Numerical modeling of oscillating Taylor bubbles

In this study, computational fluid dynamics (CFD) modeling is used to simulate Taylor bubbles rising in vertical pipes. Experiments indicate that in large diameter (0.29 m) pipes for an air–water system, the bubbles can rise in a oscillatory manner, depending on the method of air injection. The CFD models are able to capture this oscillatory behavior because the air phase is modeled as a compressible ideal gas. Insights into the flow field ahead and behind the bubble during contraction and expansion are shown. For a bubble with an initial pressure equal to the hydrostatic pressure at its nose, no oscillations are seen in the bubble as it rises. If the initial pressure in the bubble is set less than or greater than the hydrostatic pressure then the length of the bubble oscillates with an amplitude that depends on the magnitude of the initial bubble pressure relative to the hydrostatic pressure. The frequency of the oscillations is inversely proportional to the square root of the head of water above the bubble and so the frequency increases as the bubble approaches the water surface. The predicted frequency also depends inversely on the square root of the average bubble length, in agreement with experimental observations and an analytical model that is also presented. In this model, a viscous damping term due to the presence of a Stokes boundary layer for the oscillating cases is introduced for the first time and used to assess the effect on the oscillations of increasing the liquid viscosity by several orders of magnitude

Sparse Exploratory Factor Analysis

Sparse principal component analysis is a very active research area in the last decade. It produces component loadings with many zero entries which facilitates their interpretation and helps avoid redundant variables. The classic factor analysis is another popular dimension reduction technique which shares similar interpretation problems and could greatly benefit from sparse solutions. Unfortunately, there are very few works considering sparse versions of the classic factor analysis. Our goal is to contribute further in this direction. We revisit the most popular procedures for exploratory factor analysis, maximum likelihood and least squares. Sparse factor loadings are obtained for them by, first, adopting a special reparameterization and, second, by introducing additional [Formula: see text]-norm penalties into the standard factor analysis problems. As a result, we propose sparse versions of the major factor analysis procedures. We illustrate the developed algorithms on well-known psychometric problems. Our sparse solutions are critically compared to ones obtained by other existing methods

Incorporating Inductances in Tissue-Scale Models of Cardiac Electrophysiology

In standard models of cardiac electrophysiology, including the bidomain and monodomain models, local perturbations can propagate at infinite speed. We address this unrealistic property by developing a hyperbolic bidomain model that is based on a generalization of Ohm's law with a Cattaneo-type model for the fluxes. Further, we obtain a hyperbolic monodomain model in the case that the intracellular and extracellular conductivity tensors have the same anisotropy ratio. In one spatial dimension, the hyperbolic monodomain model is equivalent to a cable model that includes axial inductances, and the relaxation times of the Cattaneo fluxes are strictly related to these inductances. A purely linear analysis shows that the inductances are negligible, but models of cardiac electrophysiology are highly nonlinear, and linear predictions may not capture the fully nonlinear dynamics. In fact, contrary to the linear analysis, we show that for simple nonlinear ionic models, an increase in conduction velocity is obtained for small and moderate values of the relaxation time. A similar behavior is also demonstrated with biophysically detailed ionic models. Using the Fenton-Karma model along with a low-order finite element spatial discretization, we numerically analyze differences between the standard monodomain model and the hyperbolic monodomain model. In a simple benchmark test, we show that the propagation of the action potential is strongly influenced by the alignment of the fibers with respect to the mesh in both the parabolic and hyperbolic models when using relatively coarse spatial discretizations. Accurate predictions of the conduction velocity require computational mesh spacings on the order of a single cardiac cell. We also compare the two formulations in the case of spiral break up and atrial fibrillation in an anatomically detailed model of the left atrium, and [...].Comment: 20 pages, 12 figure

Towards Compound Identification of Synthetic Opioids in Non-targeted Screening Using Machine Learning Techniques.

The constant evolution of the illicit drug market makes the identification of unknown compounds problematic. Obtaining certified reference materials for a broad array of new analogues can be difficult and cost prohibitive. Machine learning provides a promising avenue to putatively identify a compound before confirmation against a standard. In this study, machine learning approaches were used to develop class prediction and retention time prediction models. The developed class prediction model used a Naïve Bayes architecture to classify opioids as belonging to either the fentanyl analogues, AH series or U series, with an accuracy of 89.5%. The model was most accurate for the fentanyl analogues, most likely due to their greater number in the training data. This classification model can provide guidance to an analyst when determining a suspected structure. A retention time prediction model was also trained for a wide array of synthetic opioids. This model utilised Gaussian Process Regression to predict the retention time of analytes based on multiple generated molecular features with 79.7% of the samples predicted within ± 0.1 min of their experimental retention time. Once the suspected structure of an unknown compound is determined, molecular features can be generated and input for the prediction model to compare with experimental retention time. The incorporation of machine learning prediction models into a compound identification workflow can assist putative identifications with greater confidence and ultimately save time and money in the purchase and/or production of superfluous certified reference materials

