State Of Physical System Research Articles

A high frequency builder software for arbitrary radio frequency signals.

While the frequencies accessible by signal generators steadily rise, the synthesization of complex and arbitrary waveforms with high frequency components remains challenging, especially when restricted by an external reference clock. In this article, we present a comprehensive software package combined with state-of-the-art hardware as a solution for the generation of highly sampled, arbitrary radio frequency waveforms. The software can be used to conduct both synchronous and heterodyne pump-probe experiments due to a variety of different synchronization modules. While both kinds of modules allow for standard waveforms, such as sines, pulses, and bursts, as well as any arbitrary signal, the heterodyne modules additionally are not restricted by the reference clock frequency. Both the output and the synchronization module can be adapted to support additional measurement devices. Due to the modular software structure, individual classes can be exchanged while maintaining all functionalities. The software provides a user friendly graphical interface that allows us to compose, save, and load complex arbitrary waveforms within only a few steps. The frequency selectivity provided by the software-hardware combination allows us to directly target specific excitation states of physical systems. Conducting a heterodyne scanning transmission x-ray microscopy experiment, we are able to demonstrate the capabilities of the software when paired with a high sample rate arbitrary waveform generator. The heterodyne synchronization modules allow for unlimited flexibility leveraging arbitrary waveform generation to their full power. By solving the challenges of synthesizing highly complex electromagnetic waves, the software enables a large variety of experiments to be performed more conveniently.

Quantum correlation measure based on min relative entropy for two-partition and k-partition

As a peculiar resource of quantum mechanics, quantum correlation has been applied to many aspects. In quantum information processing and quantum computing, the quantum correlation plays an extremely important role, and it has been a subject of further studies, principally due to the general belief that it is a fundamental resource for different quantum information processing tasks. In addition, correlation measure is a very important physical quantity in studying the quantum correlation. A well-defined correlation measure needs to have some necessary properties. By proving these necessary properties, we can deepen our understanding of correlation measure. As one of the key concepts of quantum information theory, relative entropy is always used to measure the uncertainty contained in the state of physical system. In order to better understand the properties and applications of correlation measure based on relative entropy, in this paper, according to the properties of the min relative entropy, we give the quantum correlation measure based on min relative entropy for two-partition and k-partition. Furthermore, we prove that it satisfies some necessary properties of quantum correlation measures, including the nonnegativity, the invariance under local unitary operators, and the monotonicity under completely positive trace-preserving. By proving these properties, we show that the given correlation measure is well-defined. Security of communication has received much attention since ancient times. In today's society, the internet, instant messaging and e-commerce applications are all related to the information security, and the information security is related to the vital interests of everyone. The information encryption is one of the important methods to ensure information security. As an important way to ensure information security, quantum channel has received more and more attention. At the end of the paper, we introduce the concept of quantum channel, and discuss the influence of quantum channel on the correlation measure based on min relative entropy under k-partition. By proposing a new correlation measure and proving the effect quantum channel on the measure, we can better describe the uncertainty contained in the state of physical system.

