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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.

This study established and examined a moderated mediation model connecting family motivation with proactive career behavior (PCB). Drawing on social cognitive career theory model of career self-management and person-environment fit theory, the authors posit that the influence of family motivation on PCB is mediated by career goal setting (CGS) and moderated by career support climate (CSC).

Using multilevel modeling techniques, data were collected from 98 leaders and 416 employees at two-time points.

Results show that family motivation has a positive direct and indirect effect on PCB through CGS. Additionally, CSC strengthens the positive effect of family motivation on PCB through CGS.

For employees, proactive career management and the establishment of clear career goals are vital for fulfilling family responsibilities. Simultaneously, for managers and organizations, creating a supportive work environment is crucial to encourage employees in career management.

While existing research highlights the detrimental effect of fulfilling family caregiving responsibilities on employees' career development, the comprehension of this relationship remains limited. Taking a family motivation perspective, this study explores the mechanisms through which family motivation stimulates employees to engage in PCB, with a specific focus on the moderating influence of CSC. The findings offer fresh insights into the role of family motivation in career development, contributing significantly to the broader literature on the intersection of family and careers.

Safety efficiency is the key to balance safety and production in construction industry; but the existing safety efficiency evaluation methods have the limitations of overestimating efficiency and ignoring undesirable outputs; therefore, according to the characteristics of safety production in construction industry, this paper innovatively develops a new cross-efficiency data envelopment analysis method to analyze safety efficiency, which can solve the limitations of traditional methods; and then the safety efficiency and its influencing factors of China's construction industry are analyzed, and some useful conclusions are obtained to improve its safety efficiency.

A new cross-efficiency data envelopment analysis method with undesirable outputs is proposed; and the two-stage efficiency analysis framework is designed.

First, the construction industries in different areas have different reasons for affecting their safety efficiency; second, the evaluation results of global safety priority tend to be more acceptable; third, frequent safety accidents and low resource utilization lead to a slow downward trend of the safety efficiency of China's construction industry in the long run; fourth, construction engineering supervision, construction industrial scale, and construction industrial structure have the significant impact on safety efficiency.

Theoretically, a new cross-efficiency data envelopment analysis method with undesirable outputs is proposed for evaluating safety efficiency; practically, the safety efficiency and its influencing factors of China's construction industry are analyzed.

To improve the accuracy of stock price trend prediction in the field of quantitative financial trading, this paper takes the prediction accuracy as the goal and avoid the enormous number of network structures and hyperparameter adjustments of long-short-term memory (LSTM).

In this paper, an adaptive genetic algorithm based on individual ordering is used to optimize the network structure and hyperparameters of the LSTM neural network automatically.

The simulation results show that the accuracy of the rise and fall of the stock outperform than the model with LSTM only as well as other machine learning models. Furthermore, the efficiency of parameter adjustment is greatly higher than other hyperparameter optimization methods.

(1) The AGA-LSTM algorithm is used to input various hyperparameter combinations into genetic algorithm to find the best hyperparameter combination. Compared with other models, it has higher accuracy in predicting the up and down trend of stock prices in the next day. (2) Adopting real coding, elitist preservation and self-adaptive adjustment of crossover and mutation probability based on individual ordering in the part of genetic algorithm, the algorithm is computationally efficient and the results are more likely to converge to the global optimum.

Using a quantitative research method, health-care workers’ (HWs) well-being was taken as the dependent variable while the two independent variables were physical quality and service environment. This study aims to focus on the well-being of HWs in their physical environment.

This study used a hybrid method that is a combination of literature review and questionnaire survey. This study used quantitative research design which is a systematic study of phenomenon by collecting quantifiable data and performing statistical, mathematical or computational techniques. This study is conducted among HWs, with doctors and nurses at four Chinese government hospitals in the southern city of Changzhou (near Shanghai) as participants.

A total of 222 responses were obtained from a random sample of HWs from four hospitals in Changzhou City, Jiangsu Province. Three-fifth of the participants (n = 133, 60%) are satisfied with hospital equipment and half (n = 111.50%) of them are satisfied with the modern technology in place. About one-third (n = 67, 30%) are satisfied with ventilation and only one-fifth (n = 44, 20%) are satisfied with hygiene. Most HWs think it was necessary to provide tests for frontline workers.

In the wake of COVID-19 in 2020, HWs are working long hours every day facing high risk of infection and stress. This research investigates the satisfaction level and the difficulties experienced by HWs based on the current physical environment setting during COVID-19 pandemic.

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.

The primary objective of this study is to investigate the association between the dimensions of intellectual capital (IC) and sustainability practices (SP) within the context of manufacturing medium and large (ML) firms in Uganda. The study aims to shed light on whether and how different dimensions of IC contribute to the adoption and implementation of SP by these firms.

This study utilized a cross-sectional and quantitative approach, collecting data through a questionnaire survey from a sample of manufacturing ML firms. The collected data underwent analysis to identify patterns and relationships using the SmartPLS structural equation modeling (SEM) technique.

The findings demonstrated that the three categories of IC (human, structural and relational capital) influence the SP of ML manufacturing enterprises in Uganda. This suggests that IC is a critical component of SP.

Manufacturing enterprises should use their IC to create strategies for sustainable solutions, such as creating new, ecologically and socially responsible products and services and improving current ones to lessen their environmental effect.

This research advances knowledge of SP by revealing if all aspects of IC are significant for the SP of manufacturing enterprises in Uganda.

This study aims to investigate the factors affecting (i.e. determinants) the continuance of mobile learning adoption in an informal setting among higher education learners from a rural region in the Philippines. It assesses the extent of the determinants of mobile learning adoption continuance and their interrelationships and the role of a personality trait (e.g. locus of control) on its determinants.

This study used a rigorous literature review method that led to a mobile learning adoption continuance model. This proposed model analyzed the perceptions of higher education learners’ experiences on mobile learning adoption during the COVID-19 pandemic (i.e. informal setting). The data collection was self-administered using an online survey from a convenience sample size of 434 using adapted questionnaire instruments. The study used factor analysis by using a structural package for social sciences (SPSS) and analysis of the moment of the structure. The effect sizes of the direct effect, simple and serial mediation and interaction effects in a path model were analyzed by using user-defined estimand and orthogonalized approaches.

The findings indicate that the effect of perceived security risks along with perceived functional benefit and learner value affect the mobile learning adoption continuance. The perceived learner value mediates the perceived functional benefit relationship on mobile learning adoption continuance. Perceived security risk indirectly affects mobile learning adoption continuance through perceived functional benefit and learner value. In addition to this, the internal locus of control strengthens the positive relationship between perceived functional benefit and mobile learning adoption continuance. However, it dampens the positive relationship of perceived learner value.

The study provides an essential foundation on the mobile learning adoption model that focuses on its continuance. This model integrated perceived security risks, functional benefits and learner value aspects of continuance intention that higher education institutions may consider in their mobile learning initiative. It further provides evidence to intensify the important moderating role of locus of control that intervenes on the determinants of mobile learning adoption continuance.

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.