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Computer simulation
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This article is about computer model within a scientific context. For simulating a computer on a computer, see emulator.
"Computer model" redirects here. For computer models of 3-dimensional objects, see 3D modeling.
Process of building a computer model, and the interplay between experiment, simulation, and theory.
Computer simulation reproduces the behavior of a system using a mathematical model. Computer simulations have become a useful tool for the mathematical modeling of many natural systems in physics (computational physics), astrophysics, climatology, chemistry and biology, human systems in economics, psychology, social science, and engineering. Simulation of a system is represented as the running of the system's model. It can be used to explore and gain new insights into new technology and to estimate the performance of systems too complex for analytical solutions.[1]
Computer simulations are computer programs that can be either small, running almost instantly on small devices, or large-scale programs that run for hours or days on network-based groups of computers. The scale of events being simulated by computer simulations has far exceeded anything possible (or perhaps even imaginable) using traditional paper-and-pencil mathematical modeling. Over 10 years ago, a desert-battle simulation of one force invading another involved the modeling of 66,239 tanks, trucks and other vehicles on simulated terrain around Kuwait, using multiple supercomputers in the DoD High Performance Computer Modernization Program.[2] Other examples include a 1-billion-atom model of material deformation;[3] a 2.64-million-atom model of the complex protein-producing organelle of all living organisms, the ribosome, in 2005;[4] a complete simulation of the life cycle of Mycoplasma genitalium in 2012; and the Blue Brain project at EPFL (Switzerland), begun in May 2005 to create the first computer simulation of the entire human brain, right down to the molecular level.[5]
Because of the computational cost of simulation, computer experiments are used to perform inference such as uncertainty quantification.[6]
Contents
[hide]
1 Simulation versus model
2 History
3 Data preparation
4 Types
5 Visualization
6 Computer simulation in science
6.1 Simulation environments for physics and engineering
7 Computer simulation in practical contexts
8 Pitfalls
8.1 Model calibration techniques
9 See also
10 References
11 Further reading
Simulation versus model[edit]
A computer model is the algorithms and equations used to capture the behavior of the system being modeled. By contrast, computer simulation is the actual running of the program that contains these equations or algorithms. Simulation, therefore, is the process of running a model. Thus one would not "build a simulation"; instead, one would "build a model", and then either "run the model" or equivalently "run a simulation".
History[edit]
Computer simulation developed hand-in-hand with the rapid growth of the computer, following its first large-scale deployment during the Manhattan Project in World War II to model the process of nuclear detonation. It was a simulation of 12 hard spheres using a Monte Carlo algorithm. Computer simulation is often used as an adjunct to, or substitute for, modeling systems for which simple closed form analytic solutions are not possible. There are many types of computer simulations; their common feature is the attempt to generate a sample of representative scenarios for a model in which a complete enumeration of all possible states of the model would be prohibitive or impossible.
Data preparation[edit]
The external data requirements of simulations and models vary widely. For some, the input might be just a few numbers (for example, simulation of a waveform of AC electricity on a wire), while others might require terabytes of information (such as weather and climate models).
Input sources also vary widely:
Sensors and other physical devices connected to the model;
Control surfaces used to direct the progress of the simulation in some way;
Current or historical data entered by hand;
Values extracted as a by-product from other processes;
Values output for the purpose by other simulations, models, or processes.
Lastly, the time at which data is available varies:
"invariant" data is often built into the model code, either because the value is truly invariant (e.g., the value of π) or because the designers consider the value to be invariant for all cases of interest;
data can be entered into the simulation when it starts up, for example by reading one or more files, or by reading data from a preprocessor;
data can be provided during the simulation run, for example by a sensor network.
Because of this variety, and because diverse simulation systems have many common elements, there are a large number of specialized simulation languages. The best-known may be Simula (sometimes called Simula-67, after the year 1967 when it was proposed). There are now many others.
Systems that accept data from external sources must be very careful in knowing what they are receiving. While it is easy for computers to read in values from text or binary files, what is much harder is knowing what the accuracy (compared to measurement resolution and precision) of the values are. Often they are expressed as "error bars", a minimum and maximum deviation from the value range within which the true value (is expected to) lie. Because digital computer mathematics is not perfect, rounding and truncation errors multiply this error, so it is useful to perform an "error analysis"[7] to confirm that values output by the simulation will still be usefully accurate.
Even small errors in the original data can accumulate into substantial error later in the simulation. While all computer analysis is subject to the "GIGO" (garbage in, garbage out) restriction, this is especially true of digital simulation. Indeed, observation of this inherent, cumulative error in digital systems was the main catalyst for the development of chaos theory.
Types[edit]
Computer models can be classified according to several independent pairs of attributes, including:
Stochastic or deterministic (and as a special case of deterministic, chaotic) – see external links below for examples of stochastic vs. deterministic simulations
Steady-state or dynamic
Continuous or discrete (and as an important special case of discrete, discrete event or DE models)
Dynamic system simulation, e.g. electric systems, hydraulic systems or multi-body mechanical systems (described primarely by DAE:s) or dynamics simulation of field problems, e.g. CFD of FEM simulations (described by PDE:s).
Local or distributed.
Another way of categorizing models is to look at the underlying data structures. For time-stepped simulations, there are two main classes:
Simulations which store their data in regular grids and require only next-neighbor access are called stencil codes. Many CFD applications belong to this category.
