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With the help of multi-body dynamics software UM, the paper uses Kik–Piotrowski model to simulate wheel-rail contact and Archard wear model for rail wear.

The CRH5 vehicle-track coupling dynamics model is constructed for the wear study of rails of small radius curves, namely 200 and 350 m in Guangzhou East EMU Depot and those 250 and 300 m radius in Taiyuan South EMU Depot.

Results show that the rail wear at the straight-circle point, the curve center point and the circle-straight point follows the order of center point > the circle-straight point > the straight-circle point. The wear on rail of small radius curves intensifies with the rise of running speed, and the wearing trend tends to fasten as the curve radius declines. The maximum rail wear of the inner rail can reach 2.29 mm, while that of the outer rail, 10.11 mm.

With the increase of the train passing number, the wear range tends to expand. The rail wear decreases with the increase of the curve radius. The dynamic response of vehicle increases with the increase of rail wear, among which the derailment coefficient is affected the most. When the number of passing vehicles reaches 1 million, the derailment coefficient exceeds the limit value, which poses a risk of derailment.

This research presents machine learning models for predicting international tourist arrivals in Indonesia during the COVID-19 pandemic using multisource Internet data.

To develop the prediction models, this research utilizes multisource Internet data from TripAdvisor travel forum and Google Trends. Temporal factors, posts and comments, search queries index and previous tourist arrivals records are set as predictors. Four sets of predictors and three distinct data compositions were utilized for training the machine learning models, namely artificial neural networks (ANNs), support vector regression (SVR) and random forest (RF). To evaluate the models, this research uses three accuracy metrics, namely root mean square error (RMSE), mean absolute error (MAE) and mean absolute percentage error (MAPE).

Prediction models trained using multisource Internet data predictors have better accuracy than those trained using single-source Internet data or other predictors. In addition, using more training sets that cover the phenomenon of interest, such as COVID-19, will enhance the prediction model's learning process and accuracy. The experiments show that the RF models have better prediction accuracy than the ANN and SVR models.

First, this study pioneers the practice of a multisource Internet data approach in predicting tourist arrivals amid the unprecedented COVID-19 pandemic. Second, the use of multisource Internet data to improve prediction performance is validated with real empirical data. Finally, this is one of the few papers to provide perspectives on the current dynamics of Indonesia's tourism demand.

The purpose of this paper is to examine the impact of structural breaks on the conditional variance of carbon emission allowance prices.

The authors employ the symmetric GARCH model, and two asymmetric models, namely the exponential GARCH and the threshold GARCH.

The authors show that the forecast performance of GARCH models improves after accounting for potential structural changes. Importantly, we observe a significant drop in the volatility persistence of emission prices. In addition, the effects of positive and negative shocks on carbon market volatility increase when breaks are taken into account. Overall, the findings reveal that when structural breaks are ignored in the emission price risk, the volatility persistence is overestimated and the news impact is underestimated.

The authors are the first to examine how the conditional variance of carbon emission allowance prices reacts to structural breaks.

Forest as a vital natural resource in China plays an irreplaceable important role in safeguarding ecological security and human survival and development. Due to the vast territory, huge population and widespread forest landscape of China, forest management is a complex system involving massive data and various management activities. To effectively implement sustainable forest management, the big data technology has been utilized to analyze China’s forestry resources. Thus, the purpose of this paper is to clarify the role of big data technology in China’s forest management.

In this paper, the authors revisited the roles of big data in forest ecosystem monitoring, forestry management system development, and forest policy implementation.

It demonstrates that big data technology has a great potential in forest ecosystem protection and management, as well as the government’s determination for forest ecosystem protection. However, to deepen the application of big data in forest management, several challenges still need to be tackled.

Thus, enhancing modern science and technology to improve big data, cloud computing, and information technologies and their combinations will contribute to tackle the challenges and achieve wisdom of forest management.

This study aims to summarize the critical issues in medical federated learning and applicable solutions. Also, detailed explanations of how federated learning techniques can be applied to the medical field are presented. About 80 reference studies described in the field were reviewed, and the federated learning framework currently being developed by the research team is provided. This paper will help researchers to build an actual medical federated learning environment.

Since machine learning techniques emerged, more efficient analysis was possible with a large amount of data. However, data regulations have been tightened worldwide, and the usage of centralized machine learning methods has become almost infeasible. Federated learning techniques have been introduced as a solution. Even with its powerful structural advantages, there still exist unsolved challenges in federated learning in a real medical data environment. This paper aims to summarize those by category and presents possible solutions.

This paper provides four critical categorized issues to be aware of when applying the federated learning technique to the actual medical data environment, then provides general guidelines for building a federated learning environment as a solution.

Existing studies have dealt with issues such as heterogeneity problems in the federated learning environment itself, but those were lacking on how these issues incur problems in actual working tasks. Therefore, this paper helps researchers understand the federated learning issues through examples of actual medical machine learning environments.

Petroleum hydrocarbons are naturally occurring flammable fossil fuels used as conventional energy sources. It has carcinogenic, mutagenic properties and is considered a hazardous pollutant. Soil contaminated with petroleum hydrocarbons adversely affects the properties of soil. This paper aim to remove pollutants from the environment is an urgent need of the hour to maintain the proper functioning of soil ecosystems.

