Search Results (5,955)

This study investigates the transmission of global risk sentiment and U.S. monetary conditions across emerging equity markets. Using Multiple Wavelet Coherence (MWC) and Quantile-on-Quantile Regression (QQR) over January 2016–December 2025, the analysis examines time–frequency co-movements and asymmetric linkages between emerging market equity indices, the CBOE Volatility Index (VIX), and the U.S. Treasury yield spread (T10Y3M). The results reveal substantial heterogeneity across markets. China, Russia, Turkey, Mexico, Egypt, and South Africa exhibit stronger long-run synchronization with external financial conditions. Saudi Arabia and Nigeria display more episodic exposure to external shocks. India, Brazil, Indonesia, and the United Arab Emirates represent intermediate cases characterized by recurrent but less persistent linkages. The findings suggest that global risk sentiment and U.S. monetary conditions affect emerging markets differently across investment horizons and periods of financial stress. The robustness analysis indicates that synchronization patterns became fragmented following the tightening cycle and rising geopolitical tensions after 2022, with less uniform spillover transmission across regions. The analysis highlights the importance of nonlinear and time-varying mechanisms in shaping financial spillovers across emerging equity markets. Full article

The growing demand for sustainable construction materials has stimulated interest in alternative binders derived from waste resources. This study investigates the use of calcined crab shell (CCS), a calcium-rich marine biowaste, as a partial replacement for Portland limestone cement. Cement pastes containing 0%, 5%, 10%, and 15% CCS were prepared and evaluated through compressive strength, water absorption, open porosity, bulk density, SEM, XRD, FTIR, and TGA analyses. The results showed that incorporating 10% CCS produced the most favorable performance, increasing compressive strength from 17.6 MPa to 33.6 MPa after 28 days of curing. This improvement was accompanied by reduced porosity, increased bulk density, and the development of a denser and more homogeneous microstructure. Physicochemical analyses suggest that CCS acts both as a filler and as a source of reactive calcium species. The CaO generated during calcination may participate in hydration processes and influence the formation of hydration products, contributing to matrix densification. In contrast, the incorporation of 15% CCS resulted in increased porosity, a less homogeneous microstructure, and lower mechanical performance. These findings indicate that replacing Portland limestone cement with up to 10% CCS can improve the properties of cement pastes while promoting the valorization of marine shell waste and reducing cement consumption, thereby supporting the development of more sustainable construction materials. Full article

This study investigates the time-varying connectedness between economic freedom and key real-financial aggregates in Tunisia, including GDP, money supply, interest rate, exchange rate, inflation, labor force, and the stock market index, using a quantile-based connectedness framework and quarterly data over the period 2010–2024. Unlike previous connectedness studies that mainly focus on macroeconomic and financial variables, this paper explicitly incorporates economic freedom as an institutional determinant within a quantile connectedness framework, thereby extending the literature on macro-financial interconnectedness in emerging economies. The empirical results reveal strong regime dependence, as the Total Connectedness Index (TCI) is substantially higher under extreme market conditions than at the median quantile. In bearish states (quantile = 0.05), economic freedom behaves primarily as a net receiver of spillovers from GDP, labor force, and interest rates, highlighting the reactive nature of institutional quality during periods of economic stress. By contrast, in bullish states (quantile = 0.95), economic freedom becomes a net transmitter of shocks, influencing money supply, inflation, and stock market dynamics, suggesting that institutional conditions amplify macro-financial interactions during expansionary phases. Under normal economic conditions (quantile = 0.50), connectedness remains moderate and relatively balanced, reflecting weaker systemic interdependence. From a theoretical perspective, the findings support the view that institutional quality is not neutral over the business cycle but evolves asymmetrically with macro-financial conditions. The study therefore contributes to the literature by demonstrating the procyclical and state-dependent role of economic freedom in shaping macro-financial spillovers in an emerging economy context. These findings also provide important policy implications for strengthening institutional resilience and macroeconomic stability in Tunisia. Full article

