Browsing by Issue Date, starting with "2025-03"
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- Prospetiva 2035 - Três Cenários para o Futuro de Leiria e OestePublication . Silva, Agostinho da; Lopes, Carla; Almeida, Isabel; Carriço, Silvia; Mouga, Teresa; Carriço, Silvia; Siopa, Jorge; Gala, Pedro; Antunes, Mário; Silva, Agostinho; Mouga, Teresa; Lopes, Carla, Alexandra Calado Lopes; Gala, Pedro; Siopa, JorgeA EM@IPLeiria é um think tank criado em 2023 para impulsionar um desenvolvimento sustentável, inovador e competitivo na região de Leiria e Oeste. Mais do que um centro de estudos, é uma fábrica de ideias e soluções, dedicada à análise dos desafios estruturais do território, à identificação de novas oportunidades e ao teste de respostas concretas para problemas reais. Como espaço de cocriação e experimentação, a EM@IPLeiria envolve diversos atores regionais, incluindo autarquias, empresas, instituições de ensino e a sociedade civil, promovendo um modelo de trabalho colaborativo e participativo na construção de estratégias para o futuro. A sua abordagem alia design thinking e prospetiva estratégica, permitindo antecipar tendências, conceber cenários e testar soluções inovadoras antes da sua aplicação em larga escala.
- The JPEG Pleno Learning-Based Point Cloud Coding Standard: Serving Man and MachinePublication . Guarda, André; M. M. Rodrigues, Nuno; Pereira, FernandoEfficient point cloud coding has become increasingly critical for multiple applications such as virtual reality, autonomous driving, and digital twin systems, where rich and interactive 3D data representations may functionally make the difference. Deep learning has emerged as a powerful tool in this domain, offering advanced techniques for compressing point clouds more efficiently than conventional coding methods while also allowing effective computer vision tasks performed in the compressed domain thus, for the first time, making available a common compressed visual representation effective for both man and machine. Taking advantage of this potential, JPEG has recently finalized the JPEG Pleno Learning-based Point Cloud Coding (PCC) standard offering efficient lossy coding of static point clouds, targeting both human visualization and machine processing by leveraging deep learning models for geometry and color coding. The geometry is processed directly in its original 3D form using sparse convolutional neural networks, while the color data is projected onto 2D images and encoded using the also learning-based JPEG AI standard. The goal of this paper is to provide a complete technical description of the JPEG PCC standard, along with a thorough benchmarking of its performance against the state-of-the-art, while highlighting its main strengths and weaknesses. In terms of compression performance, JPEG PCC outperforms the conventional MPEG PCC standards, especially in geometry coding, achieving significant rate reductions. Color compression performance is less competitive but this is overcome by the power of a full learning-based coding framework for both geometry and color and the associated effective compressed domain processing.
- A Double Deep Learning-Based Solution for Efficient Event Data Coding and ClassificationPublication . Seleem, Abdelrahman; Guarda, André; M. M. Rodrigues, Nuno; Pereira, FernandoEvent cameras have the ability to capture asynchronous per-pixel brightness changes, usually called "events", offering advantages over traditional frame-based cameras for computer vision tasks. Efficiently coding event data is critical for practical transmission and storage, given the very significant number of events captured. This paper proposes a novel double deep learning-based solution for efficient event data coding and classification, using a point cloud-based representation for events. Moreover, since the conversions from events to point clouds and back to events are key steps in the proposed solution, novel tools are proposed and their impact is evaluated in terms of compression and classification performance. Experimental results show that it is possible to achieve a classification performance for decompressed events which is similar to the one for original events, even after applying a lossy point cloud codec, notably the recent deep learning-based JPEG Pleno Point Cloud Coding standard, with a clear rate reduction. Experimental results also demonstrate that events coded using the JPEG standard achieve better classification performance than those coded using the conventional lossy MPEG Geometry-based Point Cloud Coding standard for the same rate. Furthermore, the adoption of deep learning-based coding offers future high potential for performing computer vision tasks in the compressed domain, which allows skipping the decoding stage, thus mitigating the impact of compression artifact
- Synthetic image generation for effective deep learning model training for ceramic industry applicationsPublication . Gaspar, Fábio; Daniel Carreira; Rodrigues, Nuno; Miragaia, Rolando; Ribeiro, José; Costa, Paulo; Pereira, AntónioIn the rapidly evolving field of machine learning engineering, access to large, high-quality, and well-balanced labeled datasets is indispensable for accurate product classification. This necessity holds particular significance in sectors such as the ceramics industry, in which effective production line activities are paramount and deep learning classification mechanisms are particularly relevant for streamlining processes; but real-world image samples are scarce and difficult to obtain, hindering dataset building and consequently model training and deployment. This paper presents a novel approach for dataset building in the context of the ceramic industry, which involves employing synthetic images for building or complementing datasets for image classification problems. The proposed methodology was implemented in CeramicFlow, an innovative computer graphics rendering pipeline designed to create synthetic images by employing computer-aided design models of ceramic objects and incorporating domain randomization techniques. As a result, a fully synthetic image dataset named Synthetic CeramicNet was created and validated in real-world ceramic classification problems. The results demonstrate that synthetic images provide an adequate basis for datasets and can significantly reduce reliance on real-world data when developing deep learning approaches for image classification problems in the ceramic industry. Furthermore, the proposed approach can potentially be applied to other industrial fields.
