Infectious diseases are a major burden to global health. Understanding their mechanisms and being able to predict and intervene epidemic outbreaks is an important challenge for researchers and decision makers alike. It should not be too hard either―if we include human contact patterns, the mechanisms of contagion and the typical features of the disease, we could model most infectious-disease related phenomena. Of these three components, the network epidemiology of the last decade has shown that our limited understanding of human contact patterns is probably the most important focus are for advancing infectious disease epidemiology. We will discuss what is known about human contact patterns and how to include this knowledge in epidemic modeling. First, we discuss recent work on what the epidemiologically most important temporal structures of human contacts are. We use about 80 empirical temporal network datasets, several arguably important for disease spreading, and scan the entire parameter space of disease-spreading models. By comparing to null-models, we identify important, simple temporal patterns that affect disease spreading stronger than the bursty interevent time distributions. Furthermore, we investigate how to eliminate the temporal information to make an as relevant static network as possible. After all, static network epidemiology has more methods and results than temporal network epidemiology and it for some purposes it is necessary. We find that an “exponential threshold” representation almost always the best performance, but time-sliced network (with a carefully chosen window, usually considerably different than the sampling time of the data) works almost as good. In contrast, networks of concurrent contacts do not seem to carry so important information.
The LSG-1200A compact goniophotometer is used to measure the luminous intensity distribution curve, intensity data, beam angle and other photometric for Chip LED, LED Module, LED Spotlight and all other light which spread angle is no more than 180 degree.
Online Detection of Shutdown Periods in Chemical Plants: A Case StudyManuel Martín
In process industry, chemical processes are controlled and monitored by using readings from multiple physical sensors across the plants. Such physical sensors are also supplemented by soft sensors, i.e. adaptive predictive models, which are often used for computing hard-to-measure variables of the process. For soft sensors to work well and adapt to changing operating conditions they need to be provided with relevant data. As production plants are regularly stopped, data instances generated during shutdown periods have to be identified to avoid updating these predictive models with wrong data. We present a case study concerned with a large chemical plant operation over a 2 years period. The task is to robustly and accurately identify the shutdown periods even in case of multiple sensor failures. State-of-the-art methods were evaluated using the first half of the dataset for calibration purposes and the other half for measuring the performance. Results show that shutdowns (i.e. sudden changes) can be quickly detected in any case but the detection delay of startups (i.e. gradual changes) is directly related with the choice of a window size.
The compact goniophotometer of LSG-1200A is used to measure the luminous intensity distribution curve, intensity data, spread angle and other parameters for Chip LED, LED Module, LED Spotlight and all other light which beam angle is no more than 180 degree.
The LSG-1200A compact goniophotometer is used to measure the luminous intensity distribution curve, intensity data, beam angle and other photometric for Chip LED, LED Module, LED Spotlight and all other light which spread angle is no more than 180 degree.
Online Detection of Shutdown Periods in Chemical Plants: A Case StudyManuel Martín
In process industry, chemical processes are controlled and monitored by using readings from multiple physical sensors across the plants. Such physical sensors are also supplemented by soft sensors, i.e. adaptive predictive models, which are often used for computing hard-to-measure variables of the process. For soft sensors to work well and adapt to changing operating conditions they need to be provided with relevant data. As production plants are regularly stopped, data instances generated during shutdown periods have to be identified to avoid updating these predictive models with wrong data. We present a case study concerned with a large chemical plant operation over a 2 years period. The task is to robustly and accurately identify the shutdown periods even in case of multiple sensor failures. State-of-the-art methods were evaluated using the first half of the dataset for calibration purposes and the other half for measuring the performance. Results show that shutdowns (i.e. sudden changes) can be quickly detected in any case but the detection delay of startups (i.e. gradual changes) is directly related with the choice of a window size.
The compact goniophotometer of LSG-1200A is used to measure the luminous intensity distribution curve, intensity data, spread angle and other parameters for Chip LED, LED Module, LED Spotlight and all other light which beam angle is no more than 180 degree.
