Search Results (6,264)

The article discusses the methods for classifying processes for testing and processing metals by plastic deformation, based on the characteristics of their stress–strain state. The basic methods for determining the stress and strain states using fundamental scalar quantities representing the stress and strain tensors are discussed. Equations have been derived for the quantitative determination of the type of stress–strain state through a combination of principal stresses, represented as the strain rigidity of the deformation mode. A deformable work-hardening alloy, AA7075, from the database Quantor Form 8.2.4 software product, is used, which is deformed at room temperature with an analysis of elastic–plastic deformations. A classification of deformation processes for testing and processing metals by plastic deformation is proposed, using the stress triaxiality parameter and the strain rigidity coefficient. Some 2D and 3D diagrams have been created based on simulation modeling of plastic deformation processes using virtual tools, allowing the grouping of processes according to the measured principal stresses and their combinations, which represent the stress triaxiality and strain rigidity of the deformation mode. By determining the type of grouping in these diagrams and the change in the stress–strain state with increasing strain levels, the characteristic features of the deformation processes used in materials testing and in the processing metals by plastic deformation of metals/alloys have been confirmed. Full article

Stain variability in digital pathology affects both cross-center diagnostic consistency and the robustness of downstream computational analysis. In this work, we formulate stain normalization as a variational inverse problem and derive a Screened Poisson Normalization (SPN) model from the steady-state reaction–diffusion mechanism underlying histological staining. In the CIE $L^{*}{a}^{*}{b}^{*}$ space, the model couples a gradient-domain fidelity term with a chromatic anchoring term, yielding a screened Poisson equation that preserves tissue morphology while enforcing color consistency. We prove that the corresponding variational problem is well-posed in $H^1\left(\Omega \right)$ and stable with respect to perturbations of the input data. We further show that the screening term induces an intrinsic localization length ${\ell }_c\sim {\lambda }_c^{-1/2}$, so that boundary perturbations decay exponentially away from tile interfaces. Based on this locality, we develop a non-overlapping tiled DCT-based spectral solver for gigapixel whole-slide images, enabling consistent tile-wise stain normalization and seamless whole-slide reassembly without heuristic boundary blending. Experiments on multi-scanner, multi-protocol, and archival-fading pathology datasets show that SPN achieves stable stain normalization with competitive chromatic alignment and strong preservation of diagnostically relevant microstructure, particularly in full-slide and tiled reconstruction settings. Supplementary experiments on synthetic pathology-like images further support the robustness of SPN under controlled color perturbations and indicate good generalization across diverse staining variations. Full article

In air jet looms, relay nozzles are critical components in governing airflow velocity and air consumption during the weft insertion process. Although computational fluid dynamics (CFD) offers high-fidelity simulation for aerodynamic analysis, its computational burden hinders its practicality in iterative aerodynamic design of relay nozzles. To address the challenge, this study proposes a data-driven framework integrating a Chebyshev polynomial Kolmogorov–Arnold Network (Chebyshev KAN) surrogate model with an Improved Multi-objective Red-billed Blue Magpie Optimizer (IMORBMO). The accuracy of the Chebyshev KAN model was benchmarked against conventional multilayer perceptrons (MLP), convolutional neural networks (CNN), and the standard Kolmogorov–Arnold Network (KAN). Experimental results demonstrate that the Chebyshev KAN model achieves the lowest mean absolute error (MAE) of 0.103 for airflow velocity and 0.115 for air consumption. Building upon the non-dominated sorting and crowding distance strategies, IMORBMO was developed, incorporating an adaptive mutation mechanism by information entropy for improvement of convergence, diversity, and uniformity of the Pareto-optimal solutions. Comprehensive evaluations on the ZDT and WFG benchmark suites confirm that the IMORBMO consistently attains the best and highly competitive performance, yielding the lowest generation distance (GD), inverted generational distance (IGD) values and the highest hypervolume (HV). Applied to the aerodynamic optimization of a relay nozzle, the proposed framework delivers an optimal aerodynamic design that increases airflow velocity by 10.5% while reducing air consumption by 15.4%, as verified by CFD simulation. The steady-state flow field was simulated by solving the Reynolds-Average NavierStokes equations with the k−ω turbulent model, utilizing Fluent 2025.R2. No-slip wall, inlet pressure and outlet pressures are boundary conditions to the relay nozzle surfaces. This work establishes a computationally efficient and accurate optimization paradigm that holds significant promise for aerodynamic design and other complex real-world engineering applications. Full article

