Physical Systems Research Articles

Deterministic Network–Computation–Manufacturing Interaction Mechanism for AI-Driven Cyber–Physical Production Systems

Deterministic Network–Computation–Manufacturing Interaction Mechanism for AI-Driven Cyber–Physical Production Systems

Influence of image forces on charge-dipole interaction in two-layered systems.

Interaction between a fixed point electric charge Q and a freely rotating point electric dipole with the magnitude P pinned near a plane interface between two dispersionless insulators with different dielectric permittivities ɛ1 and ɛ2 has been considered. It was shown that, as a result of this interaction and the interaction of the dipole with the polarization charges induced at the interface by the charge Q and the dipole itself, there arise regions where the dipole can possess either one or two equilibrium orientations. The spatial distributions of the electrostatic dipole energy Wtotal under the combined action of the charge Q and the induced interface polarization charges, as well as the equilibrium dipole orientations (orientation maps), the boundaries between the regions with different numbers of dipole orientations, and their evolution with the variation of problem parameters (the charge and dipole magnitudes, the mismatch between ɛ1 and ɛ2, and the charge-interface distance) were calculated. It was shown that there can emerge local minima of Wtotal, which may play the role of traps for dipoles (in particular, excitons in layered structures), and the corresponding requirements for the problem parameters were found. Most results were obtained in analytical form. The model can be applied to various physical systems, for instance, polar molecules, excitons, and trions in layered structures.

Highly-integrable analogue reservoir circuits based on a simple cycle architecture

Physical reservoir computing is a promising solution for accelerating artificial intelligence (AI) computations. Various physical systems that exhibit nonlinear and fading-memory properties have been proposed as physical reservoirs. Highly-integrable physical reservoirs, particularly for edge AI computing, has a strong demand. However, realizing a practical physical reservoir with high performance and integrability remains challenging. Herein, we present an analogue circuit reservoir with a simple cycle architecture suitable for complementary metal-oxide-semiconductor (CMOS) chip integration. In several benchmarks and demonstrations using synthetic and real-world data, our developed hardware prototype and its simulator exhibit a high prediction performance and sufficient memory capacity for practical applications, showing promise for future applications in highly integrated AI accelerators.

Smart City as Cooperating Smart Areas: On the Way of Symbiotic Cyber–Physical Systems Environment

Smart City as Cooperating Smart Areas: On the Way of Symbiotic Cyber–Physical Systems Environment

Novel techniques to analyze dynamical properties of quantum chaos with peculiar evidence of hybrid systems confinement

Recent results demonstrate the dynamical peculiarities of the quantum chaos within the hybrid systems by chaotic parameters and probe the pattern formation under the influence of condensation. The complex dynamic behavior of the considered systems was determined with numerical simulation and presented an efficient technique that studied fractional systems comprising chaos-coherence fractions. The findings divulge the peculiar association between the coherence structure and the correlations at finite relative momenta. Thus the present study helps to explore the partially chaos hybrid systems in order to stimulate the experimental applications of nonlinear phenomena. The coherent-chaotic parameters can be measured by examining the chaos peculiarities that possess explicit relations with the condensations to demonstrate the environs of the physical systems. We investigate the influence of the multiplicities, chaos, momentum and temperature of the nonlinear system on the coherent-chaotic normalized correlations. The chaotic parameters are suppressed considerably with the coherence fraction and it appears numerically zero at maximum condensation and one at ideal chaos emissions. We procure that the meaningful parameters decrease significantly with the multiplicity of the nonlinear systems and increase with the momentum in the specified regimes. The identical multiplicity leads to contemplating the coherence and thus the normalized chaotic parameters within its spectacular influences exhibit significance worth contemplating in earnest. The findings underscore the significance of cogitating correlations in deciphering the nonlinear system characteristics and bestowing extraordinary perceptiveness into the convoluted essence of complex systems. The contemplated methodology can be applied to evaluating and analyzing the nonlinear systems and such an innovative approach computes the problems of celestial mechanics, heartbeats and chemical reactions in engineering and medical fields.

