Energy Minimization Research Articles

A multi-objective indirect neural adaptive processes control design for minimization of energy consumption: An experimental validation on a transesterification reactor

Nowadays, energy management environment is a very important issue that technologies have focused on in order to save costs and minimize energy waste. This objective can be achieved by means of an energy resource management approach through an appropriate optimization technique. However, energy savings can conflict with other objective functions, to solve the problem a multi-objective optimization that considers the minimization of the control energy can be adopted. In this paper, we propose to use a multi-objective indirect neural adaptive control concept for nonlinear systems with unknown dynamics. The control scheme consists of an adaptive instantaneous neural emulator and neural controller built on fully connected real-time recurrent learning networks (RTRL). The multi-objective particle swarm optimization (MOPSO) algorithm is used as a mechanism to find NE and NC adaptive learning rates that optimize the proposed objective functions: control energy minimization and closed-loop preservation. Comparative studies and experimental validation on a semi-batch reactor, used to generate cleaner biofuels, are performed to confirm the effectiveness of the proposed strategy.

Wetting phenomena of droplets and gas bubbles: Contact angle hysteresis based on varying liquid-solid and solid-gas interfacial tensions.

Wetting phenomena have been widely observed in our daily lives, such as dew on lotus leaf, and applied in technical applications, e.g., ink-jet printing. In contrast to constant liquid-solid and solid-gas interfacial tensions in Young's law, we here focus on the wetting phenomena by considering varying fluid-solid interfacial tensions. We analyze the energy landscape, the map of Young's contact angle, the number and corresponding contact angle of local energy minima, and the contact angle hysteresis for a liquid droplet on a solid substrate in a gas phase. In addition, a gas bubble on a solid substrate in a liquid phase has been scrutinized from the aspect of surface energy minimization. The wetting effect has been regarded, where the liquid and gas species penetrate into the solid phase on the microscopic scale [F. Wang and B. Nestler, Phys. Rev. Lett. 132, 126202 (2024)]. We assume the liquid-solid and the solid-gas interfacial tensions to be a function dependent on the volume fraction of the liquid and the gas species and investigate the impact of the size ratio of the droplet to the solid surface that is overlooked in existing theories on the wetting phenomena. Our finding sheds light on the microscopic origin of the contact angle hysteresis and the droplet size effect on the wetting phenomena.

RocMLMs: Predicting Rock Properties Through Machine Learning Models

AbstractMineral phase transformations significantly alter the bulk density and elastic properties of mantle rocks and consequently have profound effects on mantle dynamics and seismic wave propagation. These changes in the physical properties of mantle rocks result from evolution in the equilibrium mineralogical composition, which can be predicted by the minimization of the Gibbs Free Energy with respect to pressure (P), temperature (T), and chemical composition (X). Thus, numerical models that simulate mantle convection and/or probe the elastic structure of the Earth's mantle must account for varying mineralogical compositions to be self‐consistent. Yet coupling Gibbs Free Energy minimization (GFEM) approaches with numerical geodynamic models is currently intractable for high‐resolution simulations because prediction speeds of widely‐used GFEM programs (100–102 ms) are impractical in many cases. As an alternative, this study introduces machine learning models (RocMLMs) that have been trained to predict thermodynamically self‐consistent rock properties at arbitrary PTX conditions between 1–28 GPa and 773–2,273 K, and dry mantle compositions ranging from fertile (lherzolitic) to refractory (harzburgitic) end‐members define9d with a large data set of published mantle compositions. RocMLMs are 101–103 times faster than GFEM calculations or GFEM‐based look‐up table approaches with equivalent accuracy. Depth profiles of RocMLMs predictions are nearly indistinguishable from reference models PREM and STW105, demonstrating good agreement between thermodynamic‐based predictions of density, Vp, and Vs and geophysical observations. RocMLMs are therefore capable, for the first time, of emulating dynamic evolution of density, Vp, and Vs due to partial melting and refertilization of dry mantle rocks in high‐resolution numerical geodynamic models.

