Computational Modeling Approach Research Articles

Observations and computational multi‐phase modelling in tropical river settings show complex channel changes downstream from rainfall‐triggered landslides

AbstractAlluvial river channels respond to changes in sediment supply by adjusting their geometry. Landslide sediment delivery and geomorphic response of river channels during floods are poorly understood and rarely examined in tropical settings. We investigate the impact of landslides on channel geomorphic changes during an extreme typhoon‐induced flood event in the Philippines, specifically the complex geomorphic response of the Antamok River to Typhoon Mangkhut in September 2018, which triggered >500 landslides in the Ambalanga catchment. The catchment has a legacy of anthropogenic modifications, such as extensive small‐scale (artisanal) mining and tailings storage facilities (TSFs) from large‐scale mining activities.We use a novel mix of mapping and computational modelling approaches to test the hypothesis that landslide sediment delivery is a major control on channel geomorphic change. Pre‐ and post‐event imagery show that the overall active channel area increased by 35.9% and the mean active channel width increased by 9.1 m. Spatially, we find no clear relationship between landslide sediment input or unit stream power and channel width geomorphic change, with longitudinal changes in active channel width complicated by TSFs. Multi‐phase modelling using r.avaflow revealed how landslide sediment delivery and TSFs interacted with the flow to generate the observed patterns of channel change. The model simulated channel incision in the upper parts of the catchment (up to 0.78 m) and deposition in the TSFs (up to 1.73 m).Our findings demonstrate that well‐established methods (e.g., stream power threshold) fail to fully explain channel width geomorphic changes, particularly for anthropogenically altered catchments. Integrating techniques, such as landslide mapping and multi‐phase computational modelling improves understanding of sediment supply's role in channel width change during extreme events. Numerical simulations also demonstrate that conventional assumptions of increased erosion and deposition with rising flow discharge are inaccurate with large sediment input, highlighting instead the effectiveness of multi‐phase models.

Strategies to optimize the intricate thermoelectric properties of 2D tetragonal silicene for energy harvesting: a computational modeling approach

Strategies to optimize the intricate thermoelectric properties of 2D tetragonal silicene for energy harvesting: a computational modeling approach

Development of a user-friendly hydraulic model for simulating hybrid water distribution networks: a transition toward sector sustainability

Abstract The conventional computational approach for modeling and simulating drinking water distribution networks (WDNs) is time-consuming and prone to human errors. Therefore, this study developed and implemented a hydraulic modeling tool, the ‘Kyema-Net model’, based on the modified Hardy Cross algorithm in Visual Basic, to simulate a hybrid WDN with the inclusion of pipe cost assessment. The model's accuracy was validated by comparing its outputs with those of EPANET 2.2 software. The Kyema-Net model and EPANET 2.2 simulation results were comparable. Considering the WDN case study, the proposed model initialized nodal inflows in proportions of 60 and 40% for any two pipes exiting the node, reducing the computational steps. The model executed three trials to achieve the final solution against four trials for EPANET 2.2. The mean absolute percentage error, coefficient of determination (R2), and root mean square error metrics for flow velocity were ∼0.00%, 1.00, and ∼0.00 m, respectively. The modified resilience index values ranged between 0.30 and 1.09. Implementing the proposed hydraulic modeling tool in designing and/or rehabilitating WDNs demonstrated compliance with numerous social-, economic-, and technological-related Sustainable Development Goals. This would assist various water sector stakeholders in the sustainable planning, operation, and management of WDNS globally.

A computational meshfree RPIM approach for phase-field modeling of brittle fracture

A computational meshfree RPIM approach for phase-field modeling of brittle fracture

