Multi-scale pressure analysis and fluidization quality characterization of dry dense medium fluidized bed

Coal beneficiation is the source technology of clean processing and utilization of coal. Dry coal beneficiation is an important way for efficient separation and upgrading of easily sliming coal in arid area. Dry dense medium fluidized bed forms a certain density of gas-solid fluidized bed by updraft-driven heavy medium particles fluidization, thus achieving coal separation according to bed density. The uniformity and stability of bed density, namely the bed fluidization quality, is the key to determine the separation accuracy. Due to the disturbance of airflow, bubbles, moving internals, feeding and other factors, the fluidization behavior of the bed is complex and changeable, and the pressure signal shows non-uniformity, non-linearity and multi-scale characteristics. Based on the characteristics of axial differential transmission and lateral equivalent diffusion of pressure signal in dry dense medium fluidized bed, the fluctuation characteristics of axial differential pressure were studied emphatically, and a quantitative characterization method of fluidization quality was proposed. The results show that: Based on time domain analysis, the probability density distribution of total pressure drop in Geldart A type separation fluidized bed is close to normal distribution. When the bed is in the particulate expansion, due to the uneven distribution of contact force between particles, the probability density shows the right deviation and the peak, deviating from the normal distribution. Through frequency domain analysis, it is found that the dominant frequency of bubbles dominates the whole axial interval of fluidized bed at the later stage of bed expansion. After complete fluidization, the dominant frequency of bubbles only controls the central region of the bed. The dominant frequency of bed concentration signal changes obviously along the bed axial distribution. Combined with the results of time-domain and frequency-domain signal analysis, a fluidization quality characterization model was proposed, where the standard deviation of axial fluctuation is weighted and averaged, and the dominant frequency of sub-bed concentration is taken as the weight value. This model can comprehensively evaluate the uniformity and stability of density distribution of dry dense medium fluidized bed, and provide strong support for the steady-state control and accurate separation of dry dense medium fluidized bed

The gut microbiota as a potential biomarker for methamphetamine use disorder: evidence from two independent datasets

BackgroundMethamphetamine use disorder (MUD) poses a considerable public health threat, and its identification remains challenging due to the subjective nature of the current diagnostic system that relies on self-reported symptoms. Recent studies have suggested that MUD patients may have gut dysbiosis and that gut microbes may be involved in the pathological process of MUD. We aimed to examine gut dysbiosis among MUD patients and generate a machine-learning model utilizing gut microbiota features to facilitate the identification of MUD patients.MethodFecal samples from 78 MUD patients and 50 sex- and age-matched healthy controls (HCs) were analyzed by 16S rDNA sequencing to identify gut microbial characteristics that could help differentiate MUD patients from HCs. Based on these microbial features, we developed a machine learning model to help identify MUD patients. We also used public data to verify the model; these data were downloaded from a published study conducted in Wuhan, China (with 16 MUD patients and 14 HCs). Furthermore, we explored the gut microbial features of MUD patients within the first three months of withdrawal to identify the withdrawal period of MUD patients based on microbial features.ResultsMUD patients exhibited significant gut dysbiosis, including decreased richness and evenness and changes in the abundance of certain microbes, such as Proteobacteria and Firmicutes. Based on the gut microbiota features of MUD patients, we developed a machine learning model that demonstrated exceptional performance with an AUROC of 0.906 for identifying MUD patients. Additionally, when tested using an external and cross-regional dataset, the model achieved an AUROC of 0.830. Moreover, MUD patients within the first three months of withdrawal exhibited specific gut microbiota features, such as the significant enrichment of Actinobacteria. The machine learning model had an AUROC of 0.930 for identifying the withdrawal period of MUD patients.ConclusionIn conclusion, the gut microbiota is a promising biomarker for identifying MUD and thus represents a potential approach to improving the identification of MUD patients. Future longitudinal studies are needed to validate these findings

Separation and Recovery of Fine Particles from Waste Circuit Boards Using an Inflatable Tapered Diameter Separation Bed

Recovering particle materials from discarded printed circuit boards can enhance resource recycling and reduce environmental pollution. Efficiently physically separating and recovering fine metal particles (−0.5 mm) from the circuit boards are a key recycling challenge. To do this, a new type of separator, an inflatable tapered diameter separation bed, was developed to study particle motion and separation mechanisms in the bed’s fluid flow field. For 0.5–0.25 mm circuit board particles, metal recovery rates ranged from 87.56 to 94.17%, and separation efficiencies ranged from 87.71 to 94.20%. For 0.25–0.125 mm particles, metal recovery rates ranged from 84.76 to 91.97%, and separation efficiencies ranged from 84.74 to 91.86%. For superfine products (−0.125 mm), metal recovery rates ranged from 73.11 to 83.04%, and separation efficiencies ranged from 73.00 to 83.14%. This research showed that the inflatable tapered diameter separation bed achieved efficient particle separation and can be used to recover fine particles under a wide range of operational conditions. The bed offers a new mechanical technology to recycle valuable materials from discarded printed circuit boards, reducing environmental pollution

Separation and Recovery of Fine Particles from Waste Circuit Boards Using an Inflatable Tapered Diameter Separation Bed

Recovering particle materials from discarded printed circuit boards can enhance resource recycling and reduce environmental pollution. Efficiently physically separating and recovering fine metal particles (−0.5 mm) from the circuit boards are a key recycling challenge. To do this, a new type of separator, an inflatable tapered diameter separation bed, was developed to study particle motion and separation mechanisms in the bed's fluid flow field. For 0.5-0.25 mm circuit board particles, metal recovery rates ranged from 87.56 to 94.17%, and separation efficiencies ranged from 87.71 to 94.20%. For 0.25-0.125 mm particles, metal recovery rates ranged from 84.76 to 91.97%, and separation efficiencies ranged from 84.74 to 91.86%. For superfine products (−0.125 mm), metal recovery rates ranged from 73.11 to 83.04%, and separation efficiencies ranged from 73.00 to 83.14%. This research showed that the inflatable tapered diameter separation bed achieved efficient particle separation and can be used to recover fine particles under a wide range of operational conditions. The bed offers a new mechanical technology to recycle valuable materials from discarded printed circuit boards, reducing environmental pollution

Surveillance of Hepatitis E Virus Contamination in Shellfish in China

Background: Hepatitis E virus (HEV) has been confirmed to be a zoonotic virus of worldwide distribution. HEV contamination in the water environment has not been well examined in China. The objective of this study was to evaluate HEV contamination in shellfish in a coastal area of China. Such contamination would be significant for evaluating public health risks. Methods: samples of three species shellfish were collected from thirteen points of estuarine tidal flats around the Bohai Gulf and screened for HEV RNA using an in-house nested RT-PCR assay. The detected HEV-positive samples were further verified by gene cloning and sequencing analysis. Results: the overall HEV-positive detection rate is approximately 17.5% per kilogram of shellfish.  HEV was more common among S. subcrenata (28.2%), followed by A. granosa (14.3%) and R. philippinarum (11.5%). The phylogenetic analysis of the 13 HEV strains detected revealed that gene fragments fell into two known 4 sub-genotypes (4b/4d) groups and another unknown group. Conclusions: 13 different sub-genotype 4 HEVs were found in contaminated shellfish in the Bohai Gulf rim. The findings suggest that a health risk may exist for users of waters in the Bonhai area and to consumers of shellfish.  Further research is needed to assess the sources and infectivity of HEV in these settings, and to evaluate additional shellfish harvesting areas