Search Results (1,216)

The rapid growth of Waste Electrical and Electronic Equipment (WEEE) in the European Union highlights the need for a rigorous understanding of its long-term dynamics and the role of innovation in shaping its trajectory. This study investigates how innovation influences the dynamics of WEEE generation in the European Union. We develop an innovation-adjusted mathematical model of e-waste as a stock flow system and prove the existence and global stability of a unique positive equilibrium. The model analytically generates an environmental Kuznets-type turning point and shows that innovation reduces waste accumulation by accelerating effective depreciation. To link the theoretical results with empirical patterns, we embed the model in a STIRPAT panel specification using annual data for 27 EU member states from 2013 to 2023, where EU Eco-innovation Index (EEI) serves as a composite index which directly captures policy-driven green technology and circular economy activities, aligning precisely with our theoretical framework. We also extend the quasi-demeaning transformation to panels with correlated shocks and establish its consistency under a factor structured error process. The empirical estimates confirm a positive effect of income on WEEE at lower development levels and a negative coefficient on its squared term, consistent with an inverted U pattern, while innovation is associated with lower waste intensity. These findings demonstrate how mathematical modeling can strengthen the interpretation of macro panel evidence on circularity and provide a basis for future optimization of innovation driven sustainability transitions. Full article

Digital twins are emerging as a pivotal technology for the performance optimization, predictive maintenance, and real-time monitoring of wind turbines. However, the accuracy of these virtual representations critically depends on the availability of the power coefficient ($C_p$) curve, a key descriptor of a turbine’s aerodynamic efficiency. This information is often proprietary and not disclosed by manufacturers, posing a significant barrier to the development of high-fidelity digital twins. This study addresses this critical gap by proposing a novel framework for estimating $C_p$ curves using operational Supervisory Control and Data Acquisition (SCADA) data. The proposed methodology utilizes a parameterized mathematical formulation to model the $C_p$ curve and employs the Adam optimizer to robustly tune the model’s parameters against real-world operational data. The framework was evaluated through a two-pronged process. First, the model’s accuracy was assessed using synthetic SCADA data from a high-fidelity simulator under ideal conditions, demonstrating excellent agreement with an $R^2$ exceeding 0.99 and a normalized Mean Absolute Percentage Error (nMAPE) ranging from 4.38% to 6.03%. Second, its practical performance was evaluated using real SCADA data from a commercial wind turbine, where it maintained high accuracy with an $R^2$ ranging from 0.89 to 0.98 and an nMAPE of 3.27% to 5.97%. The findings demonstrate that the proposed methodology can effectively reconstruct a turbine’s aerodynamic characteristics without proprietary manufacturer data. This research offers a viable pathway for operators and researchers to create accurate, turbine-specific digital twins, thereby enabling enhanced performance monitoring, advanced control optimization, and predictive maintenance for more efficient and reliable wind energy production. Full article

This study presents a novel approach for interpreting static load tests (SLT) of piles using Artificial Neural Networks (ANNs) integrated with the Meyer and Kowalow load-settlement mathematical model. Reliable estimation of pile bearing capacity and settlement behavior is critical for safe and economical geotechnical design, particularly given the nonlinear and heterogeneous nature of soils. Traditional SLT interpretation methods, such as Chin-Kondner, Decourt, and hyperbolic fitting approaches, provide useful extrapolation of the ultimate capacity but are sensitive to test termination levels and parameter estimation uncertainties. The Meyer and Kowalow function offers a robust mathematical representation of the load-settlement curve, allowing decomposition of the total pile resistance into the shaft and base components. In this work, ANN models were trained to solve both the direct and inverse forms of the Meyer and Kowalow problem, enabling rapid identification of constitutive parameters (initial stiffness, nonlinearity coefficient, and ultimate capacity) from measured SLT data. Numerical experiments demonstrated that networks with a single hidden layer achieved accurate predictions with low RMSE for both training and test sets. The proposed ANN-based framework facilitates improved parameter identification, supports partial-load SLT interpretation, and provides a practical tool for engineers seeking the reliable prediction of pile performance under service loads. Full article

