Sample Dataset Research Articles

Maternal chorioamnionitis and the risk for necrotizing enterocolitis in the United States: A national cohort study

BackgroundNecrotizing enterocolitis (NEC) is a major cause for morbidity and mortality among newborn infants. Chorioamnionitis is a perinatal complication that is associated with preterm delivery. Few reports have studied chorioamnionitis as a possible risk factor for NEC. Further investigation is needed to fully understand this association. ObjectiveTo examine the association of chorioamnionitis with NEC in newborn infants. MethodsWe used National Inpatient Sample (NIS) datasets produced by the federal Healthcare Cost and Utilization Project (HCUP). We identified infants born to mothers diagnosed with chorioamnionitis and infants born to mothers who did not have chorioamnionitis. The odds ratios (OR) to develop NEC in infants born to mothers affected by chorioamnionitis were calculated using chi square and Fisher Exact tests in the overall sample and in subgroups of different birthweight (BW) categories. The association was re-evaluated using logistic regression models to control for confounding variables. ResultsThe study identified 18,973,800 newborn infants admitted during the years 2016–2020. Among infants born to mothers with chorioamnionitis, NEC occurred in 0.9 % compared to 0.1 % in infants born to mothers without chorioamnionitis, (adjusted OR = 1.12, CI:1.02–1.15, p = 0.01). The prevalence of NEC in infants born to mothers with chorioamnionitis varied by the birth weight category, mainly for BW category 2500-4499 g (aOR = 1.61, CI:1.44–1.80, p < 0.001). ConclusionMaternal chorioamnionitis is associated with increased incidence of NEC, particularly in the BW category 2500-4499 g. Further studies are needed to examine the pathophysiological factors underlying this association.

Hybrid optimal feature selection-based iterative deep convolution learning for COVID-19 classification system

The COVID-19 pandemic has necessitated the development of innovative and efficient methods for early detection and diagnosis. Integrating Internet of Things (IoT) devices and applications in healthcare has facilitated various functions. This work aims to employ practical artificial intelligence (AI) approaches to extract meaningful information from the vast amount of IoT data to perform disease prediction tasks. However, traditional AI methods need help in feature analysis due to the complexity and scale of IoT data. So, this work implements the optimal iterative COVID-19 classification network (OICC-Net) using machine learning optimization and deep learning approaches. Initially, the preprocessing operation normalizes the dataset with uniform values. Here, random forest infused particle swarm-based black widow optimization (RFI-PS-BWO) algorithm was used to get the disease-specific patterns from SARS-CoV-2 (SC2), and other disease classes, where patterns of the SC2 virus are very similar to those of other virus classes. In addition, an iterative deep convolution learning (IDCL) feature selection method is used to distinguish features from the RFI-PS-BWO data. This iterative process enhances the performance of feature selection by providing improved representation and reducing the dimensionality of the input data. Then, a one-dimensional convolutional neural network (1D-CNN) was employed to classify and identify the extracted features from SC2 with no virus classes. The 1D-CNN model is trained using a large dataset of COVID-19 samples, enabling it to learn intricate patterns and make accurate predictions. It was tested and found that the proposed OICC-Net system is more accurate than current methods, with a score of 99.97 % for F1-score, 100 % for sensitivity, 100 % for specificity, 99.98 % for precision, and 99.99 % for recall.

Risky lane-changing behavior recognition based on stacking ensemble learning on snowy and icy surfaces

Risky lane-changing (LC) behavior adversely affects traffic safety, especially on snowy and icy surfaces. However, due to the particularity of the snowy and icy surfaces and the scarcity of data, research on risky lane-changing behavior (RLCB) under extreme scenarios is insufficient. Therefore, this study presents a novel research framework aimed at selecting key risk characterization indicators (RCIs) and identifying RLCB on highways using driving simulation data on snowy and icy surfaces. A highway LC scenario was established on snowy and icy surfaces using a driving simulator, and 1200 sets of LC sample data were extracted. From the perspectives of parameter importance and correlation, 12 key RCIs with high importance and low inter-correlation were selected using the C4.5 decision tree algorithm and Pearson correlation analysis method. The RLCB recognition model was developed using the Stacking ensemble learning method and then compared with traditional recognition algorithms. The results show that the accuracy of the recognition model based on the Stacking ensemble learning model is significantly better than that of traditional algorithms, with a recognition accuracy of 98.33%. This finding can provide the basis for developing LC warning systems for intelligent connected vehicles on snowy and icy surfaces.

