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Transforming raw EHR data into machine learning model-ready inputs requires considerable effort. One widely used EHR database is Medical Information Mart for Intensive Care (MIMIC). Prior work on MIMIC-III cannot query the updated and improved MIMIC-IV version. Besides, the need to use multicenter datasets further highlights the challenge of EHR data extraction. Therefore, we developed an extraction pipeline that works on both MIMIC-IV and eICU Collaborative Research Database and allows for model cross validation using these 2 databases. Under the default choices, the pipeline extracted 38,766 and 126,448 ICU records for MIMIC-IV and eICU, respectively. Using the extracted time-dependent variables, we compared the Area Under the Curve (AUC) performance with ​​prior works on clinically relevant tasks such as in-hospital mortality prediction. METRE achieved comparable performance with AUC 0.723-0.888 across all tasks with MIMIC-IV. Additionally, when we evaluated the model directly on MIMIC-IV data using a model trained on eICU, we observed that the AUC change can be as small as +0.019 or -0.015. Our open-source pipeline transforms MIMIC-IV and eICU into structured data frames and allows researchers to perform model training and testing using data collected from different institutions, which is of critical importance for model deployment under clinical contexts. The code used to extract the data and perform training is available here: https://github.com/weiliao97/METRE., Competing Interests: Declaration of Competing Interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2023 Elsevier Inc. All rights reserved.)

Objective: To develop a model based on machine learning to predict surgical site infection (SSI) risk in patients after lumbar spinal surgery (LSS)., Methods: Patients who developed postoperative SSI after LSS in the First Affiliated Hospital of Nanchang University between December 2010 and December 2019 were retrospectively reviewed. Preoperative and intraoperative variables, including age, diabetes mellitus, hypertension, body mass index, previous spinal surgery history, surgical duration, number of fused segments, blood loss, and surgical procedure were analyzed. Six machine learning algorithms-logistic regression, multilayer perceptron, decision tree, random forest, gradient boosting machine, and extreme gradient boosting-were used to build prediction models. The performance of the models was evaluated using the area under the curve, accuracy, precision, sensitivity, and F1 score. A web predictor was developed based on the best-performing model., Results: The study included 288 patients who underwent LSS, of whom 144 developed SSI and 144 did not develop SSI. The extreme gradient boosting model offers the best predictive performance among these 6 models (area under the curve = 0.923, accuracy = 0.860, precision = 0.900, sensitivity = 0.834, F1 score = 0.864). An extreme gradient boosting model-based web predictor was developed to predict SSI in patients after LSS., Conclusions: This study developed a machine learning model and a web predictor for predicting SSI in patients after LSS, which may help clinicians screen high-risk patients, provide personalized treatment, and reduce the incidence of SSI after LSS., (Copyright © 2022 Elsevier Inc. All rights reserved.)

Objective: To develop a machine learning (ML) pipeline based on radiomics to predict Coronavirus Disease 2019 (COVID-19) severity and the future deterioration to critical illness using CT and clinical variables., Materials and Methods: Clinical data were collected from 981 patients from a multi-institutional international cohort with real-time polymerase chain reaction-confirmed COVID-19. Radiomics features were extracted from chest CT of the patients. The data of the cohort were randomly divided into training, validation, and test sets using a 7:1:2 ratio. A ML pipeline consisting of a model to predict severity and time-to-event model to predict progression to critical illness were trained on radiomics features and clinical variables. The receiver operating characteristic area under the curve (ROC-AUC), concordance index (C-index), and time-dependent ROC-AUC were calculated to determine model performance, which was compared with consensus CT severity scores obtained by visual interpretation by radiologists., Results: Among 981 patients with confirmed COVID-19, 274 patients developed critical illness. Radiomics features and clinical variables resulted in the best performance for the prediction of disease severity with a highest test ROC-AUC of 0.76 compared with 0.70 (0.76 vs. 0.70, p = 0.023) for visual CT severity score and clinical variables. The progression prediction model achieved a test C-index of 0.868 when it was based on the combination of CT radiomics and clinical variables compared with 0.767 when based on CT radiomics features alone ( p < 0.001), 0.847 when based on clinical variables alone ( p = 0.110), and 0.860 when based on the combination of visual CT severity scores and clinical variables ( p = 0.549). Furthermore, the model based on the combination of CT radiomics and clinical variables achieved time-dependent ROC-AUCs of 0.897, 0.933, and 0.927 for the prediction of progression risks at 3, 5 and 7 days, respectively., Conclusion: CT radiomics features combined with clinical variables were predictive of COVID-19 severity and progression to critical illness with fairly high accuracy., Competing Interests: The authors have no potential conflicts of interest to disclose., (Copyright © 2021 The Korean Society of Radiology.)

