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In view of the unknown environmental parameters and uncertain interference during gripping by the manipulator, it is difficult to obtain an effective gripping force with the traditional impedance control method. To avoid this dilemma, the purpose of this study is to propose an adaptive control strategy based on an adaptive neural network and a PID search optimization algorithm for unknown environments.

The method is based on a variable impedance model, and a new impedance model is established using a radial basis function (RBF) neural network to estimate unknown parameters of the impedance model. The approximation errors of the adaptive neural network and the uncertain disturbance are effectively suppressed by designing the adaptive rate. In the meantime, auxiliary variables are constructed for Lyapunov stability analysis and adaptive controller design, and PSA is used to ensure the stability of the adaptive impedance control system. Based on the Lyapunov stability criterion, the adaptive im-pedance control system is proved to have progressive tracking convergence property.

Through comparative simulations and experiments, the superiority of the proposed adaptive control strategy in position and force tracking has been verified. For objects with low flexibility and light-weight (such as a coke, a banana and a nectarine), this control method demonstrates errors of less than 10%.

This paper uses RBF neural networks to estimate unknown parameters of the impedance model in real-time, enhancing system adaptability. Neural network weights are updated online to suppress errors and disturbances. Auxiliary variables are designed for Lyapunov stability analysis. The PSA algorithm is used to adjust controller parameters in real-time. Additionally, comparative simulations and experi-ments are designed to analyze and validate the performance of controller.

This study aims to empirically examine the impact of internal corporate governance mechanisms (ICGM) related to the size of the board, board composition, CEO duality and audit committee independence as a single metric on a firm’s investment decisions.

This study attempts to develop an internal corporate governance quality index comprising 10 items under four main ICGMs – size and independence of the board, CEO duality and audit committee independence – employing panel data analysis to investigate its impact on the investment decisions in 301 nonfinancial firms listed in six emerging capital markets in the Gulf Cooperation Council (GCC) member countries for the years 2015–2020. Data were extracted from sample companies' websites, stock markets, annual reports and Refinitiv database.

This study provides convincing evidence that effective ICGMs minimize inefficient investment and ultimately boost investment efficiency. The findings remain consistent even after considering the potential endogeneity bias.

This study provides empirical evidence on investment efficiency in the GCC region and emphasizes the importance of high-quality ICGMs in reducing inefficient investment. By examining the impact of ICGMs on investment inefficiencies, this study contributes to the corporate governance literature. The GCC region's unique economic and social contexts, with its growing economies, are considered to shed light on this issue.

The lubrication performance of static and dynamic pressure thrust bearings is improved by introducing texture on the sealing edge.

Through model building, meshing and boundary condition setting, the influence of square texture on oil film lubrication performance was simulated and analyzed, and an improved algorithm was applied to perform optimization of lubrication performance.

The findings of this study reveal that the optimum lubrication performance is attained when adjusting the parameters of the square texture to 0.12 mm, 0.1 mm, 1 mm and 34 mm. In such circumstances, the thrust bearing with square textures demonstrates an increase in loading capacity of around 19% and a temperature reduction of about 2ºC compared to a smooth thrust bearing.

The original Reynolds equation is revised, and the influence of square texture on the physical field of oil film is analyzed, considering the turbulence state and cavitation phenomenon. The multi-objective function under square texture parameters was established using BP neural network, and the improved multi-objective salp swarm algorithm was used to optimize the process parameters.

Young construction project management practitioners (CPMPs) have unique, complex, and notable occupational mental health problems. However, there were few studies about the intervention for occupational mental health of CPMPs, and traditional intervention modes often failed to achieve significant effects. Therefore, this study aims to propose a new and effective intervention method.

Job crafting intervention was used to design an intervention experiment. A total of 72 CPMPs participated in a 10-week randomized controlled trial in China. Descriptive statistics and repeated measures analysis of variance were used to verify the effectiveness of job crafting intervention (JCI) on job crafting behaviors, job burnout, and work engagement with consideration of the impact of time.

