As a new vitamin A derivative， hydroxypinacolone retinoate （HPR） has the disadvantages of poor photothermal stability and low water solubility， resulting in poor transdermal therapeutic effects.In order to improve the transdermal performance of HPR， it was combined with tocopherol acetate （VE）， and HPR/VE composite nanoemulsions were prepared using high⁃gravity technology.The average particle size of the HPR/VE nanoemulsions （with mass fractions of HPR in the range 0.6%-3.7%） prepared under the optimized conditions of a high‑gravity level of 8 and a cyclic emulsification time of 5 min was in the range 89.7-131 nm， the polydispersity index （PDI） was 0.13-0.15， and the droplets had a regular spherical morphology.There were no significant changes in the particle size， drug loading of HPR， pH and zeta potential of the HPR/VE nanoemulsions after being stored at 4 ℃ and 25 ℃ for 3 months. After centrifugation at 3 000 r/min for 30 min，the particle size and PDI of the nanoemulsion had not changed significantly， and there was no crystal precipitation or emulsion stratification.Skin penetration experiments showed that the cumulative penetration amount of the HPR/VE nanoemulsion within 12 hours was 8.33 μg/cm²， and the intradermal retention amount was 17.07 µg/cm². The cumulative penetration amounts within 12 hours are 2 times and 4 times that of the commercially available emulsion and the crude drug， respectively. The intradermal retention amounts are 1.5 times and 2.8 times that of the commercially available emulsion and the crude drug， respectively.The results show that the HPR/VE nanoemulsion prepared using high‑gravity technology has the characteristics of small particle size， uniform distribution and regular morphology， and exhibits good storage stability and skin permeability， suggesting it has potential applications in the field of transdermal drug delivery.

The boiling points of benzene and n⁃hexane are similar， and they readily form an azeotropic mixture. Current separation processes have high energy consumption and low separation efficiency.In order to efficiently separate the benzene/n⁃hexane system， an asymmetric dicationic ionic liquid 1⁃（3⁃（trimethylammonium）propan⁃1⁃yl）⁃3⁃methylpiperidine dibromide （［N₁₁₁C₃MPi］Br₂） （a hydrogen bond acceptor） was synthesized. ［N₁₁₁C₃MPi］Br₂ was subsequently mixed with ethylene glycol （a hydrogen bond donor） in different proportions to prepare a deep eutectic solvent ［N₁₁₁C₃MPi］Br₂/ethylene glycol.The density and viscosity of the deep eutectic solvent were measured in the temperature range 288.15-323.15 K. The results show that the density of the deep eutectic solvent decreased linearly with increasing temperature， and the viscosity decreased gradually. At 313.15 K， the viscosity of ［N₁₁₁C₃MPi］Br₂/ethylene glycol （molar ratio of 1∶6） was only 25.57 mPa·s.Using the deep eutectic solvent as the extractant， the conditions for separation of benzene/n⁃hexane were optimized by orthogonal tests， namely a separation time of 1.5 h， separation temperature of 313.15 K， feed ratio （volume ratio of deep eutectic solvent to benzene/n‑hexane solution） of 1∶1. Under the optimized conditions， the distribution coefficient and selectivity of benzene were 9.29 and 120.83， respectively， which were significantly higher than those reported using sulfolane and other ionic liquid systems.

Although UV flash is currently the most rigorous flash algorithm in the field of dynamic simulation， the algorithm suffers from problems such as poor stability and significant solution errors.By fully considering the influence of composition changes on various factors in the separation process， the traditional UV flash algorithm has been improved by simplifying the iterative equations and optimizing the calculation order， and applied to the dynamic simulation of distillation columns. A dynamic simulation model of distillation columns based on the improved UV flash algorithm was proposed.The improved UV flash algorithm was verified using four flash cases under different operating conditions and one actual industrial distillation column case. The results show that the improved UV flash algorithm outperforms the traditional UV flash algorithm in terms of computational efficiency， accuracy and robustness.Compared with the conventional UV flash algorithm， the calculation results for the distillation column model based on the improved UV flash algorithm are closer to the simulation results of the HYSYS distillation column dynamic model. The calculation time is reduced from 1.331 s to 0.310 s， and the relative errors of the simulation results of the intensity variable and the final product composition are less than 1% and 1.5%， respectively. The results show that the improved UV flash algorithm is more suitable for calculations of the dynamic simulation of a distillation column， and can provide a reliable flash algorithm for the establishment of a digital twin model of the dynamic process in a distillation column.