Background: Surface roughness in turning operations is still verified predominantly after machining, which limits the possibility of timely corrective intervention. Methods: This study examined whether normal-direction peak-to-peak vibration displacement can serve as a practical low-frequency indicator of surface roughness during finish turning of EN AW-2011 aluminum alloy. The analysis was based on 190 synchronized displacement-roughness observation pairs obtained in one controlled experimental campaign on a CQ6230 conventional precision lathe, using a VB-8206SD displacement logger mounted radially on the tool holder and contact profilometry measurements reported as Ra and Rz. The analytical workflow included explicit quality-control safeguards for malformed rows, missing values, and obvious artefacts; in the present dataset, these checks did not indicate a failure state that would invalidate the main calculations. The workflow combined descriptive statistics, moving-average trend inspection, low-frequency FFT and STFT descriptors, Pearson correlation analysis, and ordinary least squares regression. Results: The displacement signal exhibited a mean value of 0.0446 mm with a standard deviation of 0.0256 mm and showed strong within-dataset linear relations with roughness parameters: Ra = 14.204 + 24.191 V (R² = 0.9929, RMSE = 0.052 µm) and Rz = 63.207 + 105.253 V (R² = 0.9905, RMSE = 0.264 µm). Conclusions: The results support setup-specific roughness-related process-state assessment using low-rate normal-direction displacement measurements. However, because the 190 records represent a time-ordered synchronized sequence rather than 190 independent cutting trials, and because no separate validation set was available, the fitted equations should be interpreted as descriptive within-setup calibration rather than as universally validated predictive models. Full article

Influencer marketing research has shown that source-related evaluations matter, yet less is known about how specific influencer cues are translated into consumer responses through differentiated internal psychological states. Drawing on the Stimulus–Organism–Response (S-O-R) framework, this study examines how influencer attractiveness and expertise shape consumer responses through parasocial interaction and trust. Attractiveness is conceptualized as a social-affective cue, whereas expertise is conceptualized as a competence-based cue. Parasocial interaction is modeled as a relational organismic state, and trust is modeled as a reliance-oriented organismic state. Survey data were collected from 532 Taiwanese social media users with prior experience following influencers and analyzed using partial least squares structural equation modeling (PLS-SEM). The results show that attractiveness positively predicts parasocial interaction, expertise positively predicts trust, and parasocial interaction further contributes to trust. Trust, in turn, positively influences loyalty, purchase intention, and recommendation intention, with the strongest effect observed for recommendation intention. These findings suggest that influencer effectiveness is better understood as a differentiated cue–mechanism–response process rather than as a generalized source-evaluation effect. By distinguishing attractiveness from expertise and by modeling parasocial interaction and trust as conceptually distinct but sequentially connected organismic states, this study provides a more precise S-O-R account of how influencer evaluations are translated into relational, transactional, and advocacy-oriented consumer responses. Full article

Against the backdrop of low-carbon transportation and urban logistics transformation, metro-based freight is regarded as an important pathway for emission reduction. This paper constructs a tripartite evolutionary game model involving the government, logistics enterprises, and metro operators, and analyzes multi-agent strategy evolution and the influence of key parameters using replicator dynamics equations and numerical simulation. The results show that well-designed subsidies and penalties can effectively promote a stable state characterized by “active government intervention, active response from logistics enterprises, and low-carbon integrated passenger and freight transportation by metro operators”. Reducing the cost of transformation can improve evolutionary efficiency, while excessively high subsidies may weaken the government’s willingness to intervene. This study provides insights for optimizing low-carbon transportation policies and supporting the development of metro-based freight systems. Full article

This study develops and tests factor-based and holistic theoretical models to explain the relationships between Spatial Quality Satisfaction (SQS), Overall Dormitory Satisfaction (ODS), and Place Attachment (PA) in student dormitories. Data collected from 450 students residing in five state-run dormitories in Kırklareli, Turkey, via three 5-point Likert-type scales (validated by expert review and a pilot study), were analyzed using structural equation modeling, following exploratory factor analysis, confirmatory factor analysis, and Cronbach’s alpha validations. Findings from the factor-based Model 1 indicate that eight of the ten SQS factors do not significantly influence ODS and PA when considered individually, whereas “Emotional and Psychological Atmosphere” and “Flexibility of Use” emerge as key determinants. Additionally, this model reveals that ODS has a strong and positive effect on PA. Results from the holistic Model 2 demonstrate that SQS, when treated as an integrated construct, has a robust and significant effect on both ODS and PA, with ODS playing a significant mediating role in the relationship between SQS and PA. Overall, the findings suggest that students perceive spatial quality as a whole. Therefore, improving student dormitories through a holistic SQS approach is more effective in enhancing PA and ODS than interventions focused on individual spatial dimensions. Full article

