Computational Model Research Articles

SPLHRNMTF: robust orthogonal non-negative matrix tri-factorization with self-paced learning and dual hypergraph regularization for predicting miRNA-disease associations

MicroRNAs (miRNAs) have been demonstrated to be closely related to human diseases. Studying the potential associations between miRNAs and diseases contributes to our understanding of disease pathogenic mechanisms. As traditional biological experiments are costly and time-consuming, computational models can be considered as effective complementary tools. In this study, we propose a novel model of robust orthogonal non-negative matrix tri-factorization (NMTF) with self-paced learning and dual hypergraph regularization, named SPLHRNMTF, to predict miRNA-disease associations. More specifically, SPLHRNMTF first uses a non-linear fusion method to obtain miRNA and disease comprehensive similarity. Subsequently, the improved miRNA-disease association matrix is reformulated based on weighted k-nearest neighbor profiles to correct false-negative associations. In addition, we utilize L2,1\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$L_{2,1}$$\\end{document} norm to replace Frobenius norm to calculate residual error, alleviating the impact of noise and outliers on prediction performance. Then, we integrate self-paced learning into NMTF to alleviate the model from falling into bad local optimal solutions by gradually including samples from easy to complex. Finally, hypergraph regularization is introduced to capture high-order complex relations from hypergraphs related to miRNAs and diseases. In 5-fold cross-validation five times experiments, SPLHRNMTF obtains higher average AUC values than other baseline models. Moreover, the case studies on breast neoplasms and lung neoplasms further demonstrate the accuracy of SPLHRNMTF. Meanwhile, the potential associations discovered are of biological significance.

Computational Modeling of Ammonia Dispersion in the Atmosphere in Cases of Accidental Leakage during Road Transport

Computational Modeling of Ammonia Dispersion in the Atmosphere in Cases of Accidental Leakage during Road Transport

Two-dose priming immunization amplifies humoral immunity by synchronizing vaccine delivery with the germinal center response

Prolonging exposure to subunit vaccines during the primary immune response enhances humoral immunity. Escalating-dose immunization (EDI), administering vaccines every other day in an increasing pattern over 2 weeks, is particularly effective but challenging to implement clinically. Here, using an HIV Env trimer/saponin adjuvant vaccine, we explored simplified EDI regimens and found that a two-shot regimen administering 20% of the vaccine followed by the remaining 80% of the dose 7 days later increased T FH responses 6-fold, antigen-specific germinal center (GC) B cells 10-fold, and serum antibody titers 10-fold compared with bolus immunization. Computational modeling of T FH priming and the GC response suggested that enhanced activation/antigen loading on dendritic cells and increased capture of antigen delivered in the second dose by follicular dendritic cells contribute to these effects, predictions we verified experimentally. These results suggest that a two-shot priming approach can be used to substantially enhance responses to subunit vaccines.

Data-driven discovery of dynamics from time-resolved coherent scattering

Coherent X-ray scattering (CXS) techniques are capable of interrogating dynamics of nano- to mesoscale materials systems at time scales spanning several orders of magnitude. However, obtaining accurate theoretical descriptions of complex dynamics is often limited by one or more factors—the ability to visualize dynamics in real space, computational cost of high-fidelity simulations, and effectiveness of approximate or phenomenological models. In this work, we develop a data-driven framework to uncover mechanistic models of dynamics directly from time-resolved CXS measurements without solving the phase reconstruction problem for the entire time series of diffraction patterns. Our approach uses neural differential equations to parameterize unknown real-space dynamics and implements a computational scattering forward model to relate real-space predictions to reciprocal-space observations. This method is shown to recover the dynamics of several computational model systems under various simulated conditions of measurement resolution and noise. Moreover, the trained model enables estimation of long-term dynamics well beyond the maximum observation time, which can be used to inform and refine experimental parameters in practice. Finally, we demonstrate an experimental proof-of-concept by applying our framework to recover the probe trajectory from a ptychographic scan. Our proposed framework bridges the wide existing gap between approximate models and complex data.

