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Crystal PY-26 - History

Crystal PY-26 - History



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Crystal

Quartz, transparent and colorless or with a slight tinge of color, used as a gem.

(PY-26: 1. 226'; b. 34'; dr. 13'; s. 18 k.; a. 2 3")

Crystal (PY-25) was built as Vida in 1929 by Pusey and Jones Co., Wilmington, Del.; acquired by the Navy 15 January 1942; and commissioned 21 February 1942 Lieutenant Commander O. B. Drotning, USNR (Ret.), in command.

Crystal arrived at Pearl Harbor 1 May for duty with the Hawaiian Sea Frontier. She served on patrol and escort duty in the island area, escorting Army transports and merchant vessels to outlying islands; transported civilian workers and servicemen; and joined in exercises and drills with destroyers. From 1 December 1943 to 14 April 1944 she based at Midway for patrol duty and exercises and training with submarines. After overhaul at Pearl Harbor she returned to operations under the Hawaiian Sea Frontier, adding weather station patrols to her duties. On 8 November 1945 she got underway for the west coast, arriving at San Francisco 17 November. Crystal was decommissioned there 6 March 1946 and transferred to the Maritime Commission 2 April 1947.

Taber; commissioned 19 December 1946, Lieutenant Commander R. W. Paine, Jr., in command; and reported to the Atlantic Fleet.

After shakedown training off New London, Cubera arrived at Key West, Fla., 19 March 1946. She tested sonar equipment, provided services to experimental antisubmarine warfare development projects in the Florida Straits, and joined in fleet exercises until 4 July 1947 when she sailed to Philadelphia Naval Shipyard for extensive modernization.

Returning to Key West 9 March 1948 Cubera continued to operate locally out of this port, as well as taking part in fleet exercises in the Caribbean and Atlantic until 3 July 1952 when she arrived at Norfolk, her new home port. Through 1957 Cubera conducted local operations, and participated in fleet exercises in the Caribbean, as well as cruising to Sydney, Nova Scotia, in June 1955. During 1959 and 1960, she was assigned to TF "Alfa," a force conducting constant experiments to improve antisubmarine warfare techniques. With this group she cruised the western Atlantic from Nova Scotia to Bermuda.


The Menpo Project provides a wrapper around VLFeat: it's called cyvlfeat.

To install cyvlfeat, we strongly suggest you use conda:

conda install -c menlo cyvlfeat

If you don't want to use conda, your mileage will vary. In particular, you must satisfy the linking/compilation requirements for the package, which include the vlfeat dynamic library.

In other words, the nice thing about installing with conda is that it will install (and link) VLFeat dependencies as well.

It may not include all functionalities of VLFeat. Current State as of March 2017:

  • fisher
    • fisher
    • set_simd_enabled, get_simd_enabled, cpu_has_avx, cpu_has_sse3, cpu_has_sse2
    • get_num_cpus,
    • get_max_threads, set_num_threads, get_thread_limit
    • hog
    • kmeans
    • kmeans_quantize
    • ikmeans, ikmeans_push
    • hikmeans, hikmeans_push
    • dsift
    • sift

    Crystal PY-26 - History

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    Scheme 1

    a Reaction conditions: 1 (0.5 mmol), S8 (154 mg, 0.6 mmol), CuI (10 mg, 0.05 mmol), o-phen (18 mg, 0.1 mmol), Cs2CO3 (163 mg, 0.5 mmol), DMF (5.0 mL), 100 °C. Yields are of isolated products after purification by column chromatography.

    b Reaction run on a 2.0 mmol scale.

    In addition to aliphatic groups, the reaction tolerated various aromatic substituents bearing either electron-donating (methyl, methoxy, iso-propyl) or electron-withdrawing substituents, such as nitro and chloro, as well as heteroaryl motifs. All of these substrates underwent the cascade coupling/cyclization smoothly to afford products 2aq in good to excellent yields (62–90%, Scheme 1). The structure of product 2 was supported through single-crystal X-ray diffraction analysis of 2l, as shown in Scheme 1.

