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- A new antibacterial protocol based on Cymbopogon nardus (L.) rendle essential oil and UV-C radiation with potential application for hatching eggsPublication . Oliveira , Gabriel da Silva; McManus , Concepta; Delgado, F.M.G.; Domingues , Joana; Oliveira Filho , Josemar Gonçalves de; Salgado, Cristiane Batista; Sousa , Heloisa Alves de Figueiredo; Santos, Vinícius Machado dosBackground/Objectives: Hatching egg sanitization protocols are primarily developed to protect embryos during the embryonic stage from exposure to horizontally transmitted pathogenic bacteria, thereby ensuring their survival. This study aimed to evaluate the antibacterial efficacy of Cymbopogon nardus (L.) Rendle (Poaceae) essential oil, alone or combined with UV-C radiation, as a potential sanitization protocol for hatching eggs. Methods: Unwashed table eggs, used as an experimental model, were sprayed with C. nardus essential oil at two concentrations (0.625–1.25%) and exposed to UV-C radiation for different exposure times (0–10 min). Mesophilic bacterial load and the physical characteristics of the eggshell were subsequently evaluated. Results: A significant reduction in the mesophilic bacterial load on the eggshell surface was observed following the application of the treatments. C. nardus essential oil promoted reductions greater than 2 log10 CFU/mL, demonstrating strong antimicrobial activity. The application of UV-C radiation alone resulted in a progressive decrease in the bacterial population, reaching approximately 2 log10 CFU/mL after 10 min of exposure. However, the combination of the essential oil and UV-C radiation showed the highest antibacterial efficacy, reducing bacterial counts to below 1 log10 CFU/mL, indicating that the combination of the two technologies was more effective than either treatment applied alone. No significant macroscopic changes were observed in the physical characteristics of the eggshells. Conclusions: The combined application of C. nardus essential oil and UV-C radiation on the eggshell surface may represent a promising sanitization protocol with potential application for hatching eggs.
- From pollen to patterns: authentication and geographical classification of stingless bee honey by melissopalynology and chemometricsPublication . Hernández Zarta, Héctor Hernán; Caruso, Sebastian; Ressutte, Jéssica; Luz, Cynthia F.P. da; Anjos, O.; Spinosa, Wilma A.; Galvan, DiegoThe geographical origin of stingless bee honey influences its physicochemical, biochemical, and sensory characteristics. In this study, we propose a chemometric approach for the geographical classification and authentication of Brazilian stingless bee honey based on melissopalynological profiling. A dataset of honey samples from five Brazilian states (Par´a, Maranh˜ao and Amazonas in the North; Rio Grande do Sul and Paran´a in the South) and five genera of stingless bees was assembled. Pollen types were identified and quantified under photonic microscopy, and the relative frequencies of key pollen types were used as input variables. Principal Component Analysis (PCA) revealed clear geographical differentiation, with Northern honeys associated with pollen types such as Tapirira guianensis, Triplaris, and Protium, whereas Southern were differentiated from Eucalyptus, Allophylus edulis, and Mimosa scabrella. Partial Least Squares Discriminant Analysis (PLS-DA) modeling achieved a satisfactory classification between Northern and Southern honeys, with zero misclassifications. In addition, a one-class classification using Data-Driven Soft Independent Modeling of Class Analogy (DD-SIMCA) successfully authenticated honeys from Rio Grande do Sul, yielding perfect metrics in sensitivity, specificity, and accuracy. These findings indicate that pollen composition, coupled with robust chemometric analyses, provides reliable markers for the geographical classification and authentication of stingless bee honey.
