Efficient Computational Method Research Articles

Toward Reliable Conformational Energies of Amino Acids and Dipeptides─The DipCONFS Benchmark and DipCONL Datasets.

Simulating peptides and proteins is becoming increasingly important, leading to a growing need for efficient computational methods. These are typically semiempirical quantum mechanical (SQM) methods, force fields (FFs), or machine-learned interatomic potentials (MLIPs), all of which require a large amount of accurate data for robust training and evaluation. To assess potential reference methods and complement the available data, we introduce two sets, DipCONFL and DipCONFS, which cover large parts of the conformational space of 17 amino acids and their 289 possible dipeptides in aqueous solution. The conformers were selected from the exhaustive PeptideCS dataset by Andris et al. [ J. Phys. Chem. B 2022, 126, 5949-5958]. The structures, originally generated with GFN2-xTB, were reoptimized using the accurate r2SCAN-3c density functional theory (DFT) composite method including the implicit CPCM water solvation model. The DipCONFS benchmark set contains 918 conformers and is one of the largest sets with highly accurate coupled cluster conformational energies so far. It is employed to evaluate various DFT and wave function theory (WFT) methods, especially regarding whether they are accurate enough to be used as reliable reference methods for larger datasets intended for training and testing more approximated SQM, FF, and MLIP methods. The results reveal that the originally provided BP86-D3(BJ)/DGauss-DZVP conformational energies are not sufficiently accurate. Among the DFT methods tested as an alternative reference level, the revDSD-PBEP86-D4 double hybrid performs best with a mean absolute error (MAD) of 0.2 kcal mol-1 compared with the PNO-LCCSD(T)-F12b reference. The very efficient r2SCAN-3c composite method also shows excellent results, with an MAD of 0.3 kcal mol-1, similar to the best-tested hybrid ωB97M-D4. With these findings, we compiled the large DipCONFL set, which includes over 29,000 realistic conformers in solution with reasonably accurate r2SCAN-3c reference conformational energies, gradients, and further properties potentially relevant for training MLIP methods. This set, also in comparison to DipCONFS, is used to assess the performance of various SQM, FF, and MLIP methods robustly and can complement training sets for those.

Stochastic delayed analysis of coronavirus model through efficient computational method

Stochastic delayed modeling has a significant non-pharmaceutical intervention to control transmission dynamics of infectious diseases and its results are close to the reality of nature. The covid-19 has been controlled globally but there is still a threat and appears in different variants like omicron and SARS-CoV-2 etc. globally. This article, considered pattern a mathematical model based on Susceptible, Infected, and recovered populations with highly nonlinear incidence rates. we studied the dynamics of the coronavirus model; a newly proposed version is a stochastic delayed model that is based on nonlinear stochastic delayed differential equations (SDDEs). Transition probabilities and parametric perturbation methods were used for the construction of the stochastic delayed model. The fundamental properties like positivity, boundedness, existence and uniqueness, and stability results of equilibria of the model with certain conditions of reproduction number are studied regularly. Also, the extinction and persistence of disease are studied with the help of well-known theorems. The numerical methods used to find a visualization of results due to the complexity of stochastic delayed differential equations. Furthermore, for computational analysis, we implemented existing methods in the literature and compared their results with the proposed method like nonstandard finite difference for stochastic delayed model. The proposed method restores all dynamical properties of the model with a free choice of time steps.

Improving prediction performance of general protein language model by domain-adaptive pretraining on DNA-binding protein

DNA-protein interactions exert the fundamental structure of many pivotal biological processes, such as DNA replication, transcription, and gene regulation. However, accurate and efficient computational methods for identifying these interactions are still lacking. In this study, we propose a method ESM-DBP through refining the DNA-binding protein sequence repertory and domain-adaptive pretraining based the general protein language model. Our method considers the lacking exploration of general language model for DNA-binding protein domain-specific knowledge, so we screen out 170,264 DNA-binding protein sequences to construct the domain-adaptive language model. Experimental results on four downstream tasks show that ESM-DBP provides a better feature representation of DNA-binding protein compared to the original language model, resulting in improved prediction performance and outperforming the state-of-the-art methods. Moreover, ESM-DBP can still perform well even for those sequences with only a few homologous sequences. ChIP-seq on two predicted cases further support the validity of the proposed method.

