Introduction In recent years, with the rapid advancement of urbanization in China and the successive implementation of major national strategies such as the Belt and Road Initiative, the Sichuan-Xizang Railway, and the South-to-North Water Diversion Project, the potential risks and losses from geological disasters have continued to rise. Secondary disasters-including landslides, mudslides, and barrier lakes triggered by earthquakes-have significantly intensified the overall impact, posing severe challenges to disaster monitoring, early warning, emergency response, recovery, and reconstruction efforts. In this context, how to leverage new information technologies to achieve in-depth mining and application of geological disaster data has become a critical issue in disaster risk reduction and sustainable crisis management.Methods This study focuses on topic modeling and sentiment analysis of disaster-related data, using geological disasters in China as a background. First, it reviews the recent advances in topic modeling and sentiment analysis techniques. Then, based on data characteristics and applicability, two major social media platforms-Weibo (Sina Weibo) and Rednote (Xiaohongshu)-are selected as primary data sources. The advantages of the LDA topic model (e.g., its unlabeled and multi-topic capabilities) and the lightweight processing efficiency of the SnowNLP sentiment analysis algorithm are discussed. As a case study, the "1 center dot 07 '' earthquake in Xigaze, Tibet, in 2025 is analyzed. The LDA model is used to conduct multi-topic classification and clustering visualization of Weibo disaster topic data. Combined with the SnowNLP sentiment analysis algorithm, the phased sentiment evolution judgment application is carried out using the 6-month Rednote comment data.Results The LDA model effectively extracts geological disaster-related themes-such as emergency response and post-disaster recovery-and that sentiment analysis technology can reveal phase-based patterns in public emotions.Conclusion These findings provide scientific support for geological disaster emergency management and public opinion guidance. The research also expands the application potential of topic modeling and sentiment analysis in the field of geological disasters and offers a direction for future integration and optimization of multimodal social media data.

Landslide is a typical geological disaster distributed in most countries worldwide. Due to long-term natural weathering and human engineering disturbances, the instability of landslides is prone to occur. Once monitoring and disposal methods are implemented inappropriately, they can lead to landslide hazards, seriously threatening the safety of people’s lives and property. For a long time, extensive research on landslide monitoring has been conducted from various countries, providing crucial technical support for reducing the incidence and severity of landslide hazards. However, considering the complex geological conditions of actual landslides and the direct impact of internal and external factors such as rainfall, storms, and earthquakes, the early warning accuracy of landslide hazards is still relatively low. Therefore, based on advanced research achievements, it is significant to carry out research on the current status and development trends of landslide monitoring technology. Based on the Web of Science core database, this study quantitatively analyzes the advanced research achievements in global landslide monitoring in the past decade using bibliometric analysis. A systematic analysis of landslide monitoring technology development is conducted according to each study’s publication time, keywords, and countries. On this basis, a multi-dimensional monitoring system for landslides was proposed, which utilizes the complementary advantages of multi-dimensional monitoring technology to achieve all-round, high-precision, and real-time monitoring of landslides. Finally, taking the Xinpu landslide in the Three Gorges Region of China as an example, a multi-source and multi-field-monitoring experiment was conducted. The application of landslide multi-field-monitoring technology provides an essential reference for monitoring, early warning, as well as the scientific prevention and control of landslide hazard.

The viscoelastic tissue under dual-frequency ultrasound excitation affects the acoustic cavitation of a single gas-vapor bubble. To investigate the effect of the cavitation dynamics, the Gilmore-Akulichev-Zener (GAZ) model is coupled with the Peng-Robinson equation of state (PR EOS). Results indicate that the GAZ-PR EOS model can accurately estimate the bubble dynamics by comparing with the Gilmore PR EOS and GAZ-Van der Waals (VDW) EOS model. Furthermore, the acoustic cavitation effect in different viscoelastic tissues is investigated, including the radial stress at the bubble wall, the temperature, pressure, and the number of water molecules inside the bubble. Results show that the creep recovery and the relaxation of the stress caused by viscoelasticity can affect the acoustic cavitation of the bubble, which could inhibit the bubble's expansion and reduce the internal temperature and pressure within the bubble. Moreover, the effect of dual-frequency ultrasound on the cavitation of single gas-vapor bubbles is studied. Results suggest that dual-frequency ultrasound could increase the internal temperature of bubbles, the internal pressure of bubbles, and the radial stress at the bubble wall. More importantly, there is a specific optimal combination of frequencies for particular viscoelasticity by exploring the impact of different dual-frequency ultrasound combinations and tissue viscoelasticity on the acoustic cavitation of a single gas-vapor bubble. In conclusion, this study helps to provide theoretical guidance for dual-frequency ultrasound to improve acoustic chemical and mechanical effects, and further optimize its application in acoustic sonochemistry and ultrasound therapy.

