Computational Model 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.

Deep Learning Meets Machine Learning: A Synergistic Approach towards Artificial Intelligence

The evolution of artificial intelligence (AI) has progressed from rule-based systems to learning-based models, integrating machine learning (ML) and deep learning (DL) to tackle complex data-driven tasks. This review examines the synergy between ML, which utilizes algorithms like decision trees and support vector machines for structured data, and DL, which employs neural networks for processing unstructured data such as images and natural language. The combination of these paradigms through hybrid ML-DL models has enhanced prediction accuracy, scalability, and automation across domains like healthcare, finance, natural language processing, and robotics. However, challenges such as computational demands, data dependency, and model interpretability remain. This paper discusses the benefits, limitations, and future potential of ML and DL and also provides a review study of a hybrid model makes use of both techniques (machine learning & deep learning) advantages to solve complicated problems more successfully than one could on its own. To boost performance, increase efficiency, or address scenarios where either ML or DL alone would not be able to manage, this approach combines deep learning structures with conventional machine learning techniques.

Predicting cell type-specific epigenomic profiles accounting for distal genetic effects.

Understanding how genetic variants affect the epigenome is key to interpreting GWAS, yet profiling these effects across the non-coding genome remains challenging due to experimental scalability. This necessitates accurate computational models. Existing machine learning approaches, while progressively improving, are confined to the cell types they were trained on, limiting their applicability. Here, we introduce Enformer Celltyping, a deep learning model which incorporates distal effects of DNA interactions, up to 100,000 base-pairs away, to predict epigenetic signals in previously unseen cell types. Using DNA and chromatin accessibility data for epigenetic imputation, Enformer Celltyping outperforms current best-in-class approaches and generalises across cell types and biological regions. Moreover, we propose a framework for evaluating models on genetic variant effect prediction using regulatory quantitative trait loci mapping studies, highlighting current limitations in genomic deep learning models. Despite this, Enformer Celltyping can also be used to study cell type-specific genetic enrichment of complex traits.

Two dimensional computational model of calcium and IP3 interacting system dynamics in an obese hepatocyte cell

Abstract Calcium ion (Ca$^{2+}$) signaling is crucial in regulating numerous cellular processes vital for preserving structural integrity and functional equilibrium across diverse cell types. Both the calcium stores and mitochondria play significant roles in this signaling pathway. The calcium source may be in the form of a blip or a puff depending on the various conditions of the cellular systems. The one dimensional model of calcium dynamics with IP$_{3}$ gives crucial insight of feedback mechanisms influencing calcium homeostasis. In order to obtain deeper insights of local impacts of various mechanisms and feedbacks in hepatocyte cell, it is necessary to develop the models in higher dimensions. In order to get more deeper insights, two dimensional model is proposed assuming the phenomena to be uniform along z dimension. This research presents a two-dimensional computational model to analyse the interactive system dynamics of inositol 1,4,5-trisphosphate (IP$_{3}$) and Ca$^{2+}$, aiming to assess how these signaling patterns influence hepatocyte functionality which allows to incorporate puff type of calcium source under both obese and normal physiological states. It further examines the implications of calcium signaling on NADH synthesis, ATP production, and degradation rates. Numerical simulations are executed utilising the Crank-Nicolson method for temporal analysis and the Linear Finite Element Method for spatial analysis. Additionally, the study conducts a comparative analysis of calcium signaling between obese and normal hepatocyte. The findings offer enhanced insights into the interactive system dynamics of IP$_{3}$ and Ca$^{2+}$ in hepatocytes, elucidating the effects of various parameter alterations on cellular behaviour in both states.

