Abstract
Spectral imaging is a technique that captures spectral information from a scene and maps it onto a 2D image, featuring the potential to reveal hidden features and properties of objects that are invisible to the human eye, such as elemental and molecular compositions. Augmented reality (AR), on the other hand, is a technology that enhances the perception of reality by superimposing digital information on the physical world. While these technologies have different purposes, they can be considered one and the same in terms of providing an user-centric extension of reality. Spectral imaging provides the information that can reveal the underlying nature of objects, while AR provides the method of visualization that can display the information in an intuitive and interactive way. In this work, we present a novel Unity toolkit that combines spectral imaging and a HoloLens 2 AR device to create an interactive and immersive experience for the user. The toolkit enables the interactive visualization of various elemental maps of a 3D rock model in AR using a simple and intuitive interface. With this technique, the user can select a sample model and an elemental map from a preloaded asset library and then see the map projected onto the rock model in AR, using simple interactions such as zoom adjustment, rotation, and pan of the models to explore features and properties in detail. The toolkit offers several advantages, including better contextual interpretation of the spectral data by placing it in relation to the shape and texture of the rock, increased user engagement and curiosity through the creation of a realistic and immersive experience, and ease of decision-making through the provision of comparative tools. In short, by combining spectral imaging and AR, we present an innovative approach that can enrich the user experience and expand the user knowledge of the environment.

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The aquaculture industry faces significant challenges related to sustainability, productivity, and fish welfare. Key issues include managing environmental conditions, disease, pests, and data integration from various sensors and monitoring systems. The BigFish project aims to address these challenges through advanced analytics and machine learning, focusing on three case studies in Atlantic salmon farms: predicting oxygen levels, reducing sea lice infestations, and improving data interaction and visualization. Predictive models for oxygen levels and sea lice infestation, as well as natural language interfaces for data visualization, demonstrate the potential for improved decision-making and management practices in aquaculture. Early results indicate the effectiveness of these approaches, highlighting the importance of data-driven solutions in enhancing industry sustainability and productivity. © 2024 IEEE.

Abstract
Organizations aim to achieve operational efficiency capable of responding to high market competitiveness. The implementation of automation systems in organizational processes is a key factor in improving operational efficiency. This paper intends to contribute for a better understanding of the adoption of automation systems in organizations and analyze their impact on employee performance, considering the conditions under which they were implemented. The methodology for this study was qualitative research, in which semi-structured exploratory interviews conducted with employees from the Accounts Receivable department of automotive sector companies were carried out. The main goal was to understand their perception of the use of automation systems in their work tasks. The results of this research led to the conclusion that automation systems, even when underutilized, are beneficial in reducing repetitive and manual tasks. Nevertheless, the way in which they are implemented has a direct impact on the motivation of employees to use them.

Abstract
Energy and materials are increasingly important in industrialized countries, and they impact the economy, sustainability, and people's future. The purpose of this work was to study the relationship between energy and the circular economy using methods such as Pearson's correlation and a principal component analysis. Thus, 12 strong correlations were found, with 5 of them between the following relevant variables from two different subjects: the correlations of the raw material consumption, the domestic material consumption, and the material import dependency with the final energy consumption in transport (0.81, 0.92, and 0.81); the correlation of the circular material use rate with the final energy consumption in households (0.70); and the correlation of the material import dependency with the final energy consumption in industry (0.89). The time series forecast was only conclusive for the waste generated, showing that it will increase in the next 10 years.