Predictive Performance Research Articles

Multi-phase CT-Angiography outperforms angiographic careggi collateral score and predicts functional outcome in acute ischemic stroke.

Collaterals are a strong determinant of clinical outcome in acute ischemic stroke (AIS) patients undergoing Endovascular Treatment (EVT). Careggi Collateral Score (CCS) is an angiographic score that demonstrated to be superior to the widely suggested ASITN/SIR score. Multi-phase CT-Angiography (mCTA) could be alternatively adopted for collateral assessment. We investigated whether mCTA had an equivalent predictive performance for functional outcome compared to CCS. Consecutive AIS patients undergoing EVT for large vessel occlusion within 24h from onset were analyzed. Receiver operating characteristic curves and multivariable logistic regression were investigated to evaluate the predictive performance of mCTA collateral score (range 0-5) and CCS (range 0-4) for good functional outcome (three-months modified Rankin Scale 0-2). We included 201 subjects (59.7% females, mean age 75), of whom 96 (47.7%) had good outcome at three-months. Both CCS (OR = 14.4, 95% CI = 6.3-33.8) and mCTA (OR = 23.8, 95% CI = 10.1-56.4) collateral scores were independent predictors of outcome. The AUC of CCS was 0.80 (95% CI 0.73-0.86) and the best cut-off was ≥ 3 (87% sensitivity, 71% specificity), while the AUC of mCTA collateral score was 0.84 (95% CI 0.78-0.90) with an optimal cut-off of ≥ 4 (85% sensitivity, 87% specificity). Patients with good mCTA collaterals experienced smaller (16.6 vs. 63.7 mL, p < 0.001) infarct lesion as compared to those with mCTA poor collaterals. mCTA discriminative ability for three-months 0-2 mRS was found to be comparable to CCS. mCTA appears a valid, non-invasive imaging modality for evaluating collaterals of AIS patients potentially eligible for EVT.

Predictors of Symptom Reduction and Remission Among People with Anxiety: Secondary Analyses from a Randomized Controlled Trial.

Despite the substantial disease burden of anxiety disorders, only limited or conflicting data on prognostic factors is available. Most studies include patients in the secondary healthcare sector thus, the generalizability of findings is limited. The present study examines predictors of symptom reduction and remission in patients with anxiety disorders in a primary care setting. 214 patients with anxiety disorders, recruited as part of the Collabri Flex trial, were included in secondary analyses. Data on potential predictors of anxiety symptoms at 6-month follow-up was collected at baseline, including patient characteristics related to demography, illness, comorbidity, functional level, life quality, and self-efficacy. The outcomes were symptom reduction and remission. Univariate and multivariate linear and logistic regression analyses were conducted to assess the associations between predictor variables and the outcome, and machine-learning methods were also applied. In multiple linear regression analysis, anxiety severity at baseline (β = -6.05, 95% CI = -7.54,-4.56, p < 0.001) and general psychological problems and symptoms of psychopathology (SCL-90-R score) (β = 2.19, 95% CI = 0.24,4.14, p = 0.028) were significantly associated with symptom change at 6months. Moreover, self-efficacy was associated with the outcome, however no longer significant in the multiple regression model. In multiple logistic regression analysis, anxiety severity at baseline (OR = 0.54, 95% CI = -1.13,-0.12, p = 0.018) was significantly associated with remission at 6months. There was no predictive performance of the machine-learning models. Our study contributes with information that could be valuable knowledge for managing anxiety disorders in primary care.

Identifying subtypes and developing prognostic models based on N6-methyladenosine and immune microenvironment related genes in breast cancer