Objectives: This study aimed to establish a machine learning prediction model that can be used to predict bone metastasis (BM) in patients with newly diagnosed thyroid cancer (TC)., Methods: Demographic and clinicopathologic variables of TC patients in the Surveillance, Epidemiology, and End Results database from 2010 to 2016 were retrospectively analyzed. On this basis, we developed a random forest (RF) algorithm model based on machine-learning. The area under receiver operating characteristic curve (AUC), accuracy score, recall rate, and specificity are used to evaluate and compare the prediction performance of the RF model and the other model., Results: A total of 17,138 patients were included in the study, with 166 (0.97%) developed bone metastases. Grade, T stage, histology, race, sex, age, and N stage were the important prediction features of BM. The RF model has better predictive performance than the other model (AUC: 0.917, accuracy: 0.904, recall rate: 0.833, and specificity: 0.905)., Conclusions: The RF model constructed in this study could accurately predict bone metastases in TC patients, which may provide clinicians with more personalized clinical decision-making recommendations. Machine learning technology has the potential to improve the development of BM prediction models in TC patients., (© 2021 The Authors. Cancer Medicine published by John Wiley & Sons Ltd.)

Machine learning has emerges as a novel method for model development and has potential to be used to predict and control the performance of anaerobic digesters. In this study, several machine learning algorithms were applied in regression and classification models on digestion performance to identify determinant operational parameters and predict methane production. In the regression models, k-nearest neighbors (KNN) algorithm demonstrates optimal prediction accuracy (root mean square error = 26.6, with the dataset range of 259.0-573.8), after narrowing prediction coverage by excluding extreme outliers from the validation set. In the classification models, logistic regression multiclass algorithm yields the best prediction accuracy of 0.73. Feature importance reveals that total carbon was the determinant operational parameter. These results demonstrate the great potential of using machine learning algorithms to predict anaerobic digestion performance., (Copyright © 2019 Elsevier Ltd. All rights reserved.)

Neural networks are powerful solutions to many scientific applications; however, they usually require long model training time due to large training data sets or large model size. Research has been focused on developing numerical optimization algorithms and parallel processing to reduce the training time. In this work, we propose a multi-resolution strategy that can reduce the training time by training the model with the reduced-resolution data samples at the beginning and later switching to the original resolution data samples. This strategy is motivated by the observation that coarser versions of many applications can be solved faster than their denser counterparts, and the solution to a coarser problem could be used to initialize the solution to the denser problem. When applying the idea to neural network training, coarse data can have a similar effect on the learning curves at the early stage as the dense data but requires less time. Once the curves no longer improve significantly, our strategy switches to using the data in original resolution. The key in this process is the ability to generate multiple resolutions of a problem automatically, which could usually be done with scientific applications with spatial and temporal continuity. We use two real-world scientific applications, CosmoFlow and DeepCAM, to evaluate the proposed mixed-resolution training strategy. Our experiment results demonstrate that the proposed training strategy effectively reduces the end-to-end training time while achieving a comparable accuracy to that of the training only with the original data. While maintaining the same model accuracy, our multi-resolution training strategy reduces the end-to-end training time up to 30% and 23% for CosmoFlow and DeepCAM, respectively.

Quickly identifying astronomical transients from synoptic surveys is critical to many recent astrophysical discoveries. However, each of the data processing pipelines in these surveys contains dozens of stages with highly varying time and space requirements. Properly predicting the resources required to run these pipelines is critical for the allocation of computing resources and reducing the discovery response time. We propose a machine learning strategy for this prediction task and demonstrate its effectiveness using a set of timing measurements from the intermediate Palomar Transient Factory (iPTF) workflow. The proposed model utilizes the spatiotemporal correlation of astronomical images, where nearby patches of the sky (space) are likely to have a similar number of objects of interest and workflows executed in the recent past (time) are likely to use a similar amount of time because the machines and data storage systems are likely to be in similar states. We capture the relationship among these spatial and temporal features in a Bayesian network and study how they impact the prediction accuracy. This Bayesian network helps us to identify the most influential features for predictions. With proper features, our models achieve errors close to the random variance boundary within batches of images taken at the same time, which can be regarded as the intrinsic limit of prediction accuracy.

