Gold seeds were prepared using chloroauric acid as raw material, and the seed growth method was used to successfully prepare trisoctahedralgold nanoparticles(TOH-Au NPs)with high-index crystal faces.Firstly, trisoctahedral gold nanoparticles with high-index facets of {331}, {441}, and {431} were successfully fabricated through a seed-mediated growth method by precisely controlling the molar ratio of Au³⁺/ascorbic acid(AA)in the reaction system. The surface facets of the as-prepared nanoparticles were systematically characterized using transmission electron microscopy(TEM)combined with projection angle analysis and deconvolution of cyclic voltammetry(CV)curves, which confirmed the successful formation of high-index facets. Furthermore, the influence of cetyltrimethylammonium chloride(CTAC)concentration on the formation of TOH-Au NPs was investigated. The results demonstrated that the increase of AA content significantly promoted the formation of high-index crystal planes and the increasing the CTAC concentration improved the monodispersity of the nanoparticles. This study provided a robust strategy for the controllable synthesis of gold nanoparticles with specific high-index facets, laying a solid foundation for their potential applications in catalysis, sensing, and surface-enhanced Raman spectroscopy(SERS).

Based on UPLC-QE-Orbitrap-MS, the chemical components of Resina Draconis were identified and the analytical method was established for simultaneous determination of the main components in Resina Draconis. The chromatographic column employed is an ACQUITY UPLC BEH C18(1.7 μm, 2.1×100 mm)column, utilizing a mobile phase of acetonitrile-water containing 0.1% formic acid. A high-energy electrospray ionization source(HESI)used and negative ion mode scanning was applied. By integrating the precise molecular masses of each compound, the fragment information from secondary mass spectrometry, the data reported in the literature, and comparisons with reference substances, the chemical components of Resina Draconis were identified, and a method for the simultaneous determination of the mass fractions of loureirin A, loureirin B, loureirin C, loureirin D, resveratrol, and 7,4'-dihydroxyflavone in Resina Draconis was established based on UPLC-QE-Orbitrap-MS technique. The quantitative analysis method used 0.1% formic acid water(A)and 0.1% formic acid acetonitrile(B)as mobile phase, gradient elution program: 0-2 min, 22%-45%B; 2-6 min, 45%B; 6-7 min, 45%-95%B; 7-8 min, 95%-22%B; 8-11 min, 22%B. Flow rate was set as 0.25 mL/min and column temperature was set as 35 ℃. The inject volume was set as 5 μL. HESI source and parallel reaction monitoring(PRM)scan in negative ion mode was applied, with capillary temperature set as 350 ℃, spray voltage set as 2.8 kV, S-lens level set as 35 kV, sheath gas flow rate set as 35 arb, auxiliary gas flow rate set as 10 arb. The results showed that 25 compounds, 22 flavonoids and 3 stilbenes were identified, 9 of which were compared with the reference standard and identified exactly. Loureirin A, loureirin B, loureirin C, loureirin D, resveratrol, and 7,4'-dihydroxyflavone were determined to be the index components of mass fractions. The linear relationship between the six components was good(r≥0.999 2)in the mass concentration range, and the precision, repeatability and stability met the relevant requirements. The recoveries were 92.2%-105.0%. The method was feasible to determine the mass fractions of 14 batches of Resina Draconis. The mass fractions of loureirin B is 0.41%-7.25%, which is the highest component among the 6 index components. In summary, based on systematic analysis of the chemical components of Resina Draconis, the mass fractions determination methods of 6 main components were established in this study to provide reference for the pharmacodynamic material basis and quality control of Resina Draconis.

This study successfully prepared a nasal thermosensitive in situ gel of Acorus tatarinowii volatile oil. The complexation process parameters of Acorus tatarinowii volatile oil were optimized using orthogonal design method. Meanwhile, taking the mass fractions of poloxamer 407(P407)and poloxamer 188(P188)as the key factors and phase transition temperature as the index, the binomial fitting was carried out using central composite design-response surface methodology. Based on this, the preparation process of the nasal thermosensitive in situ gel was further optimized, and its inhibitory activity against γ-aminobutyric acid transaminase(GABA-T)was assessed. The results indicated that the optimal inclusion complexation process parameters for Acorus tatarinowii volatile oil were as follows: the feeding ratio of volatile oil to β-cyclodextrin(β-CD)was 1 mL∶5 g, the feeding ratio of β-CD to H₂O was 1 g∶35 mL, and the complexation temperature was 30 ℃. When the mass fraction of P407 was 19.13% and the mass fraction of P188 was 1.08%, the phase transition temperature of the prepared in situ gel was 32 ℃, which had a promising application prospect. Moreover, the gel exhibited a dose-dependent inhibitory effect on GABA-T, with an IC₅₀ value of 215.7 μg/mL. The thermosensitive nasal in situ gel preparation process established in this study was efficient and feasible, providing a solid theoretical basis and technical support for the development of Acorus tatarinowii volatile oil and its application in the treatment of nervous system diseases.

