Search results

It is quite universal for high-speed turnouts to be exposed to the wear of the stock rail of the switch rail during the service process. The wear will cause the change of railhead profile and the relative positions of the switch rail and the stock rail, which will directly affect the wheel–rail contact state and wheel load transition when a train passes the turnout and will further impose serious impacts on the safety and stability of train operation. The purpose of this paper is to provide suggestions for wear management of high-speed turnout.

The actual wear characteristics of switch rails of high-speed turnouts in different guiding directions were studied based on the monitoring results on site; the authorized wear limits for the switch rails of high-speed turnout were studied through derailment risk analysis and switch rail strength analysis.

The results show that: the major factor for the service life of a curved switch rail is the lateral wear. The wear characteristics of the curved switch rail of a facing turnout are significantly different from those of a trailing turnout. To be specific, the lateral wear of the curved switch rail mainly occurs in the narrower section at its front end for a trailing turnout, but in the wider section at its rear end when for a facing turnout. The maximum lateral wear of a dismounted switch rail from a trailing turnout is found on the 15-mm wide section and is 3.9 mm, which does not reach the specified limit of 6 mm. For comparison, the lateral wear of a dismounted switch rail from a facing turnout is found from the 35-mm wide section to the full-width section and is greater than 7.5 mm, which exceeds the specified limit. Based on this, in addition to meeting the requirements of maintenance rules, the allowed wear of switch rails of high-speed turnout shall be so that the dangerous area with a tangent angle of wheel profile smaller than 43.6° will not contact the switch rail when the wheel is lifted by 2 mm. Accordingly, the lateral wear limit at the 5-mm wide section of the curved switch rail shall be reduced from 6 mm (as specified) to 3.5 mm.

The work in this paper is of reference significance to the research on the development law of rail wear in high-speed turnout area and the formulation of relevant standards.

The purpose of this paper is to exploit a database of pre-existing designs to accelerate parametric optimization of antenna structures is investigated.

The usefulness of pre-existing designs for rapid design of antennas is investigated. The proposed approach exploits the database existing antenna base designs to determine a good starting point for structure optimization and its response sensitivities. The considered method is suitable for handling computationally expensive models, which are evaluated using full-wave electromagnetic (EM) simulations. Numerical case studies are provided demonstrating the feasibility of the framework for the design of real-world structures.

The use of pre-existing designs enables rapid identification of a good starting point for antenna optimization and speeds-up estimation of the structure response sensitivities. The base designs can be arranged into subsets (simplexes) in the objective space and used to represent the target vector, i.e. the starting point for structure design. The base closest base point w.r.t. the initial design can be used to initialize Jacobian for local optimization. Moreover, local optimization costs can be reduced through the use of Broyden formula for Jacobian updates in consecutive iterations.

The study investigates the possibility of reusing pre-existing designs for the acceleration of antenna optimization. The proposed technique enables the identification of a good starting point and reduces the number of expensive EM simulations required to obtain the final design.

The proposed design framework proved to be useful for the identification of good initial design and rapid optimization of modern antennas. Identification of the starting point for the design of such structures is extremely challenging when using conventional methods involving parametric studies or repetitive local optimizations. The presented methodology proved to be a useful design and geometry scaling tool when previously obtained designs are available for the same antenna structure.

A novel framework for expedited antenna optimization with an iterative prediction-correction scheme is proposed. The methodology is comprehensively validated using three real-world antenna structures: narrow-band, dual-band and wideband, optimized under various design scenarios.

The keystone of the proposed approach is to reuse designs pre-optimized for various sets of performance specifications and to encode them into metamodels that render good initial designs, as well as an initial estimate of the antenna response sensitivities. Subsequent design refinement is realized using an iterative prediction-correction loop accommodating the discrepancies between the actual and target design specifications.

The presented framework is capable of yielding optimized antenna designs at the cost of just a few full-wave electromagnetic simulations. The practical importance of the iterative correction procedure has been corroborated by benchmarking against gradient-only refinement. It has been found that the incorporation of problem-specific knowledge into the optimization framework greatly facilitates parameter adjustment and improves its reliability.

The proposed approach can be a viable tool for antenna optimization whenever a certain number of previously obtained designs are available or the designer finds the initial effort of their gathering justifiable by intended re-use of the procedure. The future work will incorporate response features technology for improving the accuracy of the initial approximation of antenna response sensitivities.

The proposed optimization framework has been proved to be a viable tool for cost-efficient and reliable antenna optimization. To the knowledge, this approach to antenna optimization goes beyond the capabilities of available methods, especially in terms of efficient utilization of the existing knowledge, thus enabling reliable parameter tuning over broad ranges of both operating conditions and material parameters of the structure of interest.

This study aims to examine the impact of migration growth on environmental outcomes and local governance and assess how well the existing local municipal governance has responded to the environmental impact of increased migration influx in Ontario, Canada using the annual data during 2012–2021.

This study used the grey relational analysis (GRA) to examine the correlation degree between migrant growth, environmental outcomes and local governance, used coupling coordination degree model (CCDM) to access to what extent the existing local governance systems have responded to the environmental impact of immigrant growth.

Results show that higher immigrant populations are associated with worse environmental outcomes and the need for more municipal environmental investment and service. The present local municipal environmental service in Ontario lags behind in response to the environmental impacts of increased migration. Good local governance practices and environmental services are required to improve the environmental adaptation capacity of host countries to migrant influx.

Climate change has been regarded as an important driver of internal and international human migration. The mass influxes of migrants will threaten cities’ environmental quality and put considerable pressure on municipal services. This study provides empirical evidence for Ontario’s municipal environmental governance and relevant authorities on how to deal with the environmental impact of increased migration and contributes to call the attention of other countries to the urban environmental pressure caused by migration influx due to the changing climate world wide.

