Computational Framework Research Articles

Continuation of nonlinear normal modes using reduced-order models based on generalized characteristic value decomposition

AbstractOver the past two decades, data-driven reduced-order modeling (ROM) strategies have gained significant traction in the nonlinear dynamics community. Currently, several challenges in physical interpretation and data availability remain overlooked in current methodologies. This work proposes a novel ROM methodology based on a newly proposed generalized characteristic value decomposition (GCVD) to address these obstacles. The GCVD-ROM approach proposes a new perspective toward data-driven ROMs via characterization of the dynamics before any ROM considerations are made. In doing so, a significant degree of versatility is inherited in the GCVD-ROM strategy, allowing our models to reproduce the full-scale dynamics in different regions of the parameter space at the cost of a single training data set. Our approach utilizes computationally efficient free-decay data sets alongside a windowed-decomposition scheme, allowing us to extract energy-dependent modal structures for use in model-order reduction. This is accomplished using the physically insightful characteristic values provided by the GCVD, which are shown to be directly related to the system poles at a particular response amplitude. This natural metric, paired with a resonance tracking scheme, allows us to address the difficulties associated with physical interpretation and data availability without sacrificing the convenient aspects of linear projection-based model order reduction. A computational framework for the continuation and bifurcation analysis using linear projection-based ROMs is also presented, permitting us to deploy rigorous analysis and bifurcation studies to verify that our ROMs reproduce the intrinsic complexity of full-scale systems. A detailed walk-through of the GCVD-ROM approach is demonstrated on a simple system where important practical considerations and implementation details are discussed using a concrete example. The discretized von Kármán beam and shallow arch partial differential equations are also used to explore complicated scenarios involving modal coupling across disparate time scales and internal resonances.

Core and accessory genomic traits of Vibrio cholerae O1 drive lineage transmission and disease severity

In Bangladesh, Vibrio cholerae lineages are undergoing genomic evolution, with increased virulence and spreading ability. However, our understanding of the genomic determinants influencing lineage transmission and disease severity remains incomplete. Here, we developed a computational framework using machine-learning, genome scale metabolic modelling (GSSM) and 3D structural analysis, to identify V. cholerae genomic traits linked to lineage transmission and disease severity. We analysed in-patients isolates from six Bangladeshi regions (2015-2021), and uncovered accessory genes and core SNPs unique to the most recent dominant lineage, with virulence, motility and bacteriophage resistance functions. We also found a strong correlation between V. cholerae genomic traits and disease severity, with some traits overlapping those driving lineage transmission. GSMM and 3D structure analysis unveiled a complex interplay between transcription regulation, protein interaction and stability, and metabolic networks, associated to lifestyle adaptation, intestinal colonization, acid tolerance and symptom severity. Our findings support advancing therapeutics and targeted interventions to mitigate cholera spread.

A vaccination-based COVID-19 model: Analysis and prediction using Hamiltonian Monte Carlo

Compartmental models have emerged as robust computational frameworks and have yielded remarkable success in the fight against COVID-19. This study proposes a vaccination-based compartmental model for COVID-19 transmission dynamics. The model reflects the specific stages of COVID-19 infection and integrates a vaccination strategy, allowing for a comprehensive analysis of how vaccination rates influence the disease spread. We fit this model to daily confirmed COVID-19 cases in Tennessee, United States of America (USA), from June 4 to November 26, 2021, in a Bayesian inference approach using the Hamiltonian Monte Carlo (HMC) algorithm. First, excluding vaccination dynamics from the model, we estimated key epidemiological parameters like infection, recovery, and disease-induced death rates. This analysis yielded a basic reproduction number (R0) of 1.5. Second, we incorporated vaccination dynamics and estimated the vaccination rate for three vaccines: 0.0051 per day for both Pfizer and Moderna and 0.0059 per day for Janssen. The fitted curves show reductions in the epidemic peak for all three vaccines. Pfizer and Moderna vaccines bring the peak down from 8,029 infected cases to 5,616 infected cases, while the Janssen vaccine reduces it, to 6,493 infected cases. Simulations of the model by varying the vaccination rate and vaccine efficacy were performed. A highly effective vaccine (95% efficacy) with a daily vaccination rate of 0.006 halved COVID-19 infections, reducing cases from 8,029 to around 4,000. The results also show that the model's prediction accuracy for new observations improves with the number of observed data used to train the model.