Analyzing Foam Injection Dynamics in Porous Media: A Combined Computational and Experimental Approach

The study of foam injection dynamics into porous media holds numerous applications, with its primary use being the optimization of the oil extraction process. Additionally, it finds utility in various other domains, including soil decontamination techniques. This study focuses on evaluating foam injection through low-cost experiments and computational models. The model integrates a classic two-phase immiscible fluid flow in a porous media framework, augmented by a mobility reduction factor to simulate foam’s impact on gas phase mobility. Simple experiments were conducted to assess water displacement using both gas and gas-foam injection methods. Results revealed that while gas injection tends to form preferential paths rapidly, leading to gas disruption, foam injection generates a more compact front, enhancing sweep efficiency by mitigating preferential path formation. The computational model successfully reproduced the experimental findings qualitatively. This research introduces a cost-effective experiment suitable for didactic purposes, illustrating complex concepts and underscoring the complementary nature of experimental and computational methodologies in advancing engineering techniques.

Validation and Verification of Computational Model for the Numerical Simulation of the Operational Principle of a Submerged Horizontal Plate Device

This study describes the validation and verification of a two-dimensional numerical wave channel with a Submerged Horizontal Plate (SHP). The SHP is a fixed, submerged structure with a dual function: it acts as a breakwater and as a wave energy converter. To carry out the validation and verification procedures for the computational modeling, experimental and numerical results obtained from the literature were used. A relative error of 2.14% for the maximum axial velocity below the SHP was obtained in the validation, while an average absolute error of 0.0043 m/s for the axial velocity variation below the SHP was identified in the verification. Therefore, it can be inferred that the validation and verification stages were carried out successfully. So, the model is suitable for the numerical simulation of the operational principle of the SHP, both on a laboratory scale and on a real scale, allowing the investigation of its fluid dynamic behavior.

Dynamic capacity sharing with multi-wavelength integrated transmitters in hybrid datacenter networks

In datacenters, bursty and unevenly distributed traffic may lead to serious network performance degradation. Various methods, including reconfigurable optical circuit switching (OCS), traffic control techniques, valiant load balancing (VLB), and so on, have been proposed to solve this problem. Based on these solutions, our method makes a trade-off between cost and performance. In this paper, we propose to use multi-wavelength tunable transmitters in our previously proposed modular arrayed waveguide grating (AWG)-based interconnection network. We discuss how the multiple wavelengths can be shared in the network and then propose a computational model to study its blocking probability. Closed-form equations for low network load cases are also derived to provide the analytical expression for the blocking probability. We verify the accuracy of our computational model through simulations. Comparing the blocking probability of networks with and without multi-wavelength integrated transmitters, we show that network performance can be considerably improved after replacement. When traffic burstiness is 1.25 and traffic skewness is 0.08, the blocking probability is reduced from 0.14 to 3.60×10−3 after replacing in each sending module one fixed laser with multi-wavelength tunable transmitters with four wavelengths. Furthermore, we also discuss how different factors influence the blocking probability and the maximum load with the given network performance requirement.

Influence of surface curvature on the impact force of water droplet

Although the global market for wind energy is growing rapidly, leading-edge erosion is a critical issue hindering the development of wind power. The impact force of a droplet colliding with flat surfaces has been investigated in previous studies. However, the impact force exerted on curved surfaces, such as that experienced by eroded wind turbine blades, is not well understood. This study discusses the relationship between the impact force generated on a solid surface by a water droplet and the radius of curvature of the impacting surface. The impact force by a droplet was measured using a force sensor mounted on semi-cylindrical caps with different radii of curvature. The measurement results showed that the impact force decreased as the radius of curvature decreased. A computational fluid dynamics model solving incompressible flows showed that, unlike the case of a curved surface, the initial momentum of the droplet was mostly transferred to the flat surface. This resulted in a high impulse for an impact with a flat surface. The falling droplet was blocked by the surface, and the lateral jet was accelerated sideward. This acceleration was moderate for curved surfaces. When colliding with a flat surface, a higher impact force was generated owing to the wider area of the excited surface pressure compared with that of the curved surface. Finally, the relationship between the peak of the impact force and the surface curvature was derived, suggesting that the force peak is inversely proportional to the curvature.

