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1. Moringa oleifera f -sand Filters for Sustainable Water Purification

   https://doi.org/10.1021/acs.estlett.7b00490  

   Xiong, Boya ; Piechowicz, Bethany ; Wang, Ziyuhan ; Marinaro, Rose ; Clement, Emma ; Carlin, Taylor ; Uliana, Adam ; Kumar, Manish ; Velegol, Stephanie Butler ( January 2018 , Environmental Science & Technology Letters)
2. X -Band Quasi-Elliptic Non-Reciprocal Bandpass Filters (NBPFs)

   https://doi.org/10.1109/TMTT.2021.3081021  

   Ashley, Andrea ; Psychogiou, Dimitra ( July 2021 , IEEE Transactions on Microwave Theory and Techniques)
3. Evaluating the role of total organic carbon in predicting the treatment efficacy of biosand filters for the removal of Vibrio cholerae in drinking water during startup

   https://doi.org/10.1111/jam.13909  

   Danley-Thomson, A.A. ; Huang, E.C. ; Worley-Morse, T. ; Gunsch, C.K. ( September 2018 , Journal of Applied Microbiology)
4. Floral organs act as environmental filters and interact with pollinators to structure the yellow monkeyflower ( Mimulus guttatus ) floral microbiome

   https://doi.org/10.1111/mec.15280  

   Rebolleda Gómez, María ; Ashman, Tia‐Lynn ( November 2019 , Molecular Ecology)

   Assembly of microbial communities is the result of neutral and selective processes. However, the relative importance of these processes is still debated. Microbial communities of flowers, in particular, have gained recent attention because of their potential impact to plant fitness and plant‐pollinator interactions. However, the role of selection and dispersal in the assembly of these communities remains poorly understood. Here, we evaluated the role of pollinator‐mediated dispersal on the contribution of neutral and selective processes in the assembly of floral microbiomes of the yellow monkeyflower (Mimulus guttatus). We sampled floral organs from flowers in the presence and absence of pollinators within five different serpentine seeps in CA and obtained 16S amplicon data on the epiphytic bacterial communities. Consistent with strong microenvironment selection within flowers we observed significant differences in community composition across floral organs and only a small effect of geographic distance. Pollinator exposure affected the contribution of environmental selection and depended on the rate and intimacy of interactions with flower visitors. This study provides evidence of the importance of dispersal and within‐flower heterogeneity in shaping epiphytic bacterial communities of flowers, and highlights the complex interplay between pollinator behaviour, environmental selection and additional abiotic factors in shaping the epiphytic bacterial communities of flowers.

    

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5. A Statistical Hypothesis Testing Strategy for Adaptively Blending Particle Filters and Ensemble Kalman Filters for Data Assimilation

   https://doi.org/10.1175/MWR-D-22-0108.1  

   Kurosawa, Kenta ; Poterjoy, Jonathan ( January 2023 , Monthly Weather Review)

   Abstract Particle filters avoid parametric estimates for Bayesian posterior densities, which alleviates Gaussian assumptions in nonlinear regimes. These methods, however, are more sensitive to sampling errors than Gaussian-based techniques such as ensemble Kalman filters. A recent study by the authors introduced an iterative strategy for particle filters that match posterior moments—where iterations improve the filter’s ability to draw samples from non-Gaussian posterior densities. The iterations follow from a factorization of particle weights, providing a natural framework for combining particle filters with alternative filters to mitigate the impact of sampling errors. The current study introduces a novel approach to forming an adaptive hybrid data assimilation methodology, exploiting the theoretical strengths of nonparametric and parametric filters. At each data assimilation cycle, the iterative particle filter performs a sequence of updates while the prior sample distribution is non-Gaussian, then an ensemble Kalman filter provides the final adjustment when Gaussian distributions for marginal quantities are detected. The method employs the Shapiro–Wilk test to determine when to make the transition between filter algorithms, which has outstanding power for detecting departures from normality. Experiments using low-dimensional models demonstrate that the approach has a significant value, especially for nonhomogeneous observation networks and unknown model process errors. Moreover, hybrid factors are extended to consider marginals of more than one collocated variables using a test for multivariate normality. Findings from this study motivate the use of the proposed method for geophysical problems characterized by diverse observation networks and various dynamic instabilities, such as numerical weather prediction models. Significance Statement Data assimilation statistically processes observation errors and model forecast errors to provide optimal initial conditions for the forecast, playing a critical role in numerical weather forecasting. The ensemble Kalman filter, which has been widely adopted and developed in many operational centers, assumes Gaussianity of the prior distribution and solves a linear system of equations, leading to bias in strong nonlinear regimes. On the other hand, particle filters avoid many of those assumptions but are sensitive to sampling errors and are computationally expensive. We propose an adaptive hybrid strategy that combines their advantages and minimizes the disadvantages of the two methods. The hybrid particle filter–ensemble Kalman filter is achieved with the Shapiro–Wilk test to detect the Gaussianity of the ensemble members and determine the timing of the transition between these filter updates. Demonstrations in this study show that the proposed method is advantageous when observations are heterogeneous and when the model has an unknown bias. Furthermore, by extending the statistical hypothesis test to the test for multivariate normality, we consider marginals of more than one collocated variable. These results encourage further testing for real geophysical problems characterized by various dynamic instabilities, such as real numerical weather prediction models. 

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