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The proportional distribution of social labor is a general law governing human social and economic activities, also a law discovered by Marxist political economy that governs socialist economic operations and development based on public ownership.

This law draws on Marx's vision of future society, but how it is adopted is not only subject to the way a country's economy interacts but also to the influence of a country's historical and cultural traditions. Generations of the CPC and state leaders since Mao Zedong have made unremitting explorations for its application.

As socialism with Chinese characteristics enters a new era, the Party Central Committee with Comrade Xi Jinping at the core adheres to the standpoints, viewpoints and methods of Marxist political economy, draws from the splendid Chinese traditional culture that values integrity, peace and harmony of all, builds on the reality of China's socialist market economy development, has summed up the features of socialist economy development with Chinese characteristics, and has proposed the five-sphere integrated plan, the four-pronged comprehensive strategy.

The new development concept of “innovation, coordination, green development, openness, and sharing” for socialism with Chinese characteristics, all reflecting the Party's deepening understanding of coordinated development, the gradual formation of the general thought and policy methods of the country's economic regulations based on the coordination and balance of economic structure, the continuous explorations to open a new chapter of contemporary Marxist political economy, China's experience and wisdom, and the Party's confidence in the theories it applies, the road it takes, its system and its culture. The coordination and balance of economic structure are a major theoretical innovation of socialist political economy with Chinese characteristics in the new era.

The COVID-19 pandemic, which appeared in China in late 2019, has affected the world psychologically, socially and economically in 2020. Tourism is one of the areas where the effects of COVID-19 have been felt most clearly. The study aims to determine the effect of negative problem orientation (NPO) and perceived risk related to the COVID-19 pandemic on travel and destination visit intention.

This study employed a convenience and probabilistic sampling method for collecting data from 531 respondents using an online questionnaire. Partial least square structural equation modeling (PLS-SEM) was used for testing research model.

According to the findings, NPO and perceived risk related to the pandemic were found to have direct and indirect effects on the travel behavior of tourists. The results of this research provide theoretical and practical implications for hospitality and travel businesses on topics such as the psychological effects of the pandemic and the travel behaviors of tourists.

It is estimated that the pandemic will also affect tourist behavior due to its effects on human psychology. For this reason, a study conducted in the context of tourist behavior theories is expected to contribute to the literature, managers and future of the tourism.

High-speed maglev technology can address the issues of adhesion, friction, vibration and high-speed current collection in traditional wheel-rail systems, making it an important direction for the future development of high-speed rail technology.

This paper elaborates on the demand and significance of developing high-speed maglev technology worldwide and examines the current status and technological maturity of several major high-speed maglev systems globally.

This paper summarizes the challenges in the development of high-speed maglev railways in China. Based on this analysis, it puts forward considerations for future research on high-speed maglev railways.

This paper describes the development status and technical maturity of several major high-speed maglev systems in the world for the first time, summarizes the existing problems in the development of China's high-speed maglev railway and on this basis, puts forward the thinking of the next research of China's high-speed maglev railway.

This papers aims to study lattice structures in terms of geometric variables, manufacturing variables and material-based variants and their correlation with compressive behaviour for their application in a methodology for the design and development of personalized elastic therapeutic products.

Lattice samples were designed and manufactured using extrusion-based additive manufacturing technologies. Mechanical tests were carried out on lattice samples for elasticity characterization purposes. The relationships between sample stiffness and key geometric and manufacturing variables were subsequently used in the case study on the design of a pressure cushion model for validation purposes. Differentiated areas were established according to patient’s pressure map to subsequently make a correlation between the patient’s pressure needs and lattice samples stiffness.

A substantial and wide variation in lattice compressive behaviour was found depending on the key study variables. The proposed methodology made it possible to efficiently identify and adjust the pressure of the different areas of the product to adapt them to the elastic needs of the patient. In this sense, the characterization lattice samples turned out to provide an effective and flexible response to the pressure requirements.

This study provides a generalized foundation of lattice structural design and adjustable stiffness in application of pressure cushions, which can be equally applied to other designs with similar purposes. The relevance and contribution of this work lie in the proposed methodology for the design of personalized therapeutic products based on the use of individual lattice structures that function as independent customizable cells.

For billing purposes, heavy-haul locomotives in Sweden are equipped with on-board energy meters, which can record several parameters, e.g., used energy, regenerated energy, speed and position. Since there is a strong demand for improving energy efficiency in Sweden, data from the energy meters can be used to obtain a better understanding of the detailed energy usage of heavy-haul trains and identify potential for future improvements.

To monitor energy efficiency, the present study, therefore, develops key performance indicators (KPIs), which can be calculated with energy meter data to reflect the energy efficiency of heavy-haul trains in operation. Energy meter data of IORE class locomotives, hauling highly uniform 30-tonne axle load trains with 68 wagons, together with additional data sources, are analysed to identify significant parameters for describing driver influence on energy usage.

Results show that driver behaviour varies significantly and has the single largest influence on energy usage. Furthermore, parametric studies are performed with help of simulation to identify the influence of different operational and rolling stock conditions, e.g., axle loads and number of wagons, on energy usage.

