Search

In recent years, outliers caused by manual operation errors and equipment acquisition failures often occur, bringing challenges to big data analysis. In view of the difficulties in identifying and correcting outliers of multi-source data, an intelligent identification and order-sensitive correction method of outliers from multi-data sources based on historical data mining was proposed. First, an intelligent identification method of outliers of single-source data is proposed based on neural tangent kernel K-means (NTKKM) clustering. The original data is mapped to high-dimensional feature space using Neural Tangent Kernel, where the features of outliers are acquired by K-means clustering to realize the accurate identification of outliers. Second, an order-sensitive missing value imputation framework for multi-source data (OMSMVI) was proposed. The similarity graph of sources with missing data was constructed based on multidimensional similarity analysis, and the filling order decision was transformed into an optimization problem to realize the optimal filling order decision of missing values in multi-source data. Finally, a neighborhood-based imputation (NI) algorithm is proposed. Based on the traditional KNN filling algorithm, neighboring nodes of sources with missing data are flexibly selected to the achieve accurate correction of outliers. The case experiment was operated on actual power grid data, and the results show that the proposed clustering method can identify outliers more accurately, and the determined optimal imputation sequence has higher accuracy, which provide a feasible new idea for the identification and correction of outliers in the process of data preprocessing. [ABSTRACT FROM AUTHOR]

This study aimed to investigate the feasibility of predicting oxygen 6-methylguanine-DNA methyltransferase (MGMT) promoter methylation in diffuse gliomas by developing a deep learning approach using MRI radiomics. A total of 111 patients with diffuse gliomas participated in the retrospective study (56 patients with MGMT promoter methylation and 55 patients with MGMT promoter unmethylation). The radiomics features of the two regions of interest (ROI) (the whole tumor area and the tumor core area) for four sequences, including T1 weighted image (T1WI), T2 weighted image (T2WI), apparent diffusion coefficient (ADC) maps, and T1 contrast-enhanced (T1CE) MR images were extracted and jointly fed into the residual network. Then the deep learning method was developed and evaluated with a five-fold cross-validation, where in each fold, the dataset was randomly divided into training (80%) and validation (20%) cohorts. We compared the performance of all models using area under the curve (AUC) and average accuracy of validation cohorts and calculated the 10 most important features of the best model via a class activation map. Based on the ROI of the whole tumor, the predictive capacity of the T1CE and ADC model achieved the highest AUC value of 0.85. Based on the ROI of the tumor core, the T1CE and ADC model achieved the highest AUC value of 0.90. After comparison, the T1CE combined with the ADC model based on the ROI of the tumor core exhibited the best performance, with the highest average accuracy (0.91) and AUC (0.90) among all models. The deep learning method using MRI radiomics has excellent diagnostic performance with a high accuracy in predicting MGMT promoter methylation in diffuse gliomas. [ABSTRACT FROM AUTHOR]

Recently, a two-dimensional (2D) heterostructure has been widely investigated as a photocatalyst to decompose water using the extraordinary type-II band structure. In this work, the MoTe2/PtS2 van der Waals heterostructure (vdWH) is constructed with different stacking structures. Based on density functional calculations, the stacking-dependent electronic characteristic is explored, so that the MoTe2/PtS2 vdWH possesses type-I and type-II band structures for the light-emitting device and photocatalyst, respectively, with decent stacking configurations. The band alignment of the MoTe2/PtS2 vdWH is also addressed to obtain suitable band edge positions for water-splitting at pH 0. Furthermore, the potential drop is investigated, resulting from charge transfer between the MoTe2 and PtS2, which is another critical promotion to prevent the recombination of the photogenerated charges. Additionally, the MoTe2/PtS2 vdWH also demonstrates a novel and excellent optical absorption capacity in the visible wavelength range. Our work suggests a theoretical guide to designing and tuning the 2D heterostructure using photocatalytic and photovoltaic devices. [ABSTRACT FROM AUTHOR]

In this paper, the optical and electronic properties of WSSe/BSe heterostructure are investigated by first-principles calculations. The most stable stacking pattern of the WSSe/BSe compounds is formed by van der Waals interaction with a thermal stability proved by ab initio molecular dynamics simulation. The WSSe/BSe heterostructure exhibits a type-I band alignment with direct bandgap of 2.151 eV, which can improve the effective recombination of photoexcited holes and electrons. Furthermore, the band alignment of the WSSe/BSe heterostructure can straddle the water redox potential at pH 0–8, and it has a wide absorption range for visible light. In particular, the solar-to-hydrogen efficiency of the WSSe/BSe heterostructure is obtained at as high as 44.9% at pH 4 and 5. All these investigations show that the WSSe/BSe heterostructure has potential application in photocatalysts to decompose water. [ABSTRACT FROM AUTHOR]