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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 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 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/

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.

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.