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Background: Vibrio parahaemolyticus is a marine bacterium causing seafood-associated gastrointestinal illness in humans and acute hepatopancreatic necrosis disease (AHPND) in shrimp. Bacteriophages have emerged as promising biocontrol agents against V. parahaemolyticus.

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Computational tools, particularly electromagnetic (EM) solvers, are now commonplace in antenna design. While ensuring reliability, EM simulations are time-consuming, leading to high costs associated with EM-driven procedures like parametric optimization or statistical design. Various techniques have been developed to address this issue, with surrogate modeling methods garnering particular attention due to their potential advantages.

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With the rapid development of Internet of Things (IoT) technology, embedded devices in various computer vision scenarios can realize real-time target detection and recognition tasks, such as intelligent manufacturing, automatic driving, smart home, and so on. YOLOv8, as an advanced deep learning model in the field of target detection, has attracted much attention for its excellent detection speed, high precision, and multi-task processing capability. However, since IoT embedded devices typically own limited computing resources, direct deployment of YOLOv8 is a big challenge, especially for real-time detection tasks.

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All-perovskite tandem solar cells (APTSCs) offer the potential to surpass the Shockley-Queisser limit of single-junction solar cells at low cost. However, high-performance APTSCs contain unstable methylammonium (MA) cation in the tin-lead (Sn-Pb) narrow bandgap subcells. Currently, MA-free Sn-Pb perovskite solar cells (PSCs) show lower performance compared with their MA-containing counterparts.

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Inhibition of the target of rapamycin (TOR/mTOR) protein kinase by the drug rapamycin extends lifespan and health span across diverse species. However, rapamycin has potential off-target and side effects that warrant the discovery of additional TOR inhibitors. TOR was initially discovered in Saccharomyces cerevisiae (yeast) which contains two TOR paralogs, TOR1 and TOR2.

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