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The objective of this study was to identify the most suitable high‐oleic rapeseed blended oil for frying. Six high‐oleic rapeseed blended oils were formulated based on a polyunsaturated fatty acid/saturated fatty acid ratio of 2.0 and an n‐3 fatty acid/n‐6 fatty acid ratio of 1:4–1:6, after which the physiochemical properties, frying performance, tocopherol content, and hazardous substance contents were evaluated. The blended oil containing high‐oleic rapeseed oil: soybean oil: palm oil (85:12:3) had a reasonable fatty acid composition, better oxidative stability index (9.05 h), higher total tocopherol content (0.3 mg/kg), and longer time to reach a total polar component content of 27% (29.3 h). Moreover, after frying for 30 h, the peroxide value (7.25 mmol/kg), residual oil rate (0.57%), trans‐fatty acid content (0.37%), malondialdehyde content (1.57 μg/kg), and 3‐monochloro‐1,2‐propanediol ester content (0.24 mg/kg) were lower than those of the other samples. Principal component analysis revealed that the quality of the high‐oleic rapeseed blended oil was positively correlated with benzo[a]pyrene, 18:2, and 18:3, and negatively correlated with the residual oil rate, trans‐fatty acids, and 18:1 content. This high‐oleic rapeseed blended oil is potentially suitable for frying. [ABSTRACT FROM AUTHOR]

This study presents the development and validation of a robust semi-supervised learning framework specifically designed for the automated segmentation and classification of sandstone thin section images from the Yanchang Formation in the Ordos Basin. Traditional geological image analysis methods encounter significant challenges due to the labor-intensive and error-prone nature of manual labeling, compounded by the diversity and complexity of rock thin sections. Our approach addresses these challenges by integrating the GL-SLIC algorithm, which combines Gabor filters and Local Binary Patterns for effective superpixel segmentation, laying the groundwork for advanced component identification. The primary innovation of this research is the semi-supervised learning model that utilizes a limited set of manually labeled samples to generate high-confidence pseudo labels, thereby significantly expanding the training dataset. This methodology effectively tackles the critical challenge of insufficient labeled data in geological image analysis, enhancing the model's generalization capability from minimal initial input. Our framework improves segmentation accuracy by closely aligning superpixels with the intricate boundaries of mineral grains and pores. Additionally, it achieves substantial improvements in classification accuracy across various rock types, reaching up to 96.3% in testing scenarios. This semi-supervised approach represents a significant advancement in computational geology, providing a scalable and efficient solution for detailed petrographic analysis. It not only enhances the accuracy and efficiency of geological interpretations but also supports broader hydrocarbon exploration efforts. [ABSTRACT FROM AUTHOR]

Capturing pseudonoise (PN) synchronous signals is an important issue in spreading spectrum underwater acoustic communication. In this letter, local waveform domain information is combined with multi‐strategy to enhance the Doppler tolerance of conventional Partial Matched Filtering‐Fast Fourier Transform (PMF‐FFT) acquisition algorithm under low signal‐to‐noise ratio (SNR). First, an optimized algorithm based on partial reconstruction in waveform domain was proposed, such that the Doppler tolerance increased by 38.9% when the SNR = −15 dB. Second, a local baseband multi‐waveform compression algorithm was proposed to further enhance the Doppler parallel search ability multiply. At last, frequency offset estimation was completed after searching the multi‐waveform serially. The effectiveness of the proposed algorithm was verified through the experiment at sea. [ABSTRACT FROM AUTHOR]

Obesity is a metabolic disease with excess weight. LncRNA SNHG14 is abnormally expressed in numerous diseases. This research aimed to enucleate the lncRNA SNHG14 role in obesity. Adipocytes were treated with free fatty acid (FFA) to establish an in vitro model for obesity. Mice were fed a high‐fat diet to construct an in vivo model. Gene levels were determined using quantitative real‐time PCR (RT‐PCR). The protein level was checked by western blot. The lncRNA SNHG14 role in obesity was assessed using western blot and enzyme‐linked immunosorbent assay. The mechanism was estimated by Starbase, dual‐luciferase reporter gene assay, and RNA pull‐down. LncRNA SNHG14 function in obesity was estimated using mouse xenograft models, RT‐PCR, western blot, and enzyme‐linked immunosorbent assay. LncRNA SNHG14 and BACE1 levels were increased, but the miR‐497a‐5p level was decreased in FFA‐induced adipocytes. Interference with lncRNA SNHG14 reduced endoplasmic reticulum (ER) stress‐related molecules GRP78 and CHOP expressions in FFA‐induced adipocytes, and decreased IL‐1β, IL‐6, and TNF‐α expressions, indicating that lncRNA SNHG14 knockdown mitigated FFA‐induced ER stress and inflammation in adipocytes. Mechanistically, lncRNA SNHG14 combined with miR‐497a‐5p, and miR‐497a‐5p targeted BACE1. Meanwhile, lncRNA SNHG14 knockdown reduced levels of GRP78, CHOP, IL‐1β, IL‐6, and TNF‐α, while cotransfection with anti‐miR‐497a‐5p or pcDNA‐BACE1 abolished these trends. Rescue assays illustrated that lncRNA SNHG14 knockdown relieved FFA‐induced adipocyte ER stress and inflammation through miR‐497a‐5p/BACE1. Meanwhile, lncRNA SNHG14 knockdown restrained adipose inflammation and ER stress caused by obesity in vivo. LncRNA SNHG14 mediated obesity‐induced adipose inflammation and ER stress through miR‐497a‐5p/BACE1. [ABSTRACT FROM AUTHOR]