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TAYLOUR, JOSEPH, commodore of the Newfoundland convoy, 1709; b. c. 1662; d. 1734.
Taylour went to sea in the merchant service at the age of ten, and on 26 March 1690 entered the Royal Navy. He took command of the Charles, a frigate of 32 guns, in 1703 and soon began to show the skill and audacity in taking numerous prizes that were to earn him a place in naval annals. During 1708 Taylour did valuable service in Spain, and the following year was appointed commodore of the Newfoundland convoy.
Along with his commission as commander-in-chief went the usual instructions, or Heads of Enquiry, dated 9 June 1709. Taylour made little systematic attempt to answer these questions as his arrival in Newfoundland on 16 Aug. 1709 found him plunged in troubles of a more serious nature. The attack by Saint-Ovide de Brouillan [Monbeton*] in the winter of 1708–9 had devastated not only the fortifications, but the town as well. All that had not been ransomed had been burned or destroyed. The garrison commander, Thomas Lloyd, had been shipped as a prisoner to Placentia (Plaisance). Taylour had several meetings with the despondent inhabitants and persuaded them not to abandon the colony. He then proceeded to repair the fortifications, employing the crews of the Litchfield and of the Rye. It was already late in the season and materials were almost unobtainable. He drew naval stores to help him with the work, and remarked that Her Majesty had not been put to much expense. The new Fort William was 180 feet square, and included four streets of houses inside the perimeter. There were even "trunks" or chutes to send shells which fell on the ramparts down into the ditch where they would burst harmlessly. "In the opinion of all that had seen it before it was destroy'd by the enemie," said Taylour, "[it] is now much more defenceable." Captain John Moody, viewing it the following year, bore witness to "the diligence of Commodore Taylour." Probably the value of the new fort was largely psychological. It gave the inhabitants somewhere to rebuild their huts with at least the illusion of safety, and it must have discouraged the French to see St John's being rebuilt so soon after its wholesale destruction.
On the recommendation of the inhabitants, Taylour appointed John Collins as governor, and when he left Newfoundland he recommended that any of the inhabitants who would flout the governor's authority once the commodore was out of the way, should be kept in check. Taylour returned to the routine duties of a naval captain for the duration of the War of the Spanish Succession.
Joseph Taylour died on 23 May 1734. Despite Taylour's recommendations, his son, Thomas, did not achieve his father's success in the navy. To his contemporaries, as to naval historians, Joseph Taylour was an extremely successful exponent of the petite guerre-the single ship action; but for Canadians his importance lies in his re-establishment of St John's.
G.B., Admiralty, List of sea officers, 1660–1815. PRO, C.O. 194/4, 195/5; B.T. Journal, 1708/9–1714/15; CSP, Col., 1708–9, 1710–11. Prowse, History of Nfld.
© 1969–2023 University of Toronto/Université Laval
Cite This Article
Carson I. A. Ritchie, "TAYLOUR, JOSEPH," in Dictionary of Canadian Biography, vol. 2, University of Toronto/Université Laval, 2003–, accessed September 29, 2023, http://www.biographi.ca/en/bio/taylour_joseph_2E.html.
The citation above shows the format for footnotes and endnotes according to the Chicago manual of style (16th edition). Information to be used in other citation formats:
|Author of Article:
|Carson I. A. Ritchie
|Title of Article:
|Publication Name:
|Dictionary of Canadian Biography, vol. 2
|University of Toronto/Université Laval
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|Year of revision:
|Access Date:
|September 29, 2023


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Additional questions-and-answers:
In "From Stargazers to Starships" on astronomy, physics and spaceflight.
Magnetic Shielding
Magnets of different shapes
On building an electromagnet
Electric and Magnetic Energy
What materials does a magnet pull?
What is the smallest magnet possible?
Are electric storms an "electromagnetic" phenomenon?
How can steady magnetic fields induce electric currents?
Does the Earth's magnetic field rotate?
How come a magnetic field can block particle radiation but not light?
What is a "magnetic moment"?
Electromagnetic Waves and Electromagnetic Induction
What are "frozen" magnetic field lines?
Why doesn't magnetism affect electro-magnetic waves?
Eddy Currents
When is Earth an Insulator and when a Conductor?
Earth's field--Magnetic or Electromagnetic?
2. Particles and Radiation in Space
Cosmic Rays
How are ions produced?
What exactly does "Radiation" Mean?
Radiation Belts and Manned Space Flight
Apollo Astronauts and radiation
Cosmic ray research using balloons
Geiger counters for locating lost objects
What is "Radiation"?
When and how were positive ions discovered?
Radius of particle gyration
Deflection of a beam of Electrons in the Earth's Field
3. Earth's internal Magnetic Field
Measuring the Earth's magnetic field
The strength of the Earth's field
Capturing the Energy of the Solar Wind
4. Earth's outer Magnetic Field
Universal Time and Magnetic Local Time
What are "geomagnetic conjugate points"?
Mapping the magnetosphere using a surface network?
Waves in the Magnetosphere
Electric field due to electromagnetic induction
5. The Radiation Belt
About the "Starfish" artificial radiation belt
What if the Radiation Belt Reached the Ground... ?
Radiation Belts and Manned Space Flight
Radiation Belt and Brazil
Space tether to remove trapped radiation?
6. Sun and solar wind
Solar Eclipses
Use of solar wind for space propulsion
Capturing the Energy of the Solar Wind
About the Upcoming Solar Maximum
Any connection between Solar Wind and Solar Flares?
Can anything solid be carried by the solar wind?
Blocking of the Solar Wind by our Moon?
The Speed of the Solar Wind
How can the intensely hot Sun be magnetic?
Does our magnetic field stop the atmosphere from getting blown away? Questions about the Solar Corona:
(1) Why don't its particles separate by weight?
(2) What accelerates the solar wind?
Who Discovered Sunspots? Sound waves on the Sun?
Do interplanetary field lines guide the solar wind back?
The solar wind and solar escape velocity
History of Cosmic ray Research
A Russian space tether experiment?
What causes sunspots?
What causes sunspots
A solar wind contribution to global warming?
What is the Radius of the Sun's magnetosphere?
Can upper atmosphere atoms join solar wind?
Magnetic field of the Sun
Flux Transfer Events (FTEs)
7. History of Space Science
8. Planetary Magnetic Fields
Magnetic effects from other planets
Blocking of the Solar Wind by our Moon?
Does our magnetic field stop the atmosphere from getting blown away? Dynamo currents at Jupiter's moons
Magnetic connections between planets and the Sun
How does electromagnetic induction occur?
9. Instruments
Measuring the Earth's magnetic field
Magnetometer for Observing Magnetic Storms
A working model of the magnetosphere?
Magnetic health products
Geiger counters for locating lost objects
Man-made triggering of radio emissions
"Soda-Bottle Magnetometer"
A Russian space tether experiment?
Space Tether
Geiger Counters (1) (Followed by Geiger Counters (2)
How to test a Geiger counter
Building a Terrella experiment like Birkeland's
10. Spaceflight and Space Science
Synchronous satellites
Use of solar wind for space propulsion
Magnetic shielding against neutral matter?
