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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.

The purpose of this paper is to combine triboelectric nanogeneration technology with ball bearing structure to achieve energy collection and fault monitoring.

In this paper, according to the rotation mode of ball bearings, the freestanding mode of triboelectric nanogeneration is selected to design and manufacture a novel triboelectric nanogeneration device Rolling Ball Triboelectric Nanogenerator (RB-TENG) which combines rotary energy collection with ball bearing fault self-sensing.

The 10,000s continuous operation experiment of the RB-TENG is carried out to verify its robustness. The accurate feedback relationship between the RB-TENG and rotation velocity can be demonstrated by the fitting comparison between the theoretical and experimental electrical signal periods at a certain time. By comparing the output electrical signals of the normal RB-TENG and the rotor spalling RB-TENG and polytetrafluoroethylene (PTFE) balls with different degrees of wear at 500 r/min, it can be concluded that the RB-TENG has an ideal monitoring effect on the radial clearance distance of bearings. The spalling fault test of the RB-TENG stator inner ring and rotor outer ring is carried out.

Through coupling experiments of rotor spalling fault of the RB-TENG and PTFE balls fault with different degrees of wear, it can be seen that when rotor spalling fault occurs, balls wear has a greater impact on the normal operation of the RB-TENG, and it is easier to identify. The fault self-sensing ability of the RB-TENG can be obtained, which is expected to provide an effective scheme for monitoring the radial wear clearance distance of ball bearings.

The peer review history for this article is available at: https://publons.com/publon/10.1108/ILT-08-2024-0295/

Job satisfaction is essential in understanding turnover intentions. Previous studies reveal that highly educated hospitality employees generally have lower levels of job satisfaction, indicating that the antecedents of job satisfaction may be different from hospitality managers and frontline employees. This study compared the different antecedents of job satisfaction for housekeeping managers and employees.

This study used a mixed-methods approach for a two-part study. The researchers recruited housekeeping managers for the exploratory survey. The results of open-end questions helped us build a custom dictionary for the text mining of comments from Glassdoor.com. Finally, a multilinear regression of themes from housekeeping employees’ ratings on Glassdoor.com was conducted to understand the antecedents of job satisfaction for housekeeping managers and employees.

The results of the exploratory survey indicated that the housekeeping department has an urgent need for organizational support and training. The text-mining revealed organizational support impacts both managers and frontline employees, while training impacts managers more than employees. Finally, the regression analysis showed compensation, business outlook, senior management, and career opportunity impacted both groups. However, work-life balance only influenced managers.

With a large number of employees at low salaries, housekeeping departments have a higher-than-average turnover rate for lodging. This study is among the first to compare the antecedents of managers’ and frontline employees’ job satisfaction in the housekeeping department, extending Social Exchange Theory. It provides suggestions for the housekeeping department to decrease turnover intentions.

The competitiveness of destinations is crucial for tourism. In this context, the study aims to uncover customer satisfaction, experiences, feelings, and thoughts by conducting a comparative analysis of social media comments from various competitive tourism destinations.

Big data research was conducted to answer the research questions. The data was collected on a social media platform focusing on three destinations in the Mediterranean region. Three methods were employed to analyse the data: sentiment analysis, topic modelling, and named-entity recognition.

This study addressed traveller satisfaction levels. It identified the topics concerning each destination, examined the emotions expressed by travellers about these topics, explored the potential impact on future behaviour, and investigated the features of the destinations and satisfaction levels about these features. It also identified the prominent food and beverage names in destinations and explored tourists’ preferences regarding these foods and beverages.

The limitations of this study relate to the sample. The data used in this study was solely obtained from a single social media platform and focused on English-only comments. Further research that includes different social media platforms for hotel categories and considers reviews in local languages could capture a broader range of customer opinions and experiences.

Policymakers can gain insight into a destination’s position in the competitive landscape. This study has numerous implications for policymakers in the relevant destinations and managers in the design and implementation of services.

The findings of this study can have broader societal implications if considered and implemented by decision-makers and tourism businesses in the context of competitiveness.

The study’s originality lies in integrating multiple disciplines and comparing tourism destinations using big data. This study improves the understanding of competitiveness in three specific Mediterranean destinations. Previous research has focused on different contexts in these Mediterranean destinations. Therefore, the study fills this gap by focusing simultaneously on all three destinations in the context of competitiveness.