ppmbrighten(1) ppmbrighten(1) NAME ppmbrighten - change an images Saturation and Value from an HSV map SYNOPSIS ppmbrighten [-n] [-s <+- saturation>] [-v <+- value>] <ppmfile> DESCRIPTION Reads a portable pixmap as input. Converts the image from RGB space to HSV space and changes the Value by <+- value> as a percentage. Likewise with the Saturation. Doubling the Value would involve ppmbrighten -v 100 to add 100 percent to the Value. The 'n' option normalizes the Value to exist between 0 and 1 (normal‐ ized). SEE ALSO pgmnorm(1), ppm(5) AUTHOR Copyright (C) 1990 by Brian Moffet Copyright (C) 1989 by Jef Poskanzer. Permission to use, copy, modify, and distribute this software and its documentation for any purpose and without fee is hereby granted, provided that the above copyright notice appear in all copies and that both that copyright notice and this permission notice appear in supporting documen‐ tation. This software is provided "as is" without express or implied warranty. NOTES This program does not change the number of colors. 20 Nov 1990 ppmbrighten(1)


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Renner* is licensed under the SIL open font licence. This allows for both personal and commercial use, without a different licence. It also makes Renner* available for free for both. I would recommend reading up on the SIL open font licence if you are not familiar and have any questions, but briefly, it allows personal and commercial use, redistribution, and modification. You cannot sell the font as is, or use the name Renner* for a different font. To skip the payment process, simply put a zero in the box below. However, it might be nice to consider contributing to this work.
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VE Wiki documentation¶
The purpose of this wiki is to provide an in-depth technical resource for those developing strategic travel models in Oregon built on the VisionEval (VE) platform. Oregon specific policies and data inputs are used throughout.
This wiki focuses on the VisionEval Regional Strategic Planning Model (VE-RSPM), intended for metropolitan planning organizations (MPO) staff, statewide planners, local planners, and other decision makers interested in regional scenario planning to assess the impacts of different transportation and land use policies.
More information on the VisionEval project can be accessed at the following links:
_Documentation of the Pooled Funds VE Development https://github.com/VisionEval/VisionEval/wiki _Development of VisionEval: https://github.com/VisionEval/VisionEval-Dev The VisionEval project website: https://visioneval.org/ The current binary release of VisionEval available for download: https://visioneval.org/category/download.html This wiki is intended to sit between the detailed information on the VisionEval GitHub site and the planning level documents which discuss the benefits and reasoning for using a strategic model. This VE Primer is the introduction to the detailed tool information which will be linked to throughout this technical wiki: https://github.com/VisionEval/VisionEval/wiki/VisionEval-Primer
Revised Combined Wiki Outline – January 19, 2021
- Introduction (the why)
- 1.1 Oregon transportation and environmental planning.
1.1.1. Target rule and concepts/ policies to reduce GHG.
1.1.2. Rulemaking (TBD).
1.2 What is VisionEval: VE-RSPM and its relationship to other models.
- 1.3 Scenario planning options in Oregon (describing briefly the purpose and process, showing the options are progressive)
1.3.1. Local Baseline Assessment to STS Vision (least intense, planning 1-4).
1.3.2. Strategic Assessment (scenario planning 1-5).
1.3.3. Preferred scenario (most intense, planning 1-6).
Scenario Planning Process.
2.1. Establish Scenario Planning Framework.
2.1.1. Stakeholders.
2.1.2. Data and projects that inform the models.
2.1.3. Principles, issues and outcomes (this is about framing the discussion around issues and strategies people are most interested in).
2.1.4. Strategies for GHG reductions. (This is describing “levers” and the info/data we require to code them; a non-exhaustive list may include: TDM, Vehicle technologies and powertrains, Land Use, Transportation System, ITS, Transit).
2.2. Develop Evaluation Criteria.
2.3. Set Up Scenario Modeling
2.3.1. Base Year (link to section 3.3)
2.3.2. Future Baseline Reference Scenario (link to section 3.5)
2.3.3. Develop and Evaluate Alternative Scenarios (link to section 3.7)
2.4. Develop and Select a Preferred Scenario (link to 3.7 and set up 4.0)
2.4.1. Sensitivity Testing
2.4.2. Key Paths
VisionEval – Developing a Model
3.1. Model Geography and inputs by geography
3.2. Set up Base Model
3.2.1. socio-economic
3.2.2. travel data
3.3. Model Calibration (base year) and Validation (testing sensitivity to key variables)
3.3.1. Base Year Outputs
3.3.2. Calibration variables and sources of data
3.4. Modeling the GHG reduction strategies (this would be formats and sources of data for the policies listed in section 2/ planning process).
3.5. Set Up Scenarios (future)
3.5.1. Future Baseline Reference Scenario
3.5.2. Target Rule Scenario
3.5.3. Alternative Scenarios (future policies and exploratory scenario planning)
3.6. Analyzing & Visualizing Outputs
3.6.1. Comparing scenarios
3.6.2. Model Outputs
3.6.3. Visualizing outputs
3.7. Finalizing and Sharing Results (for modelers)
Reporting and Implementing Results
4.1. Identify Key Paths and Strategies
4.1.1. Group Polies, strategies and actions into Key Paths
4.1.2. Identify High ROI Strategies
4.2. Linking To Transportation and Land Use Plans
4.3. Monitoring Process
4.4. Documenting and Sharing Results
Case studies
Support and Resources (This would be consolidated links and contact information, like bibliography)


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{{thought_result}}
You wanna contribute some more to this book of thoughts and be an inspiration to someone??? HURRY UP ..You can contribute


Keifer William Morris
<EMAIL_ADDRESS>To obtain a position as a programmer
Intern: Public Library of Johnston County and Smithfield (Nov 2016 - Present)
Office Associate: Food Lion, Smithfield, NC (Aug 2016 - Present)
Volunteer Coordinator/Library Assistant II: PLJCS (Apr 2012 - Aug 2016)
Languages: HTML, CSS, C/C++
Networking: Basic router/switch configuration, implementing home/office LANs
Building PCs based on user needs and intended device use
Troubleshoot PCs and small networks
AAS, Information Technology, Johnston Community College
109 N Lakeside Drive, Smithfield, NC 27577 | Tel:<PHONE_NUMBER>


I've pursued multiple full stack development side projects covering a variety of technologies and frameworks from Postgres to Django to Heroku.
Through my experience at Quantopian I've given multiple talks on basic data science principles, crafted machine learning stock prediction models, and lead development of the open source Python package for analyzing stock alpha factors.
While at Nelson Irrigation I led I lead a team of interns to build a never before tried method of automatically collecting sprinkler uniformity and performance data.
Alphalens is a Python package designed to evaluate equity return predicting factors. These factors are commonly used in alpha factor modeling. The main function of Alphalens is to surface the most relevant statistics and plots about an alpha factor, including:
Alphalens is ideally ran inside a Jupyter notebook with just a few lines of code:
import alphalens
alphalens.tears.create_factor_tear_sheet(my_factor, pricing)
Status: active
Harvust is a farm working app that connects workers with growers. Harvust provides an online marketplace to source farm labor. Harvust passes the efficiency gains of providing a platform that finds the best labor, tracks production, and pays the workers onto the worker and grower.
Status: prelaunch
Big Picture is a collaborative project between me and my friend Taso. Our joint passion for movies combined with his design skill and my programming skill led us to create this beautiful self-sustaining website for browsing movies.
Status: active
Twitter: @__jameschris
Email<EMAIL_ADDRESS>Github: jameschristopher


To simulate two different clients chatting, open this page in two different browsers (or two different personas in Chrome)
Server: BetterChatExample.cs, Client: index.vue.html index.vue.js


I’ve never cheated on my wife since we married – Brother Sammy
by Qwame BenedictGhanaian Gospel singer Brother Sammy aka the Nation’s worshipper has reacted to some allegations on social media that he has been sleeping with video vixen cum actress Rosemond Brown aka Akuapem Poloo.
It would be remembered that actress Xandy Kamel some weeks ago dropped a bombshell that the gospel singer was secretly sleeping with Akuapem Poloo and that he has been ‘chopping’ her ‘basabasa’.
According to Brother Sammy in his reaction, all the allegations leveled against him about having an intimate relationship with Akuapem Poloo are false. Adding that he has never cheated on his wife since they got married.
He continued by saying that Akuapim Poloo doesn’t know his house as been claimed and he only met her on a movie set.
Brother Sammy revealed that he always gets saddened when he is accused of such an immoral act because he had never cheated on his wife since he got married.


