Energy Function Research Articles

A new approach to medical image fusion based on the improved Extended difference-of-Gaussians combined with the Coati optimization algorithm

The synthesis of medical images plays a pivotal role in image-based disease diagnosis. In recent years, numerous medical image synthesis methods have been proposed. Nevertheless, images generated from the proposed synthesis methods often suffer from shortcomings, including low image quality, reduced brightness and contrast, and loss of vital information. In this paper, we propose a novel approach to tackle the aforementioned challenges in medical image synthesis. Initially, the input images are decomposed into two components: low-frequency and high-frequency components using the Weighted mean curvature filter (WMCF). Subsequently, we propose a synthesis rule for the high-frequency components based on the combination of the Extended difference-of-Gaussians (XDoG) filter, the Structure tensor (ST), and the Local energy (LE) function. Additionally, we employ a novel adaptive synthesis rule, based on the Coati optimization algorithm (COA), to synthesize the low-frequency components. We conducted four experiments using 90 pairs of medical images. The experimental results demonstrate that our proposed method not only effectively enhances image quality, brightness, and contrast but also better preserves crucial details such as boundaries, edges, and the original image’s structure when compared to the most recently published methods.

A study the 16 O + 166 E R , 16 O + 197 Au Systems Using Calculations of Quasi-elastic Scattering

Specific systematic studies on the nuclear potential parameter for the heavy-ion reactions which includes the systems have been achieved by using large-angle quasi elastic scattering at deep sub-barrier energies close to the Coulomb barrier height. The elastic scattering barrier distribution Dqelas a function of energy has also been calculated in this theoretical study. The single-channel (SC) and coupled-channels calculations have been carried out to elicit the nuclear potential. The chi-square method X2 has been used find the best value of the nuclear potential in comparison with the experimental data. The best values of the nuclear potential were found from the calculations of the single channels for systems: 16O+166ER , 16O+197Au, which are equal to (60.5, 100) MeV, respectively.

Accurate prediction of RNA secondary structure including pseudoknots through solving minimum-cost flow with learned potentials

Pseudoknots are key structure motifs of RNA and pseudoknotted RNAs play important roles in a variety of biological processes. Here, we present KnotFold, an accurate approach to the prediction of RNA secondary structure including pseudoknots. The key elements of KnotFold include a learned potential function and a minimum-cost flow algorithm to find the secondary structure with the lowest potential. KnotFold learns the potential from the RNAs with known structures using an attention-based neural network, thus avoiding the inaccuracy of hand-crafted energy functions. The specially designed minimum-cost flow algorithm used by KnotFold considers all possible combinations of base pairs and selects from them the optimal combination. The algorithm breaks the restriction of nested base pairs required by the widely used dynamic programming algorithms, thus enabling the identification of pseudoknots. Using 1,009 pseudoknotted RNAs as representatives, we demonstrate the successful application of KnotFold in predicting RNA secondary structures including pseudoknots with accuracy higher than the state-of-the-art approaches. We anticipate that KnotFold, with its superior accuracy, will greatly facilitate the understanding of RNA structures and functionalities.

Designing and modeling of a new MSMA vibration energy transducer

The conversion of mechanical vibrational energy into electrical energy to power wireless electronic devices using the smart material magnetic shape memory alloy (MSMA) has garnered substantial attention. This paper presents a vibration energy transducer founded on the inverse effect of MSMA, elucidating the principle of power generation. The variations in martensite and magnetic domain characteristics within MSMA were analyzed, and a constitutive model was established for the MSMA vibration energy transducer, integrating the thermodynamic theory of the Gibbs free energy function. Although this model performs well in predicting experimental outcomes, it falls short in capturing all features of the experimental data. To comprehensively encompass these features, the PSO-XGBoost machine learning approach was introduced to train the experimental data by incorporating factors such as stress, magnetic field, and induced voltage. An experimental prototype of the MSMA vibration energy transducer is fabricated, and the predictions of both models are compared with the collected experimental data, validating the accuracy of the model and indicating the enhanced effectiveness of machine learning methods in prediction. This research not only validates the correctness of the models but also emphasizes the potential for more precise predictions using machine learning methods, thereby establishing a robust foundation for the thorough study and broader application of MSMA vibration energy transducers.

