Landslide disasters have garnered significant attention due to their extensive devastating impact, leading to a growing emphasis on the prompt and precise identification and detection of landslides as a prominent area of research. Previous research has primarily relied on human–computer interactions and visual interpretation from remote sensing to identify landslides. However, these methods are time-consuming, labor-intensive, subjective, and have a low level of accuracy in extracting data. An essential task in deep learning, semantic segmentation, has been crucial to automated remote sensing image recognition tasks because of its end-to-end pixel-level classification capability. In this study, to mitigate the disadvantages of existing landslide detection methods, we propose a multiscale attention segment network (MsASNet) that acquires different scales of remote sensing image features, designs an encoder–decoder structure to strengthen the landslide boundary, and combines the channel attention mechanism to strengthen the feature extraction capability. The MsASNet model exhibited an average accuracy of 95.13% on the test set from Bijie’s landslide dataset, a mean accuracy of 91.45% on the test set from Chongqing’s landslide dataset, and a mean accuracy of 90.17% on the test set from Tianshui‘s landslide dataset, signifying its ability to extract landslide information efficiently and accurately in real time. Our proposed model may be used in efforts toward the prevention and control of geological disasters.

Lithium metal batteries (LMBs) are recognized to be crucial for secondary battery technology targeting electric vehicles and portable electronic devices. However, the undesirable growth of lithium dendrites would result in reduced capacity, short‐circuit, and overheating, seriously hindering the practical applications of LMBs. To address this issue, a neoteric lithiophilic interlayer on a commercial polypropylene separator is presented for the first time, which is constructed by amorphous CoB nanoparticles decorated reduced graphene oxide nanosheets (CoB@rGO). Density Functional Theory calculations and experimental analysis reveal remarkable lithiophilicity features for CoB@rGO and provide multiple Li deposition sites and improved electrolyte wettability, which facilitates the formation of durable solid electrolyte interphase (SEI), reduces side reactions, and improves Li⁺ flux regulation for long‐term cycling stability in LMBs. Taking advantage of these merits, the symmetric Li//Li cell with CoB@rGO/PP separator exhibits stable cycling for up to 1600 h at 1 mA cm⁻² with 1 mAh cm⁻². Employed with CoB@rGO separator, the Li//LiFePO₄ full cell with a high LiFePO₄ loading of 11 mg cm⁻² delivers a high initial specific capacity of 115.3 mAh g⁻¹ and a low decay rate of 0.08% per cycle after 200 cycles even at a high rate of 2C.

The generation of vector beams using metasurfaces is crucial for the manipulation of light fields and has significant application potential, ranging from classical physics to quantum science. This paper introduces a novel dielectric metasurface composed of quarter‐wave plate (QWP) meta‐atoms, known as a QWP metasurface, designed to generate focused vector beams (VBs) of Bell‐like states under right circularly polarized illumination. The propagation phase imparted on both the co‐ and cross‐polarized components of the output field constructs hyperbolic and helical phase profiles with topological charge l _p , whereas the Pancharatnam–Berry (PB) phase acts only on the cross‐polarized component to construct another helical phase profile with topological charge l _g . Thus, the co‐ and cross‐polarized components form two orthogonal vector vortex (VV) modes with topological charges l _p and l _p + l _g , respectively. When the parameter conditions are satisfied by matching the incident polarization chirality σ and topological charges l _p and l _g , the focused VBs of Bell‐like states are generated by simultaneously manipulating the two VV modes, in contrast to existing QWP metasurfaces. The polarization states of the generated VBs are manipulated using the initial orientation angle θ ₀ of the meta‐atom. Overall, this research provides an innovative strategy for metasurface design, enhancing the functionality of metasurface devices for a broader range of application scenarios.

Objectives: Descending necrotizing mediastinitis (DNM) is a serious and progressive infection involving the neck and chest and with high mortality if not treated quickly and properly. The aim of this study is to share our practices for managing this condition. Methods: We retrospectively evaluated 9 patients diagnosed with DNM in our hospital between January 2006 and October 2019. Age, gender, origin of infection, length of hospital stay, microorganisms present, type of surgical treatment, and clinical outcomes were reviewed. Results: All patients underwent surgery to drain neck and mediastinal secretions and collections. Three (33.3%) patients were treated with transcervical drainage alone, and 6 (66.7%) patients were treated with combined transcervical and transthoracic drainage. Reoperations were reported in 3 (33.3%) cases. The average length of hospital stay was 22.78 ± 10.05 days (range: 9-40 days). The average length of intensive care unit stay was 6.44 ± 10.10 days (range: 0-25 days). There were no in-hospital deaths, and all patients were discharged home with good outcomes. Conclusions: To improve the prognosis of DNM, we suggest early and adequate debridement of all affected areas along with the proper use of antibiotics. A multidisciplinary approach involving both cardiothoracic and ENT surgeons is also required.

Background: During X-ray computed tomography (CT) scans, the metal implants in the patient's body will produce severe artifacts, which reduce the image quality and interferes with the doctor's judgment. Therefore, it is necessary to develop an algorithm for removing metal artifacts in CT images and reconstructing high-quality images. Objective: In this article, we proposed a generative adversarial networks (GANs)-based metal artifact reduction algorithm for the image domain, Res-U-Net GANs. This method can effectively suppress noise and remove metal artifacts in CT images. Methods: Our new approach includes a generator and a discriminator. The generator contains several residual blocks, a U-Net structure and skip connections. And a weighted joint loss function is also used for training. These structures can reduce metal artifacts in images, improve image quality, and restore implant details. Results: We use SSIM, PSNR and RMSE to evaluate the performance of the proposed method. The mean SSIM, PSNR and RMSE of the testing set images are 0.977, 39.044 and 0.011, respectively. And the trained model which is compiled and encapsulated, also show excellent performance in processing clinical data sets, which can remove metal artifacts in clinical CT images. Conclusion: We consider that the proposed algorithm can remove metal artifacts in CT images and restore image details, which is very helpful for radiologists.