Climate change will increase the frequency of drought, heat waves, and wildfires. We intended to analyse how fire recurrence and/or induced water stress can affect seed germination and root cell division in Pinus pinaster Aiton. Seeds from stands with no prior fire history and from post-fire regeneration (in areas burnt once, twice, and thrice) in northern Portugal were germinated in distilled water (control) and polyethylene glycol (PEG) to simulate water stress for four weeks, followed by a recovery period. Roots were analysed cytogenetically. The germination index of the Pinus pinaster seeds was not statistically influenced by the induction of osmotic stress, nor by the fire recurrence of the stands. The mean germination time (MGT) was 10–29 days and 1–36 days for the stress and recovery periods, respectively, and increased with PEG concentration. The 20% PEG treatment inhibited root growth after germination. The 10% PEG treatment induced a high frequency of cytogenetic anomalies, mostly in the sites which experienced fire exposure. While fire recurrence did not affect the germination rate, it seemed to reduce the water stress response, negatively impacting cell division and impair root growth

Computer networking is a central topic in computer science courses curricula offered by higher education institutions. Network virtualization and simulation tools, like GNS3, allows students and practitioners to test real world networking configuration scenarios and to configure complex network scenarios by configuring virtualized equipments, such as routers and switches, through each one's virtual console. The configuration of advanced network topics in GNS3 requires that students have to apply basic and very repetitive IP configuration tasks in all network equipments. As the network topology grows, so does the amount of network equipments to be configured, which may lead to logical configuration errors. In this paper we propose an extension for GNS3 network virtualizer, to automatically generate a valid configuration of all the network equipments in a GNS3 scenario. Our implementation is able to automatically produce an initial IP and routing configuration of all the Cisco virtual equipments by using the GNS3 specification files. We tested this extension against a set of networked scenarios which proved the robustness, readiness and speedup of the overall configuration tasks. In a learning environment, this feature may save time for all networking practitioners, both beginners or advanced, who aim to configure and test network topologies, since it automatically produces a valid and operational configuration for all the equipments designed in a GNS3 environment.

The advent of Industry 4.0 has created a need for more flexible and adaptable manufacturing systems. This paper proposes the integration of AAS (Asset Administration Shells), SBM (Skill-based manufacturing) and OPC UA (Open Platform Communications Unified Architecture), to enable more flexible manufacturing systems. The integration of these concepts provides a solution for achieving faster and easier dynamic reconfiguration in manufacturing systems, which is essential for fulfilling the demand of customization and flexibility in modern production systems. An Asset Administration Shell provides a standardized structure for describing assets and their administration, while Skill-based manufacturing enables the deployment of task-oriented machines that can self-configure, self-diagnose, and self-optimize their performance. The use of OPC UA as a communication protocol ensures that these systems can communicate with one another in a secure and reliable way. This paper presents a conceptual framework for the integration of these three open technologies. This framework contributes to having a single interface and source of information for every asset, which can lead to increased efficiency by reducing changeover times, thus reducing the overall cost in flexible manufacturing system scenarios. Future work will focus on the implementation and validation of this framework in a real-world manufacturing setting.

This paper addresses an approach to teaching embedded systems programming through a challenge-based competition involving robots. This pedagogical project distinguishes itself by incorporating international students from three international institutions through the Blended Intensive Program (BIP). The research findings indicate that this approach yields excellent results regarding student engagement and learning outcomes. The challenge-based program effectively promotes students’ creative problem-solving abilities by combining theoretical instruction with hands-on experience in a competitive setting.

Climate change affects global temperature and precipitation patterns that influence the intensity and, in some cases, the frequency of extreme environmental events, such as forest fires, hurricanes and storms. These events can be particularly conducive to the increase of plant pests and diseases, which causes significant production losses. So, the early detection of pests is of the main importance to reduce pest losses and implement more safe control management strategies protecting the crop, human health, and the environment (e.g., precision in the pesticide application). Nowadays, pests’ detection and prediction are mainly based on counting insects on attacked organs or in traps by experts, but this is a costly and time-consuming task for extensive and geographically dispersed olive groves. Machine learning algorithms, using image analysis, can be used for autonomous pests’ detection and counting. In the present practical work, YOLOv5 was chosen to detect and count the olive fly adults (Bactrocera oleae Rossi), a key pest of olives. YOLOv5s architecture of YOLO’s algorithm was used to test its efficiency in olive fly detection on a mobile deployment solution. The results obtained were quite satisfactory, and the experimental results obtained have been analyzed and presented, encompassing a set of metrics such as precision, recall, and the mean average precision. This study will be extended for other pests and disease detection in future work. Also, this solution will be integrated into a web-based information and management service (with sensors and e-traps) that remotely detect the presence and severity of pest attacks.

The adoption of effective policies and access to relevant information are critical to improving strategic management and performance monitoring in Higher Education Institutions (HEIs), which is essential to promote data-driven decision-making. This article describes how an HEI can carry out the validation process when implementing a Business Intelligence (BI) system, and provides a detailed guide to doing so. Through a case study at the University of Trás-os-Montes and Alto Douro, a structured roadmap is validated, which acts as a visual and sequential guide to facilitate the effective implementation of BI solutions. The validation carried out through semi-structured interviews with experts and evaluation of dashboards by user groups, not only confirms the applicability and efficiency of the proposed model but also emphasises its practical relevance, providing valuable insights for the adaptation and use of BI in different educational contexts.