Acoustic Resonance Tuning by High-Order Lorentzian Mixing.

Nanoscale mechanical resonators have attracted a great deal of attention for signal processing, sensors, and quantum applications. Recent progress in ultrahigh-Q acoustic cavities in nanostructures allows strong interactions with various physical systems and advanced functional devices. Those acoustic cavities are highly sensitive to external perturbations, and it is hard to control those resonance properties since those responses are determined by the geometry and material. In this paper, we demonstrate a novel acoustic resonance tuning method by mixing high-order Lorentzian responses in an optomechanical system. Using weakly coupled phononic-crystal acoustic cavities, we achieve coherent mixing of second- and third-order Lorentzian responses, which is capable of the fine-tunability of the bandwidth and peak frequency of the resonance with a tuning range comparable to the acoustic dissipation rate of the device. This novel resonance tuning method can be widely applied to Lorentzian-response systems and optomechanics, especially active compensation for environmental fluctuation and fabrication errors.

Dark solitons and their bound states in a nonlinear fiber with second- and fourth-order dispersion

We study the excitations of dark solitons in a nonlinear optical fiber with the second- and fourth-order dispersion, and find the emergence of striped dark solitons (SDSs) and some multi-dark-soliton bound states. The SDSs can exhibit time-domain oscillating structures on a plane wave, and they have two types: the ones with or without the total phase step, while the multi-dark-soliton bound states exhibit different numbers of amplitude humps. By the modified linear stability analysis, we regard the SDSs as the results of the competition between periodicity and localization, and analytically give their existence condition, oscillation frequency, and propagation stability, which show good agreements with numerical results. We also provide a possible interpretation of the formation of the existing striped bright solitons (SBSs), and find that SBS will become the pure-quartic soliton when its periodicity and localization carry equal weight. Our results provide the theoretical support for the experimental observation of striped solitons in nonlinear fibers, and our method can also guide the discovery of striped solitons in other physical systems.

Dynamic mirroring: unveiling the role of digital twins, artificial intelligence and synthetic data for personalized medicine in laboratory medicine.

In recent years, the integration of technological advancements and digitalization into healthcare has brought about a remarkable transformation in care delivery and patient management. Among these advancements, the concept of digital twins (DTs) has recently gained attention as a tool with substantial transformative potential in different clinical contexts. DTs are virtual representations of a physical entity (e.g., a patient or an organ) or systems (e.g., hospital wards, including laboratories), continuously updated with real-time data to mirror its real-world counterpart. DTs can be utilized to monitor and customize health care by simulating an individual's health status based on information from wearables, medical devices, diagnostic tests, and electronic health records. In addition, DTs can be used to define personalized treatment plans. In this study, we focused on some possible applications of DTs in laboratory medicine when used with AI and synthetic data obtained by generative AI. The first point discussed how biological variation (BV) application could be tailored to individuals, considering population-derived BV data on laboratory parameters and circadian or ultradian variations. Another application could be enhancing the interpretation of tumor markers in advanced cancer therapy and treatments. Furthermore, DTs applications might derive personalized reference intervals, also considering BV data or they can be used to improve test results interpretation. DT's widespread adoption in healthcare is not imminent, but it is not far off. This technology will likely offer innovative and definitive solutions for dynamically evaluating treatments and more precise diagnoses for personalized medicine.

Data fusion based on short-term memory Kalman filtering using intermittent-displacement and acceleration signal with a time-varying bias