Biofilm formation is a major contributor to antibiotic resistance in Klebsiella pneumoniae, posing a serious challenge to current therapeutic strategies. Thus, this study aims to evaluate the antibiofilm activity of three essential oils Thymus hirtus Willd. Ssp. algeriensis Boiss, Syzygiuma romaticum, and Eucalyptus globulus against four clinical isolates of ESBL-producing K. pneumoniae, along with the reference strain K. pneumoniae ATCC 700603. The antibiofilm activity of essential oils was assessed with crystal violet assay using MICs ranging from 3.38 ± 0.2 to 27.1 ± 0.56 mg/mL, 2 ± 0.19 to 32 ± 0.55 mg/mL, and 13.78 ± 0.62 to 110.25 ± 3.37 mg/mL, for TEO, SEO and EEO, respectively. In vitro tests showed that S. aromaticum EO and T. algeriensis EO exhibited the best anti-adhesive activity with a percentage of up to 75.39%, while no difference was observed between the EO in their eradication activity. Microscopic observations confirmed the disorganization of the biofilm after treatment with T. algeriensis. The molecular docking analysis of the three EOs main compounds with MrkH, SdiA and MrkD revealed that SdiA was the most favorable target, with p-cymene (−7.7 kcal/mol), α-pinene (−7.5 kcal/mol), and eucalyptol (−7.1 kcal/mol) showing the strongest binding affinities. Thymol and p-cymene showed also a favorable affinity with MrkD. Overall, p-cymene and α-pinene demonstrated the most favorable binding profiles, whereas linalool exhibited the weakest predicted interactions. These results highlight the promising potential of these EOs, as multi-target antibiofilm agents against MDR- K. pneumoniae biofilms. Full article

The cold V-bending of Grade 4 titanium bone plates at room temperature is a critical forming operation that must be optimized to control strain localization and springback and to reduce the risk of surface cracking. This study proposes a combined experimental, numerical, and artificial intelligence-based approach for the analysis and optimization of this process. Tensile tests were first performed to characterize the mechanical behavior of the material and to calibrate the constitutive law used in the finite element model. The numerical model was then validated through comparison with experimental V-die bending results. A design of experiments was subsequently applied to investigate the effects of sheet thickness, die shoulder distance, punch radius, and punch displacement on two key responses: equivalent plastic strain (PEEQ) and spring back. The results show that sheet thickness and die shoulder distance are the most influential parameters. In addition, artificial neural network models were developed to predict process responses, and Bayesian regularization showed the best overall predictive performance among the tested ANN training algorithms, namely Levenberg–Marquardt, Bayesian regularization, and scaled conjugate gradient. The proposed framework provides a basis for optimizing the forming of titanium orthopedic implants. Full article

Background/Objectives: The precise identification of Alzheimer’s disease (AD) stages through clinical data is crucial for early diagnosis and suitable therapy. This classification remains troublesome due to overlap in cognitive profiles across different phases of illness progression. This study presents a comprehensive and advanced diagnostic system, termed FLAME, featuring an enhanced federated learning architecture for privacy-preserving multi-institutional implementation. It provides a systematic review of machine learning (ML) and deep learning (DL) models for the classification of five stages of Alzheimer’s disease (AD). The models include cognitively normal (CN), subjective memory complaints (SMC), early mild cognitive impairment (EMCI), late mild cognitive impairment (LMCI), and Alzheimer’s disease (AD). Methods: Sixteen traditional machine learning models and eleven deep learning architectures—including FT-Transformer and NODE—were evaluated using a structured clinical dataset comprising 362 features. A hybrid ensemble was created at the probability level by combining the two top-performing models, LightGBM and a five-layer DNN. The weights of this ensemble were automatically optimised using a Genetic Algorithm (GA) with Macro-F1 as the fitness criterion, confirmed stable across 30 independent runs ($w^{★}=0.5024\pm 0.0001$). A federated learning architecture was then established, deploying the DNN across non-IID clients while keeping LightGBM centralised. We examine four distinct aggregation algorithms: FedAvg, FedProx, FedNova, and SCAFFOLD. Results: Among all deep learning architectures, FT-Transformer achieved the highest standalone performance (accuracy = 0.7810, $\kappa$ = 0.7081). The five-layer deep neural network (DNN) was selected as the DL representative for the hybrid ensemble. LightGBM attained superior machine learning performance (accuracy = 0.8156, $\kappa$ = 0.7537), confirmed deterministic across 10 seeds. The LightGBM vs. XGBoost difference is not statistically significant (McNemar $p=0.4227$). The GA-optimised hybrid ensemble (w = 0.685) surpassed both individual baselines across all evaluation metrics. The FedNova hybrid design achieved superior overall performance in federated configurations, surpassing all centralised arrangements in accuracy (accuracy = 0.8213, $\kappa$ 0.7614). Conclusions: Evolutionary ensemble optimisation combined with federated learning provides a robust, scalable, and privacy-preserving solution for AD stage classification, offering a clinically viable framework for real-world multi-institutional decision-support systems. However, the AD class remains severely under-recalled across all configurations (F1 ≤ 0.21), identifying this as the primary open challenge for clinical translation. Full article