Intelligent Cybernetic System Using Gestalt Processing

Purpose of the study. The aim of the study is to create and develop modern cyber physical systems. The evolution of cyber physical systems (CPS) is associated with the development of a cognitive approach within the framework of the application of mechanisms used by humans to solve their daily tasks. In the cognitive approach to working with cyber physical systems, gestalt is considered as one of the ways of solving modern tasks within the framework of the new Industry 4.0 technology. In the cognitive approach a simple task is considered for cyber physical systems of the Internet of Things (CPS IoT) with gestalt processing. When investigating such a task for a simple cyber physical system, it will be possible to use a gestalt with a simple structure. The complication of the task and structure of gestalt can occur with the development of CPS IoT. The article examines an intelligent cyber physical system of the Internet of Things using methods of gestalt processing of their states - a picture of the world, while solving various problems of the Internet of Things.Materials and research methods. To solve tasks within the framework of a cognitive approach to the construction and development of cyber physical systems, new methods and developments of specialists in the field of intelligent systems are required. In the context of Industry 4.0 technologies, the Internet of Things the gestalt processing of CPS IoT is considered. Within the framework of the cognitive approach sensory images, concept-representations, concept-scenarios, concept-gestalts of cyber physical systems are used to interact with the real world. It is important to use concept gestalts that can reflect CPS IoT with new emergent properties. CPS IoT gestalt refers to a certain state of the cyber physical system and its habitat, which occurs when a need arises and closes after the need is satisfied. The main task of gestalt processing for a cyber physical system is to satisfy its needs. The solution to this problem includes: the organization of the collection and the direct collection of the necessary elements for the formation of the gestalt, and later for its closure; the formation of the gestalt; the closure of the gestalt. For the accumulation of experience, its use and development, it is proposed to use machine learning methods. Machine learning results can be presented in the form of concept representations, concept scenarios.Results. The concepts-gestalts of CPS IoT, gestalt processing of CPS IoT are proposed within the framework of the cognitive approach. As the main stages of gestalt processing, the article highlights: - preparation of initial data for the formation of the need for CPS IoT: - formation of an imaginative perception - a picture of the world, including the current state of CPS IoT and necessary for the closure of the gestalt; - formation of gestalt; – formation of initial data for planning the control actions necessary for closing the CPS IoT gestalt; - implementation of control actions to close the CPS IoT gestalt; - saving the gestalt processing scenario for possible reuse in the future. These stages of gestalt processing relate to IoT CPS of any nature and are focused on any tasks of the Internet of Things. The demo example shows the use of gestalt processing for CPS IoT with a simple model without training.Conclusion. The article discusses the cognitive approach that refers to the use and development of intelligent cyber physical systems for the Internet of things and the Internet of everything. A method related to the gestalt processing of CPS IoT situations is proposed, which allows recognizing a need, and forming of a gestalt. Based on the generated CPS IoT gestalt, control actions are planned to close the CPS IoT gestalt. The implementation of the proposed approach, development and use of gestalt concepts will allow to reflect CPS IoT with new emergent properties.

4-dimensional lattice models for the quantum range

Some finite subspace models L are presented for quantum structures which replace the use of countable infinite Hilbert space H dimensions. A maximal Boolean sublattice, called block, is 24, where its four atoms directly above 0εL, base vectors of H in 24 are drawn as four points on an interval. Blocks can overlap in one or two atoms. Different kinds of operators can map one block onto another and interpretations are given such that subspaces can carry on their base vector tuple real, complex or quaternionic numbers, energies, symmetries and generate coordinate lines. Describing states of physical systems is done using L and its applications for dynamical modelling. They don‘t need the infinte dimensional vectors of H. L has in the first model 11 blocks and 24 atoms (figure 1). They correspond to the 24 elements of the tetrahedral S4 symmetry. S4 arises from a spin-line rgb-graviton whirl operator with center at the tip of a tetrahedron and a nucleon triangle base with three quarks as vertices. The triangles factor group D3 of S4 is due to the CPT Klein normal subgroup Z2 x Z2 of S4 . It has a strong interaction SI rotor for the nucleons inner dynamics which is used for integrating functions, exchanging energies of nucleon with its environment and setting barycentrical coordinates in the triangle. At their intersection B as barycenter sets a Higgs boson or field the rescaled quark mass of a nucleon. Each factor class of one element from D3 assigns to it a color charge, a coordinate, an energy vector and a symmetry. Symmetries attached can be different according to interactions involved. Every atom of L has then a specific character with different properties.Three characters are added to octonian base vectors, listed by their indices as n = 0,1,…,7, and named for the atoms of L as na, nb, nc. The structure and element attributes of the finite subspace lattices L are desribed in many examples and models which technical constructed run macroscopically. Several models are described below. Example, the color charge whirl as rgb-graviton projection operator maps the block 2c3b5a6a to 0a1a2a3a. The symmetries change dimension from 3x3- to 2x2-matrices. From SU(3) are λ1 on 3b mapped to the SU(2) x-coordinate Pauli matrix σ1, from λ2 on 5a to σ2 y-coordinate and from λ3 on 6a to σ3 z-coordinate of real Euclidean space R³. The SU(3) matrices have complex w3 = z +ict, w2 = (iy,f), w1 = (x,m) coordinates. In figure 3 is shown how a rotation of two proton tetrahedrons for fusion changes the two linearly independent wj vectors to the 1-dimensional x,y,z base vectors. In deuteron then on one coordinate line sit with Cooper paire u-d-quarks at the ends the Heisenberg coupled energy or space vector rays 15 (x,m), m mass measured in kg, x in meter, 23 (iy,E(rot)), E(rot) rotational energy measured in Joule J, y in meter, 46 (ict,f), t time measured in seconds, f = 1/∆t frequency s inverse time interval measured in Hz. The six color charges are red r on +x as octonian coordinate 1, green g on +y as 2 , blue b on -z as 6, turquoise on -x as 5, magenta on -y as 3, yellow on +z as 4..

