This paper aims to review the technology, developments, and applications of spin exchange relaxation free (SERF) atomic magnetometers. The generalized comparisons between the SERF magnetometer and the other types of weak magnetic sensors are elaborated. Further, the fundamental composition and operational principle of the SERF atomic magnetometer are elucidated. The development history and recent technological developments with respect to several aspects, including the alkali metal vapor source, atom heating method, pump and probe lasers, and magnetic shielding system, are presented. The classification and respective advantages and disadvantages of these technologies are described in detail. Finally, the existing and potential applications, as well as the prospective future development trends are presented.

This study presents the development of a SERF atomic co-magnetometer with a magnetic shielding system. The shielding system includes multiple cylindrical passive magnetic shield and active magnetic compensation coils. The multilayer shield, which adopts a structural style comprising a barrel body and end cover, is applied to attenuate the external magnetic field to less than 10 nT or ideally close to zero. For the purpose of degaussing, pumping, and probing light paths, several openings must be made on the magnetic shield. A hybrid approach combining theoretical calculations and the finite element method is proposed to analyze the shielding properties, considering the influence of openings. To evaluate the shielding properties of the magnetic shield, two different simplified finite element models are established and analyzed. The simulation results are compared with theoretical and experimental data, and the results are in good agreement. The modeled results show that the openings bear a considerable influence on the shielding performance, and thus, their effect cannot be ignored. The axial magnetic shielding factor with several openings is roughly 4.4×104, which is about two orders of magnitude lower than that without openings. Further, a rotation sensitivity of 5.86×10–7rad·s–1·Hz–1/2 is obtained with the open-hole magnetic shield. This sensitivity corresponds to a magnetic sensitivity of 27.8 fT/Hz1/2. Based on the results, possible improvements are suggested for future designs.

The performance of a multilayer magnetic shield directly affects and limits the sensitivity improvement of an atomic magnetometer. To better meet the requirements of spin-exchange relaxation free atomic magnetometer for the environmental magnetic field, the magnetic shield should be optimized. At present, the optimizations have focused only on a single objective, such as the axial shielding factor. However, the importance of other goals should not be neglected. In this paper, multiobjective optimization of the shield is carried out to obtain a better comprehensive performance. First, according to the structural characteristics of the multilayer shield, a multiobjective optimization model is established. Then, a multiobjective genetic algorithm is utilized to optimize the shield. After optimization, a Pareto optimal solution set is obtained. Furthermore, depending on the desired design requirements, two sets of optimal combinations of target values and variable parameters are selected, based on the fuzzy comprehensive evaluation method and the lowest magnetic noise. This method can obtain a balance between different optimization objectives and effectively improve the comprehensive performance of the shield.

Clustered federated learning has garnered significant attention as an effective strategy for enhancing model performance in non-independent and identically distributed (non-IID) data scenarios. This approach improves model performance in such environments by calculating the similarity between users and clustering them into multiple groups. However, several challenges arise when implementing this method, particularly in balancing flexibility, communication costs, and model performance. To address these issues, this paper proposes a novel hierarchical federated learning framework that balances both network and model performance. The framework performs principal component analysis (PCA) on device-side image datasets to assess the similarity of private data across devices and, in conjunction with network performance measurements, dynamically adjusts communication strategies to minimize latency while ensuring stable model performance. By weighting similarity and communication metrics, the framework optimizes communication efficiency without significantly compromising model performance. To validate the proposed method’s effectiveness, we employed three publicly available datasets and compared it against four baseline methods. The experimental results demonstrate that SC-Fed (segmented clustering-federated learning) achieves a maximum accuracy improvement of 7.56% over baseline methods, while also reducing the average waiting time by 54.6%. These results indicate that the proposed algorithm significantly enhances the applicability and efficiency of clustered federated learning in practical training scenarios.

Background: Breast cancer (BC) is one of the most diagnosed malignancies and a leading cause of cancer-related mortality among women worldwide, thereby posing a substantial threat to women’s health worldwide. However, clinically robust diagnostic biomarkers with high sensitivity and specificity, as well as well-validated molecular targets for targeted therapy, remain limited. Methods: BC transcriptomic data from seven GEO datasets and the TCGA-BRCA cohort (n = 1231) were integrated for analysis. After batch-effect correction, candidate genes were screened through DEA, WGCNA, and PPI networks analysis. An ensemble machine learning (ML) framework incorporating 127 algorithmic combinations was constructed, and SHAP analysis was applied to identify hub genes. Further analyses included functional enrichment, immune infiltration, miRNA regulatory network analysis, and SMR analysis. The expression patterns were validated using single-cell transcriptome data. Drug repositioning analysis and AI-assisted virtual screening were performed to prioritize compounds with favorable drug-like properties. The predicted binding modes of candidate compounds with CHEK1 were assessed by molecular docking. Results: Thirty core genes were obtained through differential expression, WGCNA, and PPI screening. Integrated ML (127 algorithms) determined the optimal model (AUC = 0.919), and SHAP identified nine feature genes, among which CHEK1 and KIF23 showed preliminary diagnostic potential across four external cohorts (AUC: 0.625–0.938). Functional enrichment indicated that both are enriched in the cell cycle and p53 pathways, closely associated with BRCA1/ATR; immune infiltration revealed significant correlations with macrophages and CD8⁺ T cells, with hsa-miR-15a-5p and hsa-miR-607 being common upstream regulatory miRNAs. SMR analysis supported a causal relationship between CHEK1 expression and BC genetic susceptibility (p_SMR < 0.05, p_HEIDI > 0.05); single-cell analysis confirms its heterogeneous expression. AI-assisted virtual screening identified 25 A-grade computational candidate compounds from 171 candidates. Molecular docking suggested that Olaparib and LY294002 can form favorable interactions with the CHEK1 active pocket. Conclusions: The study identified CHEK1 as a key diagnostic gene for BC through 127 ML algorithms and SMR causal inference. By combining AI-assisted virtual screening and molecular docking, computational candidate compounds targeting CHEK1 were prioritized. These findings represent hypothesis-generating in silico predictions and require experimental validation before any therapeutic conclusions can be drawn.