Wuhan Municipal Engineering Design & Research Institute

A novel corrugated steel-reinforced concrete pipe that enhances electromagnetic shielding performance compared to the conventional reinforced concrete power pipes is developed and presented in this paper. In order to investigate the pipe’s behaviour under jacking and service conditions, the critical axial compression and flexural moment distributions were represented by two separate flat segments of a circular pipe cross-section, respectively. A total of six column specimens were designed for axial compression testing, while another four beam specimens were prepared for four-point bending tests to examine the bending behaviour. Prior to testing, all specimens were subjected to standard curing, and the material properties of steel and concrete were determined via standard tests. The load versus deformation curves of column specimens, the moment versus deflection curves of beam specimens, and the corresponding failure modes were obtained from the tested specimens. It was revealed that the load-carrying capacities of the corrugated steel-reinforced concrete cross-sections were comparable to those of the conventional reinforced concrete counterparts. Advanced finite element (FE) models incorporating the mechanical properties of encased corrugated steel plates (CSPs) and the damage development of concrete were developed and were validated against the experimental failure modes and load-carrying capacities. Based on both experimental and numerical results, the load-carrying capacity of corrugated steel-reinforced concrete cross-sections was evaluated by referring to Chinese standard GB/T 11836 and American standard ASTM C76. The experimental and numerical finding can pave the way for further research and applications of this novel type of corrugated steel-reinforced concrete pipe.

Glioblastoma (GBM) is the most prevalent type of malignant primary brain tumor. Preclinical research serves a key role in investigating the development and mechanism of GBM tumor. However, the dynamic and non‑invasive evaluation of tumors in animals faces challenges, such as the limited sensitivity of clinical instruments and insufficient spatial resolution for mouse brain tumors. The present study aimed to establish an in vivo mouse GBM model and evaluate the model using high resolution small animal positron emission tomography‑computed tomography (PET‑CT) and magnetic resonance imaging (MRI). Metabolism was compared between the normal brain and tumor tissue by using 1H‑magnetic resonance spectroscopy (1H‑MRS). T2‑weighted imaging (T2WI) MRI detected the tumor in the brain 7 days after injection of GL261 cells, with tumor sizes of 1.263, 4.917 and 13.85 mm3 on days 7, 14 and 21, respectively. 1H‑MRS demonstrated that the levels of tissue metabolites such as lactate and total choline increased, while those representing neurological function of the brain such as total N‑acetylaspartate decreased in tumor compared with the normal brain tissues. PET‑CT imaging confirmed the tumor detected by MRI. At 6‑120 min post 18F‑fluorodeoxyglucose (FDG) administration, the standard uptake value (SUV) in tumor tissue gradually increased, while the SUV value in normal brain tissue gradually decreased. SUV in the liver and kidneys decreased, while SUV in the bladder increased in a time‑dependent manner. Pharmacokinetic analysis showed that the distribution of FDG in brain and tumor tissue conformed to a two‑tissue compartment model. This model consists of a plasma compartment and two tissue compartments representing free FDG and phosphorylated FDG within brain or tumor tissue. The model parameters are defined as follows: Fractional blood volume (vB)=3.6%, k1 (forward transport rate)=1.844, k2 (reverse transport rate)=3.844 and k3 (phosphorylation rate)=0.280 in brain and vB=2.3%, k1=0.797, k2=2.722 and k3=0.319 in tumor tissue, respectively. The tumors observed by MRI and PET‑CT imaging were ultimately confirmed through morphological and pathological analysis. Compared with normal brain tissue, glioma tissue exhibited significantly elevated glucose transporter type 1 protein levels. In conclusion, the model was confirmed by high‑resolution small animal PET‑CT and MRI, as well as morphological and pathological approaches.

ABSTRACT The CaAl 2 O 4 –CaAl 4 O 7 (CA–CA 2 ) tailing slag was adopted to replace CaCO 3 , synthesizing calcium hexaluminate (CA 6 ) ceramics through reactive sintering with Al 2 O 3 . Digital image correlation (DIC) technology was employed to monitor volumetric effects in real time, while phases and morphology were combined to elucidate the densification mechanism. The results demonstrate that reducing intermediate reactions while modifying the crystal structure of CA 6 through doping can significantly enhance densification. Thermal expansion, in situ CA, CA 2 , and CA 6 formation sequentially dominated the volumetric expansion. Using CA–CA 2 tailing slag as the calcium source reduced the expansion caused by CA and CA 2 formation. The trace TiO 2 and MgO impurities inherently present in the CA–CA 2 tailing slag dissolved into CA 6 lattice, promoting a morphological transition from plate‐like to equiaxed grains, thereby further enhancing densification. Dense CA 6 ceramics with an apparent porosity of 2.4% and bulk density of 3.39 g·cm −3 were successfully prepared via one‐step sintering at 1700°C and performed good alkali corrosion resistance.

Meningeal lymphatic vessels (mLVs) contribute to brain immune surveillance; however, their structural and functional alterations in brain metastasis remain incompletely defined. Here, we optimized a magnetic resonance imaging protocol to quantitatively analyze mLV structure and drainage function. Using murine lung adenocarcinoma brain metastasis models and clinical imaging data, we reveal significant mLV disruption surrounding the superior sagittal sinus (SSS), which results in impaired drainage to deep cervical lymph nodes (dCLNs). Among ten cervical lymph-node levels assessed clinically, level IIA lymph nodes most accurately reflect mLV drainage efficiency. Functionally, mLV ablation compromised intrathecal chemotherapy efficacy in mice, while clinically, greater mLV structural and functional impairment correlates with disease progression in lung adenocarcinoma brain metastasis. Collectively, our findings demonstrate that lung adenocarcinoma brain metastases disrupt SSS-adjacent mLVs (mLVs-SSS), which potentially facilitates metastatic progression through compromised immune surveillance.

Recently, large vision-language models (VLMs) have made marked strides in vision-language capabilities through visual instruction tuning, showing great promise in the field of remote sensing image interpretation. However, existing remote sensing vision-language models (RSVLMs) often fall short in capturing the complex characteristics of remote sensing scenes. While some recent RSVLMs, such as EarthGPT and LHRSBot, have started to incorporate multiscale features, they typically adopt relatively shallow fusion strategies and lack tightly integrated mechanisms for fine-grained vision-language alignment. In this paper, we present Aquila, an advanced vision-language foundation model designed to enable richer visual feature representation and more precise vision-language feature alignment for remote sensing images. Our approach introduces a learnable hierarchical spatial feature integration (SFI) module that supports high-resolution image inputs and aggregates multi-scale visual features, allowing for the detailed representation of complex visual information. Additionally, the SFI module is repeatedly integrated into the layers of the large language model (LLM) to achieve deep vision-language feature alignment, without compromising the model’s performance in natural language processing tasks. These innovations significantly improve the model’s ability to learn from image–text data. We validate the effectiveness of Aquila through extensive quantitative experiments and qualitative analyses, demonstrating its superior performance.