Exploring the mechanism of Tetrastigma hemsleyanum in treating viral meningitis based on network pharmacology and molecular docking technology. Active ingredients of T. hemsleyanum were collected from the China National Knowledge Infrastructure （CNKI） and PubMed databases. The Traditional Chinese Medicine Systems Pharmacology Database and Analysis Platform （TCMSP） was utilized to search for these active ingredients and identify their corresponding targets. Therapeutic targets for viral meningitis were obtained from the Online Mendelian Inheritance in Man （OMIM）， DisGeNET， and GeneCards databases. Cytoscape software was employed to construct a “drug - component - target - disease” network and a “component - target - pathway” network. Gene Ontology （GO） functional enrichment and kyoto Encyclopedia of Genes and Genomes （KEGG） pathway enrichment analyses were performed using R language. A total of 12 major active components and 181 potential targets were screened from T. hemsleyanum. By intersecting these with 796 viral meningitis - related targets， 86 common drug - disease targets were identified. The protein - protein interaction （PPI） network highlighted key target proteins such as tumor protein 53 （TP53），interleukin - 6 （IL-6）， AKT serine/threonine kinase 1 （AKT1）， jun proto - oncogene （JUN）， and interleukin - 1 Beta （IL-1β）. KEGG enrichment analysis revealed significant pathways， including the AGE - RAGE signaling pathway， hepatitis B， TNF signaling pathway， and human cytomegalovirus infection. Therefore， T. hemsleyanum may exert its potential therapeutic effects against viral meningitis through a multi - component， multi - target， and multi - pathway mechanism.

The codon usage preferences and differences among cultivated and wild barley， Hordeum vulgare var. Coleste， H. vulgare ssp. vulgare and H. vulgare ssp. spontaneum， were analyzed based on the coding sequences of their chloroplast genomes. The results demonstrated that the cytosine and guanine （GC） content at different codon positions （GC1， GC2， GC3） exhibited a gradient decrease across the three species （46.74%/46.80%/46.63%， 39.47%/39.43%/39.43%， 29.80%/29.75%/30.25%）. All three species shared 31 highly preferred codons （RSCU > 1）， with 29 of these codons ending in A/U， indicating a significant preference for NNA/NNU endings. The effective codon number （ENC） values of the chloroplast genomes （47.14， 47.02， 47.75） and the proportion of genes with ENC > 45 （39， 39， 42） suggested a relatively weak overall codon preference. Neutral plotting， ENC-plot， and PR2-plot analyses confirmed that natural selection was the primary driving force behind the formation of codon preferences. Furthermore， two cultivated barley exhibited high convergence in terms of GC composition， ENC distribution， and optimal codons （including specific GCA/AGA）. This suggests that artificial selection through purifying selection has enhanced the genetic stability of beneficial traits， offering new insights into the molecular mechanisms underlying barley domestication.

A chromatographic column （250 mm × 4.6 mm， 5 μm） with octadecylsilane - bonded silica gel as the filler was used， with methanol and a volume fraction of 1% phosphoric acid aqueous solution as the mobile phase， to establish a method for determining the mass fraction of arbutin in tea using high - performance liquid chromatography （HPLC）. The method demonstrates a good linear relationship within the mass concentration range of 0.349 to 402.788 μg/mL， with satisfactory precision， stability， and repeatability. Furthermore， the extraction method for arbutin in tea was optimized with respect to extraction procedures and solvents. This method not only provides a reliable analytical technique for determining the mass fraction of arbutin in tea but also offers new insights for detecting active ingredients in tea and other plant - based products.

Inflammatory bowel disease （IBD） is a prevalent functional gastrointestinal disorder in clinical settings. Current research on the pathogenesis of IBD mainly focuses on genetic susceptibility， psychosocial stress， visceral hypersensitivity， dysregulation of the brain - gut axis， dysbiosis of the gut microbiota， and abnormalities in the intestinal mucosal immune system. However， these pathogenic mechanisms are not discrete entities. As research advances， the aberrant regulation of the brain - gut axis is increasingly recognized. This article delves into the correlation between gut microbiota dysbiosis and dysregulation of the brain - gut axis in IBD， offering compelling evidence for the interconnected pathogenic mechanisms involving the brain - gut axis and gut microbiota dysfunction in IBD.

