J. Exp. Theor. Anal., Volume 3, Issue 3 (September 2025) – 5 articles

Background: Different treatments may be required for paroxysmal versus non-paroxysmal atrial fibrillation. However, they may be difficult to distinguish on an electrocardiogram (ECG). Machine learning methods may aid in differentiating these conditions, yet current approaches either do not preserve patient privacy or tend to make the unrealistic assumption of uniform data. Methods: We create a non-independently and identically distributed dataset for paroxysmal versus non-paroxysmal atrial fibrillation detection. Two baselines (a centralized classifier and a federated classifier) are trained, and the performances of classifiers pretrained on shared data before federated training are compared. Results: The centralized classifier outperforms all other models ($p<0.001$), while the federated model is the worst-performing model ($p<0.0001$). Classifiers that are pretrained on 10%, 30%, and 50% of shared data (CNN-10, CNN-30, CNN-50, respectively) perform better than the purely federated model ($p<0.0001$ for all models). Furthermore, no performance difference is observed between any of the models trained on shared data (the null hypothesis of a one-way analysis of variance test between the shared data models is not rejected, $p=0.954$). Conclusions: The partial sharing of data in creating federated machine learning models may significantly improve performance. Furthermore, the volume of data required to be shared may be relatively small. Full article

Seed priming is an innovative pre-planting technique to improve germination and accelerate early seedling growth, offering a sustainable and eco-friendly alternative to chemical treatments. In this study, silver nanoparticles (AgNPs) were synthesized using flower extracts of neem plants for the first time, alongside the conventional neem leaf extract-based AgNPs, and their comparative efficacy was evaluated in wheat seed priming. The biosynthesized AgNPs were characterized through UV–Vis spectroscopy, Fourier Transform Infrared Spectroscopy (FTIR), X-ray Diffraction (XRD), Field Emission Scanning Electron Microscopy (FESEM), Energy-Dispersive Spectroscopy (EDS), Dynamic Light Scattering (DLS), and zeta potential analysis to confirm their formation, stability, and surface functionality. Wheat seeds were primed with varying concentrations (25, 50, 75, 100 mg/L) of flower-mediated nanoparticles (F-AgNPs) and leaf-mediated nanoparticles (L-AgNPs). Effects on seed germination, seedling growth, plant pigments, secondary metabolites, and antioxidant enzyme activities were systematically investigated. The results indicated that F-AgNP priming treatment significantly enhanced wheat seedlings’ performances in comparison to L-AgNPs, which could be attributed to the difference in phytochemical profiles in the extracts. This study contributes a comparative experimental analysis highlighting the potential of biogenic AgNPs—particularly those derived from neem flower extract—offering a promising strategy for enhancing seedling establishment in wheat through seed priming. Full article

Material extrusion additive manufacturing (MEAM) has emerged as a versatile and widely adopted 3D printing technology due to its cost-effectiveness and ability to process a diverse range of materials. However, achieving consistent part quality and repeatability remains a challenge, mainly due to variations in process parameters and material behavior during fabrication. In-situ monitoring and advanced process control systems have been increasingly integrated into MEAM to address these issues, enabling real-time detection of defects, optimization of printing conditions, reliability of fabricated parts, and enhanced control over mechanical properties. This review examines the state-of-the-art in-situ monitoring techniques, including thermal imaging, vibrational sensing, rheological monitoring, printhead positioning, acoustic sensing, image recognition, and optical scanning, and their integration with process control strategies, such as closed-loop feedback systems and machine learning algorithms. Key challenges, including sensor accuracy, data processing complexity, and scalability, are discussed alongside recent advancements and their implications for industrial applications. By synthesizing current research, this work highlights the critical role of in-situ monitoring and process control in advancing the reliability and precision of MEAM, paving the way for its broader adoption in high-performance manufacturing. Full article

Renewable fuels produced from animal- and plant-based edible oils have emerged as an alternative to oil and natural gas. Burgeoning interest in renewables can be attributed to the rapid depletion of fossil fuels caused by the global energy demand and the environmental advantages of renewables, specifically reduced emissions of greenhouse gases. An important property of the feedstock that is crucial for the conversion of edible oils to renewable fuels is the total acid number (TAN), as even a small increase in TAN for the feedstock can lead to corrosion of the catalyst in the refining process. Currently, the TAN is determined by potentiometric titration, which is time-consuming, expensive, and requires the preparation of reagents. As part of an effort to promote the use of renewable fuels, a partial least squares regression method with orthogonal signal correction to remove spectral information related to the sample background was developed to determine the TAN from the mid-infrared (IR) spectra of the feedstock. Digitally blended mid-IR spectral data were generated to fill in regions of the PLS calibration where there were very few samples. By combining experimental and digitally blended mid-IR spectral data to ensure adequate sample representation in all regions of the spectra–property calibration and better understand the spectra–property relationship through the identification of sample outliers in the original data that can be difficult to detect because of swamping, a PLS regression model for TAN (R² = 0.992, cross-validated root mean square error = 0.468, and bias = 0.0036) has been developed from 118 experimental and digitally blended mid-IR spectra of commercial feedstock. Thus, feedstock whose TAN value is too high for refining can be flagged using the proposed mid-IR method, which is faster and easier to use than the current titrimetric method. Full article

Mining by-products present both an environmental challenge and a resource opportunity. This review investigates their potential application in road pavement construction, focusing on materials such as fly ash, slag, sulphur, red mud, tailings, and silica fume. Drawing from laboratory and field studies, the review examines their roles across pavement layers—subgrade, base, subbase, asphalt mixtures, and rigid pavements—emphasising mechanical properties, durability, moisture resistance, and ageing performance. When properly processed or stabilised, many of these wastes meet or exceed conventional performance standards, contributing to reduced use of virgin materials and greenhouse gas emissions. However, issues such as variability in composition, leaching risks, and a lack of standardised design protocols remain barriers to adoption. This review aims to consolidate current research, evaluate practical feasibility, and identify directions for future studies that would enable the responsible and effective reuse of mining waste in transportation infrastructure. Full article