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In the field of multimaterial optical fibers, the demand for high‐performance single‐crystal core glass‐clad multimaterial optical fibers is increasing. However, the applications of single‐crystal fibers are restricted by the complex fabrication processes and the slow growth of single‐crystal materials. Here, a two‐step method is demonstrated to achieve single‐crystal tellurium (Te) core fibers with high crystal quality over the length of the fiber. This method starts with the thermal drawing of a fiber preform into polycrystalline Te core multimaterial fibers (precursor fibers) that are long and mechanically stable. A 532‐nm continuous laser is then employed to recrystallize the Te core in the precursor fiber into a single crystal state along the entire length of the fiber. Experimental studies of these single‐crystal fibers demonstrate that the single‐crystal Te core fibers possess high transmittance (>90% at 2–10 μm) and high thermoelectric performance (ZT values from 0.03 to 0.13 at 300 K temperature). They are superior to previous reports and our previous work. This method works for fabricating various single‐crystal fibers and has important applications in the field of optical fibers, functional fibers, and their integrated devices. [ABSTRACT FROM AUTHOR]

It's a major public health problem of global concern that malignant gliomas tend to grow rapidly and infiltrate surrounding tissues. Accurate grading of the tumor can determine the degree of malignancy to formulate the best treatment plan, which can eliminate the tumor or limit widespread metastasis of the tumor, saving the patient's life and improving their prognosis. To more accurately predict the grading of gliomas, we proposed a novel method of combining the advantages of 2D and 3D Convolutional Neural Networks for tumor grading by multimodality on Magnetic Resonance Imaging. The core of the innovation lies in our combination of tumor 3D information extracted from multimodal data with those obtained from a 2D ResNet50 architecture. It solves both the lack of temporal-spatial information provided by 3D imaging in 2D convolutional neural networks and avoids more noise from too much information in 3D convolutional neural networks, which causes serious overfitting problems. Incorporating explicit tumor 3D information, such as tumor volume and surface area, enhances the grading model's performance and addresses the limitations of both approaches. By fusing information from multiple modalities, the model achieves a more precise and accurate characterization of tumors. The model I s trained and evaluated using two publicly available brain glioma datasets, achieving an AUC of 0.9684 on the validation set. The model's interpretability is enhanced through heatmaps, which highlight the tumor region. The proposed method holds promise for clinical application in tumor grading and contributes to the field of medical diagnostics for prediction., Competing Interests: Competing Interests: The authors have declared that no competing interest exists., (© The author(s).)