Advanced Computational Methods Research Articles

Prognostic stratification of glioblastoma patients by unsupervised clustering of morphology patterns on whole slide images furthering our disease understanding

IntroductionGlioblastoma (GBM) is a highly aggressive malignant tumor of the central nervous system that displays varying molecular and morphological profiles, leading to challenging prognostic assessments. Stratifying GBM patients according to overall survival (OS) from H&E-stained whole slide images (WSI) using advanced computational methods is challenging, but with direct clinical implications.MethodsThis work is focusing on GBM (IDH-wildtype, CNS WHO Gr.4) cases, identified from the TCGA-GBM and TCGA-LGG collections after considering the 2021 WHO classification criteria. The proposed approach starts with patch extraction in each WSI, followed by comprehensive patch-level curation to discard artifactual content, i.e., glass reflections, pen markings, dust on the slide, and tissue tearing. Each patch is then computationally described as a feature vector defined by a pre-trained VGG16 convolutional neural network. Principal component analysis provides a feature representation of reduced dimensionality, further facilitating identification of distinct groups of morphology patterns, via unsupervised k-means clustering.ResultsThe optimal number of clusters, according to cluster reproducibility and separability, is automatically determined based on the rand index and silhouette coefficient, respectively. Our proposed approach achieved prognostic stratification accuracy of 83.33% on a multi-institutional independent unseen hold-out test set with sensitivity and specificity of 83.33%.DiscussionWe hypothesize that the quantification of these clusters of morphology patterns, reflect the tumor's spatial heterogeneity and yield prognostic relevant information to distinguish between short and long survivors using a decision tree classifier. The interpretability analysis of the obtained results can contribute to furthering and quantifying our understanding of GBM and potentially improving our diagnostic and prognostic predictions.

The Challenges of Engaging African American Communities During a Public Health Crisis: The Role of Government Information, COVID-19 Discourse, and Emotional Content on Social Media

ABSTRACT During a public health crisis, social media platforms play a pivotal role in circulating information and influencing public reactions. This research investigates the dynamics of public engagement with COVID-19-related content and government information sources within African American online communities, a population that has experienced significant health risks and inequities. Using advanced computational research methods, we analyzed 199,542 posts from 1,152 communities created between January 2020 and December 2022. The present research focused on the presence of COVID-19-related content, government information sources, the emotions of anger and fear in these posts, and their associations with user engagement metrics. The results indicated that posts discussing COVID-19 and those incorporating government information sources tend to receive lower levels of engagement. On the other hand, posts with higher levels of anger generated more shares and comments. The findings suggest a “triple disadvantage” in user engagement for social media messages that reference government sources and discuss public health risks without delivering strong negative emotions. These patterns are crucial not only for understanding the challenges faced by the at-risk population but also for aiding researchers and practitioners in developing more effective communication strategies during public health crises.

Characterization of curing status of commercial tire compounds with vein graphite powder and its particle sizes—Experimental and computational study

AbstractThe use of solid tires in demanding industrial applications, subjected to substantial mechanical loads, poses significant challenges due to heat generation from hysteresis and tread friction. Managing this heat is crucial to mitigate the risk of tire blowouts and layer separation. This research investigates the impact of introducing vein graphite powder, varying in particle size, into a commercially used solid tire compound, both experimentally and computationally. The research findings presented in this paper reveal substantial changes in thermal conductivity, specific heat capacity, rate constant, induction time, and the order of reaction in solid tires. Contemporary industry trends involve predictive methods for monitoring the curing process in tire manufacturing. A tire curing simulation model based on finite (FE) element analysis is developed. FE modeling is favored due to its accuracy and adaptability, especially when dealing with the intricate geometry and multi‐layered, multi‐compound structure of tires. The complex interplay between heat transfer and curing processes is effectively addressed using user subroutines (UMATHT) in commercial FE software like ABAQUS. In this analysis, thermal conductivity, heat capacity, order of reaction, rate constant, and induction time of the commercial tire compound are considered as temperature‐dependent variables. The computational model not only demonstrates its potential to significantly enhance the efficiency and quality of tire manufacturing processes but also contributes to a deeper understanding of the curing behavior of graphite powder‐based solid tire compounds. Consequently, it provides a valuable tool for optimizing tire manufacturing procedures and ensuring the safe and reliable performance of solid tires under demanding operational conditions. This research bridges the gap between the traditional commercial tire compound and the use of vein graphite powder in tire manufacturing and advanced computational methods, facilitating improvements in tire quality and safety.