Solutions manual for hydrologic analysis and design 4th edition by mc cuen ib...frazob
Solutions manual for hydrologic analysis and design 4th edition by mc cuen ibsn 9780134313122
download at: https://goo.gl/hA8T7p
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According to LM-79, GB, CIE & IEC Standards. Test intensity distribution curve in 3D & export IES files for DiaLux. It already combined 1m length dark room and used to test the lamps which diameter is less than 18cm
Epidemic processes on switching networksNaoki Masuda
Presentation slides for the following two papers:
- Leo Speidel, Konstantin Klemm, Víctor M. Eguíluz, Naoki Masuda.
New Journal of Physics, 18, 073013 (2016).
- Tomokatsu Onaga, James P. Gleeson, Naoki Masuda.
Physical Review Letters, 119, 108301 (2017).
Based on the rugged HUMIREL humidity sensor, HTG3535CH is a dedicated humidity and temperature plug and play transducer designed for OEM applications where a reliable and accurate measurement is needed. Direct interface with a micro-controller is made possible with the module’s humidity linear voltage and direct NTC outputs. HTG3535CH is designed for high volume and demanding applications.
Three Step Table Validation Pharmasug 2007snail_chen
I wrote this when I joined Forest in 2006. It extracts the main body of Table and compares it with the validator\'s output. It can pinpoint the differences and is extremely efficient and accurate. Valuable tool for large table validation. Presented at Pharmasug 2007, Denver
A Study on the Short Run Relationship b/w Major Economic Indicators of US Eco...aurkoiitk
The objective of this study
was to develop an economic indicator system for the US
economy that will help to forecast the turning points in the
aggregate level of economic activity. Our primary concern
is to study the short run relationship between the major
economic indicators of US economy (eg: GDP, Money
Supply, Unemployment Rate, Inflation rate, Federal Fund
Rate, Exchange Rate, Government Expenditure &
Receipt, Crude Oil Price, Net Import & Export).
Similar to From temporal to static networks, and back (20)
Temporal network epidemiology: Subtleties and algorithmsPetter Holme
The SIR and SIS models are the canonical model of epidemics of infections that make people immune upon recovery. Many open questions in computational epidemiology concern the underlying contact structure’s impact on models like the SIR or SIS. Temporal networks constitute a theoretical framework capable of encoding structures both in the networks of who could infect whom and when these contacts happen. In this talk, we discuss the detailed assumptions behind such simulations—how to make them comparable with analytically tractable formulations of the SIR model, and at the same time, as realistic as possible. We also discuss fast algorithms for such simulations and the challenges in improving them.
This is one segment of a talk where I presented the history of computational social science:
* The origins of computer simulations.
* The trouble to publish computational studies in the 1960s.
* The peak enthusiasm for computer simulations after "Limits of Growth"
* The precursors of social-media data science in the 1980's
Important spreaders in networks: exact results on small graphsPetter Holme
To be able to control spreading phenomena (like the spreading of diseases and information) in networks it is important to identify influential spreaders. What "important" means depends on what is spreading and what kind of countermeasures that are available. In this work, we let the susceptible-infected-removed (SIR) model represent the spreading dynamics and contrast three different definitions of importance: Influence maximization (the expected outbreak size given a set of seed nodes), the effect of vaccination (how much deleting nodes would reduce the expected outbreak size) and sentinel surveillance (how early an outbreak could be detected with sensors at a set of nodes). We calculate the exact expressions of these quantities, as functions of the SIR parameters, for all connected graphs of three to seven nodes. We obtain the smallest graphs where the optimal node sets are not overlapping. We find that: node separation is more important than centrality for more than one active node, that vaccination and influence maximization are the most different aspects of importance, and that the three aspects are more similar when the infection rate is low. Furthermore, we discuss similar approaches to study the extinction times in the susceptible-infected- susceptible model.
A paradox of importance in network epidemiologyPetter Holme
Talk at the International Conference on Computational Social Science, Helsinki, June 9, 2015. On YouTube here (Plenary II): https://www.youtube.com/channel/UCUGsbLwL4G2CQQfk95oZjVw
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Invited at Physics of Social Complexity (PoSCo), Pohang, Korea, January 28 2015. Presenting the paper by Mondani, Holme, Liljeros (2014) http://journals.plos.org/plosone/article?id=10.1371/journal.pone.0100527
The Chakra System in our body - A Portal to Interdimensional Consciousness.pptxBharat Technology
each chakra is studied in greater detail, several steps have been included to
strengthen your personal intention to open each chakra more fully. These are designed
to draw forth the highest benefit for your spiritual growth.