State and parameter estimation of stochastic physical systems from uncertain and indirect measurements

State and parameter estimation of stochastic physical systems from uncertain and indirect measurements

Control the quality of polymers based on the model of Dzeno

Numerical methods for solving mathematical problems describing the state of complex physical systems in modern digital production can be used to assess the quality and viability of materials used in additive manufacturing. In this article, the Dzeno model is chosen, as the physical behavior of the body under dynamic load and the gradual attenuation of the load.

Quantifying the Particle Aspect of Quantum Systems

The possibility of a quantum system to exhibit properties that are akin to both the classically held notions of being a particle and a wave, is one of the most intriguing aspects of the quantum description of nature. These aspects have been instrumental in understanding paradigmatic natural phenomena as well as to provide nonclassical applications. A conceptual foundation for the wave nature of a quantum state has recently been presented, through the notion of quantum coherence. We introduce here a parallel notion for the particle nature of a quantum state of an arbitrary physical system. We provide elements of a resource theory of particleness, and give a quantification of the same. Finally, we provide evidence for a complementarity between the particleness thus introduced, and the coherence of an arbitrary quantum state.

Error covariance tuning in variational data assimilation: application to an operating hydrological model

Because the true state of complex physical systems is out of reach for real-world data assimilation problems, error covariances are uncertain and their specification remains very challenging. These error covariances are crucial ingredients for the proper use of data assimilation methods and for an effective quantification of the a posteriori errors of the state estimation. Therefore, the estimation of these covariances often involves at first a chosen specification of the matrices, followed by an adaptive tuning to correct their initial structure. In this paper, we propose a flexible combination of existing covariance tuning algorithms, including both online and offline procedures. These algorithms are applied in a specific order such that the required assumption of current tuning algorithms are fulfilled, at least partially, by the application of the ones at the previous steps. We use our procedure to tackle the problem of a multivariate and spatially-distributed hydrological model based on a precipitation-flow simulator with real industrial data. The efficiency of different algorithmic schemes is compared using real data with both quantitative and qualitative analysis. Numerical results show that these proposed algorithmic schemes improve significantly short-range flow forecast. Among the several tuning methods tested, recently developed CUTE and PUB algorithms are in the lead both in terms of history matching and forecast.

Statistical Physics for Medical Diagnostics: Learning, Inference, and Optimization Algorithms.

It is widely believed that cooperation between clinicians and machines may address many of the decisional fragilities intrinsic to current medical practice. However, the realization of this potential will require more precise definitions of disease states as well as their dynamics and interactions. A careful probabilistic examination of symptoms and signs, including the molecular profiles of the relevant biochemical networks, will often be required for building an unbiased and efficient diagnostic approach. Analogous problems have been studied for years by physicists extracting macroscopic states of various physical systems by examining microscopic elements and their interactions. These valuable experiences are now being extended to the medical field. From this perspective, we discuss how recent developments in statistical physics, machine learning and inference algorithms are coming together to improve current medical diagnostic approaches.