Accurate displacement information plays a vital role in various applications from structural health monitoring to damage detection in physical systems. Due to practical reasons, displacement sensing is often performed using sensors that provide less accuracy or a low sampling rate. This study proposes an algorithm for displacement signal estimation that fuses high sampling rate acceleration data (which consists of unknown time-varying bias) with low sampling rate displacement data. In contrast to the present literature, the state vector in the proposed state-space formulation consists of short-term memory of the past realizations of displacement and velocity, and bias terms which allow (i) online estimation of the displacements by avoiding multi-rate Kalman filtering, and (ii) estimate time-varying bias in the measured acceleration signal. The state equations comprise the finite difference approximations of the time derivative of velocity and displacement which act as a moving average filter to provide smooth estimates, thus preventing the need for offline smoothing procedures. By inclusion of bias terms in the state vector, the drifts in displacement and velocity caused due to time-varying bias in measured acceleration are easily eliminated. The proposed method was found to provide accurate estimates of displacement, velocity, and time-varying bias in measured acceleration signals when tested on different types of bias, including but not limited to constant, linear, and parabolic variations. Additional tests on three oscillators subjected to three earthquake ground motions showed significantly better state estimates for stiff oscillators and improved estimates for flexible oscillators. Experimental validation on a base-excited two-story frame further affirms the capability of the method to estimate the hidden bias in the measured acceleration and provide an accurate estimate of displacement.

Beyond Dynamics: Learning to Discover Conservation Principles

Abstract The discovery of conservation principles is crucial for understanding the fundamental behavior of both classical and quantum physical systems across numerous domains. This paper introduces an innovative method that merges representation learning and topological analysis to explore the topology of conservation law spaces. Notably, the robustness of our approach to noise makes it suitable for complex experimental setups and its aptitude extends to the analysis of quantum systems, as successfully demonstrated in our paper. We exemplify our method’s potential to unearth previously unknown conservation principles and endorse interdisciplinary research through a variety of physical simulations. In conclusion, this work emphasizes the significance of data-driven techniques in deepening our comprehension of the principles governing classical and quantum physical systems.

Exploring the physical characteristics and nonlinear wave dynamics of a (3+1)-dimensional integrable evolution system

This study comprehensively explores the [Formula: see text]-dimensional Mikhailov–Novikov–Wang [Formula: see text] integrable equation, with the primary objective of elucidating its physical manifestations and establishing connections with analogous nonlinear evolution equations. The investigated model holds significant physical meaning across various disciplines within mathematical physics. Primarily, it serves as a fundamental model for understanding nonlinear wave propagation phenomena, offering insights into wave behaviors in complex media. Moreover, its relevance extends to nonlinear optics, where it governs the dynamics of optical pulses and solitons crucial for optical communication and signal processing technologies. Employing analytical methodologies, namely the unified [Formula: see text], Khater II ([Formula: see text]hat.II) method, and He’s variational iteration [Formula: see text] method, both numerical and analytical solutions are meticulously examined. Through this investigation, the intricate behaviors of the equation are systematically unveiled, shedding illuminating insights on various physical phenomena, notably including wave propagation in complex media and nonlinear optics. The outcomes not only underscore the efficacy of the analytical techniques deployed but also accentuate the equation’s pivotal role in modeling a broad spectrum of nonlinear wave dynamics. Consequently, this research significantly advances our comprehension of complex physical systems governed by nonlinear dynamics, thereby contributing notably to interdisciplinary pursuits in mathematical physics.

Dynamics of propagation patterns: An analytical investigation into fractional systems

Recent years have seen a growing interest in fractional differential equations, particularly the fractional Chaffee-Infante ([Formula: see text]) equation, pivotal for understanding dynamics governed by fractional orders in specific physical systems. Exploring solitary wave solutions, this study employs the extended Khater method and truncated Mittag-Leffler function properties to formulate tailored solutions for the ([Formula: see text]) model. Through a traveling wave ansatz, the equation transforms into a nonlinear ordinary differential equation, revealing intricate propagation patterns of solitary waves. Visual representations aid comprehension, while rigorous validation ensures solution precision, ultimately providing a comprehensive understanding of system responses to external stimuli. This study effectively integrates analytical and numerical methodologies to derive precise solitary wave solutions, with significant implications for advancing comprehension of complex phenomena in various disciplines governed by fractional-order dynamics.