Breast cancer (BC) is the most prevalent cancer in women globally. The tumor microenvironment (TME), comprising epithelial tumor cells and stromal elements, is vital for breast tumor development. N6-methyladenosine (m6A) modification plays a key role in RNA metabolism, influencing its various aspects such as stability and translation. There is a notable link between m6A methylation and immune cells in the TME, although this relationship is complex and not fully deciphered. In this research, BC expression and clinicopathological data from TCGA were scrutinized to assess expression profiles, mutations, and CNVs of 31 m6A genes and immune microenvironment-related genes, examining their correlations, functions, and prognostic impacts. Lasso and Cox regression identified prognostic genes for constructing a nomogram. Single-cell analyses mapped the distribution and patterns of these genes in BC cell development. We investigated associations between gene-derived risk scores and factors like immune infiltration, TME, checkpoints, TMB, CSC indices, and drug response. As a complement to computational analyses, in vitro experiments were conducted to confirm these expression patterns. We included 31 m6A regulatory genes and discovered a correlation between these genes and the extent of immune cell infiltration. Subsequently, a 7-gene risk score was generated, encompassing HSPA2, TAP1, ULBP2, CXCL1, RBP1, STC2, and FLT3. It was observed that the low-risk group exhibited better overall survival (OS) in BC, with higher immune scores but lower tumor mutational burden (TMB) and cancer stem cell (CSC) indices, as well as lower IC50 values for commonly used drugs. To enhance clinical applicability, age and stage were incorporated into the risk score, and a more comprehensive nomogram was constructed to predict OS. This nomogram was validated and demonstrated good predictive performance, with area under the curve (AUC) values for 1-year, 3-year, and 5-year OS being 0.848, 0.807, and 0.759, respectively. Our findings highlight the profound impact of prognostic-related genes on BC immune response and prognostic outcomes, suggesting that modulation of the m6A-immune pathway could offer new avenues for personalized BC treatment and potentially improve clinical outcomes.

Prioritising deteriorating patients using time-to-event analysis: prediction model development and internal–external validation

BackgroundBinary classification models are frequently used to predict clinical deterioration, however they ignore information on the timing of events. An alternative is to apply time-to-event models, augmenting clinical workflows by ranking patients by predicted risks. This study examines how and why time-to-event modelling of vital signs data can help prioritise deterioration assessments using lift curves, and develops a prediction model to stratify acute care inpatients by risk of clinical deterioration.MethodsWe developed and validated a Cox regression for time to in-hospital mortality. The model used time-varying covariates to estimate the risk of clinical deterioration. Adult inpatient medical records from 5 Australian hospitals between 1 January 2019 and 31 December 2020 were used for model development and validation. Model discrimination and calibration were assessed using internal–external cross validation. A discrete-time logistic regression model predicting death within 24 h with the same covariates was used as a comparator to the Cox regression model to estimate differences in predictive performance between the binary and time-to-event outcome modelling approaches.ResultsOur data contained 150,342 admissions and 1016 deaths. Model discrimination was higher for Cox regression than for discrete-time logistic regression, with cross-validated AUCs of 0.96 and 0.93, respectively, for mortality predictions within 24 h, declining to 0.93 and 0.88, respectively, for mortality predictions within 1 week. Calibration plots showed that calibration varied by hospital, but this can be mitigated by ranking patients by predicted risks.ConclusionTime-varying covariate Cox models can be powerful tools for triaging patients, which may lead to more efficient and effective care in time-poor environments when the times between observations are highly variable.

Utilization of international normalized ratio-derived formula to predict plasma rivaroxaban level-Validation study and real-world experience.

Specific assays of plasma rivaroxaban level are not always readily available with short turnaround time, which hamper the management of urgent clinical situations. In this study, we aimed to build a predictive formula of plasma rivaroxaban levels from international normalized ratio (INR) value and validated in real world clinical situations. Ninety-four patients who were taking rivaroxaban participated in the study. Patients were randomized into testing cohort and validation cohorts. The prediction formula was built from the testing cohort and then validated in validation cohort. The predictive performance was further validated on real-world clinical requests. The root mean square error (RMSE) of the predictive formula for the testing and validation cohorts were 61.81 and 69.32 ng/mL, respectively. The sensitivity and specificity for the formula to predict the threshold plasma rivaroxaban level of 75 ng/mL were 95% (95% CI: 85.4%-100%) and 87.5% (95% CI: 71.3%-100%), respectively, in real-world clinical situations. Plasma rivaroxaban level of threshold level of 75 ng/mL can be calculated from prediction formula by INR value with satisfactory accuracy and it can be used to guide the decision for reversal.

Computational Hemodynamics-Based Growth Prediction for Small Abdominal Aortic Aneurysms: Laminar Simulations Versus Large Eddy Simulations.