In recent years, with the superposition of extreme climate, earthquakes, engineering disturbance and other effects, global landslide disasters occur frequently. Due to reservoir landslides being mostly in a multi-field coupling environment, the temperature field will impact the deformation and seepage fields, thereby affecting the stability of the reservoir landslide. The variation in the landslide’s surface temperature also directly affects the stress and deformation of deep rock masses. If hidden dangers are not detected in time, and corresponding measures are implemented, it is easy to cause landslide instability. In order to clarify the temperature measurement performance of different optic-electric sensors and the application characteristics of layout techniques, laboratory calibration tests of temperature sensors under different adhesives and attachment materials are carried out in this paper. It was found that the test data of the iron bar had the best effect among the four attachment materials overall. Therefore, the bar with a high-stiffness material should be preferred when selecting a pipe fitting as the fiber Bragg grating (FBG) temperature attachment in the borehole. However, considering the high requirements for the durability of sensors and layout techniques in on-site monitoring, the long-term stability of the adhesives used in actual monitoring needs to be improved. At the same time, it was found that the platinum 100 (PT100) temperature sensor has relatively higher testing accuracy (A: 0.15 + 0.002 × |t|; B: 0.30 + 0.005 × |t|), a larger temperature measurement range (−200~+850 °C) and better temperature measurement stability when compared to conventional sensors. Moreover, its resistance value has a good linear relationship with temperature. Finally, the Xinpu landslide in the Three Gorges Reservoir area was selected as the research object for on-site monitoring. There was a high correlation between the on-site monitoring results with the laboratory calibration test results. Therefore, through the performance test of optic-electric sensors in reservoir landslide temperature fields, more accurate solutions can be provided for selecting sensors and designing layout techniques to monitor the underground temperature field of landslides under different geological conditions. Thereby, grasping the real-time state information of the reservoir landslide temperature field is achieved accurately, providing an important reference for early warning, prediction, prevention and the control of reservoir landslide disasters.

In order to determine the explosion value in the confined space, this time the simulation model of the deep learning algorithm is used to study it. The research status of deep learning algorithm is first expounded, and the numerical record of gas explosion in confined space is constructed accordin g to computer technology. In order to ensure the optimization of numerical processing, the deep learning algorithm is used to process the simulation data to ensure the accuracy of the explosion value. In order to further test the numerical accuracy of the numerical model of gas limited space explosion, the comparison of different values in the constrained space is carried out, and the efficiency and accuracy of the deep learning algorithm are tested. The test results show the application of deep learning algorithm. The accuracy of the explosion value is further guaranteed and needs further application.

The parachute system has been widely applied in modern armament design, especially for the fuel-air explosives. Because detonation of fuel-air explosives occurs during flight, it is necessary to investigate the influences of the parachute-non-bomb drop process on flight characteristics to ensure successful dynamic detonation. In this paper, the major characteristics of parachute non -bomb phase were explored with airdrop. Based on the influences, the equations for parachute-non-bomb trajectory were established to predict the position of parachute-non-bomb. The characteristics of parachute-bomb and parachute-non-bomb process were analyzed and discussed. The analysis results showed that the parachute-bomb, including parachute and bomb component, swung periodically in the falling stability phase and the parachute-non-bomb trajectory was a straight line toward an angle. The attitude angle, parachute-non-bomb velocity, and delay time were the major influencing factors of the parachute-non bomb trajectory. Parachute-non-bomb velocity was less than parachute-bomb velocity. The parachute-bomb velocity was35.73m/s, which was 3.53 m/s less than that of the parachute non -bomb velocity, 32.2m/s.

The parachute system has been widely applied in modern armament design, especially for the fuel-air explosives. Because detonation of fuel-air explosives occurs during flight, it is necessary to investigate the influences of the initiation point to ensure successful dynamic detonation. In fact, the initiating position exist the falling area in the fuels, due to the error of influencing factors. In this paper, the major influencing factors of initiation point were explored with airdrop and the regularity between initiation point area and factors were obtained. Based on the regularity, the volume equation of initiation point area was established to predict the range of initiation point in the fuel. The analysis results showed that the initiation point appeared area, scattered on account of the error of attitude angle, secondary initiation charge velocity, and delay time. The attitude angle was the major influencing factors on a horizontal axis. On the contrary, secondary initiation charge velocity and delay time were the major influencing factors on a horizontal axis. Overall, the geometries of initiation point area were sector coupled with the errors of the attitude angle, secondary initiation charge velocity, and delay time.

This paper presents an experiment system in the open field, which comprises a charge structure (approximately 166.2 kg), a high-speed camera subsystem, and a pressure measurement subsystem. Through a series of experiments under the cylindrical clouds with different diameters, heights, and diameter-to-height ratios (D : H), the influences of various cloud shapes on the overpressure field were analyzed and discussed. Based on the experimental results, the overpressure field was divided into two zones: detonation wave zone and shock wave zone. It is found that the overpressure of shock waves at the same distance from the explosion center increased with the diameters, but the variations of heights had little impact on the overpressure. Therefore, the pancake-shaped cloud of fuel-air mixtures is the optimal shape for obtaining the wider overpressure field. Moreover, it is found that the overpressure field gets the maximum under the diameter-to-height ratios of 5.7 in the same distance within the studied range.