GraphPPL.jl: A Probabilistic Programming Language for Graphical Models

This paper presents GraphPPL.jl, a novel probabilistic programming language designed for graphical models. GraphPPL.jl uniquely represents probabilistic models as factor graphs. A notable feature of GraphPPL.jl is its model nesting capability, which facilitates the creation of modular graphical models and significantly simplifies the development of large (hierarchical) graphical models. Furthermore, GraphPPL.jl offers a plugin system to incorporate inference-specific information into the graph, allowing integration with various well-known inference engines. To demonstrate this, GraphPPL.jl includes a flexible plugin to define a Constrained Bethe Free Energy minimization process, also known as variational inference. In particular, the Constrained Bethe Free Energy defined by GraphPPL.jl serves as a potential inference framework for numerous well-known inference backends, making it a versatile tool for diverse applications. This paper details the design and implementation of GraphPPL.jl, highlighting its power, expressiveness, and user-friendliness. It also emphasizes the clear separation between model definition and inference while providing developers with extensibility and customization options. This establishes GraphPPL.jl as a high-level user interface language that allows users to create complex graphical models without being burdened with the complexity of inference while allowing backend developers to easily adopt GraphPPL.jl as their frontend language.

Recognition and Scoring Physical Exercises via Temporal and Relative Analysis of Skeleton Nodes Extracted from the Kinect Sensor

Human activity recognition is known as the backbone of the development of interactive systems, such as computer games. This process is usually performed by either vision-based or depth sensors. So far, various solutions have been developed for this purpose; however, all the challenges of this process have not been completely resolved. In this paper, a solution based on pattern recognition has been developed for labeling and scoring physical exercises performed in front of the Kinect sensor. Extracting the features from human skeletal joints and then generating relative descriptors among them is the first step of our method. This has led to quantification of the meaningful relationships between different parts of the skeletal joints during exercise performance. In this method, the discriminating descriptors of each exercise motion are used to identify the adaptive kernels of the Constrained Energy Minimization method as a target detector operator. The results indicated an accuracy of 95.9% in the labeling process of physical exercise motions. Scoring the exercise motions was the second step after the labeling process, in which a geometric method was used to interpolate numerical quantities extracted from descriptor vectors to transform into semantic scores. The results demonstrated the scoring process coincided with the scores derived by the sports coach by a 99.5 grade in the R2 index.

An adaptive snow ablation-inspired particle swarm optimization with its application in geometric optimization

In response to the shortcomings of particle swarm optimization (PSO), such as low execution efficiency and difficulty in overcoming local optima, this paper proposes a multi-strategy PSO method incorporating snow ablation operation (SAO), known as SAO-MPSO. Firstly, Cubic initialization is performed on particles to obtain a good initial environment. Subsequently, SAO and PSO are combined in parallel, and a balanced search mechanism led by multiple sub-populations is devised, significantly improving the search efficiency of overall population. Finally, the degree day method of SAO is introduced, and particles are endowed with memory of environmental changes to prevent premature convergence of PSO, while balancing the exploration and exploitation (ENE) capabilities in later phases. All adaptive parameters are used throughout this method in place of fixed parameters to improve the robustness and adaptability. For a comprehensive analysis of SAO-MPSO, its good ENE ability is verified on CEC 2020 and CEC 2022 and this method is compared with existing improved PSO versions on both test sets. The results show that SAO-MPSO has certain advantages in the comparison of similar improved algorithms. In order to further validate the strength of SAO-MPSO in dealing with nonlinear optimization problems (OPs) with strong constraints, firstly, based on the ball Wang-Ball (BWB) curve, a combined BWB (CBWB) curve is constructed, and a construction method for CBWB curves that satisfy G1 and G2 continuity is derived. Then, with the energy minimization and scale parameters of the CBWB curve as the optimization objective and variables respectively, a shape optimization model that satisfies G2 continuity is established. Finally, three numerical optimization examples based on this model are solved using SAO-MPSO and compared with 10 other methods. The results show that the energy obtained by SAO-MPSO is the smallest, which verifies the effectiveness of this method applied to shape OPs of CBWB curve.

NMR Coupling Constants, Karplus Equations, and Adjusted MD Statistics: Detecting Diagnostic Torsion Angles for the Solution Geometry of 6-[α-d-Mannopyranosyl]-d-Mannopyranose (Mannobiose).