    With respect to the substituents on the benzene ring, we were delighted to find that various groups, such as methyl, methoxy, chloro, and fluoro, at either the five- or seven-position could be employed, furnishing the corresponding benzodithiole products 2rad in 75–91% yields (Scheme 1). Additionally, a pyridine derivative was also an effective coupling/cyclization partner, affording product 2ae in 91% yield.

    To evaluate possible further applications of the developed protocol, several benzodithioles were transformed into their corresponding BDT derivatives (3ae) in high yields via acidic hydrolysis (Scheme 1). The developed protocol undoubtedly provides an efficient and practical method for the preparation of these valuable and medicinally relevant compounds. Furthermore, the synthetic conversion of 3a into the important compounds 4(22) (Beaucage’s reagent) and 5(23) was attained in good yield by reacting with m-CPBA and hydrogen peroxide, respectively.

    Next, we reasoned that the corresponding selenium analogue of 2 would be accessible by replacing the sulfur source with an appropriate selenium source. Intriguingly, conducting the reaction under the optimized reaction conditions using Se powder instead of S8 provided (Z)-N-aryl-3H-benzo[d][1,2]thiaselenol-3-imines 6 rather than (Z)-N-aryl-3H-benzo[c][1,2]thiaselenol-3-imines. The structure of product 6u was supported by X-ray diffraction analysis. (See Scheme 2.) Gratifyingly, a variety of substituted aromatic motifs, such as alkylphenyl (e.g., methyl, isopropyl, and tert-butyl), alkoxyphenyl (e.g., methoxy and ethoxy), and mono- and dihalogenated phenyl (e.g., F and Cl) reacted smoothly to give the desired products under the optimized reaction conditions. A total of 30 benzothiaselenoles were obtained in moderate to high yields (56–78%, Scheme 2).


    Direct assessment of mantle boron and lithium contents and distribution by SIMS analyses of peridotite minerals

    The importance of Li and B geochemistry has long been recognised, owing specifically to their characteristic behaviours during processes involving fluid phases. However, the lack of a set of validated reference data for the “normal” Earth mantle has hampered the development of models for Li and B metasomatic effects on mantle rocks. In particular, the concentration of B in the mantle is still a matter of debate. An estimate of 0.1 ppm B seems to be consistent as a source for non-arc basalts, but to date such data have not been directly confirmed. Li and B literature contents for peridotites are derived from samples whose non-metasomatized character has not been established for both elements, due to the aforementioned lack of a comprehensive metasomatism model for Li and B.

    We have looked at two groups of mantle rocks, with and without clear compositional evidence that they are metasomatized. The latter rocks provide the best constraints on “normal” mantle B and Li contents. We propose a diagnostic diagram based on (Ce/B) vs. (Li/Yb) as measured by SIMS in peridotite clinopyroxenes, which is useful in identifying metasomatized samples. After discovering samples with no metasomatic alteration, which are considered representative of the “normal” mantle, we derived for each mantle mineral phase (ol, opx, cpx and sp) the Mg#, Li and B partial-melting evolution trends. Additionally, considering that the “normal” mantle rocks have evolved through partial melting only, we assessed Li and B contents in the parental mantle of our samples, and assumed that the calculated values (1.6–1.8 ppm Li and 0.07–0.10 ppm B) are representative of the contents in MORB mantle sources. These new data are consistent with current melting models of fertile peridotites.


    1. On your mobile device, open the Google Play Store app .
    2. Tap Menu Account.
    3. Tap Purchase history.

    Note: Order numbers aren't available on Google Play at this time. If you need your order number to request a refund, follow the instructions under "Using pay.google.com" below.

    Note: Order numbers aren't available on Google Play at this time. If you need your order number to request a refund, follow the instructions under "Using pay.google.com" below.

    1. Go to pay.google.com.
    2. Find Other purchase activity.
    3. Select View purchases.
    4. Select an order to see your receipt.

