A study of the mechanism of filtration

No abstract available

Fragment reattachment, reproductive status, and health indicators of the invasive colonial tunicate Didemnum vexillum with implications for dispersal

This manuscript is not subject to U.S. copyright. The definitive version was published in Biological Invasions 14 (2012): 2133-2140, doi:10.1007/s10530-012-0219-8.The invasive colonial tunicate Didemnum vexillum is now widespread in coastal and offshore waters of New England, USA. D. vexillum can inflict ecological and economic damage through biofouling and habitat modification. Natural and anthropogenic processes that fragment colonies of D. vexillum may be accelerating the spread of this invader. Reattachment success and fragment viability were confirmed in the laboratory after four weeks of suspension in experimental aquaria. The shape of suspended D. vexillum fragments progressed from flattened to globular spheres and then flattened again after reattachment to the substrate. Reproductive activity, confirmed by the presence of eggs and larvae, was observed for fragments suspended up to three weeks suggesting that D. vexillum is capable of reproducing while in a fragmented, suspended state. An index of colony health was used to monitor change in D. vexillum health while in suspension. Overall, colony health declined with time in suspension although colonies that appeared dead (black and gray in overall color) still contained a substantial number of healthy live zooids. These results suggest that activities that cause fragmentation can significantly facilitate the spread of D. vexillum. Coastal managers should consider reducing or eliminating, when practical, activities that return fragmented colonies of D. vexillum to the water. In-water cleaning of biofouling and dredging are likely expediting the spread of this invasive species unless biofouling can be contained and removed from the water.This research was funded by the NOAA Aquatic Invasive Species Program

Uncertainty in geometry of fibre preforms manufactured with Automated Dry Fibre Placement (ADFP) and its effects on permeability

Resin transfer moulding is one of several processes available for manufacturing fibre-reinforced composites from dry fibre reinforcement. Recently, dry reinforcements made with Automated Dry Fibre Placement have been introduced into the aerospace industry. Typically, the permeability of the reinforcement is assumed to be constant throughout the dry preform geometry whereas in reality it possesses inevitable uncertainty due to variability in geometry. This uncertainty propagates to the uncertainty of the mould filling and the fill time, one of the important variables in resin injection. It makes characterisation of the permeability and its variability an important task for design of the resin transfer moulding process. In this study, variability of the geometry of a reinforcement manufactured using Automated Dry Fibre Placement is studied. Permeability of the manufactured preforms is measured experimentally and compared to stochastic simulations based on an analytical model and a stochastic geometry model. The simulations showed that difference between the actual geometry and the designed geometry can result in 50% reduction of the permeability. The stochastic geometry model predicts results within 20% of the experimental values

Estimation of hydraulic conductivity and its uncertainty from grain-size data using GLUE and artificial neural networks

peer reviewedaudience: researcher, professionalVarious approaches exist to relate saturated hydraulic conductivity (Ks) to grain-size data. Most methods use a single grain-size parameter and hence omit the information encompassed by the entire grain-size distribution. This study compares two data-driven modelling methods, i.e.multiple linear regression and artificial neural networks, that use the entire grain-size distribution data as input for Ks prediction. Besides the predictive capacity of the methods, the uncertainty associated with the model predictions is also evaluated, since such information is important for stochastic groundwater flow and contaminant transport modelling. Artificial neural networks (ANNs) are combined with a generalized likelihood uncertainty estimation (GLUE) approach to predict Ks from grain-size data. The resulting GLUE-ANN hydraulic conductivity predictions and associated uncertainty estimates are compared with those obtained from the multiple linear regression models by a leave-one-out cross-validation. The GLUE-ANN ensemble prediction proved to be slightly better than multiple linear regression. The prediction uncertainty, however, was reduced by half an order of magnitude on average, and decreased at most by an order of magnitude. This demonstrates that the proposed method outperforms classical data-driven modelling techniques. Moreover, a comparison with methods from literature demonstrates the importance of site specific calibration. The dataset used for this purpose originates mainly from unconsolidated sandy sediments of the Neogene aquifer, northern Belgium. The proposed predictive models are developed for 173 grain-size -Ks pairs. Finally, an application with the optimized models is presented for a borehole lacking Ks data

Finite difference calculations of permeability in large domains in a wide porosity range.