Accurate and fast quaternion fractional-order Franklin moments for color image analysis

Quaternion type moments (QTM) based on hypergeometric functions and harmonic functions have attracted interest in the imaging field. The Franklin system is a complete orthonormal system consisting of piecewise linear continuous functions with Haar wavelet collocation points. However, due to the lack of efficient computational methods and explicit expressions, research on piecewise polynomials with Haar wavelet collocation points remains underexplored. Furthermore, the impact of magnitude and phase of quaternion on the color image features extracted by QTM has not been comprehensively explored. To address these problems, a fast and accurate computation method for Franklin functions is proposed. Quaternion fractional-order Franklin moments (QFFM) are proposed and using the Minkowski distance as a prior parameter for selecting unit pure quaternions. Experiments are conducted to demonstrate the superior performance of QFFM in image reconstruction and color image classification tasks compared with QTM-based and deep learning-based methods. Moreover, with the help of experiments, it is confirmed the effectiveness of using the Minkowski distance to select magnitude and phase of quaternion.

An Efficient Hybrid Method for Calculating the Focal Field of a Cassegrain Antenna

The evaluation of the focal field of Cassegrain antennas is crucial for the design and optimization of the complex feeds in quasi-optical systems. However, employing traditional physical optics methods generates high computational complexity and is inefficient. An accurate and efficient calculation method of Cassegrain antennas’ focal fields that involves a range of incidence angles is proposed, which integrates ray tracing and vector diffraction integration (RT-VDI) theories. It can calculate the focal field in any given or predefined incident direction, not limited to the case of axial incidence. In addition, the use of the equivalent parabolic theory greatly simplifies the process of integral calculation. Moreover, ray tracing and integration operations are executed upon the calculation of the reflector to further improve efficiency. Numerical examples are presented to demonstrate the accuracy and efficiency of the proposed method.

Utilizing AfDesign for Developing a Small Molecule Inhibitor of PICK 1-PDZ.

The PICK1 PDZ domain has been identified as a potential drug target for neurological disorders. After many years of effort, a few inhibitors, such as TAT-C5 and mPD5, have been discovered experimentally to bind to the PDZ domain with a relatively high binding affinity. With the rapid growth of computational research, there is an urgent need for more efficient computational methods to design viable ligands that target proteins. Recently, a newly developed program called AfDesign (part of ColabDesign) at https:// github.com/sokrypton/ColabDesign), an open-source software built on AlphaFold, has been suggested to be capable of generating ligands that bind to targeted proteins, thus potentially facilitating the ligand development process. To evaluate the performance of this program, we explored its ability to target the PICK1 PDZ domain, given our current understanding of it. We found that the designated length of the ligand and the number of recycles play vital roles in generating ligands with optimal properties. Utilizing AfDesign with a sequence length of 5 for the ligand produced the highest comparable ligands to that of prior identified ligands. Moreover, these designed ligands displayed significantly lower binding energy compared to manually created sequences. This work demonstrated that AfDesign can potentially be a powerful tool to facilitate the exploration of the ligand space for the purpose of targeting PDZ domains.

Uncertainty quantification for natural frequency and mode of variable angle tow composite plates with random and interval uncertainties

Variable angle tow (VAT) composite laminated structures are produced by automated tow-placement technology which can take advantage of the designability of composites to meet the aerospace industry’s need for dynamic properties. Complex production techniques inevitably lead to uncertainties in material properties due to manufacturing defects. The aim of this work is to present an efficient computational method for estimating the dynamical properties of VAT composite laminated plates due to multiscale hybrid uncertainties. This method consists of perturbation stochastic finite element method and particle swarm optimization combined with Mori-Tanaka homogenization. The degree of influence of the multiscale parameters on the dynamic properties of VAT composites with different structural forms has been obtained by sensitivity analyses of the frequency and mode. The method is applied to investigate variations in natural frequency and mode of a variety of VAT composite laminated plates with different fiber tow paths, layers, and boundary conditions. The results indicate that variations in natural frequencies and mode shapes are strongly linked to fiber tow paths and boundary conditions. The frequency and modes of the VAT show a nonlinear variation with angle. This method can quickly obtain the probability distribution intervals of the frequency, which is of great significance for assessing the reliability of VAT composites laminated plates dynamic designs.