We investigate the effects of dark matter on the properties of strange quark stars within the framework of general relativity with two fluids coupled only by gravity. Adopting the color–flavor-locked model for strange quark matter and considering both fermionic (free fermion gas) and bosonic (polytropic) equations of state for dark matter, we systematically study the structure and tidal deformability of dark matter-admixed strange stars. Our results show that the presence of dark matter significantly modifies the mass–radius relations, with the maximum mass of dark matter-admixed strange stars exhibiting a non-monotonic dependence on the dark matter mass fraction $\chi$, which reaches a minimum at an intermediate value of $\chi$. The tidal deformability $\Lambda$ of dark matter-admixed strange stars shows complex behavior depending on both the stellar mass and dark matter fraction, with $\Lambda -\beta$ (the compactness parameter) relations deviating from the universal relations observed for pure strange stars or dark stars. Our findings demonstrate that dark matter-admixed strange stars with different configurations but identical masses and radii can be distinguished by their tidal deformabilities, providing potential observational signatures for detecting dark matter in compact astrophysical objects. The results are compared with current astrophysical constraints from gravitational wave observations and pulsar measurements. Full article

We prove the existence of a positive ground state solution for a fractional $(p,q)$-Laplacian Choquard equation that features both a singularity and an upper critical exponent. The proof relies on a combination of the Nehari manifold technique and Ekeland’s variational principle. Full article

This study examines the impact of explainable artificial intelligence (XAI) features on user satisfaction and purchase intention in Saudi mobile shopping applications, utilising the stimulus–organism–response (S–O–R) framework. With the increasing reliance on AI-driven decision support in e-commerce, enhancing transparency, fairness, trustworthiness, and interpretability has become crucial for shaping consumer perceptions and behavioural responses. The research employed a quantitative methodology using partial least squares structural equation modelling (PLS-SEM) to examine the relationships among stimulus factors, cognitive and affective states, consumer satisfaction, and purchase intention. In a survey of 597 respondents from Jeddah and Makkah, Saudi Arabia, the findings highlight that fairness and bias detection, trustworthiness, and transparency significantly influence consumers’ cognitive and affective states, which in turn enhance satisfaction and intention to purchase. Consumer satisfaction emerged as a critical mediator, reinforcing the role of positive emotional and cognitive experiences in driving purchase behaviours. However, interpretability showed limited impact, suggesting that consumers may prioritise fairness and trustworthiness over technical clarity of explanations. Theoretically, this study contributes to advancing knowledge on the role of XAI in consumer behaviour by integrating fairness, transparency, and affective responses into the S–O–R paradigm. From a managerial perspective, the results underscore the importance for mobile shopping platforms to design AI systems that foster trust, reduce perceived bias, and ensure transparency, thereby improving consumer engagement and purchase outcomes. By addressing gaps in interpretability and transparency, businesses can strengthen user trust and loyalty, ultimately enhancing competitive advantage in Saudi Arabia’s rapidly growing e-commerce sector. Full article

Precise manipulation of two-phase flow in micro-confined electrowetting pixels is limited by contact angle hysteresis (CAH). To elucidate this non-equilibrium process, we establish a high-fidelity electrohydrodynamic (EHD) phase-field simulation framework. The model rigorously couples Navier–Stokes equations with molecular kinetic theory (MKT) to characterize energy dissipation at the three-phase contact line (TCL) and further integrates charge transport kinetics. Numerical results reveal CAH is driven by physical pinning and interfacial charge trapping, with the latter dominating interfacial retreat and causing significant residual displacement. Furthermore, analysis shows alternating current (AC) waveforms mitigate charge accumulation and promote depinning via micro-oscillations, minimizing the hysteresis loop compared to direct current (DC) waveforms. Additionally, an overdrive strategy utilizing a suprathreshold Maxwell stress pulse rapidly overcomes static friction. This strategy significantly improves transient dynamics, substantially reducing the time to reach 90% of the steady-state target from 19.6 ms (under standard DC waveform driving) to 7.4 ms. This work provides a comprehensive theoretical basis and design criteria for optimizing active driving strategies in optofluidic and digital microfluidic systems. Full article

