Abstract
The assignment of appropriate reviewers to academic articles, known as the reviewer assignment problem (RAP), has become a crucial issue in academia. While there has been much research on RAP, there has not yet been a systematic literature review (SLR) examining the various approaches, techniques, algorithms and discoveries related to this topic. To conduct the SLR, we identified and evaluated relevant articles from four databases using defined inclusion and exclusion criteria. We analysed the selected articles and extracted information, and assessed their quality. Our review identified 67 articles on RAP published in conferences and journals up to mid-2022. As one of the main challenges in RAP is acquiring open data, we have studied the data sources used by researchers and found that most studies use real data from conferences, bibliographic databases and online academic search engines. RAP is divided into two main phases: (1) finding/recommending expert reviewers and (2) assigning reviewers to submitted manuscripts. In Phase 1, we have identified that decision support systems, recommendation systems, and machine learning-oriented approaches are more commonly used due to better results. In Phase 2, heuristics and metaheuristics are the approaches that present better results and are consequently more commonly used by researchers. Based on the analysed studies, we have identified potential areas for future research that could lead to improved results. Specifically, we suggest exploring the application of deep neural networks for calculating the degree of correspondence and using the Boolean satisfiability problem to optimise the attribution process.

Abstract
This paper presents a new hybridization approach to improve CEVESA, a multi-zonal hydro-thermal equilibrium model for the joint dispatch of energy and secondary reserve capacity for the Iberian Electricity Market (MIBEL). Like similar fundamental models, CEVESA provides market prices that typically show an average systematic bias compared to real market prices. This is because these models do not always capture the true variable production costs of the generation units or the additional markups that generation companies may include in their pricing strategy. Based on real market outcomes, this paper proposes a new methodology built on a previous hybridization approach that estimated a constant monthly markup per thermal offering unit [1]. This new methodology is based on a functional estimation of the offering unit cost (or bidding price), using as input the initial CEVESA production costs based on the fuel and emissions commodities' prices, correcting the power plants' markup.

Abstract
This Special Issue originates from the international conference 2021 Joint EuCNC & 6G Summit (Joint European Conference on Networks and Communications and 6G Summit), which was held in June 2021 in virtual format. The Technical Programme Chairs of the conference selected the best papers and invited authors to submit an extended version of their paper by at least one-third of their length. Only the top ranked papers were invited to this Special Issue, in order to fulfil its purpose. The main target was to collect and present quality research contributions in the most recent activities related to technologies, systems and networks beyond 5G. Through this Special Issue, the state-of-the-art is presented and the new challenges highlighted, regarding the latest advances on systems and network perspectives that are already being positioned beyond 5G, bridging as well with the evolution of 5G, including applications and trials. Therefore, the motivation for this Special Issue is to present the latest and finest results on the evolution of research of mobile and wireless communications, coming, but not exclusively (since Joint EuCNC & 6G Summit is a conference open to the whole research community), from projects co-financed by the European Commission within its R&D programmes.

Abstract
Learning to program requires diligent practice and creates room for discovery, trial and error, debugging, and concept mapping. Learners must walk this long road themselves, supported by appropriate and timely feedback. Providing such feedback in programming exercises is not a humanly feasible task. Therefore, the early and steadily growing interest of computer science educators in the automated assessment of programming exercises is not surprising. The automated assessment of programming assignments has been an active area of research for over a century, and interest in it continues to grow as it adapts to new developments in computer science and the resulting changes in educational requirements. It is therefore of paramount importance to understand the work that has been performed, who has performed it, its evolution over time, the relationships between publications, its hot topics, and open problems, among others. This paper presents a bibliometric study of the field, with a particular focus on the issue of automatic feedback generation, using literature data from the Web of Science Core Collection. It includes a descriptive analysis using various bibliometric measures and data visualizations on authors, affiliations, citations, and topics. In addition, we performed a complementary analysis focusing only on the subset of publications on the specific topic of automatic feedback generation. The results are highlighted and discussed.

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