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Abstract The success and cost‐effectiveness of kelp forest restoration hinges on understanding the colonization ecology of kelps, particularly with respect to dispersal potential, recruitment success, and subsequent establishment. To gain needed insight into these processes we examined spatial patterns and temporal trajectories of the colonization of a large artificial reef by the giant kelpMacrocystis pyrifera. The 151 ha artificial reef complex was constructed in three phases over 21 years, enabling dispersal, recruitment, and subsequent establishment to be examined for a wide range of environmental conditions, dispersal distances, and source population sizes. Natural colonization of all phases of the artificial reef by giant kelp was rapid (within 1 year) and extended across the entire 7‐km‐long reef complex. Colonization density declined with distance from the nearest source population, but only during the first phase when the distance from the nearest source population was ≤3.5 km. Despite this decline, recruitment on artificial reef modules farthest from the source population was sufficient to produce dense stands of kelp within a couple of years. Experimental outplanting of the artificial reef with laboratory‐reared kelp embryos was largely successful but proved unnecessary, as the standing biomass of kelp resulting from natural recruitment exceeded that observed on nearby natural reefs within 2–3 years of artificial reef construction for all three phases. Such high potential for natural colonization following disturbance has important implications for kelp forest restoration efforts that employ costly and logistically difficult methods to mimic this process by active seeding and transplanting. 

The advancement of wireless networking has significantly enhanced beamforming capabilities in Autonomous Unmanned Aerial Systems (AUAS). This paper presents a simple and efficient classical algorithm to route a collection of AUAS or drone swarms extending our previous work on AUAS. The algorithm is based on the sparse factorization of frequency Vandermonde matrices that correspond to each drone, and its entries are determined through spatiotemporal data of drones in the AUAS. The algorithm relies on multibeam beamforming, making it suitable for large-scale AUAS networking in wireless communications. We show a reduction in the arithmetic and time complexities of the algorithm through theoretical and numerical results. Finally, we also present an ML-based AUAS routing algorithm using the classical AUAS algorithm and feed-forward neural networks. We compare the beamformed signals of the ML-based AUAS routing algorithm with the ground truth signals to minimize the error between them. The numerical error results show that the ML-based AUAS routing algorithm enhances the accuracy of the routing. This error, along with the numerical and theoretical results for over 100 drones, provides the basis for the scalability of the proposed ML-based AUAS algorithms for large-scale deployments. 

A quantitative model can play an essential role in controlling critical quality attributes of products and in designing the associated processes. One of the challenges in designing a dry granulation process is to find the optimal balance between improving powder flowability and sacrificing powder tabletability, both of which are highly affected by ribbon solid fraction and granule size distribution (GSD). This study is focused on developing a hybrid machine learning (ML)-assisted mechanistic model to predict ribbon solid fraction, GSD, and throughput for the purpose of implementing model predictive control of an integrated continuous dry granulation tableting process. It is found that the predictability of ribbon solid fraction and throughput are improved when modification is made to Johanson’s model by incorporating relationships between roll compaction parameters and ribbon elastic recovery. Such relationships typically are either not considered or assumed to be a constant in the models reported in the literature. To describe the nature of the bimodal size distribution of roller compactor granules instead of only using traditional 𝐷𝐷10, 𝐷𝐷50 and 𝐷𝐷90 values, the GSD is represented by a bimodal Weibull distribution with five fitting parameters. Furthermore, these five GSD parameters are predicted by ML models. The results indicate the ribbon solid fraction and screen size are the two most significant factors affecting GSD.