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This study aims to propose a calibration method to enhance the positioning accuracy in dual-robot collaborative operations, aiming to address the challenge of drilling hole spacing errors in spacecraft core cabin brackets that require an accuracy of less than 0.5 mm.

Initially, the cooperative error of dual robots is defined. Subsequently, an integrated model is constructed that encompasses the kinematic model errors of the dual robots, as well as the establishment errors of the base and tool frames. A calibration method for optimizing the cooperative accuracy of dual robots is proposed.

The application of the proposed method satisfies the collaborative drilling requirements for the spacecraft core cabin. The average cooperative positioning error of the dual robots was reduced from 0.507 to 0.156 mm, with the maximum value and standard deviation decreasing from 1.020 and 0.202 mm to 0.603 and 0.097 mm, respectively. Drilling experiments conducted on a core cabin simulator demonstrated that after calibration, the maximum hole spacing error was reduced from 1.219 to 0.403 mm, with all spacing errors falling below the 0.5 mm threshold, thus meeting the requirements.

This paper addresses the drilling accuracy requirements for spacecraft core cabins by using a calibration method to reduce the cooperative error of dual robots. The algorithm has been validated through experiments using ER 220 robots, confirming its effectiveness in fulfilling the drilling task requirements.

This study aims to investigate the impact of market competitiveness on investment efficiency, and the moderating role of ownership and regulatory structures.

In this study, the Herfindahl–Hirschman Index (HHI), Lerner Index (LI) and industry-adjusted Lerner Index (LIIA) were used to measure market competitiveness. The research population consisted of companies listed on Tehran Stock Exchange (TSE). Using a systematic elimination, 199 companies were selected within eight years during 2014–2021.

The results showed that market competitiveness (based on the LI, LIIA and HHI) positively affected investment efficiency. Moreover, institutional ownership and managerial ownership affected the relationship between market competitiveness (based on all proxies of market competitiveness) and investment efficiency. Blockholders’ ownership also moderated the relationship between market competitiveness (based on LIIA and HHI) and investment efficiency. The hypothesis testing had robustness based on additional analyses.

In recent years, competitive environment and the ownership structure of companies have changed to a certain degree, paving the way for the private sector to enter many areas of activity especially in emerging Asian markets. Moreover, investment drivers and investment efficiency in developed markets may not be generalized to emerging Asian markets. Therefore, the present findings can show the significance of this research to fill the existing gap in the literature and provide insights into ownership and regulatory structures as a governance mechanism in market competitiveness and investment efficiency.

Scholars and practitioners alike are paying attention to entrepreneurial orientation (EO) as an antecedent of the financial performance of SMEs. Other factors foster and improve SMEs' financial performance. This paper aims to shed the light on other two different strategic orientations that may help enhance SMEs' financial performance in addition to EO, namely; market orientation (MO) and brand orientation (BO).

The three different important strategic orientations are explored through two different studies. The first study was conducted to determine the different effects of the three orientations on SMEs' financial performance. Data were collected using a questionnaire among a convenient sample (131) of business owners/managers, and next PLS-SEM was used for data analysis. The financial performance of firms in the second study is hypothesized to be an outcome of a combination of different strategic orientations; therefore, the fsQCA method is applied to explore the causal recipes of those orientations.

The paper concluded that the three different strategic orientations are collectively, of paramount importance to strategic managers of SMEs.

The brand, market and EOs have been discussed discretely in previous studies and this study attempted to provide managers/owners of SMEs with a holistic view of the three different orientations and the amalgamation among them to be beneficial for better financial performance.

This paper attempts to combine the application of artificial intelligence in predicting and evaluating the classification of surrounding rock grades and provides guidance for subsequent support design and reinforcement support operations.

This paper discusses the use of BPNN as the primary tool, combined with three swarm bionic optimization algorithms (GA, PSO, GWO), to solve stability evaluation and grade prediction of surrounding rock in ultra-deep roadway excavation.

Taking the Great Wall ore group as the core and the Shanghaimiao mining area as the extension, the optimal model is applied to the classification of surrounding rock grade in ultra-deep roadway engineering. Prediction results show that the performance of BPNN models is excellent.

Due to the limitations of geological conditions and construction environment in deep coal mines, the period of roadway excavation is too long, resulting in less data collection.

The prediction results can provide guidance for the excavation method, support scheme correction and reinforcement support scheme design of deep coal mine roadway engineering.

It provides guidance for deep mining of coal mine (the premise of surrounding rock support stability), so as to ensure the economic and safety benefits of coal enterprises.