Optimal Configuration of Multi-Energy Storage in an Electric–Thermal–Hydrogen Integrated Energy System Considering Extreme Disaster Scenarios

Extreme disasters have become increasingly common in recent years and pose significant dangers to the integrated energy system’s secure and dependable energy supply. As a vital part of an integrated energy system, the energy storage system can help with emergency rescue and recovery during major disasters. In addition, it can improve energy utilization rates and regulate fluctuations in renewable energy under normal conditions. In this study, the sizing scheme of multi-energy storage equipment in the electric–thermal–hydrogen integrated energy system is optimized; economic optimization in the regular operating scenario and resilience enhancement in extreme disaster scenarios are also considered. A refined model of multi-energy storage is constructed, and a two-layer capacity configuration optimization model is proposed. This model is further enhanced by the integration of a Markov two-state fault transmission model, which simulates equipment defects and improves system resilience. The optimization process is solved using the tabu chaotic quantum particle swarm optimization (TCQPSO) algorithm to provide reliable and accurate optimization results. The results indicate that addressing severe disaster situations in a capacity configuration fully leverages the reserve energy function of energy storage and enhances system resilience while maintaining economic efficiency; furthermore, adjusting the load loss penalty coefficients offers a more targeted approach to the balancing of the system economy and resilience. Thus, new algorithmic choices and planning strategies for future research on enhancing the resilience of integrated energy systems under extreme disaster scenarios are provided.

Why Do Empirical Valence Bond Simulations Yield Accurate Arrhenius Plots?

Computer simulations of the temperature dependence of enzyme reactions using the empirical valence bond (EVB) method have proven to give very accurate results in terms of the thermodynamic activation parameters. Here, we analyze the reasons for why such simulations are able to correctly capture activation enthalpies and entropies and how sensitive these quantities are to parametrization of the reactive potential energy function. We examine first the solution reference reaction for the enzyme ketosteroid isomerase, which corresponds to the acetate catalyzed deprotonation of the steroid in water. The experimentally determined activation parameters for this reaction turn out to be remarkably well reproduced by the calculations. By modifying the EVB potential so that the activation and reaction free energies become significantly shifted, we show that the activation entropy is basically invariant to such changes and that ΔS⧧ is instead determined by the specific mixture of the underlying force fields in the transition state region. The coefficients of this mixture do not change appreciably when the EVB potential is modified within reasonable limits, and hence, the estimate of ΔS⧧ becomes very robust. This is further verified by examining a more complex concerted hydride and proton transfer reaction in the enzyme hydroxybutyrate dehydrogenase.

How to measure the free energy and partition function from atom-atom correlations

How to measure the free energy and partition function from atom-atom correlations

Biaxial nematic order in fundamental measure theory.

Liquid crystals consisting of biaxial particles can exhibit a much richer phase behavior than their uniaxial counterparts. Usually, one has to rely on simulation results to understand the phase diagram of these systems since very few analytical results exist. In this work, we apply fundamental measure theory, which allows us to derive free energy functionals for hard particles from first principles and with high accuracy, to systems of hard cylinders, cones, and spherotriangles. We provide a general recipe for incorporating biaxial liquid crystal order parameters into fundamental measure theory and use this framework to obtain the phase boundaries for the emergence of orientational order in the considered systems. Our results provide insights into the phase behavior of biaxial nematic liquid crystals and, in particular, into methods for their analytical investigation.

Optimizing dual-energy CT technique for iodine-based contrast-to-noise ratio, a theoretical study.