To investigate the therapeutic effects of Zengye Chengqi Decoction on chronic transit - type constipation (STC) in rats and its underlying molecular mechanisms, particular attention was paid to the regulation of water channel protein expression, specifically AQP3 and AQP9. An STC rat model was established via intragastric administration of Compound Diphenoxylate Tablets. Eighty - four SD rats were randomly assigned to seven groups: normal control, model control, low - dose Zengye Chengqi Decoction (9.72 g/kg/d), medium - dose Zengye Chengqi Decoction (19.44 g/kg/d), high - dose Zengye Chengqi Decoction (38.88 g/kg/d), positive drug control (Itopride 13.5 mg/kg/d), and a combination group receiving high - dose Zengye Chengqi Decoction plus U0126 (0.1 mg/kg/d). The intervention lasted for 14 days. Results demonstrated that the medium - and high - dose Zengli Chengqi Decoction groups, as well as the combination group, significantly increased fecal output and water content (P < 0.05), shortened the time to first black stool excretion, enhanced intestinal propulsion rates, and improved colonic myoelectric activity (increased frequency and reduced amplitude). Molecular mechanism studies revealed that Zengye Chengqi Decoction inhibits the MAPK/ERK signaling pathway, downregulates AQP3 expression, and upregulates AQP9 expression (P < 0.05). Notably, the efficacy of the high - dose Zengye Chengqi Decoction group was comparable to that of the positive drug group (P > 0.05). In conclusion, Zengye Chengqi Decoction may improve intestinal transmission function in STC rats by modulating the MAPK/ERK - AQP3/AQP9 pathway, providing a potential experimental basis for clinical applications.

Soil contamination with heavy metal is one of widespread global issues. impacting not only food safety but also the safety of Chinese medicinal herbs. At present， the problem of heavy metal exceedances in Chinese medicinal herbs is becoming increasingly severe， warranting a comprehensive review of the types， levels， and potential causes of contamination in plant - based Chinese herbal medicines to address market gaps in China. The findings reveal that heavy metal contamination is both widespread and serious， particularly in rhizome - based medicinal materials， with cadmium （Cd） being the most prevalent pollutant. In addition， the excessive presence of heavy metals in Chinese herbal medicines was directly related to the heavy metal content of planting soils. Therefore， strengthening regulatory measures on heavy metal exceedances offers a potential solution to mitigate the contamination of Chinese medicinal herbs.

Radiation damage to the body is rapid and difficult to recover， which is more important than treatment. This paper summarizes and analyzes the understanding of traditional Chinese medicine on radiation injury， the protective effect of single traditional Chinese medicine or single component and compound prescription in radiation injury， and the research and development of traditional Chinese medicine radiation protective agents under the background of big data era， in order to provide references for the in - depth research of traditional Chinese medicine against radiation injury， the research and development and clinical application of traditional Chinese medicine radiation protective agents.

The sample analysis of traditional Chinese medicine（TCM） is complex， and the pretreatment is the key to obtain satisfactory results for subsequent instrumental analysis. The purification effect of traditional direct extraction method is poor， and the matrix effect interferes greatly. Solid phase extraction has good purification effect， but the high cost， complex operation and time consuming limit the speed of batch screening of harmful residues. At present， QuEChERS（quick， easy， cheap， effective， rugged and safe） technology has been widely used in the rapid pretreatment of food， agricultural products and environmental samples， and has been partially used in the safety detection of TCM. Due to the complexity of sample matrix and the applicability of new materials， the universality of QuEChERS technology is still insufficient. In this paper， the application of QuEChERS technology in the analysis of pesticide residues， veterinary drug residues， mycotoxins and endogenous risk components of TCM was expounded. In order to promote its further application in combination with instrumental analysis， and provide new ideas for green pre-treatment of TCM safety testing.

Based on the data provided by the pelletizing process of an iron and steel enterprise， this paper analyzes the influence of the pelletizing process and the consumption of iron concentrate， pulverized coal， binder and solvent on the emission concentration of sulfur dioxide. The stepwise regression analysis of each influencing factor is carried out by using SPSS software， and a linear regression model affecting the balance of sulfur dioxide in the pellet process is established.

Sentiment analysis is a core task in natural language processing， involving the assessment of emotions or sentiment expressed within texts. In the current research on sentiment analysis， most models rely on bidirectional encoder representations from transformers （BERT） as a feature extractor， focusing mainly on relatively simple binary or ternary tasks. To address fine - grained sentiment classification， the paper introduces a new hybrid dual-channel gated recurrent unit and convolutional neural network （GRU - CNN） sentiment analysis model（GGC）. This model uses generative pre-trained transformer （GPT） as a feature extractor， capturing the deeper meanings in the text more precisely. Based on this， the text features extracted are fed into multi - channel GRU and CNN， capturing both global and local features respectively. The model also incorporates an attention mechanism， which dynamically fuses these two types of features. This mechanism allows the model to allocate different weights to different parts according to their importance， thus capturing key emotional information in the text more accurately. Experimental results show that this method achieves excellent performance in sentiment analysis tasks.