An Agent-Based Model of Human–Whale Interactions in the North Atlantic

We created an agent-based model (ABM; a computational model) for human–whale interactions in the North Atlantic area. We specifically looked at boats, lobster lines, and rogue fishnet interactions with the North Atlantic right whales (Eubalaena glacialis), and we assessed the conditions under which such interactions are more likely to result in injuries and fatalities for this critically endangered whale, such as the density of ships in the water. ABMs are a methodology particularly useful in data poor problems (where machine learning cannot be used), and are based on rules of interactions between various agents (human and nonhuman) and/or a specific physical environment; they can showcase and assess potential future scenarios. We informed our model with current data from the North Atlantic Right Whale Consortium and the National Oceanic and Atmospheric Administration and present our preliminary findings here.

Integrative Review-Based Conceptual Modeling: An Agent-Based Modeling Synthesis of Dynamic Energy Tariff Research and Models

Adoption of dynamic energy tariffs by households is crucial for the transition to carbon-neutral energy systems. Influencing the adoption patterns of these tariffs necessitates an examination of the drivers, decision components, and contextual factors influencing household decisions. Few computational models address this comprehensively, often omitting non-financial decision variables. Moreover, methodologically robust integrative reviews on this topic are scarce. To address this gap, this paper develops a concept-centered integrative review methodology aimed at deriving computer models for socio-techno-economic simulations of household adoption of sustainable technologies. The methodology encompasses five sequential phases: Setup, Literature Search, Analysis, Synthesis and Conceptual Model, and Discussion. To illustrate the methodology, it is applied to the case of household adoption of dynamic energy tariffs, resulting in an abstract conceptual model adaptable to local contexts. The review reveals a lack of consensus on modeled tariffs but highlights the significance of tariff complexity, relative advantage, household heterogeneity, and various agent properties. It also identifies potential improvements in model fundamentals, particularly spatial modeling. The developed process model focuses on the stages ‘knowledge’, ‘decision’, and ‘reevaluation’. The article contributes by presenting a comprehensive review scheme and delivering a concept-centered integrative review along with an explicit conceptual model derived from it.

Quasinormal mode expansion in thin elastic plates

Elastic wave manipulation using large arrays of resonators is driving the need for advanced simulation and optimization methods. To address this we introduce and explore a robust framework for wave control: quasinormal modes (QNMs). Specifically, we consider the problem for thin elastic plates, where the Green's function formalism is well known and readily exploited to solve multiple scattering problems. By studying the associated nonlinear eigenvalue problem we derive a dispersive QNM expansion, providing a reduced-order model for efficient forced response computations which reveals physical insight into the resonant mode excitation. Furthermore, we derive eigenvalue sensitivities with respect to resonator parameters and apply a gradient-based optimization to design quasi-bound states in the continuum and position eigenfrequencies precisely in the complex plane. Scattering simulations validate our approach in structures such as graded line arrays and quasicrystals. Drawing on QNM concepts from electromagnetism we demonstrate significant advances in elastic metamaterials, highlighting their potential for tailored wave manipulation. Published by the American Physical Society 2024

Multimodal fusion-powered English speaking robot

IntroductionSpeech recognition and multimodal learning are two critical areas in machine learning. Current multimodal speech recognition systems often encounter challenges such as high computational demands and model complexity.MethodsTo overcome these issues, we propose a novel framework-EnglishAL-Net, a Multimodal Fusion-powered English Speaking Robot. This framework leverages the ALBEF model, optimizing it for real-time speech and multimodal interaction, and incorporates a newly designed text and image editor to fuse visual and textual information. The robot processes dynamic spoken input through the integration of Neural Machine Translation (NMT), enhancing its ability to understand and respond to spoken language.Results and discussionIn the experimental section, we constructed a dataset containing various scenarios and oral instructions for testing. The results show that compared to traditional unimodal processing methods, our model significantly improves both language understanding accuracy and response time. This research not only enhances the performance of multimodal interaction in robots but also opens up new possibilities for applications of robotic technology in education, rescue, customer service, and other fields, holding significant theoretical and practical value.

Prognostic value of morphology and hemodynamics in moyamoya disease for long-term outcomes and disease progression.