Cognitive Structures of Space-Time.

In physics, the analysis of the space representing states of physical systems often takes the form of a layer-cake of increasingly rich structure. In this paper, we propose an analogous hierarchy in the cognition of spacetime. Firstly, we explore the interplay between the objective physical properties of space-time and the subjective compositional modes of relational representations within the reasoner. Secondly, we discuss the compositional structure within and between layers. The existing evidence in the available literature is reviewed to end with some testable consequences of our proposal at the brain and behavioral level.

Cooperative Transmission of Energy-Constrained IoT Devices in Wireless-Powered Communication Networks

In Internet of Things (IoT) systems, a number of sensor devices monitor the physical system states and exchange information with each other. The main limitation is that the IoT devices are generally energy constrained since those are powered with batteries. To address this energy problem, we consider a cooperative wireless-powered communication network (WPCN), which consists of three phases: 1) downlink (DL) energy transfer from a multi-antenna access point (AP); 2) data sharing among IoT devices; and 3) uplink (UL) information transfer from single-antenna IoT devices. Based on the shared data and the harvested energy, the single-antenna IoT devices in the neighborhood cooperate to form a virtual antenna array in order to transmit their information simultaneously to the multi-antenna AP using a multiple-input multiple-output (MIMO) technique in the UL information transfer phase. In this study, the transmit covariance matrices (i.e., beamforming vectors and the corresponding transmit power allocation) used for both DL energy transfer and UL information transfer are jointly designed to maximize the UL capacity based on the Lagrangian method. Furthermore, the time allocation for each phase is optimized based on a stochastic gradient method. In the numerical results, it is shown that our proposed beamforming scheme and the stochastic time allocation can achieve near-optimal performance.

Temperature–energy-space sampling molecular dynamics: deterministic and single-replica method utilizing continuous temperature system

For enhanced sampling of physical system (PS) states, we utilized the coupled Nosé–Hoover (NH) molecular dynamics equations of motion (EOM), wherein the heat-bath temperature for the PS fluctuates according to an arbitrary predetermined weight. The coupled NH is defined by suitably combining the NH EOM of the PS and the NH EOM of the temperature system (TS), where the inverse heat-bath temperature β is a dynamical variable. In this study, we developed a method to determine the effective weight for β for enhanced sampling of PS states. The method, based on ergodic theory, is reliable, and eliminates the need for time-consuming iterative procedures and resource-consuming replica systems. The resulting TS potential in a two dimensional (β, ϵ)-space makes a valley-shape, where the potential minimum path provides a route for β and ϵ and guides their motions. β oscillates around the potential minima for each energy ϵ, and the motion of β derives a motion of ϵ and receives the ϵ’s feedback, which leads to a mutual boost effect. Thus, it also provides a specific dynamical mechanism to explain the features of enhanced sampling such that the temperature-space ‘random walk’ enhances the energy-space ‘random walk.’ These mutual dynamics between β and ϵ naturally arise from the static probability theory formalism of double density dynamics that was previously developed, where the Liouville equation with an arbitrarily given probability density function is the fundamental polestar. Numerical examples using a model system and explicitly solvated protein system verify the reliability and simplicity of the method.

Practical quantum computation of chemical and nuclear energy levels using quantum imaginary time evolution and Lanczos algorithms

Various methods have been developed for the quantum computation of the ground and excited states of physical and chemical systems, but many of them require either large numbers of ancilla qubits or high-dimensional optimization in the presence of noise. The quantum imaginary-time evolution (QITE) and quantum Lanczos (QLanczos) methods proposed in Motta et al. (2020) eschew the aforementioned issues. In this study, we demonstrate the practical application of these algorithms to challenging quantum computations of relevance for chemistry and nuclear physics, using the deuteron-binding energy and molecular hydrogen binding and excited state energies as examples. With the correct choice of initial and final states, we show that the number of timesteps in QITE and QLanczos can be reduced significantly, which commensurately simplifies the required quantum circuit and improves compatibility with NISQ devices. We have performed these calculations on cloud-accessible IBM Q quantum computers. With the application of readout-error mitigation and Richardson error extrapolation, we have obtained ground and excited state energies that agree well with exact results obtained from diagonalization.