In order to study the interaction between particles with different sizes in a cyclone separator and the influence of coarse particles on the separation efficiency， the local flow at the inlet of the separator is simplified as a wall‑attached jet， and the coarse particles are idealized as rigid spheres. The method of large eddy simulation and discrete phase model coupling is employed to study the disturbance of near⁃wall balls on micron⁃sized particles for gap ratios of 0.125， 0.25 and 0.5 between the ball and the wall.The results show that the flow field fluctuates significantly in the characteristic attenuation region of the jet （θ=20°-75° in the circumferential direction）.In the range θ=20°-30°， with increasing gap ratio， the tangential velocity distribution behind the ball gradually approaches that without the ball. When the gap ratio is 0.5， the difference between the tangential velocity distribution and that without the ball is the smallest.When the gap ratio is 0.25， the peak value of vorticity in the wake vortex area behind the ball is the largest， and the ball has the maximum influence on the intensity of the jet flow direction vortex.For particles with d _p=125-250 μm， the ball has little effect on the residence time or the number of escaped particles.Compared with the absence of the ball， the average residence time of particles with d _p=125-250 μm is reduced， the percentage of escaped particles is increased， and the separation efficiency is improved. When the gap ratio is 0.25， the residence time of particles is the shortest， and the percentage of escaped particles is the highest.

Commercial montmorillonite Cloisite^® 30B （C30B） was activated by hexamethylenediisocyanate （HDI）， isophorone diisocyanate （IPDI） and toluene diisocyanate （TDI）， and then terminated and polymerized by caprolactam （CLA）. Nine types of modified C30B were prepared， and their structures were characterized by FT⁃IR， SAXS， TGA to identify the optimum modified C30B. The effects of C30B and modified C30B on the crystallization properties， mechanical properties and water absorption of nylon 6（PA6） were studied. Both C30B and modified C30B can improve the mechanical strength and crystallization rate of PA6， and promote a polymorphic transformation. Furthermore， the uniform dispersion of the montmorillonite and the increased crystallinity reduced the water absorption of the composite. Compared with PA6/C30B（mass ratio 199/1）， the elastic modulus of PA6/C30B‑TDI（mass ratio 199/1） composite increased by about 12.6%， the yield strength increased by about 9.2%， and the elongation at break increased by about 72.6%， showing that using C30B⁃TDI results in more effective modification of the mechanical properties of PA6.

The rapid capacity decay of high-nickel （Ni≥90%） layered oxides during long cycling limits their commercial application. Doping has been widely studied as a method to improve the electrochemical properties of cathode materials.This study explored the position-specific introduction of magnesium ions （Mg²⁺） to improve the structural stability of LiNi_0.9Co_0.05Mn_0.05O₂ cathode materials. Experimental results showed that， in coin-type lithium-ion half-cells， Mg-doped cathode materials exhibited superior electrochemical performance compared to undoped materials. Notably， after 100 cycles at a rate of 0.5 C， the 0.3% Mg²⁺-doped LiNi_0.9Co_0.05Mn_0.05O₂ cathode material retained 74.06% of its initial specific capacity. Physical characterization and analysis revealed that the doping treatment changes the exposure and lattice structure of the active crystal surfaces of the materials， effectively suppressing cation mixing and unfavorable phase transitions， thereby enhancing the cycling stability of the material. These changes contribute to improved long-term cycling performance of the battery. The doping method proposed in this study effectively improves the performance of high-nickel cathode materials and provides guidelines for the development of higher energy density lithium-ion batteries （LIBs）.

Organic silicon high-temperature resistant coatings have a wide range of applications in many fields. However， due to the cracking of organic silicon resin at high temperatures， the corrosion resistance of organic silicon high temperature-resistant coatings decreases after heating， and they cannot meet the requirements of some harsh application scenarios. In order to address the above problems， a two-component titanium ester-modified siloxane resin was used as a film-forming material， combined with reactive fillers， low-melting fillers and anti-corrosion fillers to prepare a high temperature （600 ℃） and corrosion-resistant coating. Heat resistance tests demonstrated that the resulting coatings can withstand heating for more than 500 hours at 600 ℃. In other tests， the coatings demonstrated a resistance to neutral salt spray and heat and humidity for over 1 200 hours， both before and after heat resistance tests. Optical microscopy and electrochemical impedance spectroscopy revealed the mechanism of temperature and corrosion-resistance. These coatings exhibit excellent heat resistance and corrosion resistance， and have potential for a long service life in high-temperature and corrosive environments.

