Search Results (3,615)

Variational models describe deformation and stability through the first and second variations in an underlying functional, but the relationship between these responses is seldom expressed as an intrinsic equilibrium quantity of the model itself. A canonical curvature–strain representation for equilibrium ratios arising in variational field settings is developed. For a twice Fréchet differentiable functional and an admissible perturbation generator, strain is defined as normalized first-order response and curvature as normalized second-order response along the generator direction. Their quotient defines a curvature–strain ratio that measures proportional balance between deformation and curvature within the model. The main result shows that this curvature–strain ratio is a canonical representative of a response ratio already implicit in the variational data. Under canonical normalization, the curvature–strain ratio coincides with the quotient of second- and first-order response, and stationarity of the curvature–strain ratio is equivalent to proportional stationarity of that response quotient along the admissible flow. A further theorem establishes transfer of local isolation: when the second-variation operator satisfies standard hypotheses such as compact resolvent and non-degeneracy of the constrained extremum, isolated equilibrium ratios persist in the curvature–strain representation for the same operator-theoretic reasons. Quadratic scalar and Maxwell-type models illustrate the construction. The paper establishes a mathematically controlled curvature–strain representation of equilibrium ratios within ordinary variational theory, with emphasis on the analysis of variational response and equilibrium balance. Full article

This paper studies the effects of leasing on credit risk and access to credit. The repossession of a leased asset is generally easier than the enforcement of collateral associated with securing a standard loan agreement. We argue that this greater efficiency in enforcement mitigates, ceteris paribus, the counterparty’s moral hazard. To test this hypothesis, we developed a credit rationing model in which income is privately observed and non-verifiable, and financial intermediaries share credit risk information about borrowers. Financial contracts that are more rapidly enforced, such as in leasing, enable the screening of relatively safer projects or credit rationing reduction. We provide empirical evidence consistent with this prediction for the Italian credit market and considerations for the effects of monetary policy variables on the model’s equilibrium. Full article

Hybrid rocket engines offer a compromise between safety, controllability, and performance, making them attractive for small-scale propulsion systems. However, oxidizer selection remains a critical early-stage design decision that cannot be determined solely from ideal thermodynamic metrics. This study presents a comparative analysis of three oxidizers—nitrous oxide (N₂O), gaseous oxygen (GOX), and liquid oxygen (LOX)—for a 1 kN-class hybrid rocket engine using HDPE fuel under identical operating conditions. Equilibrium combustion performance was first evaluated using NASA Chemical Equilibrium with Applications (CEA) to determine optimal oxidizer-to-fuel ratios and theoretical specific impulse. These results were subsequently refined using Rocket Propulsion Analysis (RPA) to incorporate finite combustion chamber geometry and non-ideal nozzle expansion effects. The equilibrium analysis predicts maximum specific impulses of approximately 260 s for N₂O/HDPE and nearly 300 s for oxygen-based systems. However, finite-geometry modelling indicates that practical performance is reduced by approximately 5–8%, yielding delivered specific impulses of about 275 s for GOX and 272 s for LOX. The results demonstrate that although oxygen (GOX and LOX) provides higher thermodynamic performance, the practical advantage of LOX over GOX becomes marginal at the kilonewton scale. Consequently, oxidizer selection for small hybrid engines should be treated as a system-level trade-off involving performance, infrastructure complexity, and operational safety. Full article

