Search Results (5,740)

Chatter vibration in machining operations has been identified as one of the major obstacles to improving surface quality and productivity. Therefore, efficiently and accurately predicting stable cutting regions is becoming increasingly important, especially in high-speed milling processes. In this study, on the basis of a predictor–corrector scheme, the following three error correction methods are developed for milling stability analysis: the Correction Hamming–Milne-based method (CHM), the Correction Adams–Milne-based method (CAM) and the Predictor–Corrector Hamming–Adams–Milne-based method (PCHAM). Firstly, we employ the periodic delay differential equations (DDEs), which are usually adopted to describe mathematical models of milling dynamics, and the time period of the coefficient matrix is divided into two unequal subintervals based on an analysis of the vibration modes. Then, the Hamming method and the fourth-order implicit Adams–Moulton method are separately utilized to predict the state term, and the Milne method is adopted to correct the state term. Based on local truncation error, combining the Hamming and Milne methods creates a CHM that can more precisely approximate the state term. Similarly, combining the fourth-order implicit Adams–Moulton method and the Milne method creates a CAM that can more accurately approximate the state term. More importantly, the CHM and the CAM are employed together to acquire the state transition matrix. Thereafter, the effectiveness and applicability of the three error correction methods are verified by comparing them with three existing methods. The results demonstrate that the three error correction methods achieve higher prediction accuracy without sacrificing computational efficiency. Compared with the 2nd SDM, the calculation times of the CHM, CAM and PCHAM are reduced by around 56%, 56% and 58%, respectively. Finally, verification experiments are carried out using a CNC machine (EMV650) to further validate the reliability of the proposed methods, where ten groups of cutting tests illustrate that the stability lobes predicted by the three error correction methods exhibit better agreement with the experimental results. Full article

In this paper, an intelligent simulation method for chloride ion diffusion behavior in marine concrete is established based on a physical information neural network. The dimensionless constraint equation is constructed to solve the influence of different physical parameter dimensions on the generalization ability of the model. The performance of the simulation method is verified by field measured data. The influence of different exposure ages and chloride ion diffusion coefficients on chloride ion diffusion behavior is quantified. The temporal and spatial distribution characteristics of chlorine ion (C) in concrete under a multi-dimensional diffusion state are analyzed, and the reliability model is further constructed to evaluate the degradation law of the service performance of marine concrete. The results show that the dimensionless physical information neural network model can effectively simulate the diffusion behavior and spatial–temporal distribution of C in marine concrete. The maximum error between the predicted value and the experimental value obtained by the method proposed in this paper is less than 15%. The dimension problem of high-order nonlinear equations can be solved by Non-PINN, with the maximum error value less than 5%. The spatial–temporal distributions of C on different exposed surfaces under a multi-dimensional diffusion state are independent of each other. The service performance of marine concrete will increase with an increase in slag content and protective layer thickness, and decrease with an increase in surface chloride ion concentration. Full article

Batteries are important components of electrochemical energy storage systems used in mobile devices, electric vehicles, and power generation systems. This paper proposes a compact battery model dedicated to SPICE. This model takes into account properties of a real battery, such as limited electrical capacity, limited charge and discharge current, limited voltage change at its terminals, the self-discharge effect, the dependence of the battery’s internal resistance on its state of charge, and an influence of temperature on its characteristics. The developed model is presented, along with equations describing the parameters of its components. The results of experimental verification of the correctness of the developed model for different types of batteries are presented and discussed. Good agreement was achieved between the calculation and measurement results for AGM, LiPo, LiFePO₄, and Na-ion batteries. High accuracy of the proposed model was demonstrated for all tested batteries. Full article

We study equations with real polynomials of arbitrary degree, such that each coefficient has a small, individual error; this may originate, for example, from imperfect measuring. In particular, we study the influence of the errors on the roots of the polynomials. The errors are modeled by imprecisions of Sorites type: they are supposed to be stable to small shifts. We argue that such imprecisions are appropriately reflected by (scalar) neutrices, which are convex subgroups of the nonstandard real line; examples are the set of infinitesimals, or the set of numbers of order $\epsilon$, where $\epsilon$ is a fixed infinitesimal. The Main Theorem states that the imprecisions of the roots are neutrices, and determines their shape. Full article

