Abstract
Climate change has led to the need to transition to clean technologies, which depend on an number of critical metals. These metals, such as nickel, lithium, and manganese, are essential for developing batteries. However, the scarcity of these elements and the risks of disruptions to their supply chain have increased interest in exploiting resources on the deep seabed, particularly polymetallic nodules. As the identification of these nodules must be efficient to minimize disturbance to the marine ecosystem, deep learning techniques have emerged as a potential solution. Traditional deep learning methods are based on the use of convolutional layers to extract features, while recent architectures, such as transformer-based architectures, use self-attention mechanisms to obtain global context. This paper evaluates the performance of representative models from both categories across three tasks: detection, object segmentation, and semantic segmentation. The initial results suggest that transformer-based methods perform better in most evaluation metrics, but at the cost of higher computational resources. Furthermore, recent versions of You Only Look Once (YOLO) have obtained competitive results in terms of mean average precision.

Abstract

Abstract
In recent years, non-residential buildings have increasingly adopted renewable energy generation systems to align with the European Union's goal of achieving carbon neutrality by 2050. However, energy storage systems playa fundamental role in maximising the use of the generated renewable energy. Due to their high acquisition costs, adequately sizing these systems is essential. Moreover, applying an optimal scheduling strategy for energy storage operation can significantly improve the economic viability of such systems by reducing energy-related costs. In this paper, a MILP-based optimisation algorithm-incorporating battery lifespan constraints-is applied to a reference commercial building to schedule the operation of the storage system. A sensitivity analysis on the installed photovoltaic power and energy storage capacity is performed to evaluate their impact on the economic and operational performance of the optimisation algorithm under different sizing configurations. © 2025 Elsevier B.V., All rights reserved.

Abstract
Digital transformation has been one of the main trends in organizations in recent years, and digital literacy is a critical factor in the success of this transformation. Digital transformation involves the use of digital technologies to improve an organization’s processes, products, and services. For this transformation to be successful, it is necessary for employees to have knowledge of and skills in digital technologies. Digital literacy allows employees to understand technologies and their applications, know how to use them efficiently and safely, evaluate and select the most appropriate digital tools for each task, and be prepared to deal with problems and challenges that arise in the digital environment. This study investigates the relationship between digital transformation and digital literacy through a Systematic Literature Review conducted in accordance with Kitchenham’s guidelines. A total of 54 articles, published from 2018, were analyzed from databases such as Scopus, Science Direct, IEEE and Springer. The results reveal that digital literacy significantly influences the success of digital transformation, particularly in areas such as employee adaptability, innovation capacity, and digital tool integration. Key mediating and moderating factors identified include organizational learning culture, leadership support, ongoing training programs, and technological infrastructure. Based on these findings, an explanatory model was developed that maps the interaction between these variables and their impact on digital transformation outcomes. The study offers practical implications for organizations seeking to enhance their digital maturity: investing in employee digital literacy development, aligning leadership strategies with digital initiatives, and fostering a supportive culture for digital adoption are crucial steps. Thus, this study is relevant because it seeks to understand how digital literacy can impact Digital Transformation in organizations and, through the construction of an explanatory model, allows the identification of variables that influence this relationship by developing strategies to improve the digital literacy of employees in organizations. © 2025 Elsevier B.V., All rights reserved.

Abstract
Recent studies have focused on forecasting power demand in buildings to enhance energy management. However, the literature still lacks comparative analyses of power demand forecasting algorithms. In addition, more case studies involving different building typologies are needed, as each building exhibits distinct behavior and load profiles. This paper aims to develop machine learning models to forecast the power demand of a large shopping center in the northern region of Portugal. The main objective is to compare the performance of several machine learning models. The results are promising, demonstrating adequate performance even during most holidays.

Abstract
As different eBPF libraries keep emerging, developers are left with the hard task of choosing the right one. Until now, this choice has been based on functional requirements (e.g., programming language support, development workflow), while quantitative metrics have been left out of the equation. In this paper, we argue that efficiency metrics such as performance, resource usage, and data collection fidelity also need to be considered for making an informed decision. We show it through an experimental study comparing five popular libraries: bpftrace, BCC, libbpf, ebpf-go, and Aya. For each, we implement three representative eBPF-based tools and evaluate them under different storage I/O workloads. Our results show that each library has its own strengths and weaknesses, as their specific features lead to distinct trade-offs across the selected efficiency metrics. These results further motivate experimental studies to increase the community's understanding of the eBPF ecosystem. © 2025 Elsevier B.V., All rights reserved.