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We introduce Arenite, a novel physics-based approach for modeling sandstone structures. The key insight of our work is that simulating a combination of stress and multi-factor erosion enables the generation of a wide variety of sandstone structures observed in nature. We isolate the key shape-forming phenomena: multi-physics fabric interlocking, wind and fluvial erosion, and particle-based deposition processes. Complex 3D structures such as arches, alcoves, hoodoos, or buttes can be achieved by creating simple 3D structures with user-painted erodable areas and vegetation and running the simulation. We demonstrate the algorithm on a wide variety of structures, and our GPU-based implementation achieves the simulation in less than 5 minutes on a desktop computer for our most complex example. 

We propose a novel approach for the computational modeling of lignified tissues, such as those found in tree branches and timber. We leverage a stateof the-art strand-based representation for tree form, which we extend to describe biophysical processes at short and long time scales. Simulations at short time scales enable us to model different breaking patterns due to branch bending, twisting, and breaking. On long timescales, our method enables the simulation of realistic branch shapes under the influence of plausible biophysical processes, such as the development of compression and tension wood. We specifically focus on computationally fast simulations of woody material, enabling the interactive exploration of branches and wood breaking. By leveraging Cosserat rod physics, our method enables the generation of a wide variety of breaking patterns. We showcase the capabilities of our method by performing and visualizing numerous experiments. 

Accurate tree species identification through bark characteristics is essential for effective forest management, but traditionally requires extensive expertise. This study leverages artificial intelligence (AI), specifically the EfficientNet-B3 convolutional neural network, to enhance AI-based tree bark identification, focusing on northern red oak (Quercus rubra), hackberry (Celtis occidentalis), and bitternut hickory (Carya cordiformis) using the CentralBark dataset. We investigated three environmental variables—time of day (lighting conditions), bark moisture content (wet or dry), and cardinal direction of observation—to identify sources of classification inaccuracies. Results revealed that bark moisture significantly reduced accuracy by 8.19% in wet conditions (89.32% dry vs. 81.13% wet). In comparison, the time of day had a significant impact on hackberry (95.56% evening) and northern red oak (80.80% afternoon), with notable chi-squared associations (p < 0.05). Cardinal direction had minimal effect (4.72% variation). Bitternut hickory detection consistently underperformed (26.76%), highlighting morphological challenges. These findings underscore the need for targeted dataset augmentation with wet and afternoon images, alongside preprocessing techniques like illumination normalization, to improve model robustness. Enhanced AI tools will streamline forest inventories, support biodiversity monitoring, and bolster conservation in dynamic forest ecosystems. 

We introduce RGB2Point, an unposed single-view RGB image to a 3D point cloud generation based on Transformer. RGB2Point takes an input image of an object and generates a dense 3D point cloud. Contrary to prior works based on CNN layers and diffusion-denoising approaches, we use pre-trained Transformer layers that are fast and generate high-quality point clouds with consistent quality over available categories. Our generated point clouds demonstrate high quality on a real-world dataset, as evidenced by improved Chamfer distance (51.15%) and Earth Mover’s distance (36.17%) metrics compared to the current state-of the-art. Additionally, our approach shows a better quality on a synthetic dataset, achieving better Chamfer distance (39.26%), Earth Mover’s distance (26.95%), and F-score (47.16%). Moreover, our method produces 63.1% more consistent high-quality results across various object categories compared to prior works. Furthermore, RGB2Point is computationally efficient, requiring only 2.3GB of VRAM to reconstruct a 3D point cloud from a single RGB image, and our implementation generates the results 15,133× faster than a SOTA diffusion-based model. 

Differences in canopy architecture play a role in determining both the light and water use efficiency. Canopy architecture is determined by several component traits, including leaf length, width, number, angle, and phyllotaxy. Phyllotaxy may be among the most difficult of the leaf canopy traits to measure accurately across large numbers of individual plants. As a result, in simulations of the leaf canopies of grain crops such as maize and sorghum, this trait is frequently approximated as alternating 180 angles between sequential leaves. We explore the feasibility of extracting direct measurements of the phyllotaxy of sequential leaves from 3D reconstructions of individual sorghum plants generated from 2D calibrated images and test the assumption of consistently alternating phyllotaxy across a diverse set of sorghum genotypes. Using a voxel-carving-based approach, we generate 3D reconstructions from multiple calibrated 2D images of 366 sorghum plants representing 236 sorghum genotypes from the sorghum association panel. The correlation between automated and manual measurements of phyllotaxy is only modestly lower than the correlation between manual measurements of phyllotaxy generated by two different individuals. Automated phyllotaxy measurements exhibited a repeatability of R2 ¼ 0.41 across imaging timepoints separated by a period of two days. A resampling based genome wide association study (GWAS) identified several putative genetic associations with lower-canopy phyllotaxy in sorghum. This study demonstrates the potential of 3D reconstruction to enable both quantitative genetic investigation and breeding for phyllotaxy in sorghum and other grain crops with similar lant architectures. 

The application of extended reality (XR) technology in education has been growing for the last two decades. XR offers immersive and interactive visualization experiences that can enhance learning by making it engaging. Recent technological advances have led to the availability of high-quality and affordable XR headsets. These advancements have spurred a wave of research focused on designing, implementing, and validating XR educational interventions. Limited literature focuses on the recent trends of XR within science, technology, engineering, and mathematics (STEM) education. Thus, this paper presents an umbrella review that explores the exploding field of XR and its transformative potential in STEM education. Using six online databases, the review zoomed in on 17 out of 1972 papers on XR for STEM education, published between 2020 and 2023, following the Preferred Reporting Items for Systematic reviews and Meta-Analyses (PRISMA) guidelines. The results highlighted the types of XR technology applied (i.e., virtual reality and augmented reality), the specific STEM disciplines involved, the focus of each study reviewed, and the major findings from recent reviews. Overall, the educational benefits of using XR technology in STEM education are apparent: XR boosts student motivation, facilitates learning engagement, and improves skills, for example. However, using XR in education still has challenges that must be addressed, such as the physical discomfort of the learner wearing the XR headset and technical glitches. Besides revealing trends of using XR in STEM education, this umbrella review encourages reflection on current practices and suggests ways to apply XR to STEM education effectively.