To explore the medication rule and pharmacological activity of the core combination of chrysanthemi flos,the drug frequency, complex network and association rules of Chrysanthemum prescriptions were analyzed. The network pharmacology research method was used to construct the "herbs-key target-disease" of core combination drugs. Combined with the forecast results, an in vitro cell model was used to evaluate the pharmacological effects of chrysanthemi flos drug combinations. A total of 92 prescriptions containing chrysanthemi flos were obtained, involving 212 herbs, and 32 herbs with frequency greater than or equal to 10.The association rules showed that the sustain and confidence degree of chrysanthemi flos-Glycyrrhizae radix et rhizoma were the highest, and chrysanthemi flos-glycyrrhizae radix et rhizoma-schizonepetae herba, chrysanthemi flos-glycyrrhizae radix et rhizoma-schizonepetae herba-chuanxiong rhizoma, chrysanthemi flos-glycyrrhizae radix et rhizome-schizonepetae herba-chuanxiong rhizome-saposhnikoviae radix were next to each other, respectively. Network pharmacology analysis showed that the core drug combination of chrysanthemi flos-glycyrrhizae radix et rhizoma could treat tumors, digestive system diseases, nervous system diseases and other diseases. In vitro cell activity study showed that the combination of chrysanthemi flos and glycyrrhizae radix et rhizoma had a better inhibition rate on NO levels than the single drug. Compared with the single use of chrysanthemi flos, the combination of drugs showed more significantactivity, reflecting the scientificity of compatibility of TCM in clinic.

The pharmacological method of traditional Chinese medicine serum was used to explore the effect of Banxiaxiexin Decoction (BXD) on the proliferation of different gastric cancer cells in vitro. Additionally,the preparation conditions of BXD drug-containing serums were examined toobserve the pharmacodynamics of gastric cancer cells. Blood was collected at intervals of 30, 60, 90, 120, and 150 minutes after high, medium, and low intragastric gavage of BXD to prepare drug-containing serums. Based on the selected gavage doses and blood collection times, MGC803, MKN45, AGS, and HGC27 cells were exposed to 5%~50% concentrations of drug-containing serum, and CCK8 assay was employed to detect the inhibitory effects of different gavage doses, blood collection times, and volume fractions of BXD drug-containing serum on the proliferation of gastric cancer cells. The test results showed that compared with other time points, all BXD drug-containing serums had the strongest inhibitory effect on gastric cancer cells at the blood collection time of 120 minutes. Compared with the low-dose BXD group, the medium-dose BXD group had a strong inhibitory effect on gastric cancer cells, and there was no significant difference between the medium-dose and high-dose BXD groups. Based on the analysis of the inhibitory effects of 10 concentrations ranging from 5% to 50% on different gastric cancer cells, the IC₅₀ values of all drug-containing serums collected 120 minutes after gavage in the medium-dose BXD group were 20%. Analysis of the inhibitory effects of different gavage doses, blood collection times, and volume fractions on different gastric cancer cells revealed that a medium dose of BXD, blood collection time of 120 minutes,and a volume fraction of 20% had the strongest inhibitory effect on gastric cancer cells. Therefore, the medicated serum with a blood collection time of 120 minutes and a volume fraction of 20% in the medium-dose BXD group had the best pharmacodynamic effect on gastric cancer cells.

Based on bioinformatics, this study validates the mechanism of action of Haitongpi-Tougucao (compound Haitongpi) in inhibiting lipopolysaccharide (LPS)-induced ferroptosis in rat inflammatory chondrocytes. Bioinformatics tools were used to predict the mechanism of action of ferroptosis in osteoarthritis and identify pathways for validation. The key techniques used were as follows: the detection of ferrous ion content and reduced glutathione (GSH) content using relevant kits; the detection of cell viability and the levels of related cytokines IL-1β, IL-6, and TNF-α using the enzyme-linked immunosorbent assay (ELISA) after dosing; and the use of protein immunoblotting (western blot, WB) to detect the protein expression levels of NLRP3, Caspase-1, ASC, and GPX4, a gene that inhibits ferroptosis, related to the NLRP3 inflammasome pathway in each group. The results revealed that the ferrous ion content was significantly decreased,while the GSH content was significantly increased; the ELISA experiment showed that the levels of inflammatory factors IL-1β, IL-6, and TNF-α were decreased in each group administered with the drug compared with those in the model group; the WB results showed that the expression levels of NLRP3, Caspase-1, and ASC proteins were significantly decreased and GPX4 protein expression levels were significantly increased in each group administered with a specific dosage of the drug compared with those in the model group. Therefore, the compound Haitongpi can intervene in the ferroptosis of inflammatory chondrocytes by mediating the NLRP3 inflammasome pathway, thereby achieving the purpose of osteoarthritis treatment.