    Abstract

    Quantitative structure–property relationships (QSPR) for calculating temperature dependence of surface tension (σ) of ionic liquids (ILs) in terms of group contributions (GCs) is proposed and broadly presented. A statistical learning method including stepwise multiple linear regression and two machine learning methods including feed-forward artificial neural network and least-squares support vector machine was applied to express σ as a function of GCs. The models were developed using the largest experimental data compilation reported thus far (570 ILs, 1008 datasets, 6114 data points). The GC assignments, the “reference + correction” modeling scheme, as well as the model validation protocol were adopted from the previous contributions of the series [ Paduszyński, K. Ind. Eng. Chem. Res. 2019 , 58, 5322−5338 Paduszyński, K. Ind. Eng. Chem. Res. 2019 , 58, 17049–17066]. The influence of the chemical family of both cation and anion on the quality of predictions is discussed. The potential applications of the proposed model in estimating the critical temperature of ILs are discussed. Finally, the obtained model is confronted with other methods reported in the literature. In particular, an extensive comparative analysis is presented in the case of the selected QSPRs accounting for atomic contributions and topological descriptors.


    Volatility 2.6 Commands

    If using Windows, rename the it’ll be volatility.exe.

    List all commands

    Get Profile of Image

    volatility -f image.mem imageinfo

    List Processes in Image

    volatility -f image.mem –profile=x pslist

    List Processes in process tree format

    volatility -f image.mem –profile=x pslist

    List Processes by scanning image for EPROCESS blocks

    volatility -f image.mem –profile=x psscan

    List Processes Command Line Arguments

    volatility -f image.mem –profile=x cmdline

    List Registry files in memory

    volatility -f image.mem –profile=x hivelist

    Dump Registry files in memory

    Get Virtual Address from the hivelist command first

    volatility -f image.mem –profile=x dumpregistry -o <virtual memory offset> –dump-dir=./

    List specific Process DLLs and Command Line Arguments

    volatility -f image.mem –profile=x dlllist -p x

    List SIDs (primary token and user account name) used to start specific process

    volatility -f image.mem –profile=x getsids -p x

    Dump Process

    volatility -f image.mem –profile=x procdump -p xx –dump-dir==.

    Dump Memory Section

    volatility -f image.mem –profile=x memdump-p xx –dump-dir==.

    SIFT specific commands, Windows version of Volatility doesn’t have these

    Identify processes with potentially wrong path, parent, cmdline

    vol.py -f image.mem –profile=x malprocfind

    Look for processes with most amounts of “false”

    Visualize processes

    vol.py -f image.mem –profile=x pstotal –cmd –output=dot –output-file=graph.dot

    Compare baseline memdump to suspect memdump, to identify processes that were present in suspect memdump, but not in baseline memdump

    vol.py -f image.mem –profile=x -B baseline.img processbl -U 2>>error.log

    Compare baseline memdump to suspect memdump to identify processes that were present in both baselin and suspect memdump

    vol.py -f image.mem –profile=x -B baseline.img processbl 2>>error.log

    2>>error.log = output error to error.log

    Look at PFound column. “True” if process can be found in baseline. False if it’s not.


    Seissuite 0.1.29

    SeisSuite
    ========================
    This project is dedicated to provide a Python framework for seismic noise tomography,
    based on [ObsPy](https://github.com/obspy/obspy/wiki) and numerical Python packages
    such as [numpy](http://www.numpy.org/) and [scipy](http://www.scipy.org/).

    Requirements
    ------------
    The code is developped and tested on Ubuntu (but should run on other platforms as well)
    with Python 2.7.

    In addition to [Python 2.7](https://www.python.org/download/releases/2.7/), you need
    to install the following packages:

    - [numpy](http://www.numpy.org/) >= 1.8.2
    - [scipy](http://www.scipy.org/) >= 0.13.3
    - [matplotlib](http://matplotlib.org/) >= 1.3.1
    - [ObsPy](https://github.com/obspy/obspy/wiki) >= 0.9.2
    - [pyshp](https://github.com/GeospatialPython/pyshp)
    - [pyproj](https://code.google.com/p/pyproj/) >= 1.8.9
    - [pyPdf](http://pybrary.net/pyPdf/)

    It is recommended to install these packages with `pip install . ` or with your
    favourite package manager, e.g., `apt-get install . `.

    Optionally, you may want to install:
    - [Computer Programs in Seismology](http://www.eas.slu.edu/eqc/eqccps.html)
    to be able to invert your dispersion maps for a 1-D shear velocity model,
    as these programs take care of the forward modelling.