- Targeting skin aging: Anti-senescent potential and key enzyme modulation of Lavandula essential oilsPublication . Silva, Jorge M. Alves; Domingues, Joana; Delgado, F.M.G.; Ribeiro, Teresa; Cruz, Teresa; Salgueiro, Lígia; Girão, Henrique; Zuzarte, MónicaIntroduction: Skin aging is an inherent biological process linked to chronological aging that can be accelerated by external factors. Cellular senescence and skin enzymes are crucial players in this process and, therefore, relevant preventive and therapeutic targets. In this scenario, aromatic plants and their essential oils have gained attention as promising agents in the management of skin aging. The present study aims to explore the anti-senescent potential of Lavandula stoechas subsp. luisieri and Lavandula pedunculata essential oils on skin fibroblasts and their effect on relevant enzymes, given their anti-inflammatory effects. Methods: The chemical characterization of the essential oils was carried out by gas chromatography-mass spectrometry and their effect evaluated on etoposide-induced senescent fibroblasts during the recovery phase. Relevant markers of cellular senescence were analyzed, namely, β-galactosidase activity through a colorimetric assay, nuclear alterations by immunofluorescence, SASP secretion through ELISA, expression levels of cell cycle inhibitors by WB analysis and ultrastructural alterations using transmission electron microscopy. Modulation of key enzyme activity was determined using an in chemico approach. Results: The essential oil of L. pedunculata was characterized by high amounts of fenchone and camphor whereas that of L. stoechas subsp. luisieri showed a very peculiar chemical profile rich in rare necrodane derivatives and lavandulyl acetate. Both essential oils were effective in decreasing most of the senescence markers but L. stoechas subsp. luisieri strikingly attained significant effects at very low concentrations (6.25 μg/mL). Conclusion: These results highlight the potential of L. stoechas subsp. luisieri to mitigate cellular senescence and delay skin aging, thereby contributing to the valorization of this underexplored species in cosmeceutical and pharmaceutical applications.
- Predictive modeling of terrestrial radiation for optimizing solar-powered irrigation under limited data in Adrar Region (Algeria)Publication . Meziani, Assia; Mega, Nabil; Miloudi, Abdelmonen; Duarte, A.C.An accurate estimation of the radiation is a prerequisite for the net radiation balance, evapotranspiration modeling, and optimal scheduling of water extraction by solar-powered irrigation, especially in the water-scarce Saharan zone. In this study, we developed a predictive model to estimate daily terrestrial radiation at the surface of the Adrar region in Algeria. We used a training set of 25 years of data (2000–2025) from 10 stations (Adrar, Tamantit, Sidi Ahmed Timmi, Fenoughil, Zaouiet Kounta, Reggane, Gharmianou, Tittaf, Ikiss, Kassbet Lahrar) with only three features: soil temperature (0–7 cm), air temperature (2 m), and vapor pressure deficit. The robustness of the models was ensured by a time-based split. The random forest (RF), gradient boosting (GB), and extra trees (ET) tree-based models were evaluated on the generalization set. RF and ET exhibited the best performance (R = 0.92, rootean square error—RMSE = 31.85 W/m2, Nash–Sutcliffe efficiency—NSE = 0.84 for RF; R = 0.92, RMSE = 32.33 W/m2, NSE = 0.84 for ET), whereas GB exhibited poor performance (R = 0.90, RMSE = 36.67 W/m2, NSE = 0.79). Finally, we proposed a map for solar-powered irrigation optimization. In particular, we demonstrated that the southern part of the Adrar region (Reggane, Zaouiet Kounta) has high potential for solar-powered irrigation (more than 350 W/m2). This study contributes to hyper-arid agricultural regions in Algeria through water conservation and the utilization of renewable energy.
- Spatiotemporal analysis and machine learning prediction of reference evapotranspiration in Khenchela, Algeria: Comparison of MLR, GRNN, and LSTM modelsPublication . Meziani, Assia; Mega, Nabil; Miloudi, Abdelmonen; Duarte, A.C.; Khechekhouche, AbderahamaneReference evapotranspiration (ET₀) is a key parameter for water management in semi-arid regions with variable climates. This study analyzed the spatiotemporal dynamics of annual ET₀ in the Khenchela region of north-eastern Algeria (2000–2024). ET₀ was computed using the FAO-56 Penman–Monteith (PM) method. Spatial patterns were mapped using Inverse Distance Weighting (IDW). Meteorological data from 16 stations were used to train three models: Multiple Linear Regression (MLR), Generalized Regression Neural Network (GRNN), and Long Short-Term Memory (LSTM) to predict ET₀. The regional mean annual ET₀ increased by 7.2% from 2010 to 2019 decadal average (1 490 mm/year) to the 2020-2024 period (1597 mm/year), contributing to a cumulative 25-year increase of 7% from 2000 to 2009 baseline with hotspots in Babar 2 reaching ~2194 mm/year. The Mann–Kendall test confirmed significant upward trends (p < 0.05) driven by rising temperatures and declining relative humidity. All models performed well (R² > 0.965, RMSE < 0.49 mm/day, RSR < 0.20), with LSTM showing superior accuracy (R² > 0.987, RMSE < 0.232 mm/day, NSE ≈ 0.991, WI > 0.909). The superior performance of LSTM is attributed to its inherent capability to capture temporal autocorrelation and long-term dependencies in climatic time-series data. These findings support adaptive irrigation and drought mitigation in semi-arid regions of northern Africa.