ScPanel: a tool for automatic identification of sparse gene panels for generalizable patient classification using scRNA-seq datasets.

Single-cell RNA sequencing (scRNA-seq) technologies can generate transcriptomic profiles at a single-cell resolution in large patient cohorts, facilitating discovery of gene and cellular biomarkers for disease. Yet, when the number of biomarker genes is large, the translation to clinical applications is challenging due to prohibitive sequencing costs. Here, we introduce scPanel, a computational framework designed to bridge the gap between biomarker discovery and clinical application by identifying a sparse gene panel for patient classification from the cell population(s) most responsive to perturbations (e.g. diseases/drugs). scPanel incorporates a data-driven way to automatically determine a minimal number of informative biomarker genes. Patient-level classification is achieved by aggregating the prediction probabilities of cells associated with a patient using the area under the curve score. Application of scPanel to scleroderma, colorectal cancer, and COVID-19 datasets resulted in high patient classification accuracy using only a small number of genes (<20), automatically selected from the entire transcriptome. In the COVID-19 case study, we demonstrated cross-dataset generalizability in predicting disease state in an external patient cohort. scPanel outperforms other state-of-the-art gene selection methods for patient classification and can be used to identify parsimonious sets of reliable biomarker candidates for clinical translation.

High-dimensional Ageome Representations of Biological Aging across Functional Modules.

The aging process involves numerous molecular changes that lead to functional decline and increased disease and mortality risk. While epigenetic aging clocks have shown accuracy in predicting biological age, they typically provide single estimates for the samples and lack mechanistic insights. In this study, we challenge the paradigm that aging can be sufficiently described with a single biological age estimate. We describe Ageome, a computational framework for measuring the epigenetic age of thousands of molecular pathways simultaneously in mice and humans. Ageome is based on the premise that an organism's overall biological age can be approximated by the collective ages of its functional modules, which may age at different rates and have different biological ages. We show that, unlike conventional clocks, Ageome provides a high-dimensional representation of biological aging across cellular functions, enabling comprehensive assessment of aging dynamics within an individual, in a population, and across species. Application of Ageome to longevity intervention models revealed distinct patterns of pathway-specific age deceleration. Notably, cell reprogramming, while rejuvenating cells, also accelerated aging of some functional modules. When applied to human cohorts, Ageome demonstrated heterogeneity in predictive power for mortality risk, and some modules showed better performance in predicting the onset of age-related diseases, especially cancer, compared to existing clocks. Together, the Ageome framework offers a comprehensive and interpretable approach for assessing aging, providing insights into mechanisms and targets for intervention.

Risk substance identification of asphalt VOCs integrating machine learning and network pharmacology

Asphalt releases volatile organic compounds (VOCs) during paving processes, posing risks to workers and the environment. The complex composition of asphalt and the evolving of VOCs present challenges in accurately assessing their potential environmental and health impacts using traditional experimental approaches. This study aimed to develop a robust computational framework integrating machine learning and network pharmacology to predict the risks from the asphalt VOCs. The results show that the MACCS+XGBoost model achieved the highest predictive performance, with an accuracy of 0.85, balanced accuracy of 0.84, sensitivity of 0.83, specificity of 0.84, and F1-score of 0.84 in the external validation. The network pharmacology analysis revealed that the identified VOCs with reproductive toxicity potential may disrupt key processes such as spermatogenesis, ovarian function, and hormonal regulation, providing mechanistic insights into their potential impacts. This advancement supports a proactive approach to environmental protection and fosters the transition towards a more sustainable, low-carbon transportation.

Multi-modal remote perception learning for object sensory data.

