約 5,256,375 件
https://w.atwiki.jp/tsuvoc/pages/1666.html
X-FILES えっくすふぁいるず （Jnk, 03）伊集院率いる、番組スタッフによる草野球チーム。名称の由来は米国のテレビドラマシリーズ「The X-FILES」。ユニフォームを作るにあたりスポンサーを探したところ、同ドラマの日本代理店が「チーム名をこれにするなら」という条件を出したことから。後に「花王ヘルシアX-FILES」に改名している。
https://w.atwiki.jp/220yearsafterlove/pages/58.html
http //20yearsafterlove.blog111.fc2.com/blog-entry-312.html
https://w.atwiki.jp/mydefrag_jp/pages/18.html
原文 http //www.mydefrag.com/Scripts-FileBoolean.html 更新日 2010/12/12 (ここで取り扱っている内容の原文をコピーした日付です) (...) Combine file booleans into a single boolean. Syntax ( FILEBOOLEAN ) Example FileSelect Size(10000000,0) and ( Name("-.zip") or Name("-.arj") ) FileActions ... FileEnd See also FileSelect FileBoolean FileActions All Select all the items (files, directories) that have not yet been placed in a previous zone. Syntax all Example FileSelect All FileActions ... FileEnd See also FileSelect FileBoolean FileActions Archive Select all the items that have the "archive" attribute set (yes) or not set (no). Applications use this attribute to mark files for backup or removal. Syntax Archive(yes) Archive(no) Example FileSelect # Select all the items that have the "archive" attribute. Archive(yes) FileActions .... FileEnd See also FileSelect FileBoolean FileActions AverageFragmentSize Select all the items that have an average number of bytes per fragment between the minimum (first number) and the maximum (second number). If the second number is zero then the maximum is infinity. For example, if an item is 300 bytes in size and has 3 fragments then it has an average fragment size of 100 bytes. Syntax AverageFragmentSize(NUMBER , NUMBER) Example FileSelect # Select all the items that have an average fragment size between 100 and 1000 bytes. AverageFragmentSize(100,1000) FileActions .... FileEnd See also FileSelect FileBoolean FileActions Compressed Select all the items that have the "compressed" attribute set (yes) or not set (no). For a file the attribute indicates if the file is compressed by the build-in Windows compression. For directories the attribute is the default for new files (directories by themselves cannot be compressed). Syntax Compressed(yes) Compressed(no) Example FileSelect # Select all the items that are compressed with the built-in Windows compression. Compressed(yes) FileActions .... FileEnd See also FileSelect FileBoolean FileActions CreationDate Select all the items that were created between the minimum time (first parameter) and the maximum time (second parameter). If the first parameter is empty then the minimum time is the beginning of time. If the second parameter is empty then the maximum time is infinity. - The creation date can be newer than the last-changed date, for example when a file was downloaded, or unpacked from an archive (such as zip or arj). Syntax CreationDate(DATETIME , DATETIME) Example FileSelect # Select all the items that were created less than 10 days ago. CreationDate(10 days ago,now) FileActions .... FileEnd See also FileSelect FileBoolean FileActions Directory Select all the directories (yes) or all the other files (no). Please note that this boolean does not select the files in a directory, but the directory itself. Directories and files are separate entities. Directories cannot be moved (defragmented, optimized) on FAT32 volumes. This is a known limitation of the Windows defragmentation API and not a bug in MyDefrag. Moving directories is slower than moving files of the same size, presumably because Windows has to update indexes and links in the MFT. Syntax Directory(yes) Directory(no) Example FileSelect # Select all the directories. Directory(yes) FileActions .... FileEnd See also FileSelect FileBoolean FileActions DirectoryName STRINGにマッチする名前を持つディレクトリをすべて選択し、そのディレクトリ以下にあるすべてのファイルとサブディレクトリを選択します。 STRINGにはワイルドカードとして"*"(0文字以上の任意の文字)と"?"(１文字の任意の文字)を選択できます STRINGにはスラッシュ・バックスラッシュ(および\マーク)を含めないでください。これはすべてのファイルのファイル名について比較しますが、このファイル名には(ファイルパスではないので)スラッシュなどが含まれていません。 この関数はすべてのハードリンク ファイル名を一つのアイテム(二つ名を持ち、同時に違う場所に存在するが、その実体は同じファイル)として扱います。ログファイルには最初に見つかった名前が載ります, so it may appear as if the function has selected some wrong items. この関数はソフトリンク(ジャンクション・シンボリックリンク）を追従しません。 Syntax DirectoryName(STRING) Example FileSelect # Select everything in the "Program Files" directory. DirectoryName("Program Files") FileActions .... FileEnd See also DirectoryPath FileName FullPath FileSelect FileBoolean FileActions DirectoryPath STRINGにマッチするフルパスを持つディレクトリをすべて選択します、そしてそのディレクトリ以下にあるすべてのファイルとサブディレクトリを選択します。