Computational modeling of dorsal root ganglion stimulation using an Injectrode

Objective. Minimally invasive neuromodulation therapies like the Injectrode, which is composed of a tightly wound polymer-coated Platinum/Iridium microcoil, offer a low-risk approach for administering electrical stimulation to the dorsal root ganglion (DRG). This flexible electrode is aimed to conform to the DRG. The stimulation occurs through a transcutaneous electrical stimulation (TES) patch, which subsequently transmits the stimulation to the Injectrode via a subcutaneous metal collector. However, it is important to note that the effectiveness of stimulation through TES relies on the specific geometrical configurations of the Injectrode-collector-patch system. Hence, there is a need to investigate which design parameters influence the activation of targeted neural structures. Approach. We employed a hybrid computational modeling approach to analyze the impact of Injectrode system design parameters on charge delivery and neural response to stimulation. We constructed multiple finite element method models of DRG stimulation, followed by the implementation of multi-compartment models of DRG neurons. By calculating potential distribution during monopolar stimulation, we simulated neural responses using various parameters based on prior acute experiments. Additionally, we developed a canonical monopolar stimulation and full-scale model of bipolar bilateral L5 DRG stimulation, allowing us to investigate how design parameters like Injectrode size and orientation influenced neural activation thresholds. Main results. Our findings were in accordance with acute experimental measurements and indicate that the minimally invasive Injectrode system predominantly engages large-diameter afferents (Aβ-fibers). These activation thresholds were contingent upon the surface area of the Injectrode. As the charge density decreased due to increasing surface area, there was a corresponding expansion in the stimulation amplitude range before triggering any pain-related mechanoreceptor (Aδ-fibers) activity. Significance. The Injectrode demonstrates potential as a viable technology for minimally invasive stimulation of the DRG. Our findings indicate that utilizing a larger surface area Injectrode enhances the therapeutic margin, effectively distinguishing the desired Aβ activation from the undesired Aδ-fiber activation.

Prioritising national healthcare service issues from free text feedback – A computational text analysis & predictive modelling approach

Patient experience surveys have become a key source of evidence for supporting decision-making and continuous quality improvement within healthcare services. To harness free-text feedback collected as part of these surveys for additional insights, text analytics methods are increasingly employed when the data collected is not amenable to traditional qualitative analysis due to volume. However, while text analytics techniques offer good predictive capabilities, they have limited explanatory features often required in formal decision-making contexts, such as programme monitoring or evaluation. To overcome these limitations, this study integrates computational text and predictive modelling as part of a Computational Grounded Theory method to determine the effect of quality gaps in care dimensions and their prioritisation from free-text feedback. The feedback was collected as part of a national survey to support decisions on continuous improvement in Maternity Services in Ireland. Our approach enables (1) operationalising the service quality lexicon in the context of maternity care to explain the effect of quality gaps in care dimensions on overall satisfaction from free-text comments; and (2) extending the service quality lexicon with two organisational and political decision-making concepts: “Salience” and “Valence”, for prioritising perceived quality gaps. These methodological affordances enable the extension of service quality theory to explicitly support the prioritisation of improvement decisions which before now required additional decision frameworks. Results show that tangibles-, process-, and reliability-related care issues have the highest importance in our study context. We also find that hospital contexts partly determine the relative importance of gaps in care dimensions.

Abstract 6724: Mechanistic validation of MAIT cell engagers in HUB patient-derived organoids (PDO) co-culture model

Abstract In the field of cancer immunotherapy, bispecific antibodies have emerged as a promising approach for redirecting T-cells to target and combat tumors. However, their efficacy is limited to only a subset of patients. A major challenge that hinders the development of bispecific antibody targets is the lack of preclinical models that preserve patient-specific tumor antigens and recapitulate the complex interaction between the human immune system and tumor cells. HUB Organoids technology presents a valuable platform for novel immunotherapies testing and validation as they are directly derived from patient tumor tissues, accurately reflecting patient-specific tumor antigens. BIOMUNEX Pharmaceuticals has developed the BiXAb® technology platform, a computational modeling approach that rapidly generates bispecific antibodies from various monospecific monoclonal antibodies, eliminating the need for extensive engineering. This platform includes a bivalent T cell engager specifically targeting MAIT cells and human epithelial growth factor receptor (HER2), a tumor antigen highly expressed in some colorectal cancer (CRC) patients. This innovative approach forms an efficient immunological synapse, allowing exclusive redirection of MAIT cells to directly eliminate cancer cells. In a collaborative research initiative between HUB Organoids® and BIOMUNEX Pharmaceuticals, the mechanisms by which BiXAb® induce tumor cell killing were confirmed using CRC patient-derived organoids (HUB Organoids® or PDOs) co-cultured with allogenic MAIT cells in an in vitro model. The study results depicted BiXAb® induced MAIT-cell activation in a dose-dependent manner, as detected by FACS and ELISA. Furthermore, BiXAb®-mediated MAIT-cell-specific killing of CRC PDOs was observed in a HER2-dependent manner, determined using an image-based viability assay. In conclusion, the co-culture platform of CRC PDOs-MAIT cells described here effectively captured the activation and targeted killing of MAIT cells mediated by BiXAb®, underscoring the added value of this PDO-based co-culture platform in developing novel immunotherapies. Citation Format: Yasmine Abouleila, Julie Prigent, Mayke Doorn, Claire Germain, Sylvia Boj, Simon Plyte, Carla Verissimo. Mechanistic validation of MAIT cell engagers in HUB patient-derived organoids (PDO) co-culture model [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 1 (Regular Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(6_Suppl):Abstract nr 6724.