When it comes to interpreting visual input, intelligent systems make use of contextual scene learning, which significantly improves both resilience and context awareness. The management of enormous amounts of data is a driving force behind the growing interest in computational frameworks, particularly in the context of autonomous cars. The purpose of this study is to introduce a novel approach known as Deep Fused Networks (DFN), which improves contextual scene comprehension by merging multi-object detection and semantic analysis. To enhance accuracy and comprehension in complex situations, DFN makes use of a combination of deep learning and fusion techniques. With a minimum gain of 6.4% in accuracy for the SUN-RGB-D dataset and 3.6% for the NYU-Dv2 dataset. Findings demonstrate considerable enhancements in object detection and semantic analysis when compared to the methodologies that are currently being utilized.

MGCN-PolyA: An Integrated Computational Framework for Predicting Poly(A) Signals with Multiscale-gated Convolutional Networks

Background: The accurate recognition of the polyadenylation signal (PAS) from DNA sequences is essential for understanding gene transcriptional regulation. A variety of machine learning-based computational methods have been developed to predict PAS in recent years; however, their performance and their generalization ability are unsatisfactory. It is highly desirable to design more preferable computational approaches for PAS prediction. Methods: In this work, we developed an integrated framework MGCN-PolyA for PAS prediction across four species, including Homo sapiens, Bos taurus, Mus musculus, and Drosophila melanogaster. MGCN-Poly(A) benefits from the diversity of feature engineering and the effectiveness of the model architecture. We combined features from different perspectives, such as word embedding, One-hot encoding, K-mer frequency, and Enhanced Nucleic Acid Composition (ENAC), which complement each other and provide rich and comprehensive information for model learning. In model architecture, MGCN-Poly(A) leverages a two-channel multi-scale gated convolutional network to effectively learn high-level feature representations at different scales, and then combines the statistical features to predict PAS using random forest algorithm. These designs not only speed up network training, but also improves the generalization ability Results: The benchmarking experiments on the independent test datasets demonstrate that MGCNPolyA outperforms other state-of-the-art algorithms in identifying PAS. MGCN-PolyA has the highest accuracy on all test datasets, and its excellent performance on cross-species validation also demonstrates the robustness of our model. Conclusion: Extracting features from different perspectives is important for PAS recognition, and the integration of DNNs and shallow machine learning algorithms can improve the model performance.

A QR Code for the Brain: A dynamical systems framework for computing neurophysiological biomarkers.

Neural circuits are often considered the bridge connecting genetic causes and behavior. Whereas prenatal neural circuits are believed to be derived from a combination of genetic and intrinsic activity, postnatal circuits are largely influenced by exogenous activity and experience. A dynamical neuroelectric field maintained by neural activity is proposed as the fundamental information processing substrate of cognitive function. Time series measurements of the neuroelectric field can be collected by scalp sensors and used to mathematically quantify the essential dynamical features of the neuroelectric field by constructing a digital twin of the dynamical system phase space. The multiscale nonlinear values that result can be organized into tensor data structures, from which latent features can be extracted using tensor factorization. These latent features can be mapped to behavioral constructs to derive digital biomarkers. This computational framework provides a robust method for incorporating neurodynamical measures into neuropsychiatric biomarker discovery.

Assessing the impact of driving behaviors and traffic conflicts on vehicle emissions at non-signalized intersections using a trajectory-based computational framework

Vehicle emissions can rise due to traffic conflicts and aggressive driving behaviors, such as frequent acceleration and deceleration. This issue is particularly pronounced at non-signalized intersections with a high proportion of non-motorized vehicles. In this study, we propose a framework that integrates a microscopic vehicle emission model with trajectory data. By utilizing trajectory data collected from a non-signalized intersection in Shanghai, we analyzed vehicle emissions linked to driving behaviors and traffic conflicts. Our findings reveal that pre-braking at the entrance of non-signalized intersections can significantly reduce vehicle emissions, lowering them by nearly 80 % for straight maneuvers. However, this reduction is less substantial for turning maneuvers. Additionally, conflicts involving more than two types of targets lead to a significant increase in vehicle emissions. On average, stop-and-go emissions are 1.13 % higher than those resulting from traffic conflicts. Interestingly, when non-motorized vehicles constitute more than 80 % of the traffic volume, stop-and-go emissions fall below those generated by traffic conflicts. The results of this study provide valuable insights for optimizing eco-driving strategies and advancing towards a low-carbon transportation system.

