| YOED-Youth Education |
| C |
14861 |
YOED |
2500 |
001 |
24M |
3.000 |
Planning and Assessment |
MW |
12:40 pm-02:05 pm |
15 |
15 |
0 |
0 |
0 |
0 |
Nadine Elizabeth Harris (P) |
01/17-05/04 |
COE 144 |
Experiential Learning and Credit from 4-Year College |
| NR |
14862 |
YOED |
2500 |
002 |
24M |
3.000 |
Planning and Assessment |
MW |
02:20 pm-03:45 pm |
15 |
12 |
3 |
0 |
0 |
0 |
Nadine Elizabeth Harris (P) |
01/17-05/04 |
COE 144 |
Experiential Learning and Credit from 4-Year College |
| C |
14863 |
YOED |
2500 |
003 |
24M |
3.000 |
Planning and Assessment |
T |
04:30 pm-07:30 pm |
15 |
15 |
0 |
0 |
0 |
0 |
Donald Snead (P) |
01/17-05/04 |
COE 146 |
Credit from 4-Year College |
| NR |
13202 |
YOED |
3000 |
002 |
24M |
3.000 |
Classroom Management |
TR |
11:20 am-12:45 pm |
12 |
9 |
3 |
0 |
0 |
0 |
Charlene Jewell True (P) |
01/17-05/04 |
COE 253 |
College of Educ Course Fee and Credit from 4-Year College and Upper-Division Work |
| NR |
17288 |
YOED |
3000 |
003 |
24M |
3.000 |
Classroom Management |
TR |
09:40 am-11:05 am |
12 |
10 |
2 |
0 |
0 |
0 |
Charlene Jewell True (P) |
01/17-05/04 |
COE 253 |
College of Educ Course Fee and Credit from 4-Year College and Upper-Division Work |
| NR |
15811 |
YOED |
3000 |
006 |
24M |
3.000 |
Classroom Management |
M |
04:30 pm-07:30 pm |
12 |
10 |
2 |
0 |
0 |
0 |
Lawrence J Kernagis (P) |
01/17-05/04 |
COE 148 |
College of Educ Course Fee and Credit from 4-Year College and Upper-Division Work |
| C |
12913 |
YOED |
3300 |
001 |
24M |
3.000 |
Problem Based Instructional Strategies |
TR |
11:20 am-12:45 pm |
12 |
15 |
-3 |
0 |
0 |
0 |
Terry M Weeks (P) |
01/17-05/04 |
COE 248 |
College of Educ Course Fee and Credit from 4-Year College and Upper-Division Work |
| NR |
12914 |
YOED |
3300 |
002 |
24M |
3.000 |
Problem Based Instructional Strategies |
TR |
09:40 am-11:05 am |
12 |
11 |
1 |
0 |
0 |
0 |
Terry M Weeks (P) |
01/17-05/04 |
COE 248 |
College of Educ Course Fee and Credit from 4-Year College and Upper-Division Work |
| NR |
13605 |
YOED |
3300 |
003 |
24M |
3.000 |
Problem Based Instructional Strategies |
TR |
08:00 am-09:25 am |
12 |
10 |
2 |
0 |
0 |
0 |
Terry M Weeks (P) |
01/17-05/04 |
COE 248 |
College of Educ Course Fee and Credit from 4-Year College and Upper-Division Work |
| NR |
12713 |
YOED |
3520 |
001 |
24M |
3.000 |
Knowing and Learning in Science and Mathematics |
TR |
08:00 am-09:25 am |
20 |
10 |
10 |
0 |
0 |
0 |
Laura Blackwell Clark (P) |
01/17-05/04 |
COE 110 |
College of Educ Course Fee and Credit from 4-Year College and Upper-Division Work |
| NR |
12734 |
YOED |
3550 |
001 |
24M |
3.000 |
Classroom Interactions in Mathematics and Science |
MW |
12:40 pm-02:05 pm |
12 |
9 |
3 |
0 |
0 |
0 |
Ryan S Jones (P), Sarah Elizabeth Millsap |
01/17-05/04 |
COE 146 |
College of Educ Course Fee and Credit from 4-Year College and Upper-Division Work |
| C |
13155 |
YOED |
4020 |
002 |
24M |
6.000 |
Residency I: Grades K-12 |
R |
08:00 am-04:15 pm |
4 |
4 |
0 |
0 |
0 |
0 |
Heather Kaye Dillard (P) |
01/17-05/04 |
LRC 101 |
College of Educ Course Fee and Credit from 4-Year College and Upper-Division Work |
| |
|
|
|
|
|
|
|
R |
04:30 pm-07:30 pm |
|
|
|
|
|
|
Heather Kaye Dillard (P) |
01/17-05/04 |
COE 164 |
College of Educ Course Fee and Credit from 4-Year College and Upper-Division Work |
| NR |
14727 |
YOED |
4020 |
003 |
24M |
6.000 |
Residency I: Grades K-12 |
W |
08:00 am-04:15 pm |
4 |
2 |
2 |
20 |
10 |
10 |
Heather Kaye Dillard (P) |
01/17-05/04 |
LRC 101 |
College of Educ Course Fee and Credit from 4-Year College and Upper-Division Work |
| |
|
|
|
|
|
|
|
W |
04:30 pm-07:30 pm |
|
|
|
|
|
|
Heather Kaye Dillard (P) |
01/17-05/04 |
COE 160 |
College of Educ Course Fee and Credit from 4-Year College and Upper-Division Work |
| |
|
|
|
|
|
|
|
W |
04:35 pm-07:30 pm |
|
|
|
|
|
|
Heather Kaye Dillard (P) |
01/17-05/04 |
COE 251 |
College of Educ Course Fee and Credit from 4-Year College and Upper-Division Work |
| NR |
16127 |
YOED |
4020 |
005 |
24M |
6.000 |
Residency I: Grades K-12 |
R |
08:00 am-04:15 pm |
4 |
3 |
1 |
24 |
12 |
12 |
Nadine Elizabeth Harris (P) |
01/17-05/04 |
LRC 101 |
College of Educ Course Fee and Credit from 4-Year College and Upper-Division Work |
| |
|
|
|
|
|
|
|
R |
04:30 pm-07:30 pm |
|
|
|
|
|
|
Nadine Elizabeth Harris (P) |
01/17-05/04 |
COE 164 |
College of Educ Course Fee and Credit from 4-Year College and Upper-Division Work |
| |
|
|
|
|
|
|
|
R |
04:35 pm-07:30 pm |
|
|
|
|
|
|
Nadine Elizabeth Harris (P) |
01/17-05/04 |
COE 251 |
College of Educ Course Fee and Credit from 4-Year College and Upper-Division Work |
| C |
13156 |
YOED |
4030 |
001 |
24M |
9.000 |
Residency I: Grades 7-12 |
MW |
08:00 am-04:15 pm |
4 |
4 |
0 |
20 |
10 |
10 |
Ashlee Brooke Ensey Hover (P) |
01/17-05/04 |
TBA TBA |
College of Educ Course Fee and Credit from 4-Year College and Upper-Division Work |
| |
|
|
|
|
|
|
|
W |
04:30 pm-07:30 pm |
|
|
|
|
|
|
Ashlee Brooke Ensey Hover (P) |
01/17-05/04 |
COE 160 |
College of Educ Course Fee and Credit from 4-Year College and Upper-Division Work |
| |
|
|
|
|
|
|
|
W |
04:35 pm-07:30 pm |
|
|
|
|
|
|
Ashlee Brooke Ensey Hover (P) |
01/17-05/04 |
COE 253 |
College of Educ Course Fee and Credit from 4-Year College and Upper-Division Work |
| C |
13157 |
YOED |
4030 |
002 |
24M |
9.000 |
Residency I: Grades 7-12 |
TR |
08:00 am-04:15 pm |
4 |
4 |
0 |
24 |
12 |
12 |
Nadine Elizabeth Harris (P) |
01/17-05/04 |
TBA TBA |
College of Educ Course Fee and Credit from 4-Year College and Upper-Division Work |
| |
|
|
|
|
|
|
|
R |
04:30 pm-07:30 pm |
|
|
|
|
|
|
Nadine Elizabeth Harris (P) |
01/17-05/04 |
COE 164 |
College of Educ Course Fee and Credit from 4-Year College and Upper-Division Work |
| |
|
|
|
|
|
|
|
R |
04:35 pm-07:30 pm |
|
|
|
|
|
|
Nadine Elizabeth Harris (P) |
01/17-05/04 |
COE 253 |
College of Educ Course Fee and Credit from 4-Year College and Upper-Division Work |
| C |
14729 |
YOED |
4030 |
003 |
24M |
9.000 |
Residency I: Grades 7-12 |
MW |
08:00 am-04:15 pm |
4 |
4 |
0 |
20 |
10 |
10 |
Ashlee Brooke Ensey Hover (P) |
01/17-05/04 |
TBA TBA |
College of Educ Course Fee and Credit from 4-Year College and Upper-Division Work |
| |
|
|
|
|
|
|
|
W |
04:30 pm-07:30 pm |
|
|
|
|
|
|
Ashlee Brooke Ensey Hover (P) |
01/17-05/04 |
COE 160 |
College of Educ Course Fee and Credit from 4-Year College and Upper-Division Work |
| |
|
|
|
|
|
|
|
W |
04:35 pm-07:30 pm |
|
|
|
|
|
|
Ashlee Brooke Ensey Hover (P) |
01/17-05/04 |
COE 253 |
College of Educ Course Fee and Credit from 4-Year College and Upper-Division Work |
| NR |
17264 |
YOED |
4030 |
004 |
24M |
9.000 |
Residency I: Grades 7-12 |
TR |
08:00 am-04:15 pm |
4 |
3 |
1 |
24 |
12 |
12 |
Nadine Elizabeth Harris (P) |
01/17-05/04 |
TBA TBA |
College of Educ Course Fee and Credit from 4-Year College and Upper-Division Work |
| |
|
|
|
|
|
|
|
R |
04:30 pm-07:30 pm |
|
|
|
|
|
|
Nadine Elizabeth Harris (P) |
01/17-05/04 |
COE 164 |
College of Educ Course Fee and Credit from 4-Year College and Upper-Division Work |
| |
|
|
|
|
|
|
|
R |
04:35 pm-07:30 pm |
|
|
|
|
|
|
Nadine Elizabeth Harris (P) |
01/17-05/04 |
COE 253 |
College of Educ Course Fee and Credit from 4-Year College and Upper-Division Work |
| NR |
17265 |
YOED |
4030 |
005 |
24M |
9.000 |
Residency I: Grades 7-12 |
TR |
08:00 am-04:15 pm |
4 |
2 |
2 |
24 |
12 |
12 |
Nadine Elizabeth Harris (P) |
01/17-05/04 |
TBA TBA |
College of Educ Course Fee and Credit from 4-Year College and Upper-Division Work |
| |
|
|
|
|
|
|
|
R |
04:30 pm-07:30 pm |
|
|
|
|
|
|
Nadine Elizabeth Harris (P) |
01/17-05/04 |
COE 164 |
College of Educ Course Fee and Credit from 4-Year College and Upper-Division Work |
| |
|
|
|
|
|
|
|
R |
04:35 pm-07:30 pm |
|
|
|
|
|
|
Nadine Elizabeth Harris (P) |
01/17-05/04 |
COE 253 |
College of Educ Course Fee and Credit from 4-Year College and Upper-Division Work |
| NR |
15848 |
YOED |
4040 |
001 |
24M |
4.000 |
Residency I: MTeach |
W |
08:00 am-12:00 pm |
4 |
2 |
2 |
20 |
4 |
16 |
Nancy Gail Caukin (P), Heather Kaye Dillard |
01/17-05/04 |
TBA TBA |
College of Educ Course Fee and Credit from 4-Year College and Upper-Division Work |
| |
|
|
|
|
|
|
|
W |
04:30 pm-07:30 pm |
|
|
|
|
|
|
Nancy Gail Caukin (P), Heather Kaye Dillard |
01/17-05/04 |
COE 247 |
College of Educ Course Fee and Credit from 4-Year College and Upper-Division Work |
| NR |
13158 |
YOED |
4040 |
002 |
24M |
4.000 |
Residency I: MTeach |
W |
08:00 am-12:00 pm |
4 |
2 |
2 |
20 |
4 |
16 |
Nancy Gail Caukin (P) |
01/17-05/04 |
TBA TBA |
College of Educ Course Fee and Credit from 4-Year College and Upper-Division Work |
| |
|
|
|
|
|
|
|
W |
04:30 pm-07:30 pm |
|
|
|
|
|
|
Nancy Gail Caukin (P) |
01/17-05/04 |
COE 247 |
College of Educ Course Fee and Credit from 4-Year College and Upper-Division Work |
| NR |
12915 |
YOED |
4050 |
001 |
24M |
3.000 |
Project-Based Instruction in Mathematics and Science |
TR |
04:20 pm-05:45 pm |
20 |
4 |
16 |
0 |
0 |
0 |
Nancy Gail Caukin (P), Mark M LaPorte |
01/17-05/04 |
COE 247 |
College of Educ Course Fee and Credit from 4-Year College and Upper-Division Work |
| NR |
11210 |
YOED |
6680 |
001 |
24M |
3.000 |
Issues and Trends in Teaching and Learning |
W |
04:30 pm-07:30 pm |
18 |
12 |
6 |
0 |
0 |
0 |
Meredith Anne S Higgs (P) |
01/17-05/04 |
COE 142 |
College of Educ Course Fee |


How long would you like to procrastinate?
- Under 2 minutes
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- More than 10 minutes
What music style are you most likely to enjoy on a Saturday night out?
- Richard Wagner
- Karaoke: My Heart Will Go On
- Weird Al Yankovic
- Pink Floyd
- Macklemore
You forgot to go grocery shopping and the dinner party is in an hour--what do you do?
- I can whip up something with this old mustard and frozen dumplings.
- It's not delivery, it's DiGiorno.
- Did I say dinner party, I meant beer tasting!
- I'll serve a vegatable medely from my garden.
- Just enough time for Julia Child's Coq au Vin.
First thing you’d do if you won 10 million dollars?

