omniomni/omni-engineering huggingface dataset:
Model of computation. In computer science, and more specifically in computability theory and computational complexity theory, a model of computation is a model which describes how an output of a mathematical function is computed given an input. A model describes how units of computations, memories, and communications are organized. The computational complexity of an algorithm can be measured given a model of computation. Using a model allows studying the performance of algorithms independently of the variations that are specific to particular implementations and specific technology.

Categories
Models of computation can be classified into three categories: sequential models, functional models, and concurrent models.

Sequential models
Sequential models include:
 Finite-state machines
 Post machines (Post–Turing machines and tag machines).
 Pushdown automata
 Register machines
 Random-access machines
 Turing machines
 Decision tree model

Functional models
Functional models include:
 Abstract rewriting systems
 Combinatory logic
 General recursive functions
 Lambda calculus

Concurrent models
Concurrent models include:
 Actor model
 Cellular automaton
 Interaction nets
 Kahn process networks
 Logic gates and digital circuits
 Petri nets
 Process calculus
 Synchronous Data Flow

Some of these models have both deterministic and  nondeterministic variants. Nondeterministic models  correspond to limits of certain sequences of finite computers, but do not correspond to any subset of finite computers; they are used in the study of computational complexity of algorithms.

Models differ in their expressive power; for example, each function that can be computed by a finite-state machine can also be computed by a Turing machine, but not vice versa.

Uses
In the field of runtime analysis of algorithms, it is common to specify a  computational model in terms of primitive operations allowed which have unit cost, or simply unit-cost operations. A commonly used example is the random-access machine, which has unit cost for read and write access to all of its memory cells. In this respect, it differs from the above-mentioned Turing machine model.

See also
 Stack machine (0-operand machine)
 Accumulator machine (1-operand machine)
 Register machine (2,3,... operand machine)
 Random-access machine
 Abstract machine
 Cell-probe model
 Robertson–Webb query model
 Chomsky hierarchy
 Turing completeness

References

Further reading 
 
 

 
Computational complexity theory
Computability theory
Minitab. Minitab is a statistics package developed at the Pennsylvania State University by researchers Barbara F. Ryan, Thomas A. Ryan, Jr., and Brian L. Joiner in conjunction with Triola Statistics Company in 1972. It began as a light version of OMNITAB, a statistical analysis program by National Institute of Standards and Technology.

History 

Minitab Statistical Software is a statistics package developed at the Pennsylvania State University by researchers Barbara F. Ryan, Thomas A. Ryan, Jr., Brian L. Joiner in 1972. The project received funding from the Triola Statistics Company. It began as a light version of OMNITAB, a statistical analysis program by NIST, which was conceived by Joseph Hilsenrath in years 19621964 for the IBM 7090. The documentation for the latest version of OMNITAB, OMNITAB 80, was last published in 1986, and there has been no significant development since then.

Minitab is distributed by Minitab, LLC, a privately owned company headquartered in State College, Pennsylvania. As of 2024, Minitab LLC had subsidiaries in the Netherlands, UK, France, Germany, Hong Kong, Japan and Australia.

Interoperability 
Minitab, LLC also produces other software that can be used in conjunction with Minitab; Minitab Connect helps businesses centralize and organize their data, Quality Trainer is an eLearning package that teaches statistical concepts, Minitab Workspace provides project planning and visualization tools, and Minitab Engage is a tool for Idea and Innovation Management, as well as managing Six Sigma and Lean manufacturing deployments.

In October 2020, Minitab launched the first cloud-based version of its statistical software. As of June 2021, the Minitab Desktop app is only available for Windows, with a former version for MacOS (Minitab 19.x) no longer being supported.

