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Nature-based treatment technologies such as denitrifying woodchip bioreactors (WBRs) are employed to manage nitrogen (N) pollution from agricultural nonpoint sources. Due to variability in environmental conditions like temperature and discharge, it is challenging to achieve consistent treatment effectiveness with these passive systems. To improve nitrate (NO3–) load reductions in a field-scale WBR in New York State during cool spring weather, we designed a system for controlled exogenous carbon (C) dosing, allowing rates of C dosing to respond in real time to changing discharge and NO3– concentrations. Treatment efficiencies for NO3–, acetate mass balances, and other bioreactor properties were monitored from April 5 to June 10, 2023. Biostimulation with 7.5 mg C/L acetate (assuming complete mixing of injected acetate with bioreactor pore water) increased NO3– removal rates up to 5-fold compared to a model-based scenario of baseline bioreactor performance, and were as high as 0.4 mg NO3––N L–1 h–1 while water temperatures were <12 °C. Increasing acetate concentrations beyond 7.5 mg C/L did not confer a clear improvement in NO3– removal rates. Cumulative N load reductions increased from 11.3% under the baseline scenario without C dosing to 24.1% with C dosing. The mass ratio of metabolized C to additional N removal was 2.5:1, although the total dosed C/N mass ratio was 5.1:1 due to incomplete acetate utilization in the reactor. We found evidence that C dosing could enhance the future release of dissolved organic N (DON) and dissolved organic C related to biofilm sloughing. The expense of acetate, with a cost efficiency of 86 USD/kg N, was the main cost driver of the real-time control approach. Our results demonstrate the potential of real-time control of C dosing to meaningfully improve nonpoint source N removal during cool spring conditions but also highlight opportunities for methods to improve acetate utilization efficiencies in order to improve the overall cost-effectiveness of the approach. 

The hexagonal cells built by honey bees and social wasps are an example of adaptive architecture; hexagons minimize material use, while maximizing storage space and structural stability. Hexagon building evolved independently in the bees and wasps, but in some species of both groups, the hexagonal cells are size dimorphic—small worker cells and large reproductive cells—which forces the builders to join differently sized hexagons together. This inherent tiling problem creates a unique opportunity to investigate how similar architectural challenges are solved across independent evolutionary origins. We investigated how 5 honey bee and 5 wasp species solved this problem by extracting per-cell metrics from 22,745 cells. Here, we show that all species used the same building techniques: intermediate-sized cells and pairs of non-hexagonal cells, which increase in frequency with increasing size dimorphism. We then derive a simple geometric model that explains and predicts the observed pairing of non-hexagonal cells and their rate of occurrence. Our results show that despite different building materials, comb configurations, and 179 million years of independent evolution, honey bees and social wasps have converged on the same solutions for the same architectural problems, thereby revealing fundamental building properties and evolutionary convergence in construction behavior. 

Soft robots actuate themselves and their world through induced pressure and strain, and can often sense these quantities as well. We hypothesize that coordination in a tightly coupled collective of soft robots can be achieved with purely proprioceptive sensing and no direct communication. In this paper, we target a platform of soft pneumatic modules capable of sensing strain on their perimeter, with the goal of using only the robots' own soft actuators and sensors as a medium for distributed coordination. However, methods for modelling, sensing, and controlling strain in such soft robot collectives are not well understood. To address this challenge, we introduce and validate a computationally efficient spring-based model for two-dimensional sheets of soft pneumatic robots. We then translate a classical consensus algorithm to use only proprioceptive data, test in simulation, and show that due to the physical coupling between robots we can achieve consensus-like coordination. We discuss the unique challenges of strain sensors and next steps to bringing these findings to hardware. These findings have promising potential for smart materials and large-scale collectives, because they omit the need for additional communication infrastructure to support coordination. 

Large-scale construction projects can benefit from having a team of heterogeneous building robots operating autonomously and cooperatively on unstructured environments. In this work, we propose a flexible system architecture, MARSala, that allows teams of distributed mobile robots to construct motion support structures in large and unstructured environments using purely local interactions. The paper primarily focuses on the deliberative layer of the architecture which provides a means for formulating a construction project as a motion support structure construction problem. We implemented the architecture in simulation and demonstrated the benefits of such a formulation in two different construction scenarios operating in large unstructured environments. 

Honeybees are renowned for their perfectly hexagonal honeycomb, hailed as the pinnacle of biological architecture for its ability to maximize storage area while minimizing building material. However, in natural nests, workers must regularly transition between different cell sizes, merge inconsistent combs, and optimize construction in constrained geometries. These spatial obstacles pose challenges to workers building perfect hexagons, but it is unknown to what extent workers act as architects versus simple automatons during these irregular building scenarios. Using automated image analysis to extract the irregularities in natural comb building, we show that some building configurations are more difficult for the bees than others, and that workers overcome these challenges using a combination of building techniques, such as: intermediate-sized cells, regular motifs of irregular shapes, and gradual modifications of cell tilt. Remarkably, by anticipating these building challenges, workers achieve high-quality merges using limited local sensing, on par with analytical models that require global optimization. Unlike automatons building perfectly replicated hexagons, these building irregularities showcase the active role that workers take in shaping their nest and the true architectural abilities of honeybees. 

Multiscale Granular Stacking (MSGS) is a technology for assembling planetary-surface infrastructure from unprocessed regolith. The unprocessed grains serve directly as additive manufacturing feedstock in a process that exploits their natural variation in size and shape. With precise, single-grain scanning, computation, and packing, MSGS minimizes and potentially eliminates the need for adhesives, fluids, and other binders, saving the associated mass and energy. Preliminary calculations suggest that MSGS requires less mass transport and energy for construction than traditional terrestrial building methods, drastically reducing the reliance on earth resources for sustaining a deep-space human presence and long-term exploration goals. Constructing a desired structure may require stacking millions of grains which demands extensive computation. Packing solutions with many objects exist in the literature, e.g. some versions of the knapsack problem. However, micro- to macro-scale particle dry stacking itself has never been investigated, let alone in the context of space additive manufacturing. Modeling these fabrication process dynamics as a discrete-step linear system allows for tuning of parameters such as build speed, surface finish, and contour smoothing while providing the opportunity to leverage controls theory for determining system convergence, steady-state error, and overshoot of desired build height. This paper details attempts to bring multivariable control theory to bear on additive manufacturing by using feedback on overall build geometry, a technique proven to yield more accurate results than using feedback at the process level in traditional additive manufacturing.