Behavior Of Systems Research Articles

Anti-windup higher derivative Newton-based extremum seeking under input saturation

The physical constraint of actuators in practical systems, such as actuator magnitude saturation, can significantly impact the behaviour of control systems. To address this issue, a fast learning mechanism is introduced in this paper for constrained input in higher derivatives Newton-based extremum seeking. The approach employs a constrained optimisation method with an anti-windup loop based on a wide range of penalty functions to adapt the search and prevent the violation of constraints, thereby avoiding windup of integral action in a controller. The practical asymptotic stability of the proposed algorithm is proven through a modified singular perturbation method, and its effectiveness is validated through simulations.

Problem-tailored Simulation of Energy Transport on Noisy Quantum Computers

The transport of conserved quantities like spin and charge is fundamental to characterizing the behavior of quantum many-body systems. Numerically simulating such dynamics is generically challenging, which motivates the consideration of quantum computing strategies. However, the relatively high gate errors and limited coherence times of today's quantum computers pose their own challenge, highlighting the need to be frugal with quantum resources. In this work we report simulations on quantum hardware of infinite-temperature energy transport in the mixed-field Ising chain, a paradigmatic many-body system that can exhibit a range of transport behaviors at intermediate times. We consider a chain with L=12 sites and find results broadly consistent with those from ideal circuit simulators over 90 Trotter steps, containing up to 990 entangling gates. To obtain these results, we use two key problem-tailored insights. First, we identify a convenient basis–the Pauli Y basis–in which to sample the infinite-temperature trace and provide theoretical and numerical justifications for its efficiency relative to, e.g., the computational basis. Second, in addition to a variety of problem-agnostic error mitigation strategies, we employ a renormalization strategy that compensates for global nonconservation of energy due to device noise. We discuss the applicability of the proposed sampling approach beyond the mixed-field Ising chain and formulate a variational method to search for a sampling basis with small sample-to-sample fluctuations for an arbitrary Hamiltonian. This opens the door to applying these techniques in more general models.

Directional Superradiance in a Driven Ultracold Atomic Gas in Free Space

Ultracold atomic systems are among the most promising platforms that have the potential to shed light on the complex behavior of many-body quantum systems. One prominent example is the case of a dense ensemble illuminated by a strong coherent drive while interacting via dipole-dipole interactions. Despite being subjected to intense investigations, this system retains many open questions. A recent experiment carried out in a pencil-shaped geometry [Ferioli Nat. Phys. 19, 1345 (2023)] has reported measurements that have seemed consistent with the emergence of strong collective effects in the form of a “superradiant” phase transition in free space, when looking at the light-emission properties in the forward direction. Motivated by the experimental observations, we carry out a systematic theoretical analysis of the steady-state properties of the system as a function of the driving strength and atom number N. We observe signatures of collective effects in the weak-driving regime, which disappear with increasing drive strength as the system evolves into a single-particle-like mixed state comprised of randomly aligned dipoles. Although the steady state features some similarities to the reported superradiant-to-normal nonequilibrium transition, also known as cooperative resonance fluorescence, we observe significant qualitative and quantitative differences, including a different scaling of the critical drive parameter (from N to N). We validate the applicability of a mean-field treatment to capture the steady-state dynamics under currently accessible conditions. Furthermore, we develop a simple theoretical model that explains the scaling properties by accounting for interaction-induced inhomogeneous effects and spontaneous emission, which are intrinsic features of interacting disordered arrays in free space. Published by the American Physical Society 2024

Pendekatan Matematika yang Digunakan pada Biologi

Despite their apparent differences, mathematics and biology are increasingly being used in tandem to model and analyze a wide range of biological processes. This article addresses the use of multiple methods, including population modeling, differential equations, mathematical genetics, and epidemiology, to the study of biology, a field known as biomathematics. Genetic dynamics and the behavior of biological systems, such as population expansion and disease transmission, are understood through mathematical modeling. SIR and SEIR models are used in epidemiology to describe how infectious diseases spread throughout a population. This journal emphasizes the value of mathematical approaches in comprehending intricate biological systems and the necessity of interdisciplinary research between mathematics and biology to address both theoretical and practical issues through a review of the literature. The purpose of this article is to explain the relationship between mathematics and biology and the application of mathematical models in the analysis of biological phenomena. This article uses a literature study writing method by recording research problems, analyzing, and collaborating different thoughts.