- Cleaning the house
- Carpentry repairs
- Taking a Code Academy class online
- Meditating or working out
- Binge-watching Parks and Rec
If you were stuck on a desert island with only one movie, which would it be?
- Monty Python and the Holy Grail
- Interstellar
- Fantastic Mr. Fox
- A Beautiful Mind
- A Brief History of Time
For which gameshow are you the perfect contestant?
- American Ninja Warrior
- Jeopardy
- Hell's Kitchen
- Wheel of Fortune
- Survivor: Whidbey Island


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Analysis¶
In this part we perform a mathematical analysis of the family of curves we found previously.
Definitions¶
Let \(p \in \mathbb N, p > 0\) and \(q \in \mathbb N, q > 0\). Let \(d = \gcd(p, q)\), \(p' = \frac{p}{d}\) and \(q' = \frac{q}{d}\). Let \(r \in \mathbb R\) and \(\delta_r \in \mathbb R\) such that \(0 < \delta_r < r\). Let’s define the following family of functions:
Let \(\Gamma_k\) be the graph of \(r_k\) in polar coordinates, that is the graph of \(\vec{r_k} : \theta \mapsto r_k(\theta) \cdot \vec u(\theta)\) where \(\vec u(\theta)\) is the unit vector at polar angle \(\theta\).
Intersections¶
For \((m, n) \in \mathbb Z^2\), \(\Gamma_m\) and \(\Gamma_n\) intersect if and only if \(\exists \theta_1, \theta_2 \in \mathbb R^2, \vec{r_m}(\theta_1) = \vec{r_n}(\theta_2)\).
\(0 < \delta_r < r\) and \(\cos\) is between \(-1\) and \(1\) so \(r_k(\theta) > 0\) and we can drop the second case.
\(\vec u\) is \(2 \pi \mbox{-periodic}\) and \(\cos\) is even and \(2 \pi \mbox{-periodic}\) so:
Case 1¶
So if we can find \(a\) and \(b\) such that \(n - m = a \cdot p + b \cdot q\), then \(\Gamma_m\) and \(\Gamma_n\) have an infinite number of intersection points and are identical.
According to Bézout’s identity, \(\exists (a, b) \in \mathbb Z, n - m = a \cdot p + b \cdot q\) if and only if \(n - m\) is a multiple of \(d\).
Reduction of the family¶
Applying this to \(n = m + d\) proves that \(\Gamma_{n + d}\) is identical to \(\Gamma_n\). It’s sufficient to consider intersections between \(\Gamma_m\) and \(\Gamma_n\) for \((m, n) \in [0, d-1]^2\) and \(m \le n\).
Periodicity of \(r_k\)¶
By construction, \(r_k\) is \(2 \cdot q \cdot \pi \mbox{-periodic}\).
For \(m = n\), we can use \(a = q'\) and \(b = -p'\). Then we have \(\theta_2 = \theta_1 + 2 \cdot q' \cdot \pi\). So \(r_k\) is \(2 \cdot q' \cdot \pi \mbox{-periodic}\). It’s sufficient to consider intersections where \((\theta_1, \theta_2) \in \left[0, 2 \cdot q' \cdot \pi\right[^2\).
Case 2¶
So this case correspond to “real”, individual intersection points.
Let’s reduce the domain we need to search for \(a\) and \(b\). So far, we have:
Replacing \(\theta_1\) and \(\theta_2\) in \(-2 \cdot q' \cdot \pi \lt \theta_2 - \theta_1 \lt 2 \cdot q' \cdot \pi\) and isolating \(a\), we obtain \(-q' + 1 \le a \lt q'\).
Then, replacing \(\theta_1\) and \(\theta_2\) in \(0 \le \theta_1 \lt 2 \cdot q' \cdot \pi\) and \(0 \le \theta_2 \lt 2 \cdot q' \cdot \pi\) and isolating b, we obtain:
We can regroup those two conditions in:
So to obtain all intersections, it’s enough to iterate on \(a\) and \(b\) in those ranges. Note that if \(m = n\), this will give us each intersection twice. We can consider only the cases where \(\theta_1 \lt \theta_2\), which reduces the range of \(a\) to \(1 \le a \lt q'\).
This is just a necessary condition on \(a\) and \(b\). We have proven that we obtain all intersections, but @todoc we might want to prove that we don’t get duplicates.
import fractions
import math
import matplotlib.pyplot as plt
import numpy as np
P = 7
Q = 8
plt.figure(figsize=(P, Q))
for p in range(1, P + 1):
for q in range(1, Q + 1):
d = fractions.gcd(p, q)
p_prime = p / d
q_prime = q / d
r = []
for k in range(d):
r.append(
lambda theta, k=k: 2 + np.cos((p * theta - 2 * k * np.pi) / q)
)
theta = np.arange(0, 2 * q_prime * np.pi, 0.01)
intersections = []
# Note that these 4 imbricated loops are only O(p*q)
for m in range(d):
for n in range(m, d):
if m == n:
min_a = 1
else:
min_a = -q_prime + 1
max_a = q_prime
for a in range(min_a, max_a):
min_b = int(math.ceil(float(abs(a) * p - m - n) / q))
max_b = 2 * p_prime - int(math.floor(float(abs(a) * p + m + n) / q))
for b in range(min_b, max_b):
theta_1 = (b * q - a * p + m + n) * np.pi / p
theta_2 = (b * q + a * p + m + n) * np.pi / p
assert 0 <= theta_1 < 2 * q_prime * np.pi
assert 0 <= theta_2 < 2 * q_prime * np.pi
assert m != n or theta_1 < theta_2
intersections.append((theta_1, r[m](theta_1)))
assert len(intersections) == p * (q - 1) # @todoc Explain
sp = plt.subplot(Q, P, (q - 1) * P + p, polar=True)
for k in range(d):
sp.plot(theta, r[k](theta))
sp.plot(
[theta for theta, r in intersections],
[r for theta, r in intersections],
"r."
)
sp.set_rmin(0)
sp.set_rmax(3.1)
sp.set_yticks([1, 2, 3])
sp.set_yticklabels([])
sp.set_xticklabels([])
sp.spines['polar'].set_visible(False)
plt.tight_layout()


Battery & Charging
Just like the other models in the Renault Z.E. Range, ZOE is fitted with a lithium-ion battery, offering great performance. It is very simple to charge, even when it is fully discharged. It can also be recycled at the end of its life.
Purchase options
ZOE Battery
FOR MORE INFORMATION ON
Battery Hire
How it works
Hiring a battery keeps monthly costs affordable.
This ZOE offering allows you to buy the vehicle and lease the battery, which not only reduces the purchase price, but also gives maximum peace of mind. You only pay for what you need – you tell us how many miles you’ll do and how long you’d like to keep your Z.E.; we personalise your battery hire monthly payment to meet your own individual requirements.
Battery performance guaranteed to at least 75% of it’s original charge capacity. If not, we’ll repair or replace it. Complete 24/7 roadside assistance , even if you run out of charge. You enjoy your Z.E. while we keep its heart healthy. It’s that easy.
| Contract Term | Up to 7,500 miles per annum | 9,000 miles | 10,500 miles | 12,000 miles |
|---|---|---|---|---|
| 36 months+ | £70 | £77 | £85 | £93 |
| 24 months | £80 | £87 | £95 | £103 |
| 12 months | £90 | £97 | £105 | £113 |
Battery Rental for ZOE including VAT (with Assistance for all breakdowns)*
*Additional combinations of terms and mileages are available, please consult your local Renault ZE dealer. Finance and hire facilities are provided by RCI Financial Services Limited t/a Renault Finance and are subject to status.
| Contract Term | Up to 3,000 miles per annum |
|---|---|
| 36 months+ | £45 |
| 24 months | £55 |
| 12 months | £65 |
Full Purchase
How it works
We also off the ZOE i which allows you to buy the vehicle and the battery.
- No on-going monthly payments, just one simple payment.
- No mileage limit in the battery contract.
- Battery guarantee of 5 years/62,000 miles (whichever comes first), this includes the battery performance guarantee to at least 70% of its original capacity.
- Roadside assistance, breakdown only.
| Version | Total Retail Price After PICG |
|---|---|
| i Expression |
£18,442.80 incl. VAT |
| i Dynamique ZEN |
£20,043.60 incl. VAT |
| i Dynamique INTENS | £20,043.60 incl. VAT |
*Additional combinations of terms and mileages are available, please consult your local Renault ZE dealer. Finance and hire facilities are provided by RCI Financial Services Limited t/a Renault Finance and are subject to status.
British Gas
Z.E. Charging Partner
The optimum partner in charging Z.E. vehicles.
Wall box included customers
No fuss, clean, easy - these are all things that we love about Renault ZOE.
Exclusively for retail customers, a combination of government funding and Renault funding means our charging partner, British Gas, can install a free 7KW tethered wall box at your home when you order a ZOE.
Its 7kW capacity means that your ZOE’s battery can be recharged from completely flat to 100% in just 3 to 4 hours, and Z.E. Ready certification means it’ll work perfectly with the car.
As long as you have a domestic property with off-street parking and an O2 data signal, you’re eligible – British Gas will check all this for you.
The process is simple , once your ZOE is ordered, British Gas will contact you automatically and schedule your installation at your convenience. The entire process normally takes 2-3 weeks once your ZOE has been ordered.
Non-Wall box included customers
What if you are not a retail customer?
Don’t worry , there is a simple form for you to fill out on the British Gas website to arrange for a home charge point installation.
Don't forget though, there’s a whole network of thousands of charging points available nationwide, as well as a network of over 600 rapid chargers, enabling you to charge up to 80% in just 30 minutes.
Charging
No fuss, clean and easy, for your everyday life.
Charging Network
A charging network is a network of publicly accessible charge points of various amounts of power. This system has been created through the combination of work by network providers, car manufacturers and the government.
In total there are over 6500 charge points spread across the UK that are accessible to the public through the use of R.F.I.D cards and smart phone applications.
The main manufacturers of these are:
Ecotricity
Ecotricity has its own charging network called ‘the Ecotricity Electric Highway’. This is a growing network of over 500 rapid chargers that are free to use. The points are located along motorways and aim to connect EV drivers across the UK.
Pod Point
The Pod Point network is one of the UK’s earliest EV charging networks. A £12.50 annual registration will give you access to about 1200 charging points spread over the UK. It works with a number of different providers to offer a range of charging options from 3kW to 43kW. Pod Point has recently announced that they will be switching to a Pay as you go scheme by 2015.
Polar
Charge Master Network (powered by polar energy) is one of the UK’s largest charging networks and has about 4000 charging points in the UK. There are several membership options to keep you connected. There is the option of a £20 annual subscription or simply using the pay as you go. To register for your Polar membership card: please e-mail here or telephone 01582 400 331 during office hours to obtain an application form.
3 ways to charge
Charging the ZOE is simple and flexible due to new chameleon charger technology. This means it can take charge from multiple types of charge points.
The first charging method is through your home wall box, provided by British Gas and is installed before your car is delivered. This is a 7 KW charger that can fully charge the ZOE in 3-4 hours. We recommend this is where you should do the majority of your charging so that topping up the car can be effortlessly integrated into your daily routine.
The second way to charge is by using a public charging post, of which there are already over 3,000 nationwide . These chargers range from 3KW - 22KW and can charge your car from 8 - 1 hours respectively. This type is ideal for round trips, such as going to and from one destination.
The final way to charge your ZOE is through rapid charging. These appliances (43KW capacity) are placed systematically at key locations such as motorway service stations for long journeys and have the ability to charge the battery to 80% in just half an hour. With over 100 rapid chargers installed nationwide already, this growing network will allow you to put in some serious mileage with your ZOE.
Z.E. Equipment
Designed for everyday life.
CHAMELEON CHARGER
Discover the Renault-patented Chameleon charger, which is able to adapt to the various available charging power levels. This innovation allows you to choose one of three modes to charge your ZOE: Standard Charge (full charge in 6 to 9 hours), 22kW Medium Charge (80% charge in 1 hour) and 43 kW Fast Charge (80% charge in 30 minutes).
CHARGING CABLE WITH Z.E. BAG
The supplied charging cable allows you to carry out a standard charge at a Z.E. Ready* certified charging wall box (version 1.2 or later version and with minimum power of 3kW) or a public charge point. It also allows you to carry out a medium charge (22 kW) on a public charge point. However, you may only carry out a fast charge (43 kW) with the cable connected to public charge points.
* Z.E. Ready is a trademark registered by Renault displayed on the wall boxes installed for the charging of electric vehicles in the Z.E. range. It contains the list of technical specifications and compliance tests to be carried out by the partner in order to be certified Z.E. Ready.
RANGE OPTIMIZER: HEAT PUMP
One system for two functions: to refresh the passenger compartment, the system works according to the same principle as standard modern air conditioning. To warm it up, the system works in reverse in order to capture the ambient heat in the air outside the vehicle. You thus minimise the energy drained from the battery and preserve your vehicle's driving range.
RANGE OPTIMIZER: REGENERATIVE BRAKING
The battery charges with every deceleration, when you lift your foot off the accelerator and when you press the brake pedal.
RANGE OPTIMIZER: ENERGY™ E-V MICHELIN TYRES
Enjoy ultra-low rolling resistance without modifying braking capacity or road-holding!
PASSENGER COMPARTMENT PRE-CONDITIONING
Programme the heating or air-conditioning whilst your ZOE is still charging. This way, the energy used for thermal comfort is not drained from the battery.
ECO MODE
An 'Eco' button lets you reduce the car's dynamic performance and the power of the air-conditioning or heating to extend your driving range.
ECONOMETER
Thanks to the econometer located on the screen behind the steering wheel, you can view your energy consumption in real-time. The screen changes colour according to your driving style and you can customise it according to 3 configurations.
Ready to take ZOE for a spin?
Go even further