Network pharmacology, molecular docking, and experimental verification were employed to investigate the mechanism by which Resina Draconis promotes the healing of diabetic foot wounds. The TCMSP database was utilized to screen the chemical constituents and targets of Resina Draconis. Disease targets were retrieved from the GeneCards and DisGeNET databases using keywords such as “diabetic foot ulcer” and “diabetic wound”. A “component-disease-target” network diagram was created using the intersections of component targets and disease targets. Bioinformatic analysis predicted the mechanism of Resina Draconis in facilitating wound healing. Molecular docking was used to investigate the combination of the components of Resina Draconis and key targets. Mouse macrophages were used to study the effects of Resina Draconis and its main components on the expression of the receptor of advanced glycation end products(RAGE)protein. The results showed that 226 common disease targets were obtained by network pharmacological screening. KEGG enrichment analysis suggested that the effect of Resina Draconis on diabetic ulcers may be associated with the AGE-RAGE pathway. The results of molecular docking demonstrated that loureirin A, loureirin B, loureirin C, loureirin D, resveratrol, and 7,4'-dihydroxyflavone exhibited good binding activity with the key target RAGE. Resina Draconis and its main components could significantly reverse the abnormal expression of RAGE protein in mouse macrophages induced by advanced glycation end products(AGEs). In conclusion, the mechanism of Resina Draconis in promoting healing of diabetic foot ulcers was associated with the regulation of the AGE-RAGE pathway and the inhibition of the abnormal expression of the RAGE protein caused by the accumulation of advanced glycation end products(AGEs).

To optimize dynamic network topology construction and resource allocation, a long short-term prediction-based topology construction and control(LSPTCC)framework is proposed. Long short-term memory(LSTM)network and Informer models are employed for long short-term prediction of multivariate time series. Temporal dependencies and non-stationary fluctuations in the data are accurately captured. Based on the prediction results, the enhanced capacity constrained design(ECCD)algorithm is used to construct a minimum spanning tree(MST)and to optimize the connections between nodes to minimize overall transmission losses. Additionally, a maximum network flow algorithm is applied to dynamically adjust resource allocation, ensuring efficient utilization of resources under fluctuating conditions. Experiments conducted on a photovoltaic consumption dataset demonstrate that the proposed framework can accurately predict power generation and consumption. The power transmission losses are effectively reduced by optimizing both the topology and resource allocation. The efficiency and robustness of the proposed algorithms are validated.

In order to improve the optimization performance and stability of the pelican optimization algorithm, a hybrid strategy-based pelican optimization algorithm is proposed. In the generation mechanism of candidate solutions for the pelican algorithm, the preference weight strategy is employed to guide the candidate solutions towards exploring diversity. A random search strategy is used to update partial alternative solution positions, which helps these candidate solutions with poor objective function values escape local optima. A large-range adaptive search radius strategy has been applied to enhance the algorithm, increasing the probability for each candidate solution to discover better solutions. Combined with comparative algorithms, optimization experiments are conducted on 18 test functions. The optimization results are analyzed and Wilcoxon rank sum tests are performed to verify that this improved Pelican optimization algorithm has better optimization performance and stability.

In response to the issues of large parameter size, low detection performance, and inconvenience for deployment on mobile devices in most existing visual assistance system algorithms, a lightweight visual assistance algorithm is designed based on YOLOv7-tiny. The receptive field block(RFB)is used in the network to fuse the feature information of different scales to improve the detection accuracy of objects of different resolution sizes. The non-linearity of activation function sigmoid linear unit(Silu)is used to enhance the fitting ability of the model, improve the learning speed and detection accuracy of the model. Finally, the depth-wise convolution(DWConv)with better performance is selected by comparison experiment to achieve the lightweight of the model. The experimental results show that the parameters of the improved lightweight model are reduced by 52.1% compared with the original model, and the best detection performance is obtained. Compared with other mainstream object detection algorithms, this algorithm achieves more accurate real-time detection of indoor objects with 2.90 M parameters.

How to quickly and accurately identify the climax chapter has become a common problem faced by the majority of readers in their reading choices. In view of this, the method of identifying the climax chapters of Chinese long novel on the basis of accurately portraying the plot of Chinese long novel is explored, which consists of two parts, namely, key element extraction and climax chapter recognition, where the former includes the extraction of key elements such as viewpoint and non-viewpoint passages, keywords of the chapter, main characters, etc., and the latter, based on the establishment of the chapter plot description matrix, introduces the BiGRU model and the multi-head attention mechanism to realize the climax chapter recognition of Chinese long novel. Comparative experiments on Jin Yongs novel corpus show that the proposed method in this paper has better recognition performance compared with models such as Naive Bayesian(NB), Support Vector Machine(SVM), pre-trained model named Roberta-large, and Bi-directional Long Short-Term Memory(BiLSTM). Ablation experiments validate the effectiveness of the main components of the proposed method.

