Entropy Component Research Articles

Chemical process fault detection based on kernel entropy component analysis combined with cumulative parameters difference

AbstractThe nonlinearity, dynamics, and coupling characteristics in chemical systems render traditional fault detection methods inadequate for meeting the requirements of safe production. To address this problem, a fault detection method based on kernel entropy component analysis (KECA) combined with cumulative parameter difference (CPD) is proposed. First, the important variation information of the original data is retained based on the information theory. Second, the CPD statistics are calculated by comprehensively comparing the differences in key parameters between two datasets. Finally, these statistics are applied to process monitoring. It is worth noting that the CPD can selectively count the information differences of the parameters, and smooth out individual extreme differences through an asymmetric sliding window. In addition, two simulation experiments with a numerical case and the Tennessee Eastman process (TEP) are used to verify the fault detection performance of KECA‐CPD. The experimental results clearly show the effectiveness of the fault detection performance of KECA‐CPD.

ClipQ: Clipping Optimization for the Post-Training Quantization of Convolutional Neural Network

In response to the issue that post-training quantization leads to performance degradation in mobile deployment, as well as the problem that the balanced consideration of quantization deviation by Clipping optimization techniques limits the improvement of quantization accuracy, this article proposes a novel clipping optimization method named ClipQ, which pays different attention to the parameters, aiming to preferentially reduce the quantization deviation of important parameters. The attention of the weight is positively related to its absolute value. Channel information entropy and principal component analysis are used to characterize the channel attention and spatial attention of activations, respectively. In addition, the particle swarm algorithm is applied in weight clipping to adjust the search step size and direction adaptively. ClipQ achieves high-precision quantization with very few calibration samples (<=50) and low time cost. Meanwhile, it does not bring extra computation, which is friendly to hardware. The experimental evaluation on image classification, semantic segmentation, and object detection shows that ClipQ outperforms other state-of-the-art clipping techniques, such as KL, ACIQ, and MSE. In 8-bit quantization, the average precision loss is 0.31% for image classification and 0.22% for object detection. More notably, it achieves almost lossless accuracy in semantic segmentation tasks.

Entropy is an important design principle in the photosystem II supercomplex

Photosystem II (PSII) can achieve near-unity quantum efficiency of light harvesting in ideal conditions and can dissipate excess light energy as heat to prevent the formation of reactive oxygen species (ROS) under light stress. Understanding how this pigment-protein complex accomplishes these opposing goals is a topic of great interest that has so far been explored primarily through the lens of the system energetics. Despite PSII's known flat energy landscape, a thorough consideration of the entropic effects on energy transfer in PSII is lacking. In this work, we aim to discern the free energetic design principles underlying the PSII energy transfer network. To accomplish this goal, we employ a structure-based rate matrix and compute the free energy terms in time following a specific initial excitation to discern how entropy and enthalpy drive ensemble system dynamics. We find that the interplay between the entropy and enthalpy components differ among each protein subunit, which allows each subunit to fulfill a unique role in the energy transfer network. This individuality ensures that PSII can accomplish efficient energy trapping in the reaction center (RC), effective nonphotochemical quenching (NPQ) in the periphery, and robust energy trapping in the other-monomer RC if the same-monomer RC is closed. We also show that entropy, in particular, is a dynamically tunable feature of the PSII free energy landscape accomplished through regulation of LHCII binding. These findings help rationalize natural photosynthesis and provide design principles for more efficient solar energy harvesting technologies.

Surface Free Energies and Entropy of Aqueous CaCO3 Interfaces.

This work uses a recently proposed methodology to calculate the free energies of calcite and aragonite interfaces with water. This method properly includes the entropic contributions, ignored or approximated in previous work. By including this entropic component, we show that the aqueous calcite {101̅4} surface has a lower free energy than any of the aragonite surfaces. This resolves the discrepancies in previous simulation work that suggested that an aragonite nucleus would be more stable than a calcite one. Our analysis of the water structure highlights the generally greater entropic contribution to the interfacial free energy at the aragonite/water interface than at the calcite one. These methods are applied to a range of temperatures to examine how the solution temperature alters the interfacial energies. Our results are then discussed in the context of calcium carbonate nucleation and polymorph-morphology selection under different environmental conditions.

An Improved Kernel Entropy Component Analysis for Damage Detection Under Environmental and Operational Variations.

