Computational Effort Research Articles

Blockchain-supported online banking scheme

Online banking not merely enables customers to make transactions within a short time and access transactional information anywhere but also facilitates financial institutions to simplify transaction procedures and reduce their operating expenses. Common concerns by both customers and financial institutions are the secure authentication measures for protecting sensitive financial information from cybersecurity threats and maintaining their trust in a secure environment. This paper presents a blockchain-supported scheme to establish secure authentication and mutual trust for handling online banking processes. The proposed solution’s model design, inference process, proof method, security analysis, and performance evaluation are included in this study. With the decentralized applications and smart contracts, that benefit from disruptive innovations in blockchain technology, the proposed scheme enables financial institutions to provide efficient, immutable, transparent, and secure online financial services for customers. In addition, the computational efforts of the proposed solution show that high performance can be achieved in executing financial transactions while safeguarding sensitive data and retaining trust in a decentralized environment which is some of the most significant weak security of the existing authentication schemes for online banking platforms.

Thermal-electrical analogy for simulations of superconducting power cables

Due the integration of superconducting technologies into electrical networks around the world, its precise simulations in power grids are increasingly becoming a desirable outcome. Over the years, sophisticated methods have been used to model superconducting power cables and, in this way, predict its behavior under different conditions. Most of the available models, however, are not advisable to be used and embedded in power system simulators. In this manuscript, we focus on the development of a method specifically for modeling superconducting cables in traditional power grid simulators. We start developing the model through a case example, within the basic concepts are introduced and later expanded to general cases. The model also incorporates the transient behavior of the cooling media, which is mandatory for such cables. It has been observed that the proposed model requires less computational effort and is able to deliver accurate results when compared to more advanced methods.

Guiding vector field and self-structuring learning network-based formation control of underactuated surface vessels in static obstacle environments with flexible performances

To address the problem of underactuated surface vessel (USV) formation control in static obstacle environments with model uncertainties and time-varying external disturbances, a model-free formation control strategy is proposed in this paper. First, based on the guiding vector field (GVF), a composite GVF is developed to guide USV formation to the desired position and to avoid multiple static obstacles. Second, a flexible constraint strategy is introduced, and the constraint boundary conditions are appropriately relaxed to avoid singularities in the obstacle environment. Then, based on the Mexican hat wavelet function, the self-structuring fuzzy Mexican hat wavelet cerebellar model articulation controller (SCMAC), and a self-structuring fuzzy Mexican hat wavelet brain emotional learning controller (SBELC), are proposed to achieve model-free control. In addition, the self-structuring algorithm is embedded into SCMAC and SBELC to achieve autonomous optimization of the controller structure and to reduce the computational effort of the control system. The salient features in the proposed control strategy are as follows. First, the proposed model-free formation control strategy does not have to rely on accurate model information. Second, collisions are effectively avoided, and good control performance is guaranteed even under the influence of disturbances and static obstacles. Third, the proposed self-structuring algorithm achieves automatic construction of the controller structure. Finally, the signals in the control system are proven to be bounded, and the simulation results verify the feasibility and superiority of the proposed model-free control strategy.

Representation-theoretical characterization of canonical custodial symmetry in NHDM potentials

By considering the basis-covariant constituents of N-Higgs-doublet potentials, we derive necessary and sufficient conditions for canonical SO(4)C Custodial Symmetry (CS) of potentials with N>2 doublets, based on representation-theoretical and geometrical relations. In essence, our characterization relates the presence of canonical CS to basis-covariant vectors corresponding to particular bases of the defining representation of the orthogonal Lie algebras. For N=3,4 and 5, the conditions demand little computational effort to be evaluated, and we provide practical algorithms that may be efficiently implemented in a computer program, for deciding whether or not a potential is custodial-symmetric.

