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Path planning is an important part of UAV mission planning. The main purpose of this paper is to overcome the shortcomings of the standard particle swarm optimization (PSO) such as easy to fall into the local optimum, so that the improved PSO applied to the UAV path planning can enable the UAV to plan a better quality path.

Firstly, the adaptation function is formulated by comprehensively considering the performance constraints of the flight target as well as the UAV itself. Secondly, the standard PSO is improved, and the improved particle swarm optimization with multi-strategy fusion (MFIPSO) is proposed. The method introduces class sigmoid inertia weight, adaptively adjusts the learning factors and at the same time incorporates K-means clustering ideas and introduces the Cauchy perturbation factor. Finally, MFIPSO is applied to UAV path planning.

Simulation experiments are conducted in simple and complex scenarios, respectively, and the quality of the path is measured by the fitness value and straight line rate, and the experimental results show that MFIPSO enables the UAV to plan a path with better quality.

Aiming at the standard PSO is prone to problems such as premature convergence, MFIPSO is proposed, which introduces class sigmoid inertia weight and adaptively adjusts the learning factor, balancing the global search ability and local convergence ability of the algorithm. The idea of K-means clustering algorithm is also incorporated to reduce the complexity of the algorithm while maintaining the diversity of particle swarm. In addition, the Cauchy perturbation is used to avoid the algorithm from falling into local optimum. Finally, the adaptability function is formulated by comprehensively considering the performance constraints of the flight target as well as the UAV itself, which improves the accuracy of the evaluation model.

The study delves into the influence of wear cycles on these parameters. The purpose of this paper is to identify characteristic patterns of σ_RS and ε_PEEQ that discern varying wear situations, thereby contributing to the enrichment of wear theory. Furthermore, the findings serve as a foundational basis for nondestructive and in situ wear detection methodologies, such as nonlinear ultrasonic detection, known for its sensitivity to σ_RS and ε_PEEQ.

This paper elucidates the wear mechanism through the lens of residual stress (σ_RS) and plastic deformation within distinct fretting regimes, using a two-dimensional cylindrical/flat contact model. It specifically explores the impact of the displacement amplitude and cycles on the distribution of residual stress and equivalent plastic strain (ε_PEEQ) in both gross slip regime and partial slip regimes.

Therefore, when surface observation of wear is challenging, detecting the σ_RS trend at the center/edge, region width and ε_PEEQ distribution, as well as the maximum σ_RS distribution along the depth, proves effective in distinguishing wear situations (partial or gross slip regimes). However, discerning wear situations based on ε_PEEQ along the depth direction remains challenging. Moreover, in the gross slip regime, using σ_RS distribution or ε_PEEQ along the width direction rather than the depth direction can effectively provide feedback on cycles and wear range.

This work introduces a novel perspective for investigating wear theory through the distribution of residual stress (σ_RS) and equivalent plastic strain (ε_PEEQ). It presents a feasible detection theory for wear situations using nondestructive and in situ methods, such as nonlinear ultrasonic detection, which is sensitive to σ_RS and ε_PEEQ.

The peer review history for this article is available at: https://publons.com/publon/10.1108/ILT-01-2024-0005/

The purpose of this study is to investigate the application of non-destructive testing methods in measuring bearing oil film thickness to ensure that bearings are in a normal lubrication state. The oil film thickness is a crucial parameter reflecting the lubrication status of bearings, directly influencing the operational state of bearing transmission systems. However, it is challenging to accurately measure the oil film thickness under traditional disassembly conditions due to factors such as bearing structure and working conditions. Therefore, there is an urgent need for a nondestructive testing method to measure the oil film thickness and its status.

This paper introduces methods for optically, electrically and acoustically measuring the oil film thickness and status of bearings. It discusses the adaptability and measurement accuracy of different bearing oil film measurement methods and the impact of varying measurement conditions on accuracy. In addition, it compares the application scenarios of other techniques and the influence of the environment on detection results.

Ultrasonic measurement stands out due to its widespread adaptability, making it suitable for oil film thickness detection in various states and monitoring continuous changes in oil film thickness. Different methods can be selected depending on the measurement environment to compensate for measurement accuracy and enhance detection effectiveness.