Best orbit for a Space Station
Can Space Plasma help Spacecraft Propulsion?
Gamma ray bursts
Magnetic effects of our galaxy
11. Risks and Dangers
Radiation Hazards to Air Crews
The Ozone Hole and the Magnetic Field
What is a "REM"?
Radiation Belts and Manned Space Flight
Apollo Astronauts and radiation
Risks of Spaceflight: Fry or Freeze... ?
Risks from stormy "Space Weather"
Radiation Belt and Brazil
Was the magnetosphere involved in the hole in the ozone layer?
Effects of Radiation beyond the Van Allen Belts
Solar wind effects on our lives
Weaker global magnetic field--higher cosmic ray dosage?
More about the Year 2012
"Radiation Remediation"?
12. Magnetic Storms and Polar Aurora
Skywriting by Aurora
Radio Propagation
When and where can I see "Northern Lights"?
"Importance of auroras to society"
Magnetic storms and headaches
Experimental simulation of the polar aurora
Can Polar Aurora be seen in Atlanta, Georgia?
Harnessing the energy of the Aurora?
Why has the aurora been so frequent lately?
Is the Polar Cusp visible to the eye?
Building a Terrella experiment like Birkeland's
Flux Transfer Events (FTEs)
13. Energy
The Ozone Hole and the Magnetic Field
Electric and Magnetic Energy
Free Energy from the Earth's Magnetic Field?
Why no aurora at the magnetic poles?
Harvesting electrons from power lines?
Harnessing the energy of the Aurora?
Magnetic induction by the Magnetosphere
Fluorescent lightbulbs
14. Plasma
How does one contain a Plasma?
Can plasma physics explain ball lightning?
Is fire a plasma?
Why does Plasma Follow Field Lines?
What are "frozen" magnetic field lines?
Fluorescent lightbulbs
Can Space Plasma help Spacecraft Propulsion?
Fluorescent tubes lighting up near high voltage
15. Proposed Applications of Magnetism
Skywriting by Aurora
Does the magnetosphere affect weather? (a)
Magnetism and weather (b)
Magnetic health products
Did astronauts use articifial magnetic shields"?
Man-made triggering of radio emissions
Is space debris electrically charged?
Magnetic induction by the Magnetosphere
Space tether to remove trapped radiation?
101. Miscellaneous
Alternate theory of the Sun and solar wind
Lining-up of Planets
How do Magnetic Reversals affect Animal Migrations?
Ozone and the Magnetic Field (1)
Ozone and the Magnetic Field (2): The "ozone hole"
There are electromagnetic waves all around us!
Eddy Currents
Project to show that Iron rusts faster in Fresh Water
When is Earth an Insulator and when a Conductor?
Who invented AC?
Fluorescent tubes lighting up near high voltage
Gamma ray bursts
Magnetic effects of our galaxy
If you have a relevant question of your own, you can send it to education("at" symbol)phy6.org
Back to the main list
"Exploration" home page ......... "Exploration" index
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I stopped watching the news...its so emotional and they repeat the same thing over n over n over! There is so much goodness in th world...why does the media focus on the evil!!!
Morgan Freeman's brilliant take on what happened yesterday :
"You want to know why. This may sound cynical, but here's why.
people who would otherwise just off themselves in their basements see the news and want to top it by doing something worse, and going out in a memorable way. Why a grade school? Why children? Because he'll be remembered as a horrible monster, instead of a sad nobody.
CNN's article says that if the body count "holds up", this will rank as the second deadliest shooting behind Virginia Tech, as if statistics somehow make one shooting worse than another. Then they post a video interview of third-graders for all the details of what they saw and heard while the shootings were happening. Fox News has plastered the killer's face on all their reports for hours. Any articles or news stories yet that focus on the victims and ignore the killer's identity? None that I've seen yet. Because they don't sell. So congratulations, sensationalist media, you've just lit the fire for someone to top this and knock off a day care center or a maternity ward next.
You can help by forgetting you ever read this man's name, and remembering the name of at least one victim. You can help by donating to mental health research instead of pointing to gun control as the problem. You can help by turning off the news."


text:
"If I could I'd tell you now
there are no roads that do not bend
the days like flowers bloom and fade
and they do not come again.
We've only got these times we're living in...."
Words and music by Kate Wolf (1981)
Kate Wolf was a wonderful songwriter who died far too early. She wrote some beautifully evocative songs, many of which were set in Central California where I live. My list of road songs includes quite a few of her works, including "Across the Great Divide" (here is a YouTube version with Nanci Griffith and Emmylou Harris), and Redtail Hawk (here is Kate singing the song).
"These Times We're Living In" is a song about a long-term loving relationship, but the lines spoke to me in a geological sense while I was sifting through the images I shot in Zion National Park last July (the canyon was originally called Mukuntuweap). We were on our vagabonding journey across the geology of Nevada, Utah and Colorado, and we were rapidly approaching the end of our trip. The words remind me not so much of how little time we have, but more the luck of living in the times that we do.
In the geologic sense...
Can you see the white scar crossing the forest in the center of the picture above? It's also in the top photo on the left side. It is the debris from a very recent rockfall. I don't know if it is the same rockfall, but not long ago we were camped in the lower valley we heard rumbling and crashing sounds in the distance, and a few moments later a huge dust cloud rolled down the valley. Zion had just become a slightly larger valley.


text:
This is the home page for the electronic records held by the Guild of One-Name Studies on behalf of the Newbon one-name study.
The archive holder is Mr Stephen Willis (Member no. 4907).
He can be contacted by e-mail at
Further contact details may be found in the Guild on-line register.
A summary of the one-name study can be found here.
Further information about the one-name study may be found on the web site of the archive holder www.newbonfamilyhistory.com.
The following records are available for search in this archive:
Archive contents last updated 14th December 2008.
The Guild Electronic Archive (or just the "Guild Archive") allows members of the Guild to present some of the records from their one-name study to Internet users by placing the information on the Guild web site. Members may choose to allow access to these records to everybody or to be restricted to fellow members of the Guild.
At present, the archive allows archivists to store Birth, Marriage and Death records from England & Wales (from 1837) and Scotland (from 1855), Civil Probate Records from England & Wales (from 1858), and Census Records from England & Wales (1841 to 1901). Further types of records may be included in the future.
An archive may be set up for any surname registered with the Guild. There is no charge to Guild members for this facility.
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text:
Monte Carlo methods, or Monte Carlo experiments, are a broad class of computational algorithms that rely on repeated random sampling to obtain numerical results. The underlying concept is to use randomness to solve problems that might be deterministic in principle. They are often used in physical and mathematical problems and are most useful when it is difficult or impossible to use other approaches. Monte Carlo methods are mainly used in three problem classes:[1] optimization, numerical integration, and generating draws from a probability distribution.
In physics-related problems, Monte Carlo methods are useful for simulating systems with many coupled degrees of freedom, such as fluids, disordered materials, strongly coupled solids, and cellular structures (see cellular Potts model, interacting particle systems, McKean–Vlasov processes, kinetic models of gases).