If the underlying graph is not a regular grid, the model may belong to the meshfree method class.
Equations define the relationships between elements of the modeled system and attempt to find a state in which the system is in equilibrium. Such models are often used in simulating physical systems, as a simpler modeling case before dynamic simulation is attempted.
Dynamic simulations model changes in a system in response to (usually changing) input signals.
Stochastic models use random number generators to model chance or random events;
A discrete event simulation (DES) manages events in time. Most computer, logic-test and fault-tree simulations are of this type. In this type of simulation, the simulator maintains a queue of events sorted by the simulated time they should occur. The simulator reads the queue and triggers new events as each event is processed. It is not important to execute the simulation in real time. It is often more important to be able to access the data produced by the simulation and to discover logic defects in the design or the sequence of events.
A continuous dynamic simulation performs numerical solution of differential-algebraic equations or differential equations (either partial or ordinary). Periodically, the simulation program solves all the equations and uses the numbers to change the state and output of the simulation. Applications include flight simulators, construction and management simulation games, chemical process modeling, and simulations of electrical circuits. Originally, these kinds of simulations were actually implemented on analog computers, where the differential equations could be represented directly by various electrical components such as op-amps. By the late 1980s, however, most "analog" simulations were run on conventional digital computers that emulate the behavior of an analog computer.
A special type of discrete simulation that does not rely on a model with an underlying equation, but can nonetheless be represented formally, is agent-based simulation. In agent-based simulation, the individual entities (such as molecules, cells, trees or consumers) in the model are represented directly (rather than by their density or concentration) and possess an internal state and set of behaviors or rules that determine how the agent's state is updated from one time-step to the next.
Distributed models run on a network of interconnected computers, possibly through the Internet. Simulations dispersed across multiple host computers like this are often referred to as "distributed simulations". There are several standards for distributed simulation, including Aggregate Level Simulation Protocol (ALSP), Distributed Interactive Simulation (DIS), the High Level Architecture (simulation) (HLA) and the Test and Training Enabling Architecture (TENA).
Visualization[edit]
Formerly, the output data from a computer simulation was sometimes presented in a table or a matrix showing how data were affected by numerous changes in the simulation parameters. The use of the matrix format was related to traditional use of the matrix concept in mathematical models. However, psychologists and others noted that humans could quickly perceive trends by looking at graphs or even moving-images or motion-pictures generated from the data, as displayed by computer-generated-imagery (CGI) animation. Although observers could not necessarily read out numbers or quote math formulas, from observing a moving weather chart they might be able to predict events (and "see that rain was headed their way") much faster than by scanning tables of rain-cloud coordinates. Such intense graphical displays, which transcended the world of numbers and formulae, sometimes also led to output that lacked a coordinate grid or omitted timestamps, as if straying too far from numeric data displays. Today, weather forecasting models tend to balance the view of moving rain/snow clouds against a map that uses numeric coordinates and numeric timestamps of events.
Similarly, CGI computer simulations of CAT scans can simulate how a tumor might shrink or change during an extended period of medical treatment, presenting the passage of time as a spinning view of the visible human head, as the tumor changes.
Other applications of CGI computer simulations are being developed to graphically display large amounts of data, in motion, as changes occur during a simulation run.
Computer simulation in science[edit]
Computer simulation of the process of osmosis
Generic examples of types of computer simulations in science, which are derived from an underlying mathematical description:
a numerical simulation of differential equations that cannot be solved analytically, theories that involve continuous systems such as phenomena in physical cosmology, fluid dynamics (e.g., climate models, roadway noise models, roadway air dispersion models), continuum mechanics and chemical kinetics fall into this category.
a stochastic simulation, typically used for discrete systems where events occur probabilistically and which cannot be described directly with differential equations (this is a discrete simulation in the above sense). Phenomena in this category include genetic drift, biochemical or gene regulatory networks with small numbers of molecules. (see also: Monte Carlo method).
multiparticle simulation of the response of nanomaterials at multiple scales to an applied force for the purpose of modeling their thermoelastic and thermodynamic properties. Techniques used for such simulations are Molecular dynamics, Molecular mechanics, Monte Carlo method, and Multiscale Green's function.
Specific examples of computer simulations follow:
statistical simulations based upon an agglomeration of a large number of input profiles, such as the forecasting of equilibrium temperature of receiving waters, allowing the gamut of meteorological data to be input for a specific locale. This technique was developed for thermal pollution forecasting.
agent based simulation has been used effectively in ecology, where it is often called "individual based modeling" and is used in situations for which individual variability in the agents cannot be neglected, such as population dynamics of salmon and trout (most purely mathematical models assume all trout behave identically).
time stepped dynamic model. In hydrology there are several such hydrology transport models such as the SWMM and DSSAM Models developed by the U.S. Environmental Protection Agency for river water quality forecasting.
computer simulations have also been used to formally model theories of human cognition and performance, e.g., ACT-R.
computer simulation using molecular modeling for drug discovery.[8]
computer simulation for studying the selective sensitivity of bonds by mechanochemistry during grinding of organic molecules.[9]
Computational fluid dynamics simulations are used to simulate the behaviour of flowing air, water and other fluids. One-, two- and three-dimensional models are used. A one-dimensional model might simulate the effects of water hammer in a pipe. A two-dimensional model might be used to simulate the drag forces on the cross-section of an aeroplane wing. A three-dimensional simulation might estimate the heating and cooling requirements of a large building.
