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Freshwater with high quality is crucial for both public health and aquatic biodiversity. However, freshwater resources face numerous challenges, including the proliferation of harmful algal blooms (HABs) caused by various cyanobacterial species that are generally triggered by human activities like agricultural runoff and wastewater. Native algicidal microbiomes may offer potential solutions, although challenges remain in utilizing microbial resources to mitigate HABs in freshwater environments. The combination of synthetic microbial community and probiotic development approaches with robust machine learning tools could allow us to harness native microbiomes to address water quality issues caused by HABs in large water bodies. A meta-analysis of around 100 research studies regarding algicidal bacteria-algae interactions was conducted to quantitatively assess the potential of taxonomically diverse microbial species in controlling HABs in freshwater ecosystems. Meta-analysis findings revealed that diverse species from common freshwater bacterial phyla such as Actinobacteria, Bacteroidota, Firmicutes, and Proteobacteria exhibited 50100 % algicidal activity against different algal species depending on interacting species and environmental conditions. Algicidal taxa (mainly against Microcystis aeruginosa) from both Actinobacteria and Firmicutes primarily included Actinomycetes and Bacillus species. However, Bacteroidota and alpha/beta Proteobacteria exhibited algicidal activity against a broader range of algal species, thus highlighting their potential for controlling multi-species HABs in freshwater environments. Based on this quantitative analysis, the current review puts forward synthetic microbial communities and machine-learning based frameworks to develop microbial solutions for protecting freshwater resources from HABs invasions. 

Traditional multi-agent path finding (MAPF) methods try to compute entire collision free start-goal paths, with several algorithms offering completeness guarantees. However, computing partial paths offers significant advantages including faster planning, adaptability to changes, and enabling decentralized planning. Methods that compute partial paths employ a windowed approach and only try to find collision free paths for a limited timestep horizon. While this improves flexibility, this adaptation introduces incompleteness; all existing windowed approaches can become stuck in deadlock or livelock. Our main contribution is to introduce our framework, WinC-MAPF, for Windowed MAPF that enables completeness. Our framework leverages heuristic update insights from single-agent real-time heuristic search algorithms and agent independence ideas from MAPF algorithms. We also develop Single-Step Conflict Based Search (SS-CBS), an instantiation of this framework using a novel modification to CBS. We show how SS-CBS, which only plans a single step and updates heuristics, can effectively solve tough scenarios where existing windowed approaches fail. 

The rising frequency of natural disasters demands efficient and accurate structural damage assessments to ensure public safety and expedite recovery. Human error, inconsistent standards, and safety risks limit traditional visual inspections by engineers. Although UAVs and AI have advanced post-disaster assessments, they still lack the expert knowledge and decision-making judgment of human inspectors. This study explores how expertise shapes human–building interaction during disaster inspections by using eye tracking technology to capture the gaze patterns of expert and novice inspectors. A controlled, screen-based inspection method was employed to safely gather data, which was then used to train a machine learning model for saliency map prediction. The results highlight significant differences in visual attention between experts and novices, providing valuable insights for future inspection strategies and training novice inspectors. By integrating human expertise with automated systems, this research aims to improve the accuracy and reliability of post-disaster structural assessments, fostering more effective human–machine collaboration in disaster response efforts. 

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Semi‐arid grasslands on the Mongolian Plateau are expected to experience high inputs of anthropogenic reactive nitrogen in this century. It remains unclear, however, how soil organisms and nutrient cycling are directly affected by N enrichment (i.e., without mediation by plant input to soil) vs. indirectly affected via changes in plant‐related inputs to soils resulting from N enrichment. To test the direct and indirect effects of N enrichment on soil organisms (bacteria, fungi and nematodes) and their associated C and N mineralization, in 2010, we designated two subplots (with plants and without plants) in every plot of a six‐level N‐enrichment experiment established in 1999 in a semi‐arid grassland. In 2014, 4 years after subplots with and without plant were established, N enrichment had substantially altered the soil bacterial, fungal and nematode community structures due to declines in biomass or abundance whether plants had been removed or not. N enrichment also reduced the diversity of these groups (except for fungi) and the soil C mineralization rate and induced a hump‐shaped response of soil N mineralization. As expected, plant removal decreased the biomass or abundance of soil organisms and C and N mineralization rates due to declines in soil substrates or food resources. Analyses of plant‐removal‐induced changes (ratios of without‐ to with‐plant subplots) showed that micro‐organisms and C and N mineralization rates were not enhanced as N enrichment increased but that nematodes were enhanced as N enrichment increased, indicating that the effects of plant removal on soil organisms and mineralization depended on trophic level and nutrient status. Surprisingly, there was no statistical interaction between N enrichment and plant removal for most variables, indicating that plant‐related inputs did not qualitatively change the effects of N enrichment on soil organisms or mineralization. Structural equation modelling confirmed that changes in soil communities and mineralization rates were more affected by the direct effects of N enrichment (via soil acidification and increased N availability) than by plant‐related indirect effects. Our results provide insight into how future changes in N deposition and vegetation may modify below‐ground communities and processes in grassland ecosystems.