Computational Data Research Articles

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.

The Effect of U-Bend Zone, Rotation, and Corrugation on Two-Pass Channel Flow

Abstract Further consideration on the two-pass channel flow is still necessary due to the complexity of 180-deg turn, rotation, and wall ribs. Numerical investigations have repeatedly revealed differences from experiments, with the primary focus of the smooth walls. Thus, this work deals with newly added ribs and three-equation variant of the shear stress transport (SST) k–ω model to the current fluid flow and heat transfer depending on an existing experiment as a reference. The adapted turbulence model thought to be more susceptible to U-bend zone, rotation, and wall corrugation is applied using comsolmultiphysics program. A two-pass profile with leafy characteristics, derived from a prior work by the first author, is implemented for the first time and contrasted against alternative corrugation designs. The findings demonstrated that applying the suggested model reduces the percentage error between the computational and experimental data to less than 20%. The Nusselt numbers computed at different leafy-corrugated channel divisions are augmented to 30% with 70% surface temperature reduction; however, the friction penalty rises too.

Structure confirmation, reactivity, bacterial mutagenicity and quantification of 2,2,4-tribromo-5-hydroxycyclopent-4-ene-1,3-dione in drinking water.

The presence of two new disinfectant by-product (DBP) groups in the UK was recently shown using non-target analysis, halogenated-hydroxycyclopentenediones and halogenated-methanesulfonic acids. In this work, we confirmed the structure of 2,2,4-tribromo-5-hydroxycyclopent-4-ene-1,3-dione (TBHCD), and quantified it together with dibromomethanesulfonic acid at 122 ± 34 and 326 ± 157 ng L-1 on average in London's drinking water, respectively (n = 21). We found TBHCD to be photolabile and unstable in tap water and at alkaline pH. Furthermore, spectral and computational data for TBHCD and three other halogenated-hydroxycyclopentenediones indicated they could act as a source of radicals in water and in the body. Importantly, TBHCD was calculated to have a 14.5 kcal mol-1 lower C-Br bond dissociation enthalpy than the N-Br bond of N-bromosuccinimide, a common radical substitution reagent used in organic synthesis. TBHCD was mutagenic in Salmonella/microsome assays using strains TA98, TA100 and TA102. This work reveals the unique features, activity and toxicity of trihalogenated hydroxycyclopent-4-ene-1,3-diones, prompting a need to more comprehensively assess their risks.

Analysing trends of computational urban science and data science approaches for sustainable development

AbstractUrban computing with a data science approaches can play a pivotal role in understaning and analyzing the potential of these methods for strategic, short-term, and sustainable planning. The recent development in urban areas have progressed towards the data-driven smart sustainable approaches to resolve the complexities around urban areas. The urban system faces severe challenges and these are complicated to capture, predict, resolve and deliver. The current study advances an unconventional decision-support framework to integrate the complexities of science, urban sustainability theories, and data science, with a data-intensive science to incorporate grassroots initiatives for a top-down policies. This work will influence the urban data analytics to optimize the designs and solutions to enhance sustainability, efficiency, resilience, equity, and quality of life. This work emphasizes the significant trends of data-driven and model-driven decision support systems. This will help to address and create an optimal solution for multifaceted challenges of an urban setup within the analytical framework. The analytical investigations includes the research about land use prediction, environmental monitoring, transportation modelling, and social equity analysis. The fusion of urban computing, intelligence, and sustainability science is expected to resolve and contribute in shaping resilient, equitable, and future environmentally sensible eco-cities. It examines the emerging trends in the domain of computational urban science and data science approaches for sustainable development being utilized to address urban challenges including resource management, environmental impact, and social equity. The analysis of recent improvements and case studies highlights the potential of data-driven insights with computational models for promoting resilient sustainable urban environments, towards more effective and informed policy-making. Thus, this work explores the integration of computational urban science and data science methodologies to advance sustainable development.

