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With the growth of water conservancy and the increasing demand for information, handling and representing large volumes of water-related data has become complex. Particularly, scheduling textual data often exists in natural language form, lacking clear structure and standardization. Processing and utilizing such diverse data necessitates extensive domain knowledge and professional expertise. To tackle this challenge, a method based on large language model has been proposed to construct a knowledge graph for water engineering scheduling. This approach involves collecting and preprocessing scheduling rule data at the data layer, leveraging large language models to extract embedded knowledge, constructing the ontology at the conceptual layer, and extracting the “three-step” method prompt strategy at the instance layer. Under the interaction of the data, conceptual, and instance layers, high-performance extraction of rule texts is achieved, and the construction of the dataset and knowledge graph is completed. Experimental results show that the F1 value of the extraction method in this paper reaches 85.5%, and the effectiveness and rationality of the modules of the large language model are validated through ablation experiments. This graph integrates dispersed water conservancy rule information, effectively handles unstructured textual data, and offers visualization querying and functionality tracing. It aids professionals in assessing water conditions and selecting appropriate scheduling schemes, providing valuable support for conservancy decision-making and intelligent reasoning.

Fiber-reinforced composites offer the benefits of high strength, high stiffness, lightweight, superior damping performance, and great design capability when compared to metal. The rigidity characteristics of the composite laminate in different directions may be adjusted to meet the requirements of the application by using appropriate materials and arranging the lay-up sequence. As a result, the purpose of this work is to explore the influence of lay-up type on propeller performance in terms of both hydrodynamic and structural performance. A transient fluid-structure interaction (FSI) algorithm based on the finite element method (FEM) combined with the computational fluid dynamics (CFD) technique is developed and used for the analysis of composite propellers. The hydrodynamic performance of the propeller is compared to that of a metallic material. Propeller propulsion efficiency, structural deformation, equivalent stress, and damage performance of different lay-up options under three different operating situations are compared. In addition, it is presented a parametric optimization approach to get the most appropriate lay-up program for composite blades with the best hydrodynamic properties and structural performance.

The rapid development of technology allows people to obtain a large amount of data, which contains important information and various noises. How to obtain useful knowledge from data is the most important thing at this stage of machine learning (ML). The problem of unbalanced classification is currently an important topic in the field of data mining and ML. At present, this problem has attracted more and more attention and is a relatively new challenge for academia and industry. The problem of unbalanced classification involves classifying data when there is insufficient data or severe category distribution deviations. Due to the inherent complexity of unbalanced data sets, more new algorithms and tools are needed to effectively convert a large amount of raw data into useful information and knowledge. Unbalanced data set is a special case of classification problem, in which the distribution between classes is uneven, and it is difficult to classify data accurately. This article mainly introduces the research on the processing method of computer algorithms based on the processing method of unbalanced data sets based on ML, aiming to provide some ideas and directions for the processing of computer algorithms based on unbalanced data sets based on ML. This article proposes a research strategy for processing unbalanced data sets based on ML, including data preprocessing, decision tree data classification algorithm, and C4.5 algorithm, which are used to conduct research experiments on processing methods for unbalanced data sets based on ML. The experimental results in this article show that the accuracy rate of the decision tree C4.5 algorithm based on ML is 94.80%, which can be better used for processing unbalanced data sets based on ML.

Chikungunya virus (CHIKV) is a reemerging arbovirus causing disease on a global scale, and the potential for its epidemics remains high. CHIKV has caused millions of cases and heavy economic burdens around the world, while there are no available approved antiviral therapies to date. In this study, nifuroxazide, an FDA-approved antibiotic for acute diarrhea or colitis, was found to significantly inhibit a variety of arboviruses, although its antiviral activity varied among different target cell types. Nifuroxazide exhibited relatively high inhibitory efficiency in yellow fever virus (YFV) infection of the hepatoma cell line Huh7, tick-borne encephalitis virus (TBEV) and west nile virus (WNV) infection of the vascular endothelial cell line HUVEC, and CHIKV infection of both Huh7 cells and HUVECs, while it barely affected the viral invasion of neurons. Further systematic studies on the action stage of nifuroxazide showed that nifuroxazide mainly inhibited in the viral replication stage. In vivo, nifuroxazide significantly reduced the viral load in muscles and protected mice from CHIKV-induced footpad swelling, an inflammation injury within the arthrosis of infected mice. These results suggest that nifuroxazide has a potential clinical application as an antiviral drug, such as in the treatment of CHIKV infection. [ABSTRACT FROM AUTHOR]

Ulcerative colitis (UC) is a complicated and recurrent intestinal disease. Currently available drugs for UC treatment are scarce, therefore, novel therapeutic drugs for the UC are urgently to be developed. Gingerenone A (GA) is a phenolic compound known for its anti‐inflammatory effect, but its effect on UC remains unknown. Here, it is shown that GA protects mice against UC, which is closely associated with inhibiting intestinal mucosal inflammation and enhancing intestinal barrier integrity in vivo and in vitro. Of note, RNA sequencing analysis demonstrates an evident correlation with IL‐17 signaling pathway after GA treatment, and this effect is further corroborated by Western blot. Mechanistically, GA directly interacts with IL‐17RA protein through pull‐down, surface plasmon resonance analysis and molecular dynamics simulation. Importantly, lentivirus‐mediated IL‐17RA/Act1 knock‐down or GA co‐treatment with brodalumab/ixekizumab significantly impairs the protective effects of GA against DSS‐induced inflammation and barrier dysfunction, suggesting a critical role of IL‐17RA signaling for GA‐mediated protection against UC. Overall, these results indicate that GA is an effective agent against UC mainly through the direct binding of IL‐17RA to inhibit inflammatory signaling activation. [ABSTRACT FROM AUTHOR]