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Content-type: text/html
Manpage of VOATLAS
VOATLAS
Section: User Commands (1)
Updated: Feb 2013
Index
Return to Main Contents
NAME
voatlas - Query the SkyView Image service for an all-sky image
SYNOPSIS
voatlas [<opts>] [<field> | <pos>]
OPTIONS
The voatlas application accepts the following options:
- -h, --help
-
Print a help summary to the terminal and exit. No processing is done
following this flag.
- -b BPASS,--band BPASS
-
Bandpass of image to retrieve. Allowed values are 'optical', 'infrared' (or
bandpass name is used.
- -p,--survey SURVEY
-
Survey program name. The list of available surveys is described at
http://skyview.gsfc.nasa.gov/cgi-bin/survey.pl.
- -g,--graphic
-
Get a graphic image (i.e. a JPEG), the default is to get a FITS image.
- -n,--naxis NPIX
-
Set returned image size. The default image size returned is 512x512.
- -s,--size SIZE
-
Field size to generate. The SIZE value may be appended with an 's',
(default) respectively. The default size is 0.25 degrees.
- -F,--field FIELD
-
Name of the field (e.g. an object name like 'M33') to query. The object
name must be resolvable with a task like VOSESAME, the position returned
will be the center of the query.
- -R RA,--ra RA
-
The RA position of the query.
- -D DEC,--dec DEC
-
The Dec position of the query.
- -P RA,DEC,--pos RA,DEC
-
Specify the query position as a comma-delimited string of RA and DEC.
- -S,--samp
-
Broadcast resulting image as a SAMP message. The image will be broadcast
using the 'image.load.fits' mtype.
- -v,--verbose
-
Print verbose output during execution.
- -o NAME, --output output
-
Specify the filename of the downloaded image. If not specified a name
created from the field name or position will be used.
DESCRIPTION
The voatlas task queries the NASA SkyView all-sky survey data for
an image of the requested sky position in the requested bandpass. The
position may be given as the name of an object who's coordinate can be
resolved, or as an explicit position in RA and Dec (ICRS decimal degrees).
The default field of view will be 0.25 degrees but may be changed using
the -s option (with an optional 's', 'm' or 'd' on the value to
specify arcseconds, arcminutes or degrees respectively). The default
image will be in FITS format unless the -g option is set to request
a graphic such as a JPEG, the default size will be 512x512 pixels unless the
-n option is used to set a different size (the image will always
be square so only one value is required). The resulting image may be
broadcast using the SAMP 'image.load.FITS' mtype by setting the -S
option.
The -b flag is used to select the desired bandpass, if not set then
and optical image from the DSS2B survey is used by default. Allowed values
for the bandpass string and their corresponding survey names are:
Bandpass ShortName Title
optical DSS Digital Sky Survey
infrared 2MASSK Two-micron All Sky Survey (K-band)
x-ray PSPC2INT ROSAT PSPC (2-deg cutoff)
euv EUV83 EUV Explorer83A
gamma-ray EGRET1000 Energetic Gamma-Ray Event Telescope: Hard
radio 4850MHZ 4850 MHz Survey - GB6/PMN
If the -p option is used to specify a particular survey program, the
bandpass parameter is ignored. The -p flag reserves the name 'list'
as a means to have the task return a list of available data for the field.
This list contains the survey name and the mime-type of the available image.
Script tasks can use this information to determine the best available
image for the field.
If no input file is specified the VOTable will be read from the stdin,
results will be written to stdout unless the -o (or --output)
names an output file. The task will only process a single VOTable and may
not be used to convert multiple files in a single call.
RETURN STATUS
On exit the voatlas task will return a zero indicating success, or a
one indicating an error.
EXAMPLES
- 1) Display an image of M83 on Aladin using SAMP
-
% voatlas -S m83 or % voatlas --samp m83
- 2) Get a 256x256 JPEG image of the Sombrero galaxy
-
% voatlas -o gal.jpg -n 256 --graphic sombrero
- 3) Get a 20 arcmin Wise 2.2micron survey image of m101
-
% voatlas -s 20m --survey=wise22 m101
- 4) Get a radio image of 3c273, image will be '3c273.fits'
-
% voatlas --band=radio 3c273
- 5) List (verbose) the survey images available for ngc1234
-
% voatlas --survey=list -v ngc1234
BUGS
1) There is currently no convenient way to get a list of surveys without
specifying a position, this is due to the way Skyview is implemented.
2) There is currently no convenient way to select other than the first
available image.
Revision History
Feb 2013 - First public release
Author
Michael Fitzpatrick (fitz@noao.edu), Feb 2013
SEE ALSO
vodata, vocatalog, voimage, vospectra
Index
- NAME
-
- SYNOPSIS
-
- OPTIONS
-
- DESCRIPTION
-
- RETURN STATUS
-
- EXAMPLES
-
- BUGS
-
- Revision History
-
- Author
-
- SEE ALSO
-
This document was created by
man2html,
using the manual pages.
Time: 05:13:22 GMT, April 14, 2013


---
title: Parent Resources
description: Parent Resources
layout: slideshow
permalink: /parentsandteachers/parentresources
lang: en
lang-ref: parentresources
es: /padresymaestros/recursosparapadres
slides:
Financial Relief:
name: |-
Financial
Relief
text:
- Financial and Resource Aid for Families in SouthEast LA and Oakland/Richmond – Link
- Baby Essentials – Link
Housing:
text:
- Rent Relief for Tenants or Property Owners In Los Angeles County – Link
- Housing for ELAC Students – For more information contact Celia Cruz at<EMAIL_ADDRESS>- LA County COVID-19 Rent Relief – Link
Food Assistance:
name: |-
Food
Assistance
text:
Utilities:
text:
- Get 20% off your SoCal Gas if you were affected by the pandemic – Link
- Free Youth Photography Classes for South Gate Students – Link
Tech:
text:
- Samsung Student/Parent Discount – Link
- Apple Student Discount – Link
- Microsoft Student Discount + Free Microsoft Office – Link
- Dell Student Discount – Link
- HP Student Discounts – Link
- Amazon Prime FREE for 6 months and then 50% off – Link
- Spotify + Hulu + ShowTime for $5/month – Link
- Apple Music for Students – Link
- Best Buy Student Discounts – Link
- Adobe Creative Cloud Student Discount – Link
Undocumented Immigrants:
name: |-
Undocumented
Immigrants
fontscale: .75
text:
- Vital Wellness Gatherings – Immigrants Rising – Link
- Emergency Medi-Cal – Link
- La Clinica – Link
- Disaster Funds for Undocumeted Families – Link
- More resources – Link
Businesses:
text:
- Los Angeles County Small Business/Non-Profit/Street Vendor Grants – Link
- Bell Gardens Business and Resident Grants – Link
---


December 27
Don't Answer
And be not conformed to this world: but be ye transformed by the renewing of your mind, that ye may prove what is that good, and acceptable, and perfect, will of God.
Romans 12:2
Be not conformed to this world. If you live like the world, you'll be overcome by the world. It's as simple as that. So don't let the world squeeze you into its own mold.
Instead..."be transformed" by the entire renewal of your mind.  In other words, when you see something in the Word, say, "I agree with that. From now on I'm acting on that instead of what I used to think."
For example, when sin calls your name, don't answer. Agree with the Word instead and say, "I'm dead to that, Devil. You can't pull me into it anymore!"
As you do that, you'll find yourself changing. You will find yourself living like a world overcomer!
Scripture Reading: Romans 6:1-11


Shocking: mass fight in Brazilian youth game
by BeSoccerPalmeiras and Sao Paulo played in the under 17 Paulista final and it went to penalties. After the away win, the players from both team fought and there was chaos.
Violence once again hits football. There are moments of tension in which footballers lose their heads.
In the U17 Paulista final, Palmeiras and Sao Paulo were completely out of order. The players were part of a huge fight on the pitch and an investigation has begun.
The governing body have cited three Palmeiras players after confronting the Sao Paulo players during their celebrations. Other members of the club have not been identified.
Featured links


The BouncingBall implements the following equation:
der(h) = v
der(v) = -g
when h <= 0 then h := 0 and v := -e * v
when v < v_min then h := 0 and v := 0
whith the variables
| Variable | Start | Unit | Causality | Variability | Description |
|---|---|---|---|---|---|
| h | 1 | m | output | continuous | Position of the ball |
| v | 0 | m/s | output | continuous | Velocity of the ball |
| g | 9.81 | m/s2 | parameter | fixed | Gravity acting on the ball |
| e | 0.7 | parameter | tunable | Coefficient of restitution | |
| v_min | 0.1 | m/s2 | parameter | constant | Velocity below which the ball stops bouncing |
The plot shows the reference result computed with FMPy.


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{%- endblock %}


About Me
Bio: Hi, my names is James Wang and I live in Seattle with my wife and 2 kids.
Education History: University of Michigan
Overview of your Job Experience: USMC, Consulting, AWS
Goals: Become a Software Developer with my focus being on Python coding
Top Ten Favorite Food
- Steak
- Sushi
- Crab Legs
- Cocktail Shrimp
- Poke
- Teriyaki Chicken
- Kimchee
- Pot Roast
- Ribs
- Ice Cream Sandwiches