Molecular dynamics modelling of interacting magnetic nanoparticles for investigating equilibrium and dynamic ensemble properties

Magnetic nanoparticles have the potential to be used in various biomedical applications, including magnetic drug targeting and hyperthermia for cancer treatment. In both cases, precise prediction of the local particle concentration is required to plan a successful therapy, which is a challenging task due to several complex flow phenomena. Computational macroscopic and microscopic models have been developed to support this process, but they have limitations in terms of interactions implementation, micromagnetic dynamics or computational costs. This study aims to develop a model that can represent micromagnetic dynamics and being employed to study equilibrium properties of particle ensembles in viscous media. Therefore, a kinetic Monte Carlo method in combination with Langevin equations is used. So, magnetization dynamics and mechanical motion can be simulated in combination very efficiently. The new model is validated with another model based on the stochastic Landau-Lifshitz-Gilbert equation. Various interaction potentials like Van der Waals, steric and electrostatic interactions are included to study their specific influence on structural properties. Also, methods to control the errors while integrating the equations of motion and using the Ewald method for calculating interaction quantities are implemented. As a validation we studied equilibrium and time dependent properties of non-interacting particle ensembles as well as errors made by the Ewald-method used for interaction quantities calculation. In all studies we found very good agreement of the simulation results with the theoretical predictions. The developed models can now be used for investigating equilibrium and dynamic properties of ferrofluids, or more general for biomedical research for magnetic drug targeting, hyperthermia, or imaging techniques. Furthermore, the new hybrid model can be also used for simulations in the range of milliseconds with reasonable computational effort.

Multi-Level Protocol for Mechanistic Reaction Studies Using Semi-Local Fitted Potential Energy Surfaces.

In this work, we propose a multi-level protocol for routine theoretical studies of chemical reaction mechanisms. The initial reaction paths of our investigated systems are sampled using the Nudged Elastic Band (NEB) method driven by a cheap electronic structure method. Forces recalculated at the more accurate electronic structure theory for a set of points on the path are fitted with a machine learning technique (in our case symmetric gradient domain machine learning or sGDML) to produce a semi-local reactive potential energy surface (PES), embracing reactants, products and transition state (TS) regions. This approach has been successfully applied to a unimolecular (Bergman cyclization of enediyne) and a bimolecular (SN2 substitution) reaction. In particular, we demonstrate that with only 50 to 150 energy-force evaluations with the accurate reference methods (here complete-active-space self-consistent field, CASSCF, and coupled-cluster singles and doubles, CCSD) it is possible to construct a semi-local PES giving qualitative agreement for stationary-point geometries, intrinsic reaction coordinates and barriers. Furthermore, we find a qualitative agreement in vibrational frequencies and reaction rate coefficients. The key aspect of the method's performance is its multi-level nature, which not only saves computational effort but also allows extracting meaningful information along the reaction path, characterized by zero gradients in all but one direction. Agnostic to the nature of the TS and computationally economic, the protocol can be readily automated and routinely used for mechanistic reaction studies.

CPOpt: A modular framework for genetic algorithm optimization and post-optimization analysis in complex charged particle optical design

This article reports on the development of an optimization framework for charged particle optics design (CPOpt). The optimization part of CPOpt involves a custom-developed Python multi-objective genetic algorithm (MOGA), which incorporates NSGA-II to optimize for multiple objectives, such as particle transmission and beam spot size. The simultaneous tuning of different types of parameters, such as electrode voltages and geometrical parameters, is facilitated by creating isolated groups of parameters. Modeling of parameter configurations is done with the charged particle optics simulation tool SIMION and a ZeroMQ-based communication interface enables a fully automated interaction with the MOGA. The post-optimization analysis part of CPOpt provides standardized and broadly applicable methods to identify suitable solutions, based on the outcome of the optimization part. Global sensitivity analysis, based on the Delta Moment-Independent-Measure in combination with dedicated Latin hypercube sampling, allows higher-level information about parameter sensitivity and robustness to be extracted, as well as effective randomized local optimization. Demonstrated on a two-lens system with intermediate aperture, the CPOpt framework effectively optimized the two conflicting objectives of reduced beam spot size and increased transmission at the detector plane for 12 parameters, covering both electrode voltages and geometrical parameters. The optimization process was performed within less than 11h on a workstation, while all the simulations required for the post-optimization analysis took another 7h. The additional computational effort for the post-optimization analysis can contribute to deliver solutions with high performance and stability.

