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Intelligent manufacturing, defined as the integration of manufacturing with modern information technologies such as 5G, digitalization, networking, and intelligence, has grown in popularity as a means of boosting the productivity, intelligence, and flexibility of traditional manufacturing processes. The steel industry is a necessary support for modern life and economic development, and the Chinese steel industry’s capacity has expanded to roughly half of global production. However, the Chinese steel industry is now confronted with high labor costs, massive carbon emissions, a low level of intelligence, low production efficiency, and unstable quality control. Therefore, China’s steel industry has launched several large-scale intelligent manufacturing initiatives to improve production efficiency, product quality, manual labor intensity, and employee working conditions. Unfortunately, there is no comprehensive overview of intelligent manufacturing in China’s steel industry. We began this research by summarizing the construction goals and overall framework for intelligent manufacturing of the steel industry in China. Following that, we offered a brief review of intelligent manufacturing for China’s steel industry, as well as descriptions of two typical intelligent manufacturing models. Finally, some major technologies employed for intelligent production in China’s steel industry were introduced. This research not only helps to comprehend the development model, essential technologies, and construction techniques of intelligent manufacturing in China’s steel industry, but it also provides vital inspiration for the manufacturing industry’s digital and intelligence updates and quality improvement.

Fatty acid synthase (FASN) is highly expressed in multiple types of human cancers and is recognized as one of the targets for treating cancer metastasis. α-Linolenic acid is an omega-3 essential fatty acid and it possesses various biological activities. The present study was designed to reveal the effects of α-linolenic acid on osteosarcoma and to reveal whether the mechanism of α-linolenic acid in anticancer activity may be related to FASN inhibition. The cytotoxicity of α-linolenic acid was assessed in osteosarcoma MG63, 143B, and U2OS cells. Cell viability was detected by the MTT assay. The protein expression level was detected by western blotting. Flow cytometry, Annexin V/propidium iodide dual staining, and Hoechst 33258 staining were performed to assess the apoptotic effects. Wound healing assay was applied to detect the inhibitory effect of α-linolenic acid on osteosarcoma cells migration. The results showed that α-linolenic acid downregulated FASN expression. α-Linolenic acid inhibited osteosarcoma cell proliferation and migration in a dose-dependent manner. In addition, α-linolenic acid regulated endoplasmic reticulum transmembrane receptors and signal protein expression in osteosarcoma cells. The findings of the present study suggested that α-linolenic acid suppresses osteosarcoma cell proliferation and metastasis by inhibiting FASN expression, which provides a basis as a potential target for osteosarcoma treatment.

The Dongpu Depression is a typical petroliferous depression characterized by remarkable variations in sedimentary salinity. To investigate hydrocarbon generation characteristics of source rocks in different salinity environments, 20 source rock samples were collected and analyzed for organic geochemical. Combining trace elements, Three representative samples were further selected for gold-tube pyrolysis experiments to obtain hydrocarbon generation kinetic parameters. Integrating constraints from kinetic parameters and thermal history, basin modeling was conducted to systematically compare the hydrocarbon generation characteristics of source rocks in different salinity environments. Results show that activation energy for gaseous hydrocarbon generation is widely distributed, while that for liquid hydrocarbon generation is relatively concentrated. Hydrocarbon yields follow the order: saline environment (SE)> brackish environment (BE)> freshwater environment (FE). High-quality source rock intervals with hydrocarbon generation intensity exceeding 2 × 10⁶ t/km² are recognized as key targets for subsequent oil and gas exploration in the region. This study provides valuable implications for hydrocarbon exploration and resource assessment in comparable saline lacustrine basins.