Harnessing the Distributed Computing Paradigm for Laser-Induced Breakdown Spectroscopy

Laser-induced breakdown spectroscopy allows fast and versatile elemental analysis, standing as a promising technique for a wide range of applications both at the research and industry levels. Yet, its high operation speed comes with a high throughput of data, which introduces some challenges at the level of the data processing domain, mainly due to the large computational load and data volume. In this work, we analyze and discuss opportunities of distributed computing paradigms and resources to address some of these challenges, covering most of the procedures usually employed in typical applications. We infer the possible impact of such computing resources by presenting some metrics of simple processing prototypes running in state-of-the-art computing facilities. Our results allow us to conclude that, while underexplored so far, these computing resources may allow for the development of tools for timely research and analysis in demanding applications and introduce novel solutions toward a more agile working environment.

Evaluation of reduced-order models for the rapid aerodynamic analysis of supersonic and hypersonic bodies

Abstract This paper presents a comprehensive evaluation of reduced-order models (ROMs) for the determination of pressure coefficient distributions on supersonic and hypersonic bodies. The study investigates the limitations, aerodynamic precision and computational performance associated with various methodologies, ranging from simplistic Newtonian theory-based approaches to more advanced first and second-order shock-expansion theories. Validation is performed by comparing computed results with experimental and computational data for pressure distributions, drag and lift coefficients and centres of pressure for fundamental geometries and authentic vehicle design over a wide range of freestream conditions. The study also includes a comprehensive computational complexity analysis, demonstrating the superiority of finite-element ROM approaches over traditional finite-volume computational fluid dynamics (CFD) simulations. The primary objective of this paper is to scrutinise the extension of these methodological classes to the low supersonic regime. Hence, thermo-chemical reactions within the flow are disregarded, and the ideal gas law is adopted. A value of $\gamma = 1.4$ is chosen for consistency and comparability across the analyses. The proposed ROMs show remarkable potential for reducing high-speed simulation execution times by four orders of magnitude, maintaining accuracy within 20 per cent and as low as 1 per cent. The study unveils three key findings: first, the accuracy degradation of Newtonian-based theories for inclined elements, particularly around 45 degrees, and their reduced dependency on Mach number at large inclination. Secondly, the study presents novel insights into the impact of shock-wave-Mach-wave interactions on pressure distribution calculations, emphasising the Mach number as a crucial metric governing recompression effects. Lastly, the study demonstrates the exceptional accuracy of DeJarnette’s method, providing ${C_P}$ results within 2 per cent for a wide range of conditions, offering an attractive alternative to the Taylor-Maccoll equation.

Neural mechanisms underlying robust target selection in response to microstimulation of the oculomotor system.

Despite its prevalence in studying the causal roles of different brain circuits in cognitive processes, electrical microstimulation often results in inconsistent behavioral effects. These inconsistencies are assumed to be due to multiple mechanisms, including habituation, compensation by other brain circuits, and contralateral suppression. Considering the presence of reinforcement in most experimental paradigms, we hypothesized that interactions between reward feedback and microstimulation could contribute to inconsistencies in behavioral effects of microstimulation. To test this, we analyzed data from electrical microstimulation of the frontal eye field of male macaques during a value-based decision-making task and constructed network models to capture choice behavior. We found evidence for microstimulation-dependent adaptation in saccadic choice, such that in stimulated trials, monkeys' choices were biased toward the target in the response field of the microstimulated site (Tin ). In contrast, monkeys showed a bias away from Tin in non-stimulated trials following microstimulation. Critically, this bias slowly decreased as a function of the time since the last stimulation. Moreover, microstimulation-dependent adaptation was influenced by reward outcomes in preceding trials. Despite these local effects, we found no evidence for the global effects of microstimulation on learning and sensitivity to the reward schedule. By simulating choice behavior across various network models, we found a model in which microstimulation and reward-value signals interact competitively through reward-dependent plasticity can best account for our observations. Our findings indicate a reward-dependent compensatory mechanism that enhances robustness to perturbations within the oculomotor system and could explain the inconsistent outcomes observed in previous microstimulation studies.Significance Statement Electrical microstimulation has been used to study the causal contributions of certain brain areas or circuits to cognition and behavior. Nonetheless, the overall impact of microstimulation on behavior remains inconclusive, hinting at neural mechanisms that interact with experimental perturbation of neural activity. We hypothesized that this interaction could be driven by the reward feedback animals receive while performing tasks, either with or without external perturbations. Using computational modeling and data from microstimulation during a reward-dependent decision-making task, we found microstimulation and reward-value signals competitively interact within the oculomotor system. This interaction enhances the system's robustness to both internal and external perturbations. Our results have important implications for employing microstimulation in basic and clinical research.