See also 
 List of statistical packages
 Comparison of statistical packages

References

Further reading 
 "Minitab Statistical Software Features – Minitab." Software for Statistics, Process Improvement, Six Sigma, Quality – Minitab. N.p., n.d. Web. 11 Apr. 2011.
 Groebner, David F., Mark L. Berenson, David M. Levine, Timothy C. Krehbiel, and Hang Lau. Applied management statistics. Custom ed. Boston, MA: Pearson Custom Publishing/Pearson/Prentice Hall, 2008. Print
 Akers, Michael D (2018), Exploring, Analysing and Interpreting Data with Minitab 18 (1st ed.), United Kingdom, Compass Publishing.

External links 
 

Statistical software
Quality
Pennsylvania State University
Windows-only proprietary software
Kubota. is a Japanese multinational corporation based in Osaka. It was established in 1890. The corporation produces many products including tractors and other agricultural machinery, construction equipment, engines, vending machines, pipe, valves, cast metal, pumps, and equipment for water purification, sewage treatment and air conditioning.

Kubota engines are in both diesel and gasoline or spark ignition forms, ranging from the tiny 0.276-liter engine to 6.1-liter engine, in both air-cooled and-liquid cooled designs, naturally-aspirated and forced induction. Cylinder configurations are from single cylinder to inline six cylinders, with single cylinder to four-cylinder being the most common. Those engines are widely used in agricultural equipment, construction equipment, tractors, and marine propulsion.

The corporation is listed on the first section of Tokyo Stock Exchange and is a constituent of the TOPIX 100 and Nikkei 225.

One of its notable contributions was to the construction of the Solar Ark. Kubota is also known for designing 3D graphics chips in the 1990s.

Kubota Tractor Corporation is the American corporation for marketing and distributing Kubota engineered products in the United States, headquartered in Grapevine, TX. This includes products manufactured in the United States by Kubota's American Manufacturing Corporation, called Kubota Manufacturing of America Corporation Headquartered in Gainesville, GA.

Gallery

References

External links 
 

Agricultural machinery manufacturers of Japan
Tractor manufacturers of Japan
Electric tractors
Lawn and garden tractors
Construction equipment manufacturers of Japan
Engine manufacturers of Japan
Diesel engine manufacturers
Marine engine manufacturers
Electric motor manufacturers
Pump manufacturers
Defense companies of Japan
Japanese brands
Manufacturing companies based in Osaka
Companies based in Osaka Prefecture
Companies listed on the Osaka Exchange
Companies listed on the Tokyo Stock Exchange
Companies in the Nikkei 225
Japanese companies established in 1890
Vehicle manufacturing companies established in 1890
Manufacturing companies established in 1890
Conglomerate companies of Japan
Gas engine manufacturers
Electrical generation engine manufacturers
Automotive transmission makers
Driver wrapper. A driver wrapper is a subroutine in a software library that functions as an adapter between an operating system and a driver, such as a device driver, that was not designed for that operating system. It can enable the use of devices for which no drivers for the particular operating system are available. In particular,  Microsoft Windows is the dominant family of operating systems for IBM PC compatible computers, and many devices are supplied with drivers for Windows but not other operating systems.

Windows driver wrappers for Linux
Several open-source software projects allow using Microsoft Windows drivers under another operating system, such as Linux.

Examples include network drivers for wireless cards (such as NDISwrapper for Linux or Project Evil for FreeBSD) and the NTFS file system (see Captive NTFS).

The common thread among these examples is the use of wrapper technology, which allows execution of the drivers in a foreign environment.
Limitations for driver wrappers include inability to function at real time. An example of this limitation includes latency problems as those associated with attempts to make compatible with Linux the ZoomR16 audio DAW sound recorder and control surface.

See also

 Compatibility layer
 Wrapper function
 Wrapper library

References

Device drivers
Subroutines
System software
Ore railway station. Ore railway station serves Ore in East Sussex, England. It is on the Marshlink Line, and train services are provided by Southern, with a single peak hour service operated by Southeastern.

Third rail 750 V DC electrification from Hastings ends here.

History
The station opened in 1888 and was electrified in 1935.

