The realization of high-performance electromagnetic wave (EMW) absorbing materials is particularly dependent on the regulation of interface structures, and the construction of multi-component heterogeneous interfaces is an effective strategy to achieve high-performance EMW absorption in materials. In this study, a three-dimensional flower-like ZnS/Co1 -x S@C composite material was prepared by hydrothermal method, stepwise gas-phase selenization, and gas-phase sulfidation processes. The formation of multiple heterogeneous interfaces through stepwise gas-phase selenization and sulfidation enhanced the interface polarization ability of the material and significantly improved the electromagnetic wave loss efficiency. The results show that the ZnS/Co1 -x S@C composite material has excellent EMW absorption performance, with a minimum reflection loss (RLmin ) of -53.75 dB at 3.4 mm and a maximum effective absorption bandwidth (EABmax ) of 7.28 GHz at 3.2 mm. Density functional theory (DFT) calculations verified that the built-in electric field constructed at the heterogeneous interface strengthened the interface polarization loss. Meanwhile, the radar cross-section (RCS) simulation further demonstrated that the material has good electromagnetic stealth properties, providing a new idea for the research on the construction of multiple heterogeneous interface sulfide composites. (c) 2026 Published by Elsevier Ltd on behalf of The editorial office of Journal of Materials Science & Technology.
We study sufficient conditions for the generic rigidity of a graph G expressed in terms of (i) its minimum degree δ(G), or (ii) the parameter η(G)=minuv∉E(deg(u)+deg(v)). For each case, we seek the smallest integers f(n,d) (resp. g(n,d)) such that every n-vertex graph G with δ(G)≥f(n,d) (resp. η(G)≥g(n,d)) is rigid in Rd.Krivelevich, Lew, and Michaeli conjectured that there is a constant K>0 such that f(n,d)≤n2+Kd for all pairs n,d. We give an affirmative answer to this conjecture by proving that K=1 suffices. For n≥29d, we obtain the exact result f(n,d)=⌈n+d−22⌉.Next, we prove that g(n,d)≤n+3d for all pairs n,d, and establish g(n,d)=n+d−2 when n≥d(d+2). For d=2,3, we determine the exact values of f(n,d) and g(n,d) for all n, confirming another conjecture of Krivelevich, Lew, and Michaeli in these low-dimensional special cases.As an application, we prove that the Erdős-Rényi random graph G(n,1/2) is a.a.s. rigid in Rd for d=d(n)∼732n. This result provides the first linear lower bound for d(n), and it answers a question of Peled and Peleg.
Given a graph F, an r-uniform hypergraph 3L is a Berge-F if there is a bijection phi : E(F) -> E(3L) such that e subset of phi(e) for each e is an element of E(F). Given a family .F of r-uniform hypergraphs, an r-uniform hypergraph is .F-free if it does not contain any member of .F as a subhypergraph. The Tur & aacute;n number of .F is the maximum number of hyperedges in an .F-free r-graph on n vertices. Let Ms+1 denote a matching of size s + 1, i.e., the graph consisting of s+ 1 independent edges. Khormali and Palmer [European J. Combin. 102 (2022) 103506] completely determined the Tur & aacute;n number of Berge matchings for sufficiently large n. Subsequently, Kang, Ni, and Shan [Discrete Math. 345 (2022) 112901] determined the exact value of the Tur & aacute;n number of Berge-Ms+1 for all n when r < s-1 or r > 2s +2. In this paper, we settle the final open case s < r < 2s + 1, thereby completing the determination of the Tur & aacute;n number of Berge matchings. (c) 2026 Elsevier B.V. All rights are reserved, including those for text and data mining, AI training, and similar technologies.
Achieving an excellent strength-ductility synergy remains a central challenge for metastable face-centered cubic (FCC) high-entropy alloys (HEAs). In this study, a thermomechanical processing route consisting of warm rolling followed by annealing was employed to introduce pre-designed defect architectures into two metastable HEAs: a transformation-induced plasticity Cr20 Mn20 Fe20 Co34.5 Ni5 C0.5 HEA and a transformation- and twinning-induced plasticity Cr20 Mn20 Fe20 Co34 Ni5 C1 HEA. This processing produces unrecrystallized microstructures containing high densities of nano-twins and dislocations, leading to substantial yield strength enhancement while retaining good ductility compared with fully recrystallized counterparts. The C1 HEA achieves a higher yield strength of 1174 MPa with a total elongation of 22.2 %, whereas the C0.5 HEA exhibits a lower yield strength of 894 MPa but a higher total elongation of 31.9 %. Differences in mechanical response are associated with distinct deformation-induced microstructural evolution during tensile loading. In the C0.5 HEA, a higher fraction of deformation-induced martensite with multiple variants is developed, whereas in the C1 HEA, the transformation is more limited and predominantly involves single-variant martensite, which is associated with lower strain hardenability. These results demonstrate that tailoring defect structures via thermomechanical processing provides an effective pathway for optimizing the strength-ductility synergy in metastable FCC HEAs. (c) 2026 Published by Elsevier Ltd on behalf of The editorial office of Journal of Materials Science & Technology.
Cross-modal remote sensing image-text retrieval (CMRSITR) plays a critical role in extracting semantic information from Earth observation data. However, applying parameter-efficient prompt tuning to this task faces a critical causal chain of failures: (1) the limited trainable parameters induce representation instability on noisy remote sensing data, (2) this instability hinders the bridging of the persistent modality gap, and (3) the resulting misalignment renders traditional sampling strategies ineffective, leading to the mining of false negatives. To break this dependency chain, we introduce Progressive Representation Stabilization and Alignment (ProSA), a novel framework that enforces a stabilize-align-discriminate curriculum. ProSA first employs a Hierarchical Uncertainty Modeling (HUM) module to inject adaptive noise, smoothing the optimization landscape to establish representation stability. Building upon this stable foundation, an Adversarial Modality Alignment (AMA) module is introduced to bridge the modality gap via a gradient reversal mechanism. Finally, only within the stabilized and aligned feature space, a Hard Negative Mining (HNM) module is activated to refine fine-grained discriminability by targeting valid hard negatives. Extensive experiments on three benchmark datasets demonstrate that ProSA effectively resolves these challenges, achieving state-of-the-art performance among parameter-efficient methods and competitive results against computation-heavy full fine-tuning baselines.