To explore the relationship between morphological and hemodynamic parameters, baseline characteristics, and long-term outcomes in patients with moyamoya disease (MMD) using a computational fluid dynamics model. We retrospectively reviewed 129 patients at Beijing Tiantan hospital between July 2020 and December 2021. Perioperative clinical variables and Suzuki stage were recorded. Logistic regression analysis was employed to identify the risk factors for unfavorable long-term outcomes. The association between morphological, CT perfusion parameters, hemodynamic parameters and the Suzuki stage, clinical variables of MMD was also analyzed. Patients with high relative Wall Shear Stress (rWSS) were older and had more cases with higher Suzuki stage and worse follow-up mRS scores (p < 0.05). High rWSS at the terminal ICA and diabetes mellitus were identified as independent predictors of unfavorable long-term outcomes [OR = 3.039(1.191-7.754), p = 0.020; OR = 3.164(1.141-8.723), p = 0.027, respectively]. ROC analysis demonstrated that predictive models incorporating rWSS improved AUC values, with the highest AUC in Model 2 (AUC = 0.889). High rWSS was significantly associated with future TIA and stroke events (p = 0.032). We speculated that high rWSS and diabetes mellitus were independent risk factors for unfavorable long-term outcomes in patients with MMD. rWSS and morphological parameters are crucial for predicting MMD progression and understanding its pathogenesis.

Hydrogel-based Soft Robotics for Surgical Machinery

Hydrogel-based soft robotics represent a transformative approach in surgical machinery, offering unparalleled adaptability, biocompatibility, and precision for minimally invasive procedures. Hydrogels, with their high water content and tunable mechanical properties, mimic soft biological tissues, making them ideal for applications in delicate surgical environments. This research explores the integration of hydrogel materials into soft robotic systems, focusing on their fabrication, actuation mechanisms, and performance in surgical applications. Key advancements include the development of stimuli-responsive hydrogels that enable precise control of movement and force, enhancing the capability to navigate complex anatomical structures. The study also examines computational models for simulating hydrogel behavior and optimizing robotic designs. While the potential benefits of hydrogel-based soft robotics in improving patient outcomes are significant, challenges remain in ensuring durability, scalability, and reliable control systems. This research aims to address these limitations by integrating advanced materials science with robotic engineering. By doing so, it contributes to the evolution of surgical technologies, paving the way for safer and more effective procedures.

Physical reservoir computing: a tutorial

AbstractThis tutorial covers physical reservoir computing from a computer science perspective. It first defines what it means for a physical system to compute, rather than merely evolve under the laws of physics. It describes the underlying computational model, the Echo State Network (ESN), and also some variants designed to make physical implementation easier. It explains why the ESN model is particularly suitable for direct physical implementation. It then discusses the issues around choosing a suitable material substrate, and interfacing the inputs and outputs. It describes how to characterise a physical reservoir in terms of benchmark tasks, and task-independent measures. It covers optimising configuration parameters, exploring the space of potential configurations, and simulating the physical reservoir. It ends with a look at the future of physical reservoir computing as devices get more powerful, and are integrated into larger systems.

Influence of material orientation, loading angle, and single-shot repetition of laser shock peening on surface roughness properties

Titanium alloy Ti6Al4V is extensively utilized in biomedical applications due to its excellent biocompatibility, corrosion resistance, and mechanical properties. The design of dental implant surface textures has changed throughout time to address issues with oral rehabilitation in both healthy and damaged bones. The longevity of an implant is significantly impacted by surface roughness. This study examines the use of laser shock peening (LSP) as a surface modification technique to improve the mechanical properties of implants. A numerical model is developed using the commercial finite elements software in ABAQUS/Explicit for simulating dynamic conditions. The aim of the study is to develop surface roughness parameters using computational methods such as studies have not yet been contemplated. The single shot angle, shot repeat, time, material orientation, and laser power are applied for the first time simultaneously to evaluate the impact of material orientation and loading angles on surface roughness parameters. The study showed that the developed computational model’s compressive residual stress was −578.45 MPa, while the experimental samples were −592.18 MPa. Consequently, the difference between the computational and experimental results was 2.32%. Without regard to material orientation or angle, the compressive residual stress of the samples under examination was found to be −578.450 MPa after three repetitions and to decreased to −1.620 MPa after four. These results demonstrate that by varying the material orientation and loading angle, the Ra value may be increased four times.

