ESTGV - DI - Artigo em revista científica, indexada ao WoS/Scopus
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- Environmental and Economic Assessment of Desktop vs. Laptop Computers: A Life Cycle ApproachPublication . Domingos Ferreira, Miguel; Domingos, idalina; Leite dos Santos, Lenise Maria; Barreto Ana; Ferreira, JoséThis study evaluates and compares the environmental and economic implications of desktop and laptop computer systems throughout their life cycles using screening life cycle assessment (LCA) and life cycle costing (LCC) methodologies. The functional unit was defined as the use of one computer system for fundamental home and small-business productivity tasks for over four years. The analysis considered the production, use, and end-of-life phases. The results showed the desktop system had a higher overall carbon footprint (679.1 kg CO2eq) compared to the laptop (286.1 kg CO2eq). For both systems, manufacturing contributed the largest share of the emissions, followed by use. Desktops exhibited significantly higher use phase emissions, due to greater energy consumption. Life cycle cost analysis revealed that laptops had slightly lower total costs (EUR 593.88) than desktops (EUR 608.40) over the 4-year period, despite higher initial investment costs. Sensitivity analysis examining different geographical scenarios highlighted the importance of considering regional factors in the LCA. Manufacturer-provided data generally showed lower carbon footprint values than the modeled scenarios. This study emphasizes the need for updated life cycle inventory data and energy efficiency improvements to reduce the environmental impacts of computer systems. Overall, laptops demonstrated environmental and economic advantages over desktops in the defined usage cases.
- Olive Tree (Olea europaea) Pruning: Chemical Composition and Valorization of Wastes Through LiquefactionPublication . Domingos, idalina; Domingos Ferreira, Miguel; Ferreira, José; Esteves, Bruno; MDPIOlive tree branches (OB) and leaves (OL) from the Viseu region (Portugal) were studied for their chemical composition and liquefaction behavior using polyalcohols. Chemical analysis revealed that OL contained higher ash content (4.08%) and extractives, indicating more bioactive compounds, while OB had greater α-cellulose (30.47%) and hemicellulose (27.88%). Lignin content was higher in OL (21.64%) than OB (16.40%). Liquefaction experiments showed that increasing the temperature from 140 ◦C to 180 ◦C improved conversion, with OB showing a larger increase (52.5% to 80.9%) compared to OL (66% to 72%). OB reached peak conversion faster, and the optimal particle size for OB was 40–60 mesh, while OL performed better at finer sizes. OL benefited more from higher solvent ratios, whereas OB achieved high conversion with less solvent. FTIR analysis confirmed that acid-catalyzed liquefaction breaks down lignocellulosic structures, depolymerizes cellulose and hemicellulose, and modifies lignin, forming hydroxyl, aliphatic, and carbonyl groups. These changes reflect progressive biomass degradation and the incorporation of polyalcohol components, converting solid biomass into a reactive, polyol-rich liquid. The study highlights the distinct chemical and processing characteristics of olive branches and leaves, informing their potential industrial applications.
- Life Cycle Assessment of Pig Production in Central Portugal: Environmental Impacts and Sustainability ChallengesPublication . Leite dos Santos, Lenise Maria; Domingos Ferreira, Miguel; Domingos, idalina; Oliveira Verónica; Rodrigues Carla; Ferreira António; Ferreira, José; MDPIPig farming plays a crucial socioeconomic role in the European Union, which is one of the largest pork exporters in the world. In Portugal, pig farming plays a key role in regional development and the national economy. To ensure future sustainability and minimize environmental impacts, it is essential to identify the most deleterious pig production activities. This study carried out a life cycle assessment (LCA) of pig production using a conventional system in central Portugal to identify the unitary processes with the greatest environmental impact problems. LCA followed the ISO 14040/14044 standards, covering the entire production cycle, from feed manufacturing to waste management, using 1 kg of live pig weight as the functional unit. The slurry produced is used as fertilizer in agriculture, replacing synthetic chemical fertilizers. Results show that feed production, raising piglets, and fattening pigs are the most impactful phases of the pig production cycle. Fodder production is the stage with the greatest impact, accounting for approximately 60% to 70% of the impact in the categories analyzed in most cases. The environmental categories with the highest impacts were freshwater ecotoxicity, human carcinogenic toxicity, and marine ecotoxicity; the most significant impacts were observed for human health, with an estimated effect of around 0.00045 habitants equivalent (Hab.eq) after normalization. The use of more sustainable ingredients and the optimization of feed efficiency are effective strategies for promoting sustainability in the pig farming sector.