Phase change material (PCM)-based latent heat storage (LHS) systems help address the mismatch between renewable energy supply and thermal demand. However, their practical implementation is constrained by the strongly nonlinear and multiphysics nature of phase change, which makes high-fidelity simulations and real-time applications computationally expensive. This review examines mathematical reduced-order modeling (ROM) as an effective strategy to overcome this limitation by combining physics-based simplifications, projection methods, interpolation techniques, and data-driven models for PCM-based LHS systems. While physical simplifications (such as dimensional reduction and effective property approximations) represent an important first layer of model reduction, the primary focus of this work is on the mathematical ROM methodologies that operate on the governing equations after such physical simplifications have been applied. The review covers approaches including two-temperature non-equilibrium and analytical thermal-resistance models, Proper Orthogonal Decomposition (POD), CFD-derived look-up tables, kriging and ε-NTU grey/black-box metamodels, and machine-learning methods such as artificial neural networks and gradient-boosted regressors trained from CFD data. These ROM techniques have been applied to packed beds, PCM-integrated heat exchangers, finned enclosures, triplex-tube systems, and solar thermal components, achieving speed-ups from tens to over 80,000 times faster than full CFD simulations while maintaining prediction errors typically below 5% or within sub-Kelvin temperature deviations. A critical comparative analysis exposes the fundamental trade-off between interpretability, data dependence, and computational efficiency, leading to a practical decision-making framework that guides method selection for specific applications such as design optimization, real-time control, and system-level simulation. Remaining challenges—including accurate representation of phase change nonlinearity, moving phase boundaries, multi-timescale dynamics, generalization across geometries, experimental validation, and integration into industrial workflows—motivate a structured roadmap for future hybrid physics–machine learning developments, standardized validation protocols, and pathways toward industrial deployment. Full article

The increasing release of non-biodegradable heavy metals, particularly lead (Pb²⁺) and cadmium (Cd²⁺), poses severe risks to ecosystems and human health. Herein, we present a sustainable “treating-waste-with-waste” strategy that simultaneously addresses heavy-metal contamination in water and the accumulation of expanded perlite waste. Expanded perlite waste was directly converted into a high-purity, low-silica CHA zeolite via a simple, one-pot, template-free hydrothermal conversion. The resulting sodium-exchanged material (Na-CHA-p) demonstrated excellent Pb²⁺ and Cd²⁺ removal performance, featuring ultrafast adsorption kinetics (reaching equilibrium within 5 min for both ions), high adsorption capacities (555.6 mg·g⁻¹ for Pb²⁺ and 211.0 mg·g⁻¹ for Cd²⁺), and superior selectivity. This study demonstrates an efficient pathway for the high-value utilization of perlite waste and highlights the strong potential of waste-derived CHA zeolites as advanced adsorbents for heavy-metal wastewater remediation. Full article

The relationship between economic development and environmental degradation in Brazil was studied over the period 1970–2022, using ecological footprint (EF) as an environmental indicator. A contribution to the scientific literature exists here because biomass energy (BIO) has been separated from other types of renewable energy sources, and environmental policy stringency (EPS) and energy depletion (END) have been simultaneously analyzed for their joint impacts on EF in Brazil. In this research, four hypotheses were formulated for the relationships of: GDP, BIO, EPS, RE, and END with EF. The ARDL method was used in this analysis due to the different orders of integration for some of the variables and sample size limitations, both of which make alternative cointegration techniques inappropriate. All four hypotheses were supported in the empirical estimates of this study. In the long run, increases in GDP will result in increased EF, decreases in BIO and EPS will decrease EF, and no long-run relationship exists between RE and EF. However, RE has a short-term rebound effect. Increases in END will increase EF, indicating the environmental costs associated with the extraction and consumption of non-renewable resources. The statistically significant error correction term also supports the idea that there will be a quick adjustment towards the long-run equilibrium. The implications of these results suggest that Brazil continues to operate within a stage of growth driven primarily by scale rather than intensity, yet well-regulated biomass energy and strict environmental regulations provide a pathway for achieving decoupling in alignment with SDG 13 and SDG 15. Full article

