Search Results (401)

Carbon fiber textile-reinforced engineered cementitious composite (CTR-ECC) is widely utilized in structural strengthening applications due to its advantages of low weight and high strength. A comprehensive understanding of its mechanical behavior under off-axis tension is crucial for addressing the prevalent off-axis stress states in engineering practice. This paper presents an experimental investigation on the off-axis tensile properties of CTR-ECC. Specimens were fabricated with four off-axis angles: 0°, 15°, 30°, and 45°. The study revealed three main findings: (1) Under axial (0°) loading, failure is characterized by yarn fracture and interface slip, whereas off-axis tension induces a stable progressive delamination failure in textile-reinforced ECC systems. (2) While CTR-ECC exhibits higher tensile strength than plain ECC at all angles, its strength decreases significantly as the off-axis angle increases (e.g., a 27.1% reduction at 15°). Off-axis layouts, however, substantially improve energy absorption, with strain energy density increasing by up to 368.4% at 30°. (3) A phenomenological constitutive model was developed, which can adequately capture the stress–strain response of CTR-ECC under various off-axis angles, with coefficients of determination (R²) exceeding 0.9 in all cases. These results provide important insights into the failure mechanisms and performance design of CTR-ECC under off-axis tension conditions. Full article

We introduce a local phase-field framework for spin-entanglement correlations. In this framework, the relevant hidden variable is an internal scalar phase associated with each fermion and derived from two underlying real fields. The fields are assumed to evolve locally in ordinary spacetime. When a particle pair is produced at a common spacetime event, the pair acquires a shared phase-locking condition at creation; after separation, the two internal phases evolve independently and no nonlocal interaction is introduced. Spin measurements by Stern–Gerlach analyzers are modeled as local filtering operations. Each local response depends only on the internal phase carried by the particle and on the orientation of the local analyzer. The local response function A(α,λ) = cos(λ − 2α) is derived from the spinorial transformation law of the underlying real field pair and the projection geometry of the detector interaction; it is not a phenomenological ansatz. From these deterministic local responses we derive an analog correlator. The raw product moment of the continuous detector outputs evaluates to ⟨AB⟩ = −½ cos 2(α − β), which satisfies classical Clauser-Horne-Shimony-Holt (CHSH) bounds. After Pearson normalization—the operationally appropriate correlation measure for continuous analog detector outputs, justified by channel-contrast physics and scale invariance—the normalized correlator yields E(α,β) = −cos 2(α − β), matching the quantum singlet correlator in functional form. When this normalized correlator is inserted into the CHSH expression, it yields the numerical value 2√2. This result is a structural consequence of the reduced marginal variance of continuous response functions relative to the unit-variance dichotomic observables assumed in Bell’s derivation; it does not constitute a violation of Bell’s inequality. The model does not reproduce quantum singlet statistics at the level of binary detector outcomes, where the correlator takes a triangular rather than cosine form. The contribution is therefore ontological and conceptual rather than predictive. The framework preserves parameter independence and no-signaling throughout. It provides a concrete real-field ontology for spin correlations based on internal phase structure, and it demonstrates that the functional form of the quantum singlet correlation can be obtained from a strictly local deterministic description, provided that the detector responses are treated as continuous analog quantities and normalized accordingly. We compare the model with earlier phase-based approaches and discuss experimental configurations—including time-resolved and multi-stage Stern–Gerlach measurements—that could in principle probe the proposed internal-phase dynamics at the pre-registration level. Full article

Accurate prediction of the behavior of alloys and metal matrix composites during high-temperature deformation requires strict consideration of the loading history. To address this problem, a hybrid rheological model for flow stress prediction has been developed, combining a phenomenological description of the yield stress with a recurrent neural network based on the NARX (Nonlinear AutoRegressive with eXogenous inputs) architecture. The memory effect is formed by expanding the input parameters with the response values from the previous step. The identification of the weight coefficients of the NARX neural network is implemented by training an equivalent multilayer perceptron. To improve the generalization ability of the model and eliminate its dependence on a fixed discretization step, the training dataset includes data obtained under non-monotonic changes in the strain rate over time and a variable time interval. The article justifies the structure of the model input parameters, excluding the accumulated strain from the input set due to its lack of informativeness during active softening processes. Verification of the hybrid model on the 7075/2.5% TiC composite in the temperature range of 300–500 °C demonstrated an average relative error of 1.5% when predicting modes that were not involved in the training. The predicted flow stress values fall within the experimental scatter interval of ±5% and accurately reproduce the local features of the flow stress curves. The proposed model and its identification technique provide correct consideration of the deformation history under the complex interaction of hardening and softening processes. Full article

