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Bench scale and flame manikin tests are two typical methods to evaluate thermal protective performance (TPP) of fire protective clothing. However, flame manikin test is limited to be widely used for its complication and high cost. The purpose of this paper is to develop a method to evaluate the thermal performance of protective clothing from the bench scale test results and garment parameters, which predicts the body burn injuries without conducting flame manikin tests.

Bench scale and flame manikin tests’ data were collected from the previous research literature and then statistical analysis was performed to quantitatively investigate the correlations between the two test methods. Equations were established to predict the TPP values accounting for the effects of entrapped air gap and thermal shrinkage. Fitting analysis was conducted to analyze the relationship between the predicted TPP values and total burn injury. Finally, a method to predict total burn injury from the TPP values was proposed and validated.

The results showed that when the TPP value was predicted with the effects of air gap and thermal shrinkage considered, there was an approximate linear relationship between the predicted TPP values and total burn injury from the manikin test. Therefore, the prediction model of burn injury was developed based on the correlation analysis and verified with a generally good accuracy.

This paper presented a new prediction method to evaluate the thermal performance of protective clothing, which saved significant time and cost compared to the conventional methods. It can provide useful information for burn injury prediction of protective clothing.

The present study assesses the impact resistance of the shear thickening fluids-filled (STFs-filled) foam through drop-hammer impact tests.

The maximum residual impact load and specific impact energy absorption rate of STF-filled foam are studied with varying thickness (4–14 mm), densities (0.35–0.6 g/cm3) and hardness (40–50 Rockwell Hardness C Scale (HRC)) under different ambient temperatures (−20−20 °C) and impact energies (25–75 J).

The following conclusions are obtained from this study: (1) the higher the impact energy, the greater the maximum residual impact force and energy absorption efficiency of the material; (2) the impact resistance of STF-filled foam can be improved with the decrease of ambient temperature, achieving the highest energy absorption rate at −10?. (3) STF-filled foam substrate has the highest impact resistance, the lowest maximum residual impact force and the highest energy absorption coefficient when the density is 0.35  g/cm3, the hardness is 45HC and the thickness is 10 mm.

This is the first paper to analyze the impact of both environmental factors and material properties on the impact resistance of STF-filled foam. The results show that the decrease in temperature and the increase in hardness can enhance the impact resistance of STF-filled foam.

With the continuous increase in the operating speed of high-speed trains, the wear and tear of rails on high-speed lines has also gradually deteriorated. At present, the phenomenon of asymmetric wear of rails in high-speed lines is relatively serious. This paper aims to analyze the effect of three typical rail profile wear on vehicle operation performance.

To solve this problem, by analyzing the wheel-rail contact relationship and establishing a vehicle dynamics model, the influence of worn typical rail profiles on the vehicle’s dynamic performance and carbody abnormal vibration is analyzed. Additionally, the effect of worn rail profiles on wheel wear is analyzed using a wear model.

The results showed that, compared to the standard rail profile, the three typical wear rail profiles show an increase in normal contact stress. The ride and safety indexes of the three rail profiles also increased compared with the CHN60 profile. The rail Profile 3 does not affect carbody vibration, while Profile 1 and Profile 2 can cause hunting vibrations of the carbody, with the main vibration frequencies around 7 Hz. The wheel wear depths under three typical rail profiles are 1.185 mm, 1.11 mm and 1.058 mm.

The effect of the measured typical rail profiles on the vehicle’s performance is analyzed, particularly in terms of abnormal vibrations and wheel wear. This analysis can provide guidance for the long-term maintenance of the rail system.

The peer review history for this article is available at: https://publons.com/publon/10.1108/ILT-08-2023-0270/

The purpose of this paper is to propose a data prediction framework for scenarios which require forecasting demand for large-scale data sources, e.g., sensor networks, securities exchange, electric power secondary system, etc. Concretely, the proposed framework should handle several difficult requirements including the management of gigantic data sources, the need for a fast self-adaptive algorithm, the relatively accurate prediction of multiple time series, and the real-time demand.

First, the autoregressive integrated moving average-based prediction algorithm is introduced. Second, the processing framework is designed, which includes a time-series data storage model based on the HBase, and a real-time distributed prediction platform based on Storm. Then, the work principle of this platform is described. Finally, a proof-of-concept testbed is illustrated to verify the proposed framework.

Several tests based on Power Grid monitoring data are provided for the proposed framework. The experimental results indicate that prediction data are basically consistent with actual data, processing efficiency is relatively high, and resources consumption is reasonable.

This paper provides a distributed real-time data prediction framework for large-scale time-series data, which can exactly achieve the requirement of the effective management, prediction efficiency, accuracy, and high concurrency for massive data sources.

The serious wear problem of heavy-haul freight vehicle wheels affects the safety and economy of vehicle operation. This paper aims to study wheel wear evolution law and the influence of line parameters on wheel wear of heavy-haul freight, and provide the basis for operation and line maintenance.

The wheel wear test data of heavy-haul freight vehicles were analyzed. Then a heavy-haul freight vehicle dynamic model was established. The line parameters influencing wheel wear in heavy-haul freight vehicles were also analyzed by the Jendel wear model, and the effects of rail cant, rail gauge, rail profile and line ramp on wheel wear were analyzed.