Dual-energy CT (DECT) systems provide valuable material-specific information by simultaneously acquiring two spectral measurements, resulting in superior image quality and contrast-to-noise ratio (CNR) while reducing radiation exposure and contrast agent usage. The selection of DECT scan parameters, including x-ray tube settings and fluence, is critical for the stability of the reconstruction process and hence the overall imagequality. The goal of this study is to propose a systematic theoretical method for determining the optimal DECT parameters for minimal noise and maximum CNR in virtual monochromatic images (VMIs) for fixed subject size and total radiationdose. The noise propagation in the process of projection based material estimation from DECT measurements is analyzed. The main components of the study are the mean pixel variances for the sinogram and monochromatic image and the CNR, which were shown to depend on the Jacobian matrix of the sinograms-to-DECT measurementsmap. Analytic estimates for the mean sinogram and monochromatic image pixel variances and the CNR as functions of tube potentials, fluence, and VMI energy are derived, and then used in a virtual phantom experiment as an objective function for optimizing the tube settings and VMI energy to minimize the image noise and maximize theCNR. It was shown that DECT measurements corresponding to kV settings that maximize the square of Jacobian determinant values over a domain of interest lead to improved stability of basis material reconstructions. Instances of non-uniqueness in DECT were addressed, focusing on scenarios where the Jacobian determinant becomes zero within the domain of interest despite significant spectral separation. The presence of non-uniqueness can lead to singular solutions during the inversion of sinograms-to-DECT measurements, underscoring the importance of considering uniqueness properties in parameter selection. Additionally, the optimal VMI energy and tube potentials for maximal CNR was determined. When the x-ray beam filter material was fixed at 2 mm of aluminum and the photon fluence for low and high kV scans were considered equal, the tube potential pair of 60/120 kV led to the maximal iodine CNR in the VMI at 53keV. Optimizing DECT scan parameters to maximize the CNR can be done in a systematic way. Also, choosing the parameters that maximize the Jacobian determinant over the set of expected line integrals leads to more stable reconstructions due to the reduced amplification of the measurement noise. Since the values of the Jacobian determinant depend strongly on the imaging task, careful consideration of all of the relevant factors is needed when implementing the proposedframework.

Quantum Monte Carlo study of correlation energy and pair correlation function at various electron-positron density ratios: Accurate calculation of positron annihilation lifetimes in solids

Quantum Monte Carlo study of correlation energy and pair correlation function at various electron-positron density ratios: Accurate calculation of positron annihilation lifetimes in solids

Coupling deformation analysis of self-morphing bilayers with mismatch strain

Self-morphing bilayers driven by the inner mismatch strain have inspired numerous actuators and soft robots in the four-dimensional (4D) printing field. Analyzing their coupling deformation is crucial for the design of actuators, but current methods face inefficiencies or accuracy issues. This study provides convenient and efficient approaches, from both deformation theory and model aspects, to analyze the coupling deformations of self-morphing bilayers. First, we derived a simplified energy expression for self-morphing bilayers to reflect their deformation process. This concise form permits direct derivation of some deformation theories for self-morphing bilayers, thereby significantly simplifying the complexity of deformation analysis. Second, we established the deformation simulation model of bilayers by discretizing and minimizing the simplified energy expression. The checkerboard pattern method, a new discrete differential geometric (DDG) approach, was introduced and improved to discretize the derived energy expression. The Levenberg-Marquardt (LM) algorithm was used to establish the deformation simulation model for bilayers by minimizing the discrete energy function. Further, a series of numerical simulations of bending bilayers were conducted. Results validated that the proposed model achieved a faster simulation speed compared to the finite element method (FEM)-based model in our previous work, and was more accurate for finite strain situations. Finally, three bio-inspired bilayers and a bio-inspired gripper (successfully grasped a sphere) were designed using the resulting model, demonstrating its practical application capabilities. These proposed methods hold substantial potential to facilitate the advancement of the design field of 4D printing and actuators.

Adaptive iterative learning control for high‐order nonlinear systems with different types of uncertainties

AbstractThe present work aims at investigating the adaptive iterative learning control (AILC) design for uncertain high‐order fully actuated nonlinear systems. In order to show the design principles, three types of nonlinear systems are considered, including systems with just parametric uncertainty, systems with both parametric and input distribution uncertainties as well as systems with both parametric uncertainty and an unknown control gain matrix. For different systems, the corresponding AILC schemes are proposed with different techniques in dealing with various system uncertainties, for which the convergence analysis are conducted rigorously based on the composite energy function. The effectiveness of the proposed AILC strategies is verified through numerical simulations.

Isotope Effects on State-to-State Photodissociation Dynamics of D2S in Its First Absorption Band.

