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In terms of understanding the new issues emerging in the practice of monetary policies and how to evaluate the latest theories of monetary policy, this paper proposes referring to Das Kapital and developing a monetary policy theory grounded in Marxist political economy.

Based on the discussion of interest-bearing capital in Das Kapital and using a heterogeneous agent model, this paper tries to explain the determining mechanism of interest rate, leverage ratio, and asset price.

The research finds that if there are differences in the techniques possessed by capital, the resulting disparities in production efficiency will lead to differences in profit rates and further influence the functional choices of capital in the movement of social total capital. Thus, with the formation of lending relationships, interest rates, leverage ratios, and asset prices will be endogenously determined simultaneously. Moreover, as the degree of technological diffusion influences the industrial capitalists’ willingness to take loans as well as the level of profit rates, there may be counter-cyclical changes in the returns on productive investment and financial investment at different stages of the technology life cycle, contributing to diverting funds out of the real economy. Besides, this paper discusses the challenges, tools, and goals of monetary policy within the credit money system.

Clarify the intrinsic mechanism of the functional differentiation of capital determined by heterogeneous technologies and exogenous capital-labor relation and analyze the impact of capital differentiation on the economy.

This study examines whether and how government fiscal pressure influences corporate charitable giving (CCG).

The authors exploit sub-national tax revenue sharing changes as exogenous variations to government’s fiscal pressure at the city level and then construct a quasi difference-in-differences (DiD) model to conduct the analysis based on a sample that consists of 14,168 firm-year observations in China during the period of 2003 to 2012.

The authors found that firms increase charitable donations when local governments face higher fiscal pressure. Such effects are more pronounced for firms that have stronger demand for political connectedness in the sample period. Furthermore, this study’s findings suggest that the timing strategy of donating helps firms to lower the effective tax rate and to build stronger political connections. In addition, donating firms outperform non-donating firms in terms of bank loan access and market reputation.

The authors contribute to at least three lines of literature: first, extend the understanding of timing strategies of corporate charitable behaviors; second, contribute to the literature studying the “crowd out” effect between government-provided charitable funds and private donations; finally, contribute to the emerging literature exploring the financial interests associated with corporate donation strategy (Claessens et al., 2008; Cull et al., 2015).

The integration of the digital economy and the real economy has been a key focus in promoting digital economic development. It denotes a comprehensive digital transformation of national economic activities regarding technological infrastructure and production modes, which is crucial for establishing a modern economic system, advancing industrial infrastructure and modernizing industrial chains.

Firstly, the study delves into the internal logic behind the emergence of the new development dynamic resulting from digital technology's evolution. Secondly, it explores the mechanism of mutual promotion and support between the new development dynamic and the digital economy based on China's shift in focus from international engagement to the domestic economy during different stages of industrialization. Subsequently, it analyzes the characteristics and critical factors of digital economy development and examines the macro-, meso- and micro-level constraints on these factors. Finally, the paper explores approaches to promoting digital economy development while constructing the new development dynamic and provides relevant policy suggestions.

The construction of the new development dynamic and the development of the digital economy are inextricably linked, and only by mutually reinforcing each other can they provide an inexhaustible impetus for China's high-quality economic development.

The new development dynamic and the digital economy development form an indivisible whole. The new development dynamic creates the necessary conditions for digital economy development and promotes the formation of digital production modes. In turn, the development of the digital economy should strive to improve the mainstay position of the domestic economy, enhance the synergy between the domestic economy and international engagement, upgrade value chains while improving the supply and the industrial chains in China and ensure a parallel increase in labor income alongside improved productivity.

The purpose is using generative adversarial network (GAN) to solve the problem of sample augmentation in the case of imbalanced bearing fault data sets and improving residual network is used to improve the diagnostic accuracy of the bearing fault intelligent diagnosis model in the environment of high signal noise.

A bearing vibration data generation model based on conditional GAN (CGAN) framework is proposed. The method generates data based on the adversarial mechanism of GANs and uses a small number of real samples to generate data, thereby effectively expanding imbalanced data sets. Combined with the data augmentation method based on CGAN, a fault diagnosis model of rolling bearing under the condition of data imbalance based on CGAN and improved residual network with attention mechanism is proposed.

The method proposed in this paper is verified by the western reserve data set and the truck bearing test bench data set, proving that the CGAN-based data generation method can form a high-quality augmented data set, while the CGAN-based and improved residual with attention mechanism. The diagnostic model of the network has better diagnostic accuracy under low signal-to-noise ratio samples.

A bearing vibration data generation model based on CGAN framework is proposed. The method generates data based on the adversarial mechanism of GAN and uses a small number of real samples to generate data, thereby effectively expanding imbalanced data sets. Combined with the data augmentation method based on CGAN, a fault diagnosis model of rolling bearing under the condition of data imbalance based on CGAN and improved residual network with attention mechanism is proposed.