This paper focus on the strategic selection of content release models for video platforms, accounting for consumers dual heterogeneity in platform preferences and service preferences. By constructing a game-theoretic model grounded in new content development, it examines competitive platforms decision-making under both exclusive release and co-release strategies. From the dual perspectives of individual platforms and the overall ecosystem, the research thoroughly investigates optimal content release strategies while designing effective coordination mechanisms to align platform-level decisions with system-wide optimality, concurrently analyzing how strategic choices influence subscription pricing, advertising duration, and new content investment. Findings indicate that when unit licensing fees fall within a moderate range, platforms achieve consensus on optimal release strategies, whereas divergence emerges beyond this threshold; under specific conditions, appropriate profit distribution ensures alignment between platform strategies and system-wide optima. Furthermore, under exclusive release models, content-creating platforms consistently maintain higher optimal subscription prices and longer advertising durations than non-creating platforms. Conversely, co-release strategies lead to reduced platform investments in new content development, decreased total advertising duration across platforms, and convergence in subscription pricing and advertising durations among participating platforms. Finally, numerical simulations demonstrate that as the proportion of premium members, advertising fees, and consumer switching costs increase, the probability of platforms reaching strategic consensus exhibits a U-shaped trajectory—characterized by an initial decline followed by a subsequent increase.

Considering the characteristics of consumer time sensitivity differences, a Stackelberg game model was constructed for two scenarios: retailers only selling on the spot(not cooperating with the platform)and retailers simultaneously selling on the spot(cooperating with the platform). The optimal pricing and ordering strategies of retailers for spot products under different scenarios were analyzed. For the scenario of simultaneous selling on the spot, the drainage effect brought by self pickup of pre-sale stores was introduced, and the optimal discount coefficient, optimal pre-sale cycle, and optimal commission of group leaders for community group buying platforms were given. Research has found that after retailers cooperate with community group buying platforms, the optimal pricing of spot products increases, and the upper limit of consumer time sensitivity to spot products decreases; Only when the pre-sale traffic rate and consumer expectations for pre-sale products are high, can retailers and platforms have the possibility of cooperation. After cooperation, the demand for spot products decreases, which helps to reduce the ordering risk of physical retailers and lower inventory costs.

Under the "dual carbon" goals context, examining the evolutionary game relationship between the government and enterprises within the green financial market using game theory holds significant implications for advancing the green transformation of the macroeconomy. This study employs a phase diagram analysis within the game decision-making theoretical framework and utilizes MATLAB software for empirical analysis. Numerical simulations were conducted to model the dynamic game process between the government and enterprises under green financial support. The research demonstrates that:(1)Within the bilateral regulatory game formed by the government and enterprises under green financial support, the decisions of both players exhibit mutual influence.(2)The government formulates green policies by referencing the statistics on penalized enterprises, while the net revenue of green projects is contingent on their social benefits.(3)Enterprises chosen green innovation strategies are significantly influenced by parameters related to their own profitability and the level of governmental green financial support. The findings provide valuable insights for incentivizing enterprises to pursue green innovation, establishing a dual-agent coordination mechanism within the green financial market system, and forming an effective interactive game framework between the government and enterprises.

By integrating variational mode decomposition(VMD), the dung beetle optimization(DBO)and the bidirectional gated recurrent unit(BiGRU), a price prediction model(VMD-DBO-BiGRU)is proposed, aiming to enhance iron ore futures prediction accuracy. First, VMD is employed to decompose the iron ore futures price series into different modal components, extracting multi-scale price features while suppressing noise. Then, DBO is utilized to optimize the hyperparameters of the BiGRU model, mitigating the risk of local optimum associated with traditional optimization methods. Finally, the optimized BiGRU model is applied to predict each decomposed component, and the final futures price prediction is obtained by linear reconstruction of the predicted results. Experimental findings demonstrate that the proposed model significantly improves prediction accuracy in both single-step and multi-step forecasting, consistently outperforming benchmark models. Moreover, this model provides robust technical support for hedging and investment decision-making in enterprises such as a steel plant in Shandong, contributing to risk mitigation and improved decision efficiency.

The blow-up phenomenon of solutions to a class of fourth-order reaction-diffusion equations are considered. By deriving a Sobolev inequality suitable for high-dimensional spaces, constructing appropriate auxiliary functions, and employing differential inequality techniques, a lower bound for the blow-up time of solutions is provided.

Let M be a discrete-time normal martingale that has the chaotic representation property and S(M)⊂L2(M)⊂S^*(M) a Gelfand triple associated with M. Let L(S(M),S^*(M)) be the space of all continuous linear operators from S(M) to S^*(M) and O be an open subset of R^d. The differential of operator-valued functions from O to L(S(M),S^*(M)) is investigated. Mainly using the 2D-Fock transform as a tool, a differentiability characterization theorem is obtained for operator-valued functions from O to L(S(M),S^*(M)).