Granular systems confined in a shallow box and subjected to vertical vibration provide an attractive geometry for studying fluidized granular media. In this configuration, grains acquire kinetic energy in the vertical direction through collisions with the confining walls, and this energy is subsequently transferred to the horizontal degrees of freedom via interparticle collisions. In recent years, the so-called $\mathsf{\Delta }$-model has been introduced as a simplified yet effective description of the dynamics of granular systems in such geometries. This review presents the results obtained from kinetic theory for the granular $\mathsf{\Delta }$-model. To model the energy transfer mechanism, a fixed velocity increment $\mathsf{\Delta }$ is added to the normal component of the relative velocity during collisions. In this way, the vertical motion is effectively integrated out while retaining the collisional energy injection characteristic of the confined setup. This mechanism compensates for the energy loss due to inelastic collisions and leads to stable homogeneous steady states that can be analyzed within the framework of kinetic theory. The Enskog kinetic equation is formulated for this model and first analyzed in homogeneous steady states, yielding the stationary temperature and the equation of state. The dynamics of inhomogeneous states is then investigated using the Chapman–Enskog method, from which the Navier–Stokes transport coefficients are derived. The theory is further extended to granular mixtures, in which particles may differ in mass, size, restitution coefficient, or in the value of $\mathsf{\Delta }$. In this case, the phenomenology becomes richer; for example, energy equipartition is violated even in homogeneous steady states. The mixture dynamics is studied through the corresponding Navier–Stokes equations, and the associated transport coefficients are obtained in the low-density regime. The analysis of the hydrodynamic equations shows that, in agreement with simulations, the homogeneous state is linearly stable. Moreover, the intrinsically nonequilibrium nature of the model leads to the violation of Onsager reciprocity relations in granular mixtures. The theoretical predictions exhibit in general good agreement with both molecular dynamics simulations and direct simulation Monte Carlo results. Full article

This study addresses the multi-stage minimum control energy problem for chain-of-integrator systems, where the objective is to minimize the L²-norm of the control input over a fixed terminal time, subject to boundary conditions and a sequence of intermediate state constraints. Through rigorous variational analysis, we establish the existence and uniqueness of a global optimal solution and characterize it as a piecewise polynomial. Building on this analytical foundation, we reformulate the optimization problem into a system of linear equations, effectively bypassing the need for traditional iterative solvers. We then propose a direct construction method capable of solving this system with a linear computational complexity of O(M). Numerical experiments demonstrate the superior efficiency and robustness of the proposed method. Compared to state-of-the-art methods, our method significantly reduces the computational burden, making it highly viable for high-stake, real-time applications. Full article

The purpose of this paper is to present the development of an engineering level code for calculating hypersonic aerodynamics and convective heating, HI-Mach. Novel to this paper are the use of analytic methods for streamline tracing and the direct differentiation of geometric sensitivities for both forces and heat load. Independent panel inclination methods calculate the pressure distribution on the surface of a hypersonic vehicle. Normal shock relations provide the thermodynamic state on each panel. Streamlines are integrated using closed-form streamline equations. Flat plate formulas corrected for compressibility calculate the skin friction coefficient and acreage heat flux on each panel. Formulas for heating on stagnation points and lines, including effects of ellipticity and sweep, are used to calculate stagnation region heating. A method for obtaining the sensitivities of a quantity of interest with respect to the geometry in a hypersonic panel code is described. These are obtained using direct analytical derivatives. The approach is precise and has been thoroughly tested against finite differencing. HI-Mach provides results orders of magnitude faster than would be obtained by CFD. Results from HI-Mach are analyzed and compared to experimental results for the HL-20 lifting body geometry. For the aerodynamic characteristics, HI-Mach predicted force coefficients within 12% of experimental results at $M\infty =4.5$ and 21% at $M_{\infty }=1.6$. Heating results for the HL-20 match experimental and CFD results to within 20% over a wide range of operating conditions. Full article

In this work, we study the effect of the symmetry slope on the observables of weakly and strongly magnetized neutron stars within the chaotic magnetic field approximation. We investigate the impact of the symmetry energy slope in the equation of state, as well as on the observables of neutron stars, by calculating their masses, radii, redshifts, tidal deformabilities, and fundamental-mode gravitational-wave frequencies. We show that the effect of the magnetic field is strong on low mass stars, producing a softer equation of state and correspondingly lower values of radii. Furthermore, the magnetic field also causes a significant drop in the dimensionless tidal parameter even when the effects on the radii are small. At the end of the paper, we discuss the effects of the magnetic field on neutron stars’ universal relations. Full article