The solubilities of 4, 4'-dimethylbenzophenone (4, 4'-DMBP) in 10 single organic solvents and 1 binary organic solvent (ethanol+ethyl acetate) were determined by a static equilibrium method at 293. 15-333. 15 K under atmospheric pressure. The solubility of 4, 4'-DMBP and temperature were correlated by activity coefficient models (Wilson equation and NRTL equation) and empirical models (Apelblat equation and Van't Hoff equation), and the corresponding model parameters were obtained. The results showed that the order of solubility of 4, 4'-DMBP in single solvents at room temperature (298. 15 K) was as follows: benzene > propyl acetate > ethyl acetate > methyl acetate > 1-butanol > 2-butanol > 1-propanol > 2-propanol > ethanol > methanol. The binary solvent of ethanol and ethyl acetate had no solubilization and desolvation effect on 4, 4'-DMBP. The fitting effect of the Wilson model in single solvents was the best, with a relative standard deviation (ARD) of 1. 13%, and a root mean square deviation (RMSD) of 0. 19%. Analysis of solvation effects and preferential solvation effects showed that the contributions of π^* and V_Sδ_H² /(100RT) to the solubility were 46. 43% and 46. 38%, respectively, confirming that their contribution to the solubility of 4, 4'-DMBP was dominant. In the binary solvent of ethanol and ethyl acetate, when the ethanol content is high, 4, 4'-DMBP is preferentially solvated by ethyl acetate.

In natural gas transportation, low-temperature separation methods (J-T valve refrigeration, mechanical refrigeration) are often used to separate liquid hydrocarbons and water to control the dew point and ensure pipeline transportation safety. Given the significant differences between the two processes, we have simulated and analyzed the J-T valve refrigeration and mechanical refrigeration from both energy and economic aspects. The results show that the hydrocarbon dew point drop of the two processes is close, while the water dew point drop of the mechanical refrigeration process is higher. On the basis of exergy analysis, the J-T valve refrigeration process has a higher exergy destruction (5 159 kW) than the mechanical refrigeration process (1 961 kW). The mechanical refrigeration process has a higher system heat transfer efficiency, which makes its overall efficiency higher than J-T valve refrigeration. Detailed analysis shows that the avoidable energy destruction (69. 8%) of the mechanical refrigeration process is higher than the J-T valve refrigeration process (66. 7%), with greater improvement potential and lower cost. In terms of capital investment, the mechanical refrigeration process uses an external refrigeration system to provide cooling capacity, resulting in higher equipment purchase cost, but it retains the pressure energy of the feed gas. Under the same export conditions, the operating cost and the comprehensive investment cost of the mechanical refrigeration process is lower than the J-T valve refrigeration process. Over the whole operation cycle, the mechanical refrigeration process resulted in a cost advantage of 812. 64×10⁴ dollars. A sensitivity analysis of the two processes shows that the cooling performance of the refrigerant does not continue to increase with increasing flow rate. As the formation pressure decreases, the effect of using differential pressure of natural gas is less than that of external refrigerant refrigeration.

In order to dynamically analyze the content of organic matter during the composting process in real time, tofu residue was used as the substrate supplemented with fully fermented kitchen waste powder for mixed aerobic composting. The near-infrared spectral data for composting samples were collected at different treatment stages. The original spectra were pretreated by a normalization method, a first-order differential method and a second-order differential method. Backward interval partial least squares (biPLS), synergy interval partial least squares (siPLS) and interval partial least squares (iPLS) were used to construct quantitative analysis models of the near-infrared spectral absorbance and organic matter content. The results show that the model established using the second-order differential pretreatment method combined with iPLS has the best performance. The best characteristic band corresponding to the 23rd sub-interval was 5 832-6 086 cm^-1, the correlation coefficient of the calibration set (R_c) was 0. 986 1, the root mean square error of cross-validation (RMSECV) was 0. 824 7, the correlation coefficient of the prediction set (R_p) was 0. 964 7, the root mean square error (RMSEP) was 0. 445 7, and the relative predictive deviation (RPD) was 3. 8. The results show that the established model has good stability and reliability. The second-order differential pretreatment method combined with iPLS can effectively optimize the spectral modeling area, improve the prediction ability of the model, and realize the rapid determination of the organic matter content in compost samples.

