ESTGV - DEMGI - Artigo em revista científica, indexada ao WoS/Scopus
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- A study of mixed mode interlaminar fracture on nanoclay enhanced epoxy/glass fiber compositesPublication . Silva, H.; Ferreira, J. A. M.; Costa, J. D. M.; Capela, C.Fiber reinforced laminate are widely used in aerospace, automobile and marine industries, despite its poor interlaminar fracture toughness (IFT), as consequence of the absence of fibers to sustain transverse load. One way recently explored with relative success in order to improve IFT is the use of nanoparticles to reinforce the matrix. Present paper intends to assess and discuss the fracture toughness on mixed mode loading of fiber glass mats/nanoclay enhanced epoxy matrix laminates. The matrix used was the epoxy resin Biresin® CR120 combined with the hardener CH120-3, the fiber glass was triaxial mats ETXT 450 and the nanoparticles were montmorillonite nanoclay (NC). The results were discussed in order to understand the effects of the percentage of nanoclay and the shear load quantified in terms of the GII/GI ratio on the total fracture toughness G. The incorporation of a small quantity of NC into matrices improves significantly mixed-mode IFT for all loading mode ratios GII/G. The total fracture toughness G increases with the mode II loading component and a linear mixed-mode fracture criteria reproduces the Gc against GII/G relationship.
- Fatigue behaviour of glass fibre reinforced epoxy composites enhanced with nanoparticlesPublication . Borrego, L.P.; Costa, J.D.M.; Ferreira, J.A.M.; Silva, H.Nanoparticle reinforcement of the matrix in laminates has been recently explored to improve mechanical properties, particularly the interlaminar strength. This study analyses the fatigue behaviour of nanoclay and multiwalled carbon nanotubes enhanced glass/epoxy laminates. The matrix used was the epoxy resin Biresin CR120, combined with the hardener CH120-3. Multiwalled carbon nanotubes (MWCNTs) 98% and organo-montmorillonite Nanomer I30 E nanoclay were used. Composites plates were manufactured by moulding in vacuum. Fatigue tests were performed under constant amplitude, both under tension–tension and three points bending loadings. The fatigue results show that composites with small amounts of nanoparticles addition into the matrix have bending fatigue strength similar to the obtained for the neat glass fibre reinforced epoxy matrix composite. On the contrary, for higher percentages of nanoclays or carbon nanotubes addition the fatigue strength tend to decrease caused by poor nanoparticles dispersion and formation of agglomerates. Tensile fatigue strength is only marginally affected by the addition of small amount of particles. The fatigue ratio in tensio –tension loading increases with the addition of nanoclays and multi-walled carbon nanotubes, suggesting that both nanoparticles can act as barriers to fatigue crack propagation.
- Interlaminar Adhesive Strength of Nano-Reinforced Glass/Epoxy LaminatesPublication . Silva, H.; Ferreira, J. A. M.; Costa, J. D. M.; Capela, C.Interlaminar fracture is significantly influenced by the adhesive strength of fiber=matrix interfaces. Critical strain energy release rate (GC) is the most common parameter used to quantify the interfacial strength. However, subcritical debonding can occur at lower mechanical loads than those required for interlaminar fracture toughness (IFT). This study was performed using nanoclayreinforced epoxy=glass fiber laminates in order to analyze the influence of the addition of nanoclay and hydro aging on IFT and subcritical crack growth. Hydro aging was done immersinga batch of specimens in distilled water at 25 C for 30 days. Mode I IFT was significantly improved by the incorporation of nanoclays into the resin, the improvement reaching 31% for 3% of nanoclays content. The results of subcritical debonding were plotted in terms of da=dt versus G curves, for dry materials and long term hydro aged composites. Hydro aged composites exhibit not only a reduction of GIc, of about 14% for 3% of nanoclays, but also a higher subcritical crack propagation rate. The addition of nanoparticles reduces subcritical crack propagation rate.
- Mixed Mode interlayer fracture of glass fiber/nano-enhanced epoxyPublication . Silva, H.; Ferreira, J. A. M.; Capela, C.; Richardson, M.O.WIncreasing interlaminar fracture toughness (IFT) has long been an important goal in the fiber reinforced composites field. For that purpose some research has recently explored the use of nanoparticle reinforced matrices to improve interlaminar strength. In this present paper a small quantity of nanoclays (NC) and multiwalled carbon nanotubes (MWCNT) were used in order to enhance the IFT of glass fiber/epoxy composite laminates. The composites sheets were produced by a vacuum molding process. Mode I, Mode II, and Mixed-Mode I/II tests were performed to determine critical strain Energy release rates, using double cantilever beam, end-notched flexure, and Mixed-Mode Bending specimens, respectively. Significant improvements in IFT were obtained for all loading modes by the incorporation of NC into the epoxy resin, whilst MWCNT produced only moderate improvements. For Mode I, IFT improvement by the incorporation of nanoparticle fillers, reached about 31% for 3 wt% of NC and 17% for 1 wt% of MWCNT. In Mode II the increase was about 50% for 3 wt% of NC and 30% for 1 wt% of MWCNT. The dispersion of small quantities NC and MWCNT into matrices significantly improved Mixed-Mode IFTs for all loading mode ratios GII/G. The total fracture toughness G increased under Mode II loading components and linear Mixed-Mode fracture criteria reproduced the Gc versus Mode ratios GII/G and GI versus GII relationship.
