Automated Scoring of Translations with BERT Models: Chinese and English Language Case Study

With the wide application of artificial intelligence represented by deep learning in natural language-processing tasks, the automated scoring of translations has also advanced and improved. This study aims to determine if the BERT-assist system can reliably assess translation quality and identify high-quality translations for potential recognition. It takes the Han Suyin International Translation Contest as a case study, which is a large-scale and influential translation contest in China, with a history of over 30 years. The experimental results show that the BERT-assist system is a reliable second rater for massive translations in terms of translation quality, as it can effectively sift out high-quality translations with a reliability of r = 0.9 or higher. Thus, the automated translation scoring system based on BERT can satisfactorily predict the ranking of translations according to translation quality and sift out high-quality translations potentially shortlisted for prizes

Mesoporous carbon layer encapsulated SnSe nanosheets via covalent bonds for high-performance sodium ion batteries

Sodium ion batteries (SIBs) have been widely studied because of their low cost, low standard redox potential, and abundant sodium availability. However, the structural rupture during the Na+ insertion/extraction processes and the poor conductivity of the anode material limit its cycling stability and rate capability. Herein, SnSe@C was prepared by high-temperature annealing with dopamine hydrochloride as the carbon source, while SnSe was prepared by a protein reduction method. The carbon layer not only works as a protective layer to limit the volume expansion of SnSe and reduce the dissolution of Na2Se and poly-selenides generated during the discharge process in the electrolyte, but also as a conductive matrix to expedite electron transfer, thereby boosting the cycling stability and rate capability of SnSe@C. Benefiting from the above advantages, SnSe@C exhibits a specific capacity of 211.3 mAh g−1 at 0.1 A g−1 after 110 cycles and outstanging rate capability (210.1 mAh g−1 at 5.0 A g−1 and capacity retention rate of 63.2% from 0.1 to 1.0 A g−1). This study not only proposes an idea for promoting the cycling stability and rate capability of SnSe, but also paves the way for providing anodic materials with a stable structure for SIBs

RMSRGAN: A Real Multispectral Imagery Super-Resolution Reconstruction for Enhancing Ginkgo Biloba Yield Prediction

Multispectral remote sensing data with abundant spectral information can be used to compute vegetation indices to improve the accuracy of Ginkgo biloba yield prediction. The limited spatial resolution of multispectral cameras restricts the detail capture over wide farmland, but super-resolution (SR) reconstruction methods can enhance image quality. However, most existing SR models have been trained on images processed from downsampled high-resolution (HR) images, making them less effective in reconstructing real low-resolution (LR) images. This study proposes a GAN-based super-resolution reconstruction method (RMSRGAN) for multispectral remote sensing images of Ginkgo biloba trees in real scenes. A U-Net-based network is employed instead of the traditional discriminator. Convolutional block attention modules (CBAMs) are incorporated into the Residual-in-Residual Dense Blocks (RRDBs) of the generator and the U-Net of the discriminator to preserve image details and texture features. An unmanned aerial vehicle (UAV) equipped with a multispectral camera was employed to capture field multispectral remote sensing images of Ginkgo biloba trees at different spatial resolutions. Four matching HR and LR datasets were created from these images to train RMSRGAN. The proposed model outperforms the traditional models by achieving superior results in both quantitative evaluation metrics (peak signal-to-noise ratio (PSNR) is 32.490, 31.085, 27.084, 26.819, and structural similarity index (SSIM) is 0.894, 0.881, 0.832, 0.818, respectively) and qualitative evaluation visualization. Furthermore, the efficiency of our proposed method was tested by generating individual vegetation indices (VIs) from images taken before and after reconstruction to predict the yield of Ginkgo biloba. The results show that the SR images exhibit better R2 and RMSE values than LR images. These findings show that RMSRGAN can improve the spatial resolution of real multispectral images, increasing the accuracy of Ginkgo biloba yield prediction and providing more effective and accurate data support for crop management

A Large-Range and High-Sensitivity Fiber-Optic Fabry–Perot Pressure Sensor Based on a Membrane-Hole-Base Structure

In the field of in situ measurement of high-temperature pressure, fiber-optic Fabry–Perot pressure sensors have been extensively studied and applied in recent years thanks to their compact size and excellent anti-interference and anti-shock capabilities. However, such sensors have high technological difficulty, limited pressure measurement range, and low sensitivity. This paper proposes a fiber-optic Fabry–Perot pressure sensor based on a membrane-hole-base structure. The sensitive core was fabricated by laser cutting technology and direct bonding technology of three-layer sapphire and develops a supporting large-cavity-length demodulation algorithm for the sensor’s Fabry–Perot cavity. The sensor exhibits enhanced sensitivity, a simplified structure, convenient preparation procedures, as well as improved pressure resistance and anti-harsh environment capabilities, and has large-range pressure sensing capability of 0–10 MPa in the temperature range of 20–370 °C. The sensor sensitivity is 918.9 nm/MPa, the temperature coefficient is 0.0695 nm/(MPa∙°C), and the error over the full temperature range is better than 2.312%

Double F-P interference optical fiber Temperature and gas pressure sensor based on hollow core Bragg fiber.

A dual Fabry-Perot interferometric (FPI) temperature-gas pressure dual parametric sensor based on a hollow core Bragg fiber (HCBF) is introduced and validated experimentally for gas pressure monitoring in high-temperature settings. This dual parametric sensor employs a single mode fiber-hollow core Bragg fiber-single mode fiber (SMF-HCBF-SMF) structure to form a dual FPI, while the gas flow channel of the HCBF were processed by Femtosecond laser to make the sensor sensitive to gas pressure. Additionally, a novel white light interference demodulation technique is developed to demodulate the dual parametric sensor. Experimental findings corroborate theoretical predictions, the pressure measurement range of the proposed sensor reaches 0~10 MPa, the temperature measurement reaches 600°C. Pressure sensitivity greater than 50 nm/MPa over the full temperature rang. The high temperature and high sensitivity resistance of the proposed sensor highlights its potential for monitoring gas pressure in extreme high temperature environments