The quantitative solution conformations of 2-(hydroxymethyl)-tetrahydropyran, α-methyl-d-mannopyranoside, and 6-[α-d-mannopyranosyl]-d-mannopyranose (mannobiose) are described. Parametrized Karplus equations for redundant spin pairs across the terminal ω-torsion and the glycosidic ω-torsion for mannobiose are developed, including ω/θ-hypersurfaces for the terminal hydroxymethylene group. Experimental NMR data, algorithmic spectral simulation (clustered Hamiltonian method), molecular dynamics (MD) simulations (GLYCAM06), energy minimizations by DFT, and adjusted torsion angle populations weighted over the Karplus-type equations are used. We demonstrate that spectral simulation is a powerful tool in the refinement of initial J values obtained from static GAIO DFT calculations. We also show that only as few as one of multiple redundant torsions can be diagnostic for conformational analysis of the disaccharide.

Energy Minimization of RIS-Assisted Cooperative UAV–USV MEC Network

Energy Minimization of RIS-Assisted Cooperative UAV–USV MEC Network

A multi-strategy optimizer for energy minimization of multi-UAV-assisted mobile edge computing

Disasters in remote areas often cause damage to communication facilities, which presents significant challenges for rescue efforts. As flexible mobile devices, unmanned aerial vehicles (UAVs) can provide temporary network services to address this issue. This paper studies the use of UAVs as mobile base stations to offer offload computing services for disaster relief devices in affected areas. To ensure reliable communication between disaster relief devices and UAVs, we construct a multi-UAV-assisted mobile edge computing (MEC) system with the objective of minimizing system energy consumption. Inspired by swarm intelligence principles, we propose a multi-strategy optimizer (MSO) that defines various population search functions and employs superior neighborhood methods for population updates. Experimental results demonstrate that MSO achieves superior system energy efficiency and exhibits greater stability compared to several state-of-the-art swarm intelligence algorithms.

Entropy generation minimization and activation energy in magneto-thermo-solutal stratified Ellis hybrid nanofluid flow through a stretching stenotic artery: Insights into cardiovascular disease

This article explores the impact of binary chemical reactions with activation energy on the flow of magnetized mono and hybrid nanofluids within a porous artery under the effects of viscous dissipation, nonlinear thermal radiation, and Joule heating. The main novelty of this study lies in considering a non-Newtonian Ellis fluid model of blood through a stretching stenotic artery consisting of gold (Au) and silver (Ag) nanoparticles. Additionally, velocity slip, convective temperature, and concentration boundary conditions are applied at the arterial surface. The proposed model compares the performance of Ag/Blood nanofluid and Au-Ag/Blood hybrid nanofluid models. The governing equations are solved using the bvp4c built-in solver in MATLAB, which employs advanced numerical techniques and algorithms tailored for BVPs, ensuring quicker convergence with minor errors and accurate solutions. Graphs are plotted for concentration, temperature, velocity, entropy minimization, skin friction coefficient, Nusselt number, and Sherwood number for various physical parameters. The results show that with increased nonlinear thermal radiation (0.1≤Nr≤2.0), thermal Biot number (0.1≤Bi1≤0.3), and Eckert number (0.01≤Ec≤0.2), the thermal distribution profile exhibits a rising trend. In contrast, with increasing fluctuations in the chemical reaction rate parameter (0.1≤Rc≤0.3) and Schmidt number (1.0≤Sc≤3.0), the concentration distribution profile exhibits a decreasing trend. In terms of mass and heat transfer efficiency, the Au-Ag/Blood hybrid nanofluid flow outperforms the Ag/Blood nanofluid model. More entropy is observed for Au-Ag/Blood compared to Ag/Blood in the stenotic artery. Heat energy may be created by targeting the injured area and avoiding harm to nearby tissues. Localized heating can widen arteries and increase blood flow to the affected locations, potentially aiding cardiovascular health management and personalized therapy. This study has various implications for the design of drug delivery systems, biophysical models, innovative materials, and our knowledge of fluid dynamics and heat transfer. However, nanofluid/hybrid nanofluid stability is challenging for current researchers since this modeled flow can be considered over a shrinking artery or in the trihybrid model in future studies.

