Word error rate (WER) is a common metric of the performance of a speech recognition or machine translation system. The WER metric typically ranges from 0 to 1, where 0 indicates that the compared pieces of text are exactly identical, and 1 (or larger) indicates that they are completely different with no similarity. This way, a WER of 0.8 means that there is an 80% error rate for compared sentences. The general difficulty of measuring performance lies in the fact that the recognized word sequence can have a different length from the reference word sequence (supposedly the correct one). The WER is derived from the Levenshtein distance, working at the word level instead of the phoneme level. The WER is a valuable tool for comparing different systems as well as for evaluating improvements within one system. This kind of measurement, however, provides no details on the nature of translation errors and further work is therefore required to identify the main source(s) of error and to focus any research effort. This problem is solved by first aligning the recognized word sequence with the reference (spoken) word sequence using dynamic string alignment. Examination of this issue is seen through a theory called the power law that states the correlation between perplexity and word error rate. Word error rate can then be computed as: W E R = S + D + I N = S + D + I S + D + C {\displaystyle {\mathit {WER}}={\frac {S+D+I}{N}}={\frac {S+D+I}{S+D+C}}} where S is the number of substitutions, D is the number of deletions, I is the number of insertions, C is the number of correct words, N is the number of words in the reference (N=S+D+C) The intuition behind 'deletion' and 'insertion' is how to get from the reference to the hypothesis. So if we have the reference "This is wikipedia" and hypothesis "This _ wikipedia", we call it a deletion. Note that since N is the number of words in the reference, the word error rate can be larger than 1.0, namely if the number of insertions I is larger than the number of correct words C. When reporting the performance of a speech recognition system, sometimes word accuracy (WAcc) is used instead: W A c c = 1 − W E R = N − S − D − I N = C − I N {\displaystyle {\mathit {WAcc}}=1-{\mathit {WER}}={\frac {N-S-D-I}{N}}={\frac {C-I}{N}}} Since the WER can be larger than 1.0, the word accuracy can be smaller than 0.0. == Experiments == It is commonly believed that a lower word error rate shows superior accuracy in recognition of speech, compared with a higher word error rate. However, at least one study has shown that this may not be true. In a Microsoft Research experiment, it was shown that, if people were trained under "that matches the optimization objective for understanding", (Wang, Acero and Chelba, 2003) they would show a higher accuracy in understanding of language than other people who demonstrated a lower word error rate, showing that true understanding of spoken language relies on more than just high word recognition accuracy. == Other metrics == One problem with using a generic formula such as the one above, however, is that no account is taken of the effect that different types of error may have on the likelihood of successful outcome, e.g. some errors may be more disruptive than others and some may be corrected more easily than others. These factors are likely to be specific to the syntax being tested. A further problem is that, even with the best alignment, the formula cannot distinguish a substitution error from a combined deletion plus insertion error. Hunt (1990) has proposed the use of a weighted measure of performance accuracy where errors of substitution are weighted at unity but errors of deletion and insertion are both weighted only at 0.5, thus: W E R = S + 0.5 D + 0.5 I N {\displaystyle {\mathit {WER}}={\frac {S+0.5D+0.5I}{N}}} There is some debate, however, as to whether Hunt's formula may properly be used to assess the performance of a single system, as it was developed as a means of comparing more fairly competing candidate systems. A further complication is added by whether a given syntax allows for error correction and, if it does, how easy that process is for the user. There is thus some merit to the argument that performance metrics should be developed to suit the particular system being measured. Whichever metric is used, however, one major theoretical problem in assessing the performance of a system is deciding whether a word has been “mis-pronounced,” i.e. does the fault lie with the user or with the recogniser. This may be particularly relevant in a system which is designed to cope with non-native speakers of a given language or with strong regional accents. The pace at which words should be spoken during the measurement process is also a source of variability between subjects, as is the need for subjects to rest or take a breath. All such factors may need to be controlled in some way. For text dictation it is generally agreed that performance accuracy at a rate below 95% is not acceptable, but this again may be syntax and/or domain specific, e.g. whether there is time pressure on users to complete the task, whether there are alternative methods of completion, and so on. The term "Single Word Error Rate" is sometimes referred to as the percentage of incorrect recognitions for each different word in the system vocabulary. == Edit distance == The word error rate may also be referred to as the length normalized edit distance. The normalized edit distance between X and Y, d( X, Y ) is defined as the minimum of W( P ) / L ( P ), where P is an editing path between X and Y, W ( P ) is the sum of the weights of the elementary edit operations of P, and L(P) is the number of these operations (length of P).