Spatio‐temporal occupancy models with INLA

Abstract Modern methods for quantifying, predicting and mapping species distributions have played a crucial part in biodiversity conservation. Occupancy models have become a popular choice for analysing species occurrence data due to their ability to separate out the observational error induced by imperfect detection of a species, and the sources of bias affecting the occupancy process. However, the spatial and temporal variation in occupancy that is not accounted for by environmental covariates is often ignored or modelled through simple spatial structures as the computational costs of fitting explicit spatio‐temporal models is too high. In this work, we demonstrate how Integrated Nested Laplace Approximation (INLA) may be used to fit complex spatio‐temporal occupancy models and how the R‐INLA package can provide a user‐friendly interface to make such complex models available to users. We show how occupancy models, provided some simplification on the detection process is assumed, can be framed as latent Gaussian models and, as such, benefit from the powerful INLA framework. A large selection of complex modelling features, and random effect models, including spatio‐temporal models, have already been implemented in R‐INLA. These also become available for occupancy models, providing the user with an efficient, reliable and flexible toolbox. We illustrate how INLA provides a flexible and computationally efficient framework for developing and fitting complex occupancy models using two complex case studies. Through these, we show how different spatio‐temporal models that include spatial‐varying trends, smooth terms and spatio‐temporal random effects can be fitted to aggregated detection/non‐detection data. At the cost of limiting the complexity of the detection model structure, INLA can incorporate a range of rather complex structures in the ecological process of interest and hence, extend the functionality of occupancy models. The limitations of occupancy models in terms of scalability for large spatio‐temporal data sets remain a challenge and an active area of research. INLA‐based occupancy models provide an alternative inferential and computational framework to fit complex spatio‐temporal occupancy models. The need for new and more flexible computationally efficient approaches to fit such models makes INLA an attractive option for addressing complex ecological problems, and a promising area of research.

Multi-omics features of immunogenic cell death in gastric cancer identified by combining single-cell sequencing analysis and machine learning

Gastric cancer (GC) is a prevalent malignancy with high mortality rates. Immunogenic cell death (ICD) is a unique form of programmed cell death that is closely linked to antitumor immunity and plays a critical role in modulating the tumor microenvironment (TME). Nevertheless, elucidating the precise effect of ICD on GC remains a challenging endeavour. ICD-related genes were identified in single-cell sequencing datasets and bulk transcriptome sequencing datasets via the AddModuleScore function, weighted gene co-expression network (WGCNA), and differential expression analysis. A robust signature associated with ICD was constructed using a machine learning computational framework incorporating 101 algorithms. Furthermore, multiomics analysis, including single-cell sequencing analysis, bulk transcriptomic analysis, and proteomics analysis, was conducted to verify the correlation of these hub genes with the immune microenvironment features of GC and with GC invasion and metastasis. We screened 59 genes associated with ICD and developed a robust ICD-related gene signature (ICDRS) via a machine learning computational framework that integrates 101 different algorithms. Furthermore, we identified five key hub genes (SMAP2, TNFAIP8, LBH, TXNIP, and PIK3IP1) from the ICDRS. Through single-cell analysis of GC tumor s, we confirmed the strong correlations of the hub genes with immune microenvironment features. Among these five genes, LBH exhibited the most significant associations with a poor prognosis and with the invasion and metastasis of GC. Finally, our findings were validated through immunohistochemical staining of a large clinical sample set, and the results further supported that LBH promotes GC cell invasion by activating the epithelial–mesenchymal transition (EMT) pathway.

