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Simil is case sensitive. If you want to ignore case, convert both strings to uppercase or lowercase before calling Simil. At its core, Simil is a longest common substring or LCS algorithm, and its performance can be expected to be on par with that class of algorithms. Anecdotally, we know that using Simil to test a candidate company name against 20,000 company names takes less than a second. Simil has good performance and is easy to understand. It also has several weaknesses, including the following: The result value is abstract. Therefore it ll take some trial and error to find a good threshold value above which you d consider two strings similar enough to take action. For data such as company names, I recommend a starting Simil value of about 0.75. For the organic chemistry names, we found that 0.9 gave us better results.

This is a mystery value that is hard to generate What you might expect it to mean that there s no selection because the selection is collapsed to an insertion point actually has another name No object is selected, and the selection is collapsed to an insertion point This is a block selection part of one or more paragraphs is selected This is the type of selection you get by OPTION-dragging through part of several paragraphs (for example, to remove leading spaces from several one-line paragraphs) This is a normal selection for example, a word, a paragraph, or several paragraphs Part or all of one or more columns in a table is selected One or more full rows in a table is selected A frame is selected A floating shape or text box is selected An inline shape is selected

It s insensitive for very small strings. For example, Adams and Ramos have three out of five characters in common, so the Simil value is 0.6. Most people wouldn t call those names similar. It treats every letter the same, without regard for vowels or consonants, or for letters that often occur together, or for the location in the string, or any other criteria. Some other algorithms do; for example, in the English language the letters Q and U nearly always occur together and in that order, so much so that they could almost be considered a single letter. In a more comprehensive algorithm, such occurrences could be given special consideration. SOUNDEX is another algorithm that does take into account that some consonants are almost the same (for example, d and t). Simil can t be precalculated, always requires a table scan, and can t take advantage of indexes. This may be a problem for large datasets.

Avalanche Effect Sometimes, a diode conducts when it is reverse-biased The greater the reverse-bias voltage, the more like an electrical insulator a P-N junction gets up to a point But if the reverse bias rises past a specific critical value, the voltage overcomes the ability of the junction to prevent the flow of current, and the junction conducts as if it were forward-biased This phenomenon is called the avalanche effect because conduction occurs in a sudden and massive way, something like a snow avalanche on a mountainside The avalanche effect does not damage a P-N junction (unless the voltage is extreme) It s a temporary thing When the voltage drops back below the critical value, the junction behaves normally again Some components are designed to take advantage of the avalanche effect In other cases, the avalanche effect limits the performance of a circuit In a device designed for voltage regulation, called a Zener diode, you ll hear about the avalanche voltage or Zener voltage specification This can range from a couple of volts to well over 100 V Zener diodes are often used in voltage-regulating circuits For rectifier diodes in power supplies, you ll hear or read about the peak inverse voltage (PIV) or peak reverse voltage (PRV) specification It s important that rectifier diodes have PIV ratings great enough so that the avalanche effect will not occur (or even come close to happening) during any part of the ac cycle

selection normal selection column selection row selection frame selection shape selection inline shape