This study aims to take Chinese higher vocational colleges professional group leaders as the research subjects to analyze the components of their key competencies, develop the competency model of professional group leaders (PGL), and analyze the main factors influencing the model. It provides a powerful help for improving the scientific level of the construction and management of the teaching staff in higher vocational colleges and filling the gap in the research on the quality and ability of Chinese professional group leaders. A mixed research method is deployed in this study. Data are collected with the help of a self-administrated questionnaire and a semi-structured interview based on grounded theory. Data analysis involves structural equation modeling using AMOS, complemented by qualitative coding in NVivo. It concludes that the competency development model of professional group leaders comprises two main dimensions: explicit competencies and implicit competencies. Explicit competencies include cross-border adaptability (CBA), resource integration ability (RIA), innovation and development practice ability (IDPA), management leadership ability (MLA), and interdisciplinary scientific research ability (ISRA). Implicit competencies include personality attitude (PA), and intrinsic motivation (IM). The study fills a significant gap in the literature by providing a detailed model of competency for professional group leaders in the context of higher vocational education, offering a practical framework for improving the training and management of teaching staff and promoting the development of professional groups effective in vocational colleges.

Political representation is responsible for choices regarding the supply and the management of transport infrastructure, but its decisions are sometimes in conflict with the will and the general interest expressed by citizens. This situation has progressively prompted the use of specific corrective measures in order to obtain socially sustainable decisions, such as the deliberative procedures for the appraisal of public goods. The standard Stated Choice Modelling Technique (SCMT) can be used to estimate the community appreciation for public goods such as transport infrastructure; but the application of the SCMT in its standard form would be inadequate to provide an estimation that expresses the general interest of the affected community. Hence the need to adapt the standard SCMT on the basis of the operational conditions imposed by deliberative appraisal procedures. Therefore, the general aim of the paper is to outline the basic conditions on which a modified SCMT with deliberative procedure can be set up. Firstly, the elements of the standard SCMT on which to make the necessary adjustments are identified; subsequently, modifications and additions to make to the standard technique are indicated; finally, the contents of an extensive program of experimentation are outlined.

This study investigates the role of property quality in shaping booking intentions within the dynamic landscape of the hospitality sector. A comprehensive approach, integrating qualitative and quantitative methodologies, is employed, utilising Airdna’s dataset spanning from July 2016 to June 2020. Multiple regression models, including interaction terms, are applied to scrutinise the moderating role of property quality. The study unveils unexpected findings, particularly a counterintuitive negative correlation between property quality and booking intentions in Model 7, challenging conventional assumptions. Theoretical implications call for a deeper exploration of contextual nuances and psychological intricacies influencing guest preferences, urging a re-evaluation of established models within hospitality management. On a practical note, the study emphasises the significance of continuous quality improvement and dynamic strategies aligned with evolving consumer expectations. The unexpected correlation prompts a shift towards more context-specific approaches in understanding and managing guest behavior, offering valuable insights for both academia and the ever-evolving landscape of the hospitality industry.

This paper investigates the implementation of ijarah muntahiyah bittamlik (IMBT) as an infrastructure project financing scheme within the Public-Private Partnership (PPP) models from a collaborative governance perspective. This paper follows a case study methodology. It focuses on two Indonesian non-toll road infrastructure projects, i.e., the preservation of the East Sumatra Highway projects, each in South Sumatra province and Riau province. The findings revealed that Indonesia’s infrastructure development priorities and its vision to become a global leader in Islamic finance characterized the system context that shaped the implementation of IMBT as an infrastructure project financing scheme within the PPP-AP model. Key drivers include leadership from the government, stakeholder interdependence, and financial incentives for the partnering business entity to adopt off-balance sheet solutions. Principled engagement, shared motivation, and the capacity for joint action characterized the collaboration dynamics, leading to detailed collaborative actions crucial for implementing IMBT as a financing scheme.

Falling is one of the most critical outcomes of loss of consciousness during triage in emergency department (ED). It is an important sign requires an immediate medical intervention. This paper presents a computer vision-based fall detection model in ED. In this study, we hypothesis that the proposed vision-based triage fall detection model provides accuracy equal to traditional triage system (TTS) conducted by the nursing team. Thus, to build the proposed model, we use MoveNet, a pose estimation model that can identify joints related to falls, consisting of 17 key points. To test the hypothesis, we conducted two experiments: In the deep learning (DL) model we used the complete feature consisting of 17 keypoints which was passed to the triage fall detection model and was built using Artificial Neural Network (ANN). In the second model we use dimensionality reduction Feature-Reduction for Fall model (FRF), Random Forest (RF) feature selection analysis to filter the key points triage fall classifier. We tested the performance of the two models using a dataset consisting of many images for real-world scenarios classified into two classes: Fall and Not fall. We split the dataset into 80% for training and 20% for validation. The models in these experiments were trained to obtain the results and compare them with the reference model. To test the effectiveness of the model, a t-test was performed to evaluate the null hypothesis for both experiments. The results show FRF outperforms DL model, and FRF has same accuracy of TTS.