Adaptive algorithms for drone flight control under communication constraints and information incompleteness

Abstract With the rapid increase in the use of drones in various applications, including commercial and governmental, and the increasing probability of communication failures and contingencies, research becomes critical to ensure the safety and efficiency of their operations. The aim of this research is to develop adaptive drone flight control algorithms capable of operating effectively under conditions of limited communication and incomplete information to ensure reliable and safe autonomous operation of these systems. The applied methods include computer modelling and simulation, analytical, statistical, functional, deductive and descriptive methods. The study found that the use of performance evaluation methods for complex systems enables the identification of safety and performance criteria for drones, and drone flight control provides basic principles and methods that can be adapted for drones, including autopiloting and navigation. In addition, analyses of satellite communication and navigation prove the need to consider the limitations of this technology when developing drone control algorithms. The combination of these techniques allows for more robust and adaptive drone control systems that can function effectively in complex environments such as communication limitations and incomplete information. Additionally, it was found that the integration of adaptive control algorithms based on these methods allows drones to effectively adapt to variable environmental conditions and make decisions quickly even when communication is lost or information is limited.

Bifurcation and sensitivity analysis along with the numerical simulations of susceptible breaking-out quarantine treatment recovered computer model

Bifurcation and sensitivity analysis along with the numerical simulations of susceptible breaking-out quarantine treatment recovered computer model

Improving computational models of deep brain stimulation through experimental calibration.

Deep brain stimulation has become a well-established clinical tool to treat movement disorders. Nevertheless, the knowledge of processes initiated by the stimulation remains limited. To address this knowledge gap, computational models are developed to gain deeper insight. However, their predictive power remains constrained by model uncertainties and a lack of validation and calibration. Exemplified with rodent microelectrodes, we present a workflow for validating electrode model geometry using microscopy and impedance spectroscopy in vitro before implantation. We address uncertainties in the tissue distribution and dielectric properties and outline a concept for calibrating the computational model based on in vivo impedance spectroscopy measurements. The standard deviation of the volume of tissue activated across the 18 characterized electrodes was approximately 32.93%, underscoring the importance of electrode characterization. Thus, the workflow significantly enhances the model predictions' credibility of neural activation exemplified in a rodent model. Computational models are frequently employed without validation or calibration, relying instead on manufacturers' specifications. Our approach provides an accessible method to obtain a validated and calibrated electrode geometry, which significantly enhances the reliability of the computational model that relies on this electrode. By reducing the uncertainties of the model, the accuracy in predicting neural activation is increased. The entire workflow is realized in open-source software, making it adaptable for other use cases, such as deep brain stimulation in humans. Additionally, the framework allows for the integration of further experiments, enabling live updates and refinements to computational models.

CRBPSA: CircRNA-RBP interaction sites identification using sequence structural attention model.

