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Robotic arms play a crucial role in various industrial operations, such as sorting, assembly, handling and spraying. However, traditional robotic arm control algorithms often struggle to adapt when faced with the challenge of dynamic obstacles. This paper aims to propose a dynamic obstacle avoidance method based on reinforcement learning to address real-time processing of dynamic obstacles.

This paper introduces an innovative method that introduces a feature extraction network that integrates gating mechanisms on the basis of traditional reinforcement learning algorithms. Additionally, an adaptive dynamic reward mechanism is designed to optimize the obstacle avoidance strategy.

Validation through the CoppeliaSim simulation environment and on-site testing has demonstrated the method's capability to effectively evade randomly moving obstacles, with a significant improvement in the convergence speed compared to traditional algorithms.

The proposed dynamic obstacle avoidance method based on Reinforcement Learning not only accomplishes the task of dynamic obstacle avoidance efficiently but also offers a distinct advantage in terms of convergence speed. This approach provides a novel solution to the obstacle avoidance methods for robotic arms.

Coal is a critical global energy source, and fluctuations in its price significantly impact related enterprises' profitability. This study aims to develop a robust model for predicting the coal price index to enhance coal purchase strategies for coal-consuming enterprises and provide crucial information for global carbon emission reduction.

The proposed coal price forecasting system combines data decomposition, semi-supervised feature engineering, ensemble learning and deep learning. It addresses the challenge of merging low-resolution and high-resolution data by adaptively combining both types of data and filling in missing gaps through interpolation for internal missing data and self-supervision for initiate/terminal missing data. The system employs self-supervised learning to complete the filling of complex missing data.

The ensemble model, which combines long short-term memory, XGBoost and support vector regression, demonstrated the best prediction performance among the tested models. It exhibited superior accuracy and stability across multiple indices in two datasets, namely the Bohai-Rim steam-coal price index and coal daily settlement price.

The proposed coal price forecasting system stands out as it integrates data decomposition, semi-supervised feature engineering, ensemble learning and deep learning. Moreover, the system pioneers the use of self-supervised learning for filling in complex missing data, contributing to its originality and effectiveness.

This paper aims to examine whether managerial sentiment, extracted from annual reports, is associated with corporate risk-taking in the context of Chinese companies. This study expands the vein of literature on overconfidence theory.

By leveraging textual analysis on Chinese listed companies’ annual reports, the authors construct firm-level managerial sentiment during 2007 and 2021 to examine how managerial sentiment influences corporate risk-taking after control for firm characteristics. Corporate risk-taking is denoted by corporate investment engagements: capital expenditures and net fixed asset investment.

Results show that incentives for corporate risk-taking are likely to increase with the positive managerial sentiment and decrease with the negative sentiment in companies’ annual reports. Positive managerial sentiment is associated with over-/under-investment and low/high investment efficiency. Further additional tests show that the managerial sentiment effect only holds during low economic uncertain years and samples of private-owned firms. Furthermore, the robust tests indicate that there is no endogenous issue between managerial sentiment and corporate risk-taking.

Annual report textual-based managerial sentiment may not perfectly reflect managers’ lower frequency sentiment (e.g. weekly, monthly and quarterly sentiment). Future studies could attempt to capture managers’ on-time sentiment by using media sources and corporate disclosures.

To the best of the authors’ knowledge, this paper is the first research to provide insights into supervising managers’ corporate decisions by observing their textual information usage in corporate disclosure. Moreover, the approach of measuring managerial sentiment might be a solution to monitoring managerial class.

This paper contributes to the literature on accounting and finance studies, adding another piece of empirical evidence on content analysis by examining a unique language and institutional context (i.e. China). Besides, the paper notes that in line with the English version disclosure, based on Chinese semantic words, managerial sentiment in the Chinese-speaking world has magnitude on corporate decisions. The research provides insights into supervising managers’ corporate decisions by observing their textual information usage in corporate disclosure. Moreover, the approach to measuring managerial sentiment may be a practical solution to monitoring managerial class.