This paper reviews the basic principles and latest applications of optical, electrical and acoustic measurement of oil film thickness and status. It analyzes applicable measurement methods for oil film under different conditions. It discusses the future trends of detection methods, providing possible solutions for bearing oil film thickness detection in complex engineering environments.

Usability is one of the key concerns in the development of mobile learning applications. The aim of this paper is to design and validate a usability evaluation questionnaire (UEQ) for mobile learning applications.

The UEQ was developed in four stages: selecting primary studies and extracting usability problems, thematic analysis, creating UEQ items and validation and reliability using confirmatory factor analysis (CFA).

CFA to derive the model fit was computed using AMOS to test the construct validity. The model-fit values were within their respective expected acceptance levels. To assess the reliability of the instrument item loadings, the internal consistency coefficients such as Cronbach’s alpha, McDonald’s Omega and composite reliability were considered. Indicator loadings ranged between 0.735 and 0.933, fulfilling the threshold of above 0.7.

The study provides a novel UEQ for mobile learning applications, which can be used by developers and in academic research to assess mobile learning applications.

This study aims to propose a research model integrating technology acceptance model 3 (TAM3) constructs and human aspects of humanoid service robots (HSRs), measured by the Godspeed questionnaire series and tested across two hotel properties in Japan and the USA.

Potential participants were approached randomly by email invitation. A final sample size of 395 across two hotels, one in Japan and the other in the USA, was obtained, and the data were analysed using structural equation modelling.

The results confirm that perceived usefulness, driven by subjective norms and output quality, and perceived ease of use, driven by perceived enjoyment and absence of anxiety, are the immediate direct determinants of users’ re-patronage intentions for HSRs. Results also showed that users prefer anthropomorphism, perceived intelligence and the safety of an HSR for reusing it.

The findings have practical implications for the hospitality industry, suggesting multiple attributes of an HSRs that managers need to consider before deploying them in their properties.

The current study proposes an integrated model determining factors that affect the re-patronage of HSRs in hotels.

Drawing on the cognitive–affective–conative framework, this study aims to develop a model of service robot acceptance in the hospitality sector by incorporating both cognitive evaluations and affective responses.

A mixed-method approach combining qualitative and quantitative methods was used to develop measurement and test research hypotheses.

The results show that five cognitive evaluations (i.e. cuteness, coolness, courtesy, utility and autonomy) significantly influence consumers’ positive affect, leading to customer acceptance intention. Four cognitive evaluations (cuteness, interactivity, courtesy and utility) significantly influence consumers’ negative affect, which in turn positively affects consumer acceptance intention.

This study provides significant implications for the design and implementation of service robots in the hospitality and tourism sector.

Different from traditional technology acceptance models, this study proposed a model based on the hierarchical relationships of cognition, affect and conation to enhance knowledge about human–robot interactions.

This study aims to explore the attitudes and perceptions of Chinese coffee consumers towards robot baristas, considering the proliferation of automated entities within China's coffee sector.

Employing the extended Technology Acceptance Model 2 as its theoretical framework, this research conducts in-depth interviews with 30 Chinese coffee consumers. The laddering technique is utilized, supplemented by video simulation. Thematic analysis is subsequently employed to scrutinize the data.

The findings delineate six pivotal themes encapsulating Chinese coffee consumers' perceptions of robot baristas – Perceived Introvert Friendliness, Perceived Novelty, Perceived Intellectual Discrepancies, Perceived Efficiency and Reliability, Perceived Emotional Disconnection, and Perceived Labour Market Disruption. Moreover, six motivational themes are identified - Social Status Boosting, Openness to Experience, Ease of Use, Tech-Driven Affordability, Reliable and Uncompromising Quality, and Resistance to Overbearing Service.

The study is limited by its focus on a specific cultural context. Future research could explore cross-cultural perspectives.

The findings of this study offer guidance on how to market and position robotic barista services to appeal to consumer preferences and drive adoption.

Understanding consumer perceptions of robotic baristas has broader social implications, particularly in terms of labour market disruption and the potential impact on traditional coffee professions. Businesses can navigate the social implications of automation more effectively and foster greater acceptance of technological innovations within society.

