Development Of Computational Models Research Articles

The history of hydrological studies on the Mekong floodplains – from colonial experiments to computational models

ABSTRACT This paper investigates the interwoven history of hydrological studies and water infrastructure development on the Mekong floodplains. On the basis of an extensive literature review, archival work, and key informant interviews, we unravel the making of an expert-led understanding of the Mekong floodplains – from the detailed reports written by colonial engineers during the French protectorate in the late 19th century to the development of the first computational models, developed in the 1960s on punch-cards and in Fortran. We show how these studies not only reflect the objectives of successive powers and their relationships to local communities but also shaped the landscape and hydrology of the Mekong floodplains and continue to do so through a variety of water infrastructure development projects. This serves as a call to conceive of hydrological knowledge as contingent rather than as a neutral depiction of physical processes that exists independently from the conditions of its production.

Diffuse Optical Spectroscopy: Technology and Applications: introduction to the feature issue

Welcome to the 2024 Feature Issue on Diffuse Optical Spectroscopy: Technology and Applications in Biomedical Optics Express! This feature issue provides an exemplary sample of established and emerging DOS technologies as well as their biomedical applications through 27 contributed research papers and 1 invited review article. DOS researchers are inherently multidisciplinary, advancing topics spanning the basic theory of light-tissue interactions, computational modeling, technique and system development and preclinical and clinical applications. You will find this full range of topics represented in this feature issue.

Artificial Intelligence and Omics in Malignant Gliomas.

The availability of large scale multi-omics data requires development of computational models to infer valuable biological insights for the implementation of precision medicine. Artificial intelligence (AI) refers to a host of computational algorithms that is becoming a major tool capable of integrating large genomic, transcriptomic, proteomic, and metabolomic data. Machine learning (ML) is the most significant AI algorithm in health sciences have exploded, specifically due to the recent progress made by deep learning. Although the use of AI/ML tools in GBM-omics is still at an early stage, a comprehensive discussion of how AI can be used to unravel various aspects of GBM (intratumor heterogeneity, biomarker discovery, survival prediction, and treatment optimization) would be highly relevant to both researchers and clinicians. Here, we aim to review the different AI-based techniques that have been used to study GBM pathogenesis using multi-omics data over the last decade. We first summarize different types of GBM related omics resources that can be used to develop AI models. We then discuss various AI applications for multi-omics data in order to enhance GBM precision medicine. Finally, we discuss the technical and ethical challenges that limit its application and ways to improve its implementation in clinics.

Development of novel computational models based on artificial intelligence technique to predict liquids mixtures separation via vacuum membrane distillation

The fundamental objective of this paper is to use Machine Learning (ML) methods for building models on temperature (T) prediction using input features r and z for a membrane separation process. A hybrid model was developed based on computational fluid dynamics (CFD) to simulate the separation process and integrate the results into machine learning models. The CFD simulations were performed to estimate temperature distribution in a vacuum membrane distillation (VMD) process for separation of liquid mixtures. The evaluated ML models include Support Vector Machine (SVM), Elastic Net Regression (ENR), Extremely Randomized Trees (ERT), and Bayesian Ridge Regression (BRR). Performance was improved using Differential Evolution (DE) for hyper-parameter tuning, and model validation was performed using Monte Carlo Cross-Validation. The results clearly indicated the models’ effectiveness in temperature prediction, with SVM outperforming other models in terms of accuracy. The SVM model had a mean R2 value of 0.9969 and a standard deviation of 0.0001, indicating a strong and consistent fit to the membrane data. Furthermore, it exhibited the lowest mean squared error, mean absolute error, and mean absolute percentage error, signifying superior predictive accuracy and reliability. These outcomes highlight the importance of selecting a suitable model and optimizing hyperparameters to guarantee accurate predictions in ML tasks. It demonstrates that using SVM, optimized with DE improves accuracy and consistency for this specific predictive task in membrane separation context.

Neuroscientific insights about computer vision models: a concise review.

The development of biologically-inspired computational models has been the focus of study ever since the artificial neuron was introduced by McCulloch and Pitts in 1943. However, a scrutiny of literature reveals that most attempts to replicate the highly efficient and complex biological visual system have been futile or have met with limited success. The recent state-of the-art computer vision models, such as pre-trained deep neural networks and vision transformers, may not be biologically inspired per se. Nevertheless, certain aspects of biological vision are still found embedded, knowingly or unknowingly, in the architecture and functioning of these models. This paper explores several principles related to visual neuroscience and the biological visual pathway that resonate, in some manner, in the architectural design and functioning of contemporary computer vision models. The findings of this survey can provide useful insights for building futuristic bio-inspired computer vision models. The survey is conducted from a historical perspective, tracing the biological connections of computer vision models starting with the basic artificial neuron to modern technologies such as deep convolutional neural network (CNN) and spiking neural networks (SNN). One spotlight of the survey is a discussion on biologically plausible neural networks and bio-inspired unsupervised learning mechanisms adapted for computer vision tasks in recent times.