Other examples include modeling phenomena with significant uncertainty in inputs such as the calculation of risk in business and, in mathematics, evaluation of multidimensional definite integrals with complicated boundary conditions. In application to systems engineering problems (space, oil exploration, aircraft design, etc.), Monte Carlo–based predictions of failure, cost overruns and schedule overruns are routinely better than human intuition or alternative "soft" methods.[2]
In principle, Monte Carlo methods can be used to solve any problem having a probabilistic interpretation. By the law of large numbers, integrals described by the expected value of some random variable can be approximated by taking the empirical mean (a.k.a. the 'sample mean') of independent samples of the variable. When the probability distribution of the variable is parameterized, mathematicians often use a Markov chain Monte Carlo (MCMC) sampler.[3][4][5] The central idea is to design a judicious Markov chain model with a prescribed stationary probability distribution. That is, in the limit, the samples being generated by the MCMC method will be samples from the desired (target) distribution.[6][7] By the ergodic theorem, the stationary distribution is approximated by the empirical measures of the random states of the MCMC sampler.
In other problems, the objective is generating draws from a sequence of probability distributions satisfying a nonlinear evolution equation. These flows of probability distributions can always be interpreted as the distributions of the random states of a Markov process whose transition probabilities depend on the distributions of the current random states (see McKean–Vlasov processes, nonlinear filtering equation).[8][9] In other instances we are given a flow of probability distributions with an increasing level of sampling complexity (path spaces models with an increasing time horizon, Boltzmann–Gibbs measures associated with decreasing temperature parameters, and many others). These models can also be seen as the evolution of the law of the random states of a nonlinear Markov chain.[9][10] A natural way to simulate these sophisticated nonlinear Markov processes is to sample multiple copies of the process, replacing in the evolution equation the unknown distributions of the random states by the sampled empirical measures. In contrast with traditional Monte Carlo and MCMC methodologies, these mean-field particle techniques rely on sequential interacting samples. The terminology mean field reflects the fact that each of the samples (a.k.a. particles, individuals, walkers, agents, creatures, or phenotypes) interacts with the empirical measures of the process. When the size of the system tends to infinity, these random empirical measures converge to the deterministic distribution of the random states of the nonlinear Markov chain, so that the statistical interaction between particles vanishes.
Despite its conceptual and algorithmic simplicity, the computational cost associated with a Monte Carlo simulation can be staggeringly high. In general the method requires many samples to get a good approximation, which may incur an arbitrarily large total runtime if the processing time of a single sample is high.[11] Although this is a severe limitation in very complex problems, the embarrassingly parallel nature of the algorithm allows this large cost to be reduced (perhaps to a feasible level) through parallel computing strategies in local processors, clusters, cloud computing, GPU, FPGA, etc.[12][13][14][15]
Monte Carlo methods vary, but tend to follow a particular pattern:
- Define a domain of possible inputs
- Generate inputs randomly from a probability distribution over the domain
- Perform a deterministic computation on the inputs
- Aggregate the results
For example, consider a quadrant (circular sector) inscribed in a unit square. Given that the ratio of their areas is π/4, the value of π can be approximated using a Monte Carlo method:[16]
- Draw a square, then inscribe a quadrant within it
- Uniformly scatter a given number of points over the square
- Count the number of points inside the quadrant, i.e. having a distance from the origin of less than 1
- The ratio of the inside-count and the total-sample-count is an estimate of the ratio of the two areas, π/4. Multiply the result by 4 to estimate π.
In this procedure the domain of inputs is the square that circumscribes the quadrant. One can generate random inputs by scattering grains over the square then perform a computation on each input (test whether it falls within the quadrant). Aggregating the results yields our final result, the approximation of π.
There are two important considerations:
- If the points are not uniformly distributed, then the approximation will be poor.
- The approximation is generally poor if only a few points are randomly placed in the whole square. On average, the approximation improves as more points are placed.
Uses of Monte Carlo methods require large amounts of random numbers, and their use benefitted greatly from pseudorandom number generators, which are far quicker to use than the tables of random numbers that had been previously used for statistical sampling.
Before the Monte Carlo method was developed, simulations tested a previously understood deterministic problem, and statistical sampling was used to estimate uncertainties in the simulations. Monte Carlo simulations invert this approach, solving deterministic problems using probabilistic metaheuristics (see simulated annealing).
An early variant of the Monte Carlo method was devised to solve the Buffon's needle problem, in which π can be estimated by dropping needles on a floor made of parallel equidistant strips. In the 1930s, Enrico Fermi first experimented with the Monte Carlo method while studying neutron diffusion, but he did not publish this work.[17]
In the late 1940s, Stanislaw Ulam invented the modern version of the Markov Chain Monte Carlo method while he was working on nuclear weapons projects at the Los Alamos National Laboratory. In 1946, nuclear weapons physicists at Los Alamos were investigating neutron diffusion in the core of a nuclear weapon.[17] Despite having most of the necessary data, such as the average distance a neutron would travel in a substance before it collided with an atomic nucleus and how much energy the neutron was likely to give off following a collision, the Los Alamos physicists were unable to solve the problem using conventional, deterministic mathematical methods. Ulam proposed using random experiments. He recounts his inspiration as follows:
The first thoughts and attempts I made to practice [the Monte Carlo Method] were suggested by a question which occurred to me in 1946 as I was convalescing from an illness and playing solitaires. The question was what are the chances that a Canfield solitaire laid out with 52 cards will come out successfully? After spending a lot of time trying to estimate them by pure combinatorial calculations, I wondered whether a more practical method than "abstract thinking" might not be to lay it out say one hundred times and simply observe and count the number of successful plays. This was already possible to envisage with the beginning of the new era of fast computers, and I immediately thought of problems of neutron diffusion and other questions of mathematical physics, and more generally how to change processes described by certain differential equations into an equivalent form interpretable as a succession of random operations. Later [in 1946], I described the idea to John von Neumann, and we began to plan actual calculations.[18]
Being secret, the work of von Neumann and Ulam required a code name.[19] A colleague of von Neumann and Ulam, Nicholas Metropolis, suggested using the name Monte Carlo, which refers to the Monte Carlo Casino in Monaco where Ulam's uncle would borrow money from relatives to gamble.[17] Monte Carlo methods were central to the simulations required for the Manhattan Project, though severely limited by the computational tools at the time. Von Neumann, Nicholas Metropolis and others programmed the ENIAC computer to perform the first fully automated Monte Carlo calculations, of a fission weapon core, in the spring of 1948.[20] In the 1950s Monte Carlo methods were used at Los Alamos for the development of the hydrogen bomb, and became popularized in the fields of physics, physical chemistry, and operations research. The Rand Corporation and the U.S. Air Force were two of the major organizations responsible for funding and disseminating information on Monte Carlo methods during this time, and they began to find a wide application in many different fields.