In order to investigate the influence of notched structures on creep performance under long-term high-temperature conditions, durability tests were conducted on ring-notched and hole-containing thin tubular specimens of directional columnar-grain DZ411 alloy at 850 °C and 930 °C. The results were compared with those of smooth round rod specimens at same temperatures and stress levels, to evaluate the impact of notched structures described above on the rupture life. Based on the experimental data, a finite element subroutine was developed using a macroscopic phenomenological creep model to simulate the creep deformation behavior of the structural components. The stress relaxation characteristics of the two types of notched structures were analyzed. The results show that the ring-notched structure exhibits significant stress relaxation, leading to a “notch strengthening” effect, which improves the endurance property; conversely, the small-hole structure shows insufficient stress relaxation, resulting in “notch weakening” and a reduction in the endurance property. The developed subroutine demonstrates sufficient engineering accuracy in notch creep simulation. Using creep strain as the fracture criterion, the predicted endurance life showed a deviation from experimental results within the acceptable engineering range, indicating that the subroutine has sufficient engineering accuracy. Full article

This paper examines whether a compact electron–proton configuration (“small hydrogen”) with a characteristic radius of a few femtometers is excluded by basic relativistic kinematics and simple stationarity constraints. Motivated by earlier discussions of formally deep relativistic energy scales in Dirac-based treatments, a phenomenological, virial-inspired energy-balance framework that incorporates relativistic kinetic energy, finite-size regularization of the central field, and order-of-magnitude spin–magnetic and spin–orbit contributions is developed in this paper. Within this framework, self-consistent characteristic scales associated is obtained with a hypothetical compact configuration without invoking Dirac or quantum-electrodynamics (QED) bound-state eigenvalues. The resulting scales—namely, a central energy scale of about 260 keV and a characteristic spin-dependent scale of order ΔE_spin ≈ 100 ± 20 keV—define concrete experimental and observational energy ranges of interest. The present study does not establish the existence, formation probability, lifetime, or dynamical stability of such states. Rather, it shows that relativistic kinematics, finite-size effects, and virial-inspired stationarity constraints do not, by themselves, rule out compact stationary electron–proton configurations within the assumptions of the model. If such states were realized in nature and possessed radiative or interaction channels, those states may have implications for astrophysics, fusion concepts, and dark-matter phenomenology. Full article

We report a curious numerical observation: If atomic nuclei are modelled as connect-sums of threefoil knots with alternating chirality, the ropelength of the composite knot—a purely geometric quantity requiring no quantum mechanics—tracks the experimental binding-energy curve from hydrogen to uranium. A two-parameter fit to 50 nuclei gives $R^2=0.9998$ (coefficient of determination; 1 = perfect fit) and $\mathrm{RMS}=6.9\mathrm{MeV}$ (root-mean-square deviation between model and experiment), comparable to the five-parameter Bethe–Weizsäcker formula ($\mathrm{RMS}=8.3\mathrm{MeV}$) at less than half the parameter count. Out-of-sample predictions for ${}^{244}\mathrm{Pu}$ and ${}^{252}\mathrm{Cf}$, not used in the fit, are accurate to $0.4\mathrm{MeV}$ and $8.4\mathrm{MeV}$, respectively. What makes the observation worth reporting is not the fit itself, but the range of nuclear phenomenology that emerges uninstructed from the topology: saturation, surface energy, isospin pairing, odd-even staggering, and geometric analogues of nuclear isomers all appear as consequences of the connect-sum construction, without additional assumptions. We catalogue these correspondences, assess which are structural and which may be coincidental, and identify concrete numerical tests that would distinguish the two possibilities. Full article

