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The combination of improved PSO (IPSO) algorithm and artificial neural network (ANN) model for intelligent monitoring of the bearing performance of the hydrostatic turntable.

This paper proposes an artificial neural network model based on IPSO algorithm for intelligent monitoring of hydrostatic turntables.

The theoretical model proposed in this paper improves the accuracy of the working performance of the static pressure turntable and provides a new direction for intelligent monitoring of the static pressure turntable. Therefore, the theoretical research in this paper is novel.

Theoretical novelties: an ANN model based on the IPSO algorithm is designed to monitor the load-bearing performance of a static pressure turntable intelligently; this study show that the convergence accuracy and convergence speed of the IPSO-NN model have been improved by 52.55% and 10%, respectively, compared to traditional training models; and the proposed model could be used to solve the multidimensional nonlinear problem in the intelligent monitoring of hydrostatic turntables.

The peer review history for this article is available at: https://publons.com/publon/10.1108/ILT-03-2024-0081/

The purpose of this paper is to design a climbing robot connected by a connecting rod mechanism to achieve multi-functional tasks such as obstacles crossing and climbing of power transmission towers.

A connecting rod type gripper has been designed to achieve stable grasping of angle steel. Before grasping, use coordination between structures to achieve stable docking and grasping. By using the alternating movements of two claws and the middle climbing mechanism, the climbing and obstacle crossing of the angle steel were achieved.

Through a simple linkage mechanism, a climbing robot has been designed, greatly reducing the overall mass of the robot. It can also carry a load of 1 kg, and the climbing mechanism can perform stable climbing. The maximum step distance of the climbing robot is 543 mm, which can achieve the crossing of angle steel obstacles.

A transmission tower climbing mechanism was proposed by analyzing the working environment. Through the locking ability of the screw nut, stable clamping of the angle steel is achieved, and a pitch mechanism is designed to adjust the posture of the hand claw.

The government plays an important role in the financing process of small and medium enterprises (SMEs), but the current government-enterprise cooperation (GEC) mechanism cannot well solve the financing problem of SMEs. In addition, since government-enterprise cooperation is a long-term dynamic process, this study aims to explore the cooperation strategy between the government and core enterprises in supply chain finance (SCF) under the dynamic structure.

Considering both parties have the characteristics of disappointment aversion, our research constructs a game theory model of government subsidy and the effort of the enterprise to implement SCF based on differential game and studies different game strategies in the non-cooperative game, the Stackelberg game and the cooperative game.

Our findings show that the government subsidy can significantly spur the enterprise in the supply chain to implement SCF. We also find that the limitation of the government subsidy exists. In addition, the optimal strategy, the optimal benefit and the total benefit of the financing system formed by the government and the enterprise in the cooperative game are better than those in the non-cooperative game. Pareto optimality is achieved.

The limitations of this paper are: (1) In theory, this paper only takes the government and enterprise as game subjects without considering other participants in the supply chain. In addition, only disappointment aversion is taken into consideration while in reality the participants often exhibit multiple behavior preferences. (2) In methodology, only the numerical solution is given through the solution algorithm and all parameters are assumed to be determined as time changes for the convenience of calculation.

The government can motivate enterprises in the supply chain to implement SCF by providing subsidies. However, it should be noted that excessive subsidy will make the enterprise dependent on the government and as a result, decrease the effort level to implement SCF. Thus, it is necessary for the government to keep track of the business conditions of the enterprise to make a subsidy strategy. In addition, the government can reduce the impact of disappointing aversion by making more targeted policies and taking risk management measures.

On one hand, it is necessary for the government to keep track of the business conditions of the enterprise to make a subsidy strategy. In addition, the government can reduce the impact of their own disappointing aversion by making more targeted policies and adopting risk management measures. On the other, it is important for the enterprise to improve the regulatory mechanism and optimize the compensation structure of decision-makers to inhibit the impact of decision-makers' disappointing aversion.

This study is the first to investigate the mechanism of GEC in the setting of SCF based on a differential game. Furthermore, our study provides a theoretical basis for the government and enterprises to cooperate in SCF.

This paper provides the latest systematic literature review (SLR) of prevailing studies on the interrelationship among executive compensation, financial performance and sustainable business practices. This SLR is done in three parts: (1) examine the theories employed by previous studies; (2) identify the unique variables employed by researchers in analysing this interrelationship and (3) explore potential opportunities for further study in the field.

This study conducted an SLR analysing studies from the Web of science, Scopus and EBSCO in over 20 countries from 2009 to 2022 published in several top-ranked journals. We utilised various search strings using the key phrases “executive compensation”, “CEO Pay”, “financial performance” and “sustainable business practices”. The initial sample of 27,210 was filtered with our meticulous inclusion and exclusion criteria to produce a list of 161 studies.