While the quantum emergence of spacetime is becoming a major research topic in physics, the quantum emergence of intelligence has not been widely researched in quantum information science (QIS). Following causal-logical quantum gravity theory, bipolar entropy vs. entropy and negative entropy (or negentropy) are reviewed and distinguished for quantum emergence/submergence of quantum agent (QA) and quantum intelligence (QI) in algebraic terms. This work refers to QA as an entangled bipolar string/superstring in bipolar dynamic equilibrium (BDE) and QI being centered on logically definable causality in regularity, mind-light-matter unity, and brain-universe similarity. ER = EPR is extended to ER ≥≥ EPR for the mathematical scalability of bipolar strings and their collective entanglement. The extension leads to a number of conjectures, testable predictions, and theorems. The term “equilibraton” is proposed as a type of EPR or bipolar generic string to serve as an entropic stitch to collectively hold the universe together as a quantum entanglement in BDE with ubiquitous, regulated local emergence and submergence of QA&QI. Equilibraton leads to the concept of bipolar entropy square—a complete entropic solution to the background issue in quantum gravity. With complete background independence, energy/information conservational bipolar entropy, energy/information invariance, bipolar entropy non-additivity, and equilibrium-based plateau concavity are introduced. The nature of the one-dimensional arrow of time is conjectured. As a unification of order and disorder for equilibrium-based regulation, bipolar entropy bridges QA&QI to agentic AI, where quantum-bio-economics can be viewed as a topological intervention of a natural dynamic equilibrium in a social or natural world. Use cases are reviewed to illustrate the practical and theoretical aspects of bipolar entropy in business management, quantum-bio-economics, quantum cryptography, physics, and biology. Eddington–Einstein’s comments on entropy are revisited. It is expected that bipolar entropy will bring quantum emergence/submergence to agentic AI&QI for entangled machine thinking and imagination as a naturally scalable and testable foundation of real-world quantum gravity, quantum information science (QIS), quantum cognition, and quantum biology (QCQB) to enhance Large Language AI Models (LLMs) and machine intelligence. Full article

Cosmology is living through fascinating times, where new observations from ground and space telescopes are questioning the established paradigm, the so-called $\mathsf{\Lambda }$ Cold Dark Matter model. The particle nature of Dark Matter is severely constrained by underground experiments, while recent observations by galaxy surveys indicate that the cosmological constant ($\mathsf{\Lambda }$) may not be constant after all. Furthermore, observations at high redshift of fully formed galaxies with massive black holes at their centers by the James Webb Space Telescope, as well as black holes with unexpected properties observed by the LIGO-Virgo gravitational wave detectors, are driving an in-depth revision of our assumptions in models of structure formation and the evolution of the Universe. I propose exploring two new paradigms to account for Dark Matter and Dark Energy, based on known physics, without introducing new particles into the Standard Model of Particle Physics. I will extend the primordial spectrum of fluctuations to small scales with new statistical properties to provide a viable Primordial Black Hole scenario for Dark Matter, and will include non-equilibrium thermodynamics in the expanding Universe, in the form of General Relativistic Entropic Acceleration, to explain Dark Energy. My proposal could provide a unified explanation for a plethora of interrelated multi-epoch, multi-scale, and multi-probe observations from present and future Gravitational Wave detectors, Large Scale Structure observatories, and Cosmic Microwave Background experiments. It emphasizes the need to develop new theoretical ideas hand-in-hand with observations to acquire a deeper understanding of our universe. If these ideas are correct, they will open a new window into the early universe and a new fundamental understanding of gravity in the late universe. Full article

