Search Results (80)

Cancer-associated fibroblasts (CAFs) restructure collagen hydrogels via actomyosin-driven fibril bundling and crosslinking, increasing polymer density to generate mechanical stress that accelerates tumor proliferation. Conventional hydrogel models lack spatial heterogeneity, thus obscuring how localized stiffness gradients regulate cell cycle progression. To address this, we developed a collagen hydrogel-based microtissue platform integrated with programmable microstrings (single/double tethering), enabling real-time quantification of gel densification mechanics and force transmission efficiency. Using this system combined with FUCCI cell cycle biosensors and molecular perturbations, we demonstrate that CAF-polarized contraction increases hydrogel stiffness (350 → 775 Pa) and reduces pore diameter (5.0 → 1.9 μm), activating YAP/TAZ nuclear translocation via collagen–integrin–actomyosin cascades. This drives a 2.4-fold proliferation increase and accelerates G1/S transition in breast cancer cells. Pharmacological inhibition of YAP (verteporfin), actomyosin (blebbistatin), or collagen disruption (collagenase) reversed mechanotransduction and proliferation. Partial rescue upon CYR61 knockdown revealed compensatory effector networks. Our work establishes CAF-remodeled hydrogels as biomechanical regulators of tumor growth and positions gel-based mechanotherapeutics as promising anti-cancer strategies. Full article

Temperature dependencies of the density, heat capacity at constant pressure, and isobaric thermal expansion coefficient are investigated for two liquid metal nuclear reactor coolants: pure sodium and sodium–potassium eutectic alloy (31.9 at. %Na). The variational method of the thermodynamic perturbation theory is used for the calculations. The calculations were carried out in temperature ranges of 373–1673 K for Na and 273–1573 K for 0.319Na-0.681K. The accuracy of two local pseudopotentials and three exchange–correlation functions is estimated. It is shown that two combinations between the pseudopotential and exchange–correlation function can be recommended for predicting the properties at high temperatures for which experimental information is absent. Full article

LK-99, with chemical formula Pb_10−xCu_x(PO₄)₆O, was recently reported to be a room-temperature superconductor. While this claim has met with little support in a flurry of ensuing work, a variety of calculations (mostly based on density-functional theory) have demonstrated that the system possesses some unusual characteristics in the electronic structure, in particular flat bands. We have established previously that within DFT, the system is insulating with many characteristics resembling the classic cuprates, provided the structure is not constrained to the P3(143) symmetry nominally assigned to it. Here we describe the basic electronic structure of LK-99 within self-consistent many-body perturbative approach, quasiparticle self-consistent GW (QSGW) approximation and their diagrammatic extensions. QSGW predicts that pristine LK-99 is indeed a Mott/charge transfer insulator, with a bandgap gap in excess of 3 eV, whether or not constrained to the P3(143) symmetry. When Pb₉Cu(PO₄)₆O is hole-doped, the valence bands modify only slightly, and a hole pocket appears. However, two solutions emerge: a high-moment solution with the Cu local moment aligned parallel to neighbors, and a low-moment solution with Cu aligned antiparallel to its environment. In the electron-doped case the conduction band structure changes significantly: states of mostly Pb character merge with the formerly dispersionless Cu d state, and high-spin and low spin solutions once again appear. Thus we conclude that with suitable doping, the ground state of the system is not adequately described by a band picture, and that strong correlations are likely. Irrespective of whether this system class hosts superconductivity or not, the transition of Pb₁₀(PO₄)₆O from being a band insulator to Pb₉Cu(PO₄)₆O, a Mott insulator, and multi-determinantal nature of doped Mott physics make this an extremely interesting case-study for strongly correlated many-body physics. Full article

This work presents a comprehensive mathematical framework for symmetrized neural network operators operating under the paradigm of fractional calculus. By introducing a perturbed hyperbolic tangent activation, we construct a family of localized, symmetric, and positive kernel-like densities, which form the analytical backbone for three classes of multivariate operators: quasi-interpolation, Kantorovich-type, and quadrature-type. A central theoretical contribution is the derivation of the Voronovskaya–Santos–Sales Theorem, which extends classical asymptotic expansions to the fractional domain, providing rigorous error bounds and normalized remainder terms governed by Caputo derivatives. The operators exhibit key properties such as partition of unity, exponential decay, and scaling invariance, which are essential for stable and accurate approximations in high-dimensional settings and systems governed by nonlocal dynamics. The theoretical framework is thoroughly validated through applications in signal processing and fractional fluid dynamics, including the formulation of nonlocal viscous models and fractional Navier–Stokes equations with memory effects. Numerical experiments demonstrate a relative error reduction of up to 92.5% when compared to classical quasi-interpolation operators, with observed convergence rates reaching $\mathcal{O}\left(n^{-1.5}\right)$ under Caputo derivatives, using parameters $\lambda =3.5$, $q=1.8$, and $n=100$. This synergy between neural operator theory, asymptotic analysis, and fractional calculus not only advances the theoretical landscape of function approximation but also provides practical computational tools for addressing complex physical systems characterized by long-range interactions and anomalous diffusion. Full article