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const ( INVMAP_TOL = 1.0e-10 // tolerance for inverse mapping function INVMAP_NIT = 25 // maximum number of iterations for inverse mapping )
constants
const ( VTK_VERTEX = 1 VTK_POLY_VERTEX = 2 VTK_LINE = 3 VTK_POLY_LINE = 4 VTK_TRIANGLE = 5 VTK_TRIANGLE_STRIP = 6 VTK_POLYGON = 7 VTK_PIXEL = 8 VTK_QUAD = 9 VTK_TETRA = 10 VTK_VOXEL = 11 VTK_HEXAHEDRON = 12 VTK_WEDGE = 13 VTK_PIRAMID = 14 VTK_QUADRATIC_EDGE = 21 VTK_QUADRATIC_TRIANGLE = 22 VTK_QUADRATIC_QUAD = 23 VTK_QUADRATIC_TETRA = 24 VTK_QUADRATIC_HEXAHEDRON = 25 )
const MINDET = 1.0e-14 // minimum determinant allowed for dxdR
constants
func CheckDSdR(tst *testing.T, shape *Shape, r []float64, tol float64, verbose bool)
CheckDSdR checks dSdR derivatives of shape structures
func CheckDSdx(tst *testing.T, shape *Shape, xmat [][]float64, x []float64, tol float64, verbose bool)
CheckDSdx checks G=dSdx derivatives of shape structures
func CheckIsop(tst *testing.T, shape *Shape, C [][]float64, Cnat [][]float64)
CheckIsop checks isoparametric property
C -- [4][2] elements coordinates of corners (not control points) Cnat -- [2][4] natural coordinates of corners
func CheckShape(tst *testing.T, shape *Shape, tol float64, verbose bool)
CheckShape checks that shape functions evaluate to 1.0 @ nodes
func CheckShapeFace(tst *testing.T, shape *Shape, tol float64, verbose bool)
CheckShapeFace checks shape functions @ faces
func Hex20(S []float64, dSdR [][]float64, R []float64, derivs bool, idxface int)
Hex20 calculates the shape functions (S) and derivatives of shape functions (dSdR) of hex20 elements at {r,s,t} natural coordinates. The derivatives are calculated only if derivs==true.
func Hex8(S []float64, dSdR [][]float64, R []float64, derivs bool, idxface int)
Hex8 calculates the shape functions (S) and derivatives of shape functions (dSdR) of hex8 elements at {r,s,t} natural coordinates. The derivatives are calculated only if derivs==true.
func Lin2(S []float64, dSdR [][]float64, R []float64, derivs bool, idxface int)
Lin2 calculates the shape functions (S) and derivatives of shape functions (dSdR) of lin2 elements at {r,s,t} natural coordinates. The derivatives are calculated only if derivs==true.
func Lin3(S []float64, dSdR [][]float64, R []float64, derivs bool, idxface int)
Lin3 calculates the shape functions (S) and derivatives of shape functions (dSdR) of lin3 elements at {r,s,t} natural coordinates. The derivatives are calculated only if derivs==true.
func Lin4(S []float64, dSdR [][]float64, R []float64, derivs bool, idxface int)
Lin4 calculates the shape functions (S) and derivatives of shape functions (dSdR) of lin4 elements at {r,s,t} natural coordinates. The derivatives are calculated only if derivs==true.
func Lin5(S []float64, dSdR [][]float64, R []float64, derivs bool, idxface int)
Lin5 calculates the shape functions (S) and derivatives of shape functions (dSdR) of lin5 elements at {r,s,t} natural coordinates. The derivatives are calculated only if derivs==true.
func Qua12(S []float64, dSdR [][]float64, R []float64, derivs bool, idxface int)
Qua12 calculates the shape functions (S) and derivatives of shape functions (dSdR) of qua12 elements at {r,s,t} natural coordinates. The derivatives are calculated only if derivs==true.
func Qua16(S []float64, dSdR [][]float64, R []float64, derivs bool, idxface int)
Qua16 calculates the shape functions (S) and derivatives of shape functions (dSdR) of qua16 elements at {r,s,t} natural coordinates. The derivatives are calculated only if derivs==true.
func Qua4(S []float64, dSdR [][]float64, R []float64, derivs bool, idxface int)
Qua4 calculates the shape functions (S) and derivatives of shape functions (dSdR) of qua4 elements at {r,s,t} natural coordinates. The derivatives are calculated only if derivs==true.
func Qua8(S []float64, dSdR [][]float64, R []float64, derivs bool, idxface int)
Qua8 calculates the shape functions (S) and derivatives of shape functions (dSdR) of qua8 elements at {r,s,t} natural coordinates. The derivatives are calculated only if derivs==true.
func Qua9(S []float64, dSdR [][]float64, R []float64, derivs bool, idxface int)
Qua9 calculates the shape functions (S) and derivatives of shape functions (dSdR) of qua9 elements at {r,s,t} natural coordinates. The derivatives are calculated only if derivs==true.
func Tet10(S []float64, dSdR [][]float64, R []float64, derivs bool, idxface int)
Tet10 calculates the shape functions (S) and derivatives of shape functions (dSdR) of tet10 elements at {r,s,t} natural coordinates. The derivatives are calculated only if derivs==true.
func Tet4(S []float64, dSdR [][]float64, R []float64, derivs bool, idxface int)
Tet4 calculates the shape functions (S) and derivatives of shape functions (dSdR) of tet4 elements at {r,s,t} natural coordinates. The derivatives are calculated only if derivs==true.
func Tri10(S []float64, dSdR [][]float64, R []float64, derivs bool, idxface int)
Tri10 calculates the shape functions (S) and derivatives of shape functions (dSdR) of tri10 elements at {r,s,t} natural coordinates. The derivatives are calculated only if derivs==true.
func Tri15(S []float64, dSdR [][]float64, R []float64, derivs bool, idxface int)
Tri15 calculates the shape functions (S) and derivatives of shape functions (dSdR) of tri15 elements at {r,s,t} natural coordinates. The derivatives are calculated only if derivs==true.
func Tri3(S []float64, dSdR [][]float64, R []float64, derivs bool, idxface int)
Tri3 calculates the shape functions (S) and derivatives of shape functions (dSdR) of tri3 elements at {r,s,t} natural coordinates. The derivatives are calculated only if derivs==true.
func Tri6(S []float64, dSdR [][]float64, R []float64, derivs bool, idxface int)
Tri6 calculates the shape functions (S) and derivatives of shape functions (dSdR) of tri6 elements at {r,s,t} natural coordinates. The derivatives are calculated only if derivs==true.
type Ipoint []float64 // len==4 => [r, s, t, w]
Ipoint implements integration point data: natural coordinates and weight
type Shape struct { // geometry Type string // name; e.g. "lin2" Func ShpFunc // shape/derivs function callback function FaceType string // geometry of face; e.g. "qua8" => "lin3" Gndim int // geometry of shape; e.g. "lin3" => gnd == 1 (even in 3D simulations) Nverts int // number of vertices in cell; e.g. "qua8" => 8 VtkCode int // VTK code VtkNverts int // number of vertices to use in VTK file; e.g. "qua9" => 8 vertices NatCoords [][]float64 // natural coordinates [gndim][nverts] // face data FaceFunc ShpFunc // face shape/derivs function callback function FaceNvertsMax int // max number of vertices on face FaceLocalVerts [][]int // face local vertices [nfaces][...] FaceFlip []bool // [nfaces] flip normal // basic type => for plotting or LBB cells BasicType string // geometry of basic cell; e.g. "qua8" => "qua4" BasicNverts int // number of vertices in basic cell; e.g. 8 => 4 BasicVtkCode int // VTK code of basic cell // geometry: for seams (3D-edges) SeamType int // geometry of seam (3D-edge); e.g. "hex8" => "lin2" SeamLocalVerts [][]int // seam (3d-edge) local vertices [nseams][nVertsOnSeam] // scratchpad: volume S []float64 // [nverts] shape functions G [][]float64 // [nverts][gndim] G == dSdx. derivative of shape function J float64 // Jacobian: determinant of dxdr DSdR [][]float64 // [nverts][gndim] derivatives of S w.r.t natural coordinates DxdR [][]float64 // [gndim][gndim] derivatives of real coordinates w.r.t natural coordinates DRdx [][]float64 // [gndim][gndim] dRdx == inverse(dxdR) // scratchpad: line Jvec3d []float64 // Jacobian: norm of dxdr for line elements (size==3) Gvec []float64 // [nverts] G == dSdx. derivative of shape function // scratchpad: face Sf []float64 // [FaceNvertsMax] shape functions values Fnvec []float64 // [gndim] face normal vector multiplied by Jf DSfdRf [][]float64 // [FaceNvertsMax][gndim-1] derivatives of Sf w.r.t natural coordinates DxfdRf [][]float64 // [gndim][gndim-1] derivatives of real coordinates w.r.t natural coordinates // NURBS Nurbs *gm.Nurbs // pointer to NURBS structure => indicates that this shape strucutre is based on NURBS NurbsFaces []*gm.Nurbs // boundaries (surfaces) of NURBS [normalTo0, normalTo0, normalTo1, normalTo1, normalTo2, normalTo2] Span []int // NURBS knots' indices defining cell/element; e.g. [2, 3, 1, 2] for x-quad/y-lin cell Ibasis []int // indices of basis functions corresponding to Span == local indices of control points SpanFace [][]int // NURBS knots' indices defining cell/element; e.g. [2, 3, 1, 2] for x-quad/y-lin cell IbasisFace [][]int // indices of basis functions corresponding to Span == local indices of control points U []float64 // [gndim] NURBS' parametric space coordinates }
Shape holds geometry data
func Get(geoType string, goroutineId int) *Shape
Get returns an existent Shape structure
Note: 1) returns nil on errors 2) use goroutineId > 0 to get a copy
func GetShapeNurbs(nurbs *gm.Nurbs, nrbfaces []*gm.Nurbs, span []int) (o *Shape)
GetShapeNurbs returns a shape structure based on NURBS
Note: span are the local ids of control points in NURBS defining elements Note: FaceLocalVerts does not work for internal surfaces; only those @ boundaries
func (o *Shape) AxisymGetRadius(x [][]float64) (radius float64)
AxisymGetRadius returns the x0 == radius for axisymmetric computations
Note: must be called after CalcAtIp
func (o *Shape) AxisymGetRadiusF(x [][]float64, idxface int) (radius float64)
AxisymGetRadiusF (face) returns the x0 == radius for axisymmetric computations
Note: must be called after CalcAtFaceIp
func (o *Shape) CalcAtFaceIp(x [][]float64, ipf Ipoint, idxface int) (err error)
CalcAtFaceIp calculates face data such as Sf and Fnvec
Input: x[ndim][nverts+?] -- coordinates matrix of solid element ipf -- local/natural coordinates of face idxface -- local index of face Output: Sf and Fnvec
func (o *Shape) CalcAtIp(x [][]float64, ip Ipoint, derivs bool) (err error)
CalcAtIp calculates volume data such as S and G at natural coordinate r
Input: x[ndim][nverts+?] -- coordinates matrix of solid element ip -- integration point Output: S, DSdR, DxdR, DRdx, G, and J
func (o *Shape) CalcAtR(x [][]float64, R []float64, derivs bool) (err error)
CalcAtR calculates volume data such as S and G at natural coordinate r
Input: x[ndim][nverts+?] -- coordinates matrix of solid element R[3] -- local/natural coordinates Output: S, DSdR, DxdR, DRdx, G, and J
func (o *Shape) CellBryDist(R []float64) float64
CellBryDist returns the shortest distance between R and the boundary of the cell in natural coordinates TODO: test this
func (o *Shape) CellBryDistDeriv(dfdR []float64, R []float64)
func (o *Shape) Extrapolator(E [][]float64, ips []Ipoint) (err error)
Extrapolator computes the extrapolation matrix for this Shape with a combination of integration points 'ips'
Note: E[nverts][nip] must be pre-allocated
func (o *Shape) FaceIpRealCoords(x [][]float64, ipf Ipoint, idxface int) (y []float64)
FaceIpRealCoords returns the real coordinates (y) of an integration point @ face TODO: check this function
func (o Shape) GetCopy() *Shape
GetCopy returns a new copy of this shape structure
func (o *Shape) GetIps(nips, nipf int) (ips, ipf []Ipoint, err error)
GetIps returns a set of integration points
If the number (nips) of integration points is zero, it returns a default set
func (o *Shape) GetIpsNatCoordsMat(ips []Ipoint) (ξh [][]float64)
GetIpsNatCoordsMat returns the matrix (\hat{ξ}) with natural coordinates of interation points, augmented by one column which is filled with ones [nip][ndim+1]
func (o *Shape) GetNodesNatCoordsMat() (ξ [][]float64)
GetNodesNatCoordsMat returns the matrix (ξ) with natural coordinates of nodes, augmented by one column which is filled with ones [nverts][ndim+1]
func (o *Shape) GetShapeMatAtIps(ips []Ipoint) (N [][]float64)
GetShapeMatAtIps returns a matrix formed by computing the shape functions at all integration points [nip][nverts]
func (o *Shape) InvMap(r, y []float64, x [][]float64) (err error)
InvMap computes the natural coordinates r, given the real coordinate y
Input: y[ndim] -- are the 2D/3D point coordinates x[ndim][nverts+?] -- coordinates matrix of solid element Output: r[3] -- are the natural coordinates of given point
func (o *Shape) IpRealCoords(x [][]float64, ip Ipoint) (y []float64)
IpRealCoords returns the real coordinates (y) of an integration point
func (o *Shape) NurbsFaceFunc(S []float64, dSdR [][]float64, r []float64, derivs bool, idxface int)
NurbsFaceFunc implements shape/deriv functions for faces of NURBS
func (o *Shape) NurbsFunc(S []float64, dSdR [][]float64, r []float64, derivs bool, idxface int)
NurbsFunc implements shape/deriv functions for NURBS
type ShpFunc func(S []float64, dSdR [][]float64, r []float64, derivs bool, idxface int)
ShpFunc is the shape functions callback function