Leak localization in District Heating Networks integrating physical model-based and data driven-based methods: Impact of dataset construction on model performance

Leaks in district heating networks result in waste of water and heat, and even threaten personal safety. Various machine learning-based methods have been proposed to realize leak localization and gain satisfying performance based on a large amount of leak samples. However, the creation of such samples demands a substantial investment of time and computational resources, presenting challenges for real-time applications. Previous studies have rarely addressed the influence of leak dataset construction and efficient creation methods. To fill this limited area, this paper conducts a comparative analysis on the variables in leak dataset and proposes an effective method for sample dataset generation, complemented by a novel accuracy evaluation that incorporates the topological relationship among pipelines. A multi-source looped heating network is employed to do the case study. The results demonstrate that the proposed method achieves satisfactory leak localization performance with a significantly reduced training set. Furthermore, by segmenting the physical pipeline into multiple virtual pipelines and using the adjacent evaluation metric, the leak localization method achieves a 75 m error with an accuracy of 0.998.

Enhanced Risk of Gastroesophageal Reflux Disease and Esophageal Complications in the Ulcerative Colitis Population.

Background: Although heartburn and reflux are frequently reported in ulcerative colitis [UC], the correlation between UC and gastroesophageal reflux disease [GERD], and its complications, esophageal stricture and Barrett's esophagus [BE], is not well understood. This study aims to examine the prevalence and associated risk of GERD and its complications within the UC population. Methods: We analyzed the National Inpatient Sample (NIS) dataset, consisting of 7,159,694 patients, comparing GERD patients with and without UC to those without GERD. We assessed the degree of colonic involvement in UC and the occurrence of esophageal complications. Bivariate analyses were conducted using the chi-squared test or Fisher exact test (two-tailed). Results: A higher prevalence of GERD (23.0% vs. 16.5%) and GERD phenotypes, such as non-erosive reflux disease (NERD) (22.3% vs. 16%) and erosive esophagitis (EE) (1.2% vs. 0.6%), was found in UC patients (p < 0.01), including pancolitis, proctitis, proctosigmoiditis, left-sided colitis, and indetermined UC (with undefined colonic involvement). UC patients were more likely to develop GERD (1.421), NERD (1.407), and EE (1.681) (p < 0.01). A higher prevalence of esophageal stricture (16.9 vs. 11.4 per 10,000 patients) and BE without dysplasia (94.5 vs. 39.3 per 10,000 patients) was found in UC (p < 0.05). The odds of developing BE without dysplasia were higher (1.892) in patients with UC (p < 0.01), including ulcerative pancolitis, proctitis, and indeterminate UC (OR of 1.657, 3.328, and 1.996, respectively) (p < 0.05). Conclusions: Our study demonstrates an increased risk of developing GERD and its complications in UC. This highlights the importance of vigilant monitoring and early intervention to minimize associated GERD-related risks in patients with UC.

Rapid prediction for the transient dispersion of leaked airborne pollutant in urban environment based on graph neural networks

Rapidly predicting airborne pollutant dispersion in urban is vital for ventilation design and evacuation planning. Computational fluid dynamics (CFD) simulations are commonly used to provide accurate predictions, but the computational cost is too high. Although graph neural networks (GNNs) provide fast predictions of flow fields by manipulating unstructured mesh on GPU, they suffer from high memory usage and accuracy decreases when applied to large-scale urban scenes. Moreover, it is difficult for GNNs to learn the coupled relationship between wind field and pollutant concentration field. We propose a multi-objective GNN model as CFD surrogate to rapidly predict the transient dispersion of airborne pollutant under the influence of complex wind field patterns in urban environment. Based on random urban layouts generated by a 2D bin packing algorithm, we employ a validated CFD model to construct a sample dataset of wind fields and concentration fields. We leverage graph pooling and multi-scale feature fusion to improve prediction accuracy, and subgraph partitioning of both wind field and concentration field to reduce GPU memory usage. The results show that our GNN model at its best runs 1–2 orders of magnitude faster than CFD simulation with accuracy evaluation metrics R2=0.92, and achieves 70 % GPU memory reduction.