Results showed that the intervention increased social resources, thus effectively reducing job burnout and promoting work engagement. Time also had a significant impact on cynicism, dedication, and social resources.

The authors should promote the habit of job crafting in CPMPs. Furthermore, in order to facilitate their job crafting, the authors should increase structural and social resources for them, and the authors can also encourage them to undertake challenging demands to increase their self-efficacy and the sense of achievement.

The authors bring into light a new psychological intervention approach among CPMPs, which integrates the advantages of the guidance in traditional organized intervention methods and the proactivity in individual spontaneous job crafting. The authors verify the efficacy of the JCI among CPMPs and help propose countermeasures and suggestions to improve the occupational mental health of CPMPs.

Besides ensuring traditional project objectives, expatriate construction professionals (EXCPs) working on international projects face challenges adapting to unfamiliar environments with varying construction standards, work practices and cultural values. This puts them at a high risk of job burnout. Thus, this study aims to investigate the antecedents and outcomes of EXCPs' job burnout in the international construction industry.

Based on the Job demands-resource model (JD-R), a theoretical framework was developed. Industry-specific stressors and expatriate management practices were identified using a literature review and interviews. The authors then used a questionnaire survey to collect data from Chinese EXCPs. Exploratory factor analysis, confirmatory factor analysis and structural equation modeling were then utilized to test hypotheses.

The findings indicate that early-career EXCPs experience the most severe levels of job burnout. The paths analysis proved the direct and indirect mitigating effects of expatriate management practices on job burnout, and EXCP's job burnout was associated with poor job performance and decreased intention to stay in the international assignment.

While prior research has explored job burnout among construction professionals working on domestic projects, little attention has been given to EXCPs and their unique challenges. This study aims to fill this critical gap in the literature by offering a unique perspective on the antecedents and outcomes of job burnout among EXCPs in international contexts and presents a significant contribution to understanding and addressing occupational health issues faced by EXCPs.

This paper aims to improve the refractive index sensor performance for analytes with large refractive index by adopting the technology of microstructured fiber (MF) and surface plasmon resonance (SPR).

The structure adopts an MF with a hexagonal lattice cladding structure composed of all-circular air holes, and three defect regions are introduced. The liquid analyte that needs to be tested is filled in the defect area. The surface plasmon polarition mode is generated and coupled with the core mode, thus forming a refractive index sensing channel. When the resonance conditions are satisfied, the resonance wavelength will be changed with the refractive index of the liquid analyte. All parameters that may affect the performance of the sensor are numerical simulated, and the structure is optimized through a large number of calculations.

The results demonstrate that the maximum dynamic sensitivity (S_R) can reach to 24,260 nm/RIU, and the average sensitivity (S_R-AV) can reach to 18,046 nm/RIU when the refractive index range is from 1.42 to 1.47. Besides, the sensitivity linearity (R²) is approximately 0.965, and its resolution is 4.1 × 10^–6 RIU. The comparison with some literature results shown that the proposed sensor has certain advantages over the sensors reported in these literatures.

This work proposed an SPR-based refractive index sensor with a simple MF structure. It has a certain reference significance for the design and optimization of SPR-based MF sensors. Moreover, owing to its simple structure, high refractive index sensitivity and linear sensing performance, this sensor will play an important role in the detection of high refractive index liquid analytes.

The purpose of this study is to investigate the influence of rotational speed on the oil film stability of the hydrostatic rotary table having double rectangular oil pads. The oil film stability is evaluated based on the oil film stiffness under constant load condition and the displacement response amplitude of the oil film under disturbance load condition.

The oil film stability theoretical equations of the double rectangular oil cavity are deduced such as oil film stiffness, damping and dynamic equations. A simulation model is developed to analyze the relationship among oil film temperature, oil film pressure fields and oil film stability. The user-defined function programs are used to control the rotational speed, lubricant viscosity and oil film thickness during the simulation. In addition, an experimental rig is built to test the simulation results.