A rail cant of 1:40 results in less wheel wear; an increase in the rail gauge can reduce wheel wear; and when matched with the CHN60 rail, the wear depth is relatively small. A decrease of 9.21% in wheel wear depth when matched with the CHN60 rail profile. The ramp of the heavy-haul line is necessary to consider for calculating wheel wear. When the ramp is considered, the wear depth increases by 8.47%. The larger the ramp, the greater the braking force and therefore, the greater of the wheel wear.

This paper first summarizes the wear characteristics of wheels in heavy-haul freight vehicles and then systematically analyzes the effect of line parameters on wheel wear. In particular, this study researched the effects of rail cant, rail gauge, rail profile and line ramp on wheel wear.

The peer review history for this article is available at: https://publons.com/publon/10.1108/ILT-02-2024-0038/

The research objective of this paper is to investigate the direct and indirect impacts of green finance on agricultural carbon total factor productivity (ACTFP) within the framework of the carbon peaking and carbon neutrality (dual carbon) goals, while also identifying the driving factors through an exponential decomposition of ACTFP, aiming to provide policy recommendations to enhance financial support for low-carbon agricultural development.

In this paper, the Global Malmquist Luenberger (GML) Index method was employed to analyze and decompose the ACTFP, while the direct and spillover effects of China’s green finance pilot policy (GFPP) on ACTFP were assessed using the difference-in-differences (DID) method and the spatial differences-in-differences (SDID) method, respectively.

After the implementation of the GFPP, the ACTFP in the pilot area has experienced significant improvement, with the enhancement of technical efficiency serving as the main driving force. In addition, the GFPP exhibits a positive low-carbon spatial spillover effect, indicating it benefits ACTFP in both the pilot and adjacent areas.

Within the framework of the dual carbon goals, the paper highlights agriculture as a significant carbon emitter. ACTFP is assessed by considering the agricultural carbon emission factor as the sole non-desired output, and the impact of the GFPP on ACTFP is investigated through the DID method, thereby providing substantial validation of the hypotheses inferred from the mathematical model. Subsequently, the spillover effects of GFPP on ACTFP are analyzed in conjunction with the spatial econometric model.

With the increased speed and mileage of high-speed lines, the problem of rail wear is increasing. In actual operation, a large number of abnormal wear phenomena occur on both vehicles and rails during fixed line operation; therefore, the purpose of the study is to explored the rail wear for a variety of vehicles running in mixed operation.

This paper used the universal mechanism multibody dynamics software to establish the CRH2 high speed train (HST) and the CRH3 HST vehicle dynamic models, respectively. The mixed running of HSTs on the effect of rail wear evolution law was analyzed. The rail wear of the two vehicles with different curve radii, different wheel diameters and different under-rail stiffness was compared and analyzed.

The result showed that the rail wear of CRH3 HST is greater than that of CRH2 HST. The rail wear in the tangent track under mixed operation conditions is 25.4% less than when CRH3 HST operated independently. When there is a 1-mm wheel diameter difference, the maximum rail wear of CRH2 HST and CRH3 HST increases by 263% and 44%, respectively. The amount of rail wear is proportional to the under-rail stiffness, and the position of the maximum wear is almost unchanged.

Most studies on the evolution law of rail wear are conducted for a single vehicle type and a single line. This study explored the mixed running of HSTs on the effect of rail wear evolution law.

The peer review history for this article is available at: https://publons.com/publon/10.1108/ILT-08-2023-0276/

Against the background of actively promoting the reform of mixed ownership in China, this study regards government ownership in private sector enterprises (PSEs) as an important political connection mechanism and examines private holding listed companies in high-polluting industries that sold China A-shares from 2012 to 2019.

Using regression models such as Tobit and negative binomial estimation, the research empirically examines the impact of government ownership in PSEs on the corporate fulfillment of their environmental responsibilities.

Government ownership can effectively promote PSEs to fulfill their environmental responsibilities. Government ownership, as a corporate-level political connection mechanism, enables the government to provide firms with more environmental protection subsidies and environmental tax incentives, encouraging firms to fulfill their environmental responsibilities. When considering the policy risks faced by PSEs, government ownership effectively reduces the impact of policy uncertainty on firms’ fulfillment of environmental responsibilities. Additionally, verifying the economic development level of the city in which the firm is located makes the positive impact of government ownership on fulfillment of environmental responsibilities of PSEs in regions with lower economic development levels more significant.

Unlike existing studies that generally use the personal political identity of entrepreneurs to measure the political connections of PSEs, this study regards government ownership in PSEs as an important political connection mechanism. It provides a useful reference for China to formulate environmental protection policies for PSEs.

The purpose of this paper is to simplify the computation of parameter estimation in the grey linear regression model and solve the problem that the development coefficient could not be computed in some sequence data, such as short‐term traffic flow.

Starting from the limitation that can be identified in the equation and analyzing the range using the method to estimate parameters, this paper researches the modelling mechanism and the other forms which are equivalent with the original form. At the same time, this paper gives an estimation method and gets the relationship in various forms and the relationship between the model and GM(1,1) model.

For the grey linear regression model, there exists a new method of parameter identification and three other forms as follows: the original form, the Whitenization equation and the connotation form.

The method of parameter identification exposed in the paper expanded the scope of the application of the grey linear regression model, and it can be used to model and forecast the urban road short‐time traffic flow.

This paper has solved some complicated problems such as the parameter estimation computation in the grey linear regression model. In addition, three kinds of representation forms of the model and its relationship between the model and GM(1,1) have also been presented. Finally, its application of the model in a short‐term traffic flow prediction has shown its superiority.