Accurate prediction of protein function using statistics-informed graph networks

Understanding protein function is pivotal in comprehending the intricate mechanisms that underlie many crucial biological activities, with far-reaching implications in the fields of medicine, biotechnology, and drug development. However, more than 200 million proteins remain uncharacterized, and computational efforts heavily rely on protein structural information to predict annotations of varying quality. Here, we present a method that utilizes statistics-informed graph networks to predict protein functions solely from its sequence. Our method inherently characterizes evolutionary signatures, allowing for a quantitative assessment of the significance of residues that carry out specific functions. PhiGnet not only demonstrates superior performance compared to alternative approaches but also narrows the sequence-function gap, even in the absence of structural information. Our findings indicate that applying deep learning to evolutionary data can highlight functional sites at the residue level, providing valuable support for interpreting both existing properties and new functionalities of proteins in research and biomedicine.

Zonewise surrogate-based optimization of box-constrained systems

Complex physical or numerical systems may exhibit distinct behaviors in various zones of their design spaces. We present an algorithm that uses multiple cluster-based surrogates for optimizing such box-constrained systems. It partitions the design space into multiple clusters using K-means clustering and develops a separate surrogate for each cluster. It then uses these surrogates to sample additional points in the design space whose function evaluations guide the search for a global optimum. Clustering, surrogate construction, and smart sampling are employed iteratively to add sample points until a pre-defined threshold. The best solution from these points estimates a global optimum. An extensive test bed of 52 box-constrained functions was used to evaluate and compare the algorithm's performance and computational requirements with sixteen derivative-free optimization solvers. The best version of our algorithm surpassed all sixteen solvers in optimization accuracy for a fixed number of evaluations and demanded lower computational effort than fifteen.

The Orbitron: A crossed-field device for co-confinement of high energy ions and electrons

To explore the confinement of high-energy ions above the space charge limit, we have developed a hybrid magnetic and electrostatic confinement device called an Orbitron. The Orbitron is a crossed-field device combining aspects of magnetic mirrors, magnetrons, and orbital ion traps. Ions are confined in orbits around a high-voltage cathode with co-rotating electrons confined by a relatively weak magnetic field. Experimental and computational investigations focus on reaching ion densities above the space charge limit through the co-confinement of electrons. The experimental apparatus and suite of diagnostics are being developed to measure the critical parameters, such as plasma density, particle energy, and fusion rate for high-energy, non-thermal plasma conditions in the Orbitron. Initial results from experimental and computational efforts have revealed the need for cathode voltages on the order of 100–300 kV, leading to the development of a custom high voltage, ultra-high vacuum bushing rated for 300 kV.

Building extraction from remote sensing imagery: advanced squeeze-and-excitation residual network based methodology

Extracting buildings from remote sensing imagery (RSI) is an essential task in a wide range of applications, such as urban and monitoring. Deep learning has emerged as a powerful tool for this purpose, and in this research, we propose an advanced building extraction method based on SE-ResNet18 and SE-ResNet34 architectures. These models were selected through a rigorous comparative analysis of various deep learning models, including variations of residual networks (ResNet), squeeze-and-excitation residual networks (SE-ResNet), and visual geometry group (VGG), for their high performance in all metrics and their computational efficiency. Our proposed methodology outperformed all other models under consideration by a significant margin, demonstrating its robustness and efficiency. It achieved superior results with less computational effort and time, a testament to its potential as a powerful tool for semantic segmentation tasks in remote sensing applications. An extensive comparative evaluation involving a wide range of state-of-the-art works further validated our method’s effectiveness. Our method achieved an unparalleled intersection over union (IoU) score of 88.51%, indicative of its exceptional accuracy in identifying and segmenting buildings within the Wuhan University (WHU) building dataset. The overall performance of our method, which offers an excellent balance between high performance and computational efficiency, makes it a compelling choice for researchers and practitioners in the field.

