3D Trajectory Design for UAV-Assisted Oblique Image Acquisition

In this correspondence, we consider a new unmanned aerial vehicle (UAV)-assisted oblique image acquisition system where a UAV is dispatched to take images of multiple ground targets (GTs). To study the three-dimensional (3D) UAV trajectory design for image acquisition, we first propose a novel UAV-assisted oblique photography model, which characterizes the image resolution with respect to the UAV's 3D image-taking location. Then, we formulate a 3D UAV trajectory optimization problem to minimize the UAV's traveling distance subject to the image resolution constraints. The formulated problem is shown to be equivalent to a modified 3D traveling salesman problem with neighbourhoods, which is NP-hard in general. To tackle this difficult problem, we propose an iterative algorithm to obtain a high-quality suboptimal solution efficiently, by alternately optimizing the UAV's 3D image-taking waypoints and its visiting order for the GTs. Numerical results show that the proposed algorithm significantly reduces the UAV's traveling distance as compared to various benchmark schemes, while meeting the image resolution requirement

Noema formIng Cluster survEy (NICE): Discovery of a starbursting galaxy group with a radio-luminous core at z=3.95

The study of distant galaxy groups and clusters at the peak epoch of star formation is limited by the lack of a statistically and homogeneously selected and spectroscopically confirmed sample. Recent discoveries of concentrated starburst activities in cluster cores have opened a new window to hunt for these structures based on their integrated IR luminosities. Hereby we carry out the large NOEMA (NOrthern Extended Millimeter Array) program targeting a statistical sample of infrared-luminous sources associated with overdensities of massive galaxies at z>2, the Noema formIng Cluster survEy (NICE). We present the first result from the ongoing NICE survey, a compact group at z=3.95 in the Lockman Hole field (LH-SBC3), confirmed via four massive (M_star>10^10.5M_sun) galaxies detected in CO(4-3) and [CI](1-0) lines. The four CO-detected members of LH-SBC3 are distributed over a 180 kpc physical scale, and the entire structure has an estimated halo mass of ~10^13Msun and total star formation rate (SFR) of ~4000Msun/yr. In addition, the most massive galaxy hosts a radio-loud AGN with L_1.4GHz, rest = 3.0*10^25W/Hz. The discovery of LH-SBC3 demonstrates the feasibility of our method to efficiently identify high-z compact groups or forming cluster cores. The existence of these starbursting cluster cores up to z~4 provides critical insights into the mass assembly history of the central massive galaxies in clusters.Comment: 7 pages, 7 figures, submitted to A&

ALS Point Cloud Semantic Segmentation Based on Graph Convolution and Transformer With Elevation Attention

Semantic segmentation of airborne point clouds is crucial for 3D scene reconstruction and remote sensing in surveying applications. Current deep learning methods for point clouds primarily focus on effectively aggregating local neighborhood information. However, they often overlook the fusion of global context information and elevation features, which are vital for airborne point clouds. In this study, we propose Dense-LGEANet, a novel network with dense connected architecture and multiscale feature supervision based on our designed LGEA module. The key component of our LGEA module is the combination of the graph convolution block and the transformer block with elevation attention. It can effectively fuse local neighborhood information and global context information to improve the accuracy of semantic segmentation of airborne point cloud. Moreover, the designed dense connected network architecture can enhance the feature extraction capability for point cloud objects at different scales by facilitating interactions between multiple up-sampling and down-sampling layers. We have conducted multiple experiments on the public point cloud dataset. Experimental results show that our method can achieve an mIoU of 58.5% and an mF1 of 72.0% on the ISPRS Vaihingen 3D dataset, while an mIoU of 67.2% and an mF1 of 78.3% on the LASDU dataset. It demonstrates the superior performance of our network and the effectiveness of the proposed feature enhancement module and network architecture

An Automatic Concrete Crack-Detection Method Fusing Point Clouds and Images Based on Improved Otsu’s Algorithm