The theory of more sophisticated mean-field type particle Monte Carlo methods had certainly started by the mid-1960s, with the work of Henry P. McKean Jr. on Markov interpretations of a class of nonlinear parabolic partial differential equations arising in fluid mechanics.[21][22] We also quote an earlier pioneering article by Theodore E. Harris and Herman Kahn, published in 1951, using mean-field genetic-type Monte Carlo methods for estimating particle transmission energies.[23] Mean-field genetic type Monte Carlo methodologies are also used as heuristic natural search algorithms (a.k.a. metaheuristic) in evolutionary computing. The origins of these mean-field computational techniques can be traced to 1950 and 1954 with the work of Alan Turing on genetic type mutation-selection learning machines[24] and the articles by Nils Aall Barricelli at the Institute for Advanced Study in Princeton, New Jersey.[25][26]
Quantum Monte Carlo, and more specifically diffusion Monte Carlo methods can also be interpreted as a mean-field particle Monte Carlo approximation of Feynman–Kac path integrals.[27][28][29][30][31][32][33] The origins of Quantum Monte Carlo methods are often attributed to Enrico Fermi and Robert Richtmyer who developed in 1948 a mean-field particle interpretation of neutron-chain reactions,[34] but the first heuristic-like and genetic type particle algorithm (a.k.a. Resampled or Reconfiguration Monte Carlo methods) for estimating ground state energies of quantum systems (in reduced matrix models) is due to Jack H. Hetherington in 1984[33] In molecular chemistry, the use of genetic heuristic-like particle methodologies (a.k.a. pruning and enrichment strategies) can be traced back to 1955 with the seminal work of Marshall N. Rosenbluth and Arianna W. Rosenbluth.[35]
The use of Sequential Monte Carlo in advanced signal processing and Bayesian inference is more recent. It was in 1993, that Gordon et al., published in their seminal work[36] the first application of a Monte Carlo resampling algorithm in Bayesian statistical inference. The authors named their algorithm 'the bootstrap filter', and demonstrated that compared to other filtering methods, their bootstrap algorithm does not require any assumption about that state-space or the noise of the system. We also quote another pioneering article in this field of Genshiro Kitagawa on a related "Monte Carlo filter",[37] and the ones by Pierre Del Moral[38] and Himilcon Carvalho, Pierre Del Moral, André Monin and Gérard Salut[39] on particle filters published in the mid-1990s. Particle filters were also developed in signal processing in 1989–1992 by P. Del Moral, J. C. Noyer, G. Rigal, and G. Salut in the LAAS-CNRS in a series of restricted and classified research reports with STCAN (Service Technique des Constructions et Armes Navales), the IT company DIGILOG, and the LAAS-CNRS (the Laboratory for Analysis and Architecture of Systems) on radar/sonar and GPS signal processing problems.[40][41][42][43][44][45] These Sequential Monte Carlo methodologies can be interpreted as an acceptance-rejection sampler equipped with an interacting recycling mechanism.
From 1950 to 1996, all the publications on Sequential Monte Carlo methodologies, including the pruning and resample Monte Carlo methods introduced in computational physics and molecular chemistry, present natural and heuristic-like algorithms applied to different situations without a single proof of their consistency, nor a discussion on the bias of the estimates and on genealogical and ancestral tree based algorithms. The mathematical foundations and the first rigorous analysis of these particle algorithms were written by Pierre Del Moral in 1996.[38][46]
Branching type particle methodologies with varying population sizes were also developed in the end of the 1990s by Dan Crisan, Jessica Gaines and Terry Lyons,[47][48][49] and by Dan Crisan, Pierre Del Moral and Terry Lyons.[50] Further developments in this field were described in 1999 to 2001 by P. Del Moral, A. Guionnet and L. Miclo.[28][51][52]
There is no consensus on how Monte Carlo should be defined. For example, Ripley[53] defines most probabilistic modeling as stochastic simulation, with Monte Carlo being reserved for Monte Carlo integration and Monte Carlo statistical tests. Sawilowsky[54] distinguishes between a simulation, a Monte Carlo method, and a Monte Carlo simulation: a simulation is a fictitious representation of reality, a Monte Carlo method is a technique that can be used to solve a mathematical or statistical problem, and a Monte Carlo simulation uses repeated sampling to obtain the statistical properties of some phenomenon (or behavior). Examples:
- Simulation: Drawing one pseudo-random uniform variable from the interval [0,1] can be used to simulate the tossing of a coin: If the value is less than or equal to 0.50 designate the outcome as heads, but if the value is greater than 0.50 designate the outcome as tails. This is a simulation, but not a Monte Carlo simulation.
- Monte Carlo method: Pouring out a box of coins on a table, and then computing the ratio of coins that land heads versus tails is a Monte Carlo method of determining the behavior of repeated coin tosses, but it is not a simulation.
- Monte Carlo simulation: Drawing a large number of pseudo-random uniform variables from the interval [0,1] at one time, or once at many different times, and assigning values less than or equal to 0.50 as heads and greater than 0.50 as tails, is a Monte Carlo simulation of the behavior of repeatedly tossing a coin.
Kalos and Whitlock[55] point out that such distinctions are not always easy to maintain. For example, the emission of radiation from atoms is a natural stochastic process. It can be simulated directly, or its average behavior can be described by stochastic equations that can themselves be solved using Monte Carlo methods. "Indeed, the same computer code can be viewed simultaneously as a 'natural simulation' or as a solution of the equations by natural sampling."
Monte Carlo and random numbers
The main idea behind this method is that the results are computed based on repeated random sampling and statistical analysis. The Monte Carlo simulation is, in fact, random experimentations, in the case that, the results of these experiments are not well known. Monte Carlo simulations are typically characterized by many unknown parameters, many of which are difficult to obtain experimentally.[56] Monte Carlo simulation methods do not always require truly random numbers to be useful (although, for some applications such as primality testing, unpredictability is vital).[57] Many of the most useful techniques use deterministic, pseudorandom sequences, making it easy to test and re-run simulations. The only quality usually necessary to make good simulations is for the pseudo-random sequence to appear "random enough" in a certain sense.
What this means depends on the application, but typically they should pass a series of statistical tests. Testing that the numbers are uniformly distributed or follow another desired distribution when a large enough number of elements of the sequence are considered is one of the simplest and most common ones. Weak correlations between successive samples are also often desirable/necessary.
Sawilowsky lists the characteristics of a high-quality Monte Carlo simulation:[54]
- the (pseudo-random) number generator has certain characteristics (e.g. a long "period" before the sequence repeats)
- the (pseudo-random) number generator produces values that pass tests for randomness
- there are enough samples to ensure accurate results
- the proper sampling technique is used
- the algorithm used is valid for what is being modeled
- it simulates the phenomenon in question.
Pseudo-random number sampling algorithms are used to transform uniformly distributed pseudo-random numbers into numbers that are distributed according to a given probability distribution.
Low-discrepancy sequences are often used instead of random sampling from a space as they ensure even coverage and normally have a faster order of convergence than Monte Carlo simulations using random or pseudorandom sequences. Methods based on their use are called quasi-Monte Carlo methods.
