AIMC Topic: China

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Natural and anthropogenic influences on short-term forest growth status: Evidence and mechanisms from China.

Journal of environmental management
The relationship between forest growth and natural-anthropogenic drivers remains controversial, and the relative importance of these factors on short-term forest growth at large scales still unclear. This uncertainty hinders the development of effect...

Exploring Aerosol Vertical Distributions and Their Influencing Factors: Insight from MAX-DOAS and Machine Learning.

Environmental science & technology
Understanding aerosol vertical distribution is crucial for aerosol pollution mitigation but is hindered by limited observational data. This study employed multiaxis differential optical absorption spectroscopy (MAX-DOAS) technology with a coupled rad...

Prediction of depression risk in middle-aged and elderly Cardiovascular-Kidney-Metabolic syndrome patients by social and environmental determinants of health: an interpretable machine learning approach using longitudinal data from China.

Journal of health, population, and nutrition
BACKGROUND: Cardiovascular-Kidney-Metabolic (CKM) syndrome is a systemic disease characterized by pathophysiological interactions between the cardiovascular system, chronic kidney disease, and metabolic risk factors. In China, the prevalence of CKM i...

Assessing the transfer of Cd and As from co-contaminated soil to peanut (Arachis hypogaea L.): prediction models and soil thresholds.

Environmental pollution (Barking, Essex : 1987)
In China, the co-contamination of soil with cadmium (Cd) and arsenic (As) is one of the most severe forms of combined pollution. Modeling the transfer of Cd and As from co-contaminated soil to crops has not been thoroughly studied. In this study, fiv...

Performance of DeepSeek-R1 and ChatGPT-4o on the Chinese National Medical Licensing Examination: A Comparative Study.

Journal of medical systems
Large Language Models (LLMs) have a significant impact on medical education due to their advanced natural language processing capabilities. ChatGPT-4o (Chat Generative Pre-trained Transformer), a mainstream Western LLM, demonstrates powerful multimod...

Flavor characterization of aged Citri Reticulatae Pericarpium from core regions: An integrative approach utilizing GC-IMS, GC-MS, E-nose, E-tongue, and chemometrics.

Food chemistry
This study utilized GC-MS, GC-IMS, E-nose, and E-tongue to analyze the flavor characteristics of Guangchenpi (GCP) from five core producing areas aged 5 to 40 years. Key findings include: W1W, W2S, and W5S sensors in the e-nose and bitter, umami, swe...

Mitigating bias induced by missing data in new-generation geostationary satellite monitoring of ground-level NO via machine learning.

Environmental pollution (Barking, Essex : 1987)
The Geostationary Environment Monitoring Spectrometer (GEMS) has revolutionized air quality monitoring with hourly resolution from geostationary Earth orbit (GEO). However, satellite-derived air quality data often face limitations and biases due to m...

Double-edged sword? Heterogeneous effects of digital technology on environmental regulation-driven green transformation.

Journal of environmental management
In the context of China's dual carbon goal, enterprises' green transformation is a key path to advancing the nation's high-quality economic development. A majority of existing studies have regarded digital technology as a homogeneous variable, and th...

Machine-learning model for predicting left atrial thrombus in patients with paroxysmal atrial fibrillation.

BMC cardiovascular disorders
OBJECTIVE: Left atrial thrombus (LAT) poses a significant risk for stroke and other thromboembolic complication in patients with atrial fibrillation (AF). This study aimed to evaluate the incidence and predictors of LAT in patients with paroxysmal AF...

Development of a neural network-based risk prediction model for mild cognitive impairment in older adults with functional disability.

BMC public health
BACKGROUND: Mild Cognitive Impairment (MCI) is a critical transitional stage between normal aging and Alzheimer's disease, and its early identification is essential for delaying disease progression.