An understanding of statistical thermodynamic molecular theory is fundamental to the appreciation of molecular solutions. Development of the Potential Distribution Theorem (PDT) allows this complex subject to be simplified to down-to-earth presentations of molecular theory.
Notable, and sometimes controversial, computer simulations used in science include: Donella Meadows' World3 used in the Limits to Growth, James Lovelock's Daisyworld and Thomas Ray's Tierra.
In social sciences, computer simulation is an integral component of the five angles of analysis fostered by the data percolation methodology,[10] which also includes qualitative and quantitative methods, reviews of the literature (including scholarly), and interviews with experts, and which forms an extension of data triangulation.
Simulation environments for physics and engineering[edit]
Graphical environments to design simulations have been developed. Special care was taken to handle events (situations in which the simulation equations are not valid and have to be changed). The open project Open Source Physics was started to develop reusable libraries for simulations in Java, together with Easy Java Simulations, a complete graphical environment that generates code based on these libraries.
Computer simulation in practical contexts[edit]
Computer simulations are used in a wide variety of practical contexts, such as:
analysis of air pollutant dispersion using atmospheric dispersion modeling
design of complex systems such as aircraft and also logistics systems.
design of noise barriers to effect roadway noise mitigation
modeling of application performance[11]
flight simulators to train pilots
weather forecasting
forecasting of risk
simulation of electrical circuits
simulation of other computers is emulation.
forecasting of prices on financial markets (for example Adaptive Modeler)
behavior of structures (such as buildings and industrial parts) under stress and other conditions
design of industrial processes, such as chemical processing plants
strategic management and organizational studies
reservoir simulation for the petroleum engineering to model the subsurface reservoir
process engineering simulation tools.
robot simulators for the design of robots and robot control algorithms
urban simulation models that simulate dynamic patterns of urban development and responses to urban land use and transportation policies. See a more detailed article on Urban Environment Simulation.
traffic engineering to plan or redesign parts of the street network from single junctions over cities to a national highway network to transportation system planning, design and operations. See a more detailed article on Simulation in Transportation.
modeling car crashes to test safety mechanisms in new vehicle models.
crop-soil systems in agriculture, via dedicated software frameworks (e.g. BioMA, OMS3, APSIM)
The reliability and the trust people put in computer simulations depends on the validity of the simulation model, therefore verification and validation are of crucial importance in the development of computer simulations. Another important aspect of computer simulations is that of reproducibility of the results, meaning that a simulation model should not provide a different answer for each execution. Although this might seem obvious, this is a special point of attention in stochastic simulations, where random numbers should actually be semi-random numbers. An exception to reproducibility are human-in-the-loop simulations such as flight simulations and computer games. Here a human is part of the simulation and thus influences the outcome in a way that is hard, if not impossible, to reproduce exactly.
Vehicle manufacturers make use of computer simulation to test safety features in new designs. By building a copy of the car in a physics simulation environment, they can save the hundreds of thousands of dollars that would otherwise be required to build and test a unique prototype. Engineers can step through the simulation milliseconds at a time to determine the exact stresses being put upon each section of the prototype.[12]
Computer graphics can be used to display the results of a computer simulation. Animations can be used to experience a simulation in real-time, e.g., in training simulations. In some cases animations may also be useful in faster than real-time or even slower than real-time modes. For example, faster than real-time animations can be useful in visualizing the buildup of queues in the simulation of humans evacuating a building. Furthermore, simulation results are often aggregated into static images using various ways of scientific visualization.
In debugging, simulating a program execution under test (rather than executing natively) can detect far more errors than the hardware itself can detect and, at the same time, log useful debugging information such as instruction trace, memory alterations and instruction counts. This technique can also detect buffer overflow and similar "hard to detect" errors as well as produce performance information and tuning data.
Pitfalls[edit]
Although sometimes ignored in computer simulations, it is very important to perform a sensitivity analysis to ensure that the accuracy of the results is properly understood. For example, the probabilistic risk analysis of factors determining the success of an oilfield exploration program involves combining samples from a variety of statistical distributions using the Monte Carlo method. If, for instance, one of the key parameters (e.g., the net ratio of oil-bearing strata) is known to only one significant figure, then the result of the simulation might not be more precise than one significant figure, although it might (misleadingly) be presented as having four significant figures.
Model calibration techniques[edit]
The following three steps should be used to produce accurate simulation models: calibration, verification, and validation. Computer simulations are good at portraying and comparing theoretical scenarios, but in order to accurately model actual case studies they have to match what is actually happening today. A base model should be created and calibrated so that it matches the area being studied. The calibrated model should then be verified to ensure that the model is operating as expected based on the inputs. Once the model has been verified, the final step is to validate the model by comparing the outputs to historical data from the study area. This can be done by using statistical techniques and ensuring an adequate R-squared value. Unless these techniques are employed, the simulation model created will produce inaccurate results and not be a useful prediction tool.
