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Robust edge states of periodic crystals with Dirac points fixed at the corners or centers of the Brillouin zones have drawn extensive attention. Recently, researchers have observed a special edge state associated with Dirac cones degenerated at the high symmetric boundaries of the first irreducible Brillouin zone. These nodal points, characterized by vortex structures in the momentum space, are attributed to the unavailable band crossing protected by mirror symmetry. By breaking the time reversal symmetry with intuitive rotations, valley-like states can be observed in a pair of inequivalent insulators. In this paper, an improved direct inverse design method is first applied to realize the valley-like states. Compared with the conventional strategy, the preparation of transition structures with degeneracy points is skipped. By introducing the quantitative gauge of mode inversion error, insulator pairs are directly obtained without manually tuning the structure with Dirac cone features.
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http://dx.doi.org/10.3390/ma15196697 | DOI Listing |
BMJ Open
September 2025
Department of Interventional Radiology, The First Hospital of China Medical University, Shenyang, Liaoning, China
Background: Advanced-stage hepatocellular carcinoma (HCC) with high tumour burden and portal vein tumour thrombus (PVTT) is usually associated with poor survival outcomes. Rapid tumour control usually benefits long-term outcomes, which could be hardly achieved by solely systematic targeted and immunotherapy in current guidelines. Hepatic arterial infusion chemotherapy (HAIC) is reported as an effective intervention for rapid decrease of tumour burden.
View Article and Find Full Text PDFSci Adv
September 2025
Department of Mechanics, School of Mechanical Engineering, Tianjin University, Tianjin 300350, China.
Acoustic tweezers leverage acoustic radiation forces for noncontact manipulation. One of the core bottlenecks in multidimensional manipulation is the lack of a systematic design methodology, which prevents the generation of an acoustic field that simultaneously meets the collaborative control requirements of multi-degree-of-freedom forces and torques, making it difficult to achieve precise control under conditions of stable suspension, high-frequency rotation, and complex spatial constraints. To address this challenge, we develop an end-to-end inverse design methodology for acoustic tweezers based on coding metasurfaces, establishing a dual-objective, dual-scale optimization paradigm.
View Article and Find Full Text PDFPLoS One
September 2025
The Institute of Port Information Digitalization, China Liaoning Port Group Co. Ltd., Dalian, Liaoning, China.
Background: Underwater environments face challenges with image degradation due to light absorption and scattering, resulting in blurring, reduced contrast, and color distortion. This significantly impacts underwater exploration and environmental monitoring, necessitating advanced algorithms for effective enhancement.
Objectives: The study aims to develop an innovative underwater image enhancement algorithm that integrates physical models with deep learning to improve visual quality and surpass existing methods in performance metrics.
JAMA Netw Open
September 2025
Oncostat U1018, Institut National de la Santé et de la Recherche Médicale (INSERM), Ligue Contre le Cancer, Paris-Saclay University, Villejuif, France.
Importance: Antibiotics, steroids, and proton pump inhibitors (PPIs) are suspected to decrease the efficacy of immunotherapy.
Objective: To explore the association of comedications with overall survival (OS) in patients with advanced non-small-cell lung cancer (NSCLC).
Design, Setting, And Participants: This nationwide retrospective cohort study used target trial emulations of patients newly diagnosed with NSCLC from January 2015 to December 2022, identified from the French national health care database.
J Chem Inf Model
September 2025
Songshan Lake Materials Laboratory, Dongguan 523808, PR China.
Large language models (LLMs) have demonstrated transformative potential for materials discovery in condensed matter systems, but their full utility requires both broader application scenarios and integration with ab initio crystal structure prediction (CSP), density functional theory (DFT) methods and domain knowledge to benefit future inverse material design. Here, we develop an integrated computational framework combining language model-guided materials screening with genetic algorithm (GA) and graph neural network (GNN)-based CSP methods to predict new photovoltaic material. This LLM + CSP + DFT approach successfully identifies a previously overlooked oxide material with unexpected photovoltaic potential.
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