Abstract
In the digital age, the training in companies can be facilitated through a proper system to the company's demand. A learning platform personalized to the profile of employees can facilitate the selection of training that tailored to their roles. This research aims to investigate the existence of adaptation and personalization of learning management systems (LMS) in enterprise context, that facilitate the selection of learning's content suited for employees' roles. This study focuses on literature reviewto understand the importance of a personalizedLMSin company, especially in selection of content that adequate to role of each employee.

Abstract
The Portuguese Open University, together with the University of Algarve, both in Portugal, launched a doctoral program in digital media art taught by a hybrid approach, adopting open distance asynchronous teaching based on an e-learning platform along with face-to-face sessions (doctoral retreats) with the duration of one week at the final of each academic year. This program has been established in 2012 pioneering the innovation of applying asynchronous online teaching in practical and some labour intensive disciplines such are programming sensors or planning art installation exhibitions while creating an online learning community. In this article we aim at presenting the doctoral program by emphasizing not only the overall project but specifically the pedagogic model and strategy adopted while providing examples of concrete teaching/learning scenarios implemented including the doctoral retreats.

Abstract
The growth of subtransmission network aims at satisfying load growth, maintaining a contingency level, and providing a high quality and reliable electricity service. Utilities direct the investments to reinforce this system and thus a meshed network with multiple-point feeding to the transmission system arises. At this point, an efficient alternative to achieve these objectives is to carry out a diagnosis of the network architecture and, taking advantage of the switching capability, to plan the switching of the subtransmission lines. An optimal subtransmission switching approach is proposed based on constrained multi-objective optimization that deals with energy losses and reliability, in addition to using information on the characteristics of loads and generation. A simulation-based optimization framework is constructed using the non-dominated genetic classification algorithm NSGA-II in the optimization phase and reliability assessment during simulation phase. As a result, a set of non-dominated solutions approximating the Pareto front is obtained, which allows the planner to make decisions based on its priorities and needs. The performance of the proposal is assessed with a real subtransmission system of an Ecuadorian power utility. This approach to the operational planning of a meshed subtransmission network constitutes a powerful decision-making tool that could be adopted by distribution utilities.

Abstract
The paper examines the relationship between gamification – the use of game elements in non-gaming contexts – and innovation teams’ outcomes. It builds on psychological and teamwork theories, arguing that gamification overcomes collaboration issues and generates multiple positive outcomes, particularly in coordination, alignment, engagement, and teams’ motivation. The research follows a qualitative theory-driven using a case study of an innovation project. The conceptual model built through the findings offers valuable insights about applying gamification in innovation teams, namely: i) surprising teams with such a new and playful approach reduces stress among team members; ii) rules and time constraints play a crucial role in teams’ coordination by avoiding dispersion and enhancing focused efforts. The paper provides a set of testable theoretical propositions derived from the conceptualization of gamification in the context of innovation teams. It supports innovation managers interested in measuring gamification outcomes in teams. © 2022 China Ordnance Society

Abstract
Messaging services are usually provided within social network platforms and allow these platforms to collect additional information about users, such as what time, for how long, with whom, and where a user communicates. This information allows the identification of users and is available to the messaging service provider even when communication is encrypted end-to-end. Thus, a gap still exists for alternative messaging services that enable anonymous and confidential communication and that are independent of a specific online service. Online services can still be used to support this messaging service, but in a way that enables users to communicate anonymously and without the knowledge and scrutiny of the online services. In this paper, we propose messaging using steganography and online services to support anonymous and confidential communication. In the proposed messaging service, only the sender and the receiver are aware of the existence of the exchanged data, even if the online services used or other third parties have access to the exchanged secret data containers. This work reviews the viability of using existing online services to support the proposed messaging service. Moreover, a proof-of-concept of the proposed message service is implemented and tested using two online services acting as proxies in the exchange of encrypted information disguised within images and links to those images. The obtained results confirm the viability of such a messaging service.

Abstract
Optical tweezers use light to trap and manipulate mesoscopic scaled particles with high precision making them a useful tool in a plethora of natural sciences, with emphasis on biological applications. In principle, the Brownian-like dynamics reflect trapped particle properties making it a robust source of information. In this work, we exploit this information by plotting histogram based images of 250ms of position or displacement used as input to a Convolution Neural Network. Results of 2-fold stratified cross-validation show satisfying classifications between sizes or types of particles: Polystyrene and Polymethilmethacrylate thus highlighting the potential of CNN approaches in faster and non-invasive applications in intelligent opto and microfluidic devices using optical trapping tools.