Environmental effects often trigger false alarms in vibration-based damage detection methods used for structural health monitoring (SHM). While conventional techniques like Principal Component Analysis (PCA) and cointegration have been somewhat effective in addressing this issue, challenges such as measurement noise, nonlinear behavior, and non-Gaussian data distribution continue to affect their performance. To address these limitations, a novel damage detection method combining Variational Mode Decomposition (VMD) and Dynamic Kernel Entropy Component Analysis (DKECA) is proposed. The proposed method initially uses the VMD technique to remove seasonal patterns and noise from the modal frequencies. Subsequently, a DKECA model is constructed based on a time-delay data matrix, and the principal components that maximize the Rényi entropy in the high-dimensional space are selected. Using these principal components, a damage detector developed from the T2 statistic is used to determine damage indices for SHM. The effectiveness of the proposed method is verified through both a simulated 7-DOF model and real-world data from the Z24 bridge, with comparative studies highlighting its advantages over existing techniques.

Molecular Insights into the Adsorption of Deposit Control Additives from Hydrocarbon Fuels.

Engine deposits can reduce performance and increase emissions, particularly for modern direct-injection fuel delivery systems. Surfactants known as deposit control additives (DCAs) adsorb and self-assemble on the surface of deposit precursors to keep them suspended in the fuel. Here, we show how molecular simulations can be used to virtually screen the ability of surfactants to bind to polyaromatic hydrocarbons, comprising a major class of carbonaceous deposits. We use molecular dynamics with the adaptive biasing force method to generate the potential of mean force as a function of the vertical distance between the surfactants and deposits in gasoline and diesel fuel surrogates. We find that a zwitterionic surfactant outperforms a conventional polyisobutylene succinimide for binding to these aromatic species. The amine groups in the succinimide headgroup only weakly adsorb on the polyaromatic deposit, while additional functional groups in the zwitterionic surfactant, particularly the quarternary ammonium ion, markedly enhance the binding strength. We decompose the adsorption free energies of the surfactants into their entropic and enthalpic components, to find that the latter dominates the attraction from these non-aqueous solvents. The adsorption free energy of both surfactants is slightly weaker from n-hexadecane (diesel) than iso-octane (gasoline), which is due to the larger steric barrier from stronger molecular layering of the former on the deposit. Density functional theory calculations of the adsorption of DCA fragments validate the force field used in the molecular dynamics simulations and provide further insights into the nature of the intermolecular interactions. The approach introduced here shows considerable promise for accelerating the discovery of novel DCAs to facilitate more advanced fuel formulations to reduce emissions.

Membrane-mediated interactions between arc-shaped particles strongly depend on membrane curvature.

Besides direct molecular interactions, proteins and nanoparticles embedded in or adsorbed to membranes experience indirect interactions that are mediated by the membranes. Membrane-mediated interactions between curvature-inducing proteins or nanoparticles can lead to assemblies of particles that generate highly curved spherical or tubular membrane shapes, but have mainly been quantified for planar or weakly curved membranes. In this article, we systematically investigate the membrane-mediated interactions of arc-shaped particles adsorbed to a variety of tubular and spherical membrane shapes with coarse-grained modelling and simulations. These arc-shaped particles induce membrane curvature by binding to the membrane with their inner, concave side akin to N-BAR domain proteins. We determine both the pairwise interaction free energy, which includes entropic contributions due to rotational entropy loss at close particle distances, and the pairwise interaction energy without entropic components from particle distributions observed in the simulations. For membrane shapes with small curvature, the membrane-mediated interaction free energies of particle pairs exceed the thermal energy kBT and can lead to particle ordering and aggregation. The interactions strongly decrease with increasing curvature of the membrane shape and are minimal for tubular shapes with membrane curvatures close to the particle curvature.

Understanding non-reducible N2 in the mechanism of Mo-nitrogenase.

In my proposed mechanism of Mo-nitrogenase there are two roles for separate N2 molecules. One N2 diffuses into the reaction zone between Fe2 and Fe6 where a strategic gallery of H atoms can capture N2 to form the Fe-bound HNNH intermediate which is then progressively hydrogenated through intermediates containing HNNH2, NH and NH2 entities and then two NH3 in sequence. The second N2 can be parked in an N2-pocket about 3.2 Å from Fe2 or bind end-on at the exo coordination site of Fe2. This second N2 is outside the reaction zone, not exposed to H atom donors, and so is 'non-reducible'. Here density functional calculations using a 485+ atom model describe the thermodynamics for non-reducible N2 moving between the N2-pocket and the exo-Fe2 position, for the resting state and 19 intermediates in the mechanism. The entropy component is estimated and included. The result is that for all intermediates with ligation by H or NHx at the endo-Fe2 position the free energy for association of non-reducible N2 at exo-Fe2 is negative. There remains some uncertainty about the status of exo-Fe2-N2 during the step in which H2 exchanges with the incoming reducible N2, where at least two unbound molecules are present. At Fe2 it is evident that attainment of octahedral coordination stereochemistry dominates the binding thermodynamics for non-reducible N2. Possibilities for experimental support of these computational conclusions are discussed.

