Abstract
Objective: This study investigates the applicability of systematic methods in the identification and evaluation of studies on sustainable urban mobility, providing subsidies to guide managers and policymakers in the development of efficient and environmentally responsible public policies.   Method: The methodology adopted for this research comprises a Systematic Literature Review (SLR) associated with the Fuzzy-TOPSIS method, a multi-criteria model capable of evaluating and prioritizing studies considering the imprecision inherent in decision-making processes. The PICO technique was used to define the analysis criteria, and the PRISMA protocol ensured the transparency and replicability of the results. Six criteria were established in the qualitative analyses for treatment in the Fuzzy-TOPSIS method.   Results and Discussion: The proposed approach proved effective in selecting the most relevant studies. The discussion points to the need to integrate Fuzzy-TOPSIS with complementary methods, such as DEMATEL and Social Network Analysis (SNA), in order to improve the modeling of causal relationships and strengthen the reliability of prioritization.   Research Implications: The results offer important insights for urban planning and the formulation of public policies, contributing to energy efficiency, reducing GHG emissions and improving the quality of public transport.   Originality/Value: The innovation of this study lies in the combination of quantitative and qualitative approaches to the analysis of sustainable mobility, providing a robust benchmark that can positively influence practices and strategies in urban management.

Abstract
This chapter proposes a day-ahead scheduling framework in an energy hub (EH), integrating different energy conversion and storage technologies to efficaciously fulfill various types of load demands. The mentioned EH is capable of synchronously managing electrical, cooling, and heat load demands. The system is equipped with a combined heat and power (CHP) generating unit that efficiently supplies both heat and electricity. Furthermore, there are an electric heat pump and a boiler that also supply the heating load, while the heater is specifically employed for direct heating usage. The system includes an absorption chiller to supply a cooling load. This chiller absorbs waste heat from the CHP unit, resulting in improved energy efficiency. Battery storage systems enable the efficient use of energy by storing surplus power during times of low demand for future consumption. In addition, solar photovoltaic panels are included to capture renewable energy, therefore decreasing reliance on traditional energy sources and mitigating environmental consequences. The EH also includes a saltwater desalination technology operating together with the energy network to ensure the supply of freshwater, which is especially vital in dry areas. The desalination process is fueled by both renewable and produced thermal energy, thus maximizing resource use and reducing operating costs. The presented scheduling model has been formulated within a mixed-integer linear programming framework, implemented in GAMS, and solved by using the CPLEX solver to ensure optimal operation and minimum computational burden. This chapter provides a broad guideline of how the integrated systems operate. © 2025 Elsevier B.V., All rights reserved.

Abstract
This paper presents a fast and accurate optimization technique for optimal power flow (OPF) that can be conveniently applied to transmission and distribution systems. The method is based on the branch flow and DC optimal power flow (DCOPF) models. As the branch flow model is independent of the bus voltage angle, the model needs further development to enable use in meshed transmission systems. Thus, this paper adds the bus voltage angle constraint as a key constraint to the branch flow model so that the voltage angle can also be used in the power flow model in addition to the voltage magnitude control. The problem is based on second-order programming and modeled as a quadratically-constrained programming (QCP) problem solved using the CPLEX solver in GAMS. The functionality of the proposed model is tested utilizing four standard distribution systems, three transmission systems, a combined transmission-distribution network. The studied distribution systems include the 33-bus, 69-bus, 118-bus distribution (118-D) test systems, and 730-bus distribution system (730-D). Additionally, the studied transmission systems include 9-bus, 30-bus, and 118-bus transmission (118-T) test systems. The combined transmission-distribution system included the 9-bus transmission system with three connected distribution systems. The simulation results obtained from the developed technique are compared to those obtained from a conventional optimal flow model. The power losses and the absolute error of the solution are used as the two metrics to compare the methods' performance for distribution networks. The absolute error of the solution derived from the proposed hybrid OPF compared to MATPOWER for the 33-bus system is 0.00198 %. For the 69-bus system, the error is 0.00044 %. In addition, for the 118-D and 730-D systems, the absolute errors are 0.0026 %, and 0.05 %, respectively. For the transmission network, the operating costs and the solution absolute error are the two metrics used for comparing the proposed hybrid OPF model and MATPOWER. The results indicate the superior performance of the hybrid OPF model to the Newton-Raphson method in MATPOWER in terms of operating cost. In this regard, cost reductions relative to values given by MATPOWER are 0.0005 %, 0.838 %, and 0.015 %, for the 9-bus, 30-bus, and 118-T systems, respectively. The simulation studies demonstrate the performance of the presented branch flow-based model in solving the OPF problem with accurate results.

