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This paper’s objective optimization problem belongs to the encoding problem of real numbers, and the classical particle swarm optimization algorithm is chosen. The establishment of intelligent learning model was finally completed through the assumption of model components, initial model establishment, model cycle evaluation and validation. First, after the implementation of the E-GPPE-C education model, the test was conducted on the educated people, and the AI literacy questionnaire was also distributed to the students before and after the implementation of the model education. Second, a statistical analysis explores which technologies will most likely be applied to higher education. Finally, the variability of higher education subject relations under the influence of AI and related ethical issues were explored. The results show that the highest score on the students’ final test was 96. 20 students scored more than 80 points, indicating that half of the students had a solid grasp of the knowledge and applications related to the E-GPPE-C model, and that there was a vast difference between the degree of AI knowledge and skill mastery before and after the implementation of education. Natural language understanding, computer vision, and biometrics are the three technologies most suitable for application in higher education.

Polysaccharides from Semen Astragali complanati (SACP) were obtained by hot water extraction followed by ethanol precipitation and separated by fractional precipitation with 20% and 25% ethanol into two fractions: SACP-20 and SACP-25, which was the major one with higher purity. Their physicochemical properties were determined by colorimetric methods, and their molecular mass distribution, conformation in solution and monosaccharide composition were analyzed by size exclusion chromatography coupled with muti-angle laser light scattering (SEC-MALLS) and high-performance anion exchange chromatography coupled with pulsed amperometric detection (HPLC-PAD). Results indicated that SACP-25 was a homogenous polysaccharide with total sugar content of 87.40% and was composed of galactose (42.92%) and mannose (57.08%). Meanwhile, it showed a random coil conformation in 0.1 mol/L NaNO3. Analysis by methylation combined with gas chromatography-mass spectrometry (GC-MS) and nuclear magnetic resonance (NMR) spectroscopy revealed that the main chain of SACP-25 was composed of β-1,4-linked-mannopyranose (Manp) with galactopyranose (Galp) side chains attached to C-6 of the Manp residues. This study indicated that Semen A. complanati is good natural source of galactomannans as a water-soluble dietary fiber. The results of this study may provide theoretical support for the ordered structure of polysaccharides and also provide a theoretical foundation for the development and application of SACP in the functional food and pharmaceutical industries.

Background and purpose: Golgi membrane protein 1 (GOLM1) plays the role of an oncogene in lung adenocarcinoma (LUAD), however, the effects of GOLM1 on the proliferation, invasion and migration of LUAD cells and its mechanism are not clear yet. This study investigated the effects of GOLM1 on the proliferation, invasion and migration of LUAD cells and its mechanism of action. Methods: We selected cancer tissues and corresponding paracancerous tissue specimens from 90 LUAD patients who underwent surgical resection in Karamay Central Hospital from April 2019 to April 2021. The expression of GOLM1 protein in LUAD tissues and paracancerous tissues was detected by immunohistochemistry, and the relationship between GOLM1 protein expression and clinicopathological characteristics of LUAD tissues was analyzed. Western blot was used to detect the expression of GOLM1 protein in human lung epithelial cells BEAS-2B and human lung adenocarcinoma H460, A549, PG49 and H1299 cells. Lung adenocarcinoma A549 cells in logarithmic growth stage were randomly divided into blank group (cells not transfected), GOLM1 small interfering RNA negative control (si-NC) group (cells transfected with si-NC), GOLM1 small interfering RNA (si-GOLM1) group (cells transfected with si-GOLM1), insulin-like growth factor-1 (IGF-1) group [10 μmol/L phosphatidylinositol 3-kinase (PI3K)/protein kinase B (AKT)/mammalian target of rapamycin (mTOR) signaling pathway activator IGF-1 for 30 min] and si-GOLM1+IGF-1 group (after treatment with 10 μmol/L IGF-1 for 30 min, si-GOLM1 was transfected). Cell counting kit-8 method was used to detect cell proliferation in each group of lung adenocarcinoma A549 cells. Scratch test was used to detect cell migration in each group of lung adenocarcinoma A549 cells. Transwell experiment was used to detect cell invasion in each group of lung adenocarcinoma A549 cells. Western blot was used to detect the expressions of GOLM1 and PI3K/AKT/mTOR signaling pathway related proteins in each group of lung adenocarcinoma A549 cells. Xenograft model was constructed by subcutaneously injecting A549 cells on the right side of BALB/c nude mice, which were divided into: nude mice blank group, nude mice si-NC group, nude mice si-GOLM1 group, nude mice IGF-1 group, nude mice si-GOLM1+IGF-1 group, with 6 mice in each group. The nude mice were sacrificed six weeks after injection, the tumor was collected, and the weight and volume of the tumor were measured. Results: The results of immunohistochemistry showed that the positive expression rate of GOLM1 protein was significantly higher in LUAD tissues than in adjacent tissues (P0.05). Compared with BEAS-2B cells, the relative expression level of GOLM1 protein in human lung adenocarcinoma H460, A549, PG49 and H1299 cells was significantly increased (P0.05). Compared with the nude mice blank group and nude mice si-NC group, the mass and volume of transplanted tumors in the nude mice si-GOLM1 group were significantly reduced, while the mass and volume of transplanted tumors in the nude mice IGF-1 group were significantly increased (P

Nowadays, computational intelligence-assisted autonomous assessment of education quality has become a more and more general concern in the area of smart education management. As education quality assessment is a complicated process with multiple heterogeneous factors, it remains challenging to make effective assessment using simple information modality and criteria. To deal with this issue, this paper introduces multiscale deep learning, and proposes a novel data-driven autonomous assessment framework for education quality. In particular, four aspects of heterogeneous indicators are selected as the basic indexes and a dilated convolutional neural network structure is formulated to perform multiscale feature extraction. Then, the structural equation is adopted to make multivariate characterization and output the final assessment results. At last, some simulations are carried out on realistic education operation data to evaluate the efficiency of the proposed autonomous assessment framework. Two aspects of findings can be deduced from the results. For one thing, multisource fusion of multiple indicators well makes sense to autonomous education assessment. For another, multiscale deep learning can provide some beneficial promotion to assessment efficiency. [ABSTRACT FROM AUTHOR]