Image scaling
In computer graphics and digital imaging, image scaling is the resizing of a digital image. In video technology, the magnification of digital material is known as upscaling or resolution enhancement. When scaling a vector graphic image, the graphic primitives that make up the image can be rendered using geometric transformations at any resolution with no loss of image quality. When scaling a raster graphics image, a new image with a higher or lower number of pixels must be generated. In the case of decreasing the pixel number (scaling down), this usually results in a visible quality loss. From the standpoint of digital signal processing, the scaling of raster graphics is a two-dimensional example of sample-rate conversion, the conversion of a discrete signal from a sampling rate (in this case, the local sampling rate) to another. == Mathematical == Image scaling can be interpreted as a form of image resampling or image reconstruction from the view of the Nyquist sampling theorem. According to the theorem, downsampling to a smaller image from a higher-resolution original can only be carried out after applying a suitable 2D anti-aliasing filter to prevent aliasing artifacts. The image is reduced to the information that can be carried by the smaller image. In the case of up sampling, a reconstruction filter takes the place of the anti-aliasing filter. A more sophisticated approach to upscaling treats the problem as an inverse problem, solving the question of generating a plausible image that, when scaled down, would look like the input image. A variety of techniques have been applied for this, including optimization techniques with regularization terms and the use of machine learning from examples. == Algorithms == An image size can be changed in several ways. === Nearest-neighbor interpolation === One of the simpler ways of increasing image size is nearest-neighbor interpolation, replacing every pixel with the nearest pixel in the output; for upscaling, this means multiple pixels of the same color will be present. This can preserve sharp details but also introduce jaggedness in previously smooth images. 'Nearest' in nearest-neighbor does not have to be the mathematical nearest. One common implementation is to always round toward zero. Rounding this way produces fewer artifacts and is faster to calculate. This algorithm is often preferred for images which have little to no smooth edges. A common application of this can be found in pixel art. === Bilinear and bicubic interpolation === Bilinear interpolation works by interpolating pixel color values, introducing a continuous transition into the output even where the original material has discrete transitions. Although this is desirable for continuous-tone images, this algorithm reduces contrast (sharp edges) in a way that may be undesirable for line art. Bicubic interpolation yields substantially better results, with an increase in computational cost. === Sinc and Lanczos resampling === Sinc resampling, in theory, provides the best possible reconstruction for a perfectly bandlimited signal. In practice, the assumptions behind sinc resampling are not completely met by real-world digital images. Lanczos resampling, an approximation to the sinc method, yields better results. Bicubic interpolation can be regarded as a computationally efficient approximation to Lanczos resampling. === Box sampling === One weakness of bilinear, bicubic, and related algorithms is that they sample a specific number of pixels. When downscaling below a certain threshold, such as more than twice for all bi-sampling algorithms, the algorithms will sample non-adjacent pixels, which results in both losing data and rough results. The trivial solution to this issue is box sampling, which is to consider the target pixel a box on the original image and sample all pixels inside the box. This ensures that all input pixels contribute to the output. The major weakness of this algorithm is that it is hard to optimize. === Mipmap === Another solution to the downscale problem of bi-sampling scaling is mipmaps. A mipmap is a prescaled set of downscaled copies. When downscaling, the nearest larger mipmap is used as the origin to ensure no scaling below the useful threshold of bilinear scaling. This algorithm is fast and easy to optimize. It is standard in many frameworks, such as OpenGL. The cost is using more image memory, exactly one-third more in the standard implementation. === Fourier-transform methods === Simple interpolation based on the Fourier transform pads the frequency domain with zero components (a smooth window-based approach would reduce the ringing). Besides the good conservation (or recovery) of details, notable are the ringing and the circular bleeding of content from the left border to the right border (and the other way around). === Edge-directed interpolation === Edge-directed interpolation algorithms aim to preserve edges in the image after scaling, unlike other algorithms, which can introduce staircase artifacts. Examples of algorithms for this task include New Edge-Directed Interpolation (NEDI), Edge-Guided Image Interpolation (EGGI), Iterative