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Supervisor consultation is conducive to promoting employee voice. This research examines an underlying mediating mechanism and a boundary condition that strengthens the influence of supervisor consultation.

Two-wave survey data were collected from 302 full-time employees. Path analysis was adopted to test the hypotheses.

Supervisor consultation is positively related to employee voice. Employee felt obligation for constructive change (FOCC) serves as a mediating mechanism. Supervisor consultation is more positively related to employee FOCC and subsequent voice when managerial openness is higher than lower.

This study highlights the incremental mediating effect of FOCC beyond that of two alternative mediators between supervisor consultation and employee voice and the moderating role of managerial openness in affecting the influence of supervisor consultation.

This study aims to prepare thymol-based deep eutectic solvents (DESs) and use them as lubricates for friction and wear tests to simulate the wear conditions of hybrid bearings.

Through the characterization and analysis of the morphology of wear scars and the elemental composition of friction films, the tribological behavior and wear mechanism of sample materials as lubricants were investigated and the anti-wear mechanism of thymol-based DESs was discussed.

The findings demonstrate that because of the formation of a fluid lubrication film and excellent kinematic viscosity, the lubrication effect of the prepared DES is improved by about 50% compared to the base lubricating oil. The prepared [Ch]Cl-thymol DES has a better anti-friction and lubrication effect than citric-thymol, EG-thymol and urea-thymol DESs, with an average friction coefficient of about 0.04.

In this work, the friction reduction properties of thymol-based DESs were studied as lubricants for the first time, and the lubrication mechanism of sample materials was investigated.

In this study, an indoor sensor information fusion positioning system of the quadrotor unmanned aerial vehicle (UAV) was investigated to solve the problem of unstable indoor flight positioning.

The presented system was built on Light Detection and Ranging (LiDAR), Inertial Measurement Unit (IMU) and LiDAR-Lite devices. Based on this, one can obtain the aircraft's current attitude and the position vector relative to the target and control the attitudes and positions of the UAV to reach the specified target positions. While building a UAV positioning model relative to the target for indoor positioning scenarios under limited Global Navigation Satellite Systems (GNSS), the system detects the environment through the NVIDIA Jetson TX2 (Transmit Data) peripheral sensor, obtains the current attitude and the position vector of the UAV, packs the data in the format and delivers it to the flight controller. Then the flight controller controls the UAV by calculating the posture to reach the specified target position.

The authors used two systems in the experiment. The first is the proposed UAV, and the other is the Vicon system, our reference system for comparison purposes. Vicon positioning error can be considered lower than 2 mm from low to high-speed experiments. After comparison, experimental results demonstrated that the system could fully meet the requirements (less than 50 mm) in real-time positioning of the indoor quadrotor UAV flight. It verifies the accuracy and robustness of the proposed method compared with that of Vicon and achieves the aim of a stable indoor flight preliminarily.

Vicon positioning error can be considered lower than 2 mm from low to high-speed experiments. After comparison, experimental results demonstrated that the system could fully meet the requirements (less than 50 mm) in real-time positioning of the indoor quadrotor UAV flight. It verifies the accuracy and robustness of the proposed method compared with that of Vicon and achieves the aim of a stable indoor flight preliminarily.

This paper aims to focus on solving the path optimization problem by modifying the probabilistic roadmap (PRM) technique as it suffers from the selection of the optimal number of nodes and deploy in free space for reliable trajectory planning.

Traditional PRM is modified by developing a decision-making strategy for the selection of optimal nodes w.r.t. the complexity of the environment and deploying the optimal number of nodes outside the closed segment. Subsequently, the generated trajectory is made smoother by implementing the modified Bezier curve technique, which selects an optimal number of control points near the sharp turns for the reliable convergence of the trajectory that reduces the sum of the robot’s turning angles.

The proposed technique is compared with state-of-the-art techniques that show the reduction of computational load by 12.46%, the number of sharp turns by 100%, the number of collisions by 100% and increase the velocity parameter by 19.91%.

The proposed adaptive technique provides a better solution for autonomous navigation of unmanned ground vehicles, transportation, warehouse applications, etc.

Visually impaired people usually struggle with doing daily tasks due to a lack of visual cues. For image captioning assistive applications, most applications require an Internet connection for the image captioning generation function to work properly. In this study, we developed MyUEVision, an application that assists visually impaired people by generating image captions that can work with and without the Internet. This work also involves reviewing some image captioning models for this application.

The author has selected and experimented with three image captioning models for online models and two image captioning models for offline models. The user experience (UX) design was designed based on the problems faced by visually impaired users when using mobile applications. The application is developed for the Android platform, and the offline model is integrated into the application for the image captioning generation function to work offline.

After conducting experiments for selecting online and offline models, ExpansionNet V2 is chosen for the online model and VGG16 + long short-term memory (LSTM) is chosen for the offline model. The application is then developed and assessed, and the results show that the application can generate image captions with or without the Internet, providing the best result when having an Internet connection, and the image is captured in good lighting with a few objects.

MyUEVision stands out for its both online and offline functionality. This approach ensures the image captioning generator works with or without the Internet, setting it apart as a unique solution to address the needs of visually impaired individuals.

More people share their tourism experiences on social media today than in the past, and as a result, more people follow these posts in their trip planning. However, studies into tourists' intention to follow such posts are scarce. Therefore, this study investigates the antecedents influencing social media users' intentions to follow tourism-related posts (TRPs) when planning their trips.