Copula Approximate Bayesian Computation Using Distribution Random Forests

Ongoing modern computational advancements continue to make it easier to collect increasingly large and complex datasets, which can often only be realistically analyzed using models defined by intractable likelihood functions. This Stats invited feature article introduces and provides an extensive simulation study of a new approximate Bayesian computation (ABC) framework for estimating the posterior distribution and the maximum likelihood estimate (MLE) of the parameters of models defined by intractable likelihoods, that unifies and extends previous ABC methods proposed separately. This framework, copulaABCdrf, aims to accurately estimate and describe the possibly skewed and high-dimensional posterior distribution by a novel multivariate copula-based meta-t distribution based on univariate marginal posterior distributions that can be accurately estimated by distribution random forests (drf), while performing automatic summary statistics (covariates) selection, based on robustly estimated copula dependence parameters. The copulaABCdrf framework also provides a novel multivariate mode estimator to perform MLE and posterior mode estimation and an optional step to perform model selection from a given set of models using posterior probabilities estimated by drf. The posterior distribution estimation accuracy of the ABC framework is illustrated and compared with previous standard ABC methods through several simulation studies involving low- and high-dimensional models with computable posterior distributions, which are either unimodal, skewed, or multimodal; and exponential random graph and mechanistic network models, each defined by an intractable likelihood from which it is costly to simulate large network datasets. This paper also proposes and studies a new solution to the simulation cost problem in ABC involving the posterior estimation of parameters from datasets simulated from the given model that are smaller compared to the potentially large size of the dataset being analyzed. This proposal is motivated by the fact that, for many models defined by intractable likelihoods, such as the network models when they are applied to analyze massive networks, the repeated simulation of large datasets (networks) for posterior-based parameter estimation can be too computationally costly and vastly slow down or prohibit the use of standard ABC methods. The copulaABCdrf framework and standard ABC methods are further illustrated through analyses of large real-life networks of sizes ranging between 28,000 and 65.6 million nodes (between 3 million and 1.8 billion edges), including a large multilayer network with weighted directed edges. The results of the simulation studies show that, in settings where the true posterior distribution is not highly multimodal, copulaABCdrf usually produced similar point estimates from the posterior distribution for low-dimensional parametric models as previous ABC methods, but the copula-based method can produce more accurate estimates from the posterior distribution for high-dimensional models, and, in both dimensionality cases, usually produced more accurate estimates of univariate marginal posterior distributions of parameters. Also, posterior estimation accuracy was usually improved when pre-selecting the important summary statistics using drf compared to ABC employing no pre-selection of the subset of important summaries. For all ABC methods studied, accurate estimation of a highly multimodal posterior distribution was challenging. In light of the results of all the simulation studies, this article concludes by discussing how the copulaABCdrf framework can be improved for future research.

Electro-magnetohydrodynamic (EMHD) Darcy–Forchheimer flow of Sutterby nanofluid with variable thermal conductivity over a stretching sheet: Finite difference approach

This paper examines the enactment of a two-dimensional, steady, non-Newtonian nanofluid flow over a stretching sheet. The utilization of magnetic influences acting in the direction normal to the Darcy–Forchheimer boundary layer flow of the Sutterby nanofluid with variable thermal conductivity is taken into consideration. This study takes into account the effects of thermophoresis and Brownian motion. The study found that a 15% increase in magnetic field strength resulted in a 10% increase in heat transfer rate. Similarly, a 20% increase in nanoparticle volume percentage causes a 12% increase in the convective heat transfer coefficient. The problem’s model is formulated in the form of partial differential equations (PDEs), transformed via similarity transformation into nonlinear ordinary differential equations (ODEs). Applying the finite difference method yields the solution to reduced equations. EMHD Darcy–Forchheimer flow and nanofluid dynamics are combined in cutting-edge technology. This is important for many industrial and technical applications. Moreover, providing a strong computational framework provides a precise simulation of the flow behavior. By providing insights into the intricate interactions between electromagnetic forces, porous medium effects, and variable thermal conductivity in nanofluids flow across a stretched sheet, this study makes an essential contribution to the science of fluid dynamics. This research is very significant and enlightening for researchers and professionals who are interested in the design and optimization of heat transfer systems. The fluid flow is examined thoroughly and graphically, and the relationship between the profiles of velocity, temperature, and concentration and other important physical limitations is investigated. The effect of various physical parameters on concentration, velocity, temperature, skin friction, Nusselt number and heat flow coefficient is verified and examined using graphs and tables.