Seventy-six indole aryl sulfone compounds with anti-human immunodeficiency virus (HIV) activity were selected from the literature and their structures were characterized by molecular structure descriptors. Using stepwise regression analysis, 10 parameters were selected from 28 structural parameters as the final variables of the regression equation, and a quantitative relationship model between the molecular structure and anti-HIV activity of indole aryl sulfone compounds was constructed. The multiple correlation coefficient R of the model was 0. 904 9, indicating that the model had a good fitting effect and could reflect the correlation between the molecular structure and biological activity of indole aryl sulfone compounds. The results of leave-one-out (LOO) cross-validation showed that R was 0. 856 7, and the standard deviation Std was 0. 445 5, indicating that the model had good stability and reliability and can be used to predict the anti-HIV activity of indole aryl sulfone compounds.

High-resolution two-dimensional particle image velocimetry (PIV) has been used to study the flow field formed by a disc impeller with a stirring Reynolds number Re=1 300 in a square stirred tank with a side length L of 220 mm, and the high-resolution flow field at the Kolmogorov scale was obtained. Based on the flow field data and the isotropic assumption, the turbulent kinetic energy distribution was obtained. The turbulent kinetic energy dissipation rates calculated by the direct definition method and the large eddy PIV method were compared. The results show that when the off-bottom height C=0. 15L, the flow pattern of the disc impeller presents a unique single-cycle flow pattern, and the main vortex of the fluid is a single vortex structure. The maximum value of the turbulent kinetic energy in the stirred tank appears at the end of the blade discharge flow. At the Kolmogorov spatial analytical scale, the turbulent kinetic energy dissipation rate calculated by the direct definition method is larger than that calculated by the large eddy PIV method, and the peak values of the turbulent kinetic energy dissipation rate are located at the blade end and the discharge flow region. The results of direct numerical simulation verify that the isotropic assumptions used in these two calculation methods are reasonable.

As one of the most used plastic alloys, the flammability of polycarbonate (PC)/acrylonitrile butadiene styrene (ABS) (70/30 mass ratio) blend (PC/ABS) alloys is a threat to the safety of people's lives and property during its service, so it is necessary to improve the flame retardancy of the PC/ABS alloys. Herein, poly-ethylenimine (PEI)-grafted graphene oxide nanosheets (PEI-GO) were fabricated to decorate aluminum hypophosphite (AHP) through a self-assembly strategy. PEI-GO@AHP hybrids were prepared as a flame retardant for PC/ABS blends. The addition of 8% mass ratio PEI-GO@AHP to PC/ABS resulted in a material with a UL-94 V-0 rating and a limiting oxygen index (LOI) of 29. 1%. The peak heat release rate (PHRR) and total heat release (THR) were decreased by 41. 0% and 19. 6%, respectively. Furthermore, the PEI-GO nanosheets reduced the phosphine (PH₃) concentration during combustion. This work provides a simple but effective way to fabricate flame-retardant PC/ABS blends.

Thermoplastic polyamide elastomer (TPAE)/inorganic particle microcellular foams with low shrinkage and high expansion ratio have been prepared using supercritical CO₂ as a blowing agent. The effects of inorganic particles on the foaming and anti-shrinkage behavior of TPAE were studied using scanning electron microscopy, contact angle measurements, rotary rheometry, true density measurements, and intermittent foaming experiments. The results show that compared with Talc as a standard, calcium silicate (CaSiO₃), calcium carbonate (CaCO₃), and wollastonite (WI) all have higher interfacial tension with TPAE, showing apparent thermodynamic incompatibility and improving the melt viscoelasticity of TPAE. Analysis of the mechanism of formation of the open-cell structure of the composite foams shows that their open-cell content increases with the increasing interfacial tension between the inorganic particles and the TPAE matrix, the diameter of inorganic particles, and the distribution density. The formation of an open-cell structure can accelerate the exchange rate of CO₂ and air, and reduce the shrinkage of the TPAE foams. Therefore, the introduction of inorganic particles not only improves the cell density and uniformity of the cell structure of TPAE foam but also endows the TPAE foam with an open-cell content of more than 90%, an expansion ratio of 20, and a shrinkage ratio of less than 5%, which significantly enhances the dimensional stability.