Establishing a mathematical model of a Francis turbine is the foundation for the simulation of hydropower station operation and is of great significance for the analysis of the hydropower station’s transient process. Currently, in engineering practice, the model is often established based on the comprehensive characteristic curves of the Francis turbine provided by the manufacturer, using the external characteristic method. Traditional modeling methods mostly adopt manual reading of points or the use of dedicated numerical software for curve tracing to discretely sample the comprehensive characteristic curves of the turbine. This method is labor-intensive, inefficient, and relies on manual experience, with a small sample size, which, to some extent, affects the accuracy and reliability of the numerical processing results and cannot meet the needs of transient process simulation analysis. To address these shortcomings, this paper proposes a refined modeling method based on image numerical processing and an RBF neural network. Taking the HLA685 Francis turbine as an example, the method first uses image processing to achieve large-scale automated discrete sampling of the turbine’s high-efficiency zone characteristic data, then reasonably extends the small-opening and low-speed regions, and finally uses the RBF neural network method for interpolation and extrapolation to obtain the full characteristic data. This method can effectively improve the efficiency and accuracy of comprehensive characteristic modeling of the turbine and has good reference significance for the comprehensive characteristic modeling of blade-type machinery. Full article

The standard methods for determining the basic chemical composition of wood are well-established, but include processes that demand a great deal of time and diverse chemical reagents. TGA and DTG analyses, in contrast, offer precise results in less time. This study was designed to identify the primary components and results of the proximal analysis of wood from three species –Acacia farnesiana, A. pennatula and Albizia plurijuga—using TGA with deconvolution of the DTG curve and a chemical method. Higher heating value (HHV) was determined using a bomb calorimeter and mathematical models. Elemental organic and inorganic analyses were conducted. No statistically significant differences appeared in the results of the TGA-DTG and chemical methods for the wood in terms of cellulose, lignin, and volatile material content. Results were especially accurate in the samples of A. pennatula and A. plurijuga for hemicelluloses, extractives, and moisture. Regarding HHV, the wood of A. plurijuga showed no statistically significant differences between the bomb calorimeter test, calculations as a function of chemical composition, or the proximal analysis. Elemental organic results were C = 43.76–46.65%; H = 6.70–6.95%; O = 46.06–48.95%; N = 0.21–0.42%; and S = 0.06–0.11%. For the inorganic fraction we identified 18 elements in the ash. We conclude that the TGA-DTG method made it possible to obtain results in a short time with no need for the numerous reagents that chemical processes require. Findings suggest that in the absence of a bomb calorimeter, the best model for calculating HHV is proximal analysis. Full article

This study aimed to evaluate and compare the performance of five mathematical models: Gompertz, Ørskov & McDonald, Brody, Richards, and the Dual Pool Logistic model, in describing the in vitro gas production kinetics of Capiaçu elephant grass (Pennisetum purpureum Schum ‘BRS Capiaçu’) silages. The effect of including dehydrated cashew pseudo-fruit on the in vitro degradation curves was also assessed. A completely randomized design was adopted, using Capiaçu silages containing 0%, 10%, 20%, or 30% dehydrated cashew pseudo-fruit. Rumen fermentation kinetics were measured through cumulative in vitro gas production. Model performance was evaluated using the Akaike Information Criterion (AIC), coefficient of determination (R²), concordance correlation coefficient (CCC), and mean square prediction error (MSPE). Accuracy (pMSPE) and precision (AIC) were also considered. The Richards model performed best with the lowest AIC (1119.07) and MSPE (0.246) and the highest R² (0.917) and CCC (0.966). It was over 350 times more likely to provide a correct fit (p < 0.05) compared to the other models. Significant differences (p < 0.05) were observed between degradation curves as a function of the pseudo-fruit inclusion level. Increasing pseudo-fruit inclusion improved silage composition, raising total digestible nutrients (from 54.6% to 67.1%) and reducing neutral detergent fiber (from 58.5% to 42.3%), which directly enhanced fermentation kinetics. These results indicate that the Richards model is the most suitable for describing the fermentation kinetics of Capiaçu elephant grass silages. Moreover, linking model performance to practice, the Richards model provides a reliable tool for determining optimal inclusion levels of dehydrated cashew pseudo-fruit (up to 30%), supporting better silage nutritional quality and more efficient feed utilization in ruminant production systems. Full article