Aiming at the problem of multi - robot multi - objective path planning， a multi - robot multi - objective path planning algorithm based on algorithm fusion is proposed. The algorithm adopts the improved A^* algorithm for global path planning， the improved simulated annealing algorithm for planning multiple robots to ensure the shortest path for the farthest robot in the case of combining the optimal global paths， and uses the DWA algorithm for local path planning to realize the local motion obstacle avoidance of robots. In order to verify the feasibility and superiority of the proposed algorithm， simulation experiments are carried out. The experimental results show that the improved A^* algorithm reduces the number of turns， turning angles， traversal nodes， and planned distance by 16.66%， 41.24%， 23.3%， and 0.77% respectively compared to the classical A^* algorithm. The improved simulated annealing algorithm reduces simulation time， longest path length， and total path by 12.09%， 22.26%， and 5.74% respectively compared to the traditional simulated annealing algorithm. The DWA algorithm can achieve multi robot local path planning and obstacle avoidance.

To address the issues in the Informer model， which does not account for the non - stationarity and frequency domain information in real - world data， a non - stationary long-term time series prediction model is proposed. The core idea involves encoding improvements and frequency domain enhancement. To restore non - stationary information to temporal dependencies， the model uses the time absolute position encoding to extract interdependencies between time points. Additionally， the frequency domain enhancement with channel attention， based on the discrete cosine transform， adaptively captures the interdependencies between channels in the frequency domain， thereby improving predictability. Experimental results show that， compared to other models， the proposed model achieves an average reduction of 58.4% in mean squared error （MSE） on the dataset， with a maximum reduction of 66.5%.

With the rapid development of China's economy， the mileage scale of roads and railways is becoming larger and larger. As an important node of the road network， bridges have also achieved very rapid development. At the same time， because the bridge structure will be eroded and damaged by strain， temperature and humidity， it often causes crack diseases in the bridge structure. Therefore， based on the above reasons， it is necessary to detect the crack disease of the bridge structure in order to reasonably arrange personnel to maintain and maintain the bridge. In this paper， the full convolution neural network model is designed， and the crack image data set is used to train and verify the model， so as to achieve the purpose of crack recognition， and has high accuracy and recall. The full convolution neural network model can be used to identify the forms of three bridge crack diseases， and has great detection advantages compared with other crack identification methods. The theoretical analysis of this paper and the identification and detection technology for bridge crack diseases have positive guiding significance and application value for relevant research.

With the widespread application of artificial Intelligence - generated Content （AIGC） technology in the field of education， more and more students have adopted it as a learning tool for daily use. While it has significantly improve the efficiency of students' knowledge acquisition and expand students' diversified learning paths， AIGC also has many negative effects， especially in AI dependence，posing significant risks to the improvement of students' cognitive ability.From the perspective of Bloom's cognitive classification and triune brain theory， this paper takes the survey of universities in Yunnan Province as a case study to deeply explore the problems and causes that dependence on AIGC may lead to the obstruction of students' development of higher - order cognitive abilities such as critical thinking， the weakening of teacher - student interaction and social relationship， and the widening of the digital ability gap among students. On this basis， from multiple dimensions such as teaching and content process design， curriculum construction and evaluation mechanism， this paper proposes coping strategies to crack the AI - dependent risk and promote the achievement of vocational college students' training goals， providing theoretical basis and practical guidance for the effective application and optimization of AIGC technology in the field of education.

The study focuses on the analysis of the effect of social support on college students' exercise motivation， the relationship between social subjective support， social objective support， and the degree of utilization of social support on college students' dimensions of motivation is sorted out， and the mediating role of exercise motivation in the social support and the amount of physical activity is explored. In this paper， we mainly use the literature method， questionnaire survey method， mathematical statistics method to analyze the gender differences in the study， use correlation analysis to verify the index correlation between the indicators of the dimensions of social support and college students' internal motivation， external motivation， and no motivation in sports， conduct social support regression analysis on the model of the different dimensions of motivation， and construct a model of sports motivation for the mediating effect test. The study found that： exercise motivation has a mediating effect between college students' social support and physical activity； the dimensions of social support have a positive effect on college students' exercise motivation； and there is a significant difference between college male and female students' acquisition of social support and participation in physical activity.