Abstract

Abstract
Planning consists of thinking about the future and allows territories to be better prepared to take advantage of opportunities and face challenges that arise. In Portugal, tourism is one of the pillars of the economy, generating wealth and creating various job openings. In recent years, this destination has won several international awards and distinctions due to the quality of services and tourism offerings. Part of this success is due to the planning carried out by the entity responsible, Turismo de Portugal. This study aims to analyse the content and structure of national tourism plans implemented in Portugal since 2000. Furthermore, we want to understand: (1) the vision outlined for the Portuguese territory and the changes it has undergone in recent decades; (2) the methodologies that were used in the formation process of these plans, for instance, if public auscultation was used; (3) the main objectives defined for the territory and which were the actions that have been defined to achieve them. A qualitative methodology of document analysis was used, combined with the presentation of a case study related to tourism planning at a national level. The results show the growing importance of the tourism sector for the Portuguese economy. Since 2020, the growing involvement of stakeholders in the construction of strategic plans has also been evident through public consultation and an emphasis on sustainability practices in the tourism sector.

Abstract

Abstract
Measuring gas and liquid flow rate is paramount in various scientific and industrial applications. This work presents an optical fiber flowmeter based on a graphene oxide (GO) coated Michelson interferometer. The interferometer is fabricated using a long-period fiber grating (LPFG) followed by a GO-coated single-mode fiber (SMF). By radiating the GO coating, it experiences photothermic effect that induces local heating of the film. This results in a variation in the effective refractive index in the cladding modes, which induces a phase shift on the interferometer spectrum. When a gas flow is introduced near the coated fiber, the hot-wire region will experience a reduction in temperature proportional to the flow rate. The flowmeter exhibited a linear wavelength shift to the flow rate with an absolute sensitivity of 17.4 +/- 0.8 pm/(L.min-1) for gas flow rates between 2 and 8 L/ min. Furthermore, the dynamic response of the sensor was studied, attaining a maximum response time of 1.1 +/- 0.4 s

Abstract
Energy storage systems, integrated in Renewable Energy Communities (REC), are enabling the development of operation strategies together with Photovoltaic (PV) systems. Additionally, Local Energy Markets (LEM) are emerging mechanisms to enable local energy trading in RECs, the integration of storage systems can increase the community energy savings and profits. In this context, a market environment was modelled as a Markov Decision Process (MDP). In this scope, an Agent Based Model (ABM) using the Q-Learning mechanism was used to implement and to simulate a LEM and its interaction with the Wholesale Market (WSM), also considering an architecture with storage systems. The developed model was tested considering real data regarding energy consumption and PV generation. The paper describes and discusses the obtained market strategy and the profits that can be obtained with this approach.

Abstract
Cloud computing is a modern technology that has become popular today. A large number of requests has made it essential to propose a resources allocation framework for arriving requests. The network can be made more efficient and less costly this way. The cloud-edge paradigm has been considered a growing research area in the computing industry in recent years. The increase in the number of customers and requests for cloud data centers (CDCs) has created the need for robust servers and low power consumption mechanisms. Ways to reduce energy in the CDC having appropriate algorithms for resource allocation. The purpose of this study was to develop an intelligent method for dynamic resource allocation using Takagi-Sugeno-Kang (TSK) neural-fuzzy systems and ant colony optimization (ACO) techniques to reduce energy consumption by optimizing resource allocation in cloud networks. It predicts future loads using a drop-down window to track CPU usage. By optimizing virtual machine migration, ACO can reduce energy consumption. Simulations are provided by examining the implementation and a variety of parameters such as the number of requests made wasted resources, and requests rejected. In this paper, we propose the use of virtual machine migration to accomplish two main goals: evacuating additional and non-optimal virtual machines (scaling and shutting down additional active physical machines) and solving the resource granulation problem. We evaluated and compared our results with literature for rejection rates of virtual and physical machine applications. The performances of our algorithms are compared to different criteria such as performance in request rejection, dynamic CPU resource allocation with reinforcement learning, multi-objective resource allocation, NSGAIII, Whale optimization and Forecast Particle Swarm allocation. A comparison of some evaluation criteria showed that the proposed method is more efficient than other methods.