Sexual conflict maintains variation at an insecticide resistance locus

Background: The maintenance of genetic variation through sexually antagonistic selection is controversial, partly because specific sexually-antagonistic alleles have not been identified. The Drosophila DDT resistance allele (DDT-R) is an exception. This allele increases female fitness, but simultaneously decreases male fitness, and it has been suggested that this sexual antagonism could explain why polymorphism was maintained at the locus prior to DDT use. We tested this possibility using a genetic model and then used evolving fly populations to test model predictions. Results: Theory predicted that sexual antagonism is able to maintain genetic variation at this locus, hence explaining why DDT-R did not fix prior to DDT use despite increasing female fitness, and experimentally evolving fly populations verified theoretical predictions. Conclusions: This demonstrates that sexually antagonistic selection can maintain genetic variation and explains the DDT-R frequencies observed in nature

Towards Developing an Aerial Mapping System for Stockpile Volume Estimation in Cement Plants

Integrated manufacturing systems such as cement processes are heavily dependent on stockpiles of different materials that serve as inputs to the different stages of production. Accurate estimation of material volume contained in these stockpiles is central to process profitability and waste elimination/minimisation. However, accurate estimation of stock within the cement industry is challenging owing to the unevenness of stock shapes and harsh environmental conditions (e.g. dust, temperature, humidity, etc.). This work provides a set of results obtained from preliminary investigation into the feasibility of deploying a low-cost aerial system to estimate stockpile volumes in open and semi-confined spaces within cement plants. An outdoor stockpile was first mapped using GPS for localisation, while 1D LiDAR and barometer were used for the stockpile height estimation. Visual inspection of the reconstructed stockpile surface showed strong correspondence to the actual stockpile. A second mission was conducted in a semi-confined space. The reconstructed surface appearance was inaccurate due to GPS-related issues; however, the volume was still estimated with reasonable accuracy, within 2.4% error. Future recommendations on upgrading the developed system to work within confined spaces are provided

The evolution of distributed sensing and collective computation in animal populations

Many animal groups exhibit rapid, coordinated collective motion. Yet, the evolutionary forces that cause such collective responses to evolve are poorly understood. Here, we develop analytical methods and evolutionary simulations based on experimental data from schooling fish. We use these methods to investigate how populations evolve within unpredictable, time-varying resource environments. We show that populations evolve toward a distinctive regime in behavioral phenotype space, where small responses of individuals to local environmental cues cause spontaneous changes in the collective state of groups. These changes resemble phase transitions in physical systems. Through these transitions, individuals evolve the emergent capacity to sense and respond to resource gradients (i.e. individuals perceive gradients via social interactions, rather than sensing gradients directly), and to allocate themselves among distinct, distant resource patches. Our results yield new insight into how natural selection, acting on selfish individuals, results in the highly effective collective responses evident in nature.National Science Foundation (NSF)Office of Naval ResearchArmy Research OfficeHuman Frontier Science ProgramNSFJames S McDonnell Foundatio

Multi-modal characterization of rapid anterior hippocampal volume increase associated with aerobic exercise.

The hippocampus has been shown to demonstrate a remarkable degree of plasticity in response to a variety of tasks and experiences. For example, the size of the human hippocampus has been shown to increase in response to aerobic exercise. However, it is currently unknown what underlies these changes. Here we scanned sedentary, young to middle-aged human adults before and after a six-week exercise intervention using nine different neuroimaging measures of brain structure, vasculature, and diffusion. We then tested two different hypotheses regarding the nature of the underlying changes in the tissue. Surprisingly, we found no evidence of a vascular change as has been previously reported. Rather, the pattern of changes is better explained by an increase in myelination. Finally, we show hippocampal volume increase is temporary, returning to baseline after an additional six weeks without aerobic exercise. This is the first demonstration of a change in hippocampal volume in early to middle adulthood suggesting that hippocampal volume is modulated by aerobic exercise throughout the lifespan rather than only in the presence of age related atrophy. It is also the first demonstration of hippocampal volume change over a period of only six weeks, suggesting gross morphometric hippocampal plasticity occurs faster than previously thought

Modeling organic carbon accumulation rates and residence times in coastal vegetated ecosystems

Coastal vegetated “blue carbon” ecosystems can store large quantities of organic carbon (OC) within their soils; however, the importance of these sinks for climate change mitigation depends on the OC accumulation rate (CAR) and residence time. Here we evaluate how two modeling approaches, a Bayesian age-depth model alone or in combination with a two-pool OC model, aid in our understanding of the time lines of OC within seagrass soils. Fitting these models to data from Posidonia oceanica soil cores, we show that age-depth models provided reasonable CAR estimates but resulted in a 22% higher estimation of OC burial rates when ephemeral rhizosphere OC was not subtracted. This illustrates the need to standardize CAR estimation to match the research target and time frames under consideration. Using a two-pool model in tandem with an age-depth model also yielded reasonable, albeit lower, CAR estimates with lower estimate uncertainty, which increased our ability to detect among-site differences and seascape-level trends. Moreover, the two-pool model provided several other useful soil OC diagnostics, including OC inputs, decay rates, and transit times. At our sites, soil OC decayed quite slowly both within fast cycling (0.028 ± 0.014 yr−1) and slow cycling (0.0007 ± 0.0003 yr−1) soil pools, resulting in OC taking between 146 and 825 yr to transit the soil system. Further, an estimated 85% to 93% of OC inputs enter slow-cycling soil pools, with transit times ranging from 891 to 3,115 yr, substantiating the importance of P. oceanica soils as natural, long-term OC sinks

Reactive flow and homogenization in anisotropic media

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