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The use of robots to map disaster-stricken environments can prevent rescuers from being harmed when exploring an unknown space. In addition, mapping a multi-robot environment can help these teams plan their actions with prior knowledge. The present work proposes the use of multiple unmanned aerial vehicles (UAVs) in the construction of a topological map inspired by the way that bees build their hives. A UAV can map a honeycomb only if it is adjacent to a known one. Different metrics to choose the honeycomb to be explored were applied. At the same time, as UAVs scan honeycomb adjacencies, RGB-D and thermal sensors capture other data types, and then generate a 3D view of the space and images of spaces where there may be fire spots, respectively. Simulations in different environments showed that the choice of metric and variation in the number of UAVs influence the number of performed displacements in the environment, consequently affecting exploration time and energy use.
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http://dx.doi.org/10.3390/s20030907 | DOI Listing |
Neuroimage Rep
September 2025
Arizona State University, Tempe, AZ, 85287, USA.
Non-intrusive neuroimaging technology offers fast and robust diagnostic tools for neuro-disorder disease diagnosis, such as Attention-Deficit/Hyperactivity Disorder (ADHD). Resting-state functional magnetic imaging (rs-fMRI) has been demonstrated to have great potential for such applications due to its unique capability and convenience in providing spatial-temporal brain imaging. One critical challenge of using rs-fMRI data is the high dimensionality for both spatial and temporal domains.
View Article and Find Full Text PDFbioRxiv
August 2025
Institute for Quantitative and Computational Biosciences, University of California, Los Angeles.
Manifold learning builds on the "manifold hypothesis," which posits that data in high-dimensional datasets are drawn from lower-dimensional manifolds. Current tools generate global embeddings of data, rather than the local maps used to define manifolds mathematically. These tools also cannot assess whether the manifold hypothesis holds true for a dataset.
View Article and Find Full Text PDFManifold learning builds on the "manifold hypothesis," which posits that data in high-dimensional datasets are drawn from lower-dimensional manifolds. Current tools generate global embeddings of data, rather than the local maps used to define manifolds mathematically. These tools also cannot assess whether the manifold hypothesis holds true for a dataset.
View Article and Find Full Text PDFLight Sci Appl
August 2025
NEST, CNR-Istituto Nanoscienze and Scuola Normale Superiore, Piazza San Silvestro 12, 56127, Pisa, Italy.
Collective oscillations of massless charge carriers in two-dimensional materials-Dirac plasmon polaritons (DPPs)-are of paramount importance for engineering nanophotonic devices with tunable optical response. However, tailoring the optical properties of DPPs in a nanomaterial is a very challenging task, particularly at terahertz (THz) frequencies, where the DPP momentum is more than one order of magnitude larger than that of the free-space photons, and DDP attenuation is high. Here, we conceive and demonstrate a strategy to tune the DPP dispersion in topological insulator metamaterials.
View Article and Find Full Text PDFSmall
August 2025
Institute of Chemistry, Martin Luther University Halle-Wittenberg, Kurt-Mothes-Straße 2, 06120, Halle, Germany.
Periodic tessellations of the 2D plane by nonsymmetric polygons are of particular interest for both the 2D Kelvin problem, which is critical to system linearity and elasticity, and reticular chemistry to diverse the topological varieties. Here, a new liquid crystalline honeycomb phase, representing a monohedral tiling by nonsymmetric hexagonal cells resulting from the soft self-assembly of nine consecutive end-to-end hydrogen-bonded p-terphenyl rods is reported. The new phase structure is characterized by POM, DSC as well as SAXS, with which the electron density map is reconstructed and discussed.
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