Computational Model Research Articles

HMMF: a hybrid multi-modal fusion framework for predicting drug side effect frequencies

BackgroundThe identification of drug side effects plays a critical role in drug repositioning and drug screening. While clinical experiments yield accurate and reliable information about drug-related side effects, they are costly and time-consuming. Computational models have emerged as a promising alternative to predict the frequency of drug-side effects. However, earlier research has primarily centered on extracting and utilizing representations of drugs, like molecular structure or interaction graphs, often neglecting the inherent biomedical semantics of drugs and side effects.ResultsTo address the previously mentioned issue, we introduce a hybrid multi-modal fusion framework (HMMF) for predicting drug side effect frequencies. Considering the wealth of biological and chemical semantic information related to drugs and side effects, incorporating multi-modal information offers additional, complementary semantics. HMMF utilizes various encoders to understand molecular structures, biomedical textual representations, and attribute similarities of both drugs and side effects. It then models drug-side effect interactions using both coarse and fine-grained fusion strategies, effectively integrating these multi-modal features.ConclusionsHMMF exhibits the ability to successfully detect previously unrecognized potential side effects, demonstrating superior performance over existing state-of-the-art methods across various evaluation metrics, including root mean squared error and area under receiver operating characteristic curve, and shows remarkable performance in cold-start scenarios.

Exploring the inhibitory action of betulinic acid on key digestive enzymes linked to diabetes via in vitro and computational models: approaches to anti-diabetic mechanisms

ABSTRACT Phytochemicals are now increasingly exploited as remedial agents for the management of diabetes due to side effects attributable to commercial antidiabetic agents. This study investigated the structural and molecular mechanisms by which betulinic acid exhibits its antidiabetic effect via in vitro and computational techniques. In vitro antidiabetic potential was analysed via on α-amylase, α-glucosidase, pancreatic lipase and α-chymotrypsin inhibitory assays. Its structural and molecular inhibitory mechanisms were investigated using Density Functional Theory (DFT) analysis, molecular docking and molecular dynamics (MD) simulation. Betulinic acid significantly (p < 0.05) inhibited α-amylase, α-glucosidase, pancreatic lipase and α-chymotrypsin enzymes with IC50 of 70.02 μg/mL, 0.27 μg/mL, 1.70 μg/mL and 8.44 μg/mL, respectively. According to DFT studies, betulinic acid possesses similar reaction in gaseous phase and water due to close values observed for highest occupied molecular orbital (HOMO) and lowest occupied molecular orbital (LUMO) and the chemical descriptors. The dipole moment indicates that betulinic acid has high polarity. Molecular electrostatic potential surface revealed the electrophilic and nucleophilic attack-prone atoms of the molecule. Molecular dynamic studies revealed a stable complex between betulinic acid and α-amylase, α-glucosidase, pancreatic lipase and α-chymotrypsin. The study elucidated the potent antidiabetic properties of betulinic acid by revealing its conformational inhibitory mode of action on enzymes involved in the onset of diabetes.

Advanced Image Analysis and Machine Learning Models for Accurate Cover Factor and Porosity Prediction in Knitted Fabrics: Tailored Applications in Sportswear, Swimwear, and Casual Wear

This paper presents a study focused on developing robust algorithms for cover factor and porosity calculation through digital image analysis. Computational models based on machine learning for efficient cover factor prediction based on fabric parameters have also been developed. Five algorithms were devised and implemented in MATLAB: the single threshold algorithm (ST); multiple linear threshold algorithms, ML-1 and ML-2; and algorithms with multiple thresholds obtained by the Otzu method, MT-1 and MT-2. These algorithms were applied to knitted fabrics used for football, swimming, and leisure. Algorithms ML-1 and MT-1, employing multiple thresholds, outperformed the single threshold algorithm. The ML-1 variant yielded the highest average porosity value at 95.24%, indicating the importance of adaptable thresholding in image analysis. Comparative analysis revealed that algorithm variants ML-2 and MT-2 obtain lower cover factors compared to ML-1 and MT-1 but can detect potential void areas in fabrics with higher reliability. Algorithm MT-1 proved to be the most sensitive when it came to distinguishing between different fabric samples. Computational models that were developed based on random tree, random forest, and SMOreg machine learning algorithms predicted cover factor based on fabric parameters with up to 95% accuracy.

