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Un epigramma per Wilamowitz. Sulle tracce di Wilhelm Hoffmann (1835-1900)
No full textAmong the letters in Wilamowitz’ Nachlass attributed to Martha Hofmann is a leaflet containing
a Latin poem, sent to him on 2.2.1899, in gratitude for the gift of the newly published Griechische Tragoedien. It was signed «Elpenor» – i.e., «Hoffmann», according to a widely
circulated joking etymology deriving the surname from hoffen. The author is Wilhelm Hoffmann
(1835-1900), a teacher at the Sophien-Gymnasium in Berlin and close friend of Hermann Usener,
who composed occasional poems in both modern and ancient languages. Hoffmann had known
Wilamowitz since 1869 and corresponded with him on several occasions, notably discussing the
use of rhymes in translations of Greek tragedies
Crop-“mini livestock” mixed system: a sustainable way to enhance soil quality and plant growtht
No full textThe International Conference on Computational Science and its Applications - ICCSA 2025
No full textThis study proposes an ensemble machine learning model to analyse the association between respiratory Emergency Room (ER) admissions and environmental factors, such as air pollution and weather-climatic conditions. The analysed climatic variables include air temperatures Tmin, Tmax, Taverage, atmospheric pressure P, relative humidity RH, and levels of CO, O3, PM10, and NO2. The data were processed as daily averages to ensure consistency and comparability in the analyses. Data on ER, provided by the Policlinico of Bari, cover the period from 2013 to 2023.
The analysis was conducted using ensemble learning techniques, applying three regression models: Random Forest, XGBoost, and Adaboost. The models were trained on a pre-processed database using a 7-day exponential moving average (EMA7) to obtain a more stable time series. Model hyperparameters were optimized through Bayesian optimization. Among the analysed models, XGBoost showed high predictive capacity in test sets. In particular, the R2 value was 0.772, while the MAE was 0.049 cases/day.
Applying SHAP (SHapley Additive exPlanations) analysis to the XGBoost model allowed us to identify the most important variables influencing hospital admissions and their related patterns. The most relevant features, ranked by importance, were: low values of average air temperature and atmospheric pressure, and high values of CO. The SHAP method, and in particular the use of Bee Swarm plots, were used to globally interpret the results obtained by the model and allowed us to reach the above results. Furthermore, in order to determine for the most important features the values that cause an increase in admission to the emergency room for respiratory diseases, a local analysis was carried out by applying the LIME model which allowed us to say that the greater onset of respiratory diseases is associated with average temperatures lower than 12.28 °C, atmospheric pressure values lower than or equal to 1006.81 hPa and CO concentrations greater than 0.84 mg/m3
Review and Intercomparison of Machine Learning Applications for Short-term Flood Forecasting
No full textAmong natural hazards, floods pose the greatest threat to lives and livelihoods. To reduce
flood impacts, short-term flood forecasting can contribute to early warnings that provide
communities with time to react. This manuscript explores how machine learning (ML) can
support short-term flood forecasting. Using two methods [strengths, weaknesses, opportu-
nities, and threats (SWOT) and comparative performance analysis] for different forecast
lead times (1–6, 6–12, 12–24, and 24–48 h), we evaluate the performance of machine
learning models in 94 journal papers from 2001 to 2023. SWOT reveals that the best
short-term flood forecasting was produced by hybrid, random forest (RF), long short-term
memory (LSTM), artificial neural network (ANN), and adaptive neuro-fuzzy inference
system (ANFIS) approaches. The comparative performance analysis, meanwhile, favors
convolutional neural network, ANFIS, multilayer perceptron, k-nearest neighbors algo-
rithm (KNN), hybrid, LSTM, ANN, and support vector machine (SVM) at 1–6 h; hybrid,
ANFIS, ANN, and LSTM at 6–12 h; SVM, hybrid, and RF at 12–24 h; and hybrid and RF
at 24–48 h. In general, hybrid approaches consistently perform well across all lead times.
Trends such as hybridization, model selection, input data selection, and decomposition
seem to improve the accuracy of models. Furthermore, effective stand-alone ML models
such as ANN, SVM, RF, genetic algorithm, KNN, and LSTM, provide better outcomes
through hybridization with other ML models. By including different machine learning
models and parameters such as environmental, socio-economical, and climatic parameters,
the hybrid system can produce more accurate flood forecasting, making it more effective
for early warning operational purpose
Microalgae: Green Engines for Achieving Carbon Sequestration, Circular Economy, and Environmental Sustainability—A Review Based on Last Ten Years of Research
No full textFeeding a growing global population requires sustainable, innovative, and cost-effective solutions, especially in light of the environmental damage and nutrient imbalances caused by excessive chemical fertilizer use. Microalgae have gained prominence due to their phylogenetic diversity, physiological adaptability, eco-compatible characteristics, and potential to support regenerative agriculture and mitigate climate change. Functioning as biofertilizers, biostimulants, and bioremediators, microalgae accelerate nutrient cycling, improve soil aggregation through extracellular polymeric substances (EPSs), and stimulate rhizospheric
microbial diversity. Empirical studies demonstrate their ability to increase crop yields by 5–25%, reduce chemical nitrogen inputs by up to 50%, and boost both organic carbon content and enzymatic activity in soils. Their application in saline and degraded lands further promotes resilience and ecological regeneration. Microalgal cultivation platforms offer scalable in situ carbon sequestration, converting atmospheric carbon dioxide (CO2) into biomass with potential downstream vaporization into biofuels, bioplastics, and biochar, aligning with circular economy principles. While the commercial viability of microalgae is challenged by high production costs, technical complexities, and regulatory gaps, recent breakthroughs in cultivation systems, biorefinery integration, and strain optimization highlight promising pathways forward. This review highlights the strategic importance of microalgae in enhancing climate resilience, promoting agricultural sustainability, restoring soil health, and driving global bioeconomic transformation
Hermetia illucens meal from different substrates for replacing fishmeal: study on zebrafish as fish model
No full text
Uncovering alternative physiological and molecular strategies to cope with water stress in olive tree
No full textIn recent years, the ongoing climate changes have made it crucial to rethink agriculture in a more sustainable way. This includes reducing and optimizing the use of resources, including water, through the identification and selection of genotypes more tolerant to abiotic stresses. Although considered a xerophytic species, the olive tree requires an adequate water supply to ensure both quantity and quality production. Drought-tolerant olive cultivars have been identified through breeding programs; however, the key molecular mechanisms involved in this tolerance remain largely unknown. To investigate in depth the plant responses to drought, six cultivars of different genetic backgrounds were grown in controlled conditions and exposed to water stress as well as inoculated with arbuscular mycorrhizal fungi (AMF). The physiological responses to drought stress varied among cultivars, as expected, showing complementary and/or alternative strategies, even according to AMF inoculation. This approach allowed us to identify two contrasting olive tree cultivars in response to drought stress (“Frantoio” and “Arbequina” as susceptible and tolerant, respectively). Transcriptomic profiles comparison of these cultivars enabled us to identify differentially expressed genes (DEG) with key roles in the regulation of metabolic pathways involved in drought tolerance, useful to support future olive tree breeding programs. Interestingly, the AMF inoculum was able to alleviate water stress damages mainly in the susceptible cultivar; this effect involved the more important plant physiological responses