Tiandi Science & Technology (China)

Accurate prediction of steel material parameters during heat treatment is essential for reliable finite element analysis (FEA) and process optimisation. Conventional experimental measurements and empirical models are often costly, time-consuming, and difficult to generalise to complex chemistries, microstructures, and temperature ranges. In this work, a data-driven prediction model is established using a comprehensive dataset that integrates simulation and experimental data, covering 18 elemental compositions, three typical microstructures, and a wide temperature range. Six key parameters are predicted simultaneously: thermal conductivity, specific heat capacity, yield stress, coefficient of thermal expansion, Young’s modulus, and density. Five machine learning models are evaluated, among which XGBoost shows the best performance for thermal parameters, while Gradient Boosting provides the highest accuracy for mechanical properties. After hyperparameter optimisation with grid search and cross-validation, all models achieve R² values above 0.99 and relative prediction errors within 5 %. An integrated Steel Materials Data Management System (S-MDMS) is further developed to combine data storage, visualisation, and online property prediction. The proposed model provides an efficient route for rapid acquisition and application of steel parameters in FEA-based heat treatment design and process optimisation.

UAV technology has shown significant value in the field of highway safety monitoring. With the surge of traffic flow, traditional monitoring means are difficult to cope with the complex security management challenges, and UAVs provide innovative solutions to improve monitoring efficiency and security by virtue of high mobility, long endurance, highdefinition image acquisition and intelligent analysis capabilities. By analyzing the domestic and international application examples (such as Erguang Expressway “drone station” anti-icing to protect smoothness, Renxin Expressway Beidou drone inspection, Shaoxing Expressway “Iron Wing Warrior Team” police fast), the drone in the rapid response to accidents, abnormal events active discovery, secondary accident risk reduction and optimization of road traffic efficiency effectiveness. Reducing and optimizing the efficiency of road traffic is very effective, and it can efficiently carry out daily inspection, traffic monitoring and emergency response tasks. However, the technology still faces challenges such as insufficient endurance, weak adaptability to bad weather, data processing bottlenecks and lagging regulations. Targeted countermeasures such as optimizing battery technology, enhancing environment-adaptive design, building an intelligent data processing platform and improving regulations are proposed to promote the standardization and scale development of the technology. The study shows that the in-depth integration of UAVs and intelligent transportation system can not only significantly improve the intelligent level of highway safety management, but also provide key technical support for the construction of an efficient, safe and sustainable modern transportation system.

In the process of in situ stress fracturing drilling in coal mines, obtaining downhole vibration data not only improves drilling efficiency but also plays a key role in ensuring operational safety. Nevertheless, the energy supply techniques used in current vibration detectors reduce operational performance and escalate excavation expenses. This research proposes a self-powered vibration sensor based on the triboelectric nanogenerator, designed for the operational environment of coal mine in situ stress fracturing drilling. It can simultaneously detect axial and lateral vibration frequencies, and the inclusion of redundant sensing units provides the sensor with high reliability. Experimental outcomes demonstrate that the device functions across a frequency span of 0 to 11 Hz, maintaining error margins for frequency and amplitude under 4%. Furthermore, it functions reliably in environments where temperatures are under 150 °C and humidity is under 90%, proving its strong resilience to environmental factors. In addition, the device possesses self-generating potential, achieving a maximum voltage of 68 V alongside an output current of 51 nA. When connected to a 6 × 107 Ω load, the maximum output power can reach 3.8 × 10-7 W. Unlike traditional subsurface oscillation detectors, the proposed unit combines self-generation capabilities with highly reliable measurement characteristics, making it more suitable for practical drilling needs.