- STICKY COSTS IN THE CLASSROOM: RETHINKING MANAGEMENT ACCOUNTING EDUCATION FOR REAL-WORLD FINANCIAL CHALLENGESPublication . Lucas, Ana; Azevedo, Graça; Oliveira, J; Lima Santos, LuísIn recent years, research on cost behavior in accounting has advanced significantly, particularly with the introduction of the concept of “sticky costs.” These costs exhibit asymmetry, meaning they increase more rapidly with rising activity levels than they decrease with falling activity. This phenomenon challenges cost management as it complicates earnings predictability and financial stability for organizations. While the concept has gained traction in management accounting literature, its integration into higher education curricula, specifically in degree programs in accounting and management, remains limited. This study aims to analyze the incorporation of the sticky costs concept into the curricula of management accounting courses within degrees in management and accounting at Portuguese universities. The empirical research will involve analyzing the course syllabi to assess how topics related to the asymmetrical behavior of costs are addressed, either explicitly or implicitly, and to determine how these concepts can be better integrated into academic programs to enrich student learning. The study will evaluate the extent to which new theoretical approaches to cost behavior are integrated into the curriculum, comparing them with traditional models that classify costs as either fixed or variable. Furthermore, this research will explore the pedagogical implications of teaching sticky costs within management accounting curricular units, discussing how this knowledge can improve students’ understanding of the cost dynamics within real-world organizations. The study will also assess whether properly addressing sticky costs can better prepare students to tackle the complex financial challenges faced by organizations, particularly in today’s dynamic economic environments. This study also contributes to the broader conversation around the United Nations Sustainable Development Goals (SDGs), specifically SDG 4, which aims to ensure inclusive, equitable, and quality education for all. By integrating concepts such as sticky costs into management accounting curricula, the study seeks to promote a more relevant and practical education, equipping students with a deeper understanding of the financial challenges organizations face. Furthermore, by addressing the financial sustainability of organizations, this research indirectly supports SDG 8, which aims to promote sustained, inclusive, and sustainable economic growth, as well as increased productivity and decent work. The proposed curriculum updates not only enhance the quality of education in management accounting but also reinforce the role of higher education institutions as agents of change, fostering more responsible business practices aligned with global sustainability goals. This research will contribute to improving the academic formation of future professionals in accounting and management, providing both theoretical insights and practical recommendations for curriculum design. Ultimately, it seeks to align educational practices with the evolving needs of the business world, ensuring that students are equipped with the tools necessary for navigating complex financial landscapes and contributing to sustainable economic development.
- Monitoring Revenue Management Practices in the Restaurant Industry—A Systematic Literature ReviewPublication . Malheiros, Cátia; Gomes, Conceição; Lima Santos, Luís; Campos, Filipa Alexandra Gomes deThe research of revenue management (RM) practices is widespread in the accom modation sector, but not in the restaurant industry. This study aims to ascertain which RM practices are the most used in the restaurant industry, organizing them by clusters, identifying those that imply profit maximization and describing the challenges of their implementation. Mixed methods were used as the methodology through a systematic literature review, which was submitted to a brief descriptive analysis and content anal ysis. Data were retrieved from the Scopus database, and, using the PRISMA diagram, 70 papers were collected for comprehensive analysis of their content. The results of the studies identified five main areas of RM and 21 practices, some specific to the restaurant industry, with reservations and meal duration management being the most used practices. Reservations have been implemented in many restaurants but are not a reality for all of them. A well-managed meal duration increases restaurant capacity. Furthermore, customer satisfaction implies the success of all other practices since customers must understand and accept the RM practices for their success. As a theoretical implication, this study contributes to the development of research into the RM practices of restaurants, and as practical im plications, restaurant managers should implement the following practices: meal duration management, indicators, and table mix. This study contributes to future research, such as analyzing the relationship between sustainability and RM, applying RM to the beverages department, and including RM in consumer behavior in the context of future crises.
- Introducing Exploratory Teaching in Preservice Teacher Education Through Lesson StudyPublication . Duarte, Nicole; Ponte, João Pedro da; Faria, FilipaIn exploratory teaching, the pupils learn from their work on tasks that aim to introduce new concepts, procedures, representations and mathematical ideas. Lesson study, with its focus on teachers’ collaborative and reflective work around issues of pupil learning, is a powerful formative process that may be used in preservice teacher education, sustaining an exploratory teaching approach. In this article, we present a lesson study experience in preservice teacher education, addressing the case of a preservice teacher who is preparing to teach in grades 1–6. Our aim is to identify what key aspects of knowledge of teaching practice does the preservice teacher use when preparing and leading an exploratory lesson during her participation in a lesson study. Our methodology is qualitative, with data collected from the lesson study sessions and the collection of documents produced during the lesson study work, and the data are analysed using a model that presents the key aspects of knowledge of teaching practice. The results show that the structure and the activities carried out in the lesson study, such as designing the lesson, selecting the task and anticipating questions to be posed to pupils, promoted the use of the preservice teacher’s didactic knowledge regarding the phases of an exploratory lesson.