Model calibration is achieved by adjusting any available parameters in order to adjust how the model operates and simulates the process. For example, in traffic simulation, typical parameters include look-ahead distance, car-following sensitivity, discharge headway, and start-up lost time. These parameters influence driver behavior such as when and how long it takes a driver to change lanes, how much distance a driver leaves between his car and the car in front of it, and how quickly a driver starts to accelerate through an intersection. Adjusting these parameters has a direct effect on the amount of traffic volume that can traverse through the modeled roadway network by making the drivers more or less aggressive. These are examples of calibration parameters that can be fine-tuned to match characteristics observed in the field at the study location. Most traffic models have typical default values but they may need to be adjusted to better match the driver behavior at the specific location being studied.
Model verification is achieved by obtaining output data from the model and comparing them to what is expected from the input data. For example, in traffic simulation, traffic volume can be verified to ensure that actual volume throughput in the model is reasonably close to traffic volumes input into the model. Ten percent is a typical threshold used in traffic simulation to determine if output volumes are reasonably close to input volumes. Simulation models handle model inputs in different ways so traffic that enters the network, for example, may or may not reach its desired destination. Additionally, traffic that wants to enter the network may not be able to, if congestion exists. This is why model verification is a very important part of the modeling process.
The final step is to validate the model by comparing the results with what is expected based on historical data from the study area. Ideally, the model should produce similar results to what has happened historically. This is typically verified by nothing more than quoting the R-squared statistic from the fit. This statistic measures the fraction of variability that is accounted for by the model. A high R-squared value does not necessarily mean the model fits the data well. Another tool used to validate models is graphical residual analysis. If model output values drastically differ from historical values, it probably means there is an error in the model. Before using the model as a base to produce additional models, it is important to verify it for different scenarios to ensure that each one is accurate. If the outputs do not reasonably match historic values during the validation process, the model should be reviewed and updated to produce results more in line with expectations. It is an iterative process that helps to produce more realistic models.
Validating traffic simulation models requires comparing traffic estimated by the model to observed traffic on the roadway and transit systems. Initial comparisons are for trip interchanges between quadrants, sectors, or other large areas of interest. The next step is to compare traffic estimated by the models to traffic counts, including transit ridership, crossing contrived barriers in the study area. These are typically called screenlines, cutlines, and cordon lines and may be imaginary or actual physical barriers. Cordon lines surround particular areas such as a city's central business district or other major activity centers. Transit ridership estimates are commonly validated by comparing them to actual patronage crossing cordon lines around the central business district.
Three sources of error can cause weak correlation during calibration: input error, model error, and parameter error. In general, input error and parameter error can be adjusted easily by the user. Model error however is caused by the methodology used in the model and may not be as easy to fix. Simulation models are typically built using several different modeling theories that can produce conflicting results. Some models are more generalized while others are more detailed. If model error occurs as a result, in may be necessary to adjust the model methodology to make results more consistent.
In order to produce good models that can be used to produce realistic results, these are the necessary steps that need to be taken in order to ensure that simulation models are functioning properly. Simulation models can be used as a tool to verify engineering theories, but they are only valid if calibrated properly. Once satisfactory estimates of the parameters for all models have been obtained, the models must be checked to assure that they adequately perform the intended functions. The validation process establishes the credibility of the model by demonstrating its ability to replicate actual traffic patterns. The importance of model validation underscores the need for careful planning, thoroughness and accuracy of the input data collection program that has this purpose. Efforts should be made to ensure collected data is consistent with expected values. For example, in traffic analysis it is typical for a traffic engineer to perform a site visit to verify traffic counts and become familiar with traffic patterns in the area. The resulting models and forecasts will be no better than the data used for model estimation and validation.
See also[edit]
A 48-hour computer simulation of Typhoon Mawar using the Weather Research and Forecasting model
Software portal
List of computer simulation software
Emulator
Energy modeling
Stencil code
Virtual prototyping
Web-based simulation
References[edit]
This article includes a list of references, but its sources remain unclear because it has insufficient inline citations. Please help to improve this article by introducing more precise citations. (May 2008) (Learn how and when to remove this template message)
Jump up ^ Strogatz, Steven (2007). "The End of Insight". In Brockman, John. What is your dangerous idea?. HarperCollins. ISBN 9780061214950
Jump up ^ " "Researchers stage largest Military Simulation ever" Archived 2008-01-22 at the Wayback Machine., Jet Propulsion Laboratory, Caltech, December 1997,
Jump up ^ "Molecular Simulation of Macroscopic Phenomena". Archived from the original on 2013-05-22.
Jump up ^ "Largest computational biology simulation mimics life's most essential nanomachine" (news), News Release, Nancy Ambrosiano, Los Alamos National Laboratory, Los Alamos, NM, October 2005, webpage: LANL-Fuse-story7428 Archived 2007-07-04 at the Wayback Machine..
Jump up ^ "Mission to build a simulated brain begins" Archived 2015-02-09 at the Wayback Machine., project of the institute at the École Polytechnique Fédérale de Lausanne (EPFL), Switzerland, New Scientist, June 2005.
Jump up ^ Santner, Thomas J; Williams, Brian J; Notz, William I (2003). The design and analysis of computer experiments. Springer Verlag.
Jump up ^ John Robert Taylor (1999). An Introduction to Error Analysis: The Study of Uncertainties in Physical Measurements. University Science Books. pp. 128–129. ISBN 0-935702-75-X. Archived from the original on 2015-03-16.
