Search

Aiming at the problems of large discreteness of early warning results and prediction positions of rock burst, a multi-source information fusion early warning technology is studied, and a multi-source information fusion depth prediction model based on machine learning algorithm is established. Through the analysis of rock burst events, the main causes, characteristics and influencing factors of rock burst accidents are analyzed. Taking the compressive strength, tensile strength, elastic energy and ground stress of coal and rock as the main indicators of rock burst prediction, the depth neural grid prediction model is established to determine the weight of different prediction indicators. Through big data analysis and limited data, the occurrence of rock burst is predicted. Taking the occurrence of rock burst is predicted. Taking a coal mine in Shaanxi Province as an example, the depth neural network model is verified, and the effectiveness and correctness of the prediction model of rock burst are verified through field measurement.

Introduction: The homeostasis of the microbiome in lower respiratory tract is crucial in sustaining normal physiological functions of the lung. Different pulmonary diseases display varying degrees of microbiome imbalance; however, the specific variability and clinical significance of their microbiomes remain largely unexplored. Methods: In this study, we delineated the pathogen spectrum and commensal microorganisms in the lower respiratory tract of various pulmonary diseases using metagenomic sequencing. We analyzed the disparities and commonalities of the microbial features and examined their correlation with disease characteristics. Results: We observed distinct pathogen profiles and a diversity in lower airway microbiome in patients diagnosed with cancer, interstitial lung disease, bronchiectasis, common pneumonia, Nontuberculous mycobacteria (NTM) pneumonia, and severe pneumonia. Discussion: This study illustrates the utility of Metagenomic Next-generation Sequencing (mNGS) in identifying pathogens and analyzing the lower respiratory microbiome, which is important for understanding the microbiological aspect of pulmonary diseases and essential for their early and precise diagnosis. [ABSTRACT FROM AUTHOR]

Recurrence posed an important challenge to pulmonary tuberculosis (PTB) control in China. The prospective study aimed to identify potential risk factors and to explore the value of QuantiFERON-TB Gold Plus (QFT-Plus) in identifying at-risk individuals with treated prior PTB history. All eligible individuals aged ≥18 years who had been diagnosed with PTB before 2016 in Zhongmu County, where with an average level of TB prevalence in China, were included and received baseline survey including chest radiography, QuantiFERON-TB Gold In-Tube (QFT-GIT) and QFT-Plus, then PTB recurrence was tracked through a 2-year follow-up. Half of 1068 (52.34%, 559/1068) included eligible participants were QFT-Plus positive at baseline and 21 of them recurred active TB in 2-year follow-up. Individuals aged ≥ 60 years, who had a recent history of TB and smokers were associated with increased risk of TB recurrence with an adjusted odds ratio (aOR) of 3.97 (95% confidence interval (CI): 1.29–12.24), 7.71 (95% CI: 1.74–34.25) and 4.56 (95% CI: 1.62–12.83), respectively. Compared to QFT-Plus negatives, those who were TB2+/TB1- (aOR = 15.34) exhibited stronger association with the risk of TB recurrence than those who were TB1+/TB2+ (aOR = 6.06). A dose response relationship was also found between the risk of TB recurrence with the baseline level of TB2-TB1 (p for trend < 0.001). High burden of TB infection and high risk of PTB recurrence were observed in the study population. Those with recent onset of prior TB, elderly smokers and QFT-Plus positives especially with TB2 single positive deserved further attention in active TB surveillance. [ABSTRACT FROM AUTHOR]

Composite material La0.7Sr0.3MnO3/α-Fe2O3 was prepared by high-temperature calcination method and its structure, electromagnetic parameters, and electromagnetic wave absorption performance were studied. Further preliminary exploration was conducted on the electromagnetic wave absorption effect of this powder for fabric coatings. The results showed that the significant absorption performance of La0.7Sr0.3MnO3/α-Fe2O3 is achieved when the powder and paraffin are mixed in a mass ratio of 7:3, and they can absorb over 99.9% of incident electromagnetic waves at a thickness of 5.0 mm and 9.5 mm, respectively. The absorption performance is excellent. In addition, within the range of 1-18 GHz and 1.0-10 mm, the total effective absorption (≤-10 dB) bandwidth of the sample La0.7Sr0.3MnO3/α-Fe2O3 covers 91.8% of the test bandwidth, achieving effective absorption of radar multi frequency bands. The excellent absorption performance of composite materials is attributed to the La0.7Sr0.3MnO3/α-Fe2O3. The combined effect of dielectric loss and magnetic loss of La0.7Sr0.3MnO3/α-Fe2O3 achieves effective absorption of electromagnetic waves. The fabrics prepared by coating method and plain weave methods can also absorb more than 90% of incident electromagnetic waves at 5.84 GHz, providing new ideas for the design and research of composite absorbing material and absorbing fabrics. [ABSTRACT FROM AUTHOR]