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Single-use plastics (SUPs) are used widely in cancer research laboratories. They are cheap, durable, and lightweight, and until now have been considered disposable items. This, however, contributes significantly to unsustainable waste production.

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Antimicrobial peptides (AMPs) are key components of innate immunity across all domains of life. Natural and synthetic AMPs are receiving renewed attention in efforts to combat the antimicrobial resistance (AMR) crisis and the loss of antibiotic efficacy. The gram-negative pathogen Pseudomonas aeruginosa is one of the most concerning infecting bacteria in AMR, particularly in people with cystic fibrosis (CF) where respiratory infections are difficult to eradicate and associated with increased morbidity and mortality.

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Research on leakage and diffusion behavior of hydrogen doped natural gas in integrated pipeline corridors based on data drive.

Sci Rep

January 2025

Northwest Sichuan Gas District of Southwest Oil and Gasfield Company, Jiangyou, 621700, China.

With the wide application of hydrogen-doped natural gas (HBNG) in end users, laying pipelines in urban, comprehensive pipe corridors has become increasingly common. However, the leakage and diffusion of hydrogen-doped natural gas in confined or semi-confined spaces (e.g.

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In a dilute two-dimensional electron gas, Coulomb interactions can stabilize the formation of a Wigner crystal. Although Wigner crystals are topologically trivial, it has been predicted that electrons in a partially filled band can break continuous translational symmetry and time-reversal symmetry spontaneously, resulting in a type of topological electron crystal known as an anomalous Hall crystal. Here we report signatures of a generalized version of the anomalous Hall crystal in twisted bilayer-trilayer graphene, whose formation is driven by the moiré potential.

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As a necessary part of intelligent control of a joint station, the automatic identification of abnormal conditions and automatic adjustment of operation schemes need to judge the running state of the system. In this paper, a combination of Particle Swarm Optimization (PSO) and Gray Wolf Optimizer (GWO) is proposed to optimize the Backpropagation Neural Network (BP) model (PSO-GWO-BP) and a pressure drop prediction model for the joint station export system is established using PSO-GWO-BP. Compared with the traditional hydraulic calculation modified (THCM) models and other machine learning algorithms, the PSO-GWO-BP model has significant advantages in prediction accuracy.

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