Building credibility of computational models in cardiovascular medicine through verification and validation

Building credibility of computational models in cardiovascular medicine through verification and validation Cardiovascular diseases affect 15 million people in Europe and digital solutions are now seen as very useful tools in the search for new drugs and medical devices. SimCardioTest is a 4-year project funded by the European Commission that aims to develop credible computer modelling and simulation approaches on a cloud-based platform for testing cardiac drugs and devices in silico.

Beyond Trolleyology: The CNI Model of Moral-Dilemma Responses.

How do people make judgments about actions that violate moral norms yet maximize the greater good (e.g., sacrificing the well-being of a small number of people for the well-being of a larger number of people)? Research on this question has been criticized for relying on highly artificial scenarios and for conflating multiple distinct factors underlying responses in moral dilemmas. The current article reviews research that used a computational modeling approach to disentangle the roles of multiple distinct factors in responses to plausible moral dilemmas based on real-world events. By disentangling sensitivity to consequences, sensitivity to moral norms, and general preference for inaction versus action in responses to realistic dilemmas, the reviewed work provides a more nuanced understanding of how people make judgments about the right course of action in moral dilemmas.

Immunomodulatory and anticytokine therapeutic potential of three Indian spices constituents and its hyaluronic acid conjugates for prevention and post COVID-19 complications: a computational modeling approach.

Targeted drug delivery to SARS-CoV-2 host target proteins for preventing or blocking COVID-19 infection is making serious concern during COVID-19 pandemic and its consequent waves around the globe. People seek reliable, effective folkloric preventive medication for immediate and precautionary relief from COVID-19. These folkloric medicines were effective and saved many patients during the past COVID-19 pandemic. The current research study aims to deliver antiviral Indian spices phytocompounds and their hyaluronic acid conjugates to human host target proteins (ACE-2, TNF-α, IL-6, IL-1β, PAR-1) of SARS-CoV-2 to inhibit virus propagation and also to regulate early clinical complications of COVID-19. Targeted drug delivery of hyaluronic acid conjugated traditional natural bioactive agent produces more effective and fewer side effects in delivering novel drugs to human host proteins of COVID-19. In silico molecular docking study of six phytocompounds from three Indian spices and standard drug atazanavir and its hyaluronic acid conjugates reveals that phytocompounds and its hyaluronic acid conjugates possess high affinity to binding pockets of SARS-CoV-2 human host targets with more binding affinity scores. Most notably HA cyclocurcumin exhibit a docking score -9.9 kcal/mol against ACE-2 (PDB ID 1R42) target protein similarly HA-Hydrazinocurcumin exhibit a docking score -9.8 kcal/mol against PAR-1 (PDB ID 3VW7). ADMET validation of phytocompounds and their hyaluronic acid conjugates reveals its best pharmacokinetic profile over standard antiviral drug especially HA cyclocurcumin conjugate possesses high HIA (86%) and good pharmacokinetic profiles. DFT analysis affirms the reason behind the higher binding affinity of hyaluronic acid conjugates of spices phytocompounds towards all screened target proteins especially HA-hydrazinocurcumin conjugate possess high softness (19.1570 eV) and low hardness (0.0522 eV) values. Finally, MD simulation of best-docked compounds against ACE-2 and PAR-1 target protein revealed that hyaluronic acid conjugates of Indian spices compounds exhibit stable RMSD values and more protein-ligand interactions during simulation than hyaluronic acid conjugates of drug atazanavir.Communicated by Ramaswamy H. Sarma.