In Jude 17-23 Jude shifts from piling up examples of false teachers from the Old Testament to a series of practical exhortations that flow from apostolic instruction. He preserves for us what may well have been part of the apostolic catechism for the first generation of Christ-followers. In these instructions Jude exhorts the believer to deal with 3 different groups of people: scoffers who are "devoid of the Spirit", believers who have come under the influence of scoffers and believers who are so entrenched in false teaching that they need rescue and pose some real spiritual risk for the rescuer. In all of this Jude emphasizes Jesus' call to rescue straying sheep, leaving the 99 safely behind and pursuing the 1.
The Book of Joshua is the sixth book in the Hebrew Bible and the Old Testament, and is the first book of the Deuteronomistic history, the story of Israel from the conquest of Canaan to the Babylonian exile.
Exploring the Mindfulness Understanding Its Benefits.pptxMartaLoveguard
Slide 1: Title: Exploring the Mindfulness: Understanding Its Benefits
Slide 2: Introduction to Mindfulness
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Slide 3: Benefits of Mindfulness for Mental Well-being
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Slide 4: Benefits of Mindfulness for Physical Health
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Slide 5: Impact of Mindfulness on Relationships
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Slide 6: Mindfulness Techniques and Practices
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Slide 7: Incorporating Mindfulness into Daily Life
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Slide 8: Summary: Embracing Mindfulness for Full Living
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Regular mindfulness practice can help achieve a fuller and more satisfying life.
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The PBHP DYC ~ Reflections on The Dhamma (English).pptxOH TEIK BIN
A PowerPoint Presentation based on the Dhamma Reflections for the PBHP DYC for the years 1993 – 2012. To motivate and inspire DYC members to keep on practicing the Dhamma and to do the meritorious deed of Dhammaduta work.
The texts are in English.
For the Video with audio narration, comments and texts in English, please check out the Link:
https://www.youtube.com/watch?v=zF2g_43NEa0
What Should be the Christian View of Anime?Joe Muraguri
We will learn what Anime is and see what a Christian should consider before watching anime movies? We will also learn a little bit of Shintoism religion and hentai (the craze of internet pornography today).
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Lesson 9 - Resisting Temptation Along the Way.pptxCelso Napoleon
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Adult Bible Lessons 2nd quarter 2024 CPAD
MAGAZINE: THE CAREER THAT IS PROPOSED TO US: The Path of Salvation, Holiness and Perseverance to Reach Heaven
Commentator: Pastor Osiel Gomes
Presentation: Missionary Celso Napoleon
Renewed in Grace
The Good News, newsletter for June 2024 is hereNoHo FUMC
Our monthly newsletter is available to read online. We hope you will join us each Sunday in person for our worship service. Make sure to subscribe and follow us on YouTube and social media.
6. Sociopatterns gallery
P H Y S I C A L P R O X I M I T Y
Prostitution
Sociopatterns conference
Hospital system
N = 16,730, L = 50,632, T = 6.0y
N = 113, L = 20,818, T = 59h
N = 159(8), L = 6,027(350), T = 7.3(1)h
N = 293,878, L = 64,625,283, T = 3,570dReality mining
N = 63, L = 26,260, T = 8.6h
7. ELECTRONIC COMMUNICATION
N = 57,189, L = 444,162, T = 112.0d
Bornholdt’s e-mail
Eckmann’s e-mail
N = 3,188, L = 115,684, T = 81.6d
Filmtipset forum
N = 7,084, L = 1,412,401, T = 8.61y
Filmtipset messages
Pussokram dating
N = 28,972, L = 529,890, T = 512.0d
QX dating
N = 80,683, L = 4,337,203, T = 63.7d
N = 35,624, L = 472,496, T = 8.27y
Facebook wall posts
N = 293,878, L = 876,993, T = 1591d
12. GOOD REPRESENTATION:
RANKING OF IMPORTANT
VERTICES CONSERVED
FOR ALL PARAMETER VALUES:
MEASURE AVG OUTBREAK SIZE
WHEN SPREADING STARTS AT i
FOR ALL PARAMETER VALUES:
MEASURE DEGREE OF i
FOR ALL PARAMETER VALUES:
MEASURE CORENESS OF i
degree 4
coreness 0
coreness 2
coreness 3
coreness 4
static importance
optimal params.
dynamic
importance
Spearman
rank correlation
coefficent
=
Quality of
representation
20. STEP 1 Assign stubs to vertices from a
random number distribution.