All functions
|
|
ebnm_ash
|
ebnm_ash |
|
ebnm_pl
|
EBNM using point-laplace prior, from ebnm package. |
|
ebnm_pn
|
ebnm_pn |
|
flash_add_factors_from_data
|
Add factors to a flash fit object based on data. |
|
flash_add_fixed_f
|
Add a set of fixed factors to a flash fit object. |
|
flash_add_fixed_l
|
Add a set of fixed loadings to a flash fit object. |
|
flash_add_greedy
|
Fit Empirical Bayes Matrix Factorization (greedy algorithm) |
|
flash_add_lf
|
Add factors or loadings to f. |
|
flash_backfit
|
Refines a fit of the flash model to data by "backfitting". |
|
flash_f_sampler
|
Generates sampler for F |
|
flash_fill
|
Use a flash fit to fill in missing entries. |
|
flash_get_k
|
Get number of factors in a fit object. |
|
flash_get_ldf
|
flash_get_ldf |
|
flash_get_lf
|
Return the estimated LF' matrix. |
|
flash_get_objective
|
Get objective function from flash data and flash fit objects. |
|
flash_get_pve
|
flash_get_pve |
|
flash_l_sampler
|
Generates sampler for L |
|
flash_lf_sampler
|
Generates LF sampler |
|
flash_plot_factors
|
Plot the factor/loading pairs from flash results. |
|
flash_plot_pve
|
Scree plot. |
|
flash_set_data
|
Set up data for reading into flash. |
|
flash_update_precision
|
Update precision parameter. |
|
flash_zero_out_factor
|
Zero out a factor from f. |
|
flash
|
Fit Empirical Bayes Matrix Factorization |
|
plot_one_factor
|
Factor plot. |
|
udv_random
|
udv_random |
|
udv_si_svd
|
udv_si |
|
udv_si
|
udv_si |
|
udv_svd
|
udv_svd |


Shopping list
Cooking instructions
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| Package / Feature | Classic | Ultimate | Perfect |
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After EffectsPost production processing, including retouch, adding glance and applying variery of unique effects to photos |
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Photo AlbumPackage includes 8x12 inch album complete with photos |
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Photo BookPackage includes 13x13 inch Bevelled matt designer album. |
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Digital BookBeautiful Digital DVD Slideshow with unique effects and transitions |
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PRICE |
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| Package / Feature | Classic | Ultimate | Perfect |
|---|---|---|---|
Full ScenarioFull video coverage beginning at the Grooms home and then onto the Brides home, followed by the ceremony and unlimited locations. |
|||
After EffectsPost production processing, including retouch, adding glance and applying variery of unique effects to video |
|||
Video DVD MasteredClassy mastered video DVD with effects |
|||
PRICE |
$229999 |
$279999 |
$339999 |
I dreamed of a wedding of elaborate elegance,
A church filled with family and friends.
I asked him what kind of a wedding he wished for,
He said one that would make me his wife.
Antony and Melissa.
I remember when I was in school, they would ask,
'What are you going to be when you grow up?'
and then you'd have to draw a picture of it.
I drew a picture of myself as a bride.
Antony and Melissa.
I remember when I was in school, they would ask,
'What are you going to be when you grow up?'
and then you'd have to draw a picture of it.
I drew a picture of myself as a bride.
Jack and Lisa.
I remember when I was in school, they would ask,
'What are you going to be when you grow up?'
and then you'd have to draw a picture of it.
I drew a picture of myself as a bride.
John and Jessica.
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Pellentesque congue lacinia urna, sit amet pharetra nibh accumsan sed. Nunc porttitor pellentesque metus sed hendrerit.
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*Plese note: All Filds Required


#include <block.hpp>
Definition at line 29 of file block.hpp.
◆ block_num()
| uint32_t graphene::chain::block_header::block_num |
( |
| ) |
const |
|
inline |
◆ digest()
| digest_type graphene::chain::block_header::digest |
( |
| ) |
const |
◆ num_from_id()
| uint32_t graphene::chain::block_header::num_from_id |
( |
const block_id_type & |
id | ) |
|
|
static |
◆ extensions
◆ previous
◆ timestamp
| fc::time_point_sec graphene::chain::block_header::timestamp |
◆ transaction_merkle_root
| checksum_type graphene::chain::block_header::transaction_merkle_root |
◆ witness
The documentation for this class was generated from the following files:
- libraries/chain/include/graphene/chain/protocol/block.hpp
- libraries/chain/protocol/block.cpp