Rewiring cattle movements to limit infection spread

Cattle tracing databases have become major resources for representing demographic processes of livestock and assessing potential risk of infections spreading by trade. The herds registered in these databases are nodes of a network of commercial movements, which can be altered to lower the risk of disease transmission. In this study, we develop an algorithm aimed at reducing the number of infected animals and herds, by rewiring specific movements responsible for trade flows from high- to low-prevalence herds. The algorithm is coupled with a generic computational model based on the French cattle movement tracing database (BDNI), and used to describe different scenarios for the spread of infection within and between herds from a recent outbreak (epidemic) or a five-year-old outbreak (endemic). Results show that rewiring successfully contains infections to a limited number of herds, especially if the outbreak is recent and the estimation of disease prevalence frequent, while the respective impact of the parameters of the algorithm depend on the infection parameters. Allowing any animal movement from high to low-prevalence herds reduces the effectiveness of the algorithm in epidemic settings, while frequent and fine-grained prevalence assessments improve the impact of the algorithm in endemic settings. Our approach focusing on a few commercial movements is expected to lead to substantial improvements in the control of a targeted disease, although changes in the network structure should be monitored for potential vulnerabilities to other diseases. This general algorithm could be applied to any network of controlled individual movements liable to spread disease.

Propagation Effect Analysis of Existing Cracks in Box Girder Bridges Based on the Criterion of Compound Crack Propagation

Cracking in concrete box girder bridges will have a significant impact on the safety and durability of the structure, and many box girder bridges which are in service have undergone varying degrees of cracking. Currently, the safety design of actual bridge projects place an emphasis on the stress or the load value of a cross section at the limit value specified in the code for safety control. This design method assumes that the member itself is of uniform and continuous material and is internally undamaged. However, the bridge structure is more or less cracked to varying degrees during the period from casting to construction to operation of the concrete members. In this paper, a finite element computational model of a three-span prestressed concrete box girder bridge with existing cracks is established based on the fracture mechanics theory, and the critical parameters of crack extension are introduced to evaluate the extension state of cracks. At the same time, the extended stability of the existing cracks of the box girder bridge is analyzed by considering the temperature effect, vehicle loading, and prestressing loss, and the sensitivity of crack extension under each working condition is investigated. The results show that, with the increase in crack length and depth, the crack expansion is promoted, but the effect is relatively small, and the maximum stress intensity factor is only 6.48 MPa mm1/2. Under the multi-factor coupling effect, the cracks show a composite crack expansion dominated by type I cracks, the longitudinal cracks of the existing base plate are in a stable state, the maximum value of the crack expansion critical parameter of the vertical cracks of the webs reaches 1.087, and there is a tendency to expand locally. The maximum value of the critical parameter for crack extension of the vertical crack in the web plate reaches 1.087, and there is a tendency towards local expansion. The crack extension evaluation criteria proposed in this paper have a certain reference value for crack extension research on similar concrete box girder bridges and provide a scientific basis for the optimized design of similar bridges.

Mapping causal pathways with structural modes fingerprint for perovskite oxides

Abstract Causality is innate to determination of fundamental mechanism controlling any physical phenomena. However, combining causality within the standard practices of computational modeling to understand structure-functionality connections is extremely rare. This work proposes a fingerprint based on key structural modes for ABO3-type perovskite oxides and its derivatives, combined with causal models, for predicting Kohn-Sham energies. Our study of causal models capture the inherent coupling between structural modes such as rotation, tilt and antiferroelectric displacements, responsible for phase transition, polarization, magnetization and metal-insulator transition, exhibited by these materials. Although developed for modeling specific functionality, this method is universally applicable to derive other functionalities and even different material classes while tracking hidden causal mechanisms via structural distortions.