Jump up ^ Atanasov AG, Waltenberger B, Pferschy-Wenzig EM, Linder T, Wawrosch C, Uhrin P, Temml V, Wang L, Schwaiger S, Heiss EH, Rollinger JM, Schuster D, Breuss JM, Bochkov V, Mihovilovic MD, Kopp B, Bauer R, Dirsch VM, Stuppner H. doi:10.1016/j.biotechadv.2015.08.001 Discovery and resupply of pharmacologically active plant-derived natural products: A review.] Biotechnol Adv. 2015, PMID 26281720.
Jump up ^ Mizukami, Koichi ; Saito, Fumio ; Baron, Michel. Study on grinding of pharmaceutical products with an aid of computer simulation Archived 2011-07-21 at the Wayback Machine.
Jump up ^ Mesly, Olivier (2015). Creating Models in Psychological Research. United States: Springer Psychology: 126 pages. ISBN 978-3-319-15752-8
Jump up ^ Wescott, Bob (2013). The Every Computer Performance Book, Chapter 7: Modeling Computer Performance. CreateSpace. ASIN 1482657759. ISBN 1482657759. Archived from the original on 2015-07-26. CS1 maint: ASIN uses ISBN (link)
Jump up ^ Baase, Sara. A Gift of Fire: Social, Legal, and Ethical Issues for Computing and the Internet. 3. Upper Saddle River: Prentice Hall, 2007. Pages 363–364. ISBN 0-13-600848-8.
Further reading[edit]
Wikimedia Commons has media related to Computer simulation.
"A Resource Allocation Framework for Experiment-Based Validation of Numerical Models," Journal of Mechanics of Advanced Materials and Structures (Taylor & Francis).
Young, Joseph and Findley, Michael. 2014. "Computational Modeling to Study Conflicts and Terrorism." Handbook of Research Methods in Military Studies edited by Soeters, Joseph; Shields, Patricia and Rietjens, Sebastiaan. pp. 249–260. New York: Routledge,
R. Frigg and S. Hartmann, Models in Science. Entry in the Stanford Encyclopedia of Philosophy.
E. Winsberg Simulation in Science. Entry in the Stanford Encyclopedia of Philosophy.
A.K. Hartmann, Practical Guide to Computer Simulations, Singapore: World Scientific, 2009
S. Hartmann, The World as a Process: Simulations in the Natural and Social Sciences, in: R. Hegselmann et al. (eds.), Modelling and Simulation in the Social Sciences from the Philosophy of Science Point of View, Theory and Decision Library. Dordrecht: Kluwer 1996, 77–100.
E. Winsberg, Science in the Age of Computer Simulation. Chicago: University of Chicago Press, 2010.
P. Humphreys, Extending Ourselves: Computational Science, Empiricism, and Scientific Method. Oxford: Oxford University Press, 2004.
James J. Nutaro (2011). Building Software for Simulation: Theory and Algorithms, with Applications in C++. John Wiley & Sons. ISBN 978-1-118-09945-2.
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Extended-hours trading
From Wikipedia, the free encyclopedia
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Not to be confused with Late trading.
Extended-hours trading is stock trading that happens either before or after the normal trading hours of a stock exchange, i.e., pre-market trading or after-hours trading.
After-hours trading is the name for buying and selling of securities when the major markets are closed. [1] Since 1985, the regular trading hours for major exchanges in the United States, such as the New York Stock Exchange and the Nasdaq Stock Market, have been from 9:30 a.m. to 4:00 p.m. Eastern Time (ET).[2] Pre-market trading occurs from 4:00 a.m. to 9:30 a.m. ET, although the majority of the volume and liquidity come to the pre-market at 8:00AM ET.[3][4] After-hours trading on a day with a normal session occurs from 4:00 p.m. to 8:00 p.m. ET.[4] Market makers and specialists generally do not participate in after hours trading, which can limit liquidity.[5]
Example chart of extended hours trading, via Google Finance
Trading outside regular hours is not a new phenomenon but used to be limited to high-net-worth investors and institutional investors like mutual funds.[6] The emergence of private trading systems, known as electronic communication networks (ECNs), has allowed individual investors to participate in after-hours trading.
Financial Industry Regulatory Authority (FINRA) members who voluntarily enter quotations during the after-hours session are required to comply with all applicable limit order protection and display rules (e.g., the Manning Rule and the U.S. Securities and Exchange Commission order handling rules).[7]
See also[edit]
List of market opening times
List of stock exchanges
References[edit]
Jump up ^ Sulthan, .A (2017). Stock Market Dictionary (1 ed.). Sulthan Academy. p. 229. ISBN 1522022864.
Jump up ^ "About Us: History". NYSE. Retrieved 2007-04-11.
Jump up ^ "Pre-Market Trading". Investors Underground. Retrieved 11 November 2016.
^ Jump up to: a b "Nasdaq Trading Schedule". NASDAQ. Retrieved 2011-08-02.
Jump up ^ "After Hours Trading". Investors Underground. Retrieved 11 November 2016.
Jump up ^ "SEC.gov". United States Securities and Exchange Commission.
Jump up ^ Barclay, Michael J. (2003). "Price Discovery and Trading After Hours". University of California, Berkeley.