A synergistic approach to optimizing the performance of a concentrating solar segmented variable area leg thermoelectric generator using numerical methods and neural networks

AbstractThis study presents an optimized design for segmented variable area leg thermoelectric modules using finite element methods and Bayesian regularized neural networks. We explored the impact of geometry and thermal parameters on module performance using ANSYS software, identifying optimal parameters for power output and efficiency. Key findings revealed the higher influence of geometric parameters and confirmed the advantages of segmented thermoelectric generators for high-temperature applications like concentrated solar systems. With this optimization, power output and efficiency of the module increased by 875% and 165%, respectively, under 25 Suns. To refine the optimization process, a Bayesian regularized neural network was utilized, proving effective in predicting module performance with a low mean squared error and high coefficient of determination. This research provides important insights into high-performance thermoelectric modules for sustainable energy applications, demonstrating the significant role of advanced computational methods in energy solutions.

Cross-section induced uncertainties in neutron source mission rate calculations

Analysis presented in the paper is focused on the characterization of uncertainties induced by cross-section data, which contributing to the overall uncertainty of the correction factors. Advances in computational methods and computational power shifted the calculation of correction factors among the standard steps of the manganese bath method what decrease an interest about this part of the method. Anyway, with the development of computational tools, the cross-section data were also improved, and new evaluations of nuclear data libraries include more information about the cross-section data covariances. Therefore, the propagation of uncertainties induced by neutron cross-sections can be carried out within standard transport calculation of the manganese sulphate bath model. In this paper, the super-sequence SAMPLER module that implements stochastic techniques is used to assess the uncertainty in computed results. Reaction rates on all nuclides of the solution are computed in 400 cases with uncertain parameters and the results are evaluated by an auxiliary tool. Consideration of nuclear data uncertainty in calculations is a general trend that requires the attention of nuclear researchers and should draw attention in metrology. Additional 1.5 % (one-sigma) uncertainty is introduced to the overall uncertainty through correction factor.

Predicting associations between CircRNA and diseases through structure-aware graph transformer and path-integral convolution

A series of biological experiments has demonstrated that circular RNAs play a crucial regulatory role in cellular processes and may be potentially associated with diseases. Uncovering these connections helps in understanding potential disease mechanisms and advancing the development of treatment strategies. However, in biology, traditional experiments face limitations in terms of efficiency and cost, especially when enumerating possible associations. To address these limitations, several computational methods have been proposed, but existing methods only measure from a nodal perspective and cannot capture structural similarities between edges. In this study, we introduce an advanced computational method called SATPIC2CD for analyzing potential associations between circular RNAs and diseases. Specifically, we first employ an Structure-Aware Graph Transformer (SAT), which extracts five predefined metapath representations before calculating attention. This adaptive network integrates structural information into the original self-attention by aggregating information within and between paths. Subsequently, we use Path Integral Convolutional Networks (PACN) to integrate feature information for all path weights between two nodes. Afterward, we complement the network node features with feature loss and feature smoothing using Gated Recurrent Units (GRU) and node centrality. Finally, a Multi-Layer Perceptron (MLP) is employed to obtain the ultimate prediction scores for each circular RNA-disease pair. SATPIC2CD performs remarkably well, with an accuracy of up to 0.9715 measured by the Area Under the Curve (AUC) in a 5-fold cross-validation, surpassing other comparative models. Case studies further emphasize the high precision of our method in identifying circular RNA-disease associations, laying a solid foundation for guiding future biological research efforts.

