Browsing by Issue Date, starting with "2025-11-11"
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- Wastewater stressors shape the microbiome of aerobic granular sludgePublication . Miranda, Catarina; Maia, Alexandra S.; Tiritan, Maria Elizabeth; Amorim, Catarina L.; Castro, Paula M. L.Aerobic granular sludge (AGS) is a revolutionary biological treatment technology, which, due to its competitive advantages, has been increasingly implemented in full-scale wastewater treatment plants (WWTPs). WWTPs are frequently challenged by multiple stressors, including pharmaceuticals and fluctuating salinity, which not only increase the complexity of wastewater but also impair the biological treatment performance. However, the impact of such stressors on the compositional dynamics and ecological succession of AGS microbial communities remains understudied. In this study, over four months a laboratory-scale AGS reactor was fed with domestic wastewater of fluctuating salinity that sporadically contained pharmaceuticals (tramadol and venlafaxine) and their metabolites at μg/L levels. The effect of these stressors on treatment performance and bacteriome dynamics was assessed. The reactor performance was stable, achieving high removal efficiencies for organic carbon (89.0 ± 3.1%) and ammonium (99.8 ± 0.2%). Pharmaceuticals and their metabolites were removed by up to 85% and did not impair reactor performance. At the microbiome level, distinct bacterial niches emerged over time in response to wastewater compositional changes, reflecting the ecological flexibility of AGS bacteriome under stressful conditions. In the presence of multiple stressors, a diverse core emerged in the bacteriome, including nutrient removal-related taxa (e.g., Nitrosomonas and Nitrobacter), which were crucial for sustaining effective nitrification. Additionally, the enrichment of Paracoccus during pharmaceutical exposure underscored its key role in protecting functional bacteria and preserving granular system integrity. This study highlights the potential of AGS systems to handle complex and fluctuating wastewater compositions, thanks to a versatile microbiome that sustains high treatment efficiency and prevents operational failure.
- Artificial intelligence for algorithmic trading digital assets: evidence from the Counter-Strike 2 skin marketPublication . Guede-Fernández, Federico; Wagle, Yash; Dias, Pedro; Giordano, Ana Paula; Henriques, Lúcio; Costa, Gonçalo; Azevedo, SaloméIntroduction: The Counter-Strike 2 skin market has developed into a multi-billion-dollar digital asset ecosystem, characterized by high volatility, low liquidity, and pricing inefficiencies that differ substantially from traditional financial markets. Despite the growing economic relevance of virtual items, no previous study has systematically examined the use of artificial intelligence for skin trading. Methods: This work designs and evaluates an automated trading system that applies deep learning models, specifically Long Short-Term Memory networks and Neural Hierarchical Interpolation for Time Series, to forecast skin prices and guide trading decisions. A dataset of 12,000 unique skins from the Steam Market, covering the period from May 2024 to April 2025, was collected using the CSGOskins.gg application programming interface. To reflect real market conditions, the trading strategy incorporated the Steam Market restrictions of a seven-day minimum holding period and a ten percent transaction cost, and was benchmarked against a traditional buy-and-hold strategy. Backtesting was performed multiple time horizons of two, three, and 6 months. Portfolio selection was based on risk and return criteria, including a Sharpe ratio greater than one, a Sortino ratio greater than two, and a return on investment above five percent. Results: Artificial intelligence consistently outperforms buy-and-hold, particularly in smaller, more concentrated portfolios and over longer time horizons. For example, in 6-month simulations, artificial intelligence portfolios achieved returns approaching 20%, compared to 5% to 10% for buy-and-hold, with excess returns as high as 75% in small portfolios. Larger portfolios reduced absolute returns but improved risk-adjusted performance, confirming that diversification enhances stability while diluting raw profitability. Analysis of portfolio composition by rarity further revealed that artificial intelligence favors moderately rare and liquid skins such as Mil-Spec, resembling mid-cap equity investment strategies, while buy-and-hold accumulates rarer skins, analogous to small-cap holdings that rely on scarcity premiums. Discussion: These findings highlight that even in virtual goods markets, the trade-offs between return, risk, and diversification reflect established principles of modern portfolio theory. The study demonstrates both the feasibility and the potential of artificial intelligence-based trading systems in the Counter-Strike 2 skin economy, contributing methodological advances and practical insights for participants in this emerging digital asset market.
- Alegabilidade e cognoscibilidade de factos supervenientes em recurso de apelação civilPublication . Teixeira, Joana Catarina Fernandes; Faria, Maria Rita Camarate de Campos Lynce deThe purpose of this assignment is to analyse the possibility of alleging and knowing supervening facts in the context of civil appeals in the Portuguese legal system. The importance of this research stems from the decisive role that facts play in the judicial decision, which can influence whether the parties' claims are upheld or dismissed. The dissertation aims to explore the admissibility of supervening facts in appeals, the limitations imposed by the law and the implications of accepting or rejecting the admissibility of supervening facts. To this end, the fundamental principles of civil procedure and the classification of facts. The study is based on a rigorous analysis of doctrine, case law and applicable legislation, culminating in a reasoned position on the subject.