1 reference to PerformWSManPluginShell
System.Management.Automation (1)
engine\remoting\fanin\WSManPluginFacade.cs (1)
529
WSManPluginInstance.
PerformWSManPluginShell
(pluginContext, requestDetails, flags, extraInfo, startupInfo, inboundShellInformation);


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Package org.apache.sis.referencing.factory.sql
Factories for geodetic objects defined in a SQL database, typically the EPSG dataset.
This package provides support for all codes prefixed by
"EPSG:"
in the Apache SIS's
list of authority codes.
The main class in this package is EPSGFactory
,
which requires a DataSource
providing connections to an EPSG database.
Connection to the database
By default Apache SIS uses Apache Derby,
but the database can also be PostgreSQL or MS-Access.
The database connection is obtained by the first of the following data sources which is found:
- If a JNDI context exists,
the
DataSource
registered under the"java:comp/env/jdbc/SpatialMetadata"
name. - If the
SIS_DATA
environment variable is defined, a JDBC connection for the"jdbc:derby:$SIS_DATA/Databases/SpatialMetadata"
URL. - If the
"derby.system.home"
property is defined, a JDBC connection for the"jdbc:derby:SpatialMetadata"
URL. - If the
org.apache.sis.non-free:sis-embedded-data
module is present on the classpath, a read-only connection to the database in the JAR file.
The EPSG dataset
A widely-used factory is the EPSG geodetic dataset.
EPSG codes are numerical identifiers.
For example "EPSG:4326"
is the EPSG identifier for the "WGS 84" geographic CRS.
As an extension, the Apache SIS implementation accepts names as well as numeric identifiers.
For example the two following method calls fetch the same object:
createProjectedCRS("27572")
createProjectedCRS("NTF (Paris) / Lambert zone II")
Note: names may be ambiguous since the same name may be used for more than one object.
This is the case of "WGS 84" for example. If such an ambiguity is found, an exception will be thrown.
For more determinism, the numerical codes are preferred.
How deprecated entries are handled
When an error is discovered in a Coordinate Reference System (CRS) definition, the EPSG group does not apply the
correction directly on the erroneous object (unless the correction is very minor).
Instead, the erroneous object is deprecated and a new one is created.
Apache SIS handles deprecated objects as below:
- Deprecated objects are not listed in the collection returned by the
EPSGDataAccess.getAuthorityCodes(…)
method. - All method expecting an EPSG code in argument accept also the codes of deprecated objects.
- If a deprecated object is created by a call to
EPSGDataAccess.createFoo(…)
, a warning will be logged with a message proposing a replacement.
- Since:
- 0.7
- See Also:
org.apache.sis.metadata.sql
Defined in the sis-referencing
module
-
Class Summary Class Description EPSGDataAccess Data Access Object (DAO) creating geodetic objects from a JDBC connection to an EPSG database.EPSGFactory A geodetic object factory backed by the EPSG database.InstallationScriptProvider Provides SQL scripts needed for creating a local copy of a dataset.SQLTranslator Converts the SQL statements from MS-Access dialect to standard SQL.


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-
Should not be added if on oral anticoagulants
-
Generally Not recommended risk \<20%
-
Can be considered for high-risk primary prevention: taking into account harms and benefits
-
Strongly recommended people with established cardiovascular disease
- reduces risk of cardiovascular event by about 25%
after angina or MI
- low dose aspirin
risk >20%
- commence aspirin
contraindications:
-
Aspirin allergies/intolerance
-
active peptic ulceration
-
uncontrolled BP and other major bleeding risks)
risks
-
intracranial haemorrhage = 2/1000 / year
-
extracranial haemorrhage 1-2/1000
- most non-fatal
-
upper GI bleed/perforation
- 2x increase
Ticagrlor (Brilinta)
-
reversibly inhibits platelet adenosine diphosphate p2y12 receptors
-
rapid inhibition of platelet activation and aggregation
-
clopidogrel = prodrug
-
CYP2c19
-
30-40% people of Maori, PI and aisan decent
-
reduced CYP2c19
-
compared to 15% Eruopeans
-
-
-
-
co-administered with aspirin
- dual anti-platelet
-
indicated prevention of atherothromboci events in adult patients with ACS
- N-STEACS/STEACS
-
Special authority
-
Patient has recently been diagnosed with STEACS/STEACS
- days/weeks compared to months
-
Fibrinolytic therapy not been given in the last 24 hours and is not planned
-
-
Evidence
-
PLATO
-
ticagrelor + aspirin > clopidogrel + aspirin
-
lower risk of ischaemic events and death
-
absolute risk reduction 16%
-
RR 16%
-
NNT 54
-
-
-
-
load with 180mg
-
90mg bd with or without food
-
Black box warning
- should not be used concurrrently with doses of aspirin >100mg
-
contraindications
-
active bleeding
-
history of intracranial haemorrhage
-
no data on safety in those who have had fibrinolytic thearpy
-
-
cautions
-
increase risk of bleeding
-
nsaids
-
?brady cardia
-
excluded from PLATO
-
used with caution unless PPM fitted
-
-
-
Interactions
-
CYP3A4
-
weak inhibitor
-
-
Adverse effects
-
Bleeding
-
Dyspnoea
-
increase risk of dyspnoea compared to clopidogrel
-
?mechanism
-
-
elevations in Cr and urate
-


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Our Purpose
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Incidents by state {{$ctrl.newTest}}
Incidents over time
Search:
State
All
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Incident Number
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Caller
Opened
State
{{incident.number}}
{{incident.urgency}}
{{incident.caller}}
{{incident.opened_at | date}}
{{incident.state | getState}}