External links[edit]
"Trade After-Hours". Invest FAQ.
[hide]
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This article incorporates public domain material from the United States Government document "https://www.sec.gov/investor/pubs/afterhours.htm".

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Kim Matula
From Wikipedia, the free encyclopedia
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Kim Matula
Kim Matula at the 2014 Daytime Emmy Nominees Reception
Born
Kimberly Marie Matula
(1988-08-23) August 23, 1988 (age 29)
Fort Worth, Texas, U.S.
Other names
Kim Matula
Occupation
Actress
Years active
2006–present
Known for
Hope Logan on The Bold and the Beautiful
Kimberly Marie "Kim" Matula (born August 23, 1988) is an American actress, best known for her role as Hope Logan in the CBS daytime soap opera The Bold and the Beautiful.
Contents
[hide]
1 Life and career
2 Filmography
3 References
4 External links
Life and career[edit]
Matula was born in Fort Worth, Texas to David and Karin Matula. She is of Norwegian ancestry.[citation needed] Matula has always dreamed of acting and created videos of her acting. She studied film at the University of Texas at Arlington, but dropped out because her acting career was taking off.[1] In 2008, Matula appeared in the Lifetime movie Queen Sized, and in next year moved to Los Angeles, then in 2010, she landed the role of Hope Logan in the CBS daytime soap opera The Bold and the Beautiful.[1][2]
In 2014, Matula was nominated for a Daytime Emmy Award for Outstanding Younger Actress in a Drama Series.[3] In her two final years on the soap, Matula's character had major storylines. In November 2014, it was announced that Matula had not renewed her contract and would be departing the series for a career in primetime television and film.[4][5]
In 2016, Matula joined the cast of the second season of the critically acclaimed Lifetime dark comedy-drama series, Unreal.[6]
As of 2018[update] she has a leading role in the Fox comedy series LA to Vegas.
Filmography[edit]
Year
Title
Role
Notes
2006 Savage Spirit Megan
2008 Queen Sized Tara Television film
2009 How I Met Your Mother Girl Episode: "Definitions"
2010–2014, 2015, 2016 The Bold and the Beautiful Hope Logan Series regular: January 11, 2010 – December 12, 2014
Guest: April 17 and 20, 2015 and March 16, 2016
Nominated — Daytime Emmy Award for Outstanding Younger Actress in a Drama Series (2014)
2010 Crown thy Good Claire Short film
2010 Spilt Milk Sabrina
2010 Cool Wheels Stacy McFuggins Short film
2011 The Defenders Mia Episode: "Nevada v. Doug the Mule"
2014 Broken Strings Wind / Heidi Lewis Short film
2014 Dawn Patrol Donna
2014 Almost Royal Herself Episode: "Los Angeles"
2015 Maybe Someday Lacey
2016 UnReal Tiffany Series regular (Season 2)
2018 LA to Vegas Veronica "Ronnie" Messing Main role
TBA Fighting with My Family Jeri-Lyn Filming
References[edit]
^ Jump up to: a b "Kim Matula 411". Soap Opera Digest. 2010-01-11. Archived from the original on 2014-08-18. Retrieved 2014-06-30.
Jump up ^ "Kimberly Matula Playing Hope Logan on The Bold and the Beautiful - Soaps.com". Soaps.sheknows.com. Retrieved 2014-06-30.
Jump up ^ "The 41st Annual Daytime Entertainment Emmy Award Nominations". New York: emmyonline.org and National Academy of Television Arts and Sciences. May 1, 2014. Archived from the original on June 23, 2014. Retrieved May 1, 2014.
Jump up ^ "No Hope For The Future: Kim Matula Quits The Bold and The Beautiful!". Daytime Confidential. Retrieved 9 November 2014.
Jump up ^ "Kim Matula Leaving The Bold and the Beautiful!". Michael Fairman On-Air On-Soaps. Retrieved 9 November 2014.
Jump up ^ Elizabeth Wagmeister. "'UnReal' Season 2 Cast: Meagan Tandy & Kim Matula to Play Contestants - Variety". Variety. Retrieved 19 February 2016.
External links[edit]
Wikimedia Commons has media related to Kimberly Matula.
Kim Matula on IMDb
Retrieved from "https://en.wikipedia.org/w/index.php?title=Kim_Matula&oldid=833303997"
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Transatlantic crossing
From Wikipedia, the free encyclopedia
Jump to: navigation, search
For other uses of the term Transatlantic, see Transatlantic.
The Transatlantic crossings are passages of passengers and cargo across the Atlantic Ocean between the Americas and Europe or Africa. The majority of passenger traffic is across the North Atlantic between North America and Western Europe. Centuries after the dwindling of sporadic Viking trade with Markland, a regular and lasting transatlantic trade route was established in 1566 with the Spanish West Indies fleets, following the Voyages of Christopher Columbus.
