Abstract
In this work, we present the design, laboratory tests, and the field trial results of a power-over-fiber (PoF) low power instrument transformer (LPIT) for voltage and current measurements in the medium voltage distribution networks. The new proposed design of this power-over-fiber LPIT aims to overcome the drawbacks presented by the previous technologies, such as the continuous operation (measuring and data transmission) for a wide current range conducted in the medium voltage transmission lines, damage due to lightning strikes, accuracy dependency on vibration, position and temperatures. The LPIT attends the accuracy criteria of IEC 61869-10 and IEC 61869-11 in terms of current and voltage accuracy and it attends the practical criteria adopted by Utilities companies including voltage measurements without removing the coating of the covered conductors. The PoF based LPIT was developed to be applied at 11.9 kV, 13.8 kV, and 23.0 kV phase-to-phase nominal voltages, and in two current ranges 1.25–30 A and 37.5–900 A. The digital data transmission of current, voltage, and temperature from the sensing unit to the processing unit uses a special synchronism technique and it is performed by two 62.5 µm multimode fibers in 850 nm. The optical powering in 976 nm is also performed by one 62.5 µm multimode fiber from the processing unit to the sensor unit. We presented all details of the sensor design and its laboratory characterization in terms of accuracy and temperature correction. We also presented the results of field tests of the sensor made in two different conditions: in a standard distribution network and an experimental hybrid fiber/power distribution network. We believe that these studies aim to incorporate optical fiber and devices, digital technologies, communications systems in electrical systems driving their evolution.

Abstract
The digital twin has been gaining significant attention from the academia and industry sectors in the last few years. The digital twin concept enables monitoring, diagnosis, optimisation, and decision support tasks to improve industrial systems operation. One of the identified challenges in this field is the need to improve the decision support cycle by decreasing decision-making time and improving the accuracy of recommendations by considering human intervention in the cycle. Bearing this in mind, the paper explores the use of trust models to improve the recommendation cycle in the digital twin. For this purpose, a literature overview on trust applied in recommendation systems was performed, focusing on the concept, its properties and previous models. Considering this analysis, a trust-based model is specified in a digital twin artificial intelligence-based recommendation system.

Abstract
In a multimodal world the contact time between the teacher and the students is not always sufficient to ensure the effectiveness of the learning process. For the assimilation of concepts, students often endeavor on a search for the materials that best suit their learning needs. With the application of new technologies in teaching, study materials and support platforms are increasingly abundant and diverse. Additionally, recommendation algorithms overwhelm students with several options, sometimes hard to resist and select, especially after the COVID-19 restrictions, where the amount of connected time as increased. In this context, it is important for the teacher, to know which methods and materials the students use when they are autonomously developing their knowledge and skills. A survey was conducted within a group of engineering students at a Portuguese higher education institution with the main goal of characterizing the study habits and the materials that students. The obtained results are here reported and analyzed and compared with previous results from pre-pandemic study.

Abstract
Building management systems (BMSs) are being implemented broadly by industries in recent decades. However, BMSs focus on specific domains, and when installed on the same building, they lack interoperability to work on a centralized user interface. On the other hand, BMSs interoperability allows the implementation of complex rules based on multi-domain contexts. The Building's Reasoning for Intelligent Control Knowledge-based System (BRICKS) is a context-aware semantic rule-based system for the intelligent management of buildings' energy and security. It uses ontologies and semantic web technologies to interact with different domains, taking advantage of cross-domain knowledge to apply context-based rules. This work upgrades the previously presented version of BRICKS by including services for energy consumption and generation forecast, demand response, a configuration user interface (UI), and a dynamic building monitoring and management UI. The case study demonstrates BRICKS deployed at different aggregation levels in the authors' laboratory building, managing a demand response event and interacting autonomously with other BRICKS instances. The results validate the correct functioning of the proposed tool, which contributes to the flexibility, efficiency, and security of building energy systems.

Abstract
The European Foundation for Quality Management (EFQM) 2020 model is a comprehensive and updated business model that encompasses sustainability and shares features with Industry 4.0, emphasizing transformation and improved organizational performance, yet with different theoretical and practical foundations. This research highlights the EFQM 2020 model's novelties and its relationships/implications with the Industry 4.0 paradigm, contributing to the Quality 4.0 body of knowledge. Several linkages between the EFQM 2020 model and Industry 4.0 have been identified, namely, at the criteria level and guidance points, which can support successful digital transformation by combining quality and excellence with Industry 4.0. However, given the model's generic and non-prescriptive nature, there is no specific reference to the nine Industry 4.0 pillars. Additionally, the links between direction and organizational culture and leadership criteria and driving performance and transformation are not evident, which might be a concern for business and technology transformation strategies. Managing knowledge, skills, and capabilities is critical for the successful adoption of Industry 4.0. The EFQM model adds a strategic and technologically unbiased perspective to Industry 4.0, providing an integrated business excellence framework for Quality 4.0. With empirical support of the model application, future research is recommended to develop this subject further.

Abstract
Due to the complexity and great relevance of the transmission network expansion planning (TNEP) for electrical systems, this topic remains on the focus of the academic and industry communities. Therefore, this paper proposes a new approach to deal efficiently with the basic formulation of this problem, combining low computational effort and good quality of the obtained solutions. In this approach four factors contribute to solve TNEP problem more efficiently: (i) the investment decisions are selected using a new Constructive Heuristic Algorithm (CHA); (ii) the proposed CHA includes two stages, using the relaxation of the decision integers variables through the hyperbolic tangent function and the setting of its function's slope; (iii) the performance index that was adopted was modified regarding what was reported in the literature; (iv) the use of the primal-dual interior point optimization technique allows the representation of the nonlinearities in the problem: transmission power losses and the hyperbolic tangent function (investment decision). The quality and effectiveness of the proposed algorithm is verified using two real power systems, where the proposed CHA is able to lead to better quality solutions than the ones reported in the literature.