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This paper analyzes the main contradictions and challenges in the development of China's alumina industry in terms of resources, production costs and environmental governance, compares the production cost of China's alumina with that of other countries, analyzes the pollutants produced in the production process of alumina, and discusses the related problems. On this basis, this paper puts forward the strategies and suggestions for the sustainable development of China's alumina industry, that is, adopt advanced technology and equipment to solve the problem of bauxite resource shortage; Improve safety protection facilities to prevent accidents and hazards; Solve the problem of deep purification and standard discharge of alumina industrial flue gas; Implement the safe storage and resource utilization of alumina industrial solid waste; Increase the depth recovery efficiency of waste heat in alumina production; Implement all safety management measures and strengthen the ability to prevent accidents. By adopting the above strategies, the sustainable development strategy of China's alumina industry can be realized.

On the basis of a comprehensive review of the production and technology status of China's alumina industry, the main contradictions and challenges in the development of China's alumina industry in terms of resources, energy and environmental governance are analyzed, and the production cost is compared and analyzed worldwide. It is believed that the main factors restricting the sustainable development of alumina industry in China are the lack of sufficient high-quality bauxite resources in China, the production technology needs to be further optimized, and the generated solid and hazardous waste is difficult to realize harmless and resource utilization. In this regard, this paper puts forward the strategy for the sustainable development of China's alumina industry, that is, strictly control the disorderly and rapid expansion of production capacity and reasonable layout, optimize the resource and energy supply structure, implement the industrial development strategy of high quality, energy saving and low consumption to improve the core competitiveness, and accelerate the realization of the standard discharge and resource utilization of waste gas, wastewater and solid waste. The results of this paper are of great significance to the strategy of industrial structure adjustment and sustainable development of alumina industry in China.

The Promulgation of the policy of "integration between industry and education ", local colleges and universities has changed the talent training mode. This requires universities to carry out reform and innovation in the cultivation of talents (especially Internationalized talents). This paper analyzes the demand types of local Internationalized talents, Combined with the problems existing in the management of innovative talents and Internationalized talents training in colleges and universities, Put forward the new train of thought of extrovert talent training, so as to promote the organic integration of industrial development and college education, which has certain reference value and practical significance to Actively promote "combination of industry and Education, combination of work and Study". Based on the analysis of the needs of Chinese internationalized people, this paper puts forward the demand types of Internationalized talents, and the training elements, objectives, modes and countermeasures of Internationalized talents. This paper gives a basic definition of extraverted talents and summarizes several types of extraverted talents. At the same time, on the basis of analyzing the needs of Chinese internationalized people, this paper puts forward the demand types of Internationalized talents, and puts forward the training elements, objectives, models and countermeasures of Internationalized talents.

Background Mycoplasma pneumoniae pneumonia (MPP) can cause a variety of intrapulmonary and extrapulmonary injuries, including thrombotic diseases, which seriously endanger the health of children. And for assessing the coagulation status, routine coagulation parameters has great limitations, while thromboelastography (TEG) can be comprehensively and rapidly, but there are few data on TEG used in condition evaluation in children with MPP. Objective To explore the value of TEG in condition assessment of children with MPP. Methods Two hundred and twelve MPP children were recruited from the Affiliated Changzhou No.2 People's Hospital of Nanjing Medical University from July 2019 to July 2021, including 52 with severe condition and 160 with non-severe condition. Comparisons were made between two groups in terms of demographics, platelet count, routine coagulation parameters, and TEG parameters. The association of TEG parameters with platelet count and routine coagulation parameters was assessed. Multivariate Logistic regression analysis was used to explore influence factors for the condition of MPP. ROC analysis was used for assess the value of those identified risk factors for predicting the condition of MPP. Results Both groups had no significant differences in sex ratio and mean age (P>0.05) . Compared with children with non-severe condition, those with severe conditionhad shortened reaction time (R) and kinetic time (K) , and elevated maximum amplitude (MA) , α-angle, clotting index (CI) , fibrinogen (FIB) , prothrombin time (PT) , international normalized ratio (INR) , D-dimer (D-D) (P

