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Emergency decision support techniques play an important role in complex and safety-critical systems such as nuclear power plants (NPPs). Emergency decision-making is not a single method but a framework comprising a combination of various technologies. This paper presents a review of various methods for emergency decision support systems in NPPs. We first discuss the theoretical foundations of nuclear power plant emergency decision support technologies. Based on this exposition, the key technologies of emergency decision support systems in NPPs are presented, including training operators in emergency management, risk assessment, fault detection and diagnosis, multi-criteria decision support, and accident consequence assessment. The principles, application, and comparative analysis of these methods are systematically described. Additionally, we present an overview of emergency decision support systems in NPPs across different countries and feature profiles of prominent systems like the Real-Time Online Decision Support System for Nuclear Emergencies (RODOS), the Accident Reporting and Guiding Operational System (ARGOS), and the Decision Support Tool for Severe Accidents (Severa). Then, the existing challenges and issues in this field are summarized, including the need for better integration of risk assessment, methods to enhance education and training, the acceleration of simulation calculations, the application of large language models, and international cooperation. Finally, we propose a new decision support system that integrates Level 1, 2, and 3 probabilistic safety assessment for emergency management in NPPs.

Nuclear energy plays an important role in global energy supply, especially as a key low-carbon source of power. However, safe operation is very critical in nuclear power plants (NPPs). Given the significant impact of human-caused errors on three serious nuclear accidents in history, artificial intelligence (AI) has increasingly been used in assisting operators with regard to making various decisions. In particular, data-driven AI algorithms have been used to identify the presence of accidents and their root causes. However, there is a lack of an open NPP accident dataset for measuring the performance of various algorithms, which is very challenging. This paper presents a first-of-its-kind open dataset created using PCTRAN, a pre-developed and widely used simulator for NPPs. The dataset, namely nuclear power plant accident data (NPPAD), basically covers the common types of accidents in typical pressurised water reactor NPPs, and it contains time-series data on the status or actions of various subsystems, accident types, and severity information. Moreover, the dataset incorporates other simulation data (e.g., radionuclide data) for conducting research beyond accident diagnosis.Measurement(s)nuclear power plant accident dataTechnology Type(s)nuclear power plant similulationFactor Type(s)accident

In nuclear power plants, the loss-of-coolant accident (LOCA) stands out as the most prevalent and consequential incident. Accurate breach size diagnosis is crucial for the mitigation of LOCAs, and identifying the cause of an accident can prevent catastrophic consequences. Traditional methods mostly focus on combining model algorithms and utilize intricate composite model neural network architectures. However, it is crucial to investigate whether greater complexity necessarily leads to better performance. In addition, the consideration of the impact of dataset construction and data preprocessing on model performance is also needed for model building. This paper proposes a framework named DeepLOCA-Lattice to experiment with different preprocessing approaches to fundamental deep learning models for a comprehensive analysis of the diagnosis of LOCA breach size. The DeepLOCA-Lattice involves data preprocessing via the lattice algorithm and equal-interval partitioning and deep-learning-based models, including the multi-layer perceptron (MLP), recurrent neural networks (RNNs), convolutional neural networks (CNNs), and the transformer model in LOCA breach size diagnosis. After conducting rigorous ablation experiments, we have discovered that even rudimentary foundational models can achieve accuracy rates that exceed 90%. This is a significant improvement when compared to the previous models, which yield an accuracy rate of lower than 50%. The results interestingly demonstrate the superior performance and efficacy of the fundamental deep learning model, with an effective dataset construction approach. It elucidates the presence of a complex interplay among diagnostic scales, sliding window size, and sliding stride. Furthermore, our investigation reveals that the model attains its highest accuracy within the discussed range when utilizing a smaller sliding stride size and a longer sliding window length. This study could furnish valuable insights for constructing models for LOCA breach size estimation.