In an effort to assess the impact of random number quality on Monte Carlo simulation outcomes, astrophysical researchers tested cryptographically-secure pseudorandom numbers generated via Intel's RDRAND instruction set, as compared to those derived from algorithms, like the Mersenne Twister, in Monte Carlo simulations of radio flares from brown dwarfs. RDRAND is the closest pseudorandom number generator to a true random number generator. No statistically significant difference was found between models generated with typical pseudorandom number generators and RDRAND for trials consisting of the generation of 107 random numbers.[58]
Monte Carlo simulation versus "what if" scenarios
There are ways of using probabilities that are definitely not Monte Carlo simulations – for example, deterministic modeling using single-point estimates. Each uncertain variable within a model is assigned a "best guess" estimate. Scenarios (such as best, worst, or most likely case) for each input variable are chosen and the results recorded.[59]
By contrast, Monte Carlo simulations sample from a probability distribution for each variable to produce hundreds or thousands of possible outcomes. The results are analyzed to get probabilities of different outcomes occurring.[60] For example, a comparison of a spreadsheet cost construction model run using traditional "what if" scenarios, and then running the comparison again with Monte Carlo simulation and triangular probability distributions shows that the Monte Carlo analysis has a narrower range than the "what if" analysis.[example needed] This is because the "what if" analysis gives equal weight to all scenarios (see quantifying uncertainty in corporate finance), while the Monte Carlo method hardly samples in the very low probability regions. The samples in such regions are called "rare events".
Convergence[citation needed]
Convergence can be checked with the Gelman-Rubin diagnostic or the Geweke diagnostic.
Monte Carlo methods are especially useful for simulating phenomena with significant uncertainty in inputs and systems with many coupled degrees of freedom. Areas of application include:
Physical sciences
Monte Carlo methods are very important in computational physics, physical chemistry, and related applied fields, and have diverse applications from complicated quantum chromodynamics calculations to designing heat shields and aerodynamic forms as well as in modeling radiation transport for radiation dosimetry calculations.[61][62][63] In statistical physics, Monte Carlo molecular modeling is an alternative to computational molecular dynamics, and Monte Carlo methods are used to compute statistical field theories of simple particle and polymer systems.[35][64] Quantum Monte Carlo methods solve the many-body problem for quantum systems.[8][9][27] In radiation materials science, the binary collision approximation for simulating ion implantation is usually based on a Monte Carlo approach to select the next colliding atom.[65] In experimental particle physics, Monte Carlo methods are used for designing detectors, understanding their behavior and comparing experimental data to theory. In astrophysics, they are used in such diverse manners as to model both galaxy evolution[66] and microwave radiation transmission through a rough planetary surface.[67] Monte Carlo methods are also used in the ensemble models that form the basis of modern weather forecasting.
Monte Carlo methods are widely used in engineering for sensitivity analysis and quantitative probabilistic analysis in process design. The need arises from the interactive, co-linear and non-linear behavior of typical process simulations. For example,
- In microelectronics engineering, Monte Carlo methods are applied to analyze correlated and uncorrelated variations in analog and digital integrated circuits.
- In geostatistics and geometallurgy, Monte Carlo methods underpin the design of mineral processing flowsheets and contribute to quantitative risk analysis.[19]
- In fluid dynamics, in particular rarefied gas dynamics, where the Boltzmann equation is solved for finite Knudsen number fluid flows using the direct simulation Monte Carlo[68] method in combination with highly efficient computational algorithms.[69]
- In autonomous robotics, Monte Carlo localization can determine the position of a robot. It is often applied to stochastic filters such as the Kalman filter or particle filter that forms the heart of the SLAM (simultaneous localization and mapping) algorithm.
- In telecommunications, when planning a wireless network, the design must be proven to work for a wide variety of scenarios that depend mainly on the number of users, their locations and the services they want to use. Monte Carlo methods are typically used to generate these users and their states. The network performance is then evaluated and, if results are not satisfactory, the network design goes through an optimization process.
- In reliability engineering, Monte Carlo simulation is used to compute system-level response given the component-level response.
- In signal processing and Bayesian inference, particle filters and sequential Monte Carlo techniques are a class of mean-field particle methods for sampling and computing the posterior distribution of a signal process given some noisy and partial observations using interacting empirical measures.[70]
Climate change and radiative forcing
The Intergovernmental Panel on Climate Change relies on Monte Carlo methods in probability density function analysis of radiative forcing.[71]
Computational biology
Monte Carlo methods are used in various fields of computational biology, for example for Bayesian inference in phylogeny, or for studying biological systems such as genomes, proteins,[72] or membranes.[73] The systems can be studied in the coarse-grained or ab initio frameworks depending on the desired accuracy. Computer simulations allow us to monitor the local environment of a particular molecule to see if some chemical reaction is happening for instance. In cases where it is not feasible to conduct a physical experiment, thought experiments can be conducted (for instance: breaking bonds, introducing impurities at specific sites, changing the local/global structure, or introducing external fields).
Computer graphics
Path tracing, occasionally referred to as Monte Carlo ray tracing, renders a 3D scene by randomly tracing samples of possible light paths. Repeated sampling of any given pixel will eventually cause the average of the samples to converge on the correct solution of the rendering equation, making it one of the most physically accurate 3D graphics rendering methods in existence.
Applied statistics
The standards for Monte Carlo experiments in statistics were set by Sawilowsky.[74] In applied statistics, Monte Carlo methods may be used for at least four purposes:
- To compare competing statistics for small samples under realistic data conditions. Although type I error and power properties of statistics can be calculated for data drawn from classical theoretical distributions (e.g., normal curve, Cauchy distribution) for asymptotic conditions (i. e, infinite sample size and infinitesimally small treatment effect), real data often do not have such distributions.[75]
- To provide implementations of hypothesis tests that are more efficient than exact tests such as permutation tests (which are often impossible to compute) while being more accurate than critical values for asymptotic distributions.
- To provide a random sample from the posterior distribution in Bayesian inference. This sample then approximates and summarizes all the essential features of the posterior.
- To provide efficient random estimates of the Hessian matrix of the negative log-likelihood function that may be averaged to form an estimate of the Fisher information matrix.[76][77]
Monte Carlo methods are also a compromise between approximate randomization and permutation tests. An approximate randomization test is based on a specified subset of all permutations (which entails potentially enormous housekeeping of which permutations have been considered). The Monte Carlo approach is based on a specified number of randomly drawn permutations (exchanging a minor loss in precision if a permutation is drawn twice—or more frequently—for the efficiency of not having to track which permutations have already been selected).
Artificial intelligence for games
Monte Carlo methods have been developed into a technique called Monte-Carlo tree search that is useful for searching for the best move in a game. Possible moves are organized in a search tree and many random simulations are used to estimate the long-term potential of each move. A black box simulator represents the opponent's moves.[78]
The Monte Carlo tree search (MCTS) method has four steps:[79]
- Starting at root node of the tree, select optimal child nodes until a leaf node is reached.
- Expand the leaf node and choose one of its children.
- Play a simulated game starting with that node.
- Use the results of that simulated game to update the node and its ancestors.
The net effect, over the course of many simulated games, is that the value of a node representing a move will go up or down, hopefully corresponding to whether or not that node represents a good move.
