Optimizing the Quality of Machine Learning for Identifying the Share of Biogenic and Fossil Carbon in Solid Waste.

Insights into carbon sources (biogenic and fossil carbon) and contents in solid waste are vital for estimating the carbon emissions from incineration plants. However, the traditional methods are time-, labor-, and cost-intensive. Herein, high-quality data sets were established after analyzing the carbon contents and infrared spectra of substantial samples using elemental analysis and attenuated total reflectance-Fourier transform infrared spectroscopy (ATR-FTIR), respectively. Then, five classification and eight regression machine learning (ML) models were evaluated to recognize the proportion of biogenic and fossil carbon in solid waste. Using the optimized data preprocessing approach, the random forest (RF) classifier with hyperparameter tuning ranked first in classifying the carbon group with a test accuracy of 0.969, and the carbon contents were successfully predicted by the RF regressor with R2 = 0.926 considering performance-interpretability-computation time competition. The above proposed algorithms were further validated with real environmental samples, which exhibited robust performance with an accuracy of 0.898 for carbon group classification and an R2 value of 0.851 for carbon content prediction. The reliable results indicate that ATR-FTIR coupled with ML algorithms is feasible for rapidly identifying both carbon groups and content, facilitating the calculation and assessment of carbon emissions from solid waste incineration.

Contribution of microglial reaction to increased nociceptive responses in high-fat-diet (HFD)-induced obesity in male mice.

The progressive increase in the prevalence of obesity in the population can result in increased healthcare costs and demands. Recent studies have revealed a positive correlation between pain and obesity, although the underlying mechanisms still remain unknown. Here, we aimed to clarify the role of microglia in altered pain behaviors induced by high-fat diet (HFD) in male mice. We found that C57BL/6CR mice on HFD exhibited enhanced spinal microglial reaction (increased cell number and up-regulated expression of p-p38 and CD16/32), increased tumor necrosis factor-α (TNF-α) mRNA and brain-derived neurotrophic factor (BDNF) protein expression as well as a polarization of spinal microglial toward a pro-inflammatory phenotype. Moreover, we found that using PLX3397 (a selective colony-stimulating factor-1 receptor (CSF1R) kinase inhibitor) to eliminate microglia in HFD-induced obesity mice, inflammation in the spinal cord was rescued, as was abnormal pain hypersensitivity. Intrathecal injection of Mac-1-saporin (a saporin-conjugated anti-mac1 antibody) resulted in a decreased number of microglia and attenuated both mechanical allodynia and thermal hyperalgesia in HFD-fed mice. These results indicate that the pro-inflammatory functions of spinal microglia have a special relevance to abnormal pain hypersensitivity in HFD-induced obesity mice. In conclusion, our data suggest that HFD induces a classical reaction of microglia, characterized by an enhanced phosphorylation of p-38 and increased CD16/32 expression, which may in part contribute to increased nociceptive responses in HFD-induced obesity mice.

Rapid determination of moisture content of multi-source solid waste using ATR-FTIR and multiple machine learning methods.

Rapid determination of moisture content plays an important role in guiding the recycling, treatment and disposal of solid waste, as the moisture content of solid waste directly affects the leachate generation, microbial activities, pollutants leaching and energy consumption during thermal treatment. Traditional moisture content measurement methods are time-consuming, cumbersome and destructive to samples. Therefore, a rapid and nondestructive method for determining the moisture content of solid waste has become a key technology. In this work, an attenuated total reflectance-Fourier transform infrared spectroscopy (ATR-FTIR) and multiple machine learning methods was developed to predict the moisture content of multi-source solid waste (textile, paper, leather and wood waste). A combined model was proposed for moisture content regression prediction, and the applicability of 20 combinations of five spectral preprocessing methods and four regression algorithms were discussed to further improve the modeling accuracy. Furthermore, the prediction result based on the water-band spectra was compared with the prediction result based on the full-band spectra. The result showed that the combination model can efficiently predict the moisture content of multi-source solid waste, and the R2 values of the validation and test datasets and the root mean square error for the moisture prediction reached 0.9604, 0.9660, and 3.80, respectively after the hyperparameter optimization. The excellent performance indicated that the proposed combined models can rapidly and accurately measure the moisture content of solid waste, which is significant for the existing waste characterization scheme, and for the further real-time monitoring and management of solid waste treatment and disposal process.