The effect of congruent vs. incongruent distractor positioning on electrophysiological signals during perceptual decision-making

Key event-related potentials (ERPs) of perceptual decision-making such as the centroparietal positivity (CPP) elucidate how evidence is accumulated towards a given choice. Furthermore, this accumulation can be impacted by visual target selection signals such as the N2 contralateral (N2c). How these underlying neural mechanisms of perceptual decision-making are influenced by the spatial congruence of distractors relative to target stimuli remains unclear. Here, we used electroencephalography (EEG) in humans of both sexes to investigate the effect of distractor spatial congruency (same vs. different hemifield relative to targets) on perceptual decision-making. We confirmed that responses for perceptual decisions were slower for spatially incongruent vs. congruent distractors of high salience. Similarly, markers of target selection (N2c peak amplitude) and evidence accumulation (CPP slope) were found to be lower when distractors were spatially incongruent vs. congruent. To evaluate the effects of congruency further, we applied drift diffusion modelling to participant responses, which showed that larger amplitudes of both ERPs were correlated with shorter non-decision times when considering the effect of congruency. The modelling also suggested that congruency’s effect on behaviour occurred prior to and during evidence accumulation when considering the effects of the N2c peak and CPP slope. These findings point to spatially incongruent distractors, relative to congruent distractors, influencing decisions as early as the initial sensory processing phase, then continuing to exert an effect as evidence is accumulated throughout the decision-making process. Overall, our findings highlight how key electrophysiological signals of perceptual decision-making are influenced by the spatial congruence of target and distractor.Significance StatementWhen making perceptual decisions, we often need to focus on task-relevant targets while ignoring irrelevant distractors. In this study, we show that salient distractors in the opposite visual hemifield capture attention with consequences for the speed and accuracy of decisions. Electrophysiological signals of perceptual decision-making were attenuated by spatially incongruent distractors relative to congruent distractors. Computational modelling suggested that the influence of spatial congruency of target and distractor on the electrophysiological markers of target selection and evidence accumulation occurred during the initial sensory processing phase preceding evidence accumulation, which continued as evidence was accumulated throughout the decision process. Our findings, therefore, suggest that the spatial congruence of target and distractor impacts the dynamics of electrophysiological signals of perceptual decision-making.

Artificial intelligence methods in diagnosis of retinoblastoma based on fundus imaging: a systematic review and meta-analysis.

Artificial intelligence (AI) algorithms for the detection of retinoblastoma (RB) by fundus image analysis have been proposed as a potentially effective technique to facilitate diagnosis and screening programs. However, doubts remain about the accuracy of the technique, the best type of AI for this situation, and its feasibility for everyday use. Therefore, we performed a systematic review and meta-analysis to evaluate this issue. Following PRISMA 2020 guidelines, a comprehensive search of MEDLINE, Embase,ClinicalTrials.gov and IEEEX databases identified494studies whose titles and abstracts were screened for eligibility. We included diagnostic studies that evaluated the accuracy of AI in identifying retinoblastoma based on fundus imaging. Univariate and bivariate analysis was performed using the random effects model. The study protocol was registered in PROSPERO under CRD42024499221. Six studies with 9902 fundus images were included, of which 5944 (60%) had confirmed RB. Only one dataset used a semi-supervised machine learning (ML) based method, all other studies used supervised ML, three using architectures requiring high computational power and two using more economical models. The pooled analysis of all models showed a sensitivity of 98.2% (95% CI: 0.947-0.994), a specificity of 98.5% (95% CI: 0.916-0.998) and an AUC of 0.986 (95% CI: 0.970-0.989). Subgroup analyses comparing models with high and low computational power showed no significant difference (p=0.824). AI methods showed a high precision in the diagnosis of RB based on fundus images with no significant difference when comparing high and low computational power models, suggesting a viability of their use. Validation and cost-effectiveness studies are needed in different income countries. Subpopulations should also be analyzed, as AI may be useful as an initial screening tool in populations at high risk for RB, serving as a bridge to the pediatric ophthalmologist or ocular oncologist, who are scarce globally. What isknown Retinoblastoma is the most common intraocular cancer in childhood and diagnostic delay is the main factor leading to a poor prognosis. The application of machine learning techniques proposes reliable methods for screening and diagnosis of retinal diseases. What isnew The meta-analysis of the diagnostic accuracy of artificial intelligence methods for diagnosing retinoblastoma based on fundus images showed a sensitivity of 98.2% (95% CI: 0.947-0.994) and a specificity of 98.5% (95% CI: 0.916-0.998). There was no statistically significant difference in the diagnostic accuracy of high and low computational power models. The overall performance of supervised machine learning was best than unsupervised, although few studies were available on the second type.