SpendingMonitor
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Discoverthreejs.com
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{% extends "base.html" %} {% block title %}File not found{% endblock %} {% block content %}
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| 1 |
Chen_2020 |
Distribution of the 2019-nCoV Epidemic and Correlation with Population Emigration from Wuhan, China |
Zeliang Chen; Qi Zhang; Yi Lu; Xi Zhang; Wenjun Zhang; Cheng Guo; Conghui Liao; Qianlin Li; Xiaohu Han; Jiahai Lu |
2020 |
2020-02-12 |
BioRxiv |
Y |
|
|
10.1101/2<IP_ADDRESS>21824 |
o7pz0rst |
0.969279 |
Lin_Q_2020, Ma_X_2020 |
Chen_2020, He_J_2020, Wang_2020 |
| 2 |
Chen_2020 |
Distribution of the COVID-19 epidemic and correlation with population emigration from wuhan, China |
Chen, Ze-Liang; Zhang, Qi; Lu, Yi; Guo, Zhong-Min; Zhang, Xi; Zhang, Wen-Jun; Guo, Cheng; Liao, Cong-Hui; Li, Qian-Lin; Han, Xiao-Hu; Lu, Jia-Hai |
2020 |
2020-01-01 |
None |
Y |
PMC7147281 |
32118644.0 |
10.1097/cm9.0000000000000782 |
yjay3t38 |
0.930114 |
Lin_Q_2020, Ma_X_2020 |
Chen_2020, He_J_2020 |
| 3 |
Wang_2020 |
Epidemiology of 2019 novel coronavirus in Jiangsu Province, China after wartime control measures: A population-level retrospective study |
Wang, Ke-wei; Gao, Jie; Wang, Hua; Wu, Xiao-long; Yuan, Qin-fang; Guo, Fei-yu; Zhang, Zhi-jie; Cheng, Yang |
2020 |
2020-04-05 |
PMC |
Y |
|
|
10.1016/j.tmaid.2020.101654 |
ftkhfywk |
0.925288 |
Ma_X_2020, Lin_Q_2020 |
|
| 4 |
Lin_B_2020 |
Epidemiological Trends of Coronavirus Disease 2019 in China |
Bilin Lin; Huanhuan Zhong; Yueyan Ni; Lulu Liu; Jinjin Zhong; Xin Su |
2020 |
2020-03-17 |
BioRxiv |
Y |
|
|
10.1101/2<IP_ADDRESS>35642 |
v5qz7ft9 |
0.917188 |
Ma_X_2020, Lin_Q_2020, Xu_L_2020 |
Wang_2020, youbin_2020 |
| 5 |
youbin_2020 |
Epidemic Situation of Novel Coronavirus Pneumonia in China mainland |
Liu youbin; Li bao hong |
2020 |
2020-02-18 |
BioRxiv |
Y |
|
|
10.1101/2<IP_ADDRESS>24034 |
s12vwz8m |
0.911187 |
Ma_X_2020, Lin_Q_2020 |
Lin_B_2020 |
| 6 |
Jin_G_2020 |
The impact of traffic isolation in Wuhan on the spread of 2019-nCov |
Gehui Jin; Jiayu Yu; Liyuan Han; Shiwei Duan |
2020 |
2020-02-05 |
BioRxiv |
Y |
|
|
10.1101/2<IP_ADDRESS>20438 |
kctgr529 |
0.906888 |
Lin_Q_2020 |
Ai_S_2020, Chen_2020, Xu_L_2020 |
| 7 |
Du_W_2020 |
Epidemic update of COVID-19 in Hubei Province compared with other regions in China |
Du, Wenjun; Han, Shaolei; Li, Qiang; Zhang, Zhongfa |
2020 |
2020-04-20 |
PMC |
Y |
|
|
10.1016/j.ijid.2020.04.031 |
h13v8i14 |
0.827375 |
Ma_X_2020, Lin_Q_2020 |
|
| 8 |
Ding_2020 |
Association of Population Migration and Coronavirus Disease 2019 Epidemic Control |
Yu Ding; Sihui Luo; Xueying Zheng; Ping Ling; Tong Yue; Zhirong Liu; Jianping Weng |
2020 |
2020-02-20 |
BioRxiv |
Y |
|
|
10.1101/2<IP_ADDRESS>24661 |
toizrp89 |
0.811582 |
Ma_X_2020, Tomie_2020, Lin_Q_2020, Jin_G_2020 |
|
| 9 |
Liu_T_2020 |
Time-varying transmission dynamics of Novel Coronavirus Pneumonia in China |
Tao Liu; Jianxiong Hu; Jianpeng Xiao; Guanhao He; Min Kang; Zuhua Rong; Lifeng Lin; Haojie Zhong; Qiong Huang; Aiping Deng; Weilin Zeng; Xiaohua Tan; Siqing Zeng; Zhihua Zhu; Jiansen Li; Dexin Gong; Donghua Wan; Shaowei Chen; Lingchuan Guo; Yan Li; Limei Sun; Wenjia Liang; Tie Song; Jianfeng He; Wenjun Ma |
2020 |
2020-01-26 |
BioRxiv |
Y |
|
|
10.1101/2<IP_ADDRESS>9787 |
3e2soc6w |
0.763426 |
Lin_Q_2020, Ma_X_2020 |
Wang_2020, He_J_2020 |
| 10 |
Ai_S_2020 |
Population movement, city closure and spatial transmission of the 2019-nCoV infection in China |
Siqi Ai; Guanghu Zhu; Fei Tian; Huan Li; Yuan Gao; Yinglin Wu; Qiyong Liu; Hualiang Lin |
2020 |
2020-02-05 |
BioRxiv |
Y |
|
|
10.1101/2<IP_ADDRESS>20339 |
f5yjz0zp |
0.686950 |
Jin_G_2020, Lin_Q_2020, Tomie_2020 |
Chen_2020, He_J_2020, Wang_2020 |
| 11 |
Sun_H_2020 |
Tracking and Predicting COVID-19 Epidemic in China Mainland |
Haoxuan Sun; Yumou Qiu; Han Yan; Yaxuan Huang; Yuru Zhu; Song Xi Chen |
2020 |
2020-02-20 |
BioRxiv |
Y |
|
|
10.1101/2<IP_ADDRESS>24257 |
t2x4yfrv |
0.686301 |
Lin_Q_2020, Ma_X_2020 |
He_J_2020, Peng_2020 |
| 12 |
Wang_2020 |
Effects of Chinese strategies for controlling the diffusion and deterioration of novel coronavirus-infected pneumonia in China |
Xiaoqiang Wang; Weitian Tian; Xin Lv; Yumiao Shi; Xiaoxin Zhou; Weifeng Yu; Diansan Su; Jie Tian |
2020 |
2020-03-12 |
BioRxiv |
Y |
|
|
10.1101/2<IP_ADDRESS>32755 |
odl1nv4i |
0.683804 |
Tomie_2020, Jin_G_2020 |
He_J_2020, Lin_B_2020 |
| 13 |
Hu_Z_2020 |
Evaluation and prediction of the COVID-19 variations at different input population and quarantine strategies, a case study in Guangdong province, China |
Hu, Zengyun; Cui, Qianqian; Han, Junmei; Wang, Xia; Sha, Wei E.I.; Teng, Zhidong |
2020 |
2020-04-22 |
PMC |
N |
|
|
10.1016/j.ijid.2020.04.010 |
b59uexhr |
0.682706 |
Tomie_2020 |
|
| 14 |
Jiang_2020 |
How does the outbreak of 2019-nCoV spread in mainland China? A retrospective analysis of the dynamic transmission routes |
Xiandeng Jiang; Le Chang; Yanlin Shi |
2020 |
2020-03-06 |
BioRxiv |
Y |
|
|
10.1101/2<IP_ADDRESS>29645 |
4k1i6y98 |
0.678674 |
Lin_Q_2020, Ma_X_2020 |
He_J_2020 |
| 15 |
zheming_2020 |
A simple model to assess Wuhan lock-down effect and region efforts during COVID-19 epidemic in China Mainland |
Yuan zheming; Chen Yuan |
2020 |
2020-03-03 |
BioRxiv |
Y |
|
|
10.1101/2<IP_ADDRESS>29561 |
3kp3ptbn |
0.676214 |
Ma_X_2020, Xu_L_2020 |
He_J_2020, Hou_J_2020, Huang_2020 |
| 16 |
Kraemer_2020 |
The effect of human mobility and control measures on the COVID-19 epidemic in China |
Kraemer, Moritz U. G.; Yang, Chia-Hung; Gutierrez, Bernardo; Wu, Chieh-Hsi; Klein, Brennan; Pigott, David M.; du Plessis, Louis; Faria, Nuno R.; Li, Ruoran; Hanage, William P.; Brownstein, John S.; Layan, Maylis; Vespignani, Alessandro; Tian, Huaiyu; Dye, Christopher; Pybus, Oliver G.; Scarpino, Samuel V. |
2020 |
2020-03-25 |
COMM-USE |
Y |
PMC7146642 |
32213647.0 |
10.1126/science.abb4218 |
s0w95qdn |
0.658458 |
|
|
| 17 |
Lin_Q_2020 |
Estimating the daily trend in the size of the COVID-19 infected population in Wuhan |
Qiushi Lin; Taojun Hu; Xiao-Hua Zhou |
2020 |
2020-02-13 |
BioRxiv |
Y |
|
|
10.1101/2<IP_ADDRESS>22277 |
lvjveaob |
0.644747 |
Ma_X_2020 |
Wang_2020, He_J_2020, Ping_2020, Lin_B_2020 |
| 18 |
Ma_X_2020 |
Emergency Management of the Prevention and Control of Novel Coronavirus Pneumonia in Specialized Branches of Hospital |
Ma, Xiuqing; Li, Shiyu; Yu, Shaobin; Ouyang, Ying; Zeng, Lin; Li, Xiao; Li, Hai |
2020 |
2020-03-23 |
COMM-USE |
Y |
PMC7161861 |
32147890.0 |
10.1111/acem.13958 |
2zanmu5z |
0.614619 |
|
|
| 19 |
Hou_J_2020 |
Changing transmission dynamics of COVID-19 in China: a nationwide population-based piecewise mathematical modelling study |
Jiawen Hou; Jie Hong; Boyun Ji; Bowen Dong; Yue Chen; Michael P Ward; Wei Tu; Zhen Jin; Jian Hu; Qing Su; Wenge Wang; Zheng Zhao; Shuang Xiao; Jiaqi Huang; Wei Lin; Zhijie Zhang |
2020 |
2020-03-30 |
BioRxiv |
Y |
|
|
10.1101/2<IP_ADDRESS>45757 |
kuf8p4e1 |
0.612038 |
Tomie_2020, Lin_Q_2020 |
He_J_2020 |
| 20 |
Cheng_2020 |
China's fight against COVID-19: What we have done and what we should do next? |
Sixiang Cheng; Yuxin Zhao; Atipatsa Chiwanda Kaminga; Pingxin Zhang; Huilan Xu |
2020 |
2020-03-30 |
BioRxiv |
Y |
|
|
10.1101/2<IP_ADDRESS>46086 |
j9525ohh |
0.611681 |
|
Devaux_2020, Memish_2020 |
| 21 |
Yang_2020 |
Analysis of epidemiological characteristics of coronavirus 2019 infection and preventive measures in Shenzhen China: a heavy population city |
Kai Yang; Lingwei Wang; Furong Li; Dandan Chen; Xi Li; Chen Qiu; Rongchang Chen |
2020 |
2020-03-03 |
BioRxiv |
Y |
|
|
10.1101/2<IP_ADDRESS>28555 |
3wvcuboc |
0.609397 |
Ma_X_2020, Lin_Q_2020 |
Wang_2020, Wang_2020 |
| 22 |
Zhang_2020 |
Evolving epidemiology of novel coronavirus diseases 2019 and possible interruption of local transmission outside Hubei Province in China: a descriptive and modeling study |
Juanjuan Zhang; Maria Litvinova; Wei Wang; Yan Wang; Xiaowei Deng; Xinghui Chen; Mei Li; Wen Zheng; Lan Yi; Xinhua Chen; Qianhui Wu; Yuxia Liang; Xiling Wang; Juan Yang; Kaiyuan Sun; Ira M. Longini; M. Elizabeth Halloran; Peng Wu; Benjamin J. Cowling; Stefano Merler; Cecile Viboud; Alessandro Vespignani; Marco Ajelli; Hongjie Yu |
2020 |
2020-02-23 |
BioRxiv |
Y |
|
|
10.1101/2<IP_ADDRESS>26328 |
ragcpbl6 |
0.602441 |
Lin_Q_2020, Muniz-Rodriguez_2020, Ma_X_2020, Tomie_2020 |
Zhang_2020, Wang_2020 |
| 23 |
Roosa_2020 |
Real-time forecasts of the COVID-19 epidemic in China from February 5th to February 24th, 2020 |
Roosa, K.; Lee, Y.; Luo, R.; Kirpich, A.; Rothenberg, R.; Hyman, J.M.; Yan, P.; Chowell, G. |
2020 |
2020-02-14 |
None |
Y |
PMC7033348 |
32110742.0 |
10.1016/j.idm.2020.02.002 |
0zw3ukpx |
0.602382 |
Lin_Q_2020, Tomie_2020, Brüssow_2020 |
Liu_Q_2020 |
| 24 |
Moradzadeh_2020 |
The challenges and considerations of community-based preparedness at the onset of COVID-19 outbreak in Iran, 2020 |
Moradzadeh, Rahmatollah |
2020 |
2020-04-03 |
COMM-USE |
Y |
PMC7167485 |
32242790.0 |
10.1017/s0950268820000783 |
i49m4y0e |
0.597982 |
|
|
| 25 |
Zhao_2020 |
Modeling the Epidemic Dynamics and Control of COVID-19 Outbreak in China |
Shilei Zhao; Hua Chen |
2020 |
2020-02-29 |
BioRxiv |
Y |
|
|
10.1101/2<IP_ADDRESS>28639 |
ry3m6x8c |
0.594592 |
Lin_Q_2020, Ma_X_2020, Tomie_2020 |
He_J_2020 |
| 26 |
FANG_2020 |
Human Mobility Restrictions and the Spread of the Novel Coronavirus (2019-nCoV) in China |
HANMING FANG; LONG WANG; YANG YANG |
2020 |
2020-03-26 |
BioRxiv |
Y |
|
|
10.1101/2<IP_ADDRESS>42424 |
ucwuptgg |
0.587344 |
Lin_Q_2020, Jin_G_2020 |
Ai_S_2020 |
| 27 |
Li_X_2020 |
The lockdown of Hubei Province causing different transmission dynamics of the novel coronavirus (2019-nCoV) in Wuhan and Beijing |
Xinhai Li; Xumao Zhao; Yuehua Sun |
2020 |
2020-02-11 |
BioRxiv |
Y |
|
|
10.1101/2<IP_ADDRESS>21477 |
uqgrjjwp |
0.571954 |
Lin_Q_2020 |
Wang_2020, Wang_2020, He_J_2020 |
| 28 |
Shao_2020 |
Beware of asymptomatic transmission: Study on 2019-nCoV prevention and control measures based on extended SEIR model |
Peng Shao; Yingji Shan |
2020 |
2020-01-28 |
BioRxiv |
Y |
|
|
10.1101/2<IP_ADDRESS>3169 |
x443k65a |
0.564012 |
|
Ji_X_2020 |
| 29 |
Muniz-Rodriguez_2020 |
Epidemic doubling time of the COVID-19 epidemic by Chinese province |
Kamalich Muniz-Rodriguez; Gerardo Chowell; Chi-Hin Cheung; Dongyu Jia; Po-Ying Lai; Yiseul Lee; Manyun Liu; Sylvia K. Ofori; Kimberlyn M. Roosa; Lone Simonsen; Cecile G. Viboud; Isaac Chun-Hai Fung |
2020 |
2020-02-06 |
BioRxiv |
Y |
|
|
10.1101/2<IP_ADDRESS>20750 |
vpouyyev |
0.553791 |
Gao_Y_2020 |
Liu_Q_2020, Hou_J_2020 |
| 30 |
Ying_2020 |
Spread and control of COVID-19 in China and their associations with population movement, public health emergency measures, and medical resources |
Songmin Ying; Fei Li; Xinwei Geng; Zhouyang Li; Xufei Du; Haixia Chen; Sisi Chen; Min Zhang; Zhehua Shao; Yinfang Wu; Madiha Zahra Syeda; Fugui Yan; Luanqing Che; Bin Zhang; Jian Lou; Shaobin Wang; Zhengming Chen; Wen Li; Ye Shen; Zhihua Chen; Huahao Shen |
2020 |
2020-02-27 |
BioRxiv |
Y |
|
|
10.1101/2<IP_ADDRESS>27623 |
mopydeq5 |
0.542450 |
Ma_X_2020 |
He_J_2020, Wang_2020, Chen_2020 |
| 31 |
He_Y_2020 |
Moving-average based index to timely evaluate the current epidemic situation after COVID-19 outbreak |
Yun-ting He; Hao He; Jing Zhai; Xiao-jin Wang; Bing-shun Wang |
2020 |
2020-03-26 |
BioRxiv |
Y |
|
|
10.1101/2<IP_ADDRESS>27730 |
bcde0u7u |
0.540724 |
|
Lin_B_2020 |
| 32 |
Zhang_2020 |
Evolving epidemiology and transmission dynamics of coronavirus disease 2019 outside Hubei province, China: a descriptive and modelling study |
Zhang, Juanjuan; Litvinova, Maria; Wang, Wei; Wang, Yan; Deng, Xiaowei; Chen, Xinghui; Li, Mei; Zheng, Wen; Yi, Lan; Chen, Xinhua; Wu, Qianhui; Liang, Yuxia; Wang, Xiling; Yang, Juan; Sun, Kaiyuan; Longini, Ira M; Halloran, M Elizabeth; Wu, Peng; Cowling, Benjamin J; Merler, Stefano; Viboud, Cecile; Vespignani, Alessandro; Ajelli, Marco; Yu, Hongjie |
2020 |
2020-04-02 |
PMC |
Y |
|
|
10.1016/s1473-3099(20)30230-9 |
ur7tu7g1 |
0.539828 |
Ma_X_2020, Lin_Q_2020, Tomie_2020 |
Zhang_2020 |
| 33 |
Cao_Z_2020 |
Incorporating Human Movement Data to Improve Epidemiological Estimates for 2019-nCoV |
Zhidong Cao; Qingpeng Zhang; Xin Lu; Dirk Pfeiffer; Lei Wang; Hongbing Song; Tao Pei; Zhongwei Jia; Daniel Dajun Zeng |
2020 |
2020-02-09 |
BioRxiv |
Y |
|
|
10.1101/2<IP_ADDRESS>21071 |
q14x0i2c |
0.533354 |
Lin_Q_2020 |
Hou_J_2020 |
| 34 |
He_D_2020 |
The relative transmissibility of asymptomatic cases among close contacts |
He, Daihai; Zhao, Shi; Lin, Qianying; Zhuang, Zian; Cao, Peihua; Wang, Maggie H; Yang, Lin |
2020 |
2020-04-18 |
PMC |
Y |
|
|
10.1016/j.ijid.2020.04.034 |
8pjoz64q |
0.529314 |
|
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| 35 |
Xu_H_2020 |
Basic reproduction number of 2019 Novel Coronavirus Disease in Major Endemic Areas of China: A latent profile analysis |
Honglv Xu; Min Yuan; Liya Ma; Meng Liu; Yi Zhang; Wenwen Liu; Hong Gan; Fangbiao Tao |
2020 |
2020-04-17 |
BioRxiv |
Y |
|
|
10.1101/2<IP_ADDRESS>60228 |
zpjkwecx |
0.528062 |
Lin_Q_2020, Tomie_2020 |
|
| 36 |
Wang_2020 |
Incidence of novel coronavirus (2019-nCoV) infection among people under home quarantine in Shenzhen, China |
Wang, Jingzhong; Liao, Yi; Wang, Xiaoyang; Li, Yichong; Jiang, Dan; He, Jianfan; Zhang, Shunxiang; Xia, Junjie |
2020 |
2020-04-02 |
PMC |
Y |
|
|
10.1016/j.tmaid.2020.101660 |
m3nqa321 |
0.525263 |
|
Wang_2020 |
| 37 |
Kong_2020 |
Epidemic prevention and control measures in China significantly curbed the epidemic of COVID-19 and influenza |
Xiang Sha Kong; Feng Liu; Hai Bo Wang; Rui Feng Yang; Dong Bo Chen; Xiao Xiao Wang; Feng Min Lu; Hui Ying Rao; Hong Song Chen |
2020 |
2020-04-14 |
BioRxiv |
Y |
|
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10.1101/2<IP_ADDRESS>58859 |
12xdtllc |
0.523709 |
|
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| 38 |
Li_J_2020 |
Estimation of the epidemic properties of the 2019 novel coronavirus: A mathematical modeling study |
Jinghua Li; Yijing Wang; Stuart Gilmour; Mengying Wang; Daisuke Yoneoka; Ying Wang; Xinyi You; Jing Gu; Chun Hao; Liping Peng; Zhicheng Du; Dong Roman Xu; Yuantao Hao |
2020 |
2020-02-20 |
BioRxiv |
Y |
|
|
10.1101/2<IP_ADDRESS>24315 |
nzynerfu |
0.522922 |
Ma_X_2020 |
Wang_2020 |
| 39 |
Bernard_Stoecklin_2020 |
First cases of coronavirus disease 2019 (COVID-19) in France: surveillance, investigations and control measures, January 2020 |
Bernard Stoecklin, Sibylle; Rolland, Patrick; Silue, Yassoungo; Mailles, Alexandra; Campese, Christine; Simondon, Anne; Mechain, Matthieu; Meurice, Laure; Nguyen, Mathieu; Bassi, Clément; Yamani, Estelle; Behillil, Sylvie; Ismael, Sophie; Nguyen, Duc; Malvy, Denis; Lescure, François Xavier; Georges, Scarlett; Lazarus, Clément; Tabaï, Anouk; Stempfelet, Morgane; Enouf, Vincent; Coignard, Bruno; Levy-Bruhl, Daniel |
2020 |
2020-02-13 |
None |
Y |
PMC7029452 |
32070465.0 |
10.2807/1560-7917.es.2<IP_ADDRESS>0094 |
jdlt0xcj |
0.521251 |
|
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| 40 |
Tomie_2020 |
Understanding the present status and forecasting of COVID―19 in Wuhan |
Toshihisa Tomie |
2020 |
2020-02-14 |
BioRxiv |
N |
|
|
10.1101/2<IP_ADDRESS>22251 |
lmoekp1m |
0.508545 |
Jin_G_2020, Lin_Q_2020, Ma_X_2020 |
Liu_Q_2020, Xu_L_2020, Huang_2020, Wang_2020 |
| 41 |
Lin_H_2020 |
Trends in Transmissibility of 2019 Novel Coronavirus-infected Pneumonia in Wuhan and 29 Provinces in China |
Huazhen Lin; Wei Liu; Hong Gao; Jinyu Nie; Qiao Fan |
2020 |
2020-02-25 |
BioRxiv |
Y |
|
|
10.1101/2<IP_ADDRESS>26468 |
eedk54xf |
0.508491 |
|
He_J_2020, Wang_2020, Lin_B_2020 |
| 42 |
Ping_2020 |
Epidemiologic Characteristics of COVID-19 in Guizhou, China |
Kaike Ping |
2020 |
2020-03-06 |
BioRxiv |
Y |
|
|
10.1101/2<IP_ADDRESS>28944 |
tne83uu0 |
0.504898 |
Tomie_2020, Lin_Q_2020 |
Tian_2020, He_J_2020 |
| 43 |
You_C_2020 |
Estimation of the Time-Varying Reproduction Number of COVID-19 Outbreak in China |
Chong You; Yuhao Deng; Wenjie Hu; Jiarui Sun; Qiushi Lin; Feng Zhou; Cheng Heng Pang; Yuan Zhang; Zhengchao Chen; Xiao-Hua Zhou |
2020 |
2020-02-11 |
BioRxiv |
Y |
|
|
10.1101/2<IP_ADDRESS>21253 |
juiomzb3 |
0.495325 |
Tomie_2020, batista_2020, Griette_2020 |
Hou_J_2020, Liu_Q_2020 |
| 44 |
Feng_2020 |
Is home isolation appropriate for preventing the spread of COVID-19? |
Feng, Zhan-hui; Cheng, Yong-ran; Ye, Lan; Zhou, Meng-Yun; Wang, Ming-Wei; Chen, Juan |
2020 |
2020-04-08 |
PMC |
Y |
|
|
10.1016/j.puhe.2020.03.008 |
dxiyqf9d |
0.494574 |
|
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| 45 |
Zhang_2020 |
Comparison of the spatiotemporal characteristics of the COVID-19 and SARS outbreaks in mainland China |
Xi Zhang; Hua-Xiang Rao; Yuwan Wu; Yubei Huang; Hongji Dai |
2020 |
2020-03-26 |
BioRxiv |
Y |
|
|
10.1101/2<IP_ADDRESS>34058 |
brwmpm5a |
0.483028 |
|
He_J_2020 |
| 46 |
Fu_X_2020 |
Simulating and Forecasting the Cumulative Confirmed Cases of SARS-CoV-2 in China by Boltzmann Function-based Regression Analyses |
Xinmiao Fu; Qi Ying; Tieyong Zeng; Tao Long; Yan Wang |
2020 |
2020-02-18 |
BioRxiv |
Y |
|
|
10.1101/2<IP_ADDRESS>23564 |
9j5yda3k |
0.480344 |
Lin_Q_2020, Tomie_2020, Jin_G_2020 |
|
| 47 |
Hu_F_2020 |
The Estimated Time-Varying Reproduction Numbers during the Ongoing Epidemic of the Coronavirus Disease 2019 (COVID-19) in China |
Fu-Chang Hu; Fang-Yu Wen |
2020 |
2020-04-17 |
BioRxiv |
Y |
|
|
10.1101/2<IP_ADDRESS>61838 |
nlpeyh5e |
0.480296 |
|
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| 48 |
Cao_L_2020 |
Estimation of instant case fatality rate of COVID-19 in Wuhan and Hubei based on daily case notification data |
Lei Cao; Ting-ting Huang; Jun-xia Zhang; Qi Qin; Si-yu Liu; Hui-min Xue; Ya-xin Gong; Chang-hua Ning; Xiao-tong Shen; Jia-xin Yang; Yan-ni Mi; Xue Xiao; Yongxiao Cao |
2020 |
2020-03-13 |
BioRxiv |
Y |
|
|
10.1101/2<IP_ADDRESS>34215 |
wxm8kqnd |
0.477488 |
Ma_X_2020, Lin_Q_2020, Tomie_2020 |
|
| 49 |
Wang_2020 |
Strongly heterogeneous transmission of COVID-19 in mainland China: local and regional variation |
Yuke Wang; Peter F.M. Teunis |
2020 |
2020-03-16 |
BioRxiv |
Y |
|
|
10.1101/2<IP_ADDRESS>33852 |
j181i5pr |
0.477426 |
Jin_G_2020, Tomie_2020, Lin_Q_2020 |
He_J_2020, Ping_2020 |
| 50 |
Xu_B_2020 |
Epidemiological data from the COVID-19 outbreak, real-time case information |
Xu, Bo; Gutierrez, Bernardo; Mekaru, Sumiko; Sewalk, Kara; Goodwin, Lauren; Loskill, Alyssa; Cohn, Emily L.; Hswen, Yulin; Hill, Sarah C.; Cobo, Maria M.; Zarebski, Alexander E.; Li, Sabrina; Wu, Chieh-Hsi; Hulland, Erin; Morgan, Julia D.; Wang, Lin; O’Brien, Katelynn; Scarpino, Samuel V.; Brownstein, John S.; Pybus, Oliver G.; Pigott, David M.; Kraemer, Moritz U. G. |
2020 |
2020-03-24 |
COMM-USE |
Y |
PMC7093412 |
32210236.0 |
10.1038/s41597-020-0448-0 |
l63z3btv |
0.463551 |
|
Ping_2020 |
| 51 |
Li_X_2020 |
Potential of large “first generation” human‐to‐human transmission of 2019‐nCoV |
Li, Xingguang; Zai, Junjie; Wang, Xiaomei; Li, Yi |
2020 |
2020-02-14 |
PMC |
N |
PMC7166825 |
31997390.0 |
10.1002/jmv.25693 |
475nei28 |
0.462215 |
Lin_Q_2020, Ma_X_2020 |
Lu_R_2020 |
| 52 |
Qiu_J_2020 |
A nationwide survey of psychological distress among Chinese people in the COVID-19 epidemic: implications and policy recommendations |
Qiu, Jianyin; Shen, Bin; Zhao, Min; Wang, Zhen; Xie, Bin; Xu, Yifeng |
2020 |
2020-03-06 |
NONCOMM |
Y |
PMC7061893 |
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10.1136/gpsych-2020-100213 |
q8p187qx |
0.455973 |
|
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| 53 |
Pan_X_2020 |
Lessons learned from the 2019-nCoV epidemic on prevention of future infectious diseases |
Pan, Xingchen; Ojcius, David M.; Gao, Tianyue; Li, Zhongsheng; Pan, Chunhua; Pan, Chungen |
2020 |
2020-03-31 |
PMC |
Y |
PMC7102576 |
32088333.0 |
10.1016/j.micinf.2020.02.004 |
d13bq4ij |
0.450339 |
|
Wang_2020 |
| 54 |
Park_2020 |
Spatial Visualization of Cluster-Specific COVID-19 Transmission Network in South Korea During the Early Epidemic Phase |
James Yeongjun Park |
2020 |
2020-03-20 |
BioRxiv |
Y |
|
|
10.1101/2<IP_ADDRESS>38638 |
901m6zw0 |
0.444431 |
