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This study aims to optimize the preparation conditions and modify the nanofiltration (NF) membranes to prepare high-performance polysulfone/sulfonated polysulfone composite nanofiltration (PSF/SPSF-NF) membranes through interfacial polymerization.

Investigating the impacts of anhydrous piperazine (PIP) concentration, trimesoyl chloride (TMC) concentration and basement membrane type on NF membrane performance, the optimal membrane was prepared. In addition, nano-SiO₂ was added to the active separation layer to modify the NF membranes.

The comprehensive performance of PSF/SPSF-NF membranes was optimized when the concentration of PIP was 0.75 Wt.% and the concentration of TMC was 0.15 Wt.%, at which time the water flux was 66.1 L·m⁻²·h⁻¹ and the retention rate of Na₂SO₄ was 98.1%. The comprehensive performance of polysulfone/sulfonated polysulfone-SiO₂ nanofiltration (PSF/SPSF-SiO₂-NF) membranes was optimized when the blending ratio of nano-SiO₂ to PIP was 2:3, with a pure water flux of 81.9 L·m⁻²·h⁻¹ and a Na₂SO₄ retention rate of 95.9%. Compared to polysulfone nanofiltration (PSF-NF) membranes and PSF/SPSF-NF membranes, NF membranes with nano-SiO₂ increased the flux recovery rate by 22.9% and 8.7%.

PSF/SPSF-SiO2-NF membrane exhibits excellent antifouling properties.

There is currently no literature available on the preparation of NF membranes using polysulfone/sulfonated polysulfone (PSF/SPFS) as a substrate. This provides a method for modifying NF membranes, starting with the modification of the basement membrane and then modifying the active separation layer.

The purpose of this paper is to show how description logics (DLs) can be applied to formalizing the information bearing capability (IBC) of paths in entity-relationship (ER) schemata.

The approach follows and extends the idea presented in Xu and Feng (2004), which applies DLs to classifying paths in an ER schema. To verify whether the information content of a data construct (e.g. a path) covers a semantic relation (which formulates a piece of information requirement), the principle of IBC under the source-bearer-receiver framework is presented. It is observed that the IBC principle can be formalized by constructing DL expressions and examining constructors (e.g. quantifiers).

Description logic can be used as a tool to describe the meanings represented by paths in an ER schema and formalize their IBC. The criteria for identifying data construct distinguishability are also discovered by examining quantifiers in DL expressions of paths of an ER schema.

This paper focuses on classifying paths in data schemas and verifying their formalized IBC by using DLs and the IBC principle. It is a new point of view for evaluation of data representation, which looks at the information borne by data but not data dependencies.

To reduce energy consumption, the time needed of drying, and the loss of ß-carotene content, and ascorbic acid content, response surface methodology (RSM) was employed for optimization.

To reduce energy consumption, the time needed of drying, and the loss of ß-carotene content, and ascorbic acid content, response surface methodology (RSM) was employed for optimization.

The results show that the optimum solar-assisted heat pump drying (SAHPD) conditions for drying pumpkin slice were: drying temperature of 67.40 °C, loading density of 1.05 kg/m³, and material thickness of 4 mm. Under these conditions, slice of pumpkin were dried in 440.637 min, where the unit energy consumption, ascorbic acid content, and ß-carotene content were 16.737 kJ/g, 25.682 mg/ (100–g dried sample), and 10.202 mg/g, respectively. The structure of the samples dried using the optimized SAHPD method exhibited a more complete cell morphology than those dried using heat pump drying when examined using scanning electronic microscopy.

This suggests that the optimized SAHPD conditions used in this study are important for production and processing.

Internet marketing underground industry users refer to people who use technology means to simulate a large number of real consumer behaviors to obtain marketing activities rewards illegally, which leads to increased cost of enterprises and reduced effect of marketing. Therefore, this paper aims to construct a user risk assessment model to identify potential underground industry users to protect the interests of real consumers and reduce the marketing costs of enterprises.

Method feature extraction is based on two aspects. The first aspect is based on traditional statistical characteristics, using density-based spatial clustering of applications with noise clustering method to obtain user-dense regions. According to the total number of users in the region, the corresponding risk level of the receiving address is assigned. So that high-quality address information can be extracted. The second aspect is based on the time period during which users participate in activities, using frequent item set mining to find multiple users with similar operations within the same time period. Extract the behavior flow chart according to the user participation, so that the model can mine the deep relationship between the participating behavior and the underground industry users.

Based on the real underground industry user data set, the features of the data set are extracted by the proposed method. The features are experimentally verified by different models such as random forest, fully-connected layer network, SVM and XGBOST, and the proposed method is comprehensively evaluated. Experimental results show that in the best case, our method can improve the F1-score of traditional models by 55.37%.

This paper investigates the relative importance of static information and dynamic behavior characteristics of users in predicting underground industry users, and whether the absence of features of these categories affects the prediction results. This investigation can go a long way in aiding further research on this subject and found the features which improved the accuracy of predicting underground industry users.