Mesoscale explorer: Visual exploration of large-scale molecular models.

The advent of cryo-electron microscopy (cryo-EM) and cryo-electron tomography (cryo-ET), coupled with computational modeling, has enabled the creation of integrative 3D models of viruses, bacteria, and cellular organelles. These models, composed of thousands of macromolecules and billions of atoms, have historically posed significant challenges for manipulation and visualization without specialized molecular graphics tools and hardware. With the recent advancements in GPU rendering power and web browser capabilities, it is now feasible to render interactively large molecular scenes directly on the web. In this work, we introduce Mesoscale Explorer, a web application built using the Mol* framework, dedicated to the visualization of large-scale molecular models ranging from viruses to cell organelles. Mesoscale Explorer provides unprecedented access and insight into the molecular fabric of life, enhancing perception, streamlining exploration, and simplifying visualization of diverse data types, showcasing the intricate details of these models with unparalleled clarity.

Development of a Novel Transonic Fan Casing Making Use of Rapid Prototyping and Additive Manufacturing

Additive manufacturing (AM) presents significant cost savings and lead time reductions because of features inherent to the manufacturing process. The technology lends itself to rapid prototyping due to the streamlined workflow of quickly implementing design changes. Compared to traditional machining, AM techniques are simpler in execution for design engineers because they do not require detailed engineering drawings and they typically make use of the nominal geometry in computer models. A novel transonic fan casing assembly has been developed that makes use of AM inserts surrounding the rotor to provide an opportunity to cost-effectively change the corresponding flowpath. The rapid prototyping design philosophy developed from this work will allow for numerous experimental studies into the effects that different design parameters of casing geometries have on fan aerodynamic performance. A fan stage representative of a small turbofan engine was successfully tested with smooth-walled, additively manufactured inserts as a baseline case for future configurations. Before installing the 3D printed casing assembly, computational thermal stress analysis was performed to reduce the risk in implementation due to the demanding environment associated with the rotor. AM components and materials typically have nonlinear mechanical properties, adding to the complexity of the structural analysis. As part of the research, steady aerodynamic performance was measured over the entire relevant operating range of the fan.

Research on novel hybrid structure AACMM based on 3-RPS parallel mechanism

Abstract The articulated arm coordinate measuring machine (AACMM), widely utilized in the field of industrial measurement, is assessed based on key performance indicators such as measurement accuracy, measurement space, and flexibility. To enhance these aspects of the AACMM, this paper introduces a novel type of AACMM. The new model is based on a 3-RPS (revolute joint R, prismatic joint P, spherical joint S) parallel mechanism (3-RPSPM). The working principle of the 3-RPSPM is detailed, and a corresponding computational model is established. An algorithm is proposed based on this mathematical model to determine the theoretical achievable accuracy of the 3-RPSPM. Additionally, a spatial error analysis of the 3-RPSPM is conducted to verify its compliance with AACMM accuracy requirements and to demonstrate a certain level of accuracy improvement. Through simulation analysis, the differences in measurement accuracy, measurement space, and flexibility between the new type of AACMM based on the 3-RPS parallel mechanism (3-RPSAACMM) and traditional AACMMs are compared. The results indicate that the 3-RPSAACMM, with its novel parallel structure free from series accumulation errors, not only maintains measurement flexibility but also achieves improvements in measurement accuracy and space. The hybrid structure adopted by the new 3-RPSAACMM offers a new direction for the future development of AACMMs, presenting significant research value and application prospects.

SeqSeg: Learning Local Segments for Automatic Vascular Model Construction.