Ore station was once adjoined to the north by a depot for electric trains but this closed in May 1986 following the electrification of the Hastings to Tonbridge line and relocation of electric units for servicing and cleaning to St Leonards depot.

Facilities 
Today, there are two platforms linked by a footbridge. Both platforms have shelters and modern help points. Ore station has been unstaffed since a booking office fire in the late 1980s, although the station has a self-service ticket machine available for ticket purchases. The station has step-free access to the Ashford bound platform only.

The area has in the past been a hotspot for vandalism but is now covered comprehensively by CCTV. It is scheduled for re-development in the future which may involve the station being renamed to either Hastings East or Ore Valley.

Services 

Ore station is the terminus for electric East Coastway services.
Trains terminating and restarting at this station run forward into a turnaround siding which runs adjacent to the Marshlink Line as far as Ore Tunnel. Ore is also the start of the single track section on the Marshlink Line.

Off-peak, all services at Ore are operated by Southern using  DMUs and  EMUs.

The typical off-peak service in trains per hour is:
 1 tph to  via 
 1 tph to  (semi-fast)
 1 tph to  (stopping)
 1 tph to 

The station is also served by a single early morning service to London Cannon Street via , operated by Southeastern.

References

External links 

Transport in Hastings
Railway stations in East Sussex
DfT Category F2 stations
Former South Eastern Railway (UK) stations
Railway stations in Great Britain opened in 1888
Railway stations served by Govia Thameslink Railway
Railway stations served by Southeastern
Structurae. Structurae is an online database containing pictures and information about structural and civil engineering works, and their associated engineers, architects, and builders.

Overview
Structurae was founded in 1998 by Nicolas Janberg, who had studied civil engineering at Princeton University. In March 2012, Structurae was acquired by , a subsidiary of John Wiley & Sons, Inc., with Janberg joining the company as Structurae's editor-in-chief. At that time, the web site received more than one million pageviews per month, and was available in English, French and German. In 2015, Janberg bought the site back to operate it as a freelancer again.

Buildings in the Structurae database

References

External links

1998 establishments in Germany
Architecture websites
German websites
Architecture databases
Online databases
Databases in Germany
Internet properties established in 1998
Project Monterey. Project Monterey was an attempt to build a single Unix operating system that ran across a variety of 32-bit and 64-bit platforms, as well as supporting multi-processing.  Announced in October 1998, several Unix vendors were involved; IBM provided POWER and PowerPC support from AIX, Santa Cruz Operation (SCO) provided IA-32 support, and Sequent added multi-processing (MP) support from their DYNIX/ptx system. Intel Corporation provided expertise and ISV development funding for porting to their upcoming IA-64 (Itanium Architecture) CPU platform, which was yet to be released at that time.  The focus of the project was to create an enterprise-class UNIX for IA-64, which at the time was expected to eventually dominate the UNIX server market.

By March 2001, however, "the explosion in popularity of Linux  ... prompted IBM to quietly ditch" this; all involved attempted to find a niche in the rapidly developing Linux market and moved their focus away from Monterey. Sequent was acquired by IBM in 1999. In 2000, SCO's UNIX business was purchased by Caldera Systems, a Linux distributor, who later renamed themselves the SCO Group. In the same year, IBM eventually declared Monterey dead. Intel, IBM, Caldera Systems, and others had also been running a parallel effort to port Linux to IA-64, Project Trillian, which delivered workable code in February 2000. In late 2000, IBM announced a major effort to support Linux.

In May 2001, the project announced the availability of a beta test version AIX-5L for IA-64, basically meeting its original primary goal. However, Intel had missed its delivery date for its first Itanium processor by two years, and the Monterey software had no market.

With the exception of the IA-64 port and Dynix MP improvements, much of the Monterey effort was an attempt to standardize existing versions of Unix into a single compatible system.  Such efforts had been undertaken in the past (e.g., 3DA) and had generally failed, as the companies involved were too reliant on vendor lock-in to fully support a standard that would allow their customers to leave for other products.  With Monterey, two of the key partners already had a niche they expected to continue to serve in the future: POWER and IA-64 for IBM, IA-32 and IA-64 for SCO.