STRINGにはワイルドカードとして"*"(0文字以上の任意の文字)と"?"(１文字の任意の文字)を選択できます。 この条件構文はDirectoryName()とよく似ていますが、これはディレクトリ名ではなくフルパスで比較するために多少遅いです。 STRINGはディレクトリのフルパスと比較され、マッチするでしょう。(The STRING is compared with and must match the full path of the directories.) ドライブレターにマッチするようなマスクを確認してください。ディレクトリパスというのは"c \windows\System32"といったような物のことです。バックスラッシュの追跡はしないことを覚えておいてください。(訳注 自信がないのでエロイ人お願いします。) この関数はすべてのハードリンク ファイル名を一つのアイテム(二つ名を持ち、同時に違う場所に存在するが、その実体は同じファイル)として扱います。ログファイルには最初に見つかった名前が載ります, so it may appear as if the function has selected some wrong items. この関数はソフトリンク(ジャンクション・シンボリックリンク）を追従しません。 Syntax DirectoryPath(STRING) Example FileSelect # Select everything in the "? \Program Files" directory. DirectoryPath("? \Program Files") FileActions .... FileEnd See also DirectoryName FileName FullPath FileSelect FileBoolean FileActions Encrypted Select all the items that have the "encrypted" attribute set (yes) or not set (no). For a file the attribute indicates if the file is encrypted by the build-in Windows encryption. For directories the attribute is the default for new files (directories by themselves cannot be encrypted). Syntax Encrypted(yes) Encrypted(no) Example FileSelect # Select all the items that have the "encrypted" attribute. Encrypted(yes) FileActions .... FileEnd See also FileSelect FileBoolean FileActions
https://w.atwiki.jp/api_programming/pages/145.html
http //developer.garmin.com/downloads/connect-iq/monkey-c/doc/Toybox/Attention.html Module Toybox AttentionDefined Under Namespace Constant Summary 関数（要約） 関数（詳細）(Object) backlight(onOff) バックライトを点灯／消灯する (Object) playTone(tone) ビープ音を鳴らす (Object) vibrate(vibe) Use the vibe motor Module Toybox Attention The Tone module allows for making pre-defined sounds. Not all devices support this API. Since 1.0.0 App Types Widget,App Defined Under Namespace Classes VibeProfile Constant Summary Supported Devices All except vivoactive TONE_KEY = 0 Indicates that a key was pressed. Since 1.0.0 TONE_START = 1 Indicates that an activity has started. Since 1.0.0 TONE_STOP = 2 Indicates that an acitivty has stopped. Since 1.0.0 TONE_MSG = 3 Indicates that a message is available. Since 1.0.0 TONE_ALERT_HI = 4 An alert ending with a high note. Since 1.0.0 TONE_ALERT_LO = 5 An alert ending with a low note. Since 1.0.0 TONE_LOUD_BEEP = 6 A loud beep. Since 1.0.0 TONE_INTERVAL_ALERT = 7 Indicates a change in interval. Since 1.0.0 TONE_ALARM = 8 Indicates an alarm has triggered. Since 1.0.0 TONE_RESET = 9 Indicates that the activity was reset. Since 1.0.0 TONE_LAP = 10 Indicates that the user has completed a lap. Since 1.0.0 TONE_CANARY = 11 An annoying sound to get the users attention. Since 1.0.0 TONE_TIME_ALERT = 12 An alert that a time threshold has been met. Since 1.0.0 TONE_DISTANCE_ALERT = 13 An alert that a distance threshold has been met. Since 1.0.0 TONE_FAILURE = 14 Indicates that the activity was a failure. Since 1.0.0 TONE_SUCCESS = 15 Indicates that the activity was a success. Since 1.0.0 TONE_POWER = 16 The power on tone. Since 1.0.0 TONE_LOW_BATTERY = 17 Indicates that the device has low battery power. Since 1.0.0 TONE_ERROR = 18 Indicates an error occurred. Since 1.0.0 関数（要約） (Object) backlight(onOff) バックライトを点灯／消灯する (Object) playTone(tone) ビープ音を鳴らす (Object) vibrate(vibe) Use the vibe motor. 関数（詳細） (Object) backlight(onOff) バックライトを点灯／消灯する ParametersonOff (Boolean) true to turn on backlight, false otherwise. Since 1.0.0 Supported Devices All devices (Object) playTone(tone) ビープ音を鳴らす Parameterstone TONE_XXX value to play Since 1.0.0 Supported Devices All except vivoactive (Object) vibrate(vibe) Use the vibe motor Parametersvibe (Array) Array of VibeProfile objects to play in sequence. Maximum of 8 supported. Since 1.0.0 Supported Devices All non-Edge devices
https://w.atwiki.jp/kobapan/pages/240.html
