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  1. Batteries are prevalent energy storage devices, and their failures can cause huge losses such as the shutdown of entire systems. Therefore, the prognostic health management of batteries to increase their availability is highly desirable. This work focuses on improving the serviceability of batteries for wireless sensor networks (WSNs) deployed in remote and hard‐to‐reach places. We propose an active management strategy such that the batteries in a network will attain similar end‐of‐life times, in addition to lifetime extension. The fundamental idea is to adaptively adjust the node quality‐of‐service (QoS) to actively manage their degradation processes, while ensuring a minimum level of network QoS. The framework first executes a prognostic algorithm that can predict the remaining useful life (RUL) of a battery, given its assigned node‐level QoS. A Bayesian optimization framework with an augmented Lagrangian method has been adopted to efficiently solve the developed black‐box constrained optimization problem. A Matlab Simulink model based on a truss bridge structure health monitoring network is built considering the battery aging and temperature effects. Compared with the benchmark models, the proposed strategy demonstrates a more extended network lifespan and uniform working time ratio.

     
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    Free, publicly-accessible full text available December 6, 2025
  2. 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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    Free, publicly-accessible full text available July 1, 2025
  3. Abstract

    This work seeks to capture how an expert interacts with a structure during a facade inspection so that more detailed and situationally-aware inspections can be done with autonomous robots in the future. Eye tracking maps where an inspector is looking during a structural inspection, and it recognizes implicit human attention. Experiments were performed on a facade during a damage assessment to analyze key, visually-based features that are important for understanding human-infrastructure interaction during the process. For data collection and analysis, experiments were conducted to assess an inspector’s behavioral changes while assessing a real structure. These eye tracking features provided the basis for the inspector’s intent prediction and were used to understand how humans interact with the structure during the inspection processes. This method will facilitate information-sharing and decision-making during the inspection processes for collaborative human-robot teams; thus, it will enable unmanned aerial vehicle (UAV) for future building inspection through artificial intelligence support.

     
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  5. Abstract

    Although microbes influence plant growth, little is known about the impact of microbial diversity on plant fitness trade-offs, intraspecific-interactions, and soil nutrient dynamics in the context of biodiversity-ecosystem functioning (BEF) research. The BEF theory states that higher species richness can enhance ecosystem functioning. Thus, we hypothesize that rhizobacterial species richness will alter sorghum (Sorghum bicolorL.) growth, soil nutrient dynamics and interactions (antagonism or synergism) in a nutrient-poor greenhouse soil. Using six rhizobacterial species in a BEF experiment, we tested the impact of a species richness gradient (0, 1, 3, 5 or 6 species per community) on plant growth, nutrient assimilation, and soil nutrient dynamics via seed-inoculation. Our experiment included, one un-inoculated control, six rhizobacterial monoculture(Pseudomonas poae, Pseudomonas sp., Bacillus pumilus., Pantoea agglomerance., Microbacterium sp.,andSerratia marcescens),and their nine mixture treatments in triplicate (48). Rhizobacterial species richness enhanced per pot above- or below-ground dry mass. However, the per plant growth and plant nutrient assimilation declined, most likely, due to microbial-driven competitive interactions among sorghum plants. But nevertheless, some rhizobacterial monoculture and mixture treatments improved per plant (shoot and root) growth and nutrient assimilation as well. Soil nutrient contents were mostly lower at higher plant-associated rhizobacterial diversity; among these, the soil Zn contents decreased significantly across the rhizobacterial diversity gradient. Rhizobacterial diversity promoted synergistic interactions among soil nutrients and improved root–soil interactions. Overall, our results suggest that a higher rhizobacterial diversity may enhance soil–plant interactions and total productivity under resource limited conditions.

     
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  6. 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. 
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