Real System Research Articles

Cluster consensus of finite-field networks with stochastic communication topology

Most of the multi-agent systems (MASs) in real life are with limited storage and communication capabilities; studies on such systems, including consensus problem, however have been mainly based on real number field models. In this article, the cluster consensus problem of MASs with stochastic communication topology over finite-field known as the cluster consensus of finite-field networks (FFNs), is considered. Using the semi-tensor product of matrices, the dynamics of FFNs with stochastic communication topology are equivalently converted into a logical algebraic form, which facilitates further studies. Then a permutation system is introduced. The relationship between cluster consensus of FFNs with stochastic communication topology and the set stability with probability one (SSPO) of the permutation system is revealed. In such an approach, some verifiable criteria can be derived for the cluster consensus of FFNs with stochastic communication topology, without requesting the connectivity of the whole network. Finally, an example is presented to validate the main results.

Monte Carlo Simulation Via Visual Basic Application

Objective: The objective of this study is to implement the Monte Carlo Simulation method using Microsoft Excel's Visual Basic for Applications (VBA). This approach enables users to simulate complex systems easily and obtain statistical outputs for decision-making. Theoretical Framework: Implementing the results of the changes we desire on a real-life system through trial and error can lead to costly and risky outcomes. Simulation allows us to develop a computer model of an existing system, enabling us to understand the system's behavior. Method: Monte Carlo Simulation, a widely used simulation technique, utilizes random sampling to estimate possible outcomes of a process or experiment. Monte Carlo Simulation utilizes random sampling to estimate the outcomes of an experiment. Results and Discussion: The purpose of this study is to implement the Monte Carlo Simulation method using MS Excel Visual Basic Application. When the user interacts with the Excel interface by clicking the "Calculate" button, the system generates random numbers based on predefined probability distributions. Subsequently, statistical measures such as the mean, variance, standard deviation, median, mode, range, and extreme values (maximum and minimum) are calculated using VBA. Research Implications: Thanks to the developed application, Monte Carlo Simulation applications can be calculated easily. Originality/Value: This study contributes to the literature by implementing the Monte Carlo Simulation method using Microsoft Excel's Visual Basic for Applications (VBA). This approach enables users to simulate complex systems easily and obtain statistical outputs for decision-making. These statistics provide insights into the underlying randomness and variability of the system, which aids in decision-making processes.

Memory Effects Explain the Fractional Viscosity Dependence of Rates Associated with Internal Friction: Simple Models and Applications to Butane Dihedral Rotation.

Barrier-crossing rates of biophysical processes, ranging from simple conformational changes to protein folding, often deviate from the Kramers prediction of an inverse viscosity dependence. In many recent studies, this has been attributed to the presence of internal friction within the system. Previously, we showed that memory-dependent friction arising from the nonequilibrium solvation of a single particle crossing a smooth one-dimensional barrier can also cause such a deviation and be misinterpreted as internal friction. Here we introduce a simple diatom model and show that even in the absence of explicit solvent, internal memory effects arise due to coupling of the reaction coordinate motion with frictionally orthogonal degrees of freedom. This results in a fractional viscosity dependence and a deviation from Kramers' theory, typically attributed to the presence of internal friction. This model therefore mimics several biological processes where a local conformational change of a biomolecule is often influenced by its surroundings. This gives rise to an apparent "internal friction" commonly measured in terms of empirical fitting parameters α and σ. We propose a microscopic measure of this internal friction using Grote-Hynes theory which employs memory-dependent friction. We use butane to demonstrate the effect of coupling strength on the internal friction in realistic systems. This model therefore can serve the purpose of understanding internal friction in biological systems in terms of such coupling.

Anomalies of average symmetries: entanglement and open quantum systems

Symmetries and their anomalies are powerful tools for understanding quantum systems. However, realistic systems are often subject to disorders, dissipation and decoherence. In many circumstances, symmetries are not exact but only on average. This work investigates the constraints on mixed states resulting from non-commuting average symmetries. We will focus on the cases where the commutation relations of the average symmetry generators are violated by nontrivial phases, and call such average symmetry anomalous. We show that anomalous average symmetry implies degeneracy in the density matrix eigenvalues, and present several lattice examples with average symmetries, including XY chain, Heisenberg chain, and deformed toric code models. In certain cases, the results can be further extended to reduced density matrices, leading to a new lower bound on the entanglement entropy. We discuss several applications in the contexts of many body localization, quantum channels, entanglement phase transitions and also derive new constraints on the Lindbladian evolution of open quantum systems.

Nondirect-Product Local Diabatic Representation with Smolyak Sparse Grids.