Questionnaires were collected from 402 social media users who had followed TRPs for their trip planning. Data were then analyzed using partial least squares structural equation modeling (PLS-SEM) and artificial neural networks.

The authors found that blogger–user fit and users' involvement with TRPs influenced behavior components (attitudes toward TRPs and intention to follow TRPs) via assessment components (bloggers' credibility and content quality), and the authors developed a framework to explain this relationship.

The findings advance prior studies by investigating (1) the antecedents of intention to follow TRPs when trip planning, (2) the two main social media elements – bloggers and posts – to understanding the role of social media on travel behavior and (3) involvement with TRPs and their impacts on travel behavior. This study contributes to the research on social media and tourism marketing and proposes practical indications for bloggers, social media platforms and destination marketing organizations.

A lack of suitably qualified Building Information Modelling (BIM) professionals is understood to be a major barrier towards higher uptakes of BIM in the Australian construction industry. In response, Australian universities have tried to integrate the teaching of BIM into construction-related curricula, but with limited success. The acknowledged impediment is the lingering mismatch between what universities offer and what industry actually needs. However, the exact nature of that mismatch has yet to be identified. This study addresses that knowledge gap. It assesses both the current status of BIM competencies among university graduates and explores how BIM education at Australian universities may be improved to deliver BIM work readiness, as required by the industry.

This paper employed a qualitative research approach, utilizing 17 semi-structured interviews with experts in the Australian BIM industry. The Person-Organization (PO) fit theory, which emphasizes the congruence between individual and organizational characteristics, was utilized as a theoretical framework to examine the compatibility between “demand” and “ability” perspectives. The resulting data were analysed using this theoretical framework to gain insights into the PO fit perspectives in relation to BIM industry practices.

Findings reveal that graduates are generally competent regarding the use of BIM software. However, employers require much more than software skills, and expect recruits to have the capability to implement BIM as a process according to information management standards. Specifically, graduates are significantly deficient in matters of BIM protocols, collaboration and coordination, information workflows as well as completion and handover procedures.

This study is the first of its kind that bridges the gap between industry expectations and university education, in the Australian context, moving beyond the common discourse in education literature, which is exclusively focused on assessing students’ perceptions about BIM.

This study aims to enhance the classification and processing of online appeals by employing a deep-learning-based method. This method is designed to meet the requirements for precise information categorization and decision support across various management departments.

This study leverages the ALBERT–TextCNN algorithm to determine the appropriate department for managing online appeals. ALBERT is selected for its advanced dynamic word representation capabilities, rooted in a multi-layer bidirectional transformer architecture and enriched text vector representation. TextCNN is integrated to facilitate the development of multi-label classification models.

Comparative experiments demonstrate the effectiveness of the proposed approach and its significant superiority over traditional classification methods in terms of accuracy.

The original contribution of this study lies in its utilization of the ALBERT–TextCNN algorithm for the classification of online appeals, resulting in a substantial improvement in accuracy. This research offers valuable insights for management departments, enabling enhanced understanding of public appeals and fostering more scientifically grounded and effective decision-making processes.

This study examined the factors militating against walking as a form of active mobility in Akure, Nigeria. For questionnaire administration, from the 548,315 population of Akure, two hundred and seventy-four (274) household heads representing 0.05% of the entire population of Akure were sampled with the aid of a structured and self-administered questionnaire using a systematic sampling technique. The elicited data were analysed using descriptive and inferential statistics. From the analysis, the major impediments to walking were the socio-cultural belief that anybody walking was poor, followed by accessibility to motorised transport, and inadequate pedestrian facilities. These situations have significantly deterred people from seeing walking as an active form of mobility in the study area. Thus, the study recommends a strong sensitisation and awareness programme to robustly enlighten people on the need to embrace walking as a form of urban mobility. Also, the government should adequately and sustainably invest more in pedestrian facilities that will promote the culture of walking among people and/or road users in Akure, Nigeria. More importantly, for inclusiveness in urban planning, road infrastructure should be designed alongside other road elements to ensure seamless negotiations between pedestrians and vehicles without any form of conflict.

This paper aims to introduce a custom-designed integrated nucleic acid detection polymerase chain reaction (PCR) instrument for clinical detection applications.

The PCR instrument can make rapid, sensitive, low-cost and quantitative molecular diagnosis compared with the current routine test flow from the pipette, series reagent to RT-PCR by manual manipulation. By integrating the multichannel automatic pipetting module, heat amplification module and real-time fluorescence detection module for the first time, the custom-designed integrated nucleic acid detection PCR instrument can achieve sample collection, subpackage, mixing, extracting, measuring and result presentation.

The multichannel automatic pipetting module was assembled with an accuracy of 0.4% (2 microliters) for accuracy measurement. Besides, the accuracy and sensitivity of nucleic acid using integrated low-cost nucleic acid detection PCR instruments were checked with COV-2019 virus (staining method) and African swine fever virus (probe method) under different concentrations.

Because of its high cost, complex system and bulky laboratory settings, including sample subpackage, mixing, extracting, measuring and finally result in presentation, the current nucleic acid detection system is not suitable for field operation and disease diagnosis in remote areas. The group independently designed and assembled an integrated low-cost multichannel nucleic acid detection PCR instrument, including a multichannel automatic pipetting module, a heat amplification module and a real-time fluorescence detection module.

The above equipment showed better reliability compared with commercial qPCR. These results can lay the foundation for functional, fast and low-cost PCR equipment for trace measurements.