The ability of micro-organisms to degrade petroleum hydrocarbons makes it possible to use these microorganisms to clean the environment from petroleum pollution. For preparing this review, research papers and review articles related to petroleum hydrocarbons degradation by micro-organisms were collected from journals and various search engines.

Various physical and chemical methods are used for remediation of petroleum hydrocarbons contaminants. However, these methods have several disadvantages. This paper will discuss a novel understanding of petroleum hydrocarbons degradation and how micro-organisms help in petroleum-contaminated soil restoration. Bioremediation is recognized as the most environment-friendly technique for remediation. The research studies demonstrated that bacterial consortium have high biodegradation rate of petroleum hydrocarbons ranging from 83% to 89%.

Proper management of petroleum hydrocarbons pollutants from the environment is necessary because of their toxicity effects on human and environmental health.

This paper discussed novel mechanisms adopted by bacteria for biodegradation of petroleum hydrocarbons, aerobic and anaerobic biodegradation pathways, genes and enzymes involved in petroleum hydrocarbons biodegradation.

Under the background of the overall increase of China's economic policy uncertainty and the urgent need for the transformation and upgrading of the substantial economy, this paper studies the time-varying causality between China's economic policy uncertainty and the growth of the substantial economy through bootstrap rolling window causality test, further refines economic policies and studies the causal differences between different types of economic policies and substantial economic growth, refining the conclusions of previous studies.

This paper first studies the causal relationship between China's economic policy uncertainty and substantial economic growth in the full sample period through bootstrap Granger causality test. Then, the paper tests the short-term and long-term stability of the parameters of the VAR model, and it is found that the model parameters are unstable in both the short and long term, so the results of the Granger causality test of the full sample are not credible. Finally, we conduct a dynamic test of the causal relationship between China's economic policy uncertainty and substantial economic growth by means of rolling window, so as to comprehensively analyze the dynamic characteristics and sudden changes of the relationship between them.

The research shows that economic policy uncertainty in China has a significant inhibiting effect on the growth of substantial economy. Growth in the substantial economy will drive up economic policy uncertainty before 2016 and restrain it after that. In addition, this paper further subdivides economic policy uncertainty to explore the causal differences between different types of economic policy uncertainty and substantial economic growth. The test results show that the relationship between them has obvious policy heterogeneity. The fiscal policy uncertainty and the monetary policy uncertainty, as the main policy means in China, has a significant impact on the growth rate of substantial economy in multiple ranges, but the effect time is short. Although trade policy uncertainty has a significant impact on the growth rate of substantial economy only during the financial crisis, the effect lasts for a long time. The impact of exchange rate and capital account policy uncertainty on the growth rate of substantial economy is mainly reflected after 2020.

The values of this paper are as follows: First, the economic policy uncertainty is combined with the growth of substantial economy, which makes up the gap of previous studies. Second, the economic policy uncertainty is further subdivided. The paper explores the causal differences between different types of economic policy uncertainties and the growth of substantial economy, so as to make the research more detailed. Finally, different from the previous static analysis, this paper uses dynamic model to examine the relationship between China's economic policy uncertainty and the growth of substantial economy from a dynamic perspective, with richer research conclusions.

The purpose of this paper is to show that the droplet impact phenomenon is important for the advancement of industrial technologies in many fields such as spray cooling and ink jet printing. Droplet bouncing on the nonwetting surfaces is a special phenomenon in the impact process which has attracted lots of attention.

In this work, the authors fabricated two kinds of representative nonwetting surfaces including superhydrophobic surfaces (SHS) and a slippery liquid-infused porous surface (SLIPS) with advanced UV laser processing.

The droplet bouncing behavior on the two kinds of nonwetting surfaces were compared in the experiments. The results indicate that the increasing Weber number enlarges the maximum droplet spreading diameter and raises the droplet bounce height but has no effect on contact time.

In addition, the authors find that the topological SHS and SLIPS with the laser-processed microwedge groove array produce asymmetric droplet bouncing with opposite offset direction. Microdroplets can be continuously transported without any additional driving force on such a topological SLIPS. The promising method for manipulating droplets has potential applications for the droplet-based microfluidic platforms.

The intended nationally determined contributions (INDCs) is a major outcome of the Paris Agreement on international cooperation to reduce emissions, and is likely to be the future scenario for carbon emissions. This paper aims to obtain the fine spatial pattern of carbon emissions in 2030, identify hot spots and analyze changes of carbon emissions with a spatial grid method.

Based on the integrated quantified INDCs of each economy in 2030, the authors predict the population density pattern in 2030 by using the statistics of current population density, natural growth rates and differences in population growth resulting from urbanization within countries. Then the authors regard population density as a comprehensive socioeconomic indicator for the top-bottom allocation of the INDC data to a 0.1° × 0.1° grid. Then, the grid spatial pattern of carbon emissions in 2030 is compared with that in 2016.

Under the unconditional and conditional scenarios, the global carbon emission grid values in 2030 will be within [0, 59,200.911] ktCO₂ and [0, 51,800.942] ktCO_2, respectively; eastern China, northern India, Western Europe and North America will continue to be the major emitters; grid carbon emissions will increase in most parts of the world compared to 2016, especially in densely populated areas.

While many studies have explored the overall global carbon emissions or warming under the INDC scenario, attention to spatial details is also required to help us make better emissions attributions and policy decisions from the perspective of the grid unit rather than the administrative unit.