Abstract
Recently, deep learning has proven to be a successful technique especially in medical image analysis. This paper aims to highlight the importance of deep learning architectures in lung disease diagnosis using CXR images. Related articles were identified through searches of electronic resources, including IEEE, Springer, Elsevier, PubMed, Nature and, Hindawi digital library. The inclusion of articles was based on high-performance artificial intelligence models, developed for the classification of possible findings in CXR images published from 2018 to 2023.After the quality assessment of papers, 129 articles were included according to PRISMA guidelines. Papers were studied by types of lung disease, data source, algorithm type, and outcome metrics. Three main categories of computer-aided lung disease detection were covered: traditional machine learning, deep learning-based methods, and combination of aforementioned methods for all lung diseases.The results showed that various pre-trained networks including ResNet, VGG, and DenseNet, are the most frequently used CNN architectures and would result in a notable increase in sensitivity and accuracy. Recent research suggests that utilizing a combination of deep networks with a robust machine learning classifier can outperform deep learning approaches that rely solely on fully connected neural networks as their classifier. Finally, the limitations of the existing literature and potential future research opportunities in possible findings in CXR images using deep learning architectures are discussed in this systematic review.

Abstract
PurposeWhile academics and practitioners increasingly recognize the impacts of gamification on customer experience (CX), its role in the customer journey remains undeveloped. This article aims to identify how gamification can leverage each customer journey stage, integrate the findings into a conceptual model and propose future research opportunities.Design/methodology/approachSince CX and customer journey are interrelated concepts, the authors rely on CX research to identify research themes that provide insights to propose the conceptual model. A systematic review of 154 articles on the interplay between gamification and CX research published from 2013 to 2022 was performed and analyzed by thematic content analysis. The authors interpreted the results according to the service customer journey stages and the taxonomy of digital engagement practices.FindingsThis article identified five main thematic categories that shape the conceptual model (design, customer journey stages, customer, technology and context). Gamification design can support customer value creation at any customer journey stage. While gamification can leverage brand engagement at the pre-service stage by enhancing customer motivation and information search, it can leverage service and brand engagement at the core and post-service stages by enhancing customer participation and brand relationships. Moreover, customer-, technology- and context-related factors influence the gamified service experience in the customer journey.Originality/valueThis article contributes to a conceptual integration between gamification and customer journey. Additionally, it provides opportunities for future research from a customer journey perspective.

Abstract
This paper introduces a mathematical model designed to optimise the operation of natural gas distribution networks, considering the injection of hydrogen in multiple nodes. This proposal is devised to optimise the quantity of hydrogen injected to maintain pressure, gas flows, and gas quality indexes (Wobbe Index (WI) and the Higher Heating Value (HHV)) within admissible limits. The model has been applied to a gas network case study with three distinct scenarios and implemented using Python. The findings from the case study show the maximum permissible volume of hydrogen in the network, quantify the total savings in natural gas, and estimate the reduction in carbon dioxide emissions. © 2023 IEEE.

Abstract
In this work, an optical fiber flowmeter based on a Michelson interferometer is presented. The Michelson interferometer uses a long period fiber grating (LPFG) to couple light to the cladding modes followed by a section of a GO-coated single mode fiber (SMF). By radiating the GO thin film, it will increase its temperature changing the effective refractive index of the optical cavity of the Michelson interferometer. By placing the sensor on a gas flow, its temperature surface will decrease in a proportional manner to the flow rate. The sensor was studied in both static and dynamic dry nitrogen flow, attaining an absolute sensitivity of 17.4 ± 0.8 pm/(L.min-1) and a maximum response time of 1.1 ± 0.4 s.

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
Distribution networks are vulnerable to natural hazards which can cause major social and economic consequences. Identifying vulnerable areas and developing operational strategies, such as dispatching mobile energy systems, can help mitigate the effects of extreme events. Conventional approaches, mainly N-1/N-2 contingency security analysis, are efficient but they do not fully provide a comprehensive picture of the stochastic nature of the hazard impact. Stochastic approaches are more accurate but in general they are computationally expensive and hence not practical for the resilient operational decision-making of distribution system operators. Therefore, this paper develops a novel framework based on an adjacency-resource matrix (ARM) and an unsupervised machine learning algorithm to first identify vulnerable nodes. Next, these vulnerable nodes are utilized in the mitigation stage in order to minimize the expected energy not served (EENS) against a natural hazard. The efficiency of the proposed framework is tested on a 125-node Portuguese distribution system.