The verification process is mandatory in the critical software realm. To improve this process, static analysis tools can make significant contributions. Static analysis meets a variety of goals, including error detection, security analysis, and program verification, which is why standards for critical software development recommend the use of static analysis to identify errors that are difficult to detect at run-time. Thus, this chapter presents a case study on the use of Frama-C as a static analyzer for formal verification of critical software and a lightweight semantic-extractor tool; the former uses an abstract interpretation technique, and the latter allows for the extraction of semantic information to provide a better understanding of source code. In practical terms, the chapter shows how Frama-C can support the development life cycle of an inertial system in aerospace applications, reporting a list of pros and cons. The final results indicate the benefits obtained in terms of software safety, software quality assurance and, consequently, the software verification process.

The exponential growth in digital data—from mobile devices, social media and IoT devices—has created an escalation of information travelling from the edge to the core layer. The core layer plays a crucial and central role in storage, analytics, and service delivery. Key Value Store (KVS) as RocksDB, Cassandra, among others, are widely used in the core layer due to their high throughput and low-latency solutions for managing and storing real-time data of large volumes of small-sized data. Recent efforts have been made to reduce the carbon footprint of data centres while improving their energy efficiency. However, due to the end of the Dennard scaling, reducing hardware energy consumption comes at the cost of worse performance. A shift to improving the energy efficiency of data centres through a software-centric approach is expected. As KVS support a significant share of services hosted in data centres, it is crucial to evaluate and improve their energy efficiency. Nonetheless, improvements in KVS have focused on performance and have neglected the energy impacts of their decisions. To the best of our knowledge, no existing KVS is solely designed with energy efficiency in mind, and there are no studies evaluating the energy profile of existing KVS. Evaluating the energy of these systems requires access to fine-grained energy monitoring tools, capable of capturing the energy of internal events. Notwithstanding, existing tools cannot capture energy at such fine granularity. To facilitate the development of energy-aware KVS, we present an in-depth experimental study that characterises the energy consumption patterns of RocksDB, a widely used LSM-Tree KVS. We also present PowMon, a fine-grain energy monitoring tool capable of monitoring energy at the application, thread, and event levels to support this study’s energy readings. The study shows that energy efficiency is highly dependent on workload, data distribution, and hardware properties, underscoring the need for profiling when choosing the most energy-efficient configuration setup. Furthermore, the insights in this work can be used by KVS designers to create a more energy-aware KVS and also enable them to compare energy efficiency results against a RocksDB baseline efficiency benchmark.

The role of digital transformation (DT) in economic development is a vital and recurring point of research. It is particularly relevant if we consider the high percentage of digital transformation initiatives that fail to deliver the expected results, particularly in Small and Medium Enterprises (SMEs). This paper analyzes what is needed to make this transformation successful from an implementation perspective and, simultaneously, from the standpoint of obtaining the company’s expected results. This phenomenon is even more critical to decipher and understand when we look at the small and medium enterprises that face more significant challenges due to the scarcity of resources and needed skills. This work reviews a large variety of models through an extensive systematic literature review (SLR) that assess the readiness and maturity of the digital transformation of enterprises, with a focus on SMEs, with its primary objectives being (1) to review the existing studies and models that assess an organization’s maturity and readiness in the context of digital transformation, focusing on SMEs; (2) to identify if there are gaps considering the importance of the SMEs; and (3) to propose a standardized set of dimensions that should always be considered in a digital transformation assessment. The outcome of this research provides an essential contribution by identifying apparent gaps in the assessment of digital transformation in SMEs and proposing a scalable and standardized set of categories and subcategories that can be used across any future assessment model. These contributions are even more relevant when referencing minimal deep research in the context of SMEs and Digital Transformation.

A área da Aprendizagem Profunda sofreu uma enorme expansão nos últimos anos, com modelos cada vez mais complexos e datasets cada vez maiores. Devido à crescente complexidade dos modelos, foi adotado o uso das GPUs para o seu treino, tornando este processo mais eficiente. No entanto, o crescimento progressivo do poder de processamento destes dispositivos causou um aumento do seu consumo energético. Para além da GPU, o processo de treino de um modelo de AP utiliza todos os outros recursos existentes num servidor: disco, RAM e CPU, podendo ainda recorrer à rede no caso de treino distribuído e nos casos em que o dataset está em armazenamento remoto. Quando o gargalo é qualquer outro recurso que não a GPU significa que o treino dos fragmentos neste recurso é mais rápido do que a reposição de novos fragmentos, ficando a GPU inutilizada até ao momento em que esse carregamento é concluído. Por padrão, a GPU opera à frequência máxima de forma a terminar o treino o mais rápido possível mas, nesta situação, acaba por ficar inativa. Por atuar a alta frequência apresenta um elevado consumo energético durante o treino, consumindo também energia durante o período de inutilização. Através do ajuste de frequência da GPU é possível diminuir o custo energético associado ao treino de modelos. No entanto, este ajuste deverá ter em conta as características da carga de trabalho por forma a não degradar o seu tempo de execução. De forma a resolver estes problemas, esta dissertação apresenta o WAFT, um novo sistema de otimização energética de GPUs especificamente concebido para treino de modelos de AP. Este sistema recorre ao ajuste dinâmico da frequência da GPU reconhecendo as várias fases de uma iteração. Adicionalmente, adapta o seu comportamento às característica do modelo e do hardware, de forma a diminuir o consumo energético sem prejudicar o tempo de execução. Através de uma avaliação experimental compreensiva, mostramos que o WAFT consegue diminuir o consumo energético até 42% em relação aos cenários usados em ambientes de produção. Ainda, quando comparado com soluções do estado da arte, o WAFT apresenta ganhos de poupança energética até 29%.