LogOW: A semi-supervised log anomaly detection model in open-world setting

Log anomaly detection is a method for finding abnormal behavior and faults in systems. However, existing methods face two main challenges: the open-world problem and the cold-start problem. The open-world problem means that the test set may contain new classes that are not in the training set, while the cold-start problem means that the initial training data are scarce, both for normal and abnormal log sequences. Most existing methods assume a closed-world setting and rely on sufficient normal data, which limits their adaptability to new log environments.We propose LogOW, a novel log anomaly detection model that can learn from a few normal log sequences. The model finds emerging normal log sequences in the open-world setting through the open-world sample retrieval module. Through the incremental pre-training module, these log sequences are fine-tuned in an online mode for model parameters.First, we train a basic model from normal log sequences using Masked-Language Modeling(MLM). During the testing phase, we then combine the anomaly score and the uncertainty score obtained through a novel dynamic multi-mask to distinguish closed-world normal log sequences from the test set. Next, we cluster the open-world log sequences based on fused sequence and count features, and identify the abnormal ones and the new normal ones. Finally, we update our model with the new normal sequences in the next time period. Experiments on three log datasets and real-world airport logs show that our model outperforms traditional models in the open-world and lack of training data setting.

Dynamical analysis of the Rulkov model with quasiperiodic forcing

This paper investigates the dynamical behavior of the Rulkov neuron model under quasiperiodic forcing, focusing on the emergence and mechanisms of strange nonchaotic attractors (SNAs). SNAs are characterized by fractal geometry without sensitive dependence on initial conditions, and their formation mechanisms, such as torus-doubling, fractal, bubble, Heagy–Hammel, and multi-intermittency routes, are explored in detail. The study employs Lyapunov exponents, singular continuous spectrum, and phase sensitivity to detect and characterize the strange and nonchaotic properties of attractors. Numerical simulations reveal the transition dynamics between strange nonchaotic, quasiperiodic, and chaotic states, emphasizing the role of transient dynamics in understanding the complex behavior of neural systems under multifrequency stimuli. The findings provide deeper insights into the intricate dynamical processes in neuron models, potentially aiding the development of advanced models for neural response prediction in varying environments.

Effects of value-driven social learning on cooperation in the prisoner's dilemma games.

Despite the growing attention and research on the impact of Q-learning-based strategy updating on the evolution of cooperation, the joint role of individual learners and social learners in evolutionary games has seldom been considered. Here, we propose a value-driven social learning model that incorporates a shape parameter, β, to characterize the degree of radicalism or conservatism in social learning. Using the prisoner's dilemma game on a square lattice as a paradigm, our simulation results show that the cooperation level has a non-trivial dependence of β, density ρ, and dilemma strength b. We find that both β and ρ have nonmonotonic effects on cooperation; specifically, moderate levels of radicalism in social learning can facilitate cooperation remarkably, and when slightly conservative, can form a favorable cooperation region with the appropriate ρ. Moreover, we have demonstrated that social learners play a key role in the formation of network reciprocity, whereas individual learners play a dual role of support and exploitation. Our results reveal a critical balance between individual learning and social learning that can maximize cooperation and provide insights into understanding the collective behavior in multi-agent systems.