Soliton solutions of nonlinear stochastic Fitz-Hugh Nagumo equation

This study deals with the stochastic Fitz-Hugh Nagumo (FHN) equation and its multiple soliton solutions. The underlying model has numerous applications in neuroscience that express the pulse behavior of neurons. In general, different kinds of noise affect neurons, e.g., oscillations in the opening and closing of ion stations within cell membranes and the fluctuations of the different conductivities in the system. This fluctuation creates a sequence of stochastic excitation. Various applications are branching Brownian motion process, flame propagation, nuclear reactor theory, autocatalytic chemical reaction, mobility in neurons, population growth in the open environment, and liquid environment. So, need of the hour to consider the FHN equation under the impact of noise. The ϕ6\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\\phi ^6$$\\end{document}-model expansion method is used to extract the analytical solutions that give a dynamic attitude of the transmission for the nerve impulses of a nervous system. The different constraint conditions for the existence of these solutions are also discussed. The solutions of this model are represented in hyperbolic, trigonometric, and rational forms. The 2 and 3-dimensional behavior of these solutions are depicted by choosing the different values of parameters. The impact of noise on the physical system is analyzed and its real-world applications are discussed. The spikes in the solutions are controlled through the Borel function. These important results will open a new horizon of research for the young researchers.

Smoothing in linear multicompartment biological processes subject to stochastic input

Many physical and biological systems rely on the progression of material through multiple independent stages. In viral replication, for example, virions enter a cell to undergo a complex process comprising several disparate stages before the eventual accumulation and release of replicated virions. While such systems may have some control over the internal dynamics that make up this progression, a challenge for many is to regulate behavior under what are often highly variable external environments acting as system inputs. In this work, we study a simple analog of this problem through a linear multicompartment model subject to a stochastic input in the form of a mean-reverting Ornstein-Uhlenbeck process, a type of Gaussian process. By expressing the system as a multidimensional Gaussian process, we derive several closed-form analytical results relating to the covariances and autocorrelations of the system, quantifying the smoothing effect discrete compartments afford multicompartment systems. Semianalytical results demonstrate that feedback and feedforward loops can enhance system robustness, and simulation results probe the intractable problem of the first passage time distribution, which has specific relevance to eventual cell lysis in the viral replication cycle. Finally, we demonstrate that the smoothing seen in the process is a consequence of the discreteness of the system, and does not manifest in systems with continuous transport. While we make progress through analysis of a simple linear problem, many of our insights are applicable more generally, and our work enables future analysis into multicompartment processes subject to stochastic inputs. Published by the American Physical Society 2024

6G Cyber Physical System Based Smart Healthcare Modelling by Mobile Edge Network and Artificial Intelligence

6G Cyber Physical System Based Smart Healthcare Modelling by Mobile Edge Network and Artificial Intelligence

A linear response framework for quantum simulation of bosonic and fermionic correlation functions

Response functions are a fundamental aspect of physics; they represent the link between experimental observations and the underlying quantum many-body state. However, this link is often under-appreciated, as the Lehmann formalism for obtaining response functions in linear response has no direct link to experiment. Within the context of quantum computing, and via a linear response framework, we restore this link by making the experiment an inextricable part of the quantum simulation. This method can be frequency- and momentum-selective, avoids limitations on operators that can be directly measured, and can be more efficient than competing methods. As prototypical examples of response functions, we demonstrate that both bosonic and fermionic Green’s functions can be obtained, and apply these ideas to the study of a charge-density-wave material on the ibm_auckland superconducting quantum computer. The linear response method provides a robust framework for using quantum computers to study systems in physics and chemistry.