A novel higher-order efficient computational method for pricing European and Asian options

A novel higher-order efficient computational method for pricing European and Asian options

A novel efficient calculation method for rotor aeroacoustic characteristics based on multiple aerodynamic surfaces source model

Based on the simplified acoustic source model of multiple aerodynamic surfaces, an efficient calculation method for rotor aeroacoustic characteristics has been established. Firstly, the aerodynamic characteristics database of the airfoil is obtained based on the Computational Fluid Dynamics (CFD) method. The inflow environment of each blade element is calculated using the free wake method, and the distribution of chord-wise loading is obtained by combining the established database. Subsequently, based on the F1A equation, a simplified formula for loading noise with pressure difference as a parameter is derived. The thickness noise is obtained from the blade shape and operating conditions, and the blade surface mesh is constructed by using the adaptive curvature distribution strategy of airfoil points. The validation is carried out by comparing with experimental values in the hover and forward flight states. Then, A hybrid weighted interpolation scheme is proposed based on inverse distance weighted interpolation (IDW) and bidirectional linear interpolation method, which is suitable for capturing blade/vortex interaction (BVI) noise. A set of parameters suitable for engineering applications is obtained through the parametric sensitivity analysis. The computational time and memory usage are reduced to 1/47 and 1/4 of the original amount, respectively. Finally, Using the above method, the influence of rotational speeds (ω) on rotor aeroacoustic characteristics are studied. The results show that ω and sound pressure level (SPL) are linearly related and the SPL decreases by approximately 5 dB for every 0.1 Ω decrease in ω. The rotational speed ω≈90 %Ω can effectively suppress rotor aeroacoustic levels, benefiting the stealth design of helicopters.

Flexible Modeling of Nonstationary Extremal Dependence using Spatially-Fused LASSO and Ridge Penalties

Statistical modeling of a nonstationary spatial extremal dependence structure is challenging. Parametric max-stable processes (MSPs) are common choices for modeling spatially-indexed block maxima, where an assumption of stationarity is usual to make inference feasible. However, this assumption is unrealistic for data observed over a large or complex domain. We develop a computationally-efficient method for estimating extremal dependence using a globally nonstationary but locally-stationary MSP construction, with the spatial domain divided into a fine grid of subregions, each with its own dependence parameters. We use LASSO (L 1) or ridge (L 2) penalties to obtain spatially-smooth parameter estimates. We then develop a novel data-driven algorithm to merge homogeneous neighboring subregions. The algorithm facilitates model parsimony and interpretability. To make our model suitable for high-dimensional data, we exploit a pairwise likelihood to perform inference and discuss its computational and statistical efficiency. We apply our proposed method to model monthly maximum temperature data at over 1400 sites in Nepal and the surrounding Himalayan and sub-Himalayan regions; we show significant improvements in model fit compared to a stationary model. Furthermore, we demonstrate that the estimated merged partition is interpretable from a geographic perspective and leads to better model diagnostics by adequately reducing the number of parameters.

