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The hoist cage is used to lift miners in a coal mine’s auxiliary shaft. Monitoring miners’ unsafe behaviors and their status in the hoist cage is crucial to production safety in coal mines. In this study, a visual detection model is proposed to estimate the number and categories of miners, and to identify whether the miners are wearing helmets and whether they have fallen in the hoist cage. A dataset with eight categories of miners’ statuses in hoist cages was developed for training and validating the model. Using the dataset, the classical models were trained for comparison, from which the YOLOv5s model was selected to be the basic model. Due to small-sized targets, poor lighting conditions, and coal dust and shelter, the detection accuracy of the Yolov5s model was only 89.2%. To obtain better detection accuracy, k-means++ clustering algorithm, a BiFPN-based feature fusion network, the convolutional block attention module (CBAM), and a CIoU loss function were proposed to improve the YOLOv5s model, and an attentional multi-scale cascaded feature fusion-based YOLOv5s model (AMCFF-YOLOv5s) was subsequently developed. The training results on the self-built dataset indicate that its detection accuracy increased to 97.6%. Moreover, the AMCFF-YOLOv5s model was proven to be robust to noise and light.

As thermoplastic materials are widely used in buildings, the fire hazards of thermoplastic materials are increasingly becoming a central issue in fire safety research due to their unique pyrolysis and melting mechanisms. In this paper, the features and common types of thermoplastic materials are introduced first. Then, the combustion behavior of thermoplastic materials is theoretically analyzed based on the empirical formulas and heat balance equations, such as the pyrolysis kinetics, ignition time, melting and dripping, flame, burning rate and mass loss rate, temperature and heat flow, gas products, and influencing factors. The influencing factors basically include the sample properties (width, incline angle, and thickness, etc.), the façade structure (sidewalls, curtain wall, etc.), the ambient conditions (altitude, pressure, and gravity, etc.), and the flame retardant treatment. Similarly, this study also illustrates the vertical and horizontal flame spread behavior of the thermoplastic materials and the influencing factors. The utilized methods include the experimental methods, the analytical methodologies, and the approaches for numerical simulation. Finally, the problems encountered at this stage and worthy of further study in the future are presented.

In this paper, to identify the roof unconfined compressive strength (UCS) in the process of coal mine roadway support in real-time and optimize the real-time drilling speed while drilling, this paper proposes and establishes a drilling test method for assessing the uniaxial compressive strength (UCS) of a roof. This method can be used to optimize the speed of drilling. Moreover, a mathematical model of the power output is developed for a roof-strata identification system with a drilling test system. The results were as follows: (1) the system was able to identify the uniaxial compressive strength of roof rock; (2) the pressure of the drill leg of the pneumatic bolt did not match the output power of the pneumatic motor, the pneumatic motor could not reach the maximum power point, and the insufficient thrust of the pneumatic leg led to failure of the maximum output power of the pneumatic motor; (3) to increase the output power of the air motor and thus improve the drilling speed, we applied a booster valve for the system. The experimental results show that the power of the air motor has a linear relationship with drilling speed. In this way, the speed of the drill can be increased by increasing the motor power.

This paper presents the design and experimental study of a voice coil motor (VCM)-based Stewart platform used for active vibration isolation. The high precision payloads carried on the satellites always require an extremely stable environment to work properly. Installing a vibration isolation device between the vibration sources and precision payloads is an efficient method for dissipating vibration energy. A Stewart platform with active damping is designed to isolate the vibration transferring from the satellite to the payloads in six degrees-of-freedom. First, the kinematics and dynamical equations of a Stewart platform with spherical joints at both the base and top of each leg are established with Newton-Euler Method in task space and joint space. Second, the H∞ Control Theory is employed to design a robust controller for the linearized system with parameter uncertainty, noise and sensor errors. Finally, an experimentation study on the vibration of the payload supported by a Stewart platform with VCM actuator is conducted. The feasibility and effectiveness of the vibration isolation system are verified by comparing the amplitude-frequency characteristics of the active control system with that of the passive control system and the system without damping.

Furfural is a versatile platform molecule for the synthesis of various chemicals and fuels, and it can be produced by acid-catalyzed dehydration of xylose derived from renewable biomass resources. A series of metal salts and ionic liquids were investigated to obtain the best combination of catalyst and solvent for the conversion of xylose into furfural. A furfural yield of 71.1% was obtained at high xylose loading (20 wt%) from the single-phasic reaction system whereby SnCl4 was used as catalyst and ionic liquid 1-ethyl-3-methylimidazolium bromide (EMIMBr) was used as reaction medium. Moreover, the combined catalyst consisting of 5 mol% SnCl4 and 5 mol% MgCl2 also produced a high furfural yield (68.8%), which was comparable to the furfural yield obtained with 10 mol% SnCl4. The water⁻organic solvent biphasic systems could improve the furfural yield compared with the single aqueous phase. Although these organic solvents could form biphasic systems with ionic liquid EMIMBr, the furfural yield decreased remarkably compared with the single EMIMBr phase. Besides, the EMIMBr/SnCl4 system with appropriate water was also efficient to convert xylan and lignocellulosic biomass corn stalk into furfural, obtaining furfural yields as high as 57.3% and 54.5%, respectively.