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- Fatigue analysis of sustainable bituminous pavements with artificial and recycled aggregatesPublication . Teijón-López-Zuazo, Evelio; Vega-Zamanillo, Ángel; Santos, C.C.; Gómez-Carrascal, DavidThe circular economy represents a significant opportunity to enhance the mechanical properties of bituminous mixtures, thereby contributing to sustainable development. This research compares the behaviour of traditional bituminous mixtures with sustainable ones that reuse recycled materials, industrial waste products, or additives that improve mechanical or rheological properties. The methodology employed comprised the acquisition of fatigue resistance laws from 4-point bending tests on prismatic specimens. This facilitated the analytical determination of the number of axles of 13 tons that the section of pavement with sustainable material can support for comparison with the axles supported in the conventional mix. The findings corroborate the utilization of sustainable bituminous mixtures in pavement sections, employing the maximum circularity criterion. The fatigue laws calculated must permit the use of different calculation methods or other applications in green infrastructures, such as cycling lanes or pedestrian areas. On sections with an AADT of between 800 and 25 HV/day, all of the analyzed bituminous mixtures with sustainable materials prolong the service life of the road. There were increases in service life of between 25.5% and 6.6%, respectively, which satisfactorily achieved an increase in pavement service life based on the criterion of maximum circularity.
- Fire safety regulations in Brazil: Analysis of the occupancy classification of buildingsPublication . Minervino, Bernardete de Lourdes Ferreira; Santos, C.C.; von Krüger, Paulo Gustavo; Rodrigues, João Paulo CorreiaFire safety in a building depends on several factors, including its use and occupancy, which fire protection systems are present in the building and the state of maintenance of these systems. Considering the fact that Brazil has 27 federal states and each of them has its own fire safety legislation, it is common for there to be divergences that result in different protection requirements depending on the state where the building is located. This study analyzes Brazilian state fire safety legislation with regard to the sizing of fire protection systems for buildings, considering the parameters used for such sizing. It then identifies the differences in classification in terms of the use and occupation of buildings in the 27 federal states; and presents a proposal for a standardized classification for the whole country, taking into account the convergences that already exist in state legislation. The main objective is to suggest a discussion starter for the process of standardizing fire safety parameters in Brazil.
- High frequency ultrasonic condition monitoring framework based on edge-computing and telemetry stack approachPublication . Spencer, Geoffrey; Torres, Pedro; Pinto, Vítor H.; Gonçalves, GilThis paper presents initial developments towards a high-frequency condition monitoring framework designed for Autonomous Mobile Robots (AMRs) in Smart Factory environments. The proposed approach focuses on data acquisition and edge-level processing at the ultrasound range specifically (>20 kHz), using Micro-Electro-Mechanical System (MEMS) sensors. The system integrates real-time data acquisition, embedded fixed-point frequency-domain processing via a 1024-point FFT, and the integration of Industrial Internet-of-Things (IIoT) infrastructure based on the TIG (Telegraf, InfluxDB, and Grafana) stack, for data aggregation and remote visualization. To ensure timing precision at a sampling rate of 160 kHz, a software-based calibration routine is implemented to compensate for microcontroller overhead. Furthermore, the architecture’s alignment with IEEE 1451 principles is discussed to support interoperable and scalable sensor integration. Experimental results validate the reliable acquisition and processing of ultrasonic signals up to 80 kHz using controlled acoustic sources. This work provides a foundational infrastructure for condition-based monitoring, enabling future development of automated anomaly detection for mechanical components, such as bearings, which exhibit early-stage fault signatures in the ultrasonic spectrum.