Streamlining Computational Fragment-Based Drug Discovery through Evolutionary Optimization Informed by Ligand-Based Virtual Prescreening.

Recent advances in computational methods provide the promise of dramatically accelerating drug discovery. While mathematical modeling and machine learning have become vital in predicting drug-target interactions and properties, there is untapped potential in computational drug discovery due to the vast and complex chemical space. This paper builds on our recently published computational fragment-based drug discovery (FBDD) method called fragment databases from screened ligand drug discovery (FDSL-DD). FDSL-DD uses in silico screening to identify ligands from a vast library, fragmenting them while attaching specific attributes based on predicted binding affinity and interaction with the target subdomain. In this paper, we further propose a two-stage optimization method that utilizes the information from prescreening to optimize computational ligand synthesis. We hypothesize that using prescreening information for optimization shrinks the search space and focuses on promising regions, thereby improving the optimization for candidate ligands. The first optimization stage assembles these fragments into larger compounds using genetic algorithms, followed by a second stage of iterative refinement to produce compounds with enhanced bioactivity. To demonstrate broad applicability, the methodology is demonstrated on three diverse protein targets found in human solid cancers, bacterial antimicrobial resistance, and the SARS-CoV-2 virus. Combined, the proposed FDSL-DD and a two-stage optimization approach yield high-affinity ligand candidates more efficiently than other state-of-the-art computational FBDD methods. We further show that a multiobjective optimization method accounting for drug-likeness can still produce potential candidate ligands with a high binding affinity. Overall, the results demonstrate that integrating detailed chemical information with a constrained search framework can markedly optimize the initial drug discovery process, offering a more precise and efficient route to developing new therapeutics.

Evaluation of a Microcontroller-based Smart Wearable Device in College Students' Sports Forging Application

INTRODUCTION: The widespread use of smart wearable devices in various fields, including healthcare and sports, underscores the importance of their application in enhancing physical exercise among college students. Recent advancements in technology have facilitated the development of sophisticated methods to assess and predict physical activity outcomes, making their evaluation increasingly critical.OBJECTIVES: This study aims to develop a reliable assessment model for smart wearable devices used in college students' sports activities. The objective is to accurately predict and evaluate the effectiveness of these devices in improving students' physical health and promoting lifelong sports habits. Ultimately, the research seeks to integrate advanced computational methods to enhance the accuracy of physical exercise assessments.METHODS: The research introduces a novel assessment model that combines a zebra behavior-based heuristic optimization algorithm with a convolutional neural network (CNN). By analyzing user behavior data from wearable devices, the model constructs an evaluation index system tailored for college sports activities. The approach optimizes the parameters of the CNN using the zebra optimization algorithm, ensuring enhanced prediction accuracy.RESULTS: The evaluation model demonstrated high accuracy, with a significant improvement in predicting the outcomes of physical exercises among college students. Comparative analyses with traditional methods revealed that the new model reduced prediction errors and increased real-time performance metrics. Specifically, the model achieved a lower root mean square error (RMSE) in simulation tests, indicating more precise assessments. Figures and statistical data provided in the study illustrate the model's superior performance across various parameters.CONCLUSION: The developed assessment model significantly advances the application of smart wearable devices in monitoring and enhancing college students' physical activities. By integrating cutting-edge algorithms, the study not only improves the accuracy of exercise assessments but also contributes to the broader understanding of technology's role in health and fitness education. Future research could further refine this model by incorporating additional sensors and data points to expand its applicability and robustness.

Automated Machine Learning and Explainable AI (AutoML-XAI) for Metabolomics: Improving Cancer Diagnostics.

