http://hdl.handle.net/10071/2106 2026-04-18T00:46:59Z 2026-04-18T00:46:59Z Machado Alexandre, I. http://hdl.handle.net/10071/36929 2026-04-16T11:59:29Z 2026-01-01T00:00:00Z

Título próprio: Dementia: Harnessing the power of slow memory to prevent disease progression Autoria: Machado Alexandre, I. Resumo: As life expectancy increases among industrialized countries, age-related illnesses such as Alzheimer’s have become more common. Alzheimer’s, a common type of dementia, encompasses progressive cognitive loss, impacting memory, reasoning, and social behavior. This article explores the integration of slow memory within the MEM+ application, designed to stimulate memory and cognitive functions in people with Alzheimer’s dementia. This article presents a methodological approach to incorporating slow memory exercises into MEM*, ensuring adaptability for people with dementia across different stages of the disease. The applied methodologies encompass iterative design processes, cognitive task evaluations, and feedback collection from health professionals and people with dementia. The application’s effectiveness was assessed through a combination of quantitative cognitive impact assessments and qualitative feedback, focusing on the impact on memory retention and cognitive performance. Findings suggest that a slow memory approach, when combined with digital cognitive tools, can play a crucial role in enhancing engagement among people with dementia.

2026-01-01T00:00:00Z Gatti, F. Pedro, J. Costa, N. Cancela, L. http://hdl.handle.net/10071/36917 2026-04-16T10:21:19Z 2026-01-01T00:00:00Z

Título próprio: Design of filterless horseshoe networks optimized for interoperable coherent pluggable transceivers Autoria: Gatti, F.; Pedro, J.; Costa, N.; Cancela, L. Resumo: The continuous growth of traffic in metro networks is increasing the need for cost-effective, scalable, and power-efficient optical solutions. Filterless optical networks (FONs) have emerged as a promising architecture for metro-aggregation and metro-access domains, thanks to their low complexity and reliance on passive optical components. However, their inherent broadcast nature introduces key challenges, including spectrum waste, limited power equalization, and significant noise accumulation, particularly when coherent pluggable transceivers are employed. This work provides a detailed assessment of FON performance using state-of-the-art multi-source agreement (MSA)-compliant coherent modules, evaluating both point-to-point (P2P) and digital subcarrier multiplexing (DSCM)-based point-to-multipoint (P2MP) architectures. A novel optical amplifier (OA) optimization algorithm is proposed to balance expressed and added signal power in FON, accounting for optical power saturation effects and optical performance degradation due to limited power at the receiver input. The analysis highlights the substantial impact of transmitter out-of-band (OB) noise in FONs and its detrimental accumulation during multi-channel colorless aggregation, which can limit network capacity. In scenarios with lower capacity requirements, P2MP architectures demonstrate superior performance, benefiting from reduced insertion loss and lower OB noise accumulation while offering enhanced scalability compared with P2P solutions. Overall, the study highlights that FONs combined with coherent pluggables can support cost-efficient and scalable metro solutions, provided that OB noise, power imbalance, and amplifier operation are properly addressed through optimized design strategies.

2026-01-01T00:00:00Z Awais, M. Postolache, O. A. Oliveira, S. M. http://hdl.handle.net/10071/36914 2026-04-15T15:07:36Z 2026-01-01T00:00:00Z

Título próprio: Quantum-enhanced learning: Leveraging von Neumann entropy for enhanced graph neural network performance Autoria: Awais, M.; Postolache, O. A.; Oliveira, S. M. Resumo: Graph Neural Networks (GNNs) have established themselves as powerful tools for learning from graph-structured data. However, their reliance on local message-passing mechanisms leads to over-squashing—the compression of exponentially growing neighborhood information into fixed-size vectors—which severely limits long-range dependency modeling. We introduce the Quantum-Inspired Graph Neural Network (QGNN) with a novel Quantum Entanglement Loss (QEL) function that addresses this challenge through a fundamentally different mechanism than existing approaches. Unlike spectral regularization (which enforces smoothness) or maximum entropy methods (which encourage representation diversity), QEL minimizes the von Neumann entropy of the node embedding correlation matrix, thereby concentrating eigenvalues in dominant eigenmodes that preserve global structural patterns. This entropy minimization creates direct information pathways between distant but functionally related nodes, effectively bypassing multi-hop bottlenecks. We evaluate QGNN on both standard benchmarks (Cora, Citeseer, PPI, Electronic Circuits) and the Long Range Graph Benchmark (LRGB) suite, which features graphs with average diameters up to 56.99 (Peptides). On LRGB datasets, QGNN achieves substantial improvements: 37.6% relative MAE reduction on Peptides-struct compared to GCN, 4.0% improvement over Graph Transformers (GraphGPS), and notably, 97% better performance than GCN on node pairs separated by 7+ hops. Despite these gains, QGNN requires only 20–30% additional computational overhead compared to standard GCN, while being 5–6 ×  faster than Graph Transformer approaches. Our results establish entropy-based regularization as a principled and efficient approach for long-range dependency modeling in graphs.

2026-01-01T00:00:00Z Barros, J. Turner, C. http://hdl.handle.net/10071/36913 2026-04-15T14:57:44Z 2026-01-01T00:00:00Z

Título próprio: Network algorithm to model automotive supply chain structure Autoria: Barros, J.; Turner, C. Resumo: A network algorithm that models the structure of automotive supply chains, compiled from a proprietary database, is presented. An initial structural analysis was conducted using key performance indicators, including average path length, clustering coefficient, and degree distribution, to assess network configurations. The networks were then partitioned into subnetworks, with an emphasis on reflecting the operational dynamics of supply chain activities. Regression analysis was applied to each subnetwork, using the number of vertices as the independent variable, to develop an algorithm for generating synthetic networks. These synthetic constructs serve as benchmarks for the automotive sector and have shown a strong average correlation (0.94) with the structure of actual supply networks. This methodological contribution provides tools for analysing and optimising supply chain structures that underpin automotive engineering and manufacturing, ensuring robustness and efficiency in vehicle production systems. The prevalence of tree-like structures within supply networks challenge conventional beliefs regarding the complexity of automotive supply chains and prompts further investigation into the determinants of their resilience.

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