Curvature-Based Interpolation (ICBI), and Directional Cubic Convolution Interpolation (DCCI). A 2013 analysis found that DCCI had the best scores in peak signal-to-noise ratio and structural similarity on a series of test images. === hqx === For magnifying computer graphics with low resolution and/or few colors (usually from 2 to 256 colors), better results can be achieved by hqx or other pixel-art scaling algorithms. These produce sharp edges and maintain a high level of detail. === Vectorization === Vector extraction, or vectorization, offers another approach. Vectorization first creates a resolution-independent vector representation of the graphic to be scaled. The resulting SVG vector file can then be exported and rendered at any required resolution without quality loss, serving directly as production-ready artwork for scalable display & printing. This technique is used by Adobe Illustrator, Live Trace, and Inkscape. Scalable Vector Graphics are well suited to simple geometric images, while photographs do not fare well with vectorization due to their complexity. === Deep convolutional neural networks === This method uses machine learning for more detailed images, such as photographs and complex artwork. Programs that use this method include waifu2x, Imglarger and Neural Enhance. Demonstration of conventional vs. waifu2x upscaling with noise reduction, using a detail of Phosphorus and Hesperus by Evelyn De Morgan. [Click image for full size] AI-driven upscaling software allows detail and sharpness to be added to historical photographs, where it is not present in the original. The availability of AI upscaling tools has led to confusion where a person believes that the upscaled version of a blurry image is genuinely showing them the subject of the original photograph. In 2025 a user of the social media site X posted an AI-upscaled version of a low resolution photo of Donald Trump that they had zoomed in on, and asked if anyone could "explain what the hell is happening to his forehead". Experts noted that the image had been distorted by the upscaling process, and that such tools "inevitably have to invent, or at least recreate, details that were or were not there". == Applications == === General === Image scaling is used in, among other applications, web browsers, image editors, image and file viewers, software magnifiers, digital zoom, the process of generating thumbnail images, and when outputting images through screens or printers. === Video === This application is the magnification of images for home theaters for HDTV-ready output devices from PAL-Resolution content, for example, from a DVD player. Upscaling is performed in real time, and the output signal is not saved. === Pixel-art scaling === As pixel-art graphics are usually low-resolution, they rely on careful placement of individual pixels, often with a limited palette of colors. This results in graphics that rely on stylized visual cues to define complex shapes with little resolution, down to individual pixels. This makes scaling pixel art a particularly difficult problem. Specialized algorithms were developed to handle pixel-art graphics, as the traditional scaling algorithms do not take perceptual cues into account. Since a typical application is to improve the appearance of fourth-generation and earlier video games on arcade and console emulators, many are designed to run in real time for small input images at 60 frames per second. On fast hardware, these algorithms are suitable for gaming and other real-time image processing. These algorithms provide sharp, crisp graphics, while minimizing blur. Scaling art algorithms have been implemented in a wide range of emulators such as HqMAME and DOSBox, as well as 2D game engines and game engine recreations such as ScummVM. They gained recognition with game
Mike Little
Mike Little (born 12 May 1962) is an English web developer and writer. He is the co-founder of the free and open source web publishing software WordPress. == Biography == Mike Little was born in Manchester, England in 1962 to a Nigerian father, who was a mathematics lecturer and musician, and an English mother who worked as a primary school teacher. Little was placed into foster care when he was four months of age, and was later adopted by the same family. He grew up on a council estate in Brinnington, Stockport, and was educated at Stockport School. In 2003, Little and Matt Mullenweg started working on a project in which they built on b2/cafelog and later named it WordPress, releasing the first version on 27 May 2003. Little states that, despite not being invited to join his co-founder's for-profit business Automattic, he and Mullenweg remain on good terms. He clarified: "I don’t want it to sound like he cheated me out of something or ripped me off in some way. He didn’t." In June 2013, Little was awarded the SAScon's "Outstanding Contribution to Digital" award for his part in co-founding and developing WordPress. Little has been described as "modest" and living in "virtual anonymity". He has one daughter. He identifies as a follower of Stoicism and a humanist, and in 2021, he became a patron of charity Humanists UK.
