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Slide 1 of 5 Publicación Acceso Abierto
Assessing Banana-Based Activated Carbon as a Biomaterial for the Adsorption of Drug Metabolites in Wastewater: Simulation of an Industrial-Scale Packed Column
(Processes, 2025-12-30) Tejada - Tovar, C; Villabona Ortiz, Angel; Coronado Hernández, Óscar Enrique; Haeckermann-Ruiz, Esmeralda; Méndez-Anillo, Rafael; Grupo de Investigación Sistemas Ambientales e Hidráulicos (GISAH)
The presence of paracetamol and ciprofloxacin in aquatic ecosystems is a cause for great concern due to their harmful effects on human health. The objectives of this investigation are to simulate an industrial-scale adsorption bed for the competitive removal of these pharmaceutical metabolites from effluents using banana-based activated carbon as the adsorbent. Aspen Adsorption simulation software (v.1) was used to model an industrial-scale packed-bed column under different conditions. Freundlich and Langmuir isothermal models were used in combination with the linear driving force (LDF) kinetic formulation. Adsorption efficiencies of 89.57% for paracetamol and 89.57% for ciprofloxacin were achieved using the Freundlich-LDF model, while the Langmuir-LDF model presented efficiencies of 89.60% for paracetamol and 89.59% for ciprofloxacin. This study used machine learning algorithms, combined with analyses of multiple statistical indicators (R2, RMSE, and MAE), to evaluate model performance. Coefficient of determination (R2) values of up to 0.99 were observed in validation and testing. The application of these mathematical models yielded high removal efficiencies, demonstrating the potential of this approach for drug-contaminated effluent remediation and for forecasting the performance of packed columns at scaled-up levels.
Slide 2 of 5 Publicación Acceso Abierto
Derivative-aligned anticipation of forbush decreases from entropy and fractal markers
(Instrumentation and Methods for Astrophysics, 2026-01-15) Sierra Porta, David; Perez Navarro, Juan Diego; Grupo de Investigación Gravitación y Matemática Aplicada; Semillero de Investigación en Astronomía y Ciencia de Datos
We develop a feature-based framework to anticipate Forbush decreases (FDs) in one-minute neutron monitor records by tracking sliding-window invariants from information theory, scaling, and geome- try. For each station we compute marker time series—including Shannon, spectral, approximate and sample entropy; Lempel–Ziv complexity; correlation dimension; and Higuchi and Katz fractal dimen- sions—smooth them with an exponentially weighted moving average, and analyze their within-station standardized first differences. Timing is referenced to an operational alignment time t0 defined as the minimum of the smoothed count first difference, and marker leads are reported in minutes (ℓ∗ < 0 indicates anticipation). Station-level detectability is defined on a pre-t0 window using a robust z-score detector with bilateral threshold and persistence, requiring neither cross-correlation nor hypothesis testing.
We apply the pipeline to two FD episodes with broad station coverage (2023-04-23 and 2024-05-10; 28 stations each). Across events, a compact CORE panel exhibits consistently high detection rates and predominantly negative lead distributions, with median leads of order several hours depending on the invariant and event. Lead dispersion across stations is substantial (interquartile ranges typically spanning a few hours), underscoring the value of station-wise criteria and distributional summaries rather than single-station inference. Representative marker trajectories confirm that early flagging corresponds to sustained pre-t0 excursions in marker differences, not merely tabulated artifacts. The approach is reproducible from open code, operates on native station units without cross-station homogenization, and is qualitatively stable to sensitivity sweeps of windowing, smoothing, and detector parameters. These results support derivative-aligned invariant panels as practical early-warning complements to amplitude-threshold methods in space-weather nowcasting.
Slide 3 of 5 Publicación Acceso Abierto
Complexity and scaling descriptors as diagnostic predictors of heliophysical indices across solar-cycle timescales
(Advances in Space Research, 2026-02-10) Sierra Porta, David; Canedo Verdugo, Maximiliano; Herrera Acevedo, Daniel David; Grupo de Investigación Gravitación y Matemática Aplicada; Semillero de Investigación en Astronomía y Ciencia de Datos
Heliophysical variability emerges from a coupled, multiscale system in which changes in the solar atmosphere and heliospheric plasma translate into measurable signatures in widely used activity indices. Operational space-weather workflows often summarize this variability through amplitudes and a small set of bulk solar-wind covariates, yet important dynamical information may also reside in the evolving \emph{morphology} of the signals. We examine whether shape descriptors computed from heliophysical time series provide information beyond classical amplitude summaries and standard bulk solar-wind covariates. Using daily OMNIWeb-era records spanning 1964--2025, we compute ten sliding-window descriptors under a past-only convention, designed to capture complementary aspects of temporal morphology such as irregularity, roughness, and long-range dependence. The descriptor set combines entropy measures, fractal-dimension estimators, the Hurst exponent, and Lempel--Ziv (LZ) complexity, yielding a compact representation of time-series structure that is not reducible to amplitude alone. The window length is treated as a methodological hyperparameter and selected through a target-specific sensitivity analysis that jointly favors competitive out-of-sample RMSE and stable permutation-importance rankings across neighboring windows.
Two complementary learners, gradient boosting and a multilayer perceptron, are used as diagnostic probes to quantify permutation-based feature relevance under chronological splitting and training-only preprocessing. Across three targets (F10.7, Sunspot Number, and Dst), shape descriptors consistently rank among the most informative predictors, often matching or exceeding the relevance of standard solar-wind inputs. The most robust signals arise from LZ complexity and a compact subset of entropy/fractal measures, whose windowed trajectories track solar-cycle phases with characteristic lead--lag behaviour. Correlation analyses on both levels and standardised first differences expose redundancy within descriptor families and reduce spurious associations driven by shared nonstationarity, motivating a family-level interpretation of relevance rather than causal attribution. Overall, the results indicate that heliophysical time-series morphology encodes dynamical information complementary to amplitude- and bulk-plasma descriptions, suggesting compact, instrument-light features for augmenting future space-weather modelling pipelines.
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