Accurate prediction of rain attenuation in short-range millimeter-wave (mmWave) wireless links is critical for ensuring reliable performance under rainy conditions. This study presents a measurement-based model to precisely characterize the impact of rainfall on wireless channels. A dedicated measurement system is developed to capture channel responses under controlled rainfall conditions. Based on the measurement data and the framework of the International Telecommunication Union Radiocommunication Sector (ITU-R) Recommendation P.838-3 model, the proposed model optimizes model parameters and incorporates wet antenna effects. Comparison with the ITU model demonstrates that the proposed model improves attenuation prediction accuracy by over 70% for short-range mmWave links, while cross-band validation confirms its applicability. The proposed model provides reliable technical support for wireless system design in complex meteorological environments.

Accurate modeling of wireless channels is crucial for the design and optimization of railway wireless communication systems. Ray-tracing (RT) is a widely used technique for generating multipath channel characteristics, but the high computational resource requirements make it difficult to meet real-time demands. To address this issue, a super-resolution (SR) model based on the Graph Neural Network (GNN) for generating multipath channel data is proposed. The model leverages the ability of GNNs to process graph-structured data, mapping the multipath propagation process to the graph representation. RT generates low-resolution multipath data, which is then improved by the GNN-based SR model to produce high-resolution channel characteristics. Experimental results in the railway scenario demonstrate that the proposed method significantly reduces computation time while maintaining high accuracy. The GNNbased SR model performs excellently in power, spatial, and angular domains, providing a more efficient and accurate solution for railway wireless communication systems.

Abstract Breast cancer intrinsic subtyping is critical for precision therapy and prognostic assessment. We applied the DNA-level multi-omics classifier UGES (Unified Genetic and Epigenetic Subtyping) [1] to two independent cohorts, TCGA (n = 931) and METABRIC (n = 1134), using a hierarchical learning strategy to progressively distinguish Basal-like, HER2-enriched, Luminal A, and Luminal B subtypes. UGES demonstrated excellent overall classification performance, achieving an AUC of 0.963 on the combined test set. In the challenging task of discriminating Luminal A from Luminal B tumors, UGES reached an AUC of 0.948, substantially outperforming a single-step strategy (0.828), highlighting the benefit of the stepwise approach. Compared with PAM50, UGES reclassified 11.57% of samples, with the most notable change being 11.25% of PAM50 Luminal A samples reassigned to UGES Luminal B. Additionally, UGES improved the sensitivity for HER2-enriched subtype recognition by 28.92%. Survival analyses revealed that UGES-defined subtypes provided stronger prognostic discrimination, with significant survival differences observed across most subtype pairs, particularly for HER2 compared with other subtypes. These findings demonstrate that UGES offers a robust and clinically relevant framework for breast cancer subtyping across independent cohorts, providing a reliable tool for real-world data analysis and clinical decision-making in precision oncology. References [1] Chang X., Xie J., Duan H., Li K., Liu X., Xiong Y., Bai X., Ning K., Xia L.C. ‘A unified genetic and epigenetic model to predict breast cancer intrinsic subtypes using large DNA-level multi-omics data and hierarchical learning.’ IEEE Transactions on Computational Biology and Bioinformatics 2025; early access. Doi:10.1109/TCBBIO.2025.3613591.

Breast cancer subtyping presents a significant clinical and scientific challenge. The prevalent expression-based Prediction Analysis of the Microarray of 50 genes (PAM50) system and its Immunohistochemistry (IHC) surrogate tests showed substantial inconsistencies and did not apply to the rapidly progressing circulating tumor DNA screenings. We developed Unified Genetic and Epigenetic Subtyping (UGES), a new intrinsic subtype classifier, by integrating large-scale DNA-level omics data with a hierarchy learning algorithm. Our benchmarks showed that both multi-step hierarchical learning and using all DNA-level alteration data are crucial, improving the overall AUC score by over 8.3% compared to the one-step multi-classification method. Based on these insights, we developed UGES, a three-step classifier based on 50831 DNA features of 2065 samples, including mutations, copy number aberrations, and methylations. UGES achieved an overall AUC score of 0.963 and greatly improved the clinical stratification of real-world patients, as each subtype strata's survival difference became statistically more significant, P = 9.7e-55 (UGES) vs. 2.2e-47 (PAM50). Finally, UGES identified 52 subtype-specific DNA biomarkers that can be targeted in early screening technology to expand the time window for precision care. The UGES code is freely available at https://github.com/labxscut/UGES.

Millimeter-wave (mmWave) automotive radar can detect the surrounding environment using highly directional, high-frequency electromagnetic waves. With these advantages, mmWave radar has become an important component of autonomous driving and integrated sensing systems. Designing systems and sensing algorithms requires the application of realistic channel models and simulation. In this paper, the propagation channel is measured, characterized, and modeled for mmWave automotive radar with typical configurations in scenarios. Based on the channel measurements in urban street and expressway environments, ray-tracing (RT) technology is verified for modeling important objects regarding radar cross-section and echo power. A hybrid channel model is proposed by integrating RT with the stochastic modeling of targets and surrounding environments, which significantly improves simulation efficiency and provides more flexibility for virtual tests in various complex environments.

