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This study intends to explore the influence of temperature and molar ratio on the competitive adsorption of CH4/O2/N2 gas in coal. It probed into the competitive adsorption characteristics of CH4/O2 and N2/O2 binary mixed gas in Xiaolongtan lignite of Yunnan under different temperatures (303.15~383.15 K)and pressures (0~480 kPa)through grand canonical Monte Carlo (GCMC)and molecular simulation methods. Results show that : ① Within the range of test temperature and pressure, temperature will inhibit the adsorption of CH4, O2 and N2 by coal, and the adsorption capacity of coal to three gases is CH4>O2>N2. ② The selectivity coefficient of CH4/O2 adsorption is basically independent of the gas molar ratio, and shows decrease with the increase of temperature. The adsorption selectivity of N2/O2 on coal under different molar ratios was not significantly related to temperature and molar ratio. ③ With the increase of adsorption capacity, the isosteric adsorption heat of any component in the binary mixed gas shows linear increase. Under the same adsorption capacity, larger molar ratio of CH4/O2 and N2/O2 would lead to lower adsorption heat. However, when the adsorption capacity of CH4 is less than 0.029 mmol / g, the isosteric adsorption heat of CH4 gas shows little correlation with the molar ratio. The research reveals the influence mechanism of competitive adsorption behavior of CH4 and air on low temperature oxidation of coal.

With the increase of burial depth of coal seam, the gas pressure and gas content of coal seam tend to rise, resulting in the serious problem of gas concentration exceeding the limit.Generally, adding surfactant aqueous solution is an important technical means to reduce coal seam gas emission.In this paper, the application of surfactant aqueous solution in the prevention and control of coal mine gas disaster is analyzed by the literature survey method.The results are as follow: ① based on the consideration of safety and gentleness effects, the descending order of the optimization of surfactants is amphoteric and biological, non-ionic, anionic and cationic, and most non-ionic and amphoteric surfactants have good compound performance.② In the test concentration range, except for biological surfactants, an inflection point for the variations of surface tension and contact angle with solution concentration can be observed for other types of surfactant aqueous solutions, and the corresponding critical micelle concentration (CMC) is 0.1 %.Moreover, the descending order of the wettability effects of these surfactants is anionic, non-ionic, amphoteric, cationic and biological.③ The addition of surfactant aqueous solution can significantly inhibit the desorption-diffusion capacity of gas in coal, which is closely related to the wetting spreading effect and blocking effect of aqueous solution.

Background: Chronic infections by pathogenic microorganisms play a significant role in cancer development, disrupting the body's immune system and microenvironment. This interference impairs the body's ability to eliminate these microorganisms promptly, allowing them to persist by evading immune defenses. Aims: This study aimed to explore how chronic pathogenic infections influence the immune microenvironment, impacting tumorigenesis, cancer progression, and treatment strategies. Additionally, it seeks to investigate the effects of these infections on specific immune checkpoints and identify potential targets for immunotherapy. Methods: We conducted searches, readings, and detailed analyses of key terms in databases like PubMed and Web of Science to evaluate the impact of chronic infections by pathogenic microorganisms on the immune microenvironment. Results: Our analysis demonstrates a significant association between persistent chronic infections by pathogenic microorganisms and tumorigenesis. Notable impacts on the immune microenvironment include changes in immune cell function and the regulation of immune checkpoints, offering insights into potential targets for cancer immunotherapy. Discussion: This study highlights the complex relationship between chronic infections and cancer development, presenting new opportunities for cancer immunotherapy by understanding their effects on the immune microenvironment. The influence of these infections on immune checkpoints emphasizes the crucial role of the immune system in cancer treatment. Conclusion: Chronic infections by pathogenic microorganisms greatly affect the immune microenvironment, tumorigenesis, and cancer treatment. Unraveling the underlying mechanisms can unveil potential targets for immunotherapy, improving our comprehension of the immune response to cancer and potentially leading to more effective cancer treatments in the future. [ABSTRACT FROM AUTHOR]

Localizing defects in long-range pipelines is essential to reduce the inspection time and develop timely repair strategies. The acoustic emission (AE) method is employed to pinpoint the position of defects in pipelines. The conventional 1-D localization algorithm requires time of arrival differences between two sensors, which may not be accurately captured due to the dispersive nature of the pipe structures. The geometric variations such as elbows and welds can influence the propagating elastic waves and, consequently, arrival time. In this study, an AE source localization approach using a deep learning model is developed to tackle the influences of sensor-source distance and geometric variables. The multi-task learning model first identifies the impact of the elbow and subsequently integrates this information when predicting the source location. The proposed model is evaluated on a complex piping system, which features welded elbows in its connections. Incorporating the elbow effect into the model shows a notable improvement in overall accuracy, rising from 53% (conventional method) to 94% (proposed multi-task learning method). [ABSTRACT FROM AUTHOR]

Growth curve models have been widely used to analyse longitudinal data in social and behavioural sciences. Although growth curve models with normality assumptions are relatively easy to estimate, practical data are rarely normal. Failing to account for non‐normal data may lead to unreliable model estimation and misleading statistical inference. In this work, we propose a robust approach for growth curve modelling using conditional medians that are less sensitive to outlying observations. Bayesian methods are applied for model estimation and inference. Based on the existing work on Bayesian quantile regression using asymmetric Laplace distributions, we use asymmetric Laplace distributions to convert the problem of estimating a median growth curve model into a problem of obtaining the maximum likelihood estimator for a transformed model. Monte Carlo simulation studies have been conducted to evaluate the numerical performance of the proposed approach with data containing outliers or leverage observations. The results show that the proposed approach yields more accurate and efficient parameter estimates than traditional growth curve modelling. We illustrate the application of our robust approach using conditional medians based on a real data set from the Virginia Cognitive Aging Project. [ABSTRACT FROM AUTHOR]