https://ri.ufs.br/jspui/handle/riufs/2491 Wed, 30 Sep 2026 23:07:36 GMT 2026-09-30T23:07:36Z https://ri.ufs.br/jspui/handle/riufs/26172 Título: Guidelines for adopting micro frontends Autor(es): Santos, Luiz Felipe Cirqueira dos Abstract: Context: The size and structure of software development teams directly impact the design and construction of systems. According to Conway’s Law, the internal organization of teams influences the architecture of the systems developed, reflecting their communication flows. In the context of frontend architectures, approaches such as monolithic frontends, microservices, and microfrontends present different trade-offs in terms of performance, modularity, and scalability. While smaller teams tend to benefit from monolithic architectures due to their simplicity and agility, larger and distributed teams may adopt microfrontends to promote greater autonomy, scalability, and organizational alignment. This alignment between organizational structure and architecture is essential to ensure systems that are technically viable and aligned with the operational reality of teams. Problem: The integration between backend and frontend in microservices-based architectures is complex and requires solutions that balance modularity, specialization, and governance without compromising team cohesion. There is a lack of consolidated guidelines to support the adoption of microfrontends, both in the literature and in industrial practice. Objective: To propose a set of guidelines for the adoption of microfrontends in microservices-based environments, aiming to reduce risks, maximize benefits, and align architectural decisions with organizational structure. Method: The research was conducted based on the Design Science Research (DSR) methodology, focusing on the development and validation of an artifact. The following steps were carried out: (i) Systematic Literature Mapping, to characterize the state of the art; (ii) Multivocal Systematic Review, integrating academic and industrial evidence; (iii) Survey with industry professionals, to understand the state of practice; and (iv) empirical studies, including a controlled experiment and a case study, to evaluate performance and analyze the applicability of the proposed guidelines. Results: The results indicate that microfrontends are more suitable for organizations with higher maturity in DevOps practices, distributed teams, and systems with multiple business domains. Architectures based on monolithic applications were observed to provide better initial performance, whereas microfrontend-based architectures offer modularization mechanisms that may support both technical and organizational scalability, as evidenced by the literature and industry reports. The proposed guidelines encompass aspects of architecture, governance, integration, quality assurance, and team organization, and are grounded in evidence derived from the literature, industry practice, and experimental studies conducted in a controlled environment. Conclusion: The adoption of microfrontends should be context-driven and aligned with the organizational structure, as advocated by Conway’s Law. The proposed guidelines provide a structured set of recommendations grounded in academic, industrial, and experimental evidence, supporting architectural decision-making across different organizational contexts. The results obtained provide evidence regarding the technical aspects of the architecture and indicate the need for further studies in real-world environments to evaluate organizational and operational factors associated with microfrontend adoption. Tue, 21 Jul 2026 00:00:00 GMT https://ri.ufs.br/jspui/handle/riufs/26172 2026-07-21T00:00:00Z https://ri.ufs.br/jspui/handle/riufs/26169 Título: Geração de dados totalmente sintéticos para alta segura neonatal: uma metodologia orientada por conhecimento baseada em redes bayesianas Autor(es): Santos, Jean Louis Silva Abstract: Context: Neonatal hospital discharge is a critical stage in the transition of care, requiring multidimensional assessment of biophysiological, preventive, and healthcare-related conditions. Problem: Although Artificial Intelligence-based Clinical Decision Support Systems have the potential to reduce variability in professional judgment, their development in neonatology is limited by the scarcity of labeled records, legal restrictions on the use of sensitive health data, and the cold start problem. Motivation: The generation of fully synthetic data emerges as an alternative to support research, simulation, and computational development without direct exposure of real medical records. Justification: In sensitive domains such as neonatology, synthetic approaches guided only by data may depend on prior real datasets, reproduce biases, or generate clinically implausible combinations, making a knowledge-driven, interpretable, and auditable strategy relevant. Methodology: This dissertation proposes and evaluates a Bayesian Network-based methodology for generating fully synthetic data applied to decision support in safe neonatal discharge, without using real medical records in the modeling process. The methodology was developed incrementally, starting from an initial model with 11 clinical variables (Core11) and advancing to an expanded version with 18 variables (Core18). The causal network topology, Conditional Probability Tables (CPTs), normative weights, and clinical audit rules were defined based on specialized literature, normative documents, and clinical knowledge curation. The evaluation used internal validation protocols, including statistical fidelity, conditional inference, predictive utility under a Train on Synthetic, Test on Synthetic (TSTS) regime, SHAP-based explainability, threshold analysis, borderline case auditing, multivariate topological fidelity, and clinical edit checks. Results: The main experiments were conducted with 10,000 synthetic records in each model version. In Core11, the results indicated approval in protocols S1 and S2, a mean Kappa of 0.9445 in protocol S3, Hellinger Distance approved in 11/11 variables, and PCD-MAD of 0.0088 in protocol S7. In Core18, the expansion maintained good statistical, topological, and inferential