The fabrication of self-biased microwave devices demands ferrite materials that integrate high magnetic performance with low-loss characteristics at operational frequencies. M-type barium hexaferrites are promising candidates for millimeter-wave self-biased circulators, yet simultaneously optimizing their saturation magnetization, ferromagnetic resonance (FMR) linewidth, dielectric loss, and thermal conductivity remains a key challenge. To address this issue, Cu-Zn-Sn co-substituted BaFe11.5CuxZn0.25-xSn0.25O19 (x = 0.05-0.20) were synthesized. The optimal composition (x = 0.10) achieves a high saturation magnetization of 65.95 emu g-1, a narrow FMR linewidth of similar to 500 Oe at 9 GHz, a low dielectric loss (epsilon '' approximate to 0.970 at 1 MHz), and a thermal conductivity of 1.944 W m(-1) K-1. Magnetic-field-assisted texturing applied to the x = 0.10 composition induces strong c-axis orientation, as evidenced by significantly enhanced (00l) diffraction peaks and {000l} pole figure analysis, yielding a high remanence ratio (Mr/Ms). The improved remanence ratio arises from preferential grain alignment along the easy magnetization axis, which is critical for self-biased circulator operation. These findings demonstrate that multi-element co-doping combined with texture control provides an effective strategy for balancing the key magnetic, dielectric, and thermal properties of BaM hexaferrites, establishing a viable material platform for millimeter-wave self-biased circulators. (c) 2026 Published by Elsevier Ltd on behalf of The editorial office of Journal of Materials Science & Technology.
RGB-D salient object detection aims to localize visually salient regions by integrating appearance-semantic cues from RGB images and geometric-spatial cues from depth maps. However, existing methods still face two challenges. First, depth quality is often spatially non-uniform, and fixed or coarse-grained fusion strategies may propagate local depth noise into saliency predictions. Second, the complementarity between RGB and depth varies with scene content, object structure, and local depth quality. To address these issues, we propose UCMNet, a dual spatial reliability-and-complementarity modeling framework for RGB-D salient object detection. Specifically, SACF predicts a pixel-wise depth-reliability proxy from local texture, edge, and geometric-consistency cues at the shallow fusion stage, thereby adaptively adjusting RGB-D fusion weights. CCM constructs an explicit complementarity map via bidirectional cross-modal conditional reconstruction during decoding, characterizing spatially varying reconstruction residuals between modalities. PMSM enhances high-level structural representation through orientation-selective filtering and multi-scale semantic aggregation. DHAR further uses the complementarity map to modulate center-surround responses, enhancing foreground-related structures and suppressing background interference. Experiments on six public RGB-D salient object detection benchmarks show that UCMNet achieves competitive performance on most metrics and exhibits improved robustness under synthetic depth degradation, low-contrast scenes, and cluttered backgrounds.
Federated Learning (FL) enables collaborative model training without centralizing raw data and has become an important paradigm for privacy-sensitive intelligent systems. Nevertheless, its distributed architecture, heterogeneous data, and unreliable participants introduce substantial robustness and security challenges, ranging from communication noise and system failures to poisoning, privacy inference, and Byzantine attacks. In this paper, we present a structured survey of robust federated learning. We first organize the literature through a threat-oriented taxonomy that covers poisoning attacks, privacy leakage risks, Byzantine behavior, and scenario-specific robustness challenges. We explicitly analyze stronger adversaries that collude, coordinate poisoning campaigns, replace models behind secure aggregation, and adapt their optimization to defense-specific weaknesses. We then review representative defense mechanisms, including robust aggregation, backdoor defense, privacy-preserving training, secure aggregation, and personalized robustness enhancement. Particular attention is given to three recurring tensions in the literature: robustness under Non-IID data, robustness-efficiency trade-offs, and robustness-privacy trade-offs. We further discuss robust federated learning in wireless and IoT environments, where communication uncertainty and resource constraints become first-order concerns. We examine how existing robustness mechanisms translate, or fail to translate, to foundation-model-based FL, federated fine-tuning of large language models, federated reinforcement learning, agentic federated systems, and federated retrieval-augmented generation. Finally, we summarize open challenges and outline future research directions for building more reliable, efficient, and deployable federated learning systems.
Third-element doping is an effective strategy for promoting low-temperature L 10 ordering in FePt nanoparticles, but the underlying dopant-regulated diffusion kinetics and atomic migration mechanisms remain insufficiently understood. In this work, density functional theory (DFT) calculations and experiments are combined to elucidate these processes. The calculations reveal distinct defect-mediated diffusion mechanisms. Fe preferentially migrates through nearest-neighbor vacancy jumping, whereas Pt favours a lower-energy antisite-assisted pathway. A five-step strategy integrating thermodynamic, structural, magnetic, and ordering-transition screening with DFT prediction and experimental verification is established to identify promising dopants. Large-radius dopants reduce migration barriers by more than 20% and enhance low-temperature diffusion coefficients by over three orders of magnitude. Atomic radius is identified as the dominant geometric descriptor of migration barriers, while electronegativity and valence electron number provide secondary electronic contributions. Experimental results further support the predicted trends. Large-radius elements, such as Bi and Pb, can promote L 10 ordering at low temperatures and achieve a high ordering degree and coercivity. In contrast, Mn shows limited ordering, whereas Co-, Ni-, and Cu-doped systems retain the disordered fcc phase, which is broadly consistent with the calculated trends. This work provides a framework for dopant selection and low-temperature synthesis of ordered intermetallic nanoparticles. (c) 2026 Published by Elsevier Ltd on behalf of The editorial office of Journal of Materials Science & Technology.
Radio-frequency (RF) and microwave dielectric ceramics are key materials for high-frequency electronic devices. Among them, perovskites outperform conventional dielectrics due to their high quality factor ( Q = 1/dielectric loss) and exceptional reliability. However, their practical application is limited by the trade-off between Q & times; f and the temperature coefficient of resonant frequency ( rf ), as well as by their high densification temperature ( >= 1500 degrees C). Here, a synergistic strategy combining B-site complex-ion modification with composite sintering-aid engineering is proposed. The optimized 95CZT05CMN composition exhibits excellent microwave dielectric properties of dielectric constant sr = 32.4, Q & times; f = 34,240 GHz, and rf = + 16.8 ppm/ degrees C at 1540 degrees C. More importantly, the 5G1L0.5C-modified ceramics can be densified at 950-1050 degrees C while retaining favorable dielectric properties ( sr = 27.3-31.9, Q & times; f = 12,920-13,400 GHz, rf = -6.1 to -11.6 ppm/ degrees C), together with a high flexural strength of 212 MPa. Furthermore, a C-band dielectric resonator antenna delivers radiation efficiency above 85%, while multilayer ceramic capacitors (MLCCs) fabricated show excellent C0G-type stability ( AC / C25 degrees C within +/- 0.3%) and a dielectric loss of similar to 0.06% at 1 MHz. These results establish a viable route toward low sintering temperature, low-loss and thermally stable CaZrO3 -based perovskites for advanced RF and integrated electronic applications. (c) 2026 Published by Elsevier Ltd on behalf of The editorial office of Journal of Materials Science & Technology.