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|
Hunt & Krueger (2014,TAMUC)
|
Caruso et al. (2013) Study 1
|
very close |
money priming (instruction background) |
ML1 #1 |
30 |
d = .77 ± .74 |
87 |
d = -.26 ± .42 |
no signal - inconsistent |
exposure to money vs. control (instruction background) |
system justification scale |
|
Cheong (2014,Help)
|
Caruso et al. (2013) Study 1
|
very close |
money priming (instruction background) |
ML1 #2 |
30 |
d = .77 ± .74 |
102 |
d = -.24 ± .39 |
no signal - inconsistent |
exposure to money vs. control (instruction background) |
system justification scale |
|
Devos (2014,SDSU)
|
Caruso et al. (2013) Study 1
|
very close |
money priming (instruction background) |
ML1 #3 |
30 |
d = .77 ± .74 |
162 |
d = -.21 ± .40 |
no signal - inconsistent |
exposure to money vs. control (instruction background) |
system justification scale |
|
Swol (2014,Wisc)
|
Caruso et al. (2013) Study 1
|
very close |
money priming (instruction background) |
ML1 #4 |
30 |
d = .77 ± .74 |
96 |
d = -.20 ± .40 |
no signal - inconsistent |
exposure to money vs. control (instruction background) |
system justification scale |
|
John & Skorinko (2014,WPI)
|
Caruso et al. (2013) Study 1
|
very close |
money priming (instruction background) |
ML1 #5 |
30 |
d = .77 ± .74 |
87 |
d = -.19 ± .42 |
no signal - inconsistent |
exposure to money vs. control (instruction background) |
system justification scale |
|
Kappes (2014,LSE)
|
Caruso et al. (2013) Study 1
|
very close |
money priming (instruction background) |
ML1 #6 |
30 |
d = .77 ± .74 |
277 |
d = -.16 ± .39 |
no signal - inconsistent |
exposure to money vs. control (instruction background) |
system justification scale |
|
Davis & Hicks (2014,TAMU) Study 1
|
Caruso et al. (2013) Study 1
|
very close |
money priming (instruction background) |
ML1 #7 |
30 |
d = .77 ± .74 |
187 |
d = -.16 ± .24 |
no signal - inconsistent |
exposure to money vs. control (instruction background) |
system justification scale |
|
Klein et al. (2014,UFL)
|
Caruso et al. (2013) Study 1
|
very close |
money priming (instruction background) |
ML1 #8 |
30 |
d = .77 ± .74 |
127 |
d = -.15 ± .35 |
no signal - inconsistent |
exposure to money vs. control (instruction background) |
system justification scale |
|
Vranka (2014,Charles)
|
Caruso et al. (2013) Study 1
|
very close |
money priming (instruction background) |
ML1 #9 |
30 |
d = .77 ± .74 |
84 |
d = -.14 ± .43 |
no signal - inconsistent |
exposure to money vs. control (instruction background) |
system justification scale |
|
Packard (2014,Laurier)
|
Caruso et al. (2013) Study 1
|
very close |
money priming (instruction background) |
ML1 #10 |
30 |
d = .77 ± .74 |
112 |
d = -.14 ± .37 |
no signal - inconsistent |
exposure to money vs. control (instruction background) |
system justification scale |
|
Cemalcilar (2014,KU)
|
Caruso et al. (2013) Study 1
|
very close |
money priming (instruction background) |
ML1 #11 |
30 |
d = .77 ± .74 |
113 |
d = -.13 ± .36 |
no signal - inconsistent |
exposure to money vs. control (instruction background) |
system justification scale |
|
Bocian & Frankowska (2014,SWPSON) Study 2
|
Caruso et al. (2013) Study 1
|
very close |
money priming (instruction background) |
ML1 #12 |
30 |
d = .77 ± .74 |
169 |
d = -.12 ± .30 |
no signal - inconsistent |
exposure to money vs. control (instruction background) |
system justification scale |
|
Huntsinger & Mallett (2014,Luc)
|
Caruso et al. (2013) Study 1
|
very close |
money priming (instruction background) |
ML1 #13 |
30 |
d = .77 ± .74 |
146 |
d = -.07 ± .33 |
no signal - inconsistent |
exposure to money vs. control (instruction background) |
system justification scale |
|
Schmidt & Nosek (2014, MTURK)
|
Caruso et al. (2013) Study 1
|
very close |
money priming (instruction background) |
ML1 #14 |
30 |
d = .77 ± .74 |
1000 |
d = -.06 ± .12 |
no signal - inconsistent |
exposure to money vs. control (instruction background) |
system justification scale |
|
Hovermale & Joy-Gaba (2014,VCU)
|
Caruso et al. (2013) Study 1
|
very close |
money priming (instruction background) |
ML1 #15 |
30 |
d = .77 ± .74 |
108 |
d = -.05 ± .40 |
no signal - inconsistent |
exposure to money vs. control (instruction background) |
system justification scale |
|
Vianello & Galliani (2014,UNIPD)
|
Caruso et al. (2013) Study 1
|
very close |
money priming (instruction background) |
ML1 #16 |
30 |
d = .77 ± .74 |
144 |
d = -.04 ± .34 |
no signal - inconsistent |
exposure to money vs. control (instruction background) |
system justification scale |
|
Schmidt & Nosek (2014,PI)
|
Caruso et al. (2013) Study 1
|
very close |
money priming (instruction background) |
ML1 #17 |
30 |
d = .77 ± .74 |
1329 |
d = -.03 ± .11 |
no signal - inconsistent |
exposure to money vs. control (instruction background) |
system justification scale |
|
Bernstein (2014,abington)
|
Caruso et al. (2013) Study 1
|
very close |
money priming (instruction background) |
ML1 #18 |
30 |
d = .77 ± .74 |
84 |
d = .00 ± .43 |
no signal - inconsistent |
exposure to money vs. control (instruction background) |
system justification scale |
|
Vaughn (2014,Ithaca)
|
Caruso et al. (2013) Study 1
|
very close |
money priming (instruction background) |
ML1 #19 |
30 |
d = .77 ± .74 |
90 |
d = .01 ± .40 |
no signal - inconsistent |
exposure to money vs. control (instruction background) |
system justification scale |
|
Rutchick (2014,CSUN)
|
Caruso et al. (2013) Study 1
|
very close |
money priming (instruction background) |
ML1 #20 |
30 |
d = .77 ± .74 |
96 |
d = .01 ± .40 |
no signal - inconsistent |
exposure to money vs. control (instruction background) |
system justification scale |
|
Adams & Nelson (2014,PSU)
|
Caruso et al. (2013) Study 1
|
very close |
money priming (instruction background) |
ML1 #21 |
30 |
d = .77 ± .74 |
95 |
d = .01 ± .42 |
no signal - inconsistent |
exposure to money vs. control (instruction background) |
system justification scale |
|
Brumbaugh & Storbeck (2014,QCCUNY) Study 1
|
Caruso et al. (2013) Study 1
|
very close |
money priming (instruction background) |
ML1 #22 |
30 |
d = .77 ± .74 |
103 |
d = .03 ± .36 |
no signal - inconsistent |
exposure to money vs. control (instruction background) |
system justification scale |
|
Levitan (2014,Oxy)
|
Caruso et al. (2013) Study 1
|
very close |
money priming (instruction background) |
ML1 #23 |
30 |
d = .77 ± .74 |
123 |
d = .03 ± .39 |
no signal - inconsistent |
exposure to money vs. control (instruction background) |
system justification scale |
|
Smith (2014,OSU)
|
Caruso et al. (2013) Study 1
|
very close |
money priming (instruction background) |
ML1 #24 |
30 |
d = .77 ± .74 |
107 |
d = .10 ± .38 |
no signal - inconsistent |
exposure to money vs. control (instruction background) |
system justification scale |
|
Kurtz (2014,JMU)
|
Caruso et al. (2013) Study 1
|
very close |
money priming (instruction background) |
ML1 #25 |
30 |
d = .77 ± .74 |
174 |
d = .11 ± .30 |
no signal - inconsistent |
exposure to money vs. control (instruction background) |
system justification scale |
|
Brumbaugh & Storbeck (2014,QCCUNY2) Study 2
|
Caruso et al. (2013) Study 1
|
very close |
money priming (instruction background) |
ML1 #26 |
30 |
d = .77 ± .74 |
86 |
d = .13 ± .43 |
no signal - inconsistent |
exposure to money vs. control (instruction background) |
system justification scale |
|
Wichman (2014,WKU)
|
Caruso et al. (2013) Study 1
|
very close |
money priming (instruction background) |
ML1 #27 |
30 |
d = .77 ± .74 |
103 |
d = .18 ± .26 |
no signal - inconsistent |
exposure to money vs. control (instruction background) |
system justification scale |
|
Davis & Hicks (2014,TAMUON) Study 2
|
Caruso et al. (2013) Study 1
|
very close |
money priming (instruction background) |
ML1 #28 |
30 |
d = .77 ± .74 |
225 |
d = .18 ± .36 |
no signal - inconsistent |
exposure to money vs. control (instruction background) |
system justification scale |
|
Pilati (2014,brasilia)
|
Caruso et al. (2013) Study 1
|
very close |
money priming (instruction background) |
ML1 #29 |
30 |
d = .77 ± .74 |
120 |
d = .18 ± .40 |
no signal - inconsistent |
exposure to money vs. control (instruction background) |
system justification scale |
|
Furrow & Thompson (2014,MSVU)
|
Caruso et al. (2013) Study 1
|
very close |
money priming (instruction background) |
ML1 #30 |
30 |
d = .77 ± .74 |
85 |
d = .19 ± .43 |
no signal - inconsistent |
exposure to money vs. control (instruction background) |
system justification scale |
|
Brandt et al. (2014,Tilburg)
|
Caruso et al. (2013) Study 1
|
very close |
money priming (instruction background) |
ML1 #31 |
30 |
d = .77 ± .74 |
80 |
d = .21 ± .44 |
no signal - inconsistent |
exposure to money vs. control (instruction background) |
system justification scale |
|
Bocian & Frankowska (2014,SWPS) Study 1
|
Caruso et al. (2013) Study 1
|
very close |
money priming (instruction background) |
ML1 #32 |
30 |
d = .77 ± .74 |
79 |
d = .21 ± .45 |
no signal - inconsistent |
exposure to money vs. control (instruction background) |
system justification scale |
|
Nier (2014,Conncoll)
|
Caruso et al. (2013) Study 1
|
very close |
money priming (instruction background) |
ML1 #33 |
30 |
d = .77 ± .74 |
95 |
d = .23 ± .40 |
no signal - inconsistent |
exposure to money vs. control (instruction background) |
system justification scale |
|
Woodzicka (2014,WL)
|
Caruso et al. (2013) Study 1
|
very close |
money priming (instruction background) |
ML1 #34 |
30 |
d = .77 ± .74 |
90 |
d = .24 ± .41 |
no signal - inconsistent |
exposure to money vs. control (instruction background) |
system justification scale |
|
Schmidt & Nosek (2014,UVA)
|
Caruso et al. (2013) Study 1
|
very close |
money priming (instruction background) |
ML1 #35 |
30 |
d = .77 ± .74 |
81 |
d = .30 ± .44 |
no signal - inconsistent |
exposure to money vs. control (instruction background) |
system justification scale |
|
Morris (2014,McDaniel)
|
Caruso et al. (2013) Study 1
|
very close |
money priming (instruction background) |
ML1 #36 |
30 |
d = .77 ± .74 |
98 |
d = .40 ± .40 |
no signal - inconsistent |
exposure to money vs. control (instruction background) |
system justification scale |
|
Rohrer et al. (2015) Study 1
|
Caruso et al. (2013) Study 1
|
very close |
money priming (instruction background) |
individual effort |
30 |
d = .77 ± .74 |
136 |
d = -.07 ± .34 |
no signal - inconsistent |
exposure to money vs. control (instruction background) |
system justification scale |
|
Rohrer et al. (2015) Study 2
|
Caruso et al. (2013) Study 2
|
very close |
money priming (scrambled sentence) |
individual effort |
168 |
d = .43 ± .30 |
420 |
d = .06 ± .19 |
no signal - inconsistent |
exposure to money vs. control (scrambled sentence) |
belief in a just world scale |
|
Schuler & WÃ¤nke (in press) Study 2
|
Caruso et al. (2013) Study 2
|
very close |
money priming (scrambled sentence) |
individual effort |
168 |
d = .43 ± .30 |
115 |
d = -.09 ± .39 |
no signal - inconsistent (imprecise) |
exposure to money vs. control (scrambled sentence) |
belief in a just world scale |
|
Rohrer et al. (2015) Study 3
|
Caruso et al. (2013) Study 3
|
very close |
money priming (scrambled sentence) |
individual effort |
80 |
d = .49 ± .44 |
156 |
d = -.06 ± .31 |
no signal - inconsistent |
exposure to money vs. control (scrambled sentence) |
social dominance orientation scale |
|
Rohrer et al. (2015) Study 4
|
Caruso et al. (2013) Study 4
|
very close |
money priming (instruction background) |
individual effort |
48 |
d = .69 ± .58 |
116 |
d = .13 ± .37 |
no signal - inconsistent |
exposure to money vs. control (instruction background) |
Fair Market Ideology scale |
|
Grenier et al. (2012)
|
Vohs et al. (2006) Study 3
|
close |
money priming (scrambled sentence) |
individual effort |
39 |
d = .65 ± .65 |
40 |
d = .07 ± .62 |
no signal - consistent |
exposure to money vs. control (scrambled sentence) |
helping others (coding sheets for RA) |