Modeling ion transport through membrane channels is crucial for understanding cellular processes, and Poisson–Nernst–Planck (PNP) equations provide a fundamental continuum framework for such ionic fluxes. We investigate a quasi-one-dimensional steady-state PNP system for two oppositely charged ion species, focusing on how large permanent charges within the channel and realistic boundary conditions impact ion transport. In contrast to classical models that impose ideal electroneutrality at the channel ends (a simplification that eliminates boundary layers near the membrane interfaces), we adopt relaxed neutral boundary conditions that allow small charge imbalances at the boundaries. Using asymptotic analysis treating the large permanent charge as a singular perturbation, we derive explicit first-order expansions for each ionic flux, incorporating boundary layer parameters $(\sigma ,\rho )$ to quantify slight deviations from electroneutrality. This analysis enables a qualitative characterization of individual cation and anion flux behaviors. Notably, we identify two critical transmembrane potentials, $V_{1c}$ and $V_{2c}$, at which the cation and anion fluxes, respectively, vanish, signifying flux-reversal thresholds that delineate distinct monotonic regimes in the flux-voltage response; these critical values depend on the permanent charge magnitude and the boundary layer parameters. We further show that both ionic fluxes exhibit saturation: as the applied voltage becomes extreme, each flux approaches a finite limiting value, with the saturation level modulated by the degree of boundary charge imbalance. Moreover, allowing even small boundary charge deviations reveals non-intuitive discrepancies in flux behavior relative to the ideal electroneutral case. For example, in certain parameter regimes, a large permanent charge that enhances an ionic current under strict electroneutral conditions will instead suppress that current under relaxed-neutral conditions (and vice versa). This new analytical framework exposes subtle yet essential nonlinear dynamics that classical electroneutral assumptions would otherwise obscure. It provides deeper insight into the interplay between large fixed charges and boundary-layer effects, emphasizing the importance of incorporating such realistic boundary conditions to ensure accurate modeling of ion transport through membrane channels. Numerical simulations are performed to provide more intuitive illustrations of our analytical results. Full article

In the polyurethane industry, catalytically generated carbodiimides can modify the properties of isocyanate and, thus, the resulting foams. In this work, a kinetic reaction study was carried out to investigate the formation of a simple, bifunctional carbodiimide from a widely used polyurethane raw material: 4,4′-methylenediphenyl diisocyanate (MDI). The experimental section outlines a catalytic process, using a 3-methyl-1-phenyl-2-phospholene-1-oxide (MPPO) catalyst in ortho-dichlorobenzene (ODCB) solvent, to model industrial circumstances. The reaction produces carbon dioxide, which was observed using gas volumetry at between 50 and 80 °C to obtain kinetic data. A detailed regression analysis with linear and novel nonlinear fits showed that the initial stage of the reaction is second-order, and the temperature dependence of the rate constant is $k(T)=(3.4\pm 3.8)\mathrm{‧}{10}^6\cdot e^{{\displaystyle \frac{-7192\pm 389}{T}}}$. However, the other isocyanate group of MDI reacts with new isocyanate groups and the reaction deviates from the second-order due to oligomer (polycarbodiimide) formation and other side reactions. A linearized Arrhenius equation was used to determine the activation energy of the reaction, which was E_a = 60.4 ± 3.0 kJ mol⁻¹ at the applied temperature range, differing by only 4.6 kJ mol⁻¹ from a monoisocyanate-based carbodiimide. In addition to experimental results, computationally derived thermochemical data (from simplified DFT and IRC calculations) were applied in transition state theory (TST) for a comprehensive prediction of rate constants and Arrhenius parameters. As a result, it was found that the activation energy of the carbodiimide bond formation reaction from theoretical and experimental results was independent of the number and position of isocyanate groups, which is consistent with the principle of equal reactivity of functional groups. Full article