Abstract
This work presents a bio-inspired Autonomous Underwater Vehicle (AUV) concept called Raya that enables high manoeuvrability required for close-range inspection and intervention tasks, while fostering endurance for long-range operations by enabling efficient navigation. The AUV has an estimated terminal velocity of 0.82 m/s in an optimal environment, and a capacity to acquire visual data and sonar measurements in all directions. Raya was designed with the potential to incorporate an electric manipulator arm of 6 degrees of freedom (DoF) for free-floating underwater intervention. Smart and biologically inspired principles applied to morphology and a strategic thruster configuration assure that Raya is capable of manoeuvring in all 6 DoFs even when equipped with a manipulator with a 5 kg payload. Extensive experiments were conducted using simulation tools and real-life environments to validate Raya's requirements and functionalities. The stresses and displacements of the rigid bodies were analysed using finite element analysis (FEA), and an estimation of the terminal forward velocity was achieved using a dynamic model. To assess the accuracy of the perception system, a reconstruction task took place in an indoor pool, resulting in a 3D reconstruction with average length, width, and depth errors below 1. 5%. The deployment of Raya in the ATLANTIS Coastal Testbed and Porto de Leix & otilde;es allowed the validation of the propulsion system and the gathering of valuable 2D and 3D data, thus proving the suitability of the vehicle for operation and maintenance (O&M) activities of underwater structures.

Abstract
The increasing penetration of electric vehicles will be accompanied for a wide deployment of charging infrastructures. Large charging demand brings formidable challenges to existing power networks, driving them near to their operational limits. In this regard, it becomes pivotal developing novel energy management strategies for active distribution networks that take into account the strategic behaviour of parking lots. This paper focuses on this issue, developing a novel energy management tool for distribution networks encompassing distributed generators and parking lots. The new proposal casts as a tri-level game equilibrium framework where the profit maximization of lots is implicitly considered, thus ensuring that network-level decisions do not detract the profit of parking owners. The original tri-level model is reduced into a tractable single-level mixed-integer-linear programming by combining equivalent primal-dual and first-order optimality conditions of the distribution network and parking operational models. This way, the model can be solved using off-the-shelf solvers, with superiority against other approaches like metaheuristics. The developed model is validated in well-known 33-, and 85-bus radial distribution systems. Results show that, even under unfavourable conditions with limited distributed generation, charging demand is maximized, thus preserving the interests of parking owners. Moreover, the model is further validated through a number of simulations, showing its effectiveness. Finally, it is demonstrated that the developed tool scales well with the size of the system, easing its implementation in real-life applications.

Abstract
This paper examines a series of bachelor's and master's thesis projects from the supervisor's perspective, focusing on how Augmented Reality (AR) and Mixed Reality (MR) can enhance industrial robotics engineering education. While industrial robotics systems continue to evolve and the need for skilled robotics engineers grows, teaching methods have not changed. Mostly, higher education in robotics engineering still relies on funding industrial robots or otherwise on traditional 2D tools that do not effectively represent the complex spatial interactions involved in robotics. This study presents a comparative analysis of seven thesis projects integrating MR technologies to address these challenges. All projects were supervised by the lead author and showcase different approaches and learning outcomes, building on insights from previous work. This comparison outlines the benefits and challenges of using MR for robotics engineering education. Additionally, it shares key takeaways from a supervisory standpoint as an evolutionary process, offering practical insights for fellow educators/supervisors guiding MR-based robotics education projects.