- Neural network approximation based on ANFIS and Geographic Information System mapping for reliable evapotranspiration prediction in Khenchela, AlgeriaPublication . Meziani, Assia; Mega, Nabil; Miloudi, Abdelmonen; Duarte, A.C.; Khechekhouche, AbderahamaneAccurate estimation of reference evapotranspiration (ET0) is critical for sustainable water resource management, irrigation scheduling, and climate adaptation in heterogeneous semi-arid regions. This study presents a streamlined neural network (NN) approximation inspired by the Adaptive Neuro-Fuzzy Inference System (ANFIS) for predicting daily ET0 in Khenchela province, northeastern Algeria. Utilizing meteorological and soil data from 2000 to 2024 at 16 representative stations (Babar (1), Babar (2), Babar (3), Baghai, Bouhmama, Chechar, Djellal, El Hamma, Kais, Khenchela, Khirane, M’sara, Remila, Tamza, Taouzient, and Zaoui), sourced from the Open-Meteo Historical Weather API, the model employs inputs including air temperature, relative humidity, precipitation, wind speed, sunshine duration, terrestrial radiation, soil temperature, and soil moisture. The NN was trained to closely approximate the FAO-56 Penman-Monteith reference ET0 values computed directly by the API. Performance evaluation yielded strong agreement across stations: R2 > 0.96, RMSE 0.22 - 0.46 mm/day, NSE > 0.95, RSR < 0.13, and Willmott’s index 0.88 - 0.93, with peak accuracy (R2 > 0.99, RMSE < 0.24 mm/day) at high-elevation sites. Spatial patterns, mapped via GIS-based inverse distance weighting interpolation, revealed pronounced topographic and aridity-driven variability, confirmed by Emberger and De Martonne indices. This computationally efficient NN offers a scalable surrogate for FAO-56 calculations in data-limited, heterogeneous environments, supporting precision irrigation, drought monitoring, and adaptive strategies in semi-arid North Africa and Mediterranean regions.
- Effects of DEM Resolution on the characterization of a small agroforestry basin for hydrological modelling: The case of Idanha—PortugalPublication . Duarte, A.C.; Ferreira, Carla; Vitali, GiulianoDigital elevation models (DEMs) are key fundamental inputs in hydrological modelling, yet the influence of spatial resolution on basin delineation and process representation remains insufficiently understood, particularly in small catchments. This study investigates the influence of DEM resolution on topographic characterization and hydrological response in a small agroforestry basin in central Portugal. Three DEMs with resolutions of 5 m, 10 m, and 30 m were generated from contour data and satellite sources and processed using the TOPAZ-based TopAGNPS delineation framework. The sensitivity of basin structure to delineation parameters—critical source area (CSA) and minimum source channel length (MSCL)—was assessed, and the resulting configurations were used as inputs to the AnnAGNPS model. Results show that DEM resolution strongly influences the representation of hydrological cells and stream reaches. Increasing resolution from 30 m to 5 m leads to a nearly doubling of average cell slope and increases reach slope by more than four times, with corresponding changes in drainage network density and connectivity. Loglinear relationships were identified between slope and contributing area, as well as between slope and reach length, consistent with established geomorphic scaling laws. Hydrological simulations further indicate that resolution-dependent delineation significantly influences runoff, erosion, and peak discharge estimates, with finer resolutions increasing sensitivity to parametrization. Among land-cover scenarios, desertified conditions generate substantially higher runoff and peak flows compared to naturalized and forested conditions. Overall, the findings demonstrate that DEM resolution, together with preprocessing and delineation choices, exerts a critical control on hydrological model outputs. These effects are particularly pronounced in low-relief, human-influenced catchments, where coarse-resolution DEMs may lead to systematic underestimation of hydrological responses. The study highlights the need for resolution-aware modelling strategies and careful parametrization to improve the reliability and transferability of hydrological simulations.