Modern data mining techniques using machine learning (ML) and deep learning (DL) algorithms have been shown to excel in the regression-based task of materials property prediction using various materials representations. In an attempt to improve the predictive performance of the deep neural network model, researchers have tried to add more layers as well as develop new architectural components to create sophisticated and deep neural network models that can aid in the training process and improve the predictive ability of the final model. However, usually, these modifications require a lot of computational resources, thereby further increasing the already large model training time, which is often not feasible, thereby limiting usage for most researchers. In this paper, we study and propose a deep neural network framework for regression-based problems comprising of fully connected layers that can work with any numerical vector-based materials representations as model input. We present a novel deep regression neural network, iBRNet, with branched skip connections and multiple schedulers, which can reduce the number of parameters used to construct the model, improve the accuracy, and decrease the training time of the predictive model. We perform the model training using composition-based numerical vectors representing the elemental fractions of the respective materials and compare their performance against other traditional ML and several known DL architectures. Using multiple datasets with varying data sizes for training and testing, We show that the proposed iBRNet models outperform the state-of-the-art ML and DL models for all data sizes. We also show that the branched structure and usage of multiple schedulers lead to fewer parameters and faster model training time with better convergence than other neural networks. Scientific contribution: The combination of multiple callback functions in deep neural networks minimizes training time and maximizes accuracy in a controlled computational environment with parametric constraints for the task of materials property prediction. [ABSTRACT FROM AUTHOR]

Objective Preterm infants are at high risk of neuromotor disorders. Recent advances in digital technology and machine learning algorithms have enabled the tracking and recognition of anatomical key points of the human body. It remains unclear whether the proposed pose estimation model and the skeleton-based action recognition model for adult movement classification are applicable and accurate for infant motor assessment. Therefore, this study aimed to develop and validate an artificial intelligence (AI) model framework for movement recognition in full-term and preterm infants. Methods This observational study prospectively assessed 30 full-term infants and 54 preterm infants using the Alberta Infant Motor Scale (58 movements) from 4 to 18 months of age with their movements recorded by 5 video cameras simultaneously in a standardized clinical setup. The movement videos were annotated for the start/end times and presence of movements by 3 pediatric physical therapists. The annotated videos were used for the development and testing of an AI algorithm that consisted of a 17-point human pose estimation model and a skeleton-based action recognition model. Results The infants contributed 153 sessions of Alberta Infant Motor Scale assessment that yielded 13,139 videos of movements for data processing. The intra and interrater reliabilities for movement annotation of videos by the therapists showed high agreements (88%–100%). Thirty-one of the 58 movements were selected for machine learning because of sufficient data samples and developmental significance. Using the annotated results as the standards, the AI algorithm showed satisfactory agreement in classifying the 31 movements (accuracy = 0.91, recall = 0.91, precision = 0.91, and F1 score = 0.91). Conclusion The AI algorithm was accurate in classifying 31 movements in full-term and preterm infants from 4 to 18 months of age in a standardized clinical setup. Impact The findings provide the basis for future refinement and validation of the algorithm on home videos to be a remote infant movement assessment. [ABSTRACT FROM AUTHOR]

Modern machine learning (ML) and deep learning (DL) techniques using high-dimensional data representations have helped accelerate the materials discovery process by efficiently detecting hidden patterns in existing datasets and linking input representations to output properties for a better understanding of the scientific phenomenon. While a deep neural network comprised of fully connected layers has been widely used for materials property prediction, simply creating a deeper model with a large number of layers often faces with vanishing gradient problem, causing a degradation in the performance, thereby limiting usage. In this paper, we study and propose architectural principles to address the question of improving the performance of model training and inference under fixed parametric constraints. Here, we present a general deep-learning framework based on branched residual learning (BRNet) with fully connected layers that can work with any numerical vector-based representation as input to build accurate models to predict materials properties. We perform model training for materials properties using numerical vectors representing different composition-based attributes of the respective materials and compare the performance of the proposed models against traditional ML and existing DL architectures. We find that the proposed models are significantly more accurate than the ML/DL models for all data sizes by using different composition-based attributes as input. Further, branched learning requires fewer parameters and results in faster model training due to better convergence during the training phase than existing neural networks, thereby efficiently building accurate models for predicting materials properties. [ABSTRACT FROM AUTHOR]

Nuclear receptor coactivator 6 (NCOA6), a coactivator of numerous nuclear receptors and transcription factors, regulates multiple critical cellular functions. Nuclear receptor coactivator 6 is dysregulated in various cancers, including hepatocellular carcinoma (HCC); however, its role remains largely unknown. Here we reported that NCOA6 was highly expressed in HCC compared to the adjacent liver tissue, and NCOA6 overexpression was significantly correlated with poor HCC prognosis. Experiments revealed that the knockdown of NCOA6 damaged the proliferation, migration, and invasion of HCC cells. Multiomics and immune infiltration analyses showed a close relationship between NCOA6 expression, multiple cancer‐related malignant pathways, and the immunosuppressive microenvironment. Finally, we established an effective NCOA6‐related microRNA (miRNA) signature to distinguish HCC from hepatitis\liver cirrhosis patients. To the best of our knowledge, this study is the first to provide a comprehensive analysis of NCOA6 expression in HCC. We found that NCOA6 plays an important role in HCC development and has a potential mechanism of action. Establishing an NCOA6‐related miRNA signature will help develop novel diagnostic strategies for HCC patients. [ABSTRACT FROM AUTHOR]