Irregular migration across the Mediterranean Sea causes thousands of deaths annually, mostly due to shipwrecks involving structurally inadequate vessels navigating under severe meteo-marine conditions. The forensic investigation of human remains recovered in such contexts is particularly challenging due to advanced decomposition and the absence of documentary evidence linking victims to a specific departure event. In the present study, a methodology is developed and validated for reconstructing the most probable departure location of human remains recovered at sea, through the integration of backward Lagrangian drift simulations with large-scale oceanographic and atmospheric datasets provided by the Copernicus Marine Service (CMEMS). The methodology was applied to five bodies recovered in the Aeolian Islands area (Sicily, Italy) between March and June 2024. Simulations were performed using the OpenDrift Leeway model, with an ensemble of several drifters released across five temporal offsets per recovery site. Results were synthesised through a drift probability metric $P_d$ and a newly proposed Hydrodynamic Connectivity Index (HCI), cross-referenced with documented shipwreck incidents and complemented by a wave climate analysis. The methodology successfully identified the port of Bizerte (Tunisia) and the shipwreck event of 5–6 February 2024 as the most probable origin, in full agreement with independent forensic findings, demonstrating the reliability of the proposed approach for forensic reconstruction of shipwreck events in the central Mediterranean and the possibility of being used as aid in recovering further remains. Full article

The increasing penetration of photovoltaic (PV) systems requires accurate and stable short-term forecasting to ensure reliable grid operation under operational constraints. This paper investigates short-horizon multi-step PV power forecasting using one full year of high-resolution (5 min) real-world data from a 111-kW grid-connected rooftop installation. The forecasting problem is formulated as a direct multi-output supervised learning task with a 30 min prediction horizon. A comprehensive comparative evaluation is conducted across baseline (persistence), tree-based (XGBoost), and deep learning architectures (LSTM, GRU, and Temporal Convolutional Networks—TCN). Results show that deep learning models significantly outperform conventional baselines, with LSTM achieving the lowest normalized RMSE (≈10.3%), while TCN provides a competitive trade-off between predictive accuracy, temporal stability, and computational efficiency. The direct multi-step formulation was adopted to reduce potential error propagation effects commonly observed in recursive forecasting approaches. Beyond forecasting accuracy, the study evaluates computational complexity and inference latency to assess practical deployability in resource-constrained environments. The proposed framework demonstrates that high-resolution real-world PV forecasting can achieve both strong predictive performance and operational feasibility. These findings contribute to the development of robust short-term forecasting strategies for distributed renewable energy systems operating under regulatory export constraints. Full article

This study investigates the electronic and nonlinear optical properties of coupled GaN/AlN quantum dots using a numerical approach based on coordinate transformation combined with the finite difference method (FDM). The Schrödinger equation is solved to determine the electronic energy levels and wave functions of the system, which are subsequently used to evaluate the nonlinear optical rectification (NOR) response. Since numerical simulations become computationally expensive for large quantum dot systems, several regression-based models, including Polynomial Regression, Ridge Regression, LASSO, and Elastic Net, are trained on high-fidelity numerical data. These models learn the relationship between structural parameters and the resulting electronic and optical properties, enabling fast and reliable predictions for larger quantum dot configurations. The predictive performance of the ML models is assessed by comparing their results with the numerical simulations, showing excellent agreement while significantly reducing computational effort. The proposed hybrid physics–machine learning framework therefore provides an efficient and reliable approach for predicting the electronic and nonlinear optical behavior of coupled GaN/AlN quantum dots. Full article

