In order to enhance the service life of TC4 alloy under harsh high-temperature conditions, CrNiMnTiAl-yTi3SiC2 (y = 5, 7.5, 10 wt%; TS5, TS7.5 and TS10) composite coatings were fabricated on TC4 alloy via laser cladding additive manufacturing. The microstructural evolution, tribological properties, and high-temperature oxidation resistance were investigated. The results show that the TS7.5 exhibits the best high-temperature wear resistance, with a 62% reduction in wear compared to the substrate, corresponding to an approximately four-fold improvement in wear resistance at 600 degrees C. This is attributed to the internal dispersion strengthening, together with the synergistic effect of a uniformly distributed dislocation network structure and a high proportion of high-angle grain boundaries, which effectively hinder dislocation motion, alleviate local stress concentration, and significantly enhance the coating strength and wear resistance. Besides, the TS10 demonstrates superior oxidation resistance at 800 degrees C, achieving a 59% reduction in oxidation weight gain relative to the substrate. This results from the higher Ti3SiC2 content provides sufficient SiO2, which integrates with Cr2O3, TiO2, Al2O3 and other oxides to construct dense composite oxide scale that effectively suppresses oxidation reactions. By investigating the underlying mechanisms of tribological properties and oxidation resistance, this work not only extends the service life of TC4 alloy but also provides new theoretical support for enhancing coating performance.
Amid the global push for sustainable construction, bamboo has been widely studied as a rapidly renewable, high-performance building material. This paper reviews the published literature on bamboo’s journey from plant to structural component. The review first examines the fundamental botanical characteristics of bamboo, explaining the relationship between its hierarchical structure—from molecular to cellular levels—and its mechanical properties. It then transitions to an analysis of sustainable forestry, harvesting practices, the structural uses of round bamboo, and the manufacturing processes for engineered bamboo products, such as glued laminated bamboo and bamboo scrimber. Critical performance challenges are addressed by evaluating modern treatment methods that enhance durability, dimensional stability, and fire resistance. Structural applications are explored in detail, covering traditional building typologies, innovative connection systems, and the emerging potential for bamboo in mid- and high-rise as well as long-span structures. The life cycle perspective is completed by examining end-of-life scenarios, including reuse, recycling, and energy recovery. Ultimately, the review situates bamboo within the global context by examining resource distribution, trade, and the policy frameworks required to promote its widespread adoption. By consolidating research across multiple disciplines, this paper highlights the significant potential of bamboo to contribute to a low-carbon built environment. It concludes that a holistic, science-based approach is essential for overcoming remaining challenges and fully integrating this versatile material into modern construction practices.
Blind super-resolution (SR) of remote sensing images is highly challenging due to the complex and spatially variant degradations caused by diverse imaging conditions, atmospheric effects, and sensor characteristics. Most existing methods assume uniform degradation across the entire image or rely on global degradation representations, which fail to capture the intrinsic spatial heterogeneity in real-world remote sensing scenarios. To address this issue, a novel spatial degradation field modeling framework is proposed, in which spatially varying degradations are explicitly characterized at the patch level. Specifically, a spatially adaptive degradation estimation network (SADEN) is designed to estimate patch-wise degradation representations, which are further organized into a spatial degradation field to describe the degradation distribution across the image. Based on this representation, an adaptive degradation adjustment module (ADAM) is introduced to dynamically modulate feature responses conditioned on the estimated degradation. The module incorporates both spatial modulation (SM) and channel modulation (CM) mechanisms, enabling the network to adaptively refine spatial structures and channel-wise feature responses under complex degradation patterns. Extensive experiments across multiple benchmark remote sensing datasets demonstrate that the proposed method effectively captures spatially varying degradation patterns and consistently outperforms state-of-the-art blind super-resolution approaches in both quantitative performance and visual quality. Moreover, its effectiveness is further validated through evaluations on real-world scenarios as well as downstream semantic segmentation tasks.
Unfair negative reviews can harm businesses by damaging brand image and reducing customer trust. This research examines how tourism and hospitality businesses should respond to different types of unfair reviews through three scenario-based experiments. The findings reveal a congruity effect: rational responses are more effective in improving third-party consumers’ purchase intentions for low-arousal unfair reviews, while emotional responses are more effective for high-arousal ones. The results further show that social distance mediates this effect. In addition, in high-arousal cases, weak victimization responses are more suitable than strong reprimands. These findings enrich theories related to Communication Accommodation Theory and contribute to the literature on unfair negative reviews. They also provide practical guidance for tourism and hospitality businesses on how to effectively handle online consumer unfair reviews.
Artificial intelligence (AI) plays an increasingly important role in marketing, acting as both a product designer and an endorser, yet research predominantly examines these roles in isolation. This study addresses this gap by investigating how consumers’ adoption of AI-designed products is jointly shaped by textual designer disclosure (i.e., AI- vs. human-designed) and visual endorser cues (i.e., human-like vs. animated avatars). This research proposes that consumers integrate these two modalities into a single evaluation, and their adoption intention is shaped by the congruence between textual and visual cues. Drawing on schema-congruity and curiosity theories, this research tests a designer-endorser congruence effect across four studies. Study 1 (N = 240) demonstrates the congruence effect: for an AI-designed product, consumers show higher adoption intention when it is endorsed by an animated (vs. human-like) endorser, whereas the effect is reversed for human-designed products. Study 2 (N = 156) illustrates that perceptual curiosity serves as a mechanism: AI’s involvement in design stimulates curiosity, which is then sustained by presenting the endorser in a congruent, animated form. Study 3 (N = 156) validates the mediating effect of perceptual curiosity alongside cognitive mechanisms, i.e., perceived trustworthiness and uncanniness. Study 4 (N = 340) examines how this congruence effect varies across consumers with different dispositional traits and shows that this effect is stronger among those with high anxiety about AI. These findings make a theoretical contribution by introducing designer-endorser congruence as a determinant of product adoption in AI-mediated marketing, and by identifying curiosity as an alternative affective mechanism that complements prior information-processing perspectives. In practice, this study provides insights for leveraging avatar endorsement to induce curiosity and to encourage the adoption of AI-designed products.