Because of the low accuracy in intrusion detection, a model of extreme learning machine based on the optimization of quantum beetle swarm algorithm is proposed. First of all, this paper proposes a quantum beetle swarm optimization algorithm, which introduces quantum mechanics and combines the advantages of beetle antennae search and particle swarm optimization. In this way, the individual can learn both their own experience and group experience, which enables the beetle to move purposefully and instructively, and improves the convergence performance of the algorithm. In extreme learning machine, it is more difficult to solve the problem in high-dimensional data. This paper proposed an improved extreme learning machine that uses the least squares QR algorithm to decompose the matrix, which can reduce the computational complexity of the traditional extreme learning machine. The improved extreme learning machine model optimized by quantum beetle swarm optimization algorithm is applied to intrusion detection, and the simulation results show that the model proposed in this paper can significantly improve detection accuracy and increase convergence rate. [ABSTRACT FROM AUTHOR]

Artificial intelligence (AI) and machine learning (ML) have been increasingly used in materials science to build predictive models and accelerate discovery. For selected properties, availability of large databases has also facilitated application of deep learning (DL) and transfer learning (TL). However, unavailability of large datasets for a majority of properties prohibits widespread application of DL/TL. We present a cross-property deep-transfer-learning framework that leverages models trained on large datasets to build models on small datasets of different properties. We test the proposed framework on 39 computational and two experimental datasets and find that the TL models with only elemental fractions as input outperform ML/DL models trained from scratch even when they are allowed to use physical attributes as input, for 27/39 (≈ 69%) computational and both the experimental datasets. We believe that the proposed framework can be widely useful to tackle the small data challenge in applying AI/ML in materials science. Artificial intelligence and machine learning can greatly enhance materials property prediction and discovery. Here the authors propose cross-property transfer learning to build accurate models for dozens of properties with limited data availability. [ABSTRACT FROM AUTHOR]

aimed to develop and validate a machine learning model for predicting bone metastases (BM) in prostate cancer (PCa) patients. Methods: Demographic and clinicopathologic variables of PCa patients in the Surveillance, Epidemiology and End Results (SEER) database from 2010 to 2017 were retrospectively analyzed. We used six different machine learning algorithms, including Decision tree (DT), Random forest (RF), Multilayer Perceptron (MLP), Logistic regression (LR), Naive Bayes classifiers (NBC), and eXtreme gradient boosting (XGB), to build prediction models. External validation using data from 644 PCa patients of the First Affiliated Hospital of Nanchang University from 2010 to 2016. The performance of the models was evaluated using the area under receiver operating characteristic curve (AUC), accuracy score, sensitivity (recall rate) and specificity. A web predictor was developed based on the best performance model. Results: A total of 207,137 PCa patients from SEER were included in this study. Of whom, 6725 (3.25%) developed BM. Gleason score, Prostate-specific antigen (PSA) value, T, N stage and age were found to be the risk factors of BM. The XGB model offered the best predictive performance among these 6 models (AUC: 0.962, accuracy: 0.884, sensitivity (recall rate): 0.906, and specificity: 0.879). An XGB model-based web predictor was developed to predict BM in PCa patients. Conclusion: This study developed a machine learning model and a web predictor for predicting the risk of BM in PCa patients, which may help physicians make personalized clinical decisions and treatment strategy for patients. [ABSTRACT FROM AUTHOR]

Radiation encephalopathy (RE) is an important potential complication in patients with nasopharyngeal carcinoma (NPC) who undergo radiotherapy (RT) that can affect the quality of life. However, a functional imaging biomarker of pre-symptomatic RE has not yet been established. This study aimed to assess radiation-induced gray matter functional alterations and explore fractional amplitude of low-frequency fluctuation (fALFF) as an imaging biomarker for predicting or diagnosing RE in patients with NPC. A total of 60 patients with NPC were examined, 21 in the pre-RT cohort and 39 in the post-RT cohort. Patients in the post-RT cohort were further divided into two subgroups according to the occurrence of RE in follow-up: post-RT non−RE (n = 21) and post-RT REprovedinfollow−up (n = 18). Surface-based and volume-based fALFF were used to detect radiation-induced functional alterations. Functional derived features were then adopted to construct a predictive model for the diagnosis of RE. We observed that surface-based fALFF could sensitively detect radiation-induced functional alterations in the intratemporal brain regions (such as the hippocampus and superior temporal gyrus), as well as the extratemporal regions (such as the insula and prefrontal lobe); however, no significant intergroup differences were observed using volume-based fALFF. No significant correlation between fALFF and radiation dose to the ipsilateral temporal lobe was observed. Support vector machine (SVM) analysis revealed that surface-based fALFF in the bilateral superior temporal gyri and left insula exhibited impressive performance (accuracy = 80.49%) in identifying patients likely to develop RE. We conclude that surface-based fALFF may serve as a sensitive imaging biomarker in the prediction of RE. [ABSTRACT FROM AUTHOR]