Based on network pharmacology, molecular docking, and in vitro experiments, this study explores the molecular mechanism of quercetin against colorectal cancer through the p53 signaling pathway. The drug targets quercetin, and the disease targets colorectal cancer, which was obtained via the Traditional Chinese Medicine Systems Pharmacology Database and Analysis Platform and Gene Cards database, respectively. The common drug and disease targets were mapped using a Venn diagram, and the protein-protein interaction network map was constructed with the help of the String database and Cytoscape_v3.7.2 software. At the same time, GO and KEGG enrichment analysis, molecular docking, core target expression, and survival analysis were also performed. Finally, cell proliferation activity, level of apoptosis, cell cycle arrest, and changes in the expression of core targets and key proteins of the p53 pathway were detected through cellular experiments. Network pharmacology suggests that AKT1 and TP53 are the core targets of quercetin against colorectal cancer, GO and KEGG analysis demonstrate that quercetin is mainly involved in the PI3K/Akt and p53 signaling pathways, molecular docking demonstrates that quercetin exhibits strong binding activity with the core targets AKT1 and TP53, and TP53 is found to be both highly expressed in colorectal cancerand also affect the survival and prognosis of patients with colorectal cancer. The results of cellular experiments show that quercetin can inhibit the proliferation of HCT-116 cells, induce G₀/G₁ cell-cycle arrest in HCT-116 cells, and promote apoptosis. This mechanism may regulate core targets such as TP53 and AKT1, activate the p53 signaling pathway, participate in the proliferation and apoptosis of HCT-116 cells, and thus function to resist colorectal cancer.

To improve the corrosion resistance of traditional pure Zn coatings, we used 30 mm diameter Q195 welded pipes as the substrate and prepared a series of hot-dip galvanized alloy coatings by adding trace amounts of alloy elements such as Al, Ni, and Re to the Zn bath. First, the main factors affecting corrosion resistance were identified through a four-factor and three-level orthogonal experiment. Then, the experiment was further improved for the primary factors, and single-factor experiments were conducted to obtain the optimal parameter combination. Finally, the microstructure characteristics and corrosion resistance of the coating were studied and analyzed using methods such as high and low temperature humidity test, neutral salt spray test, metallographic analysis, and scanning electron microscopy. Results indicate that the introduction of alloying elements suppresses the growth of ζ layer, which makes the coating structure compact, and improves the corrosion resistance of the coating. The coating prepared in this study could remain rustless throughout a 72 h salt spray test and a 120 h humidity test. The process for preparing the alloy coating is same as the existing production process for traditional Zn coatings.

Although regression analysis can predict some drape indicators, they have problems such as low prediction accuracy and the inability to calculate some indicators. To overcome these issues, this study proposes a new method using genetic algorithm to optimize BP neural network (GA-BP neural network) to improve the prediction accuracy of real fabric drape. In this study, we designed a GA-BP neural network model, selected 100 pure cotton woven fabric samples from the fabric database, including 80 training samples, 10 test samples, and 10 validation samples, used the genetic algorithm to optimize the parameters of the neural network, and used correlation analysis to optimize sample input parameters to improve the prediction performance of the model. The results of the drape coefficient prediction for the 10 test samples show that compared with the traditional BP neural network, the average absolute percentage error of the BP neural network optimized by the genetic algorithm decreased from 12.74% to 7.03%. Furthermore, we used an empirical equation to identify error cycles and concluded that the optimal number of hidden layer nodes is 9. This study indicates that the GA-BP neural network can effectively improve the accuracy of fabric drape prediction and has important application value for the virtualization of fabric drape performance.

