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Microbially Induced Desaturation and Precipitation (MIDP) through denitrification is an emerging ground improvement method in which indigenous nitrate reducing bacteria are stimulated to introduce biogas, biominerals and biomass in the soil matrix. In this study, a numerical model is developed to evaluate the effect of biogas, biominerals and biomass on the hydraulic properties of soils treated with MIDP. The proposed model couples the biochemical conversions to changes of porosity and water saturation and predicts changes in permeability through two separate power law equations. Experimental studies from the literature are used to calibrate the model. Comparing the results with other studies on bioclogging or biomineralization in porous media reveals that the combined production of biogas, biomass, and biominerals results in efficient clogging, in the sense that only a small amount of products leads to a substantial permeability reduction. Based on this comparison, the authors postulate that biogenic gas bubbles preferably form within the larger pore bodies. The presence of biogenic gas in the larger pore bodies forces calcium carbonate minerals and biomass to be formed mainly at the pore throats. The interaction between the different phases results in more efficient clogging than observed in other studies which focus on a single product only.

 

Abstract The highly diverse Solanaceae family contains several widely studied models and crop species. Fully exploring, appreciating, and exploiting this diversity requires additional model systems. Particularly promising are orphan fruit crops in the genus Physalis, which occupy a key evolutionary position in the Solanaceae and capture understudied variation in traits such as inflorescence complexity, fruit ripening and metabolites, disease and insect resistance, self-compatibility, and most notable, the striking inflated calyx syndrome (ICS), an evolutionary novelty found across angiosperms where sepals grow exceptionally large to encapsulate fruits in a protective husk. We recently developed transformation and genome editing in Physalis grisea (groundcherry). However, to systematically explore and unlock the potential of this and related Physalis as genetic systems, high-quality genome assemblies are needed. Here, we present chromosome-scale references for P. grisea and its close relative Physalis pruinosa and use these resources to study natural and engineered variations in floral traits. We first rapidly identified a natural structural variant in a bHLH gene that causes petal color variation. Further, and against expectations, we found that CRISPR–Cas9-targeted mutagenesis of 11 MADS-box genes, including purported essential regulators of ICS, had no effect on inflation. In a forward genetics screen, we identified huskless, which lacks ICS due to mutation of an AP2-like gene that causes sepals and petals to merge into a single whorl of mixed identity. These resources and findings elevate Physalis to a new Solanaceae model system and establish a paradigm in the search for factors driving ICS. 

In machine learning, we often face the situation where the event we are interested in has very few data points buried in a massive amount of data. This is typical in network monitoring, where data are streamed from sensing or measuring units continuously but most data are not for events. With imbalanced datasets, the classifiers tend to be biased in favor of the main class. Rare event detection has received much attention in machine learning, and yet it is still a challenging problem. In this paper, we propose a remedy for the standing problem. Weighting and sampling are two fundamental approaches to address the problem. We focus on the weighting method in this paper. We first propose a boosting-style algorithm to compute class weights, which is proved to have excellent theoretical property. Then we propose an adaptive algorithm, which is suitable for real-time applications. The adaptive nature of the two algorithms allows a controlled tradeoff between true positive rate and false positive rate and avoids excessive weight on the rare class, which leads to poor performance on the main class. Experiments on power grid data and some public datasets show that the proposed algorithms outperform the existing weighting and boosting methods, and that their superiority is more noticeable with noisy data. 

Engaging with mathematical modeling can support learners to collaboratively explore mathematics in integrated ways as well as generate mathematical ideas and representations that may be useful in everyday life. Although several studies provide diverse insights into teaching and learning mathematical modeling, research has yet to be conducted on the mathematical modeling learning opportunities available to secondary mathematics preservice teachers (PTs) in mathematics and education courses in teacher education programs. This study investigates the mathematical modeling learning opportunities reported by 48 instructors and ten focus groups of 37 PTs. Multiple data sources (e.g., interview transcripts, syllabi, tasks, and exams) collected from universities were used to achieve triangulation in this case study of secondary preparation programs. When asked about mathematical modeling, both PTs and instructors reported rich examples of mathematical modeling from the opportunities afforded by their respective programs. Both also reported modeling experiences that were not mathematical modeling, such as word problems, representations, or demonstrations. Along with the study's particular themes and examples, common mathematical modeling opportunities recalled by PTs and instructors are elaborated in our findings. This study intends to begin a discussion of possible pathways for providing rich opportunities for PTs to engage in mathematical modeling.

 

Because of their limited spatial resolution, numerical weather prediction and climate models have to rely on parameterizations to represent atmospheric turbulence and convection. Historically, largely independent approaches have been used to represent boundary layer turbulence and convection, neglecting important interactions at the subgrid scale. Here we build on an eddy‐diffusivity mass‐flux (EDMF) scheme that represents all subgrid‐scale mixing in a unified manner, partitioning subgrid‐scale fluctuations into contributions from local diffusive mixing and coherent advective structures and allowing them to interact within a single framework. The EDMF scheme requires closures for the interaction between the turbulent environment and the plumes and for local mixing. A second‐order equation for turbulence kinetic energy (TKE) provides one ingredient for the diffusive local mixing closure, leaving a mixing length to be parameterized. Here, we propose a new mixing length formulation, based on constraints derived from the TKE balance. It expresses local mixing in terms of the same physical processes in all regimes of boundary layer flow. The formulation is tested at a range of resolutions and across a wide range of boundary layer regimes, including a stably stratified boundary layer, a stratocumulus‐topped marine boundary layer, and dry convection. Comparison with large eddy simulations (LES) shows that the EDMF scheme with this diffusive mixing parameterization accurately captures the structure of the boundary layer and clouds in all cases considered.