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The detailed anisotropic dispersion of the low-temperature, low-energy magnetic excitations of the candidate spin-triplet superconductor UTe is revealed using inelastic neutron scattering. The magnetic excitations emerge from the Brillouin zone boundary at the high symmetry and points and disperse along the crystallographic -axis. In applied magnetic fields to at least = 11 T along the , the magnetism is found to be field-independent in the ( 0) plane.

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In this study, we investigate the thermoelectric properties of functionalized multi-walled carbon nanotubes (F-MWCNTs) dispersed over a flexible substrate through a facile vacuum filtration route. To improve their interfacial adhesion and dispersion, F-MWCNTs underwent hot-pressing. The heat-treatment has improved the nanotubes' connections and subsequently reduced porosity as well, which results in an increasing electrical conductivity upon increasing temperature of hot-pressing.

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sp-carbon-linked covalent organic frameworks (spc-COFs) are crystalline porous polymers with repeat organic units linked by sp carbons, and have attracted increasing interest due to their robust skeleton and tunable semiconducting properties. Single-crystalline spc-COFs with well-defined structures can represent an ideal platform for investigating fundamental physics properties and device performance. However, the robust olefin bonds inhibit the reversible-reaction-based crystal self-correction, thus yielding polycrystalline or amorphous polymers.

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Background: Devices that help educate young doctors and enable safe, minimally invasive surgery are needed. Eureka is a surgical artificial intelligence (AI) system that can intraoperatively highlight loose connective tissues (LCTs) in the dissected layers and nerves in the surgical field displayed on a monitor. In this study, we examined whether AI navigation (AIN) with Eureka can assist trainees in recognizing nerves during colorectal surgery.

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The Insulated Gate Bipolar Transistor (IGBT) is a crucial power semiconductor device, and the integrity of its internal structure directly influences both its electrical performance and long-term reliability. However, the precise semantic segmentation of IGBT ultrasonic tomographic images poses several challenges, primarily due to high-density noise interference and visual distortion caused by target warping. To address these challenges, this paper constructs a dedicated IGBT ultrasonic tomography (IUT) dataset using Scanning Acoustic Microscopy (SAM) and proposes a lightweight Multi-Scale Fusion Network (LMFNet) aimed at improving segmentation accuracy and processing efficiency in ultrasonic images analysis.

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