Search

Purpose: Firstly, this study diagnosed professional competence amongst Chinese vocational students within a broad range of the manufacturing sectors; then, the authors examined how different types of P-E fit (job, organisation and vocation) and internship quality jointly shape the newly acquired professional competences of interns. Design/methodology/approach: This study utilised the COMET methodology to conduct a large-scale assessment of professional competence amongst 961 graduates from vocational colleges who had successfully completed internships. Participants actively engaged in the data collection process by responding to questionnaires that sought contextual information concurrently. Findings: The majority of students have attained fundamental functional competencies, indicating their fulfillment of basic requirements. However, there is a tendency to overlook the cultivation of shaping competence. Three types of P-E fit and task characteristics are positively correlated with professional competence. The indirect relationship between P-E fit and professional competence mediated by task characteristics was verified through P-V fit and P-J fit except for P-O fit. Overall, the model explains 39.2% of the variance in professional competence. Originality/value: "How to promote professional competence" has been highlighted as an important topic in vocational education. This paper contributes to identify the characteristics of a quality internship program for vocational colleges and firms. These insights are important in considering a student-centred approach, design internships programmes that better fit their own abilities, needs and vocations, avoiding a one-size-fits-all approach to implement internships and thus, enhance students' professional development. [ABSTRACT FROM AUTHOR]

Dempster–Shafer theory (DST), which is widely used in information fusion, can process uncertain information without prior information; however, when the evidence to combine is highly conflicting, it may lead to counter-intuitive results. Moreover, the existing methods are not strong enough to process real-time and online conflicting evidence. In order to solve the above problems, a novel information fusion method is proposed in this paper. The proposed method combines the uncertainty of evidence and reinforcement learning (RL). Specifically, we consider two uncertainty degrees: the uncertainty of the original basic probability assignment (BPA) and the uncertainty of its negation. Then, Deng entropy is used to measure the uncertainty of BPAs. Two uncertainty degrees are considered as the condition of measuring information quality. Then, the adaptive conflict processing is performed by RL and the combination two uncertainty degrees. The next step is to compute Dempster's combination rule (DCR) to achieve multi-sensor information fusion. Finally, a decision scheme based on correlation coefficient is used to make the decision. The proposed method not only realizes adaptive conflict evidence management, but also improves the accuracy of multi-sensor information fusion and reduces information loss. Numerical examples verify the effectiveness of the proposed method. [ABSTRACT FROM AUTHOR]

The Dempster-Shafer theory (DST) is an information fusion framework and widely used in many fields. However, the uncertainty measure of a basic probability assignment (BPA) is still an open issue in DST. There are many methods to quantify the uncertainty of BPAs. However, the existing methods have some limitations. In this paper, a new total uncertainty measure from a perspective of maximum entropy requirement is proposed. The proposed method can measure both dissonance and non-specificity in BPA, which includes two components. The first component is consistent with Yager's dissonance measure. The second component is the non-specificity measurement with different functions. We also prove the desirable properties of the proposed method. Besides, numerical examples and applications are provided to illustrate the effectiveness of the proposed total uncertainty measure. [ABSTRACT FROM AUTHOR]

Aerial base stations (ABSs) are expected to be important supplementary components for the 5G-and-beyond communication systems to achieve global Internet of Everything. To fully exploit the advantages of the ABSs seamless connection, in this paper, we propose a high altitude platform (HAP) and low altitude platforms (LAPs) cooperated network architecture. Specifically, the HAP can extend the network coverage and LAPs can act as relays to improve the transmission performance for hot spots. In the considered network, we investigate the sum rate maximization problem by optimizing the downlink and the uplink transmissions, respectively. In the downlink, we adopt the orthogonal frequency division multiple access technique and take the basic rate requirement into account. In addition, we optimize the height of LAPs and the spectrum allocation between the HAP and LAPs, and between the LAP and users. Then, we decouple the downlink sum rate maximation problem as three subproblems. An alternating optimization framework is designed to deal with these non-convex optimization problems, where the height and spectrum allocation are tackled in turn. In the uplink, we adopt the non-orthogonal multiple access technique, and consider the decoding threshold of the successive interference cancellation technique. Afterward, we optimize the power allocation of each user, and the spectrum allocation between users and the LAP, and between LAPs and the HAP. Finally, simulation results show the effects of spectrum allocation, height optimization and power allocation on network performance, which verify that the proposed scheme can achieve higher sum rate on both downlink and uplink in comparison with the current works. [ABSTRACT FROM AUTHOR]

This paper investigates the delay sensitive secure transmission problem in unmanned aerial vehicle (UAV)-relayed vehicular ad hoc networks (VANETs). Specifically, we take the security assurance into account and formulate this problem as a total information delay minimization problem by optimizing the UAV relay trajectory and channel allocation. To tackle this optimization problem, we propose an alternating optimization framework, where the UAV relay trajectory is solved by the Newton method, and the channel allocation is obtained by relax-and-round and sequential convex approximation methods. Finally, simulation results show the performance improvement of our algorithm compared with the current works in terms of the total information delay. [ABSTRACT FROM AUTHOR]

In this paper, we investigate tags in anti-collision applications of radio frequency identification (RFID) technology in unmanned aerial vehicle (UAV)-aided vehicular ad hoc networks (VANETs). The integration of RFID technology in UAV-aided VANETs can provide reliable traffic-related services for vehicles. However, multiple tags' simultaneous responses to a reader mounted on a UAV, denoted as tag collision, gravely affect the correct tag detection on each vehicle. Therefore, in order to decrease the collision probability and improve the throughput, we propose a multi-frequency tag identification method. In the proposed scheme, we devise a tag grouping method based on adaptive power control to make the reader dynamically match the optimal frame length. Based on the above matching results, we introduce a tag estimation method using the optimal weight to improve the accuracy of tag estimation. We theoretically analyze the closed-form expression of the security outage probability expression. Finally, our simulation results demonstrate that the proposed tag anti-collision scheme achieved significant performance superiority in terms of the throughput and identification time slots. [ABSTRACT FROM AUTHOR]

In this paper, we propose a mobile edge computing (MEC)-enabled unmanned aerial vehicle (UAV)-assisted vehicular ad hoc network (VANET) architecture, based on which a number of vehicles are served by UAVs equipped with computation resource. Each vehicle has to offload its computing tasks to the proper MEC server on the UAV due to the limited computation ability. To counter the problems above, we first model and analyze the transmission model and the security assurance model from the vehicle to the MEC server on UAV, and the task computation model of the local vehicle and the edge UAV. Then, the vehicle offloading problem is formulated as a multi-objective optimization problem by jointly considering the task offloading, the resource allocation, and the security assurance. For tackling this hard problem, we decouple the multi-objective optimization problem as two subproblems and propose an efficient iterative algorithm to jointly make the MEC selection decision based on the criteria of load balancing and optimize the offloading ratio and the computation resource according to the Lagrangian dual decomposition. Finally, the simulation results demonstrate that our proposed scheme achieves significant performance superiority compared with other schemes in terms of the successful task processing ratio and the task processing delay. [ABSTRACT FROM AUTHOR]