Modeling nonadiabatic conical intersection dynamics is critical for understanding a wide range of photophysical, photochemical, and biological phenomena. Here we develop a nonadiabatic conical intersection wave packet dynamic method in the local diabatic representation using Smolyak sparse grids. Employing sparse grids avoids the direct-product grids in configuration space and alleviates the exponential scaling of computation costs with the molecular size. Numerical demonstrations are first performed for a two-dimensional vibronic model of pyrazine, where the results using sparse grids are in excellent agreement with those using direct-product grids, with sparse grids being much faster. Moreover, we demonstrate that for a four-dimensional pyrazine model, where direct-product grids are computationally infeasible, sparse grids can provide almost exact results. The sparse grid local diabatic representation method is further applied to a realistic model system of phenol photodissociation with much more complex potential energy surfaces; the results using sparse grids still agree very well with the direct-product grids. Finally, by combining with electronic structure calculations, we apply our method to the Shin-Metiu model without quasi-diabatization. The sparse grid and direct-product grid results are in good agreement, with the sparse grid computational cost being half of the full grid.

More quantum chemistry with fewer qubits

Quantum computation is one of the most promising new paradigms for the simulation of physical systems composed of electrons and atomic nuclei, with applications in chemistry, solid-state physics, materials science, and molecular biology. This requires a truncated representation of the electronic structure Hamiltonian using a finite number of orbitals. While it is, in principle, obvious how to improve on the representation by including more orbitals, this is usually unfeasible in practice (e.g., because of the limited number of qubits available) and severely compromises the accuracy of the obtained results. Here, we propose a quantum algorithm that improves on the representation of the physical problem by virtue of second-order perturbation theory. In particular, our quantum algorithm evaluates the second-order energy correction through a series of time-evolution steps under the unperturbed Hamiltonian. An important application is to go beyond the active-space approximation, allowing to include corrections of virtual orbitals, known as multireference perturbation theory. Here, we exploit the fact that the unperturbed Hamiltonian is diagonal for virtual orbitals and show that the number of qubits is independent of the number of virtual orbitals. This gives rise to more accurate energy estimates without increasing the number of qubits. Moreover, we demonstrate numerically for realistic chemical systems that the total runtime has highly favorable scaling in the number of virtual orbitals compared to previous studies. Numerical calculations confirm the necessity of the multireference perturbation theory energy corrections to reach accurate ground-state energy estimates. Our perturbation theory quantum algorithm can also be applied to symmetry-adapted perturbation theory. As such, we demonstrate that perturbation theory can help to reduce the quantum hardware requirements for quantum chemistry. Published by the American Physical Society 2024

Closing the Loop between Plastic Waste Management and Energy Cogeneration: An Innovative Design for a Flexible Pyrolysis Small-Scale Unit

This study proposes a simplified unit that can be employed in an industrial facility for the utilization of its own abundant plastic waste, primarily from discarded packaging, to achieve full or partial energy autonomy. By converting this waste into synthetic pyrolysis oil equivalent to 91,500 L, the industry can power a combined heat and power generation unit. The proposed unit was designed with a focus on maintaining high temperatures efficiently while minimizing oxygen exposure to protect the integrity of hydrocarbons until they transform into new compounds. Pyrolysis stands as a foundational procedure, paving the way for subsequent thermochemical transformations such as combustion and gasification. This study delves into the factors affecting pyrolysis and presents analytically the mathematical formulations and relevant calculations in order to effectively design and apply a real-life system. On this basis, fuels from plastic waste can be produced, suitable for utilization in typical equipment meant to produce heat, estimated for six months’ operation and 800 MWh of electricity. This study enhances the transition towards a more circular and resource-efficient economy with technologies that unlock the latent energy contained within the discarded matter. Additionally, it demonstrates the feasibility of a moderate investment in a co-generation system for industries utilizing 568 tonnes of plastic waste per year. The design and accurate calculations of this study highlight the theoretical potential of this technology, promoting environmental sustainability and resource conservation.

A low-complexity adaptive speech dereverberation approach based on multichannel linear prediction

The adaptive multichannel linear prediction (AMCLP) technique is one of the most effective methods for speech dereverberation in real-time application systems. However, the computational complexity of the associated AMCLP algorithms is very high. In this paper, we propose a low-complexity speech dereverberation approach based on AMCLP. The coefficient matrix of the multichannel dereverberation filter is decomposed into a linear combination of a set of low-dimensional sub-filter matrices and a set of low-dimensional sub-filter vectors through Kronecker product. According to these sub-filter models, we define two sets of signal models and cost functions, from which the adaptive speech dereverberation algorithm is established. This solution not only leads to effective dereverberation performance, but also reduces the computational cost, which is very conducive to realistic speech systems.

