1. Crystal habit analysis of LiFePO4 microparticles by AFM and first-principles calculations 

   Gradwohl, Kevin-P.; Benedek, Peter; Popov, Maxim; et al. (2022)

   CRYSTENGCOMM

   LiFePO4 (LFP) microparticles with a faceted crystal habit have been fabricated by hydrothermal synthesis. Three different surfactants were employed to control the crystal habits of the particles, which ranged from cubes over different diamond-shaped platelets to pyramids. Crystallographic models of the particles with the lowest matching Miller indices were determined based on the data acquired by atomic force microscopy (AFM) and gave ...

   Journal Article

2. Ways forward for Machine Learning to make useful global environmental datasets from legacy observations and measurements 

   Andriuzzi, Walter; Hengl, Tomislav; Routh, Devin; et al. (2022)

   NATURE COMMUNICATIONS

   Advances in geospatial and Machine Learning techniques for large datasets of georeferenced observations have made it possible to produce model-based global maps of ecological and environmental variables. However, the implementation of existing scientific methods (especially Machine Learning models) to produce accurate global maps is often complex. Tomislav Hengl (co-founder of OpenGeoHub foundation), Johan van den Hoogen (researcher at ...

   Other Journal Item

3. Carbon farming: integrate biodiversity metrics 

   Coutu, Sylvain; Becker-Reshef, Inbal; Pellissier, Loic (2022)

   Nature

   Other Publication

4. Methane Production by Facultative Anaerobic Wood-Rot Fungi via a New Halomethane-Dependent Pathway 

   Huang, Xin; Liu, Xuan; Xue, Yarong; et al. (2022)

   MICROBIOLOGY SPECTRUM

   Here, we demonstrate that wood-rot fungi produce methane anaerobically without the involvement of methanogenic archaea via a new, halomethane-dependent pathway. These findings of an anaerobic fungal methane formation pathway open another avenue in methane research and will further assist with current efforts in the identification of the processes involved and their ecological implications.

   Journal Article

5. OSLO: On-the-Sphere Learning for Omnidirectional Images and Its Application to 360-Degree Image Compression 

   Bidgoli, Navid Mahmoudian; Azevedo, Roberto G. de A.; Maugey, Thomas; et al. (2022)

   IEEE Transactions on Image Processing

   State-of-the-art 2D image compression schemes rely on the power of convolutional neural networks (CNNs). Although CNNs offer promising perspectives for 2D image compression, extending such models to omnidirectional images is not straightforward. First, omnidirectional images have specific spatial and statistical properties that can not be fully captured by current CNN models. Second, basic mathematical operations composing a CNN architecture, ...

   Journal Article

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