High-throughput single-cell sequencing has distinct advantages over previous bulk sequencing technologies. It provides an opportunity for researchers to study cell heterogeneity from the level of individual cells and explain biological relationships between individual cells from a higher resolution perspective. However, single-cell data are characterized by a large number of samples, high dimensionality, and sparseness, which pose a challenge to traditional methods. Therefore, we develop a semi-supervised deep generative model with a self-attention mechanism. The use of deep learning methods allows the denoising and dimensionality reduction of high-dimensional single-cell data nonlinear. We use a neural network with a self-attention mechanism for cell type prediction. This approach facilitates the neural network to extract cell-to-cell relationship features and enhances the model’s ability to extract features. The model can generate data. We apply this ability of imputation to single-cell datasets, thus solving the sparsity problem of single-cell datasets. We have conducted experiments on several simulated and real datasets, and the experimental results show that our proposed method largely outperforms other existing methods both in terms of identifying cell types and imputation on single-cell data. Our method is scalable because it can handle large-scale single-cell datasets of more than a million quantities. This method is promising in other fields as well. All source codes used in our experiments have been deposited at https://github.com/FengLi12/scSASSL.

Accurate identification of cancer driver modules or pathways is important for controlling disease progression and timely treatment. In recent years, most approaches have been based on mutation data combined with gene interaction networks to identify cancer driver modules, but cancer-related genes tend to interact with each other, and the mutations they experience disruption their neighbors. Therefore, we propose a framework that combines network function and topological information to quantify the extent to which mutated genes disrupt their neighbors. Firstly, similarity in protein-protein interaction networks binds to high coverage and high mutual exclusivity of mutant genes, which are used to obtain the impact of the interaction between two mutant genes on biological function. Secondly, we quantified the degree of gene disruption by mutant genes in their neighborhood using an adaptive spread strength measure to obtain the gene spread strength network (GSSN). Finally, the module is extended using CFinder strategy to obtain the optimal driving module. We apply our method to 12 cancer datasets, and the experimental results show that our method outperforms the other three methods on most datasets. At the same time, we also analyze common and low-frequency driver modules in cancer.

In this paper, a foreign object detection (FOD) system based on thermal infrared imaging is designed, taking advantage of its benefits, such as strong penetration in extreme weather conditions, high accuracy of non-contact detection, and no side effect on the wireless power transfer (WPT) system. Firstly, theoretical analysis and improvements are made on image processing techniques such as bilinear interpolation, Gaussian filtering, Operational Test Support Unit (Ostu) adaptive binarization, and morphological operations. Next, the processing advantages of the enhanced algorithms are confirmed using the openCV toolbox. Then, the aforementioned image processing techniques are implemented using embedded programming via the STM32 microprocessor, and the system’s foreign object detection capability is also verified by designing experiments for the location and type of foreign object under 5kW power level. Finally, the experimental results show that the WPT foreign object detection system is effective in the case of detecting and locating foreign objects that up to the size of a half yuan coin even an M4 nut, when the sensor is 15 cm away from the foreign object and the maximum viewing angle is within 110°. At this point, the minimum required temperature deviation between foreign object and background is 7°C, as well as the detection processing time is less than 0.5s, which fully satifies the demand for foreign obiect detection in the WPT system.

An omnidirectional wireless power transfer (WPT) system using three-phase frustum-shaped transmitting coil is designed. In the system, the receiving coil is able to receive the power at any position and any angle within the charging space. The three-phase frustum-shaped transmitting coil is consisted of three half frustum-shaped coils overlapped 60°in turn. The system is powered-up by a three-phase power source, which generates a uniformly rotating magnetic field in the space to achieve omnidirectional wireless charging with high degree of freedom. Based on the circuit principle, the equivalent circuit model of the system is established, the output power is calculated, and the main factors affecting the charging efficiency are analyzed. Then, using Maxwell, a three-phase positive sequence control strategy is designed through finite element simulation, and uniform-intensity magnetic field can be obtained in the space. Mutual inductance is used to indicate the influence of the receiving coil position on the output characteristics of the system. Finally, a three-phase WPT system prototype is built, and the transmission performance of the system is tested when the receiving coil rotates and moves longitudinally inside the frustum-shaped transmitting coils. The experimental results show that the maximum charging power is 12W and the maximum charging efficiency is up to 55.2%, and the fluctuation of the charging efficiency is 9.5% as the receiving coil moving and rotating within the charging space.

For metal foreign object detection (MFOD) in wireless power transfer (WPT) system, the electromagnetic induction coil detection method has the advantages of simple structure, strong environmental adaptability, and low cost. Whether it is a passive detection scheme or an active detection scheme, the detection effect is closely related to the mutual inductance between the detection coil and the metal foreign object. Thus, the structure of the detection coil needs to be optimized. It is very meaningful to optimize the structure of the detection coil for the purpose of improving mutual inductance. In this paper, a rack-shaped detection coil is designed. Firstly, theoretical analysis is used to compare the mutual inductance between the single-turn rack and the circular metal foreign object and the mutual inductance between the rectangular detection coil and the circular metal foreign object. Secondly, simulation analysis is used to compare the mutual inductance between the rack coil pair and the metal foreign object and the mutual inductance between the rectangular coil pair and the metal foreign object. Finally, experiment shows that the single-layer rack coil can effectively detect 1 RMB coin and double-layer rack coil can accurately locate the coin on the basis of effective detection when the WPT system power is 1.5kW and the vertical distance between the detection coil and the primary coil is 15mm. 1 jiao RMB coin is the minimum size of the object that can be detected accurately by the detection system.

The structure of real-world complex networks has long been an area of interest, and one common way to describe the structure of a network has been with the k-core decomposition. The core number of a node can be thought of as a measure of its centrality and importance, and is used by applications such as community detection, understanding viral spreads, and detecting fraudsters. However, we observe that the k-core decomposition suffers from an important flaw: namely, it is calculated globally, and so if the network contains distinct regions of different densities, the sparser among these regions may be neglected.