1] Single-well push-pull tracer tests have been used to characterize the extent, fate, and transport of subsurface contamination. Analytical solutions provide one alternative for interpreting test results. In this work, an exact analytical solution to two-dimensional equations describing the governing processes acting on a dissolved compound during a modified push-pull test (advection, longitudinal and transverse dispersion, first-order decay, and rate-limited sorption/partitioning in steady, divergent, and convergent flow fields) is developed. The coupling of this solution with inverse modeling to estimate aquifer parameters provides an efficient methodology for subsurface characterization. Synthetic data for single-well push-pull tests are employed to demonstrate the utility of the solution for determining (1) estimates of aquifer longitudinal and transverse dispersivities, (2) sorption distribution coefficients and rate constants, and (3) non-aqueous phase liquid (NAPL) saturations. Employment of the solution to estimate NAPL saturations based on partitioning and non-partitioning tracers is designed to overcome limitations of previous efforts by including rate-limited mass transfer. This solution provides a new tool for use by practitioners when interpreting single-well push-pull test results.

In the automobile industry, recent years have witnessed a growing interest in developing self-parking systems. For such systems, how to accurately and efficiently detect and localize the parking-slots defined by regular line segments near the vehicle is a key and still unresolved issue. In fact, kinds of unfavorable factors, such as the diversity of ground materials, changes in illumination conditions, and unpredictable shadows caused by nearby trees, make the vision-based parking-slot detection much harder than it looks. In this paper, we attempt to solve this issue to some extent and our contributions are twofold. First, we propose a novel DCNN (Deep Convolutional Neural Networks) based parking-slot detection approach, namely DeepPS, which takes the surround-view image as the input. There are two key steps in DeepPS, identifying all the marking-points on the input image and classifying local image patterns formed by pairs of markingpoints. We formulate both of them as learning problems, which can be solved naturally by modern DCNN models. Second, to facilitate the study of vision-based parking-slot detection, a largescale labeled dataset is established. This dataset is the largest in this field, comprising 12,165 surround-view images collected from typical indoor and outdoor parking sites. For each image, the marking-points and parking-slots are carefully labeled. The efficacy and efficiency of DeepPS have been corroborated on our collected dataset. To make our results fully reproducible, all the relevant source codes and the dataset have been made publicly available at https://cslinzhang.github.io/deepps/.

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Multi-layer PDMS/PVDF composite membrane with an alternative PDMS/PVDF/non-woven-fiber/PVDF/PDMS configuration was prepared in this paper. The porous PVDF substrate was obtained by casting PVDF solution on both sides of non-woven fiber with immersion precipitation phase inversion method. Polydimethylsiloxane (PDMS) was then cured by phenyltrimethoxylsilane (PTMOS) and coated onto the surface of porous PVDF substrate one layer by the other to obtain multi-layer PDMS/PVDF composite membrane. The multi-layer composite membrane was used for ethanol recovery from aqueous solution by pervaporation, and exhibited enhanced separation performance compared with one side PDMS/PVDF composite membranes, especially in the low ethanol concentration range. The maximum separation factor of multi-layer PDMS/PVDF composite membrane was obtained at 60 degrees C, and the total flux increased exponentially along with the increase of temperature. The composite membrane gave the best pervaporation performance with a separation factor of 15, permeation rate of 450 g/m(2)h with a 5 wt.% ethanol concentration at 60 degrees C.

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