Advanced exploration of rare metal mineralization through integrated remote sensing and geophysical analysis of structurally-controlled hydrothermal alterations

Fusing multi-source (remote sensing and geophysical) data and diverse approaches validation in targeting hydrothermal alteration and structural anomalies enhances the potential for accurately detecting and characterizing mineralization zones. Sentinel 2 data and ASTER were processed for lithological and hydrothermal alteration mapping in the rare metal-rich Umm Naggat area (Egypt). Different image processing techniques were implemented, including false color composites, minimum noise fraction, band rationing, band math, mineral indices, relative absorption band depth, and constrained energy minimization. The rare metal-bearing Umm Naggat younger granite (NYG) pluton was lithologically discriminated and intra-differentiated to mafic-rich biotite granites, mafic-poor alkali feldspar granites, and albitized granites. Extensive hydrothermal alterations, such as albitization, ferrugination, propylitization, argillization, and phyllitization, overprint the NYG pluton. Normalized standard deviation, automatic lineament extractions, and trend analysis highlighted the key structural directions (NW, NNW, NNE, and NE) and distinguished the NYG pluton as a moderate to high structural density zone. The high structural density and intensive alteration zones are spatially associated and more localized within the NYG pluton than the surrounding rocks. Spatial overlay analysis confirmed that the hydrothermal alterations and fluid circulation systems are structurally-controlled. Furthermore, the hydrothermal alteration mapping and structural analysis outcomes were verified by combining fieldwork, slab polishing, petrographic investigations, and mineral chemistry through semi-quantitative scanning electron microscopy with energy dispersive spectroscopy (SEM-EDS) and quantitative electron probe microanalysis (EPMA) analysis. As a result, the hydrothermal genesis of rare metal-bearing minerals (Nb-rutile, Nb-ilmenite, and columbite) close to or incorporated within alteration minerals (chlorite, muscovite, and hematite) is confirmed from the alteration zones (propylitic, phyllic, and ferruginated). In addition, biotite muscovitization and chloritization significantly contribute to the secondary rare metal enrichment. The current study emphasizes the extensive distribution of secondary rare metal-bearing minerals within the entire NYG pluton (not only limited to the northern albitized granite as depicted by previous studies), which might shed light on these hydrothermally-altered younger granites as a new potential source for Nb and Ta in Egypt.

COSMIC Database and Structural Modeling Analysis of CYP2D6 Mutations in Human Cancers.

Single nucleotide polymorphisms (SNPs) in cytochrome P450 (CYP450) enzymes alter the metabolism of a variety of drugs. Numerous medications, including chemotherapies, are metabolized by CYP450 enzymes, making the expression of this suite of enzymes in tumor cells relevant to prescription regimens for cancer patients. We analyzed the characteristics of mutations of the CYP2D6 enzymes in cancer patients obtained from the Catalogue of Somatic Mutations in Cancer (COSMIC), including mutation type, age of the patient, tissue type, and histology. Mutations were analyzed through the Cancer-Related Analysis of Variants Toolkit (CRAVAT) software along with CHASM and VEST4 algorithms to determine the likelihood of being a driver and/or pathogenic mutation. For mutations with significant CHASM and VEST4 scores, structural analysis of each corresponding mutant protein was performed. The effect of each mutation was evaluated for its impact on the overall protein stability and ligand binding using Foldit Standalone and SwissDock, respectively. Structural analysis revealed that several missense mutations in CYP2D6 resulted in altered stability after energy minimization. Three missense mutations of CYP2D6 significantly altered docking stability and those located on alpha-helices near the docking site had a more significant impact than those not found in secondary protein structures. In conclusion, we have identified a series of mutations to CYP2D6 enzymes with possible relevance to cancer pathologies. Significance Statement CYP2D6 is responsible for the metabolism of many anti-cancer drugs. This study identified and characterized a series of mutations in the CYP2D6 enzyme that occurred in tumors. We found it likely that many of these mutations would alter enzyme function, leading to changes in drug metabolism in the tumor. We provide a basis for predicting the likelihood of a patient carrying these mutations to identify patients who may benefit from a precision medicine approach to drug selection and dosing.

