Search

With the explosion of English subjects, graduates majoring in English are now facing the dilemma of losing social and employment competitiveness. The present study found that English can combine its designated education with other subjects to improve students’ overall competence, and using the new artificial intelligence of ChatGPT may be a promising method to assist English language teaching. The research has conducted semi-structured interviews with five students studying “English Language and Data” subjects at a university in Zhejiang, China. Through the semi-structured interviews, all of the interviewees used ChatGPT during their studying life at university; they not only used ChatGPT in English major courses, but they also used it in IT-related courses more often, and they preferred using ChatGPT, believing it had more pros than cons. The results showed that ChatGPT had become a good tool for English language teaching and “English+” subject learning, and its future remains to be explored.

Background To strengthen the construction of health talents team in rural areas, a national compulsory services program (CSP) was started in 2010 in China. In 2015, the state required graduates of the CSP to participate in a 3-year standardized training for residents (residency) upon graduation. Objective To understand the participation of CSP graduates in residency, analyze the effect of residency on the pass rate of the practicing physician's examination for CSP graduates, and explore the significance of residency on the training of CSP graduates and the rural primary health workforce, so as to provide policy recommendations for the high-quality training of CSP graduates. Methods Starting in 2015, a cohort of CSP graduates was established from a total of 4 colleges undertaking CSP training programs in 3 provinces in the Midwest of China, and 1∶1 general clinical graduates who graduated the same year were selected as controls and followed up annually. Descriptive analysis was used to show the status of CSP graduates' participation in residency, the pass rate of the examination for practicing physicians, and to compare them with those of general clinical graduates. Stratified Cox regression was used to analyze the effect of attending residency on the pass rate of practicing physician exams. Results 86.69% (228/263), 86.78% (361/416), 87.79% (381/434), 91.08% (388/426), 94.43% (356/377) of the 2015—2019 CSP graduates stated that they were willing to participate in training after graduation, and 72.20% (200/277), 58.75% (151/257), 70.42% (169/240), 73.23% (145/198), 61.03% (83/136) of the same general clinical graduates stated that they were willing to participate in training after graduation. The actual participation proportions of CSP graduates in 2015—2019 in residency were 99.02% (202/204), 97.10% (301/310), 94.99% (322/339), 89.91% (285/317), 69.45% (241/347), 67.57% (100/148), 66.12% (80/121), 74.79% (89/119), 43.33% (39/90), 34.62% (27/78), respectively. The pass rate of practicing physician examination in 2015—2017 CSP graduates reached 99.23% (259/261), 94.86% (351/370), 91.74% (311/339), while the pass rate of same general clinical students was 97.74% (173/177), 95.87% (116/121), 88.24% (105/119), respectively. Cox regression results showed that after adjusting for demographic variables, family economic status, intention of studying medicine, medical education, and factors related to attending the residency, the probability of attending the residency group passing the practitioner's examination was 1.232 times higher than that of not attending the residency group 〔95%CI (1.02, 1.49), P=0.03〕. Conclusion CSP graduates' willingness to participate in residency training, the proportion of participation, and the pass rate of the qualifying examination for practicing physicians were all high. Participating in residency training can improve the ability of medical graduates and has a promoting effect on passing the qualifying examination of practicing physicians.

Deep and offshore drilling operations face complex geological formations, uncertain formation pressures, and narrow safety density windows, making them susceptible to lost circulation risks. To address these challenges, this paper introduces an innovative, intelligent lost circulation monitoring model that incorporates geological lithology information. This model not only utilizes real-time drilling parameters, but also encodes geological information such as rock type as inputs to the model. By combining these key lithological features, the model can comprehensively assess wellbore stability and reduce the lost circulation risks. In this paper, the Conditional Tabular Generative adversarial network (CTGAN) model is used to enhance the data of small-sample risk data, which can effectively expand the data distribution space and improve the performance of the model. This paper conducts a comparative analysis of intelligent monitoring results using artificial neural networks (ANNs), long short-term memory (LSTM), and temporal convolutional networks (TCNs). The results show that the TCN achieves an identification accuracy of 93.7%. Furthermore, the analysis reveals that the inclusion of lithology information significantly enhances the model's performance, resulting in a 7.1% increase in accuracy. The false alarm rate of the model can be reduced by 10.2%, considering the fluctuation of the logging curve caused by the on/off condition of the pump. This indicates that the introduction of lithology information and the condition of the pump on−off provide advantages in monitoring and identifying lost circulation risks, enabling a more precise assessment of wellbore stability and a reduction in lost circulation incidents. The method of lost circulation monitoring proposed in this paper provides an important safety guarantee for the oil drilling industry. [ABSTRACT FROM AUTHOR]