Injecting gas into the natural circulation loop of a liquid lead bismuth eutectic （LBE） can effectively improve its natural circulation ability. In order to investigate the effect of varying gas injection volumes on the heat transfer characteristics of liquid LBE flowing in the circuit and the efficiency of gas lift， numerical simulations were carried out to investigate the effect of seven different gas injection rate conditions ranging from 0 to 750 NL/h on the velocity field， gas-phase volume fraction， friction pressure drop， heat transfer characteristics， and lifting efficiency of the circuit. The calculation results show that the liquid LBE mass flow rate increases from fast to slow and then decreases slightly with the increasing gas injection rate； the gas phase volume fraction in the rising pipe and the falling pipe increases with increasing gas injection rate； the total pressure drop decreases with increasing gas injection rate， and the rate of decrease slows down， and the pressure drop in the rising pipe decreases rapidly and then rises slightly； the change of the Nu number is affected by the mass flow rate of liquid LBE and the volume fraction of the gas phase； the Nu number increases with increasing Re number in the case of low gas injection rates， and decreases with increasing volume fraction of the gas phase in the case of high gas injection rates； the efficiency of the air-lift shows decreases with increasing injection rate， and the rate of decrease slows down gradually.

To improve the efficiency and accuracy of Chinese painting seal recognition， this paper proposes a two-stage seal recognition algorithm based on EfficientNet and scale⁃invariant feature transform （SIFT）. In the initial stage of seal extraction， preprocessing techniques are utilized to optimize image quality. HSV（hue， saturation， value） color space features are employed to identify potential seal regions， followed by the application of the EfficientNet model to extract image features from these candidate regions for classification and the retrieval of seal images. In the subsequent seal matching stage， the SIFT algorithm is employed to extract image features from the seal images， and nearest neighbor matching is conducted to obtain the final seal information. A dataset comprising 4 000 images for seal extraction and a standard seal database consisting of 14 790 records of seal information are created to evaluate the algorithm’s effectiveness. The new method achieves an accuracy of 95.25% in seal image extraction and 98.20% in seal matching using the self-built dataset. Furthermore， the method affords robust handling of image rotation and scale changes.

Thyroid nodule elastography ultrasound images contain information about nodule stiffness and morphology. Accurate segmentation of these images can significantly enhance the accuracy of thyroid nodule diagnosis. While deep learning⁃based thyroid nodule ultrasound image segmentation has shown promising results， its accuracy in elastography image segmentation remains limited due to the small dataset sizes. To address the low segmentation accuracy of thyroid nodule elastography images， we propose a transfer learning⁃based method that leverages shared features between grayscale ultrasound and elastography images. We first introduce a large-kernel attention mechanism to develop a multi⁃scale feature extraction network， capturing both local structural details and long-range dependencies in thyroid nodules. U⁃Net is then employed as the backbone network to build the grayscale ultrasound segmentation model. On this basis， we use the conditional generative adversarial network （CGAN） to align the feature distributions of grayscale ultrasound images （the source domain） and elastography ultrasound images （the target domain）. A weight-sharing strategy is applied to transfer the feature extraction parameters， establishing a segmentation model for elastography ultrasound images. Experimental results show that the proposed segmentation method affords Dice coefficient， intersection over union （IOU）， Recall， and Precision values of 77.09%， 65.20%， 78.19%， and 81.05%， respectively.

Segmented annular seals are an important part of the sealing technology of rotating mechanical power equipment. At present， there is a lack of research on the characteristics and performance of segmented annular seals under high working condition parameters （high speed and high temperature）.Therefore， a fluid-solid-thermal coupling numerical analysis model of a segmented annular seal under high-speed and high-temperature conditions was established. The influence of working condition parameters （rotational speed， temperature and sealing pressure difference） on the performance of the segmented annular seal was analyzed， and the accuracy of the model was verified by experiments.The results indicate that the stress distribution in the main sealing surface of the sealing ring is relatively uniform， with the maximum stress occurring at the lap joint.Only slight deformations occur on the sealing surface， and the strain at the lip edge and the lap joint is considerable.The overall temperature distribution in the sealing ring is relatively uniform， with a significant temperature rise at the main sealing surface.The viscosity of the fluid film medium on the main sealing surface remains constant， and the medium density is a maximum at the inlet.The leakage of the segmented annular seal decreases slightly with increasing rotational speed and temperature， and increases significantly with increasing sealing pressure difference， confirming that the leakage is greatly affected by the sealing pressure difference.There is mixed lubrication on the main sealing surface of the sealing ring. There is no obvious wear on the secondary sealing surface of the graphite ring both before and after operation， whereas the wear of the main sealing surface is more severe.