Metabolomics generates complex data necessitating advanced computational methods for generating biological insight. While machine learning (ML) is promising, the challenges of selecting the best algorithms and tuning hyperparameters, particularly for nonexperts, remain. Automated machine learning (AutoML) can streamline this process; however, the issue of interpretability could persist. This research introduces a unified pipeline that combines AutoML with explainable AI (XAI) techniques to optimize metabolomics analysis. We tested our approach on two data sets: renal cell carcinoma (RCC) urine metabolomics and ovarian cancer (OC) serum metabolomics. AutoML, using Auto-sklearn, surpassed standalone ML algorithms like SVM and k-Nearest Neighbors in differentiating between RCC and healthy controls, as well as OC patients and those with other gynecological cancers. The effectiveness of Auto-sklearn is highlighted by its AUC scores of 0.97 for RCC and 0.85 for OC, obtained from the unseen test sets. Importantly, on most of the metrics considered, Auto-sklearn demonstrated a better classification performance, leveraging a mix of algorithms and ensemble techniques. Shapley Additive Explanations (SHAP) provided a global ranking of feature importance, identifying dibutylamine and ganglioside GM(d34:1) as the top discriminative metabolites for RCC and OC, respectively. Waterfall plots offered local explanations by illustrating the influence of each metabolite on individual predictions. Dependence plots spotlighted metabolite interactions, such as the connection between hippuric acid and one of its derivatives in RCC, and between GM3(d34:1) and GM3(18:1_16:0) in OC, hinting at potential mechanistic relationships. Through decision plots, a detailed error analysis was conducted, contrasting feature importance for correctly versus incorrectly classified samples. In essence, our pipeline emphasizes the importance of harmonizing AutoML and XAI, facilitating both simplified ML application and improved interpretability in metabolomics data science.

Handwritten Calculator Using Contour Detection

This project introduces a novel Handwritten Calculator that merges traditional handwritten calculations with modern computing techniques. By leveraging computer vision, machine learning, and human-computer interaction, the calculator interprets handwritten digits and operators to perform mathematical calculations. Through contour detection and Convolutional Neural Network (CNN) model prediction, the calculator offers users a seamless and intuitive way to compute. The project explores the development, applications, and impact of this innovative technology, with potential implications for education, accessibility, and software engineering. The Handwritten Calculator represents a significant advancement in computational methods, bridging the gap between analog and digital approaches to calculation.

Advances in Computational Methods for Modeling Photocatalytic Reactions: A Review of Recent Developments.

Photocatalysis is a fascinating process in which a photocatalyst plays a pivotal role in driving a chemical reaction when exposed to light. Its capacity to harness light energy triggers a cascade of reactions that lead to the formation of intermediate compounds, culminating in the desired final product(s). The essence of this process is the interaction between the photocatalyst's excited state and its specific interactions with reactants, resulting in the creation of intermediates. The process's appeal is further enhanced by its cyclic nature-the photocatalyst is rejuvenated after each cycle, ensuring ongoing and sustainable catalytic action. Nevertheless, comprehending the photocatalytic process through the modeling of photoactive materials and molecular devices demands advanced computational techniques founded on effective quantum chemistry methods, multiscale modeling, and machine learning. This review analyzes contemporary theoretical methods, spanning a range of lengths and accuracy scales, and assesses the strengths and limitations of these methods. It also explores the future challenges in modeling complex nano-photocatalysts, underscoring the necessity of integrating various methods hierarchically to optimize resource distribution across different scales. Additionally, the discussion includes the role of excited state chemistry, a crucial element in understanding photocatalysis.

Advanced Computational Methods for Radiation Dose Optimization in CT.

