Search Results (6,297)

We study an infinite-horizon consumption–investment problem in which an investor endogenously manages a consumption comfort zone above a fixed subsistence benchmark. Consumption can move freely within the prevailing admissible interval, while upward expansions of the upper endpoint are irreversible and costly. This captures downward rigidity not through a single ratcheting reference level but through the endogenous management of a sustainable expenditure range. Using the dual martingale method together with singular stochastic control, we reduce the problem to a one-sided singular control problem for the comfort-zone width process. The associated dual Hamilton–Jacobi–Bellman equation becomes a gradient-constrained free-boundary problem, which admits a one-dimensional reduction under CRRA utility. We characterize the optimal comfort-zone expansion rule, consumption policy, risky portfolio rule, and value function. Economically, the model implies infrequent upward revisions of the sustainable consumption ceiling, smoother consumption than in the frictionless Merton benchmark, and state-dependent portfolio behavior. A key implication of the additive specification is that proportional consumption flexibility shrinks as the upper endpoint rises, so higher consumption states become endogenously tighter and require a larger wealth buffer to sustain. The infinite-horizon formulation is interpreted as a stationary benchmark that isolates the economics of costly lifestyle upgrading. Full article

This paper presents on-policy and off-policy algorithms for the $H_{\infty }$ control of continuous-time mean-field stochastic Markov jump systems. Using online state and input data, these algorithms can learn control and disturbance strategies without the need for prior knowledge of system matrices. Under the standard assumptions that the system is mean-square stabilizable and detectable, we rigorously prove the monotonicity, boundedness, and convergence of the proposed iterative algorithms to obtain the unique stabilizing solution of cross-coupled generalized algebraic Riccati equations. Moreover, the off-policy algorithm features high data efficiency because the collected data can be utilized again after each iteration. Numerical simulations demonstrate the effectiveness of two algorithms and explicitly show that the off-policy algorithm achieves a faster convergence rate compared to its on-policy counterpart. Full article

Dynamic recrystallization processes are known to significantly affect both the mechanical properties and the microstructure of materials. In this paper, we investigate the influence of discontinuous dynamic recrystallization (dDRX) during deformation at high strain rates (from 10⁴ to 10⁵ s⁻¹) and elevated temperatures in pure aluminum and copper (in the range of 700–800 K for aluminum and 800–1100 K for copper). For this purpose, we propose a theoretical model in which the material is described within the framework of continuum mechanics, plastic deformations are modeled using a dislocation plasticity approach, the equation of state is represented by a neural network, and the microstructure evolution is simulated using the cellular automata method. The model is applied to uniaxial compression and tension of copper and aluminum polycrystals with an initial average grain size of 14 μm. It is shown that grain refinement occurs in all systems. The average grain size decreases from 14 μm to 4–5 μm. The distribution of plastic and total strains in the polycrystals is presented. In all considered systems, deformation localization is observed, and the localization pattern changes due to the nucleation of new grains and grain boundary surfaces during dynamic recrystallization. Full article

Strong magnetic fields and anisotropic stresses can substantially modify the structure and observable properties of compact stars. In this review, we present a unified treatment of magnetically induced anisotropy across neutron stars, hybrid stars, and white dwarfs, connecting the microphysical equation of state effects to macroscopic structure and multimessenger observables. We demonstrate that magnetic-field geometry plays a decisive role: toroidally oriented (transverse) fields enhance the maximum mass by providing additional perpendicular pressure support, whereas radially oriented fields primarily increase central compression with comparatively small mass gain. In neutron stars, anisotropy and magnetic stresses can shift phase-transition thresholds in hybrid models and enable configurations in the lower mass gap with significantly smaller magnetic energy compared to the gravitational binding energy. We further show that continuous gravitational wave emission from magnetically deformed neutron stars provides a complementary probe of internal field geometry through ellipticity-driven strain evolution. In magnetized white dwarfs, super-Chandrasekhar masses arise from the spatial redistribution of magnetic stresses rather than from globally strong magnetic energy. Taken together, these results highlight that magnetic-field geometry and matter anisotropy are as important as field strength in determining mass–radius relations, tidal deformability, gravitational wave detectability, and the emergence of extreme compact-star configurations. Full article