Ladder-like polyphenylsilsesquioxane (LPPSQ) has been synthesized by a hydrolytic polycondensation reaction under alkaline conditions using phenyltrimethoxysilane (PTMS) as the raw material. LPPSQ modified room temperature vulcanized (RTV) silicone rubber was obtained after cross-linking and curing. The product was characterized by Fourier transform infrared spectroscopy, solid-state silicon-29 nuclear magnetic resonance spectroscopy, and powder X-ray diffraction. The results showed that LPPSQ was synthesized successfully. The effect of LPPSQ on the properties of RTV silicone rubber was studied. It was found that LPPSQ can significantly improve the thermal stability and mechanical properties of RTV silicone rubber. Compared with traditional RTV silicone rubber, the elongation at break of RTV silicone rubber using LPPSQ as a crosslinking agent is more than two times higher, reaching 358%, and the toughness is significantly enhanced. In addition, in terms of thermal stability, LPPSQ also significantly increased the T_10% and T_max of RTV silicone rubber by 138. 2 ℃ and 224. 1 ℃, respectively.

Many kinds of scalp health problems seriously trouble people's daily life. At present, commercially available scalp care products often have specific drugs added to treat a single symptom and cannot comprehensively improve the various symptoms associated with scalp health problems. In addition, the scalp has a rich microbial population, and long-term use of scalp care products can cause microecological disorders on the scalp surface, leading to repeated disease episodes. In view of this, a formulation designed to reconstruct the scalp microecology was proposed. Plant antibacterial hair-nourishing liquids with various natural extracts as the main active components were prepared, and their hair care effects were investigated. The results showed that when the antibacterial component content was 2 mg/mL, the hair-nourishing liquids had a strong inhibitory effect on Malassezia furfur (inhibition rate of 94. 6%), a relatively weak inhibitory effect on Cutibacterium acnes (inhibition rate of 83. 1%), and a certain inhibitory effect on Staphylococcus epidermidis (inhibition rate of 82. 7%), indicating that the prepared hair-nourishing liquids had the effect of regulating scalp microecology. After 7 weeks of using the hair-nourishing liquids, the dandruff removal rate of the subjects could reach 94. 2%. After 1 month of using the hair-nourishing liquids, the symptoms of abnormal oil secretion were improved, the degree of scalp redness and swelling was reduced, the hair diameter was significantly increased, and the degree of hair loss was also reduced.

The signal trend term can seriously affect the accuracy of signal anomaly detection methods. Therefore, it is very important to eliminate the trend term in the process of signal preprocessing. High-pass filtering has been proved to be a simple and efficient trend term elimination method. In this work, the positive and negative interval statistical distribution of the signal is first obtained. Then, a judgment criterion for the trend item is proposed. On this basis, a real-time optimal selection method of the cut-off frequency of a high-pass filter is designed to realize the adaptive elimination of the signal trend, and the factors influencing the efficacy of the proposed method are analyzed. The algorithm was then improved to give the high convergence speed and reduced calculation amount required for practical application. Finally, the proposed method was employed using the pressure signal data in a pipeline leakage monitoring problem. The experimental results show that this method can realize real-time optimal selection of the cut-off frequency, and the calculation quantity of the algorithm is small.