The neural network is applied to rock mechanics in a deep site for the first time, which is used to solve the prediction direction of surrounding rock grade evaluation. The index of the input layer is determined by combining the “three high and one disturbance” with the on-site construction situation, which is closer to the actual project. The swarm intelligent bionic algorithms are selected to optimize the hyperparameters of back propagation neural network, so as to improve the accuracy of the models. The classification and evaluation system of surrounding rock for the Great Wall ore group is constructed, which is the core of Shanghaimiao mining area in the northwest of China, guiding the dynamic adjustment of on-site excavation and support operations.

This study aims to deepen our understanding of how chatbots’ empathy influences humans–AI relationship in frontline service encounters. The authors investigate the underlying mechanisms, including perceived anthropomorphism, perceived intelligence and psychological empowerment, while also considering variations between different stages of the customer journey (before and after purchase).

Data collection was conducted through an online survey distributed among 301 customers who had experience using AI-based service chatbot in frontline service encounters in China. The hypotheses were examined through structural equation modeling and multi-group analysis.

The findings of this study revealed the positive impacts of emotional and cognitive empathy on humans–AI relationship through perceived anthropomorphism, perceived intelligence and psychological empowerment. Furthermore, this study verified the moderating effect of the customer journey stages, such that the impacts of anthropomorphism and intelligence on humans–AI relationship displayed more strength during the pre- and post-purchase phases, respectively.

This research offers practical implications for companies: recognize and enhance empathy dimensions in AI-based service chatbot to empower human–AI relationships; boost customer empowerment in human–AI interactions; and tailor anthropomorphic features in the pre-purchase stage and improve problem-solving capability in the post-purchase stage to enrich user experiences.

This study extends relationship marketing theory and human–AI interaction frameworks by investigating the underlying mechanisms of the effect of two-dimensional empathy on human–AI relationship. This study also enriches service design theories by revealing the moderating effect of customer journey stages.

The assessment of risk to existing tunnels within the context of pit construction is influenced by a multitude of factors. The conventional fuzzy analytic hierarchy process (FAHP) method may lack precision due to its inability to incorporate the inherent randomness associated with numerous risk factors. To enhance the precision of risk evaluation for existing tunnels, this research introduces an improved FAHP approach grounded in cloud modeling theory.

We developed a risk assessment index system for existing tunnels, categorizing risk sources into three areas: hydrogeological conditions, foundation pit construction and tunnel structural bearing capacity. The system includes 11 evaluation indicators linked to these sources, with defined risk level thresholds for each. Using the cloud model, we calculated the membership degree of these indicators to risk levels, replacing traditional membership function formulas. The cloud model’s three digital characteristics (Ex, En and He) account for the randomness and ambiguity between qualitative descriptions and quantitative values, enhancing assessment accuracy. We applied hierarchical analysis to determine the weights of each risk factor and combined these with the membership degrees to evaluate overall risk levels. Engineering applications and model comparisons confirmed the method’s reliability, while sensitivity analysis identified key risk indicators affecting evaluation outcomes, allowing for targeted risk control measures to safeguard existing tunnels during foundation pit construction.

The evaluation results of engineering applications show the same results with the traditional FAHP method, which proves the reliability of the improved method. Furthermore, when comparing the evaluation result vectors between the two methods, it is observed that the outcomes of the improved method are more concentrated on a specific risk level compared to the traditional FAHP. This concentration mitigates the potential for bias in the evaluation results, thereby enhancing their accuracy. Through sensitivity analysis, four indicators were identified to have a significant influence on the evaluation result. After implementing targeted risk control measures, a downgrade in risk level to III was revealed. This aligns with the actual construction circumstances, as no safety incidents occurred in the Line 1 metro tunnel throughout the duration of the pit construction. This confirms the efficacy of the measures taken based on the evaluation results.

The novelty of this study is demonstrated through two key advancements. First, in response to the lack of a mature evaluation index system for risk assessment of existing tunnels during pit construction, the authors have meticulously curated a comprehensive risk evaluation index system. This system provides a valuable reference for the selection of appropriate risk evaluation indices in similar projects. Second, building upon the established index system, the study introduces a cloud model FAHP risk evaluation method. This method automates the generation of the membership degree between indicators and risk levels. The improved method has good reliability for the risk evaluation of existing tunnels, and it can provide decision-making reference for related studies when they carry out risk evaluations of similar projects.

A transmission tower usually experiences bolt loosening under long-term alternating cyclic load, which may lead to collapse of the tower in extreme operating conditions. The paper aims to propose a data-driven identification method for bolt looseness of complicated tower structures based on reduced-order models and numerical simulations to perceive and evaluate the health state of a tower in operation.

The equivalent stiffnesses of three types of bolt joints under various loosening scenarios are numerically determined by three-dimensional finite element (FE) simulations. The order of the FE model of a tower structure with bolt loosening is reduced by means of the component modal synthesis method, and the dynamic responses of the reducer-order model under calibration loads are simulated and used to create the dataset. An identification model for bolt looseness of the tower structure based on convolutional neural networks driven by the acceleration sensors is constructed.