Multi-scaled temporal modeling of cardiovascular disease progression: An illustration of proximal arteries in pulmonary hypertension

The progression of cardiovascular disease is intricately influenced by a complex interplay between physiological pathways, biochemical processes, and physical mechanisms. This study aimed to develop an in-silico physics-based approach to comprehensively model the multifaceted vascular pathophysiological adaptations. This approach focused on capturing the progression of proximal pulmonary arterial hypertension, which is significantly associated with the irreversible degradation of arterial walls and compensatory stress-induced growth and remodeling. This study incorporated critical characteristics related to the distinct time scales for the deformation, thus reflecting the impact of mean pressure on artery growth and tissue damage. The in-silico simulation of the progression of pulmonary hypertension was realized based on computational code combined with the finite element method (FEM) for the simulation of disease progression. The parametric studies further explored the consequences of these irreversible processes. This computational modeling approach may advance our understanding of pulmonary hypertension and its progression.

Physics based optical modeling of iron disulfide thin films

In this work, physics based optical modeling is carried out using iron disulfide thin films deposited by using a plasma-assisted, radio frequency-powered technique. Iron disulfide is a transition metal dichalcogenide material, exhibiting a variety of unique and excellent characteristics. Various characterization techniques are employed to examine the growth rate, film thickness, and behavior of as-grown iron disulfide thin films. Furthermore, the physics based optical modeling was performed using a combination of experimental techniques and computer modeling approaches. The analyzed thin films exhibit a bandgap of around 1.16 eV. The theoretically calculated values of absorbance, transmission, and reflectance show a good match with the experimental measurements. Moreover, a physics based optical model is developed based on the experimental data and is used to calculate the external quantum efficiency and the optically generated current density of the iron disulfide films to provide insight into its use as an absorber layer.

Global dynamics and computational modeling approach for analyzing and controlling of alcohol addiction using a novel fractional and fractal–fractional modeling approach

In recent years, alcohol addiction has become a major public health concern and a global threat due to its potential negative health and social impacts. Beyond the health consequences, the detrimental consumption of alcohol results in substantial social and economic burdens on both individuals and society as a whole. Therefore, a proper understanding and effective control of the spread of alcohol addictive behavior has become an appealing global issue to be solved. In this study, we develop a new mathematical model of alcohol addiction with treatment class. We analyze the dynamics of the alcohol addiction model for the first time using advanced operators known as fractal–fractional operators, which incorporate two distinct fractal and fractional orders with the well-known Caputo derivative based on power law kernels. The existence and uniqueness of the newly developed fractal–fractional alcohol addiction model are shown using the Picard–Lindelöf and fixed point theories. Initially, a comprehensive qualitative analysis of the alcohol addiction fractional model is presented. The possible equilibria of the model and the threshold parameter called the reproduction number are evaluated theoretically and numerically. The boundedness and biologically feasible region for the model are derived. To assess the stability of the proposed model, the Ulam–Hyers coupled with the Ulam–Hyers–Rassias stability criteria are employed. Moreover, utilizing effecting numerical schemes, the models are solved numerically and a detailed simulation and discussion are presented. The model global dynamics are shown graphically for various values of fractional and fractal dimensions. The present study aims to provide valuable insights for the understanding the dynamics and control of alcohol addiction within a community.

