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The linearized invariant-imbedding T-matrix method (LIITM) and linearized physical-geometric optics method (LPGOM) were applied on regular hexagonal prisms from small to large sizes to obtain the scattering properties and their partial derivatives. T-matrices and their derivatives from the LIITM are presented and discussed in the expansion order, where the minor diagonal elements are dominant. The simulation results of single-scattering properties and their corresponding linearization from both methods are compared. The mutual agreements can be treated as further verification of both linearized methods. Using extinction efficiency as the criterion, the LPGOM are convergent at the LIITM for the particle size parameter larger than 130 with a relative difference of less than 1%, with errors of about 3% and 5% for particle sizes of 50 and 30, respectively. The capability and convergence of the LIITM and LPGOM are discussed in detail based on linearized properties.
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http://dx.doi.org/10.1364/OE.473075 | DOI Listing |
Mol Divers
September 2025
Department of Pharmacognosy, College of Pharmacy, Prince Sattam Bin Abdulaziz University, 11942, Al Kharj, Saudi Arabia.
Cyclin-dependent kinase 20 (CDK20), also known as cell cycle-related kinase (CCRK), plays a pivotal role in hepatocellular carcinoma (HCC) progression by regulating β-catenin signaling and promoting uncontrolled proliferation. Despite its emerging significance, selective small-molecule inhibitors of CDK20 remain unexplored. In this study, a known CDK20 inhibitor, ISM042-2-048, was employed as a reference to retrieve structurally similar compounds from the PubChem database using an 85% similarity threshold.
View Article and Find Full Text PDFACS Appl Mater Interfaces
September 2025
Department of Materials Science and Engineering, Gwangju Institute of Science and Technology (GIST), Gwangju 61005, Republic of Korea.
Cyclic peptides (CPs) are versatile building blocks whose conformational constraints foster ordered supramolecular architectures with potential in biomedicine, nanoelectronics, and catalysis. Herein, we report the development of biomimetic antifreeze materials by conjugating CPs bearing ice-binding residues to 4-arm polyethylene glycol (PEG) via click chemistry. The concentration-dependent self-assembly of these CP-PEG conjugates induces programmable morphological transitions, forming nanotube networks above the critical aggregation concentration (CAC) and two-dimensional nanosheet networks near the CAC.
View Article and Find Full Text PDFProg Mol Biol Transl Sci
September 2025
Institute of Intelligent Machines, Chinese Academy of Science, Hefei, Anhui, P.R. China. Electronic address:
The convergence of artificial intelligence (AI) and wearable biosensors is revolutionizing personalized healthcare, enabling continuous monitoring, early detection of health issues, which enhances the efficiency of data processing and real-time decision-making. Multimodal Large Language Models (MLLMs) play a pivotal role in this ecosystem by offering advanced capabilities in analyzing complex health data, understanding nuanced health contexts, and generating tailored health recommendations instantaneously. This study provides insights into how machine learning, deep learning algorithms, and MLLM can work together to facilitate the analysis of physiologic data for real-time monitoring and early warning systems as well as complex decision support mechanisms.
View Article and Find Full Text PDFACS Nano
September 2025
Department of Electrical and Computer Engineering, Sungkyunkwan University, Suwon 16419, Republic of Korea.
Vagus nerve stimulation (VNS) is a promising therapy for neurological and inflammatory disorders across multiple organ systems. However, conventional rigid interfaces fail to accommodate dynamic mechanical environments, leading to mechanical mismatches, tissue irritation, and unstable long-term interfaces. Although soft neural interfaces address these limitations, maintaining mechanical durability and stable electrical performance remains challenging.
View Article and Find Full Text PDFJ Chem Phys
September 2025
Department of Chemistry, University of Rochester, Rochester, New York 14627, USA.
We introduce an efficient method, TTN-HEOM, for exactly calculating the open quantum dynamics for driven quantum systems interacting with highly structured bosonic baths by combining the tree tensor network (TTN) decomposition scheme with the bexcitonic generalization of the numerically exact hierarchical equations of motion (HEOM). The method yields a series of quantum master equations for all core tensors in the TTN that efficiently and accurately capture the open quantum dynamics for non-Markovian environments to all orders in the system-bath interaction. These master equations are constructed based on the time-dependent Dirac-Frenkel variational principle, which isolates the optimal dynamics for the core tensors given the TTN ansatz.
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