Selective Reduction of NO into N2 Catalyzed by the RhM2O3- Clusters (M = Ta, V, and Al): Importance of the Triatomic Lewis Acid-Base-Acid Mδ+-Rhδ--Mδ+ Site.

Catalytic NO reduction by CO into N2 and CO2 is imperative owing to the increasingly rigorous emission regulation. Identifying the nature of active sites that govern the reactivity and selectivity of NO reduction is pivotal to tailor catalysts, while it is extremely challenging because of the complexity of real-life systems. Guided by our newly discovered triatomic Lewis acid-base-acid (LABA, Ceδ+-Rhδ--Ceδ+) site that accounts for the selective reduction of NO into N2 catalyzed by the RhCe2O3- cluster in gas-phase experiments, the reactivity of the RhM2O3- (M = Ta, V, and Al) clusters in catalytic NO reduction by CO was explored. We determined theoretically that the LABA site still prevails to reduce NO to N2 mediated by RhTa2O3- and RhV2O3-, and the strong M-oxygen affinity was emphasized to construct the LABA site. An overall assessment highlights that RhV2O3- functions as a more promising catalyst because the well-fitting V-O bonding strength facilitates both elementary reactions of NO reduction and CO oxidation.

Supermassive black hole formation via collisions in black hole clusters

More than 300 supermassive black holes have been detected at redshifts larger than six, and they are abundant in the centers of local galaxies. Their formation mechanisms, however, are still rather unconstrained. A possible origin of these supermassive black holes could be mergers in dense black hole clusters, forming as a result of mass segregation within nuclear star clusters at the center of galaxies. In this study, we present the first systematic investigation of the evolution of such black hole clusters in which the effect of an external potential is taken into account. Such a potential could be the result of gas inflows into the central region; for example, as a result of galaxy mergers. We show here that the efficiency of the formation of a massive central object is mostly regulated by the ratio of cluster velocity dispersion divided by the speed of light, potentially reaching efficiencies of 0.05–0.08 in realistic systems. Our results show that this scenario is potentially feasible and may provide black hole seeds of at least 103 M⊙. We conclude that the formation of seed black holes via this channel should be taken into account in statistical assessments of the black hole population.

Analytical Models for Secular Descents in Hierarchical Triple Systems

Three-body systems are prevalent in nature, from planetary to stellar to supermassive black hole scales. In a hierarchical triple system, oscillations of the inner orbit’s eccentricity and inclination can be induced on secular timescales. Over many cycles, the octupole-level terms in the secular equations of motion can drive the system to extremely high eccentricities via the eccentric Kozai–Lidov (EKL) mechanism. The overall decrease in the inner orbit’s pericenter distance has potentially dramatic effects for realistic systems, such as tidal disruption events. We present an analytical approximation in the test-particle limit to describe individual stepwise increases in eccentricity of the inner orbit. A second approximation, also in the test-particle limit, is obtained by integrating the equations of motion and calibrating to numerical simulations to estimate the overall octupole-level time evolution of the eccentricity. The latter approach is then extended beyond the test particle to the general case. The three novel analytical approximations are compared to numerical solutions to show that the models accurately describe the form and timescale of the secular descent from large distances to a close-encounter distance (e.g., the Roche limit). By circumventing the need for numerical simulations to obtain the long-term behavior, these approximations can be used to readily estimate properties of close encounters and descent timescales for populations of systems. We demonstrate this by calculating rates of EKL-driven migration for Hot Jupiters in stellar binaries.

From ideal to realistic compact hybrid PV-CSP systems: A technoeconomic evaluation

Compact PV/CSP hybrid systems, based on the integration of both PV and CSP technologies within a single power plant, have been attracting interest in recent years, because of their potential to lower the cost of solar electricity and minimize the land footprint required. However, the objective evaluation of these technologies is complicated by the fact that they involve two separate converters, each delivering a different form of energy (dispatchable for CSP, non-dispatchable for PV). We propose here an original evaluation and optimization approach based on 3 techno-economic indicators quantifying (1) the overall conversion efficiency of these systems compared to their standalone CSP counterparts, (2) the energy production balance between PV and CSP, and (3) the extra manufacturing cost compared to standalone CSP systems. These different indicators are evaluated for two families of hybrid systems (PV Topping and PV Mirror) and for 3 geometries of concentrating optics (Linear Fresnel Lenses, Parabolic Troughs and Solar Towers). One originality of our approach lies in its ability to determine the optoelectronic properties of PV cells likely to lead to the best hybrid performance, taking into account whether or not they are able to reach their own theoretical limits. Another original feature is that the hybrid systems considered in our analysis are compared with CSP equivalents which have been independently optimized. It is shown that the integration of realistic solar cell technologies whose performances deviate significantly from their theoretical limits notably restricts the range of operating conditions for which hybrid systems could outperform their standalone CSP counterparts. We conclude this work by discussing a number of key challenges that need to be overcome in order to develop more competitive and efficient hybrid systems.