A comprehensive meta-analysis of tissue resident memory T cell shaping non-small-cell lung cancer immune microenvironment and patient prognosis.

210 Background: Tissue-resident memory T cells (TRM) are a specialized subset of long-lived memory T cells that reside in peripheral tissues. However, the impact of TRM-related immunosurveillance on the tumor-immune microenvironment (TIME) and tumor progression across various non-small-cell lung cancer (NSCLC) patient populations is yet to be elucidated. Methods: Our comprehensive analysis of multiple independent single-cell and bulk RNA-seq datasets of patient NSCLC samples generated reliable, unique TRM signatures, through which we inferred the abundance of TRM in NSCLC. A machine learning model was developed to prognosticate survival, tested in multiple independent NSCLC cohorts. Model performance was corroborated through Kaplan-Meier survival plots, receiver operating characteristic (ROC) curves, principal component analysis, and t-SNE analyses. Results: We discovered that TRM abundance is consistently positively correlated with CD4+ T helper 1 cells, M1 macrophages, and resting dendritic cells in the TIME. In addition, TRM signatures are strongly associated with immune checkpoint genes and the prognosis of NSCLC patients. A TRM-based machine learning model to predict patient survival was validated and an 18-gene risk score was further developed to effectively stratify patients into low-risk and high-risk categories, wherein patients with high-risk scores had significantly lower overall survival than patients with low-risk. The prognostic value of the risk score was independently validated by the TCGA dataset and multiple independent NSCLC patient datasets. Notably, low-risk NSCLC patients with higher TRM infiltration exhibited enhanced T-cell immunity, nature killer cell activation, and other TIME immune responses related pathways, indicating a more active immune profile benefitting from immunotherapy. However, the TRM signature revealed low TRM abundance and a lack of prognostic association among lung squamous cell carcinoma patients in contrast to adenocarcinoma, indicating that the two NSCLC subtypes are driven by distinct TIMEs. Conclusions: Altogether, this study provides valuable insights into the complex interactions between TRM and TIME and their impact on NSCLC patient prognosis. The development of a simplified 18-gene risk score provides a practical prognostic marker for risk stratification. Keywords: Tissue resident memory T cell, non-small-cell lung cancer, prognosis, tumor immune microenvironment, machine learning.

Facial soft tissue depth of a contemporary adult Greek population

Facial approximation is a technique that involves constructing the facial muscles and applying a suitable facial soft tissue depth (FSTD) dataset. To date, several FSTD studies have been conducted for varying population groups. This study aims to establish a FSTD dataset of an adult Greek population sample for the first time. The facial depths of subjects were measured on 100 head CT scans of 50 male and 50 female subjects aged from 18 to 99. The 3D head and skull models of subjects were segmented in Amira 6.1 by using histogram method. FSTDs were measured at 22 cranial landmarks (5 mid-sagittal, 17 bilateral). The FSTD dataset was generated by considering the age and sex of subjects. The impact of age and sex on the FSTD was limited. Slight inter-population depth variations were reported. Facial asymmetry calculated between the bilateral landmarks was insignificant for both male and female subjects.

Gdcov2sinex: a Python conversion tool from GipsyX’s gdcov file to SINEX file

The Solution INdependent EXchange (SINEX) file format, an international standard for the exchange of information, is essential in the Global Navigation Satellite System (GNSS) processing strategies that integrate different software applications. GNSS data processing using Jet Propulsion Laboratory’s (JPL) GipsyX software, which employs the Precise Point Positioning (PPP) technique, generates positions and covariance matrices in gdcov files, a GipsyX file format specific for storing this information. GipsyX software provides a tool, named mkDailySinex.py, to convert from gdcov file to SINEX file, but it is designed specifically for creating daily JPL SINEX files and does not function properly for non-JPL GipsyX users. I have developed a Python 3 program, named gdcov2sinex.py, which solves this problem, enabling any user to perform the SINEX conversion and, therefore, apply the processing strategies that integrate GipsyX with other software, such as GAMIT/GLOBK. The new python tool presented herein takes advantage of the capabilities of mkDailySinex.py and provides all its default options, but also expands the number of selectable options, which can be useful to the user. The source code, user manual, and a sample dataset of gdcov2sinex.py are provided as electronic supplementary material of this paper.