Background and Aims: Rapid digital transformation has generated pervasive attentional disruption in research and professional settings, raising the question of how the temporal conditions that support deep scientific thinking can be preserved. Our narrative review aimed to (i) synthesize neurobiological evidence on the mechanisms through which task-irrelevant digital interruption impairs deep thinking; (ii) discuss the conditions required for deep thinking and the potential threats posed by contemporary developments, including generative artificial intelligence-related cognitive offloading; and (iii) elaborate evidence-based, multi-level recommendations for research institutions. Methods: Targeted searches of PubMed, Google Scholar, and Web of Science (January 2010–September 2025) were conducted using terms spanning attentional neuroscience, digital distraction, neuroplasticity, and cognitive performance, supplemented by forward and backward citation tracking. Peer-reviewed empirical studies, meta-analyses, and theoretical frameworks addressing neurobiological mechanisms of sustained attention and the cognitive effects of digital interruption in professional and/or research settings were included. Results and Interpretation: Deep thinking and protected thinking time are treated as distinct constructs: the former as a sustained, integrative cognitive process supported by coordinated executive control and default mode network activity, the latter as uninterrupted temporal intervals within which that process can occur. Repeated engagement with task-irrelevant digital stimuli is associated with cortico-striatal strengthening and prefrontal-parietal under-consolidation, producing a plasticity paradox in which attentional fragmentation becomes self-reinforcing. The emergence of generative artificial intelligence introduces a qualitatively distinct threat through voluntary cognitive offloading, which reduces deep engagement independently of attentional distraction. Conclusions: Evidence-based strategies spanning individual, team, organizational, technological, and assessment levels are available to preserve protected thinking time. Direct evidence linking these intervals to specific research-impact outcomes remains limited, and institutional interventions should be prospectively evaluated. Full article

Understanding how brain activity varies across neurological and neurodevelopmental disorders requires tools capable of revealing patterns hidden in complex electroencephalographic (EEG) data. Conditions such as epilepsy, Alzheimer’s disease, dementia, and autism exhibit distinct alterations in neural oscillations and connectivity, which remain difficult to interpret in real time; therefore, this study proposes an interactive interface for intuitive exploration and analysis of disease-specific EEG dynamics. The system integrates classical signal processing techniques and computational modeling to extract spectral features, inter-electrode coherence, and spatial activation patterns, which are visualized through spectrograms, topographic maps, and connectivity graphs that update continuously. In addition, a web-based platform is incorporated to enable clinicians and technicians to store and manage patient information, including diagnosis, severity level, number of recordings, sampling frequency, recording duration, and acquisition dates, supporting structured data organization and longitudinal monitoring. The results demonstrate that the interface captures meaningful differences between disorders, with epileptic patterns showing strong synchronization and burst activity, while neurodegenerative conditions exhibit spectral slowing and reduced connectivity. Overall, the proposed framework provides an effective and accessible tool for EEG visualization, combining interactive analysis with clinical data management to support research, education, and potential clinical applications. Full article

Background: Reference values may assist practitioners in interpreting anthropometric and physical performance profiles in youth handball players within comparable sporting contexts. This study aimed to establish sex- and competitive-age-specific anthropometric and physical performance reference values for Tunisian youth handball players aged 9–15 years and to examine differences by sex and competitive age category. Methods: A total of 370 competitive youth handball players participated in this cross-sectional study (182 boys and 188 girls; U11, n = 130; U13, n = 158; U15, n = 82). Participants had at least two years of structured handball training. Assessment included body size, body composition, flexibility, squat jump, countermovement jump, 3 kg medicine ball throw, horizontal jumps, and handgrip strength. Sex, competitive age category, and sex × age category effects were examined using two-way ANOVA, with Bonferroni-adjusted post-hoc comparisons applied when appropriate. Effect sizes were reported as partial eta squared. Percentile values were calculated. Significance was set at p < 0.05. Results: Boys demonstrated higher values than girls in squat jump, (η_p² = 0.099), countermovement jump (η_p² = 0.097), medicine ball throw (η_p² = 0.202), and both dominant (η_p² = 0.073) and non-dominant handgrip strength (η_p² = 0.048, p < 0.001). Additionally, older age categories showed higher scores on all these tests (p < 0.001). Sex- and competitive-age-category-specific percentile values were established. Conclusions: The established reference values may support descriptive benchmarking and training/monitoring among comparable Tunisian youth handball players. However, these values should not be interpreted as maturity-adjusted standards or general population norms. Full article