This paper studies the use of secret key agreement for single-input single-output (SISO) visible light communication (VLC) systems. Specifically, we put forward a scheme for secret key generation and analyze the secret key rate in SISO VLC systems. First, we derive the secret key capacity bounds. Then, we analyze the achievable secret key rate distribution versus the illegal receiver’s location when the legal receiver’s location is fixed. Meanwhile, we deduce the average secret key capacity using random geometry knowledge when all the receivers’ positions are unknown. We also analyze the impact of employing a protected zone on the average secret key capacity and observe that a protected zone can obviously improve the secret performance. Finally, simulations are presented to verify the theoretical analysis.

As an infectious disease that poses a significant threat to the rapidly growing pig breeding industry, the detection of Haemophilus parasuis (HPS) is often compromised by various interfering substances present in the test sample during quantitative real-time PCR (qPCR). The rapid detection of HPS is important for the isolation of infectious pigs and their treatment. We designed and optimized a rapid qPCR test to detect the INFB gene of HPS in clinical and environmental samples on pig farms. The method was evaluated for its specificity, sensitivity, repeatability, anti-interference capability, and its ability to detect HPS in clinical samples. The results indicated that the method was specific for the detection of HPS when evaluated against pathogens and intestinal probiotics found in pig farms. By using a seven-fold dilution series of the recombinant plasmid DNA in triplicate, it was determined that the lowest limit of detection (LOD) for this method was less than 10 copies/µL. The results of inter-batch and intra-batch repeatability tests showed that the coefficient of variation (CV) was consistently below 1%. Furthermore, the impact of 14 endogenous and exogenous interfering substances on the Ct values detected by the HPS qPCR was found to be less than 5% when compared to the Ct values obtained in the absence of interfering substances. A total of 248 clinical samples were analyzed using the HPS qPCR, commercial kits, and corresponding national standards, yielding positive rates of 9.27%, 6.05%, and 9.27%, respectively. Notably, the positive and negative percent agreement between the detection method developed in this research and the national standard was 100%. These findings demonstrate that the established detection method is suitable for epidemiological research on HPS and for diagnosing clinical samples containing interfering substances, thereby providing essential technical support for the prevention and control of HPS.

In this study, we constructed an extraction process for bovine lung peptide-1 (BLP-1) derived from bovine lung tissue utilizing single-factor optimization combined with response surface methodology. We systematically analyzed its antioxidant activity, biological safety, and therapeutic mechanisms against alcoholic liver disease (ALD). In vitro experiments demonstrated that BLP-1 exhibits excellent scavenging activity against various free radicals, while exhibiting no significant cytotoxicity or hemolytic activity. In a model of H2O2-induced oxidative damage in HepG2 cells, BLP-1 significantly alleviated oxidative stress injury by upregulating the activities of intracellular antioxidant enzymes. Animal experiments further confirmed that BLP-1 significantly reduced serum levels of transaminase, inhibited the release of inflammatory factors, enhanced antioxidant enzyme activity, and ameliorated lipid peroxidation and pathological injury in ALD mice. By combining liquid chromatography-tandem mass spectrometry (LC-MS/MS) with bioinformatics, we screened 12 novel antioxidant peptides. Among these, the binding energies of GP9, FG6, and WG6 to Keap1 were −10.2, −9.7, and −8.7 kcal/mol, respectively, indicating their potential to modulate the antioxidant defense system through competitive inhibition of Keap1-Nrf2 interactions. This study provides a novel approach for the high-value utilization of bovine lung and the treatment of ALD, as well as a new source for the extraction of natural antioxidant peptides.

This study aimed to evaluate the therapeutic efficacy, safety, and biocompatibility of calcium alginate hydrogel in suprachoroidal buckling procedures performed on rabbit eyes. A total of 21 healthy New Zealand White rabbits underwent surgery, during which calcium alginate hydrogel was injected into the suprachoroidal space of one eye. Monthly ophthalmic examinations were performed for 3 months following the procedure. The assessments included external eye and fundus examinations, color fundus imaging, and optical coherence tomography. At the end of the observation period, both eyes from each rabbit were enucleated and subjected to histopathological evaluation. The surgery was successfully performed on 21 eyes, with a success rate of 80.95% (17/21). A total of 17 eyes showed favorable postoperative outcomes and no complications. The average maximum dome height is 2,470.42 ± 876.11 μm on the day of operation and 1,585.97 ± 351.93 μm 3 months postoperatively, with the average difference of 866.37 ± 592.94 μm. A total of four eyes experienced inadvertent perforations of both the choroid and retina, leading to vitreous and suprachoroidal hemorrhages. Histological evaluation confirmed the persistent presence of a suprachoroidal indentation in the treated eyes of the 17 rabbits without complications, with no observable abnormalities detected in the lenses or retinas. Calcium alginate hydrogel proved to be a safe and effective material for suprachoroidal buckling in rabbits, exhibiting excellent biocompatibility.

