Computational Scheme Research Articles

Humans adaptively select different computational strategies in different learning environments.

The Rescorla-Wagner rule remains the most popular tool to describe human behavior in reinforcement learning tasks. Nevertheless, it cannot fit human learning in complex environments. Previous work proposed several hierarchical extensions of this learning rule. However, it remains unclear when a flat (nonhierarchical) versus a hierarchical strategy is adaptive, or when it is implemented by humans. To address this question, current work applies a nested modeling approach to evaluate multiple models in multiple reinforcement learning environments both computationally (which approach performs best) and empirically (which approach fits human data best). We consider 10 empirical data sets (N = 407) divided over three reinforcement learning environments. Our results demonstrate that different environments are best solved with different learning strategies; and that humans adaptively select the learning strategy that allows best performance. Specifically, while flat learning fitted best in less complex stable learning environments, humans employed more hierarchically complex models in more complex environments. (PsycInfo Database Record (c) 2024 APA, all rights reserved).

A Fractional-Order Archimedean Spiral Moth–Flame Optimization Strategy to Solve Optimal Power Flows

This research utilizes the innovative fractional-order Archimedean spiral moth–flame optimization (FO-AMFO) technique to address the issues of the optimal reactive power dispatch (ORPD) problem. The formulated fitness function aims to minimize power losses and determine the ideal flow of reactive power for the IEEE 30- and 57-bus test systems. The extensive functions of the fractional evolutionary computing strategy are utilized to address the minimization problem of ORPD. This involves determining the control variables, such as VAR compensators, bus voltages, and the tap setting of the transformers. The effective incorporation of reactive compensation devices into traditional power grids has greatly reduced power losses; however, it has resulted in an increase in the complexity of optimization problems. A comparison of the findings indicates that swarming fractional intelligence using FO-AMFO surpassed the state-of-the-art competitors in terms of minimizing power losses.

An efficient polynomial-based verifiable computation scheme on multi-source outsourced data

With the development of cloud computing, users are more inclined to outsource complex computing tasks to cloud servers with strong computing capacity, and the cloud returns the final calculation results. However, the cloud is not completely trustworthy, which may leak the data of user and even return incorrect calculations on purpose. Therefore, it is important to verify the results of computing tasks without revealing the privacy of the users. Among all the computing tasks, the polynomial calculation is widely used in information security, linear algebra, signal processing and other fields. Most existing polynomial-based verifiable computation schemes require that the input of the polynomial function must come from a single data source, which means that the data must be signed by a single user. However, the input of the polynomial may come from multiple users in the practical application. In order to solve this problem, the researchers have proposed some schemes for multi-source outsourced data, but these schemes have the common problem of low efficiency. To improve the efficiency, this paper proposes an efficient polynomial-based verifiable computation scheme on multi-source outsourced data. We optimize the polynomials using Horner’s method to increase the speed of verification, in which the addition gate and the multiplication gate can be interleaved to represent the polynomial function. In order to adapt to this structure, we design the corresponding homomorphic verification tag, so that the input of the polynomial can come from multiple data sources. We prove the correctness and rationality of the scheme, and carry out numerical analysis and evaluation research to verify the efficiency of the scheme. The experimental indicate that data contributors can sign 1000 new data in merely 2 s, while the verification of a delegated polynomial function with a power of 100 requires only 18 ms. These results confirm that the proposed scheme is better than the existing scheme.

Application of an Extended Cubic B-Spline to Find the Numerical Solution of the Generalized Nonlinear Time-Fractional Klein–Gordon Equation in Mathematical Physics

A B-spline function is a series of flexible elements that are managed by a set of control points to produce smooth curves. By using a variety of points, these functions make it possible to build and maintain complicated shapes. Any spline function of a certain degree can be expressed as a linear combination of the B-spline basis of that degree. The flexibility, symmetry and high-order accuracy of the B-spline functions make it possible to tackle the best solutions. In this study, extended cubic B-spline (ECBS) functions are utilized for the numerical solutions of the generalized nonlinear time-fractional Klein–Gordon Equation (TFKGE). Initially, the Caputo time-fractional derivative (CTFD) is approximated using standard finite difference techniques, and the space derivatives are discretized by utilizing ECBS functions. The stability and convergence analysis are discussed for the given numerical scheme. The presented technique is tested on a variety of problems, and the approximate results are compared with the existing computational schemes.