Multiphoton synthetic lattices in multiport waveguide arrays: synthetic atoms and Fock graphs

Activating transitions between internal states of physical systems has emerged as an appealing approach to create lattices and complex networks. In such a scheme, the internal states or modes of a physical system are regarded as lattice sites or network nodes in an abstract space whose dimensionality may exceed the systems’ apparent (geometric) dimensionality. This introduces the notion of synthetic dimensions, thus providing entirely novel pathways for fundamental research and applications. Here, we analytically show that the propagation of multiphoton states through multiport waveguide arrays gives rise to synthetic dimensions where a single waveguide system generates a multitude of synthetic lattices. Since these synthetic lattices exist in photon-number space, we introduce the concept of pseudo-energy and demonstrate its utility for studying multiphoton interference processes. Specifically, the spectrum of the associated pseudo-energy operator generates a unique ordering of the relevant states. Together with generalized pseudo-energy ladder operators, this allows for representing the dynamics of multiphoton states by way of pseudo-energy term diagrams that are associated with a synthetic atom. As a result, the pseudo-energy representation leads to concise analytical expressions for the eigensystem of N photons propagating through M nearest-neighbor coupled waveguides. In the regime where N ≥ 2 and M ≥ 3 , nonlocal coupling in Fock space gives rise to hitherto unknown all-optical dark states that display intriguing nontrivial dynamics.

Comparison of the fabrication methods, formation dynamics, structure, and delivery performance of solid nanoparticles and hollow layer‐by‐layer edible/biodegradable nanodelivery systems

AbstractAimThis article summarizes and reviews recent studies on nanoparticulation of solid and hollow nanoparticles using edible proteins and carbohydrates from plant and animal origin and shares new research conducted in our laboratory on fabrication of biopolymer‐based nanodelivery systems.MethodsWe compare the properties of nanoparticles fabricated using desolvation, coacervation, and layer‐by‐layer assembly. The parameters affecting the morphological and physical states of the nanodelivery systems as well as their abilities to encapsulate bioactive compounds, and the mechanisms of the nanoparticulation process are discussed in detail.ResultsThe article offers methods that need to be used with each nanoparticulation technique along with key characterization data obtained. The effect of varying a wide range of parameters on particle size, size distribution, morphology, effect of solvents and non‐solvents, effect of surface charge of polyelectrolytes, effect of fabrication conditions are presented.ConclusionsWe believe the article gives enough detail to give the reader useful information related to the performance of all the different nanoparticles studied in our laboratory over the last 6 years.Practical applicationsBiopolymer based nanodelivery systems presented in this article, have great potential to be used in the nutraceutical, food, pharmaceutical industries as well as agriculture. The biodegradable and edible nature of these nanodelivery systems make them safe for incorporating into food and nutraceutical materials, because, the biopolymers used in their fabrication are biocompatible polymers of food origin that are generally recognized as safe (GRAS) (Food and Drug Administration, The Daily Journal of the United States Government, 2016). Therefore, use of edible polymer‐based nanoparticles would be safer and less toxic and consequently better for all these industries. For example, in the food industry, antioxidant compounds can be encapsulated which would then be incorporated into packaging materials and that would help maintain freshness and good quality and would prevent oxidative deterioration of foods to increase shelf life. Pesticides and insecticides can also be encapsulated in these nanodelivery systems for agricultural applications. Because they are biodegradable and originated from natural biopolymers, their impact on the environment and the quality of the soil would be minimized compared to current applications that use some burdensome chemicals. Biopolymer based nanodelivery systems would also be of high interest for the pharmaceutical industry. Through the efficacy and extra stability gained with encapsulation, drugs and bioactives would be better protected from the acidic environment of the stomach and be released more controllably in the intestines. Further functionalization of nanodelivery systems with recognition elements, such as antibodies or aptamers, would enable more specifically targeted delivery.