Experimental sharing of Bell nonlocality with projective measurements

Abstract \textcolor{red}{In the standard Bell experiment, two parties perform local projective measurements on a shared pair of entangled qubits to generate nonlocal correlations. However, these measurements completely destroy the entanglement, rendering the post-measurement state unable for subsequent use. For a long time, it was believed that only unsharp measurements can be used to share quantum correlations. Remarkably, recent research has shown that classical randomness assisted projective measurements are sufficient for sharing nonlocality} [Phys. Rev. Lett. 129, 230402 (2022)]. Here, by stochastically combining no more than two different projective measurement strategies, we report an \textcolor{red}{experimental} observation of \textcolor{red}{double Clauser-Horne-Shimony-Holt (CHSH) inequality violations with two measurements in a sequence made on each pair of maximally and partially entangled} polarization photons. Our results reveal that the double violation achieved by partially entangled states can be 11 standard deviations larger than that achieved by maximally entangled ones. Our scheme eliminates the requirement for entanglement assistance in previous unsharp-measurement-based sharing schemes, making it experimentally easier. Our work provides possibilities for sharing other types of quantum correlations in various physical systems with projective measurements.

Qualitative case study on the disability acceptance experiences of soldiers with disabilities

ABSTRACT Purpose This study comprehensively examines the disability acceptance experience of individuals who become disabled following accidents in the military after enlistment. Methods In-depth interviews and participative observation of two soldiers with disabilities are conducted. Data sources encompass the transcripts from these interviews, relevant news videos, and articles on the participants. A qualitative case study approach is applied to conduct both “within-case” and “cross-case” analyses. Results Although the two participants survived a crippling accidents, their military units did not actively attempt to resolve the accident. They grappled with despair and found it challenging to accept their new status as individuals with disability. Over time, they noticed changes in their personal relationships and started considering themselves burdens on their caregivers. However, despite encountering psychological challenges, which were marked by repeated setbacks and disappointments, the soldiers consistently made determined efforts to realize their objectives. Moreover, they strove to lead purposeful lives despite suffering the adversities caused by their disabilities. Conclusions This study is the first in-depth examination of the disability acceptance experiences of soldiers with disability. The insights gleaned from our in-depth interviews will help formulate psychological and physical support systems for such individuals.

Taking on a new meaning of physics mathematization for teaching in teacher education processes

This is a documented reflection that seeks to characterize an alternative conception of the ‘mathematization of physics for teaching’. The reflection was made on the academic production of the research group ‘Teaching and learning of physics’. This group has posed a sequence of research questions has been raised to find out how physics teachers understand the relationship between physics and mathematics in the physics teaching process. In 2003 they studied the idea of ‘mathematical beauty’ in the early days of quantum mechanics, suggesting that there would be a difference between the way scientists assume the physical/mathematical relationship and the way physics is taught. In 2010, they wondered if there would be research in this field that would allow transformations in teaching focused on equations as the set of mathematical–physical relationships, finding that there are at least three trends. In 2019, they verified that despite the research in the literature, many students continue with the same reductionist idea about this relationship. In 2020, they made a proposal, showing a possibility of educating the teacher’s thinking for new understandings in this regard. The main conclusion is that it is possible to develop mathematization processes in the classroom from three specific phases that educate scientific thinking. The first phase tries to make the student aware of the existence of phenomenology and describe it. The second phase educates the study of nature in a systematic way, building the meaning of the organization of a physical system. The third phase promotes explanation and argument so that students achieve an explanatory model. These phases are configured as a criterion to guide the sequence of activities in a class, a set of classes or a complete course and have been worked on and tested in ‘physics didactics’ courses in a physics teacher training course.

Delayed dynamics analysis of SEI2RS malware propagation models in cyber–Physical systems

Cyber-physical systems facilitate seamless interaction between the physical and digital elements for improved efficiency, automation, and real-time monitoring across domains. This study analyzes a novel virus-spreading model called the delayed SEI2RS model, which is specifically designed for cyber-physical systems. This model incorporates a saturated incidence rate and treatment. An emphasis of this research is to explore the impact of time delay on the transient immunity interval of restored nodes. By using the time delay associated with the transitory immunity interval of recovered nodes as the bifurcation parameter, we derive a comprehensive set of appropriate conditions to assess the local stability of the malware-existence equilibrium and determine Hopf bifurcation. The center manifold theorem and normal form theory are employed to investigate the path and stability of Hopf bifurcation. Numerical calculations were used to validate the results, providing empirical evidence for the proposed model and its implications.