- An Automated Repository for the Efficient Management of Complex DocumentationPublication . Frade, José; Antunes, MárioThe accelerating digitalization of the public and private sectors has made information technologies (IT) indispensable in modern life. As services shift to digital platforms and technologies expand across industries, the complexity of legal, regulatory, and technical requirement documentation is growing rapidly. This increase presents significant challenges in managing, gathering, and analyzing documents, as their dispersion across various repositories and formats hinders accessibility and efficient processing. This paper presents the development of an automated repository designed to streamline the collection, classification, and analysis of cybersecurity-related documents. By harnessing the capabilities of natural language processing (NLP) models—specifically Generative Pre-Trained Transformer (GPT) technologies—the system automates text ingestion, extraction, and summarization, providing users with visual tools and organized insights into large volumes of data. The repository facilitates the efficient management of evolving cybersecurity documentation, addressing issues of accessibility, complexity, and time constraints. This paper explores the potential applications of NLP in cybersecurity documentation management and highlights the advantages of integrating automated repositories equipped with visualization and search tools. By focusing on legal documents and technical guidelines from Portugal and the European Union (EU), this applied research seeks to enhance cybersecurity governance, streamline document retrieval, and deliver actionable insights to professionals. Ultimately, the goal is to develop a scalable, adaptable platform capable of extending beyond cybersecurity to serve other industries that rely on the effective management of complex documentation.
- ROI -BASED CODING OF BIOMEDICAL IMAGES FOR MACHINE ANALYSISPublication . Nicolau, Daniel Filipe da Silva; Faria, Sérgio Manuel Maciel de; Távora, Luís Miguel de Oliveira Pegado de Noronha e; Thomaz, Lucas ArrabalThe increasing volume of data acquired and generated daily in the healthcare sector, driven by technological advancements, brings significant benefits to patient diagnosis and research. However, this growth also presents considerable challenges in the analysis and processing of such data. To address these difficulties, computer vision algorithms have emerged as powerful tools, capable of automating repetitive and time-consuming tasks, enabling faster and more accurate processing. At the same time, the growing volume of data places pressure on storage and transmission capabilities, demanding efficient compression methods to minimise its size. In the literature, various approaches are found, primarily divided into two categories: lossy and lossless compression. While lossless methods ensure data integrity, they do not achieve compression rates as high as lossy algorithms. The latter, despite significantly reducing file sizes, introduces distortions that may compromise image quality, affecting the accuracy of automated systems. This dissertation focuses on two main challenges: first, evaluating the impact of image compression on the performance of biomedical computer vision systems, and second, improving compression efficiency without compromising the accuracy of these algorithms. To this end, detection and segmentation models, such as YOLOv8 and SAM, were used to analyse the effect of distortion caused by encoding on the localisation and segmentation of mitochondria in two datasets of electron microscopy images. To enhance model performance at higher compression levels, two methodologies were implemented. The first focuses on domain adaptation, fine-tuning the models to recognise and compensate for distortions introduced by compression, specifically in HEVC/H.265 and VVC/H.266 encoders. The second approach proposes contentaware encoder adaptation, allowing the assignment of different quality levels to selected regions of interest. This method aims to reduce storage and bandwidth requirements without significantly compromising the performance of deep learningbased models. Experimental results demonstrate that region-of-interest-based encoding strategies effectively reduce compression rates while maintaining model accuracy. In particular, the proposed methodologies allowed to achieve an average performance improvement of up to 23.70% for the same bpp range and a data size reduction of up to 74.96%. Additionally, a Pareto-based optimisation algorithm was proposed to determine the most suitable encoding configurations for different standards and models, ensuring a balance between compression efficiency and object detection performance.
- Ascertaining Restaurant Financial Sustainability by Analyzing Menu PerformancePublication . Gomes, Conceição; Malheiros, Cátia; Lima Santos, Luís; Campos, Filipa Alexandra Gomes deThe complexity of companies in the restaurant industry is clear, and various techniques can be used to make decisions. The analysis of performance and the optimization of restaurant menus are considered important, which is why several approaches can be used. The objective of this study is to achieve financial sustainability in the restaurant industry through menu performance analysis and identifying strategies to improve menu profitability. A qualitative methodology of a dual case study was adopted by comparing a restaurant within a hotel and a street restaurant. The results show that for restaurant owners and managers, these approaches are useful, simple, and pertinent for measuring the performance of the restaurant menu and consequently improving results. The originality of this research lies in the fact that three analysis models were applied simultaneously, allowing for an in-depth analysis of the profitability of the menus being analyzed. This study identified the most profitable items for each restaurant and the items that needed to be changed to contribute more to the profitability of the restaurant’s menu, resulting in practical implications. Through theoretical implications, this study corrects the limited knowledge about performance through the restaurant menu, creating a starting point for knowledge spreading to society. In conclusion, this research is one of the first to bridge the gap between theory and practice, taking several approaches to assess restaurant menu performances, which can be useful in restaurants to promote sustainability.