Empirical modeling and prediction of neuronal dynamics.

Mathematical modeling of neuronal dynamics has experienced a fast growth in the last decades thanks to the biophysical formalism introduced by Hodgkin and Huxley in the 1950s. Other types of models (for instance, integrate and fire models), although less realistic, have also contributed to understand neuronal dynamics. However, there is still a vast volume of data that have not been associated with a mathematical model, mainly because data are acquired more rapidly than they can be analyzed or because it is difficult to analyze (for instance, if the number of ionic channels involved is huge). Therefore, developing new methodologies to obtain mathematical or computational models associated with data (even without previous knowledge of the source) can be helpful to make future predictions. Here, we explore the capability of a wavelet neural network to identify neuronal (single-cell) dynamics. We present an optimized computational scheme that trains the ANN with biologically plausible input currents. We obtain successful identification for data generated from four different neuron models when using all variables as inputs of the network. We also show that the empiric model obtained is able to generalize and predict the neuronal dynamics generated by variable input currents different from those used to train the artificial network. In the more realistic situation of using only the voltage and the injected current as input data to train the network, we lose predictive ability but, for low-dimensional models, the results are still satisfactory. We understand our contribution as a first step toward obtaining empiric models from experimental voltage traces.

Supplemental Material for Humans Adaptively Select Different Computational Strategies in Different Learning Environments

Supplemental Material for Humans Adaptively Select Different Computational Strategies in Different Learning Environments

A Computational and Chemical Design Strategy for Manipulating Glycan-Protein Recognition.

Glycans are complex biomolecules that encode rich information and regulate various biological processes, such as fertilization, host-pathogen binding, and immune recognition, through interactions with glycan-binding proteins. A key driving force for glycan-protein recognition is the interaction between the π electron density of aromatic amino acid side chains and polarized C─H groups of the pyranose (termed the CH-π interaction). However, the relatively weak binding affinity between glycans and proteins has hindered the application of glycan detection and imaging. Here, computational modeling and molecular dynamics simulations are employed to design a chemical strategy that enhances the CH-π interaction between glycans and proteins by genetically incorporating electron-rich tryptophan derivatives into a lectin PhoSL, which specifically recognizes core fucosylated N-linked glycans. This significantly enhances the binding affinity of PhoSL with the core fucose ligand and enables sensitive detection and imaging of core fucosylated glycans in vitro and in xenograft tumors in mice. Further, the study showed that this strategy is applicable to improve the binding affinity of GafD lectin for N-acetylglucosamine-containing glycans. The approach thus provides a general and effective way to manipulate glycan-protein recognition for glycoscience applications.

Abstract SY41-02: Experimental methods for high throughput discovery and validation of tumor antigens

Abstract The discovery and validation of tumor associated T-cell antigens, which can include neoantigens, viral antigens, cancer testis antigens, and others, is fundamental to the design of both cancer vaccines and engineered cell cancer immunotherapies. However, identifying tumor specific antigens that are highly presented within tumors, and identifying and validating T cell receptors (TCRs) that are specific to those antigens, remains a non-trivial exercise. Even identifying and validating a single antigen-specific T cell clonotype for advancement into a clinic trial remains a daunting task. In this talk, I will discuss a number of new experimental and computational tools that are designed to accelerate this process. The experimental toolsets include reagents and strategies that allow for the capture and analysis of T cells specific to hundreds to thousands of tumor-associated Class I and Class II antigens simultaneously, with as many as 100 patient bloods analyzed in a single experiment. Single cell analysis of the resulting data yields a deep characterization of the captured T cells, including T cell receptor α/β genes, antigen-MHC specificity, T cell phenotype, and donor identification. This type of analysis, in turn, provides large data bases that can fuel new computational strategies that can be harnessed (at a still early stage) to predict which TCRα/β genes associate with which putative antigen-MHCs, or to extract the biochemical and biophysical characteristics of TCR-antigen pairings that influence T cell phenotype. Thus, this presentation will span both new experimental and new computational toolsets. Citation Format: James R. Heath. Experimental methods for high throughput discovery and validation of tumor antigens [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 2 (Late-Breaking, Clinical Trial, and Invited Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(7_Suppl):Abstract nr SY41-02.

Exploring Type II Diabetes Inhibitors from Genus Daphne Plant-species: An Integrated Computational Study.