- Suggestions for promoting SOC storage within the carbon farming framework: Analyzing the INFOSOLO databasePublication . Cunha, Carlos; Castanheira, Nádia Luísa; Ramos, Tiago Brito; Martinho, Vítor João Pereira Domingues; Ferreira, António José Dinis; Pereira, José Luís da Silva; Sánchez-Carreira, Maria del CarmenThe new world challenges under climate change call for eco-friendly practices that make agriculture’s economic and social dimensions compatible with environmental preservation and ecosystem resilience. Carbon farming has emerged as an interesting alternative for dealing with these new frameworks, as it promotes conservation agriculture with practices that increase carbon sequestration in soils and plants. Considering these motivations, this research intends to bring more insights into the levels of soil organic carbon (SOC) in the Portuguese context, and this variable is interrelated with land use, land attributes, and soil characteristics. Statistical information from the INFOSOLO legacy database was analyzed through statistical methodologies and machine-learning approaches. The findings provide interesting support for the stakeholders about the influence of land use and soil types on the levels of SOC.
- Designing Inclusive Smartwatch Interfaces: Guidelines for Enhancing Usability and Adoption Among Older AdultsPublication . P. Duarte , Rui; Alves, Valter; Alves, Valter; Mota, MikaelAging introduces sensory, motor, and cognitive challenges and limited familiarity with digital interfaces, often hindering older adults’ adoption of new technologies. Smartwatches, with their compact size and health monitoring features, promise to improve older adults’ quality of life. However, their small screens and complex interfaces create significant usability barriers. While guidelines for mobile and web interfaces exist, frameworks for smartwatch design still need to be explored. This study addresses this gap by proposing smartwatch-specific design guidelines for older adults. Through an analysis of user challenges, existing design principles, and smartwatch constraints, the research formulates actionable recommendations to enhance usability and user experience. The contributions include identifying key obstacles older adults face with smartwatches, evaluating the applicability of established guidelines, creating tailored design principles for small screens, and developing a design system that balances simplicity, usability, and functionality. These contributions aim to facilitate smartwatch adoption and improve the inclusivity of digital technologies for older adults.
- 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.
- 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.
- 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
- 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.
- Real-Time Gesture-Based Hand Landmark Detection for Optimized Mobile Photo Capture and SynchronizationPublication . Marques, Pedro; ANTUNES VAZ, PAULO JOAQUIM; Silva, José; Martins, Pedro; Abbasi, MaryamGesture recognition technology has emerged as a transformative solution for natural and intuitive human–computer interaction (HCI), offering touch-free operation across diverse fields such as healthcare, gaming, and smart home systems. In mobile contexts, where hygiene, convenience, and the ability to operate under resource constraints are critical, hand gesture recognition provides a compelling alternative to traditional touch based interfaces. However, implementing effective gesture recognition in real-world mobile settings involves challenges such as limited computational power, varying environmen tal conditions, and the requirement for robust offline–online data management. In this study, we introduce ThumbsUp, which is a gesture-driven system, and employ a partially systematic literature review approach (inspired by core PRISMA guidelines) to identify the key research gaps in mobile gesture recognition. By incorporating insights from deep learning–based methods (e.g., CNNs and Transformers) while focusing on low resource consumption, we leverage Google’s MediaPipe in our framework for real-time detection of 21 hand landmarks and adaptive lighting pre-processing, enabling accurate recogni tion of a “thumbs-up” gesture. The system features a secure queue-based offline–cloud synchronization model, which ensures that the captured images and metadata (encrypted with AES-GCM) remain consistent and accessible even with intermittent connectivity. Ex perimental results under dynamic lighting, distance variations, and partially cluttered environments confirm the system’s superior low-light performance and decreased resource consumption compared to baseline camera applications. Additionally, we highlight the feasibility of extending ThumbsUp to incorporate AI-driven enhancements for abrupt lighting changes and, in the future, electromyographic (EMG) signals for users with mo tor impairments. Our comprehensive evaluation demonstrates that ThumbsUp maintains robust performance on typical mobile hardware, showing resilience to unstable network conditions and minimal reliance on high-end GPUs. These findings offer new perspectives for deploying gesture-based interfaces in the broader IoT ecosystem, thus paving the way toward secure, efficient, and inclusive mobile HCI solutions.