Research on wind resistance principles and design of super high-rise buildings

Due to the high and flexible characteristics of super high-rise buildings, the structure is very sensitive to wind loads, and wind vibration effects and comfort are issues that cannot be ignored in the structural design of super high-rise buildings. The paper mainly introduces the problems in wind resistance design of super high-rise buildings. Including wind load values, wind vibration effects, and vibration control methods and applications. The result show that the wind induced vibration effect and comfort of the structure are often determined through wind tunnel tests. For super high-rise buildings with complex structural forms. There are two methods for controlling wind-induced vibration in high-rise buildings: Optimizing structural form through architectural methods; Take structural control measures. At the same time, with the cost of high-performance materials and engineering technologies, it is a challenge to find building materials and technologies that can meet wind resistance requirements while keeping costs in check. However, as computational technology continues to evolve, the wind-resistant design of tall buildings will benefit from more accurate wind-tunnel simulations and computational modelling, which will help to better predict and address wind-resistance challenges. Scientists and engineers will continue to research novel materials and structural designs to improve the wind resistance of tall buildings and reduce costs.

How Will Artificial Intelligence Shape the Future of Decision-Making in Congenital Heart Disease?

Improvements in medical technology have significantly changed the management of congenital heart disease (CHD), offering novel tools to predict outcomes and personalize follow-up care. By using sophisticated imaging modalities, computational models and machine learning algorithms, clinicians can experiment with unprecedented insights into the complex anatomy and physiology of CHD. These tools enable early identification of high-risk patients, thus allowing timely, tailored interventions and improved outcomes. Additionally, the integration of genetic testing offers valuable prognostic information, helping in risk stratification and treatment optimisation. The birth of telemedicine platforms and remote monitoring devices facilitates customised follow-up care, enhancing patient engagement and reducing healthcare disparities. Taking into consideration challenges and ethical issues, clinicians can make the most of the full potential of artificial intelligence (AI) to further refine prognostic models, personalize care and improve long-term outcomes for patients with CHD. This narrative review aims to provide a comprehensive illustration of how AI has been implemented as a new technological method for enhancing the management of CHD.

Linking Computational Fluid Dynamics Modeling to Device-Induced Platelet Defects in Mechanically Assisted Circulation.

Thrombotic and bleeding events are the most common hematologic complications in patients with mechanically assisted circulation and are closely related to device-induced platelet dysfunction. In this study, we sought to link computational fluid dynamics (CFD) modeling of blood pumps with device-induced platelet defects. Fresh human blood was circulated in circulatory loops with four pumps (CentriMag, HVAD, HeartMate II, and CH-VAD) operated under a total of six clinically representative conditions. Blood samples were collected and analyzed for glycoprotein (GP) IIb/IIIa activation and receptor shedding of GPIbα and GPVI. In parallel, CFD modeling was performed to characterize the blood flow in these pumps. Numerical indices of platelet defects were derived from CFD modeling incorporating previously derived power-law models under constant shear conditions. Numerical results were correlated with experimental results by regression analysis. The results suggested that a scalar shear stress of less than 75 Pa may have limited contribution to platelet damage. The platelet defect indices predicted by the CFD power-law models after excluding shear stress <75 Pa correlated excellently with experimentally measured indices. Although numerical prediction based on the power-law model cannot directly reproduce the experimental data. The power-law model has proven its effectiveness, especially for quantitative comparisons.

Multi-objective optimisation of a 2D backward-sfacing step channel with porous baffles

AbstractPorous baffles can be used to enhance heat transfer in various engineering applications, including electronic cooling, gas turbine blades, and chemical reactors. Also, the backward-facing step is a widely used configuration in fluid dynamics studies due to its simplicity and relevance to real-world geometries. This study examines heat transfer and flow characteristics in a backward-facing step channel featuring a heated bottom wall and two porous baffles. A computational fluid dynamics model, validated against prior research, is used to investigate flow and temperature fields. The innovation of this work lies in the application of multi-objective optimisation to search for a set of solutions that establish a trade-off between the average Nusselt number and the pressure drop. The optimisation specifically considers various parameters of the porous baffles, including height, width, distance from the step, and Darcy number, to identify optimal design configurations. Results show that porous baffles significantly improve heat transfer compared to a backward-facing step channel without them, despite an increase in pressure drop due to their presence. This work offers valuable insights into the trade-off between heat transfer performance and pressure drop, crucial for designing efficient heat transfer systems. By exploring the Pareto-Frontier, which represents various optimal design solutions, the study provides practical guidance when seeking to optimise heat transfer in backward-facing step channels with porous baffles. The findings contribute to advancing the understanding of heat transfer enhancement, highlighting the potential of porous baffles as a viable solution for improving thermal management in engineering systems.