Extracting data from scientific literature and constructing machine learning-ready (ML-ready) datasets is crucial for data-driven materials discovery. However, material properties under various compositions and processing conditions are often summarized in figures, making manual data extraction time-consuming and labor-intensive. In this study, we propose an automated figure data extraction tool to rapidly generate datasets from figures in materials science literature on Cu-Cr-X alloys used in high-end lead frames, aiming to support the development of next-generation high-strength, high-conductivity copper alloys. We collected 251 figures containing mechanical and electrical performance data from 146 papers and automatically extracted 3,018 high-quality Cu-Cr-X data records. Each record includes article metadata and composition/processing information manually extracted from text, enabling construction of a domain-specific dataset linking alloy composition, processing, and properties. The dataset summarizes 20 microalloying elements and mechanical and electrical performance under different processing conditions. This tool can significantly improve the efficiency of large-scale data accumulation in materials science.

Objective: This study aimed to investigate the role of miRNA 21-5p in regulating the differentiation of C2C12 myoblasts and intramuscular lipid droplets accumulation in myotubes.Methods: The role of miR-21-5p in the proliferation and differentiation of myofibroblasts and intracellular lipid accumulation was analyzed using bioinformatics, CCK-8 assay, quantitative real-time polymerase chain reaction, immunoblotting, immunofluorescence staining, and Oil Red O staining.Results: The analysis of porcine BodyMap transcriptome data revealed differential expression of miRNA 21-5p in skeletal muscle and adipose tissue. Bioinformatics analysis combined with a dual-luciferase reporter assay demonstrated that FBXO11 serves as a direct target of miR-21-5p. Transfection experiments involving a miR-21-5p mimic, miR-21-5p inhibitor, and si-FBXO11 in C2C12 cells showed that overexpression of miR-21-5p or silencing of FBXO11 significantly enhanced the proliferation of C2C12 cells, upregulated myogenesis-related factors, and promoted myotube formation. Furthermore, oleic acid-induced lipid accumulation in myotubes was suppressed, accompanied by reduced expression of adipogenesis-related genes. Conversely, inhibition of miR-21-5p expression produced opposite effects.Conclusion: These findings indicate that miR-21-5p promotes proliferation and differentiation while inhibiting intramyocellular lipid deposition by targeting the 3'-untranslated region of FBXO11 in myogenic cell. The results suggest that miR-21-5p could serve as a potential miRNA biomarker for regulating intramuscular adipogenesis.

The influence of co-regulating Mo and Sn on the corrosion resistance of low alloy steel in tropical marine atmospheric was investigated. The combined addition of Mo and Sn has been found to significantly improve the corrosion resistance of low alloy steel, augmenting the protective capabilities of the rust layer. This combined addition promotes the formation of protective compounds like α -FeOOH and FeCr 2 O 4 within the alloy rust layer. Furthermore, it facilitates the conversion of Cr, Ni and Cu into corrosion-resistant oxides such as Cr 2 O 3 , NiFe 2 O 4 and CuO, thereby enhancing the density of the rust layer. Additionally, as corrosion progresses over time, higher levels of Sn addition lead to increased Sn content within the inner rust layer, consequently bolstering the protective qualities of the rust layer. This comprehensive understanding sheds light on the synergistic effects of Mo and Sn in fortifying the corrosion resistance of low alloy steel, offering insights for the development of advanced corrosion-resistant materials in marine environments.