Eco-Innovative Construction: Integrating Green Roofs Design within the BIM Framework

This research delves into the integration of green roofs elements and parameters with Building Information Modeling (BIM), a pivotal advancement in sustainable urban construction. Aligning with the United Nations Sustainable Development Goals, particularly Goal 11, this study explores how this integration can address global challenges like climate change and resource depletion. Using the Dynamo Visual Programming Language within Autodesk Revit, this research develops a computational modeling approach for green roofs, focusing on their thermal and structural characteristics under varied environmental conditions. Key findings demonstrate the significant influence of substrate and drainage material combinations on green roofs’ thermal performance, highlighting the need for tailored designs based on climatic conditions. This study also emphasizes the importance of considering structural performance in both dry and saturated conditions for overall building integrity. This research identifies gaps in current practices, such as limited focus on specific green roof materials and reliance on certain software tools, suggesting the need for broader material selection and software adaptability. Future research directions include expanding material selections, exploring diverse environmental conditions, and integrating green roofs elements and parameters with various BIM software platforms. This study’s implications extend to stakeholders in the construction industry, offering a framework for architects, urban planners, and policymakers to design and implement green roofs aligned with environmental goals. This work contributes to the field by proposing a novel approach to sustainable construction, integrating ecological design with digital technology, and setting a new benchmark in the integration of green roofs design with BIM. By addressing these aspects, this research paves the way for future developments in sustainable urban construction, enhancing the efficiency, sustainability, and resilience of urban communities in line with global sustainability objectives.

Interactions between calcium-induced arrhythmia triggers and the electrophysiological-anatomical substrate underlying the induction of atrial fibrillation.

Atrial fibrillation (AF) is the most common cardiac arrhythmia and is sustained by spontaneous focal excitations and re-entry. Spontaneous electrical firing in the pulmonary vein (PV) sleeves is implicated in AF generation. The aim of this simulation study was to identify the mechanisms determining the localisation of AF triggers in the PVs and their contribution to the genesis of AF. A novel biophysical model of the canine atria was used that integrates stochastic, spontaneous subcellular Ca2+ release events (SCRE) with regional electrophysiological heterogeneity in ionic properties and a detailed three-dimensional model of atrial anatomy, microarchitecture and patchy fibrosis. Simulations highlighted the importance of the smaller inward rectifier potassium current (IK1 ) in PV cells compared to the surrounding atria, which enabled SCRE more readily to result in delayed-afterdepolarisations that induced triggered activity. There was a leftward shift in the dependence of the probability of triggered activity on sarcoplasmic reticulum Ca2+ load. This feature was accentuated in 3D tissue compared to single cells (Δ half-maximal [Ca2+ ]SR =58μM vs. 22μM). In 3D atria incorporating electrical heterogeneity, excitations preferentially emerged from the PV region. These triggered focal excitations resulted in transient re-entry in the left atrium. Addition of fibrotic patches promoted localised emergence of focal excitations and wavebreaks that had a more substantial impact on generating AF-like patterns than the PVs. Thus, a reduced IK1 , less negative resting membrane potential, and fibrosis-induced changes of the electrotonic load all contribute to the emergence of complex excitation patterns from spontaneous focal triggers. KEY POINTS: Focal excitations in the atria are most commonly associated with the pulmonary veins, but the mechanisms for this localisation are yet to be elucidated. We applied a multi-scale computational modelling approach to elucidate the mechanisms underlying such localisations. Myocytes in the pulmonary vein region of the atria have a less negative resting membrane potential and reduced time-independent potassium current; we demonstrate that both of these factors promote triggered activity in single cells and tissues. The less negative resting membrane potential also contributes to heterogeneous inactivation of the fast sodium current, which can enable re-entrant-like excitation patterns to emerge without traditional conduction block.

Genome-scale and pathway engineering for the sustainable aviation fuel precursor isoprenol production in Pseudomonas putida

Sustainable aviation fuel (SAF) will significantly impact global warming in the aviation sector, and important SAF targets are emerging. Isoprenol is a precursor for a promising SAF compound DMCO (1,4-dimethylcyclooctane) and has been produced in several engineered microorganisms. Recently, Pseudomonas putida has gained interest as a future host for isoprenol bioproduction as it can utilize carbon sources from inexpensive plant biomass. Here, we engineer metabolically versatile host P. putida for isoprenol production. We employ two computational modeling approaches (Bilevel optimization and Constrained Minimal Cut Sets) to predict gene knockout targets and optimize the “IPP-bypass” pathway in P. putida to maximize isoprenol production. Altogether, the highest isoprenol production titer from P. putida was achieved at 3.5 g/L under fed-batch conditions. This combination of computational modeling and strain engineering on P. putida for an advanced biofuels production has vital significance in enabling a bioproduction process that can use renewable carbon streams.

