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- The Paradox Between Concept Knowledge and Digital Maturity Level for Industry 4.0: The Portuguese CasePublication . Guimarães, André; Rosivalda Pereira; Maria Teresa Pereira; Afonso Carvalho; Reis, Pedro; Antonio J. Marques Marques CardosoThis 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.
- Performance Comparison of Python-Based Complex Event Processing Engines for IoT Intrusion Detection: Faust Versus StreamzPublication . Abbasi, Maryam; Cardoso, Filipe; ANTUNES VAZ, PAULO JOAQUIM; Silva, José; Sá, Filipe; Martins, PedroThe 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.
- Menus as Instruments for Communicating Endogenous Products The case of Restaurants in Pousadas de PortugalPublication . Barroco, Cristina; Gonçalves, TiagoRestaurant menus can be much more than the presentation of dishes, they can showcase a set of endogenous products, while at the same time allowing the customer to get to know a little more about the territory, through gastronomy. When done well, menus can be instruments for promoting gastronomic tourism, taking customers on authentic journeys through the flavours and knowledge of the territory. The main aim of this paper is to identify how endogenous products are being communicated in menus, using the case study of Pousadas de Portugal restaurants for this purpose. The menus of three restaurants were analysed using a grid that made it possible to identify which endogenous products were presented and how this information was transmitted to the customer. The analysis allowed us to conclude that all the restaurants offer contemporary regional cuisine representative of the territories in which they are located. All menus feature short, interesting stories about some of the dishes. The word "Regional" appears several times on the menus and the dishes' names mention some territories. To complement the analysis, 25 chefs were surveyed, who were asked about the importance of including local products in their menus. Promoting these products can help preserve and showcase the unique cultural identity of a region and can contribute to sustainable development and environmental conservation.
- Unified Data Governance in Heterogeneous Database Environments: An API-Driven Architecture for Multi-Platform Policy EnforcementPublication . Abbasi, Maryam; ANTUNES VAZ, PAULO JOAQUIM; Silva, José; Cardoso, Filipe; Sá, Filipe; Martins, Pedro; Cardoso, Filipe; Sá, Filipe; Martins, PedroModern organizations increasingly rely on heterogeneous database environments that combine relational, document-oriented, and key-value storage systems to optimize performance for diverse application requirements. However, this technological diversity creates significant challenges for implementing consistent data governance policies, regulatory compliance, and access control across disparate systems. Traditional governance approaches that operate within individual database silos fail to provide unified policy enforcement and create compliance gaps that expose organizations to regulatory and operational risks. This paper presents a novel API-driven architecture that enables unified data governance across heterogeneous database environments without requiring database-specific modifications or vendor lock-in. The proposed framework implements a centralized governance layer that coordinates policy enforcement across PostgreSQL, MongoDB, and Amazon DynamoDB systems through RESTful API interfaces. Key architectural components include differentiated access control through hierarchical API key management, automated compliance workflows for regulatory requirements such as GDPR, real-time audit trail generation, and comprehensive data quality monitoring with automated improvement mechanisms. Comprehensive experimental evaluation demonstrates the framework’s effectiveness across multiple operational dimensions. The system achieved 95.2% accuracy in access control enforcement across different data classification levels, while automated GDPR compliance workflows demonstrated 98.6% success rates with average processing times of 2.9 h. Performance evaluation reveals acceptable overhead characteristics with linear scaling patterns for PostgreSQL operations (R2 = 0.89), consistent sub-20ms response times for MongoDB logging operations, and sustained throughput rates ranging from 38.9 to 142.7 requests per second across the integrated system. Data quality improvements ranged from 16.1% to 34.3% across accuracy, completeness, consistency, and timeliness dimensions over a 12-week monitoring period, with accuracy improving by 17.8 percentage points, completeness by 13.2 percentage points, consistency by 19.7 percentage points, and timeliness by 24.5 percentage points. The duplicate detection system achieved 94.6% precision and 95.6% recall across various duplicate types, including cross-database redundancy identification. The results demonstrate that API-driven governance architectures can effectively address the persistent challenges of policy fragmentation in multi-database environments while maintaining operational performance and enabling measurable improvements in data quality and regulatory compliance. The framework provides a practical migration path for organizations seeking to implement comprehensive governance capabilities without replacing existing database infrastructure investments.
