arXiv subjects

Cátia Vaz

Publications and source records attributed to Cátia Vaz.

11 recordsLinked to original sources

arXiv2026

The Combined Van-and-Mopeds Routing Problem with Time Windows: Formulation, Variants, and Solution Methods

We introduce and study the Combined Van-and-Mopeds Routing Problem with Time Windows (CVMRPTW), a novel variant of the heterogeneous fleet vehicle routing problem motivated by last-mile parcel delivery in dense European cities. In the CVMRPTW, a single van serves as a mobile base from which on-demand mopeds are deployed to reach customers located in areas inaccessible or inefficient for vans. The primary objective is to minimise the number of mopeds used while guaranteeing that all delivery time windows are met. We propose a Mixed-Integer Linear Programming (MILP) formulation that captures vehicle synchronisation, capacity tracking, and time-window compliance through 22 structured constraint families, and extend it to three operationally motivated variants: the standard model, the active-waiting-van variant, and the common-depot variant. A lexicographic two-stage optimisation framework allows secondary objectives-route duration, combined travel time, and combined distance-to be optimised without sacrificing the primary objective. Three graph-sparsification preprocessing techniques reduce the number of variables and constraints by up to 25%. For instances beyond the practical limits of exact solvers, we develop the Cluster-Based Combined Routing (CBCR) heuristic, which decomposes the problem into a clustering phase, a cluster-level exact solve, and a route-reconstruction phase. Computational experiments on real-world instances derived from OpenStreetMap data for Lisbon and Stuttgart show that the MILP solves instances with up to 48 customers to feasibility within one hour, and that the CBCR heuristic extends tractability to instances with up to 80 customers under favourable conditions.

math.OC↗

arXiv2026

Identity-Bound Academic Credentials on Blockchain: On-Chain Issuer Accreditation with ERC-3643 and OnchainID

Verifying academic credentials remains difficult: records are held by individual institutions in proprietary systems, verification is slow and manual, and counterfeit qualifications are widespread. Blockchain-based registries have been proposed as a remedy, but existing systems tend to anchor certificate hashes without binding them to a verifiable identity, without an explicit mechanism to accredit issuing institutions, and without support for correcting or revoking credentials once issued. This paper investigates whether an infrastructure designed for regulated financial instruments can be repurposed to close these gaps. We present the design of a registry for identity-bound academic credentials that composes OnchainID self-sovereign identities (ERC-734/ERC-735) with the T-REX suite (ERC-3643): its trusted-issuer registry becomes an on-chain issuer-accreditation whitelist, and each certificate is represented as a signed, updatable claim bound to a student's identity and verifiable by any third party without a wallet, while sensitive fields are kept off-chain. We make explicit the tension between a transferable-security-token standard and non-transferable credentials, clarifying which of its guarantees carry over. We validate the design with a reference implementation covering the full certificate life cycle and evaluate it in terms of gas cost, scalability, latency, and security, quantifying the overhead relative to a hash-anchoring baseline.

cs.CR↗

arXiv2026

Approximating SPR Distance Between Phylogenetic Trees with Graph Neural Networks

Comparing phylogenetic tree topologies is essential for understanding epidemic dynamics, yet biologically meaningful distances such as the Subtree Prune and Regraft (SPR) distance are NP-hard to compute and intractable on large datasets. We investigate whether a Graph Neural Network (GNN) can approximate SPR distances in near-constant time per comparison after training. Our contributions are fourfold. First, we build and publicly release a dataset of 864 phylogenetic trees inferred with UPGMA and Neighbor-Joining over four bacterial species, spanning up to 9{,}500 isolates, together with 388 labelled tree pairs. Second, we establish a reproducible pre-processing pipeline including midpoint re-rooting, which reduces tree depth and supplies the rooting required for exact distance computation and for the model's root-based features. Third, we validate the supervision target: on small trees, where exact SPR is tractable, the unrooted phangorn::SPR.dist heuristic correlates almost perfectly with the exact rooted distance computed by rspr (Pearson $0.98$--$0.99$), making it an excellent monotonic surrogate. Lastly, we train a Siamese Graph Isomorphism Network (GIN) regressor. In-distribution, i.e., held-out trees from the same species and size range as training, it explains roughly 87--90% of the variance ($R^2 \approx 0.87$ on a held-out split; $0.90 \pm 0.19$ under stratified cross-validation), with about four times lower error than a mean-predictor baseline, and shows partial transfer to unseen species ($R^2 \approx 0.37$). Its main limitation is extrapolation to trees larger than those seen in training, where accuracy collapses. The released dataset and the validated heuristic versus exact relationship provide a reproducible basis for scaling learned SPR approximation.