H_2-H_2O demixing in Uranus and Neptune: Adiabatic structure models

Demixing properties of major planetary constituents influence the interior structure and evolution of planets. Comparing experimental and computational data on the miscibility of hydrogen and water to adiabatic profiles suggests that phase separation between these two components occurs in the ice giants Uranus and Neptune. We aim to predict the atmospheric water abundance and transition pressure between the water-poor outer envelope and the water-rich deep interior in Uranus and Neptune. We constructed seven H$_ $-H$_ $O phase diagrams from the available experimental and computational data. We computed interior adiabatic structure models and compared these to the phase diagrams to infer whether demixing occurred. We obtain a strong water depletion in the top layer due to the rain-out of water and find upper limits on the atmospheric water-mass fraction Zatm of 0.21 for Uranus and 0.16 for Neptune. The transition from the water-poor to the water-rich layer is sharp and occurs at pressures $P_Z$ between 4 and 11 GPa. Using these constraints on Zatm and $P_Z$, we find that the observed gravitational harmonics $J_2$ and $J_4$ can be reproduced if $P_Z 10$ GPa in Uranus and $ 5$ GPa in Neptune, and if the deep interior has a high primordial water-mass fraction of 0.8, unless rocks are also present. The agreement with $J_4$ is improved if rocks are confined deeper than $P_Z$, for instance, below a rock cloud level at 2000 K (20--30 GPa). These findings confirm classical few-layer models and suggest that a layered structure may result from a combination of primordial mass accretion and subsequent phase separation. Reduced observational uncertainty in $J_4$ and its dynamic contribution, atmospheric water abundance measurements from the Uranus Orbiter and Probe (UOP) or a Neptune mission, and better understanding of the mixing behaviour of constituents are needed to constrain the interiors of ice giants.

Benchmark Investigation of SCC-DFTB Against Standard DFT to Model Phononic Properties in Two-Dimensional MOFs for Thermoelectric Applications.

Despite the importance of modeling lattice thermal conductivity in predicting thermoelectric (TE) properties, computational data on heat transport, especially from first-principles, in 2D metal-organic frameworks (MOFs) remain limited due to the high computational cost. To address this, we provide a benchmark of the performance of semiempirical self-consistent-charge density functional tight-binding (SCC-DFTB) methods against density functional theory (DFT) for monolayer, serrated, AA-stacked and/or AB-stacked Zn3C6O6, Cd3C6O6, Zn-NH-MOF, and Ni3(HITP)2 MOFs. Harmonic lattice dynamics calculations, including partial atomic contributions to phonon dispersions, are evaluated with both SCC-DFTB and DFT, whereas anharmonic transport (i.e., thermal conductivity) is evaluated with SCC-DFTB only. Our findings further suggest that unlike the other stacking geometries modeled, serrated Zn3C6O6, serrated Zn-NH-MOF, and wavy serrated Ni3(HITP)2 represent stable geometries. While Zn3C6O6 and Zn-NH-MOF exhibit a higher power factor than Ni3(HITP)2 (as found in our previous work), Zn-NH-MOF shows lower thermal conductivity, resulting in the highest thermoelectric figure of merit (ZT) among the studied MOFs.

Enantioconvergent Negishi Cross-Couplings of Racemic Secondary Organozinc Reagents to Access Privileged Scaffolds: A Combined Experimental and Theoretical Study.

An enantioconvergent palladium-catalyzed Negishi cross-coupling with racemic secondary organozinc reagents has been developed, enabling access to enantioenriched 1,1-diarylalkane motifs in high yields and enantioselectivities. Computational data indicates that the racemization of organozinc compounds most likely occurs through a bridged bimolecular mechanism.