- Geochemical contamination signatures: Insights from information theory and cokriging— a compositional approachPublication . Pazo, María; Albuquerque, M.T.D.; Roque, Natália; Rita FonsecaPotentially toxic elements (PTEs) such as arsenic (As) and mercury (Hg) are among the most critical pollutants globally, threatening ecosystem integrity and human health. The Trimpancho mining system in the Iberian Pyrite Belt (W Spain) is one such hotspot, where centuries of activity have left a legacy of acid mine drainage and heavy metal dispersion. This study employs an integrated compositional, probabilistic, and spatial modeling framework to characterize and map contamination dynamics in this area with quantified uncertainty. A total of 31 water samples were collected during 2022 and 2023 from surface streams and tributaries. Concentration data were transformed using isometric log-ratio (ilr) techniques to preserve their compositional nature and avoid spurious correlations. Bayesian Networks (BNs), combined with information-theoretic metrics, were then applied to identify latent geochemical contamination patterns and quantify both aleatory and epistemic uncertainties. The key drivers identified were incorporated into a co-kriging framework, enabling spatial interpolation that accounted for over 90% of total variance and reduced epistemic uncertainty by 22.7% compared to raw-data models. The resulting spatial–temporal maps revealed distinct As– Hg contamination signatures, influenced by hydrological variability and mining legacy sources. In conclusion, this integrated approach provides a robust, uncertainty-aware methodology for detecting, interpreting, and mapping contamination patterns, offering actionable insights for environmental risk assessment and remediation planning in mining-impacted watersheds.
- Inclusive digital gaming platformPublication . Mendonça, Rodrigo; Lopes, Salvador; Oliveira, Ângela; Serra, Paulo; Fidalgo, FilipeThe lack of accessibility in digital gaming platforms remains a significant barrier to equitable user participation. To address this issue, this article presents an inclusive solution developed as a multimedia project designed to promote access to digital games for any user through the ipcb.games platform. The platform offers features that enhance accessibility, including voice-based authentication, voice-assisted registration, facial recognition, visual and auditory feedback, and a simplified interface. It also enables users to submit their own games for subsequent approval and integration. The development process followed a multimedia project methodology, structured into phases of analysis, planning, design, production, testing, and validation. The proposal was informed by a systematic review of scientific literature on digital inclusion and accessibility, complemented by a comparative analysis of existing platforms. During usability testing, the platform was evaluated by approximately 50 teachers from different educational levels, who provided highly positive feedback. Future work includes implementing voice-controlled gameplay, enabling keyboard-based navigation, re-implementing a functional eye-tracking system, and creating pedagogical groups, further strengthening the platform’s role in educational contexts.
- Empirical evidence of AI-enabled accessibility in digital gastronomy: Development and evaluation of the Receitas +Power PlatformPublication . Serra, Paulo; Oliveira, Ângela; Fidalgo, Filipe; Serra, Bruno; Infante, Tiago; Baião, LuísThis study explores how artificial intelligence can promote accessibility and inclusiveness in digital culinary environments. Centred on the Receitas +Power platform, the research adopts an exploratory, multidimensional case study design integrating qualitative and quantitative analyses. The investigation addresses three research questions concerning (i) user empowerment beyond recommendation systems, (ii) accessibility best practices across disability types, and (iii) the effectiveness of AI-enabled inclusive solutions. The system was developed following user-centred design principles and WCAG 2.2 standards, combining generative AI modules for recipe creation with accessibility features such as voice interaction and adaptive navigation. The evaluation, conducted with 87 participants, employed the System Usability Scale complemented by thematic qualitative feedback. Results indicate excellent usability (M = 80.6), high reliability (Cronbach’s α = 0.798–0.849), and moderate positive correlations between usability and accessibility dimensions (r = 0.45–0.55). Participants highlighted the platform’s personalisation, clarity, and inclusivity, confirming that accessibility enhances rather than restricts user experience. The findings provide empirical evidence that AI-driven adaptability, when grounded in universal design principles, offers an effective and ethically sound pathway toward digital inclusion. Receitas +Power thus advances the field of inclusive digital gastronomy and presents a replicable framework for human–AI co-creation in accessible web technologies.
- Flexible takt times through overlapping zones: an assessment by simulationPublication . Thürer, Matthias; Fernandes, Nuno O.; Henriques, Marcelo F.N.Flexible takt times through overlapping zones: an assessment by simulation.
