The European Physical Journal Special Topics最新文献

The problem of identifying the conditions that cause the formation of oscillatory patterns in the calcium dynamics of cells is considered. This problem is studied in this article on the basis of a stochastic version of the Li-Rinzel model of calcium dynamics. The study is carried out for mono- and bi-stability zones, where the initial deterministic version of the model has only stable equilibria as attractors. For the monostability parameter zone, conditions have been found under which even small noise destructs the equilibrium mode and forms the spiking oscillatory patterns. In the bistability zone with two coexisting stable equilibria, mechanisms of two-stage transformations from stochastic preference to noise-induced excitement are revealed. To study these stochastic phenomena, we apply the method of confidence ellipses.

The significant influence of convective transport flow between compressing plates involving both homogeneous and heterogeneous reactions, with uniform ionic distribution across the plates’ surfaces, is examined. The physical situation is elucidated through the utilization of fundamental equations governing fluid flow, including the Poisson–Boltzmann equation, the energy equation, and the Nernst–Planck equation, as well as equations pertaining to heterogeneous and homogeneous reactions. The governing equations undergo a transformation into systems of ODEs through a similarity transformation. These equations are then solved numerically utilizing the BVP4c technique for different controlling parameter values, and the outcomes are tabulated and visually represented. In addition, a homotopy analysis method (HAM) is employed to solve the resulting equations. The accuracy and validity of the HAM findings are confirmed by comparing them to solutions obtained from BVP4c numerical solver packages. Based on both homogeneous and heterogeneous chemical reactions, it is concluded that compressing plates increases the distribution of anions and cations. Physical restrictions impact vertical and horizontal velocities, as well as positive and negative charge profiles, are drawn and briefly described. Furthermore, the rate of vertical velocity near the parallel plates increases with an increase in the squeezing Reynolds number.

Deep learning algorithms will play a key role in the upcoming runs of the Large Hadron Collider (LHC), helping bolster various fronts ranging from fast and accurate detector simulations to physics analysis probing possible deviations from the Standard Model. The game-changing feature of these new algorithms is the ability to extract relevant information from high-dimensional input spaces, often regarded as “replacing the expert” in designing physics-intuitive variables. While this may seem true at first glance, it is far from reality. Existing research shows that physics-inspired feature extractors have many advantages beyond improving the qualitative understanding of the extracted features. In this review, we systematically explore automatic feature extraction from a phenomenological viewpoint and the motivation for physics-inspired architectures. We also discuss how prior knowledge from physics results in the naturalness of the point cloud representation and discuss graph-based applications to LHC phenomenology.

Here we consider the influence of simultaneous operation of convective and conductive heat and mass fluxes in a binary liquid on directional crystallization processes with a two-phase region. We consider two possible crystallization scenarios with constant and unsteady growth velocities and construct the corresponding analytical solutions in a parametric form. These solutions enable us to find such process characteristics as temperature, impurity concentration, solid-phase fraction, the laws of motion for the two-phase region boundaries dependent on material parameters and crystallization driving force, i.e. the specified system cooling conditions. The solutions obtained enable us to describe the material microstructure by means of two-phase region permeability and primary interdendritic spacing dependent on the solid-phase fraction of a solidified material. The theory under consideration also enables us to find the unfrozen liquid phase fraction of a two-phase material released in ice and permafrost melting processes, which defines the biophysical significance of the issue under study.

Heart rate variability (HRV) refers to the physiological phenomenon of variation in heartbeat duration, which can be characterized using various metrics. Considering a complex systems approach, in this study we used network modeling to quantitatively evaluate the relationship between different HRV metrics during sleep. Polysomnography recordings were performed on 24 healthy participants and their cardiac activity was sampled from the N2, N3, and REM sleep stages. Fifty-eight HRV metrics were calculated, and the relationship between each was assessed using mutual information (MI). One network was created for each sleep stage; HRV metrics constituted its nodes, and MI values were used to establish its edges. Repeated measures ANOVA was applied to each metric to assess variation between sleep stages. It was found that all three networks had characteristics of complex networks. Several communities of shared similar metrics were found across the three sleep stages. Of these, one community had the same metrics in stages N2 and N3, but in REM sleep was divided into three communities. REM sleep exhibited significant differences compared to the other sleep stages in several metrics. These preliminary findings allow us to suggest the application of this method in other HRV research contexts, which will determine its scope and limitations.