Determinant- and derivative-free quantum Monte Carlo within the stochastic representation of wavefunctions

Abstract Describing the ground states of continuous, real-space quantum many-body systems, like atoms and molecules, is a significant computational challenge with applications throughout the physical sciences. Recent progress was made by variational methods based on machine learning (ML) ansatzes. However, since these approaches are based on energy minimization, ansatzes must be twice differentiable. This (a) precludes the use of many powerful classes of ML models; and (b) makes the enforcement of bosonic, fermionic, and other symmetries costly. Furthermore, (c) the optimization procedure is often unstable unless it is done by imaginary time propagation, which is often impractically expensive in modern ML models with many parameters. The stochastic representation of wavefunctions (SRW), introduced in (Atanasova et al 2023 Nat. Commun. 14 3601), is a recent approach to overcoming (c). SRW enables imaginary time propagation at scale, and makes some headway towards the solution of problem (b), but remains limited by problem (a). Here, we argue that combining SRW with path integral techniques leads to a new formulation that overcomes all three problems simultaneously. As a demonstration, we apply the approach to generalized ‘Hooke’s atoms’: interacting particles in harmonic wells. We benchmark our results against state-of-the-art data where possible, and use it to investigate the crossover between the Fermi liquid and the Wigner molecule within closed-shell systems. Our results shed new light on the competition between interaction-driven symmetry breaking and kinetic-energy-driven delocalization.

Viscoelasticity estimation of sports prosthesis by energy-minimizing inverse kinematics and its validation by forward dynamics

In this study, we present a method for estimating the viscoelasticity of a leaf-spring sports prosthesis using advanced energy minimizing inverse kinematics based on the Piece-wise Constant Strain (PCS) model to reconstruct the three-dimensional dynamic behavior. Dynamic motion analysis of the athlete and prosthesis is important to clarify the effect of prosthesis characteristics on foot function. However, three-dimensional deformation calculations of the prosthesis and viscoelasticity have rarely been investigated. In this letter, we apply the PCS model to a prosthesis deformation, which can calculate flexible deformation with low computational cost and handle kinematics and dynamics. In addition, we propose an inverse kinematics calculation method that is consistent with the material properties of the prosthesis by considering the minimization of elastic energy. Furthermore, we propose a method to estimate the viscoelasticity by solving a quadratic programming based on the measured motion capture data. The calculated strains are more reasonable than the results obtained by conventional inverse kinematics calculation. From the result of the viscoelasticity estimation, we simulate the prosthetic motion by forward dynamics calculation and confirm that this result corresponds to the measured motion. These results indicate that our approach adequately models the dynamic phenomena, including the viscoelasticity of the prosthesis.

Cortical development in the structural model and free energy minimization.

A model of neocortical development invoking Friston's Free Energy Principle is applied within the Structural Model of Barbas etal. and the associated functional interpretation advanced by Tucker and Luu. Evolution of a neural field with Hebbian and anti-Hebbian plasticity, maximizing synchrony and minimizing axonal length by apoptotic selection, leads to paired connection systems with mirror symmetry, interacting via Markov blankets along their line of reflection. Applied to development along the radial lines of development in the Structural Model, a primary Markov blanket emerges between the centrifugal synaptic flux in layers 2,3 and 5,6, versus the centripetal flow in layer 4, and axonal orientations in layer 4 give rise to the differing shape and movement sensitivities characteristic of neurons of dorsal and ventral neocortex. Prediction error minimization along the primary blanket integrates limbic and subcortical networks with the neocortex. Synaptic flux bypassing the blanket triggers the arousal response to surprising stimuli, enabling subsequent adaptation. As development progresses ubiquitous mirror systems separated by Markov blankets and enclosed blankets-within-blankets arise throughout neocortex, creating the typical order and response characteristics of columnar and noncolumnar cortex.

Multiscale molecular modeling of chromatin with MultiMM: From nucleosomes to the whole genome

Motivation: We present a user-friendly 3D chromatin simulation model for the human genome based on OpenMM, addressing the challenges posed by existing models with use-specific implementations. Our approach employs a multi-scale energy minimization strategy, capturing chromatin's hierarchical structure. Initiating with a Hilbert curve-based structure, users can input files specifying nucleosome positioning, loops, compartments, or subcompartments.Results: The model utilizes an energy minimization approach with a large choice of numerical integrators, providing the entire genome's structure within minutes. Output files include the generated structures for each chromosome, offering a versatile and accessible tool for chromatin simulation in bioinformatics studies. Furthermore, MultiMM is capable of producing nucleosome-resolution structures by making simplistic geometric assumptions about the structure and the density of nucleosomes on the DNA.Code availability: Open-source software and the manual are freely available on https://github.com/SFGLab/MultiMM or via pip https://pypi.org/project/MultiMM/.