Plant species of the genus Daphne clasps a historical background with a potential source of bioactive phytochemicals such as flavonoids and daphnodorins. These compounds manifest a significant chemotaxonomic value in drug discovery. Their flair comprehensive pharmacological, phytochemical, biological, catalytic, and clinical utilities make them exclusively unique. This study was conducted to investigate the optimization and structure-based virtual screening of these peculiar analogs. The majority of the active constituents of medicines are obtained from natural products. Previously, before the invention of virtual screening methods or techniques, almost 80% of drugs were obtained from natural resources. Comparing reported data to drug discovery from 1981 to 2007 signifies that half of the FDA-approved drugs are obtained from natural resources. It has been reported that structures of natural products that have particularities of structural diversity, biochemical specification, and molecular properties make them suitable products for drug discovery. These products basically have unique chiral centers which increase their structural complexity than the synthesized drugs. This work aimed to probe the use of daphnodorins analogs for the first time as antidiabetic inhibitors based on significant features and to determine the potential of daphnodorin analogs as antidiabetic inhibitors through computational analysis and structure-based virtual screening. A dataset of 38 compounds was selected from different databases, including PubChem and ZINC, for computational analysis, and optimized compounds were docked against various co-crystallized structures of inhibitors, antagonists, and receptors which were downloaded from PDB by using AutoDock Vina (by employing Broyden-Fletcher-Goldfarb-Shanno method), Discovery studio visualizer 2020, PYMOL (Schrodinger). Docking results were further validated by Molecular dynamic simulation and MM-GBSA calculation. Quantitative structure-activity relationship (QSAR) was reported by using Gaussian 09W by intimating Density Functional Theory (DFT). Using this combination of multi-approach computational strategy, 14 compounds were selected as potential exclusive lead compounds, which were analyzed through ADMET studies to pin down their druglike properties and toxicity. At significant phases of drug design approaches regular use of molecular docking has helped to promote the separation of important representatives from 38 pharmaceutically active compounds by setting a threshold docking score of -9.0 kcal/mol which was used for their exposition. Subsequently, by employing a threshold it was recognized that 14 compounds proclaimed this threshold for antidiabetic activity. Further, molecular dynamic simulation, MM-GBSA, ADMET, and DFT results screened out daphnegiralin B4 (36) as a potential lead compound for developing antidiabetic agents. Our analysis took us to the conclusion that daphnegiralin B4 (36) among all ligands comes out to be a lead compound having drug-like properties among 38 ligands being non-carcinogenic and non-cytotoxic which would benefit the medical community by providing significant drugs against diabetes. Pragmatic laboratory investigations identified a new precursor to open new doors for new drug discovery.

The 2024 Magnonics Roadmap.

Magnonicsis a research field that has gained an increasing interest in both the fundamental and applied sciences in recent years. This field aims to explore and functionalize collective spin excitations in magnetically ordered materials for modern information technologies, sensing applications, and advanced computational schemes. Spin waves, also known as magnons, carry spin angular momenta that allow for the transmission, storage, and processing of information without moving charges. In integrated circuits, magnons enable on-chip data processing at ultrahigh frequencies without the Joule heating, which currently limits clock frequencies in conventional data processors to a few GHz. Recent developments in the field indicate that functional magnonic building blocks for in-memory computation, neural networks, and Ising machines are within reach. At the same time, the miniaturization of magnonic circuits advances continuously as the synergy of materials science, electrical engineering, and nanotechnology allows for novel on-chip excitation and detection schemes. Such circuits can already enable magnon wavelengths of 50 nm at microwave frequencies in a 5G frequency band. Research into non-charge-based technologies is urgently needed in view of the rapid growth of machine learning and artificial intelligence applications, which consume substantial energy when implemented on conventional data processing units. In its first part, the 2024 Magnonics Roadmap provides an update on the recent developments and achievements in the field of nano-magnonics while defining its future avenues and challenges. In its second part, the Roadmap addresses the rapidly growing research endeavors on hybrid structures and magnonics-enabled quantum engineering. We anticipate that these directions will continue to attract researchers to the field and, in addition to showcasing intriguing science, will enable unprecedented functionalities that enhance the efficiency of alternative information technologies and computational schemes.

SWISH-X, an Expanded Approach to Detect Cryptic Pockets in Proteins and at Protein-Protein Interfaces.