Prior studies have shown that computational fluid dynamics (CFD) simulations help assess patient-specific hemodynamics in abdominal aortic aneurysms (AAAs); patient-specific hemodynamic stressors are frequently used to predict an AAA's growth. Previous studies have utilized both laminar and turbulent simulation models to simulate hemodynamics. However, the impact of different CFD simulation models on the predictive modeling of AAA growth remains unknown and is thus the knowledge gap that motivates this study. Specifically, CFD simulations were performed for 70 AAA models derived from 70 patients' computed tomography angiography (CTA) data with known growth status (i.e., fast-growing [> 5mm/yr] or slowly growing [< 5mm/yr]). We used laminar and large eddy simulation (LES) models to obtain hemodynamic parameters to predict AAAs' growth status. Predicting the growth status of AAAs was based on morphological, hemodynamic, and patient health parameters in conjunction with three classical machine learning (ML) classifiers, namely, support vector machine (SVM), K-nearest neighbor (KNN), and generalized linear model (GLM). Our preliminary results estimated aneurysmal flow stability and wall shear stress (WSS) were comparable in both laminar and LES flow simulations. Moreover, computed WSS and velocity-related hemodynamic variables obtained from the laminar and LES simulations showed comparable abilities in differentiating the growth status of AAAs. More importantly, the predictive modeling performance of the three ML classifiers mentioned above was similar, with less than a 2% difference observed (p-value>0.05). In closing, our findings suggest that two different flow simulations investigated did not significantly affect outcomes of computational hemodynamics and predictive modeling of AAAs' growth status, given the data investigated.

Enhanced Air Quality Prediction through Spatio-temporal Feature Sxtraction and Fusion: A Self-tuning Hybrid Approach with GCN and GRU

Accurate prediction of air quality change is essential for air pollution control and human daily mobility. Due to the strong spatial and temporal correlation of air quality changes, existing air quality prediction methods often face the problem of low prediction accuracy due to insufficient extraction of spatio-temporal features. In this paper, we proposed a self-tuning spatio-temporal neural network (ST2NN) to enhance air quality prediction. ST2NN model consisted of four modules. First, ST2NN model constructed a temporal feature extraction module and a spatial feature extraction module based on gated recurrent unit (GRU) and graph convolutional neural network (GCN), respectively, and the two feature extraction modules adopted a parallel structure, which could effectively extract the spatio-temporal features in data. Additionally, ST2NN model constructed a feature fusion module based on gating mechanism to delineate the contribution of spatio-temporal features to the predicted values. Further, ST2NN model constructed a Hyperband hyperparameter optimization module based on Hyperband optimization algorithm to automatically adjust the network hyperparameters. The structure of ST2NN model endowed it with excellent spatio-temporal feature extraction and parameter adaptability. ST2NN model was evaluated and compared with existing models, including convolutional long short-term memory neural network (ConvLSTM), GRU, combined convolutional neural network and long short-term memory neural network (CNN-LSTM), and GCN-LSTM for air quality index (AQI) prediction using data from twelve monitoring stations in Beijing, China. Across all four evaluation indexes, ST2NN model outperformed the comparative models, improving prediction accuracy by 0.51%-10.18% (measured using R2\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$${R}^{2}$$\\end{document}). From the experimental results, it can be seen that ST2NN model constructed from the perspective of spatio-temporal feature extraction has better prediction performance compared with the existing air quality prediction model, which provides a new method for air quality prediction and has certain application value.

Prediction of protein secondary structure by the improved TCN-BiLSTM-MHA model with knowledge distillation

Secondary structure prediction is a key step in understanding protein function and biological properties and is highly important in the fields of new drug development, disease treatment, bioengineering, etc. Accurately predicting the secondary structure of proteins helps to reveal how proteins are folded and how they function in cells. The application of deep learning models in protein structure prediction is particularly important because of their ability to process complex sequence information and extract meaningful patterns and features, thus significantly improving the accuracy and efficiency of prediction. In this study, a combined model integrating an improved temporal convolutional network (TCN), bidirectional long short-term memory (BiLSTM), and a multi-head attention (MHA) mechanism is proposed to enhance the accuracy of protein prediction in both eight-state and three-state structures. One-hot encoding features and word vector representations of physicochemical properties are incorporated. A significant emphasis is placed on knowledge distillation techniques utilizing the ProtT5 pretrained model, leading to performance improvements. The improved TCN, achieved through multiscale fusion and bidirectional operations, allows for better extraction of amino acid sequence features than traditional TCN models. The model demonstrated excellent prediction performance on multiple datasets. For the TS115, CB513 and PDB (2018–2020) datasets, the prediction accuracy of the eight-state structure of the six datasets in this paper reached 88.2%, 84.9%, and 95.3%, respectively, and the prediction accuracy of the three-state structure reached 91.3%, 90.3%, and 96.8%, respectively. This study not only improves the accuracy of protein secondary structure prediction but also provides an important tool for understanding protein structure and function, which is particularly applicable to resource-constrained contexts and provides a valuable tool for understanding protein structure and function.

