Applied Sciences

Underground mining environments pose significant challenges for automated hazard detection due to low illumination, restricted visibility, and the absence of Global Navigation Satellite System (GNSS) coverage. These factors limit situational awareness and delay inspection efforts, particularly after disruptive events when rapid assessment is essential for safety. This study addresses this problem by developing a dual-pipeline framework for 2D–3D detection that uses 360° imaging and LiDAR-based machine learning to identify people, vehicles, and positional changes in underground settings without requiring personnel to re-enter hazardous areas. The objective was to create a system capable of recognizing objects and monitoring spatial changes under real underground mine conditions. The 2D component used a Ricoh Theta Z1 camera to collect panoramic images, and a YOLO (You Only Look Once) v8n model was fine-tuned using datasets representing low light, shadowed underground scenes. The 3D component employed an Ouster OS1-070-64 LiDAR sensor, and point clouds were processed through denoising, ICP alignment, surface reconstruction, manual annotation, and 2D projection. A YOLO-based model was then trained to detect objects and measure displacement between LiDAR scans. Results demonstrated strong performance for both components. The fine-tuned YOLOv8n model reliably detected personnel and vehicles despite challenging lighting and visual clutter, while the 3D pipeline localized objects in the registered LiDAR frame and quantified vehicle displacement between consecutive scans by comparing 3D bounding-box centroids after ICP alignment (displacement vector and magnitude). These findings indicate that the combined 2D–3D system can effectively support automated hazard recognition and environmental monitoring in GNSS-denied underground spaces.

This study employs a comprehensive bibliometric analysis to map the global scientific landscape of laser wakefield acceleration (LWFA) from 1990 to 2025. Using data extracted from the Web of Science (WoS) and analyzed with Bibliometrix, VOSviewer, and CiteSpace, the study identifies key publication trends, influential authors, leading countries, prominent journals, and thematic evolution within the field. The findings reveal exponential growth in LWFA-related research, driven by advances in high-power laser technology and controlled injection techniques. Network analyses demonstrate extensive international collaboration and a strong interdisciplinary structure linking plasma physics, optics, and accelerator science. Keyword co-occurrence and burst analyses highlight emerging topics such as ionization injection, dual-stage acceleration, betatron radiation, and machine learning-assisted optimization. These insights delineate both the historical progression and the dynamic frontiers of LWFA, providing a systematic understanding of its development and guiding future research toward the realization of compact, high-quality electron sources and next-generation plasma-based accelerators.

Monitoring the hydrochemistry of groundwater and the H-O isotopes in the Jingpo Lake volcanic area, China, is fundamental to studying the mechanisms of volcanic and seismic events, as well as the associated hazards. To study the hydrogeochemistry of fluids in the Jingpo Lake volcanic area, water samples from seven sites were tested for hydrogeochemistry, H-O isotopes, and radon (Rn) content. The genesis and evolution of the groundwater system were elucidated through an integrated approach employing Gibbs diagrams, ionic ratio analyses, reservoir temperature estimation (silica–enthalpy method), and inverse geochemical modeling with PHREEQC. The results showed that the dominant water chemistry type was HCO₃⁻, primarily influenced by volcanic rock weathering and deep hydrothermal activity. Spring and well water were influenced by cation exchange, adsorption, and rock weathering dissolution. The H-O isotope composition and radon content indicate that atmospheric precipitation is the main source of supply, while well water is influenced by deep fluids. According to the Na-K-Mg triangle diagram, most of the groundwater was shallow and immature, whereas the well water was partially balanced. The temperature of the geothermal water was controlled by the geothermal gradient, depending on its occurrence and circulation depth. Additionally, the equilibrium temperature of the thermal reservoir was calculated using the silica–enthalpy equation method, with the concentrations of dissolved components in the water taken into account. The temperature of the thermal reservoir of the well water and the depth of groundwater circulation were estimated. The original reservoir temperature in the study area was calculated to range from 108 °C to 156 °C, with a geothermal water-to-shallow groundwater mixing ratio of between 71% and 85%. The estimated shallow temperature ranged from 64.9 °C to 74.9 °C. These hydrogeochemical signatures reflect active water–rock interactions and the contribution of deep-seated geothermal fluids, providing robust evidence for ongoing geothermal activity in the Jingpo Lake volcanic system. The findings enhance our understanding of the recent geological evolution and present-day hydrothermal processes of this potentially active volcanic field, which establishes a crucial hydrogeochemical baseline for future monitoring and hazard assessment studies.