Study on the change of the oil-paper insulation’s AC conductivity characteristics based on dielectric response in frequency domain

Oil-immersed power equipment (transformers and high voltage bushings) are key components of power systems. Oil-paper insulation has endured complex and harsh operating conditions when used as a primary insulation system, so it is essential to determine the insulation state of oil-paper insulation system to ensure a stable operation for the power grid. The oil-paper insulation’s conductivity behavior can effectively characterize the degree of insulation deterioration. However, the relationship between the alternating current (AC) and direct current (DC) conductivity behavior for oil-paper insulation systems is still unclear in current research, resulting in limitations in the traditional evaluation model’s application. Therefore, this paper uses oil-paper insulation materials under different aging to carry out relevant research about the changing features of the complex AC conductivity under different test temperature environments. The influence of oil-paper insulation aging, test temperature, and excitation frequency on the complex AC conductivity is verified. Considering the correlation between low-frequency AC conductivity and DC conductivity, the frequency range of AC conductivity is extended by means of frequency temperature and conductance double translation. The temperature dependence of the AC conductivity behavior is defined over a wide frequency test range. The relaxation polarization process dominated the AC conductivity variation law is obtained. The impact of experimental temperature and insulation aging on the cumulative characteristic parameters of AC ionic mobility is clarified. This study theoretically corrects the traditional calculation model for AC conductivity of oil-paper insulation. The bias in conductivity calculations caused by only considering a single relaxation polarization process is eliminated. The conductivity model that can characterize multiple relaxation polarization processes in the dielectric is established. This model provides theoretical support for the later assessment of oil-paper insulation state depending on conductance behavior.

Cyclic behaviour of friction energy dissipation connection systems with precast concrete cladding panel: experimental and numerical studies

Cyclic behaviour of friction energy dissipation connection systems with precast concrete cladding panel: experimental and numerical studies

Soret and Dufour effects in two-phase boundary layer flow of non-Newtonian and Newtonian fluids with uniform shear strength ratio

BackgroundThis study numerically investigates heat and mass transfer in two-phase boundary layer shear flows, focusing on a non-Newtonian Casson fluid interacting with an adjacent Newtonian fluid under the influence of thermal radiation. By incorporating both Soret and Dufour effects, we examine how temperature gradients influence mass flux and how concentration differences affect energy flux. These coupled effects are essential for applications involving simultaneous heat and mass transport, such as chemical reactors, separation processes, and thermal management systems. MethodsThe analysis explores boundary layer convergence with varying shear intensities, utilizing the MATLAB solver “bvp4c” with appropriate similarity transformations for computational accuracy. The resulting numerical data are presented in graphical form to illustrate the impact of key parameters on flow characteristics. Additionally, variations in the Sherwood number, Nusselt number, and interfacial shear stress are depicted for each fluid across different parameter values. To evaluate the accuracy of our numerical method, we conducted a comparative analysis with the results reported by Weidman and Wang [4] and Wang [2]. This comparison demonstrated an excellent match, confirming the reliability of our approach, as shown in Table 1 and Fig. 2. Significant findingsThe results demonstrate that the fluid temperature increases with the Dufour number, while the concentration rises with the Soret number. Higher Casson parameter values lead to a reduction in interfacial shear stress in both fluids. Furthermore, the Nusselt and Sherwood numbers increase with an enhanced radiation parameter, highlighting the influence of thermal radiation on heat and mass transfer. These insights offer a valuable understanding of flow behavior in systems with coupled thermal and concentration gradients.

Study on improvement of the generator end fixed structure and optimization of pretightening process

To address recurring issues identified during comprehensive maintenance of a specific generator model—particularly the loosening of ring lead fixing structures at the generator ends, along with the detachment of felt and insulation wear—a detailed investigation was conducted. This study focused on analyzing the root causes of these problems and developing targeted improvement strategies. Taking the ring lead fixing structure as the research object, the investigation explored enhancements to the fixing structure and optimization of the bolt pretightening process. By comparing the fixing structure of faulty units with that of fault-free units and considering practical field conditions, a solution involving an increased cleat wrapping angle was proposed. The effectiveness of this improved design was verified through testing. Furthermore, the interaction of adjacent bolts during the stretching of single-head bolt stretchers was investigated for the bolt pre-tightening process. In view of the bolt pretightening process, the study explored the interaction between adjacent bolts when utilizing single-head bolt tensioners was studied, and experiments using multi-head synchronous hydraulic tensioners were conducted to evaluate their pretightening performance. The results revealed the pretightening behavior of multi-head systems, demonstrating their superior efficiency compared to single-head methods. These findings provide a robust foundation for applying such systems in industrial settings. This study aims to resolve technical challenges related to the ring lead fixing structure and the bolt pretightening process, ultimately ensuring the safe and stable operation of the generator.