Session Token Creation
For encoding/decoding data you can use this site:
Encode/Decode
ACTION:
MERCHANT:
MERCHANTUSER:
MERCHANTPASSWORD:
SESSIONTYPE
MERCHANTPAYMENTID:
CURRENCY:
AMOUNT:
RETURNURL:
CUSTOMER:
CUSTOMERNAME:
CUSTOMEREMAIL:
CUSTOMERIP
CUSTOMERPHONE:
CUSTOMERBIRTHDAY:
CUSTOMERUSERAGENT:
NAMEONCARD:
CARDPAN:
CARDEXPIRY:
CARDCVV:
BILLTOADDRESSLINE:
BILLTOCITY:
BILLTOCOUNTRY:
BILLTOPOSTALCODE:
BILLTOPHONE:
SHIPTOADDRESSLINE:
SHIPTOCITY
SHIPTOCOUNTRY:
SHIPTOPOSTALCODE:
SHIPTOPHONE:
ORDERITEMS:
Decoded ORDERITEMS param: [ { "code" : "T00D3AITCC", "name" : "Galaxy S8+", "description" : "The Samsung Galaxy S8 is Android smartphone...", "amount" : 1000, "quantity" : 1 } ]
EXTRA:
Decoded EXTRA param: {"AlwaysSaveCard":"true","IntegrationModel":"API"}


Warehouse Keeper Sorint:
These are the prizes available for level 3 members.
Moonstone Earring - M. Def.: 39 Weight: 150
Nassen's Earring - M. Def.: 48 Weight: 150
Ring of Sage - M. Def.: 32 Weight: 150
Necklace of Protection - M. Def.: 56 Weight: 150


| packrat-package | Packrat: Reproducible dependency management |
| bundle | Bundle a Packrat Project |
| clean | Remove Packages from the Library |
| disable | Disable the use of Packrat in a Project |
| extlib | Managing External Libraries |
| get_opts | Get/set packrat project options |
| init | Initialize Packrat on a new or existing R project |
| install | Install a local development package. |
| install_github | Install a package from GitHub |
| install_local | Install a Package from a Local Repository |
| migrate | Migrate to Packrat Mode |
| off | Packrat Mode |
| on | Packrat Mode |
| opts | Get/set packrat project options |
| packify | Automatically Enter Packrat Mode on Startup |
| packrat | Packrat: Reproducible dependency management |
| packrat-external | Managing External Libraries |
| packrat-mode | Packrat Mode |
| packrat-options | Get/set packrat project options |
| packrat_lib | Managing External Libraries |
| packrat_mode | Packrat Mode |
| repository-management | Add a Repository |
| repos_add | Add a Repository |
| repos_add_local | Add a Repository |
| repos_create | Create a Local, CRAN-like Repository |
| repos_list | Add a Repository |
| repos_remove | Add a Repository |
| repos_set | Add a Repository |
| repos_upload | Upload a Package to a Local CRAN-like Repository |
| restore | Apply the most recent snapshot to the library |
| search_path | Get Packages on the Search Path |
| set_opts | Get/set packrat project options |
| snapshot | Capture and store the packages and versions in use |
| status | Show differences between the last snapshot and the library |
| unbundle | Unbundle a Packrat Project |
| unused_packages | Find Unused Packages in a Project |
| user_lib | Managing External Libraries |
| with_extlib | Managing External Libraries |


| transactions {DBI} | R Documentation |
A transaction encapsulates several SQL statements in an atomic unit.
It is initiated with dbBegin()
and either made persistent with dbCommit()
or undone with dbRollback()
.
In any case, the DBMS guarantees that either all or none of the statements
have a permanent effect.
This helps ensuring consistency of write operations to multiple tables.
dbBegin(conn, ...) dbCommit(conn, ...) dbRollback(conn, ...)
conn |
A DBIConnection object, as returned by
|
... |
Other parameters passed on to methods. |
Not all database engines implement transaction management, in which case these methods should not be implemented for the specific DBIConnection subclass.
dbBegin()
, dbCommit()
and dbRollback()
return TRUE
, invisibly.
The implementations are expected to raise an error in case of failure,
but this is not tested.
In any way, all generics throw an error with a closed
or invalid connection.
In addition, a call to dbCommit()
or dbRollback()
without a prior call to dbBegin()
raises an error.
Nested transactions are not supported by DBI,
an attempt to call dbBegin()
twice
yields an error.
Actual support for transactions may vary between backends.
A transaction is initiated by a call to dbBegin()
and committed by a call to dbCommit()
.
Data written in a transaction must persist after the transaction is committed.
For example, a record that is missing when the transaction is started
but is created during the transaction
must exist
both during
and after the transaction,
and also in a new connection.
A transaction
can also be aborted with dbRollback()
.
All data written in such a transaction must be removed after the
transaction is rolled back.
For example, a record that is missing when the transaction is started
but is created during the transaction
must not exist anymore after the rollback.
Disconnection from a connection with an open transaction effectively rolls back the transaction. All data written in such a transaction must be removed after the transaction is rolled back.
The behavior is not specified if other arguments are passed to these
functions. In particular, RSQLite issues named transactions
with support for nesting
if the name
argument is set.
The transaction isolation level is not specified by DBI.


April 2017/ Past Issues
It’s that time of year again! ZTEsoft Annual Summit 2017 will be taking place at a unique castle on French riviera - Château de Crémat on May 18, 2017.
ZTEsoft Annual Summit 2017 brings together executives from major communication service providers that are using ZTEsoft innovative solutions and executive industry experts to network, learn and collaborate on becoming digital service providers. The Summit is a place where thought leaders from around the world are encouraged to share ideas, experiences, and knowledge.
 We keep them all the time from the moment we wake up in the morning until the minute we fall asleep. The digital life will always be on, always open. How do communication service providers transform themselves in an effect to enable customers to enjoy the always-on digital life? ZTEsoft believes the power of CSPs, vendors and industry players can be united together to offer unparalleled experience and competence on the way to digital success.
ZTEsoft Annual Summit incorporates a full morning of sessions for leading industry analysts. It’s a great opportunity for the delegates to learn from and network with analysts across the industry. The Summit offers a more relaxed way of engaging in thought-provoking round-table discussions and gives you access to insightful keynote speeches. ZTEsoft will also share its own vision and thoughts on how to address the industry’s most pressing issues and strategies with POWER for digital revolution.
It definitely will be an enjoyable time, together with you. Join us at the Summit to share and network with executives from major communication service providers and industry experts in a more relaxing way.
ZTEsoft Annual Summit 2017 is invitation only. For more information or questions, please contact us at<EMAIL_ADDRESS>or call at +86 139 5177 1387.
We look forward to seeing you in Nice!
ZTEsoft is dedicated to providing you with the latest, high-value information that will help you to know more about us. Contact us at<EMAIL_ADDRESS>with any questions.
If you no longer wish to receive these email, click here to unsubscribe.


The window is a border between private space and public space. Floor-to-ceiling windows give apartment residents an excellent view to the outside, but also provide people outside a great view of the inside. That presents a privacy issue.
Why do people fear the invasion of privacy? Is it because they fear that the public will piece together fragments of their personal information and figure out who they are?
A QR code has been placed on my apartment window, viewable to people standing outside. By scanning the QR code, viewers can enter my virtual room. In this room, they can identify what kind of person I am and what my life is. Different people may find various clues in this room and may reach different conclusions.


Notes
-
USE THE DEVELOPER CONSOLE
-
Use `xmeans` object to generate test data and perform clustering
-
xmeans.genTestData(dims, n) generates n uniformly random dims-dimensional points
-
xmeans.genTestData2(dims, n, k, sd) generates n, dims-dimensional points clustered around k centroids with standard deviation of sd
-
xmeans.genTestData3() generates 2d clustered data on a diagonal
-
xmeans.xmeans(data, options) returns clustering data. options is a plain object with optional properties "kMin" and "kMax", by default 2 and n/4, respectively.
-
xmeans.oxmeans(data, options) runs xmeans.xmeans(...) repeatedly looking for the best model (maximize BIC). options is a plain object that accepts optional properties for xmeans.xmeans(...) and an additional option, "iters" (default 3).
-
xmeans.render(clusters) to render the output of xmeans.xmeans(...)


Bootstrap split buttons and dropdowns on button groups are supported with the existing dropdown directives.