- Emerging contaminants in water resources: Monitoring gaps, treatment limitations and governance challenges with insights from PortugalPublication . Esperanço, Pedro; Leal, Teresa; Almeida, André; Duarte, A.C.; Lopes, Luísa Cruz; Gonçalves, José Manuel; Oliveira, MargaridaThis study provides a comprehensive overview of emerging contaminants in water resources. It includes a global perspective with specific insights from Portugal. Following PRISMA 2020 guidelines, peer-reviewed studies published between 2020 and 2025 were critically assessed to identify patterns of contamination, monitoring gaps and technological readiness levels. Results indicate frequently detected emerging contaminants including pesticides, antibiotics and antidepressants in surface water, groundwater and wastewater systems. Advanced analytical methods, particularly liquid chromatography coupled with high-resolution mass spectrometry, stands out as the main detection technique, allowing the identification of trace levels of contaminants. These techniques also support the identification of pollution patterns associated with agriculture, urban and industrial effluents. However, significant asymmetries persist between international and Portuguese research. Particularly evident in systematic monitoring networks and integrated risk assessment approaches. Conventional water/wastewater treatment plants show limited removal efficiency, while advanced oxidation processes, adsorption technologies and microalgae-based systems demonstrate promising but variable performance depending on scale and operational maturity. The findings highlight gaps between scientific advances and regulatory implementation, emphasizing the need for strengthened monitoring frameworks and technology scale-up strategies. They also call for improved integration between science, governance, and sustainability policies to ensure resilient water resource management in line with the Sustainable Development Goals.
- Machine learning models for simulating daily reference evapotranspiration in a semi-arid environment using four meteorological variables: A multi-station study in Northwestern Algeria (Tlemcen Region)Publication . Meziani, Assia; Duarte, A.C.In this study, we evaluated the use of five different ML algorithms (CatBoost, XGBoost, random forest, gradient boosting, and support vector regression [SVR]) to estimate daily ET0 based only on four independent variables: 2 m air temperature, vapor pressure deficit, 10 m wind speed, and sunshine duration. We used a total of 9132 daily values (2000–2025) from the Open-Meteo Historical Weather API (2000–2025) at 10 stations in the Tlemcen province of northwest Algeria. The dataset was divided into training, validation, and testing sets using a chronological split of 70/15/15. We estimated the performance of each algorithm by using several statistics (RMSE, MAE, R2, NSE, RSR, andWillmott Index) as well as some statistics to evaluate the potential of overfitting and the ability to reproduce the behavior observed during the training phase. CatBoost had the highest overall accuracy and the most generalized performance, with an RMSE of approximately 0.292 mm day−1, MAE of approximately 0.208 mm day−1, R2 of 0.971, and NSE of 0.971 in the test set, suggesting an extremely low risk of overfitting. The optimal CatBoost model was also used to estimate the spatial and temporal variations of monthly ET0. The results showed high interannual variability (changes from year to year from −12.815 to +8.707 mm month−1) in the semi-arid region of Tlemcen but no significant long-term trends (cumulative net change of approximately −0.021 mm month−1 over 2000–2025). Therefore, the use of CatBoost is recommended as a robust, efficient, and reliable emulator of the FAO-56 Penman–Monteith equation (ET0) for estimating ET0 in semi-arid environments with limited climate data availability, and could be particularly useful in northwestern Algeria and other semi-arid Mediterranean regions.
- Use of cactus pear (Opuntia ficus-indica) as forage in the diet of small ruminantsPublication . Pitacas, F.I.; Reis, C.M.G.; Rodrigues, A.M.The use of cactus pear (Opuntia ficus-indica (L.) Miller) has proven to be a viable alternative for feeding small ruminants, particularly in arid and semi-arid regions. It serves as an accessible source of water and nutrients, partially replacing more expensive and less available feeds, especially during periods of feed scarcity. This review examines the nutritional value and chemical composition of cactus pear cladodes and their application in small ruminant feeding. Additionally, general aspects related to planting systems, biomass production, and cladode storage are discussed. Three examples of diets incorporating Opuntia ficus-indica cladodes in small ruminant feeding are presented: Assaf sheep breed, fattening lamb, and Saanen goat breed. In these diets, the proportion of fresh O. ficus-indica cladodes varied between 51.3% and 79.2%. A literature review was conducted to evaluate the impact of cactus pear cladodes on meat and milk production and quality. In general, O. ficus-indica cladodes exhibit low levels of dry matter (DM), crude protein (CP), and neutral detergent fibre (NDF). However, they are an excellent source of energy, rich in non-fibrous arbohydrates, and have a high DM digestibility. Given the importance of DM, CP, and NDF in ruminant nutrition and the high-water content of cladodes, O. ficus-indica can be effectively integrated into diets when animals have access to dry forage and a high-CP feed source. The use of O. ficus-indica represents a promising option for feeding small ruminants, offering a sustainable solution to reduce dependence on imported feeds and contributing to a lower carbon footprint, without compromising meat and milk production or quality.