Research on the path of improving college teachers’ teaching ability in the information 2.0 era

education is the cornerstone of social progress, and the reform of education also needs to be in line with the times. With the advent of the information 2.0 era, college teachers are facing new challenges. Teachers need to make full use of information technology to improve teaching quality in practice. However, looking back at the education and teaching work in Colleges and Universities under the background of informatization, it is not difficult to find that there are many problems that affect the development process of the modernization of higher education. Based on this, this paper explores the path to improve the teaching ability of College Teachers in the information 2.0 era, hoping to provide a valuable reference for promoting the construction of college teachers

Protective Effect of a Combined Glutamine and Curcumin Formulation on Alcoholic Gastric Mucosal Damage

Objective: This study aimed to investigate the protective effect and underlying mechanism of a combined glutamine and curcumin formulation on ethanol-induced gastric mucosal damage in rats. Method: A total of fifty SPF-grade healthy SD male rats were randomly partitioned into five groups: A normal group, a model control group, a cimetidine group, a high-dose treatment group, and a low-dose treatment group. After a period of 30 days marked by oral gavage administration, all groups, with the exception of the normal group, were euthanized post anhydrous ethanol-induced modeling. The histopathological alterations in the gastric mucosa were observed via hematoxylin & eosin (H&E) staining. Furthermore, serum levels of malondialdehyde (MDA), nitric oxide (NO), and glutathione peroxidase (GSH-PX) were ascertained using a specific reagent kit. Concurrently, the concentration of prostaglandin E2 (PGE2) within the tissue and the expression levels of heme oxygenase-1 (HO-1), NADPH quinone oxidoreductase (NQO1), the antioxidant-related nuclear factor-E2-related factor 2 (Nrf2) gene, and glycogen synthase kinase-3β (GSK-3β) were evaluated. Results: In the cimetidine and high-dose treatment groups, the incidence of gastric mucosal bleeding and other forms of injury were noticeably mitigated (P<0.05) compared to the model control group, with the high-dose treatment group demonstrating a more pronounced effect. Moreover, the model control group exhibited a significant elevation in MDA content and GSH-PX activity and a concurrent decline in NO and PGE2 levels (P<0.05). The expression of antioxidant-related genes, namely, HO-1, NQO1, and Nrf2, was significantly suppressed (P<0.05), whereas GSK-3β expression was markedly increased. In contrast, in comparison to the model control group, the cimetidine and high-dose treatment groups manifested a significant reduction in MDA content and GSH-PX activity, while NO and PGE2 levels notably increased (P<0.05). The expression of the antioxidant-related genes HO-1, NQO1, and Nrf2 was significantly returned to normal (P<0.05), and GSK-3β expression was suppressed (P<0.05). Conclusion: The combined formulation appears to exert an inhibitory effect on ethanol-induced acute gastric mucosal damage. This effect is hypothesized to be associated with the Keap1-Nrf2-ARE oxidative stress signaling pathway

Two-Stream Mixed Convolutional Neural Network for American Sign Language Recognition

The Convolutional Neural Network (CNN) has demonstrated excellent performance in image recognition and has brought new opportunities for sign language recognition. However, the features undergo many nonlinear transformations while performing the convolutional operation and the traditional CNN models are insufficient in dealing with the correlation between images. In American Sign Language (ASL) recognition, J and Z with moving gestures bring recognition challenges. This paper proposes a novel Two-Stream Mixed (TSM) method with feature extraction and fusion operation to improve the correlation of feature expression between two time-consecutive images for the dynamic gestures. The proposed TSM-CNN system is composed of preprocessing, the TSM block, and CNN classifiers. Two consecutive images in the dynamic gesture are used as inputs of streams, and resizing, transformation, and augmentation are carried out in the preprocessing stage. The fusion feature map obtained by addition and concatenation in the TSM block is used as inputs of the classifiers. Finally, a classifier classifies images. The TSM-CNN model with the highest performance scores depending on three concatenation methods is selected as the definitive recognition model for ASL recognition. We design 4 CNN models with TSM: TSM-LeNet, TSM-AlexNet, TSM-ResNet18, and TSM-ResNet50. The experimental results show that the CNN models with the TSM are better than models without TSM. The TSM-ResNet50 has the best accuracy of 97.57% for MNIST and ASL datasets and is able to be applied to a RGB image sensing system for hearing-impaired people

Telecom Churn Prediction System Based on Ensemble Learning Using Feature Grouping

In recent years, the telecom market has been very competitive. The cost of retaining existing telecom customers is lower than attracting new customers. It is necessary for a telecom company to understand customer churn through customer relationship management (CRM). Therefore, CRM analyzers are required to predict which customers will churn. This study proposes a customer-churn prediction system that uses an ensemble-learning technique consisting of stacking models and soft voting. Xgboost, Logistic regression, Decision tree, and Naïve Bayes machine-learning algorithms are selected to build a stacking model with two levels, and the three outputs of the second level are used for soft voting. Feature construction of the churn dataset includes equidistant grouping of customer behavior features to expand the space of features and discover latent information from the churn dataset. The original and new churn datasets are analyzed in the stacking ensemble model with four evaluation metrics. The experimental results show that the proposed customer churn predictions have accuracies of 96.12% and 98.09% for the original and new churn datasets, respectively. These results are better than state-of-the-art churn recognition systems