Thisarticle develops and analyzes a fractional-order Leslie–Gower prey–predator–parasite system incorporating two discrete delays and nonlocal spatial diffusion. The model’s central novelty lies in the simultaneous integration of three biologically realistic features that have not previously been combined: (i) fractional-order memory effects via a Caputo derivative of order $\alpha \in (0,1]$, (ii) two distinct biological delays—an infection transmission delay ${\tau }_1$ and a predator handling delay ${\tau }_2$—and (iii) nonlocal spatial dispersal modeled through fractional Laplacian operators ${(-\Delta )}^{\gamma /2}$. This triple integration enables the model to capture long-range temporal memory, delayed biological responses, and nonlocal spatial interactions simultaneously, offering insights into dynamics that are challenging to capture with classical integer-order or single-delay formulations. The fractional Laplacian generalizes classical diffusion by allowing long-range dispersal events (Lévy flights), where individuals can occasionally move over large distances with heavy-tailed step-size distributions—a phenomenon observed in many animal movement patterns but absent from standard diffusion models. We provide rigorous proofs of solution existence, uniqueness, non-negativity, and boundedness in both temporal and spatiotemporal settings. Local asymptotic stability conditions are derived for all feasible equilibrium states via characteristic equation analysis. The coexistence equilibrium undergoes a Hopf bifurcation when either delay crosses a critical threshold, with fractional order $\alpha$ modulating the bifurcation point and post-bifurcation oscillation frequency. A Lyapunov functional demonstrates global asymptotic stability of the infection-free equilibrium under biologically interpretable conditions. Turing instability analysis reveals conditions for spontaneous pattern formation, with the fractional exponent $\gamma$ controlling pattern wavelength and correlation length. Numerical simulations validate theoretical predictions, including spatial patterns, traveling waves, and chaos. To bridge theory with potential applications, we outline a statistical framework for parameter estimation and uncertainty quantification, suggesting that $\beta$, $\alpha$, and ${\tau }_1$ may be priority targets for parameter estimation. Full article

Low-frequency seismic disturbances significantly limit the performance of precision engineering systems and active vibration isolation platforms. Model predictive control (MPC) is widely applied in such systems due to its ability to handle multi-variable dynamics and constraints. However, its performance strongly depends on model accuracy. To address this issue, this paper proposes a multilayer perceptron-enhanced model predictive control (MLP-MPC) framework for active vibration isolation. In the proposed approach, a multilayer perceptron (MLP) is trained offline to learn the mapping between the current system state and the free-response term in the MPC prediction equation. During online implementation, the trained MLP replaces the model-based free-response calculation while preserving the original quadratic programming structure of conventional MPC. The proposed method is evaluated on a single-degree-of-freedom active vibration isolation system under low-frequency sinusoidal excitation and measured seismic disturbances. The simulation results show that MLP-MPC achieves reduced running RMS tracking error and lower moving-window RMS error compared with conventional MPC and Proportional–Integral–Derivative (PID) control. The results suggest that integrating data-driven free-response estimation into predictive control provides a practical approach to enhancing the performance of low-frequency vibration suppression while maintaining computational feasibility. Full article

Driven by global decarbonisation efforts and the pursuit of carbon neutrality in metallurgical processes, this study aimed to comparatively evaluate the performance of conventional fossil-fuel-based reductants (anthracite and coke) and an unconventional biochar alternative (charcoal) for the solid-state reduction of titanomagnetite concentrate. Reduction experiments were conducted at 1100–1400 °C under an inert argon atmosphere. The results showed that coke was a more effective reductant for titanomagnetite ore at 1400 °C, yielding degrees of reduction (DOR) and metallisation (DOM) of 97.3% and 96.9%, respectively. Overall, the results indicate that all three reductants exhibited varying degrees of metallisation and reactivity. The order of reactivity observed was charcoal > anthracite > coke; charcoal showed superiority in DOR at lower temperatures and also, the reaction rate of charcoal was higher compared to other reductants used in this study. The apparent average activation energies of charcoal, anthracite and coke, at 1100–1400 °C were calculated based on the Arrhenius equation, and they are 40.2, 60.0, and 74.4 kJ/mol, respectively. The activation energy of the charcoal is less than that using other reduction agents. Overall, the interfacial chemical reaction was found to be rate-controlling for the three reductants. This suggests that using a carbon-neutral reductant, such as charcoal, can be effectively utilised to reduce titanomagnetite ore at lower temperatures and at a fast reaction rate. Full article

This study investigates the stress–strain state of rocks subjected to the impact of penetrators with diverse configurations, employing numerical simulations in the ANSYS Workbench Static Structural module. The research focuses on the interaction between roller cone drill bit teeth and rock formations during blast hole drilling. Through finite element modeling using a linear elastic constitutive model, the influence of penetrator geometry, position relative to borehole walls, angle of attack, and distance to open surfaces on rock fracture parameters is analyzed. Key quantitative findings include: the relative breaking force near the borehole wall reaches 2.8 for soft rocks (siltstones) with a 10 mm tooth diameter, and decreases to approximately 1.0 at a distance of 1.5d from the wall; the optimal angle of attack ranges from 60° to 90° depending on rock hardness; and the proximity to a free surface reduces fracture resistance to as low as 0.23 of the baseline value. Six sets of parabolic regression equations (R² > 0.95) are derived for relative breaking forces across three rock hardness groups and two tooth diameters. Optimal parameters for tooth placement, borehole bottom shapes, and operational conditions are proposed. Implementation of the recommended parameters is estimated to increase drilling efficiency by 10–20% and extend tool service life by 15–30%. The findings provide a scientific foundation for designing advanced roller cone drill bits suitable for rocks with Protodyakonov hardness indices ranging from f = 5 to f = 18. Full article