A computational neural model that incorporates both intrinsic dynamics and sensory feedback in the Aplysia feeding network.

Studying the nervous system underlying animal motor control can shed light on how animals can adapt flexibly to a changing environment. We focus on the neural basis of feeding control in Aplysia californica. Using the Synthetic Nervous System framework, we developed a model of Aplysia feeding neural circuitry that balances neurophysiological plausibility and computational complexity. The circuitry includes neurons, synapses, and feedback pathways identified in existing literature. We organized the neurons into three layers and five subnetworks according to their functional roles. Simulation results demonstrate that the circuitry model can capture the intrinsic dynamics at neuronal and network levels. When combined with a simplified peripheral biomechanical model, it is sufficient to mediate three animal-like feeding behaviors (biting, swallowing, and rejection). The kinematic, dynamic, and neural responses of the model also share similar features with animal data. These results emphasize the functional roles of sensory feedback during feeding.

A Prototype Software Framework for Transferable Computational Health Economic Models and Its Early Application in Youth Mental Health.

We are developing an economic model to explore multiple topics in Australian youth mental health policy. To help make that model more readily transferable to other jurisdictions, we developed a software framework for authoring modular computational health economic models (CHEMs) (the software files that implement health economic models). We specified framework user requirements for: a simple programming syntax; a template CHEM module; tools for authoring new CHEM modules; search tools for finding existing CHEM modules; tools for supplying CHEM modules with data; reproducible analysis and reporting tools; and tools to help maintain a CHEM project website. We implemented the framework as six development version code libraries in the programming language R that integrate with online services for software development and research data archiving. We used the framework to author five development version R libraries of CHEM modules focussed on utility mapping in youth mental health. These modules provide tools for variable validation, dataset description, multi-attribute instrument scoring, construction of mapping models, reporting of mapping studies and making out of sample predictions. We assessed these CHEM module libraries as mostly meeting transparency, reusability and updatability criteria that we have previously developed, but requiring more detailed documentation and unit testing of individual modules. Our software framework has potential value as a prototype for future tools to support the development of transferable CHEMs.Code: Visit https://www.ready4-dev.com for more information about how to find, install and apply the prototype software framework.

Theoretical Insights into Enhancing Catalytic Performance of Al-Cu Alloy for CO2 Electroreduction toward Ethene Production.

The understanding of the reaction mechanism of CO2 electroreduction (CO2RR) is essential for the precise design of catalysts for specific products with high selectivity. In this work, combined with the computational hydrogen electrode model and kinetic energy barrier calculations, CO2RR pathways on Cu(100) and Al1Cu3(100) are intensively investigated. The free energy barrier of the rate-determining step of ethylene formation is reduced from 1.08 eV for *CCOH formation on Cu(100) to 0.51 eV for *CH2OCHOH formation on Al1Cu3(100) and enhances the catalytic activity. The reaction free energy of *CO-*CO coupling is remarkably reduced from 0.86 eV on Cu(100) to -0.43 eV on Al1Cu3(100) and the coupling barrier is reduced from 0.97 to 0.37 eV, suppressing the production of gas phase CO and enhancing the production of C2 products. Furthermore, the selectivity toward C-O breaking of *CH2CHOH on Cu(100) and *CH2CH2OH on Al1Cu3(100) ensures high selectivity toward ethene rather than ethanol.

The fusion point of temporal binding: Promises and perils of multisensory accounts

Performing an action to initiate a consequence in the environment triggers the perceptual illusion of temporal binding. This phenomenon entails that actions and following effects are perceived to occur closer in time than they do outside the action-effect relationship. Here we ask whether temporal binding can be explained in terms of multisensory integration, by assuming either multisensory fusion or partial integration of the two events. We gathered two datasets featuring a wide range of action-effect delays as a key factor influencing integration. We then tested the fit of a computational model for multisensory integration, the statistically optimal cue integration (SOCI) model. Indeed, qualitative aspects of the data on a group-level followed the principles of a multisensory account. By contrast, quantitative evidence from a comprehensive model evaluation indicated that temporal binding cannot be reduced to multisensory integration. Rather, multisensory integration should be seen as one of several component processes underlying temporal binding on an individual level.