The breakdown of Project Monterey was one of the factors leading to a lawsuit in 2003, where SCO Group sued IBM over their contributions to Linux.

IBM sold only 32 Monterey licenses in 2001, and fewer in 2002.

References

External links
 

Parallel computing
Collaborative projects
Unix variants
Unix history
Discontinued operating systems
IBM operating systems
Power ISA operating systems
Computer-related introductions in 1998
Power law of practice. The power law of practice states that the logarithm of the reaction time for a particular task decreases linearly with the logarithm of the number of practice trials taken. It is an example of the learning curve effect on performance. It was first proposed as a psychological law by Snoddy (1928), used by Crossman (1959) in his study of a cigar roller in Cuba, and played an important part in the development of Cognitive Engineering by Card, Moran, & Newell (1983). Mechanisms that would explain the power law were popularized by Fitts and Posner (1967), Newell and Rosenbloom (1981), and Anderson (1982).

However, subsequent research by Heathcote, Brown, and Mewhort suggests that the power function observed in learning curves that are averaged across participants is an artifact of aggregation. Heathcote et al. suggest that individual-level data is better fit by an exponential function and the authors demonstrate that the multiple exponential curves will average to produce a curve that is misleadingly well fit by a power function. 

The power function is based on the idea that something is slowing down the learning process; at least, this is what the function suggests. Our learning does not occur at a constant rate according to this function; our learning is hindered. The exponential function shows that learning increases at a constant rate in relationship to what is left to be learned. If you know absolutely nothing about a topic, you can learn 50% of the information quickly, but when you have 50% less to learn, it takes more time to learn that final 50%.

Research by Logan suggests that the instance theory of automaticity can be used to explain why the power law is deemed an accurate portrayal of reaction time learning curves. A skill is automatic when there is one step from stimulus to retrieval. For many problem solving tasks (see table below), reaction time is related to how long it takes to discover an answer, but as time goes on, certain answers are stored within the individual's memory and they have to simply recall the information, thus reducing reaction time. This is the first theory that addresses the why of the power law of practice. 

Power function:

RT = aP−b + c

Exponential function:

RT = ae−b(P-1) + c

Where

RT = trial completion time
P = trial number, starting from 1 (for exponential functions the P-1 argument is used)
a, b, and c, are constants

Practice effects are also influenced by latency. Anderson, Fincham, and Douglass looked at the relationship between practice and latency and people's ability to retain what they learned. As the time between trials increases, there is greater decay. The latency function relates to the forgetting curve.

Latency function:

latency = A + B*Td

Where

A = asymptotic latency
B = latency that varies
T = time between introduction and testing
d = decay rate

See also

Portal: Psychology
 Learning curve
 Power law
 Forgetting
 Power Law

References

Psychological theories
Learning theory (education)
Psychology of learning
Richard T. James. Richard Thompson James (March 27, 1918 – July 13, 1974) was an American naval engineer, best known for inventing the Slinky spring toy with his wife Betty James in Haverford Township, Pennsylvania in 1943.

Education
James was born on March 27, 1918. In 1935, he graduated from Westtown School, a Quaker boarding school located in Chester County, Pennsylvania. In 1939, he graduated with a degree in mechanical engineering from Pennsylvania State University.

Career
In 1943 James was trying to develop a means for suspending sensitive shipboard instruments aboard naval vessels, even in rough seas, and was working with torsion springs when he accidentally dropped one. Seeing how the spring kept moving after it hit the ground, the idea for a toy was born. He bought a coil-winding machine and started the James Spring & Wire Company to mass-produce the Slinky. The following year, Betty came up with the name Slinky after leafing through the dictionary and thinking that the word described the motion of the spring. The couple made 400 Slinkys and convinced Gimbels department store in Philadelphia to carry the toy for Christmas 1945. Originally displayed in a static position, none of the toys sold but when Richard James stepped up to demonstrate the toy in action on a ramp, the entire first production run sold out within 90 minutes at a price of $1 each. The toy became a huge success, particularly after James left the operation and Betty took over the helm. In all, a total of 300 million Slinkys have been sold, with about a quarter million still sold world-wide every year.