On prescribing the number of singular points in a Cosserat-elastic solid

Abstract In a geometrically non-linear Cosserat model for micro-polar elastic solids, we prove that critical points of the Cosserat energy functional with an arbitrary large (finite) number of singularities do exist, whereas Cosserat energy minimizers are known to be locally Hölder continuous. To reach that goal, we first develop a technique to insert dipole pairs of singularities into smooth maps while controlling the amount of Cosserat energy needed to do so. We then use this method to force an arbitrary number of singular points into (weak) Cosserat-elastic solids by prescribing smooth boundary data. The boundary data themselves are given in such a way, that they contain no topological obstruction to regularity. Throughout this paper, we often exploit connections between harmonic maps and Cosserat-elastic solids, so that we are able to adapt and incorporate ideas of R. Hardt and F.-H. Lin for harmonic maps with singularities, as well as of F. Béthuel for dipole pairs of singularities.

Coastline target detection based on UAV hyperspectral remote sensing images

Timely and accurate monitoring of typical coastal targets using remote sensing technology is crucial for maintaining marine ecological stability. Hyperspectral target detection technology proves to be an effective tool in extracting various typical materials along the coastline. Traditional target detection methods using spectral domain information can effectively retain the intrinsic properties of the material. However, it is difficult to effectively recognize targets in homogeneous regions by using only spectral domain information, which may lead to insufficient utilization of spatial information. In this study, a detector based on signal-to-noise ratio fusion constrained energy minimization with low-rank sparse decomposition (SFLRSD) is proposed. This detector improves the separability of background and target by obtaining spatial domain information from hyperspectral images and fusing spectral domain information. First, total variation regularization and fractional Fourier transform are applied to process spatial and spectral domain information, respectively. The constrained energy minimization (CEM) detector is used to improve the separability between the target and background of the processed data. Then, the background and anomalies are represented as low-rank and sparse components, respectively, using low-rank sparse matrix factorization. This transforms the model solution into a covariance matrix problem, which is then solved using marginal distance difference (MDD) to isolate anomalous parts. Subsequently, the anomaly parts are fused with CEM detector results, weighted by their respective signal-to-noise ratios. This detection model leverages unified hyperspectral image features, enhancing spectral discreteness of anomalous targets and backgrounds. Finally, experiments on custom created hyperspectral dataset show that the proposed method outperforms other baseline methods in terms of visualization and quantitative performance. In this paper, we not only propose a new hyperspectral target detection method, but we also collect three typical marine litter of different materials by means of airborne hyperspectral remote sensing and construct four hyperspectral datasets in a real environment. All the simulation experiments in this paper are conducted in these four datasets.

Equilibrium configurations of two-dimensional bubbles in a channel: N -bubble case

Two-dimensional liquid foam systems comprised of an odd number N of bubbles arranged in a staircase configuration are studied as they are packed within a confined straight channel. Energy minimization is employed to characterize the permitted topology and geometry of the system at equilibrium. It is known that in an infinite staircase, bubble films are flat. The configuration for a finite number of bubbles, however, deviates from that of an infinite staircase at the edges. It is known that films near the edges are not flat and structures with a few bubbles (up to N = 3 ) are well characterized. To study what happens as more bubbles are added, and how the behaviour approaches that of an infinite staircase, structures comprised of N = [ 5 , 7 , … ] bubbles are studied here. For these N values, minimum and maximum bubble areas are found that can pack in a staircase configuration within a straight channel. However, for an infinite staircase and also for N = 3 , there are maximum areas, but no minima. Staircases with very short films, which make the structure susceptible to break in various ways, are also identified.

Joint Energy and Completion Time Difference Minimization for UAV-Enabled Intelligent Transportation Systems: A Constrained Multi-Objective Optimization Approach

Joint Energy and Completion Time Difference Minimization for UAV-Enabled Intelligent Transportation Systems: A Constrained Multi-Objective Optimization Approach