Creep is one of the main failure mechanisms of materials at elevated temperatures, and the creep rate curve is a key descriptor of creep deformation and damage evolution. However, existing creep models are mainly phenomenological or stage-wise, and the physical origin of the bathtub-shaped creep rate curve over the full creep process has not been systematically clarified. In this study, creep damage is treated as an aging failure process of a material system, and a physically interpretable hierarchical model is established based on statistical physics for disordered complex systems. By linking the evolution and interaction of microscopic material units with macroscopic creep behavior, the proposed model provides a unified description of the primary, secondary, and tertiary creep stages and offers a theoretical explanation for the bathtub-shaped creep rate curve. Validation using representative metallic and composite material cases shows that the model can reasonably reproduce the overall three-stage creep rate evolution, with residual sums of squares of 1.3088 and 0.5369, respectively. These results demonstrate the ability of the model to capture full-process creep behavior in different material systems. The main advantage of the proposed approach is its physical interpretability within a unified framework, while its current limitation is that the validation remains limited in scale and broader benchmark comparisons with conventional methods are still needed. This work provides a statistical perspective for creep behavior modeling and for understanding the microscopic mechanisms and interactions underlying creep degradation in structural materials. Full article

Robust numerical frameworks are essential for the simulation, design, monitoring, and control of membrane-based separation units, particularly under highly nonlinear and industrially relevant operating conditions. In this context, a comprehensive phenomenological and numerical framework is proposed for the simulation of hollow-fiber membrane modules, incorporating coupled mass, momentum (through pressure drop), and energy transport equations. The governing equations are discretized using a rigorous orthogonal collocation formulation, and the performances of two numerical solution strategies are systematically investigated for the first time to allow the in-line and real-time implementation of the model: a steady-state approach based on the Newton–Raphson method with careful treatment of initial estimates, and a pseudotransient formulation. Particularly, an original and consistent numerical treatment is introduced for the energy balance at boundaries where the permeate flow vanishes, enabling the stable incorporation of thermal effects and Joule–Thomson phenomena. The results clearly show that the steady-state Newton–Raphson approach provides the best overall performance in terms of computational efficiency, numerical robustness, and accuracy when physically consistent initial profiles are employed. In particular, the combination of a linear initial guess and a numerical mesh constituted of four collocation points yielded the most favorable balance between convergence speed, numerical robustness, and accuracy for the base-case sensitivity analysis. For monitoring-oriented applications, the numerical choice should be weighted primarily toward computational performance once physical consistency and convergence criteria are satisfied, rather than toward maximum mesh-refinement accuracy. In this context, small differences in internal-fiber profiles can be compensated through real-time permeance estimation and are negligible when compared with measurement uncertainty in real industrial processes. Under extreme operating conditions involving low concentrations, low flow rates, and highly permeable species, the pseudotransient formulation proved to be a reliable auxiliary strategy, enabling robust convergence when suitable initial guesses were not readily available. The proposed framework is validated against experimental data from the literature and subjected to extensive convergence and sensitivity analyses, providing a reliable basis for simulation and for assessing computational feasibility in in-line and real-time monitoring-oriented applications. A full demonstration of digital-twin integration, online parameter updating, reduced-order coupling, and closed-loop control is beyond the scope of the present study and will be addressed in future work. Full article

