esys-Escript User’s Guide: Solving Partial Differential Equations with Escript and Finley Release - 3.4 (r4488)

esys.escript is a python-based environment for implementing mathematical models, in particular those based on coupled, non-linear, time-dependent partial differential equations. It consists of five major components • esys.escript core library • finite element solver esys.finley (which uses fast vendor-supplied solvers or our paso linear solver library) • the meshing interface esys.pycad • a model library. • an inversion library. The current version supports parallelization through both MPI for distributed memory and OpenMP for distributed shared memory. In this release there are a number of small changes which are not backwards compatible. Please see Appendix B to see if your scripts will be affected. If you use this software in your research, then we would appreciate (but do not require) a citation. Some relevant references can be found in Appendix D. For Python3 support, see Appendix E

esys-Escript User’s Guide: Solving Partial Differential Equations with Escript and Finley Release - 3.4.2 (r4925)

esys.escript is a python-based environment for implementing mathematical models, in particular those based on coupled, non-linear, time-dependent partial differential equations. It consists of five major components • esys.escript core library • finite element solver esys.finley (which uses fast vendor-supplied solvers or our paso linear solver library) • the meshing interface esys.pycad • a model library. • an inversion library. The current version supports parallelization through both MPI for distributed memory and OpenMP for shared memory. In this release there are a number of small changes which are not backwards compatible. Please see Appendix B to see if your scripts will be affected. If you use this software in your research, then we would appreciate (but do not require) a citation. Some relevant references can be found in Appendix D. For Python 3 support, see Appendix E

esys-Escript User’s Guide: Solving Partial Differential Equations with Escript and Finley Release - 3.3.1 (r4302)

esys.escript is a python-based environment for implementing mathematical models, in particular those based on coupled, non-linear, time-dependent partial differential equations. It consists of five major components • esys.escript core library • finite element solver esys.finley (which uses fast vendor-supplied solvers or our paso linear solver library) • the meshing interface esys.pycad • a model library. • an inversion library. The current version supports parallelization through both MPI for distributed memory and OpenMP for distributed shared memory. In this release there are a number of small changes which are not backwards compatible. Please see Appendix B to see if your scripts will be affected. If you use this software in your research, then we would appreciate (but do not require) a citation. Some relevant references can be found in Appendix D. For Python3 support, see Appendix E

esys-Escript User’s Guide: Solving Partial Differential Equations with Escript and Finley Release - 4.0 (r5402)

esys.escript is a python-based environment for implementing mathematical models, in particular those based on coupled, non-linear, time-dependent partial differential equations. It consists of five major components: • esys.escript core library • finite element solvers esys.finley, esys.dudley, esys.ripley, and esys.speckley (which use fast vendor-supplied solvers or the included PASO linear solver library) • the meshing interface esys.pycad • a model library • an inversion module. All esys.escript modules should work under both python 2 and python 3, see Appendix E. The current version supports parallelization through MPI for distributed memory, OpenMP for shared memory on CPUs, as well as CUDA for some GPU-based solvers. This release comes with some significant changes and new features. Please see Appendix B for a detailed list. If you use this software in your research, then we would appreciate (but do not require) a citation. Some relevant references can be found in Appendix D

Tracking tracer motion in a 4-D electrical resistivity tomography experiment

A new framework for automatically tracking subsurface tracers in electrical resistivity tomography (ERT) monitoring images is presented. Using computer vision and Bayesian inference techniques, in the form of a Kalman filter, the trajectory of a subsurface tracer is monitored by predicting and updating a state model representing its movements. Observations for the Kalman filter are gathered using the maximally stable volumes algorithm, which is used to dynamically threshold local regions of an ERT image sequence to detect the tracer at each time step. The application of the framework to the results of 2-D and 3-D tracer monitoring experiments show that the proposed method is effective for detecting and tracking tracer plumes in ERT images in the presence of noise, without intermediate manual intervention