State-to-state photodissociation dynamics of D2S in its first absorption band were explored by utilizing recently developed diabatic potential energy surfaces (PESs). Quantum dynamics calculations, involving the first two strongly coupled 1A″ states, were executed employing a Chebyshev real wavepacket method. The nonadiabatic channel via the conical intersection (CI) is facile, direct, and fast, leading to the production of rotationally and vibrationally cold SD(X̃2Π). The calculated absorption spectrum, product state distributions, and angular distributions are in reasonable agreement with the experimental results, although some discrepancies exist at 193.3 nm. Compared with H2S, there are obvious isotope effects on rotational state distributions for D2S photodissociation in its first absorption band. Moreover, we scrutinize the variation of product state distributions as a function of photon energy and the vibrational mediated photodissociation of the parent molecule. Due to the diverse shapes of the three fundamental vibrational wave functions, photoexcited wavepackets access distinct segments of the upper-state PES, resulting in a disparate absorption spectrum and ro-vibrational distributions via the nonadiabatic transition. This study provides a comprehensive figure of the isotopic effect and wavelength dependence on the photofragmentation behaviors from D2S photodissociation, which should attract more experimental and theoretical attention to this prototypical system.

An adaptive energy regulation in a memristive map linearized from a circuit with two memristive channels

Nonlinear circuits can show multistability when a magnetic flux-dependent memristor (MFDM) or a charge-sensitive memristor (CSM) is incorporated into a one branch circuit, which helps estimate magnetic or electric field effects. In this paper, two different kinds of memristors are incorporated into two branch circuits composed of a capacitor and a nonlinear resistor, thus a memristive circuit with double memristive channels is designed. The circuit equations are presented, and the dynamics in this oscillator with two memristive terms are discussed. Then, the memristive oscillator is converted into a memristive map by applying linear transformation on the sampled time series for the memristive oscillator. The Hamilton energy function for the memristive oscillator is obtained by using the Helmholtz theorem, and it can be mapped from the field energy of the memristive circuit. An energy function for the dual memristive map is suggested by imposing suitable weights on the discrete energy function. The dynamical behaviors of the new memristive map are investigated, and an adaptive law is proposed to regulate the firing mode in the memristive map. This work will provide a theoretical basis and experimental guidance for oscillator-to-map transformation and discrete map energy calculation.

An optimized denoised bias correction model with local pre-fitting function for weak boundary image segmentation

The active contour model (ACM) plays a paramount part in grasping visual properties of images and exacting targets of interest. It is overwhelming hardship for traditional ACMs to segment images with noise, intensity inhomogeneity or low contrast and consider computation speed for practical applicability. Therefore, an optimized denoised bias correction (ODBC) model incorporating the pre-piecewise fitting function and the variational denoised term into the energy function is proposed for images with low contrast and intensity inhomogeneity. An optimized gradient descent equation and an upgraded regularization term are produced to enhance the robustness and sensitivity of this model. Experiments are conducted to validate that ODBC model possesses the superiority of reliability and speed in segmenting images with inhomogeneous intensity and strengthens the robustness to initial contours. The results manifest that mIOU of ODBC model exceeds 0.9, superior to prime ACMs and deep learning schemes, in segmenting images associated and unrelated with the dataset.

Imaging faults and fractures with the difference of fast and slow shear-wave splitting reflectivity, Δγ(S): 3D/9C survey in Midland Basin, West Texas, and 3D/3C survey in Washakie Basin, Wyoming

One of the most important applications of shear-wave (S-wave) seismic exploration has been in reservoir fracture characterization. While many advancements have been made over the past 30 years to compute and correct for the long-wavelength kinematics of S-wave splitting (SWS) (fast S-wave polarization directions and slow S-wave time delays), practically no progress has been made in imaging the short-wavelength reflectivity of fractures directly with Δγ(S). This property is the contrast in the SWS anisotropy parameter, γ(S), and represents the reflection amplitude at vertical incidence for changes in fracture density and orientation across an interface. In this article, we examine the Lupin nine-component survey in Midland Basin, Texas, for the technical reliability of imaging fractures in depth with converted P to S waves (PS waves) guided by pure-mode horizontal shear waves and vertical shear waves. Final Δγ(S) amplitude maps for each mode show sensitivity to fractures, faults, and the maximum horizontal stress direction. These maps are computed from the difference between fast and slow S-wave stacks (after SWS analysis and correction) and P-wave amplitude variation with offset gradient stacks. The S-wave difference maps identify an east–west lineament, possibly a strike-slip fault or fracture corridor, that is not observed by P-wave depth slices. Pure-mode S waves and PS waves are orders of magnitude more sensitive to Δγ(S) than P waves. We also review the development of Δγ(S) and find that it has been relatively unexploited by the exploration industry. In addition, we demonstrate that Δγ(S) can be obtained directly from the objective function of the transverse energy to correct for SWS, and show a four-component Alford rotation example from a previous PS-wave survey in the Washakie Basin, Wyoming.