Subway passenger flow is affected by many factors, and accurate passenger flow prediction data facilitates to the formulation of more efficient traffic control schemes and passenger flow control schemes. In order to improve the accuracy of passenger flow prediction, a short-term subway passenger flow prediction method based on multidimensional predictable features and temporal convolutional network-long short-term memory(TCN-LSTM) has been proposed. Considering the influence of external factors, the prediction accuracy was improved and the feature space was reduced to overcome the initial overly-complex model caused by redundant feature data. The long short-term memory(LSTM)network layer input was constructed by integrating the time series features of passenger flow extracted from the temporal convolutional network(TCN)and the set of predictable feature states. The LSTM network layer input was used to learn the long-term and short-term dependence of passenger flow and external influencing factors, so as to achieve short-term passenger flow prediction under multiple scenarios such as working days, holidays and different weather conditions. Based on the Automatic Fare Collection System(AFC)data of a subway station in a southwest city, the short-term passenger flow prediction results of ARIMA, TCN, LSTM and TCN-LSTM models were compared. The overall mean absolute error(MAE)value of the TCN-LSTM method was 27%-48% lower than the other methods, and the mean squared error(MSE)value was 13%-35% lower, and the mean absolute percentage error(MAPE)value decrease by 2. 8%-6. 7%, which indicates that the TCN-LSTM model gives a better prediction of passenger flow. In addition, comparative experiments show that incorporating the extracted predictable feature data significantly reduces the prediction error evaluation metrics of the TCN-LSTM model on the test set. Thus the proposed method can effectively improve the prediction accuracy of short-term subway passenger flow.

Compound fault diagnosis technology is one of the key ways to solve muti-failure problems in industrial equipment condition monitoring and fault diagnosis. To solve the problem that the core components of large-scale machinery and equipment groups inevitably suffer from composite faults since that they are often operated in the environment with complex working conditions, a novel composite fault diagnosis method based on nonconvex regularization and sparse component analysis is proposed in this paper. The accuracy of the sparse component analysis method is improved as much as possible by constructing a nonconvex penalty function to improve the sparsity of the signal and ensuring the global convexity of the objective function. This can generate the diagnostic results by constructing a sparse optimization framework without knowing the number of fault sources in advance. The optimal value of RMSE based on non-convex regularization in the simulation experiments is less than 0. 5, which is significantly smaller than the traditional method. Taking 900 r/min and 1 300 r/min bearing fault experiments as an example, the characteristic frequencies of the outer ring, inner ring and rolling element can be recognized effectively, which shows that the proposed method can effectively diagnose compound faults.

In order to predict the flow and heat transfer process of a double-layer molten pool at the lower head, computational fluid dynamics(CFD)numerical simulation of a corium pool research apparatus(COPRA)doublelayer molten pool experiment has been carried out for different turbulence models by adopting the solidification melting model. Through numerical calculation, the quasi-steady state temperature of the molten pool, the heat flux along the wall and the distribution of the crust were obtained. The simulation results were compared with the experimental values to evaluate the applicability and accuracy of different turbulence models, and the turbulence model was optimized. The results show that the wall-modelled large-eddy simulations(WMLES)turbulence model has the best accuracy and applicability for the simulation of the flow and heat transfer in the double-layer molten pool at the lower head. The WMLES turbulence model shows that the temperature of the oxide layer increases with the increase of the height of the molten pool. Intense turbulence is observed in the upper region of the oxide layer, and the thickest shell forms at the bottom of the molten pool.

Asthma is a chronic respiratory disease that significantly impacts children's quality of life. Timely prediction and accurate diagnosis are crucial to the health of children with asthma. However, children in the stable stage of asthma do not exhibit wheezing or other characteristic sounds during an asthma attack. Therefore, there is no significant difference in the breath sounds of children in the stable stage of asthma and those of healthy children, making it challenging for healthcare professionals to diagnose asthma using traditional auscultation methods. This study utilized a support vector machine(SVM)algorithm in machine learning to predict the presence of asthma in children. The results indicate that SVM performed well in classifying the breath sounds of asthmatic and healthy children. The accuracy of the SVM's prediction for the inspiratory phase was 96. 53%, while for the expiratory phase it was 91. 66%. This demonstrates that the SVM method is highly feasible for diagnosing childhood asthma, can improve the accuracy and efficiency of diagnosis, and can provide a reliable diagnostic tool for this field.

In this paper, the large-time behavior of the solution to the Cauchy problem for the one-dimensional compressible Navier-Stokes/Allen-Cahn system, which describes the flow of non-miscible two-phase flows with diffusion interfaces, has been studied. Using the anti-derivative and energy method, we demonstrate the existence and asymptotic stability of the viscous shock solution for one-dimensional compressible Navier-Stokes/Allen-Cahn equation.