Protein-protein interactions mediate most molecular processes in the cell, offering a significant opportunity to expand the set of known druggable targets. Unfortunately, targeting these interactions can be challenging due to their typically flat and featureless interaction surfaces, which often change as the complex forms. Such surface changes may reveal hidden (cryptic) druggable pockets. Here, we analyze a set of well-characterized protein-protein interactions harboring cryptic pockets and investigate the predictive power of current computational methods. Based on our observations, we developed a new computational strategy, SWISH-X (SWISH Expanded), which combines the established cryptic pocket identification capabilities of SWISH with the rapid temperature range exploration of OPES MultiThermal. SWISH-X is able to reliably identify cryptic pockets at protein-protein interfaces while retaining its predictive power for revealing cryptic pockets in isolated proteins, such as TEM-1 β-lactamase.

Computational synthesis of a new generation of 2D-based perovskite quantum materials

Perovskite-based optoelectronic devices have emerged as a promising energy source due to their potential for scalable production. This study introduces “perovskene,” a novel class of 2D materials derived from the ABC3-like perovskites, synthesized via a data-driven, high-throughput computational strategy. We harness machine learning and multitarget deep neural networks to systematically investigate the structure–property relations, paving the way for targeted material design and optimization in fields such as renewable energy, electronics, and catalysis. The characterization of over 1500 synthesized structures shows that more than 500 structures are stable, revealing properties such as ultra-low work function and large magnetic moment, underscoring the potential for advanced technological applications.

Reconstructing dynamics correlation network to simultaneously improve activity and stability of 2,3-butanediol dehydrogenase by design of distal interchain disulfide bonds

The complete enzyme catalytic cycle includes substrate binding, chemical reaction and product release, in which different dynamic conformations are adopted. Due to the complex relationship among enzyme activity, stability and dynamics, the directed evolution of enzymes for improved activity or stability commonly leads to a trade-off in stability or activity. It hence remains a challenge to engineer an enzyme to have both enhanced activity and stability. Here, we have attempted to reconstruct the dynamics correlation network involved with active center to improve both activity and stability of a 2,3-butanediol dehydrogenase (2,3-BDH) by introducing inter-chain disulfide bonds. A computational strategy was first applied to evaluate the effect of introducing inter-chain disulfide bond on activity and stability of three 2,3-BDHs, and the N258C mutation of 2,3-BDH from Corynebacterium glutamicum (CgBDH) was proved to be effective in improving both activity and stability. In the results, CgBDH-N258C showed a different unfolding curve from the wild type, with two melting temperatures (Tm) of 68.3 °C and 50.8 °C, 19.7 °C and 2 °C higher than 48.6 °C of the wild type. Its half-life was also improved by 14.8-fold compared to the wild type. Catalytic efficiency (kcat/Km) of the mutant was increased by 7.9-fold toward native substrate diacetyl and 8.8-fold toward non-native substrate 2,5-hexanedione compared to the wild type. Molecular dynamics simulations revealed that an interaction network formed by Cys258, Arg162, Ala144 and the catalytic residues was reconstructed in the mutant and the dynamics change caused by the disulfide bond could be propagated through the interactions network. This improved the enzyme stability and activity by decreasing the flexibility and locking more “reactive” pose, respectively. Further construction of mutations including A144G showing a 44-fold improvement in catalytic efficiency toward meso-2,3-BD confirmed the role of modifying dynamics correlation network in tunning enzyme activity and selectivity. This study provided important insights into the relationship among dynamics, enzyme catalysis and stability, and will be useful in the designing new enzymes with co-evolution of stability, activity and selectivity.

A new compact scheme-based Lax–Wendroff method for high fidelity simulations

The classical Lax–Wendroff (LW) method employs explicit second order central difference schemes to solve partial differential equations. Recently, the authors in Sengupta et al. (2023) have identified numerical parameter ranges in performing large eddy simulation (LES) using this classical LW method. In a bid to improve these numerical parameter ranges for high fidelity computations, a new non-uniform grid based compact scheme for LW method (NUCLW) is developed here. This new method uses sixth order non-uniform compact scheme (NUC6) for the first and higher order derivatives. Reported global spectral analysis confirms substantial improvement in the resolution and accuracy of NUCLW scheme over the classical LW scheme. A significant advantage of the present scheme is its ability to compute solutions for non-uniform, structured grids, and that does not suffer degradation in accuracy. The potential of the NUCLW scheme for high accuracy computations is demonstrated by solving 2D incompressible Navier–Stokes equations for the benchmark unsteady flow inside a square lid driven cavity problem for a subcritical Reynolds number of 3200 and a post-critical Reynolds number of 10,000 and simulating the instability of the Taylor–Green vortex problem. These demonstrate the ability of the NUCLW scheme to solve the Navier–Stokes equations for high fidelity simulations such as LES/DNS with enhanced ranges of numerical parameters with respect to enhanced accuracy and faster computations.

