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Bacterial communities play a crucial role in maintaining the stability of river ecosystems and driving biogeochemical cycling, exhibiting high sensitivity to environmental change. However, understanding the spatial scale effects and assembly mechanisms of riverine bacterial communities under distinct anthropogenic disturbances remains a challenge. Here, we investigated bacterial communities across three distinct watersheds [i.e., intensive urban (UW), intensive agricultural (AW), and natural (NW)] in both dry and wet seasons. We explored biogeographic patterns of bacterial communities and the influence of landscape patterns at multi-spatial scales and water chemistry on bacterial communities. Results showed that α diversity was significantly lower in UW and AW compared to NW, particularly in the dry season. A gradient of β diversity with NW > UW > AW was observed across both seasons (p < 0.05). Pseudomonadota, Bacteroidota, and Actinobacteriota were the most abundant phyla across all watersheds, with specific taxa enriched in each watershed (i.e., the class Actinobacteria was significant enrichment in UW and AW, and Clostridia in NW). The influence of landscape patterns on bacterial communities was significantly lower in human-disturbed watersheds, particularly in UW, where this influence also varied slightly from near riparian buffers to sub-watershed. Homogeneous selection and drift jointly dominated the bacterial community assembly across all watersheds, with homogeneous selection exhibiting a greater influence in UW and AW. Landscape patterns explained less variance in bacterial communities in UW and AW than in NW, and more variance was explained by water chemistry (particularly in UW). These suggest that the stronger influence of species sorting in UW and AW was driven by more allochthonous inputs of water chemistry (greater environmental stress). These findings provide a theoretical foundation for a deeper understanding of riverine bacterial community structure, spatial scale effects, and ecological management under different anthropogenic activities. [ABSTRACT FROM AUTHOR]

Grassland degradation and desertification is a complex process, including both state conversion (e.g., grasslands to deserts) and gradual within-state change (e.g., greenness dynamics). Existing studies hardly separated the two components and analyzed it as a whole based on time series vegetation index data, which cannot provide a clear and comprehensive picture for grassland degradation and desertification. Here we propose an integrated assessment strategy, by considering both state conversion and within-state change of grasslands, to investigate grassland degradation and desertification process in Central Asia. First, annual maps of grasslands and sparsely vegetated land were generated to track the state conversions between them. The results showed increasing grasslands were converted to sparsely vegetated lands from 2000 to 2014, with the desertification region concentrating in the latitude range of 43-48° N. A frequency analysis of grassland vs. sparsely vegetated land classification in the last 15 yr allowed a recognition of persistent desert zone (PDZ), persistent grassland zone (PGZ), and transitional zone (TZ). The TZ was identified in southern Kazakhstan as one hotspot that was unstable and vulnerable to desertification. Furthermore, the trend analysis of Enhanced Vegetation Index during thermal growing season (EVITGS ) was investigated in individual zones using linear regression and Mann-Kendall approaches. An overall degradation across the area was found; moreover, the second desertification hotspot was identified in northern Kazakhstan with significant decreasing in EVITGS , which was located in PGZ. Finally, attribution analyses of grassland degradation and desertification were conducted by considering precipitation, temperature, and three different drought indices. We found persistent droughts were the main factor for grassland degradation and desertification in Central Asia. Considering both state conversion and gradual within-state change processes, this study provided reference information for identification of desertification hotspots to support further grassland degradation and desertification treatment, and the method could be useful to be extended to other regions.

Iron-based shape memory alloys (Fe-SMAs), traditionally manufactured, are favored in engineering applications owing to their cost-effectiveness and ease of fabrication. However, the conventional manufacturing process of Fe-SMAs is time-consuming and raw-material-wasting. In contrast, additive manufacturing (AM) technology offers a streamlined approach to the integral molding of materials, significantly reducing raw material usage and fabrication time. Despite its potential, research on AMed Fe-SMAs remains in its early stages. This review provides updated information on current AM technologies utilized for Fe-SMAs and their applications. It provides an in-depth discussion on how printing parameters, defects, and post-printing microstructure control affect the mechanical properties and shape memory effect (SME) of AMed Fe-SMAs. Furthermore, this review identifies existing challenges in the AMed Fe-SMA approach and proposes future research directions, highlighting potential areas for development. The insights presented aim to guide improvements in the material properties of AMed Fe-SMAs by optimizing printing parameters and enhancing the SME through microstructure adjustment. [ABSTRACT FROM AUTHOR]