The European Physical Journal Special Topics最新文献

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.

This paper examines the challenge of designing (H_infty) filters for continuous systems with varying time delays. The filter design incorporates potential variations in gain due to implementation inaccuracies. The new delay-dependent (H_infty) performance is derived by using a novel Lyapunov–Krasovskii functional (LKF) and by employing novel free weighting matrices. The paper establishes existence of (H_infty) filters in terms of linear matrix inequality (LMI). To demonstrate the proposed method’s effectiveness, apply it to a vertical take-off and landing (VTOL) helicopter system in the numerical section.

Silica gels have a multitude of applications ranging from cosmetics and food science to oil and gas recovery. For proper design and application, it is important to have a thorough understanding of the underlying mechanisms of gel formation under different circumstances. The growth and structure of colloidal silica gels has been investigated using RheoSAXS to study the effect of silica concentration, NaCl concentration, temperature and shear rate. Additionally, SAXS in combination with a strong magnetic field has been applied to investigate the effect of magnetic microparticles and magnetic field on the development of the gel structure. Results indicate that the strongest effect on the gel kinetics are achieved by altering the activator concentration, here in the form of NaCl, followed by silica concentration and temperature. Small structural effects were also observed, with larger cluster sizes being produced at lower silica concentration and at higher NaCl concentration. Applying shear caused major changes both in structure as well as the macroscopic behavior of the silica, preventing the gel from reaching an arrested state, instead forming a viscous liquid. Applying a magnetic field appears to suppress the formation of larger clusters. The same effect is observed for increasing magnetic microparticle concentrations.

Graphical Abstract

Chemical-induced pulmonary toxicity, characterized by adverse respiratory effects from various drugs or chemicals, is increasingly becoming a point of concern for the pharmaceutical and chemical sectors, as well as public health. Traditional toxicity prediction methods are not only expensive but also demand significant time and effort. In response to these challenges, we focus on computational models to identify potential pulmonary toxicants early in the drug development process. Early identification of toxicity not only enhances the safety and efficiency of drugs and chemicals but also helps prevent late-stage drug withdrawals. In this study, we compared various sets of molecular descriptors and fingerprints using Mordred and RDKit software. We systematically employed feature selection techniques to identify the key molecular and structural features that significantly affect the model’s performance. We then applied a variety of tree-based ensemble machine-learning algorithms to build the proposed model, using a tenfold cross-validation methodology to enhance the model’s ability to predict pulmonary toxicity. We subsequently evaluated the proposed model’s performance using both a test set and a separate external validation set to assess reliability. The proposed optimal tree-ensemble model achieved an accuracy of 85.07% during tenfold cross-validation and 86.88% on the test set. Additionally, we applied the SHapley Additive exPlanations (SHAP) approach to gain deeper insights into the crucial molecular features influencing pulmonary toxicity predictions. Thus, the proposed model emerged as a promising tool for the early screening of potential pulmonary toxic compounds, enhancing chemical safety and providing interpretability for the predictions.

Every country is continuously experiencing the monkeypox disease that started in the United Kingdom in May 2022. A detailed understanding of the transmission mechanism is required for controlling the disease. Based on real needs, we have developed an eight-compartmental mathematical model using a system of fractional-order differential equations to understand the behavior of the disease. The fractional-order model is adopted to discuss the effect of memory in reducing monkeypox infection. The next-generation matrix method determines the basic reproduction number for human beings and beasts, which are (mathscr {R}_0^m) and ({mathscr {R}}_0^b), respectively. Depending on the numerical values of ({mathscr {R}}_0^m) and ({mathscr {R}}_0^b), the feasibility and the nature of the equilibrium points are studied. It is found that the system exhibits two transcritical bifurcations; one occurs at ({mathscr {R}}_0^b=1) for any value of ({mathscr {R}}_0^m), and the other occurs at ({mathscr {R}}_0^m=1) when ({mathscr {R}}_0^b<1). The effectiveness of the parameters has been discussed with the help of global sensitivity analysis. Further, we have investigated the optimal control policies, considering vaccination and treatment as two dynamic control variables. The incremental cost-effectiveness ratio and the infected averted ratio are determined to assess the cost-effectiveness of all practical control strategies. From the fractional-order optimal control problem, we have experienced that simultaneous use of both treatment and vaccination controls gives better results than using any single control in reducing infected humans. The global sensitivity analysis shows that controlling certain system parameters can regulate monkeypox infection. Further, our cost-effectiveness analysis shows that treatment control is the most cost-effective method for the monkeypox virus.

In dynamical systems, fractals and their features have been proven for a wide range of applications in graphical structures. In particular, self-similar graphs as well as graph polynomials play a vital role. This paper explores the characteristics of the polynomials for the family of well-known self-similar graphs, namely Sierpinski triangle graph of the (n^{text {th}}) iteration, and proposes an algorithm to compute the multivariate independence polynomials of these graphs. We employ iterative patterns from the Sierpinski triangle graph, and we implement our approach to explicitly compute the independent sets to formulate multivariate independence polynomials for iterative values of n. In addition, the inverse of these polynomials have been computed using SAGE software.

This research delves into the utilization of the matrix measure approach (MMA) for the synchronization of fractional order neural networks (FONNs) incorporating time delays. This study introduces a set of criteria for achieving control input within the slave system (FONNs), employing a novel approach based on fractional order Dini-like derivatives within the matrix measure framework. The proposed criteria formulated in fractional order, encompass diverse conditions that align with several sufficient conditions inherent in MMA. Synchronization between the slave and master system is established, ensuring the asymptotic stability between them. Finally, by presenting the numerical result, the efficacy of FONNs synchronization is obtained.

The effect of dimethyl sulfoxide (DMSO) on a model 1,2-dimyristoyl-sn-glycero-3-phosphocholine lipid membrane is investigated during a rapid supercooling from 350 to 250 K using a total of (165.0198~upmu)s all-atom molecular dynamics simulations. Our findings reveal that the addition of DMSO above a critical concentration induces significant alterations in the gel phase of the membrane at supercooled temperatures, shifting the gel phase to a fluid phase evident from area per lipid, order parameter, and d-spacing. Notably, an anomalous contraction is observed in bilayers in the presence of DMSO with the same critical concentrations as the temperature is cooled from 300 K. As the concentration of DMSO rises at supercooled temperatures, the interface becomes increasingly populated with DMSO molecules, approaching a two-dimensional percolation threshold. This process leads to an expansion in the area occupied by each lipid molecule, creating free space around the lipid tails. Subsequently, the population of DMSO and water at the hydrophobic core becomes energetically favorable at a supercooled temperature compared to the higher temperature above the critical concentration of DMSO. The higher population of DMSO and water at the interface and at the hydrophobic core increases the disorder and fluidity of the lipids and gradually changes the gel phase toward the fluid phase. Thus, our results provide the molecular mechanism of DMSO-induced fluidity of the membrane at supercooled temperature relevant for cell banking in the future.

We report temperature-induced complete de-wetting of phospholipid membranes from thermally evaporated silicon carbide (SiC) substrates, which occurs in the form of fractal patterns. Excess membrane material released as a result of de-wetting, transforms into fluid-filled membrane pockets, or leads to vesicle formation. The membrane pockets are composed of a double lipid membrane. These double bilayer superstructures, i.e. isolated membrane-enclosed fluid volumes, bring the internal contents into direct contact with the surface. This membrane morphology can be viewed as an alternative prebiotic assembly mechanism with possible implications for protocell development, where physicochemical surface interactions with internal primitive cell contents are greatly facilitated.