q-bio.PE↗

arXiv2026

AIdentifyAGE Ontology for Decision Support in Forensic Dental Age Assessment

Age assessment is crucial in forensic and judicial decision-making, particularly in cases involving undocumented individuals and unaccompanied minors, where legal thresholds determine access to protection, healthcare, and judicial procedures. Dental age assessment is widely recognized as one of the most reliable biological approaches for adolescents and young adults, but current practices are challenged by methodological heterogeneity, fragmented data representation, and limited interoperability between clinical, forensic, and legal information systems. These limitations hinder transparency and reproducibility, amplified by the increasing adoption of AI- based methods. The AIdentifyAGE ontology is domain-specific and provides a standardized, semantically coherent framework, encompassing both manual and AI-assisted forensic dental age assessment workflows, and enabling traceable linkage between observations, methods, reference data, and reported outcomes. It models the complete medico-legal workflow, integrating judicial context, individual-level information, forensic examination data, dental developmental assessment methods, radiographic imaging, statistical reference studies, and AI-based estimation methods. It is being developed together with domain experts, and it builds on upper and established biomedical, dental, and machine learning ontologies, ensuring interoperability, extensibility, and compliance with FAIR principles. The AIdentifyAGE ontology is a fundamental step to enhance consistency, transparency, and explainability, establishing a robust foundation for ontology-driven decision support systems in medico-legal and judicial contexts.

cs.AI↗

arXiv2025

Structuring Security: A Survey of Cybersecurity Ontologies, Semantic Log Processing, and LLMs Application

This survey investigates how ontologies, semantic log processing, and Large Language Models (LLMs) enhance cybersecurity. Ontologies structure domain knowledge, enabling interoperability, data integration, and advanced threat analysis. Security logs, though critical, are often unstructured and complex. To address this, automated construction of Knowledge Graphs (KGs) from raw logs is emerging as a key strategy for organizing and reasoning over security data. LLMs enrich this process by providing contextual understanding and extracting insights from unstructured content. This work aligns with European Union (EU) efforts such as NIS 2 and the Cybersecurity Taxonomy, highlighting challenges and opportunities in intelligent ontology-driven cyber defense.

cs.CR↗

arXiv2024

A modular and scalable web platform for computational phylogenetics

Phylogenetic analysis, which allow to understand the evolution of bacterial and viral epidemics, requires large quantities of data to be analysed and processed for knowledge extraction. One of the major challenges consists on the integration of the results from typing and phylogenetic inference methods with epidemiological data, namely in what concerns their integrated and simultaneous analysis and visualization. Numerous approaches to support phylogenetic analysis have been proposed, varying from standalone tools to integrative web applications that include tools and/or algorithms for executing the common analysis tasks for this kind of data. However, most of them lack the capacity to integrate epidemiological data. Others provide the ability for visualizing and analyzing such data, allowing the integration of epidemiological data but they do not scale for large data analysis and visualization. Namely, most of them run inference and/or visualization optimization tasks on the client side, which becomes often unfeasible for large amounts of data, usually implying transferring data from existing databases in order to be analysed. Moreover, the results and optimizations are not stored for reuse. We propose the PHYLOViZ Web Platform, a cloud based tool for phylogenetic analysis, that not only unifies the features of both existing versions of PHYLOViZ, but also supports structured and customized workflows for executing data processing and analyses tasks, and promotes the reproducibility of previous phylogenetic analyses. This platform supports large scale analyses by relying on a workflow system that enables the distribution of parallel computations on cloud and HPC environments. Moreover, it has a modular architecture, allowing easy integration of new methods and tools, as well as customized workflows, making it flexible and extensible.

q-bio.PE↗

arXiv2023

Phylogenetic tree distance computation over succinct representations

There are several tools available to infer phylogenetic trees, which depict the evolutionary relationships among biological entities such as viral and bacterial strains in infectious outbreaks, or cancerous cells in tumor progression trees. These tools rely on several inference methods available to produce phylogenetic trees, with resulting trees not being unique. Thus, methods for comparing phylogenies that are capable of revealing where two phylogenetic trees agree or differ are required. An approach is then to compute a similarity or dissimilarity measure between trees, with the Robinson- Foulds distance being one of the most used, and which can be computed in linear time and space. Nevertheless, given the large and increasing volume of phylogenetic data, phylogenetic trees are becoming very large with hundreds of thousands of leafs. In this context, space requirements become an issue both while computing tree distances and while storing trees. We propose then an efficient implementation of the Robinson-Foulds distance over trees succinct representations. Our implementation generalizes also the Robinson-Foulds distances to labelled phylogenetic trees, i.e., trees containing labels on all nodes, instead of only on leaves. Experimental results show that we are able to still achieve linear time while requiring less space. Our implementation is available as an open-source tool at https://github.com/pedroparedesbranco/TreeDiff.