Background: Critical illness is characterized by profound and rapidly evolving metabolic derangements driven by systemic inflammation, hypercatabolism, fluid shifts, and endocrine dysregulation. These dynamic changes markedly limit the accuracy of predictive equations, increasing the risk of both underfeeding and overfeeding. Indirect Calorimetry Energy represents the gold standard for measuring energy expenditure, while bioelectrical impedance vector analysis (BIVA) provides complementary insights into hydration status, cellular integrity, and body cell mass. In palliative care, AI-supported integration of indirect calorimetry and BIVA enables goal-concordant artificial nutrition by aligning energy delivery with real-time metabolic status while minimizing symptom burden. Artificial intelligence (AI) has emerged as a promising tool to integrate these heterogeneous data streams and support adaptive nutritional strategies. Methods: We conducted a structured narrative review of the literature published between 2000 and 2025 using PubMed, Scopus, Embase, and Web of Science. Artificial intelligence was not used to perform the literature search or study selection. Instead, AI was analyzed as a clinical and technological component within the included studies and explored as a future enabling strategy. Eligible publications involved adult critically ill patients and addressed indirect calorimetry, BIVA-derived parameters, or AI-based metabolic modeling applied to nutritional support. Given the heterogeneity of study designs and outcomes, findings were synthesized qualitatively. Results: Predictive equations showed substantial inaccuracy in unstable metabolic states, with errors frequently exceeding ±20–40%. Indirect calorimetry enabled individualized assessment of energy expenditure but remained limited by intermittent availability. Serial BIVA assessments consistently identified clinically relevant alterations in hydration status, body cell mass, and phase angle, the latter being strongly associated with adverse outcomes. Studies incorporating AI demonstrated improved integration of calorimetry, BIVA, and clinical variables, allowing identification of metabolic phenotypes, anticipation of metabolic shifts, and generation of adaptive nutritional recommendations. Conclusions: This narrative review highlights the complementary roles of Indirect Calorimetry and BIVA in characterizing metabolic needs in critical illness. Artificial intelligence does not replace these tools but enhances their clinical utility by integrating multidimensional data into dynamic, patient-specific nutritional strategies. The combined AI–IC–BIVA approach represents a promising framework for metabolic precision nutrition in the ICU, warranting prospective validation. Full article

We study the phase-space dynamics of quantum systems with $\mathrm{SU}(1,1)$ group symmetry using coherent-state representations on the Poincaré disk. The resulting evolution equation combines transport terms with nonlocal contributions generated with the spectral functions of the Casimir operator, which admit a natural interpretation as pseudodifferential operators associated with the hyperbolic Laplace–Beltrami operator. Using this pseudodifferential structure, we classify the phase-space generators according to the type of the underlying PDE: compact quadratic dynamics ($\widehat{H}\propto {\widehat{K}}_0^2$) yield a degenerate hyperbolic operator of the transport type, and noncompact dynamics ($\widehat{H}\propto {\widehat{K}}_2^2$) give rise to a mixed-order differential–pseudodifferential operator. For numerical evaluation, we reformulate the propagator as an oscillatory integral and develop two complementary strategies: a Fourier-series reduction exploiting the periodicity of compact orbits and a Levin-type spectral collocation method for the noncompact case. Both approaches are stable, accurate, and free of the stiffness issues that afflict direct PDE evolution on the Poincaré disk. Full article

We investigate the impact of ultrastrong magnetic fields on the structure of neutron stars within a density-dependent relativistic mean-field framework (DDME2). In the first case, we incorporate a magnetic field framework through Landau quantization of charged particles, yielding anisotropic pressure contributions and showing that field-induced stiffening increases stellar radii, maximum masses, and tidal deformabilities. To capture anisotropic stresses and geometric distortions, we employ axisymmetric equilibrium configurations computed with the XNS 4.0 code under the extended conformally flat condition. For magnetic field strengths up to $4.5\times {10}^{17}$ G, we analyze purely poloidal and toroidal geometries across a representative mass range (1.2–2.0 $M_{\odot }$). Axisymmetric models reveal that purely toroidal fields induce prolate deformations reaching $|\overline{e}| \approx 0.67$ for a 1.2 $M_{\odot }$ star, while purely poloidal fields drive oblate deformations with $\overline{e}\approx 0.24$, both diminishing with increasing stellar mass as greater gravitational binding resists magnetic reshaping. These macroscopic effects, combined with microphysical stiffening, have direct implications for gravitational-wave emission and systematic biases in radius measurements. Our study provides a systematic mapping between magnetic field strength, topology, and dense-matter stiffness, offering constraints relevant to multimessenger observations of magnetized neutron stars. Full article

