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The manufactured capital, usually denoted as material stocks from an industrial ecology perspective, has thus far received wide attention in sustainability and circularity science. Sustainable resource management should be rooted in detailed knowledge of manufactured capital accumulation in society at a high spatial resolution. Previous studies demonstrated that night‐time light (NTL) data provide a great opportunity for monitoring material stocks dynamics at a higher spatial resolution on the regional and global scale. However, the potential of historical–geographical refined material stocks has not been fully analyzed and explored because of the inconsistency of NTL images detected by the different satellites. In this study, based on a new set of material stocks data in China and harmonized NTL images (1992–2018), we map the national stocks of 13 bulk materials (including cement, gravel, wood, brick, sand, asphalt, glass, lime, plastic, rubber, copper, aluminum, and steel) at a 1 × 1 km resolution from 1992 to 2018. The results find that the total material stocks increased from 190,000 to 460,000 t/km2 between 1992 and 2018. Among the five end‐use sectors, buildings have the highest density of 430,000 t/km2, while domestic appliances have the lowest density of 140 t/km2. Four manufactured capital clusters, including the Yangtze River Delta, Pearl River Delta, Beijing–Tianjin–Hebei, and Chengdu–Chongqing agglomerations, possess 38% of the national total stocks in 2018, revealing an unbalanced distributed pattern of manufactured capital across China. Our results provide valuable support for policymakers and business decision‐makers on efficient resource management and urban mining. [ABSTRACT FROM AUTHOR]

In response to the unusually obvious booming problems of the rear-wheel drive vehicles during operation, the booming tests were conducted on the entire vehicle to confirm the booming states. At the same time, the bench sweep frequency tests were performed on the transmission systems of the rear-drive vehicles to study the influences of the transmission system resonance mode on the booming vibrations. Building the multi-body dynamics model of the vehicles with the help of ADAMS software, using the model after the benchmarking to perform modal analysis calculations to study the mechanism of booming vibrations, and then a plan was formulated to improve the booming vibrations, and to verify the booming vibrations on the actual vehicles. The results show that the improvement plan for the clutch stiffness and the vibrations of the drive shafts may effectively solve the booming problems of the rear-drive vehicles. [ABSTRACT FROM AUTHOR]

Objectives: This study aimed to predict severe coronavirus disease 2019 (COVID-19) progression in patients with increased pneumonia lesions in the early days. A simplified nomogram was developed utilizing artificial intelligence (AI)-based quantified computed tomography (CT)., Methods: From 17 December 2019 to 20 February 2020, a total of 246 patients were confirmed COVID-19 infected in Jingzhou Central Hospital, Hubei Province, China. Of these patients, 93 were mildly ill and had follow-up examinations in 7 days, and 61 of them had enlarged lesions on CT scans. We collected the neutrophil-to-lymphocyte ratio (NLR) and three quantitative CT features from two examinations within 7 days. The three quantitative CT features of pneumonia lesions, including ground-glass opacity volume (GV), semi-consolidation volume (SV), and consolidation volume (CV), were automatically calculated using AI. Additionally, the variation volumes of the lesions were also computed. Finally, a nomogram was developed using a multivariable logistic regression model. To simplify the model, we classified all the lesion volumes based on quartiles and curve fitting results., Results: Among the 93 patients, 61 patients showed enlarged lesions on CT within 7 days, of whom 19 (31.1%) developed any severe illness. The multivariable logistic regression model included age, NLR on the second time, an increase in lesion volume, and changes in SV and CV in 7 days. The personalized prediction nomogram demonstrated strong discrimination in the sample, with an area under curve (AUC) and the receiver operating characteristic curve (ROC) of 0.961 and a 95% confidence interval (CI) of 0.917-1.000. Decision curve analysis illustrated that a nomogram based on quantitative AI was clinically useful., Conclusion: The integration of CT quantitative changes, NLR, and age in this model exhibits promising performance in predicting the progression to severe illness in COVID-19 patients with early-stage pneumonia lesions. This comprehensive approach holds the potential to assist clinical decision-making., Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2024 Chen, Li, Wu, Liu and Huang.)