Simulations of design and high load operation of a model Francis turbine using bridged approach to turbulence modelling

Abstract Off-design operation of hydraulic turbines is contemporarily frequented for balance of variable energy intermittence in the electric grid. Being operationally highly flexible, these turbines allow a quick transition to off-design operation from the design point. However, such operational flexibility, and therefore the grid balancing capability is impeded by generation of flow instabilities like vortex breakdown during off-design operation. Vortex breakdown causes losses in efficiency and pressure recovery, pressure fluctuations and possibly mechanical vibrations in event of resonance between system natural and flow field fluctuation frequencies. While substantial experimental and numerical effort has already been made to study draft tube vortex breakdown, an accurate numerical flow characterization of the phenomenon is still a challenge. To this end, operation of a high head model Francis turbine under design and high load regimes using a bridged turbulence modelling approach is simulated. The approach allows a seamless transition between direct numerical simulation and Reynolds averaged Navier-Stokes equations. The highest attainable accuracy is limited by the mesh size. As such a satisfactory compromise between desired accuracy and invested computational effort is attained. The flow in the draft tube is free of anomalies under design specified operation. However, at high load an axial flow stagnation occurs centrally, and the flow is separated about the stagnated zone. The core of the vortex is enlarged with flow recirculation within it. Shear layers between the central stagnant zone and surrounding outflow kink and roll up transforming it into a spiral structure. In this work, a basic yet accurate numerical flow characterization of the aforementioned flow situations is achieved.

Simplified lattice Boltzmann method on multi-resolution mesh

The simplified lattice Boltzmann method (SLBM), as a recent advancement in the lattice Boltzmann community, alleviates the intrinsic limitations of the standard lattice Boltzmann method by directly evolving macroscopic properties and nicely preserving numerical stability in high-Reynolds-number simulations. To enhance its capability of handling complex hydrodynamic/acoustic problems and to reduce computational efforts, an improved version of SLBM, which is applicable on multi-resolution mesh, is developed in this article. In this method, smooth transition of physical properties across the interface of meshes with different resolutions is ensured by a novel numerical strategy, which effectively couples the interpolation and the extrapolation algorithms. In the meantime, a rescaling algorithm is proposed to fix the inconsistency of time levels induced by the usage of multi-resolution mesh. Comprehensive evaluations of the accuracy, efficiency, and stability of the new method are carried out through benchmark tests of both hydrodynamic and acoustic problems.

Collisionless Relaxation from Near-equilibrium Configurations: Linear Theory and Application to Tidal Stripping

Placed slightly out of dynamical equilibrium, an isolated stellar system quickly returns toward a steady virialized state. We study this process of collisionless relaxation using the matrix method of linear response theory. We show that the full phase-space distribution of the final virialized state can be recovered directly from the disequilibrium initial conditions, without the need to compute the time evolution of the system. This shortcut allows us to determine the final virialized configuration with minimal computational effort. Complementing this result, we develop tools to model the system's full time evolution in the linear approximation. In particular, we show that moments of the velocity distribution can be efficiently computed using a generalized moment matrix. We apply our linear methods to study the relaxation of energy-truncated Hernquist spheres, mimicking the tidal stripping of a cuspy dark matter subhalo. Comparison of our linear predictions against controlled, isolated N-body simulations shows agreement at percent level for the parts of the system where a linear response to the perturbation is expected. We find that relaxation generates a tangential velocity anisotropy in the intermediate regions, despite the initial disequilibrium state having isotropic kinematics. Our results also strengthen the case for relaxation depleting the amplitude of the density cusp, without affecting its asymptotic slope. Finally, we compare the linear theory against an N-body simulation of tidal stripping on a radial orbit, confirming that the theory still accurately predicts density and velocity dispersion profiles for most of the system.