Military communications
Military communications or military signals involve all aspects of communications, or conveyance of information, by armed forces. Examples from Jane's Military Communications include text, audio, facsimile, tactical ground-based communications, naval signalling, terrestrial microwave, tropospheric scatter, satellite communications systems and equipment, surveillance and signal analysis, security, direction finding and jamming. The most urgent purposes are to communicate information to commanders and orders from them. Military communications span from pre-history to the present. The earliest military communications were delivered by runners. Later, communications progressed to visual signals. For example, Naval ships would use flag signaling to communicate from ship to ship. These flags are a uniform set of easily identifiable nautical codes that would convey visual messages and codes between ships and from ship to shore. Then militaries discovered methods to use audible signaling to communicate with each other. This way of communicating was possible because of telegraphs. They are an electronic device that is used by a sender and when the sender presses on the telegraph key, they interrupt the current creating an audible pulse that is heard at the receiving station. The receiver then decodes the pulses to decode the messages. Since then, military communication has evolved and advanced much further. Today, there are many perspectives used to examine how troops around the world communicate. Anthony King states how Military sociologists have attempted to explain how military institutions develop and maintain high levels of social cohesion. == History == In past centuries communicating a message usually required someone to go to the destination, bringing the message. Thus, the term communication often implied the ability to transport people and supplies. A place under siege was one that lost communication in both senses. The association between transport and messaging declined in recent centuries. The first military communications involved the use of runners or the sending and receiving of simple signals (sometimes encoded to be unrecognizable). The first distinctive uses of military communications were called semaphore. Modern units specializing in these tactics are usually designated as signal corps. The Roman system of military communication (cursus publicus or cursus vehicularis) is an early example of this. Later, the terms signals and signaller became words referring to a highly-distinct military occupation dealing with general communications methods (similar to those in civil use) rather than with weapons. Present-day military forces of an informational society conduct intense and complicated communicating activities on a daily basis, using modern telecommunications and computing methods. Only a small portion of these activities are directly related to combat actions. Modern concepts of network-centric warfare (NCW) rely on network-oriented methods of communications and control to make existing forces more effective. == Military communications equipment == Drums, horns, flags, and riders on horseback were some of the early methods the military used to send messages over distances. The advent of distinctive signals led to the formation of the signal corps, a group specialized in the tactics of military communications. The signal corps evolved into a distinctive occupation where the signaller became a highly technical job dealing with all available communications methods including civil ones. In the middle 20th century radio equipment came to dominate the field. Many modern pieces of military communications equipment are built to both encrypt and decode transmissions and survive rough treatment in hostile climates. They use different frequencies to send signals to other radio stations to communicate. Radios have played a major role in military communication. Since they are capable of sending radio waves to transmit voice signals over long distances. This can be helpful for communication on the battlefield since it is a good way to send messages undetected over long distances. Radios are also very reliable because even in harsh weather conditions they are still able to help communicate among the soldiers. Militaries still use radios and continue to improve the technology because of their durability and reliability for military communication. Spelling alphabets such as the NATO phonetic alphabet are used to aid radio communications by reducing ambiguity between letters. Military communications – or "comms" – are activities, equipment, techniques, and tactics used by the military in some of the most hostile areas of the earth and in challenging environments such as battlefields, on land (compare radio in a box), underwater and also in air. Military comms include command, control and communications and intelligence and were known as the C3I model before computers were fully integrated. The U.S. Army expanded the model to C4I when it recognized the vital role played by automated computer equipment to send and receive large, bulky amounts of data. In the modern world, most nations attempt to minimize the risk of war caused by miscommunication or inadequate communication. As a result, military communication is intense and complicated and often motivates the development of advanced technology for remote systems such as satellites. Satellites have been improving and are being used more and more for communication. They are being made to have higher transmission capacity to help with their communication abilities. The military is upgrading satellites to be immune to interference during combat operations. This advancement will establish stable, high-quality information highways for long distance communication. Aircraft are also beneficial for communication, both crewed and uncrewed, as well as computers. Computers and their varied applications have revolutionized military comms. Although military communication is designed for warfare, it also supports intelligence-gathering and communication between adversaries, and thus sometimes prevents war. The six categories of military comms are: alert measurement systems cryptography military radio systems command and control signal corps network-centric warfare The alert measurement systems are various states of alertness or readiness for the armed forces used around the world during a state of war, act of terrorism or a military attack against a state. They are known by different acronyms, such as DEFCON, or defense readiness condition, used by the U.S. Armed Forces. Cryptography is the study of methods of converting messages to a form unreadable except to one who knows how to decrypt them. This ancient military comms art gained new importance with the rise of radio systems whose signals traveled far and were easily intercepted. Cryptographic software is also widely used in civilian commerce. == Commercial refile == In United States military communications systems, commercial refile refers to sending a military message via a commercial communications network. The message may come from a military network, such as a tape relay network, a point-to-point telegraph network, a radio-telegraph network, or the Defense Switched Network. Commercial refiling of a message will usually require a reformatting of the message, particularly the heading.
