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Background: Gender disparity is pervasive in academic medicine. This study aimed to assess the disparity between men and women with regard to first and senior author positions in primary studies on liver cancer over the last two decades. Methods: We conducted a review of articles published in high-impact factor journals of the field of Gastroenterology and Hepatology in 2005, 2010, 2015 and 2020. First and senior authors of all ages were considered as the study population. The authors' genders were determined using the online artificial intelligence tool genderize.io (https://genderize.io/). The disparity between men and women authors was assessed using the linear-by-linear association test. Results: 665 original articles from 10 journals were reviewed. The point prevalence of first women authors was 25.0% compared with 75.0% for men. The point prevalence of senior women authors was 16.3% compared with 83.7% for men. From 2000 to 2020, the proportion of first women authors increased 14.4% to 26.8% compared with 85.6%-73.2% for men (P = 0.009), and the proportion of senior women authors increased from 7.4% to 19.5%, compared with 92.6%-80.5% for men (P = 0.035). The factor independently associated with a reduced representation of women among first authors was the region of author. The factor independently associated with a reduced representation of women among senior authors was the impact factor of journals. Conclusion: The findings indicated a remarkable increase in the proportion of women, both first and senior authors, over the past two decades in the field of liver cancers. However, the representation of women authors in this area is far less than that of men. [ABSTRACT FROM AUTHOR]

Students' performance is an important factor for the evaluation of teaching quality in colleges. The prediction and analysis of students' performance can guide students' learning in time. Aiming at the low accuracy problem of single model in students' performance prediction, a combination prediction method is put forward based on ant colony algorithm. First, considering the characteristics of students' learning behavior and the characteristics of the models, decision tree (DT), support vector regression (SVR) and BP neural network (BP) are selected to establish three prediction models. Then, an ant colony algorithm (ACO) is proposed to calculate the weight of each model of the combination prediction model. The combination prediction method was compared with the single Machine learning (ML) models and other methods in terms of accuracy and running time. The combination prediction model with mean square error (MSE) of 0.0089 has higher performance than DT with MSE of 0.0326, SVR with MSE of 0.0229 and BP with MSE of 0.0148. To investigate the efficacy of the combination prediction model, other prediction models are used for a comparative study. The combination prediction model with MSE of 0.0089 has higher performance than GS-XGBoost with MSE of 0.0131, PSO-SVR with MSE of 0.0117 and IDA-SVR with MSE of 0.0092. Meanwhile, the running speed of the combination prediction model is also faster than the above three methods. [ABSTRACT FROM AUTHOR]

Graphitic carbon nitride (g-C3N4) used in this work was obtained by heating dicyandiamide and melamine, respectively, at different temperatures. The differences of g-C3N4 derived from different precursors in phase composition, functional group, surface morphology, microstructure, surface property, band gap and specific surface area were investigated by X-ray diffraction, Fourier transform infrared spectroscopy, scanning electron microscopy, transmission electron microscopy, X-ray photoelectron spectroscopy, UV-visible diffuse reflection spectroscopy and BET surface area analyzer, respectively. The photocatalytic discoloration of an active cationic dye, Methylene Blue (MB) under visible-light irradiation indicated that g-C3N4 derived from melamine at 500°C (CN-M500) had higher adsorption capacity and better photocatalytic activity than that from dicyandiamide at 500°C (CN-D500), which was attributed to the larger surface area of CN-M500. MB discoloration ratio over CN-M500 was affected by initial MB concentration and photocatalyst dosage. After 120 min reaction time, the blue color of MB solution disappeared completely. Subsequently, based on the measurement of the surface Zeta potentials of CN-M500 at different pHs, an active anionic dye, Methyl Orange (MO) was selected as the contrastive target pollutant with MB to reveal the synergic effect between adsorption and photocatalysis. Finally, the photocatalytic mechanism was discussed. [ABSTRACT FROM AUTHOR]