As an omnivorous fish, wild tilapia feed on both animal and plant foods, depending on what is available. To understand the nutritional function of the gut microbiota of tilapia, we used shotgun metagenomic sequencing, untargeted metabolomics analysis, and 16S rRNA gene sequencing to study the metabolites and gut microbial communities in tilapia eating different diets (artemia nauplii in the carnivorous diet; plant‐based pelleted feed [PBP] in the herbivorous diet; and a mixture of these two in the omnivorous diet). A total of 225 Nile tilapia (Oreochromis niloticus) were cultured in nine 1500 L outdoor tanks, with three replicate tanks per diet and 25 fish per tank. β‐diversity analysis revealed that there was no significant difference in microbial structure between the three diets. However, significant differences were detected in gut metabolites. The abundance of the core bacteria in tilapia intestine (genus Cetobacterium) increased significantly when an omnivorous diet was fed. Cetobacterium abundance was significantly correlated to some differential metabolites including glucose and amino acids, and significantly correlated to pathways of carbohydrate and amino acids metabolism, identified by metagenome analysis. Metagenomic binning produced seven Cetobacterium MAGs with the completeness >50% and contamination <10%, in which genes related to the above metabolites and metabolism pathways were identified. All seven MAGs contained CAZymes that could act on distinct carbohydrates, providing highly diversified capabilities to this genus to cope with both animal and plant glycans. These tilapia fed with an omnivorous diet also showed increased growth. The differential intestinal metabolites aspartate, isoleucine, ornithine, proline, nicotinate, and NAD+ could be related to the increased growth. Our results expanded the understanding of how tilapia have adapted to an omnivorous diet and may provide knowledge for improving aquaculture management. [ABSTRACT FROM AUTHOR]

The impact of molecular point groups on phase transition materials has been seldom discussed. Two organic cations with different symmetries were incorporated into a [SnCl6]2− framework. By breaking the symmetry of cations, a novel hybrid material with two successive phase transitions featuring a rare "on–off–on" nonlinear optical switch was obtained. [ABSTRACT FROM AUTHOR]

Aim: Although the interplay of insulin resistance (IR) and low muscle mass is increasingly recognized, it remains unknown whether the triglyceride-glucose (TyG) index, as an indicator of IR, is associated with low muscle mass in patients with type 2 diabetes (T2D). Our study aimed to investigate the association between TyG index and low muscle mass in hospitalized T2D patients. Methods: This cross-sectional study involved 2687 hospitalized participants with T2D. The TyG index was calculated by fasting plasma glucose (FPG) and triglyceride levels. The outcome variables were defined as appendicular skeletal muscle mass index (ASMI) and low muscle mass. To explore the relationship between TyG index and low muscle mass, we conducted the multivariate linear regression, multivariate logistic regression, and subgroup analysis. Results: In the fully adjusted multivariate linear regression, there was a negative correlation between TyG index (β=− 0.10, 95% CI: − 0.14, − 0.06) and ASMI. TyG index (OR = 1.34, 95% CI: 1.08, 1.65) had a more significant association with low muscle mass compared to FPG (OR = 1.05, 95% CI: 1.01, 1.09) and glycated hemoglobin A1c (OR = 1.07, 95% CI: 0.99, 1.15). The statistical significance of the trend persisted among the TyG index quartile groups. Subgroup analysis revealed stronger positive associations between TyG index and low muscle mass in females, individuals aged 60 years or older, those with a body mass index of 28kg/m2 or higher, and HbA1c levels of 6.5% or higher, as well as those with hypertension and dyslipidemia. Conclusion: A higher TyG index level is positively associated with a higher risk of low muscle mass, suggesting that TyG index could be a potential biomarker of low muscle mass in hospitalized T2D patients. [ABSTRACT FROM AUTHOR]