To address the core challenges in multi-robot path planning (MRPP) within large-scale, complex environments—namely path conflicts, suboptimal task allocation, and computational inefficiency—this paper introduces a Hybrid Clustering-Enhanced Brain Storm Optimization (HC-BSO) algorithm designed to improve both path quality and computational efficiency significantly. For optimizing initial task assignment, the conventional K-Means clustering method is supplanted by a hybrid clustering methodology that integrates Mini-Batch K-Means with Density-Based Spatial Clustering of Applications with Noise (DBSCAN), facilitating an efficient and robust partitioning of task points. Concurrently, we incorporate a two-stage exploration–perturbation evolutionary strategy. This strategy effectively balances global exploration with local exploitation, thereby enhancing solution diversity and search depth. Comparative analyses against the standard Brain Storm Optimization (BSO) and other prominent swarm intelligence algorithms reveal that HC-BSO exhibits significant advantages in terms of total path length, computational time, and path conflict avoidance. Notably, in large-scale, multi-task scenarios, HC-BSO consistently generates high-quality, conflict-free paths, demonstrating superior stability, convergence, and scalability. Full article

In the context of intelligent manufacturing, equipment fault early-warning technology has become a critical support for ensuring the continuity and safety of industrial production. However, with the increasing complexity of modern industrial equipment structures and the growing coupling of operational states, traditional fault warning models face significant challenges in feature recognition accuracy and adaptability. To address these issues, this study proposes a hybrid fault early-warning framework that integrates an improved bees algorithm (IBA) with a categorical boosting (CatBoost) model and kernel density estimation (KDE). The proposed framework first develops the IBA by integrating Latin Hypercube Sampling, a multi-perturbation neighborhood search strategy, and a dynamic scout bee adjustment strategy, which effectively overcomes the conventional bees algorithm (BA)’s tendency to fall into local optima. The IBA is then employed to achieve global optimization of CatBoost’s key hyperparameters. The optimized CatBoost model is subsequently used to predict equipment operational data. Finally, the KDE method is applied to the prediction residuals to determine fault thresholds. An empirical study on a deflection fault in the valve position sensor connecting rod of the mineral oil system in a gas compressor station shows that the proposed method can issue early-warning signals two hours in advance and outperforms existing advanced algorithms in key indicators such as root mean square error (RMSE), coefficient of determination (R²) and mean absolute percentage error (MAPE). Furthermore, ablation experiments verify the effectiveness of the strategies in IBA and their contribution to CatBoost hyperparameter optimization. The proposed method significantly improves the accuracy and reliability of fault prediction in complex industrial environments. Full article

The structure and density of plankton communities greatly influence carbon and nutrient cycling as well as the environmental status of lake ecosystems. This community can respond to a range of environmental drivers, including those influenced by human perturbations on local and regional scales, causing abrupt changes and imbalances. While the implications of climate and land-use changes are evident for a range of tropical lake conditions, their impacts on planktonic population dynamics are less understood. In this study, we aimed to investigate how distinctive levels of nutrients, allochthonous organic matter (OM), and sunlight availability change phytoplankton and zooplankton density and structure in a natural tropical lake. Using an in situ mesocosm facility, we manipulated the addition of nutrients and OM, in addition to sunlight availability and a combination of these treatments. We monitored limnological parameters, plankton count, and identification for 12 days. The mesocosms included eight different combinations in a 2 × 2 × 2 factorial design, each with two replicates. Inorganic nutrient addition reduced phytoplankton species richness, favoring the dominance of opportunistic species such as Chlorella sp. at much higher densities. Organic matter also increased light attenuation and caused the substitution of species and changes in dominance from Pseudanabaena catenata to Aphanocapsa elachista. On the other hand, physical shading had less influence on these communities, presenting densities similar to those found in the control mesocosms. Zooplankton presented a group dominance substitution in all mesocosms from copepod to rotifer species, and copepod growth seemed to be negatively affected by Chlorella sp. density increase. Furthermore, this community was associated with the light attenuation indices and bacterioplankton. These results indicate that tropical planktonic responses to environmental changes can effectively occur in just a few days, and the responses can be quite different depending on the nutritional source added. The punctual nutrient addition was sufficient to provide changes in this community, evidencing the strength of anthropic events associated with strong nutrient input. Understanding tropical plankton dynamics in response to environmental changes, such as those simulated in this work, is important for understanding the effects of climate and anthropogenic changes on tropical lake functioning. This knowledge can strengthen measures for the conservation of freshwater systems by allowing predictions of plankton community changes and the possible consequences for the aquatic food chain and water quality. Full article