Monte Carlo Tree Search has been used successfully to play games such as Go,[80] Tantrix,[81] Battleship,[82] Havannah,[83] and Arimaa.[84]
Design and visuals
Monte Carlo methods are also efficient in solving coupled integral differential equations of radiation fields and energy transport, and thus these methods have been used in global illumination computations that produce photo-realistic images of virtual 3D models, with applications in video games, architecture, design, computer generated films, and cinematic special effects.[85]
Search and rescue
The US Coast Guard utilizes Monte Carlo methods within its computer modeling software SAROPS in order to calculate the probable locations of vessels during search and rescue operations. Each simulation can generate as many as ten thousand data points that are randomly distributed based upon provided variables.[86] Search patterns are then generated based upon extrapolations of these data in order to optimize the probability of containment (POC) and the probability of detection (POD), which together will equal an overall probability of success (POS). Ultimately this serves as a practical application of probability distribution in order to provide the swiftest and most expedient method of rescue, saving both lives and resources.[87]
Finance and business
Monte Carlo simulation is commonly used to evaluate the risk and uncertainty that would affect the outcome of different decision options. Monte Carlo simulation allows the business risk analyst to incorporate the total effects of uncertainty in variables like sales volume, commodity and labour prices, interest and exchange rates, as well as the effect of distinct risk events like the cancellation of a contract or the change of a tax law.
Monte Carlo methods in finance are often used to evaluate investments in projects at a business unit or corporate level, or other financial valuations. They can be used to model project schedules, where simulations aggregate estimates for worst-case, best-case, and most likely durations for each task to determine outcomes for the overall project.[1] Monte Carlo methods are also used in option pricing, default risk analysis.[88][89][90] Additionally, they can be used to estimate the financial impact of medical interventions.[91]
A Monte Carlo approach was used for evaluating the potential value of a proposed program to help female petitioners in Wisconsin be successful in their applications for harassment and domestic abuse restraining orders. It was proposed to help women succeed in their petitions by providing them with greater advocacy thereby potentially reducing the risk of rape and physical assault. However, there were many variables in play that could not be estimated perfectly, including the effectiveness of restraining orders, the success rate of petitioners both with and without advocacy, and many others. The study ran trials that varied these variables to come up with an overall estimate of the success level of the proposed program as a whole.[92]
Library science
Monte Carlo approach had also been used to simulate the number of book publications based on book genre in Malaysia. The Monte Carlo simulation utilized previous published National Book publication data and book's price according to book genre in the local market. The Monte Carlo results were used to determine what kind of book genre that Malaysians are fond of and was used to compare book publications between Malaysia and Japan.[93]
Nassim Nicholas Taleb writes about Monte Carlo generators in his 2001 book Fooled by Randomness as a real instance of the reverse Turing test: a human can be declared unintelligent if their writing cannot be told apart from a generated one.
Use in mathematics
In general, the Monte Carlo methods are used in mathematics to solve various problems by generating suitable random numbers (see also Random number generation) and observing that fraction of the numbers that obeys some property or properties. The method is useful for obtaining numerical solutions to problems too complicated to solve analytically. The most common application of the Monte Carlo method is Monte Carlo integration.
Deterministic numerical integration algorithms work well in a small number of dimensions, but encounter two problems when the functions have many variables. First, the number of function evaluations needed increases rapidly with the number of dimensions. For example, if 10 evaluations provide adequate accuracy in one dimension, then 10100 points are needed for 100 dimensions—far too many to be computed. This is called the curse of dimensionality. Second, the boundary of a multidimensional region may be very complicated, so it may not be feasible to reduce the problem to an iterated integral.[94] 100 dimensions is by no means unusual, since in many physical problems, a "dimension" is equivalent to a degree of freedom.
Monte Carlo methods provide a way out of this exponential increase in computation time. As long as the function in question is reasonably well-behaved, it can be estimated by randomly selecting points in 100-dimensional space, and taking some kind of average of the function values at these points. By the central limit theorem, this method displays convergence—i.e., quadrupling the number of sampled points halves the error, regardless of the number of dimensions.[94]
A refinement of this method, known as importance sampling in statistics, involves sampling the points randomly, but more frequently where the integrand is large. To do this precisely one would have to already know the integral, but one can approximate the integral by an integral of a similar function or use adaptive routines such as stratified sampling, recursive stratified sampling, adaptive umbrella sampling[95][96] or the VEGAS algorithm.
A similar approach, the quasi-Monte Carlo method, uses low-discrepancy sequences. These sequences "fill" the area better and sample the most important points more frequently, so quasi-Monte Carlo methods can often converge on the integral more quickly.
Another class of methods for sampling points in a volume is to simulate random walks over it (Markov chain Monte Carlo). Such methods include the Metropolis–Hastings algorithm, Gibbs sampling, Wang and Landau algorithm, and interacting type MCMC methodologies such as the sequential Monte Carlo samplers.[97]
Simulation and optimization
Another powerful and very popular application for random numbers in numerical simulation is in numerical optimization. The problem is to minimize (or maximize) functions of some vector that often has many dimensions. Many problems can be phrased in this way: for example, a computer chess program could be seen as trying to find the set of, say, 10 moves that produces the best evaluation function at the end. In the traveling salesman problem the goal is to minimize distance traveled. There are also applications to engineering design, such as multidisciplinary design optimization. It has been applied with quasi-one-dimensional models to solve particle dynamics problems by efficiently exploring large configuration space. Reference[98] is a comprehensive review of many issues related to simulation and optimization.
The traveling salesman problem is what is called a conventional optimization problem. That is, all the facts (distances between each destination point) needed to determine the optimal path to follow are known with certainty and the goal is to run through the possible travel choices to come up with the one with the lowest total distance. However, let's assume that instead of wanting to minimize the total distance traveled to visit each desired destination, we wanted to minimize the total time needed to reach each destination. This goes beyond conventional optimization since travel time is inherently uncertain (traffic jams, time of day, etc.). As a result, to determine our optimal path we would want to use simulation - optimization to first understand the range of potential times it could take to go from one point to another (represented by a probability distribution in this case rather than a specific distance) and then optimize our travel decisions to identify the best path to follow taking that uncertainty into account.
Inverse problems
Probabilistic formulation of inverse problems leads to the definition of a probability distribution in the model space. This probability distribution combines prior information with new information obtained by measuring some observable parameters (data). As, in the general case, the theory linking data with model parameters is nonlinear, the posterior probability in the model space may not be easy to describe (it may be multimodal, some moments may not be defined, etc.).
When analyzing an inverse problem, obtaining a maximum likelihood model is usually not sufficient, as we normally also wish to have information on the resolution power of the data. In the general case we may have many model parameters, and an inspection of the marginal probability densities of interest may be impractical, or even useless. But it is possible to pseudorandomly generate a large collection of models according to the posterior probability distribution and to analyze and display the models in such a way that information on the relative likelihoods of model properties is conveyed to the spectator. This can be accomplished by means of an efficient Monte Carlo method, even in cases where no explicit formula for the a priori distribution is available.