Ma_X_2020 |
He_J_2020 |
| 55 |
Roda_2020 |
Why is it difficult to accurately predict the COVID-19 epidemic? |
Roda, Weston C.; Varughese, Marie B.; Han, Donglin; Li, Michael Y. |
2020 |
2020-12-31 |
PMC |
Y |
PMC7104073 |
|
10.1016/j.idm.2020.03.001 |
vx2t7jgu |
0.439584 |
|
Li_M_2020, Caccavo_2020 |
| 56 |
Li_M_2020 |
Predicting the epidemic trend of COVID-19 in China and across the world using the machine learning approach |
Mengyuan Li; Zhilan Zhang; Shanmei Jiang; Qian Liu; Canping Chen; Yue Zhang; Xiaosheng Wang |
2020 |
2020-03-20 |
BioRxiv |
Y |
|
|
10.1101/2<IP_ADDRESS>38117 |
qpt5dj8p |
0.438377 |
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Distante_2020, Zareie_2020, Zheng_2020 |
| 57 |
Jia_J_2020 |
Epidemiological Characteristics on the Clustering Nature of COVID-19 in Qingdao City, 2020: A Descriptive Analysis |
Jia, Jing; Hu, Xiaowen; Yang, Feng; Song, Xin; Dong, Liyan; Zhang, Jingfei; Jiang, Fachun; Gao, Ruqin |
2020 |
2020-03-31 |
COMM-USE |
Y |
PMC7156571 |
32228732.0 |
10.1017/dmp.2020.59 |
ohrqw3ac |
0.437528 |
Tomie_2020, Xu_L_2020 |
|
| 58 |
Wan_K_2020 |
When will the battle against novel coronavirus end in Wuhan: A SEIR modeling analysis |
Wan, Kangkang; Chen, Jing; Lu, Changming; Dong, Lanlan; Wu, Zhicheng; Zhang, Lianglu |
2020 |
2020-04-01 |
COMM-USE |
Y |
PMC7125416 |
|
10.7189/jogh.10.011002 |
j224pm1k |
0.434813 |
Tomie_2020, Scheuerl_2020, Xu_L_2020 |
zhang_2020, Yan_S_2020 |
| 59 |
Zhu_X_2020 |
Spatially Explicit Modeling of 2019-nCoV Epidemic Trend based on Mobile Phone Data in Mainland China |
Xiaolin Zhu; Aiyin Zhang; Shuai Xu; Pengfei Jia; Xiaoyue Tan; Jiaqi Tian; Tao Wei; Zhenxian Quan; Jiali Yu |
2020 |
2020-02-11 |
BioRxiv |
Y |
|
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10.1101/2<IP_ADDRESS>21360 |
6u9q0ox9 |
0.426377 |
|
Zhan_2020, Chen_2020, Ai_S_2020, Jin_G_2020 |
| 60 |
Li_R_2020 |
Prediction of the Epidemic of COVID-19 Based on Quarantined Surveillance in China |
Rui Li; Wenliang Lu; Xifei Yang; Peihua Feng; Ozarina Muqimova; Xiaoping Chen; Gang Wei |
2020 |
2020-02-29 |
BioRxiv |
Y |
|
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10.1101/2<IP_ADDRESS>27169 |
jadzias9 |
0.423546 |
Lin_Q_2020 |
Zareie_2020 |
| 61 |
Wang_2020 |
A deterministic epidemic model for the emergence of COVID-19 in China |
Meng Wang; Jingtao Qi |
2020 |
2020-03-10 |
BioRxiv |
Y |
|
|
10.1101/2<IP_ADDRESS>32854 |
6tnkz6hh |
0.421253 |
Ma_X_2020, Lin_Q_2020, Xu_L_2020 |
Wang_2020, He_J_2020 |
| 62 |
Wang_2020 |
Epidemiological characteristics of 1212 COVID-19 patients in Henan, China |
Pei Wang; Junan Lu; Yanyu Jin; Mengfan Zhu; Lingling Wang; Shunjie Chen |
2020 |
2020-02-23 |
BioRxiv |
Y |
|
|
10.1101/2<IP_ADDRESS>26112 |
1mxjklgx |
0.418543 |
Tomie_2020 |
Ping_2020 |
| 63 |
Zhu_Y_2020 |
The Risk and Prevention of Novel Coronavirus Pneumonia Infections Among Inpatients in Psychiatric Hospitals |
Zhu, Yuncheng; Chen, Liangliang; Ji, Haifeng; Xi, Maomao; Fang, Yiru; Li, Yi |
2020 |
2020-02-25 |
PMC |
Y |
PMC7056754 |
32096116.0 |
10.1007/s12264-020-00476-9 |
fe061u76 |
0.417777 |
Ma_X_2020 |
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| 64 |
Wang_2020 |
Real-time estimation and prediction of mortality caused by COVID-19 with patient information based algorithm |
Wang, Lishi; Li, Jing; Guo, Sumin; Xie, Ning; Yao, Lan; Cao, Yanhong; Day, Sara W.; Howard, Scott C.; Graff, J. Carolyn; Gu, Tianshu; Ji, Jiafu; Gu, Weikuan; Sun, Dianjun |
2020 |
2020-04-08 |
PMC |
Y |
|
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10.1016/j.scitotenv.2020.138394 |
xyowl659 |
0.417338 |
Tomie_2020, Jin_G_2020 |
Zareie_2020, Cao_L_2020 |
| 65 |
zhang_2020 |
When will the battle against novel coronavirus end in Wuhan: a SEIR modeling analysis |
lianglu zhang; kangkang wan; jing chen; changming lu; lanlan dong; zhicheng wu |
2020 |
2020-02-18 |
BioRxiv |
Y |
|
|
10.1101/2<IP_ADDRESS>23804 |
32vs98ya |
0.407071 |
Tomie_2020, Scheuerl_2020, Tay_K_2020 |
Wan_K_2020, Yan_S_2020 |
| 66 |
Li_Q_2020 |
Early Transmission Dynamics in Wuhan, China, of Novel Coronavirus–Infected Pneumonia |
Li, Qun; Guan, Xuhua; Wu, Peng; Wang, Xiaoye; Zhou, Lei; Tong, Yeqing; Ren, Ruiqi; Leung, Kathy S.M.; Lau, Eric H.Y.; Wong, Jessica Y.; Xing, Xuesen; Xiang, Nijuan; Wu, Yang; Li, Chao; Chen, Qi; Li, Dan; Liu, Tian; Zhao, Jing; Liu, Man; Tu, Wenxiao; Chen, Chuding; Jin, Lianmei; Yang, Rui; Wang, Qi; Zhou, Suhua; Wang, Rui; Liu, Hui; Luo, Yinbo; Liu, Yuan; Shao, Ge; Li, Huan; Tao, Zhongfa; Yang, Yang; Deng, Zhiqiang; Liu, Boxi; Ma, Zhitao; Zhang, Yanping; Shi, Guoqing; Lam, Tommy T.Y.; Wu, Joseph T.; Gao, George F.; Cowling, Benjamin J.; Yang, Bo; Leung, Gabriel M.; Feng, Zijian |
2020 |
2020-03-26 |
None |
Y |
PMC7121484 |
31995857.0 |
10.1056/nejmoa2001316 |
e9rt7z8d |
0.406924 |
Muniz-Rodriguez_2020, Du_Z_2020 |
Wang_2020 |
| 67 |
Zhang_2007 |
The outbreak pattern of SARS cases in China as revealed by a mathematical model |
Zhang, Zhibin |
2007 |
2007-06-16 |
PMC |
N |
PMC7117038 |
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10.1016/j.ecolmodel.2007.01.020 |
8fswufnx |
0.406198 |
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Ruan_2008, Wang_2020 |
| 68 |
Wang_2020 |
Tracking and forecasting milepost moments of the epidemic in the early-outbreak: framework and applications to the COVID-19 |
Huiwen Wang; Yanwen Zhang; Shan Lu; Shanshan Wang |
2020 |
2020-03-24 |
BioRxiv |
Y |
|
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10.1101/2<IP_ADDRESS>40139 |
fyh8gjjl |
0.405652 |
Tomie_2020 |
Roosa_2020, Peng_2020, Fanelli_2020 |
| 69 |
Liao_2020 |
A Tempo-geographic Analysis of Global COVID-19 Epidemic Outside of China |
Huipeng Liao; Gifty Marley; Yafei Si; Zaisheng Wang; Yewei Xie; Cheng Wang; Weiming Tang |
2020 |
2020-03-23 |
BioRxiv |
Y |
|
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10.1101/2<IP_ADDRESS>39602 |
fb0nwixw |
0.402875 |
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Hou_J_2020, He_J_2020, Weber_2020 |
| 70 |
Yuan_2020 |
The Framework for the Prediction of the Critical Turning Period for Outbreak of COVID-19 Spread in China based on the iSEIR Model |
George X Yuan; Lan Di; Yudi Gu; Guoqi Qian; Xiaosong Qian |
2020 |
2020-04-11 |
BioRxiv |
Y |
|
|
10.1101/2<IP_ADDRESS>54346 |
px4b6ykg |
0.401560 |
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Peng_2020, Roosa_2020, Li_M_2020 |
| 71 |
Xu_L_2020 |
Highland of COVID-19 outside Hubei: epidemic characteristics, control and projections of Wenzhou, China |
Liangde Xu; Jian Yuan; Yaru Zhang; Guosi Zhang; Fan Lu; Jianzhong Su; Jia Qu |
2020 |
2020-02-29 |
BioRxiv |
N |
|
|
10.1101/2<IP_ADDRESS>24398 |
acmfhbtm |
0.393276 |
Ma_X_2020, Lin_Q_2020 |
He_J_2020, Ping_2020, Wen_Y_2020, Wang_2020 |
| 72 |
Lin_Y_2020 |
The spatiotemporal estimation of the dynamic risk and the international transmission of 2019 Novel Coronavirus (COVID-19) outbreak: A global perspective |
Yuan-Chien Lin; Wan-Ju Chi; Yu-Ting Lin; Chun-Yeh Lai |
2020 |
2020-03-03 |
BioRxiv |
Y |
|
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10.1101/2<IP_ADDRESS>29413 |
qs7dsxn5 |
0.392024 |
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He_J_2020, Hou_J_2020, Zhang_2020 |
| 73 |
Shi_Z_2020 |
Temporal relationship between outbound traffic from Wuhan and the 2019 coronavirus disease (COVID-19) incidence in China |
Zaixing Shi; Ya Fang |
2020 |
2020-03-17 |
BioRxiv |
Y |
|
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10.1101/2<IP_ADDRESS>34199 |
hrrzztt5 |
0.390426 |
Ma_X_2020, Lin_Q_2020, Jin_G_2020 |
Chen_2020, He_J_2020 |
| 74 |
Zhan_2020 |
Modeling and Prediction of the 2019 Coronavirus Disease Spreading in China Incorporating Human Migration Data |
Choujun Zhan; Chi K. Tse; Yuxia Fu; Zhikang Lai; Haijun Zhang |
2020 |
2020-02-20 |
BioRxiv |
Y |
|
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10.1101/2<IP_ADDRESS>24570 |
tc8eru1w |
0.385839 |
batista_2020, Lin_Q_2020, Tomie_2020 |
Wang_2020 |
| 75 |
Tang_2020 |
Prediction of New Coronavirus Infection Based on a Modified SEIR Model |
Zhou Tang; Xianbin Li; Houqiang Li |
2020 |
2020-03-06 |
BioRxiv |
N |
|
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10.1101/2<IP_ADDRESS>30858 |
1mu1z4xd |
0.382826 |
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| 76 |
Li_Y_2020 |
COVID-19 Epidemic Outside China: 34 Founders and Exponential Growth |
Yi Li; Meng Liang; Xianhong Yin; Xiaoyu Liu; Meng Hao; Zixin Hu; Yi Wang; Li Jin |
2020 |
2020-03-03 |
BioRxiv |
Y |
|
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10.1101/2<IP_ADDRESS>29819 |
7reqkx3h |
0.382147 |
Tomie_2020, Griette_2020 |
Wang_2020, He_J_2020 |
| 77 |
Chen_2020 |
Air Pollution Reduction and Mortality Benefit during the COVID-19 Outbreak in China |
Kai Chen; Meng Wang; Conghong Huang; Patrick L. Kinney; Anastas T. Paul |
2020 |
2020-03-27 |
BioRxiv |
Y |
|
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10.1101/2<IP_ADDRESS>39842 |
mmvbkbjw |
0.380902 |
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| 78 |
Sahafizadeh_2020 |
Estimating the reproduction number of COVID-19 in Iran using epidemic modeling |
Ebrahim Sahafizadeh; Samaneh Sartoli |
2020 |
2020-03-23 |
BioRxiv |
Y |
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|
10.1101/2<IP_ADDRESS>38422 |
f3ikyl9d |
0.377431 |
batista_2020 |
Zareie_2020, He_J_2020 |
| 79 |
Wang_2020 |
Survival-Convolution Models for Predicting COVID-19 Cases and Assessing Effects of Mitigation Strategies |
Qinxia Wang; Shanghong Xie; Yuanjia Wang; Donglin Zeng |
2020 |
2020-04-21 |
BioRxiv |
Y |
|
|
10.1101/2<IP_ADDRESS>67306 |
tqeyx7yn |
0.376157 |
|
|
| 80 |
Zhang_2020 |
Transmission and epidemiological characteristics of Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) infected Pneumonia (COVID-19): preliminary evidence obtained in comparison with 2003-SARS |
Rongqiang Zhang; Hui Liu; Fengying Li; Bei Zhang; Qiling Liu; Xiangwen Li; Limei Luo |
2020 |
2020-02-02 |
BioRxiv |
Y |
|
|
10.1101/2<IP_ADDRESS>19836 |
mm4ngrla |
0.376099 |
batista_2020 |
|
| 81 |
Amodio_2020 |
Outbreak of Novel Coronavirus (SARS-CoV-2): First Evidences From International Scientific Literature and Pending Questions |
Amodio, Emanuele; Vitale, Francesco; Cimino, Livia; Casuccio, Alessandra; Tramuto, Fabio |
2020 |
2020-02-27 |
None |
N |
PMC7151147 |
32120965.0 |
10.3390/healthcare8010051 |
99doz9m6 |
0.371108 |
Ma_X_2020 |
Zhang_2020, Huang_2020 |
| 82 |
Zhang_2020 |
Prediction of the COVID-19 outbreak based on a realistic stochastic model |
Yuan Zhang; Chong You; Zhenghao Cai; Jiarui Sun; Wenjie Hu; Xiao-Hua Zhou |
2020 |
2020-03-13 |
BioRxiv |
Y |
|
|
10.1101/2<IP_ADDRESS>33803 |
0xzsa21a |
0.367264 |
|
Caccavo_2020 |
| 83 |
Lu_J_2020 |
Genomic epidemiology of SARS-CoV-2 in Guangdong Province, China |
Jing Lu; Louis du Plessis; Zhe Liu; Verity Hill; Min Kang; Huifang Lin; Jiufeng Sun; Sarah Francois; Moritz U G Kraemer; Nuno R Faria; John T McCrone; Jinju Peng; Qianling Xiong; Runyu Yuan; Lilian Zeng; Pingping Zhou; Chuming Liang; Lina Yi; Jun Liu; Jianpeng Xiao; Jianxiong Hu; Tao Liu; Wenjun Ma; Wei Li; Juan Su; Huanying Zheng; Bo Peng; Shisong Fang; Wenzhe Su; Kuibiao Li; Ruilin Sun; Ru Bai; Xi Tang; Minfeng Liang; Josh Quick; Tie Song; Andrew Rambaut; Nick Loman; Jayna Raghwani; Oliver Pybus; Changwen Ke |
2020 |
2020-04-04 |
BioRxiv |
Y |
|
|
10.1101/2<IP_ADDRESS>47076 |
ju9japd8 |
0.365032 |
|
|
| 84 |
Zhao_2020 |
Chinese Public Attention to COVID-19 Epidemic: Based on Social Media |
Yuxin Zhao; Huilan Xu |
2020 |
2020-03-20 |
BioRxiv |
Y |
|
|
10.1101/2<IP_ADDRESS>38026 |
q856rx6b |
0.362846 |
|
Li_M_2020, Lin_B_2020 |
| 85 |
Zhao_2020 |
Epidemic size of novel coronavirus-infected pneumonia in the Epicenter Wuhan: using data of five-countries' evacuation action |
Hongxin Zhao; Sailimai Man; Bo Wang; Yi Ning |
2020 |
2020-02-13 |
BioRxiv |
Y |
|
|
10.1101/2<IP_ADDRESS>22285 |
j60j7e9b |
0.357077 |
Lin_Q_2020, Ma_X_2020 |
Wang_2020, He_J_2020, Wang_2020 |
| 86 |
Wan_H_2020 |
Risk estimation and prediction by modeling the transmission of the novel coronavirus (COVID-19) in mainland China excluding Hubei province |
Hui Wan; Jing-an Cui; Guo-Jing Yang |
2020 |
2020-03-06 |
BioRxiv |
Y |
|
|
10.1101/2<IP_ADDRESS>29629 |
3e1ji2mw |
0.355744 |
|
Wang_2020, Zareie_2020 |
| 87 |
Li_R_2020 |
The demand for inpatient and ICU beds for COVID-19 in the US: lessons from Chinese cities |
Ruoran Li; Caitlin Rivers; Qi Tan; Megan B Murray; Eric Toner; Marc Lipsitch |
2020 |
2020-03-13 |
BioRxiv |
Y |
|
|
10.1101/2<IP_ADDRESS>33241 |
k4l45ene |
0.342779 |
|
|
| 88 |
Liu_Q_2020 |
Assessing the Tendency of 2019-nCoV (COVID-19) Outbreak in China |
Qinghe Liu; Zhicheng Liu; Deqiang Li; Zefei Gao; Junkai Zhu; Junyan Yang; Qiao Wang |
2020 |
2020-02-11 |
BioRxiv |
Y |
|
|
10.1101/2<IP_ADDRESS>21444 |
5dd89gnm |
0.342491 |
Muniz-Rodriguez_2020, Tomie_2020, Du_Z_2020 |
He_J_2020, Zareie_2020 |
| 89 |
Cao_C_2020 |
Epidemiologic Features of 135 Patients With Coronavirus Disease (COVID-19) in Tianjin, China |
Cao, Chunxia; Li, Yue; Liu, Shuyu; Fan, Haojun; Hao, Liangchen |
2020 |
2020-04-01 |
COMM-USE |
Y |
PMC7156568 |
32234107.0 |
10.1017/dmp.2020.63 |
8bamjtu6 |
0.339030 |
Ma_X_2020, Lin_Q_2020 |
|
| 90 |
Lee_P_2020 |
Estimating the real-time case fatality rate of COVID-19 using Poisson mixtures model |
Paul Hong Lee |
2020 |
2020-04-15 |
BioRxiv |
Y |
|
|
10.1101/2<IP_ADDRESS>62190 |
g1l3gi3l |
0.336685 |
Tomie_2020, Lin_Q_2020, batista_2020 |
Cao_L_2020, Zareie_2020 |
| 91 |
Wang_2020 |
Updated understanding of the outbreak of 2019 novel coronavirus (2019‐nCoV) in Wuhan, China |
Wang, Weier; Tang, Jianming; Wei, Fangqiang |
2020 |
2020-02-12 |
PMC |
Y |
PMC7167192 |
31994742.0 |
10.1002/jmv.25689 |
xuiswu2l |
0.331683 |
|
Bassetti_2020, Ai_J_2020 |
| 92 |
He_J_2020 |
Comparative Analysis of COVID-19 Transmission Patterns in Three Chinese Regions vs. South Korea,Italy and Iran |
Junyu He; Guangwei Chen; Yutong Jiang; Runjie Jin; Mingjun He; Ashton Shortridge; Jiaping Wu; George Christakos |
2020 |
2020-04-14 |
BioRxiv |
Y |
|
|
10.1101/2<IP_ADDRESS>53223 |
d9y31nlj |
0.329225 |
Tomie_2020, Ma_X_2020 |
Wang_2020, Ji_X_2020, Ping_2020 |
| 93 |
Zhu_Y_2020 |
Limited Early Warnings and Public Attention to Coronavirus Disease 2019 in China, January–February, 2020: A Longitudinal Cohort of Randomly Sampled Weibo Users |
Zhu, Yuner; Fu, King-Wa; Grépin, Karen A.; Liang, Hai; Fung, Isaac Chun-Hai |
2020 |
2020-04-03 |
COMM-USE |
Y |
PMC7171227 |
|
10.1017/dmp.2020.68 |
bgj97s5o |
0.326827 |
|
|
| 94 |
Wu_H_2020 |
Facemask shortage and the coronavirus disease (COVID-19) outbreak: Reflection on public health measures |
Huailiang Wu; Jian Huang; Casper JP Zhang; Zonglin He; Wai-kit Ming |
2020 |
2020-02-12 |
BioRxiv |
Y |
|
|
10.1101/2<IP_ADDRESS>20735 |
6r6zwfoy |
0.326065 |
|
Gao_Q_2020, Memish_2020 |
| 95 |
Zhuang_2020 |
Estimation of local novel coronavirus (COVID-19) cases in Wuhan, China from off-site reported cases and population flow data from different sources |
Zian Zhuang; Peihua Cao; Shi Zhao; Yijun Lou; Weiming Wang; Shu Yang; Lin Yang; Daihai He |
2020 |
2020-03-02 |
BioRxiv |
Y |
|
|
10.1101/2<IP_ADDRESS>30080 |
4f84pc83 |
0.325937 |
Ma_X_2020, Du_Z_2020, Amodio_2020 |
Tian_2020, Wang_2020 |
| 96 |
Zhang_2020 |
Early surveillance and public health emergency disposal measures between novel coronavirus disease 2019 and avian influenza in China: a case-comparison study |
Tiantian Zhang; Wenming Shi; Ying Wang; Ge Bai; Ruiming Dai; Qian Wang; Li Luo |
2020 |
2020-04-01 |
BioRxiv |
Y |
|
|
10.1101/2<IP_ADDRESS>46490 |
j40zj4al |
0.325174 |
|
Ai_J_2020 |
| 97 |
Bedford_2020 |
Cryptic transmission of SARS-CoV-2 in Washington State |
Trevor Bedford; Alexander L. Greninger; Pavitra Roychoudhury; Lea M Starita; Michael Famulare; Meei-Li Huang; Arun Nalla; Gregory Pepper; Adam Reinhardt; Hong Xie; Lasata Shrestha; Truong N Nguyen; Amanda Adler; Elisabeth Brandstetter; Shari Cho; Danielle Giroux; Peter D Han; Kairsten Fay; Chris D Frazar; Misja Ilcisin; Kirsten Lacombe; Jover Lee; Anahita Kiavand; Matthew Richardson; Thomas R Sibley; Melissa Truong; Caitlin R Wolf; Deborah A Nickerson; Mark J Rieder; Janet A Englund; James Hadfield; Emma B Hodcroft; John Huddleston; Louise H Moncla; Nicola F Müller; Richard A Neher; Xianding Deng; Wei Gu; Scot Federman; Charles Chiu; Jeff Duchin; Romesh Gautom; Geoff Melly; Brian Hiatt; Philip Dykema; Scott Lindquist; Krista Queen; Ying Tao; Anna Uehara; Suxiang Tong; Duncan MacCannell; Gregory L Armstrong; Geoffrey S Baird; Helen Y Chu; Keith R Jerome |
2020 |
2020-04-06 |
BioRxiv |
Y |
|
|
10.1101/2<IP_ADDRESS>51417 |
wb19vqv3 |
0.321272 |
Ma_X_2020 |
|
| 98 |
Wen_Y_2020 |
Epidemiological and clinical characteristics of COVID-19 in Shenzhen, the largest migrant city of China |
Ying Wen; Lan Wei; Yuan Li; Xiujuan Tang; Shuo Feng; Kathy Leung; Xiaoliang Wu; Xiong-Fei Pan; Cong Chen; Junjie Xia; Xuan Zou; Tiejian Feng; Shujiang Mei |
2020 |
2020-03-23 |
BioRxiv |
Y |
|
|
10.1101/2<IP_ADDRESS>35246 |
ddq2q1pg |
0.315152 |
|
|
| 99 |
Shen_2020 |
Modelling the epidemic trend of the 2019 novel coronavirus outbreak in China |
Mingwang Shen; Zhihang Peng; Yanni Xiao; Lei Zhang |
2020 |
2020-01-25 |
BioRxiv |
Y |
|
|
10.1101/2<IP_ADDRESS>6726 |
59rcogfc |
0.314407 |
|
Chinazzi_2020, Wang_2020 |
| 100 |
Shim_2020 |
Transmission potential of COVID-19 in South Korea |
Eunha Shim; Amna Tariq; Wongyeong Choi; Yiseul Lee; Gerardo Chowell |
2020 |
2020-02-29 |
BioRxiv |
Y |
|
|
10.1101/2<IP_ADDRESS>28829 |
z7a3g6e8 |
0.310802 |
Muniz-Rodriguez_2020 |
Li_P_2020, Wang_2020, Chen_2020 |
| 101 |
Kucharski_2020 |
Early dynamics of transmission and control of COVID-19: a mathematical modelling study |
Kucharski, Adam J; Russell, Timothy W; Diamond, Charlie; Liu, Yang; Edmunds, John; Funk, Sebastian; Eggo, Rosalind M; Sun, Fiona; Jit, Mark; Munday, James D; Davies, Nicholas; Gimma, Amy; van Zandvoort, Kevin; Gibbs, Hamish; Hellewell, Joel; Jarvis, Christopher I; Clifford, Sam; Quilty, Billy J; Bosse, Nikos I; Abbott, Sam; Klepac, Petra; Flasche, Stefan |
2020 |
2020-03-11 |
PMC |
Y |
|
|
10.1016/s1473-3099(20)30144-4 |
uvavdk6n |
0.310765 |
Ma_X_2020, Lin_Q_2020 |
Hou_J_2020 |
| 102 |
Li_C_2020 |
Retrospective analysis of the possibility of predicting the COVID-19 outbreak from Internet searches and social media data, China, 2020 |
Li, Cuilian; Chen, Li Jia; Chen, Xueyu; Zhang, Mingzhi; Pang, Chi Pui; Chen, Haoyu |
2020 |
2020-03-12 |
COMM-USE |
Y |
PMC7078825 |
32183935.0 |
10.2807/1560-7917.es.2<IP_ADDRESS>0199 |
d1j95rqt |
0.308083 |
Scheuerl_2020 |
Xu_B_2020, Zareie_2020 |
| 103 |
Ji_X_2020 |
Analysis of Epidemic Situation of New Coronavirus Infection at Home and Abroad Based on Rescaled Range (R/S) Method |
Xiaofeng Ji; Zhou Tang; Kejian Wang; Xianbin Li; Houqiang Li |
2020 |
2020-03-20 |
BioRxiv |
Y |
|
|
10.1101/2<IP_ADDRESS>36756 |
xqs4baou |
0.307966 |
Tomie_2020 |
He_J_2020, Wang_2020 |
| 104 |
Dehesh_2020 |
Forecasting of COVID-19 Confirmed Cases in Different Countries with ARIMA Models |
Tania Dehesh; H.A. Mardani-Fard; Paria Dehesh |
2020 |
2020-03-18 |
BioRxiv |
Y |
|
|
10.1101/2<IP_ADDRESS>35345 |
a0pf12jo |
0.307110 |
|
Zareie_2020 |
| 105 |
Yu_F_2020 |
Measures for diagnosing and treating infections by a novel coronavirus responsible for a pneumonia outbreak originating in Wuhan, China |
Yu, Fei; Du, Lanying; Ojcius, David M.; Pan, Chungen; Jiang, Shibo |
2020 |
2020-03-31 |
PMC |
Y |
PMC7102556 |
32017984.0 |
10.1016/j.micinf.2020.01.003 |
x95h80nq |
0.305322 |
|
Ai_J_2020, Zhu_N_2020, Cheng_2020 |
| 106 |
Velavan_2020 |
The COVID‐19 epidemic |
Velavan, Thirumalaisamy P.; Meyer, Christian G. |
2020 |
2020-02-16 |
PMC |
Y |
PMC7169770 |
32052514.0 |
10.1111/tmi.13383 |
e4hmo4yc |
0.305041 |
Ma_X_2020 |
Memish_2020, Arshad_Ali_2020, He_J_2020 |
| 107 |
Liu_N_2020 |
Prevalence and predictors of PTSS during COVID-19 outbreak in China hardest-hit areas: Gender differences matter |
Liu, Nianqi; Zhang, Fan; Wei, Cun; Jia, Yanpu; Shang, Zhilei; Sun, Luna; Wu, Lili; Sun, Zhuoer; Zhou, Yaoguang; Wang, Yan; Liu, Weizhi |
2020 |
2020-05-31 |
PMC |
Y |
|
|
10.1016/j.psychres.2020.112921 |
ji6cdunz |
0.302382 |
|
Jahanshahi_2020, Wen_Y_2020 |