By the 1950s, Slinky sales were declining and Richard became affiliated with an evangelical Christian sect. In 1960, Richard went to Bolivia to join Wycliffe Bible Translators, leaving behind his wife, six children, and nearly-bankrupt company in the United States. Betty James took over as CEO of James Industries.  She moved the company from Philadelphia to its current Hollidaysburg, Pennsylvania location and began an active advertising campaign, complete with the famous Slinky jingle.  She ran the company until her retirement in 1998, and was inducted into the Toy Industry Hall of Fame in 2001.

Richard James died of a heart attack in 1974 in Bolivia. Betty died on November 20, 2008, age 90 of congestive heart failure at the Hospital of the University of Pennsylvania in Philadelphia, Pennsylvania.

References 

{Reflist}

External links
 Slinky patent
 Delaware County Times article, 26 August, 1948
 Delaware County Daily Times article, 25 November 1976

1918 births
1974 deaths
Penn State College of Engineering alumni
Toy inventors
Westtown School alumni
20th-century American inventors
20th-century American engineers
Engineers from Delaware
Engineers from Pennsylvania
American expatriates in Bolivia
Self-condensation. In organic chemistry, self-condensation is an organic reaction in which a chemical compound containing a carbonyl group () acts both as the electrophile and the nucleophile in an aldol condensation. It is also called a  symmetrical aldol condensation as opposed to a mixed aldol condensation in which the electrophile and nucleophile are different species.

For example, two molecules of acetone condense to a single compound mesityl oxide in the presence of an ion-exchange resin:

For synthetic uses, this is generally an undesirable, but spontaneous and favored side-reaction of mixed aldol condensation, and special precautions are needed to prevent it.

Preventing self-condensation
In many cases, self-condensation is an unwanted side-reaction. Therefore, chemists have adopted many ways to prevent this from occurring when performing a crossed aldol reaction.

The use of a more reactive electrophile, and a non-enolizable partner
If acetophenone and benzaldehyde are put together in the presence of aqueous NaOH, only one product is formed:

This occurs because benzaldehyde lacks any enolizable protons, so it cannot form an enolate, and the benzaldehyde is much more electrophilic than any unenolized acetophenone in solution. Therefore, the enolate formed from acetophenone will always preferentially attack the benzaldehyde over another molecule of acetophenone.

Making enolate ion quantitatively

When nitromethane and acetophenone are combined using aqueous NaOH, only one product is formed:

Here, the acetophenone never gets a chance to condense with itself, because the nitromethane is so much more acidic that the nitro "enolate" is made quantitatively.  There is no known published procedure for the condensation between Acetophenone and Nitromethane.  

A similar process can also be used to prevent self-condensation between two ketones. In this case, however, the base used needs to be more powerful. A common base used is Lithium diisopropyl amide (LDA). Here it is used in order to perform the crossed condensation between acetone and cyclohexanone.

The deprotonation step using LDA is so fast that the enolate formed never gets a chance to react with any unreacted molecules of cyclohexanone. Then the enolate reacts quickly with acetone.

Silyl enol ether formation
Using LDA will not work when attempting to make enolate ion from aldehydes. They are so reactive that self-condensation will occur. One way to get around this is to turn the aldehyde into a silyl enol ether using trimethylsilyl chloride and a base, such as triethylamine, and then perform the aldol condensation. Here this tactic is employed in the condensation of acetaldehyde and benzaldehyde. A Lewis acid, such as TiCl4, must be used in order to promote condensation.

References 

Condensation reactions