In this paper, a quantum neural network with multilayer activation function is proposed by using multilayer Sigmoid function superposition and learning algorithm to adjust quantum interval. On this basis, the quasiuniform stability of fractional quantum neural networks with mixed delays is studied. According to the order of two different cases, the conditions of quasi uniform stability of networks are given by using the techniques of linear matrix inequality analysis, and the sufficiency of the conditions is proved. Finally, the feasibility of the conclusion is verified by experiments. [ABSTRACT FROM AUTHOR]

Keywords: neuroimaging; functional magnetic resonance imaging; brain network; machine learning; biomarkers EN neuroimaging functional magnetic resonance imaging brain network machine learning biomarkers 1 3 3 04/25/22 20220419 NES 220419 The human brain is a large, interactive, and complex system, supporting our daily behavior and cognitive function. Multi-Modal Neuroimaging Techniques Compared to single-modal measures, multi-modal neuroimaging analysis can provide complementary information to uncover the underlying brain mechanism. Neuroimaging, functional magnetic resonance imaging, brain network, machine learning, biomarkers. [Extracted from the article]

The application of machine learning (ML) techniques in materials science has attracted significant attention in recent years, due to their impressive ability to efficiently extract data-driven linkages from various input materials representations to their output properties. While the application of traditional ML techniques has become quite ubiquitous, there have been limited applications of more advanced deep learning (DL) techniques, primarily because big materials datasets are relatively rare. Given the demonstrated potential and advantages of DL and the increasing availability of big materials datasets, it is attractive to go for deeper neural networks in a bid to boost model performance, but in reality, it leads to performance degradation due to the vanishing gradient problem. In this paper, we address the question of how to enable deeper learning for cases where big materials data is available. Here, we present a general deep learning framework based on Individual Residual learning (IRNet) composed of very deep neural networks that can work with any vector-based materials representation as input to build accurate property prediction models. We find that the proposed IRNet models can not only successfully alleviate the vanishing gradient problem and enable deeper learning, but also lead to significantly (up to 47%) better model accuracy as compared to plain deep neural networks and traditional ML techniques for a given input materials representation in the presence of big data. [ABSTRACT FROM AUTHOR]

The application of artificial intelligence (AI) in medicine has increased rapidly with respect to tasks including disease detection/diagnosis, risk stratification, and prognosis prediction. With recent advances in computing power and algorithms, AI has shown promise in taking advantage of vast electronic health data and imaging studies to supplement clinicians. Machine learning and deep learning are the most widely used AI methodologies for medical research and have been applied in pancreatobiliary diseases for which diagnosis and treatment selection are often complicated and require joint consideration of data from multiple sources. The aim of this review is to provide a concise introduction of the major AI methodologies and the current landscape of AI research in pancreatobiliary diseases. [ABSTRACT FROM AUTHOR]

The density and configurational changes of crystal dislocations during plastic deformation influence the mechanical properties of materials. These influences have become clearest in nanoscale experiments, in terms of strength, hardness and work hardening size effects in small volumes. The mechanical characterization of a model crystal may be cast as an inverse problem of deducing the defect population characteristics (density, correlations) in small volumes from the mechanical behavior. In this work, we demonstrate how a deep residual network can be used to deduce the dislocation characteristics of a sample of interest using only its surface strain profiles at small deformations, and then statistically predict the mechanical response of size-affected samples at larger deformations. As a testbed of our approach, we utilize high-throughput discrete dislocation simulations for systems of widths that range from nano- to micro- meters. We show that the proposed deep learning model significantly outperforms a traditional machine learning model, as well as accurately produces statistical predictions of the size effects in samples of various widths. By visualizing the filters in convolutional layers and saliency maps, we find that the proposed model is able to learn the significant features of sample strain profiles. [ABSTRACT FROM AUTHOR]

Background: Early identification of critically ill neonates with poor outcomes can optimize therapeutic strategies. We aimed to examine whether machine learning (ML) methods can improve mortality prediction for neonatal intensive care unit (NICU) patients on intubation for respiratory failure. Methods: A total of 1734 neonates with respiratory failure were randomly divided into training (70%, n = 1214) and test (30%, n = 520) sets. The primary outcome was the probability of NICU mortality. The areas under the receiver operating characteristic curves (AUCs) of several ML algorithms were compared with those of the conventional neonatal illness severity scoring systems including the NTISS and SNAPPE-II. Results: For NICU mortality, the random forest (RF) model showed the highest AUC (0.939 (0.921–0.958)) for the prediction of neonates with respiratory failure, and the bagged classification and regression tree model demonstrated the next best results (0.915 (0.891–0.939)). The AUCs of both models were significantly better than the traditional NTISS (0.836 (0.800–0.871)) and SNAPPE-II scores (0.805 (0.766–0.843)). The superior performances were confirmed by higher accuracy and F1 score and better calibration, and the superior and net benefit was confirmed by decision curve analysis. In addition, Shapley additive explanation (SHAP) values were utilized to explain the RF prediction model. Conclusions: Machine learning algorithms increase the accuracy and predictive ability for mortality of neonates with respiratory failure compared with conventional neonatal illness severity scores. The RF model is suitable for clinical use in the NICU, and clinicians can gain insights and have better communication with families in advance. [ABSTRACT FROM AUTHOR]