Combined computational and synthetic strategies for the development of potent pyrazolo-pyridine derivatives as anticancer agents

PI3K comprise a group of lipid kinases mainly made up of three classes and have a particular role in signal transduction. The aberration of class IA PI3K is most frequently shown in cancer. The computation techniques have been applied for the identification of potent compounds. This involved the generation of a library of novel compounds (2700) through R-group enumeration study. The screening process utilized a combination of pharmacophore-based virtual screening and various docking algorithms, leading to the discovery of ten novel compounds. The docking studies against PI3K kinase highlighted MSA6 and MSA7 as the most promising compounds, with docking scores of -9.03 and -9.0 kcal/mol, respectively. Results of docking studies of all derivatives confirmed satisfactory binding mode of the compounds within the active site of the target protein PI3K kinase. The screened pyrazolo-pyridine derivatives (MSA1-MSA10) were synthesized and evaluated as anti-cancer agents against NSCLC using A549 cell line. MSA6 showed the dose dependent enhancement in ROS production at concentration of 10 and 20 µM in A549 cells. MSA6 triggered caspase 3 cleavage in lung cancer cells in a dose-dependent manner. MSA6 treatment at 10 and 20 µM levels boosted NF-κβ expression in the cytoplasm while decreasing it in the nucleus, as demonstrated by flow cytometry with an anti-NF-κβ antibody. The modulation of PI3K activity have been observed at 10µM and 20µM concentration of MSA6. A549 cells treated with MSA6 with 20 µM showed significant reduction in wound healing in cancerous cell. A549 cells treated with 20 µM showed significantly reduction in number of colonies compared to control group. Compound MSA6 having a methyl group at the para position of phenyl ring at imine carbon and phenyl ring substitution at nitrogen of pyrazole ring exhibited remarkable growth inhibitory activity with IC50 value of 49.23 μM. Based on the above results, pyrazolo-pyridine derivatives can be used for the further development of novel PI3K kinase inhibitors as anticancer agents.

Cooperative-centrality enabled investigations on edge-based trustworthy framework for cloud focused internet of things

Trustworthiness possesses numerous design challenges for the researchers and scientists in the global era of internet of things. The solution to deal with these challenges lies in trust management mechanisms (TMM). The integration of TMM on the Internet of things (IoT) adds a higher level of security and reliability for the resource utilization and management. On the other hand, the efficient and optimal implementation of TMM in IoT network requires significant efforts and innovations. In this paper, an innovative edge based trustworthy framework (EBTF) using novel cooperative centrality-based system (NCCBS) has been proposed and implemented for IoT networks. Trustworthiness has been computed using factors like direct trust, recommendation, global trust, and centralities. The NCCBS has been compared with existing mechanism namely: Distributive Reputation Management (DRM), Personalized Similarity Measure (PSM), and Reliable Trust Computation Mechanism (RTCM). The NCCBS outperformed the existing approaches with the adoption of dynamic weight computation scheme. The performance of IoT networks has been evaluated on parameters like recommendation weight, trust index, interaction success rate and malicious behaviour analysis. Simulations have been carried out to evaluate and validate the proposed NCCBS and it depicts optimal performance with reference to the existing schemes.

Integrative computational approaches for designing novel alpha-glucosidase inhibitors based on curculigoside A derivatives: Virtual screening, molecular docking, and molecular dynamics