- A Simulation of Data Censored Rigth Type I with Weibull DistributionPublication . Gaspar, Daniel; Andrande Ferreira, LuisIn the maintenance and reliability field, there are frequent analyses with data being censored. In reliability research, many articles do simulation, but few explain how they do it. the loss of information resulting from the unavailable exact failure times will impact negatively the efficiency of reliability analysis. This paper presents four different algorithms to generate random data with a different number of censored values. The four algorithms are compared, and tree parameters are used to select the best one. The Weibull distribution is used to generate the random numbers because it is one of the most used in reliability studies. The results of the algorithm chosen are very relevant; with a sample of n = 50 and a number of cycles of simulations m = 1000, the standard deviation is higher when the shape factor of Weibull distribution is beta = 0.5 and slowly decreases until the shape factor equals 5. The percentage error (PE), one of the indicators selected, is much higher when the percentage of censored data is c = 5%, then goes down when the shape factor increases. There is a different behaviour when censored data is C = 20% and the percentage error (PE) is higher when shape factor is beta = 1.5. This article presents an algorithm that it considers the best for simulating right-censored type-I data. The algorithm has excellent accuracy, random data i.i.d and excellent computational performance.
- Reliability Estimation Using EM Algorithm with Censored Data: A Case Study on Centrifugal Pumps in an Oil RefineryPublication . Silva, José; VAZ, PAULO; Martins, Pedro; Ferreira, LuísCentrifugal pumps are widely employed in the oil refinery industry due to their efficiency and effectiveness in fluid transfer applications. The reliability of pumps plays a pivotal role in ensuring uninterrupted plant productivity and safe operations. Analysis of failure history data shows that bearings have been identified as critical components in oil refinery pump groups. Analyzing historical failure data for such systems is a complex task due to censored data and missing information. This paper addresses the complexity of estimating the Weibull distribution parameters using the maximum likelihood method under these conditions. The likelihood equation lacks an explicit analytical solution, necessitating numerical methods for resolution. The proposed approach presented in this article leverages the expectation maximization (EM) algorithm for estimating the Weibull distribution parameters in a real-world case study of a complex engineering system. The results demonstrate the superior performance of the EM algorithm with censored data, showcasing its ability to overcome the limitations of traditional methods and provide more accurate estimates for reliability metrics. This highlights the importance of obtaining results through these methodologies in the analysis of reliability and in facilitating more informed decision making in complex systems
- Data Privacy and Ethical Considerations in Database ManagementPublication . Pina, Eduardo; Ramos, José; Jorge, Henrique; ANTUNES VAZ, PAULO JOAQUIM; Vaz, Paulo; Silva, José; Wanzeller, Cristina; Abbasi, Maryam; Martins, Pedro; Silva, José; Wanzeller Guedes de Lacerda, Ana CristinaData privacy and ethical considerations ensure the security of databases by respecting individual rights while upholding ethical considerations when collecting, managing, and using information. Nowadays, despite having regulations that help to protect citizens and organizations, we have been presented with thousands of instances of data breaches, unauthorized access, and misuse of data related to such individuals and organizations. In this paper, we propose ethical considerations and best practices associated with critical data and the role of the database administrator who helps protect data. First, we suggest best practices for database administrators regarding data minimization, anonymization, pseudonymization and encryption, access controls, data retention guidelines, and stakeholder communication. Then, we present a case study that illustrates the application of these ethical implementations and best practices in a real-world scenario, showing the approach in action and the benefits of privacy. Finally, the study highlights the importance of a comprehensive approach to deal with data protection challenges and provides valuable insights for future research and developments in this field
- Adaptive and Scalable Database Management with Machine Learning Integration: A PostgreSQL Case StudyPublication . Abbasi, Maryam; Bernardo, Marco V.; Vaz, Paulo; Silva, José; Martins, Pedro; ANTUNES VAZ, PAULO JOAQUIM; Silva, JoséThe increasing complexity of managing modern database systems, particularly in terms of optimizing query performance for large datasets, presents significant challenges that traditional methods often fail to address. This paper proposes a comprehensive framework for integrating advanced machine learning (ML) models within the architecture of a database management system (DBMS), with a specific focus on PostgreSQL. Our approach leverages a combination of supervised and unsupervised learning techniques to predict query execution times, optimize performance, and dynamically manage workloads. Unlike existing solutions that address specific optimization tasks in isolation, our framework provides a unified platform that supports real-time model inference and automatic database configuration adjustments based on workload patterns. A key contribution of our work is the integration of ML capabilities directly into the DBMS engine, enabling seamless interaction between the ML models and the query optimization process. This integration allows for the automatic retraining of models and dynamic workload management, resulting in substantial improvements in both query response times and overall system throughput. Our evaluations using the Transaction Processing Performance Council Decision Support (TPC-DS) benchmark dataset at scale factors of 100 GB, 1 TB, and 10 TB demonstrate a reduction of up to 42% in query execution times and a 74% improvement in throughput compared with traditional approaches. Additionally, we address challenges such as potential conflicts in tuning recommendations and the performance overhead associated with ML integration, providing insights for future research directions. This study is motivated by the need for autonomous tuning mechanisms to manage large-scale, hetero geneous workloads while answering key research questions, such as the following: (1) How can machine learning models be integrated into a DBMS to improve query optimization and workload management? (2) What performance improvements can be achieved through dynamic configuration tuning based on real-time workload patterns? Our results suggest that the proposed framework significantly reduces the need for manual database administration while effectively adapting to evolving workloads, offering a robust solution for modern large-scale data environments.