Uncarboxylated osteocalcin (GluOC), a small-molecule protein specifically synthesized and secreted by osteoblasts, is important in the regulation of energy metabolism. In our previous study, GluOC was shown to be effective in ameliorating dyslipidemia and hepatic steatosis in KKAy mice. However, the underlying mechanism of GluOC action on hepatocytes has not been well validated. In this study, oleic acid/palmitic acid (OA/PA)-induced HepG2 and NCTC 1469 cells were used as non-alcoholic fatty liver disease (NAFLD) cell models, and triacylglycerol (TG) levels were measured by oil red O staining, Nile Red staining, and ELISA. The fatty acid synthesis-related protein expression was detected by real-time quantitative polymerase chain reaction, Western blotting, and immunofluorescence. The results show that GluOC reduced triglyceride levels, and decreased the expression of sterol regulatory element-binding protein-1c (SREBP-1c) and stearyl-coenzyme A desaturase 1 (SCD1). si-SCD1 mimicked the lipid accumulation-reducing effect of GluOC, while overexpression of SCD1 attenuated the effect of GluOC. In addition, GluOC activated AMP-activated protein kinase (AMPK) phosphorylation to affect lipid metabolism in hepatocytes. Overall, the results of this study suggest that GluOC decreases SCD1 by activating AMPK to alleviate hepatocyte lipid accumulation, which provides a new target for improving NAFLD in further research.

Post-stroke dysphagia (PSD) is an increasingly common complication of stroke. Despite its intuitively unfavorable impact on secondary prevention medication use, limited awareness is available regarding this issue. Herein, a cross-sectional survey was conducted to determine the current use, patient-perceived needs and preferences for secondary prevention medications among PSD patients. To emphasize the unique context related to dysphagia, we recruited Chinese stroke patients with a duration of less than 5 years. These patients were initially categorized into PSD respondents with and without dysphagia. Among the 3490 eligible respondents, 42.7% reported experiencing dysphagia after stroke. Those PSD respondents were more likely to consume multiple medications and suffer from anticoagulants-associated gastrointestinal bleeding as compared to non-PSD ones (p < 0.001). More crucially, 40.2% of them had frequent difficulty in swallowing pills, 37.1% routinely crushed solid oral dosage forms (SODFs), and 23.5% coughed frequently when taking SODFs. In consequence, 87.4% responded a need for PSD-specific formulations where safe swallowing, easy swallowing, and reduced medication frequency were preferred pharmaceutical factors. These findings demonstrate an unsatisfactory situation and definite needs for PSD patients in using secondary prevention medications. Awareness should be increased to develop PSD-specific formulations for safe and effective secondary prevention.

Diabetic cardiomyopathy (DCM) is a severe complication of diabetes, in which ferroptosis is a key pathogenic mechanism. This study examines how alpha-linolenic acid (ALA), a plant-derived omega-3 polyunsaturated fatty acid, protects against damage from ferroptosis in DCM. Using an in vitro model of H9C2 cardiomyocytes treated with high glucose/palmitate, combined with a high-fat diet and mouse model of low-dose streptozotocin (STZ)-induced diabetes, this research demonstrates for the first time that ALA significantly alleviates cardiac dysfunction and prevents ferroptosis. Mechanistically, ALA inhibits STAT3 phosphorylation by activating the AMPK signaling pathway, thereby reducing NCOA4-mediated ferritinophagy and mitigating mitochondrial iron overload and reactive oxygen species accumulation. It also enhances the function of the SLC7A11/GSH/GPX4 axis, reducing lipid peroxidation (LPO)-induced ferroptosis. Collectively, these findings indicate that ALA protects against diabetic cardiomyopathy by coordinating the regulation of ferritinophagy and antioxidant defense through the AMPK-STAT3 pathway, offering a potential therapeutic strategy for disease management.

Non-alcoholic fatty liver disease (NAFLD) is the primary chronic liver disease worldwide, mainly manifested by hepatic steatosis. Hepatic lipids may be derived from dietary intake, plasma free fatty acid (FFA) uptake, or hepatic de novo lipogenesis (DNL). Currently, cellular and animal models of hepatocellular steatosis are widely used to study the pathogenesis of NAFLD and to investigate therapeutic agents. However, whether there are differences between the in vivo and in vitro models of the mechanisms that cause lipid accumulation has not been reported. We used OA/PA-induced NCTC 1469 cells and high-fat-diet-fed C57BL/6J mice to simulate a hepatocyte steatosis model of NAFLD and to detect indicators related to FFA uptake and DNL. In addition, when serological indicators were analysed in the mouse model, it was found that serum FASN levels decreased. The results revealed that, in the cellular model, indicators related to DNL were decreased, FASN enzyme activity was unchanged, and indicators related to FFA uptake were increased, including the high expression of CD36; while, in the animal model, indicators related to both FFA uptake and de novo synthesis were increased, including the high expression of CD36 and the increased protein levels of FASN with enhanced enzyme activity. In addition, after an analysis of the serological indicators in the mouse model, it was found that the serum levels of FASN were reduced. In conclusion, the OA/PA-induced cellular model can be used to study the mechanism of FFA uptake, whereas the high-fat-diet-induced mouse model can be used to study the mechanism of FFA uptake and DNL. Combined treatment with CD36 and FASN may be more effective against NAFLD. FASN in the serum can be used as one of the indicators for the clinical diagnosis of NAFLD.