Due to the ability of circRNA to bind with corresponding RBPs and play a critical role in gene regulation and disease prevention, numerous identification algorithms have been developed. Nevertheless, most of the current mainstream methods primarily capture one-dimensional sequence features through various descriptors, while neglecting the effective extraction of secondary structure features. Moreover, as the number of introduced descriptors increases, the issues of sparsity and ineffective representation also rise, causing a significant burden on computational models and leaving room for improvement in predictive performance. Based on this, we focused on capturing the features of secondary structure in sequences and developed a new architecture called CRBPSA, which is based on a sequence-structure attention mechanism. Firstly, a base-pairing matrix is generated by calculating the matching probability between each base, with a Gaussian function introduced as a weight to construct the secondary structure. Then, a Structure_Transformer is employed to extract base-pairing information and spatial positional dependencies, enabling the identification of binding sites through deeper feature extraction. Experimental results using the same set of hyperparameters on 37 circRNA datasets, totaling 671,952 samples, show that the CRBPSA algorithm achieves an average AUC of 99.93%, surpassing all existing prediction methods. CRBPSA is a lightweight and efficient prediction tool for circRNA-RBP, which can capture structural features of sequences with minimal computational resources and accurately predict protein-binding sites. This tool facilitates a deeper understanding of the biological processes and mechanisms underlying circRNA and protein interactions.

Designing a Water Pipeline to Last 100 Years or More

Key TakeawaysAlthough superior materials and advanced techniques are available, designing a pipeline to supply water for 100 years or more is not necessarily the optimal choice for a utility.Numerous factors come into play when planning for pipe design, installation, monitoring, and maintenance, not the least of which is uncertainty about the future.Tools such as computational modeling, life‐cycle cost analysis, forensic analysis, and scenario planning through digital design can help a utility create a cost‐effective pipeline plan.

The natural stability of autonomous morphology: how an attraction–repulsion dynamic emerges from paradigm cell filling

AbstractAutonomous morphology, such as inflection class systems and paradigmatic distribution patterns, is widespread and diachronically resilient in natural language. Why this should be so has remained unclear given that autonomous morphology imposes learning costs, offers no clear benefit relative to its absence and could easily be removed by the analogical forces which are constantly reshaping it. Here we propose an explanation for the resilience of autonomous morphology, in terms of a diachronic dynamic of attraction and repulsion between morphomic categories, which emerges spontaneously from a simple paradigm cell filling process. Employing computational evolutionary models, our key innovation is to bring to light the role of ‘dissociative evidence’, i.e., evidence for inflectional distinctiveness which a rational reasoner will have access to during analogical inference. Dissociative evidence creates a repulsion dynamic which prevents morphomic classes from collapsing together entirely, i.e., undergoing complete levelling. As we probe alternative models, we reveal the limits of conditional entropy as a measure for predictability in systems that are undergoing change. Finally, we demonstrate that autonomous morphology, far from being ‘unnatural’, is rather the natural (emergent) consequence of a natural (rational) process of inference applied to inflectional systems.

Application of physical and mathematical modeling in the educational process by the example of power plant simulators

This article discusses the application of digital twin technology in thermal power engineering. Special attention is paid to the influence of digital models of the existing equipment of the powerplant on the educational process in higher educational institutions and professional development of service personnel. The article summarizes the results of the introduction of power plant simulators into the curriculum, the methodology of laboratory work and the organization of training sessions. It is also shown that the introduction of physical and mathematical modeling in education and practice contributes to improving the safety and efficiency of work in the energy sector. The results of the introduction of plant simulators into the curriculum are summarized. The software of computer equipment provided for classes at a higher educational institution is considered as a tool for practicing operation skills and creating digital models. When working in a software package, the user is given the opportunity to control units of equipment individually, collectively as a whole unit, or create computational models consisting of simple blocks of equipment. The proposed mutual cooperation between software developers and universities can lead to the creation of digital models to enable retraining of specialists in the workplace, as well as increasing the level of knowledge of graduates. The result of the introduction of a virtual testing ground in a higher educational institution in order to verify the knowledge of students is considered. Thus, the introduction of digital twins into the educational process and practice of work in the thermal power industry is a promising direction for the development of the industry. It contributes to improving the level of training of specialists, ensuring the safety and efficiency of equipment operation, as well as reducing the risks of emergency situations

Retraction Note: Optical network modelling-based data analytics for network monitoring and security analysis using hybrid computing models

Retraction Note: Optical network modelling-based data analytics for network monitoring and security analysis using hybrid computing models