The Studying of Gas and Hydrotherm Parameters and the Fluid-Conductor Geochemical Features with the Help of the “SELECTOR” Program Complex on the Example of the Golovnin Calder Sources

There is a discription of dynamical physical and chemical model of “flow reactor with an ideal piston displacement” type. With its help using the SELECTOR program complex (PC) for thermal solutions of Golovnin caldera (Kunashir Island, Kuril Islands) the numerical experiments were performed. The results of two “water-rock” type (W/R) systems modeling are represented: 1 – thermal solutions with pH=1.93; 2 – thermal ones with pH=6.35. The temperature range from 200 to 15 °C and the pressure of 1 and 10 bar in both experiments were used. As a results, data on the systems phase and chemical compositions were obtained; an evolutionary sequence of physical and chemical system states was identified with long-term water/rock (W/R) interaction; a metasomatism type for systems was gained; a metasomatic column was verified with ones near the orifice of thermal solutions. Thus, the complete correlation of model data with obtained in situ one was shown.

Stabilization of Stochastic Fluctuations in Hyperbolic Systems

We consider steady states of physical systems that are described by hyperbolic balance laws.We derive control policies that damp small perturbations over time. Uncertainties in model parameters are taken into account. Theoretical results are illustrated by stabilizing a viscoplastic material.

Superposition Principle and Born’s Rule in the Probability Representation of Quantum States

The basic notion of physical system states is different in classical statistical mechanics and in quantum mechanics. In classical mechanics, the particle system state is determined by its position and momentum; in the case of fluctuations, due to the motion in environment, it is determined by the probability density in the particle phase space. In quantum mechanics, the particle state is determined either by the wave function (state vector in the Hilbert space) or by the density operator. Recently, the tomographic-probability representation of quantum states was proposed, where the quantum system states were identified with fair probability distributions (tomograms). In view of the probability-distribution formalism of quantum mechanics, we formulate the superposition principle of wave functions as interference of qubit states expressed in terms of the nonlinear addition rule for the probabilities identified with the states. Additionally, we formulate the probability given by Born’s rule in terms of symplectic tomographic probability distribution determining the photon states.

Soliton Solution of Schrödinger Equation Using Cubic B-Spline Galerkin Method

The non-linear Schrödinger (NLS) equation has often been used as a model equation in the study of quantum states of physical systems. Numerical solution of NLS equation is obtained using cubic B-spline Galerkin method. We have applied the Crank–Nicolson scheme for time discretization and the cubic B-spline basis function for space discretization. Three numerical problems, including single soliton, interaction of two solitons and birth of standing soliton, are demonstrated to evaluate to the performance and accuracy of the method. The error norms and conservation laws are determined and found to be in good agreement with the published results. The obtained results show that the approach is feasible and accurate. The proposed method has almost second order convergence. The linear stability of the method is performed using the Von Neumann method.

A graph theoretic framework for representation, exploration and analysis on computed states of physical systems

A graph theoretic perspective is taken for a range of phenomena in continuum physics in order to develop representations for analysis of large scale, high-fidelity solutions to these problems. Of interest are phenomena described by partial differential equations, with solutions being obtained by computation. The motivation is to gain insight that may otherwise be difficult to attain because of the high dimensionality of computed solutions. We consider graph theoretic representations that are made possible by low-dimensional states defined on the systems. These states are typically functionals of the high-dimensional solutions, and therefore retain important aspects of the high-fidelity information present in the original, computed solutions. Our approach is rooted in regarding each state as a vertex on a graph and identifying edges via processes that are induced either by numerical solution strategies, or by the physics. Correspondences are drawn between the sampling of stationary states, or the time evolution of dynamic phenomena, and the analytic machinery of graph theory. A collection of computations is examined in this framework and new insights to them are presented through analysis of the corresponding graphs.