Please wait, ID is currently being started and will soon have matches to review.
This page will automatically refresh to check for status updates in seconds. You may safely navigate to other pages and return here when identification reviewing is ready.
{% elif finished == 'unavailable' %}
The identification matching review you are attempting to review cannot be completed. The Annotation IDs in this
review are currently being used by UUID {{ refer_query_uuid }}
{% elif finished %}
Congratulations, identification reviewing is completed. Thank you!
View annotations
{% endif %}
{% if not REFER_DST_ENCODED and progress %}
{{ progress }} Names (PCCs)
{% endif %}
{% if session %}
Session: [ {{ session_counter }} / {{ session_limit }} ]
{% endif %}
Time Delta: {{ timedelta_str }}
Match Annotation IDs: ({{ aid1 }}, {{ aid2 }})
{% include 'review/identification_insert.html' %}
Identification Reviewing Instructions
A “Photobomb” occurs when the foreground animals in the two annotations are different, but a correct match appears in the image anyway, either between one foreground annotation and one background annotation, or between two background annotations. We only want to say that two annotations are the same animal when both are clearly the primary animals in the annotation. Therefore, when you see a photobomb, please click on the Photobomb box and then select Different Animals as the decision.
A “Scenery Match” occurs when the animals in the foreground of the annotations are not the same and the primary matching information is in the background / scenery of the annotations. In this case click the Scenery Match box and select either Different Animals or Cannot Tell, as appropriate. Note that you can click on both Photobomb and Scenery Match for one pair of annotations.
If an annotation is too poor to reliably match, please click on the corresponding Exclude button at the bottom. Such an annotation will be removed from further consideration during the id process.


