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This study aims to understand how employability capitals’ dynamics foster self-perceived employability (SPE) among students and graduates, which is still being empirically explored. Building upon the Employability Capital Growth Model and the Social Cognitive Career Theory’s career self-management model, we aimed to understand how different capitals associate by testing a serial mediation model connecting career identity (reflecting career identity capital) and SPE through the serial mediation of cultural capital and job interview self-efficacy (ISE) (an element of psychological capital).

We adopted a two-wave design involving 227 Italian University students and graduates. We recruited participants through multi-channel communication. The hypothesised relationships were analysed employing the structural equation modelling approach with the SPSS AMOS statistical package.

The results indicated that career identity, cultural capital, ISE and SPE are meaningfully related. In particular, in line with our expectations, we observed that career identity predicts cultural capital, which is positively associated with ISE which, ultimately, impacts SPE.

Our work adds to existing research by advancing the understanding of employability capitals, explaining how they interact and influence SPE, which is crucial for a sustainable transition into the workforce. At a practical level, our findings call upon, and guide, efforts from various stakeholders in the graduate career ecosystem (i.e. universities and their partners) to offer students and graduates meaningful experiences to form and use their employability capitals.

Drawing on the social cognitive model of career self-management (CSM model) and deep learning theory, the authors' study aims to investigate the effect of knowledge sharing on career adaptability and explore how and when this effect occurs. The authors proposed a moderated sequential-mediation model linking knowledge sharing, knowledge depth, self-efficacy and career adaptability, moderated by reflective thinking.

The authors used a field survey involving 611 employees working in different enterprises in China to test the authors' predictions. For supplementary validation, the authors conducted a scenario-based experiment with a distinct sample of 148 employees from Chinese enterprises. The authors used hierarchical regression analysis, ANOVA analysis and moderated sequential-mediation analysis to test the hypotheses.

The results indicated a positive effect of knowledge sharing on career adaptability, sequentially mediated through knowledge depth and self-efficacy. Additionally, reflective thinking strengthened the positive effect of knowledge sharing on knowledge depth and further moderated the sequential mediating effect.

Previous research has predominantly focused on the perspective that individuals act as “recipients” from the environment in developing career adaptability. Instead, the authors adopt a “giving” perspective by investigating the influence of knowledge sharing on career adaptability, which advances the existing knowledge on the predictors of career adaptability.

To make the robot that have real autonomous ability is always the goal of mobile robot research. For mobile robots, simultaneous localization and mapping (SLAM) research is no longer satisfied with enabling robots to build maps by remote control, more needs will focus on the autonomous exploration of unknown areas, which refer to the low light, complex spatial features and a series of unstructured environment, lick underground special space (dark and multiintersection). This study aims to propose a novel robot structure with mapping and autonomous exploration algorithms. The experiment proves the detection ability of the robot.

A small bio-inspired mobile robot suitable for underground special space (dark and multiintersection) is designed, and the control system is set up based on STM32 and Jetson Nano. The robot is equipped with double laser sensor and Ackerman chassis structure, which can adapt to the practical requirements of exploration in underground special space. Based on the graph optimization SLAM method, an optimization method for map construction is proposed. The Iterative Closest Point (ICP) algorithm is used to match two frames of laser to recalculate the relative pose of the robot, which improves the sensor utilization rate of the robot in underground space and also increase the synchronous positioning accuracy. Moreover, based on boundary cells and rapidly-exploring random tree (RRT) algorithm, a new Bio-RRT method for robot autonomous exploration is proposed in addition.

According to the experimental results, it can be seen that the upgraded SLAM method proposed in this paper achieves better results in map construction. At the same time, the algorithm presents good real-time performance as well as high accuracy and strong maintainability, particularly it can update the map continuously with the passing of time and ensure the positioning accuracy in the process of map updating. The Bio-RRT method fused with the firing excitation mechanism of boundary cells has a more purposeful random tree growth. The number of random tree expansion nodes is less, and the amount of information to be processed is reduced, which leads to the path planning time shorter and the efficiency higher. In addition, the target bias makes the random tree grow directly toward the target point with a certain probability, and the obtained path nodes are basically distributed on or on both sides of the line between the initial point and the target point, which makes the path length shorter and reduces the moving cost of the mobile robot. The final experimental results demonstrate that the proposed upgraded SLAM and Bio-RRT methods can better complete the underground special space exploration task.

Based on the background of robot autonomous exploration in underground special space, a new bio-inspired mobile robot structure with mapping and autonomous exploration algorithm is proposed in this paper. The robot structure is constructed, and the perceptual unit, control unit, driving unit and communication unit are described in detail. The robot can satisfy the practical requirements of exploring the underground dark and multiintersection space. Then, the upgraded graph optimization laser SLAM algorithm and interframe matching optimization method are proposed in this paper. The Bio-RRT independent exploration method is finally proposed, which takes shorter time in equally open space and the search strategy for multiintersection space is more efficient. The experimental results demonstrate that the proposed upgrade SLAM and Bio-RRT methods can better complete the underground space exploration task.

The future increase of chronic diseases in the world requires new challenges in the health domain to improve patients' care from the point of view of the organizational processes, clinical pathways and technological solutions of digital health. For this reason, the present paper aims to focus on the study and application of well-known clinical practices and efficient organizational approaches through an innovative model (TALIsMAn) to support new care process redesign and digitalization for chronic patients.

In addition to specific clinical models employed to manage chronic conditions such as the Population Health Management and Chronic Care Model, we introduce a Business Process Management methodology implementation supported by a set of e-health technologies, in order to manage Care Pathways (CPs) digitalization and procedures improvement.

This study shows that telemedicine services with advanced devices and technologies are not enough to provide significant changes in the healthcare sector if other key aspects such as health processes, organizational systems, interactions between actors and responsibilities are not considered and improved. Therefore, new clinical models and organizational approaches are necessary together with a deep technological change, otherwise, theoretical benefits given by telemedicine services, which often employ advanced Information and Communication Technology (ICT) systems and devices, may not be translated into effective enhancements. They are obtained not only through the implementation of single telemedicine services, but integrating them in a wider digital ecosystem, where clinicians are supported in different clinical steps they have to perform.

The present work defines a novel methodological framework based on organizational, clinical and technological innovation, in order to redesign the territorial care for people with chronic diseases. This innovative ecosystem applied in the Italian research project TALIsMAn is based on the concept of a continuum of care and digitalization of CPs supported by Business Process Management System and telemedicine services. The main goal is to organize the different socio-medical activities in a unique and integrated IT system that should be sustainable, scalable and replicable.