The best-known importance sampling method, the Metropolis algorithm, can be generalized, and this gives a method that allows analysis of (possibly highly nonlinear) inverse problems with complex a priori information and data with an arbitrary noise distribution.[99][100]
Popular exposition of the Monte Carlo Method was conducted by McCracken.[101] The method's general philosophy was discussed by Elishakoff[102] and Grüne-Yanoff and Weirich.[103]
See also
- Auxiliary field Monte Carlo
- Biology Monte Carlo method
- Direct simulation Monte Carlo
- Dynamic Monte Carlo method
- Ergodicity
- Genetic algorithms
- Kinetic Monte Carlo
- List of software for Monte Carlo molecular modeling
- Mean-field particle methods
- Monte Carlo method for photon transport
- Monte Carlo methods for electron transport
- Monte Carlo N-Particle Transport Code
- Morris method
- Multilevel Monte Carlo method
- Quasi-Monte Carlo method
- Sobol sequence
- Temporal difference learning
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|journal=ignored (help); Missing or empty
- ^ Carmona, René; Del Moral, Pierre; Hu, Peng; Oudjane, Nadia (2012). Numerical Methods in Finance. Springer Proceedings in Mathematics. Vol. 12. doi:10.1007/978-3-642-25746-9. ISBN 978-3-642-25745-2.
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- ^ Elwart, Liz; Emerson, Nina; Enders, Christina; Fumia, Dani; Murphy, Kevin (December 2006). "Increasing Access to Restraining Orders for Low Income Victims of Domestic Violence: A Cost-Benefit Analysis of the Proposed Domestic Abuse Grant Program" (PDF). State Bar of Wisconsin. Archived from the original (PDF) on 6 November 2018. Retrieved 12 December 2016.
- ^ Dahlan, Hadi Akbar (29 October 2021). "Perbandingan Penerbitan dan Harga Buku Mengikut Genre di Malaysia dan Jepun Menggunakan Data Akses Terbuka dan Simulasi Monte Carlo" (PDF). Kajian Malaysia. 39 (2): 179–202. doi:10.21315/km2021.39.2.8. S2CID 240435973.
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- ^ Bartels, Christian; Karplus, Martin (31 December 1997). "Probability Distributions for Complex Systems: Adaptive Umbrella Sampling of the Potential Energy". The Journal of Physical Chemistry B. 102 (5): 865–880. doi:10.1021/jp972280j.
- ^ Del Moral, Pierre; Doucet, Arnaud; Jasra, Ajay (2006). "Sequential Monte Carlo samplers". Journal of the Royal Statistical Society, Series B. 68 (3): 411–436. arXiv:cond-mat/0212648. doi:10.1111/j.1467-9868.2006.00553.x. S2CID 12074789.
- ^ Spall, J. C. (2003), Introduction to Stochastic Search and Optimization: Estimation, Simulation, and Control, Wiley, Hoboken, NJ. http://www.jhuapl.edu/ISSO
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External links
- Media related to Monte Carlo method at Wikimedia Commons
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This complete guide to C++ and computational finance is a major extension to Daniel J. Duffy's 2004 edition.
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Stochastic Simulation and Applications in Finance with MATLAB Programs:
Huu Tue Huynh, Van Son Lai, Issouf Soumare -
Stochastic Simulation and Applications in Finance with MATLAB Programs explains the fundamentals of Monte Carlo simulation techniques, their use in the numerical resolution of stochastic differential equations and their current applications in finance.
Python for Finance – 2nd Ed.:
Mastering Data-Driven Finance
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Yves Hilpisch shows you how to develop a full-fledged framework for Monte Carlo simulation-based derivatives and risk analytics.
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Data Analysis, Models, Simulation, Calibration and Hedging
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This book is the finance professional's guide to exploiting Python's capabilities for efficient and performing derivatives analytics.
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text:
The Portuguese coach sealed perhaps the greatest victory in his time at the Spanish side to silence the doubters who thought Madrid were setting themselves up for a big fall
By Ben Hayward | Spanish Football Writer
Writing off Jose Mourinho is clearly unwise. The Portuguese has endured one controversy after another in this, his third season at the Santiago Bernabeu, and with La Liga looking lost plus difficult second leg matches away from home to come in the Copa del Rey and the Champions League against Barcelona and Manchester United, respectively, many had already waved the 50-year-old into the Spanish sunset.
How premature.
Many criticisms can be levelled at Mourinho. His antics off the pitch have been unsavoury at times and ugly at others since he signed for Madrid in 2010. But when it comes to winning football matches, the man knows what he is doing.
It took a while. After his famous triumph with Inter over Barca in 2010 en route to the Champions League trophy and the Bernabeu hotseat, Mourinho kicked off with a 5-0 defeat at Camp Nou in his first Clasico. It was the worst defeat of his career. The loss, he said, "was easy to digest." But it must have been hard to swallow for a natural-born winner such as he is.
Mourinho's first Clasico win – and still the only time his Madrid team have kept a clean sheet against Barca – came in the final of the Copa del Rey on April 20, 2011, when Cristiano Ronaldo headed home in extra time to seal the side's first trophy under their new coach. It seems hard to believe now that Ronaldo had scored his first goal against Barca just four days earlier in the teams' 1-1 league draw at the Bernabeu.
Ronaldo, it was claimed, was not a man for the big occasion. He is now. If Mourinho is the leader on the bench for this Madrid team, Ronaldo is the undisputed leader on the pitch. The 28-year-old turned Gerard Pique inside out to win a penalty, coolly converted and added a second following a devastating counter-attack in the second half.
He now has 12 Clasico goals and is only five behind Lionel Messi, overshadowed on Tuesday and only coming close with a curling free kick late in the first half.
It will have been worrying for Barcelona fans. The Blaugrana produced a poor performance at AC Milan in midweek. At home, a reaction was expected, but never materialised, part in thanks to a disciplined defensive display from the visitors.
Mourinho has never gone without a trophy since his first full season at Porto in 2002-03 – and his Madrid team will now be favourites to overcome Atletico or Sevilla in the final of the Copa del Rey.
And on this evidence, the Champions League is also within their grasp. Sir Alex Ferguson was in the crowd to watch the game ahead of Manchester United's match against Madrid at Old Trafford. And he will not have liked what he saw. United, like Barcelona before them, had drawn the first match of their tie 1-1 in Madrid. But after this swashbuckling show from Mourinho's men, that surely means little.
For Barcelona, the concerns continue to grow. Messi was strangely subdued again, while Carles Puyol was turned inside out for the second goal by Angel Di Maria and the whole defence now looks leakier than ever. The lack of a full-time coach really is not helping, either.
For Madrid, meanwhile, it was an unforgettable night. Mourinho managed to silence his critics once again. And whatever is happening behind the scenes at the Bernabeu, more fool those who write off Jose in the battles ahead.
Follow Ben Hayward on


text:
Finally, a use for 3D printers that we can all get behind! Two services are now available that can help you generate a sex toy from scratch by using a 3D printer — which can produce preconfigured dildos of various shapes, sizes, and colors. There's even an option to create an exact replica of your very own genitals. But while the idea appears to be total win, the end results aren't completely perfect — at least not yet.
This news comes to us from Venture Beat's Ricardo Bilton, who highlights two different options:
First up is Makerlove, a website that provides free sex toy designs that can be downloaded and sent out to a 3D printer queue for on-demand gratification.