Periodic Boundary Value Problems On Thermoelastic Star Graphs And Their Solutions By Use General Function Method

This paper presents a comprehensive study on periodic boundary value problems (BVPs) in thermoelastic star graphs, utilizing the method of generalized functions. The research focuses on the behavior of rod structures subjected to thermal heating and cooling, providing a unified approach to solving various boundary value problems relevant to practical applications. We derive integral representations of generalized solutions that facilitate the determination of displacements, deformations, stress, temperature, and heat fluxes across each element of the graph. The study also addresses the modeling of force and heat sources through both regular and singular generalized functions under diverse boundary conditions. By establishing continuity and Kirchhoff conditions at the common node of the graph, we formulate the governing equations for the amplitudes of displacement and temperature. The findings highlight the versatility of the proposed method, which can be extended to a wide range of network structures, distinguishing it from existing techniques. This work contributes to the understanding of thermoelastic behavior in complex systems, with implications for engineering applications in fields such as mechanical design, materials science, and structural analysis.

Ligand Effects on the Emission Characteristics of Molecular Eu(II) Luminescence Thermometers.

Discrete molecular organometallic europium(II) complexes are promising functional materials due to their ability to behave as highly sensitive band-shift luminescence thermometers. Furthering our understanding of the design principles salient to the emission behavior of such systems is important for developing them in this emerging application. To this end, a series of pseudo-C4v-symmetric organometallic europium(II) complexes bearing systematically varying ligand sets were synthesized and characterized to probe the influence of subtle structural modification on their optical properties. Opto-structural correlation analyses via variable-temperature single-crystal X-ray diffraction and photoluminescence spectroscopy reveal a remarkable variability in properties among structurally similar complexes and a convoluted dependence of the emission characteristics on the stereoelectronic properties of the ligands. A few factors of particular influence are nevertheless identified, including the distance between the europium(II) ion and the basal plane of the square-pyramidal coordination polyhedron, the presence of pendant electron density that might further interact with the excited-state 5d orbitals, and, qualitatively, the metal-ligand flexibility of the construct. These results help to elucidate principles that govern the luminescence properties of organometallic europium(II) complexes with an eye to enabling the rational design of high-performance luminescence thermometers of this genre.

Bifurcation analysis in dynamical systems through integration of machine learning and dynamical systems theory

Abstract Characterizing the nonlinear behavior of dynamical systems near the stability boundary is a critical step toward understanding, designing, and controlling systems prone to stability concerns. Traditional methods for bifurcation analysis in both experimental systems and large-dimensional models are often hindered either by the absence of an accurate model or by the analytical complexity involved. This paper presents a novel approach that combines the theoretical frameworks of nonlinear reduced-order modeling and stability analysis in dynamical systems with advanced machine learning techniques to perform bifurcation analysis in dynamical systems. By focusing on a low-dimensional nonlinear invariant manifold, this work proposes a data-driven methodology that simplifies the process of bifurcation analysis in dynamical systems. The core of our approach lies in utilizing carefully designed neural networks to identify nonlinear transformations that map observation space into reduced manifold coordinates in its normal form where bifurcation analysis can be performed. The unique integration of analytical and data-driven approaches in the proposed method enables the learning of these transformations and the performance of bifurcation analysis with a limited number of trajectories. Therefore, this approach improves bifurcation analysis in model-less experimental systems and cost-sensitive high-fidelity simulations. The effectiveness of this approach is demonstrated across several examples.