This study reports the first documented occurrence of double-seeded fruits (DSF) in date palm (Phoenix dactylifera L.), a phenomenon distinct from previously described polyembryony. DSF were observed only in the cultivars ‘Kentichi’ and ‘Deglet nour’, where they occurred at very low frequencies (2.8 × 10⁻³% and 1.6 × 10⁻⁴%, respectively). Morphological observations indicate that DSF arise through partial or complete fusion of two carpels, resulting in syncarpic fruits that are significantly heavier and wider than single-seeded fruits, while fruit length remains unchanged. Germination rates were high and similar in both groups, but seeds from DSF germinated 8 days earlier than those from single-seeded fruits. In contrast, seedlings derived from DSF showed slower early growth. These findings identify DSF as a rare, genotype-dependent developmental variant in date palm and suggest that syncarpy influences fruit morphology, seed allocation, and germination behaviour. Full article

Wellbore instability in clay-rich intervals remains a major drilling challenge, even when the selected fluid density satisfies the conventional pressure window. This study evaluates delayed instability during exposure to a low-salinity water-based drilling fluid using outcrop-derived Aleg and El Haria materials as analogs for clay-rich Tunisian drilling intervals. Mineralogical, chemical, geotechnical, and shear strength data were integrated with a coupled stability analysis to link fluid exposure, pore pressure redistribution, effective stress modification, and hydration-induced strength degradation. The two materials exhibited contrasting hydro-mechanical behavior. El Haria is clay-rich, with 80% total clay mineral content, including 41% smectite and 47% illite/smectite mixed layers, and has a swelling pressure of 2112 kPa. Aleg is more carbonate-influenced, with 66% total clay mineral content, 28% calcite, and a lower swelling pressure of 576 kPa. Freshwater hydration strongly reduced the shear strength envelope; between approximately 15% and 45% water content, cohesion decreased by approximately 91% in Aleg and 70% in El Haria. The stability profiles show that El Haria reached r_c/a = 1.10 after 0.3 h and the critical threshold of r_c/a = 1.30 after 21.7 h, whereas Aleg remained close to r_c/a = 1.03. This defines a practical temporal stability window for planning open-hole exposure during logging, casing, and cementing operations. Full article

Invasive infections caused by Aspergillus flavus are more common in tropical and subtropical countries. The emergence of azole resistance in A. flavus complicates the management of aspergillosis, as azoles are the first-line and empirical therapy. The aim of this study was to investigate the molecular mechanisms underlying azole resistance in A. flavus, focusing on the cyp51C gene. We screened 34 molecularly confirmed A. flavus isolates obtained from patients with invasive aspergillosis for cyp51C gene expression by real-time RT-qPCR and for mutations by PCR sequencing. Molecular modeling and docking studies were performed using SWISS-MODEL, SwissDock, and I-TASSER software. Susceptibility testing revealed that 14.71% and 8.82% of isolates were resistant to itraconazole and posaconazole, respectively, with 5.88% exhibiting cross-resistance. The mRNA expression of cyp51C was upregulated (>2.5-fold) in five of the six resistant strains (83.33%). Hyperexpression of cyp51C was significantly more frequent among resistant isolates than among susceptible isolates (Fisher’s exact test, p = 0.014). Sequencing identified ten point mutations, including six synonymous and four non-synonymous substitutions. The non-synonymous mutations M54T and S240A were detected in the protein sequences of both resistant and susceptible isolates. Notably, D254N and I285V were observed exclusively in resistant isolates and in susceptible isolates with itraconazole MICs near the epidemiological threshold. Homology modeling and 3D structure prediction of the mutated Cyp51C protein demonstrated interactions with itraconazole, posaconazole, and voriconazole. Importantly, I-TASSER analysis indicated that the I285V substitution is located near the itraconazole binding site. Simultaneous overexpression of the cyp51A, cyp51B and cyp51C genes was observed in 33.33% of resistant isolates. These findings suggest that multiple target genes and mechanisms may act concurrently to confer azole resistance in A. flavus. Overall, this study supports the hypothesis that azole resistance in A. flavus is multifactorial and highlights the potential value of combining mutation analysis, gene expression profiling, and structural modeling for improved molecular surveillance and antifungal resistance monitoring. Full article