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During the construction process of the China Railway Track System (CRTS) I type filling layer, the nonwoven fabric bags have been used as grouting templates for cement asphalt (CA) emulsified mortar. The porous structure of nonwoven fabrics endowed the templates with breathability and water permeability. The standard requires that the volume expansion rate of CA mortar must be controlled within 1%–3%, which can generate expansion pressure to ensure that the cavities under track slabs are filled fully. However, the expansion pressure caused some of the water to seep out from the periphery of the filling bag, and it would affect the actual mix proportion of CA mortar. The differences in physical and mechanical properties between the CA mortar under track slabs and the CA mortar formed in the laboratory were studied in this paper. The relevant results could provide important methods for the research of filling layer materials for CRTS I type and other types of ballastless tracks in China.

During the inspection of filling layer, the samples of CA mortar from different working conditions and raw materials were taken by uncovering the track slabs and drilling cores. The physical and mechanical properties of CA mortar under the filling layer of the slab were systematically analyzed by testing the electrical flux, compressive strength and density of mortar in different parts of the filling layer.

In this paper, the electric flux, the physical properties and mechanical properties of different parts of CA mortar under the track slab were investigated. The results showed that the density, electric flux and compressive strength of CA mortar were affected by the composition of raw materials for dry powders and different parts of the filling layer. In addition, the electrical flux of CA mortar gradually decreased within 90 days’ age. The electrical flux of samples with the thickness of 54 mm was lower than 500 C. Therefore, the impermeability and durability of CA mortar could be improved by increasing the thickness of filling layer. Besides, the results showed that the compressive strength of CA mortar increased, while the density and electric flux decreased gradually, with the prolongation of hardening time.

During 90 days' age, the electrical flux of the CA mortar gradually decreased with the increase of specimen thickness and the electrical flux of the specimens with the thickness of 54 mm was lower than 500 C. The impermeability and durability of the CA mortar could be improved by increasing the thickness of filling layer. The proposed method can provide reference for the further development and improvement of CRTS I and CRTS II type ballastless track in China.

The climate change effects on agricultural output in different regions of the world and have been debated in the literature of emerging economies. Recently, the agriculture sector has influenced globally through climate change and also hurts all sectors of economies. This study aims to examine and explore the impact of global climate change on agricultural output in China over the period of 1982-2014.

Different unit root tests including augmented Dickey–Fuller, Phillips–Perron and Kwiatkowski, Phillips, Schmidt and Shin are used to check the order of integration among the study variables. The autoregressive distributed lag (ARDL) bounds testing approach to cointegration and the Johansen cointegration test are applied to assess the association among the study variables with the evidence of long-run and short-run analysis.

Unit root test estimations confirm that all variables are stationary at the combination of I(0) and I(1). The results show that CO₂ emissions have a significant effect on agricultural output in both long-run and short-run analyses, while temperature and rainfall have a negative effect on agricultural output in the long-run. Among other determinants, the land area under cereal crops, fertilizer consumption, and energy consumption have a positive and significant association with agricultural output in both long-run and short-run analysis. The estimated coefficient of the error correction term is also highly significant.

China’s population is multiplying, and in the coming decades, the country will face food safety and security challenges. Possible initiatives are needed to configure the Chinese Government to cope with the adverse effects of climate change on agriculture and ensure adequate food for the growing population. In concise, the analysis specifies that legislators and policy experts should spot that the climate change would transmute the total output factors, accordingly a county or regional specific and crop-specific total factor of production pattern adaptation is indorsed.

The present empirical study is the first, to the best of the authors’ knowledge, to investigate the impact of global climate change on agricultural output in China by using ARDL bounds testing approach to cointegration and Johansen cointegration test.

In an era marked by heightened geopolitical uncertainties, such as international conflicts and economic instability, the dynamics of energy markets assume paramount importance. Our study delves into this complex backdrop, focusing on the intricate interplay the between traditional and emerging energy sectors.

This study analyzes the interconnections among green financial assets, renewable energy markets, the geopolitical risk index and cryptocurrency carbon emissions from December 19, 2017 to February 15, 2023. We investigate these relationships using a novel time-frequency connectedness approach and machine learning methodology.

Our findings reveal that green energy stocks, except the PBW, exhibit the highest net transmission of volatility, followed by COAL. In contrast, CARBON emerges as the primary net recipient of volatility, followed by fuel energy assets. The frequency decomposition results also indicate that the long-term components serve as the primary source of directional volatility spillover, suggesting that volatility transmission among green stocks and energy assets tends to occur over a more extended period. The SHapley additive exPlanations (SHAP) results show that the green and fuel energy markets are negatively connected with geopolitical risks (GPRs). The results obtained through the SHAP analysis confirm the novel time-varying parameter vector autoregressive (TVP-VAR) frequency connectedness findings. The CARBON and PBW markets consistently experience spillover shocks from other markets in short and long-term horizons. The role of crude oil as a receiver or transmitter of shocks varies over time.