Letter identity and position coding in the parafovea.

Letter position coding has been extensively examined in studies of isolated word identification, spurring the development of computational models. However, these models are largely restricted to explaining word identification in foveal vision, despite the fact that early lexical processing during reading occurs in the parafovea. We report four experiments that examined the flexibility of parafoveal letter identity and position coding using a variant of the same-different match task. Participants matched transposed- and substituted-letter strings to reference words, with the former being displayed at various retinal eccentricities for 100 ms versus 300 ms to respectively preclude or allow eye movements. The first pair of experiments demonstrated the relative difficulty of coding parafoveal letter positions as compared to their identities, as well as the standard benefit in identifying words displayed in the right visual field. The second pair of experiments further demonstrated that the location of letter-position uncertainty (i.e., transposed letters) interacts with both eccentricity and visual field. Initial letter transpositions were more easily detected in the left visual field, whereas transpositions of the final letters were more accurately detected in the right visual field. As discussed, these results are challenging for existing models of reading, which can individually account for some of our findings but not the results in their entirety. (PsycInfo Database Record (c) 2024 APA, all rights reserved).

Proceedings 13th International Workshop on Developments in Computational Models

Proceedings 13th International Workshop on Developments in Computational Models

Development of a new analytical model for circular concrete ring segments with dry joints under combined effects

AbstractPrevious models of circular ring segment joints have been inadequate in describing their loading capacity due to the lack of consideration for the interaction between shear force and bending moment. Consequently, these models have led to over‐ or underestimation of the joints' capacity. The increasing use of dry joint connection technology in wind turbine towers and prefabricated segmental bridge constructions necessitates the development of advanced computational models for these connections. This study presents a new model that considers the effects of bending moment, shear force, and torsion on the loading capacity of circular ring segment joints. Numerical simulations were conducted to validate the developed model under different load combinations, and the results demonstrate excellent agreement between the model predictions and numerical simulations.

MATHEMATICAL MODELING OF THE NON-STATIONARY THERMAL STATE OF A COMPOSITE COATING OF CLOSE TO EQUIMOLAR COMPOSITION DURING LASER REMELTING

Based on assumptions and limitations, a mathematical model of the non-stationary thermal state of a composite coating during laser remelting on a metal substrate to obtain an alloy of equimolar or close to equimolar composition has been developed. The model includes the boundary value problem of calculating the nonstationary thermal state of the temperature distribution in the solid, two-phase and liquid regions, considering the heat release of crystallization according to the theory of a quasi-equilibrium two-phase zone in the presence of porosity of a mixture of metal powders. Based on the mathematical model, the computer program “Composite coating thermal state” was created, which allows to determine the non-stationary temperature distribution in the formed coating and substrate, the depth of penetration of the high-entropy coating layer, to evaluate the shrinkage of the molten coating under different technological conditions: laser power, the diameter of its focal spot, as well as the speed of movement along the surface of the processed material. The created program also allows to determine the dynamics of the position of two-dimensional liquidus and solidus lines for each metal included in the mixture. Using the created program, a computer simulation of the process of unsteady heating of a mixture of three metal powders in the same mass fractions: nickel, chromium and iron was carried out. Non-stationary temperature fields in the composite coating are obtained, considering its penetration and heating of the substrate. The depth of penetration of all metals, including the most low-melting one, was determined during laser remelting on a metal substrate in the production of a high-entropy alloy. The practical significance of the developed computer model lies in its use to predict the penetrationdepth of a composite coating under specified technological modes of laser remelting. At the same time, its adaptation is necessary, based on experimental data on the porosity and thickness of the coating before and after melting of simple metal systems with similar thermophysical properties.