A Novel Efficient Computational Method for the Electron Optics System in Multibeam Klystrons With Applications to Manufacturing Sensitivities

A Novel Efficient Computational Method for the Electron Optics System in Multibeam Klystrons With Applications to Manufacturing Sensitivities

Integrated optimization of pilot and pilot carrier routing in seaports

Pilotage is an important service for vessels to enter and leave seaports. When vessels entering or leaving their berths, pilots are assigned to provide assistance on board to maneuver the vessels. Pilot carriers, such as pilot boats and helicopters are utilized to transport pilots from the pilot station to the vessels at appointed ground. With the increasing number of calling vessels, the pilotage plan should be made properly considering the limited pilots and carriers resources in seaports to enhance timing reliability, which is a key port performance indicator. This paper studies the pilot and pilot carrier routing problem arising in seaports, where both the routes of pilots and carriers are determined jointly to achieve the lowest pilotage costs. The required service time intervals of vessels, the synchronization between pilots and carriers and the limited working duration for both pilots and carriers are considered. The problem is formulated as an arc-flow model, and a hybrid multi-start adaptive large neighborhood search and local search algorithm (mALNS-LS) is developed. Efficient departure time and cost computation methods are designed based on a set of forward and backward functions, and a model-based post optimization is applied. Computational experiments verify the performance of the proposed mALNS-LS in terms of solution quality and efficiency.

Calculation Method and Simulation Study of Optical Efficiency of Fixed-Heaven Mirror Field

Calculation Method and Simulation Study of Optical Efficiency of Fixed-Heaven Mirror Field

X-SLAM: Scalable Dense SLAM for Task-aware Optimization using CSFD

We present X-SLAM, a real-time dense differentiable SLAM system that leverages the complex-step finite difference (CSFD) method for efficient calculation of numerical derivatives, bypassing the need for a large-scale computational graph. The key to our approach is treating the SLAM process as a differentiable function, enabling the calculation of the derivatives of important SLAM parameters through Taylor series expansion within the complex domain. Our system allows for the real-time calculation of not just the gradient, but also higher-order differentiation. This facilitates the use of high-order optimizers to achieve better accuracy and faster convergence. Building on X-SLAM, we implemented end-to-end optimization frameworks for two important tasks: camera relocalization in wide outdoor scenes and active robotic scanning in complex indoor environments. Comprehensive evaluations on public benchmarks and intricate real scenes underscore the improvements in the accuracy of camera relocalization and the efficiency of robotic navigation achieved through our task-aware optimization. The code and data are available at https://gapszju.github.io/X-SLAM.

A trajectory similarity computation method based on GAT-based transformer and CNN model

Trajectory similarity computation is very important for trajectory data mining. It is applied into many trajectory mining tasks, including trajectory clustering, trajectory classification and trajectory search etc. So efficient trajectory similarity computation method is very useful for improving trajectory mining result. Nowadays many trajectory similarity computation methods have been proposed. But most of them can not be applied into long trajectories similarity calculation efficiently. So a new algorithm called TrajGAT is proposed. This algorithm can calculate similarity for long trajectories. It treats long trajectory as a long sequence. By doing so, long-term dependency of long trajectory is considered by this algorithm while computing similarity value. But, the spatial feature of long trajectories is not considered. As long trajectory can be presented in many different shapes, if two long trajectories are judged as similar trajectories, the outline shape of these two trajectories should be similar as well. To solve this problem, a new trajectory similarity computation method is proposed in this paper. This method not only takes the long-term dependence feature into consideration, but also considers the outline feature of long trajectory. The proposed method employs GAT-based transformer to extract long-term dependence feature from long trajectory. And it applies Convolutional Neural Network to extract outline feature.

PreDBP-PLMs: Prediction of DNA-binding proteins based on pre-trained protein language models and convolutional neural networks

The recognition of DNA-binding proteins (DBPs) is the crucial step to understanding their roles in various biological processes such as genetic regulation, gene expression, cell cycle control, DNA repair, and replication within cells. However, conventional experimental methods for identifying DBPs are usually time-consuming and expensive. Therefore, there is an urgent need to develop rapid and efficient computational methods for the prediction of DBPs. In this study, we proposed a novel predictor named PreDBP-PLMs to further improve the identification accuracy of DBPs by fusing the pre-trained protein language model (PLM) ProtT5 embedding with evolutionary features as input to the classic convolutional neural network (CNN) model. Firstly, the ProtT5 embedding was combined with different evolutionary features derived from the position-specific scoring matrix (PSSM) to represent protein sequences. Then, the optimal feature combination was selected and input to the CNN classifier for the prediction of DBPs. Finally, the 5-fold cross-validation (CV), the leave-one-out CV (LOOCV), and the independent set test were adopted to examine the performance of PreDBP-PLMs on the benchmark datasets. Compared to the existing state-of-the-art predictors, PreDBP-PLMs exhibits an accuracy improvement of 0.5 % and 5.2 % on the PDB186 and PDB2272 datasets, respectively. It demonstrated that the proposed method could serve as a useful tool for the recognition of DBPs.