The purpose of this paper is to develop initial evidence about the nature and features of crowdfunding in China, given it is largely unregulated regulatory frameworks.

The paper used extensive desk research using data collected from the public and private sectors, after which the data was analyzed parallel to existing academic literature, that is, institutional context by Bruton et al. (2014). This paper uncovered patterns of development, profiling crowdfunding platforms, examining the regulatory landscape and providing antecedents of successful crowdfunding projects in China.

When the traditional financial markets are hard to reach, micro, small and medium enterprises (MSMEs) were starved for capital. Crowdfunding can play a major role in funding and risk sharing. It is an innovative and dynamic vehicle for MSMEs as well as enthusiastic investors in China. Since its initial introduction to China in 2009, crowdfunding has gained substantial popularity in a relatively short period. Currently, there is still not an identifiable guideline on how to delineate the significance of the crowdfunding platform. The development of crowdfunding in China faces a few unresolved key issues. As researchers exploring this phenomenon in new ways, crowdfunding platforms can be enhanced in a manner that benefits the capital seeker, investors and society as a whole.

There is a dearth of information on start-up crowdfunding in Asia. With little data available to analyze, so this paper hopes to contribute to knowledge and provide valuable information to researchers and industry representations. Crowdfunding represents a potentially disruptive change in the way that new ventures are funded. This paper represents an initial analysis in the study of new ventures in China. Finally, the authors provide recommendations for entrepreneurs, investors and policymakers as well as researchers and practitioners with suggestions about yet unexplored avenues of research.

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.

In this study, we examine whether work from home (WFH) had an impact on firm productivity during the COVID-19 period.

We employ a panel fixed-effect model using 79,201 firm-quarter observations in a cross-country setting of 68 countries.

First, we find that firms that employed WFH contributed to real sector growth during the pandemic due to greater capital expenditure compared to otherwise. Second, we find that WFH amenable firms turned over assets better than less WFH amenable firms.

To the best of our knowledge, this is the first study to examine the impact of WFH on firms’ investment and efficiency using a cross-country setting.

With the rapid development of internet technology, open online social networks provide a broader platform for information spreading. While dissemination of information provides convenience for life, it also brings many problems such as security risks and public opinion orientation. Various negative, malicious and false information spread across regions, which seriously affect social harmony and national security. Therefore, this paper aims to minimize negative information such as online rumors that has attracted extensive attention. The most existing algorithms for blocking rumors have prevented the spread of rumors to some extent, but these algorithms are designed based on entire social networks, mainly focusing on the microstructure of the network, i.e. the pairwise relationship or similarity between nodes. The blocking effect of these algorithms may be unsatisfactory in some networks because of the sparse data in the microstructure.

An algorithm for minimizing the influence of dynamic rumor based on community structure is proposed in this paper. The algorithm first divides the network into communities, and integrates the influence of each node within communities and rumor influence probability to measure the influence of each node in the entire network, and then selects key nodes and bridge nodes in communities as blocked nodes. After that, a dynamic blocking strategy is adopted to improve the blocking effect of rumors.

Community structure is one of the most prominent features of networks. It reveals the organizational structure and functional components of a network from a mesoscopic level. The utilization of community structure can provide effective and rich information to solve the problem of data sparsity in the microstructure, thus effectively improve the blocking effect. Extensive experiments on two real-world data sets have validated that the proposed algorithm has superior performance than the baseline algorithms.

As an important research direction of social network analysis, rumor minimization has a profound effect on the harmony and stability of society and the development of social media. However, because the rumor spread has the characteristics of multiple propagation paths, fast propagation speed, wide propagation area and time-varying, it is a huge challenge to improve the effectiveness of the rumor blocking algorithm.

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.

The purposes of this research are to study the theory and method of multi-attribute index system design and establish a set of systematic, standardized, scientific index systems for the design optimization and inspection process. The research may form the basis for a rational, comprehensive evaluation and provide the most effective way of improving the quality of management decision-making. It is of practical significance to improve the rationality and reliability of the index system and provide standardized, scientific reference standards and theoretical guidance for the design and construction of the index system.

Using modern methods such as complex networks and machine learning, a system for the quality diagnosis of index data and the classification and stratification of index systems is designed. This guarantees the quality of the index data, realizes the scientific classification and stratification of the index system, reduces the subjectivity and randomness of the design of the index system, enhances its objectivity and rationality and lays a solid foundation for the optimal design of the index system.

Based on the ideas of statistics, system theory, machine learning and data mining, the focus in the present research is on “data quality diagnosis” and “index classification and stratification” and clarifying the classification standards and data quality characteristics of index data; a data-quality diagnosis system of “data review – data cleaning – data conversion – data inspection” is established. Using a decision tree, explanatory structural model, cluster analysis, K-means clustering and other methods, classification and hierarchical method system of indicators is designed to reduce the redundancy of indicator data and improve the quality of the data used. Finally, the scientific and standardized classification and hierarchical design of the index system can be realized.

The innovative contributions and research value of the paper are reflected in three aspects. First, a method system for index data quality diagnosis is designed, and multi-source data fusion technology is adopted to ensure the quality of multi-source, heterogeneous and mixed-frequency data of the index system. The second is to design a systematic quality-inspection process for missing data based on the systematic thinking of the whole and the individual. Aiming at the accuracy, reliability, and feasibility of the patched data, a quality-inspection method of patched data based on inversion thought and a unified representation method of data fusion based on a tensor model are proposed. The third is to use the modern method of unsupervised learning to classify and stratify the index system, which reduces the subjectivity and randomness of the design of the index system and enhances its objectivity and rationality.