Continuous evolution of Fermi arcs in a minimal ideal photonic Weyl medium

Propagation properties of electromagnetic waves in an optical medium are mainly determined by the contour of equal-frequency states in k\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$${\\boldsymbol{k}}$$\\end{document}-space. In photonic Weyl media, the topological surface waves lead to a unique open arc of the equal-frequency contour, called the Fermi arc. However, for most realistic Weyl systems, the shape of Fermi arcs is fixed due to the constant impedance of the surrounding medium, making it difficult to manipulate the surface wave. Here we demonstrate that by adjusting the thickness of the air layer sandwiched between two photonic Weyl media, the shape of the Fermi arc can be continuously changed from convex to concave. Moreover, we show that the concave Fermi-arc waves can be used to achieve topologically protected electromagnetic pulling forces over a broad range of angles in the air layer. Our finding offers a generally applicable strategy to shape the Fermi arc in photonic Weyl media.

Unsupervised Machine Learning in the Analysis of Nonadiabatic Molecular Dynamics Simulation.

The all-atomic full-dimensional-level simulations of nonadiabatic molecular dynamics (NAMD) in large realistic systems has received high research interest in recent years. However, such NAMD simulations normally generate an enormous amount of time-dependent high-dimensional data, leading to a significant challenge in result analyses. Based on unsupervised machine learning (ML) methods, considerable efforts were devoted to developing novel and easy-to-use analysis tools for the identification of photoinduced reaction channels and the comprehensive understanding of complicated molecular motions in NAMD simulations. Here, we tried to survey recent advances in this field, particularly to focus on how to use unsupervised ML methods to analyze the trajectory-based NAMD simulation results. Our purpose is to offer a comprehensive discussion on several essential components of this analysis protocol, including the selection of ML methods, the construction of molecular descriptors, the establishment of analytical frameworks, their advantages and limitations, and persistent challenges.

Assessing the Accuracy of Quantum Dynamics Performed in the Time-Dependent Basis Representation.

A full quantum-mechanical (QM) description of large amplitude nuclear motion, associated with chemical reactions or isomerization of high-dimensional molecular systems, is inherently challenging due to the exponential scaling of the QM complexity with system size. To ameliorate the scaling bottleneck in studies of realistic systems, typically modeled in the configuration space, the nuclear wave functions are represented in terms of time-dependent basis functions. Such bases are expected to give an accurate description with a modest number of basis functions employed, by adapting them to the wave function solving the time-dependent Schrödinger equation. It is not, however, straightforward to estimate the accuracy of the resulting solution: in QM the energy conservation, a convenient such measure for a classical trajectory evolving in a time-independent potential, is not a sufficient criterion of the dynamics' accuracy. In this work, we argue that the expectation value of the Hamiltonian's "variance", quantifying the basis completeness, is a suitable practical measure of the quantum dynamics' accuracy. Illustrations are given for several chemistry-relevant test systems, modeled employing time-independent as well as time-dependent bases, including the coupled and variational coherent states methods and the quantum-trajectory guided adaptable Gaussians (QTAG) as the latter basis type. A novel semilocal definition of the QTAG basis time-evolution for placing the basis functions "in the right place at the right time" is also presented.

Adaptive coupling between neurons widens the entrainment range of the suprachiasmatic nucleus.

In many realistic systems, such as neural networks in the brain, the coupling strength between neurons is not fixed, but adaptively adjusts according to their activities. The suprachiasmatic nucleus (SCN), as the main clock in the mammalian brain, has been found to be a plastic neural network, and the coupling strength between neurons is highly dynamical. An important function of the SCN is entrainment, reflecting the ability of the SCN to synchronize with the external light-dark cycle. The entrainment ability is reflected by the entrainment range, which is a period range for the external light-dark cycle to which the SCN can entrain. In this article, we investigated whether the entrainment range of the SCN is affected by the adaptive coupling. We use a modified Kuramoto model with external light-dark cycle. We found that when the light sensitivity is larger than the fixed coupling strength (the coupling strength without adaptive rules), adaptive coupling can widen the entrainment range. Our findings help to understand the impact of the adaptive coupling between oscillatorty neurons on the collective behavior of the SCN, and provides a possible explanation for the plasticity of coupling in the master clock network.