Computational modeling of cardiovascular function has become a critical part of diagnosing, treating and understanding cardiovascular disease. Most strategies involve constructing anatomically accurate computer models of cardiovascular structures, which is a multistep, time-consuming process. To improve the model generation process, we herein present SeqSeg (sequential segmentation): a novel deep learning-based automatic tracing and segmentation algorithm for constructing image-based vascular models. SeqSeg leverages local U-Net-based inference to sequentially segment vascular structures from medical image volumes. We tested SeqSeg on CT and MR images of aortic and aortofemoral models and compared the predictions to those of benchmark 2D and 3D global nnU-Net models, which have previously shown excellent accuracy for medical image segmentation. We demonstrate that SeqSeg is able to segment more complete vasculature and is able to generalize to vascular structures not annotated in the training data.

Exploring the potential of QSAR in the discovery of novel green TADF materials: an experimental and theoretical study

Thermally activated delayed fluorescence (TADF) materials have garnered significant attention in developing high-efficiency organic light-emitting diodes (OLEDs) for next-generation displays. Despite the progress in TADF research, the increasing complexity of molecular structures poses challenges in material design and synthesis. This study explores the potential of quantitative structure–activity relationship (QSAR) calculations, particularly AutoQSAR, to streamline TADF OLED material selection and design. By leveraging the predictive capabilities of QSAR, we aimed to enhance the accuracy and efficiency of material development. We employed computational modeling, synthesis, and device fabrication to evaluate the performance of a newly developed green TADF dopant material. Our findings indicate that QSAR-guided design predicts material properties effectively and optimizes OLED performance. The synthesized TADF material demonstrated promising efficiency, highlighting the advantages of integrating QSAR calculations into the material discovery process. This study underscores the feasibility of QSAR methodologies in the context of OLED materials, suggesting a pathway for faster and more accurate development. Future improvements in QSAR techniques and collaborative efforts between computational and experimental research will be essential in driving further advancements in OLED technology.

Understanding the variability of the proximal femoral canal: A computational modeling study.

Statistical shape modeling (SSM) offers the potential to describe the morphological differences in similar shapes using a compact number of variables. Its application in orthopedics is rapidly growing. In this study, an SSM of the intramedullary canal of the proximal femur was built, with the aim to better understanding the complexity of its shape which may, in turn, enhance the preoperative planning of total hip arthroplasty (THA). This includes the prediction of the prosthetic femoral version (PFV) which is known to be highly variable amongst patients who have undergone THA. The model was built on three dimensional (3D) models of 64 femoral canals which were generated from pelvic computed tomography images including the proximal femur in the field of view. Principal component analysis (PCA) was performed on the mean shape derived from the model and each segmented canal. Five prominent modes of variations representing approximately 84% of the total 3D variations in the population of shapes were found to capture variability in size, proximal torsion, intramedullary femoral anteversion, varus/valgus orientation, and distal femoral shaft twist/torsion, respectively. It was established that the intramedullary femoral canal is highly variable in its size, shape, and orientation between different subjects. PCA-driven SSM is beneficial for identifying patterns and extracting valuable features of the femoral canal.

Fast Computer Model Calibration using Annealed and Transformed Variational Inference

Computer models play a crucial role in numerous scientific and engineering domains. To ensure the accuracy of simulations, it is essential to properly calibrate the input parameters of these models through statistical inference. While Bayesian inference is the standard approach for this task, employing Markov chain Monte Carlo methods often encounters computational hurdles due to the costly evaluation of likelihood functions and slow mixing rates. Although variational inference (VI) can be a fast alternative to traditional Bayesian approaches, VI has limited applicability due to boundary issues and local optima problems. To address these challenges, we propose flexible VI methods based on deep generative models that do not require parametric assumptions on the variational distribution. We embed a surjective transformation in our framework to avoid posterior truncation at the boundary. Additionally, we provide theoretical conditions that guarantee the success of the algorithm. Furthermore, our temperature annealing scheme and fine-tuning can prevent being trapped in local optima through a series of intermediate posteriors and weight adjustment. We apply our method to infectious disease models and a geophysical model, illustrating that the proposed method can provide fast and accurate inference compared to its competitors.