Ensemble Learning of Multiple Deep CNNs Using Accuracy-Based Weighted Voting for ASL Recognition

More than four million people worldwide suffer from hearing loss. Recently, new CNNs and deep ensemble-learning technologies have brought promising opportunities to the image-recognition field, so many studies aiming to recognize American Sign Language (ASL) have been conducted to help these people express their thoughts. This paper proposes an ASL Recognition System using Multiple deep CNNs and accuracy-based weighted voting (ARS-MA) composed of three parts: data preprocessing, feature extraction, and classification. Ensemble learning using multiple deep CNNs based on LeNet, AlexNet, VGGNet, GoogleNet, and ResNet were set up for the feature extraction and their results were used to create three new datasets for classification. The proposed accuracy-based weighted voting (AWV) algorithm and four existing machine algorithms were compared for the classification. Two parameters, α and λ, are introduced to increase the accuracy and reduce the testing time in AWV. The experimental results show that the proposed ARS-MA achieved 98.83% and 98.79% accuracy on the ASL Alphabet and ASLA datasets, respectively

Ensemble Learning of Multiple Deep CNNs Using Accuracy-Based Weighted Voting for ASL Recognition

More than four million people worldwide suffer from hearing loss. Recently, new CNNs and deep ensemble-learning technologies have brought promising opportunities to the image-recognition field, so many studies aiming to recognize American Sign Language (ASL) have been conducted to help these people express their thoughts. This paper proposes an ASL Recognition System using Multiple deep CNNs and accuracy-based weighted voting (ARS-MA) composed of three parts: data preprocessing, feature extraction, and classification. Ensemble learning using multiple deep CNNs based on LeNet, AlexNet, VGGNet, GoogleNet, and ResNet were set up for the feature extraction and their results were used to create three new datasets for classification. The proposed accuracy-based weighted voting (AWV) algorithm and four existing machine algorithms were compared for the classification. Two parameters, &alpha; and &lambda;, are introduced to increase the accuracy and reduce the testing time in AWV. The experimental results show that the proposed ARS-MA achieved 98.83% and 98.79% accuracy on the ASL Alphabet and ASLA datasets, respectively

High-efficiency electrocatalyst for N2 conversion to NH3 based on Au nanoparticles loaded on defective WO3x

In this work, Au nanoparticles (NPs) grown on defective WO3-x (Au/WO3-x) show superior electrocatalytic N2 reduction activity under ambient conditions in 0.1 M KOH. At -0.2 V versus the reversible hydrogen electrode (vs. RHE), the Au/WO3-x catalyst achieves a large NH3 yield of 23.15 μg h-1 mg-1 and a high faradaic efficiency (FE) of 14.72%. Further density functional theory (DFT) calculations indicate that electron transfer between the Au nanoparticles and defective WO3-x promotes the activation of N2 molecules effectively

Novel magnetic Fe3O4/g-C3N4/MoO3 nanocomposites with highly enhanced photocatalytic activities: Visible-light-driven degradation of tetracycline from aqueous environment.

In the present work, a series of magnetically separable Fe3O4/g-C3N4/MoO3 nanocomposite catalysts were prepared. The as-prepared catalysts were characterized by XRD, EDX, TEM, FT-IR, UV-Vis DRS, TGA, PL, BET and VSM. The photocatalytic activity of photocatalytic materials was evaluated by catalytic degradation of tetracycline solution under visible light irradiation. Furthermore, the influences of weight percent of MoO3 and scavengers of the reactive species on the degradation activity were investigated. The results showed that the Fe3O4/g-C3N4/MoO3 (30%) nanocomposites exhibited highest removal ability for TC, 94% TC was removed during the treatment. Photocatalytic activity of Fe3O4/g-C3N4/MoO3 (30%) was about 6.9, 5, and 19.9-fold higher than those of the MoO3, g-C3N4, and Fe3O4/g-C3N4 samples, respectively. The excellent photocatalytic performance was mainly attributed to the Z-scheme structure formed between MoO3 and g-C3N4, which enhanced the efficient separation of the electron-hole and sufficient utilization charge carriers for generating active radials. The highly improved activity was also partially beneficial from the increase in adsorption of the photocatalysts in visible range due to the combinaion of Fe3O4. Superoxide ions (·O2-) was the primary reactive species for the photocatalytic degradation of TC, as degradation rate were decreased to 6% in solution containing benzoquinone (BQ). Data indicate that the novel Fe3O4/g-C3N4/MoO3 was favorable for the degradation of high concentrations of tetracycline in water