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The purpose of this study is to determine the effect of ventilation openings and fire intensity on heat transfer and fluid flow within the microclimate between 3D human body and clothing.

On account of interaction effects of fire and ventilation openings on heat transfer process, a 3D transient computational fluid dynamics model considering the real shape of human body and clothing was developed. The model was validated by comparing heat flux history and distribution with experimental results. Heat transfer modes and fluid flow were investigated under three levels of fire intensity for the microclimate with ventilation openings and closures.

Temperature distribution on skin surface with open microclimate was heavily depended on the heat transfer through ventilation openings. Higher temperature for the clothing with confined microclimate was affected by the position and direction of flames injection. The presence of openings contributed to the greater velocity at forearms, shanks and around neck, which enhanced the convective heat transfer within microclimate. Thermal radiation was the dominant heat transfer mode within the microclimate for garment with closures. On the contrary, convective heat transfer within microclimate for clothing with openings cannot be neglected.

The findings provided fundamental supports for the ease and pattern design of the improved thermal protective systems, so as to realize the optimal thermal insulation of the microclimate on the garment level in the future.

The outcomes broaden the insights of results obtained from the mesoscale models. Different high skin temperature distribution and heat transfer modes caused by thermal environment and clothing structure provide basis for advanced thermal protective clothing design.

The purpose of this paper is to gain an in-depth understanding of the research progress, hotspots and future trends in the field of functional clothing.

The records of 4,153 pieces of literature related to functional clothing were retrieved from Web of Science by using a comprehensive retrieval strategy. A piece of software, CiteSpace was used as a tool to visualize the results of specific terms, such as author, institution and keyword. By analyzing the knowledge maps with several indicators, the intellectual basis and research fronts for the functional clothing domain could then be demonstrated.

The result indicated that functional clothing was a popular research field, with approximately 500 papers published worldwide in 2020. Its main research area was material science and involved public environmental and occupational health, engineering, etc. showing the characteristic of multi-interdisciplinary. Textile Research Journal and International Journal of Clothing Science and Technology were the top two journals in this field. The USA, China, Australia, England and Germany have been active and frequently cooperating with each other. Donghua University, the Hong Kong Polytechnic University and NASA, with the largest number of publications, were identified as the main research drivers. According to the co-citation analysis, thermal stress, nanogenerator and electrospinning were the topics of most cited articles during the past 20 years.

The findings identified smart clothing and protective clothing to be the research frontiers in the field of functional clothing, which deserved further study in the future.

The outcomes offered an overview of the research status and future trends of the functional clothing field. It could not only provide scholars with convenience in identifying research hotspots and building potential cooperation in the follow-up research, but also assist beginners in searching core scholars and literature of great significance.

The purpose of this paper is to determine the effect of flash fire exposure on the mechanical properties of single-layer thermal protective clothing.

The full-scale flame manikin tests were performed to simulate flash fire exposure. Two typical fire-resistant fabrics were investigated. The manikin was divided into seven body parts and the specimens meeting the requirements of tensile and tear strength standards were sampled. Fabric thickness, mass per unit area, tensile strength and tear strength were measured and analyzed.

The results revealed the significant influence of heat flux on both of tensile and tear strength. However, the regression analysis indicated the low R² of the liner models. When the tensile and tear strength retention were reorganized based on the body parts, both of the multiple linear regression models for tensile and tear strength showed higher R² than the one-variable linear regressions. Furthermore, the R² of the multiple linear regression model for tear strength retention was remarkably higher than that of the tensile strength.

The findings suggested that greater attention should be paid to the local part of human body and more factors such as the air gap should be considered in the future thermal aging of firefighters’ clothing studies.

The outcomes provided useful information to evaluate the mechanical properties of thermal protective clothing and predict its service life.

The paper aims to reveal the relationship among energy efficiency, thermal comfort and thermal regulation of electrically heated footwear and to investigate influencing factors on the energy efficiency and thermal comfort.

A finite volume model was proposed to simulate the two-dimensional heat transfer in electrically heated footwear (EHF) under an extremely cold condition. The model domain consists of three-layer footwear materials, a heating pad, a sock material, an air gap and skin tissues. Model predictions were verified by experimental data from cold-contact exposure. Then the influencing factors on the energy efficiency and thermal comfort were investigated through parametric analysis.

The paper demonstrated that the skin temperature control (STC) mode provided superior thermal comfort compared to the heating pad temperature control (HPTC) mode. However, the energy efficiency for the HPTC mode with a heating temperature of 38 °C was 18% higher than the STC mode. The energy efficiency of EHF while reaching the state of thermal comfort was strongly determined by the arrangement and connection of heating elements, heating temperature, thickness and thermal conductivity of footwear materials.

The findings obtained in this paper can be used to engineer the EHF that provides optimal thermal comfort and energy efficiency in cold environments.

The purpose of this study is to predict the thermal protective performance (TPP) of flame-retardant fabric more economically using machine learning and analyze the factors affecting the TPP using model visualization.

A total of 13 machine learning models were trained by collecting 414 datasets of typical flame-retardant fabric from current literature. The optimal performance model was used for feature importance ranking and correlation variable analysis through model visualization.

Five models with better performance were screened, all of which showed R2 greater than 0.96 and root mean squared error less than 3.0. Heat map results revealed that the TPP of fabrics differed significantly under different types of thermal exposure. The effect of fabric weight was more apparent in the flame or low thermal radiation environment. The increase in fabric weight, fabric thickness, air gap width and relative humidity of the air gap improved the TPP of the fabric.

The findings suggested that the visual analysis method of machine learning can intuitively understand the change trend and range of second-degree burn time under the influence of multiple variables. The established models can be used to predict the TPP of fabrics, providing a reference for researchers to carry out relevant research.