During the critical period of agricultural green transformation, clarifying the evolutionary logic of farmers’ green production behavior under a multi-stakeholder framework provides significant insights for implementing “Dual Carbon” goals, establishing long-term mechanisms for high-quality agricultural development, and resolving deep-seated contradictions in agricultural non-point source pollution. Based on the social co-governance and public participation framework, this paper constructs a tripartite evolutionary game model involving government departments, farmer groups, and the general public, grounded in cost–benefit analysis, social governance friction, and evolutionary game theory. Through simulation, the study explores the equilibrium states and the specific impacts of varying parameter values on stable points. The findings reveal that: (1) The “interest price scissors” (benefit disparity) between green and conventional production is the key determinant of farmers’ strategic equilibrium. Once this structural contradiction is resolved, green production becomes the optimal strategy. (2) Farmers are highly sensitive to marginal cost–benefit fluctuations, leading to a sequential behavioral cascade: farmers retreat first, followed by the government, and finally the public. (3) Public participation cost is the pivotal variable for activating the co-governance mechanism, and the application of digital governance tools determines the time required to reach equilibrium. (4) A “Success Paradox” exists in government regulation; incentive mechanisms must be adjusted promptly after initial success. (5) Integrated policy combinations outperform single instruments; breaking the “locked-in” state requires a policy shock of sufficient intensity. This research offers a theoretical basis and policy enlightenment for optimizing the social co-governance landscape and promoting sustainable agricultural modernization. Full article

In the joint operation of multiple virtual power plants, after day-ahead optimal dispatch is completed, some participants may engage in speculative behaviors such as misreporting profit contribution data to obtain greater benefits during profit distribution, thereby undermining fairness. To address this issue, this paper constructs a profit distribution model designed to prevent speculation. An improved Nash bargaining equilibrium algorithm based on a third-party trading intermediary is proposed to curb speculative actions. Furthermore, a dual-layer monitoring mechanism centered on profit deviation is established, which can effectively identify both single-day speculative behaviors and long-term systematic speculative trends, thereby triggering verification procedures. This forms a closed-loop management mechanism for speculation prevention—“detection, monitoring, analysis, verification”—ensuring fair profit distribution among participants within virtual power plants. Case study results demonstrate that the proposed method achieves an average deviation of only 2.32% compared to the profit distribution outcome under non-speculative conditions. In contrast, commonly used methods such as the Shapley value method, nucleolus method, and Nash–Harsanyi bargaining solution exhibit an average deviation as high as 18.44%. The research presented in this paper enables the detection of speculative behaviors among participants and facilitates verification, significantly enhancing the fairness and rationality of profit distribution. Full article

The segregation of brittle Laves phases remains a critical bottleneck limiting the performance of additive manufacturing (AM) nickel-based superalloys. While its evolution is governed by complex transient physical fields within the melt pool, a quantitative kinetic correlation between processing parameters and microstructural features is currently lacking. In this study, a high-fidelity multiphysics numerical model was developed to establish a cross-scale mapping logic of “Process-Physical Field-Microstructure” by dissecting the global distribution of temperature gradient (G) and solidification rate (R) along the quasi-steady-state melt pool boundary. It is revealed that increasing the scanning speed synergistically enhances R while compressing G. Beyond driving a transition from oriented columnar dendrites to refined mixed-dendritic structures, this shift effectively blocks the continuous enrichment channels of Nb and Mo elements by compressing the “kinetic time window” for solute redistribution. Consequently, the morphology of the Laves phase is forced to evolve from a continuous interconnected chain-like network into dispersed isolated particles. This research clarifies the kinetic essence of microstructural evolution under non-equilibrium solidification, providing critical physical criteria for the precise intervention of deleterious phases and the regulation of microstructural consistency in high-performance AM components. Full article

Microfluidic electrokinetic flows play a central role in applications such as lab-on-a-chip diagnostics, microelectronics cooling, and biomedical sample manipulation. These systems involve intricate heat transfer processes, including Joule heating from ionic currents, temperature-driven flow instabilities, and coupled thermal–fluid interactions, that crucially affect device performance, reliability, and scalability. Current challenges include non-equilibrium charge dynamics, incomplete thermophysical property data for complex fluids, and thermal crosstalk in integrated platforms. This review summarizes the literature published over the past 20 years, with occasional reference to earlier work, covering the fundamental mechanisms of heat generation and dissipation in electrokinetic microflows; analytical, numerical, and experimental approaches for characterizing thermal effects; and discussion on the limitations and application-driven opportunities. It also highlights open questions and future research directions and offers a comprehensive view of design principles and guidelines for developing robust, thermally optimized electrokinetic microfluidic technologies. Full article

