University of Monastir

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.

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.

Abstract Background: Vascular endothelial growth factor A (VEGFA) contributes to angiogenesis and exerts neurotrophic and neuroprotective effects. Altered VEGFA expression has been implicated in neurodegenerative processes, including Parkinson´s disease (PD), yet findings from peripheral transcriptomic studies remain inconsistent. Data on individuals at increased risk, particularly first-degree relatives (FDRs), are limited. Objective: To evaluate peripheral blood VEGFA mRNA expression in patients with PD and their asymptomatic FDRs compared with healthy controls, and to assess age-related patterns and diagnostic performance. Methods: Peripheral blood VEGFA expression was analyzed in 123 participants (PD patients, FDRs, healthy controls) using one-step RT-qPCR normalized to B2M . Relative expression values were log-transformed (logVEGFA). Group differences, age correlations, and diagnostic discrimination were assessed using ANOVA, Pearson correlation, and receiver operating characteristic (ROC) analysis. Results: Both PD patients and FDRs showed significantly higher logVEGFA levels than healthy controls (p = 0.004), with the most pronounced differences observed in participants aged ≤ 60 years (p < 0.001). A positive correlation between age and logVEGFA was detected only in healthy controls. In participants aged ≤ 60 years, logVEGFA demonstrated excellent discrimination between PD patients and healthy individuals (AUC 0.897; 95% CI 0.771–1.000; sensitivity 100%, specificity 73.3%). Conclusions: Peripheral VEGFA expression is elevated in both PD patients and FDRs, particularly in younger individuals. Age-stratified VEGFA assessment may support early identification of individuals at risk of PD and complement existing prodromal biomarkers, pending longitudinal validation.

Naphthalene diimides (NDIs) are a class of electron-deficient aromatic compounds widely explored for applications in organic electronics, photovoltaics, and metal-organic frameworks (MOFs), owing to their high electron affinity, robust stability, and tunable optoelectronic properties. Precise modulation of the HOMO-LUMO energy gap is critical to optimize their performance, yet accurate prediction remains challenging due to the limitations of conventional density functional theory (DFT) approaches, including functional sensitivity and computational cost. In this study, we present a hybrid DFT-machine learning (ML) framework to efficiently and accurately predict the energy gaps of 195 structurally diverse NDI derivatives with a broad spectrum of electron-donating and electron-withdrawing substituents. DFT calculations were performed only to compute the reference HOMO-LUMO gap used as the regression target. All machine-learning input features were generated solely from the molecular SMILES representations using RDKit-based cheminformatics descriptors. Among the tested algorithms, LightGBM achieved the best performance (R 2 = 0.86, RMSE = 0.25 eV), exhibiting robust generalization, low error dispersion, and meaningful uncertainty estimates. SHAP analysis revealed that topological, electronic, and steric descriptors play a central role in modulating the energy gap. Compared to baseline models, our hybrid approach significantly reduces computational time while capturing complex structure-property relationships. Beyond virtual screening, the model offers mechanistic interpretability and enables the rational design of NDI with tailored gaps for optoelectronic and MOF-based applications. This work lays the foundation for integrating active learning, transfer learning, and uncertainty quantification in future workflows targeting π-conjugated systems with high functional diversity. • LightGBM+RDKit predicts B3LYP/6-31G(d,p) gaps for 195 NDI derivatives. • External test vs literature gaps (n=32) with gap-type labeling (optical/electrochem). • SHAP reveals topological and E-state descriptors governing NDI gap modulation. • Break-even analysis: net CPU-time savings after screening ∼200 candidates. • Workflow enables fast pre-screening of NDI MOF linkers without new DFT runs.

Hydroclimate variability in northern Africa, driven by the interactions between the mid-latitude westerlies and the West African Monsoon (WAM) and by variations in their intensity, represents a key control on hominin dispersal. However, the response of these atmospheric circulation systems to changing boundary conditions and their influence on precipitation during past climate states remain poorly constrained, largely because most well-dated terrestrial hydroclimate records are limited to the Holocene interglacial period. Here, we present a 300,000-year hydroclimate record from northernmost Africa based on speleothem growth in Tunisia. This record captures alternating humid and arid phases across multiple glacial–interglacial cycles, as well as superimposed millennial-scale variability. Speleothem growth occurred predominantly during interglacials, indicating humid conditions, whereas growth hiatuses correspond to arid glacial periods. The cave record suggests that humid phases in northernmost Africa were primarily driven by a southward displacement and intensification of the mid-latitude westerlies and the Mediterranean storm track during interglacials. Comparison with Saharan palaeolake records further indicates that both enhanced winter westerlies and a strengthened summer WAM were required to generate widespread wet conditions across northern Africa during interglacial periods. These results highlight the combined influence of winter and summer moisture sources in shaping long-term hydroclimate variability and defining habitable corridors across northern Africa over the past 300,000 years.