Research on Autonomous Navigation of Unmanned Aerial Vehicles Based on Correlation-Based Image Comparison Methods

Relevance. Autonomous navigation of unmanned aerial vehicles (UAVs) is one of the key challenges in the modern aerospace industry. Specifically, for small UAVs, the task of autonomous navigation becomes even more complex due to limitations in computational resources and sensor capabilities. Optimizing navigation methods under such conditions is a pressing issue that requires solutions capable of providing high performance with minimal resource consumption.Objective. Improving the efficiency of autonomous navigation for small UAVs by using correlation methods for image comparison. Achieving this objective is linked to the development and evaluation of algorithms that provide high-speed and accurate navigation with limited computational resources. The work uses methods such as the autocorrelation function, Pearson correlation, the structural similarity index, and a simple neural network for image comparison.Solution. The research showed that the autocorrelation-based approach demonstrates the best performance under low computational resources. It ensures high-speed processing of the entire image and shows optimal detection accuracy. Compared to other methods presented in the study, the autocorrelation approach is capable of working not only with noise in the "reference" map but also of using alternative areas with altered patterns as detected regions.The scientific novelty of the study is determined by the systematic comparison of various methods applied to the task of image comparison for small UAVs with limited computational resources. Unlike well-known works in the field of correlation-extreme systems, this research focuses on the use of a "reference map" and a search area, representing two different images of the same terrain taken from different sources. This is a key difference, as most methods are not highly efficient in processing such images where patterns may differ significantly.The practical significance of the developed algorithm lies in the fact that the proposed autocorrelation-based method can be used by developers of autonomous small UAV control systems to reduce computational load and increase data processing speed.

Media image transmission and privacy protection for internet of things security

Internet of Things (IoT) is becoming more popular with the increase in advancements of technology and is widely used in various sectors. The data are collected from the real environment and then transmitted over the networks. Due to their restricted resources, processing capacity, and memory, IoT gadgets are susceptible to certain security risks. Several encryption methodologies were established to provide safe communication between IoT devices with minimal computing cost and bandwidth consumption, due to the rise in media date. This research proposes a revolutionary compact pixel crypto (CPC) technique that can accommodate the modest transmission speeds of IoT gadgets. The suggested techniques reduces the computational burden and data quantity by encrypting the image data using pixel driven selective encryption and bloke scanning compression. The suggested method’s effectiveness is examined using the network simulator (NS-2) platform. According to the findings of the experiments, the suggested method outperforms the previous approaches in terms of both packet rate and energy usage. The proposed approach shortens the time needed for node side encryption and decryption operations with improving the system’s energy effectiveness and connectivity performance.

Non‐linear time history analyses of a rigid block isolated with unbonded fiber‐reinforced elastomeric isolators (UFREIs): A comparison between 3D finite element and phenomenological models

AbstractNumerical modeling represents a pivotal tool in the seismic analysis and design of structural systems, enabling the detailed prediction and examination of structural responses under seismic loading. This research conducts a comparative analysis of two numerical modeling approaches aimed at simulating the seismic response of unbonded fiber‐reinforced elastomeric isolators (UFREIs). The research focuses on a finite element (FE) model developed using Abaqus and a developed phenomenological model implemented in OpenSees, outlining the development and calibration processes for each. The FE model is developed based on simple rubber material testing data, while the phenomenological model is calibrated using experimental results from cyclic shear tests conducted on the UFREI device and the FE model. The primary objective of this study is to assess the effectiveness of these modeling approaches in predicting UFREI behavior under seismic conditions. This evaluation entails comparing model predictions with experimental data obtained from unidirectional shake table tests performed on a rigid block isolated by two UFREIs. This paper highlights the distinct advantages and limitations of each model in simulating UFREI dynamic responses during seismic events. Furthermore, it provides insights into the modeling techniques and discusses the computational demands and data requirements of each model, thereby aiding in their application to various aspects of seismic analysis and design.

Low-rank iterative infilling for zero echo-time (ZTE) imaging.

A new referenceless low-rank reconstruction technique has been introduced to address the issue of missing samples within the Zero Echo Time (ZTE) dead-time gap. The proposed method reformulates the in-filling of the missing samples as an inverse problem subject to low-rank constraints. Its performance and robustness are evaluated through a comparative analysis that combines Monte Carlo computational simulations and data obtained from in vivo experiments. The proposed method is tested for dead-time gaps ranging up to 4.5 Nyquist dwells, across signal-to-noise ratio levels of 5, 10, 15, and 20 dB. Consistently superior performance is observed across all cases compared to algebraic and parallel imaging methods. The speed for convergence decreases exponentially as the dead-time gap expands. The proposed method enables artifact-free reconstruction up to dead-time gap of 4 Nyquist dwells and thereby supports ZTE imaging up to an imaging bandwidth of kHz (assuming transmit and receive switching less than 30 s). It demonstrates superior performance compared to algebraic and parallel imaging methods.