For Sex, there are three choices:
Male = Generate Male NPCs onlyFemale = Generate Female NPCs onlyRandom = Generate NPCs of either sex
The default value is Random.
Warning
Your inputted data is case-sensitive.
For How many NPCs, choose a value from 1 to 100.
The default value is 10.
For Characteristic Roll, you may enter a roll type that you are familiar with when doing your Traveller character generation. The common Characteristic Roll is 2d6. Not so common rolls are Boon and 1d6+6.
Warning
Whichever roll type you enter, keep in mind that the roll result range must not be larger than 2 - 15.
The generated NPCs will output in this format:
NPC names typically will start with a title, depending on their career or their social standing. Characters of nobility will have longer names and include the homeworld they are from.
Next is the NPC's UPP, their sex, and age. The social class that the NPC was raised in is shown, along with how many terms they were in a career.
And last is the NPC's list of skills.


MISSISSIPPI MASALA A film review by Ken Johnson Copyright 1992 Ken Johnson
130 min., R, Drama, 1991 Director: Mira Nair Cast: Denzel Washington, Roshan Seth, Sarita Chudhury, Sharmila Tangore, Charles S. Dutton
Indian (from India) Sarita Chudhury, who moved from Africa to Mississippi with her family 18 years before, meets Denzel Washington, a Black who owns his own rug cleaning business. The two fall in love, but their relationship is threatened by Sarita Chudhury's father, who dislikes Blacks.
MISSISSIPPI MASALA is by the director of SALAAM BOMBAY!, and I feel that it is a very good film, and definitely worth seeing. I almost didn't go to see this film, because the title, at least to me, sounded boring, and I had only seen one preview for the film and it didn't tell too much. I give MISSISSIPPI MASALA a four on a scale of zero to five. MISSISSIPPI MASALA is rated R for explicit language, adult situations, and mild violence.
When I saw the advertisements for MISSISSIPPI MASALA I wasn't quite sure of what it was going to be about. It looked like it was either going to be a remake of ROMEO AND JULIET or an immigrant drama. It was more of a remake of ROMEO AND JULIET than an immigrant drama, but it really wasn't either.
Denzel Washington gave a great performance, along with the rest of the cast. What I think that I liked most about the film, was the screenplay. It was very well crafted and it held my interest throughout the film. Also the locations for the film were well-picked. I don't think that the film would have been nearly as good if the film was set in a rich neighborhood.
I think that MISSISSIPPI MASALA deserved a better release than it got, at least in my area. My local art/import theater picked it up and played it for four days. If given the chance, I think that the film would have done very well in a real movie theater, and gotten a bigger audience.
Ken J<EMAIL_ADDRESS>.
The review above was posted to the
rec.arts.movies.reviews newsgroup (de.rec.film.kritiken for German reviews).
The Internet Movie Database accepts no responsibility for the contents of the
review and has no editorial control. Unless stated otherwise, the copyright
belongs to the author.
Please direct comments/criticisms of the review to relevant newsgroups.
Broken URLs inthe reviews are the responsibility of the author.
The formatting of the review is likely to differ from the original due
to ASCII to HTML conversion.
Related links: index of all rec.arts.movies.reviews reviews


Getting Started
Usage
README
Components
Contact Form A
Popup Box A
Theta Tools
Home
Contributing
Documentation
Home
/
Documentation
/ Getting Started / Usage


Reports failed method calls or assertions in tests. It helps to detect the failed line in code faster and start debugging it immediately if needed.
Example:
@Test fun foo() { assertEquals(1, 0) // highlighted }


JavaScript is another incredibly popular language. Many websites that you use every day rely on JavaScript including Twitter, Gmail, Spotify, Facebook, and Instagram according to General Assembly.


Searching is the act of sending a request which delegates to one or more searchers. The results from the searches is then returned.
The SummaSearcher defines the outer interface, SearchNode defines the underlying searchers.
SearchNodeFactory - constructs SearchNodes from properties. SummaSearcherImpl - handles queueing of requests to a single SearchNode. SearchNodeAggregator - contains one or more SearchNodes that are searched in parallel or in sequence, depending on setup. SearchNodeImpl - base inplementation of SearchNode that handles threading and open/close/warmup coordination.


An awesome website is coming soon to this address!
The web hosting service for this web page is provided by SiteGround.com. Our client is still working on their website, but nice of you to come by. Check back again!
If you are the owner of this domain name and would like to remove this page, you simply have to upload your website into the /public_html folder of your SiteGround hosting account. Login Here.


The new update to Spotify changed how albums appear in your Library. Albums that previously appeared in your Library are now gone. This website will help you add them back to your Library.


At PetFirst, we want to care for your pets from puppyhood throughout all nine of their lives.
Unlike some of our competitors, we don't have an upper age restriction for enrollment. We allow dogs and cats ages 8 weeks and older to enroll in our pet insurance plans, making it easier to afford veterinary care for the long haul. Though your premiums may increase as your dog or cat gets older, your pet's coverage will remain the same and never will be reduced or limited due to age.
Sometimes the first sign of medical trouble is behavior changes. Detecting these changes can be critical in early diagnosis and treatment. According to the American Veterinary Medical Association (AVMA), if you're caring for senior dogs or cats and your pet is showing any of the signs below, it may be an indicator that something else is wrong:
Cancer is responsible for nearly 50% of deaths of pets older than 10 years of age. So, as your dog or cat gets older, their rate of cancer increases as well. Our plans cover cancer treatments, as well as the diagnostic tests like x-rays, blood tests and biopsies (as long as the cancer isn't showing symptoms or diagnosed prior to the effective date of the policy). Cancer can be expensive, and catching it early can help decrease your treatment costs.


 Doing so will return you to the Select File and Validate page and you will need to resubmit your HMDA File for validation.
Do you really want to reset your session?


Soil Compositions of Physical and Chemical Characteristics
soils
A data frame with 48 rows and 14 variables:
Group
grouping variable with 12 levels, corresponding to the combinations of Contour and Depth
Contour
character variable with 3 levels: Depression Slope Top
Depth
character variable with 4 levels: 0-10 10-30 30-60 60-90
Gp
character variable with 12 levels, giving abbreviations for the groups: D0 D1 D3 D6 S0 S1 S3 S6 T0 T1 T3 T6
Block
a factor with levels 1 2 3 4
pH
soil pH
N
total nitrogen in %
Dens
bulk density in gm/cm$^3$
P
total phosphorous in ppm
Ca
calcium in me/100 gm.
Mg
magnesium in me/100 gm.
K
phosphorous in me/100 gm.
Na
sodium in me/100 gm.
Conduc
conductivity
Rdatasets from vincentarelbundock github
Soil characteristics as measured on samples from three types of contours (Top, Slope, and Depression) and at four depths (0-10cm, 10-30cm, 30-60cm, and 60-90cm). The area was divided into 4 blocks, in a randomized block design.


Sketchpad
So I've always been fascinated with the HTML5 Canvas tag. My plan is to make a mouse and touch friendly sketchpad!
Wishlist
- Get the canvas loaded!
- Get mouse click drawing working
- Get drawing working
- Basic toolbar with presets
- Customizeable brushes, pens, markers, etc.
- Undo/Redo/History
- Save/Load with JSON file
- Animated Drawings
- Graphing Calculator Mode!
Without further adieu...let's get started!


Simple NWJS project that enables intranet and extranet websites send ZPL labels to network printers.
Needing to replace the printer, contact the technical support.
Simple NWJS project that enables intranet and extranet websites send ZPL labels to network printers.
Needing to replace the printer, contact the technical support.


Reference Id: SFR34/2012
Publication type: Performance tables
Publication data: Local authority data
Region: England
Release date: 13 December 2012
Coverage status: Final/provisional
Publication status: Published
The primary school performance tables show:
-
results from the key stage 2 (KS2) tests in reading and mathematics
-
KS2 teacher assessments in English, reading, writing, mathematics and science; and
-
KS1 to 2 progress measures in English and mathematics.
-
key measures for sub-groups of pupils in each school, including disadvantaged pupils, low-, middle- and high-attaining pupils, boys, girls, pupils with English as an additional language and pupils who have been in the school throughout the whole of years 5 and 6 (non-mobile pupils). Additional school-level data is also available, including:
-
information on the expenditure of each maintained school open for the full financial year 2011 to 2012. The expenditure data takes the form of spend per pupil for a wide range of expenditure categories including funding and income, education staff spend and learning resources and curriculum spend
-
the numbers of teachers, teaching assistants and other school staff. It also provides the pupil-teacher ratio and the mean gross salary of full-time teachers;
-
information on the characteristics of the pupils attending the school;
-
pupil absence data for each school.
The data can be viewed and downloaded from the
school performance tables
section.
Data at national and local authority level of key stage 2 national curriculum assessments from 2011 is also available .
Jayne Middlemas
0114 274 2117
jayne.middlemas@education.gsi.gov.uk


How To Play
Each player has 3 'seeds' . Place your 'seed' on the board by clicking an empty location.
After the second player plays his/her third 'seed', you can move you 'seed' from one location to another
by
clicking on the the 'seed' you want to move, then click the new location.
For Online players, you can:
- Create a new game: You can create a new game by filling a session password. You will
be
given a session name and password. You can then give the session name and password to your friend.
- Join a game: You can join a game by filling the session name and password. You will be asked if you
want to join the session. Click the join button.
- Note: Only two players are allowed in a session.


Farming Simulator 20's Switch Release Gets a Pokémon-Themed Trailer
by Alistair JonesFarming Simulator is coming to Switch next month, so what’s a marketing team to do to mark that occasion but riff on one of Nintendo’s most successful IPs of all time? Clearly, the answer is ‘not a lot’, because publisher Focus home Interactive has just released a trailer inspired by Pokémon.
In case you were wondering, that ‘inspiration’ isn’t something that the trailer leans particularly far away from, opening to the slogan “gotta farm ‘em all,” complete with its own take on the series blue and yellow text.
From there, things get even more egregious, as players are encouraged to collect and then presumably evolve their farm vehicles (tractor, bigger tractor, thresher (?) is my favourite evolution line). If heavy machinery isn’t your jam, you can also raise a host of animals, although I think I prefer Game Freak’s takes on Wooloo and Rapidash. Of course, no Pokémon parody would be complete without a nod to the anime, so Ash’s iconic baseball cap switcheroo makes an appearance.
This isn’t the first time Farming Simulator’s trailer have leaned towards the bombastic, but I’m pretty sure this blows last year’s Fast and Furious-themed trailer out of the water.