Background and Aim: False positives (FPs) pose a significant challenge in the application of artificial intelligence (AI) for polyp detection during colonoscopy. The study aimed to quantitatively evaluate the impact of computer‐aided polyp detection (CADe) systems' FPs on endoscopists. Methods: The model's FPs were categorized into four gradients: 0–5, 5–10, 10–15, and 15–20 FPs per minute (FPPM). Fifty‐six colonoscopy videos were collected for a crossover study involving 10 endoscopists. Polyp missed rate (PMR) was set as primary outcome. Subsequently, to further verify the impact of FPPM on the assistance capability of AI in clinical environments, a secondary analysis was conducted on a prospective randomized controlled trial (RCT) from Renmin Hospital of Wuhan University in China from July 1 to October 15, 2020, with the adenoma detection rate (ADR) as primary outcome. Results: Compared with routine group, CADe reduced PMR when FPPM was less than 5. However, with the continuous increase of FPPM, the beneficial effect of CADe gradually weakens. For secondary analysis of RCT, a total of 956 patients were enrolled. In AI‐assisted group, ADR is higher when FPPM ≤ 5 compared with FPPM > 5 (CADe group: 27.78% vs 11.90%; P = 0.014; odds ratio [OR], 0.351; 95% confidence interval [CI], 0.152–0.812; COMBO group: 38.40% vs 23.46%, P = 0.029; OR, 0.427; 95% CI, 0.199–0.916). After AI intervention, ADR increased when FPPM ≤ 5 (27.78% vs 14.76%; P = 0.001; OR, 0.399; 95% CI, 0.231–0.690), but no statistically significant difference was found when FPPM > 5 (11.90% vs 14.76%, P = 0.788; OR, 1.111; 95% CI, 0.514–2.403). Conclusion: The level of FPs of CADe does affect its effectiveness as an aid to endoscopists, with its best effect when FPPM is less than 5. [ABSTRACT FROM AUTHOR]

Background and Aim: Early whitish gastric neoplasms can be easily misdiagnosed; differential diagnosis of gastric whitish lesions remains a challenge. We aim to build a deep learning (DL) model to diagnose whitish gastric neoplasms and explore the effect of adding domain knowledge in model construction. Methods: We collected 4558 images from two institutions to train and test models. We first developed two sole DL models (1 and 2) using supervised and semi‐supervised algorithms. Then we selected diagnosis‐related features through literature research and developed feature‐extraction models to determine features including boundary, surface, roundness, depression, and location. Then predictions of the five feature‐extraction models and sole DL model were combined and inputted into seven machine‐learning (ML) based fitting‐diagnosis models. The optimal model was selected as ENDOANGEL‐WD (whitish‐diagnosis) and compared with endoscopists. Results: Sole DL 2 had higher sensitivity (83.12% vs 68.67%, Bonferroni adjusted P = 0.024) than sole DL 1. Adding domain knowledge, the decision tree performed best among the seven ML models, achieving higher specificity than DL 1 (84.38% vs 72.27%, Bonferroni adjusted P < 0.05) and higher accuracy than DL 2 (80.47%, Bonferroni adjusted P < 0.001) and was selected as ENDOANGEL‐WD. ENDOANGEL‐WD showed better accuracy compared with 10 endoscopists (75.70%, P < 0.001). Conclusions: We developed a novel system ENDOANGEL‐WD combining domain knowledge and traditional DL to detect gastric whitish neoplasms. Adding domain knowledge improved the performance of traditional DL, which provided a novel solution for establishing diagnostic models for other rare diseases potentially. [ABSTRACT FROM AUTHOR]