Computational Math Modeling and AI Optimization for Better Decision-Making: Applications in Machine Learning

The integration of computational mathematical modeling, optimization, machine learning (ML), data analysis, and artificial intelligence (AI) offers transformative opportunities to solve complex problems across diverse fields. This paper delves into the development and application of advanced modeling techniques that blend traditional mathematical approaches with advanced AI algorithms to enhance predictive accuracy, optimize resource allocation, and improve decision-making processes in areas such as logistics, finance, engineering, and healthcare. By leveraging data-driven insights, these models can simulate real-world scenarios and identify optimal solutions more effectively than conventional methods. The paper also explores how AI-enhanced modeling can impact broader systems by reshaping industry practices, influencing frameworks, and potentially challenging established societal norms. Eventually, the paper argues for a balanced and informed deployment of AI-driven modeling techniques to maximize their benefits while addressing potential risks and limitations.

Optimized multi-head self-attention mechanism for SOH prediction in lithium-ion battery energy storage systems within microgrids

Estimating the State of Health (SOH) of lithium batteries is crucial for the safety and stability of the microgrids. Currently, existing data-driven methods struggle to predict across multiple groups of lithium batteries varying in type, capacity, and degree of degradation, as the prediction process requires the collection of extensive operational data for processing and computation. This paper analyzes the characteristics of lithium battery storage units within the microgrids and proposes a novel prediction method based on an improved attention mechanism. In the operational process, we collected the data under constant current (CC) and constant voltage (CV) charging modes to obtain health indicators with high correlation. We proposed a novel optimized multi-head attention method, designated OM-Attention, which employs a multi-head self-attention-based model to compute multiple sets of lithium batteries within a network node in a simultaneous manner. To validate the effectiveness of the OM-Attention method, we randomly selected four lithium batteries from the NASA public dataset for multiple experiments. Compared to SOH prediction methods, our approach exhibits superior performance with reduced Mean Absolute Error (MAE), Root Mean Square Error (RMSE), and Mean Square Error (MSE) to 4.16%, 5.02%, and 0.302×10−4, respectively. Additionally, the R2-score achieved by OM-Attention is 97.7%.

Data-driven parameter identification of an equivalent mechanical model for large amplitude liquid sloshing

The accurate extraction of model parameters is vital to ensure that the equivalent mechanical model can precisely describe the dynamic behavior of large amplitude liquid sloshing. In this paper, the parameters of the moving pulsating ball model (MPBM), which is an equivalent mechanical model used to represent the large amplitude liquid sloshing, are extracted using a combination of computational fluid dynamics (CFD), experimental data, and data-driven algorithms. The arbitrary Lagrangian-Eulerian finite element method (ALE-FEM) is adopted to simulate three-dimension large amplitude liquid sloshing in the tank with high precision. The calculated results from the presented algorithm are compared with the experimental data to verify the reliability and validity. The typical parameters required by the MPBM mainly include equivalent sloshing mass, equivalent ball radius, and damping coefficient. These parameters are extracted by using a data-driven parameter optimization algorithm, which is based on the numerical calculation results of ALE-FEM. The accuracy of the MPBM with the equivalent model parameters extracted by using data-driven parameter optimization algorithm is investigated under two types of excitations: harmonic excitation and step excitation. The results show that the MPBM with equivalent model parameters extracted by a data-driven parameter optimization algorithm can precisely imitate the large amplitude liquid sloshing behavior and the method presented can provide significant reference for the overall design of spacecraft dynamics system.