- Work faster, work in parallel, or work overtime? An assessment of short-termPublication . Fernandes, Nuno O.; Thürer, Matthias; Costa, FedericaMany firms use short-term capacity adjustments to deal with demand changes over time, and a broad literature assesses when and where to adjust capacity. This study highlights that this may be dependent on the type of capacity flexibility used to actually realize the adjustment. By comparing for the first-time capacity adjustments by speeding up processing rates, working in parallel, or using overtime, significant differences in the operational performance are identified, with the latter resulting in the best percentage tardy performance. This provides important insights for the interpretation of the existing literature, guides the future literature, and helps managers to make better decisions.
- The DDMRP replenishment model: An assessment by simulationPublication . Fernandes, Nuno O.; Djabi, Suleimane; Thürer, Matthias; Ávila, Paulo; Ferreira, Luís Pinto; Silva, Sílvio C.Demand-Driven Material Requirements Planning (DDMRP) has been proposed as a solution for managing uncertainty and variability in supply chains by combining decoupling, buffer management and demand-driven planning principles. A key element of DDMRP is its inventory replenishment model, which relies on dynamically adjusted inventory buffers rather than fixed stock levels. However, parameterization of these buffers often involves subjective choices, raising concerns about consistency and performance. This paper assesses the DDMRP replenishment model through discrete-event simulation of a multi-echelon, capacity-constrained production system. Two alternative formulations of the safety stock term in the red zone are compared: the original factor-based approach and a revised formula that incorporates measurable variability coefficients. While both safety stock formulations yield similar numerical results, the revised formula enhances transparency and reduces subjectivity. Assessing the impact of introducing a buffer for components in addition to a finished goods buffer further shows that the componentes buffer can reduce finished goods inventory requirements while maintaining service levels. These findings contribute to a better understanding of the DDMRP replenishment model, offering practical insights for parameter selection and supply chain design.
- Big Data-based recommendation systemsPublication . Barata, Luís; Mata, Diogo; Nunes, Jorge; Rodrigues, LucasIntroduction: With the exponential growth of data, digital platforms increasingly rely on Big Data technologies to personalize user experiences and improve the accuracy of item recommendations. Recommendation systems play a critical role in e-commerce, entertainment, and social media by analyzing user interactions, behaviors, and preferences. However, the complexity of large-scale data processing and the diversity of filtering techniques pose significant challenges to achieving high performance and scalability. Objectives: This study aims to analyze how Big Data technologies are applied in modern recommendation systems, emphasizing their role in enhancing personalization and system performance. The work seeks to identify the main data collection strategies, algorithms, and tools adopted in recent research, as well as to assess how these systems address challenges related to scalability and real-time processing. Methods: A systematic literature review was conducted using the IEEE Xplore database, focusing on articles published between 2015 and 2025. The search targeted studies combining Big Data and recommendation systems within e-commerce contexts. Out of 49 retrieved publications, 10 met the inclusion criteria, and 5 were ultimately selected after applying exclusion filters. Each selected paper was analyzed regarding its objectives, employed algorithms, data sources, and achieved outcomes. Results: The reviewed studies demonstrate a wide variety of approaches and technologies, including Hadoop and Spark frameworks for large-scale data processing, deep learning models such as DeepLearning4j for real-time prediction, and classical data mining algorithms like K-Means, Apriori, and FP-Growth. Hybrid methods combining collaborative and content-based filtering were shown to overcome limitations such as the cold start and data sparsity problems. Scalability was addressed through distributed processing and optimization techniques like network pruning and parallel computation. These systems achieved higher recommendation precision and responsiveness across different e-commerce and media applications. Conclusions: The analysis confirms that Big Data–driven recommendation systems are essential for enhancing user engagement and conversion in digital platforms. By integrating data mining, machine learning, and distributed processing technologies, these systems deliver efficient, adaptive, and context-aware recommendations. Future developments should continue exploring hybrid and deep learning–based approaches to further improve scalability, personalization, and computational performance in increasingly complex digital ecosystems.