Abstract
The Multiple Traveling Salesman Problem (mTSP) is an interesting combinatorial optimization problem due to its numerous real-life applications. It is a problem where m salesmen visit a set of n cities so that each city is visited once. The primary purpose is to minimize the total distance traveled by all salesmen. This paper presents a hybrid approach called GABC-LS that combines an evolutionary algorithm with the swarm intelligence optimization ideas and a local search method. The proposed approach was tested on three instances and produced some better results than the best-known solutions reported in the literature.

Abstract
Over the years, methods and algorithms have been extensively studied to solve variations of the rectangular twodimensional strip packing problem (2D-SPP), in which small rectangles must be packed inside a larger object denominated as a strip, while minimizing the space necessary to pack all rectangles. In the rectangular 2D-SPP, constraints are used to restrict the packing process, satisfying physical and real-life practical conditions that can impact the material cutting. The objective of this paper is to present an extensive literature review covering scientific publications about the rectangular 2D-SPP constraints in order to provide a useful foundation to support new research works. A systematic literature review was conducted, and 223 articles were selected and analyzed. Real-life practical constraints concerning the rectangular 2D-SPP were classified into seven different groups. In addition, a bibliometric analysis of the rectangular 2D-SPP academic literature was developed. The most relevant authors, articles, and journals were discussed, and an analysis made concerning the basic constraints (orientation and guillotine cutting) and the main solving methods for the rectangular 2D-SPP. Overall, the present paper indicates opportunities to address real-life practical constraints.

Abstract
Today's power grid is in a transitional stage to cater to the needs of energy efficiency, climate change, and environmental targets. In the process of designing the future power grid, one of the most fundamental models to be utilized is AC optimal power flow (AC-OPF). Since the feasible space of AC-OPF is non-convex, the optimization models developed using it often result in multiple local minima. To avoid such computational challenges in solving optimization models, various relaxation methods have been developed in the past. In the literature, these relaxation methods are mainly tested on specific networks. However, the scalability of relaxation techniques on branch-flow-based AC-OPF is yet to be explored. In this context, this paper compares the performance of different relaxation methods with the well-established MATPOWER AC-OPF solver in terms of the mean square error (MSE), maximum squared error, minimum and maximum values of voltage magnitude, and the average simulation time. In addition, the scalability of these models is tested on various radial and mesh networks with nodes ranging from 33 to 6655 nodes and 9 to 6515 nodes, respectively. In this manner, the trade-off between computational complexity and solution accuracy is demonstrated and analyzed in depth. This provides an enhanced understanding of the suitability and efficiency of the compared relaxation methods, helping, in turn, the efficiency of optimization models for varying sizes and types (i.e., radial or meshed) of networks.

Abstract
This paper deals with an optimal operation of a microgrid in the electricity market and presents the communication between the distribution market operator and microgrid operator. The distribution market operator controls and manages the electricity market established in the distribution level, determining the amount of both electricity price and power exchange between market participants. The microgrid operator is able to purchase active and reactive power from the local distribution market. An effective short-term scheduling of the microgrid is implemented to ensure optimal operation. A risk based stochastic model is used to model the prevailing uncertainties such as loads, wind generation, and main-grid availability in a market-based operation framework. Moreover, in this model, a linearized AC power flow is added to the mathematical formulations to offer a comprehensive solution to the security-constraint operation of the microgrid. The stochastic operation strategy is formulated as a mixed integer linear programming problem. Regarding the uncertainty modeling, the substation equipment failure is modeled with Monte-Carlo algorithm. The effectiveness of the risk-based stochastic method is demonstrated using a microgrid test bed in the presence of demand response resources, dispatchable and wind generation units as well as energy storage system. The results demonstrate demand response program can significantly reduce the operation cost in worst scenarios. Also, it is indicated that the risk averse decisions reduce the risk of experiencing costly scenario. The proposed framework incorporating the distribution market constraints reduces the uncertainty in real-time operation as it can specified the required energy before running the problem. The deviations from assigned energy to the microgrid are penalized through the distribution market operator.

Abstract
Engineering aims to improve the quality of daily life on a planet with limited resources. This chapter describes how the European Project Semester offered by the School of Engineering of the Porto Polytechnic contributes to make engineering undergraduates conscious of their environmental and societal impact. The program is a project-based active learning framework where problem-solving is supported by intercultural communication and creativity, ethical and sustainable reasoning, and global contextual analysis. This analysis shows that it fosters scientific, technical, and interpersonal competencies, emphasizing ethical and sustainable design as recommended by several reference institutions through the testimonials left on the project reports and the evolution perceived by the participants during the semester. These findings are supported by a sample of 45 projects conducted by 228 students from 23 countries and 65 degrees. All projects aimed to have a lasting impact on the future engineering practice of participants and, somehow, transform the world positively.

Abstract
The Portuguese General Directorate for Book, Archives and Libraries (DGLAB) has selected CIDOC CRM as the basis for its next-generation digital archive management software. Given the ontological foundations of the Conceptual Reference Model (CRM), a graph database or a triplestore was seen as the best candidate to represent a CRM-based data model for the new software. We thus decided to compare several of these databases, based on their maturity, features, performance in standard tasks and, most importantly, the Object-Graph Mappers (OGM) available to interact with each database in an object-oriented way. Our conclusions are drawn not only from a systematic review of related works but from an experimental scenario. For our experiment, we designed a simple CRM-compliant graph designed to test the ability of each OGM/database combination to tackle the so-called diamond-problem in Object-Oriented Programming (OOP) to ensure that property instances follow domain and range constraints. Our results show that (1) ontological consistency enforcement in graph databases and triplestores is much harder to achieve than in a relational database, making them more suited to an analytical rather than a transactional role; (2) OGMs are still rather immature solutions; and (3) neomodel, an OGM for the Neo4j graph database, is the most mature solution in the study as it satisfies all requirements, although it is also the least performing.