In the pursuit of bridging the energy demand gap and striving for a pristine environment, ammonia fuel has emerged as one of the most promising fuels of the future. Zero carbon emissions, high energy density, and low production and transportation costs make it a promising candidate. However, challenges persist regarding the overall efficiency of pure ammonia combustion. This paper proposes a regenerative cycle in an ammonia gas turbine that matches the reheat Rankine cycle, considering the maximum temperature of the exhaust gas from the turbine and phase transition temperature of liquid ammonia in the turbine cycle. We conducted a thermodynamic analysis and evaluated the system performance based on the first and second laws of thermodynamics and analyzed the influence of the inlet temperature and pressure of the ammonia gas turbine on the overall cycle performance. The results indicate that the combined cycle has improved the efficiency of the ammonia gas turbine by up to 33.38% and the maximum efficiency achieved by the combined thermodynamic cycle is 60.13%,when the inlet temperature of an ammonia gas turbine does not exceed 1 400 ℃ and the inlet pressure remains below 0.5 MPa. Furthermore, the combined cycle exhibits outstanding thermodynamic properties and energy recovery rates. Additionally, the efficiency of the regenerative cycle increases with increasing the inlet temperature and pressure of the ammonia gas turbine, provided that the inlet pressure does not exceed 5 MPa. New perspectives have been proposed to enhance the operational efficiency of ammonia-powered gas turbines and promote the efficient utilization of ammonia as a fuel. This study proposes novel perspectives towards enhancing the efficient utilization of ammonia fuel and the actual efficiency of ammonia gas turbine cycles, providing a forward-looking exploration for the energy utilization of ammonia gas turbine systems.

Currently, the steam pipelines in cigarette factories are characterized by numerous points, extensive lengths, and broad coverage. The thermal conversion factor of these pipelines is high, and their steam energy consumption accounts for a large proportion of the total energy consumption. Therefore, investigating the performance of the insulation layer of steam pipes is of considerable importance for improving steam utilization efficiency and reducing heat loss in the steam pipe network. In this study, the thermal conductivities of insulation layers made of four insulation materials were measured using the steady-state method at different temperatures to elucidate the relationship between the thermal conductivity of an insulation material and the steam temperature, thereby identifying the efficient insulation materials suitable for application scenarios. The appropriate insulation layer thickness was determined using the maximum allowable heat loss method and economic thickness method. Moreover, the thermal conductivities of insulation layers with different service lives were measured. Results indicate that the thermal conductivity increased linearly with the increasing service life. Factors causing the deterioration of insulation layer performance were incorporated into the model to study the relationship between the operating cost of an insulation layer and its outer diameter and service life. For insulation layers with different designed service lives, their optimal outer diameters and operating costs were calculated using the economic thickness method. Results show that considering material aging factors in the design of insulation layer thickness can reduce cumulative costs by 10.7% within the designed service life. However, when the service life expires, the operating cost of a design that considered the aging issue is higher than that of a design that did not consider the aging issue owing to increased heat loss as a result of aging of the insulation layer. The insulation layer can be designed to reduce steam heat loss and improve steam utilization efficiency as well as provide theoretical guidance for the green, low-carbon, and high-quality development of cigarette factories.

Supercritical CO₂ plays an important role in many applications such as nuclear power generation, solar power generation, cryogenic refrigeration, and aerospace. Currently, the majority of studies on supercritical CO₂ convective heat transfer in tubes focus on the temperature range near the critical point, while the heat transfer patterns at high temperature and pressure far from the critical point remain unclear and need to be further studied. In this study, numerical simulations were performed to analyze the effects of mass flow, inlet temperature, system pressure, heat flux density, and tube diameter on the convective heat transfer coefficient at high temperature and pressure, as well as the effects of buoyancy and flow acceleration caused by operating conditions on the heat transfer characteristics. The results show that the convective heat transfer coefficient increases with increasing mass flow, inlet temperature, system pressure, and heat flux density. The difference in convective heat transfer coefficient gradually grows along the flow direction under different heat flux densities. Convective heat transfer coefficient decreases with increasing tube diameter. Compared with the heat transfer patterns near the critical point, heat flux density and tube diameter exert different effects on the convective heat transfer coefficient. In general, the effects of pressure on the convective heat transfer coefficient are small. This study provides significant values to understand the law of supercritical fluid heat transfer and guide the design of efficient and safe heat exchanger.