Large underwater vehicles, designed for multiple cruising speeds, are required to operate under diverse conditions such as full speed, surfacing, diving, and hovering. This demands that the axial flow pumps used in these applications have a broad operational range, typically functioning efficiently from 0.1 times rated flow to 1.5 times rated flow. In the process of adjusting operational conditions, axial flow pumps may experience rotating stall phenomena. Importantly, the presence of tip leakage vortices within the pump markedly influences the internal flow dynamics. To assess the impact of tip leakage vortices on the internal flow field under varied operational states, this study delves into the inherent link between tip leakage vortices and pressure pulsation across three specific scenarios: optimal, critical stall, and deep stall conditions. Analyzing from the perspective of the vorticity transport equation, it is found that the compression–expansion term dictates the core strength of tip leakage vortices, while the viscous dissipation factor determines the frequency of pressure pulsation. With an increase in the core strength of tip leakage vortices, a gradual rise in pressure pulsation is observed; in optimal scenarios, the core of tip leakage vortices progressively shifts toward the interior of the clearance, keeping the pulsation amplitude at each monitoring point within the blade tip clearance at integer multiples of the blade passing frequency. During critical stall and deep stall scenarios, the viscous dissipation effect of tip leakage vortices contributes to the emergence of high-frequency harmonic components within pressure pulsation. Full article

Controlling autonomous vehicles to follow a desired lateral trajectory presents a significant challenge. Developers of lateral control systems often find it difficult to simultaneously bring both lateral error and heading angle error close to zero while smoothly following the curvature of the road. This paper introduces the design and development of a control strategy for lateral trajectory following using the backstepping control method, which successfully achieves the goal of stabilization and tracking. The controller comprises a backstepping feedback control law to regulate the errors and stabilize the vehicle by controlling the yaw rate, along with a dynamic feedforward component to compensate for road curvature and further eliminate steady-state errors on curved roads. The controller is built upon the dynamic bicycle model, enhanced by integrating the error dynamics into the state space equation, which allows for the inclusion of errors as state variables. The global uniform stability of the feedback control law is proven using Lyapunov stability theory and the LaSalle–Yoshizawa theorem. The stability and tracking performance of the controller are validated through simulation and experimental results obtained from a test vehicle on a public highway. Full article

This study examines the spatio-temporal evolution of precipitation in the State of Guanajuato, Mexico, from 1981 to 2016 by analyzing monthly series from 65 meteorological stations. A rigorous data quality protocol was implemented, selecting stations with more than 30 years of continuous data and less than 10% missing values. Multiple Imputation by Chained Equations (MICE) with Predictive Mean Matching was applied to handle missing data, preserving the statistical properties of the time series as validated by Kolmogorov–Smirnov tests ($p=1.000$ for all stations). Homogeneity was assessed using Pettitt, SNHT, Buishand, and von Neumann tests, classifying 60 stations (93.8%) as useful, 3 (4.7%) as doubtful, and 2 (3.1%) as suspicious for monthly analysis. Breakpoints were predominantly clustered around periods of instrumental changes (2000–2003 and 2011–2014), underscoring the necessity of homogenization prior to trend analysis. The Trend-Free Pre-Whitening Mann–Kendall (TFPW-MK) test was applied to account for significant first-order autocorrelation (${\rho }_1 > 0.3$) present in all series. The analysis revealed no statistically significant monotonic trends in monthly precipitation at any of the 65 stations ($\alpha =0.05$). While 75.4% of the stations showed slight non-significant increasing tendencies (Kendall’s $\tau$ range: 0.0016 to 0.0520) and 24.6% showed non-significant decreasing tendencies ($\tau$ range: −0.0377 to −0.0008), Sen’s slope estimates were negligible (range: −0.0029 to 0.0111 mm/year) and statistically indistinguishable from zero. No discernible spatial patterns or correlation between trend magnitude and altitude ($\rho =-0.022$, $p>0.05$) were found, indicating region-wide precipitation stability during the study period. The integration of advanced imputation, multi-test homogenization, and robust trend detection provides a comprehensive framework for hydroclimatic analysis in semi-arid regions. These findings suggest that Guanajuato’s severe water crisis cannot be attributed to declining precipitation but rather to anthropogenic factors, primarily unsustainable groundwater extraction for agriculture. Full article