install node.js visit Node.js or wget http //nodejs.org/dist/v0.12.0/node-v0.12.0.tar.gz tar xf node-v0.12.0.tar.gz cd node-v0.12.0 ./configure make sudo make install update npm sudo npm install npm -g install Grunt CLI sudo npm install -g grunt-cli install Grunt bake cd path/to/your/project npm install grunt-bake --save-dev create Gruntfile.js in your project root module.exports = function(grunt) { // Project configuration. grunt.initConfig( { bake { your_target { files { // files to from, ... "index.html" "app/index.html", "mobile.html" "app/mobile.html" } }, }, }); // Load the plugin grunt.loadNpmTasks( "grunt-bake" ); // Default task(s). grunt.registerTask( default , [ bake ]);}; create app/index.html !--(bake includes/head.html title="おらホームページ")-- !--(bake includes/foot.html)-- create app/includes/head.html html head title {{title}} title /head body !--(bake contents.html)-- create app/includes/foot.html /body /html create app/includes/contents.html div id="container" hello /div run grunt $ grunt and this bake task will create index.html html head title おらホームページ title /head body div id="container" hello /div /body /html 参考 Getting started - Grunt The JavaScript Task Runner MathiasPaumgarten/grunt-bake grunt-rsync jedrichards/grunt-rsync npm install grunt-rsync module.exports = function(grunt) { // Project configuration. grunt.initConfig( { bake { // bake config }, rsync { options { exclude [ app , node_modules , README.txt , package.json , Gruntfile.js , .htaccess ], recursive true, syncDest false, // コピー先に存在しないファイルを削除しない }, dist { options { src "./", // コピー元ディレクトリ dest "~/www", // コピー先ディレクトリ host "username@host", // コピー先ホスト // private-key を使えるようにしておく ~/.ssh/id_rsa } }, } }); grunt.loadNpmTasks( grunt-rsync ); // Default task(s). grunt.registerTask( default , [ bake , rsync ]);};
https://w.atwiki.jp/220yearsafterlove/pages/51.html
http //20yearsafterlove.blog111.fc2.com/blog-entry-305.html
https://w.atwiki.jp/mrfrtech/pages/53.html
Market Scenario In its research report, Market Research Future (MRFR), asserts that the AI in Construction Market Research 2020 is slated to grow exponentially over the review period, securing a considerable market valuation of USD 2.01 billion, and a healthy 35% CAGR over the review period. Novel coronavirus has actually AI in Construction Market Research to open new avenues for those firms that are on the lookout for solutions that are reliable, efficiently managed, scalable, and are subscription-based, to remain more focused on the core business. The AI in Construction Market is bearing lesser impact of the COVID-19 outbreak compared to most other segments of the tech world. In a nutshell, COVID-19 impact on managed services has been fruitful, with the market growth enhanced than before. Given the prevalent lockdown situation, managed services vendors are now investing heavily in remote-centric worker solutions, which can make the market highly resilient in the coming years, even as the world is currently rushing to achieve a COVID-19 breakthrough. Request a Free Sample @ https //www.marketresearchfuture.com/sample_request/6035 Segmentation The AI in construction market is differentiated by component, technology, organization size, deployment, stage, and application. On the basis of stage, the market is segmented into construction stage, pre-construction, and post-construction. Based on the component, the AI in construction market is bifurcated as solutions and services. The solution segment is categorized as demand forecasting, virtual assistant, revenue estimation, design planning, predictive maintenance, and others. The service sub-segment comprised implementation services, training consulting, and other support services. In terms of technology, the market is segregated into machine learning deep learning, neural networks, and natural learning programming (NLP). Based on the deployment, the market is divided into on-cloud and on-premises. Based on the organization size, the market is bifurcated into large enterprises, and small medium enterprise (SMEs). On the basis of application, the market is categorized as, project management, schedule management, risk management, equipment management, building information management, and supply chain management. Competitive Outlook The major market players operating in the global market as identified by MRFR are Oracle Corporation (U.S), IBM Corporation (U.S.), SAP SE (Germany), Alice Technologies.