Cracks are one of the main distresses that occur on concrete surfaces. Traditional methods for detecting cracks based on two-dimensional (2D) images can be hampered by stains, shadows, and other artifacts, while various three-dimensional (3D) crack-detection techniques, using point clouds, are less affected in this regard but are limited by the measurement accuracy of the 3D laser scanner. In this study, we propose an automatic crack-detection method that fuses 3D point clouds and 2D images based on an improved Otsu algorithm, which consists of the following four major procedures. First, a high-precision registration of a depth image projected from 3D point clouds and 2D images is performed. Second, pixel-level image fusion is performed, which fuses the depth and gray information. Third, a rough crack image is obtained from the fusion image using the improved Otsu method. Finally, the connected domain labeling and morphological methods are used to finely extract the cracks. Experimentally, the proposed method was tested at multiple scales and with various types of concrete crack. The results demonstrate that the proposed method can achieve an average precision of 89.0%, recall of 84.8%, and F1 score of 86.7%, performing significantly better than the single image (average F1 score of 67.6%) and single point cloud (average F1 score of 76.0%) methods. Accordingly, the proposed method has high detection accuracy and universality, indicating its wide potential application as an automatic method for concrete-crack detection

Cyclodextrin-Based Metal-Organic Frameworks (CD-MOFs) in Pharmaceutics and Biomedicine

Metal-organic frameworks (MOFs) show promising application in biomedicine and pharmaceutics owing to their extraordinarily high surface area, tunable pore size, and adjustable internal surface properties. However, MOFs are prepared from non-renewable or toxic materials, which limit their real-world applications. Cyclodextrins (CDs) are a typical natural and biodegradable cyclic oligosaccharide and are primarily used to enhance the aqueous solubility, safety, and bioavailability of drugs by virtue of its low toxicity and highly flexible structure, offering a peculiar ability to form CD/drug inclusions. A sophisticated strategy where CD is deployed as a ligand to form an assembly of cyclodextrin-based MOFs (CD-MOFs) may overcome real-world application drawbacks of MOFs. CD-MOFs incorporate the porous features of MOFs and the encapsulation capability of CD for drug molecules, leading to outstanding properties when compared with traditional hybrid materials. This review focuses on the inclusion technology and drug delivery properties associated with CD-MOFs. In addition, synthetic strategies and currently developed uses of CD-MOFs are highlighted as well. Also, perspectives and future challenges in this rapidly developing research area are discussed

Recycling preparative isolation of six bicyclol active metabolites from SD rat urine using macroporous resin, offline 2D LPLC/HPLC, and prep-HPLC combined with pharmacodynamic evaluation of two active metabolites

In our previous study, several bicyclol (BIC) metabolites were found to possess higher solubility, security, and efficacy than the parent drug. However, further research can’t be conducted without monomeric metabolites. In the current study, a highly efficient preparative approach for six BIC-active metabolites from Sprague-Dawley (SD) rat urine was developed. First, 1000 mL of urine was purified and concentrated to 50 mL using microporous resin. Second, middle chromatogram isolated (MCI) GEL®CHP20P adsorbent was used to create a low-pressure liquid chromatography (LPLC) column, which was combined with high-performance liquid chromatography (HPLC) to build an offline 2D system to visualize the separation process. Samples were segmented into 25 tubes and merged into three fractions. Then, recycling preparative HPLC was applied to the monomeric preparation to improve the efficiency. The prepared metabolites possessed high purity (greater than98%), and were verified by nuclear magnetic resonance (NMR). Finally, an isoniazid (INH)-induced liver injury zebrafish model was established to evaluate the efficiency of the BIC, M7, and M8 metabolites. The M7 metabolite exhibited a higher efficiency than BIC in histopathology, gene expression, and aminotransferase levels. Consequently, this study provided a strategy that integrating modern analytical techniques to prepare metabolites for discovering high value candidate compounds from biological metabolism