Jie Yang

Optimizing thermal comfort for gloves requires precise predictions of localized thermal resistance ( R _ct ), yet traditional experimental methods are costly and limited in capturing complex interactions. This study developed a computational framework employing Bayesian-optimized machine learning models, including multiple linear regression (MLR), decision tree (DT), generalized additive model (GAM), support vector regression (SVR), and deep learning (DL), to predict localized R _ct of firefighting gloves. Utilizing 1680 measurements from firefighting gloves, predictive accuracy and stability were evaluated. DL demonstrated the highest predictive accuracy ( R ² = 0.924690, mean absolute error [MAE] = 0.009970), closely followed by SVR ( R ² = 0.913098, MAE = 0.011209) and GAM ( R ² = 0.909959, MAE = 0.011472), whereas DT and MLR exhibited lower accuracy. Response surface analysis revealed that GAM provides consistent and interpretable predictions in sparse data contexts; DL captures detailed, complex interactions but requires careful management of potential overfitting; SVR represents an intermediate choice, balancing nonlinear accuracy with smoothness but still vulnerable to occasional nonphysical predictions in sparse data scenarios. Key predictors were air layer thickness, glove thickness, wind speed, glove section, and surface area. Thermal resistance increased consistently with air layer thickness, whereas higher wind speeds reduced insulation. Predictions highlighted the little finger as the most critical region, with R _ct decreasing from 0.145 to 0.112 K m ² /W as the wind speed increased from 0 to 2 m/s. Future studies should incorporate experimental validation under realistic firefighting scenarios, expand dataset diversity, and apply physically constrained models to enhance prediction robustness.

This paper proposes an optimization model for temperature of water electrolyzers in pursuit of maximum power-to-hydrogen efficiency. Unlike the traditional operating strategy which controls constant temperature, the model aims at obtaining optimal temperature according to the variation of supplied current. In order to realize the maximum efficiency, the temperature-dependent electrochemical model of the electrolyzer, as well as the thermal model of the heater, is established. The optimal temperature occurs where the electrochemical loss and the thermal loss meet the balance. From the simulation results, the proposed model can acquire optimal temperature based on the supplied current, which can be applied to the scene of fluctuating power supply, e.g. wind and solar power. The test results show that up to 15% efficiency improvement can be achieved with the proposed temperature control method, compared with the traditional constant temperature control.

This study proposed an extended multi–layer heat transfer model to simulate skin burns of firefighters during firefighting. The proposed model takes into account the effect of fabric movement frequencies, fabric movement amplitudes and human body movement speeds on the heat transfer between the skin and the heat source under low–level radiative exposure. The simulation performance was validated against the simulations in the published literature in terms of the heat transfer in the multi–layer fabric system, skin temperature and skin burns. The results indicated that the fabric periodic movement caused by human body movement decreased the time to skin burns and the skin temperature increased with increasing fabric movement amplitude. During firefighting, the time to 2nd degree burn was 33.3–35.2% shorter at medium human body movement speed than at low and high movement speeds. Furthermore, at low movement speeds, the time to 2nd degree burn was negatively associated with fabric movement amplitude, whereas it was delayed by 12.9–29.8% at the fabric movement amplitude of 2.5 mm at medium and high human body movement speeds. This research provides foundational knowledge for the development of a new generation of firefighters’ protective clothing (FPC) and the assessment of skin burns in firefighters.

Objectives. The effects of personal protective clothing (PPC) on firefighters’ gait were investigated to develop high-performance PPC. Methods. Thirteen participants participated in human trials with three types of PPC (firefighter protective clothing [FPC], semi-enclosed chemical protective clothing [CPC_semi] and fully enclosed chemical protective clothing [CPC_full]) and a T-shirt (control clothing [CON]). A three-dimensional (3D) motion capture system was used to obtain gait parameters (step length, step width, stride frequency, gait speed and toe-out angle) and the range of motion (ROM) of the joints (hip, knee and ankle). Results. PPC produced an increase in step width (23.4%, p > 0.05), but the gait speed (9.1%) and stride frequency (6.4%) decreased compared with the CON results. ROM is affected by the PPC type and joint. FPC and CPC_semi had no significant effect in terms of ROM of the hip and knee besides the landing angle of the knee. However, CPC_full had a significant effect on the maximum extension angle of the hip and maximum flexion angle of the knee, which reached up to 27.2%. Conclusion. The ROM of the firefighter’s lower limbs was limited by PPC. This study offers insights into next-generation PPC design and development, as well as guidelines for training and firefighting.

In this work, a numerical hand-glove-mitten model is developed to simulate human hand physiological responses in various cold environments. The model was validated against data from published literature and human trials conducted at -20, -40, and -60 °C in a chamber. Results demonstrated that the model could reasonably predict cold-induced vasodilation (CIVD) responses at 0 °C. At -20 °C, the model predicted skin temperature with the root mean square deviation (RMSD) falling within the measurement SD for both the entire/local hand except at the posterior hand. At -40 and -60 °C, the model could predict the trend of the skin temperatures of the whole/local hand, but the RMSD was greater than the SD for the majority of the predictions. A parametric study revealed that the palm and posterior hand exhibited higher skin temperatures than fingers, while the thumb had the lowest skin temperature of the fingers for all cases except for the case with a 3.5 clo mitten at -60°C. The proposed numerical hand-glove-mitten model provides fundamental knowledge for cold stress prediction and protective gloves development, which stands to improve the safety and health of industrial workers, firefighters, first responders, and troops.