This study aims to study the influence of friction influencing factors between the wire rope and the liner on the safe use of the wire rope, which can provide guidance for the reliability design of the lifting system with strong dynamic response such as high speed, heavy load, etc., and improve the friction-driven stability of the system.

In this paper, the friction mechanism of wire rope and liner under the condition of excitation is investigated by means of wire rope-liner friction-vibration experimental platform and dynamic viscoelastic test of liner.

The results show that: With increasing excitation frequency, the friction between the three liner materials (G30, K25, PU) and the wire rope decreased, and the wear of the surface shape of the liners was greater. The dynamic thermomechanical analysis (DMA) test results showed that the viscoelasticity of the three liner materials increased when the frequency was increased.

Wire ropes are widely used in deep shaft hoisting and building elevators. Its operational reliability depends on whether there is sufficient friction between the wire rope and the friction liner, and whether the friction liner has good wear resistance. The study of the friction between the wire rope and the liner influencing factors is of great significance for the safe service of the wire rope.

The related results can provide guidance for the reliability design of lifting systems with strong dynamic response, such as high speed and heavy load, to improve the friction drive stability of the system.

With the increase of mining depth, to improve the transportation efficiency of the hoist used in deep and ultra-deep mines, as well as to ensure the safety and reliability of its operation, it is crucial that the large friction hoisting equipment has sufficient friction between the wire rope and the friction lining, as well as whether the friction lining has a good abrasion resistance.

Considering the influence of deformation error, the target poses must be corrected when compensating for positioning error but the efficiency of existing positioning error compensation algorithms needs to be improved. Therefore, the purpose of this study is to propose a high-efficiency positioning error compensation method to reduce the calculation time.

The corrected target poses are calculated. An improved back propagation (BP) neural network is used to establish the mapping relationship between the original and corrected target poses. After the BP neural network is trained, the corrected target poses can be calculated with short notice on the basis of the pose correction similarity.

Under given conditions, the calculation time when the trained BP neural network is used to predict the corrected target poses is only 1.15 s. Compared with the existing algorithm, this method reduces the calculation time of the target poses from the order of minutes to the order of seconds.

The proposed algorithm is more efficient while maintaining the accuracy of the error compensation.

This method can be used to quickly position the error compensation of a large parallel mechanism.

This study aims to explore the different effects of inter-community group networks and intra-community group networks on group innovation.

The authors used a pooled panel dataset of 12,111 self-organizing innovation groups in 463 game product creative workshop communities from Steam support to test the hypothesis. The pooled ordinary least squares (OLS) model is used for analyzing the data.

The results show that network constraint is negatively associated with the innovation performance of online groups. The average path length of the inter-community group network negatively moderates the relationship between network constraint and group innovation, while the average path length of the intra-community group network positively moderates the relationship between network constraint and group innovation. In addition, both the network density of inter-community group networks and intra-community group networks can negatively moderate the negative relationship between network constraint and group innovation.

The findings of this study suggest that network structural characteristics of inter-community networks and intra-community networks have different effects on online groups’ product innovation, and therefore, group members should consider their inter- and intra-community connections when choosing other groups to form a collaborative innovation relationship.

This literature review explores the potential of gamification in workplace learning beyond formal training. The study also highlights research gaps and opportunities for scholars to develop new theories and methodologies to enhance the understanding and application of gamification in workplace learning. It provides guidance for managers to use gamification to enhance learning and engagement. Ultimately, this review presents gamification as a promising field of study to increase individual and organizational performance.

Literature review of 6625 papers in the timeframe 1990–2020, with an update to include papers published in 2023.

This article examines the impact of gamification beyond formal learning and its potential to enhance employee productivity and well-being in the workplace. While there has been extensive research on gamification in formal learning contexts, little is known about its impact on informal learning. The study argues that the context of gamification is crucial to extending its effects and discusses the role, antecedents and consequences of game design elements in the workplace. The article also explores how the learning context relates to employee learning during work. Further research is necessary to investigate the impact of individual characteristics on work experience and performance.