An identification model for bolt looseness of the tower structure based on convolutional neural networks driven by the acceleration sensors is constructed and the applicability of the model is investigated. It is shown that the proposed method has a high identification accuracy and strong robustness to data noise and data missing. Meanwhile, the method is less dependent on the number and location of sensors and is easier to apply in real transmission lines.

This paper proposes a data-driven identification method for bolt looseness of a complicated tower structure based on reduced-order models and numerical simulations. Non-linear relationships between equivalent stiffness of bolted joints and bolt preload depicting looseness are obtained and reduced-order model of tower structure with bolt looseness is established. Finally, this paper investigates applicability of identification model for bolt looseness.

The purpose of this paper is to study and report the effects of silver (Ag) content, glass phase particle size and Ag/antimony-doped tin oxide (ATO) particle size on the properties of ATO thick film resistor pastes, especially on the microstructure, square resistance, temperature coefficient of resistance (TCR), resistance temperature curve and other properties of the pastes.

Thick film resistor pastes with different Ag content, glass phase particle size and ATO particle size were printed on stainless steel substrates by screen printing technology, and a series of Ag/ATO thick film resistors (TFRs) were obtained after high-temperature sintering. The electrical properties of TFRs were evaluated. The microstructure development, square resistance, TCR and other properties of the developed TFRs were evaluated with the change in Ag content and the particle size.

The results show that with the increase of Ag content, the square resistance of the pastes decreases and the TCR increases. The change rate of resistance after resintering is less than 4%, and the pastes show excellent antiaging properties. Meanwhile, with the increase of the particle size of the glass phase, the square resistance decreases first and then increases, and the TCR increases first and then decreases, which has little effect on the conductive behavior. The increase in ATO particle size leads to an increase in the square resistance of TFRs and a decrease in the TCR.

This paper provides a useful evaluation of the square resistance, TCR and other properties of Ag/ATO thick film resistor pastes, which are related to the Ag content, glass phase particle size and ATO particle size of the developed TFRs. The thick film resistor pastes with zero TCR can be obtained using Ag/ATO as the functional phase without Pd or Pt.

This paper aims to facilitate reliable online diagnosis of early faults in the stator winding inter-turn short circuits of induction motors (IMs) under various operating conditions.

A novel fault characteristic component, the characteristic current amplitude, is proposed for the fault. Defined as the product of short-circuit coefficient and short-circuit current, the characteristic current is derived from the positive and negative-sequence components of the stator-side current and voltage.

Simulation models of the IMs pre- and postfault, along with an experimental platform for the motor’s inter-turn short circuit, were established. The characteristic current amplitude proves more robust against voltage unbalance and load variations, which offers enhanced reliability and sensitivity for early fault diagnosis of inter-turn short circuit in IMs stator windings.

A novel feature is proposed. Compared with negative-sequence current, which is considered as a traditional fault feature, the characteristic current amplitude exhibits a greater robustness against the imbalanced conditions, which simultaneously possesses the attributes of both reliability and expeditiousness in fault detection.

The purpose of this paper is to investigate the existence, uniqueness and stability of solutions to a class of Riemann–Liouville fractional differential equations with anti-periodic boundary conditions of variable order (R-LFDEAPBCVO). The study utilizes standard fixed point theorems (FiPoTh) to establish the existence and uniqueness of solutions. Additionally, the Ulam-Hyers-Rassias (Ul-HyRa) stability of the considered problem is examined. The obtained results are supported by an illustrative example. This research contributes to the understanding of fractional differential equations with variable order and anti-periodic boundary conditions, providing valuable insights for further studies in this field.

This paper (1) defines the Riemann–Liouville fractional differential equations with anti-periodic boundary conditions of variable order (R-LFDEAPBCVO); (2) discusses the existence and uniqueness of solutions to these equations using standard FiPoTh; (3) investigates the stability of the considered problem using the Ul-HyRa stability concept (Ul-HyRa); (4) provides a detailed explanation of the design and methodology used to obtain the results and (5) supports the obtained results with a relevant example.

The authors confirm that no funds, grants or any other form of financial support were received during the preparation of this manuscript.

The originality/value of our paper lies in its contribution to the field of fractional differential equations. Specifically, we address the existence, uniqueness and stability of solutions to a class of Riemann–Liouville fractional differential equations with anti-periodic boundary conditions of variable order. By utilizing standard FiPoTh and investigating Ul-HyRa stability, we provide novel insights into this problem. The results obtained are supported by an example, further enhancing the credibility and applicability of your findings. Overall, our paper adds to the existing knowledge and understanding of Riemann–Liouville fractional differential equations with anti-periodic boundary conditions, making it valuable to the scientific community.