Systematic analysis of constitutive models of brain tissue materials based on compression tests

It's crucial to understand the biomechanical properties of brain tissue to comprehend the potential mechanisms of traumatic brain injury. This study, distinct from homogeneous models, integrates axonal coupling in both axial and transverse compressive experiments within a continuum mechanics framework to capture its intricate mechanical behaviors. Fresh porcine brains underwent unconfined compression at strain rates of 0.001/s and 0.1/s to 0.3 strain, allowing for a comprehensive statistical analysis of the directional, regional, and strain-rate-dependent mechanical properties of brain tissue. The established constitutive model, fitted to experimental data, delineates material parameters providing intuitive insights into the stiffness of gray/white matter isotropic matrices and neural fibers. Additionally, it predicts the mechanical performance of white matter matrix and axonal fibers under compressive loading. Results reveal that gray matter is insensitive to loading direction, exhibiting insignificant stiffness variations within regions. White matter, however, displays no significant differences in mechanical properties under axial and transverse loading, with an overall higher average stress than gray matter and a more pronounced strain-rate effect. Stress-strain curves indicate that, under axial compression, white matter axons primarily resist the load before transitioning to a matrix-dominated response. Under transverse loading, axonal fibers exhibit weaker resistance to lateral pressure. The mechanical behavior of brain tissue is highly dependent on loading rate, region, direction, and peak strain. This study, by combining experimentation with phenomenological modeling, elucidates certain phenomena, contributing valuable insights for the development of precise computational models.

Forward and inverse river contaminant transport modeling using group preserving scheme

Environmental concerns have necessitated the development of computational models predicting pollutant dispersion in natural water systems. Due to the ill-posed nature of the inverse contaminant transport equation, solving this equation using all stable and convergent inverse methods is impossible. Factors such as river geometry, unsteady and non-uniform flow, and tidal influences add to the complexity of the inverse problem. These factors have prompted the evaluation of Group Preserving Scheme for environmental applications. The inverse solution method derives a general equation for solving ordinary differential equations by addressing a dynamical system at negative time steps, ensuring convergence. Three test cases have been presented to evaluate Backward Group Preserving Scheme (BGPS). These have included validation using observational data from Missouri River, inverse simulation in a tidal river, and sensitivity analysis of parameters such as pollutant patterns, advection, dispersion, and decay coefficients. The dataset includes calibrated data from Missouri River, which demonstrated high accuracy, with Mean Relative Error (MRE) ranging from 2.8% to 5.0% for the inverse model. Under tidal conditions, accuracy decreases over time but remains robust, with a Nash–Sutcliffe efficiency of 0.77–0.96 and an MRE of 0.9%–5.9%. Sensitivity analysis revealed optimal model performance for Péclet numbers greater than 500. The model performed best with gradual, wide-peaked pollutant patterns and moderate decay rates (Damköhler number between 5 and 10). BGPS proves effective for transport simulation and concentration history reconstruction in complex rivers, including those with tidal influences, offering a robust tool for water contamination analysis in various flow conditions.

Advanced tumor growth modeling: A numerical study integrating phase plane analysis with finite volume method

This study investigates tumor density dynamics through computational modeling, focusing on key parameters: proliferation rate m, diffusion coefficient τ, and additional factors c and κ. Systematic exploration reveals nuanced interactions shaping tumor growth, regression, and dispersal. Higher m values accelerate proliferation, while increased τ facilitates dispersal. Parameters c and κ further refine our understanding of tumor behavior. These insights inform the development of computational models for oncological research, with implications for targeted therapeutic interventions. The aim can be realized by utilizing the propose Finite Volume Method to develop a representation of cancer models and improve the efficiency of their simulations. Furthermore, the numerical results will be validated by comparing them with the Tanh-Coth method formulation to assess the model's accuracy and efficiency. The findings suggest that accurate modeling of tumor dynamics can aid in predicting tumor progression and response to treatments, ultimately contributing to personalized medicine and more effective therapeutic strategies. Phase plane analysis shows the schemes are conditionally stable and accurate to the fourth order in time. The method demonstrates exceptional agreement and error-free findings compared to 3D plots, underscoring its potential utility in clinical and research settings.

Numerical assessment of fiber distribution effects on strain-hardening cement composites using a rate-dependent Voronoi-Cell-Lattice-Model

Strain-hardening cementitious composites, also known as high-performance fiber-reinforced cementitious composites, display exceptional strength and toughness not only under static conditions but also under high-speed dynamic loading. The mechanical properties of these composites are greatly influenced by the orientation and dispersion of embedded fibers. However, experimental analysis of complex fiber arrangements in real specimens has limitations, necessitating the development of computational models to understand the impact of fiber distribution. This study examines the effect of fiber distribution conditions on composite behavior using a Voronoi-Cell-Lattice-Model that incorporates reinforcing effects into discrete-type quasi-brittle matrix elements. Firstly, virtual fiber distribution models revealed that as the gauge length of specimens increased, weaknesses in fiber distribution were more likely to form, leading to decreased strength and strain capacity. Secondly, the model demonstrated that favorable fiber orientation relative to the loading direction and minimal sectional deviation in fiber dispersion significantly increased strain capacity and toughness. Finally, the study explored the wall-effect that may arise during specimen fabrication due to the boundary of the casting mold.