Three-Dimensional Simulation for Radon Migration in Fractured Rock Masses: A Computational Modeling Approach

Three-Dimensional Simulation for Radon Migration in Fractured Rock Masses: A Computational Modeling Approach

Engineering of glycoside hydrolase family 7 cellobiohydrolases directed by natural diversity screening

Protein engineering and screening of processive fungal cellobiohydrolases (CBHs) remain challenging due to limited expression hosts, synergy-dependency, and recalcitrant substrates. In particular, GH7 CBHs are a family critically important for the bioeconomy as well as a methodology for improving these and other difficult to engineer enzymes. Here, we target the discovery of highly active natural GH7 CBHs and engineering of variants with improved activity. Using experimentally assayed activities of genome mined CBHs, we applied sequence and structural alignments to top performers to identify key point mutations linked to improved activity. From ∼1500 known GH7 sequences, an evolutionarily diverse subset of 57 GH7 CBH genes was expressed in Trichoderma reesei and screened using a multiplexed activity screening assay. Ten catalytically enhanced natural variants were identified, produced, purified, and tested for efficacy using industrially-relevant conditions and substrates. Three key amino acids in CBHs with performance comparable or superior to Penicillium funiculosum Cel7A were identified and combinatorically engineered into P. funiculosum cel7a, expressed in T. reesei, and assayed on lignocellulosic biomass. The top performer generated using this combined approach of natural diversity genome mining, experimental assays, and computational modeling produced a 41% increase in conversion extent over native P. funiculosum Cel7A, a 55% increase over the current industrial standard T. reesei Cel7A, and 10% improvement over Aspergillus oryzae Cel7C, the best natural GH7 CBH previously identified in our laboratory.

Modeling and prediction of business success: a survey

Businesses are the driving force behind economic systems and are the lifeblood of the community. A business shares striking similarity to a living organism, including birth, infancy, rising, prosperity, and falling. The success of a business is not only important to the owners, but is also critical to the regional/domestic economic system, or even the global economy. Recent years have witnessed many new emerging businesses with tremendous success, such as Google, Apple, Facebook etc., yet millions of businesses also fail or fade out within a rather short period of time. Finding patterns/factors connected to the business rise and fall remains a long lasting question puzzling many economists, entrepreneurs, and government officials. Recent advancement in artificial intelligence, especially machine learning, has lend researchers powers to use data to model and predict business success. However, due to data driven nature of all machine learning methods, existing approaches are rather domain-driven and ad-hoc in their design and validations. In this paper, we propose a systematic review of modeling and prediction of business success. We first outline a triangle framework to showcase three parities connected to the business: Investment-Business-Market (IBM). After that, we align features into three main categories, each of which is focused on modeling a business from a particular perspective, such as sales, management, innovation etc., and further summarize different types of machine learning and deep learning methods for business modeling and prediction. The survey provides a comprehensive review of computational approaches for business performance modeling and prediction.

Segmenting mechanically heterogeneous domains via unsupervised learning.

From biological organs to soft robotics, highly deformable materials are essential components of natural and engineered systems. These highly deformable materials can have heterogeneous material properties, and can experience heterogeneous deformations with or without underlying material heterogeneity. Many recent works have established that computational modeling approaches are well suited for understanding and predicting the consequences of material heterogeneity and for interpreting observed heterogeneous strain fields. In particular, there has been significant work toward developing inverse analysis approaches that can convert observed kinematic quantities (e.g., displacement, strain) to material properties and mechanical state. Despite the success of these approaches, they are not necessarily generalizable and often rely on tight control and knowledge of boundary conditions. Here, we will build on the recent advances (and ubiquity) of machine learning approaches to explore alternative approaches to detect patterns in heterogeneous material properties and mechanical behavior. Specifically, we will explore unsupervised learning approaches to clustering and ensemble clustering to identify heterogeneous regions. Overall, we find that these approaches are effective, yet limited in their abilities. Through this initial exploration (where all data and code are published alongside this manuscript), we set the stage for future studies that more specifically adapt these methods to mechanical data.