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Compared with traditional methods relying on manual teaching or system modeling, data-driven learning methods, such as deep reinforcement learning and imitation learning, show more promising potential to cope with the challenges brought by increasingly complex tasks and environments, which have become the hot research topic in the field of robot skill learning. However, the contradiction between the difficulty of collecting robot–environment interaction data and the low data efficiency causes all these methods to face a serious data dilemma, which has become one of the key issues restricting their development. Therefore, this paper aims to comprehensively sort out and analyze the cause and solutions for the data dilemma in robot skill learning.

First, this review analyzes the causes of the data dilemma based on the classification and comparison of data-driven methods for robot skill learning; Then, the existing methods used to solve the data dilemma are introduced in detail. Finally, this review discusses the remaining open challenges and promising research topics for solving the data dilemma in the future.

This review shows that simulation–reality combination, state representation learning and knowledge sharing are crucial for overcoming the data dilemma of robot skill learning.

To the best of the authors’ knowledge, there are no surveys that systematically and comprehensively sort out and analyze the data dilemma in robot skill learning in the existing literature. It is hoped that this review can be helpful to better address the data dilemma in robot skill learning in the future.

The purpose of this study is to solve two unique but difficult partial differential equations: the foam drainage equation and the nonlinear time-fractional fisher’s equation. Through our methods, we aim to provide accurate solutions and gain a deeper understanding of the intricate behaviors exhibited by these systems.

In this study, we use a dual technique that combines the Aboodh residual power series method and the Aboodh transform iteration method, both of which are combined with the Caputo operator.

We develop exact and efficient solutions by merging these unique methodologies. Our results, presented through illustrative figures and data, demonstrate the efficacy and versatility of the Aboodh methods in tackling such complex mathematical models.

Owing to their fractional derivatives and nonlinear behavior, these equations are crucial in modeling complex processes and confront analytical complications in various scientific and engineering contexts.

Traditional case-adaptation methods have poor accuracy, low efficiency and limited applicability, which cannot meet the needs of knowledge users. To address the shortcomings of the existing research in the industry, this paper proposes a case-adaptation optimization algorithm to support the effective application of tacit knowledge resources.

The attribute simplification algorithm based on the forward search strategy in the neighborhood decision information system is implemented to realize the vertical dimensionality reduction of the case base, and the fuzzy C-mean (FCM) clustering algorithm based on the simulated annealing genetic algorithm (SAGA) is implemented to compress the case base horizontally with multiple decision classes. Then, the subspace K-nearest neighbors (KNN) algorithm is used to induce the decision rules for the set of adapted cases to complete the optimization of the adaptation model.

The findings suggest the rapid enrichment of data, information and tacit knowledge in the field of practice has led to low efficiency and low utilization of knowledge dissemination, and this algorithm can effectively alleviate the problems of users falling into “knowledge disorientation” in the era of the knowledge economy.

This study provides a model with case knowledge that meets users’ needs, thereby effectively improving the application of the tacit knowledge in the explicit case base and the problem-solving efficiency of knowledge users.

The adaptation model can serve as a stable and efficient prediction model to make predictions for the effects of the many logistics and e-commerce enterprises' plans.

This study designs a multi-decision class case-adaptation optimization study based on forward attribute selection strategy-neighborhood rough sets (FASS-NRS) and simulated annealing genetic algorithm-fuzzy C-means (SAGA-FCM) for tacit knowledgeable exogenous cases. By effectively organizing and adjusting tacit knowledge resources, knowledge service organizations can maintain their competitive advantages. The algorithm models established in this study develop theoretical directions for a multi-decision class case-adaptation optimization study of tacit knowledge.

This study aims to predict China's carbon emission intensity and put forward a set of policy recommendations for further development of a low-carbon economy in China.

In this paper, the Interaction Effect Grey Power Model of N Variables (IEGPM(1,N)) is developed, and the Dragonfly algorithm (DA) is used to select the best power index for the model. Specific model construction methods and rigorous mathematical proofs are given. In order to verify the applicability and validity, this paper compares the model with the traditional grey model and simulates the carbon emission intensity of China from 2014 to 2021. In addition, the new model is used to predict the carbon emission intensity of China from 2022 to 2025, which can provide a reference for the 14th Five-Year Plan to develop a scientific emission reduction path.