A novel real-time model for predicting acute kidney injury in critically ill patients within 12 hours.

A major challenge in prevention and early treatment of acute kidney injury (AKI) is the lack of high-performance predictors in critically ill patients. Therefore, we innovatively constructed U-AKIpredTM for predicting AKI in critically ill patients within 12h of panel measurement. The prospective cohort study included 680 patients in the training set and 249 patients in the validation set. After performing inclusion and exclusion criteria, 417 patients were enrolled in the training set and 164 patients were enrolled in the validation set finally. AKI was diagnosed by Kidney Disease Improving Global Outcomes (KDIGO) criteria. Twelve urinary kidney injury biomarkers (mALB, IgG, TRF, α1MG, NAG, NGAL, KIM-1, L-FABP, TIMP2, IGFBP7, CAF22 and IL-18) exhibited good predictive performance for AKI within 12h in critically ill patients. U-AKIpredTM, combined with three crucial biomarkers (α1MG, L-FABP and IGFBP7) by multivariate logistic regression analysis, exhibited better predictive performance for AKI in critically ill patients within 12h than the other twelve kidney injury biomarkers. The area under the curve (AUC) of the U-AKIpredTM, as a predictor of AKI within 12h, was 0.802 (95% CI: 0.771-0.833, P<0.001) in the training set and 0.844 (95% CI: 0.792-0.896, P<0.001) in validation cohort. A nomogram based on the results of the training and validation sets of U-AKIpredTM was developed which showed optimal predictive performance for AKI. The fitting effect and prediction accuracy of U-AKIpredTM was evaluated by multiple statistical indicators. To provide a more flexible predictive tool, the dynamic nomogram (https://www.xsmartanalysis.com/model/U-AKIpredTM) was constructed using a web-calculator. Decision curve analysis (DCA) and a clinical impact curve were used to reveal that U-AKIpredTM with the three crucial biomarkers had a higher net benefit than these twelve kidney injury biomarkers respectively. The net reclassification index (NRI) and integrated discrimination index (IDI) were used to improve the significant risk reclassification of AKI compared with the 12 kidney injury biomarkers. The predictive efficiency of U-AKIpredTM was better than the NephroCheck® when testing for AKI and severe AKI. U-AKIpredTM is an excellent predictive model of AKI in critically ill patients within 12h and would assist clinicians in identifying those at high risk of AKI.

Controlled Noise: Evidence of epigenetic regulation of Single-Cell expression variability.

Understanding single-cell expression variability (scEV) or gene expression noise among cells of the same type and state is crucial for delineating population-level cellular function. While epigenetic mechanisms are widely implicated in gene expression regulation, a definitive link between chromatin accessibility and scEV remains elusive. Recent advances in single-cell techniques enable the study of single-cell multiomics data that include the simultaneous measurement of scATAC-seq and scRNA-seq within individual cells, presenting an unprecedented opportunity to address this gap. This paper introduces an innovative testing pipeline to investigate the association between chromatin accessibility and scEV. With single-cell multiomics data of scATAC-seq and scRNA-seq, the pipeline hinges on comparing the prediction performance of scATAC-seq data on gene expression levels between highly variable genes (HVGs) and non-highly variable genes (non-HVGs). Applying this pipeline to paired scATAC-seq and scRNA-seq data from human hematopoietic stem and progenitor cells, we observed a significantly superior prediction performance of scATAC-seq data for HVGs compared to non-HVGs. Notably, there was substantial overlap between well-predicted genes and HVGs. The gene pathways enriched from well-predicted genes are highly pertinent to cell type-specific functions. Our findings support the notion that scEV largely stems from cell-to-cell variability in chromatin accessibility, providing compelling evidence for the epigenetic regulation of scEV and offering promising avenues for investigating gene regulation mechanisms at the single-cell level. The source code and data used in this paper can be found at https://github.com/SiweiCui/EpigeneticControlOfSingle-CellExpressionVariability. Supplementary data are available at Bioinformatics online.