Random attractors for fractional stochastic reaction–diffusion systems with fractional Brownian motion

Fractional stochastic reaction–diffusion systems involving fractional Brownian motion (fBm) provide a comprehensive modeling framework for studying complex physical and biological phenomena. In this study, we investigate the dynamic behavior of solutions to fractional stochastic reaction–diffusion systems with fBm defined on Rn. Firstly, we establish the existence and uniqueness of solutions to the fractional stochastic reaction–diffusion systems with fBm. We also obtain uniform estimates for the solutions on average. Furthermore, we construct a mean random dynamical system based on the derived solutions. Finally, we prove the existence and uniqueness of weak pullback mean random attractors. The results of our investigation contribute to a deeper understanding of the complexities involved in reaction–diffusion processes, specifically considering the effect of fBm with long-range dependence and self-similarity properties. Additionally, our findings have broader applications, particularly in areas such as analyzing the dynamic behavior of complex systems and biological models.

Ethylene polymerization catalyzed by salicylaldimine Fe or Cr complexes: A DFT study of the metal effects

Salicylaldimine transition metal catalysts are of great interest in the field of ethylene polymerization. Especially, the late transition metal Fe and the middle transition metal Cr complexes exhibited completely different catalytic behaviors. We carried out systematic DFT studies to understand the catalytic mechanism in detail and revealed the intriguing metal effect on ethylene polymerization in propagation, β-hydride elimination, and termination stages. The metal centers would lead to dissociative or associative insertion mechanisms in the chain propagation stage due to their preference for different coordination features. The metal centers also significantly influence the β-hydride elimination, i.e., the late transition metal Fe center much prefers the β-hydride elimination over the middle transition metal Cr, due to the stronger hydride affinity of the Fe center than that of the Cr center. Therefore, salicylaldimine Fe produces a highly branched product, however, salicylaldimine Cr prevents β-hydride elimination producing linear high-density polyethylene. On the other hand, the higher hydride affinity of the Fe center leads to an easy chain transfer, resulting in only oligomer for salicylaldimine Fe systems. In contrast, the chain transfer step is difficult for the Cr center, leading to a successful ethylene polymerization for the salicylaldimine Cr system. These mechanistic insights into the metal center effects well explain the distinct ethylene polymerization behaviors for salicylaldimine Fe and Cr systems, providing helpful guidelines for the design of structure-controllable polymerization catalysts with different metal centers.

Theoretical models from experiments on particle population in convective arrays

Studying suspensions of gold nanorods has provided valuable knowledge on variations in concentration and how nanoparticles behave. Analysis of UV–visible spectra has shown changes in both longitudinal and transverse peaks, indicating a reduction in nanoparticle absorption peaks as the nanoparticle concentration decreases in suspension. Furthermore, changes in droplet contact angles have elucidated a direct correlation between nanoparticle density and droplet dynamics, shedding light on the intricate interplay between concentration reduction and droplet behavior. Investigations into cetyltrimethylammonium bromide (CTAB-coated) gold nanorods have unraveled the complexities of depletion, electrostatic, and Van der Waals forces, offering valuable knowledge on the self-assembly mechanisms governing these nanomaterials. The examination of interactions within coffee stain rings has underscored the crucial role of nanoparticle density in nanostructure arrangements, influencing deposition patterns and contributing to a deeper understanding of volume fraction evolution in nanostructured systems. By establishing a quantitative framework for interpreting energy parameters and observing optimal fitting agreements with experimental data, this research provides a solid foundation for future endeavors aimed at elucidating nanomaterial behavior in suspended systems. This comprehensive overview lays the groundwork for tailored manipulation of gold nanorod properties, emphasizing the essential relationship between density and contact angle in shaping wetting phenomena and surface engineering practices.