The results show that if the Chinese government does not take effective policy measures in the future, carbon emission intensity will not achieve the set goals. The IEGPM(1,N) model also provides reliable results and works well in simulation and prediction.

The paper considers the nonlinear and interactive effect of input variables in the system's behavior and proposes an improved grey multivariable model, which fills the gap in previous studies.

In response to the challenge of reduced efficiency or failure of robot motion planning algorithms when faced with end-effector constraints, this study aims to propose a new constraint method to improve the performance of the sampling-based planner.

In this work, a constraint method (TC method) based on the idea of cross-sampling is proposed. This method uses the tangent space in the workspace to approximate the constrained manifold pattern and projects the entire sampling process into the workspace for constraint correction. This method avoids the need for extensive computational work involving multiple iterations of the Jacobi inverse matrix in the configuration space and retains the sampling properties of the sampling-based algorithm.

Simulation results demonstrate that the performance of the planner when using the TC method under the end-effector constraint surpasses that of other methods. Physical experiments further confirm that the TC-Planner does not cause excessive constraint errors that might lead to task failure. Moreover, field tests conducted on robots underscore the effectiveness of the TC-Planner, and its excellent performance, thereby advancing the autonomy of robots in power-line connection tasks.

This paper proposes a new constraint method combined with the rapid-exploring random trees algorithm to generate collision-free trajectories that satisfy the constraints for a high-dimensional robotic system under end-effector constraints. In a series of simulation and experimental tests, the planner using the TC method under end-effector constraints efficiently performs. Tests on a power distribution live-line operation robot also show that the TC method can greatly aid the robot in completing operation tasks with end-effector constraints. This helps robots to perform tasks with complex end-effector constraints such as grinding and welding more efficiently and autonomously.

This study aims to investigate two issues: (1) a nexus between climate-related financial policies (CRFP) and global value chains (GVC) and (2) the government’s policies to help countries enhance the efficient use of CRFP in improving a country’s likelihood to participate in GVC.

To investigate the connection between GVC and CRFP, the authors incorporate that backward participation is measured using foreign value-added, while domestic value-added is used to measure forward participation, quantified as proportions of gross exports. The study analyses yield significant insights across a span of 20 developing countries and 26 developed countries over the period from 2010 to 2020.

Regarding the first issue, the authors affirm the presence of a linear link between GVC and CRFP, implying that involvement in CRFP is advantageous for both backward and forward participation. Furthermore, the authors identify long-term GVC and CRFP cointegration and confirm its long-term effects. Notably, the expression of a linear relationship between GVC and CRFP appears to be stronger in developing countries.

The study findings, together with previous research, highlight the importance of financial policies relating to climate change (CRFP) in the context of economic growth. Climate change’s consequences for financial stability and GVC highlight the importance of expanded policymakers and industry participation in tackling environmental concerns.

Regarding the second issue, the study findings suggest critical policy implications for authorities by highlighting the importance of financial stability and expanded policymakers in promoting countries' participation in GVC.

This paper investigates the link between GVC performance and CRFP, offering three significant advances to previous research. Moreover, as a rigorous analytical method, this study adopts a typical error model with panel correction that accounts for cross-sectional dependency and stationarity.

The main objective of this paper is to investigate the response of human skin to an intense temperature drop at the surface. In addition, this paper aims to evaluate the efficiency of finite difference and finite volume methods in solving the highly nonlinear form of Pennes’ bioheat equation.

One-dimensional linear and nonlinear forms of Pennes’ bioheat equation with uniform grids were used to study the behavior of human skin. The specific heat capacity, thermal conductivity and blood perfusion rate were assumed to be linear functions of temperature. The nonlinear form of the bioheat equation was solved using the Newton linearization method for the finite difference method and the Picard linearization method for the finite volume method. The algorithms were validated by comparing the results from both methods.