Dissecting thrombus-directed chemotaxis and random movement in neutrophil near-thrombus motion in flow chambers.

Thromboinflammation is caused by mutual activation of platelets and neutrophils. The site of thromboinflammation is determined by chemoattracting agents release by endothelium, immune cells, and platelets. Impaired neutrophil chemotaxis contributes to the pathogenesis of Shwachman-Diamond syndrome (SDS). In this hereditary disorder, neutrophils are known to have aberrant chemoattractant-induced F-actin properties. Here, we aim to determine whether neutrophil chemotaxis could be analyzed using our previously developed ex vivo assay of the neutrophils crawling among the growing thrombi. Adult and pediatric healthy donors, alongside with pediatric patients with SDS, were recruited for the study. Thrombus formation and granulocyte movement in hirudinated whole blood were visualized by fluorescent microscopy in fibrillar collagen-coated parallel-plate flow chambers. Alternatively, fibrinogen, fibronectin, vWF, or single tumor cells immobilized on coverslips were used. A computational model of chemokine distribution in flow chamber with a virtual neutrophil moving in it was used to analyze the observed data. The movement of healthy donor neutrophils predominantly occurred in the direction and vicinity of thrombi grown on collagen or around tumor cells. For SDS patients or on coatings other than collagen, the movement was characterized by randomness and significantly reduced velocities. Increase in wall shear rates to 300-500 1/s led to an increase in the proportion of rolling neutrophils. A stochastic algorithm simulating leucocyte chemotaxis movement in the calculated chemoattractant field could reproduce the experimental trajectories of moving neutrophils for 72% of cells. In samples from healthy donors, but not SDS patients, neutrophils move in the direction of large, chemoattractant-releasing platelet thrombi growing on collagen.

Enhancement of N-Methyl Amino Acid Incorporation into Proteins and Peptides Using Modified Bacterial Ribosomes and Elongation Factor P.

N-Methylated amino acids are constituents of natural bioactive peptides and proteins. Nα-methylated amino acids appear abundantly in natural cyclic peptides, likely due to their constraint of peptide conformation and contribution to peptide stability. Peptides containing Nα-methylated amino acids have long been prepared by chemical synthesis. While such natural peptides are not produced ribosomally, recent ribosomal strategies have afforded Nα-methylated peptides. Presently, we define new strategies for the ribosomal incorporation of Nα-methylated amino acids into peptides and proteins. First, we identify modified ribosomes capable of facilitating the incorporation of six N-methylated amino acids into antibacterial scorpion peptide IsCT. Also synthesized analogously was a protein domain (RRM1) from hnRNP LL; improved yields were observed for nearly all tested N-methylated amino acids. Computational modeling of the ribosomal assembly illustrated how the distortion imposed by N-methylation could be compensated by altering the nucleotides in key 23S rRNA positions. Finally, it is known that incorporation of multiple prolines (an N-alkylated amino acid) ribosomally can be facilitated by bacterial elongation factor P. We report that supplementing endogenous EF-P during IsCT peptide and RRM1 protein synthesis gave improved yields for most of the N-methylated amino acids studied.

Exploring Education as a Complex System: Computational Educational Research with Multi-Level Agent-Based Modeling

Our study employs multi-level agent-based modeling and computational techniques to explore education as a complex system. With an underlying focus that education should be underpinned by a scientific understanding of student learning, we created computational models that simulated learning dynamics in classrooms, integrating both quantitative and qualitative insights. Through these models, we conducted experiments aligned with real classroom data to address key questions, such as “How can we effectively support the academic progress of underperforming students, who are disproportionately from low socio-economic status (SES) backgrounds, to close their multi-year achievement gap in mathematics?” Our study analyzes various instructional approaches for mathematical learning, and our findings highlight the potential effectiveness of Productive Failure as an instructional approach. Considerations of the broader applicability of computational methods in advancing educational research are also provided.