Purpose: The pulmonary function test holds significant clinical value in assessing the severity, prognosis, and treatment efficacy of respiratory diseases. However, the test is limited by patient compliance, thereby limiting its practical application. Moreover, it only reflects the current state of the patient and cannot directly indicate future health trends or prognosis. Computational fluid dynamics (CFD), combined with airway models built from medical image data, can assist in analyzing a patient’s ventilation function, thus addressing the aforementioned issues. However, current airway models have shortcomings in accurately representing the structural features of a patient’s airway. Additionally, these models exhibit geometric defects such as low smoothness, topological errors, and noise, which further reduce their usability. This study generates airway skeletons based on CT data and, in combination with convolutional surface technology, proposes an individualized airway modeling method to solve these deficiencies. This study also provides a method for predicting a patient’s lung function based on the constructed airway model and using CFD simulation technology. This study also explores the application of this method in preoperative prediction of the required extent of airway expansion for patients with large airway stenosis. Methods: Based on airway skeleton data extracted from patient CT images, a personalized airway model is constructed using convolutional surface technology. The airway model is simulated according to the patient’s clinical statistical values of pulmonary function to obtain airway simulation data. Finally, a regression equation is constructed between the patient’s measured pulmonary function values and the airway simulation data to predict the patient’s pulmonary function values based on the airway simulation data. Results: To preliminarily demonstrate the above method, this study used the prediction of FEV1 in patients with large airway stenosis as an example for a proof-of-concept study. A linear regression model was constructed between the outlet flow rate from the simulation of the stenosed airway and the patient’s measured FEV1 values. The linear regression model achieved a prediction result of RMSE = 0.0246 and R² = 0.9822 for the test set. Additionally, preoperative predictions were made for the degree of airway dilation needed for patients with large airway stenosis. According to the linear regression model, the proportion of airway radius expansion required at the stenotic position to achieve normal FEV1 was calculated as 72.86%. Conclusions: This study provides a method for predicting patient pulmonary function based on CFD simulation technology and convolutional surface technology. This approach addresses, to some extent, the limitations in pulmonary function testing and accuracy caused by patient compliance. Meanwhile, this study provides a method for preoperative evaluation of airway dilation therapy. Full article

This study explores how Instagram fashion influencers build brand loyalty through customer engagement within the framework of sustainable consumption. Grounded in the stimulus–organism–response (SOR) theory, influencer marketing is conceptualized as a stimulus that activates customer engagement (organism), which in turn enhances brand loyalty (response). A cross-sectional survey was conducted with 279 Instagram users in Palestine who actively follow fashion influencers, and the model was tested using partial least squares structural equation modeling (PLS-SEM). The findings confirm that social media influencer marketing (SMIM) significantly improves both engagement and loyalty. Customer engagement was found to be both a partial mediator and a significant moderator, such that highly engaged consumers exhibited stronger loyalty responses—suggesting intensified value alignment and emotional resonance in sustainability contexts. This study extends the prior literature by integrating the creation–consumption–contribution (C–C–C) model into the SOR framework and conceptualizing engagement as both a psychological state and a boundary condition. It contributes to sustainable consumption research by illustrating how participatory digital behaviors can foster ethical brand relationships, particularly in emerging economies. Practically, it offers strategic guidance for fashion brands and influencers to design campaigns that promote co-creation, authenticity, and eco-conscious narratives. It also emphasizes the importance of aligning influencer values with those of sustainability-minded consumers to foster long-term loyalty. By contextualizing the findings within the Palestinian market, the study highlights how cultural factors may shape engagement and sustainability perceptions, paving the way for future cross-cultural investigations. Full article