Quick Xcode 4 Tip: Deleting Derived Data to Rescue Code Completion
Quick Tip:
I had an issue on a very large project in XCode. Starting 2 days ago, all of my syntax highlighting was completely broken. This is bad, but even worse, my code completion worked only partially. I was getting autocomplete for local variables only. When calling framework methods with a lot of parameters, I rely pretty heavily on code completion. I tried all of the usual fixes.
This includes: - Doing a Project Clean (⌘⇧K) - Doing a Clean Build Folder(⌥⌘⇧K) - Restarting XCode
None of these solutions fixed the issue. After some digging, I came across this StackOverflow Post which pointed me in the right direction. Seems like sometimes the project index, which contains metadata used to display code completion suggestions (among other things), can become corrupted. The solution is to delete it. This will force XCode to regenerate it, which rebuilds the index.
Here are the steps to delete your Derived Data folder:
- Go to XCode Settings (⌘,)
- Click the Locations tab. You will see a screen like this:
- Click the small arrow next to the location of the Derived Data folder. This will open the Finder to the location of the Derived Data for any XCode projects you currently have on your Mac.
- Find the one that starts with the name of your current project and delete it.
- You should immediately see XCode start to re-index your project.
For me, after this process completed, I got all of my functionality back. Your mileage may vary, and please make sure you back everything up before deleting anything!


The $animateProvider allows developers to register JavaScript animation event handlers directly inside of a module.
When an animation is triggered, the $animate service will query the $animate service to find any animations that match
the provided name value.
|


[§482-53] Common law rights. Nothing in this chapter shall adversely affect common law rights in trade names, entity names, service marks, or trademarks, or the enforcement of those rights. [L 2008, c 108, pt of §1]


Associated Targets ({{this.disease.targetCountsTotal || this.disease.associationCount}})
Mondo Description
{{this.disease.mondoDescription}}
Uniprot Description
{{this.disease.uniprotDescription}}
Disease Ontology Description
{{this.disease.doDescription}}
Mondo Term and Equivalent IDs


643_TerryHung.py
def minnode(n, keyval, mstset):
mini =<PHONE_NUMBER>99
mini_index = None
for i in range(n):
if (mstset[i] == False and
keyval[i] < mini):
mini = keyval[i]
mini_index = i
return mini_index
def findcost(n, city):
# Array to store the parent
# node of a particular node.
parent = [None] * n
# Array to store key value
# of each node.
keyval = [None] * n
# Boolean Array to hold bool
# values whether a node is
# included in MST or not.
mstset = [None] * n
# Set all the key values to infinite and
# none of the nodes is included in MST.
for i in range(n):
keyval[i] =<PHONE_NUMBER>999
mstset[i] = False
# Start to find the MST from node 0.
# Parent of node 0 is none so set -1.
# key value or minimum cost to reach
# 0th node from 0th node is 0.
parent[0] = -1
keyval[0] = 0
# Find the rest n-1 nodes of MST.
for i in range(n - 1):
# First find out the minimum node
# among the nodes which are not yet
# included in MST.
u = minnode(n, keyval, mstset)
# Now the uth node is included in MST.
mstset[u] = True
# Update the values of neighbor
# nodes of u which are not yet
# included in MST.
for v in range(n):
if (city[u][v] and mstset[v] == False and
city[u][v] < keyval[v]):
keyval[v] = city[u][v]
parent[v] = u
# Find out the cost by adding
# the edge values of MST.
cost = 0
for i in range(1, n):
cost += city[parent[i]][i]
return cost
class Solution:
def minCostConnectPoints(self, points: List[List[int]]) -> int:
n = len(points)
m = [[0] * n for i in range(n)]
for i in range(n):
for j in range(i+1, n):
a = points[i]
b = points[j]
dist = abs(a[0] - b[0]) + abs(a[1] - b[1])
m[i][j] = dist
m[j][i] = dist
return findcost(n, m)


STAR WARS
Battle Arena
Your browser does not support the audio element.
Obi-Wan Kenobi
Hit points
120
Luke Skywalker
Hit points
100
Darth Sidious
Hit points
150
Darth Maul
Hit points
180
Attack
Reset
" "


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| Skill No | Skill Name | Media Type | Is Active |
Campaign Name | OB Skill |
SLA Seconds |
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| {{modSkillData.skillId}} | {{modSkillData.mediaTypeName}} |
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Use Screen Pops |
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|---|---|---|---|---|---|---|---|---|---|
| Disposition Name | Associated | Priority |
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| {{disp.Description}} |


I've tried sinicuichi several times before, but it was only the other night that I met the ally face to face. Like many, I've underestimated this ethnobotanical, and have never quite understood why it appears with the likes of mushrooms and morning glory seeds on Xochipili's statue. I now realize how blind I was.
The ounce of sinicuichi that had just arrived looked very green and pungent-smelling. I attempted something similiar to the traditional preparation that involves fermenting the leaves for 24 hours under the sun. About five grams of dried herb were placed with two cups cold water in an ovenproof dish. The dish was left for 3.5 hours in the oven at 'warm' (around 140 F) temperature. Finally, the liquid was microwaved on 'high' until it began boiling, after which it was strained through a fine wire mesh (to separate the leaves) and drunk.
My old sinicuichi from was yellowish-green and made a pleasant-tasting tea. This brew (from a different vendor) was a far cry from my previous batch. I was shocked. The tea was almost as bitter as calea and smelled even more disgusting! I mixed in a tablespoon of sugar and pinched my nose as I drank the vile fluid in a one or two draughts.
My patience was well rewarded. In half an hour I realized that this sinicuichi session would be very different from my previous ones. The first hint was provided by a peculiar altered perception of colors, as if empty space were darker than usual and objects were emitting some faint radiance. Then it happened: for the first time, the 'sun opened' on me! [Sinicuichi is often called the 'sun opener' although reports of yellow vision are rare.] Everything was bathed in a soft, wondrous sunshine. I went into a dark room, and put the headphones on with some favorite music. Eyes closed, I experienced a euphoria that can only be described as overwhelming--certainly as strong, if not stronger than anything I've felt with kratom or even the Plant of Joy. There was a definite bodily sensation of flowing into the music, almost like morphing into a musical note. Incredible! A truly entheogenic experience if there ever was one!
The major disadvantage of sinicuichi is its short duration. The intense euphoria lasted for only a few minutes. I was back to baseline, except for some residual relaxation, in about two hours.
Still, the experiment was most encouraging. It's possible to have a true sinicuichi experience with commercial leaf with just under four hours of preparation. The brief but extremely refreshing experience is worth all that work. One might be amazed at how powerful this often-ignored ally really is!
Experience Reports are the writings and opinions of the individual authors who submit them.
Some of the activities described are dangerous and/or illegal and none are recommended by Erowid.


Egemen Yiğit Kömürcü Computer Science and Electronics Engineering Faculy of Engineering and Natural Sciences Sabancı University Tuzla, İstanbul 34956 +90(539)8185700 Linkedin Github CV


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CS108 Xamarin Library
1.0.14
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Image information structure /summary> More...
Public Member Functions | |
| ImageMessage () | |
| Constructor More... | |
Public Attributes | |
| IntPtr | puchBuffer |
| Pointer Buffer for image More... | |
| Int32 | nBufferSize |
| Size of buffer in bytes More... | |
| ImgFormat | imageFormat |
| Image Format for returned data More... | |
| uint | dwJpegQFactor = 80 |
| JPEG Quality Factor, default 80% More... | |
| Int32 | nBytesReturned |
| Number of bytes returned. More... | |
| SIZE | imgSize |
| Size of image returned. More... | |
| Int32 | nCapturedFrames |
| Number of frames captured prior to this image More... | |
| Int32 | nGain |
| Gain value used to capture this image More... | |
| Int32 | nExposureTime |
| Exposure time used to capture this image More... | |
| Int32 | nUnderexposedPixels |
| Number of underexposed pixels in image More... | |
| Int32 | nOverexposedPixels |
| Number of overexposed pixels in image More... | |
Image information structure /summary>
| CSLibrary.Structures.ImageMessage.ImageMessage | ( | ) |
Constructor
| uint CSLibrary.Structures.ImageMessage.dwJpegQFactor = 80 |
JPEG Quality Factor, default 80%
| ImgFormat CSLibrary.Structures.ImageMessage.imageFormat |
Image Format for returned data
| SIZE CSLibrary.Structures.ImageMessage.imgSize |
Size of image returned.
| Int32 CSLibrary.Structures.ImageMessage.nBufferSize |
Size of buffer in bytes
| Int32 CSLibrary.Structures.ImageMessage.nBytesReturned |
Number of bytes returned.
| Int32 CSLibrary.Structures.ImageMessage.nCapturedFrames |
Number of frames captured prior to this image
| Int32 CSLibrary.Structures.ImageMessage.nExposureTime |
Exposure time used to capture this image
| Int32 CSLibrary.Structures.ImageMessage.nGain |
Gain value used to capture this image
| Int32 CSLibrary.Structures.ImageMessage.nOverexposedPixels |
Number of overexposed pixels in image
| Int32 CSLibrary.Structures.ImageMessage.nUnderexposedPixels |
Number of underexposed pixels in image
| IntPtr CSLibrary.Structures.ImageMessage.puchBuffer |
Pointer Buffer for image


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Evaluations with Adversary Emulation
Understanding defensive coverage of the ATT&CK knowledge base is complex. ATT&CK has an ever-growing number of techniques. Each of the techniques can be executed in many ways (i.e. procedures). Adversary emulation lets us scope an evaluation that:
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Adversaries may execute the same technique, but in very different ways. We use procedural variation in our emulations to capture the same behavior via different methods to explore the depth of ATT&CK coverage.
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