This study investigates the problem of profit maximization in a retrial queueing-inventory system. Customers who arrive at the system when there is no stock enter a retrial orbit and are treated as retrial demands. We consider two strategies for inventory replenishment: the base stock policy and the (s, S) policy. For each strategy, we first formulate the fundamental equations needed to determine the rate matrix and the steady-state probabilities. Then, we compute the system’s performance metrics and profit function. Moreover, by leveraging particle swarm optimization (PSO) and genetic algorithm (GA), we introduce an improved hybrid optimization algorithm, Improved Hybrid Particle Swarm optimization (IHPSO), to solve the profit maximization problem. This algorithm initially uses PSO, followed by GA crossover and mutation to improve performance. In comparison to the canonical PSO algorithm (CPSO), our algorithm exhibits superior global search capabilities. Finally, we conduct a numerical analysis on the optimal decision variables and the corresponding profits utilizing the IHPSO algorithm and present several interesting findings. Full article

Urban nature and nature-based solutions are increasingly promoted to enhance public space experience and urban climate resilience. In Public and semi-public indoor settings, biophilic design is considered beneficial for stress reduction and mental health restoration through the introduction of natural elements such as plants. However, research focusing on the specific visual features of plants and the underlying mechanisms remains limited. Based on 200 indoor greenery images and their multi-dimensional feature vectors, and combined with questionnaire data from 253 valid participants, this study developed a quantitative framework of plant visual features and adopted a two-level analytical approach. At the image level, linear mixed-effects models (LMMs) were used to identify how plant features influenced immediate responses. At the group level, partial least squares structural equation modelling (PLS-SEM) was employed to examine how cumulative restorative experience translated into affective states, perceived restorativeness, and behavioural intention. The results showed that Green View Index (GVI) and species richness were the most stable positive features, while plant health status, certain planting modes, and spatial layer-related features also showed significant effects. Restorative experience influenced behavioural intention mainly through positive affect and perceived restorativeness. These findings provide evidence for biophilic design, offering quantitative support for incorporating indoor public space into broader urban nature and public space framework. Full article

Background/Objectives: Osteoarthritis (OA) is an age-related degenerative joint disease whose pathogenic mechanisms remain poorly understood. Experimental evidence implicates dysregulated mechanotransduction mediated by Piezo1 and TRPV4 channels, but how their interaction with inflammation may drive pathogenic state transitions remains unknown. Here, we aimed to study whether a Piezo1–TRPV4 network can intrinsically produce distinct stable physiological and pathological regimes. Methods: Based on literature data, we developed a nonlinear dynamical model describing closed-loop interactions involving Piezo1, TRPV4, and inflammation. The system was translated into a set of ordinary differential equations and studied using stability and bifurcation analysis. Results: Computational analysis revealed bistability, allowing the system to shift from a physiological to a pathogenic regime in response to specific stimuli. Critical bifurcation parameters were linked to Piezo1 and inflammation, suggesting that the bidirectional interaction between these two components represents a key node for interventions aimed at preventing or reversing transitions from non-pathogenic to pathogenic states. Conclusions: Our results suggest that OA pathogenesis may emerge from the intrinsic nonlinear dynamics of Piezo1/TRPV4/inflammation interactions. Bifurcation analysis indicates the sensitivity of TRPV4 to the inhibitory effect of Piezo1 as a key target for preventing or reversing pathogenic state transitions. Further investigations in preclinical and clinical settings are warranted to validate the model. Full article

This paper examines the rotational motion of a compound mechanical system comprising a rigid carrier body equipped with internal gyroscopic devices and a point mass that moves along a prescribed trajectory relative to the body. The system undergoes free motion in a uniform gravitational field. We derive the complete equations of motion accounting for the constant gyrostatic torque (GT) generated by internal rotors. Using asymptotic methods, we develop approximate dynamical equations valid under two distinct physical scenarios: (i) when the moving mass is small relative to the carrier mass and executes rapid oscillations and (ii) when the mass oscillates with small amplitude near a fixed location within the body, regardless of mass ratio. The accuracy and validity range of these approximations are rigorously established. For the first scenario, we have approached the idea that gyrostatic coupling fundamentally alters the system’s integrability properties while introducing beneficial stabilization mechanisms. We characterize families of permanent rotational states and analyze their stability using linear perturbation theory. The second scenario reveals that the approximate dynamics correspond to gyrostat motion rather than the classical Euler–Poinsot case. Comprehensive numerical simulations validate theoretical predictions and demonstrate applications to spacecraft attitude control problems. The results provide practical design guidelines for gyrostabilized systems with internal moving components. Full article