This paper presents the formulation and simulation-based validation of a novel hierarchical fuzzy-adaptive Proportional–Integral–Derivative (PID) control framework for a rectilinear active mass damper, designed to enhance vibration suppression in structural applications. The proposed scheme utilizes a Linear–Quadratic Regulator (LQR)-optimized PID controller as the baseline regulator. To address the limitations of this baseline PID controller under varying seismic excitations, an auxiliary fuzzy adaptation layer is integrated to adjust the state-weighting matrices of the LQR performance index dynamically. The online modification of the state weightages alters the Riccati equation’s solution, thereby updating the PID gains at each sampling instant. The fuzzy adaptive mechanism modulates the said weighting parameters as nonlinear functions of the classical displacement error and normalized acceleration. Normalized acceleration provides fast, scalable, and effective feedback for vibration mitigation in structural control using AMDs. By incorporating the system’s normalized acceleration into the adaptation scheme, the controller achieves improved self-tuning, allowing it to respond efficiently and effectively to changing conditions. The hierarchical design enables robust real-time PID gain adaptation while maintaining the controller’s asymptotic stability. The effectiveness of the proposed controller is validated through customized MATLAB/SIMULINK-based simulations. Results demonstrate that the proposed adaptive PID controller significantly outperforms the baseline PID controller in mitigating structural vibrations during seismic events, confirming its suitability for intelligent structural control applications. Full article

The complexity of the biochemistry and the variety of possible environments make the subject of the no-growth limits of bacteria a very tough challenge. This present work addresses the problem of applying to the microbial cultures the polymer science approach, which is widespread in food technology. This requires the definition of a “dynamic state diagram” that reports the expected trends of the glass transition of two virtual polymers, which mimic the crowded cytoplasmic polymers and the polymeric meshwork of the cell envelope, respectively, versus the water content. At any given temperature, the water content at the glass transition represents the lowest limit for the relevant molecular mobility. This representation leads one to recognize that the lowest temperature to observe microbial growth coincides with that of the largest freeze-concentrated liquid phase, in line with the values predicted by the Ratkowsky empirical equation. In view of potential applications in predictive microbiology, this paper suggests an alternative interpretation for the highest tolerated temperature and the temperature of the largest specific growth rate. Full article

This study presents a comprehensive numerical investigation of excitonic states in GaAs quantum wells embedded in ${\mathrm{Al}}_x$${\mathrm{Ga}}_{1-x}$As barriers, incorporating the effects of donor and acceptor impurities, external electric and magnetic fields, and varying well widths. The electron and hole wavefunctions are computed by directly solving the Schrödinger equation using the finite element method in cylindrical coordinates, without assuming trial forms. To evaluate the exciton binding energy, the implementation and comparison of two independent approaches were performed: a numerical integration method based on elliptic function corrections, and a novel finite element electrostatic formulation using COMSOL Multiphysics v5.6. The latter computes the Coulomb interaction by solving Poisson’s equation with the hole charge distribution and integrating the resulting potential over the electron density. Both methods agree within 1% and capture the spatial and field-induced modifications in excitonic properties. The results show that quantum confinement enhances binding in narrow wells, while donor impurities and electric fields reduce binding via spatial separation of carriers. Magnetic fields counteract this effect by providing radial confinement. The FEM-based electrostatic method demonstrates high spatial accuracy, computational efficiency, and flexibility for complex heterostructures, making it a promising tool for exciton modeling in low-dimensional systems. Full article

The oxalic acid leachate of vanadium-bearing shale (OALS) is a complex system in which the ion states and coordination mechanisms of the primary metallic elements—vanadium, iron, and aluminum—are not fully understood. This study investigated the ionic speciation and coordination mechanisms of vanadium, iron, and aluminum in OALS. The results indicate that vanadium predominantly existed as VO(C₂O₄)₂²⁻ anions, iron as Fe(C₂O₄)₂⁻ and Fe(C₂O₄)₃³⁻ anions, and aluminum as Al(C₂O₄)₂⁻ and Al(C₂O₄)₃³⁻ anions. The coordination reaction processes and equations of various oxalate complexes were examined. Regardless of whether the molar ratio was 1:1 or 1:2, the iron–oxalate complex exhibited the lowest reaction Gibbs free energy (ΔG), with values of −5343.69 and −1470.72 kJ/mol, respectively. The aluminum–oxalate complex followed, with ΔG values of −5169.23 and −1318.87 kJ/mol, respectively. The vanadium–oxalate complex displayed the highest reaction ΔG, at −2760.65 and −714.12 kJ/mol, respectively. Therefore, the coordination mechanism of vanadium, iron, and aluminum with oxalate ions in OALS is such that iron coordinated with oxalate first, followed by aluminum, and finally vanadium. The research results have important guiding significance for the purification, enrichment, and coordination mechanisms of complex solutions. Full article