Development of a new hydraulic electric index for rock typing in carbonate reservoirs

Rock typing techniques have relied on either electrical or hydraulic properties. The study introduces a novel approach for reservoir rock typing, the hydraulic-electric index (HEI), which combines the strengths of traditional electrical and hydraulic rock typing methods to characterize carbonate reservoirs more accurately. By normalizing the ratio of permeability and formation resistivity factor (K/FRF) with respect to porosity, the HEI method is applied to two datasets of carbonate core samples: dataset 1 consists of 112 carbonate core samples from the Tensleep formation in the Bighorn basin of Wyoming and Montana, and dataset 2 includes 81 carbonate core samples from the Asmari formation in the south-west of Iran. Statistical analysis confirms the effectiveness of the HEI in predicting permeability, with high determination coefficients for both datasets (resulting in determination coefficients (R2) of 0.965 and 0.904 for dataset 1 and dataset 2, respectively). The results classify the rock samples into distinct rock types, nine rock types for dataset 1 and four rock types for dataset 2, and demonstrate the HEI ability to capture the relationship between hydraulic conductivity and electrical resistivity in carbonate reservoir rocks. Applying the HEI method to the validation dataset yielded highly accurate permeability predictions, with average of determination coefficients of 0.883 and 0.859 for dataset 1 and dataset 2, respectively. Validation of the HEI method further confirms (20% of the dataset was set aside for validation, while the remaining 80% was used for the rock typing approach (5 folds)) its accuracy in predicting permeability, highlighting its robust predictive capacity for estimating permeability in carbonate reservoirs.

Time-series China urban land use mapping (2016–2022): An approach for achieving spatial-consistency and semantic-transition rationality in temporal domain

The global urbanization trend is geographically manifested through city expansion and the renewal of internal urban structures and functions. Time-series urban land use (ULU) maps are vital for capturing dynamic land changes in the urbanization process, giving valuable insights into urban development and its environmental consequences. Recent studies have mapped ULU in some cities with a unified model, but ignored the regional differences among cities; and they generated ULU maps year by year, but ignored temporal correlations between years; thus, they could be weak in large-scale and long time-series ULU monitoring. Accordingly, we introduce an temporal-spatial-semantic collaborative (TSS) mapping framework to generating accurate ULU maps with considering regional differences and temporal correlations. Firstly, to support model training, a large-scale ULU sample dataset based on OpenStreetMap (OSM) and Sentinel-2 imagery is automatically constructed, providing a total number of 56,412 samples with a size of 512 × 512 which are divided into six sub-regions in China and used for training different classification models. Then, an urban land use mapping network (ULUNet) is proposed to recognize ULU. This model utilizes a primary and an auxiliary encoder to process noisy OSM samples and can enhance the model's robustness under noisy labels. Finally, taking the temporal correlations of ULU into consideration, the recognized ULU are optimized, whose boundaries are unified by a time-series co-segmentation, and whose categories are modified by a knowledge-data driven method. To verify the effectiveness of the proposed method, we consider all urban areas in China (254,566 km2), and produce a time-series China urban land use dataset (CULU) at a 10-m resolution, spanning from 2016 to 2022, with an overall accuracy of CULU is 82.42%. Through comparison, it can be found that CULU outperforms existing datasets such as EULUC-China and UFZ-31cities in data accuracies, spatial boundaries consistencies and land use transitions logicality. The results indicate that the proposed method and generated dataset can play important roles in land use change monitoring, ecological-environmental evolution analysis, and also sustainable city development.

A fast open data reduction workflow for the electron microprobe flank method to determine Fe3+/ΣFe contents in minerals

Abstract Knowing the Fe3+/ΣFe ratio in minerals is important to understand the formation and evolution of minerals and their host rocks. Variable Fe3+/ΣFe in, e.g., garnet is accompanied by a change of their characteristic FeLα and FeLβ X-ray emission lines, which can be quantified with electron microprobe measurements using the flank method. Previously, the required data reduction process to determine the Fe3+/ΣFe was complex. Here, we present a new data reduction tool taking garnet data as an example. This new Flank Reduction app is a freely available, graphical user interface (GUI)-driven, web-based application to reduce flank method data quickly and easily. The entire data reduction process is achieved in minutes compared to hours or days, as it was before. Flank Reduction provides comprehensive insights into, e.g., the flank method standards employed, data obtained, and errors through a wide array of controls and visualization tools. Documentation with comprehensive information on the flank method, data reduction, as well as training material such as video tutorials or sample data sets, are available on a dedicated webpage. Flank Reduction emphasizes the high value of Findable, Accessible, Interoperable, Reproducible (FAIR) and open research software and demonstrates how current developments in coding and app implementation can facilitate the development of powerful and expandable research software.