The purpose of this paper is to provide the extensive review on dynamic monitoring of forestry area in China.

Countermeasure and suggestions were proposed for three aspects including the establishment of data sets with unified standards, top-level design of monitoring and assessment and analysis models, and establishment of the decision support platform with multiple scenario simulation.

Finally, the authors proposed key research area in this field, i.e., improving the systematic and optimal forest management through integrating and improving the data, models and simulation platforms and coupling the data integration system, assessment system and decision support system.

The authors explored the limitation of dynamic monitoring and state of the art research on data accumulation, professional model development and the analytical platform.

This study proposes a deep learning algorithm-based model to predict the repair and maintenance costs of apartment buildings, by collecting repair and maintenance cost data that were incurred in an actual apartment complex. More specifically, a long short-term memory (LSTM) algorithm was adopted to develop the prediction model, while the robustness of the model was verified by recurrent neural networks (RNN) and gated recurrent units (GRU) models.

Repair and maintenance cost data incurred in actual apartment complexes is collected, along with various input variables, such as repair and maintenance timing (calendar year), usage types, building ages, temperature, precipitation, wind speed, humidity and solar radiation. Then, the LSTM algorithm is employed to predict the costs, while two other learning models (RNN and GRU) are taught to validate the robustness of the LSTM model based on R-squared values, mean absolute errors and root mean square errors.

The LSTM model’s learning is more accurate and reliable to predict repair and maintenance costs of apartment complex, compared to the RNN and GRU models’ learning performance. The proposed model provides a valuable tool that can contribute to mitigating financial management risks and reducing losses in forthcoming apartment construction projects.

Gathering a real-world high-quality data set of apartment’s repair and maintenance costs, this study provides a highly reliable prediction model that can respond to various scenarios to help apartment complex managers plan resources more efficiently, and manage the budget required for repair and maintenance more effectively.

Industrial robots are of great significance to the long-term development of family firms. Drawing on the lens of the principal–principal conflict, this paper aims to investigate the influence of family non-executive directors on robot adoption in Chinese family firms.

This paper selects the family firms in China from 2011 to 2019 as the sample. Furthermore, the authors manually collected the family non-executive directors and constructed the robot adoption variable utilizing data sourced from the International Federation of Robotics. In brief, this paper constructs a comprehensive framework of the mechanisms and additional tests pertaining to the influence of family non-executive directors on robot adoption.

This paper finds that family non-executive directors can promote robot adoption in family firms. The underlying mechanism analysis shows that family non-executive directors promote robot adoption by exerting financial and human effects. This paper further finds that the characteristics of family non-executive directors, such as kinship, differential shareholding and excessive directors, affect the role of family non-executive directors. Finally, robot adoption can improve future performance, and the promotional effect is more evident when family members are non-executive directors.

This paper contributes to the related literature from the following two aspects. Firstly, this paper decomposes the types of family directors to understand the role of family non-executive directors, which challenges the assumption that family board members are homogeneous in family firms. Second, this paper expands the research on the factors that influence robot adoption in emerging economies from the micro-enterprise level. In addition, the findings in this paper have managerial implications for family firms to optimize their strategic decisions with the help of the mode of board right allocation.

This study aims to review the recent advancements in high entropy alloys (HEAs) called high entropy materials, including high entropy superalloys which are current potential alternatives to nickel superalloys for gas turbine applications. Understandings of the laser surface modification techniques of the HEA are discussed whilst future recommendations and remedies to manufacturing challenges via laser are outlined.

Materials used for high-pressure gas turbine engine applications must be able to withstand severe environmentally induced degradation, mechanical, thermal loads and general extreme conditions caused by hot corrosive gases, high-temperature oxidation and stress. Over the years, Nickel-based superalloys with elevated temperature rupture and creep resistance, excellent lifetime expectancy and solution strengthening L12 and γ´ precipitate used for turbine engine applications. However, the superalloy’s density, low creep strength, poor thermal conductivity, difficulty in machining and low fatigue resistance demands the innovation of new advanced materials.

HEAs is one of the most frequently investigated advanced materials, attributed to their configurational complexity and properties reported to exceed conventional materials. Thus, owing to their characteristic feature of the high entropy effect, several other materials have emerged to become potential solutions for several functional and structural applications in the aerospace industry. In a previous study, research contributions show that defects are associated with conventional manufacturing processes of HEAs; therefore, this study investigates new advances in the laser-based manufacturing and surface modification techniques of HEA.

The AlxCoCrCuFeNi HEA system, particularly the Al0.5CoCrCuFeNi HEA has been extensively studied, attributed to its mechanical and physical properties exceeding that of pure metals for aerospace turbine engine applications and the advances in the fabrication and surface modification processes of the alloy was outlined to show the latest developments focusing only on laser-based manufacturing processing due to its many advantages.

It is evident that high entropy materials are a potential innovative alternative to conventional superalloys for turbine engine applications via laser additive manufacturing.