DeepLNE++ leveraging knowledge distillation for accelerated multi-state path-like collective variables.

Path-like collective variables (CVs) can be very effective for accurately modeling complex biomolecular processes in molecular dynamics simulations. Recently, we have introduced DeepLNE (deep-locally non-linear-embedding), a machine learning-based path-like CV that provides a progression variable s along the path as a non-linear combination of several descriptors. We have demonstrated the effectiveness of DeepLNE by showing that for simple models such as the Müller-Brown potential and alanine dipeptide, the progression along the path variable closely approximates the ideal reaction coordinate. However, DeepLNE is computationally expensive for realistic systems needing many descriptors and limited in its ability to handle multi-state reactions. Here, we present DeepLNE++, which uses a knowledge distillation approach to significantly accelerate the evaluation of DeepLNE, making it feasible to compute free energy landscapes for large and complex biomolecular systems. In addition, DeepLNE++ encodes system-specific knowledge within a supervised multitasking framework, enhancing its versatility and effectiveness.

Cathode catalyst layer aging of proton exchange membrane fuel cell during emulated start-up/shut-down phases

Start-up and Shut-down phases for Proton Exchange Membrane Fuel Cell stacks are known to damage the Cathode Catalyst Layer inducing an irreversible loss of performance. An original Accelerated Stress Test has been designed to mimic the Start-up phase in real-life system conditions. The impact of cathode potential on the degradation mechanisms of the Pt/C components within the Cathode Catalyst Layer by local electrochemical and physicochemical analyses provided a deep understanding of the cathode evolution. An unexpected cell performance improvement is observed between 1.0 and 1.2 V cathode potential, especially at high current density. A beneficial modification of the properties occurs suggesting that fresh Cathode Catalyst Layer exhibits non-optimized carbon support properties. However, a significant structure subsidence appears when the cathode potential is above 1.4 V. In this condition, modification/compaction of cathode layer due to carbon corrosion alters mass transport and empathizes water flooding through the electrode.

Understanding the transmission of bacterial agents of sapronotic diseases using an ecosystem-based approach: A first spatially realistic metacommunity model.

Pathogens such as bacteria, fungi and viruses are important components of soil and aquatic communities, where they can benefit from decaying and living organic matter, and may opportunistically infect human and animal hosts. One-third of human infectious diseases is constituted by sapronotic disease agents that are natural inhabitants of soil or aquatic ecosystems. They are capable of existing and reproducing in the environment outside of the host for extended periods of time. However, as ecological research on sapronosis is infrequent and epidemiological models are even rarer, very little information is currently available. Their importance is overlooked in medical and veterinary research, as well as the relationships between free environmental forms and those that are pathogenic. Here, using dynamical models in realistic aquatic metacommunity systems, we analyze sapronosis transmission, using the human pathogen Mycobacterium ulcerans that is responsible for Buruli ulcer. We show that the persistence of bacilli in aquatic ecosystems is driven by a seasonal upstream supply, and that the attachment and development of cells to aquatic living forms is essential for such pathogen persistence and population dynamics. Our work constitutes the first set of metacommunity models of sapronotic disease transmission, and is highly flexible for adaptation to other types of sapronosis. The importance of sapronotic agents on animal and human disease burden needs better understanding and new models of sapronosis disease ecology to guide the management and prevention of this important group of pathogens.

Impact of decoupling capacitor aging and temperature for the long-term reliability of power delivery networks

Nowadays, almost all electronic systems on printed circuit boards (PCB) adopt a vital element known as the power delivery network (PDN). However, the performance of the PDN is susceptible to variables such as the temperature and aging of its key constituents: the decoupling capacitors (decaps). Consequently, the long-term reliability of the PDN demands meticulous consideration to foresee how its performance can deviate from the initial design specifications. A realistic high-current server system is considered. It involves hundreds of decaps to achieve the required target impedance as optimally selected and laid out by the Power Integrity (PI) designer. The degradation of decap performance is analyzed by collecting experimental data from tens of decaps for each type used in the design while applying an accelerated aging process at different temperatures. The impact of aging in terms of the capacitance, parasitic inductance, and resistance of the decaps is considered to illustrate an innovative methodological design approach based on statistical analysis. Such a design approach can prevent the detrimental impact of a larger noise level due to the gradual performance degradation of the PDN over the intended life cycle of the system.