MIGP: Metapath Integrated Graph Prompt Neural Network

Graph neural networks (GNNs) leveraging metapaths have garnered extensive utilization. Nevertheless, the escalating parameters and data corpus within graph pre-training models incur mounting training costs. Consequently, GNN models encounter hurdles including diminished generalization capacity and compromised performance amidst small sample datasets. Drawing inspiration from the efficacy demonstrated by self-supervised learning methodologies in natural language processing, we embark on an exploration. We endeavor to imbue graph data with augmentable, learnable prompt vectors targeting node representation enhancement to foster superior adaptability to downstream tasks. This paper proposes a novel approach, the Metapath Integrated Graph Prompt Neural Network (MIGP), which leverages learnable prompt vectors to enhance node representations within a pretrained model framework. By leveraging learnable prompt vectors, MIGP aims to address the limitations posed by mall sample datasets and improve GNNs’ model generalization. In the pretraining stage, we split symmetric metapaths in heterogeneous graphs into short metapaths and explicitly propagate information along the metapaths to update node representations. In the prompt-tuning stage, the parameters of the pretrained model are fixed, a set of independent basis vectors is introduced, and an attention mechanism is employed to generate task-specific learnable prompt vectors for each node. Another notable contribution of our work is the introduction of three patent datasets, which is a pioneering application in related fields. We will make these three patent datasets publicly available to facilitate further research on large-scale patent data analysis. Through comprehensive experiments conducted on three patent datasets and three other public datasets, i.e., ACM, IMDB, and DBLP, we demonstrate the superior performance of the MIGP model in enhancing model applicability and performance across a variety of downstream datasets. The source code and datasets are available in the website.11https://github.com/hzw-ai/MIGP, with password: MIGPNN2024.

MAGPIE: An interactive tool for visualizing and analyzing protein-ligand interactions.

Quantitative tools to compile and analyze biomolecular interactions among chemically diverse binding partners would improve therapeutic design and aid in studying molecular evolution. Here we present Mapping Areas of Genetic Parsimony In Epitopes (MAGPIE), a publicly available software package for simultaneously visualizing and analyzing thousands of interactions between a single protein or small molecule ligand (the "target") and all of its protein binding partners ("binders"). MAGPIE generates an interactive three-dimensional visualization from a set of protein complex structures that share the target ligand, as well as sequence logo-style amino acid frequency graphs that show all the amino acids from the set of protein binders that interact with user-defined target ligand positions or chemical groups. MAGPIE highlights all the salt bridge and hydrogen bond interactions made by the target in the visualization and as separate amino acid frequency graphs. Finally, MAGPIE collates the most common target-binder interactions as a list of "hotspots," which can be used to analyze trends or guide the de novo design of protein binders. As an example of the utility of the program, we used MAGPIE to probe how different antibody fragments bind a viral antigen; how a common metabolite binds diverse protein partners; and how two ligands bind orthologs of a well-conserved glycolytic enzyme for a detailed understanding of evolutionarily conserved interactions involved in its activation and inhibition. MAGPIE is implemented in Python 3 and freely available at https://github.com/glasgowlab/MAGPIE, along with sample datasets, usage examples, and helper scripts to prepare input structures.