This study shows that oil film stability decreases with increasing rotational speed under constant load and disturbance load. The trend of oil film stability decreased slowly within 30 r/min, and then rapidly. However, since the hydrodynamic pressure effect, the decrease rate of stability is mitigated under constant load and high rotational speeds.

The conclusions can provide a theoretical basis for improving the oil film stability of machines with similar hydrostatic support structure.

The peer review history for this article is available at: https://publons.com/publon/10.1108/ILT-07-2024-0267/

Achieving accurate trajectory tracking control of robot manipulators is challenging due to dynamic model errors and uncertain payloads. This paper aims to enhance trajectory tracking performance for robots with n degrees of freedom (DOF).

This study proposes a robust motion control framework that combines uncertainty and disturbance estimator with model-based compensation. The proposed framework ensures precise trajectory tracking in robot manipulators. In addition, uncertainties in the high-DOF robot dynamics are estimated through a simple model-based compensation for system error dynamics. The stability of the closed-loop system of the proposed framework is analyzed and proved.

The results indicate that the proposed framework can significantly reduce tracking errors and increase disturbance resistance. The simulation results of a two-link robotic arm verify the effectiveness of the proposed method. The results of the experiments conducted on a seven-DOF torque-controlled Flexiv4S manipulator demonstrate the superior trajectory tracking performance and robustness of the proposed algorithm.

This study introduces a highly efficient, robust motion control framework for high-DOF robots, which can improve the trajectory tracking performance in the presence of model uncertainties.

This study explores how chatbot gender and symbolic service recovery may improve the satisfaction of angry customers in the context of service failures. It provides a strategy for companies to deploy chatbots effectively in customer anger.

This research relies upon a systematic literature review to propose three hypotheses, and we recruit 826 participants to examine the effect of chatbot gender on angry customers through one lab study and one field study.

This research shows that female chatbots are more likely to increase the satisfaction of angry customers than male chatbots in service failure scenarios. In addition, symbolic recovery (apology vs. appreciation) moderates the effect of chatbot gender on angry customers. Specifically, male (vs. female) chatbots are more effective in increasing the satisfaction of angry customers when using the apology method, whereas female (vs. male) chatbots are more effective when using the appreciation method.

The rapid advancements in artificial intelligence technology have significantly enhanced the effectiveness of chatbots as virtual agents in the field of interactive marketing. Previous research has concluded that chatbots can reduce negative customer feedback following a service failure. However, these studies have primarily focused on the level of chatbot anthropomorphism and the design of conversational texts, rather than the gender of chatbots. Therefore, this study aims to bridge that gap by examining the effect of chatbot gender on customer feedback, specifically focusing on angry customers following service failures.

Waste generation poses a significant environmental challenge in the United Arab Emirates due to the rapid urbanization, population growth and industrialization witnessed in recent decades. As a result, there has been a substantial surge in waste production. To fulfil its sustainability and circular economy aspirations in various economic domains, the UAE must prioritize efficient waste management. The purpose of this study is to assess the environmental and energy efficiency of the UAE’s economic sectors particularly within its vital energy sectors, which encompass crude oil, natural gas and mining, manufacturing and electricity, by gauging their adherence to sustainability and circularity objectives.

The authors used the data envelopment analysis input–output model to identify sectors that exhibit strong performance as well as those that are falling behind.

Based on this study, the agriculture, the crude oil, natural gas and mining sectors and financial services and banking were found to be the most efficient. The results of this study concluded that the UAE is making progress toward achieving its sustainability and circularity objectives; however, the findings suggest that more effort is needed to fully realize these goals.

By identifying high-performing and underperforming sectors, decision-makers can prioritize efforts to enhance sustainability and circularity in area of greatest need in the economy.