    - [waveloc](https://github.com/amaggi/waveloc)
    to be able to run the kurtosis and migration-based event detector and locator,
    this would enable for an automated removal of earthquake events.

    - [nonlinloc](http://alomax.free.fr/nlloc/)
    to be able to run the non-linear event detection algorithms for waveloc
    and other detection programmes.

    How to start
    ------------
    If you are reading this, then you have either directly downloaded the tar ball or
    cloned this project from github.com/boland1992/SeisSuite/
    In both cases, now you should cd into the SeisSuite directory and run the following
    line in the terminal:

    This should successfully install all of the module package files required for seissuite.
    If you wish to check for a successful installation, run this line in any python shell
    that is correctly linked to your PYTHONPATH:

    If no errors occur, then the installation has been successful.

    Next, you should start reading the example configuration file contained in:

    which contains global parameters and detailed instructions. You should then create
    your own configuration file (any name with cnf extension, *.cnf) with your
    own parameters, and place it in the same folder as the scripts. It is not advised
    to simply modify `./bin/config_example.cnf`, as any update may revert your changes.

    You may then process in recommended order (items and tools from the seissuite module can
    be used independently of these scripts to create your own application if necessary):

    - `00_setup.py` sets up the initial required file structure for the applications.

    AFTER THE FILE STRUCTURE HAS BEEN INITIALISED IT IS RECOMMENDED THAT YOU THEN PLACE YOUR
    MSEED RAW WAVEFORMS FILES IN THE ./INPUT/DATA FOLDER AND THE ASSOCIATED METADATA IN THE
    ./INPUT/XML OR THE ./INPUT/DATALESS FOLDERS.

    - `01_database_init.py` sets up the initial databases required for finding files and
    general processing. It requires MSEED files to be in the ./INPUT/DATA folder, and metadata
    to be in either the ./INPUT/XML or the ./INPUT/DATALESS folders.

    - `02_timeseries_process.py` takes seismic waveforms as input in order to first
    preprocess the waveforms and then and export cross-correlations between
    pairs of stations,

    - `03_dispersion_curves.py` takes cross-correlations as input and applies
    a frequency-time analysis (FTAN) in order to extract and export group velocity
    dispersion curves,

    - `04_tomo_inversion_testparams.py` takes dispersion curves as input and applies
    a tomographic inversion to produce dispersion maps the inversion parameters
    are systematically varied within user-defined ranges,

    - `05_tomo_inversion_2pass.py` takes dispersion curves as input and applies
    a two-pass tomographic inversion to produce dispersion maps: an overdamped
    inversion is performed in the first pass in order to detect and reject outliers
    from the second pass.

    - `06_1d_models.py` takes dispersion maps as input and invert them for a 1-D
    shear velocity model at selected locations, using a Markov chain Monte Carlo
    method to sample to posterior distribution of the model's parameters.

    The scripts rely on the Python package `pysismo`, which must thus be located
    in a place included in your PATH (or PYTHONPATH) environment variable. The easiest
    choice is of course to place it in the same folder as the scripts.

    How to update
    -------------
    The code is still experimental so you should regularly check for (and pull)
    updates. These will be backward-compatible, **except if new parameters appear
    in the configuration file**.

    **In other words, after any update, you should check whether new parameters were added
    to the example configuration file (`tomo_Brazil.cnf`) and insert them accordingly
    to your own configuration file.**

    References
    ----------
    The cross-correlation procedure of ambient noise between pairs of stations follows
    the steps advocated by Bensen et al. (2007).
    The measurement of dispersion curves is based on the frequency-time
    analysis (FTAN) with phase-matched filtering described in Levshin and Ritzwoller (2001)
    and Bensen et al. (2007).
    The tomographic inversion implements the linear inversion procedure
    with norm penalization and spatial smoothing of Barmin et al. (2001).
    The Markov chain Monte Carlo method is described by Mosegaard and Tarantola (1995),
    and the forward modelling is taken care of by the Computer Programs in Seimology
    (Herrmann, 2013).

