Calcium carbonate derived from scallop shells (SCC) represents a sustainable mineral filler for asphalt applications, offering environmental benefits through the valorisation of marine by-products generated by aquaculture and seafood-processing activities in Peru and other coastal countries. Hydrated lime (HL) is widely used to enhance adhesion and moisture resistance in asphalt mixtures; however, in Peru its acquisition and commercialisation are subject to administrative controls due to its regulated status. In this study, SCC was obtained through a physical processing route and evaluated against commercially available HL at the asphalt mastic (binder–filler) scale. Both fillers were characterised using Fourier-transform infrared spectroscopy (FTIR), thermogravimetric analysis (TGA), scanning electron microscopy (SEM), X-ray fluorescence (XRF), and X-ray diffraction (XRD). FTIR and TGA confirmed the chemical stability and thermal resistance of SCC under typical asphalt production temperatures, while SEM revealed compact granular particles favourable for binder–filler interaction within the asphalt mastic. The higher alkalinity of HL was associated with stronger chemical affinity, favouring adhesion and moisture resistance, whereas SCC exhibited high purity and a stable calcite structure, providing a physically compatible and thermally stable filler phase. Rheological characterisation of asphalt mastics using multiple stress creep recovery (MSCR) and Linear Amplitude Sweep (LAS) tests showed that SCC produced lower non-recoverable compliance (Jnr) and higher elastic recovery (R), indicating improved resistance to permanent deformation, while HL exhibited superior fatigue performance under cyclic loading. Overall, this study establishes a binder- and mastic-level predictive basis for assessing the mechanical behaviour of SCC-filled systems within a circular economy framework.
To investigate the effect of half-time cooling via single arm immersion in carbonated water (CO2WI) during intermittent exercise in the heat (34.63 ± 0.49 °C, 54.2 ± 1.8
We present Franca (pronounced Fran-ka): free one; the first fully open-source (data, code, weights) vision foundation model that matches and in many cases surpasses the performance of state-of-the-art proprietary models, e.g., DINOv2, CLIP, SigLIPv2, etc. Our approach is grounded in a transparent training pipeline inspired by Web-SSL and uses publicly available data: ImageNet-21K and a subset of ReLAION-2B. Beyond model release, we tackle critical limitations in SSL clustering methods. While modern models rely on assigning image features to large codebooks via clustering algorithms like Sinkhorn-Knopp, they fail to account for the inherent ambiguity in clustering semantics. To address this, we introduce a parameter-efficient, multi-head clustering projector based on nested Matryoshka representations. This design progressively refines features into increasingly fine-grained clusters without increasing the model size, enabling both performance and memory efficiency. Additionally, we propose a novel positional disentanglement strategy that explicitly removes positional biases from dense representations, thereby improving the encoding of semantic content. This leads to consistent gains on several downstream benchmarks, demonstrating the utility of cleaner feature spaces. Our contributions establish a new standard for transparent, high-performance vision models and open a path toward more reproducible and generalizable foundation models for the broader AI community.
3D Scene Graphs (3DSGs) have emerged as a powerful representation for spatial AI by combining geometric grounding with semantic and relational abstractions of the environment. Their expressiveness has made them relevant to a broad range of problems in robotics and computer vision, including manipulation, navigation, task planning, scene understanding, and many others. However, the field remains fragmented: different communities adopt distinct formulations, construction pipelines, and evaluation protocols, making it difficult to compare methods, identify common assumptions, and assess remaining challenges for robust real-world deployment. This survey provides a unified and critical review of 3DSGs, with particular emphasis on open challenges and future directions. We first formalize 3DSGs under a common definition and analyze the principal modeling choices that characterize existing formulations, including node and edge attributes, hierarchical structure, dynamic scene representations, and affordance-aware extensions. We then review how 3DSGs are built from raw sensory observations, discussing the most common terminologies, conventions, and techniques. Finally, we examine downstream applications and evaluation strategies, from intrinsic graph quality to task-level performance. To support the community, we also provide a dedicated website that organizes and extends the surveyed content, accessible at https://3dscenegraphs.com/.
Detecting small aerial targets (SATs) from long-range LiDAR is challenging because point density changes dramatically with motion: fast flights produce ultra-sparse returns, while hovering or slow motion yields dense local clusters, breaking fixed-voxel and static-threshold assumptions in standard 3D detectors and trackers. We introduce A3PRL, an RL-driven adaptive perception framework that closes the loop between LiDAR sensing and tracking. A3PRL builds on a sparsity-aware proposal stage with Temporal Dispersion Signatures and velocity-change cues, and deploys a lightweight 5D policy that jointly adjusts voxel resolution, detection sensitivity, and association gating based on purely label-free statistics summarizing spatio–temporal sparsity, foreground acceptance, and tracking continuity. The policy is trained with privileged supervision from ground-truth trajectories to shape a reward that balances geometric accuracy, temporal stability, and regularized acceptance, but runs fully label-free at test time. On the public MMAUD benchmark, training on V1 and evaluating on unseen V2/V3 domains, A3PRL reduces 3D localization error by about 19\% compared to its non-RL counterpart and consistently outperforms LiDAR-only and multimodal baselines under both day and night conditions. We further show that the same policy transfers to an in-house LiDAR–RTK setup and a public multi-LiDAR SAT dataset with heterogeneous scan patterns, where it maintains accurate trajectories and stable tracks under varying sparsity, while adding less than 2 ms per frame on a 10 Hz LiDAR budget.