Intended contribution of the present study is the development of a theoretical framework exploring the benefits of gamification in a work context.

For practicing managers, this paper shows how to use gamification to increase workplace learning and employee engagement, not just in the context of formal learning—as some companies already do today—but also systematically, in the context of informal learning.

This study explores the impact of gamification on informal workplace learning and emphasizes the significance of the context of gamification in extending its effects to improve individual and organizational performance.

This study aims to develop an electrochemical sensor for the detection of benzophenone (BP) as an alternative to conventional techniques that are known, expensive, complex and less sensitive.

The developed sensor is a platinum electrode modified with a plasticized polymer film based on ß-cyclodextrin, using PVC as the polymer, PEG as the plasticizer and ß-CD as the ionophore. This sensor is characterized by various techniques, such as optical microscopy, scanning electron microscopy and cyclic voltammetry. This latter is also used for analyzing kinetic processes at the electrode/electrolyte interface and to evaluate the selectivity and sensitivity of the sensor.

The results highlight the performance of our sensor. In fact, it exhibits a linear response extending from 10⁻¹⁹ to 10⁻¹³ M, with a correlation coefficient of 0.9836. What is more, it has an excellent detection limit of 10⁻¹⁹ M and a good sensitivity of 21.24 µA/M.

The results of this investigation demonstrated that the developed sensor is an analytical tool of choice for the monitoring of BP in the aqueous phase. The suggested sensor is fast, simple, reproducible and inexpensive.

This study investigated the asymmetric effects of changes in policy uncertainty on real sector variables in Brazil, China, India and South Africa.

The study used the nonlinear autoregressive distributed lag (NARDL) modeling framework.

The results showed that both in the long run and short run, rising uncertainty not only increases consumer prices significantly in these economies, but also impedes aggregate and sectoral output growths, and deters investment, employment and private consumption. Contrary to economic expectation, the results also showed that in the long run, declining uncertainty impedes aggregate and sectoral output growths in these economies, and significantly hinders employment in South Africa and Brazil. This suggests that in the long run, economic agents in these economies somewhat behave as if uncertainty is rising. The authors also found significant asymmetric effects in the response of real sector variables to uncertainty both in the long run and short run, which justifies the choice of NARDL framework for this study.

The sample is limited to Brazil, India, China and South Africa. While Brazil, India and China are three of the most prominent large emerging market economies, South Africa is the largest emerging market economy in Africa.

To lessen the adverse effects of policy uncertainty observed in the results, there is need for sound institutions and policy regimes that can promote predictable policy responses in these economies so that policy neither serves as a source of uncertainty nor as a channel through which the effects of other shocks are transmitted.

Apart from using the NARDL framework to capture the asymmetric effects of policy uncertainty, this study also accounted for the sectoral effects of uncertainty in emerging markets.

This study aims to characterize the behavior of blended concrete, including metakaolin (MK) and quarry dust (QD), as supplementary cementing materials. The study focuses on evaluating the effects of these materials on the fresh and hardened properties of concrete.

MK, a pozzolanic material, and QD, a fine aggregate by-product, are potentially sustainable alternatives for enhancing concrete performance and reducing environmental impact. The addition of different percentages of MK enhances the pozzolanic reaction, resulting in improved strength development. Furthermore, the optimum dosage of MK, mixed with QD, and mechanical properties like compressive, flexural and split tensile strength of concrete were evaluated to investigate the synergetic effect of MK and quarry dust for M20-grade concrete.

The results reveal the influence of metakaolin and QD on the overall performance of blended concrete. Cost analysis showed that the optimum mix can reduce the 7%–8% overall cost of the materials for M20-grade concrete. Energy analysis showed that the optimum mix can reduce 7%–8% energy consumption.

The effective utilization is determined with the help of the analytical hierarchy process method to find an optimal solution among the selected criteria. According to the AHP analysis, the optimum content of MK and quarry dust is 12% and 16%, respectively, performing best among all other trial mixes.