cs.DS↗

arXiv2023

On Finding Optimal (Dynamic) Arborescences

Let G = (V, E) be a directed and weighted graph with vertex set V of size n and edge set E of size m, such that each edge (u, v) \in E has a real-valued weight w(u, c). An arborescence in G is a subgraph T = (V, E') such that for a vertex u \in V, the root, there is a unique path in T from u to any other vertex v \in V. The weight of T is the sum of the weights of its edges. In this paper, given G, we are interested in finding an arborescence in G with minimum weight, i.e., an optimal arborescence. Furthermore, when G is subject to changes, namely edge insertions and deletions, we are interested in efficiently maintaining a dynamic arborescence in G. This is a well known problem with applications in several domains such as network design optimization and in phylogenetic inference. In this paper we revisit algorithmic ideas proposed by several authors for this problem, we provide detailed pseudo-code as well as implementation details, and we present experimental results on large scale-free networks and on phylogenetic inference. Our implementation is publicly available at \url{https://gitlab.com/espadas/optimal-arborescences}.

cs.DS↗

arXiv2023

phyloDB: A framework for large-scale phylogenetic analysis

phyloDB is a modular and extensible framework for large-scale phylogenetic analyses, which are essential for understanding epidemics evolution. It relies on the Neo4j graph database for data storage and processing, providing a schema and an API for representing and querying phylogenetic data. Custom algorithms are also supported, allowing to perform heavy computations directly over the data, and to store results in the database. Multiple computation results are stored as multilayer networks, promoting and facilitating comparative analyses, as well as avoiding unnecessary ab initio computations. The experimental evaluation results showcase that phyloDB is efficient and scalable with respect to both API operations and algorithms execution.

cs.DB↗

arXiv2020

Distance-based phylogenetic inference from typing data: a unifying view

Typing methods are widely used in the surveillance of infectious diseases, outbreaks investigation and studies of the natural history of an infection. And their use is becoming standard, in particular with the introduction of High Throughput Sequencing (HTS). On the other hand, the data being generated is massive and many algorithms have been proposed for phylogenetic analysis of typing data, addressing both correctness and scalability issues. Most of the distance-based algorithms for inferring phylogenetic trees follow the closest-pair joining scheme. This is one of the approaches used in hierarchical clustering. And although phylogenetic inference algorithms may seem rather different, the main difference among them resides on how one defines cluster proximity and on which optimization criterion is used. Both cluster proximity and optimization criteria rely often on a model of evolution. In this work we review, and we provide an unified view of these algorithms. This is an important step not only to better understand such algorithms, but also to identify possible computational bottlenecks and improvements, important to deal with large data sets.

cs.DS↗

arXiv2016

Beyond NGS data sharing and towards open science

Biosciences have been revolutionized by next generation sequencing (NGS) technologies in last years, leading to new perspectives in medical, industrial and environmental applications. And although our motivation comes from biosciences, the following is true for many areas of science: published results are usually hard to reproduce either because data is not available or tools are not readily available, which delays the adoption of new methodologies and hinders innovation. Our focus is on tool readiness and pipelines availability. Even though most tools are freely available, pipelines for data analysis are in general barely described and their configuration is far from trivial, with many parameters to be tuned. In this paper we discuss how to effectively build and use pipelines, relying on state of the art computing technologies to execute them without users need to configure, install and manage tools, servers and complex workflow management systems. We perform an in depth comparative analysis of state of the art frameworks and systems. The NGSPipes framework is proposed showing that we can have public pipelines ready to process and analyse experimental data, produced for instance by high-throughput technologies, but without relying on centralized servers or Web services. The NGSPipes framework and underlying architecture provides a major step towards open science and true collaboration in what concerns tools and pipelines among computational biology researchers and practitioners. We show that it is possible to execute data analysis pipelines in a decentralized and platform independent way. Approaches like the one proposed are crucial for archiving and reusing data analysis pipelines at medium/long-term. NGSPipes framework is freely available at http://ngspipes.github.io/.

cs.DC↗