Itaconate suppresses house dust mite-induced allergic airways disease and Th2 cell differentiation

Itaconate was initially identified as an antimicrobial compound produced by myeloid cells. Beyond its antimicrobial role, itaconate may also serve as a crucial metabolic and immune modulator. We therefore examined the roles of aconitate decarboxylase 1 (Acod1) and itaconate in house dust mite (HDM)-sensitized and −challenged mice, a model of T helper 2 (Th2)-driven allergic airways disease. HDM treatment induced lung Acod1 mRNA expression and bronchoalveolar lavage (BAL) itaconate levels in wild-type C57BL/6 mice. Acod1 knockout mice (Acod1-KO) with negligible BAL itaconate showed heightened HDM-induced type 2 cytokine expression, increased serum IgE, and enhanced recruitment of Th2 cells in the lung, indicating a shift towards a more pronounced Th2 immune response. Acod1-KO mice also showed increased eosinophilic airway inflammation and hyperresponsiveness. Experiments in chimeric mice demonstrated that bone marrow from Acod1-KO mice is sufficient to increase type 2 cytokine expression in wild-type mice, and that restitution of bone marrow from wild type mice attenuates mRNA expression of Th2 cytokines in Acod1-KO mice. Specific deletion of Acod1 in lysozyme-secreting macrophages (LysM-cre+Acod1flox/flox) recapitulated the exaggerated phenotype observed in whole-body Acod1-KO mice. Adoptive transfer of Acod1-KO bone marrow-derived macrophages also increased lung mRNA expression of Th2 cytokines. In addition, treatment of Th2-polarized CD4 cells with itaconate impeded Th2 cell differentiation, as shown by reduced expression of Gata3 and decreased release of IL-5 and IL-13. Finally, public datasets of human samples show lower Acod1 expression in subjects with allergic asthma, consistent with a protective role of itaconate in asthma pathogenesis. Together, these data suggest that itaconate plays a protective, immunomodulatory role in limiting airway type 2 inflammation after allergen challenge by attenuating T cell responses.

Identification of multimodal vortex optical orbital angular momentum in multimode fiber speckle patterns

Orbital angular momentum (OAM) multiplexing technology is a highly promising approach that can significantly increase the data capacity of optical communication systems. However, traditional optical communication systems are prone to atmospheric disturbances such as turbulence, which can degrade transmission quality. To mitigate this issue, multimode fibers (MMFs) have been introduced to reduce the impact of turbulence on signals. Moreover, previous studies have primarily focused on the identification of single vortex beams, facing challenges in accurately recognizing multiplexed modes. To address this challenge, this chapter proposes a multi-label image classification optimization algorithm based on transfer learning. By utilizing the pre-trained MobileNet V2 model as a feature extractor, this network structure can accurately identify 8-bit, 16-bit, and 24-bit multiplexed OAM from speckle patterns in multimode fibers, even with small sample datasets, achieving classification accuracies exceeding 95%. This method overcomes the limitations of traditional optical communication systems that are susceptible to atmospheric disturbances, providing new possibilities for long-distance transmission and increased data capacity in optical communication systems.

PAFE: A lightweight visualization-based fast malware classification method

With the development of automated malware toolkits, cybersecurity faces evolving threats. Although visualization-based malware analysis has proven to be an effective method, existing approaches struggle with challenging malware samples due to alterations in the texture features of binary images during the visualization preprocessing stage, resulting in poor performance. Furthermore, to enhance classification accuracy, existing methods sacrifice prediction time by designing deeper neural network architectures. This paper proposes PAFE, a lightweight and visualization-based rapid malware classification method. It addresses the issue of texture feature variations in preprocessing through pixel-filling techniques and applies data augmentation to overcome the challenges of class imbalance in small sample datasets. PAFE combines multi-scale feature fusion and a channel attention mechanism, enhancing feature expression through modular design. Extensive experimental results demonstrate that PAFE outperforms the current state-of-the-art methods in both efficiency and effectiveness for malware variant classification, achieving an accuracy rate of 99.25 % with a prediction time of 10.04 ms.

Machine Learning Predicts Peripherally Inserted Central Catheters-Related Deep Vein Thrombosis Using Patient Features and Catheterization Technology Features.

