation size, and reproducibility of ablation zones in ex vivo liver, kidney, and lung using a commercial device, i.e.
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Key Laboratory of Concrete and Pre-Stressed Concrete Structures of the Ministry of Education, Southeast University, Nanjing 210096, China.
Monitoring existing cracks is a critical component of structural health monitoring in bridges, as temperature fluctuations significantly influence crack development. The study of the Huai'an Bridge indicated that concrete cracks predominantly occur near the central tower, primarily due to temperature variations between the inner and outer surfaces. This research aims to develop a deep learning model utilizing Long Short-Term Memory (LSTM) neural networks to predict crack depth based on the thermal variations experienced by the main tower.
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Laboratory of Ethnic Language Intelligent Analysis and Security Governance of MOE, Minzu University of China, Beijing 100081, China.
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View Article and Find Full Text PDFPlants (Basel)
January 2025
AirTech UAV Solutions Inc., Inverary, ON K0H 1X0, Canada.
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