fidelity, with S1 approved in 7/7 tests, S2 approved in 14/14 cases, a mean Kappa of 0.9209, Hellinger Distance approved in 18/18 variables, and PCD-MAD of 0.0097. However, relevant limitations were also observed, including divergence between explanatory importance and normative weights in S3SHAP, instability of decision thresholds in protocols S4 and S5, and the occurrence of clinical edit check violations in 36.9% of the synthetic records. Conclusions: It is concluded that the proposed approach is promising as a privacy-by-design synthetic data generation methodology for neonatal cold start scenarios, especially because it preserves probabilistic and topological properties of the model without relying on real patient data. At the same time, the findings show that global statistical coherence alone does not guarantee full individual clinical plausibility. Thus, the main contribution of this dissertation lies not only in generating synthetic neonatal data, but also in formulating an auditable pipeline for modeling, validation, and identification of methodological limits, indicating the need for future mechanisms of calibration, clinical filtering, conditional resampling, and expert validation. Thu, 30 Jul 2026 00:00:00 GMT https://ri.ufs.br/jspui/handle/riufs/26169 2026-07-30T00:00:00Z https://ri.ufs.br/jspui/handle/riufs/25815 Título: Investigação de métodos para estimação de pesos de relevância para agrupamento de dados relacionais com múltiplas visões Autor(es): Silva, Vitória Teles da Abstract: Multiview data clustering is characterized as the challenge of grouping similar data and separating dissimilar data by using different representations, such as attribute vectors, feature matrices, graphs, or images, whichtogetherprovidecomplementaryinformation.Thisapproachisappliedin real-world scenarios such as social network analysis, medical image classification, recommender systems, and multimedia data integration. Data clustering is one of the most relevant tools for data analysis, and its importance is related to the faithful representation of real-world data, with the combined use of multiple views providing greater accuracy when compared to the use of a single view. The experiments show that the performance of multiview clustering algorithms strongly depends on the distance metrics and weight estimation strategies. While MRDCA-RWL is more sensitive to the adopted distance metric, methods such as MVKKM and MVSC exhibit a strong joint dependence on both distance metrics and weights, reinforcing the importance of selecting appropriate parameters. Given the above, this work addresses the need for a proper interpretation of the use of multiview data in the context of contemporary applications, aiming to investigate different methods for estimating relevance weights. These weights are then applied to the different views in the context of relational data clustering, discussing both the quality of the results and the algorithmic efficiency in comparison with the complexity of competing solutions. Based on this, the experimental results demonstrate the influence of the methodological choices on the behavior of the algorithms, with MRDCA-RWL exhibiting more stable behavior, whereas MVKKM,MVSC,andTW-KMdemonstrate sensitivity to configuration changes. Thu, 23 Jul 2026 00:00:00 GMT https://ri.ufs.br/jspui/handle/riufs/25815 2026-07-23T00:00:00Z https://ri.ufs.br/jspui/handle/riufs/25810 Título: A comparative survey of ARM and RISC-V: architectures, applications and challenges Autor(es): Meira, Andre Reges Souza Abstract: The selection of a processor instruction set architecture (ISA) has become a foundational engineering decision that shapes the performance, power efficiency, security, and supply-chain independence of modern computing systems. Three converging trends make a renewed comparison of ARM and RISC-V especially timely: the expansion of computing into safety- and securitycritical domains governed by standards such as ISO 26262 and IEC 61508; the rise of RISC-V as the first credible open alternative to ARM, with over ten billion cores shipped by 2024; and the growing demand for hardware auditability following microarchitectural attacks such as Spectre and Meltdown and supply-chain compromises such as the XZ Utils backdoor. In this master’s thesis, we provide a comprehensive, parallel-structured comparative survey of the ARM and RISC-V architectures, spanning ISA design and evolution, microarchitecture, vector processing, and virtualization, together with the application domains of robotics, artificial intelligence, and hardware security — dimensions that existing ISA-level, benchmarking, and industry analyses address only in isolation. The work is an architectural survey rather than an experimental benchmarking study, grounded in ISA specifications, peer-reviewed literature from IEEE, ACM, USENIX, and Springer venues, vendor datasheets and safety documentation, and international functional-safety standards, and organised as a chapter-by-chapter parallel evaluation of the two architectures. The analysis finds that the gap between ARM and RISC-V is increasingly one of ecosystem maturity rather than architectural capability: ARM offers a vertically integrated, broadly deployed ecosystem with hardware-enforced security defaults (PAC, MTE, BTI, CCA) and certified safety cores, whereas RISC-V offers openness, amenability to formal verification, and license-free customisation, with ISO 26262 ASIL D cores now emerging. For artificial-intelligence workloads, RISC-V demonstrates up to fourfold superior energy efficiency through custom extensions, while ARM delivers up to fifteenfold faster inference on complex networks through a mature toolchain; these figures are best-case, implementation-dependent trends rather than fixed architectural verdicts. Across robotics and security, architecture selection proves application- and certificationdependent rather than absolute, and the frontier is shifting from a binary ARM-versus-RISC-V choice toward heterogeneous designs that compose both architectures with domain-specific accelerators on a single platform. Wed, 22 Jul 2026 00:00:00 GMT https://ri.ufs.br/jspui/handle/riufs/25810 2026-07-22T00:00:00Z