WEA Manufacturing
WEA Manufacturing was the record, tape, and compact disc manufacturing arm of WEA International Inc. from 1978 to 2003, when it was sold and merged into Cinram International, a previous competitor. The last owner when the plant closed was Technicolor. == History == WEA Manufacturing Inc. was created in 1978–1979 when Warner Communications Inc. purchased two of its longtime suppliers: the record pressing plants Specialty Records Corporation (Olyphant, Pennsylvania) and Allied Record Company (Los Angeles). The company was headquartered in Olyphant, where the original plant was replaced in late 1981 by a new facility which retained the name Specialty Records Corporation. The Specialty Records Corporation name was dropped in 1996 in favor of WEA Manufacturing. The company invested in CD manufacturing in 1986, matching a $247,000 contribution by economic development corporation Ben Franklin Technology Partners to develop and implement new processes of manufacturing audio CDs and CD-ROMs. BFTP assembled a team of experts in physics, electrical engineering, and thin film technology from the University of Scranton and Lehigh University to carry out the research and development. The Olyphant plant and another plant in Alsdorf, Germany, were expanded to support CD pressing that year, with the Olyphant facility's production commencing first in September 1986. WEA Manufacturing grew to become one of the largest manufacturers of recorded media in the world. The company began manufacturing Laserdiscs in July 1991. The company's DVD division, Warner Advanced Media Operations (WAMO), helped design the high-density format used in DVDs, and manufactured some of the first DVDs in the late 1990s. The company was sold to Cinram International in October 2003 and no longer exists under the name WEA Manufacturing, but the Olyphant plant continued to operate under its new ownership. In 2005, the company was Lackawanna County's largest employer, with over 2,300 people working at the Olyphant plant. Cinram closed the former Allied plant in 2006, while Technicolor (which purchased Cinram's assets in 2015) closed the Olyphant plant in 2018. == Patents == WEA Manufacturing held U.S. patents related to compact disc manufacture: Print scanner, (1993). Interference of converging spherical waves with application to the design of light-readable information-recording media and systems for reading such media, (2004). Method of manufacturing a composite disc structure and apparatus for performing the method, (2005). Methods and apparatus for reducing the shrinkage of an optical disc's clamp area and the resulting optical disc, (2005). == Litigation == In 1990, WEA Manufacturing was sued by a Canadian firm, Optical Recording Co. (ORC), for alleged infringement of two 1971 patents related to glass mastering equipment which was used by Time Warner and WEA Manufacturing in the manufacture of approximately 450 million CDs. ORC contended that unlike five other major CD manufacturers in the U.S., Time Warner had refused to license the technology from ORC. In 1992, a jury assessed damages of 6 cents per disc, plus $4–5 million in interest.