Nested structure role in the mechanical response of spicule inspired fibers

Euplectella aspergillum marine sponge spicules are renowned for their remarkable strength and toughness. These spicules exhibit a unique concentric layering structure, which contributes to their exceptional mechanical resistance. In this study, finite element method simulations were used to comprehensively investigate the effect of nested cylindrical structures on the mechanical properties of spicules. This investigation leveraged scanning electron microscopy images to guide the computational modeling of the microstructure and the results were validated by three-point bending tests of 3D-printed spicule-inspired structures. The numerical analyses showed that the nested structure of spicules induces stress and strain jumps on the layer interfaces, reducing the load on critical zones of the fiber and increasing its toughness. It was found that this effect shows a tapering enhancement as the number of layers increases, which combines with a threshold related to the 3D-printing manufacturability to suggest a compromise for optimal performance. A comprehensive evaluation of the mechanical properties of these fibers can assist in developing a new generation of bioinspired structures with practical real-world applications.

A Review of Recent Developments on Modeling Low Impact Development (LID) Technique

Urban areas are more susceptible to flooding and water body contamination due to the detrimental effects of urbanization. As a result, a sustainable urban drainage system, also known as low impact development (LID) technique, is required. Although this technique can be extensively applied, the planning and design processes are multi-dimensional, multi-variable, and site-specific, which must consider various local conditions and factors. Consequently, these processes can be very complicated and time-consuming for professionals, necessitating support from computer modeling. This study intends to thoroughly explore the idea of LID modeling, various available computer models, and other tools for its optimization and decision-making processes. The most recent trustworthy journal publications that addressed the subjects under discussion were reviewed. This paper used the descriptive and comparative approaches as the analytical methods. According to the findings of the review, Storm Water Management Model (SWMM) is the computer model in LID modeling that is most frequently employed. This model is a fundamental package for dynamic urban rainfall-runoff modeling, and it has the benefits of being lightweight, simple to use, and an intuitive user interface. Besides, this model is public domain (free to use), open source, and interoperable with many hydro modeling applications. A specific LID editor module is also included in this model for modeling different LID units. To acquire the best LID planning and design from multiple criteria and alternatives, it is also necessary to use metaheuristic algorithms as an optimization model and a multi-criteria decision-making (MCDM) model in addition to the rainfall-runoff model. The authors believe combining the hydrologic and hydraulics models integrated with geographical information systems (GIS), metaheuristic algorithms, and MCDM is the most comprehensive and appropriate method for LID modeling in urban watersheds.

Emerging Technologies in Chemical Engineering: Advancements in Process Optimization, Sustainable Practices, and Future Innovations

The abstract encapsulates the essence of the study on emerging technologies in chemical engineering, focusing on advancements in process optimization, sustainable practices, and future innovations. Within the dynamic landscape of chemical engineering, the integration of emerging technologies has reshaped industrial processes, offering new opportunities to enhance efficiency, sustainability, and resilience. This study explores the transformative potential of emerging technologies, such as computational modelling, artificial intelligence, and bioprocess engineering, in addressing pressing societal and environmental challenges while driving innovation and sustainable development. Through a comprehensive literature review and synthesis of key findings, the study highlights the pivotal role of chemical engineering in shaping the design, optimization, and implementation of industrial processes across diverse sectors, including manufacturing, energy production, pharmaceuticals, and environmental remediation. Advancements in process optimization, facilitated by the integration of computational tools and data analytics, enable engineers to systematically analyze and control chemical processes with unprecedented precision and efficiency, leading to improvements in productivity, resource utilization, and waste reduction. The adoption of sustainable practices, such as green chemistry principles, renewable energy sources, and circular economy initiatives, reflects a growing emphasis on minimizing environmental impact and promoting resource efficiency throughout the product lifecycle. Concurrently, the exploration of future innovations in chemical engineering, including nanotechnologies, bioprocesses, and advanced materials, offers promising opportunities to address emerging challenges and drive positive change in the field. By synthesizing these key findings, the study provides valuable insights into the opportunities and challenges associated with emerging technologies in chemical engineering, guiding future research, policy, and practice towards achieving sustainable and resilient industrial processes. In essence, the study contributes to advancing knowledge and understanding of the transformative potential of emerging technologies in chemical engineering and their role in shaping a more sustainable and inclusive future for society and the planet.

Linking cognitive integrity to working memory dynamics in the aging human brain.