With the extensive development of intelligent transportation and eco-friendly travel, a low-energy task-offloading method based on edge computing in the internet of vehicles (IoV) is proposed to address the dual challenges of low-latency service demands and energy conservation in the IoV. In the context of multivehicle single-cell scenarios on public roads, this study explores the task-offloading requirements of vehicles in motion and systematically investigates the allocation of computational resources. To fully utilize computing resources, this study not only considers the computing power of vehicles but also introduces a new approach for offloading tasks to vehicle servers traveling in the same direction or parked along the roadside as well as to edge servers in roadside units. This enables the effective integration and efficient sharing of computing resources, thereby remarkably enhancing the processing capabilities of the IoV. Furthermore, this study employs an improved particle swarm optimization algorithm to optimize offloading power and task allocation ratios. Extensive simulation tests revealed that the proposed method significantly reduced the energy consumption of vehicle tasks and improved the service quality and energy efficiency of the IoV.It helps to promote green transportation and sustainable development, and lays a solid foundation for energy optimization and efficiency improvement of future intelligent transportation systems.

Energy-saving metro train control is closely related to the vertical track alignment (VTA) design, and both have a significant impact on operating costs. To further reduce operating costs based on optimized train control, this study proposed a collaborative optimization model for the VTA design phase. This model optimizes the bidirectional train control strategy and VTA of a metro section with the goal of minimizing energy consumption and maintenance costs simultaneously, while adhering to the constraints of scheduled train control and the requirements of the "Metro Design Code." Given the numerous factors affecting the maintenance costs of wheels and rails, a train-track dynamic simulation model was developed to calculate these costs. Based on this, an algorithm combining the pseudospectral method and brute force search was designed to solve the collaborative optimization model. The effectiveness of this optimization method was validated using three sections of the Guangzhou metro line. The results indicate that, compared to the method of optimizing scheduled train control alone on the actual VTA, the collaborative optimization model is more effective in saving operating costs, reducing the average operating costs by 21% across the studied sections. This study can provide novel approaches and theoretical support to further reduce metro operating costs, which contributes to promoting sustainable development of metro.

The prediction of highway tolls is affected by complex factors such as holidays and unexpected events. Traditional prediction methods often fail to fully account for intricate interactions between these multiple factors, resulting in less-than-ideal prediction accuracy. By leveraging the self-attention mechanism, large language models can better fit complex spatiotemporal data and have enhanced feature learning capabilities, making them highly effective for precise highway toll prediction. Therefore, this study proposes a highway toll prediction model based on iTransformer. This model embeds temporal information as an independent dimension into the input sequence and reverses the roles of the self-attention mechanism and feed-forward network, thereby allowing the model to more accurately capture the dynamic features of time series and correlations between multiple variables. Case studies show that the proposed model improves the average prediction accuracy by 23.47% and 17.84% compared with the SARIMA and LSTM models, respectively, in regular scenarios. In irregular scenarios, the model demonstrates even better predictive performance, improving the accuracy by 70.92% and 45.64%, respectively. A sensitivity analysis of the proposed model indicates that it is highly sensitive to the number of feed-forward network layers and stacked encoder layers but is less sensitive to changes in the number of attention heads. Thus, this study provides a new methodological approach for addressing the challenges associated with toll prediction in complex traffic environments and has significant implications in terms of improving the accuracy of highway toll predictions.

Lane detection is a remarkable practical application of computer vision technology in the field of transportation. However, existing semantic segmentation network models still face certain challenges such as insufficient accuracy and blurred edges in road semantic segmentation tasks. To address these issues, an improved lane segmentation network architecture based on the UNet model is proposed. First, a dual attention module (DAM) is introduced in the skip connections of the UNet model, which prioritizes the importance of lane lines and effectively reduces noise interference. Additionally, dynamic snake convolution (DSConv) is employed to replace traditional convolution methods, enhancing the network’s lane detection ability. To enhance the comprehensiveness and accuracy of lane detection in underexposed or dark backgrounds, an improved adaptive Gamma correction method is introduced in the image preprocessing stage. Furthermore, atrous spatial pyramid pooling (ASPP) technology is introduced at the end of the encoder to enhance network performance. Experimental results show that this model achieves an accuracy of 98.93% on the TuSimple dataset while meeting real-time requirements. Compared to five other semantic segmentation-based lane detection algorithms, the proposed algorithm demonstrates superior recognition performance, thus validating its effectiveness.