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For multimodal magnetic resonance (MR) brain tumor segmentation, current methods usually directly extract the discriminative features from input images for the tumor subregion category. However, information aliasing caused by the mutual inclusion of tumor subregions is often ignored. Moreover, existing methods usually do not take tailored efforts to highlight the single tumor subregion features. To this end, a multimodal MR brain tumor segmentation method with tumor prototype-driven and multiexpert integration is proposed. It could highlight the features of each tumor subregion under the guidance of tumor prototypes. Specifically, to obtain the prototypes with complete information, we propose a mutual transmission mechanism to transfer different modal features to each other. Furthermore, we devise a prototype-driven feature representation and fusion (PFRF) method, which implants the learned prototypes into tumor features and generates corresponding activation maps. With the activation maps, the subregion features consistent with the prototype category can be highlighted. A key information enhancement and fusion strategy with multiexpert integration is designed to further improve the segmentation performance. The strategy can integrate the features from different layers of the extra feature extraction network and the features highlighted by the prototypes. Experimental results on three competition datasets prove the superiority of the proposed method. The source code can be available at https://github.com/Linzy0227/PDMINet.

The functionalization of polyoxometalates with organic ligands provides a new-style strategy to accurately incorporate polyoxometalates with advanced functional organic moieties on their surfaces, the development of which has attracted increasing research interest due to the potential applications. A germanium tungstate Na2(H3O)6[{RuIV(bpy)}2{WO2(C2O4)}2(GeW11O39)2]·27H2O (bpy = 2,2′-bipyridine) with two ligands covalently modified was triumphantly synthesized, using the conventional one-pot hydrothermal method. It was systematically characterized by thermogravimetric analysis (TGA), elemental analysis, infrared (IR) spectroscopy, single-crystal X-ray diffraction, X-ray photoelectron spectroscopy (XPS), powder diffraction (PXRD), scanning electronic microscopy (SEM), and ultraviolet–visible (UV–vis) spectroscopy. The two-dimensional (2D) layered structure was established through hydrogen bonding and Na+bridges. Impedance measurements indicate that it displays outstanding proton conduction properties, with a splendid conductivity up to 1.24 × 10–2S·cm–1under 353 K and 90% relative humidity (RH), owing to the rich interlayer hydrogen-bond network formed by the organic ligands ({RuC10H8N2}4+and {WC2O4}4+), hydrated protons (H3O+), and crystal waters.

Domain adaptation person re-identification (Re-ID) is a challenging task, which aims to transfer the knowledge learned from the labeled source domain to the unlabeled target domain. Recently, some clustering-based domain adaptation Re-ID methods have achieved great success. However, these methods ignore the inferior influence on pseudo-label prediction due to the different camera styles. The reliability of the pseudo-label plays a key role in domain adaptation Re-ID, while the different camera styles bring great challenges for pseudo-label prediction. To this end, a novel method is proposed, which bridges the gap of different cameras and extracts more discriminative features from an image. Specifically, an intra-to-intermechanism is introduced, in which samples from their own cameras are first grouped and then aligned at the class level across different cameras followed by our logical relation inference (LRI). Thanks to these strategies, the logical relationship between simple classes and hard classes is justified, preventing sample loss caused by discarding the hard samples. Furthermore, we also present a multiview information interaction (MvII) module that takes features of different images from the same pedestrian as patch tokens, obtaining the global consistency of a pedestrian that contributes to the discriminative feature extraction. Unlike the existing clustering-based methods, our method employs a two-stage framework that generates reliable pseudo-labels from the views of the intracamera and intercamera, respectively, to differentiate the camera styles, subsequently increasing its robustness. Extensive experiments on several benchmark datasets show that the proposed method outperforms a wide range of state-of-the-art methods. The source code has been released at https://github.com/lhf12278/LRIMV.

Most existing domain generalization fault diagnosis methods concentrate on learning domain-invariant features or global feature distribution alignment. Nevertheless, this could lose vital clues related to the fault categories, and make adapting to unknown working conditions challenging. To this end, a novel approach termed feature adaptive modulation and health state prototype consistency learning (FAMPL) is proposed. Specifically, FAMPL incorporates a feature adaptive modulation module designed to generate modulation parameters, which are utilized to perform affine transformations on the acquired features, yielding modulation features. This approach aims to capture essential clues associated with specific working conditions. To further enhance the ability to distinguish between different fault categories, a specialized health state prototype learning strategy has been developed. This approach significantly refines the model's capacity for feature discrimination, making it more adept at accurately identifying and categorizing various fault types. Numerous cross-domain fault diagnosis experiments have demonstrated the superiority of FAMPL.

Most current domain adaptation methods for fault diagnosis focus on single target domain. However, test data often comes from multiple target domains, as machines work under different operating conditions, subsequently generating a more complex and extensive distribution of target data. Unfortunately, single target domain adaptation methods are not adaptive for multiple target domains adaptation (MTDA), which results in transfer performance degradation. To this end, a novel disentanglement learning with adaptive centroid alignment is proposed for MTDA. Specifically for disentanglement learning, two encoders and two classifiers are constructed independently for fault-related and domain-related feature extractions and classifications. Followed by the dual-adversarial strategy, only fault-related but domain-irrelevant features are extracted. Furthermore, to achieve the category alignment, we also propose an adaptive centroid alignment strategy, so that the feature centroids of the same fault category in different domains are enforced to be close to each other. Extensive experiments demonstrate the superiority of our proposed method compared with other popular approaches.