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Wang has focused on the relationship between Das Kapital and the political economy in the broad sense. Numerous ideas covering the political economy in the broad sense are involved in the overall structure of Das Kapital, methodology of historical materialism and analyses of the historical fate of capitalist system. The paper aims to discuss these issues.

In broad outline, the Asiatic, ancient, feudal and modern bourgeois modes of production may be designated as epochs, marking progress in the economic development of society (Wang, 2007b). Historical materialism provides a new, scientific and objective explanation for understanding the dialectical development laws of society. It is crucial for constructing the theoretical system of a political economy in the broad sense. It could be said that it is the key to solving the puzzle of the historical course of social development.

Today, economic relations between the world’s top two economies have merged with each other. How can two countries with different systems trade with each other so well? These questions can no longer be answered with traditionally narrow political economic theory. The authors have to seek these answers from the perspective of a political economy in the broad sense.

Numerous ideas covering the political economy in the broad sense are involved in the overall structure of Das Kapital, methodology of historical materialism, and analyses of the historical fate of capitalist system.

Over the past 20 years, China's infrastructure has developed at an extraordinary speed. The current literature mainly focuses on the effects of political incentives on the infrastructure. However, this paper indicates that the structural change of China's land regime is an important clue and that the supernormal development of China's infrastructure is an explicable result for that.

This paper theoretically proves that in a politically centralized and economically decentralized economic entity with a public land-ownership regime, the self-financing mechanism formed by local officials through regulation of the land-grant price is the primary factor that influences the optimal supply volume of infrastructure in a region, in addition to political and economic incentives, and whether the self-financing mechanism can be formed or not depends on the structure of a country's land regime, which can help to explain the difference between the development of infrastructure in China and that in other developing countries from a theoretical angle.

The paper suggests that the mode is facing an important transformation toward land reform and new-type urbanization construction, and the replication and promotion of China's experience in infrastructure construction are of further significance under the Belt and Road Initiative as it provides a method for helping developing countries to eliminate infrastructure bottlenecks.

Through the test of multinational panel data, the paper indicates that the structural change of China's land regime around 1990 had an overall effect on the supernormal development of infrastructure in China. The paper indicates that the “land-based development mode” of China's infrastructure indeed contributed to the supernormal development of infrastructure in China, but there are still some shortcomings in this mode.

Forecasts of commodity prices are vital issues to market participants and policy makers. Those of corn are of no exception, considering its strategic importance. In the present study, the authors assess the forecast problem for the weekly wholesale price index of yellow corn in China during January 1, 2010–January 10, 2020 period.

The authors employ the nonlinear auto-regressive neural network as the forecast tool and evaluate forecast performance of different model settings over algorithms, delays, hidden neurons and data splitting ratios in arriving at the final model.

The final model is relatively simple and leads to accurate and stable results. Particularly, it generates relative root mean square errors of 1.05%, 1.08% and 1.03% for training, validation and testing, respectively.

Through the analysis, the study shows usefulness of the neural network technique for commodity price forecasts. The results might serve as technical forecasts on a standalone basis or be combined with other fundamental forecasts for perspectives of price trends and corresponding policy analysis.

The purpose of this paper is to provide an overview of the evolution of service supply chain management from a behavioural operations perspective, pointing out future research directions for scholars.

This study searched five databases for relevant literature published between 2009 and 2018, selecting 64 papers for this review. The selected literature was categorised according to two dimensions: a service supply chain link perspective and a behavioural factor perspective. Comparative analysis was used to identify gaps in the literature, and five future research agendas were proposed.

In terms of the perspective of service supply chain link, extant literature primarily focuses on service supply and service co-ordination management, and less on service demand and integration management. In terms of the behavioural factor’s perspective, most focus on classic behaviour factors, with less attention paid to emerging behaviour factors. This paper thus proposes five research agendas: demand-oriented management and integrated supply chain-oriented behavioural research; broadening the understanding of the scope of behavioural operations; integrating the latest backgrounds and trends of service industry into the research; greater attention to behavioural operations in service sub-industries; and multimethod combination is encouraged to be used to dig into the interesting research problems.

This study constitutes the first systematic review of service supply chain research from a behavioural perspective. By categorising the literature into two dimensions, the state of existing research is evaluated with an eye towards future research avenues.

This research study aims to design a novel risk-managed time-series momentum approach. The present study also examines the time-series momentum effect in the Indian equity market. Apart from this, the study also proposes a novel risk-managed time-series momentum approach.

The study considers the adjusted monthly closing prices of the stocks listed on the Bombay Stock Exchange from January 1996 to December 2020 to formulate long-short portfolios. Newey–West t statistics were used to test the significance of momentum returns. The present research has considered standard risk factors, i.e. market, size and value, to evaluate the risk-adjusted performance of time-series momentum portfolios.