Toward aerodynamic surrogate modeling based on β-variational autoencoders

Surrogate models that combine dimensionality reduction and regression techniques are essential to reduce the need for costly high-fidelity computational fluid dynamics data. New approaches using β-variational autoencoder (β-VAE) architectures have shown promise in obtaining high-quality low-dimensional representations of high-dimensional flow data while enabling physical interpretation of their latent spaces. We propose a surrogate model based on latent space regression to predict pressure distributions on a transonic wing given the flight conditions: Mach number and angle of attack. The β-VAE model, enhanced with principal component analysis (PCA), maps high-dimensional data to a low-dimensional latent space, showing a direct correlation with flight conditions. Regularization through β requires careful tuning to improve overall performance, while PCA preprocessing helps to construct an effective latent space, improving autoencoder training and performance. Gaussian process regression is used to predict latent space variables from flight conditions, showing robust behavior independent of β, and the decoder reconstructs the high-dimensional pressure field data. This pipeline provides insight into unexplored flight conditions. Furthermore, a fine-tuning process of the decoder further refines the model, reducing the dependence on β and enhancing accuracy. Structured latent space, robust regression performance, and significant improvements in fine-tuning collectively create a highly accurate and efficient surrogate model. Our methodology demonstrates the effectiveness of β-VAEs for aerodynamic surrogate modeling, offering a rapid, cost-effective, and reliable alternative for aerodynamic data prediction.

A Hybrid Soft Sensor Framework for Real-Time Biodiesel Yield Prediction: Integrating Mechanistic Models and Machine Learning Algorithms

Biodiesel yield prediction is vital for optimizing process efficiency, minimizing costs, and maintaining product quality. Traditional methods are labor-intensive, costly, and lack real-time capabilities, leading to inefficiencies in operations. Data-driven soft sensors offer real-time prediction but require extensive, high-quality datasets, posing practical challenges. To address these limitations, this study proposes a hybrid soft sensor model that integrates mechanistic and data-driven approaches. Mechanistic models were utilized to generate computational data via MATLAB®, reducing the reliance on costly laboratory experiments. A comprehensive dataset (n=1500) comprising seven input variables—catalyst type, feedstock type, temperature, reaction time, free fatty acid (FFA) content, water content, and methanol-to-oil ratio—along with one output variable (biodiesel yield) was developed. This dataset was used to train various machine learning algorithms, with the artificial neural network (ANN) model demonstrating the highest predictive accuracy, achieving an R2 (goodness of fit) of 0.998 and root mean square error (RMSE) of 0.303. Hyperparameter tuning further enhanced the model’s performance, reducing RMSE and the mean absolute error (MAE) by 63% and 61.7%, respectively. By combining mechanistic and data-driven techniques, this hybrid model effectively overcomes the limitations of traditional and purely data-driven methods, providing a cost-effective and efficient solution for biodiesel yield prediction and data generation.

An automated computational data pipeline to rapidly acquire, score, and rank toxicological data for ecological hazard assessment.

Biological Evaluations support Endangered Species Act (ESA) consultation with the US Fish and Wildlife Service and National Marine Fisheries Service by federal action agencies, such as the USEPA, regarding impacts of federal activities on threatened or endangered species. However, they are often time-consuming and challenging to conduct. The identification of pollutant benchmarks or guidance to protect taxa for states and tribes when USEPA has not yet developed criteria recommendations is also of importance to ensure a streamlined approach to Clean Water Act program implementation. Due to substantial workloads, tight regulatory timelines, and the often-protracted length of ESA consultations, there is a need to streamline the development of biological evaluation toxicity assessments for determining the impact of chemical pollutants on ESA-listed species. Moreover, there is limited availability of species-specific toxicity data for many contaminants, further complicating the consultation process. New approach methodologies are being increasingly used in toxicology and chemical safety assessment to rapidly and cost-effectively provide data that can fill gaps in hazard and/or exposure characterization. Here, we present the development of an automated computational pipeline-RASRTox (Rapidly Acquire, Score, and Rank Toxicological data)-to rapidly extract and categorize ecological toxicity benchmark values from curated data sources (ECOTOX, ToxCast) and well-established quantitative structure-activity relationships (TEST, ECOSAR). As a proof of concept, points-of-departure (PODs) generated in RASRTox for 13 chemicals were compared against benchmark values derived using traditional methods-toxicity reference values (TRVs) and water quality criteria (WQC). The RASRTox PODs were generally within an order of magnitude of corresponding TRVs, though less concordant compared with WQC. The greatest utility of RASRTox, however, lies in its ability to quickly and systematically identify critical studies that may serve as a basis for screening value derivation by toxicologists as part of an ecological hazard assessment. As such, the strategy described in this case study can potentially be adapted for other risk assessment contexts and stakeholder needs. Integr Environ Assess Manag 2024;20:2203-2217. © 2024 Society of Environmental Toxicology & Chemistry (SETAC). This article has been contributed to by U.S. Government employees and their work is in the public domain in the USA.