In planning radiotherapy treatments, computed tomography (CT) has become a crucial tool. CT scans involve exposure to ionizing radiation, which can increase the risk of cancer and other adverse health effects in patients. Ionizing radiation doses for medical exposure must be kept "As Low As Reasonably Achievable". Very few articles on guidelines for radiotherapy-computed tomography scans are available. This paper reviews the current literature on radiation dose optimization based on the effective dose and diagnostic reference level (DRL) for head, neck, and pelvic CT procedures used in radiation therapy planning. This paper explores the strategies used to optimize radiation doses, and high-quality images for diagnosis and treatment planning. A cross-sectional study was conducted on 300 patients with head, neck, and pelvic region cancer in our institution. The DRL, effective dose, volumetric CT dose index (CTDIvol), and dose-length product (DLP) for the present and optimized protocol were calculated. DRLs were proposed for the DLP using the 75th percentile of the distribution. The DLP is a measure of the radiation dose received by a patient during a CT scan and is calculated by multiplying the CT dose index (CTDI) by the scan length. To calculate a DRL from a DLP, a large dataset of DLP values obtained from a specific imaging procedure must be collected and can be used to determine the median or 75th-percentile DLP value for each imaging procedure. Significant variations were found in the DLP, CTDIvol, and effective dose when we compared both the standard protocol and the optimized protocol. Also, the optimized protocol was compared with other diagnostic and radiotherapy CT scan studies conducted by other centers. As a result, we found that our institution's DRL was significantly low. The optimized dose protocol showed a reduction in the CTDIvol (70% and 63%), DLP (60% and 61%), and effective dose (67% and 62%) for both head, neck, and pelvic scans. Optimized protocol DRLs were proposed for comparison purposes.

Design and Application of Basketball Tactical Drill System Based on Motion Trajectory Analysis Algorithm

A basketball tactical drill system based on motion trajectory analysis algorithms employs advanced computational methods to enhance training effectiveness and strategy development. By utilizing these algorithms to analyze player movements and interactions during drills, coaches can gain valuable insights into team dynamics, spatial awareness, and decision-making processes. This analysis enables coaches to tailor drills to specific tactical objectives, such as improving defensive rotations, offensive spacing, or fast break execution. Additionally, the system can provide real-time feedback to players, helping them understand their positioning and movement patterns to refine their skills and decision-making abilities. This paper presents the design and implementation of a Basketball Tactical Drill System leveraging advanced object detection and motion trajectory analysis algorithms. The system aims to enhance basketball training by providing coaches and players with real-time insights into tactical drills and player performance. Two key algorithms, Mean-Shift and Difference Object Detection with Motion Trajectory (DOD-MT), are utilized to accurately identify and track basketballs, players, and other relevant objects during training sessions. Experimental results demonstrate the effectiveness of the proposed system, showcasing high detection accuracy, minimal false positives, and competitive processing times across various scenarios and tactical drills. The system leverages Mean-Shift and Difference Object Detection with Motion Trajectory (DOD-MT) algorithms to achieve exceptional detection accuracy, with an average of 93% across various scenarios and tactical drills. False positives are minimized, with an average of 12 per scenario. Processing times remain competitive, averaging 27 milliseconds per scenario. Integration of motion trajectory analysis provides coaches with invaluable real-time insights into player movements and interactions, facilitating data-driven coaching strategies. The system's versatility is demonstrated through successful application in a range of drills, resulting in performance improvements averaging 20%.

The Important Role of Fluid Mechanics in the Engineering Field

Fluid mechanics, a fundamental discipline within engineering, has gained increasing significance across various fields due to continuous advancements in engineering science and technology. Its principles and methodologies find widespread application in aerospace, energy development, environmental protection, biomedicine, and beyond, serving as a cornerstone for technological progress. However, despite the expanding applications of fluid dynamics, challenges persist in integrating theory with practice in certain domains. This paper aims to synthesize exemplary applications of fluid mechanics in diverse engineering fields, illustrating its crucial role in addressing practical challenges. Additionally, it explores current problems and limitations in fluid dynamics applications, such as numerical simulation accuracy, multiphase flow complexity, and fluid-structure interaction nonlinearity. Furthermore, recommendations are provided for future fluid mechanics development, emphasizing interdisciplinary collaboration, advanced computational methods, and the seamless integration of experimental techniques and theoretical research. Through these discussions, this paper endeavors to offer insights into the ongoing development of fluid mechanics, fostering its broader application and deeper exploration within engineering disciplines.

