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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]

• Model users' dynamic preferences by temporal interaction behaviors. • Incorporate auxiliary information to construct meta-path-based graphs. • Complement program and user semantics through the auxiliary information. • Obtain high-level connectivity between user and program via graph structure. With the explosion of information and users' changing interest, program sequential recommendation becomes increasingly important for TV program platforms to help their users find interesting programs. Existing sequential recommendation methods mainly focus on modeling user preferences from users' historical interaction behaviors directly, with insufficient learning about the dynamics of programs and users, while ignoring the rich semantic information from the heterogeneous graph. To address these issues, we propose the multipath-guided heterogeneous graph neural networks for TV program sequential recommendation (MHG-PSR), which can enhance the representations of programs and users through multiple paths in heterogeneous graphs. In our method, the auxiliary information is fused to supplement the semantics of program and user to obtain initial representations. Then, we explore the interactive behaviors of programs and users with temporal and auxiliary information to model the collaborative signals in the heterogeneous graph and extract the users' dynamic preferences of programs. Extensive experiments on real-world datasets verify the proposed method can effectively improve the performance of TV program sequential recommendation. [ABSTRACT FROM AUTHOR]

TV program recommendation is important for users in the face of a huge amount of information data. The existing TV program recommendation mainly relies on a collaborative filtering method to recommend through interactive data between users and programs. Although some methods utilize auxiliary information to enrich semantic features, most of them only use a single data type, which cannot capture a more diverse feature representation of the user and program. In this paper, we propose a neural TV program recommendation model with multi-source heterogeneous data, which makes full use of the multi-source heterogeneous auxiliary information. Specifically, we combine heterogeneous features derived from auxiliary information to learn a deep program representation in the program encoder module. To more accurately capture user preferences, we further utilize the personalized attention mechanism to determine the importance of different programs to the user representation based on the interaction between users and programs in the user encoder module. Extensive experiments on a real dataset of the Chinese capital show that our model can effectively improve the performance of TV program recommendations compared to the existing models. [ABSTRACT FROM AUTHOR]

Recommendation systems for TV programs play an important role in alleviating the information overload problem. Existing TV program recommendation methods either do not aggregate neighborhood information well to capture collaborative signals from interaction data, or fail to make good use of auxiliary information, because they ignore the heterogeneity of different entities and relationships. In this paper, we propose a multi-component graph collaborative filtering recommendation based on auxiliary information, which learns representations of user and program through heterogeneous data modeling and information propagation on graphs. We extract homogeneous subgraphs from the heterogeneous graph based on multiple symmetric meta-paths, learn the components of the node representation by performing graph convolution on the homogeneous subgraphs, and finally combine the components to obtain the complete user representation and program representation. Experiments on real-world datasets show that our approach can effectively improve the performance of TV program recommendations compared to the existing baselines. [ABSTRACT FROM AUTHOR]

Well-honed CNN architectures trained with massive labeled images datasets are the state-of-the-art solution in many fields. In this paper, the weights of five commonly used pre-trained models are carefully analyzed for extracting their numerical characteristics and spatial distribution law. The general characteristics are: (1) the weights of a single convolutional layer conform to the distribution of symmetric power law. (2) the power exponent at the center of its convolutional kernel is relatively large, and the power exponent decreases radially from the center. (3) the value range of power exponents between layers is continuous from - 0.5 to - 3.5 . Based on these founding, a weight initialization method is proposed in order to speed up the convergence and improve the performance of CNN models. The proposed weight initialization method is compared with several commonly used methods. Extensive experiments show that it can improve the convergence speed of the CNN models, and the model accuracy is improved by 1–3%. [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]

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]

[Display omitted] • Identification and absolute quantification of naturally derived prostate-specific antigen (PSA). • First absolute quantification of PSA that uses amino acid hydrolysis coupled with isotope-dilution mass spectrometry (IDMS), and enzymatic hydrolysis-IDMS simultaneously. • Support the development of high-order reference materials of PSA to ensure the accuracy, comparability, and traceability of testing in medical laboratories. Prostate-specific antigen (PSA) is a diagnostic marker for prostate cancer; however, because it is a macromolecular glycoprotein with complex and diverse isoforms, it is difficult to standardize clinical PSA detection results. To overcome this limitation, herein, naturally extracted PSA was characterized as free PSA (fPSA), and the PSA solution was successfully quantified by amino acid analysis coupled with isotope-dilution mass spectrometry (AAA-IDMS) and enzymatic hydrolysis-IDMS; the results could be traced to the International System of Units (SI) through absolutely quantified amino acids and peptides. After protein hydrolysis or digestion condition optimization, amino acids and signature peptides were detected by liquid chromatography-mass spectrometry with the multiple reaction monitoring mode. The mass concentrations of PSA obtained through AAA-IDMS and enzymatic hydrolysis-IDMS were (75.3 ± 1.5) µg/g (k = 2) and (74.7 ± 1.7) µg/g (k = 2), respectively. The PSA weighted average mass concentration was (75.0 ± 1.6) µg/g (k = 2). The consistency assessment between the two methods was successfully validated, ensuring absolute quantitative accuracy. This study lays the foundation for the development of high-order reference materials for the clinical detection of PSA, which can improve the accuracy, reliability, and consistency of clinical PSA test results. [ABSTRACT FROM AUTHOR]