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Rubble-pile asteroids may be the type of near-Earth object most likely to threaten Earth in a future collision event. Small-scale impact experiments and numerical simulations for large-scale impacts were conducted to clarify the size ratio of the boulder/projectile diameter effects on ejecta size–velocity distribution. A series of small-scale impact cratering experiments were performed on porous gypsum–basalt targets at velocities of 2.3 to 5.5 km·s−1. Three successive ejection processes were observed by high-speed and ultra-high-speed cameras. The momentum transfer coefficient and cratering size were measured. A three-dimensional numerical model reflecting the random distribution of the interior boulders of the rubble-pile structure asteroid is established. The size ratio (length to diameter) of the boulder size inside the asteroid to the projectile diameter changed from 0.25 to 1.7. We conducted a smoothed particle hydrodynamics numerical simulation in the AUTODYN software to study the boulder size effect on the ejecta size–velocity distribution. Simulation results suggest that the microscopic porosity on regolith affects the propagation of shock waves and reduces the velocity of ejecta. Experiments and numerical simulation results suggest that both excavation flow and spalling ejection mechanism can eject boulders (0.12–0.72 m) out of the rubble-pile asteroid. These experiments and simulation results help us select the potential impact site in a planetary defense scenario and reduce deflection risk. are comprised primarily of boulders of a range of sizes. [ABSTRACT FROM AUTHOR]

Background: Microsatellite instability-high (MSI-H) is a unique genomic status in many cancers. However, its role in the genomic features and immunotherapy in cholangiocarcinoma (CCA) is unclear. This study aimed to systematically investigate the genomic characterization and immunotherapy efficacy of MSI-H patients with CCA. Methods: We enrolled 887 patients with CCA in this study. Tumor samples were collected for next-generation sequencing. Differences in genomic alterations between the MSI-H and microsatellite stability (MSS) groups were analyzed. We also investigated the survival of PD-1 inhibitor-based immunotherapy between two groups of 139 patients with advanced CCA. Results: Differential genetic alterations between the MSI-H and MSS groups included mutations in ARID1A, ACVR2A, TGFBR2, KMT2D, RNF43, and PBRM1 which were enriched in MSI-H groups. Patients with an MSI-H status have a significantly higher tumor mutation burden (TMB) (median 41.7 vs. 3.1 muts/Mb, P < 0.001) and more positive programmed death ligand 1 (PD-L1) expression (37.5% vs. 11.9%, P < 0.001) than those with an MSS status. Among patients receiving PD-1 inhibitor-based therapy, those with MSI-H had a longer median overall survival (OS, hazard ratio (HR) = 0.17, P = 0.001) and progression-free survival (PFS, HR = 0.14, P < 0.001) than patients with MSS. Integrating MSI-H and PD-L1 expression status (combined positive score ≥ 5) could distinguish the efficacy of immunotherapy. Conclusions: MSI-H status was associated with a higher TMB value and more positive PD-L1 expression in CCA tumors. Moreover, in patients with advanced CCA who received PD-1 inhibitor-based immunotherapy, MSI-H and positive PD-L1 expression were associated with improved both OS and PFS. Trial registration: This study was registered on ClinicalTrials.gov on 07/01/2017 (NCT03892577). [ABSTRACT FROM AUTHOR]

Effective vehicle detection plays a crucial role in various applications in cities, including traffic management, urban planning, vehicle transport, and surveillance systems. However, existing vehicle detection methods suffer from low recognition accuracy, high computational costs, and excessive parameters. To address these challenges, this paper proposed a high-precision and lightweight detector along with a new dataset for construction-related vehicles. The dataset comprises 8425 images across 13 different categories of vehicles. The detector was based on a modified version of the You Only Look Once (YOLOv4) algorithm. DenseNet was utilized as the backbone to optimize feature transmission and reuse, thereby improving detection accuracy and reducing computational costs. Additionally, the detector employed depth-wise separable convolutions to optimize the model structure, specifically focusing on the lightweight neck and head components. Furthermore, H-swish was used to enhance non-linear feature extraction. The experimental results demonstrated that the proposed detector achieves a mean average precision (mAP) of 96.95% on the provided dataset, signifying a 4.03% improvement over the original YOLOv4. The computational cost and parameter count of the detector were 26.09GFLops and 16.08 MB, respectively. The proposed detector not only achieves lower computational costs but also provides higher detection when compared to YOLOv4 and other state-of-the-art detectors. [ABSTRACT FROM AUTHOR]

Ge2Sb2Te5 (GST) alloy has the ability to quickly transform between the amorphous phase and the crystalline phase; therefore, it can be used in non-volatile phase change storage. First-principles molecular dynamics was used to simulate the reversible phase change process of GST alloy, namely, amorphization and crystallization. The amorphous state was obtained by melting (∼3000 K) and cooling, and the amorphization process was characterized by the pair correlation function and bond angle distribution. The bond angles of amorphous GST were mainly distributed around 60°, 90°, and 110°, indicating the existence of octahedral and tetrahedral bonding configurations. The crystalline state was obtained by annealing at a temperature of 600 K, and the crystallization process was characterized by the pair correlation function, component disordered number, bond angle distribution, and dynamic atomic structure change. The results show that the bond angle of GST in crystallization is mainly distributed around 90°, and the structure mainly contains four-membered rings formed by Ge–Te and Sb–Te. By analyzing the optical properties, with the growth of crystallization time, the absorption coefficient, reflectivity, and conductivity of GST were significantly improved. [ABSTRACT FROM AUTHOR]

Illegal construction poses a safety hazard to both cities and people and affects the social stability and long-term stability of the country. Therefore, it is important to detect illegal buildings as early as possible. However, current illegal building detection methods generally suffer from either detection cycles or low detection accuracies. To solve these challenges, this study adopts an unusual method that detects illegal building objects to prevent illegal building behavior. A detection model, CRMNet, which is based on the anchor-free detection model CenterNet, and dataset for illegal building objects are proposed. ResNet50 is selected as the backbone for extracting futures after weighing the computational cost and detection accuracy. Furthermore, Mish, a new activation function, is used to improve the identification accuracy of illegal building objects. Experimental results show that the mean average precision (mAP) of the proposed detector on the illegal building object dataset reached 88.16%, which is higher than that of other popular object detection methods. Additionally, in contrast to mainstream target detection methods, the proposed detection method has fewer parameters and a higher detection accuracy, which can be better applied to mobile devices and smart devices. [ABSTRACT FROM AUTHOR]