Based on the parametric studies, some operational parameters which have significant impact on energy efficiency are found and then the KPIs are derived. In the end, some possible measures for improving energy performance in heavy-haul operations are given.

This study analyzes the impact of packaging-free practices in food retail stores, particularly supermarkets, on customer loyalty.

Based on the literature on the impacts of sustainable practices and corporate social responsibility (CSR) policies on consumer behavior, this study defined a set of seven hypotheses that were tested using data collected from 447 consumers that regularly buy food products at supermarkets. The data were subjected to structural equation modeling using SmartPLS.

This study confirmed that packaging-free practices positively influence brand image, brand trust, satisfaction and customer loyalty. The expected positive impacts of brand image and satisfaction on customer loyalty were also confirmed. However, the expected impact of brand trust on customer loyalty was not confirmed.

This article demonstrates how a competitive sector can reap benefits from implementing sustainable practices in the operational domain, particularly by offering packaging-free products at the point of purchase. Thus, as recommended, general retail stores (e.g. supermarkets) gradually increase the stores' offering of packaging-free food products, as this practice has been shown to have positive impacts not only on brand image, but also on customer satisfaction and loyalty.

This study extends the literature on the effects of sustainable practices on customer loyalty, by focusing on a specific practice. Furthermore, this study contributes to the advancement of research on packaging-free practices in retail by developing a research framework and providing evidence on the direct and indirect effects of this specific practice on customer loyalty.

The scalability of similarity joins is threatened by the unexpected data characteristic of data skewness. This is a pervasive problem in scientific data. Due to skewness, the uneven distribution of attributes occurs, and it can cause a severe load imbalance problem. When database join operations are applied to these datasets, skewness occurs exponentially. All the algorithms developed to date for the implementation of database joins are highly skew sensitive. This paper presents a new approach for handling data-skewness in a character- based string similarity join using the MapReduce framework. In the literature, no such work exists to handle data skewness in character-based string similarity join, although work for set based string similarity joins exists. Proposed work has been divided into three stages, and every stage is further divided into mapper and reducer phases, which are dedicated to a specific task. The first stage is dedicated to finding the length of strings from a dataset. For valid candidate pair generation, MR-Pass Join framework has been suggested in the second stage. MRFA concepts are incorporated for string similarity join, which is named as “MRFA-SSJ” (MapReduce Frequency Adaptive – String Similarity Join) in the third stage which is further divided into four MapReduce phases. Hence, MRFA-SSJ has been proposed to handle skewness in the string similarity join. The experiments have been implemented on three different datasets namely: DBLP, Query log and a real dataset of IP addresses & Cookies by deploying Hadoop framework. The proposed algorithm has been compared with three known algorithms and it has been noticed that all these algorithms fail when data is highly skewed, whereas our proposed method handles highly skewed data without any problem. A set-up of the 15-node cluster has been used in this experiment, and we are following the Zipf distribution law for the analysis of skewness factor. Also, a comparison among existing and proposed techniques has been shown. Existing techniques survived till Zipf factor 0.5 whereas the proposed algorithm survives up to Zipf factor 1. Hence the proposed algorithm is skew insensitive and ensures scalability with a reasonable query processing time for string similarity database join. It also ensures the even distribution of attributes.

This study aims to investigate whether social capital mediates the impact of financial capital on business performance in Cameroon.

The study uses quantitative data collected from 370 small businesses in Yaoundé and Douala in Cameroon. All businesses in the sample are formally registered and are in the services sector. A structural equation modelling (SEM) approach is used for the analysis.

Structural and relational capital constraints are significant mediators of formal and informal finance. The magnitude effects of relational capital are the largest, underlining information's importance in resolving small and medium enterprises’ (SMEs') financial constraints. In addition, the effect of informal finance constraints on business performance is larger in magnitude, confirming the substantial impact of informal finance on SME operations.

The paper confirms that relational and structural social capital are vital in business. However, the study did not investigate the disaggregated effects of these dimensions of social capital. Furthermore, how SMEs transition between formal and informal finance could provide further understanding of the role of social capital. A disaggregated and panel data set would help to provide additional insights.

Social capital emerges as a pivotal factor in enhancing SME access to finance. The results, therefore, confirm the relevance of a holistic approach to easing financial capital constraints for SMEs and enabling small businesses to connect more to various stakeholders to amplify business performance. In addition, the findings identified some intervention points for the governments in Cameroon as it seeks to use SMEs as its pivot for development and to catapult itself to emerging economy status in its Cameroon 2035 vision.

The value of the study lies in assessing the mediating effect of cognitive, relational and structural social capital constraints on business performance and comparing the effect of formal and informal financial constraints on business performance.

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.

This study aims to empirically investigate how difference in social trust explains the heterogeneity of intellectual property right (IPR) protection (proxied by software piracy rate) across countries. Specifically, the authors also examine whether this effect is complementary or substitute to legal and economic factors.

The authors use both ordinary least square and two-stage least square regressions to investigate this effect.

The authors find that there is also a complementary effect between trust and rule of law in reducing the violation of IPRs.

Although the literature by now has documented the solid relationship between trust and the quality of formal institutions, only few studies have explored more specific measures of institutional consequences. Thus, this study is the first study investigating the role of trust, a valuable social capital dimension, on IPR protection.