Our findings are as follows: first, most studies encompassing this subject area lack multi-theoretical perspectives with agency theory being the most dominant theoretical viewpoint; second, we observed the use of monotonous quantitative research methods, with studies heavily lacking qualitative and mixed-method research approaches; finally, there is a palpable gap in cross-country studies.

There are a few limitations that must be acknowledged. First, the inclusion criteria ensured that only articles published in the CABS journal ranking of three star and above. Thus, this review may not be a precise reflection of the EC, FP and SBPs literature scope. The inclusion criteria also limit our review to only accounting, finance, management and business-related studies about the topic. Therefore, future studies could explore studies ranked three star and below and from other subject areas.

This study contributes to the existing literature by conducting a comprehensive SLR that examines both the theoretical underpinnings and empirical evidence on this topic. It builds upon previous research and extends our understanding of the interrelationship among executive compensation, financial performance and sustainable business practices.

This study aims to examine the impact of logistics and merchant certification information on consumer behaviour in hybrid retail platforms. Furthermore, it explores the moderating role of online shopping experience on the certification effect.

The authors utilize transaction-level data from over 2.5 million consumers involving 30,000 stock keeping units (SKUs) on JD.com in March 2018. They analyse the impact of different types of certification information on consumer behaviour using ordinary linear regression and linear probability models.

The findings reveal that, compared with information without certification, (1) single logistics certification information can enhance consumers' search depth and purchase intention; (2) dual logistics and merchant certification information also has a positive impact on consumer behaviour; and (3) single certification information is more effective for inexperienced consumers, while dual certification is more effective for experienced consumers.

Theoretically, this study contributes to the literature on certification information in hybrid retail platforms and broadens information communication methods for online shopping. Our discovery is meaningful for managers in locating customers and allocating resources. In addition, we encourage online retailers to utilize certification information to engage consumer.

This study aims to examine the relationship between environmental, social and governance (ESG) disclosure, firm risk and stock market returns within the Chinese energy sector. Using a variety of econometric techniques, the study seeks to uncover the impact of ESG disclosure on risk mitigation and its influence on stock market performance.

Benchmark regression models were used to explore the associations between ESG disclosure, firm risk and stock returns. To address potential endogeneity, a generalised method of moments estimator is used. Quantile regression was used for robustness analysis.

The study reveals a negative relationship between ESG disclosure and firm risk, indicating that companies with greater ESG disclosure tend to experience reduced risk exposure. In addition, a positive association is observed between ESG disclosure and stock market returns, suggesting that companies with more comprehensive ESG disclosure practices tend to perform better in the stock market.

This study implies that investors appreciate sustainable investment and incorporate ESG practices and disclosure in decision-making. Policymakers can promote transparent ESG reporting through regulatory frameworks, fostering sustainable practices in the energy sector.

Despite the mounting concerns over carbon dioxide emissions and the energy industry’s environmental footprint, this study pioneers a comprehensive analysis of ESG disclosure within this critical sector. Delving into the relationship of ESG practices, firm risk and market returns, this research uniquely examines both risk mitigation and return enhancement, shedding new light on sustainable strategies in the energy domain.

Automatic modulation recognition (AMR) is a challenging problem in intelligent communication systems and has wide application prospects. At present, although many AMR methods based on deep learning have been proposed, the methods proposed by these works cannot be directly applied to the actual wireless communication scenario, because there are usually two kinds of dilemmas when recognizing the real modulated signal, namely, long sequence and noise. This paper aims to effectively process in-phase quadrature (IQ) sequences of very long signals interfered by noise.

This paper proposes a general model for a modulation classifier based on a two-layer nested structure of long short-term memory (LSTM) networks, called a two-layer nested structure (TLN)-LSTM, which exploits the time sensitivity of LSTM and the ability of the nested network structure to extract more features, and can achieve effective processing of ultra-long signal IQ sequences collected from real wireless communication scenarios that are interfered by noise.

Experimental results show that our proposed model has higher recognition accuracy for five types of modulation signals, including amplitude modulation, frequency modulation, gaussian minimum shift keying, quadrature phase shift keying and differential quadrature phase shift keying, collected from real wireless communication scenarios. The overall classification accuracy of the proposed model for these signals can reach 73.11%, compared with 40.84% for the baseline model. Moreover, this model can also achieve high classification performance for analog signals with the same modulation method in the public data set HKDD_AMC36.

At present, although many AMR methods based on deep learning have been proposed, these works are based on the model’s classification results of various modulated signals in the AMR public data set to evaluate the signal recognition performance of the proposed method rather than collecting real modulated signals for identification in actual wireless communication scenarios. The methods proposed in these works cannot be directly applied to actual wireless communication scenarios. Therefore, this paper proposes a new AMR method, dedicated to the effective processing of the collected ultra-long signal IQ sequences that are interfered by noise.