Applied Sciences

Bolter miners have been widely used in coal mining or excavation industries. Its efficiency is closely related to the performance of its cutting reducer, which is literally determined by the thermal behavior of the planetary gear set. Thus, this study conducts experimental investigation on the thermal behavior of a cutting reducer (produced by Zhengzhou Machinery Research Institute Transmission Technology Co., Ltd., rated input power 170 kW, transmission ratio 3.06), where the results show the high temperature rise around the intermediate shaft for unloaded condition and significant influence of the torque for loaded conditions. Then, the Finite Element Method (FEM) is used to analyze the temperature field and thermal–structural coupling of the planetary gear set. The thermal stress and deformation increase by 11.5% and 38.4%, respectively, indicating high risk of gear damage. Moreover, the load spectrum imitating the actual industrial condition is added to the KISSsoft to evaluate the reliability and contact of the planetary gear set. The findings including low safety factors of the sun gear tooth surface and planetary gear root, slipping during the sun gear and planetary gear meshing, and uneven contact fluctuations can benefit planetary gear set optimization.

Earthquake prediction remains one of the central unsolved problems in geophysics, and ionospheric variability offers a promising yet debated window into the earthquake preparation process through lithosphere–atmosphere–ionosphere coupling. Progress has been hindered by methodological limitations in prior studies, including the use of inappropriate performance metrics for highly imbalanced seismic data, the reliance on geographically and temporally narrow data, and inclusion of inherent spatial or temporal features that artificially inflate model performance while preventing the discovery of genuine ionospheric precursors. To address these challenges, we introduce a global, temporally validated machine learning framework grounded in thirty-eight years of ionospheric observations from more than a hundred ionosonde stations. We eliminate lookahead bias through strict temporal partitioning, prevent overlapping precursor windows across samples to eliminate autocorrelation artifacts and apply sophisticated feature selection to exclude spatial and temporal identifiers, enabling prevention of data leakage and coincidence effects. We investigate whether spatiotemporally invariant ionospheric precursors exist across diverse seismic regions, addressing the field’s reliance on geographically isolated case studies. Cross-regional validation shows that our models yield modest classification skill above chance levels, with our best-performing model achieving a weighted F1 score of 71% though performance exhibits pronounced sensitivity to temporal validation configuration, suggesting these results represent an upper bound on operational accuracy. While multimodal fusion with complementary precursor channels could possibly improve performance, our focus remains on establishing whether ionospheric observations alone contain learnable, region-independent seismic signatures. These findings suggest that ionospheric precursors, if they exist as universal phenomena, exhibit weaker cross-regional consistency than previously reported in case studies, raising questions about their standalone utility for earthquake prediction while indicating potential value as one component within multimodal observation systems.

Real-time indoor monitoring systems (RTIMS) are a key component of modern technological infrastructures in smart and automated buildings and facilities. They enable the continuous collection, analysis, and response to environmental data under strict time constraints, ensuring optimal system performance. These systems are designed to operate with high accuracy and low latency, making them essential in situations and events where timely decision-making is critical. Their applications range from industrial automation and production line monitoring to smart cities, smart homes, and healthcare for the elderly and disabled. The significant advances in electronics, communications, and software—particularly in Internet of Things (IoT) technologies and data transfer protocols—are reflected in the diversity of real-time monitoring systems, in terms of the parameters that can be monitored, the control and command systems that can be used, and the actuators that respond to commands. In this paper, the concepts, design, components, and working methods of these systems are discussed in detail. The latest research on real-time indoor monitoring systems published over the past five years is reviewed, resulting in the selection of 143 studies that met the inclusion criteria. This review synthesizes the technologies used for data capture, transmission, processing, storage, and visualization, as well as the approaches employed for alerts and system integration. By presenting these technical insights in a structured manner, the article provides a practical reference for researchers and practitioners aiming to design and implement real-time monitoring systems more efficiently and effectively.