A deep-learning-based surrogate model for Monte-Carlo simulations of the linear energy transfer in primary brain tumor patients treated with proton-beam radiotherapy.

This study explores the use of neural networks (NNs) as surrogate models for Monte-Carlo (MC) simulations in predicting the dose-averaged linear energy transfer (LETd) of protons in proton-beam therapy based on the planned dose distribution and patient anatomy in the form of computed tomography (CT) images. As LETdis associated with variability in the relative biological effectiveness (RBE) of protons, we also evaluate the implications of using NN predictions for normal tissue complication probability (NTCP) models within a variable-RBE context.&#xD;&#xD;Approach: The predictive performance of three-dimensional NN architectures was evaluated using five-fold cross-validation on a cohort of brain tumor patients (n=151). The best-performing model was identified and externally validated on patients from a different center (n=107). LETdpredictions were compared to MC-simulated results in clinically relevant regions of interest. We assessed the impact on NTCP models by leveraging LETdpredictions to derive RBE-weighted doses, using the Wedenberg RBE model.&#xD;&#xD;Main results: We found NNs based solely on the planned dose profile, i.e. without additional usage of CT images, can approximate MC-based LETddistributions. Root mean squared errors (RMSE) for the median LETdwithin the brain, brainstem, CTV, chiasm, lacrimal glands (ipsilateral/contralateral) and optic nerves (ipsilateral/contralateral) were 0.36, 0.87, 0.31, 0.73, 0.68, 1.04, 0.69 and 1.24~keV/μm, respectively. Although model predictions showed statistically significant differences from MC outputs, these did not result in substantial changes in NTCP predictions, with RMSEs of at most 3.2 percentage points.&#xD;&#xD;Significance: The ability of NNs to predict LETdbased solely on planned dose profiles suggests a viable alternative to the compute-intensive MC simulations in a variable-RBE setting. This is particularly useful in scenarios where MC simulation data are unavailable, facilitating resource-constrained proton therapy treatment planning, retrospective patient data analysis and further investigations on the variability of proton RBE.

Innovative graph neural network approach for predicting soil heavy metal pollution in the Pearl River Basin, China

Predicting soil heavy metal (HM) content is crucial for monitoring soil quality and ensuring ecological health. However, existing methods often neglect the spatial dependency of data. To address this gap, our study introduces a novel graph neural network (GNN) model, Multi-Scale Attention-based Graph Neural Network for Heavy Metal Prediction (MSA-GNN-HMP). The model integrates multi-scale graph convolutional network (MS-GCN) and attention-based GNN (AGNN) to capture spatial relationships. Using surface soil samples from the Pearl River Basin, we evaluate the MSA-GNN-HMP model against four other models. The experimental results show that the MSA-GNN-HMP model has the best predictive performance for Cd and Pb, with a coefficient of determination (R2) of 0.841 for Cd and 0.886 for Pb, and the lowest mean absolute error (MAE) of 0.403 mg kg−1 for Cd and 0.670 mg kg−1 for Pb, as well as the lowest root mean square error (RMSE) of 0.563 mg kg−1for Cd and 0.898 mg kg−1 for Pb. In feature importance analysis, latitude and longitude emerged as key factors influencing the heavy metal content. The spatial distribution prediction trend of heavy metal elements by different prediction methods is basically consistent, with the high-value areas of Cd and Pb respectively distributed in the northwest and northeast of the basin center. However, the MSA-GNN-HMP model demonstrates superior detail representation in spatial prediction. MSA-GNN-HMP model has excellent spatial information representation capabilities and can more accurately predict heavy metal content and spatial distribution, providing a new theoretical basis for monitoring, assessing, and managing soil pollution.

Analysis of organic and mineral nitrogen, total organic carbon and humic fractions in Ferralsols: an approach using Vis-NIR-SWIR, MIR and X-ray fluorescence spectroscopy