Na⁺,K⁺-ATPase possesses a highly conserved glycine (G358 in the α3 isoform) that—together with a nearby isoleucine (I363 in α3)—is targeted by mutations causing some of the most severe neurological phenotypes of the clinical spectrum of α3-Na⁺,K⁺-ATPase mutations. The disease mutations α3-G358V and α3-I363N affect Na⁺ and K⁺ transport to an extent incompatible with cell growth. However, alanine replacement of the corresponding glycine G363 in the α1 isoform is compatible with cell growth, allowing the effects on Na⁺,K⁺-ATPase function to be addressed using enzymatic assays on plasma membranes isolated from transfected cells. Occlusion of Na⁺ appears to be defective in mutant G363A, resulting in a reduced rate of phosphorylation from ATP. Furthermore, the mutation displaces the major conformational equilibrium of Na⁺,K⁺-ATPase such that the K⁺-occluded state is destabilized and occluded K⁺ is released faster, thereby leading to accumulation of a non-productive state without bound Na⁺ or K⁺. The critical function of the glycine can be ascribed to a strategic location at the bending point between an α helix and a β strand, where it connects the catalytic ATP hydrolysis site in the cytoplasmic P domain with the ion-binding region in the membrane and coordinates important intramolecular domain movements during the Na⁺,K⁺-ATPase transport cycle. Full article

Mechano-sorptive phenomena (MSP) refer to the coupled mechanical response of polymers under simultaneous mechanical stress and fluid sorption. The most researched MSP are environmental stress cracking (ESC) and mechano-sorptive creep (MSC). ESC initiates at regions of localized stress and solvent sorption, presenting as brittle fracture, while MSC is characterized by large, time-dependent, and partially recoverable creep associated with transient bulk sorption. ESC experiments can however also result in significant plastic deformation, in which case the term environmental stress yielding (ESY) has been used. Similarly, MSC can evolve into tertiary creep followed by rupture, in which case the phenomenon is termed mechano-sorptive creep rupture (MSCR). Both behaviors originate from solvent diffusion into the amorphous phase, leading to disruption of non-covalent interactions between polymer chains. This review bridges seemingly disconnected research to illustrate that ESC and MSC represent extremes on a continuum of MSP, rather than disparate phenomena. We identify the principles of polymer thermodynamics and experimental methods necessary to separate polymer deformation under MSC into reversible stress-induced swelling and irreversible non-equilibrium deformation. Finally, we illustrate how MSP underline the functionality of several biomimetic materials including dentin adhesives, mutable collagenous tissue, spider silk, tendons, and articular cartilage, as well the synthesis of biomimetic materials by solvent vapor annealing assisted by soft shear. Full article

Topological insulators (TIs) feature unique Dirac fermion-hosting surface states with exceptional electronic properties, rendering them promising candidates for optoelectronic and spintronic applications. Herein, we investigate the relaxation dynamics of photoexcited carriers in Bi₂Se₃ films via optical pump–terahertz (THz) probe spectroscopy (OPTP) at room temperature. Under 800 nm pump pulse excitation, the time-dependent real part of the pump excitation conductivity Δσ exhibits a positive-to-negative sign reversal as carriers relax toward equilibrium, which is further validated by frequency-dependent conductivity spectra at varied pump-probe delays. The initial positive Δσ originates dominantly from bulk carrier contributions, while the negative component at prolonged delays is ascribed to Dirac surface states, driven by enhanced scattering of photoexcited carriers. Using the Drude–Smith model to fit the differential conductivity spectra, we quantitatively extracted time-dependent transport parameters of bulk and surface states. These results unravel the comprehensive carrier relaxation mechanism in Bi₂Se₃, clarify the distinct roles of surface and bulk contributions, and lay the groundwork for designing TI-based THz devices. Full article