University of Sfax

This study examines the interrelation among gold, oil, and cryptocurrency markets and their implications for economic growth in the context of geopolitical turmoil. Employing panel data from 2000 to 2023 of exporter, importer, and mixed economies, we employ Nonlinear Autoregressive Distributed Lag (NARDL) and Panel Vector Autoregression (PVAR) to ascertain asymmetric as well as dynamic relations. Evidence shows that oil and gold price shocks exert significant effects on growth with geopolitical risk increasing volatility, while cryptocurrencies are heterogeneously resilient in panels. The results provide fresh evidence of cross-asset linkages, risk transmission mechanisms, and provide policy implications for policymakers and investors under volatile geopolitical environments.

• Identifying the type and freshness level of beef where beef is primarily sold in packaged form. • Impedance spectroscopy enables non-invasive meat assessment, existing methods often rely on raw data or complex feature sets, failing to explain the rationale behind their selection or justify their complexity. • A systematic, physics-based feature engineering methodology that can classify muscle type and freshness in bovine meat simultaneously using a single neural network. • Feature selection is primarily based on significant parameter variation measured over a 14-day period, enabling a classification accuracy of up to 91,5% using both neural networks and SVM models. • The evolution of these parameters aligns with biological fundamentals, confirming the relevance of the selected features and underlying physical principles In this paper, we propose a systematic, physics-based feature engineering methodology that can classify muscle type and freshness in bovine meat simultaneously using a single neural network. We identify a minimal, yet highly informative set of features extracted from bioimpedance spectra based on observed spectral behavior and underlying physical principles. Feature selection in this study is primarily based on significant parameter variation measured over a 14-day period, particularly between days 2 or 3 and day 14 for a binary classification of meat in the two classes safe-to-sell and not-safe-to-sell. This variation serves as a strong indicator of sensitivity and contributes to achieving a high classification rate. The key features selected include parameters from the modified Fricke model and characteristic points in the α and β frequency bands (Rex, Rin, C, Im1, Im2 and Im3). We validate the stability of these features and assess the model’s interpretability. Our analysis highlights the consistent relevance of these parameters, enabling a classification accuracy of up to 91,5% using both neural networks and SVM models. The evolution of these parameters aligns with biological fundamentals, confirming the relevance of the selected features.

Brain-computer interfaces (BCIs) were initially created to help individuals with disabilities control devices and communicate without muscle movement. Today, BCIs are used for prosthetic control, cognitive enhancement, and neurological rehabilitation. The BCI system depends on analyzing electroencephalogram (EEG) signals captured from the brain. Decoding these EEG signals is a complex process that combines multiple algorithms to extract meaningful information from these intricate and noisy signals. One of the most popular techniques is the Common Spatial Patterns (CSP), which helps preserve useful and sensitive information. This paper presents an optimized extension of the CSP model for extracting EEG data features in a multiclass setting using Riemannian geometry-based weighting. The use of weighting based on Riemannian geometry enhances the robustness of covariance matrix computation, thereby decreasing the influence of noise that can significantly distort the mean of covariance matrices in the traditional CSP method. The proposed approach is also extended by the integration of a multi-band filter bank, providing a more detailed examination of EEG signals. Three classifiers, Linear Discriminant Analysis (LDA), Random Forest Classifier (RFC), and Multi-Layer Perceptron (MLP), are employed to differentiate features across four motor imagery tasks. LDA achieves an accuracy of 80.40%, while MLP and RFC reach 80.02% and 80.90%, respectively. The results obtained using a majority vote combining the decisions of the three classifiers are 81.83% for accuracy and Recall, 82.74% for precision, and 81.87% for F1-score. The proposed architecture is evaluated using the BCI Competition IV set 2a dataset, proving its effectiveness in EEG signal classification for BCI applications.

The importance of this work lies in the application of a combined electrocoagulation/electro-Fenton (EC/EF) treatment for carwash wastewater, offering a cost-effective and efficient remediation approach. During electro-coagulation (EC), experiments were carried out in a cylindrical cell containing 450 mL of carwash wastewater. This process promotes the formation of $$Fe\left( {OH} \right)_{n}$$ (n = 2 or 3) as coagulants, along with $${\text{Fe}}^{3 + } {\text{/Fe}}^{2 + }$$ ions acting as catalysts. The effects of key operating parameters, including applied current, electrode material (Fe and Al), and initial pH, were evaluated, and both COD removal and energy consumption were assessed. In the EF stage, comparative trials were performed in stirred reactors equipped with a Pt anode and a carbon-felt cathode under O₂ bubbling to generate H₂O₂ at the cathode. The hydroxyl radicals produced at the anode surface through water oxidation and in the bulk solution via Fenton’s reagent (Fe2⁺/H₂O₂) promote the oxidative degradation of organic contaminants. The effects of the supporting electrolyte, applied current and the catalytic activity were investigated to optimize electro-Fenton efficiency. Under optimal EC conditions (I = 1 A, Fe electrode, pH = 5.7), 76.58% COD removal was achieved with an energy consumption of 0.987 kWh·m⁻3. Nearly complete COD removal (97.8%) was obtained using the combined process, where residual organics after EC were removed during electro-Fenton treatment at 0.3 A for 360 min.

In this paper, we introduce a newly proposed probability distribution, the Gamma Lindley \(q\)- distribution (\(q\)-GLD). We explore its structural properties and derive parameter estimation procedures. The proposed model is then applied in the context of quantum mechanics, where we demonstrate its suitability as a potential solution to the \(q\)-deformed Schrödinger equation. These findings reveal the distribution’s effectiveness in modeling complex physical systems within a \(q\)-deformed framework.