Gypsum-based fire protection relies on thermally activated dehydration, where chemically bound water is released and evaporated, thereby providing an endothermic heat sink that delays heat penetration through assemblies. In parallel, inorganic hydrated salts are increasingly used as flame-retardant additives in gypsum-based systems to enhance heat absorption over specific temperature ranges. Fire simulation tools and performance-based fire engineering approaches require reliable kinetic data and reaction enthalpies that can be implemented as coupled thermal–chemical source terms. However, additive-specific kinetic datasets remain limited, particularly under restricted vapor exchange conditions representative of porous construction materials. This work investigates the thermal decomposition behavior and dehydration kinetics of Aluminum Trihydrate (Al(OH)₃, ATH), Magnesium Hydroxide (Mg(OH)₂, MDH), Calcium Aluminate Sulfate (3CaO·Al₂O₃·3CaSO₄·32H₂O, CAS), and Magnesium Sulfate Heptahydrate (MgSO₄·7H₂O, ESM) with emphasis on vapor-restricted conditions representative of confined porous systems. Differential scanning calorimetry (DSC) experiments were conducted at three heating rates (2, 10, and 20 K/min for MDH, CAS and ESM and 20, 40 and 60 K/min for GB-ATH) up to 600 °C using pinhole crucibles to simulate autogenous vapor pressure. The thermal analysis indicates that ATH and MDH exhibit predominantly single-step dehydration behavior, while ESM shows a complex multi-step mechanism. Although CAS presents a single dominant thermal peak in the DSC signal, the isoconversional analysis reveals a multi-stage reaction behavior, demonstrating that peak-based interpretation alone may be insufficient for such systems. Kinetic parameters were determined using both model-free (Starink) and model-fitting approaches in accordance with the recommendations of the Kinetics Committee of the International Confederation for Thermal Analysis and Calorimetry (ICTAC). All reactions were consistently described using the Avrami–Erofeev model as an effective phenomenological representation of the conversion behavior. The extracted kinetic triplets were validated through numerical simulations, showing good agreement with experimental conversion and reaction rate data. The resulting kinetic parameters and dehydration enthalpies provide a physically consistent dataset for the description of dehydration processes under restricted vapor exchange. These results support the development of thermochemical models for gypsum-based systems; however, their transferability to full-scale assemblies remains subject to validation under coupled heat- and mass-transfer conditions. Full article

The critical brain hypothesis proposes that neural systems operate near a phase transition to optimize information processing. A key method for investigating this hypothesis is the phenomenological renormalization group (pRG), which looks for scale-invariant features across levels of coarse-graining. One such feature is the power-law scaling of eigenvalues of covariance matrices of coarse-grained variables. However, the estimation of this scaling exponent, $\mu$, often relies on linear regression over arbitrarily selected ranges of the plot of eigenvalues versus rank. This heuristic “eyeballing” introduces uncontrolled bias and complicates the interpretation of observed scaling relationships. In order to obtain a more robust estimation of $\mu$, we do not fit the standard eigenvalue-vs-rank relationship, but rather the density of eigenvalues, for which standard protocols exist for fitting power laws to empirical data distributions. We demonstrate this approach using a synthetic model that replicates the scaling signatures of neural data while providing control over the system’s exponents as well as neural data obtained from publicly available Neuropixels recordings. We also establish standards for the minimal data required to quantify power-law behavior in a pRG eigenvalue analysis. Our approach contributes a tool for understanding the fundamental limitations imposed by spatial and temporal constraints of experimental datasets, which is required to rigorously assess the neural criticality hypothesis. Full article

Flip-flow screens are widely used for the efficient separations of wet fine materials. To explore the separation characteristics of the particle and screen plate in the flip-flow screening process, a flip-flow plate impact experimental system was built. The experimental system was based on a spherical inertial measurement device and a semi-industrial flip-flow screen system. In this study, we first derive the impact mechanics equation of the flip-flow screen plate on the particle and analyze the influence of the main parameters on the maximum impact force. Subsequently, we investigated the kinematic characteristics of the particle impacted by the screen plate at different moving positions, the variation of the centerline acceleration mechanism, and determined the angular velocity in the collision process. Additionally, we further clarified the alteration in the rules of translational and rotational kinetic energy of the particles in the collision process. This study addresses a research gap in the phenomenological modelling of particulate screening process. At the same time, it provides theoretical support for the accurate control of the flip-flow screening process. Full article