Archaean metamorphic basement reservoirs, characterized by the development of natural fractures, constitute the primary target for oil and gas exploration in the Bozhong Depression, Bohai Bay Basin, Eastern China. Based on analyses of geophysical image logs, cores, scanning electron microscopy (SEM), and laboratory measurements, tectonic fractures are identified as the dominant type of natural fracture. Their development is primarily controlled by lithology, weathering intensity, and faulting. Fractures preferentially develop in metamorphic rocks with low plastic mineral content and are positively correlated with weathering intensity. Fracture orientations are predominantly parallel or subparallel to fault strikes, while localized stress perturbations induced by faulting significantly increase fracture density. Open fractures, constituting more than 60% of the total reservoir porosity, serve as both primary storage spaces and dominant fluid flow conduits, fundamentally governing reservoir quality. Consequently, spatial heterogeneity in fracture distribution drives distinct vertical zonation within the reservoir. The lithological units are ranked by fracture development potential (in descending order): leptynite, migmatitic granite, gneiss, cataclasite, diorite-porphyrite, and diabase. Diabase represents the lower threshold for effective reservoir formation, whereas overlying lithologies may function as reservoirs under favorable conditions. The large-scale compressional orogeny during the Indosinian period marked the primary phase of tectonic fracture formation. Subsequent uplift and inversion during the Yanshanian period further modified and overlaid the Indosinian structures. These structures are characterized by strong strike-slip strain, resulting in a series of conjugate shear fractures. During the Himalayan period, preexisting fractures were primarily reactivated, significantly influencing fracture effectiveness. The development model of the fracture network system in the metamorphic basement reservoirs of the study area is determined by a coupling mechanism of dominant lithology and multiphase fracturing. The spatial network reservoir system, under the control of multistage structure and weathering, is key to the formation of large-scale effective reservoirs in the metamorphic basement. Full article

An asymmetric non-singular bouncing cosmological model is proposed in the framework of a locally scale-invariant scalar-tensor version of the standard model of particle physics and gravitation. The scalar field $\varphi$ is complex. In addition to local scale invariance, the theory is U(1)-symmetric and has a conserved global charge associated with time variations of the phase of $\varphi$. An interplay between the positive energy density contributions of relativistic and non-relativistic matter and that of the negative kinetic energy associated with the phase of $\varphi$ results in a classical non-singular stable bouncing dynamics deep in the radiation-dominated era. This encompasses the observed redshifting era, which is preceded by a blueshifting era. The proposed model potentially avoids the flatness and horizon problems, as well as allowing for the generation of a scale-invariant spectrum of metric perturbations of the scalar type during a matter-dominated-like pre-bounce phase, with no recourse to an inflationary era. Full article

The Allee effect and group defense are two naturally occurring phenomena in the prey species of a predator–prey system. This research paper examines the impact of integrating the Allee effect on the dynamics of a predator–prey model, including a density-dependent functional response that reflects the defensive strategies of the prey population. Initially, the positivity and boundedness of the solutions are examined to ascertain the biological validity of the model. The presence of ecologically significant equilibrium points are established, followed by examining parametric restrictions for the local stability to comprehend the system dynamics in response to minor perturbations. A detailed computation encompasses diverse bifurcations, both of codimension one and two, which provide distinct dynamic behaviors of the model, such as oscillations, stable coexistence, and potential extinction scenarios. Numerical simulation has been provided to showcase complex dynamical behavior resulting from the Allee effect and prey group defense. Full article

Thermal relics with masses in the GeV to TeV range remain possible candidates for the Universe’s dark matter (DM). These neutral particles are often assumed to have vanishing electric and magnetic dipole moments so that they do not interact with single real photons, but the anapole moment, a static electromagnetic property whose features are akin to that of a classical toroidal solenoid, can still be non-zero, permitting interactions with single virtual photons. In some models, DM predominantly annihilates into charged standard model particles through a p-wave process mediated by the anapole moment. The anapole moment is also responsible for another interaction of interest. If a DM medium were subjected to an electric current, a DM particle whose anapole moment was aligned with the current would have lower energy than the state with an antialigned anapole moment. Given these interactions, if a collection of initially unpolarized DM particles were subjected to an electric current, then the DM medium would become partially polarized, according to the Boltzmann distribution. In such a polarized medium, DM annihilation into photons, a subdominant s-wave process realizable through higher order interactions, would be somewhat suppressed. If the local electric current existed during a time in which the DM begins to drop out of thermal equilibrium with the rest of the Universe, the suppressed annihilation could lead to a small local excess in the relic DM density relative to a current-free region. This mechanism by which the local DM density can be perturbed is novel. Using effective interactions to model a DM particle’s anapole moment and polarizabilities (responsible for s-wave annihilation into two photons), we compute the changes in the DM density produced by long- and short-lived currents around freeze out. If we employ the most stringent constraints on DM annihilation into two photons, we find that long-lived currents can result in a fractional change in the DM density on the order of ${10}^{-17}$ for DM masses around 100 GeV; for short-lived currents, this fractional change in local DM density is on the order of ${10}^{-23}$ for the same DM mass. Full article