A Cellular Automaton Simulation for Predicting Phase Evolution in Solid-State Reactions.

New computational tools for solid-state synthesis recipe design are needed in order to accelerate the experimental realization of novel functional materials proposed by high-throughput materials discovery workflows. This work contributes a cellular automaton simulation framework for predicting the time-dependent evolution of intermediate and product phases during solid-state reactions as a function of precursor choice and amount, reaction atmosphere, and heating profile. The simulation captures the effects of reactant particle spatial distribution, particle melting, and reaction atmosphere. Reaction rates based on rudimentary kinetics are estimated using density functional theory data from the Materials Project and machine learning estimators for the melting point and the vibrational entropy component of the Gibbs free energy. The resulting simulation framework allows for the prediction of the likely outcome of a reaction recipe before any experiments are performed. We analyze five experimental solid-state recipes for BaTiO3, CaZrN2, and YMnO3 found in the literature to illustrate the performance of the model in capturing reaction selectivity and reaction pathways as a function of temperature and precursor choice. This simulation framework offers an easier way to optimize existing recipes, aid in the identification of intermediates, and design effective recipes for yet unrealized inorganic solids in silico.

Effect of Water Networks On Ligand Binding: Computational Predictions vs Experiments.

Rational drug design focuses on the explanation and prediction of complex formation between therapeutic targets and small-molecule ligands. As a third and often overlooked interacting partner, water molecules play a critical role in the thermodynamics of protein-ligand binding, impacting both the entropy and enthalpy components of the binding free energy and by extension, on-target affinity and bioactivity. The community has realized the importance of binding site waters, as evidenced by the number of computational tools to predict the structure and thermodynamics of their networks. However, quantitative experimental characterization of relevant protein-ligand-water systems, and consequently the validation of these modeling methods, remains challenging. Here, we investigated the impact of solvent exchange from light (H2O) to heavy water (D2O) to provide complete thermodynamic profiling of these ternary systems. Utilizing the solvent isotope effects, we gain a deeper understanding of the energetic contributions of various components. Specifically, we conducted isothermal titration calorimetry experiments on trypsin with a series of p-substituted benzamidines, as well as carbonic anhydrase II (CAII) with a series of aromatic sulfonamides. Significant differences in binding enthalpies found between light vs heavy water indicate a substantial role of the binding site water network in protein-ligand binding. Next, we challenged two conceptually distinct modeling methods, the grid-based WaterFLAP and the molecular dynamics-based MobyWat, by predicting and scoring relevant water networks. The predicted water positions accurately reproduce those in available high-resolution X-ray and neutron diffraction structures of the relevant protein-ligand complexes. Estimated energetic contributions of the identified water networks were corroborated by the experimental thermodynamics data. Besides providing a direct validation for the predictive power of these methods, our findings confirmed the importance of considering binding site water networks in computational ligand design.

Simple model for the fracture of a polymer chain: Single-bond potential of mean force and tension-based rate constants for chain rupture.

A linear chain of N particles connected by Morse bonds with periodic boundaries in a Brownian bath is considered as a model for polymer fracture. The potential of mean force (pomf) with respect to the length of a bond and its energy and entropic components are computed via Langevin Dynamics simulations at various chain elongations λ > 1. A narrow range of λ values is identified over which equilibrium is established between an intact (i) and a fractured (f) state over a pomf barrier. While the barrier and (f) regions of the pomf are well described by a well-known (N - 1)-bond adiabatic approximation, the shape of the (i) region departs from it, exhibiting a bimodal character. A new, 1-bond adiabatic approximation is proposed to explain this. The lower envelope of the two adiabatic approximations provides an excellent description of the pomf. The relationship between (f) states for individual bonds and for the entire chain is elucidated. A new approach, based on analysis of equilibrium tension autocorrelation functions, is developed to extract rate constants for the fracture and reformation of the chain in dependence of λ. Results from it agree with tension relaxation during nonequilibrium simulations initiated at equispaced configurations of the particles. Rate constants for chain fracture conform to a Boltzmann-Arrhenius-Zhurkov dependence on the tension averaged over the intact state of the chain, the activation length being higher than estimated from pomf extrema. These findings constitute a step toward a predictive multiscale simulation scheme for fracture in polymeric materials.