Its founder, Tom Nardone, is responsible for such toys as the Hello Kitty-inspired Hello Pussycat, the tongue-shaped Organic Communicator, and the very strange Freaky Freud.
As Bilton notes, Makerlove allows for the utmost in privacy — which may be a welcome relief for those maker enthusiasts too embarrassed to go out and buy one for themselves.
There's also the New York Toy Collective, a high-end sex shop run by Chelsea Downs and Laura Parker. By using a MakerBot printer, their service allows for an almost endless array of possibilities — designs that can be altered to meet the specific needs of the individual. Customers can request changes to dimensions, color, and overall aesthetic appeal.
Bilton writes:
This process also allows them to maintain a high level of quality in their creations. Most sex toys are designed via injection molding, a massively expensive manufacturing process used to create as many products as quickly as possible. But that method is completely incompatible with New York Toy Collective's hyper-realistic silicon toys, which have to sit in a mold for longer than a day before they can be removed. Try telling that to a manufacturer focused on getting products out as fast as possible.
New York Toy Collective has also created a technique in which an exact replica of your junk can be created. Visitors to this week's 3DEA Pop Up exhibition can have their genitals scanned and replicated in silicon form.
But, as Bilton notes, the end results aren't quite perfect. First, the sex toys are slightly textured and coarse, owing to print resolutions that aren't quite high enough (although if you like it rough, however, then this might be right up your alley). And as Nardine told Venture Beat, "The resolution will improve with time. 3D printing is just like any other technology."
In addition, the material used for printing, polyvinyl alcohol, is water-soluable, meaning that water-based lubricants can't be used. And lastly, 3D printers are prohibitatively expensive; this is not something that everybody can do right now.
Images via Venture Beat and Makerlove.


text:
By Jennifer Radloff Publisher: APCNews Istanbul,Published on
Page last updated on
"Cross-country cross-cultural exchange would also help break the gender stereotypes and cultural restrictions to women's rights and build towards ending their dependency on men or others to address their digital security needs." [Mary Jane Real, WHRD IC]
From 19 to 22 March 2013, Women Human Rights Defenders met in Istanbul for a Training of Trainers workshop on digital security. Participants were selected via an internal process through the WHRD International Coalition, who had secured funds to show their commitment to building a cadre of digital security trainers within the WHRD community. There are increasing online threats to the freedom of expression and association of WHRDs and their activism. Because they are women, WHRDs face many unique threats and obstacles, which translate in the online environment as public or private sexualised threats, tracking via mobile phones, restrictions on access to content related to women's sexuality, computer confiscation, censorship and others and can prevent WHRDs from effectively carrying out their activism.
In 2012, APC conducted one of the first international surveys of the online threats faced by women human rights defenders. The response revealed that WHRDs require digital security training, the most pressing needs being for training in secure social networking in order to protect online identities and privacy and security in online campaigning. It also showed that there is a need to link offline and online security and highlighted the importance of a safe online environment for feminism, in general.
The training in Istanbul was carried out in partnership with APC, Amnesty International, Front Line Defenders and the Tactical Technology Collective.
Participants came from Nepal, Nigeria, Cameroon, Burkina Faso, Kyrgyzstan, Pakistan, Kazakhstan, India, Indonesia and Honduras and represented defenders working on LGBTI issues, land rights, protection desks, violence against women, internet rights and journalists under threat.
Through an online, pre-workshop process, participants were given tasks based on the Security-in-a-Box toolkit. Facilitators conducted a needs analysis prior to the workshop to ensure that the training was relevant to the particular security threats participants face as WHRDs. The training was a mix of building capacity on secure passwords, encryption, circumvention and preventing attacks through malware and spyware. Mobile phone security was another aspect of concern to WHRDs as many activists use mobile phones for communicating.
Participants who work in the area of internet rights and feminist activism online were constantly balancing the discussion between "the threats" versus "the tools," bringing back the issue of how critical it is for WHRDs to understand internet rights and policy advocacy if they are to be digitally secure. "Anonymity, which can be used to protect as well as infiltrate, brought up many interesting points of debate. The tools and tactics we use to protect ourselves can be used against us," said Fatima Bayat, an APC digital security trainer.
Including a feminist practice of technology and grounding activist's online work in feminist principles is an area of growing interest and development. Discussions on the difference of contextual realities and finding ways to share strategies will be one of the topics of a post-workshop online space to facilitate sharing.
Part of the agenda included WHRDs spending time defining how they were going to build the capacity of their communities through digital security trainings. Interviews were conducted with the participants to build evidence of the realities of online threats faced by WHRDs and to respond to these through advocacy at a policy and practical level.
One participant could not risk taking any unencrypted workshop materials home since the first threat she faced would be the customs officers who could confiscate her computer in order to track her movements and the content she carries.
APC has engaged with WHRDs and their digital security over the years through the Connect Your Rights campaign. In 2011 and 2012, three workshops were held in Africa, Asia and Latin America to build the capacity of WHRDs in digital security. A global dialogue for WHRDs on digital security was held prior to the 2012 AWID Forum, which facilitated the connections between APC, WHRD International Coalition, APC, Amnesty International, Front Line Defenders and the Tactical Technology Collective, giving the impetus to this workshop.


text:
Sheriff's Search and Rescue Team Recruitment Drive Continues
Santa Barbara-The Santa Barbara County Sheriff's Search and Rescue Team (SBCSAR) continues to look for more members to fill its upcoming search and rescue training academy. The two following recruitment meetings have been scheduled in North and South County.
Tomorrow Wednesday, March 19, 2014 @ 6 pm – 66 S. San Antonio Road, Santa Barbara
Wednesday, March 26, 2014 @ 6 pm – Santa Ynez Airport Pilot Lounge
SBCSAR is a volunteer branch of the Santa Barbara County Sheriff's Office and one of the most active search and rescue teams in California. This highly-trained group uses specialized training and equipment to handle a variety of emergencies, including high-angle rock rescues, car-over-the-side accidents, downed aircraft, swiftwater rescues and medical emergencies.
SBCSAR is a California Type I search and rescue team and one of only nineteen teams in California that is fully certified as a Mountain Rescue Team. In order to qualify for MRA status, a team must pass proficiency tests in Snow and Ice, Rock, and Search Operations every three years. MRA teams are viewed as the best in the country and are often requested to support search and rescue personnel of other counties.
Team members are men and women from all walks of life and are on call 24 hours a day, 365 days a year.
SBCSAR's primary jurisdiction is the wilderness and urban areas of Santa Barbara County. As such, team members have the opportunity to visit parts of the county very few ever experience to see the unique beauty of our backcountry. In addition, as part of the Mountain Rescue Association, the team responds to emergencies in other counties, states and national parks such as Yosemite, Sequoia and Kings National Parks.
As an active arm of the Sheriff's Office, the Team is called to assist on evidence searches, provides medical support for large community events, and is the primary entity to coordinate and carry out evacuations during major disasters such as wildland fires.
For more information go to www.sbcsar.org or you can also send an e-mail to email@example.com