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Studying temperature evolution and distribution range during underground coal gasification is essential to optimize process efficiency, ensure safe and stable operation and reduce environmental impact. In this paper, based on the Liyan Coal Mine underground gasification project, the moving grid setting is used to simulate the moving heat transfer process of the underground coal gasification (UCG) flame working face (FWF). The results showed that the temperature distribution within the coal wall facing the flame is relatively narrow and remains concentrated within a limited range. Temperature distribution curves for T = 100 °C and T = 600 °C initially exhibit a nonlinear increase, reaching a maximum value, followed by a nonlinear decrease, ultimately trending towards a constant value. The maximum temperature influence ranges at ∆T = 10 °C (T = 30 °C) in the roof, left coal pillar, and floor are approximately 13.0 m, 9.0 m, and 10.1 m, respectively. The temperature values at the +1 m and +2 m positions on the roof exhibit a parabolic pattern, with the height and width of the temperature curve gradually increasing. By the end of the operation at t = 190 d, the length range of temperatures exceeding 600 °C at the +1 m position is 73 m, with a maximum temperature of approximately 825 °C, while at the +2 m position it is 31 m, with a maximum temperature of approximately 686 °C. [ABSTRACT FROM AUTHOR]

Catastrophic landslides are generally evolved from potential active landslides, and early identification of active landslides over an extensive region is vital to effective prevention and control of disastrous landslides in urban areas. Interferometric Synthetic Aperture Radar (InSAR) has immense potential in mapping active landslides. However, artificial interpretation of InSAR measurements and manual recognition of active landslides are very laborious and time-consuming, with a relatively high missing and false alarms. That hinders the application of InSAR technique and the identification of active landslides in wide areas. Automatic recognition of active landslides has always been a great challenge and has been relatively rarely investigated by previous studies. This work establishes comprehensive identification indices of geoenvironmental, disaster-triggering, and surface deformation features. Moreover, it suggests a novel deep learning algorithm of SDeepFM to conduct automatic identification of active landslides across a vast and landslide-serious area of Hualong County. Some new viewpoints are suggested as follows. (1) The identification indices consist of disaster-controlling, disaster-inducing, and active deformation characteristics and are constructed in terms of the cause characteristics of active landslides. Thus, it can effectively decrease the false alarms of active landslide identification. (2) The proposed SDeepFM algorithm features a spatial-perception ability and can adequately extract and fuse the low-level and high-level semantic features. It outperforms the classification and regression tree (CART), multi-layer perceptron (MLP), convolutional neural network (CNN), and deep neural network (DNN) algorithms. The test accuracy attains 0.91, 99.73%, 90.21%, 0.92, 0.96, and 0.91 in F1-score, Accuracy, Precision, Recall, AUC, and Kappa, respectively. (3) A total of 164 active landslides are exactly recognized, and 39 active landslides are newly identified in this work. (4) In Hualong County, the characteristics of slope deformation, spatial context, lithology, tectonic movement, human activity, and topography play important roles in active landslide identification. River distribution and rainfall also contribute to active landslide recognition. [ABSTRACT FROM AUTHOR]

The document "Microalgae Biotechnology: Methods and Applications" discusses the potential of microalgae as sustainable chassis for biotechnology, emphasizing their ability to produce valuable compounds and remediate pollutants. Advances in genetic tools and techniques, such as CRISPR/Cas9, are crucial for enhancing microalgal biotechnology. The document also highlights the use of synthetic biology to produce antimicrobial peptides in microalgae, offering a sustainable solution to combat antibiotic resistance. Additionally, innovations in cost-effective microalgae cultivation techniques, such as optimized culture mediums and photobioreactors, are essential for making microalgal products economically viable. [Extracted from the article]

In the past decade, triboelectric nanogenerators (TENGs) have attracted significant attention across various fields due to their compact size, light weight, high output voltage, versatile shapes, and strong compatibility. However, substantial wear at solid–solid contact interfaces presents a major obstacle to the electrical output stability of TENGs. The objective of this study is to investigate the output performances of TENGs lubricated with TiO2-doped oleic acid. The results suggest that the triboelectrical performances of the polyimide (PI) film sliding against a steel ball under 0.1 wt% TiO2-doped oleic acid are significantly improved compared to those under dry conditions; the growth rates are 35.2%, 103.6, and 85.6%, respectively. Moreover, the coefficient of friction dropped from 0.31 to 0.066. The wear and performance enhancement mechanism are also analyzed. This study provides an effective approach to improve both the electrical performances and tribological behaviors. [ABSTRACT FROM AUTHOR]