This article proposes various new approximate analytical solutions of the boundary value problem for the non-stationary system of Nernst–Planck–Poisson (NPP) equations in the diffusion layer of an ideally selective ion-exchange membrane at overlimiting current densities. As is known, the diffusion layer in the general case consists of a space charge region and a region of local electroneutrality. The proposed analytical solutions of the boundary value problems for the non-stationary system of Nernst–Planck–Poisson equations are based on the derivation of a new singularly perturbed nonlinear partial differential equation for the potential in the space charge region (SCR). This equation can be reduced to a singularly perturbed inhomogeneous Burgers equation, which, by the Hopf–Cole transformation, is reduced to an inhomogeneous singularly perturbed linear equation of parabolic type. Inside the extended SCR, there is a sufficiently accurate analytical approximation to the solution of the original boundary value problem. The electroneutrality region has a curvilinear boundary with the SCR, and with an unknown boundary condition on it. The article proposes a solution to this problem. The new analytical solution methods developed in the article can be used to study non-stationary boundary value problems of salt ion transfer in membrane systems. The new analytical solution methods developed in the article can be used to study non-stationary boundary value problems of salt ion transport in membrane systems. Full article

To investigate deep Earth information, researchers often utilize geomagnetic observatories and satellite data to obtain the conversion function of geomagnetic sounding, C-response data, and employ traditional inversion techniques to reconstruct subsurface structures. However, the traditional gradient-based inversion produces geophysical models with artificial structure constraint enforced subjectively to guarantee a unique solution. This method typically requires the model parameterization knowledge a priori (e.g., based on personal preference) without uncertainty estimation. In this paper, we apply an efficient trans-dimensional (trans-D) Bayesian algorithm to invert C-response data from observatory and satellite geomagnetic data for the electrical conductivity structure of the Earth’s mantle, with the model parameterization treated as unknown and determined by the data. In trans-D Bayesian inversion, the posterior probability density (PPD) represents a complete inversion solution, based on which useful inversion inferences about the model can be made with the requirement of high-dimensional integration of PPD. This is realized by an efficient reversible-jump Markov-chain Monte Carlo (rjMcMC) sampling algorithm based on the birth/death scheme. Within the trans-D Bayesian algorithm, the model parameter is perturbated in the principal-component parameter space to minimize the effect of inter-parameter correlations and improve the sampling efficiency. A parallel tempering scheme is applied to guarantee the complete sampling of the multiple model space. Firstly, the trans-D Bayesian inversion is applied to invert C-response data from two synthetic models to examine the resolution of the model structure constrained by the data. Then, C-response data from geomagnetic satellites and observatories are inverted to recover the global averaged mantle conductivity structure and the local mantle structure with quantitative uncertainty estimation, which is consistent with the data. Full article

This paper explores a high-dimensional stochastic SIS epidemic model characterized by a mean-reverting, stochastic process. Firstly, we establish the existence and uniqueness of a global solution to the stochastic system. Additionally, by constructing a series of appropriate Lyapunov functions, we confirm the presence of a stationary distribution of the solution under $R_0^s>1$. Taking 3D as an example, we analyze the local stability of the endemic equilibrium in the stochastic SIS epidemic model. We introduce a quasi-endemic equilibrium associated with the endemic equilibrium of the deterministic system. The exact probability density function around the quasi-stable equilibrium is determined by solving the corresponding Fokker–Planck equation. Finally, we conduct several numerical simulations and parameter analyses to demonstrate the theoretical findings and elucidate the impact of stochastic perturbations on disease transmission. Full article

Urban ultra-low altitude airspace (ULAA) presents unique challenges for unmanned aerial vehicle (UAV) path planning due to high building density and regulatory constraints. This study analyzes and improves classical path planning algorithms for UAVs in ULAA. Experiments were conducted using A*, RRT, RRT*, and artificial potential field (APF) methods in a simulated environment based on building data from Chengdu City, China. Results show that traditional algorithms struggle in dense obstacle environments, particularly APF due to local minima issues. Enhancements were proposed: a density-aware heuristic for A*, random perturbation for APF, and a hybrid optimization strategy for RRT*. These modifications improved computation time, path length, and obstacle avoidance. The study provides insights into the limitations of classical algorithms and suggests enhancements for more effective UAV path planning in urban environments. Full article