Resolution of tonic concentrations of highly similar neurotransmitters using voltammetry and deep learning.

With advances in our understanding regarding the neurochemical underpinnings of neurological and psychiatric diseases, there is an increased demand for advanced computational methods for neurochemical analysis. Despite having a variety of techniques for measuring tonic extracellular concentrations of neurotransmitters, including voltammetry, enzyme-based sensors, amperometry, and in vivo microdialysis, there is currently no means to resolve concentrations of structurally similar neurotransmitters from mixtures in the in vivo environment with high spatiotemporal resolution and limited tissue damage. Since a variety of research and clinical investigations involve brain regions containing electrochemically similar monoamines, such as dopamine and norepinephrine, developing a model to resolve the respective contributions of these neurotransmitters is of vital importance. Here we have developed a deep learning network, DiscrimNet, a convolutional autoencoder capable of accurately predicting individual tonic concentrations of dopamine, norepinephrine, and serotonin from both in vitro mixtures and the in vivo environment in anesthetized rats, measured using voltammetry. The architecture of DiscrimNet is described, and its ability to accurately predict in vitro and unseen in vivo concentrations is shown to vastly outperform a variety of shallow learning algorithms previously used for neurotransmitter discrimination. DiscrimNet is shown to generalize well to data captured from electrodes unseen during model training, eliminating the need to retrain the model for each new electrode. DiscrimNet is also shown to accurately predict the expected changes in dopamine and serotonin after cocaine and oxycodone administration in anesthetized rats in vivo. DiscrimNet therefore offers an exciting new method for real-time resolution of in vivo voltammetric signals into component neurotransmitters.

Numerical Simulation of a Submerged Floating Tunnel: Validation and Analysis

The dynamic response analysis of submerged floating tunnels (SFTs) under seismic action is a complex two-way fluid–structure coupling problem that requires expertise in structural dynamics, fluid mechanics, and advanced computational methods. The coupled Eulerian–Lagrangian (CEL) method is a promising method for solving fluid–structure interaction problems, but its application to SFTs is not well established. Therefore, it is crucial to verify the accuracy and reliability of the CEL method in fluid–structure coupling simulations. This study verified the applicability of the CEL method for simulating one-way and two-way fluid–structure coupling cylindrical flow problems, and then applied the CEL method for the analysis of a shaking table test of a model SFT. A comparison of results obtained with the CEL method with those obtained in a previous indoor model test of an SFT demonstrates the agreement between the results of the CEL method and the overall trend of the experimental results, indicating the reliability of the method for the seismic analysis of SFTs. Moreover, the analysis of the dynamic response characteristics of SFTs under seismic conditions provides data support and a technological means for the seismic design of SFTs.

A between-wall heat transfer simulation approach based on the lattice Boltzmann method and its application in tube heat transfer analysis

Between-wall heat transfer is a fundamental physical process. As a typical coupling heat and mass transfer process, between-wall heat transfer-based problems can be solved conveniently on the basis of advanced computation methods among which the lattice Boltzmann method (LBM) has been widely used in the analysis of simple heat and mass transfer cases. To investigate the applicability of LBM in more complex solid-fluid coupling heat transfer analysis, a 3D fluid-solid-fluid model was established in this work to carry out between-wall heat transfer simulations using the doubled-population LBM. Three independent simulation regions were coupled in the heat transfer model, and the heat flux was taken as the physical quantity that transferred from the region at high temperature to that at low temperature through solid wall. The simulation results satisfactorily agree with that obtained using the commercial code FLUENT. Furthermore, an equivalent fluid-wall-fluid heat transfer model was established for simulation of heat transfer between internal and external fluids through a circular tube. All the boundaries are complete quadrilaterals on which heat transfer boundary conditions can be conveniently applied. The proposed between-wall model based on LBM can be easily established and applied in cases with internal heat source or mass force.