This study aims to use patient feature and catheterization technology feature variables to train the corresponding machine learning (ML) models to predict peripherally inserted central catheters-deep vein thrombosis (PICCs-DVT) and analyze the importance of the two types of features to PICCs-DVT from the aspect of "input-output" correlation. To comprehensively and systematically summarize the variables used to describe patient features and catheterization technical features, this study combined 18 literature involving the two types of features in predicting PICCs-DVT. A total of 21 variables used to describe the two types of features were summarized, and feature values were extracted from the data of 1,065 PICCs patients from January 1, 2021 to August 31, 2022, to construct a data sample set. Then, 70% of the sample set is used for model training and hyperparameter optimization, and 30% of the sample set is used for PICCs-DVT prediction and feature importance analysis of three common ML classification models (i.e. support vector classifier [SVC], random forest [RF], and artificial neural network [ANN]). In terms of prediction performance, this study selected four metrics to evaluate the prediction performance of the model: precision (P), recall (R), accuracy (ACC), and area under the curve (AUC). In terms of feature importance analysis, this study chooses a single feature analysis method based on the "input-output" sensitivity principle-Permutation Importance. For the mean model performance, the three ML models on the test set are P = 0.92, R = 0.95, ACC = 0.88, and AUC = 0.81. Specifically, the RF model is P = 0.95, R = 0.96, ACC = 0.92, AUC = 0.86; the ANN model is P = 0.92, R = 0.95, ACC = 0.88, AUC = 0.81; the SVC model is P = 0.88, R = 0.94, ACC = 0.85, AUC = 0.77. For feature importance analysis, Catheter-to-vein rate (RF: 91.55%, ANN: 82.25%, SVC: 87.71%), Zubrod-ECOG-WHO score (RF: 66.35%, ANN: 82.25%, SVC: 44.35%), and insertion attempt (RF: 44.35%, ANN: 37.65%, SVC: 65.80%) all occupy the top three in the ML models prediction task of PICCs-DVT, showing relatively consistent ranking results. The ML models show good performance in predicting PICCs-DVT and reveal a relatively consistent ranking of feature importance from the data. The important features revealed might help clinical medical staff to better understand and analyze the formation mechanism of PICCs-DVT from a data-driven perspective.

Dynamic prediction of fracture propagation in horizontal well hydraulic fracturing: A data-driven approach for geo-energy exploitation

Accurate and efficient prediction of hydraulic fracturing fracture propagation is of utmost importance for unconventional reservoir development. Traditional numerical simulation methods require specialized domain knowledge and technical expertise, and machine learning approach can be an alternative predictive tool for fracture propagation. Additionally, existing neural networks cannot be directly applied to hydraulic fracturing scenarios influenced by multiple coupled factors. Thus, this study presents an innovative approach, “AE-ATT-ConvLSTM”, that integrates an additional Convolutional Layer, Autoencoder, and an Attention Mechanism Feature Fusion Layer into the architecture of a Convolutional Long Short-Term Memory (ConvLSTM) sequential image prediction network. This approach aims to predict fracture propagations in different fracturing stages of horizontal wells. The proposed model is trained on a sample dataset consisting of 5000 instances of hydraulic fracturing in horizontal wells. The dataset includes crucial data such as fracture propagation images, wellbore perforation images, natural fracture images, pumping schedules, and reservoir properties. The model achieves remarkable results, with an MSE less than 15 × 10−5, a maximum SSIM of 0.93, and an average FMAE less than 48. The research findings demonstrate that this method significantly enhances prediction efficiency and provides new insights for predicting fracture morphology and optimizing fracturing design.

Attention mechanism-enhanced graph convolutional neural network for unbalanced lithology identification

In this study, we propose a novel method for identifying lithology using an attention mechanism-enhanced graph convolutional neural network (AGCN). The aim of this method is to address the limitations of traditional approaches that evaluate unbalanced lithology by improving the identification of thin layers and small samples, while providing reliable data support for reservoir evaluation. To achieve this goal, we begin by using Principal Component Analysis (PCA) with maximum and minimum distance clustering (Max-min-distance) to correct the logging curves, which compensates for the low resolution of thin layers and enhances the accuracy of stratigraphic representation. Subsequently, we transform the logging data into graph-structured data by connecting distance similarity points and feature similarity points of the logging samples. We then use the graph convolutional network (GCN) to identify lithology, leveraging both labeled and unlabeled data to enhance the ability to identify lithology in small sample datasets. Additionally, our model incorporates a channel and spatial attention mechanism that assigns weights to the graph structure during lithology identification, improving the model’s capability to discern differences across samples. To evaluate the performance of our model, we constructed a lithology dataset comprising five wells and conducted experiments. The results indicate that our approach achieves a maximum accuracy of 97.67%, surpassing the performance of a singlestructure model in lithology identification. In conclusion, our proposed method provides a promising and effective approach for unbalanced lithology identification, significantly improving accuracy levels.