Aging is accompanied by a decline of working memory, an important cognitive capacity that involves stimulus-selective neural activity that persists after stimulus presentation. Here, we unraveled working memory dynamics in older human adults (male and female) including those diagnosed with mild cognitive impairment (MCI) using a combination of behavioral modeling, neuropsychological assessment, and magnetoencephalographic (MEG) recordings of brain activity. Younger adults (male and female) were studied with behavioral modeling only. Participants performed a visuo-spatial delayed match-to-sample task under systematic manipulation of the delay and distance between sample and test stimuli. Their behavior (match/non-match decisions) was fit with a computational model permitting the dissociation of noise in the internal operations underlying the working memory performance from a strategic decision threshold. Task accuracy decreased with delay duration and sample/test proximity. When sample/test distances were small, older adults committed more false alarms than younger adults. The computational model explained the participants' behavior well. The model parameters reflecting internal noise (not decision threshold) correlated with the precision of stimulus-selective cortical activity measured with MEG during the delay interval. The model uncovered an increase specifically in working memory noise in older compared to younger participants. Furthermore, in the MCI group, but not in the older healthy controls, internal noise correlated with the participants' clinically assessed cognitive integrity. Our results are consistent with the idea that the stability of working memory contents deteriorates in aging, in a manner that is specifically linked to the overall cognitive integrity of individuals diagnosed with MCI.Significance statement Several cognitive functions decline during aging, and this process is aggravated in MCI - a condition constituting a primary risk factor for developing dementia. One function susceptible to age-related cognitive decline is working memory: the ability to maintain information online for the flexible control of behavior, which entails persistent stimulus-selective neural activity in different regions of the cerebral cortex. We used computational modeling of behavioral and neural recordings to show that the stability of working memory contents is reduced in older human subjects and predicts overall cognitive decline in MCI patients. Our findings provide new mechanistic insight into cognitive aging and MCI and highlight working memory stability as an objective marker of the mechanisms underlying cognitive impairment.

Economic impacts of meeting China's NDC through carbon taxes with alternative schemes for recycling tax revenues

This study examines the impact of meeting China's Paris Climate Agreement commitment to reduce its emission intensity by 65 % below 2005 levels by 2030 through various policy instruments. We use a dynamic recursive computable general equilibrium model to simulate the economy-wide impacts of administrative mandates and carbon taxes using different revenue recycling schemes. China's GDP in 2030 would be approximately 1 % lower than the baseline if carbon emissions were constrained by mandates. The commitment's economic costs are lower than the carbon constraint case, regardless of revenue recycling. Economic costs are the lowest when carbon tax revenue is recycled to reduce taxes on capital or corporate income. Subsidizing solar and wind energy further reduces emissions, but at a higher economic cost with a GDP loss of about 0.53 %–0.58 % from the baseline. The results highlight the importance of proper design architecture to make carbon taxes more palatable to policymakers and taxpayers.

Combining Computational Fluid Dynamics and Experimental Data to Understand Fish Schooling Behavior.

Understanding the flow physics behind fish schooling poses significant challenges due to the difficulties in directly measuring hydrodynamic performance and the three-dimensional, chaotic, and complex flow structures generated by collective moving organisms. Numerous previous simulations and experiments have utilized computational, mechanical, or robotic models to represent live fish. And existing studies of live fish schools have contributed significantly to dissecting the complexities of fish schooling. But the scarcity of combined approaches that include both computational and experimental studies, ideally of the same fish schools, has limited our ability to understand the physical factors that are involved in fish collective behavior. This underscores the necessity of developing new approaches to working directly with live fish schools. An integrated method that combines experiments on live fish schools with computational fluid dynamic (CFD) simulations represents an innovative method of studying the hydrodynamics of fish schooling. CFD techniques can deliver accurate performance measurements and high-fidelity flow characteristics for comprehensive analysis. Concurrently, experimental approaches can capture the precise locomotor kinematics of fish and offer additional flow information through particle image velocimetry (PIV) measurements, potentially enhancing the accuracy and efficiency of CFD studies via advanced data assimilation techniques. The flow patterns observed in PIV experiments with fish schools and the complex hydrodynamic interactions revealed by integrated analyses highlight the complexity of fish schooling, prompting a reevaluation of the classic Weihs model of school dynamics. The synergy between CFD models and experimental data grants us comprehensive insights into the flow dynamics of fish schools, facilitating the evaluation of their functional significance and enabling comparative studies of schooling behavior. In addition, we consider the challenges in developing integrated analytical methods and suggest promising directions for future research.