- Enhancing Visual Perception in Immersive VR and AR Environments: AI-Driven Color and Clarity Adjustments Under Dynamic Lighting ConditionsPublication . Abbasi, Maryam; Silva, José; Martins, Pedro; ANTUNES VAZ, PAULO JOAQUIM; Silva, JoséThe visual fidelity of virtual reality (VR) and augmented reality (AR) environments is essential for user immersion and comfort. Dynamic lighting often leads to chromatic distortions and reduced clarity, causing discomfort and disrupting user experience. This paper introduces an AI-driven chromatic adjustment system based on a modified U-Net architecture, optimized for real-time applications in VR/AR. This system adapts to dynamic lighting conditions, addressing the shortcomings of traditional methods like histogram equalization and gamma correction, which struggle with rapid lighting changes and real-time user interactions. We compared our approach with state-of-the-art color constancy algorithms, including Barron’s Convolutional Color Constancy and STAR, demonstrating superior performance. Experimental results from 60 participants show significant improvements, with up to 41% better color accuracy and 39% enhanced clarity under dynamic lighting conditions. The study also included eye-tracking data, which confirmed increased user engagement with AI-enhanced images. Our system provides a practical solution for developers aiming to improve image quality, reduce visual discomfort, and enhance overall user satisfaction in immersive environments. Future work will focus on extending the model’s capability to handle more complex lighting scenarios.
- Machine Learning Approaches for Predicting Maize Biomass Yield: Leveraging Feature Engineering and Comprehensive Data IntegrationPublication . Abbasi, Maryam; Vaz, Paulo; Silva, José; Martins, Pedro; Silva, José; ANTUNES VAZ, PAULO JOAQUIMThe efficient prediction of corn biomass yield is critical for optimizing crop production and addressing global challenges in sustainable agriculture and renewable energy. This study employs advanced machine learning techniques, including Gradient Boosting Machines (GBMs), Random Forests (RFs), Support Vector Machines (SVMs), and Artificial Neural Networks (ANNs), integrated with comprehensive environmental, soil, and crop management data from key agricultural regions in the United States. A novel framework combines feature engineering, such as the creation of a Soil Fertility Index (SFI) and Growing Degree Days (GDDs), and the incorporation of interaction terms to address complex non-linear relationships between input variables and biomass yield. We conduct extensive sensitivity analysis and employ SHAP (SHapley Additive exPlanations) values to enhance model interpretability, identifying SFI, GDDs, and cumulative rainfall as the most influential features driving yield outcomes. Our findings highlight significant synergies among these variables, emphasizing their critical role in rural environmental governance and precision agriculture. Furthermore, an ensemble approach combining GBMs, RFs, and ANNs outperformed individual models, achieving an RMSE of 0.80 t/ha and R2 of 0.89. These results underscore the potential of hybrid modeling for real-world applications in sustainable farming practices. Addressing the concerns of passive farmer participation, we propose targeted incentives, education, and institutional support mechanisms to enhance stakeholder collaboration in rural environmental governance. While the models assume rational decision-making, the inclusion of cultural and political factors warrants further investigation to improve the robustness of the framework. Additionally, a map of the study region and improved visualizations of feature importance enhance the clarity and relevance of our findings. This research contributes to the growing body of knowledge on predictive modeling in agriculture, combining theoretical rigor with practical insights to support policymakers and stakeholders in optimizing resource use and addressing environ mental challenges. By improving the interpretability and applicability of machine learning models, this study provides actionable strategies for enhancing crop yield predictions and advancing rural environmental governance.