    - Barmin, M. P., Ritzwoller, M. H. and Levshin, A. L. (2001).
    A fast and reliable method for surface wave tomography.
    *Pure Appl. Geophys.*, **158**, p. 1351–1375. doi:10.1007/PL00001225
    [[journal](http://link.springer.com/article/10.1007%2FPL00001225)]
    [[pdf](http://jspc-www.colorado.edu/pubs/2001/1.pdf)]

    - Bensen, G. D. et al. (2007). Processing seismic ambient noise data to obtain
    reliable broad-band surface wave dispersion measurements.
    *Geophys. J. Int.*, **169**(3), p. 1239–1260. doi:10.1111/j.1365-246X.2007.03374.x
    [[journal](http://onlinelibrary.wiley.com/doi/10.1111/j.1365-246X.2007.03374.x/abstract)]
    [[pdf](http://ciei.colorado.edu/pubs/2007/2.pdf)]

    - Herrmann, R. B., 2013. Computer Programs in Seismology: an evolving tool for
    instruction and research, *Seismol. Res. Let.*, **84**(6), p. 1081-1088
    doi: 10.1785/0220110096
    [[pdf](http://srl.geoscienceworld.org/content/84/6/1081.full.pdf+html)]
    - Levshin, A. L. and Ritzwoller, M. H. (2001). Automated detection, extraction,
    and measurement of regional surface waves. *Pure Appl. Geophys.*, **158**,
    p. 1531–1545. doi:10.1007/PL00001233
    [[journal](http://link.springer.com/chapter/10.1007%2F978-3-0348-8264-4_11)]
    [[pdf](http://ciei.colorado.edu/pubs/pageoph_01/Levshin_Ritzwoller_pag2001.pdf)]

    - Mosegaard, K. and Tarantola, A. (1995) Monte Carlo sampling of solutions to inverse
    problems, *J. Geophys. Res.*, **100**(B7), p. 12431–12447
    [[journal](http://onlinelibrary.wiley.com/doi/10.1029/94JB03097/abstract)]
    [[pdf](http://www.ipgp.fr/

    - Langet N. et al (2014). Continuous Kurtosis-Based Migration for Seismic Event Detection and Location,
    with Application to Piton de la Fournaise Volcano, La Réunion.
    * Bul. Seis. Soc. Am.*, **104**, p. 229-246. doi:10.1785/0120130107


    Crystal PY-26 - History

    The Django migration system was developed and optmized to work with large number of migrations. Generally you shouldn’t mind to keep a big amount of models migrations in your code base. Even though sometimes it causes some undesired effects, like consuming much time while running the tests. But in scenarios like this you can easily disable the migrations (although there is no built-in option for that at the moment).

    Anyway, if you want to perform a clean-up, I will present a few options in this tutorial.

    Scenario 1:

    The project is still in the development environment and you want to perform a full clean up. You don’t mind throwing the whole database away.

    1. Remove the all migrations files within your project

    Go through each of your projects apps migration folder and remove everything inside, except the __init__.py file.

    Or if you are using a unix-like OS you can run the following script (inside your project dir):

    2. Drop the current database, or delete the db.sqlite3 if it is your case.
    3. Create the initial migrations and generate the database schema:

    Scenario 2:

    You want to clear all the migration history but you want to keep the existing database.

    1. Make sure your models fits the current database schema

    The easiest way to do it is trying to create new migrations:

    If there are any pending migration, apply them first.

    2. Clear the migration history for each app

    Now you will need to clear the migration history app by app.

    First run the showmigrations command so we can keep track of what is going on:

    Clear the migration history (please note that core is the name of my app):

    The result will be something like this:

    Now run the command showmigrations again:

    You must do that for all the apps you want to reset the migration history.

    3. Remove the actual migration files.

    Go through each of your projects apps migration folder and remove everything inside, except for the __init__.py file.

    Or if you are using a unix-like OS you can run the following script (inside your project dir):

    PS: The example above will remove all the migrations file inside your project.

    Run the showmigrations again:

    4. Create the initial migrations
    5. Fake the initial migration

    In this case you won’t be able to apply the initial migration because the database table already exists. What we want to do is to fake this migration instead:


    Watch the video: The Crystal Palace: The story of a great building. AmorSciendi (August 2022).