In this study, bio-based polyurethanes (PUs) were synthesized using renewable polyols derived from sunflower seed oil, aiming to develop flexible yet robust polymeric films and scaffolds. Given their composition and favorable physico-chemical properties, these materials may represent promising candidates for the design and development of advanced biomedical systems. Two distinct oil polyols were prepared via glycerol transesterification (GM) and epoxidation (EPO) with hydrogen peroxide/glacial acetic acid, respectively. These polyols, in combination with poly(tetramethylene ether) glycol (PTMEG) and/or poly(ethylene glycol) (PEG), served as diol components in a one-step reaction with 1,6-hexamethylene diisocyanate (HDI). The structure of the polyol precursors was thoroughly characterized by MALDI-TOF MS and NMR spectroscopy, confirming successful functionalization. The resulting PU films exhibited excellent flexibility (885%) and mechanical properties (23 MPa), as evaluated by ATR-FTIR, Tensile test, DSC, DMA and SEM methods. The crosslink density of the order of 10⁻³ also contributes to the development of outstanding mechanical properties. Stress relaxation experiments were described using a stretched exponential (Kohlrausch–Williams–Watts) model to capture the viscoelastic behavior of the materials. In addition, stress vs. relative elongation curves revealing strain-hardening behavior were also analyzed and modeled mathematically to better describe the mechanical response under deformation. Furthermore, salt leaching techniques were employed to fabricate porous scaffolds. This work highlights the versatility of vegetable oil-based feedstocks in producing functional polyurethanes with tunable mechanical properties for applied polymer systems. Full article

The short-circuit tolerance capability of a transformer is a key performance indicator for ensuring the safe and stable operation of the power system. As the core component of the transformer, the mechanical stability of the windings under the huge electromagnetic force generated by the short-circuit current directly determines the short-circuit tolerance capability of the transformer. Most current research focuses on the coupling analysis of electromagnetic fields and structural fields, while ignoring the influence of temperature, a crucial variable, on the mechanical properties of the winding materials. Therefore, this study conducted tests on the transformer winding conductors, insulating materials, and silicon steel sheet materials under different temperatures, and provided a mathematical model and variation rules of elastic modulus with temperature and the B-H curves of silicon steel sheets at different temperatures. Based on this, a calculation method considering the short-circuit force of the transformer winding under different temperatures of the transformer components was proposed. This method enables precise calculations of the transformer’s mechanics under different temperatures and shows the distribution of leakage magnetic field, short-circuit force, and displacement of the winding under different transformer component temperatures. Finally, the Random Forest algorithm was used to estimate the short-circuit displacement of the transformer winding under different transformer component temperatures, and a short-circuit displacement prediction model based on temperature and impact frequency was provided. This offers a new method for evaluating the short-circuit capacity of the transformer. The feasibility of the calculation method was verified using a 750 kV transformer. Full article