Organic solar cells are an inexpensive, flexible alternative to traditional silicon‐based solar cells but disadvantaged by low power conversion efficiency due to empirical design and complex manufacturing processes. This process can be accelerated by generating a comprehensive set of potential candidates. However, this would require a laborious trial and error method of modeling all possible polymer configurations. A machine learning model has the potential to accelerate the process of screening potential donor candidates by associating structural features of the compound using molecular fingerprints with their highest occupied molecular orbital energies. In this paper, extremely randomized tree learning models are employed for the prediction of HOMO values for donor compounds, and a web application is developed.1 The proposed models outperform neural networks trained on molecular fingerprints as well as SMILES, as well as other state‐of‐the‐art architectures such as Chemception and Molecular Graph Convolution on two datasets of varying sizes. [ABSTRACT FROM AUTHOR]

Graphical abstract Flow diagram of the proposed methodology. There are two phases in the sampling process. In the first phase, two data generation algorithms are explored for dataset creation, namely partition and allocation. In the first step, we execute our data-generation algorithms (depicted by orange arrows) to generate many valid solutions for each set of constraint. In the second phase, a machine learning-guided feedback-aware sampling approach (depicted by green allows) extracts a combination of non- zero ODF dimensions. ODFs generated with these subset of combinations lead to optimal or near-optimal solutions. Abstract Microstructure sensitive design has a critical impact on the performance of engineering materials. The safety and performance requirements of critical components, as well as the cost of material and machining of Titanium components, make dovetailing of the microstructure imperative. This paper addresses the optimization of several microstructure design problems for Titanium components under specific design constraints using a feedback-aware data-driven solution methodology. In this study, the microstructure is modeled with an orientation distribution function (ODF) that measures the volumes of different crystallographic orientations. Two algorithms are used to sample the entire microstructure space followed by machine learning-aided identification of a minimal subset of ODF dimensions which is subsequently explored by targeted sampling. Conventional optimization methods lead to a unique microstructure rather than yielding a comprehensive space of optimal or near-optimal microstructures. Multiple solutions are crucial for the deployment of materials design for manufacturing as traditional manufacturing processes can only generate a limited set of microstructures. Our data sampling-based methodology not only outperforms or is on par with other optimization techniques in terms of the optimal property value, but also provides numerous near-optimal solutions, 3–4 orders of magnitude more than previous methods. Consequently, the proposed framework delivers a spectrum of optimal solutions in the microstructure space which can accelerate materials development and reduce manufacturing costs. [ABSTRACT FROM AUTHOR]

Additive manufacturing (AM) has undergone significant development over the past decades, resulting in vast amounts of data that carry valuable information. Numerous research studies have been conducted to extract insights from AM data and utilize it for optimizing various aspects such as the manufacturing process, supply chain, and real-time monitoring. Data integration into proposed digital twin frameworks and the application of machine learning techniques is expected to play pivotal roles in advancing AM in the future. In this paper, we provide an overview of machine learning and digital twin-assisted AM. On one hand, we discuss the research domain and highlight the machine-learning methods utilized in this field, including material analysis, design optimization, process parameter optimization, defect detection and monitoring, and sustainability. On the other hand, we examine the status of digital twin-assisted AM from the current research status to the technical approach and offer insights into future developments and perspectives in this area. This review paper aims to examine present research and development in the convergence of big data, machine learning, and digital twin-assisted AM. Although there are numerous review papers on machine learning for additive manufacturing and others on digital twins for AM, no existing paper has considered how these concepts are intrinsically connected and interrelated. Our paper is the first to integrate the three concepts big data, machine learning, and digital twins and propose a cohesive framework for how they can work together to improve the efficiency, accuracy, and sustainability of AM processes. By exploring latest advancements and applications within these domains, our objective is to emphasize the potential advantages and future possibilities associated with integration of these technologies in AM. • Explored how additive manufacturing evolution yields big data, leveraging ML and DT to optimize manufacturing. • Discussed the interconnection among big data, machine learning, and digital twins. • Examined ML's role in material analysis, design optimization, process optimization, defect detection, and sustainability. • Reviewed current developments for the application of digital twins to empower additive manufacturing. [ABSTRACT FROM AUTHOR]