1
2
3
4
5
6
21. STEP 2 Connect random pairs of stubs
to form a simple graph.
1
2
3
4
5
6
22. STEP 3 Create active intervals for each
edge.
(1,2)
(2,3)
(2,4)
(2,5)
(3,4)
(3,5)
(4,5)
(5,6)
time
23. STEP 4 Create a time series of contacts
from some interevent-time
distribution.
time
24. STEP 5 Split the time series into
segments proportional to the
intervals and impose the
contacts of the segments to the
intervals.
(1,2)
(2,3)
(2,4)
(2,5)
(3,4)
(3,5)
(4,5)
(5,6)
time
25. STEP 6 Forget the active intervals.
(1,2)
(2,3)
(2,4)
(2,5)
(3,4)
(3,5)
(4,5)
(5,6)
time
36. Compensate for the size bias on intervals because of finite
T0
t’
t
sampling time (t’ would only be recorded if it starts within [0,T–t’])
37. Compensate for the size bias on intervals because of finite
T0
t’
t
sampling time (t’ would only be recorded if it starts within [0,T–t’])
Compensate for the chance an interevent time t is active
0
t
at the start of the sampling is proportional to t
38. Compensate for the size bias on intervals because of finite
T0
t’
t
sampling time (t’ would only be recorded if it starts within [0,T–t’])
Compensate for the chance an interevent time t is active
0
t
at the start of the sampling is proportional to t
ti
T–tii: ti≥t
∑ /
ti
T–tii
∑
Sum up and normalize
39. 0
0.2
0.4
0.6
0.8
1
0 500 1000 1500 2000
time t (days)
predicted from
interevent times
end times
beginning times
PROSTITUTION
P(t)B
45. 0
0.1
0.2
0.3
0.1 0.2 0.90.8 10.70.60.50.40.3
0.1
1
0.01
0.001
per-contact transmission probability
durationofinfectivestage
fractionofinfectives
O r i g i n a l d a t a S I R
46. 0
0.1
0.2
0.3
0.1 0.2 0.90.8 10.70.60.50.40.3
0.1
1
0.01
0.001
per-contact transmission probability
durationofinfectivestage
fractionofinfectives
I n t e r e v e n t t i m e s S I R
47. 0
0.1
0.2
0.3
0.1 0.2 0.90.8 10.70.60.50.40.3
0.1
1
0.01
0.001
per-contact transmission probability
durationofinfectivestage
fractionofinfectives
B e g i n n i n g t i m e s S I R
48. 0
0.1
0.2
0.3
0.1 0.2 0.90.8 10.70.60.50.40.3
0.1
1
0.01
0.001
per-contact transmission probability
durationofinfectivestage
fractionofinfectives
E n d t i m e s S I R
50. 0.1 0.2 0.90.8 10.70.60.50.40.3
0.1
1
0.01
0.001
per-contact transmission probability
durationofinfectivestage
O r i g i n a l d a t a
0
0.2
0.3
0.4
averagenumberofinfections
S I S
0.1
51. 0.1 0.2 0.90.8 10.70.60.50.40.3
0.1
1
0.01
0.001
per-contact transmission probability
durationofinfectivestage
I n t e r e v e n t t i m e s
0
0.2
0.3
0.4
averagenumberofinfections
S I S
0.1
52. 0.1 0.2 0.90.8 10.70.60.50.40.3
0.1
1
0.01
0.001
per-contact transmission probability
durationofinfectivestage
B e g i n n i n g t i m e s
0
0.2
0.3
0.4
averagenumberofinfections
S I S
0.1
53. 0
0.2
0.3
0.4
0.1 0.2 0.90.8 10.70.60.50.40.3
0.1
1
0.01
0.001
per-contact transmission probability
durationofinfectivestage
averagenumberofinfections
E n d t i m e s S I S
0.1
56. Science by: Illustrations by:
Petter Holme Fredrik Liljeros Mi Jin Lee
P Holme, 2013, PLoS Comp. Biol. 9:e1003142.
P Holme, F Liljeros, 2013, arxiv:1307.6436.