Abstract
Sectorization is the division of a large area, territory or network into smaller parts considering one or more objectives. Dynamic sectorization deals with situations where it is convenient to discretize the time horizon in a certain number of periods. The decisions will not be isolated, and they will consider the past. The application areas are diverse and increasing due to uncertain times. This work proposes a conceptualization of dynamic sectorization and applies it to a distribution problem with variable demand. Furthermore, Genetic Algorithm is used to obtain solutions for the problem since it has several criteria; Analytical Hierarchy Process is used for the weighting procedure. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

Abstract
Echocardiography is the most frequently used imaging modality in cardiology. However, its acquisition is affected by inter-observer variability and largely dependent on the operator's experience. In this context, artificial intelligence techniques could reduce these variabilities and provide a user independent system. In recent years, machine learning (ML) algorithms have been used in echocardiography to automate echocardiographic acquisition. This review focuses on the state-of-the-art studies that use ML to automate tasks regarding the acquisition of echocardiograms, including quality assessment (QA), recognition of cardiac views and assisted probe guidance during the scanning process. The results indicate that performance of automated acquisition was overall good, but most studies lack variability in their datasets. From our comprehensive review, we believe automated acquisition has the potential not only to improve accuracy of diagnosis, but also help novice operators build expertise and facilitate point of care healthcare in medically underserved areas.

Abstract
Digital twin is one promising and key technology that emerged with Industry 4.0 to assist the decision-making process in multiple industries, enabling potential benefits such as reducing costs, and risk, improving efficiency, and supporting decision-making. Despite these, the decision-making approach of carrying out a what-if simulation study using digital twin models of each and every possible scenario independently is time-consuming and requires significant computational resources. The integration of recommendation systems within the digital twindriven decision-support framework can support the decision-making process by providing targeted scenario recommendations, reducing the decision-making time and imposing decision- making efficiency. However, recommendation systems have inherent challenges, such as cold-start, data sparsity, and prediction accuracy. The integration of trust and similarity measures with recommendation systems alleviates the challenges mentioned earlier, and the integration of machine learning techniques enables better recommendations through their ability to simulate human learning. Having this in mind, this paper proposes a trust-based recommendation approach using a reinforcement learning technique combined with similarity measures, which can be integrated within a digital twin-based what-if simulation decision-support system. This approach was experimentally validated by performing accurate recommendations in an industrial case study of a battery pack assembly line. The results show improvements in the proposed model regarding the accuracy of the prediction about the user rating of the recommended scenarios over the state-of-the-art recommendation approaches, particularly in coldstart and data sparsity scenarios.

Abstract
Volcanic sites on Earth provide valuable insights into the geological processes that shape our planet and can also serve as effective analogs for studying similar volcanic activity on other celestial bodies, such as the Moon. This work aims to discuss the general characterization of the Capelinhos volcanic site, in the archipelago of Azores in Portugal, showing the potential as a planetary analog. It's barren landscape, covered with pyroclastic rocks can lend itself the purpose of becoming a lunar planetary analog site, possibly even a Martian site. This geological site was formed during an eruption that occurred in 1957-58, thus the vegetation is practically absent. By examining the physical and chemical properties of its pyroclastic rocks, as well as the associated volcanic landforms, researchers of different fields can better understand lunar volcanic activity and its implications for many aspects of future lunar exploration. Although its origin is different from most of the locations on the lunar surface, since it doesn't contemplate the impactism originated regolith characteristics and associated geomorphology, it does resemble this setting for a broad range of research objectives. © 2023 International Multidisciplinary Scientific Geoconference. All rights reserved.

Abstract
Electric vehicle (EV) sales and shared mobility are increasing worldwide. Despite its challenges, e-carsharing has an opportunity to still profit in periods of low rental demand compared to traditional carsharing. The purpose of this paper is to assess the profitability of an e-carsharing company based on distribution local marginal price (DLMP) and vehicle-to-grid (V2G) that cooperates with the distribution system operator (DSO) through a two -stage stochastic model. The AC optimal power flow (ACOPF) is modeled using second-order cone program-ming (SOCP) linearized by the global polyhedral approximation. The IEEE 33 bus test system and a real Kernel distribution for the EV rental demands are used in four planning cases in the GAMS environment. The results indicate that the proposed methodology does not affect EV user satisfaction. Moreover, the planning disregarding the power grid perspective is the most profitable, but the operation may not be possible in real applications due to the high-power flows via V2G. Finally, the e-carsharing planning considering the DSO perspective increased the charging cost by 1.66 % but also reduced the DLMP peak, losses, and peak demand by 2.5 %, 1.5 %, and 5.1 %, respectively. One important conclusion is that the technical benefits brought to the DSO by the e-carsharing company could be turned into services and advantages for both agents, increasing profit and mitigating negative impacts, such as higher operational costs.

Abstract
Digital transformation plays a significant role in modernizing and improving the efficiency of ports around the world. However, digitalization also brings a set of challenges that ports must face. They have to respond to several unique challenges because of the complexity of their operations and the varying demands of stakeholders. This study seeks to identify and summarize the challenges of digital transformation processes in ports. For this purpose, the World Ports Sustainability Program database was used. The findings revealed 74 digitalization initiatives carried out by ports, which makes it possible to recognize 7 dimensions and 32 sub-dimensions of challenges to the digital transformation process. Among the identified dimensions are port infrastructure, the interconnection between various systems, the port organization model, regulation, security and privacy, market evolution, and the establishment of partnerships to implement these projects. The results of this study are relevant to mitigate the risks of the digitalization process in ports and respond to market needs that demand greater transparency and visibility of their operations.