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The stability in the operation of insulated gate bipolar transistor (IGBT) modules plays a crucial role in wind power generation. It is essential to improve the thermal performance of the heat sink of IGBT modules in wind power converters and reduce the power consumption of liquid cooling systems, in order to optimize the heat dissipation of IGBT modules in wind power converters. In this paper, a simulation model of the liquid-cooled heat sink of the IGBT module is established and the performance of three different series flow channel structures is compared by computational fluid dynamics (CFD). Moreover, based on the orthogonal test design, the three factors (channel width, channel height, and cold plate wall thickness) affecting the performance of the water-cooled plate were ranked and optimized. The results show that the water-cooled plate with double-helical-type flow channel structure has the best comprehensive performance. In addition, for the double-helical-type structure, the optimal combination of channel structure parameters about channel width, channel height, and cold plate wall thickness is obtained. After optimization, the maximum IGBT temperature, thermal resistance, and pressure drop of the cold plate are reduced by 3.13%, 5.78%, and 18.87%, respectively, compared with the double-S structure in parameter case one. The proposed methods and results are expected to provide theoretical guidance for the thermal management of IGBT modules in wind power converters. [ABSTRACT FROM AUTHOR]

Feature selection is a crucial method for discovering relevant features in high-dimensional data. However, most studies primarily focus on completely labeled data, ignoring the frequent occurrence of missing labels in real-world problems. To address high-dimensional and label-missing problems in data classification simultaneously, we proposed a semisupervised bacterial heuristic feature selection algorithm. To track the label-missing problem, a k-nearest neighbor semisupervised learning strategy is designed to reconstruct missing labels. In addition, the bacterial heuristic algorithm is improved using hierarchical population initialization, dynamic learning, and elite population evolution strategies to enhance the search capacity for various feature combinations. To verify the effectiveness of the proposed algorithm, three groups of comparison experiments based on eight datasets are employed, including two traditional feature selection methods, four bacterial heuristic feature selection algorithms, and two swarm-based heuristic feature selection algorithms. Experimental results demonstrate that the proposed algorithm has obvious advantages in terms of classification accuracy and selected feature numbers. [ABSTRACT FROM AUTHOR]

Commutability of reference materials is essential for ensuring the traceability of patient measurement results and the technical basis for the use of reference materials. Commutability is only relevant for matrixed reference material; it is a prerequisite for the accuracy and authenticity of calibration methods. In this study, we evaluated the commutability of reference materials for homocysteine. Five conventional measurement methods were applied to simultaneously measure 30 serum samples and seven homocysteine reference materials from the National Institute of Standards and Technology and the National Institute of Metrology. Liquid chromatography tandem-mass spectrometry was used as a reference method. Two methods were used to evaluate the commutability of the seven reference materials according to the Clinical and Laboratory Standards Institute EP30-A and the International Federation of Clinical Chemistry and Laboratory Medicine (IFCC) commutability assessment document. Among 35 combinations of the five conventional methods and seven reference materials, after evaluation in accordance with the EP30-A, the seven reference materials passed the commutability assessment, and 34 combinations were commutable. According to the IFCC, the commutability evaluation of 28 combinations was conclusive (commutable or non-commutable), while results for the remaining seven combinations could not be determined. The homocysteine reference materials showed good commutability. The sensitivity of the measurement procedure, measurement deviation and uncertainty, and differences in the "measurand" selected by different methods may affect the evaluation results. Additionally, different judgment standards for different methods may explain the observed variations in evaluation results. [ABSTRACT FROM AUTHOR]

Objectives: To establish a multi-classification model for precisely predicting the invasiveness (pre-invasive adenocarcinoma, PIA; minimally invasive adenocarcinoma, MIA; invasive adenocarcinoma, IAC) of lung adenocarcinoma manifesting as pure ground-glass nodules (pGGNs). Methods: By the inclusion and exclusion criteria, this retrospective study enrolled 346 patients (female, 297, and male, 49; age, 55.79 ± 10.53 (24-83)) presenting as pGGNs from 1292 consecutive patients with pathologically confirmed lung adenocarcinoma. A total of 27 clinical were collected and 1409 radiomics features were extracted by PyRadiomics package on python. After feature selection with L2,1-norm minimization, logistic regression (LR), extra w(ET) and gradient boosting decision tree (GBDT) were used to construct the three-classification model. Then, an ensemble model of the three algorithms based on model ensemble strategy was established to further improve the classification performance. Results: After feature selection, a hybrid of 166 features consisting of 1 clinical (short-axis diameter, ranked 27th) and 165 radiomics (4 shape, 71 intensity and 90 texture) features were selected. The three most important features are wavelet-HLL_firstorder_Minimum, wavelet-HLL_ngtdm_Busyness and square_firstorder_Kurtosis. The hybrid-ensemble model based on hybrid clinical-radiomics features and the ensemble strategy showed more accurate predictive performance than other models (hybrid-LR, hybrid-ET, hybrid-GBDT, clinical-ensemble and radiomics-ensemble). On the training set and test set, the model can obtain the accuracy values of 0.918 ± 0.022 and 0.841, and its F1-scores respectively were 0.917 ± 0.024 and 0.824. Conclusion: The multi-classification of invasive pGGNs can be precisely predicted by our proposed hybrid-ensemble model to assist patients in the early diagnosis of lung adenocarcinoma and prognosis. [ABSTRACT FROM AUTHOR]

Fluorescence molecular tomography (FMT) is a promising and high sensitivity imaging modality that can reconstruct the three-dimensional (3D) distribution of interior fluorescent sources. However, the spatial resolution of FMT has encountered an insurmountable bottleneck and cannot be substantially improved, due to the simplified forward model and the severely ill-posed inverse problem. In this work, a 3D fusion dual-sampling convolutional neural network, namely UHR-DeepFMT, was proposed to achieve ultra-high spatial resolution reconstruction of FMT. Under this framework, the UHR-DeepFMT does not need to explicitly solve the FMT forward and inverse problems. Instead, it directly establishes an end-to-end mapping model to reconstruct the fluorescent sources, which can enormously eliminate the modeling errors. Besides, a novel fusion mechanism that integrates the dual-sampling strategy and the squeeze-and-excitation (SE) module is introduced into the skip connection of UHR-DeepFMT, which can significantly improve the spatial resolution by greatly alleviating the ill-posedness of the inverse problem. To evaluate the performance of UHR-DeepFMT network model, numerical simulations, physical phantom and in vivo experiments were conducted. The results demonstrated that the proposed UHR-DeepFMT can outperform the cutting-edge methods and achieve ultra-high spatial resolution reconstruction of FMT with the powerful ability to distinguish adjacent targets with a minimal edge-to-edge distance (EED) of 0.5 mm. It is assumed that this research is a significant improvement for FMT in terms of spatial resolution and overall imaging quality, which could promote the precise diagnosis and preclinical application of small animals in the future. [ABSTRACT FROM AUTHOR]