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As urban greenery improves and the ecological environment is continuously optimized, road facilities are also impacted to varying degrees. For example, as vegetation grows, it causes varying degrees of obstruction to the lighting facilities on the roads. This article is based on vehicle-mounted LiDAR data and focuses on the point cloud data characteristics of different objects. Using appropriate modeling techniques, it accurately models road surfaces, green belts, streetlights, and other objects. On the Lumion platform, this system creates a 3D visualization of road scenes and examines the interplay between objects and lighting space, analyzing lit areas. Leveraging the precise 3D spatial relationships found in point clouds, it determines the effective illumination area on the ground from streetlights after object obstruction, comparing it to the theoretical illumination area. This not only visualizes the road scene but also quantifies the lighting obstruction rate. Furthermore, it assesses the lighting conditions in road scenes based on illuminance distribution, offering scientific insights and suggestions for enhancing road lighting. [ABSTRACT FROM AUTHOR]

Mobile LiDAR technology is a powerful tool that accurately captures spatial information about typical static objects in road scenes. However, the precise extraction and classification of these objects pose persistent technical challenges. In this paper, we employ a deep learning approach to tackle the point cloud classification problem. Despite the popularity of the PointNet++ network for direct point cloud processing, it encounters issues related to insufficient feature learning and low accuracy. To address these limitations, we introduce a novel layer-wise optimization network, LO-Net. Initially, LO-Net utilizes the set abstraction module from PointNet++ to extract initial local features. It further enhances these features through the edge convolution capabilities of GraphConv and optimizes them using the "Unite_module" for semantic enhancement. Finally, it employs a point cloud spatial pyramid joint pooling module, developed by the authors, for the multiscale pooling of final low-level local features. Combining three layers of local features, LO-Net sends them to the fully connected layer for accurate point cloud classification. Considering real-world scenarios, road scene data often consist of incomplete point cloud data due to factors such as occlusion. In contrast, models in public datasets are typically more complete but may not accurately reflect real-world conditions. To bridge this gap, we transformed road point cloud data collected by mobile LiDAR into a dataset suitable for network training. This dataset encompasses nine common road scene features; hence, we named it the Road9 dataset and conducted classification research based on this dataset. The experimental analysis demonstrates that the proposed algorithm model yielded favorable results on the public datasets ModelNet40, ModelNet10, and the Sydney Urban Objects Dataset, achieving accuracies of 91.2%, 94.2%, and 79.5%, respectively. On the custom road scene dataset, Road9, the algorithm model proposed in this paper demonstrated outstanding classification performance, achieving a classification accuracy of 98.5%. [ABSTRACT FROM AUTHOR]

Droplet injection methods are widely used in the applications of microelectronic manufacturing, biological engineering, and 3-D printing. This work presents a new jet dispenser driven by a single piezoelectric stack that enables the capability of drop-on-demand patterning under a high working frequency (500 Hz). Due to the special designs of the jet dispenser, a broad range of liquids, whose viscosities span more than four orders of magnitude (21–665 320 cps), can be jetted. Moreover, a coupled Coulomb damping physical model is proposed, which includes an electromechanical and a dynamic model. Both the Coulomb and the fluid–solid damping are considered in the dynamic model. In order to validate the results obtained by using MATLAB/Simulink, the experiments were carried out. The injection performance of this jet dispenser has been tested by employing a self-made jetting platform. The minimum volume of a jetting droplet is about 14.4 nL with liquid wax. The error of volume uniformity among droplets does not exceed 8%, and the error of angle trajectory is about 0.17°. Furthermore, the versatility of the jet dispenser is demonstrated by printing liquid lubricant, food, glue, silver past, and a ceramic slurry in predefined patterns. [ABSTRACT FROM AUTHOR]

Exosome-based liquid biopsy has shown great potential in the diagnosis and prognosis of tumors, thus developing a convenient and reliable detection method is crucial for its clinical application. Herein, we construct a separable visual aptasensor for the sensitive detection of exosomes by using double spherical nucleic acids (DSNAs) and terminal deoxynucleotidyl transferase (TdT). In this system, CD63 aptamer-modified magnetic beads (MNPs-Apt CD63 , the first spherical nucleic acids) could efficiently capture exosomes, and avoid the aggregation of gold nanoparticles (GNPs) induced by complex components in the reaction system through magnetic separation. As the second spherical nucleic acids, abundant nucleolin aptamer-modified GNPs (GNPs-Apt nucleolin) are used to specially identify exosomes. Then, those nucleolin aptamers can further produce long polyT sequences by the extension of TdT and induce polyA release from the GNPs-polyA (polyA-attached gold nanoparticles) by hybridization, thus resulting in the salt-induced aggregation of GNPs to produce color change for visual analysis of exosomes. Based on the above ingenious designed strategies, this method achieved a limit of detection (LOD) as low as 45 particles/μL, and showed accurate visual analysis for the clinical samples. We expect this method to provide a new opportunity for the exosomes-based point-of-care diagnosis of tumors. • Double spherical nucleic acids and aptamer endow the aptasensor with outstanding anti-interference ability and performance. • Terminal Deoxynucleotidyl Transferase assisted spherical nucleic acids enable the aptasensor with satisfactory sensitivity. • This colorimetric aptasensor show significant clinical application value by point-of-care test. • Nucleolin positive exosomes as biomarker show great potential for liquid biopsy of leukemia. [ABSTRACT FROM AUTHOR]