The findings of this study contribute directional insights for optimizing the structure of thermal protective clothing, and introduce innovative perspectives and methodologies for advancing heat transfer modeling in thermal protective clothing.

The purpose of this paper is to study heat and steam transfer in a vertical air gap and improve thermal protective performance of protective clothing under thermal radiation and hot steam.

An experiment-based model was introduced to analyze heat and moisture transfer in the vertical air gap between the protective clothing and human body. A developed test apparatus was used to simulate different air gap sizes (3, 6, 9, 12, 15, 18, 21 and 24 mm). The protective clothing with different air gap sizes was subjected to dry and wet heat exposures.

The increase of the air gap size reduced the heat and moisture transfer from the protective clothing to the skin surface under both heat exposures. The minimum air gap size for the initiation of natural convection in the dry heat exposure was between 6 and 9 mm, while the air gap size for the occurrence of natural convection was increased in the wet heat exposure. In addition, the steam mass flux presented a sharp decrease with the rising of the air gap size, followed by a stable state, mainly depending on the molecular diffusion and the convection mass transfer.

This research provides a better understanding of the optimum air gap under the protective clothing, which contributes to the design of optimum air gap size that provided higher thermal protection against dry and wet heat exposures.

In novelty detection, the autoencoder based image reconstruction strategy is one of the mainstream solutions. The basic idea is that once the autoencoder is trained on normal data, it has a low reconstruction error on normal data. However, when faced with complex natural images, the conventional pixel-level reconstruction becomes poor and does not show the promising results. This paper aims to provide a new method for improving the performance of novelty detection based autoencoder.

To solve the problem that conventional pixel-level reconstruction cannot effectively extract the global semantic information of the image, a novel model with the combination of attention mechanism and self-supervised learning method is proposed. First, an auxiliary task, reconstruct rotated image, is set to enable the network to learn global semantic feature information. Then, the channel attention mechanism is introduced to perform adaptive feature refinement on the intermediate feature map to optimize the correspondingly passed feature map.

Experimental results on three public data sets show that the proposed method has potential performance for novelty detection.

This study explores the ability of self-supervised learning methods and attention mechanism to extract features on a single class of images. In this way, the performance of novelty detection can be improved.

The purpose of this study was to evaluate the effects of repetitive transcranial magnetic stimulation (rTMS) at different frequencies on working memory (WM) and neuroelectric activity in rats.

Three rTMS protocols involving different frequencies were applied to rats, and 16-channel local field potentials (LFPs) and spikes were recorded from the prefrontal cortex (PFC) of rats in each group during the WM task. First, the behavior of rats during the T-maze task was analyzed, and then, the firing rate of spikes and the energy of the θ-band and γ-band in LFPs when rats performed the WM tasks were calculated. Finally, the spectral coherence between LFPs and spikes was analyzed by wavelet transform.

The results showed that rats in the stimulation groups needed fewer days than those in the control group to reach the task correction standard during the WM experiment (p < 0.05). High-frequency rTMS increases the firing rate of spikes and the degree of synchronization of LFPs-spikes in the θ-band and γ-band in the WM process.

This study showed that high-frequency rTMS can improve the spatial learning ability of rats, which might be due to the increased neuronal excitability of the PFC and the enhancement of co-coding between different modes of neural signals. This study is helpful for understanding the neuroregulatory mechanism of rTMS and will provide a reference for the selection of a suitable frequency for TMS treatment.

The purpose of this paper is to put forward a nvew reconfigurable multi-mode walking-rolling robot based on the single-loop closed-chain four-bar mechanism, and the robot can be changed to different modes according to the terrain.

Based on the topological analysis, singularity analysis, feasibility analysis, gait analysis and the motion strategy based on motor time-sharing control, the paper theoretically verified that the robot can switch between the four motion modes.

The robot integrates four-bar walking, self-deforming and four-bar and six-bar rolling modes. A series of simulation and prototype experiment results are presented to verify the feasibility of multiple motion modes of the robot.

The work presented in this paper provides a good theoretical basis for further exploration of multiple mode mobile robots. It is an attempt to design the multi-mode mobile robot based on single loop kinematotropic mechanisms. It is also a kind of exploration of the new unknown movement law.

This paper aims to study the influence of aerodynamics force of trains passing each other on the dynamic response of vehicle bridge coupling system based on numerical simulation and multi-body dynamics and put forward the speed threshold for safe running of train under different crosswind speeds.

The computational fluid dynamics method is adopted to simulate the aerodynamic force in the whole process of train passing each other by using dynamic grid technology. The dynamic model of vehicle-bridge coupling system is established considering the effects of aerodynamic force of train passing each other under crosswind, the dynamic response of train intersection on the bridge under crosswind is computed and the running safety of the train is evaluated.

The aerodynamic force of trains' intersection has little effects on the derailment factor, lateral wheel-rail force and vertical acceleration of train, but it increases the offload factor of train and significantly increases the lateral acceleration of train. The crosswind has a significant effect on increasing the derailment factor, lateral wheel-rail force and offload factor of train. The offload factor of train is the key factor to control the threshold of train speed. The impact of the aerodynamic force of trains' intersection on running safety cannot be ignored. When the extreme values of crosswind wind speed are 15 m·s⁻¹, 20 m·s⁻¹ and 25 m·s⁻¹, respectively, the corresponding speed thresholds for safe running of train are 350 km·h⁻¹, 275 km·h⁻¹ and 200 km·h⁻¹, respectively.

The research can provide a more precise numerical method to study the running safety of high-speed trains under the aerodynamic effect of trains passing each other on bridge in crosswind.