Understanding Defects in Amorphous Silicon with Million‐Atom Simulations and Machine Learning

AbstractThe structure of amorphous silicon (a‐Si) is widely thought of as a fourfold‐connected random network, and yet it is defective atoms, with fewer or more than four bonds, that make it particularly interesting. Despite many attempts to explain such “dangling‐bond” and “floating‐bond” defects, respectively, a unified understanding is still missing. Here, we use advanced computational chemistry methods to reveal the complex structural and energetic landscape of defects in a‐Si. We study an ultra‐large‐scale, quantum‐accurate structural model containing a million atoms, and thousands of individual defects, allowing reliable defect‐related statistics to be obtained. We combine structural descriptors and machine‐learned atomic energies to develop a classification of the different types of defects in a‐Si. The results suggest a revision of the established floating‐bond model by showing that fivefold‐bonded atoms in a‐Si exhibit a wide range of local environments–analogous to fivefold centers in coordination chemistry. Furthermore, it is shown that fivefold (but not threefold) coordination defects tend to cluster together. Our study provides new insights into one of the most widely studied amorphous solids, and has general implications for understanding defects in disordered materials beyond silicon alone.

Surface Engineering of the Mechanical Properties of Molecular Crystals via an Atomistic Model for Computing the Facet Stress Response of Solids.

Dynamic molecular crystals are an emerging class of crystalline materials that can respond to mechanical stress by dissipating internal strain in a number of ways. Given the serendipitous nature of the discovery of such crystals, progress in the field requires advances in computational methods for the accurate and high-throughput computation of the nanomechanical properties of crystals on specific facets which are exposed to mechanical stress. Here, we develop and apply a new atomistic model for computing the surface elastic moduli of crystals on any set of facets of interest using dispersion-corrected density functional theory (DFT-D) methods. The model was benchmarked against a total of 24 reported nanoindentation measurements from a diverse set of molecular crystals and was found to be generally reliable. Using only the experimental crystal structure of the dietary supplement, L-Aspartic acid, the model was subsequently applied under blind test conditions, to correctly predict the growth morphology, facet and nanomechanical properties of L-Aspartic acid to within the accuracy of the measured elastic stiffness of the crystal, 24.53 ± 0.56 GPa. This work paves the way for the computational design and experimental realization of other functional molecular crystals with tailor-made mechanical properties.

Data-driven dimensional analysis of critical heat flux in subcooled vertical flow: A two-stage machine learning approach

This study presents a novel two-stage machine learning algorithm that identifies dominant dimensionless numbers for critical heat flux (CHF) prediction, circumventing the limitations of traditional dimensional analysis by integrating the Buckingham Pi theorem with advanced computational methods, including the active subspace method and machine learning. When applied to extensive datasets, including 1438 data points from subcooled departure from nucleate boiling (DNB) conditions and validated against a non-overlapping 24,284-point lookup table dataset, our algorithm effectively discerns and validates the dimensionless numbers critical to the CHF phenomena. Notably, it identifies a significant negative correlation between a newly discovered dominant dimensionless number and the boiling number Bochf = q″w,chf / (G hfg), and introduces a new simplified scaling law for water under earth's gravitational conditions, outperforming existing models with substantial predictive accuracy. This scaling law and the identified dimensionless numbers, derived from an initial dataset of 1438 data points, exhibit exceptional generalization capabilities when validated against a significantly larger dataset of 24,284 points. These findings also emphasize the nonlinear effects of inlet quality on the boiling number Bochf. The contribution of this research lies in its potential to deepen the understanding of CHF based on data-driven fashion, with the potential to enhance thermal management and design optimization across various applications.