Unsteady Lifting-Line Theory for Camber Morphing Wings State-Space Aeroelastic Modeling

A novel frequency-domain analytical-numerical model for the aerodynamic solution of camber morphing wings is developed. The model combines an unsteady lifting-line formulation with Küssner–Schwarz aerodynamic theory to provide the pressure distribution and thus the transcendental aerodynamic matrix that relates the generalized aerodynamic forces to the Lagrangian coordinates of a given wing structural dynamics model. The state-space form of the aerodynamic loads is obtained from the rational matrix approximation of the aerodynamic matrix. This, combined with the wing structural dynamics operator, can readily provide the state-space form of the aeroelastic problem. To assess the accuracy of the proposed aerodynamic solution method, numerical investigations are performed. These consist of its application to several conventional wing configurations and wings with camber morphing, followed by the comparisons of the corresponding solutions with the predictions given by a well-validated panel-method solver for potential flows. These results are shown to be in very good agreement, thus demonstrating that the proposed approach can capture the effects due to wing tapering, sweep angle, and camber morphing, while requiring a remarkably lower computational effort. The excellent accuracy of a few-pole finite-state approximation of the aerodynamic matrix is finally proven for a swept wing with camber morphing.

Computational Methods for the Discovery and Optimization of TAAR1 and TAAR5 Ligands.

G-protein-coupled receptors (GPCRs) represent a family of druggable targets when treating several diseases and continue to be a leading part of the drug discovery process. Trace amine-associated receptors (TAARs) are GPCRs involved in many physiological functions with TAAR1 having important roles within the central nervous system (CNS). By using homology modeling methods, the responsiveness of TAAR1 to endogenous and synthetic ligands has been explored. In addition, the discovery of different chemo-types as selective murine and/or human TAAR1 ligands has helped in the understanding of the species-specificity preferences. The availability of TAAR1-ligand complexes sheds light on how different ligands bind TAAR1. TAAR5 is considered an olfactory receptor but has specific involvement in some brain functions. In this case, the drug discovery effort has been limited. Here, we review the successful computational efforts developed in the search for novel TAAR1 and TAAR5 ligands. A specific focus on applying structure-based and/or ligand-based methods has been done. We also give a perspective of the experimental data available to guide the future drug design of new ligands, probing species-specificity preferences towards more selective ligands. Hints for applying repositioning approaches are also discussed.

Phenomenological model for unburned hydrocarbon emissions from spark-ignition, pre-chamber, and dual-fuel internal combustion engines

Considering the strict regulations on the transport sector emissions, predictive models for engine emissions are essential tools to optimize high-efficient low-emission internal combustion engines (ICE) for vehicles. This aspect is of major importance, especially for developing new combustion concepts (e.g. lean, pre-chamber) or using alternative fuels. Among the gaseous emissions from spark-ignition (SI) engines, unburned hydrocarbons (uHC) are the most challenging species to model due to the complexity of the formation mechanisms. Phenomenological models are successfully used in these cases to predict emissions with a reduced computational effort. In this work, uHC phenomenological model approaches by the authors are further developed to improve the model predictivity for multiple variations including engine design, engine operating parameters, as well as different fuels and ignition methods. The model accounts for uHC contributions from piston top-land crevice, wall flame quenching, oil film fuel adsorption/desorption and features a tabulated-chemistry approach to describe uHC post-oxidation. With the support of 3D-CFD simulations, multiple novel modelling assumptions are developed and verified. The model is validated against an extensive measurement database obtained with two small-bore single-cylinder engines (SCE) fuelled with gasoline-like fuel, one with SI and one with pre-chamber, as well as against data from two different ultra-lean large-bore engines fuelled with natural gas (one equipped with a pre-chamber and one dual-fuel with a diesel pilot). The model correctly predicts the trends and absolute values of uHC emissions for all the operating conditions and the engines with an accuracy on average of 11.4%. The results demonstrate the general applicability of the model to different engine designs, the correct description of the main mechanisms contributing to fuel partial oxidation, and the potential to be extended to predict unburned fuel emissions with other fuels.