FORECASTING ECONOMIC TIME SERIES WITH EXOGENOUS VARIA-BLES: OPPORTUNITIES IN COMPUTATIONAL PARAMETRIZATION TECHNIQUES FOR SUPPORTING PUBLIC DEVELOPMENT POLICIES

This study presents a comprehensive literature review exploring the integration of exogenous variables in economic time series forecasting, focusing on supporting public development policies. It systematically examines various computational parametrization techniques incorporating these external variables, emphasizing their potential to enhance prediction accuracy and address practical challenges in policy-driven contexts. By meticulously analyzing the existing body of research, this review identifies significant gaps and outlines future opportunities for advancing computational techniques and model development. Addressing these gaps could lead to more robust and precise economic forecasting models, offering valuable insights for both academic researchers and policymakers in the formulation and implementation of effective public development strategies. This paper consolidates current knowledge and paves the way for innovative computational approaches that can revolutionize economic forecasting by integrating multidimensional data from diverse external sources.

Recent developments in computational modelling of the knee

ObjectiveModel calculations of knee joint loading range from an assumption of perfectly rigid articular surfaces to more realistic simulations of cartilage and meniscal deformation. Rigid-body musculoskeletal models simulate knee contact mechanics using the ‘bed of springs’ method from elastic foundation theory whereas finite-element models discretise each structure into a series of interconnected elements and ascribe material properties to each element. This mini-review describes some of the most recent developments in computational modelling of knee contact mechanics and suggests possible avenues for future improvements. DesignNarrative mini-review. ResultsMuscle and joint contact forces can be calculated synchronously at a reasonable computational cost (typically a few hours of CPU time) using rigid-body models and elastic foundation theory whereas similar calculations using fully deformable finite-element models can take several days and even weeks. The main computational expense incurred in finite-element musculoskeletal modelling is the solution of a muscle-force optimization problem. ConclusionCalculation of muscle and joint contact forces within the framework of a finite-element musculoskeletal model remains challenging. Integrating biomechanical data from human motion experiments with fully deformable finite-element models to simulate knee contact mechanics during dynamic activity is an evolving science. Future work should explore the use of efficient methods such as direct collocation to perform muscle-driven dynamic optimization simulations of movement using finite-element musculoskeletal models. Dynamic optimization may be combined with finite-element modelling to enable predictive simulations of movement so that the effects of changes in musculoskeletal anatomy on knee contact mechanics can be studied more systematically.

SIMPLIFIED COMPUTATIONAL TOOL FOR PREDICTING FORMATION DAMAGE IN OIL WELLS

The present study underscores the significance of regular well test analysis to ensure the accuracy and consistency of data pertaining to the permeability and skin of exploratory wells. This study advances the development of a novel computer model, ASPIRE, which predicts formation damage in oil wells by analysing well-test data. In this context, identifying blocked pore throats in the formation close to the wellbore by paraffinic deposits (scales) and its influence on permeability necessitates routine well-test analysis. The software employs a modified linear regression model developed in this study to perfectly fit the well-test data's infinitely acting linear flow (IARF) region to a straight line. The cases "CASE 1" and "CASE 2" were considered for constant rate transient drawdown well test analyses. The findings indicate that Case 1, with a high skin factor, requires remedial action, such as stimulation, to increase the formation's permeability close to the wellbore for enhanced productivity. In contrast, Case 2 has higher permeability and lower skin factor than Case 1, so stimulation is not required. In addition, R-squared analyses were conducted to validate the results obtained from the developed tool. The analyses reveal an R-square value of 1 for all cases, indicating a 100% accuracy. Therefore, the study concludes that the developed tool is instrumental in accurately predicting formation damage in oil wells.

Toward Modeling the Complexity of the Chemical Mechanism in SERS.

Surface-enhanced Raman scattering (SERS) provides detailed information about the binding of molecules at interfaces and their interactions with the local environment due to the large enhancement of Raman scattering. This enhancement arises from a combination of the electromagnetic mechanism (EM) and chemical mechanism (CM). While it is commonly accepted that EM gives rise to most of the enhancement, large spectral changes originate from CM. To elucidate the rich information contained in SERS spectra about molecules at interfaces, a comprehensive understanding of the enhancement mechanisms is necessary. In this Perspective, we discuss the current understanding of the enhancement mechanisms and highlight their interplay in complex local environments. We will also discuss emerging areas where the development of computational and theoretical models is needed with specific attention given to how the CM contributes to the spectral changes. Future efforts in modeling should focus on overcoming the challenges presented in this review in order to capture the complexity of CM in SERS.