Isolation and identification of gut symbiotic bacteria of the termite Anacanthotermes vagans (Isoptera: Hodotermitidae)

Termites play a major role in reducing and decomposing woody materials within terrestrial ecosystems by degrading lingo-cellulosic materials with the help of the microbial community of their guts. We isolated the lignin-degrading bacteria from Anacanthotermes vagans (Hagen) using liquid and solid media containing wheat straw and lignin hydrochloric acid. Cellulose-degrading bacteria were also isolated using liquid medium containing filter paper, agar-cellulose and Congo red agar-cellulose. By conducting various experiments, 16 bacterial species were isolated and subjected to different biochemical tests for comparing their growth rates. The genera Enterobacter and Klebsiella showed the highest growth rate among the rest species of isolated lignin-degrading bacteria. The species Staphylococcus lentus and Bacillus subtilis were isolated from the media containing cellulose

Derivation of lowland riparian wetland deposit architecture using geophysical image analysis and interface detection

For groundwater-surface water interactions to be understood in complex wetland settings, the architecture of the underlying deposits requires investigation at a spatial resolution sufficient to characterize significant hydraulic pathways. Discrete intrusive sampling using conventional approaches provides insufficient sample density and can be difficult to deploy on soft ground. Here a noninvasive geophysical imaging approach combining three-dimensional electrical resistivity tomography (ERT) and the novel application of gradient and isosurface-based edge detectors is considered as a means of illuminating wetland deposit architecture. The performance of three edge detectors were compared and evaluated against ground truth data, using a lowland riparian wetland demonstration site. Isosurface-based methods correlated well with intrusive data and were useful for defining the geometries of key geological interfaces (i.e., peat/gravels and gravels/Chalk). The use of gradient detectors approach was unsuccessful, indicating that the assumption that the steepest resistivity gradient coincides with the associated geological interface can be incorrect. These findings are relevant to the application of this approach in settings with a broadly layered geology with strata of contrasting resistivities. In addition, ERT revealed substantial structures in the gravels related to the depositional environment (i.e., braided fluvial system) and a complex distribution of low-permeability putty Chalk at the bedrock surface—with implications for preferential flow and variable exchange between river and groundwater systems. These results demonstrate that a combined approach using ERT and edge detectors can provide valuable information to support targeted monitoring and inform hydrological modeling of wetlands

Study on breeding of imported brood stocks Litopenaeus vannamei in Iran environment

Regarding to breeding the Litopenaeus vannamei, a total of 126 pairs of broodstocks were imported from Hawaii to Iran in 2004 and 2005, and then transferred to the Bandargah Research Station in Bushehr. The female broodstocks were ablated, and were feeded 3 times per day with cuttlefish, small size shrimp and Nereis worm with a ratio of 30% body weight. The water exchange were done 3 times per day. During the years 2004 and 2005 a total 1700000 naupli were produced of which 772000 specimens of pl13 and pl7 were harvested and then transferred to Helleh Site for carrying out the next culture project. The average naupli and postlarvae were 170000 and 92000 in proportion to each broodstock. Also the mean survival rate was estimated 54%

Lattice Boltzmann Simulation of Solid Particles Motion in a Three Dimensional Flow using Smoothed Profile Method

Three-dimensional particulate flow has been simulated using Lattice Boltzmann Method (LBM). Solid-fluid interaction was modeled based on Smoothed Profile Method (SPM) (Jafari et. al, Lattice-Boltzmann method combined with smoothed-profile method for particulate suspensions, Phys. Rev. E, 2011). In this paper a GPU code based on three-dimensional lattice Boltzmann method and smoothed profile method has been prepared due to the ability of SPM-LBM to perform locally and in parallel mode. Results obtained for sedimentation of one and two spherical particles as well as their behavior in shear flow showed excellent correspondence with previous published works. Computations for a large number of particles sedimentation showed that combination of LBM and SPM on a GPU platform can be considered as an efficient and promising computational frame work in particulate flow simulations