Determining effective hydraulic, thermal, mechanical and electrical properties of porous materials by means of classical physical experiments is often time-consuming and expensive. Thus, accurate numerical calculations of material properties are of increasing interest in geophysical, manufacturing, bio-mechanical and environmental applications, among other fields. Characteristic material properties (e.g. intrinsic permeability, thermal conductivity and elastic moduli) depend on morphological details on the porescale such as shape and size of pores and pore throats or cracks. To obtain reliable predictions of these properties it is necessary to perform numerical analyses of sufficiently large unit cells. Such representative volume elements require optimized numerical simulation techniques. Current state-of-the-art simulation tools to calculate effective permeabilities of porous materials are based on various methods, e.g. lattice Boltzmann, finite volumes or explicit jump Stokes methods. All approaches still have limitations in the maximum size of the simulation domain. In response to these deficits of the well-established methods we propose an efficient and reliable numerical method which allows to calculate intrinsic permeabilities directly from voxel-based data obtained from 3D imaging techniques like X-ray microtomography. We present a modelling framework based on a parallel finite differences solver, allowing the calculation of large domains with relative low computing requirements (i.e. desktop computers). The presented method is validated in a diverse selection of materials, obtaining accurate results for a large range of porosities, wider than the ranges previously reported. Ongoing work includes the estimation of other effective properties of porous media

Infection with hepatitis B virus carrying novel pre-S/S gene mutations in female siblings vaccinated at birth: two case reports

<p>Abstract</p> <p>Introduction</p> <p>After the initiation of a mass hepatitis B vaccination program in Taiwan, the prevalence of hepatitis B virus infection has declined progressively. However, about 1 percent of the young generation, who received hepatitis B vaccination at birth, remain carriers. Infection with vaccine-escape hepatitis B virus mutants always occurs shortly after birth. Here, we report two female siblings in whom the infection occurred in their adolescence. This report raises the question of whether a booster for hepatitis B vaccination is needed.</p> <p>Case presentation</p> <p>Two 19 and 14-year-old Taiwanese female siblings were born to a mother infected with hepatitis B virus and received a complete course of hepatitis B vaccination at birth. They remained negative for serum hepatitis B surface antigen and positive for serum anti-hepatitis B surface antibody throughout their childhood. However, both were infected with the hepatitis B virus in their adolescence. Hepatitis B virus DNA was extracted from serum samples from the mother and two siblings. Hepatitis B virus pre-S/S sequence was amplified by polymerase chain reaction followed by nucleotide sequencing. When compared with the sequence obtained from the mother, multiple amino acid substitutions located near or in the major hydrophilic region of the surface antigen were identified in the elder sister. Four of these mutations (sL97S, sL98S, sG102R, and sA159P) were novel. A novel in-frame deletion (14 amino acids deleted, pre-S 127-140) was found in the hepatitis B virus pre-S2 region in the younger sister.</p> <p>Conclusions</p> <p>Despite having received hepatitis B vaccination at birth, hepatitis B virus infection can still occur in adolescence with the emergence of novel mutations in the pre-S/S gene. This is a rare event and, to the best of our knowledge, has not been previously reported.</p

Study on the sound absorption behavior of multi-component polyester nonwovens: experimental and numerical methods

This study presents an investigation of the acoustical properties of multi-component polyester nonwovens with experimental and numerical methods. Fifteen types of nonwoven samples made with staple, hollow and bi-component polyester fibers were chosen to carry out this study. The AFD300 AcoustiFlow device was employed to measure airflow resistivity. Several models were grouped in theoretical and empirical model categories and used to predict the airflow resistivity. A simple empirical model based on fiber diameter and fabric bulk density was obtained through the power-fitting method. The difference between measured and predicted airflow resistivity was analyzed. The surface impedance and sound absorption coefficient were determined by using a 45 mm Materiacustica impedance tube. Some widely used impedance models were used to predict the acoustical properties. A comparison between measured and predicted values was carried out to determine the most accurate model for multi-component polyester nonwovens. The results show that one of the Tarnow model provides the closest prediction to the measured value, with an error of 12%. The proposed power-fitted empirical model exhibits a very small error of 6.8%. It is shown that the Delany–Bazley and Miki models can accurately predict surface impedance of multi-component polyester nonwovens, but the Komatsu model is less accurate, especially at the low-frequency range. The results indicate that the Miki model is the most accurate method to predict the sound absorption coefficient, with a mean error of 8.39%