Laplace approximation of J factors for rigid base and rigid basepair models of DNA cyclization

Laplace approximation of J factors for rigid base and rigid basepair models of DNA cyclization

The importance of the vibrational entropy in the mixing stabilization for complex ceramics

AbstractThe concept of high‐entropy materials has been introduced based on the idea that multiple principal components can be mixed through the increase in configurational entropy. Implicit in this idea is that the vibrational entropy, the other component of the mixing entropy, is small compared to the configurational entropy. To explore this relationship, we examined the mixing enthalpy, configurational entropy, and vibrational entropy of two binary ceramic systems—the transition metal carbides and transition metal diborides. We computed the vibrational entropy directly using the dynamical matrices obtained from density functional theory and the quasiharmonic approximation. The mixing vibrational entropy of the mixed diborides is at least as large as the configurational entropy while it is smaller for the carbides. Utilizing the phonon density of states, we further demonstrate the origin of the high mixing vibrational entropy arises because of a large number of new low‐frequency modes that appear in the diborides. Similar modes occur in the carbides but occur at larger frequencies. These differences ultimately arise because of the structural differences where metal atoms share nearest neighbors in the diborides, while they do not in the carbides. This increased vibrational mixing entropy dramatically enhances the mixing of the diborides and demonstrates that this type of entropy cannot be neglected when considering what stabilizes mixtures and provides a new perspective on what is considered high entropy.

Cellular Phosphate Sensing and Anion Binding by an Azacrown-Calixpyrrole Hybrid.

A hybrid receptor-sensor for anions originating from the merging of positively charged ammonium moieties for electrostatic attraction/stronger binding of azacrowns with directionality of calixpyrrole hydrogen bond donors for selectivity is investigated. As demonstrated this hybrid receptor-sensor shows a remarkable selectivity for orthophosphate even in the presence of other phosphates and anions found in cellular materials (Kassoc H2PO4 ->H2P2O7 2->AMP-≫ADP2- or ATP3- over halides, nitrate, or hydrogen sulfate; all Na+ salts in water) but also cellular polyphosphate or phospholipids. This selectivity is harnessed in a real-time monitoring of cell lysis by lysozyme, which releases orthophosphate and other phosphates and anions from the cells. This sensitive (LOD 0.4 μM) fluorescence-based microscale method compares favorably with the state-of-the-art techniques but can easily be practiced in a high-throughput screening (HTS) manner. The anion binding and selectivity in aqueous solutions were investigated by NMR and put in context with phosphate binding of the parent calix[4]pyrrole. The microscopic understanding of anion binding by the hybrid receptor was then obtained from a combination of density functional theory (DFT), classical molecular dynamics (MD) with explicit water solvation, and ab initio MD (AIMD) simulations. Correlating the NMR and fluorescence binding data with studies of solvation of the receptor, phosphate anion, and the resulting complex confirms the binding is largely driven by entropic component (TΔS) associated with receptor and anion desolvation.

Changing Your Martini Can Still Give You a Hangover.

The Martini 3.0 coarse-grained force field, which was parametrized to better capture transferability in top-down coarse-grained models, is analyzed to assess its accuracy in representing thermodynamic and structural properties with respect to the underlying atomistic representation of the system. These results are compared to those obtained following the principles of statistical mechanics that start from the same underlying atomistic system. To this end, the potentials of mean force for lateral association in Martini 3.0 binary lipid bilayers are decomposed into their entropic and enthalpic components and compared to those of corresponding atomistic bilayers that have been projected onto equivalent coarse-grained mappings but evolved under the fully atomistic forces. This is accomplished by applying the reversible work theorem to lateral pair correlation functions between coarse-grained lipid beads taken at a range of different temperatures. The entropy-enthalpy decompositions provide a metric by which the underlying statistical mechanical properties of Martini can be investigated. Overall, Martini 3.0 is found to fail to properly partition entropy and enthalpy for the PMFs compared to the mapped all-atom results, despite changes made to the force field from the Martini 2.0 version. This outcome points to the fact that the development of more accurate top-down coarse-grained models such as Martini will likely necessitate temperature-dependent terms in the corresponding CG force-field; although necessary, this may not be sufficient to improve Martini. In addition to the entropy-enthalpy decompositions, Martini 3.0 produces an incorrect undulation spectrum, in particular at intermediate length scales of biophysical pertinence.