This work aimed to develop suitable predictive models for ammonium, nitrate, total nitrogen, total organic carbon and soil humic fractions, for Ferralsols, using Vis-NIR-SWIR, MIR and X-ray fluorescence spectroscopic techniques in conjunction with machine learning algorithms, Cubist, PLSR, Random Forest and Support Vector Machine. Chemical analyzes were carried out to determine nitrate, total nitrogen, total organic carbon and chemical fractionation of soil organic matter, as well as spectral analyzes using Vis-NIR-SWIR spectroscopy, MIR and X-ray fluorescence. The spectroscopy results were processed using RStudio v. 4.1.3, applying Cusbist, PLSR, Random Forest and Support Vector Machine machine learning algorithms to create predictive models and describe spectral curves and Pearson correlation. Of the prediction models developed for nitrogen, total organic carbon and humic fractions, the PLSR and Support Vector Machine algorithms presented the best predictive performances. The descriptive analysis of the spectra identified the main absorption bands and the location of the bands sensitive to the attributes of interest. The correlation analysis proposed that the use of Vis-NIR-SWIR, MIR and XRF spectroscopic techniques were effective in predicting the contents of nitrogen, total organic carbon and humic fractions in soil with a medium sandy texture. However, it is important to highlight that each technique has its characteristic mechanism of action, Vis-NIR-SWIR and MIR detect the element based on overtones and fundamental tones, while XRF is based on the atomic number of the elements or elemental association.

DPP: A Novel Disease Progression Prediction Method for Ginkgo Leaf Disease Based on Image Sequences

Ginkgo leaf disease poses a grave threat to Ginkgo biloba. The current management of Ginkgo leaf disease lacks precision guidance and intelligent technologies. To provide precision guidance for disease management and to evaluate the effectiveness of the implemented measures, the present study proposes a novel disease progression prediction (DPP) method for Ginkgo leaf blight with a multi-level feature translation architecture and enhanced spatiotemporal attention module (eSTA). The proposed DPP method is capable of capturing key spatiotemporal dependencies of disease symptoms at various feature levels. Experiments demonstrated that the DPP method achieves state-of-the-art prediction performance in disease progression prediction. Compared to the top-performing spatiotemporal predictive learning method (SimVP + TAU), our method significantly reduced the mean absolute error (MAE) by 19.95% and the mean square error (MSE) by 25.35%. Moreover, it achieved a higher structure similarity index measure (SSIM) of 0.970 and superior peak signal-to-noise ratio (PSNR) of 37.746 dB. The proposed method can accurately forecast the progression of Ginkgo leaf blight to a large extent, which is expected to provide valuable insights for precision and intelligent disease management. Additionally, this study presents a novel perspective for the extensive research on plant disease prediction.

Prediction of diabetes mellitus using XGBoost model

With the number of health-threatening diseases and deaths on the rise, medical decision support systems continue to prove effective in improving the efficiency of physicians and other healthcare providers and supporting clinical decisions. Diabetes remains one of the leading diseases responsible for many deaths worldwide. Diabetes is characterized by elevated blood glucose levels, which can have serious consequences for other organs in the body. According to the International Diabetes Alliance (IDA), 382 million people currently have diabetes and this number is expected to double to 592 million by 2035. This paper proposes a clinical prediction model for diabetes based on machine learning (ML).As traditional ML models, we consider the most commonly used classifiers, the Logic Tree (LR) and XGBoost. We compare these ML models. On the other hand, we also use deep learning (DL) models and apply a fully convolutional neural network (DNN) for diabetes prediction and recognition. The proposed models were evaluated in the public Kaggle database: prediction performance at each level was analyzed using the Precision, Recall, AUC, accuracy and F1 metrics, and overall prediction efficiency was assessed using accuracy and its macro-average: DL, LR, The overall accuracy obtained by XGBoost was 91.88%, 88.75% and 97.12% respectively. The experimental results show that XGBoost is more effective at predicting diabetes than deep learning and LR methods.

SGANnet: Super-resolution guided asymmetric stereo matching network

With asymmetric resolution stereo images as input, existing stereo matching algorithms significantly decline in prediction performance. To address this, we introduce SGANet (Super-resolution Guided Asymmetric Stereo Matching Network), a model that employs unsupervised training methods to overcome the difficulty of acquiring ground truth disparity. For the lower resolution side, this paper designs a stereo guided super-resolution module (SGSR), where the network generates a super-resolved image enriched with details guided by the higher resolution side. Additionally, for this module, we propose a feature consistency loss based on the image's feature space to measure the similarity between the real and super-resolved images. Experimental results on the autonomous driving dataset KITTI demonstrate the effectiveness of the SGSR module and the feature consistency loss in improving the disparity prediction performance of asymmetric resolution stereo images.

