Abstract
The Iliad Digital Twins of the Ocean is a European Green Deal Project which aims at the development of an architecture and set of components, tools and services for the creation of digital twins of the ocean. The approach aims to support the emerging European Digital Twins of the Ocean (EDITO) initative with associated projects like EDITO Infra and EDITO Model lab and the overall Destination Earth (DestinE) initiative and also taking advantage of the evolving European Common Data Spaces including the Green Deal Data Space, the Copernicus Data Space and the EOSC cross domain Data Space. The paper presents the final version of the Iliad digital twin interoperability architecture based on four steps of a digital twin pipeline from Data Acquisition/Collection to Digital Twin Data Representation to Digital Twin Hybrid and Cognitive/AI Analytics Models and further to Digital Twin Visualisation and Control, which are presented together with associated Digital twin components and services.

Abstract
The Federal Institutes of Education, Science and Technology, created by Law No. 11,892 of December 2008 [1], offer a multicurricular and multi-campus education specialized in professional and technological education. Of the 679 campuses of Brazilian Federal Institutes (FI), all adopt the Integrator Project (IP) or similar, as a component of the training curriculum for the promotion and development of scientific initiation. Aimed at promoting interdisciplinarity, development of professional skills and potential for innovation combined with the theoretical knowledge acquired in the classroom. At IFRS (Instituto Federal de Educacao Ciencia e Tecnologia) - Ibiruba Campus, the classes of the Computer Technician Integrated to High School participate in Integrative Projects that cover different themes, multidisciplinary teams and diverse curricular components [2]. Combining PI with pedagogical practice, research, teaching and extension is fundamental. However, it is necessary to develop computational tools that help teachers and students for their development. In order to delimit a coherent path and clarify controversies, the first step is to perform a Systematic Review of Literature (RSL). The research presented results capable of guiding the necessary requirements for the development of a tool that establishes an effective interaction of the student to assist the creation of Integrating Projects.

Abstract
Reconfigurable Intelligent Surfaces (RISs) are expected to play a pivotal role in future indoor ultra high data rate wireless communications as well as highly accurate three-dimensional localization and sensing, mainly due to their capability to provide flexible, cost- and power-efficient coverage extension, even under blockage conditions. However, when considering beyond millimeter wave frequencies where there exists GHz-level available bandwidth, realistic models of indoor RIS-parameterized channels verified by field-trial measurements are unavailable. In this article, we first present and characterize three RIS prototypes with unit cells of half-wavelength intercell spacing, which were optimized to offer a specific nonspecular reflection with 1-, 2-, and 3-bit phase quantization at 304 GHz. The designed static RISs were considered in an indoor channel measurement campaign carried out with a 304 GHz channel sounder. Channel measurements for two setups, one focusing on the transmitter-RIS-receiver path gain and the other on the angular spread of multipath components, are presented and compared with both state-of-the-art theoretical models as well as full-wave simulation results. The article is concluded with a list of challenges and research directions for RIS design and modeling of RIS-parameterized channels at THz frequencies.

Abstract
Deep learning models are widely used in multivariate time series forecasting, yet, they have high computational costs. One way to reduce this cost is by reducing data dimensionality, which involves removing unimportant or low importance information with the proper method. This work presents a study on an explainability feature selection framework composed of four methods (IMV-LSTM Tensor, LIME-LSTM, Average SHAP-LSTM, and Instance SHAP-LSTM) aimed at using the LSTM black-box model complexity to its favour, with the end goal of improving the error metrics and reducing the computational cost on a forecast task. To test the framework, three datasets with a total of 101 multivariate time series were used, with the explainability methods outperforming the baseline methods in most of the data, be it in error metrics or computation time for the LSTM model training.

Abstract
This position paper emphasizes the critical role of professional training in facilitating the effective adoption of Generative AI (GenAI) in the corporate world. GenAI, with its ability to create new content from existing data, holds immense potential for transforming business processes, enhancing decision-making, and driving innovation. However, the adoption of GenAI faces significant challenges, including a shortage of skilled professionals, high implementation costs, data privacy concerns, and the complexity of integrating these technologies into existing systems. To address these challenges, this paper highlights the importance of comprehensive education and training programs tailored to equip employees with the necessary skills and knowledge. Such programs should focus on developing technical competencies and understanding the operational implications of GenAI. By analyzing current literature and case studies, this paper identifies key strategies for effective training and outlines best practices for integrating GenAI into corporate environments. The findings underscore the need for a strategic approach to training that aligns with the evolving demands of AI-driven innovation. This includes continuous learning and development initiatives, the promotion of a culture of innovation, and the implementation of responsible AI practices. By investing in professional training, organizations can bridge the skills gap, mitigate risks, and fully leverage the transformative potential of GenAI technologies, ultimately gaining a competitive edge in the market. Through this comprehensive exploration, the paper advocates for the integration of robust training frameworks that support the sustainable adoption of GenAI, ensuring that businesses are well-prepared to navigate the complexities and opportunities of the digital age.

Abstract
PurposeThis study explores the role of ports in achieving the United Nations Sustainable Development Goals (SDGs), focusing on the interconnection between key SDGs. It aims to characterize how port sustainability initiatives address multiple SDGs to increase their impact.Design/methodology/approachIt examines through mixed methods research how ports align their development projects with these SDGs, analyzing the policy implications of integrating economic, environmental, and social objectives. It considers 401 projects from the World Ports Sustainability Program (WPSP).FindingsThe findings indicate the most achieved SDGs and reveal six common themes that ports follow to address multiple SDGs. These characteristics include environmental sustainability, energy transition, social inclusion, digitalization, governance and ethical practices, and innovation and collaboration.Originality/valueThis study brings original contributions on the SDGs addressed by ports, considering not only the individual SDGs addressed by the sustainability initiatives promoted by ports, but also the joint effects of addressing multiple SDGs. This study also emphasizes the need for supporting these initiatives in international collaboration, green technologies, and climate resilience.