Introduction: Over 422 million people worldwide suffer from diabetes, causing 1.5 million fatalities annually. The existing medications have shortcomings, including poor glucose control and adverse effects. The present study aimed to create possible alpha-glucosidase inhibitors utilizing a curculigoside A derivative ligand-based model. Methods: A pharmacophore model was constructed utilizing the structure information of curculigoside A derivatives and PharmaGist. Subsequently, virtual screening, molecular docking, and molecular dynamics were employed to simulate the hits. Results: From six training sets, eleven pharmacophore models were developed; the model with the highest score (18.0435) was chosen for further analysis. Using the verified pharmacophore model, 270 547 chemicals from the ZINC natural product database were subjected to virtual screening. Subsequently, molecular docking was performed on the top 1000 hits with AutoDock Wizard from PyRx. This analysis unveiled 434 hits with better binding energies than acarbose, the native ligand. Subsequently, second optimal docking configurations were evaluated with AutoDock 2.4; this process yielded the discovery of three prospective inhibitors (ZINC000150350051, ZINC000008382292, and ZINC000085595291) characterized by the most advantageous binding energies. To evaluate the stability and dynamics of these ligand-receptor complexes, Gromacs 2022 molecular dynamics simulations were executed for one hundred nanoseconds. Out of the three hits, ZINC000085595291 (Hit03) exhibited good energy components and interaction stability constantly during the simulation. Conclusion: The integrated computational strategy identified promising alpha-glucosidase inhibitors in curculigoside A compounds, highlighting the potential of ZINC000085595291 (Hit03) as a potential diabetes therapeutic agent.

Multiscale nonlinear analysis of 3D orthogonal woven C/SiC composites based on the CT reconstruction

AbstractDue to the effects of manufacturing process, there are lots of distributed voids in the interior area of three‐dimensional (3D) woven C/SiC composites, leading to the difficulties in predictions of damage evolution and ultimate strength. In this work, the mesoscale geometry of 3D woven C/SiC composites is reconstructed based on the high‐resolution CT scanning, considering the yarn path, yarn profile, and void content. The CT‐based reconstructed model agrees well with the experimental observations. Then, the constitutive laws of mesoscale constituents and macroscale structure are established and implemented numerically by the user‐defined material subroutine UMAT. Finally, the established constitutive laws are applied for the reconstructed model, and the mesoscale and macroscale nonlinear progressive damage behavior of 3D woven C/SiC composite structures are predicted using the hierarchical multiscale method, which are also validated by the experiments. The established CT‐based multiscale computational scheme would be a potential tool for performance evaluation and design of composites.

Unravelling the Genetic Landscape of Hemiplegic Migraine: Exploring Innovative Strategies and Emerging Approaches

Migraine is a severe, debilitating neurovascular disorder. Hemiplegic migraine (HM) is a rare and debilitating neurological condition with a strong genetic basis. Sequencing technologies have improved the diagnosis and our understanding of the molecular pathophysiology of HM. Linkage analysis and sequencing studies in HM families have identified pathogenic variants in ion channels and related genes, including CACNA1A, ATP1A2, and SCN1A, that cause HM. However, approximately 75% of HM patients are negative for these mutations, indicating there are other genes involved in disease causation. In this review, we explored our current understanding of the genetics of HM. The evidence presented herein summarises the current knowledge of the genetics of HM, which can be expanded further to explain the remaining heritability of this debilitating condition. Innovative bioinformatics and computational strategies to cover the entire genetic spectrum of HM are also discussed in this review.

Efficient simulation of stochastic interactions among representative Monte Carlo particles

Context. Interaction processes between discrete particles are often modelled with stochastic methods such as the Representative Particle Monte Carlo (RPMC) method which simulate mutual interactions (e.g. chemical reactions, collisions, gravitational stirring) only for a representative subset of n particles instead of all N particles in the system. However, in the traditionally employed computational scheme the memory requirements and the simulation runtime scale quadratically with the number of representative particles. Aims. We want to develop a computational scheme that has significantly lower memory requirements and computational costs than the traditional scheme, so that highly resolved simulations with stochastic processes such as the RPMC method become feasible. Results. In this paper we propose the bucketing scheme, a hybrid sampling scheme that groups similar particles together and combines rejection sampling with a coarsened variant of the traditional discrete inverse transform sampling. For a v-partite bucket grouping, the storage requirements scale with n and v2, and the computational cost per fixed time increment scales with n ⋅ v, both thus being much less sensitive to the number of representative particles n. Extensive performance testing demonstrates the higher efficiency and the favourable scaling characteristics of the bucketing scheme compared to the traditional approach, while being statistically equivalent and not introducing any new requirements or approximations. With this improvement, the RPMC method can be efficiently applied not only with very high resolution but also in scenarios where the number of representative particles increases over time, and the simulation of high-frequency interactions (such as gravitational stirring) as a Monte Carlo process becomes viable.