- Validating the Use of Smart Glasses in Industrial Quality Control: A Case StudyPublication . Silva, José; Coelho, Pedro; Saraiva, Luzia; Martins, Pedro; López-Rivero, AlfonsoEffective quality control is crucial in industrial manufacturing for influencing efficiency, product dependability, and customer contentment. In the constantly changing landscape of industrial production, conventional inspection methods may fall short, prompting the need for inventive approaches to enhance precision and productivity. In this study, we investigate the application of smart glasses for real-time quality inspection during assembly processes. Our key innovation involves combining smart glasses’ video feed with a server-based image recognition system, utilizing the advanced YOLOv8 model for accurate object detection. This integration seamlessly merges mixed reality (MR) with cutting-edge computer vision algorithms, offering immediate visual feedback and significantly enhancing defect detection in terms of both speed and accuracy. Carried out in a controlled environment, our research provides a thorough evaluation of the system’s functionality and identifies potential improvements. The findings highlight that MR significantly elevates the efficiency and reliability of traditional inspection methods. The synergy of MR and computer vision opens doors for future advancements in industrial quality control, paving the way for more streamlined and dependable manufacturing ecosystems.
- An integrated and interoperable AutomationML-based platform for the robotic process of metal additive manufacturingPublication . Babcinschi, Mihail; Freire, Bernardo; Ferreira, Lucía; Señaris, Baltasar; Vidal, Felix; Vaz, Paulo; Neto, PedroIncreasingly, industry is looking to better integrate their industrial processes and related data. Interoperability is key since the organizations need to share data between them, between departments and the different stages of a given technological process. The problem is that many times there are no standard data formats for data exchange between heterogeneous engineering tools. In this paper we present an integrated and interoperable AutomationML-based platform for the robotic process of metal additive manufacturing (MAM). Data such as the MAM robot targets and process parameters are shared and edited along the different sub-stages of the process, from Computer-Aided Design (CAD), to path planning, to multiphysics simulation, to robot simulation and production. The AutomationML neutral data format allows the implementation of optimization loops connecting different sub-stages, for example the multi-physics simulation and the path planning. A practical use case using the Direct Energy Deposition (DED) process is presented and discussed. Results demonstrated the effectiveness of the proposed AutomationML-based solution.
- Revisiting Database Indexing for Parallel and Accelerated Computing: A Comprehensive Study and Novel ApproachesPublication . Abbasi, Maryam; Bernardo, Marco V.; ANTUNES VAZ, PAULO JOAQUIM; Silva, José; Martins, PedroWhile the importance of indexing strategies for optimizing query performance in database systems is widely acknowledged, the impact of rapidly evolving hardware architectures on indexing techniques has been an underexplored area. As modern computing systems increasingly leverage parallel processing capabilities, multi-core CPUs, and specialized hardware accelerators, traditional indexing approaches may not fully capitalize on these advancements. This comprehensive experimental study investigates the effects of hardware-conscious indexing strategies tailored for contemporary and emerging hardware platforms. Through rigorous experimentation on a real-world database environment using the industry-standard TPC-H benchmark, this research evaluates the performance implications of indexing techniques specifically designed to exploit parallelism, vectorization, and hardware-accelerated operations. By examining approaches such as cache-conscious B-Tree variants, SIMD-optimized hash indexes, and GPU-accelerated spatial indexing, the study provides valuable insights into the potential performance gains and trade-offs associated with these hardware-aware indexing methods. The findings reveal that hardware-conscious indexing strategies can significantly outperform their traditional counterparts, particularly in data-intensive workloads and large-scale database deployments. Our experiments show improvements ranging from 32.4% to 48.6% in query execution time, depending on the specific technique and hardware configuration. However, the study also highlights the complexity of implementing and tuning these techniques, as they often require intricate code optimizations and a deep understanding of the underlying hardware architecture. Additionally, this research explores the potential of machine learning-based indexing approaches, including reinforcement learning for index selection and neural network-based index advisors. While these techniques show promise, with performance improvements of up to 48.6% in certain scenarios, their effectiveness varies across different query types and data distributions. By offering a comprehensive analysis and practical recommendations, this research contributes to the ongoing pursuit of database performance optimization in the era of heterogeneous computing. The findings inform database administrators, developers, and system architects on effective indexing practices tailored for modern hardware, while also paving the way for future research into adaptive indexing techniques that can dynamically leverage hardware capabilities based on workload characteristics and resource availability.