Target Trial Emulation: A Call for More Widespread Use.

Causal inference methods intended for use with observational data have been widely available for decades, but barriers exist to their widespread adoption. These likely include lack of familiarity with several methodologic techniques often used in combination in these investigations such as inverse probability of treatment weighting and g-estimation, and the intensity of computational effort to employ these techniques. Even with these methods, critical design flaws undermine the ability to make valid causal inference in some studies. Identification of the need to explicitly pair study design elements with these causal inference methods led to development of a methodologic approach recently termed target trial emulation. This approach requires that investigators define a hypothetical randomized trial, emulate that hypothetical protocol in assembling the cohort and defining study elements and then conduct an analysis that attempts to mirror random treatment assignment. In the article by Heindel et al. (Am J Epidemiol. 2024;XXX(XX):XXXX-XXXX), the authors successfully emulate a target trial of systemic heparin during arteriovenous fistula creation on short-term endpoints by utilizing two existing randomized trials with key confounders available. Target trial emulation provides a framework to promote valid inference and generate high-quality contributions to the literature and its use should be expanded.

A survey on dynamic populations in bio-inspired algorithms

Population-Based Bio-Inspired Algorithms (PBBIAs) are computational methods that simulate natural biological processes, such as evolution or social behaviors, to solve optimization problems. Traditionally, PBBIAs use a population of static size, set beforehand through a specific parameter. Nevertheless, for several decades now, the idea of employing populations of dynamic size, capable of adjusting during the course of a single run, has gained ground. Various methods have been introduced, ranging from simpler ones that use a predefined function to determine the population size variation, to more sophisticated methods where the population size in different phases of the evolutionary process depends on the dynamics of the evolution itself and events occurring within the population during the run. The common underlying idea in many of these approaches, is similar: to save a significant amount of computational effort in phases where the evolution is functioning well, and therefore a large population is not needed. This allows for reusing the previously saved computational effort when optimization becomes more challenging, and hence a greater computational effort is required. Numerous past contributions have demonstrated a notable advantage of using dynamically sized populations, often resulting in comparable results to those obtained by the standard PBBIAs but with a significant saving of computational effort. However, despite the numerous successes that have been presented, to date, there is still no comprehensive collection of past contributions on the use of dynamic populations that allows for their categorization and critical analysis. This article aims to bridge this gap by presenting a systematic literature review regarding the use of dynamic populations in PBBIAs, as well as identifying gaps in the research that can lead the path to future works.

Multifidelity Methodology for Reduced-Order Models with High-Dimensional Inputs

In the early stages of aerospace design, reduced-order models (ROMs) are crucial for minimizing computational costs associated with using physics-rich field information in many-query scenarios requiring multiple evaluations. The intricacy of aerospace design demands the use of high-dimensional design spaces to capture detailed features and design variability accurately. However, these spaces introduce significant challenges, including the curse of dimensionality, which stems from both high-dimensional inputs and outputs necessitating substantial training data and computational effort. To address these complexities, this study introduces a novel multifidelity, parametric, and nonintrusive ROM framework designed for high-dimensional contexts. It integrates machine learning techniques for manifold alignment and dimension reduction—employing proper orthogonal decomposition and model-based active subspace—with multifidelity regression for ROM construction. Our approach is validated through two test cases: the 2D RAE 2822 airfoil and the 3D NASA CRM wing, assessing various fidelity levels, training data ratios, and sample sizes. Compared to the single-fidelity principal component–active subspace (PCAS) method, our multifidelity solution offers improved cost-accuracy benefits and achieves better predictive accuracy with reduced computational demands. Moreover, our methodology outperforms the manifold-aligned ROM method by 50% in handling scenarios with large input dimensions, underscoring its efficacy in addressing the complex challenges of aerospace design.