The hydraulic system of unmanned minesweepers is prone to failure which can be difficult to diagnose. An XGBoost fault diagnosis model based on an improved gold rush optimizer （GRO） algorithm is proposed to improve the optimization efficiency of the GRO by means of Piecewise chaotic mapping and the Cauchy variational strategy. A fault simulation model of the hydraulic system of an unmanned minesweeper is established， and fault data for common fault types， including solenoid valve faults， hydraulic cylinder faults， hydraulic pump internal leakage faults and filter blockages are obtained. These data are then used as the input to the diagnostic model to classify and diagnose the unmanned minesweeper hydraulic system faults. Finally， the models before and after modification are compared， and the results show that the modified diagnostic model affords improved accuracy in unmanned minesweeper hydraulic system fault diagnosis.

In the light of the problems of complex feature extraction and low accuracy of fine-grained classification in traditional industrial equipment health state assessment， this paper proposes an equipment health state assessment method based on convolutional neural networks and an improved hierarchical Softmax strategy （hierarchical softmax convolutional neural network， HSCNN）. By using convolutional neural networks（CNN） to learn feature representations from equipment health state data and mining the intrinsic features of the equipment state， and by introducing a hierarchical Softmax strategy for multilevel classification of the equipment health state， the original fine-grained classification task is converted into a hierarchical decision-making process. A Huffman tree is then used to solve the problem of the traditional hierarchical Softmax strategy， which may lead to the waste of computational resources and the degradation of the model performance， and realize the efficient and accurate assessment of the equipment health state. The proposed method was employed to assess the operational health status of large-scale coal mining equipment. Our new method of assessing the health status of equipment based on CNN and the improved hierarchical Softmax strategy has a higher accuracy than the traditional methods employing multilayer perceptron （MLP）， recurrent neural networks （RNN）， CNN， hierarchircal Softmax convolutional neural networks （CNN‑LSoftmax）， and long short-term memory networks （LSTM）.

Health⁃safety⁃environment （HSE） performance evaluation is a key tool to improve the comprehensive management level of electric power enterprises， and can effectively reduce the probability of engineering accidents and ensure the implementation of electric power construction. However， most existing techniques do not consider the credibility of each evaluation element， and hence have relatively low resolution. A method of HSE performance evaluation for electric power construction projects is proposed based on FPN⁃LSTM. Firstly， a fuzzy Petri net （FPN）， which can be regarded as the HSE performance evaluation model for electric power construction projects， is established based on an on-site HSE data statistics table. Secondly， the confidence values of initial， intermediate， and termination places in the FPN model are determined based on the percentage difference of node data and linear interpolation calculation. The HSE performance evaluation of electric power construction projects can then be carried out. Thirdly， a long short-term memory （LSTM） algorithm is introduced to train and update the confidence in FPN， thereby maximizing the optimization of HSE performance evaluation results. The HSE performance evaluation of the Luxi Branch was used to verify the FPN-LSTM model and compare it with existing models.The FPN⁃LSTM model can accurately and systematically reflect the HSE performance level of an entire electric power construction project， as well as precisely and effectively clarify the implementation and distribution characteristics of evaluation indicators at all levels. In summary， the FPN-LSTM method offers a scientific， systematic， and precise decision-making tool for HSE managers.

Metal oxide semiconductor （MOS） gas sensors are widely used in gas sensing due to their high sensitivity， low cost， and excellent stability. However， their cross-sensitivity to similar gases limits their detection accuracy in mixed gas environments. To enhance the qualitative recognition performance of gas sensors， this study focuses on a WO₃-based sensor. An arithmetic optimization algorithm （AOA） is employed to optimize the parameters of the support vector machine （SVM）， aiming to improve both accuracy and computational efficiency. Experimental results indicate that the WO₃ sensor exhibits high selectivity toward triethylamine （TEA）. When combined with the optimized SVM model， the recognition accuracy for binary gas mixtures exceeds 85%， representing a 6% improvement over the original SVM. This method demonstrates superior classification accuracy and efficiency under complex gas conditions， offering a promising approach for real-time gas detection.