- Optimizing Database Performance in Complex Event Processing through Indexing StrategiesPublication . Abbasi, Maryam; Bernardo, Marco V.; ANTUNES VAZ, PAULO JOAQUIM; Silva, José; Martins, PedroComplex event processing (CEP) systems have gained significant importance in various domains, such as finance, logistics, and security, where the real-time analysis of event streams is crucial. However, as the volume and complexity of event data continue to grow, optimizing the performance of CEP systems becomes a critical challenge. This paper investigates the impact of indexing strategies on the performance of databases handling complex event processing. We propose a novel indexing technique, called Hierarchical Temporal Indexing (HTI), specifically designed for the efficient processing of complex event queries. HTI leverages the temporal nature of event data and employs a multi-level indexing approach to optimize query execution. By combining temporal indexing with spatial- and attribute-based indexing, HTI aims to accelerate the retrieval and processing of relevant events, thereby improving overall query performance. In this study, we evaluate the effectiveness of HTI by implementing complex event queries on various CEP systems with different indexing strategies. We conduct a comprehensive performance analysis, measuring the query execution times and resource utilization (CPU, memory, etc.), and analyzing the execution plans and query optimization techniques employed by each system. Our experimental results demonstrate that the proposed HTI indexing strategy outperforms traditional indexing approaches, particularly for complex event queries involving temporal constraints and multi-dimensional event attributes. We provide insights into the strengths and weaknesses of each indexing strategy, identifying the factors that influence performance, such as data volume, query complexity, and event characteristics. Furthermore, we discuss the implications of our findings for the design and optimization of CEP systems, offering recommendations for indexing strategy selection based on the specific requirements and workload characteristics. Finally, we outline the potential limitations of our study and suggest future research directions in this domain.
- Impact of Cyberbullying on Academic Performance and Psychosocial Well-Being of Italian StudentsPublication . Ragusa, Antonio; Núñez-Rodríguez, Sandra; ANTUNES VAZ, PAULO JOAQUIM; Silva, José; Caliciotti, Virginia; González-Bernal, Jerónimo J.; López-Rivero, Alfonso J.; Petrillo, Ema; Gatto, Manuela; Obregón-Cuesta, Ana Isabel; González-Santos, JosefaCyberbullying is a growing problem in the Italian educational sector, with a prevalence of 17%. This study analyzes its impact on the psychosocial well-being and academic performance of Italian adolescents. Method: A cross-sectional study was conducted with 502 students from six schools in different Italian regions, using the European Cyberbullying Intervention Project Questionnaire (ECIPQ) to assess cyberbullying, in addition to collecting data on satisfaction, friends, and academic performance. Chi-square and ANOVA analyses were conducted to identify significant associations between the variables. Results: The analyses showed significant associations between cyberbullying and gender and in psychosocial well-being, with significant differences in personal satisfaction and body satisfaction. On the other hand, there were no significant differences in academic performance or in the ability to make new friends, although victims showed a significantly lower ability to make new friends compared to those who were neither victims nor aggressors. Conclusions: Cyberbullying has a significant impact on students’ psychosocial well-being, especially on personal satisfaction and school happiness, making it essential to implement interventions that promote safe school environments to mitigate these negative effects.
- A Case Study of a Solar Oven’s Efficiency: An Experimental ApproachPublication . Silva, José; Serrano, Luís; Martins, Pedro; Ferreira, Hugo; ANTUNES VAZ, PAULO JOAQUIM; Guerra, EmanuelThis research presents the design, construction, and experimental evaluation of a novel box-type solar oven optimized for enhanced thermal efficiency and heat retention, developed to address the challenges of sustainable cooking in temperate climates. The solar oven, measuring 120 cm × 60 cm × 45 cm, incorporates strategically designed rock wool insulation and 5 kg of steel plates as thermal mass, along with a double-glazed glass cover tilted at an experimentally optimized angle of 15° relative to the horizontal plane. Extensive experimental testing was conducted in Viseu, Portugal (40° N latitude) under varying meteorological conditions, including solar irradiance levels ranging from 400 to 900 W/m2 and wind speeds of up to 3 m/s. The results demonstrated that the oven consistently achieved internal temperatures exceeding 160 °C, with a peak temperature of 180 °C, maintaining cooking capability even during periods of intermittent cloud cover. Quantitative analysis showed that the thermal efficiency of the oven reached a peak of 38%, representing a 25–30% improvement over conventional designs. The incorporation of thermal mass reduced temperature fluctuations by up to 40%, and the enhanced insulation reduced conductive heat loss by approximately 30%. Cooking tests validated the oven’s practical effectiveness, with the successful preparation of various foods including rice (90 min), cake (120 min), vegetables (60 min), and bread (110 min). This study provides comprehensive performance data under different meteorological conditions, including detailed temperature profiles, heating rates, and thermal efficiency measurements. By addressing key limitations of prior models, particularly the challenge of temperature stability during variable solar conditions, the proposed solar oven offers a cost-effective, efficient solution that can be adapted for use in diverse climates and regions, with particular relevance to areas seeking sustainable alternatives to traditional cooking methods.