To accurately simulate the progression of pipeline corrosion, this paper proposes a three-dimensional corrosion modeling method for curved random surfaces based on spatial frequency composition. It applies this method to the inner surface of layered pipelines to emulate both the morphological characteristics and the evolution of internal corrosion. Combined with ultrasonic guided wave technology, the approach enables quantitative assessment of internal corrosion in layered pipelines. First, trigonometric series expansion and nonlinear polynomial superposition are used to characterize the roughness and curvature of the corroded surface, respectively, establishing a mathematical model capable of accurately representing complex corrosion morphologies. Next, a COMSOL–ABAQUS co-modeling approach is employed to build a finite element model of a three-layer composite pipeline consisting of a steel pipe, an insulating layer, and an anti-corrosion layer, with curved random-surface corrosion on the inner surface of the steel pipe. Finally, a wavelet packet decomposition algorithm is applied to extract features from the guided wave echo signals, creating a damage index matrix to correlate the corrosion area with the damage index quantitatively. The results show that the damage index increases steadily with the corrosion area, confirming the effectiveness of the proposed method. This study provides an alternative technical approach for high-fidelity modeling and precise assessment of pipeline corrosion detection. Full article

Alzheimer’s disease (AD) affects over 55 million individuals worldwide, yet no transformative disease-modifying therapies exist. Mathematical modelling provides a powerful framework to elucidate complex disease mechanisms, predict therapeutic outcomes, and enable precision medicine—capabilities urgently needed where multiscale spatiotemporal processes defy experimental analysis alone. We developed a mechanistic spatiotemporal model coupling four AD hallmarks: β-amyloid (Aβ) accumulation, T-protein (T-p) aggregation, neuroinflammation and electrical conductivity decline. Formulated as non-linear partial differential equations (p.d.es) on a 3-dimensional biological interpretation of non-linear terms (the ellipsoidal brain domain with biologically grounded parameters), the model was solved using eigenfunction expansion, Fourier analysis and numerical methods. Therapeutic interventions were simulated through mechanistically motivated parameter modifications and validated against longitudinal biomarker data from major cohort studies. Simulations reveal Aβ-initiated spatiotemporal cascades originating in the hippocampus and spreading radially at 0.15–0.20 cm/year, with T-pathology emerging after 2–3 years. Conductivity decline accelerates upon T-onset (year 5–7), reflecting the transition to symptomatic disease. Multimodal intervention at early symptomatic stages reduces peak Aβ by 36% and inflammation by 52% and preserves 41% more conductivity than untreated controls. Sensitivity analysis identifies Aβ production and inflammatory regulation as critical therapeutic targets, with dose–response curves demonstrating linear efficacy relationships. This biologically grounded framework explicitly links molecular pathology to functional decline, enabling patient-specific trajectory prediction through parameter calibration. The model establishes a foundation for precision medicine applications including individualized prognosis, optimal treatment timing and virtual clinical trial design, advancing quantitative systems biology of neurodegeneration. Full article

The rapid convergence of the Internet of Things (IoT), quantum computing, and artificial intelligence (AI) has amplified the urgency for lightweight yet resilient data protection mechanisms, particularly within unmanned aerial vehicles (UAV). Traditional cryptographic approaches, while mathematically secure, often fail to reconcile the competing requirements of robustness, computational efficiency, and energy sustainability when deployed on resource-constrained platforms such as drones. To address this gap, this paper proposes a novel hybrid lightweight cryptographic model that strategically integrates symmetric and asymmetric primitives in a dual-layer design. The model leverages the efficiency of lightweight authenticated encryption for high-throughput data protection, while incorporating elliptic-curve and lattice-based key exchange mechanisms to ensure both forward secrecy and post-quantum resilience. Experimental evaluation demonstrates that the proposed scheme achieves superior performance compared to conventional methods, offering reduced computational overhead, lower energy consumption, and enhanced resistance to cyber threats. Crucially, the model maintains high levels of confidentiality, integrity, and authenticity while extending operational endurance, making it particularly well-suited for next-generation UAV operating within the broader IoT ecosystem. Full article