The present research reports a substantial absolute momentum effect in the Indian equity market. However, absolute momentum strategies are exposed to occasional severe losses. The proposed time-series momentum approach not only yields 2.5 times higher return than the standard time-series momentum approach but also causes substantial enhancement in downside risks and higher-order moments.

The study's outcomes offer valuable insights for professional investors, capital market regulators and asset management companies.

This study is one of the pioneers attempting to test the time-series momentum effect in emerging economies. Besides, current research contributes to the escalating literature on risk-managed momentum by suggesting a novel revised time-series momentum approach.

The evolution of crowd intelligence is a mainly concerns issue in the field of crowd science. It is a kind of group behavior that is superior to the individual’s ability to complete tasks through the cooperation of many agents. In this study, the evolution of crowd intelligence is studied through the clustering method and the particle swarm optimization (PSO) algorithm.

This study proposes a crowd evolution method based on intelligence level clustering. Based on clustering, this method uses the agents’ intelligence level as the metric to cluster agents. Then, the agents evolve within the cluster on the basis of the PSO algorithm.

Two main simulation experiments are designed for the proposed method. First, agents are classified based on their intelligence level. Then, when evolving the agents, two different evolution centers are set. Besides, this paper uses different numbers of clusters to conduct experiments.

The experimental results show that the proposed method can effectively improve the crowd intelligence level and the cooperation ability between agents.

This paper proposes a crowd evolution method based on intelligence level clustering, which is based on the clustering method and the PSO algorithm to analyze the evolution.

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.

This paper aims to review the research on accounting professionalisation in China to develop insights into how the research is developing, offer a critique of the research to date and outline future research directions and opportunities.

This paper adopts a methodological approach of systematic literature review, as suggested by Tranfield et al. (2003) and Denyer and Tranfield (2009), to identify, select and analyse the extant literature on the Chinese public accounting profession. In total, 68 academic works were included in the review process.

This paper finds that the extant literature has produced fruitful insights into the processes and underlying motivation of accounting professionalisation in China, demonstrating that the Chinese experience has differed, to a large extent, from the hitherto mainly Anglo-American-dominated understandings of accounting professionalisation. However, due to the lack of common theoretical vernacular and an agreed upon focus, the extant literature illustrates a fragmented and contradictory picture, making attempts to accumulate prior knowledge in the field increasingly difficult.

This paper focusses only on research published in English. Consequently, the scope of review has been limited as some works published in languages other than English may be excluded.

This paper provides one of the pioneering exercises to systematically review the research on accounting professionalisation in China. It explores significant issues arising from the analysis and provides several suggestions for furthering the research effort in this field.

This paper aims to provide a systematic review of literature pertaining to how executive behavioral characteristics relate to financial reporting decisions.

The authors review 44 papers published between 2001 and 2021 in top journals that are nested in leading business, economic and accounting journals.

Through the systematic review, the authors provide a framework for the emergence of narcissism and how it relates to decision making and hence, firm performance. Additionally, this paper identifies different measures of measuring narcissism with their pros and cons and suggest that different measures lead to different outcomes in prior literature.

The study contributes to a growing stream of research on executives' attributes influence on decision making. The authors recommend that future research may focus more on the chief financial officer (CFO) role as the majority of literature in CEO based. Additionally, the authors suggest that different settings may moderate the outcomes, and the authors propose that future research may be conducted to show how the regulatory environment affects or moderates narcissism effect.

Traditional Chinese medicine (TCM) prescriptions have always relied on the experience of TCM doctors, and machine learning(ML) provides a technical means for learning these experiences and intelligently assists in prescribing. However, in TCM prescription, there are the main (Jun) herb and the auxiliary (Chen, Zuo and Shi) herb collocations. In a prescription, the types of auxiliary herbs are often more than the main herb and the auxiliary herbs often appear in other prescriptions. This leads to different frequencies of different herbs in prescriptions, namely, imbalanced labels (herbs). As a result, the existing ML algorithms are biased, and it is difficult to predict the main herb with less frequency in the actual prediction and poor performance. In order to solve the impact of this problem, this paper proposes a framework for multi-label traditional Chinese medicine (ML-TCM) based on multi-label resampling.

In this work, a multi-label learning framework is proposed that adopts and compares the multi-label random resampling (MLROS), multi-label synthesized resampling (MLSMOTE) and multi-label synthesized resampling based on local label imbalance (MLSOL), three multi-label oversampling techniques to rebalance the TCM data.

The experimental results show that after resampling, the less frequent but important herbs can be predicted more accurately. The MLSOL method is shown to be the best with over 10% improvements on average because it balances the data by considering both features and labels when resampling.

The authors first systematically analyzed the label imbalance problem of different sampling methods in the field of TCM and provide a solution. And through the experimental results analysis, the authors proved the feasibility of this method, which can improve the performance by 10%−30% compared with the state-of-the-art methods.