Search

This study is based on the panel data from 2006 to 2020 to explore the relationship between artificial intelligence (AI) and pollution emission. The study demonstrates a notable and statistically significant reduction in pollution emission intensity attributable to AI. This finding is robust, holding up under various sensitivity analyses and addressing endogeneity using instrumental variable techniques. The mechanism analysis reveals that AI's role in decreasing emissions primarily arises from three factors: the promotion of technological innovation, increased investment in emission reduction technology, and the replacement of low‐skilled labor with more efficient alternatives. We also find more substantial environmental benefits in areas with stricter environmental regulations and higher initial levels of pollution. Additionally, AI is shown to facilitate a symbiotic relationship between economic growth and environmental management. This research provides both empirical evidence and theoretical insights that reinforce the essential role of AI in driving green transformations, process upgrades, and high‐quality development. It situates these advancements within the broader context of the interplay between artificial intelligence, environmental sustainability, and economic progression. [ABSTRACT FROM AUTHOR]

In the past years, human computer interaction (HCI) research and innovation have developed substantially, leading to a number of fruitful research topics. In this paper, we surveyed the HCI research and innovation in China from a 10-year perspective. We analyzed the popular research methodology and topics among Chinese researchers, including human modeling, user interface techniques, context awareness, user acceptance and performance, user experience design, human-AI interaction, HCI applications and social influences. We also conducted a bibliography analysis on the published papers in top-tier conferences and journals, which revealed a significant rising trend, and a generally broad distribution of research types. Moreover, we described typical applications and the industry influence of the research outcomes. We concluded with implications and reflections for HCI researchers across the world and shared the future research trends envisioned by Chinese researchers. [ABSTRACT FROM AUTHOR]

• New technology to analyze a panel of 88 serum sphingolipid metabolites. • Identified a biomarker for the accurate diagnosis of WD and the evaluation of cirrhosis risk in individuals with WD. • Uncovered the sphingolipid metabolism signature in patients with WD. The diagnosis of Wilson's disease (WD) remains a challenging endeavor in clinical practice. Serum sphingolipids play a significant role in the development of liver disease. In this study, we examined the serum sphingolipid profile in patients with WD and explored the potential diagnostic utility of serum sphingolipid metabolites. These metabolites may aid in distinguishing WD patients from healthy controls and identifying those with a risk of cirrhosis. This study consecutively enrolled 26 WD patients and 88 healthy controls. We utilized high-performance liquid chromatography–tandem mass spectrometry to analyze a panel of 88 serum sphingolipid metabolites. The data were analyzed by multivariate statistical methods. Among the 88 sphingolipids metabolites analyzed, 17 sphingolipids were observed significant differences between WD and HC groups (all P < 0.05). Notably, five sphingolipids, namely S1P (d18:1), Cer (d18:2/21:0), SM41:2, sph(d18:1), and Cer (d18:2/22:0), each with an AUC exceeding 0.9, emerged as potential biomarkers for WD. Additionally, in the comparison between WD patients with and without cirrhosis, 24 sphingolipid metabolites exhibited significant differences (all P < 0.05). We identified Cer(d18:1/20:0), Cer(d18:2/22:0), Cer(d18:2/24:0), Cer(d18:2/20:0), and Cer(d18:2/18:0), each with an AUC exceeding 0.9, as potential serological markers for WD patients with cirrhosis. For enhanced clinical applicability, we propose considering Cer (d18:2/22:0) as a predictive marker applicable to both WD patients and their susceptibility to cirrhosis. This particular ceramide has exhibited strong diagnostic and predictive performance. These findings have the potential to facilitate non-invasive WD diagnosis. [ABSTRACT FROM AUTHOR]

[Display omitted] • Gas chromatography-mass spectrometry (GC-MS) remains the preeminent method for ascertaining the presence of chloropropanol. • Rapid progress in optical technologies and instrumentation has enabled the visual identification of chloropropanol. • The growing field of electrochemical analysis has emerged as a novel method of chloropropanol analysis, based on innovative technologies, such as nanomaterials and biosensors. • The reduced size and enhanced portability of analysis instruments provide promise for the future of chloropropanol analysis. Chloropropanols are among the major food contaminants, and quantifying their content in food is a key food-safety issue. In response to the demand for highly sensitive and selective analysis, the scientific community is committed to continuous innovation and optimization of various analytical techniques. This paper comprehensively reviews the latest developments in chloropropanol analysis technologies and systematically compares and analyzes the working principles, application conditions, advantages, and challenges of these methods. Gas chromatography-mass spectrometry is the preferred choice for chloropropanol analysis in complex sample matrices owing to its high resolution, sensitivity, and accuracy. Electrochemical methods provide strong support for the real-time monitoring of chloropropanols because of their high selectivity and sensitivity towards electrochemically active molecules. Other techniques offer innovative solutions for the rapid and accurate analysis of chloropropanol at different levels. Finally, innovative directions for the development of chloropropanol analysis methods for food safety are highlighted. [ABSTRACT FROM AUTHOR]