Green financial assets and clean energy play significant roles in the financial markets and reduce geopolitical risk. Our study employs a time-frequency connectedness approach to assess the interconnections among four markets' families: fuel, renewable energy, green stocks and carbon markets. We utilize the novel TVP-VAR approach, which allows for flexibility and enables us to measure net pairwise connectedness in both short and long-term horizons.

The measurement capabilities of the data envelopment analysis (DEA) models are used to train the artificial neural network (ANN) models for the best performance modeling of the sawmills in Ontario. The bootstrap DEA models measure robust technical efficiency scores and have benchmarking abilities, whereas the ANN models use abstract learning from a limited set of information and provide the predictive power.

The complementary modeling approaches of the DEA and the ANN provide an adaptive decision support tool for each sawmill.

The trained ANN models demonstrate promising results in predicting the relative efficiency scores and the optimal combination of the inputs and the outputs for three categories (large, medium and small) of sawmills in Ontario. The average absolute error in predicting the relative efficiency scores varies from 0.01 to 0.04, and the predicted optimal combination of the inputs (roundwood and employees) and the output (lumber) demonstrate that a large percentage of the sawmills shows less than 10% error in the prediction results.

The purpose of this study is to develop an integrated DEA-ANN model that can help in the continuous improvement and performance evaluations of the forest industry working under uncertain business environment.

It would take billions of miles’ field road testing to demonstrate that the safety of automated vehicle is statistically significantly higher than the safety of human driving because that the accident of vehicle is rare event.

This paper proposes an accelerated testing method for automated vehicles safety evaluation based on improved importance sampling (IS) techniques. Taking the typical cut-in scenario as example, the proposed method extracts the critical variables of the scenario. Then, the distributions of critical variables are statistically fitted. The genetic algorithm is used to calculate the optimal IS parameters by solving an optimization problem. Considering the error of distribution fitting, the result is modified so that it can accurately reveal the safety benefits of automated vehicles in the real world.

Based on the naturalistic driving data in Shanghai, the proposed method is validated by simulation. The result shows that compared with the existing methods, the proposed method improves the test efficiency by 35 per cent, and the accuracy of accelerated test result is increased by 23 per cent.

This paper has three contributions. First, the genetic algorithm is used to calculate IS parameters, which improves the efficiency of test. Second, the result of test is modified by the error correction parameter, which improves the accuracy of test result. Third, typical high-risk cut-in scenarios in China are analyzed, and the proposed method is validated by simulation.

This study aims to provide a picture of the current state of art in the use of big data for decision-making processes for the management of health-care organizations.

A systematic literature review was carried out. The research uses two analyses: descriptive analysis, describing the evolution of citations; keywords; and the ten most influential papers, and bibliometric analysis, for content evaluation, for which a cluster analysis was performed.

A total of 48 articles were selected for bibliographic coupling out of an initial sample of more than 5,000 papers. Of the 48 articles, 29 are linked on the basis of their bibliography. Clustering the 29 articles on the basis of actual content, four research areas emerged: quality of care, quality of service, crisis management and data management.

Health-care organizations believe strongly that big data can become the most effective tool for correctly influencing the decision-making processes. Thus, more and more organizations continue to invest in big data analytics, and the literature on this topic has expanded rapidly. This study seeks to provide a comprehensive picture of the different streams of literature existing, together with gaps in research and future perspectives. The literature is mature enough for an analysis to be made and provide managers with useful insights on opportunities, criticisms and perspectives on the use of big data for health-care organizations. However, to date, there is no comprehensive literature review on the big data analysis in health care. Furthermore, as big data is a “sexy catchphrase,” more clarity on its usage may be needed. It represents an important tool to be investigated and its great potential is often yet to be discovered. This study thus sheds light on emerging issues and suggests further research that may be needed.

This study aims to evaluate the characteristics of climate change in Yunnan minority areas and identify an effective path to promote sustainable livelihoods based on climate change.

Taking Yunnan Province as an example, based on the expansion of the traditional sustainable livelihood framework, the authors constructed a system dynamics (SD) model of sustainable livelihood from the six subsystems of natural, physical, financial, social, human and cultural and tested the accuracy and effectiveness of the model with data from Cangyuan County. By adjusting these parameters, five development paths are designed to simulate the future situation of the livelihood system and determine the optimal path.

Climate change has exacerbated the vulnerability of people’s livelihoods. In future, each of the five development paths will be advantageous for promoting sustainable livelihoods. However, compared with Path I (maintaining the status quo), Path III (path of giving priority to culture) and Path IV (path of giving priority to economic development) have more obvious advantages. Path II (path of giving priority to people’s lives) gradually increases the development rate by promoting people’s endogenous motivation, and Path V (path of coordinated development) is better than the other paths because of its more balanced consideration.

The analytical framework of sustainable livelihoods based on the characteristics of minority areas is broadened. By constructing a SD model of the livelihood system, the limitations of traditional static analysis have been overcome and a development path for promoting sustainable livelihoods through simulation is proposed. This study offers a theoretical framework and reference method for livelihood research against the backdrop of climate change and a decision-making basis for enhancing climate adaptability and realizing sustainable livelihoods.