Adaptive Evolutionary Computing Ensemble Learning Model for Sentiment Analysis

Standard machine learning and deep learning architectures have been widely used in the field of sentiment analysis, but their performance is unsatisfactory if the input texts are short (e.g., social media posts). Specifically, the accuracy of standard machine learning methods crucially depends on the richness and completeness of the features used to represent the texts, and in the case of short messages, it is often difficult to obtain high-quality features. Conversely, methods based on deep learning can achieve better expressiveness, but these methods are computationally demanding and often suffer from over-fitting. This paper proposes a new adaptive evolutionary computational integrated learning model (AdaECELM) to overcome the problems encountered by traditional machine learning and deep learning models in sentiment analysis for short texts. AdaECELM consists of three phases: feature selection, sub classifier training, and global integration learning. First, a grid search is used for feature extraction and selection of term frequency-inverse document frequency (TF-IDF). Second, cuckoo search (CS) is introduced to optimize the combined hyperparameters in the sub-classifier support vector machine (SVM). Finally, the training set is divided into different feature subsets for sub-classifier training, and then the trained sub-classifiers are integrated and learned using the AdaBoost integrated soft voting method. Extensive experiments were conducted on six real polar sentiment analysis data sets. The results show that the AdaECELM model outperforms the traditional ML comparison methods according to evaluation metrics such as accuracy, precision, recall, and F1-score in all cases, and we report an improvement in accuracy exceeding 4.5%, the second-best competitor.

GraphPro: An interpretable graph neural network-based model for identifying promoters in multiple species

Promoters are DNA sequences that bind with RNA polymerase to initiate transcription, regulating this process through interactions with transcription factors. Accurate identification of promoters is crucial for understanding gene expression regulation mechanisms and developing therapeutic approaches for various diseases. However, experimental techniques for promoter identification are often expensive, time-consuming, and inefficient, necessitating the development of accurate and efficient computational models for this task. Enhancing the model's ability to recognize promoters across multiple species and improving its interpretability pose significant challenges. In this study, we introduce a novel interpretable model based on graph neural networks, named GraphPro, for multi-species promoter identification. Initially, we encode the sequences using k-tuple nucleotide frequency pattern, dinucleotide physicochemical properties, and dna2vec. Subsequently, we construct two feature extraction modules based on convolutional neural networks and graph neural networks. These modules aim to extract specific motifs from the promoters, learn their dependencies, and capture the underlying structural features of the promoters, providing a more comprehensive representation. Finally, a fully connected neural network predicts whether the input sequence is a promoter. We conducted extensive experiments on promoter datasets from eight species, including Human, Mouse, and Escherichia coli. The experimental results show that the average Sn, Sp, Acc and MCC values of GraphPro are 0.9123, 0.9482, 0.8840 and 0.7984, respectively. Compared with previous promoter identification methods, GraphPro not only achieves better recognition accuracy on multiple species, but also outperforms all previous methods in cross-species prediction ability. Furthermore, by visualizing GraphPro's decision process and analyzing the sequences matching the transcription factor binding motifs captured by the model, we validate its significant advantages in biological interpretability. The source code for GraphPro is available at https://github.com/liuliwei1980/GraphPro.

Purkinje cell models: past, present and future.

The investigation of the dynamics of Purkinje cell (PC) activity is crucial to unravel the role of the cerebellum in motor control, learning and cognitive processes. Within the cerebellar cortex (CC), these neurons receive all the incoming sensory and motor information, transform it and generate the entire cerebellar output. The relatively homogenous and repetitive structure of the CC, common to all vertebrate species, suggests a single computation mechanism shared across all PCs. While PC models have been developed since the 70's, a comprehensive review of contemporary models is currently lacking. Here, we provide an overview of PC models, ranging from the ones focused on single cell intracellular PC dynamics, through complex models which include synaptic and extrasynaptic inputs. We review how PC models can reproduce physiological activity of the neuron, including firing patterns, current and multistable dynamics, plateau potentials, calcium signaling, intrinsic and synaptic plasticity and input/output computations. We consider models focusing both on somatic and on dendritic computations. Our review provides a critical performance analysis of PC models with respect to known physiological data. We expect our synthesis to be useful in guiding future development of computational models that capture real-life PC dynamics in the context of cerebellar computations.