Contents
[hide]
1 By sea
2 Transatlantic flights
3 Transatlantic cables
4 Transatlantic radio
5 Transatlantic tunnel
6 Crossings in small vessels
6.1 Zodiac
6.2 Rafts
6.3 Rowing and paddling
6.3.1 Transatlantic rowing race
7 See also
8 Citations
9 Further reading
10 External links
By sea[edit]
Main article: List of crossings of the Atlantic Ocean
Prior to the 19th century, transatlantic crossings were undertaken in sailing ships, and the journeys were time consuming and often perilous. The first trade route across the Atlantic was inaugurated by Spain a few decades after the European Discovery of America, with the establishment of the West Indies fleets in 1566, a convoy system that regularly linked its territories in the Americas with Spain for over two centuries. Portugal created a similar maritime route between its ports in Brazil and the Portuguese mainland. Other colonial powers followed, such as Britain, France and the Netherlands, as they colonized the New World.
Transatlantic crossings became faster, safer, and more reliable with the advent of steamships in the 19th century. Grand ocean liners began making regularly scheduled crossings, and soon it became a symbol of national and company status to build the largest, fastest, and most luxurious ocean liner for transatlantic crossings. The United States, United Kingdom, France, Germany and Italy built the most famous ocean liners. Examples of some famous transatlantic liners are RMS Titanic (had only made one voyage that was unsuccessful due to striking an iceberg), RMS Lusitania, RMS Mauretania, RMS Olympic, SS Rex, SS America, SS United States, RMS Queen Mary, SS Île de France, SS Normandie, RMS Queen Elizabeth, SS France, Queen Elizabeth 2, and RMS Queen Mary 2.
The Blue Riband is awarded for the record fastest crossing by transatlantic liner. The current eastbound record was set by the American ocean liner United States in July 1952: the ship made the crossing in 3 days, 10 hours, 40 minutes. Guinness Book of World Records has awarded world records to various classes such as luxury liners, sail boats, and rowing boats.
During World War II the transatlantic crossing was very important for the United Kingdom as much of Europe had been taken over by Germany and its allies preventing trade and supplies; the struggle is known as the Battle of the Atlantic.
Transatlantic flights[edit]
Main article: Transatlantic flight
Transatlantic flight surpassed ocean liners as the predominant mode of crossing the Atlantic in the mid 20th century. In 1919, the American NC-4 became the first airplane to cross the Atlantic (but in multiple stages). Later that year, a British Vickers Vimy piloted by Alcock and Brown made the first non-stop transatlantic flight from Newfoundland to Ireland. Also in 1919, the British were the first to cross the Atlantic in an airship when the R34 captained by Major George Herbert Scott of the Royal Air Force with his crew and passengers flew from East Fortune, Scotland to Mineola, Long Island, covering a distance of about 3,000 statute miles (4,800 km) in about four and a half days; he then made a return trip to England, thus also completing the first double crossing of the Atlantic (east–west–east). In 1927, Charles Lindbergh made the first solo non-stop transatlantic flight in an airplane (between New York City and Paris). The second solo piloting, and the first to carry a passenger, was Clarence Duncan Chamberlin on June 6, 1927. Edward R. Armstrong proposed a string of anchored "seadromes" to refuel planes in a crossing.
The first serious attempt to take a share of the transatlantic passenger market away from the ocean liners was undertaken by Germany. In the 1930s, Germany crossed the Atlantic with Zeppelins that could carry about 60 passengers in a similar luxurious style to the ocean liners. However, the Hindenburg disaster in 1937 put an end to transatlantic Zeppelin flights.
On June 1, 1944, two K-class blimps from Blimp Squadron ZP-14[1] of the United States Navy (USN) completed the first transatlantic crossing by non-rigid airships. The two K-ships (K-123 and K-130) left South Weymouth, MA on May 28, 1944 and flew approximately 16 hours to Naval Station Argentia, Newfoundland. From Argentia, the blimps flew approximately 22 hours to Lagens Field on Terceira Island in the Azores. The final leg of the first transatlantic crossing was about a 20-hour flight from the Azores to Craw Field in Port Lyautey (Kenitra), French Morocco.[2][3]
Beginning in the 1950s, the predominance of ocean liners began to wane when larger, jet-powered airplanes began carrying passengers across the ocean in less and less time. The speed of crossing the ocean therefore became more important than the style of crossing it. The maturing passenger Jet Age starting with the Boeing 707 reduced the typical crossing time between London and New York City to between 6.5 and 8 hours, depending on weather conditions. By the 1970s, supersonic Concorde airplanes could connect the two cities in less than 4 hours, and only one ocean liner, The RMS Queen Elizabeth 2 remained on the transatlantic route for those who favored the slower style of travel.
The economics of commercial transatlantic flying have evolved markedly since the 1950s; the introduction of widebody airliners (such as the Boeing 747 and Douglas DC-10) in the early 1970s made affordable transatlantic travel to the masses a reality. Since the 1990s, the high reliability of modern jet engines has meant that twin engine jet aircraft such as the Boeing 767, Boeing 777 and Airbus A330 have largely taken over on transatlantic routes from quad-engine jets, whilst the supersonic Concorde was ultimately doomed by its high running costs, leading to its retirement in 2003.
Transatlantic cables[edit]
Transatlantic cables are cables that have been laid along the ocean floor to connect North America and Europe. Before the advent of radio, the only means of communication across the Atlantic Ocean was to physically connect the continents with a transatlantic telegraph cable, the first of which was installed from Valentia, Ireland to Heart's Content, Newfoundland in 1858. The first transatlantic telephone cable, TAT-1, was installed in 1955. The first transatlantic fiber optic cable, TAT-8, was installed in 1988. The exchange rate between the United States dollar and British pound is still colloquially known as "cable" by financial marketeers, from the early use of the transatlantic cable for this purpose.