Open Source Cloud Computing™
Apache CloudStack is open source software designed to deploy and manage large networks of virtual machines, as a highly available, highly scalable Infrastructure as a Service (IaaS) cloud computing platform. CloudStack is used by a number of service providers to offer public cloud services, and by many companies to provide an on-premises (private) cloud offering, or as part of a hybrid cloud solution.
CloudStack is a turnkey solution that includes the entire "stack" of features most organizations want with an IaaS cloud: compute orchestration, Network-as-a-Service, user and account management, a full and open native API, resource accounting, and a first-class User Interface (UI).
CloudStack currently supports the most popular hypervisors: VMware, KVM, Citrix XenServer, Xen Cloud Platform (XCP), Oracle VM server and Microsoft Hyper-V.
Users can manage their cloud with an easy to use Web interface, command line tools, and/or a full-featured RESTful API. In addition, CloudStack provides an API that's compatible with AWS EC2 and S3 for organizations that wish to deploy hybrid clouds.
Learn more about getting involved with Apache CloudStack on the Contributing to Apache CloudStack page, or go straight to our Developer Resources page.
Apache CloudStack <IP_ADDRESS> is out!
This is current CloudStack LTS release.
If you prefer proven stability rather than the newest features, please take a look at the previous LTS release.
Previous Apache CloudStack LTS Release is <IP_ADDRESS>.
Looking for other versions of Apache CloudStack? You can find them in our archives page as well.
There's always plenty of Cloudstack Events happening in different parts of the world. These range from Meetups, User Groups to CloudStack Collaboration Conference events. Each event will feature a lot of opportunities to learn, collaborate and engage with the CloudStack community. The CloudStack Collaboration Conferences are valuable for both developers and users, and are a great way to get involved with the community.
View videos from previous CloudStack Collaboration Conferences:


