Low-Resolution License Plate Recognition (LRLPR) remains a challenging problem in real-world surveillance scenarios, where long capture distances, compression artifacts, and adverse imaging conditions can severely degrade license plate legibility. To promote progress in this area, we organized the ICPR 2026 Competition on Low-Resolution License Plate Recognition, the first competition specifically dedicated to LRLPR using real low-quality data collected under operationally relevant conditions. The competition was based on the LRLPR-26 dataset, which comprises 20,000 training tracks and 3,000 test tracks; each training track contains five low-resolution and five high-resolution images of the same license plate. Notably, a total of 269 teams from 41 countries registered for the competition, and 99 teams submitted valid entries in the Blind Test Phase. The winning team achieved a Recognition Rate of 82.13 https://icpr26lrlpr.github.io/
The rapid electrification of the automotive industry has accelerated the demand for high-energy-density lithium-ion batteries, establishing nickel-rich layered oxides like LiNi0.8Co0.1Mn0.1O2 (NCM811) as a dominant cathode chemistry. While hydrometallurgical recycling using organic acids offers an eco-friendly alternative to inorganic processes, its industrial adoption is often hindered by inherently slow reaction kinetics. This study presents a process intensification strategy for the rapid recovery of valuable metals from NCM811 using a biodegradable lactic acid and hydrogen peroxide (H2O2) system. Unlike previous studies that focused solely on final yields, the kinetic trade-off between reduction power and the "gas blocking effect" was investigated. It was found that while H2O2is essential for reducing insoluble Co3+/Ni3+/Mn4+, an excessive dosage (3.0 vol%) generates vigorous oxygen evolution that physically blocks the solid-liquid interface, reducing Co leaching efficiency from 88.6% (at 2.0 vol%) to 79.5%. Kinetic analysis using the Avrami model confirmed a surface reaction-controlled mechanism with high activation energies (75-95 kJ/mol). Based on this mechanistic insight, the process was intensified by optimizing the solid-to-liquid (S/L) ratio to 10 g/L. This adjustment was not merely for dilution but to maximize the "space-time yield" of the reactor. Under the optimized conditions (90 degrees C, 2.0 vol% H2O2, S/L 10 g/L), a remarkable Co leaching efficiency of 96.6% was achieved within only 20 min. These findings demonstrate that overcoming kinetic barriers through precise reductant control and process intensification can make organic acid leaching competitively fast for industrial application.
We provide explicit parametrisations of all Darboux transforms of Delaunay surfaces. Using the Darboux transformation on a multiple cover, we obtain this way new closed CMC surfaces with dihedral symmetry. These can be used to construct closed same-lobed CMC multibubbletons by applying Bianchi permutability.
Street layout has a significant effect on accessibility and intelligibility, which ultimately affects navigation and movement efficiency. While previous research has examined planned and unplanned street patterns, most studies focus on single-scale analyses or isolated typologies, limiting understanding of how hybrid networks function across multiple spatial levels. Addressing this gap, this study investigates the effects of hybrid planned and organically evolved street layouts on spatial accessibility in Mandalay, Myanmar. The research employs space syntax analysis to assess the citywide, township-level, and micro-scale networks through measures of angular integration, choice, axial connectivity, and intelligibility. Using the Four-Point Star Model to identify Mandalay’s distinct spatial features, a global accessibility assessment compares it to 50 other cities. The results show that grid-based layouts with central townships exhibit the highest integration and connectivity, while organic and fragmented networks, particularly in Amarapura, reduce spatial coherence and accessibility. Micro-scale analysis indicates that hybrid layouts with cul-de-sacs and distorted grids can improve accessibility when they connect effectively with secondary roads. By analysing street networks across multiple spatial scales, this research presents significant implications for efficient accessibility and transport planning in mixed-pattern cities.
Training robots in real-world is costly and time-consuming. Consequently, recent research has increasingly used simulator-based virtual environments for robot training. 3D Gaussian Splatting, which can generate fast and photorealistic 3D scenes from multi-view images, is particularly suitable for constructing such virtual environments. However, due to the inherent characteristics of splats, artifacts such as floaters and noise frequently arise, it is unsuitable to make the raw representation for simulators that rely on physics engines and collision detection. To address these limitations, we propose a pipeline composed of YOLO-based floor segmentation, planar estimation, and mesh reconstruction. The proposed method reliably identifies and flattens the floor region in the 3D Gaussian Splatting model and converts only the non-floor regions into meshes. This approach effectively reduces surface irregularities and collision errors that commonly occur in conventional gaussian-to-mesh conversion methods. When we deployed in a Unity-based simulation environment, the reconstructed mesh significantly reduced floor-related noise and improved the stability of mobile robot navigation compared to the baseline.