Performance study of organic photovoltaic/thermal system with synergistic effect of photocatalytic and thermal catalytic technology

Organic photovoltaic cells are considered to be an effective solution for maintaining excellent electrical energy output in high temperature environment. However, the application of OPV alone has a low utilization rate of the solar spectrum. The combined application of OPV and other technologies is explored to improve the utilization efficiency of the solar spectrum. Based on the above gaps, this study proposes an organic photovoltaic/thermal system with synergistic effect of photocatalytic and thermal catalytic (PC/TC-OPV/T). By integrating organic photovoltaic cells, photocatalytic technology, thermal catalysis technology and photothermal components, the utilization efficiency of the solar spectrum is improved, and the triple functions of formaldehyde removal, electrical energy and hot water are realized. Therefore, this study proposes a new air purification-thermal-electrical model, establishes the heat transfer balance equation inside the system and the air purification and mass transfer balance equation in the air duct, proposes the performance evaluation index of the system, and uses Ansys Fluent software to predict the working characteristics of the system under different operating conditions, and draw the following conclusions: (1) Under the synergistic action of photocatalytic and thermal catalytic technologies, the formaldehyde removal efficiency is 84.4%, which is 30% higher than that of photocatalytic alone. (2) The electrical efficiency of the new system benefits from high solar irradiance and environment temperature, enabling it to achieve the efficiency of 17.84% within the research scope. (3) Under the research conditions, the instantaneous water flow heating efficiency of the system is up to 69.6%.

Micro-macro analysis of Hyperelastic auxetic lattice structures under finite-strain regime

This paper addresses the intricate analysis of lattice structures, pivotal components in engineering applications, confronted by challenges arising from their diverse unit cells (UCs) and complex behavior across micromechanical and macro-scale dimensions. The present study deals with a comprehensive analysis of lattice structure with honeycomb and re-entrant auxetic unit cells under large deformation via both a robust finite element analysis (FEA) and experimental tests. The proposed FEA is developed based on the hyperelastic Mooney–Rivlin strain energy function and the novel exact motion field that can fully describe the projection of cross-section. The results demonstrate that mechanical parameters such as Young's modulus, Poisson's ratio, and shear modulus have significant nonlinear behaviors with respect to UC geometrical parameters that are crucial for optimization across varied operational conditions. Additionally, the lattices made of TPU material and fabricated by Fused Deposition Modeling are tested under three-point bending and compression considering contact interaction. The results reveal highly nonlinear responses due to instabilities in some links and material nonlinearity. Furthermore, the behavior of lattice structures is exceedingly dependent on the orientations and types of UCs. It also can be found that both of the proposed FEA and constitutive model are in good agreement with experimental data.

Large deviation principle of multiplicative Ising models on Markov–Cayley trees

In this paper, we study the large deviation principle (LDP) for two types (Type I and Type II) of multiplicative Ising models. For Types I and II, the explicit formulas for the free energy functions and the associated rate functions are derived. Furthermore, we prove that those free energy functions are differentiable, which indicates that both systems are characterized by a lack of phase transition phenomena.

Distributed Learning Control for High-Speed Trains Subject to Operation Safety Constraints.

Trajectory tracking problems related to high-speed trains (HSTs) are fundamental issues that affect the operation safety and ride comfort. This study proposes a distributed learning control scheme based on a multiagent system framework for trajectory tracking of HSTs subject to operation safety constraints. Two different connection modes are considered between the carriages: 1) a soft connection achieved via information exchange and 2) a hard connection enforced via couplers. Both relative displacement and speed constraints are carefully analyzed in the tracking control process. By constructing an appropriate barrier composite energy function, the convergence of the tracking error and satisfaction of the operation safety constraints are rigorously proven for the proposed scheme. The theoretical results are verified using numerical simulations.