Hierarchical feature aggregation with mixed attention mechanism for single-cell RNA-seq analysis

Single-cell RNA sequencing (scRNA-seq) analysis is capable of elucidating cell heterogeneity and diversity, making cell-level biological research possible. Cell type clusterization is one of the main goals of scRNA-seq analysis. However, existing methods lack a flexible aggregation mechanism to fuse node attribute features and graph structural features adaptively. They also ignore multi-scale information embedded in different layers of networks. Here, we propose AGAC (Attention-driven Graph Attentional Clustering), a unified computational framework that applies hierarchical feature aggregation with mixed attentional mechanisms to scRNA-seq data clustering. Firstly, AGAC learns the attribute features of cells through graph attention autoencoder and the graph structural features among cells through graph neural network (GNN) respectively. Secondly, AGAC utilizes the heterogeneous intelligent fusion module to fuse these two kinds of features dynamically, as well as the multi-scale intelligent fusion module to aggregate the multi-scale information embedded in different layers of the GNN adaptively. In addition, AGAC adopts a dual self-supervision module for end-to-end training and synchronous optimization. Experimental results on 13 real scRNA-seq datasets demonstrate that AGAC is more effective than existing baseline methods because it takes into account the discriminative information in the network comprehensively and generates clustering results directly. The source code of AGAC can be downloaded at https://github.com/zzyqh/AGAC.

A Classification Methodology for Assessing Countries in Terms of Tourism Competitiveness

Tourism industry is important for national economies, and, in this regard, it is vital to monitor its competitiveness. The Travel and Tourism Competitiveness Index (TTCI), developed and reported by the World Economic Forum, serves this purpose by providing a consistent framework, explanatory factors, and corresponding data sets. In this paper we exploit the past data sets of this index, first, to verify that in several countries, competitiveness in tourism and travel industries hardly changes over time. Next, we identify countries that show consistency in travel–tourism competitiveness and separate them into classes of best, worst, intermediate, and ambiguous past performance. Building on such a classification, we apply linear discriminant analysis (LDA) as an alternative to the TTCI computational framework in order to compose the new synthetic index TTCI-LDA, which assesses countries’ competitiveness. The analysis of country scores obtained from this index has revealed that ICT readiness and touristic service infrastructure are important for tourism competitiveness. The score thresholds for the best–worst country cases in each class provide additional useful information for management, benchmarking, and policy decision making.

Maximization of strength–ductility balance of dual-phase steels using generative adversarial networks and Bayesian optimization

Dual-phase (DP) steels with a soft ferrite matrix and hard martensite islands exhibit an excellent combination of strength and ductility. However, further improvement of the strength–ductility balance is desirable for automotive applications, and the optimization of the DP microstructure is crucial for enhancing mechanical properties. This study aimed to maximize the strength–ductility balance of DP steel using an integrated computational framework comprised by generative adversarial networks (GAN), finite element method (FEM), and Bayesian optimization. GAN was trained on the microstructures of real DP steels to generate synthetic microstructure images randomly mapped from latent variable vectors, and the tensile properties of the generated microstructures were evaluated using FEM. Bayesian optimization was then employed to identify latent variables yielding microstructures with the highest tensile strength (TS), uniform elongation (uEL), or strength–ductility balance (TS × uEL). The optimized microstructures of DP steels were then quantitatively analyzed to elucidate the relationship between the microstructural morphology and tensile properties. The optimal synthetic DP microstructure exhibited a TS × uEL value higher by 4027 MPa% than the highest value shown by real DP steels. Thus, the proposed integrated optimization framework enables efficient computational design of various material microstructures for maximizing desired mechanical properties.

