Instituto Politécnico de Viseu
Repositório Científico
Entradas recentes
The Paradox Between Concept Knowledge and Digital Maturity Level for Industry 4.0: The Portuguese Case
Publication . Guimarães, André; Rosivalda Pereira; Maria Teresa Pereira; Afonso Carvalho; Reis, Pedro; Antonio J. Marques Marques Cardoso
This study examines whether companies’ knowledge of the Industry 4.0 concept, geographic location, and size influence the digital maturity of Portuguese industrial firms. Data were collected through a self-assessment questionnaire based on the IMPULS model and analyzed using ordinal logistic regression and chi-square tests to test three hypotheses. The results show that none of these factors significantly affects digital maturity, suggesting that isolated variables do not fully explain digital progress and that broader contextual elements, such as support programs and internal digital strategies, may play a more decisive role. The study meets its objectives and contributes to understanding digital readiness in the Portuguese industrial context. Future research should incorporate additional variables, employ longitudinal or sector-specific approaches, and utilize qualitative methods to enhance the analysis further.
Introducción. Estudios Hispánicos en Portugal en el siglo XXI. Una Visión panorámica. Homenaje à la Profesora Maria de Lurdes Correia Fernandes
Publication . Rocha Relvas, Susana
This Introduction serves as a critical guide to the contemporary challenges and potentialities of Iberian and Hispanic Studies, grounded in a comprehensive survey of the State of the Art. The text examines the Reconfiguration of the Canon in light of new demands for representativeness and the tensions between tradition and modernity. Through the lenses of Comparative Literature and Comparative Area Studies (CAS), the Iberian Peninsula and the Hispanic world are analyzed not as monolithic blocks, but as spaces of multilingual and intercultural intersection. The discussion expands into Cultural Studies, incorporating Postcolonial and Decolonial perspectives that challenge imperial narratives and persistent power structures. Within the scope of Transatlantic Studies, the fluidity of relations between the Peninsula and the Americas is explored. Complementarily, Studies on Literary Space are addressed, focusing on spatiality as a social and symbolic construct manifested in Transits, Migrations, and Exiles, as well as the documentary richness of Travel Literature. Finally, the central role of Translation Studies is highlighted as a tool for mediation and resistance, offering an integrated vision of a discipline in constant transformation and epistemological reaffirmation.
Advances in measuring the water footprint of dairy farming
Publication . Monteiro, António; Gökdal, Özdal; Webster, J.
Performance Comparison of Python-Based Complex Event Processing Engines for IoT Intrusion Detection: Faust Versus Streamz
Publication . Abbasi, Maryam; Cardoso, Filipe; ANTUNES VAZ, PAULO JOAQUIM; Silva, José; Sá, Filipe; Martins, Pedro
The proliferation of Internet of Things (IoT) devices has intensified the need for efficient real-time anomaly and intrusion detection, making the selection of an appropriate Complex
Event Processing (CEP) engine a critical architectural decision for security-aware data pipelines. Python-based CEP frameworks offer compelling advantages through the seamless integration with data science and machine learning ecosystems; however, rigorous comparative evaluations of such frameworks under realistic IoT security workloads remain
absent from the literature. This study presents the first systematic comparative evaluation of Faust and Streamz—two Python-native CEP engines representing fundamentally different architectural philosophies—specifically in the context of IoT network intrusion detection. Faust was selected for its actor-based stateful processing model with native Kafka integration and distributed table support, while Streamz was selected for its reactive, lightweight pipeline design targeting high-throughput stateless processing, making them representative of the two dominant paradigms in Python stream processing. Although both engines target different application niches, their performance characteristics under
realistic CEP workloads have never been rigorously compared, leaving practitioners without empirical guidance. The primary evaluation employs an IoT network intrusion dataset comprising 583,485 events from 83 heterogeneous devices. To assess whether the observed performance characteristics are specific to this single dataset or generalize across different workload profiles, a secondary IoT-adjacent benchmark is included: the PaySim financial transaction dataset (6.4 million records), selected because its event schema, fraud-pattern temporal structure, and volume differ substantially from the intrusion dataset, providing a stress test for cross-workload robustness rather than a claim of domain equivalence. We acknowledge the reviewer’s valid point that a second IoT-specific intrusion dataset (such as TON_IoT or Bot-IoT) would constitute a more directly comparable validation; this is identified as a priority for future work. The load levels used in scalability experiments (up to 5000 events per second) intentionally exceed the dataset’s natural rate to stress-test each engine’s architectural ceiling and identify saturation thresholds relevant to large-scale or multi-sensor IoT deployments. We conducted controlled experiments with comprehensive statistical analysis. Our results demonstrate that Streamz achieves superior throughput at 4450 events per second with 89% efficiency and minimal resource
consumption (40 MB memory, 12 ms median latency), while Faust provides robust intrusion pattern detection with 93–98% accuracy and stable, predictable resource utilization (1.4% CPU standard deviation). A multi-framework comparison including Apache Kafka Streams and offline scikit-learn baselines confirms that Faust achieves detection quality competitive with JVM-based alternatives (Faust: 96.2%; Kafka Streams: 96.8%; absolute difference of 0.6 percentage points, not statistically significant at p = 0.318) while retaining
the Python ecosystem advantages. Statistical analysis confirms significant performance differences across all metrics (p < 0.001, Cohen’s d > 0.8). Critical scalability thresholds
are identified: Streamz maintains efficiency above 95% up to 3500 events per second, while Faust degrades beyond 2500 events per second. These findings provide IoT security engineers and system architects with actionable, empirically grounded guidance for CEP engine selection, establish reproducible benchmarking methodology applicable to futurePython-based stream processing evaluations, and advance theoretical understanding of the accuracy–throughput trade-off in stateful versus stateless Python CEP architectures.
Manual UFCD 9596 - Autor - Propriedade IEFP
Publication . Correia, João