• The enhanced segmented active constrained layer damping is proposed. • A new three-dimensional fan-section shell element is proposed. • The dependence on Euler's angular velocity of rotating hollow beam is found. • Neural network algorithm is used to optimize the structure. As an active and passive hybrid control technology, enhanced segmented active constrained layer damping (ESACLD) is an intelligent damping structure for suppressing adverse vibrations in flexible structures. In this study, the three-dimensional spatial dynamic model of a rotating hollow beam with improved ESACLD treatment is established based on the finite element method in a floating frame of reference. The damping effects of the ESACLD with both edge elements and incisions are considered in the dynamic model for the first time. Simulation results show the damping performance of the ESACLD is better than traditional active constrained layer damping with or without incisions (SACLD or ACLD). The Genetic Algorithm-Backpropagation (GA-BP) neural network is used to optimize the physical parameters of ESACLD, and the optimized model can effectively suppress the transverse and flapping bending vibration. It is also found that flapping and transverse bending vibrations exhibit differential dependence on nutation and precession angular velocities; higher nutation and lower precession velocities suppress flapping bending. The existing commercial finite element software has some problems, such as low efficiency and inaccuracy. The new finite element proposed in this paper uses a new form function to ensure accuracy in the simulation of rotation and vibration and has obvious advantages in dealing with rotational structures and complex boundary conditions. Research in this work can provide a design framework for vibration prediction and control of flexible beam-like structures. [ABSTRACT FROM AUTHOR]

The drift ratio or lateral deformation is typically applied as the indicator in order to evaluate the earthquake-induced damage, one of the most important issues is to determine the seismic performance level limits. Therefore, this study presents to predict the seismic performance level limits of RC columns by using the machine learning method. Firstly, a test database of the backbone curves of RC columns was established after collecting 754 specimens under axial and lateral loads. Then the seismic performance level limits of all the collected columns were taken out as the input values of machine learning. The correlations among the geometric, mechanical parameters and the performance limits of RC columns were analyzed based on Pearson correlation analysis and mutual information method. Afterward, regression models of seven machine learning methods were established to predict the performance level limits of RC columns, while the hyperparameters of the machine learning models were optimized by the grid search and cross-validation methods. The generalization ability of the prediction models was verified and evaluated by using mean square error, mean absolute error, maximum error and R square, meanwhile, the accuracy of the applied methods was also analyzed. The seismic performance level limits of RC columns determined by the machine learning method can comprehensively consider the influence of geometric and mechanical parameters of RC columns. Combined with the earthquake-induced deformation of RC columns, the seismic damage of RC columns can be evaluated reasonably, which is of great significance for evaluating the seismic damage of building structures. The discussion on the prediction accuracy among different machine learning algorithms is also beneficial for the deformation prediction of other RC components. • A test database of the backbone curves of 754 RC columns was established. • Seven machine learning regression algorithms were used to predict the seismic performance level limits. • Accuracy analysis of different machine learning methods provides a reference for the seismic evaluation of RC components. [ABSTRACT FROM AUTHOR]

Previous studies indicated the beneficial influence of residential greenness on cardiovascular disease (CVD), however, the association of residential greenness with cardiac conduction performance remains unclear. This study aims to examine the epidemiological associations between residential greenness and cardiac conduction abnormalities in rural residents, simultaneously exploring the role of residential greenness for cardiac health in an explainable machine learning modeling study. A total of 27,294 participants were derived from the Henan Rural Cohort. Two satellite-based indices, the normalized difference vegetation index (NDVI) and the enhanced vegetation index (EVI), were used to estimate residential greenness. Independent and combined associations of residential greenness indices and physical activities with electrocardiogram (ECG) parameter abnormalities were evaluated using the logistic regression model and generalized linear model. The Gradient Boosting Machine (GBM) and the SHapely Additive exPlanations (SHAP) were employed in the modeling study. The odds ratios (OR) and 95% confidence interval (CI) for QRS interval, heart rate (HR), QTc interval, and PR interval abnormalities with per interquartile range in NDVI were 0.896 (0.873–0.920), 0.955 (0.926–0.986), 1.015 (0.984–1.047), and 0.986 (0.929–1.045), respectively. Furthermore, the participants with higher physical activities plus residential greenness (assessed by EVI) were related to a 1.049-fold (1.017–1.081) and 1.298-fold (1.245–1.354) decreased risk for abnormal QRS interval and HR. Similar results were also observed in the sensitivity analysis. The NDVI ranked fifth (SHAP mean value 0.116) in the analysis for QRS interval abnormality risk in the modeling study. A higher level of residential greenness was significantly associated with cardiac conduction abnormalities. This effect might be strengthened in residents with more physical activities. This study indicated the cruciality of environmental greenness to cardiac functions and also contributed to refining preventive medicine and greenness design strategies. [Display omitted] • Residential greenness was negatively associated with cardiac health risk among rural Chinese. • Higher level of physical activities strengthened the beneficial effect of residential greenness. • Residential greenness was verified to be crucial for cardiac health via SHAP. [ABSTRACT FROM AUTHOR]