This study offers insights into the inclinations of Chinese coffee consumers, thereby facilitating informed decision-making and the formulation of effective strategies to expedite the adoption of robotic service.

While existing research has predominantly focused on the positive relationships between service quality and outcome variables, there has been limited investigation into the influence of food service quality on these variables or the moderating role of stadium atmosphere in the realm of sports marketing. This study aims to fill these gaps by examining the structural relationships between baseball stadium food quality, timeliness, satisfaction, and behavioral intention with an emphasis on the moderating effect of stadium atmosphere.

The data were collected from spectators attending baseball games. This study established the validity of the measurement scale through confirmatory factor analysis, factor loadings, average variance extracted, and construct reliability using Cronbach’s alpha. In addition, this study employed structural equation modeling with maximum likelihood estimation to examine positive relationships and mediating effects and used Jamovi statistical software to conduct moderation analysis.

The findings demonstrate the significant impacts of food quality on satisfaction and behavioral intention, the positive effects of timeliness on satisfaction and behavioral intention, and the notable influence of satisfaction on behavioral intention. Additionally, this study found that satisfaction partially mediates the aforementioned relationships and that stadium atmosphere moderates these pathways.

This study contributes to the literature by addressing the impact of food service quality on outcome variables within sports marketing, an area that has received limited attention. Specifically, it examines how food quality and timeliness influence consumer satisfaction and behavioral intention. Furthermore, the study highlights the moderating role of stadium atmosphere, demonstrating its potential to enhance the relationship between food quality and consumer outcomes. These findings expand our understanding of consumer behavior in sports settings, offering practical strategies for team officials to enhance fan experiences. By diversifying menu options, optimizing concession operations, and collaborating with local vendors, team officials can significantly improve food service quality and create a more engaging stadium atmosphere.

Product discounts have been widely applied in digital commerce as a method to attract and retain customers to purchase in China. Given that digital channels differ in their attributes, customers may behave differently when they respond to the same discount across channels. However, little attention has been paid to explore the heterogeneity of customer responses to discounts across channels. This study aims to fill in the gap by exploring how customers’ purchase responses to price discounts differ across digital channels.

This study applies a Poisson regression to an unbalanced data set of purchasing history from Chinese footwear brands with 3,510 customers in a two-year time window across the three digital channels (i.e. personal computer [PC], app and mobile website), with a correction for endogeneity by using the Gaussian copula method.

This paper finds that price discounts have the strongest positive effect on consumers’ purchase volumes on the PC channel, followed by the app channel, while discounts show the weakest impact in the mobile website channel. By so, this paper demonstrates that customers respond differently to online and mobile channels, and they also respond differently within mobile channels when they purchase products with price discounts.

This study is original in analyzing the difference in customers’ discount responses across digital channels, offering valuable contributions to existing research on multichannel marketing as well as mobile marketing and providing helpful insights for multichannel merchants to design digital discount strategies.

This paper aims to explore the influencing factors of public perceived efficacy of emergency infrastructure projects based on the triadic interactive determinism, and analyze the relationship among these factors.

Based on the triadic interactive determinism, we explored the factors influencing public perceived efficacy of emergency infrastructure project and empirically verified the relationship among these factors and perceived efficacy by using data drawn from a questionnaire survey of 491 residents near Leishenshan Hospital, Jiangxia District, Wuhan, China.

Prior experience, emotional response, personal expectation, public trust, context message and interactivity level, namely behavior, individual and environment, affect the perceived efficacy of public emergency infrastructure projects.

The results offer an insight into public perceived efficacy of emergency infrastructure project from the perspective of antecedents in a triadic reciprocal determinism, which provides a reference basis for the sustainable development of the emergency infrastructure projects. This study also suggests valuable practical implications to government departments to improve the quality of administrative decision-making effectively.

Although existing studies have found some influencing factors of public perceived efficacy in general infrastructure, there is still a lack of systematic carding and quantitative description of influencing factors of public perceived efficacy of emergency infrastructure projects. This study bridges this gap by exploring the determinants and their influencing relationship of public perceived efficacy especially for emergency infrastructure projects.