Acoustic emission and multiscale computation-guided tensile damage identification in woven composite laminates at cryogenic temperatures as low as 20 K

For laminated composite structures as key components of storage tanks serving at cryogenic temperature, it is crucial to identify the damage mechanisms for evaluating their mechanical properties and guiding structural design. In this work, the cryogenic tensile damage behavior of a thin-walled woven composite laminate was investigated through the acoustic emission (AE) monitoring and multiscale finite element (FE) computation at typical low temperatures of 153 K, 77 K and 20 K. We first established temperature-dependent constitutive laws for the microscale and mesoscale constituents of such composites based on experimental data, followed by the development of a hierarchical computational framework for modeling multiscale damage characteristics at different low temperatures. A fiber-optic acoustic emission measurement system was constructed to provide online monitoring of tensile damage of the woven composite laminates at cryogenic temperatures as low as 20 K. The comparations were made between the predicted cryogenic damage characteristics and in-situ AE signal analysis, assisted by scanning electron microscope (SEM) observations. The computed cryogenic damage evolution closely matched the AE signal identification results. The results indicate that fiber breakage and matrix cracking are the dominant cryogenic damage modes, and that the different low temperatures exert significant effects on the properties of the epoxy matrix, yarns and composite laminates. The combination of the AE monitoring system and the computational scheme provides a valuable tool for evaluating structural integrity and guiding the microstructural design of composite laminates used in cryogenic environments.

Prokrustean Graph: A substring index for rapid k-mer size analysis.

Despite the widespread adoption of -mer-based methods in bioinformatics, understanding the influence of -mer sizes remains a persistent challenge. Selecting an optimal -mer size or employing multiple -mer sizes is often arbitrary, application-specific, and fraught with computational complexities. Typically, the influence of -mer size is obscured by the outputs of complex bioinformatics tasks, such as genome analysis, comparison, assembly, alignment, and error correction. However, it is frequently overlooked that every method is built above a well-defined -mer-based object like Jaccard Similarity, de Bruijn graphs, -mer spectra, and Bray-Curtis Dissimilarity. Despite these objects offering a clearer perspective on the role of -mer sizes, the dynamics of -mer-based objects with respect to -mer sizes remain surprisingly elusive. This paper introduces a computational framework that generalizes the transition of -mer-based objects across -mer sizes, utilizing a novel substring index, the Prokrustean graph. The primary contribution of this framework is to compute quantities associated with -mer-based objects for all -mer sizes, where the computational complexity depends solely on the number of maximal repeats and is independent of the range of -mer sizes. For example, counting vertices of compacted de Bruijn graphs for can be accomplished in mere seconds with our substring index constructed on a gigabase-sized read set. Additionally, we derive a space-efficient algorithm to extract the Prokrustean graph from the Burrows-Wheeler Transform. It becomes evident that modern substring indices, mostly based on longest common prefixes of suffix arrays, inherently face difficulties at exploring varying -mer sizes due to their limitations at grouping co-occurring substrings. We have implemented four applications that utilize quantities critical in modern pangenomics and metagenomics. The code for these applications and the construction algorithm is available at https://github.com/KoslickiLab/prokrustean.

Seismic resilience assessment of sheet-pile wharves in liquefiable soils using different liquefaction countermeasures

Seismic resilience assessment is essential for maintaining the functionality of sheet-pile wharves in liquefiable soils, preventing significant damages and minimizing losses during earthquakes. This study delves into the seismic resilience of sheet-pile wharves, focusing specifically on the effectiveness of four different liquefaction countermeasure techniques: anchor lengths, cement deep mixing, stone columns, and soil compaction. As such, an advanced two-dimensional (2D) Finite Element (FE) computational framework is established, motivated by a typical large-scale sheet-pile wharf configuration. Within this framework, a recently developed multi-yield surfaces plasticity model is employed, with the modeling parameters calibrated through undrained stress-controlled cyclic triaxial tests and a centrifuge test. Subsequently, the impacts of these liquefaction countermeasures on the seismic resilience of the sheet-pile wharves are systematically investigated. Additionally, the effectiveness of combining longer anchor lengths with the other three mitigation techniques to enhance the seismic resilience of the sheet-pile wharves are examined. It is demonstrated that the synergistic effects of different liquefaction countermeasures can further reduce the liquefaction potential, thereby improving the seismic resilience. Overall, the FE analysis technique and the resulting insights are highly significant for the seismic resilience assessment of equivalent sheet-pile wharves in liquefiable soils, particularly when implementing such liquefaction mitigation countermeasures.