sort-viz
Sorting Algorithm Visualizations
Demo
Algorithm
Bubble
Cocktail
Heap
Insertion
Merge (Iterative)
Merge (Recursive)
Quick
Selection
Shell
Smooth
Tournament
Radix (LSD)
Sleep
Criteria
Top
Bottom
Height
Center
Red
Green
Blue
Hue
Saturation
Lightness
Luminance
Order
Ascending
Descending
Interval
ms
History
Size
Sort
Step
Shuffle
Reset
Compares
0
Swaps
0
Puts
0
Keys
0


A particularly beautiful island about 150nm north east of Okinawa.
Position 28°0'N, 129° 20'E.
The port is Koniya and has a non-floating dock for yachts.
Most things are available here.


Sascha Bates
DevOps Enthusiast, Automator of Things
Objective
Never to be bored
Personal Profile
I have over 10 years of IT experience, mainly in infrastructure design, build and automation, as well as complementary skills in *nix system administration and extensive problem solving experience. In the late 90’s I was pursuing a degree in Medieval Literature when I was drawn into full time technology work during the Internet Boom. I never looked back. Not only do I bring several years of technical experience, but also a solid communication skill set that allows me to speak clearly to all levels of technical background.
Background
Buzzwords?
Professional Experience
Principal Consultant - Bratty Redhead Consulting, July 2011
Recent Project Overview
- I write the O’Reilly Web Ops and Performance Newsletter (email only) http://radar.oreilly.com/webops
- Multiphase POC to design and build a manifest-driven deploy toolchain for hosted VMware applications using Chef Solo, Jenkins, Rundeck and VMware tools on both Ubuntu and Windows
- Project to automate ATG10/JBoss5 with Puppet for a small hosting/consulting shop
- ATG 9/JBoss 4.3 Dual retail applications - production environment standup
- Given zero documentation and aggressive deadlines, I designed and built the application production layout for two applications sharing hardware. Components included Apache, mod_jk, ATG Search and BCC install/config. Along the way I abstracted jboss port management, wrote a management script, hacked together a deployment script, while also providing detailed documentation on JBoss slimming, optimization and build instructions.
- ATG 10/JBoss 5.1 all environments design/build/automate/support
- Worked with the lead developer to design environment layouts, pushed for a documentation wiki that became wildly popular and stood up a monitoring dashboard(m/monit) and external log reader (Graylog2) to enable developers where they weren’t allowed access to servers.


A Jaguar speed controller that uses CAN protocol to control the
the attached motor. In addition to controlling speed, Jaguars
controlled by CAN can do PID and uses limit switches to control
motion.
Properties
CAN ID
The ID of the CAN device. This must match the ID of the
device configured with BDC-COMM and shown in the roboRIO WebDash.


Part 1: Access your documents
This part is easy. To access your document storage area, click "Documents" on the top left navigation bar. Your documents area will open in a new tab.
This is what your documents area will look like. In this tutorial, you will be using the following features:
- Google account navigation bar Use these links to toggle between your documents, inbox, and other Google pages
- Create new document menu Use this drop-down menu to select a new Google Doc type
- Upload button This link will take you to the upload area, where you can select your own documents and files to upload and store
- Share documents menu Use this menu to choose how you want to share your documents and files with others
- Documents storage area This area, which appears similar to your email inbox, is where you can find all your stored documents and files


Bunch of relative information and answers in your hand.
We are dedicated to practical implimentations of Thoughts, Experiments, Solutions. Products that will help farmers in improving productivity. Tell us more about your problems and we will get back to you with possible solutions. Lets colabroate and grow together,
We collected data from various resources and created a base product to help farmers in increasing profit.
Contact Details


{{ block "add_css" . }} {{ end }}
×
Blog
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Archives
×
FFT
Testament
Contact
Blog
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Archives
Bio1ixir
FFT
Testament
Contact
{{ block "main" . }} {{ end }}
Contact
Bio1ixir Google Group
Executive Head
Quick Links
IITJ website
BSBE Department
Academia- IITJ
Students' Gymkhana- IITJ
Academic Calendar
Students' Placement Cell- IITJ
Institutional Repository
B.Tech. in Bioengineering
Testament- Executive Head
Contributors Manual
FAQ- IITJ
Get Involved
Contribute on GitHub
Seed & Suggest
Request Membership
{{ block "add_scripts" . }} {{ end }}


{% raw %}{% extends 'general.html' %} {% block title %}Front page{% endblock %} {% block container %}
Use this document as a way to quickly start any new project. All you get is this text and a mostly barebones HTML document.


--- ---
Ehtiman Nuralizade
Co-founder
Nijad Huseinov
Sahila Nematli
Manager
Samira Huseinli
Emil Mirzazade
Developer
Nijat Dursunlu
Narmin Erdener
Project Member
Madina Hasanzade
Lala Karimova


Wolf cardigan tumblr succulents, beard shabby chic next level messenger bag. Church-key bushwick tousled, kinfolk swag 8-bit vexillologist dreamcatcher jianbing listicle pour-over pop-up wolf. Mixtape irony occupy pour-over typewriter, cardigan fanny pack. Cold-pressed migas blue bottle health goth. Master cleanse plaid cronut trust fund art party.
Mixtape irony occupy pour-over typewriter, cardigan fanny pack. Cold-pressed migas blue bottle health goth. Master cleanse plaid cronut trust fund art party
Some text here, with extra HTML tags such as


Data formats¶
Within peakTree basically two data formats are used to handle the trees.
Array¶
The first one is array based and used to save the trees into netcdf4 files.
Such files are the output of the conversion from Doppler spectra to trees.
A variable exists for each moment Z
, v
, width
, … with a nodes
value saved according to its index. Hence, the array might be sparse.
Z(time, height, node)
v(time, height, node)
width(time, height, node)
To reconstruct the tree, the variable parent
holds the index of a nodes parent
node. The peakTree.peakTreeBuffer.get_tree_at()
provieds a reading routine.
parent[1632,13,:] = [-1.0 0.0 0.0 -- -- 2.0 2.0]
Z[1632,13,:] = [-5.2 -17.0 -5.4 -- -- -5.6 -19.5]
Dictionary¶
The second one contains the tree as a dictionary in a 2d list (either as a numpy array with
dtype object or a javascript/json list of list with objects).
Each entry of the dictionary contains the moments of a node.
The trees are stored inside a dictionary under the key var
with the additional keys
ts
, rg
, dimlabel
and paraminfo
providing meta information.
This format is used for the interactive visualizer and grouping into particle populations.
[
times...
[
ranges...
{0: {"id": 0, "bounds": [70, 164], "prominence": 6955.19, "skew": -0.2946, "v": -0.7237,
"ldrmax": -13.8709, "width": 0.4588, "thres": -52.1102, "z": -5.2166, "ldr": -17.102,
"coords": [0]},
1: {"id": 1, "bounds": [70, 110], "prominence": 3.1156, "skew": 0.3129, "v": -1.5912,
"ldrmax": -26.2039, "width": 0.099, "thres": -27.7299, "z": -17.0026, "ldr": -25.3611,
"coords": [0, 0], "parent_id": 0},
2: {"id": 2, "bounds": [110, 164], "prominence": 25.3677, "skew": -0.0628, "v": -0.6729,
"ldrmax": -13.8709, "width": 0.3627, "thres": -27.7299, "z": -5.4885, "ldr": -16.8673,
"coords": [0, 1], "parent_id": 0},
5: {"id": 5, "bounds": [110, 126], "prominence": 22.9612, "skew": -0.1676, "v": -0.6873,
"ldrmax": -13.8709, "width": 0.3429, "thres": -27.297, "z": -5.6327, "ldr": -16.7911,
"coords": [0, 1, 0], "parent_id": 2},
6: {"id": 6, "bounds": [126, 164], "prominence": 1.3913, "skew": -0.1692, "v": 0.0501,
"ldrmax": -21.6985, "width": 0.0901, "thres": -27.297, "z": -19.5505, "ldr": -18.7583,
"coords": [0, 1, 1], "parent_id": 2}
...
]
...
]


{% extends 'base_account.html' %} {% load account %} {% load i18n %} {% block head_title %}Sign In{% endblock head_title %} {% block page_title %}Sign In{% endblock page_title %} {% block extra_head %} {% endblock %} {% block content %}
{% blocktrans with site.name as site_name %}Please sign in with one of your existing third party accounts. Or, sign up for a {{site_name}} account and sign in below: {% endblocktrans %}
{% include "socialaccount/snippets/login_extra.html" %} {% endif %}
Don't have an account? Sign up!
Resend confirmation email


{{title}}
Case has been deleted, you have no draft cases.
{% include "includes/get_help.html" %}
{% extends "layout.html" %}
{% set title = "Draft cases" %}
{% set version = "2" %}
{% block page_title %}
{{title}}
{% endblock %}
{% block service_name %}
Check and challenge your business rates valuation
{% endblock %}
{% block content %}
{{title}}
Case has been deleted, you have no draft cases.
{% include "includes/get_help.html" %}