Sol–gel phase transitions are complex far-from-equilibrium processes characterized by limited reproducibility, whose origin remains poorly understood and rarely quantified. We investigated the thermally induced sol–gel transition of agar using turbidimetry. A phenomenological model was applied to extract key kinetic parameters (maximum absorbance, maximum rate, and characteristic times) from 96 independent replicates. Variability was quantified and compared with that of an enzymatic reaction exhibiting similar sigmoidal kinetics, allowing for separation of experimental, intrinsic, and nonergodic contributions. Agar gelation displays markedly higher variability. The total variability (CV ≈ 16%) exceeds both the experimental error (1–2%) and the nonergodic contribution (≈2%), demonstrating that it predominantly arises from intrinsic process dynamics. Variability increases sharply during early stages of gelation and then evolves more gradually, indicating that stochastic nucleation and network formation pathways drive divergent kinetic trajectories despite identical initial conditions. Variability in gelation is therefore not a measurement artifact but an intrinsic hallmark of the sol–gel transition. This inherent stochasticity limits the predictive power of deterministic models, particularly at meso- and microscopic scales, and should be considered a fundamental feature of gel-forming systems. Our approach provides a quantitative framework for characterizing variability in phase transitions and may be extended to more complex biological and soft matter systems. Full article

Recently, the experimental realization of a Kapitza potential in a Bose–Einstein condensate (BEC) was reported for the first time in the literature, motivating further theoretical investigations of such a system. At the same time, in the astrophysical context, BEC dark matter models have been widely studied as a possible phenomenological explanation for the dark matter phenomena. We model the galactic structure with an inner cored profile obtained from the ground state equilibrium solution of the Schrödinger–Poisson together with a Kapitza–BEC-like interaction for the tail region. We find reasonable agreement of the model with representative galaxy rotation curves available in the SPARC catalogue. Full article

Organophosphate (OP) exposure can trigger seizures within minutes and can rapidly evolve into status epilepticus (SE). Early seizure generation is plausibly driven by acetylcholinesterase inhibition, leading to central cholinergic overstimulation. With increasing seizure duration, experimental data are consistent with a time-dependent shift toward glutamatergic maintenance (NMDA/AMPA), oxidative stress, neuroinflammation, and progressive failure of GABAergic inhibition. This framework predicts a narrow window in which benzodiazepine (BDZ) monotherapy is most effective and a rising probability of BDZ non-response when seizures are prolonged, while anti-glutamatergic strategies may retain relative efficacy later in the course. This narrative review integrates clinical phenomenology, diagnostic limitations, and mechanistic evidence to propose an operational approach for OP-related seizures and SE in emergency settings. We discuss a pragmatic “Stage 1 Plus” framing for patients presenting after prolonged seizures or in non-convulsive SE with coma. Human evidence remains limited and heterogeneous, and inference is constrained by confounding due to delayed recognition, variable decontamination/resuscitation pathways, sparse EEG confirmation, and selection bias in mass-casualty reporting. Full article

This study aims to investigate the influence of clay mineral content on the rheological properties and long-term deformation stability of clays, and to establish a unified model capable of quantitatively describing the nonlinear rheological behavior of clays with different mineral compositions. Direct shear rheological tests were conducted on specimens prepared with different mixing ratios of bentonite, kaolin, and quartz. Combined with micro-mechanism analysis, the controlling factors of clay rheological behavior were explored. The experimental results show that the creep stress threshold, elastic viscosity, and average plastic viscosity decrease significantly with increasing clay mineral content. The rheological deformation exhibits distinct nonlinear characteristics, and clay mineral content plays a controlling role in the rheological behavior. Based on experimental and mechanistic analysis, a unified rheological model was established, which reflects the material origin of rheology and captures nonlinear rheological characteristics. This model can predict the entire time-history mechanical behavior of clays with different mineral compositions across the three stages of instantaneous deformation, decay rheology, and steady-state rheology under different shear stress levels using a single set of parameters. Validation was performed through direct shear rheological tests under 50 working conditions for five types of clay specimens, demonstrating good consistency between the model calculations and experimental results. The unified rheological model reveals the material origin and physical essence of clay rheology, demonstrates high universality, and advances the understanding of the influence of mineral composition on rheology from the current phenomenological qualitative description to quantitative calculation for the first time, significantly enhancing its engineering application value. This provides a more reliable tool for predicting long-term deformation and assessing the stability of clay foundations. Full article