Tandem Money
Tandem is one of the UK's original challenger banks. Tandem is a digital bank with a mobile app, and no branches. The acquisition of Harrods Bank in 2017 allowed the company to provide services using the former's banking licence. Tandem Bank Limited is authorised by the Prudential Regulation Authority and regulated by the Financial Conduct Authority. Tandem has offices across the UK in Blackpool, Cardiff, Durham and London, employing over 500 people. == History == The company was founded by Ricky Knox, Matt Cooper and Michael Kent in 2014. In December 2016, Tandem announced that it had secured a £35 million investment from The Sanpower Group, the Chinese company that also owned the department store House of Fraser; however, £29 million of this investment was later revoked by Sanpower over concerns that the Chinese Government would object to the investment following increased restrictions on outbound investment in China. This resulted in a delay in the launch of Tandem's savings products, which, at the time of the revocation, was expected imminently and, more importantly, meant that Tandem volunteered the return of their banking license but retained all other permissions. In April 2018, Tandem launched fixed-term savings accounts, offering one-, two- and three-year terms through its app. === Acquisitions === In August 2017, it was announced that Tandem would fully acquire Harrods Bank, founded in 1893, in a deal that would bring a near-£200m loan book, over £300m of deposits and nearly £80 million of capital. Prior to its sale to Tandem Money, Harrods Bank catered for high-net-worth (HNW) individuals and operated from the Harrods store in Knightsbridge, London. It offered a variety of personal and business current and savings accounts, mortgages, foreign currency and gold bullion trading services. On 7 August 2017, Tandem Money Limited announced a deal to acquire 100% of Harrods Bank Limited shares. The purchase deal closed successfully on 11 January 2018. In March 2018, Tandem agreed to acquire Pariti Technologies Limited, developers of the Pariti money management application. In August 2020 Tandem acquired green home improvement loan specialists Allium Lending Group. It was announced on 8 February 2021 that Tandem had agreed to purchase the mortgage book from private bank Bank and Clients, consisting of 300 B&C customers for an undisclosed amount. In January 2022 Tandem Bank acquired consumer lender Oplo, creating a combined business with £1.2 billion of total assets. In April 2023, it was announced that Tandem had acquired money-sharing app Loop Money. At the time of the purchase, one of Loop's founders – Paul Pester – was also chairman at Tandem. == Features == Tandem Bank offers customers savings, mortgages, personal and secured loans, green home improvement loans and motor finance. In November 2022, the bank launched its new Tandem Marketplace, providing information and resources to help promote greener living.
Paperless society
A paperless society is a society in which paper communication (written documents, email, letters, etc.) is replaced by electronic communication and storage. The concept was first introduced by Frederick Wilfrid Lancaster in 1978. Furthermore, libraries would no longer be needed to handle printed documents. "Librarians will, in time, become information specialists in a deinstitutionalized setting". Lancaster also stated that both computers and libraries will not always give us the information that other people and living life will. == Literature == Brodman, E. (1979). Review of Toward Paperless Information Systems. Bulletin of the Medical Library Association, 67(4), 437–439. Buckland, M. K. (1980). Review of Toward Paperless Information Systems. Journal of Academic Librarianship, 5(6), 349. Grosch, A. (1979). Review of Toward Paperless Information Systems. College & Research Libraries, 40(1), 88–89. Kohl, D. F. (2004). From the editor . . . The paperless society . . . Not quite yet. Journal of Academic Librarianship, 30(3), 177–178. Lancaster, F. W. (1978a). Toward paperless information systems. New York: Academic Press. Lancaster, F. W. (1980b). The future of the librarian lies outside of the library. Catholic Library World, 51, 388–391. Lancaster, F. W. (1982a). Libraries and librarians in an age of electronics. Arlington, VA: Information Resources Press. Lancaster, F. W. (1982b). The evolving paperless society and its implications for libraries. International Forum on Information and Documentation, 7(4), 3–10. Lancaster, F. W. (1983). Future librarianship: Preparing for an unconventional career. Wilson Library Bulletin, 57, 747–753. Lancaster, F. W. (1985). The paperless society revisited. American Libraries, 16, 553–555. Lancaster, F. W. (1993). Libraries and the future: Essays on the library in the twenty-first century. New York: Haworth Press. Lancaster, F. W. (1999). Second thoughts on the paperless society. Library Journal, 124(15), 48– 50. Lancaster, F. W., & Smith, L. C. (1980c). On-Line systems in the communication process: Projections. Journal of the American Society for Information Science, 31(3), 193–200. Miall, D. S. (2001). The library versus the Internet: Literary studies under siege? Proceedings of the Modern Language Association, 116(5), 1405–1414. Salton, G. (1979). Review of Toward Paperless Information Systems. Journal of Documentation, 35(3), 250–252. Sellen, A. J., & Harper, R. H. R. (2003). The myth of the paperless office. Cambridge, MA: MIT Press. Stevens, N. D. (2006). The fully electronic academic library. College & Research Libraries, 67(1),5–14. Young, Arthur P. (2008).Aftermath of a Prediction: F. W. Lancaster and the Paperless Society LIBRARY TRENDS, 56(4),(“The Evaluation and Transformation of Information Systems: Essays Honoring the Legacy of F. W. Lancaster,” edited by Lorraine J. Haricombe and Keith Russell), pp. 843–858.