The development of urbanization has brought convenience to people, but it has also brought a lot of harmful construction solid waste. The machine vision detection algorithm is the crucial technology for finely sorting solid waste, which is faster and more stable than traditional methods. However, accurate identification relies on large datasets, while the datasets from the field working conditions are scarce, and the manual annotation cost of datasets is high. To rapidly and automatically generate datasets for stacked construction waste, an acquisition and detection platform was built to automatically collect different groups of RGB-D images for instances labeling. Then, based on the distribution points generation theory and data augmentation algorithm, a rapid-generation method for synthetic construction solid waste datasets was proposed. Additionally, two automatic annotation methods for real stacked construction solid waste datasets based on semi-supervised self-training and RGB-D fusion edge detection were proposed, and datasets under real-world conditions yield better models training results. Finally, two different working conditions were designed to validate these methods. Under the simple working condition, the generated dataset achieved an F1-score of 95.98, higher than 94.81 for the manually labeled dataset. In the complicated working condition, the F1-score obtained by the rapid generation method reached 97.74. In contrast, the F1-score of the dataset obtained manually labeled was only 85.97, which demonstrates the effectiveness of proposed approaches.

Arc faults can produce very high temperatures and can easily ignite combustible materials; thus, they represent one of the most important causes of electrical fires. The application of arc fault detection, as an emerging early fire detection technology, is required by the National Electrical Code to reduce the occurrence of electrical fires. However, the concealment, randomness and diversity of arc faults make them difficult to detect. To improve the accuracy of arc fault detection, a novel arc fault detector (AFD) is developed in this study. First, an experimental arc fault platform is built to study electrical fires. A high-frequency transducer and a current transducer are used to measure typical load signals of arc faults and normal states. After the common features of these signals are studied, high-frequency energy and current variations are extracted as an input eigenvector for use by an arc fault detection algorithm. Then, the detection algorithm based on a weighted least squares support vector machine is designed and successfully applied in a microprocessor. Finally, an AFD is developed. The test results show that the AFD can detect arc faults in a timely manner and interrupt the circuit power supply before electrical fires can occur. The AFD is not influenced by cross talk or transient processes, and the detection accuracy is very high. Hence, the AFD can be installed in low-voltage circuits to monitor circuit states in real-time to facilitate the early detection of electrical fires.

Microplastic particles produced by non-degradable waste plastic bottles have a critical impact on the environment. Reasonable recycling is a premise that protects the environment and improves economic benefits. In this paper, a multi-scale feature fusion method for RGB and hyperspectral images based on Segmenting Objects by Locations (RHFF-SOLOv1) is proposed, which uses multi-sensor fusion technology to improve the accuracy of identifying transparent polyethylene terephthalate (PET) bottles, blue PET bottles, and transparent polypropylene (PP) bottles on a black conveyor belt. A line-scan camera and near-infrared (NIR) hyperspectral camera covering the spectral range from 935.9 nm to 1722.5 nm are used to obtain RGB and hyperspectral images synchronously. Moreover, we propose a hyperspectral feature band selection method that effectively reduces the dimensionality and selects the bands from 1087.6 nm to 1285.1 nm as the features of the hyperspectral image. The results show that the proposed fusion method improves the accuracy of plastic bottle classification compared with the SOLOv1 method, and the overall accuracy is 95.55%. Finally, compared with other space-spectral fusion methods, RHFF-SOLOv1 is superior to most of them and achieves the best (97.5%) accuracy in blue bottle classification.