Detecting infrastructure hazard potential change by SAR techniques on postseismic surface deformation: A case study of 2016 Meinong earthquake in southwestern Taiwan

Over the past two decades, multi-temporal InSAR techniques have been successfully utilized to monitor surface deformation throughout earthquake cycles. As sensor resolution and observation frequency of radar satellites have significantly improved, the spatial monitoring capability has expanded from tectonic to regional and local scales. Although regions with higher deformation rates are generally considered to have greater hazard potential, from an engineering perspective, locations with abrupt displacement rate changes may pose greater risks. This paper presents a robust method to transform tectonic-scale geodetic data into engineering-scale deformation tolerance ratios for assessing infrastructure safety during the postseismic period. Using the persistent scatterer InSAR (PSI) method, we measured line-of-sight (LOS) displacements from Sentinel-1 satellite images. By correcting LOS velocities with GNSS data and constraining the velocity in the N-S direction, we inverted the 2D (E-W and UD) postseismic deformation rates for the three years following the 2016 Meinong earthquake. The 2D rates were then converted into annual deformation tolerance ratios (ADTR), with a statistically established threshold to identify infrastructure segments with high hazard potential. Additionally, we conducted time-series variation analysis on the inherent pixel unit to evaluate the state of hazard potential and characterize the spatiotemporal behavior of fault systems during the postseismic period. Our results indicated that the postseismic deformation rates in the E-W and UD directions are 1.5 and 2–3 times higher, respectively, than those in the interseismic period. The horizontal ADTR index of high-speed railway infrastructures identified a high-hazard potential segment located on the Chegualin fault in Kaohsiung, while the vertical ADTR index highlighted a high-hazard potential segment at a junction between multiple fault extensions in Tainan. Moreover, statistical time-series variation analysis revealed a gradual decrease in hazard potential after 2017, which is related to a slow earthquake that occurred in early to mid-2018, modulating regional stress during the postseismic period. Our novel method not only highlights the impact of postseismic deformation on infrastructure but also provides a crucial basis for future earthquake hazard prevention and mitigation.

Two novel semi-analytical coefficients of restitution models suited for nonlinear impact behavior in granular systems

This study proposes two novel semi-analytical coefficients of restitution (CoR) models to predict nonlinear impact behavior in granular systems. The models address energy loss at the end of the compression phase, redefining a characteristic length to enhance the equation of motion for colliding particles. The first, the inverse CoR model, derives from the inverse collision approach, introducing a length parameter for its analytical solution. The second, the energy CoR model, is proposed by integrating the damping force in conjunction with the energy conservation principles. Both models are semi-analytical since the characteristic length is numerically determined. Despite differing in derivation, both models account for initial impact velocity and material properties. Comparative analysis highlights their advantages over existing models, with validation through experimental data. Simulations confirm that the models yield accurate predictions for both metal and nonmetal contacts, at high and low impact speeds.

Dichotomy Law for a Modified Shrinking Target Problem in Beta Dynamical System

Let φ:N→(0,1] be a positive function. We consider the size of the set Ef(φ):={β>1:|Tβn(x)−f(β)|<φ(n)i.o.n}, where “i.o.n” stands for “infinitely often”, and f:(1,∞)→[0,1] is a Lipschitz function. For any x∈(0,1], it is proved that the Hausdorff measure of Ef(φ) fulfill a dichotomy law according to lim supn→∞logφ(n)n=−∞ or not, where Tβ is the β-transformation. In ergodic theory, the phenomenon of shrinking targets is crucial for understanding the long-term behavior of systems. By studying the shrinking target problem of the β dynamical system, we can reveal the relationship between randomness and determinism, which is significant for constructing more complex mathematical models. Moreover, there is a close connection between the β transformation and number theory. Investigating the contraction target problem helps uncover new properties and patterns in number theory, advancing the development of this field. In this work, we establish a significant relationship between the decay rate of the positive function φ(n) and the structural properties of the set Ef(φ). Specifically, we show that: The Hausdorff dimension of Ef(φ) either vanishes or is positive based on the behavior of φ(n) as n approaches infinity. The establishment of this dichotomy can help us more effectively understand the geometric characteristics and dynamical behavior of the system, thereby aiding our acceptance and comprehension of complex theories. Researching this shrinking target problem can help us uncover new properties in number theory, leading to a better understanding of the structure of numbers and promoting the development of related fields in number theory.