Further information: Transatlantic communications cable
Transatlantic radio[edit]
Transatlantic radio communication was first accomplished on December 12, 1901 by Guglielmo Marconi who, using a temporary receiving station at Signal Hill, Newfoundland, received a Morse code signal representing the letter "S" sent from Poldhu, in Cornwall, United Kingdom.[4] Guglielmo Marconi initiated commercial transatlantic radio communications between his high power long wave wireless telegraphy stations in Clifden Ireland and Glace Bay, Nova Scotia on 17 October 1907.
Amateur radio operators are usually credited with the discovery of transatlantic radio communication in the shortwave bands. The first successful transatlantic tests were conducted by radio amateurs in December 1921 operating in the 200 meter medium-wave band, the shortest wavelength then available to amateurs. In 1922 hundreds of North American amateurs were heard in Europe at 200 meters and at least 20 North American amateurs heard amateur signals from Europe. The first two way transatlantic shortwave radio contacts were completed by radio amateurs in November 1923, on 110 meters.
Marconi initiated the first commercial shortwave transatlantic radio communication between the UK to Canada using his Beam Wireless Service which went into commercial operation on 25 October 1926. Shortwave radio vastly increased the speed and capacity of transatlantic communications at dramatically reduced cost compared to telegraph cable and long wave radio.
Telstar was the first communications satellite to provide commercial transatlantic communications. It was launched by on July 10, 1962, the first privately sponsored space launch. Communications satellites vastly increased the speed and quality of transatlantic communication, but transatlantic fiber optic cables have carried the vast majority of transatlantic communications traffic since the early 1990s.
Transatlantic tunnel[edit]
Main article: Transatlantic tunnel
The Transatlantic Tunnel is a theoretical structure proposed several times since the late 19th century. It would be a tunnel that spans the Atlantic Ocean between New York City and the United Kingdom or France.
Crossings in small vessels[edit]
Zodiac[edit]
Alain Bombard crossed the Atlantic from East to West in 1952, spending 113 days adrift in a Zodiac, L'Hérétique.[5]
Rafts[edit]
In 1956, Henri Beaudout crossed the Atlantic from West to East, from Halifax to Falmouth, on a raft of wood and rope, L’Égaré II, in 88 days.[6]
A later Atlantic crossing by junk raft was the Son of Town Hall, in 1998.[7]
Anthony Smith and the Antiki crossed in 2011.[8]
Rowing and paddling[edit]
On June 13, 2003, the French rower Maud Fontenoy started an eastward crossing of the Atlantic from Saint-Pierre-et-Miquelon. She reached A Coruña in Spain on October 10, becoming the first woman to accomplish this feat.[9]
The Polish sexagenarian Aleksander Doba was the first recorded individual to complete a non-stop transatlantic crossing by kayak. He departed Dakar, Senegal on October 26, 2010 and arrived in Brazil 99 days later.[10]
Transatlantic rowing race[edit]
Main article: Atlantic Rowing Race
The first East–West Atlantic Rowing Race took place in 1997 from the Canary Islands to the Caribbean. It now runs once every two years or so.
The first West–East 'North' Atlantic Rowing Race took place in 2006 from New York City to Falmouth UK.
See also[edit]
Transatlantic relations
Citations[edit]
Jump up ^ Blimp Squadron 14
Jump up ^ Kline, R. C. and Kubarych, S. J., Blimpron 14 Overseas, 1944, Naval Historical Center, Navy Yard, Washington, D. C.
Jump up ^ http://www.naval-airships.org/resources/documents/NAN_vol93_no2_KShips_feature.pdf
Jump up ^ "Milestones:Transmission of Transatlantic Radio Signals, 1901". IEEE Global History Network. IEEE. Retrieved 29 July 2011.
Jump up ^ Bombard, Alain (1953). The Voyage of the Heretique. Simon and Schuster.
Jump up ^ Wadden, Marie (3 August 2012). "Three Canadians, two kittens, one raft: A little-known journey across the Atlantic". The Globe and Mail. Retrieved 19 May 2014.
Jump up ^ "Son of Town Hall, First Raft made of Scrap to Cross the North Atlantic Ocean". The Floating Neutrinos. May 30, 2006. Retrieved Sep 30, 2010.
Jump up ^ "Across the Atlantic - in a garden shed". Retrieved Mar 2, 2015.
Jump up ^ Fontenroy Foundation
Jump up ^ "64-Year-Old Kayaker Completes Trans-Atlantic Voyage". Wired. February 10, 2011. Retrieved July 2, 2012.
Further reading[edit]
Fowler Jr., William M. Steam Titans: Cunard, Collins, and the Epic Battle for Commerce on the North Atlantic (London: Bloomsbury), 2017. 358 pp
External links[edit]
Look up transatlantic crossing in Wiktionary, the free dictionary.
Wikimedia Commons has media related to North Atlantic Treaty Organisation.
"Evolution of trans-Atlantic Ships", May 1931, Popular Mechanics
Retrieved from "https://en.wikipedia.org/w/index.php?title=Transatlantic_crossing&oldid=833795855"
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