The study demonstrated the capacity of both finite difference and finite volume methods to solve the one-dimensional and highly nonlinear form of the bioheat equation. The investigation of human skin’s thermal behavior indicated that thermal conductivity and blood perfusion rate are the most effective properties in mitigating a surface temperature drop, while specific heat capacity has a lesser impact and can be considered constant.

This paper modeled the transient heat distribution within human skin in a one-dimensional manner, using temperate-dependent physical properties. The nonlinear equation was solved with two numerical methods to ensure the validity of the results, despite the complexity of the formulation. The findings of this study can help in understanding the behavior of human skin under extreme temperature conditions, which can be beneficial in various fields, including medical and engineering.

This study clarifies the intricate nature of globalization's impact on unemployment rates in South Africa. Given the heterogeneous views on globalization's effect on economic development, this study aims to offer a nuanced perspective. Furthermore, it aims to explore the mediating role of entrepreneurial development in shaping the complex relationship between globalization and unemployment.

The study employs four key indicators to measure entrepreneurial development, globalization and unemployment rates in South Africa. Hierarchical regression is used to evaluate the relationship between globalization and unemployment rates, and how entrepreneurial development mediates this relationship. Additionally, both the Sobel test and bootstrapping analyses were employed to verify and validate the mediating relationship.

The study demonstrates that globalization constitutes a crucial determinant of (un)employment rates in South Africa. The study shows that entrepreneurial development, specifically in the context of established business ownership, but not total early-stage entrepreneurial activity, exhibits an inverse relationship with unemployment rates. Moreover, it was observed that the positive impact of globalization on entrepreneurial development in South Africa becomes evident as SMEs advance to the established stage.

The study's concentration on South Africa constrains the applicability of the results to other nations.

Based on the findings of this study, it is essential for emerging economies, such as South Africa, to take measures to foster a robust entrepreneurial ecosystem that can aid in the growth and international competitiveness of young SMEs.

To the best of the authors' knowledge, this study represents the first endeavor to analyze the potential impact of entrepreneurial development, as measured by both nascent and mature SMEs, on the correlation between globalization and unemployment.

Marketing is a hot topic, and the purpose of this study is to investigate how shareholding strategies can be applied to achieve strategic synergy between firms in vertical supply chains to improve retailers’ marketing efforts from a long-term perspective.

This study constructs Stackelberg models to analyze the operating mechanisms of shareholding supply chains under forward, backward and cross-shareholding strategies. The authors analyze the effects of shareholding on prices, marketing efforts and profits, and explore the strategic preferences and outcomes of different supply chain members.

Forward/backward shareholding plays the same role as cross/nonshareholding in supply chains because the effect of the retailer’s shareholding is offset by the power status of the manufacturer, and the retailer can still profit when wholesale prices are higher than selling prices in certain cases. A manufacturer’s shareholding in a retailer can benefit consumers and improve marketing efforts by reducing retailers’ marketing costs, while a retailer’s shareholding in a manufacturer has no such effect. None of all shareholding strategies can coordinate the interests of all members; however, an effective rebate policy can resolve this problem.

The results reveal the operational mechanism of shareholding supply chains and provide reference values for managers who want to improve marketing efforts and economic performance using a shareholding strategy.

This study aims to analyze and compare the relationship between international trade in global value chains (GVC) and greenhouse gas (GHG) emissions for Brazil and China from 2000 to 2016.

The input-output method apply to multiregional tables from Eora-26 to decompose the GHG emissions of the Brazilian and Chinese productive structure.

The data reveals that Chinese production and consumption emissions are associated with power generation and energy-intensive industries, a significant concern among national and international policymakers. For Brazil, the largest territorial emissions captured by the metrics come from services and traditional industry, which reveals room for improving energy efficiency. The analysis sought to emphasize how the productive structure and dynamics of international trade have repercussions on the environmental dimension, to promote arguments that guide the execution of a more sustainable, productive and commercial development strategy and offer inputs to advance discussions on the attribution of climate responsibility.

The metrics did not capture emissions related to land use and deforestation, which are representative of Brazilian emissions.

Comparative analysis of emissions embodied in traditional sectoral trade flows and GVC, on backward and forward sides, for developing countries with the main economic regions of the world.