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The stimulation and dominance of potentially harmful phytoplankton taxa at a given locale and time are determined by local environmental conditions as well as by transport to or from neighboring regions. The present study investigated the occurrence of common harmful algal bloom (HAB) taxa within the Southern California Bight, using cross-correlation functions to determine potential dependencies between HAB taxa and environmental factors, and potential links to algal transport via local hydrography and currents. A simulation study, in which Lagrangian particles were released, was used to assess travel times due to advection by prevailing ocean currents in the bight. Our results indicate that transport of some taxa may be an important mechanism for the expansion of their distributions into other regions, which was supported by mean travel times derived from our simulation study and other literature on ocean currents in the Southern California Bight. In other cases, however, phytoplankton dynamics were rather linked to local environmental conditions, including coastal upwelling events. Overall, our study shows that complex current patterns in the Southern California Bight may contribute significantly to the formation and expansion of HABs in addition to local environmental factors determining the spatiotemporal dynamics of phytoplankton blooms.
The enormous diversity of seed traits is an intriguing feature and critical for the overwhelming success of higher plants. In particular, seed mass is generally regarded to be key for seedling development but is mostly approximated by using scanning methods delivering only two-dimensional data, often termed seed size. However, three-dimensional traits, such as the volume or mass of single seeds, are very rarely determined in routine measurements. Here, we introduce a device named phenoSeeder, which enables the handling and phenotyping of individual seeds of very different sizes. The system consists of a pick-and-place robot and a modular setup of sensors that can be versatilely extended. Basic biometric traits detected for individual seeds are two-dimensional data from projections, three-dimensional data from volumetric measures, and mass, from which seed density is also calculated. Each seed is tracked by an identifier and, after phenotyping, can be planted, sorted, or individually stored for further evaluation or processing (e.g. in routine seed-to-plant tracking pipelines). By investigating seeds of Arabidopsis (Arabidopsis thaliana), rapeseed (Brassica napus), and barley (Hordeum vulgare), we observed that, even for apparently round-shaped seeds of rapeseed, correlations between the projected area and the mass of seeds were much weaker than between volume and mass. This indicates that simple projections may not deliver good proxies for seed mass. Although throughput is limited, we expect that automated seed phenotyping on a single-seed basis can contribute valuable information for applications in a wide range of wild or crop species, including seed classification, seed sorting, and assessment of seed quality.
A comparative performance analysis of the CFD platforms OpenFOAM and FLOW-3D is presented, focusing on a 3D swirling turbulent flow: a steady hydraulic jump at low Reynolds number. Turbulence is treated using RANS approach RNG k-ε. A Volume Of Fluid (VOF) method is used to track the air–water interface, consequently aeration is modeled using an Eulerian–Eulerian approach. Structured meshes of cubic elements are used to discretize the channel geometry. The numerical model accuracy is assessed comparing representative hydraulic jump variables (sequent depth ratio, roller length, mean velocity profiles, velocity decay or free surface profile) to experimental data. The model results are also compared to previous studies to broaden the result validation. Both codes reproduced the phenomenon under study concurring with experimental data, although special care must be taken when swirling flows occur. Both models can be used to reproduce the hydraulic performance of energy dissipation structures at low Reynolds numbers.
We prove characterizations of the existence of perfect ƒ-matchings in uniform mengerian and perfect hypergraphs. Moreover, we investigate the ƒ-factor problem in balanced hypergraphs. For uniform balanced hypergraphs we prove two existence theorems with purely combinatorial arguments, whereas for non-uniform balanced hypergraphs we show that the ƒ-factor problem is NP-hard.