Research on simulation calculation method of local strength of swath ship stabilizing fins based on sub-model method

Abstract At present, there is little research on the strength simulation calculation method of local steel structure at the joint between the fin stabilizer and hull of SWATH at home and abroad. Aiming at the problems of high-stress levels and difficult local strength of the local structure at the joint of SWATH submersible and stable fin, this paper, based on APDL language, takes a SWATH ship as an example, establishes a whole ship simulation model, analyses and studies the modeling scope, modeling requirements, boundary conditions, and loading methods of the local calculation model of stable fin, and puts forward an efficient simulation calculation and analysis method, namely sub-model method, which is suitable for the local strength calculation of stable fin, and provides guidance for the structural design method of the stable fin of SWATH catamaran.

Efficient Computational Methods for Solving the One-Dimensional Parabolic Equation with Nonlocal Initial and Boundary Conditions

Efficient Computational Methods for Solving the One-Dimensional Parabolic Equation with Nonlocal Initial and Boundary Conditions

Recent advances and effectiveness of machine learning models for fluid dynamics in the built environment

ABSTRACT Indoor environmental quality is crucial for human health and comfort, necessitating precise and efficient computational methods to optimise indoor climate parameters. Recent advancements in machine learning (ML) and computational fluid dynamics (CFD) are promising. However, applying ML to complex building airflow presents challenges. This research aims to investigate the integration of ML with CFD in the context of built environment applications using a systematic review approach. It highlights a critical knowledge gap: the need to synthesise innovative approaches that address the limitations of indoor modelling using data-driven ML methods. The review examines contemporary literature, identifying current developments and suggesting potential future directions. It delves into the innovations in combining ML with CFD to predict thermal comfort and indoor air quality, uncovering key limitations such as the lack of high-quality experimental data for training and validation, the computational complexity of detailed CFD simulations, and the interpretability issues of ‘black-box’ ML models. The emergence of data-driven techniques in fluid mechanics offers promising prospects for modelling in the built environment. Future research should focus on incorporating physics-based rules in ML models, adapting turbulence closure models for indoor flows, and enhancing model validation using real-world datasets. The research emphasises the synergistic relationship between ML and CFD; it proposes pathways to overcome current limitations, aiming to enhance the precision and efficiency of indoor environment modelling through their integration.

A Survey of Computationally Efficient Graph Neural Networks for Reconfigurable Systems

Graph neural networks (GNNs) are powerful models capable of managing intricate connections in non-Euclidean data, such as social networks, physical systems, chemical structures, and communication networks. Despite their effectiveness, the large-scale and complex nature of graph data demand substantial computational resources and high performance during both training and inference stages, presenting significant challenges, particularly in the context of embedded systems. Recent studies on GNNs have investigated both software and hardware solutions to enhance computational efficiency. Earlier studies on deep neural networks (DNNs) have indicated that methods like reconfigurable hardware and quantization are beneficial in addressing these issues. Unlike DNN research, studies on efficient computational methods for GNNs are less developed and require more exploration. This survey reviews the latest developments in quantization and FPGA-based acceleration for GNNs, showcasing the capabilities of reconfigurable systems (often FPGAs) to offer customized solutions in environments marked by significant sparsity and the necessity for dynamic load management. It also emphasizes the role of quantization in reducing both computational and memory demands through the use of fixed-point arithmetic and streamlined vector formats. This paper concentrates on low-power, resource-limited devices over general hardware accelerators and reviews research applicable to embedded systems. Additionally, it provides a detailed discussion of potential research gaps, foundational knowledge, obstacles, and prospective future directions.