Affine invariance is one of the most fundamental properties of free-form curves, ensuring that transformations such as translation, scaling, rotation, and shearing preserve the essential characteristics of the geometric shape. It is exploited by almost every software that uses such curves. However, this property only holds in a theoretical, mathematical sense. The transformation of a curve calculated and displayed on computers using finite precision arithmetic and representation may not be fully identical to the curve calculated from the transformed control points. This deviation, even pixel-level inaccuracy, can cause problems in various applications, such as Computer-Aided Geometric Design, medical image processing, numerical computations, and font design, where this level of error can have serious consequences. In this paper, we study and demonstrate the extent and nature of this deviation using geometric and statistical tools on a cubic Bézier curve. We provide practical methods to mitigate this inaccuracy and decrease the error level using fast and simple alternative computations of the curve, taking advantage of the symmetry of the basis functions, elevating the degree of the curve, and using reparametrization to evaluate the curve on integer values. The effectiveness of these alternatives is evaluated by statistical methods based on 500,000 transformations. Full article

Branching structures such as vascular networks are representative morphological patterns in living systems, and they often arise from collective cell migration. Angiogenesis, the sprouting of new blood vessels from pre-existing ones, is a fundamental process in development. Experimental and theoretical studies have demonstrated that sprout formation depends on the collective movements and shapes of endothelial cells, as well as the remodelling of the extracellular matrix. Many discrete models have been proposed to describe cell dynamics, successfully reproducing vascular patterns and collective behaviours. In this study, we present a two-dimensional mathematical model that represents each endothelial cell as an ellipse and incorporates the effects of the extracellular matrix. We performed computer simulations under two scenarios: invasion from a pre-formed sprout and collective advancement into an extracellular matrix region. The results show that the extracellular matrix helps maintain linear sprout extension and suppresses the formation of dispersed or curved branches, while elongated cell shapes promote sprouting more effectively than round cells. The model also reproduces experimentally observed behaviours such as tip-cell replacement and the mixing of cells within sprouts. These findings highlight the importance of integrating cell shape and extracellular matrix remodelling to understand early blood vessel formation. Full article

Mathematical modeling of unitized regenerative fuel cells (URFCs) faces significant challenges in reconciling parameter conflicts between fuel cell (FC) and electrolysis cell (EC) modes. This study establishes a COMSOL-based multi-physics framework coupling water–gas–heat–electric transport for both operational states. The critical factors associated with the model were identified through a systematic sensitivity analysis of structural and operational parameters, including temperature, exchange current density, conductivity, porosity, and flow rates. FC modes exhibited strong sensitivity to exchange current density (27.8–40.5% performance variation) and conductivity of membrane (10.1–35.6%), while temperature degraded performance (−4.2% to −4.0%). Spatial analysis revealed temperature-induced membrane dehydration and accelerated gas depletion at electrodes, thus explaining the negative correlation. EC modes were dominantly governed by temperature (8.6–9.4%), exchange current density (13.0–16.4%), and conductivity (2.5–13.3%). Channel simulations revealed that elevated temperature contributed to enhanced liquid water fluidity, while high flow rates had a relatively limited effect on mitigating species concentration gradients. Parameter optimization guided by sensitivity thresholds (e.g., porosity > 0.4 in FC GDLs, conductivity > 222 S/m in EC modes) enabled dual-mode calibration. The model achieved <4% error in polarization curve validation under experimental conditions, demonstrating robust prediction of voltage–current dynamics. This work resolves key conflicts of URFC modeling through physics-informed parameterization to provide a foundation for efficient dual-mode system design. Full article

(1) Background: This research examines the study of crude oil generation mechanisms in the Diapir Fold Area (exaggerated diapirism alignment) through two representative cases. The geology of the respective area, along with the tectonics and the formation conditions of the hydrocarbons, is presented. (2) Methods: Based on the research of the international study and local research study, the authors simulated two sediment burial models (from the previously mentioned area), suggesting the hydrocarbon generation conditions and tracing the sediment burial curves. (3) Results: Based on these, the depths, the geological ages of the formations generating hydrocarbons, and the time in millions of years were established. (4) Conclusions: A mathematical model based on Artificial Intelligence is presented to resolve an oil generation in a diapirism area. Full article