Intrusion Detection System with Ensemble Machine Learning Approaches using VotingClassifier

Internets have become a part of our everyday life due to the advancement in the electronics and signal processing technologies during past decades. The tremendous growth of internet leads towards the network threats. Many times firewalls and anti-viruses fails to manage the network because of this Intrusion Detection System (IDS) comes to assists us. In this paper we use IDS with Ensemble methodologies utilized in machine learning involve the fusion of multiple classifiers to improve predictive performance, while voting classifiers combine predictions from individual models to reach conclusive decisions. The paper employs a voting ensemble method combing decision tree, logistic regression and support vector machine classifier models. We test our proposedmodel to classify the NSL-KDD dataset. Our ensemble methodologies of proposed algorithmproduce a good result.

Establishment and validation of a risk prediction model for delayed neurocognitive recovery associated with cerebral oxygen saturation monitoring

BackgroundDelayed neurocognitive recovery (DNR) is a common complication in patients undergoing laparoscopic surgery, and there are currently no effective therapies. It is vital to provide a reliable basis for clinical prediction. This study tried to analyse the risk factors for DNR in patients undergoing laparoscopic colorectal surgery and to establish a risk prediction model. MethodsA retrospective analysis of the clinical data and DNR status of patients undergoing laparoscopic colorectal surgery at Xiangya Hospital of Central South University from March 2018 to July 2020 was conducted. Logistic regression was performed to analyse the related risk factors for DNR post-operatively, and the predictive model of DNR post-operatively was constructed and validated internally. Patients who underwent laparoscopic colorectal surgery between January and July 2021 were also selected for external validation of the predictive model, to ultimately investigate the risk factors for DNR in patients undergoing laparoscopic colorectal surgery.ResultsThe incidence of DNR in patients undergoing laparoscopic colorectal surgery was 15.2% (31/204). The maximum variability of cerebral oxygen, age, education, and pre-existing diabetes was related to the incidence of DNR (p < 0.05). The risk prediction model of DNR after laparoscopic colorectal surgery was established. The internal and external validation showed that the discrimination was good (the AUCs were 0.751 and 0.694, respectively).ConclusionsThe risk prediction model of DNR related to cerebral oxygen saturation monitoring shows good predictive performance and clinical value, providing a basis for postoperative DNR prevention.

Improvement of Wind Power Prediction by Assimilating Principal Components of Cabin Radar Observations

Abstract Data assimilation is an important approach to improve the prediction performance of near-surface wind and wind power. Based on four-dimensional variational technique, this study proposes an approach to improve near-surface wind and wind power prediction by extracting and assimilating the principal components of cabin radar radial wind observations installed at wind turbine within wind farm. The verification for a series of cases under strong and weak vertical wind shear conditions indicates that, compared to the simulations without assimilation, the predicted ultra-short term (0–4 h) mean absolute error of near-surface wind and single turbine wind power could be reduced by 0.09–1.17 m s−1 and 53–209 kW after the assimilation of radial wind directly, while by 0.33–1.38 m s−1 and 62–239 kW after the assimilation of principal components. These illustrate that assimilating the principal components of radial wind is superior to assimilating radial wind directly, and could obviously reduce prediction error. Further investigation suggests that extracting the principal components of radial wind has marginal influences on the density and distribution of observations, but could obviously reduce the fluctuation of the observations and the correlation among the observations. The prediction improvement by assimilating the principal components of radial wind is essentially due to the assimilation of low-frequency and low-correlation information involved in the observations.

MSA-Net: A Precise and Robust Model for Predicting the Carbon Content on an As-Received Basis of Coal

The carbon content as received (Car) of coal is essential for the emission factor method in IPCC methodology. The traditional carbon measurement mechanism relies on detection equipment, resulting in significant detection costs. To reduce detection costs and provide precise predictions of Cars even in the absence of measurements, this paper proposes a neural network combining MLP with an attention mechanism (MSA-Net). In this model, the Attention Module is proposed to extract important and potential features. The Skip-Connections are utilized for feature reuse. The Huber loss is used to reduce the error between predicted Car values and actual values. The experimental results show that when the input includes eight measured parameters, the MAPE of MSA-Net is only 0.83%, which is better than the state-of-the-art Gaussian Process Regression (GPR) method. MSA-Net exhibits better predictive performance compared to MLP, RNN, LSTM, and Transformer. Moreover, this article provides two measurement solutions for thermal power enterprises to reduce detection costs.