Abstract Data-driven methods are attracting growing attention in the field of materials science. In particular, it is now becoming clear that machine learning approaches offer a unique avenue for successfully mining practically useful process-structure-property (PSP) linkages from a variety of materials data. Most previous efforts in this direction have relied on feature design (i.e., the identification of the salient features of the material microstructure to be included in the PSP linkages). However due to the rich complexity of features in most heterogeneous materials systems, it has been difficult to identify a set of consistent features that are transferable from one material system to another. With flexible architecture and remarkable learning capability, the emergent deep learning approaches offer a new path forward that circumvents the feature design step. In this work, we demonstrate the implementation of a deep learning feature-engineering-free approach to the prediction of the microscale elastic strain field in a given three-dimensional voxel-based microstructure of a high-contrast two-phase composite. The results show that deep learning approaches can implicitly learn salient information about local neighborhood details, and significantly outperform state-of-the-art methods. Graphical abstract Image 1 [ABSTRACT FROM AUTHOR]

Data-driven methods are emerging as an important toolset in the studies of multiscale, multiphysics, materials phenomena. More specifically, data mining and machine learning methods offer an efficient toolset for extracting and curating the important correlations controlling these multiscale materials phenomena in high-value reduced-order forms called process-structure-property (PSP) linkages. Traditional machine learning methods usually depend on intensive feature engineering, and have enjoyed some success in establishing the desired PSP linkages. In contrast, deep learning approaches provide a feature-engineering-free framework with high learning capability. In this work, a deep learning approach is designed and implemented to model an elastic homogenization structure-property linkage in a high contrast composite material system. More specifically, the proposed deep learning model is employed to capture the nonlinear mapping between the three-dimensional material microstructure and its macroscale (effective) stiffness. It is demonstrated that this end-to-end framework can predict the effective stiffness of high contrast elastic composites with a wide of range of microstructures, while exhibiting high accuracy and low computational cost for new evaluations. [ABSTRACT FROM AUTHOR]

This paper presents a hybrid machine learning model for predicting the pullback force in horizontal directional drilling (HDD) construction. The model combines the nondominated sorting genetic algorithm II (NSGA-II) and support vector machine (SVM). NSGA-II is used to optimize two hyperparameters in SVM. Different from other optimization algorithms, NSGA-II is a multi-objective optimizer, which considers prediction accuracy and stability. The proposed model is applied to two practical HDD projects in China. The prediction result is compared with the actual monitoring data, which shows that the mean absolute percentage errors (MAPE) are less than 7%. The primary conclusions are as follows: (1) The proposed model's accuracy and stability are better than those of the two benchmark models; (2) Machine learning models can predict the pullback force more accurately than the empirical model in the construction phase, and the maximum MAPE does not exceed 17%; (3) The running time of the proposed model is short, and it is feasible in practical application. Moreover, this paper discusses the practical application of machine learning models in HDD construction and the future development direction. • A hybrid machine learning model is proposed to predict pullback force during HDD construction. • Prediction accuracy and stability are considered. • Proposed model is applied to two HDD projects in China. [ABSTRACT FROM AUTHOR]

Burst pressure is the key to the design of pressure pipelines, and its accurate prediction is of considerable significance to pipeline safety and integrity management. Due to the different corrosion forms of different pipelines, the traditional empirical formula is difficult to predict the burst pressure with high accuracy. This paper proposes an ensemble data-driven model that can train the model based on existing pipeline parameters, corrosion characteristics, and burst pressure data. The trained model can be used to predict the burst pressure of pipelines with other pipeline parameters and corrosion characteristics. In the ensemble model design, a more advanced machine learning model, relevance vector machine, is used as the basic predictor. Multi-objective salp swarm algorithm is utilized to optimize the relevance vector machine, to improve the prediction accuracy and stability. The proposed model is applied to three datasets with different features and inputs. The results reveal that the average absolute percent errors of the proposed model in the three datasets are 3.724%, 1.788%, and 4.133%, respectively, and the standard deviations of the percentage errors are 9.07%, 2.36%, and 5.42%, respectively, which are all in a low level. The results indicate that the proposed model has high prediction accuracy and stability in different datasets, even when the data amount is small. Moreover, the Diebold-Mariano test implies that the proposed model's prediction performance is better than benchmark models, and much better than the model without the optimizer or with the single-objective optimizer. [Display omitted] • A hybrid data-driven model is proposed to predict burst pressure of pipelines. • The proposed model is applied to three datasets. • The accuracy and stability of the model are evaluated. • The proposed model is compared with five benchmark models. [ABSTRACT FROM AUTHOR]