Background The choice of polypectomy device and surveillance intervals for colorectal polyps are primarily decided by polyp size. We developed a deep learning-based system (ENDOANGEL-CPS) to estimate colorectal polyp size in real time. Methods ENDOANGEL-CPS calculates polyp size by estimating the distance from the endoscope lens to the polyp using the parameters of the lens. The depth estimator network was developed on 7297 images from five virtually produced colon videos and tested on 730 images from seven virtual colon videos. The performance of the system was first evaluated in nine videos of a simulated colon with polyps attached, then tested in 157 real-world prospective videos from three hospitals, with the outcomes compared with that of nine endoscopists over 69 videos. Inappropriate surveillance recommendations caused by incorrect estimation of polyp size were also analyzed. Results The relative error of depth estimation was 11.3% (SD 6.0%) in successive virtual colon images. The concordance correlation coefficients (CCCs) between system estimation and ground truth were 0.89 and 0.93 in images of a simulated colon and multicenter videos of 157 polyps. The mean CCC of ENDOANGEL-CPS surpassed all endoscopists (0.89 vs. 0.41 [SD 0.29]; P <0.001). The relative accuracy of ENDOANGEL-CPS was significantly higher than that of endoscopists (89.9% vs. 54.7%; P <0.001). Regarding inappropriate surveillance recommendations, the system's error rate is also lower than that of endoscopists (1.5% vs. 16.6%; P <0.001). Conclusions ENDOANGEL-CPS could potentially improve the accuracy of colorectal polyp size measurements and size-based surveillance intervals. [ABSTRACT FROM AUTHOR]

Esophagogastroduodenoscopy (EGD) screening is being implemented in countries with a high incidence of upper gastrointestinal (UGI) cancer. High‐quality EGD screening ensures the yield of early diagnosis and prevents suffering from advanced UGI cancer and minimal operational‐related discomfort. However, performance varied dramatically among endoscopists, and quality control for EGD screening remains suboptimal. Guidelines have recommended potential measures for endoscopy quality improvement and research has been conducted for evidence. Moreover, artificial intelligence offers a promising solution for computer‐aided diagnosis and quality control during EGD examinations. In this review, we summarized the key points for quality assurance in EGD screening based on current guidelines and evidence. We also outline the latest evidence, limitations, and future prospects of the emerging role of artificial intelligence in EGD quality control, aiming to provide a foundation for improving the quality of EGD screening. [ABSTRACT FROM AUTHOR]

Objectives The histopathologic diagnosis of colorectal sessile serrated lesions (SSLs) and hyperplastic polyps (HPs) is of low consistency among pathologists. This study aimed to develop and validate a deep learning (DL)–based logical anthropomorphic pathology diagnostic system (LA-SSLD) for the differential diagnosis of colorectal SSL and HP. Methods The diagnosis framework of the LA-SSLD system was constructed according to the current guidelines and consisted of 4 DL models. Deep convolutional neural network (DCNN) 1 was the mucosal layer segmentation model, DCNN 2 was the muscularis mucosa segmentation model, DCNN 3 was the glandular lumen segmentation model, and DCNN 4 was the glandular lumen classification (aberrant or regular) model. A total of 175 HP and 127 SSL sections were collected from Renmin Hospital of Wuhan University during November 2016 to November 2022. The performance of the LA-SSLD system was compared to 11 pathologists with different qualifications through the human-machine contest. Results The Dice scores of DCNNs 1, 2, and 3 were 93.66%, 58.38%, and 74.04%, respectively. The accuracy of DCNN 4 was 92.72%. In the human-machine contest, the accuracy, sensitivity, and specificity of the LA-SSLD system were 85.71%, 86.36%, and 85.00%, respectively. In comparison with experts (pathologist D: accuracy 83.33%, sensitivity 90.91%, specificity 75.00%; pathologist E: accuracy 85.71%, sensitivity 90.91%, specificity 80.00%), LA-SSLD achieved expert-level accuracy and outperformed all the senior and junior pathologists. Conclusions This study proposed a logical anthropomorphic diagnostic system for the differential diagnosis of colorectal SSL and HP. The diagnostic performance of the system is comparable to that of experts and has the potential to become a powerful diagnostic tool for SSL in the future. It is worth mentioning that a logical anthropomorphic system can achieve expert-level accuracy with fewer samples, providing potential ideas for the development of other artificial intelligence models. [ABSTRACT FROM AUTHOR]