The aim of this research is to enhance image quality by applying convolution methods to a newly generalized subclass of an analytic function, (k-Omega S^*(rho ,beta )), which incorporates the concept of the Mittag-Leffer type Poisson distribution associated with starlike functions. Image enhancement processes, such as noise reduction, sharpening, and brightening, improve the image’s suitability for display or further analysis. The proposed method demonstrates superior results based on performance metrics including PSNR (Peak Signal-to-Noise Ratio), SSIM (Structural Similarity Index), MSQE (Mean Squared Error), RMSE (Root Mean Squared Error), PCC (Pearson Correlation Coefficient), and CIR (Contrast Improvement Ratio). For the flower dataset, the technique achieves values of 20.425 for PSNR, 0.8866 for SSIM, 765.044 for MSQE, 27.143 for RMSE, 0.1310 for PCC, and 0.9794 for CIR. Similarly, for the brain dataset, the quality metrics are 24.2981 for PSNR, 0.9773 for SSIM, 268.288 for MSQE, 16.0041 for RMSE, 0.9888 for PCC, and 0.2918 for CIR.

Acute Lymphoblastic Leukemia (ALL) is a prevalent form of childhood blood cancer characterized by the proliferation of immature white blood cells that rapidly replace normal cells in the bone marrow. The exponential growth of these leukemic cells can be fatal if not treated promptly. Classifying lymphoblasts and healthy cells poses a significant challenge, even for domain experts, due to their morphological similarities. Automated computer analysis of ALL can provide substantial support in this domain and potentially save numerous lives. In this paper, we propose a novel classification approach that involves analyzing shapes and extracting topological features of ALL cells. We employ persistent homology to capture these topological features. Our technique accurately and efficiently detects and classifies leukemia blast cells, achieving a recall of 98.2% and an F1-score of 94.6%. This approach has the potential to significantly enhance leukemia diagnosis and therapy.

The study of biological systems by methods of physical kinetics and non-equilibrium thermodynamics often faces the difficulty of studying nonlinear processes due to the significant deviation from equilibrium. In this paper, the new variation approach is proposed that allows us to generalize the patterns of chemical processes taking into account the influence of non-equilibrium effects.The study of interrelated chemical reactions is considered as one of the applications of this theory. The new method has been developed for the general description of the kinetics of several reactions, taking into account their mutual influence on each other. Practical examples and model calculations for several reactions with varying the degrees of influence on each other are considered. The calculations performed for the several reactions allowed us to evaluate their mutual influence on the change in the reaction rate, as well as on the change in the reaction reagents concentration over time. The results obtained can be used to describe complex chemical reactions in various systems.

This paper introduces a discrete memristor model and verifies the correctness of the model through circuit simulation. A six-dimensional discrete neural network was built by coupling the Rulkov neuron and the KTZ neuron. Dynamical analyses show that this neural network has multiple firing patterns when the memristor parameters and coupling coefficient are varied in the appropriate ranges, such as periodic firing, quasi-periodic firing, chaotic firing, and hyperchaotic firing. In addition, the coexisting multiple firing patterns and state transition phenomena of this neural network are revealed. Finally, the complexity analysis shows that the generated chaotic sequences have high pseudo-randomness, and the hardware implementation is completed in the Digital Signal Processor (DSP). This paper provides a reference for the study of memristive neural networks and communication encryption.

Motivated by important ecological applications, we study how immigration and noise can drastically change patterns of behavior of population systems. We explore this problem on the base of the Ricker conceptual population model and focus on two questions: (i) how random immigration can change regular and chaotic dynamic regimes of survival; (ii) how random disturbances cause extinction of population. For the initial deterministic model, we overview the variety of dynamic regimes and their transformations depending on the growth rate and intensity of immigration. For the stochastic model that takes into account random fluctuations in immigration intensity, probabilistic mechanisms for transforming order into chaos are identified and the key role of chaotic transients is revealed. A parametric study of the important population phenomenon of noise-induced extinction is given. For mathematical study of the considered stochastic deformations, a new approach based on confidence domains for regular and chaotic attractors was proposed and successfully applied.