Research on entropy weight variation evaluation method for wind power clusters based on dynamic layered sorting

Research on entropy weight variation evaluation method for wind power clusters based on dynamic layered sorting

Multidimensional Analysis of Physiological Entropy during Self-Paced Marathon Running.

The pacing of a marathon is arguably the most challenging aspect for runners, particularly in avoiding a sudden decline in speed, or what is colloquially termed a "wall", occurring at approximately the 30 km mark. To gain further insight into the potential for optimizing self-paced marathon performance through the coding of comprehensive physiological data, this study investigates the complex physiological responses and pacing strategies during a marathon, with a focus on the application of Shannon entropy and principal component analysis (PCA) to quantify the variability and unpredictability of key cardiorespiratory measures. Nine recreational marathon runners were monitored throughout the marathon race, with continuous measurements of oxygen uptake (V˙O2), carbon dioxide output (V˙CO2), tidal volume (Vt), heart rate, respiratory frequency (Rf), and running speed. The PCA revealed that the entropy variance of V˙O2, V˙CO2, and Vt were captured along the F1 axis, while cadence and heart rate variances were primarily captured along the F2 axis. Notably, when distance and physiological responses were projected simultaneously on the PCA correlation circle, the first 26 km of the race were positioned on the same side of the F1 axis as the metabolic responses, whereas the final kilometers were distributed on the opposite side, indicating a shift in physiological state as fatigue set in. The separation of heart rate and cadence entropy variances from the metabolic parameters suggests that these responses are independent of distance, contrasting with the linear increase in heart rate and decrease in cadence typically observed. Additionally, Agglomerative Hierarchical Clustering further categorized runners' physiological responses, revealing distinct clusters of entropy profiles. The analysis identified two to four classes of responses, representing different phases of the marathon for individual runners, with some clusters clearly distinguishing the beginning, middle, and end of the race. This variability emphasizes the personalized nature of physiological responses and pacing strategies, reinforcing the need for individualized approaches. These findings offer practical applications for optimizing pacing strategies, suggesting that real-time monitoring of entropy could enhance marathon performance by providing insights into a runner's physiological state and helping to prevent the onset of hitting the wall.

Largest Lyapunov exponent and Shannon entropy: Two indices to analyze mixing in fluidized beds

Largest Lyapunov exponent and Shannon entropy: Two indices to analyze mixing in fluidized beds

Medium–Long-Term PV Output Forecasting Based on the Graph Attention Network with Amplitude-Aware Permutation Entropy

Medium–long-term photovoltaic (PV) output forecasting is of great significance to power grid planning, power market transactions, power dispatching operations, equipment maintenance and overhaul. However, PV output fluctuates greatly due to weather changes. Furthermore, it is frequently challenging to ensure the accuracy of forecasts for medium–long-term forecasting involving a long time span. In response to the above problems, this paper proposes a medium–long-term forecasting method for PV output based on amplitude-aware permutation entropy component reconstruction and the graph attention network. Firstly, the PV output sequence data are decomposed by ensemble empirical mode decomposition (EEMD), and the decomposed intrinsic mode function (IMF) subsequences are combined and reconstructed according to the amplitude-aware permutation entropy. Secondly, the graph node feature sequence is constructed from the reconstructed subsequences, and the mutual information of the node feature sequence is calculated to obtain the graph node adjacency matrix which is applied to generate a graph sequence. Thirdly, the graph attention network is utilized to forecast the graph sequence and separate the PV output forecasting results. Finally, an actual measurement system is used to experimentally verify the proposed method, and the outcomes indicate that the proposed method, which has certain promotion value, can improve the accuracy of medium–long-term forecasting of PV output.

Fractional-order state space reconstruction: a new frontier in multivariate complex time series

This paper presents a novel approach to the phase space reconstruction technique, fractional-order phase space reconstruction (FOSS), which generalizes the traditional integer-order derivative-based method. By leveraging fractional derivatives, FOSS offers a novel perspective for understanding complex time series, revealing unique properties not captured by conventional methods. We further develop the multi-span transition entropy component method (MTECM-FOSS), an advanced complexity measurement technique that builds upon FOSS. MTECM-FOSS decomposes complexity into intra-sample and inter-sample components, providing a more comprehensive understanding of the dynamics in multivariate data. In simulated data, we observe that lower fractional orders can effectively filter out random noise. Time series with diverse long- and short-term memory patterns exhibit distinct extremities at different fractional orders. In practical applications, MTECM-FOSS exhibits competitive or superior classification performance compared to state-of-the-art algorithms when using fewer features, indicating its potential for engineering tasks.