(U.S.), Microsoft Corporation (U.S.), Autodesk (U.S.), Aurora Computer Services(U.K), eSUB (U.S.), Smartvid.io(U.S.),and Building System Planning (U.S.). Some other market players who are involved in AI construction market are Jaroop, Deepomatic, Lili.Ai, Predii, Assignar, Coins Global, Beyond Limits, Doxel Askporter, Bentley Systems, Plangrid, and Renoworks Software Regional Analysis The geographical overview of the global market has been analyzed in four major regions, comprising the Asia Pacific, North America, Europe, and the rest of the world. On the building industry, North America is believed to have substantial growth in the AI, with the U.S. and Canada being the sector leading countries. Regional domination is due to increased investment by companies such as IBM Corporation, Oracle Corporation and many others, which invest directly in the advancement of technologies such as neural networks and machine learning in research and development. However, Asia Pacific is also expected to experience a strong market growth rate. The leading countries in this field are China, Japan, South Korea and India. The market growth is due to rise in demand by the region to improve smart city projects which require better facilities that boost the real estate sector. Table of Contents 1Executive Summary 2Scope of the Report 2.1Market Definition 2.2Scope of the Study 2.2.1Research objectives 2.2.2Assumptions Limitations 2.3Markets Structure Continued…. Browse Full Report Details @ https //www.marketresearchfuture.com/reports/ai-in-construction-market-6035 List of Tables Table1 Global AI In Construction Market By Region, 2020-2027 Table2 North America AI In Construction Market By Country, 2020-2027 Table3 Europe AI In Construction Market By Country, 2020-2027 Continued… List of Figures FIGURE 1 Global AI In Construction Software Market Segmentation FIGURE 2 Forecast Methodology FIGURE 3 Porter’s Five Forces Analysis of Global AI In Construction Software Market Continued… Trending #MRFR Report** https //ictmrfr.blogspot.com/2022/04/geofencing-market-companies-growth-with.html https //blogfreely.net/pranali004/telecom-expense-management-market-size-impressive-cagr-changing-business-scope https //postheaven.net/pranali004/financial-app-industry-impressive-cagr-changing-business-needs-scope-of https //market-research-future.tribe.so/post/openstack-service-market-research-impressive-cagr-changing-scope-of-current--6263de46791566c10c79891e https //www.scutify.com/articles/2022-04-24-infrastructure-as-a-service-industry-cagr-changing-business-scope-of-current-and-future-industry- About Market Research Future Market Research Future (MRFR) has created a niche in the world of market research. It is counted among the top market research companies that offer well-researched and updated market research reports and insights to businesses of all sizes. What sets us apart is our super-responsive team that offers quality work keeping clients abridged of the prospective challenges and opportunities in various markets. Our team is adept in their space as well as patiently listens to every client. The best part is they know their work inside out and possess the expertise to guide the client in the right direction and achieve results on a tight deadline. We are a one-stop solution for all your data research needs. Our team does not believe in the “one size fits all” approach to creating a report that is detailed and concise. We handle 13 industry verticals including Healthcare, Chemicals and Materials, Information and Communications Technology, Semiconductor and Electronics, Energy and Power, Food, Beverages Nutrition, Automobile, Consumer and Retail, Aerospace and Defense, Industrial Automation and Equipment, Packaging Transport, Construction, and Agriculture. With our unique approach for every market report, we aim to reach the zenith in qualitative business intelligence and syndicated market research. Contact Market Research Future (Part of Wantstats Research and Media Private Limited) 99 Hudson Street, 5Th Floor New York, NY 10013 United States of America 1 628 258 0071 (US) 44 2035 002 764 (UK) Email sales@marketresearchfuture.com Website https //www.marketresearchfuture.com
https://w.atwiki.jp/mrfrtech/pages/125.html
Market Scenario In its research report, Market Research Future (MRFR), asserts that the AI in Construction Market Research 2020 is slated to grow exponentially over the review period, securing a considerable market valuation of USD 2.01 billion, and a healthy 35% CAGR over the review period. Novel coronavirus has actually AI in Construction Market Research to open new avenues for those firms that are on the lookout for solutions that are reliable, efficiently managed, scalable, and are subscription-based, to remain more focused on the core business. The AI in Construction Market is bearing lesser impact of the COVID-19 outbreak compared to most other segments of the tech world. In a nutshell, COVID-19 impact on managed services has been fruitful, with the market growth enhanced than before. Given the prevalent lockdown situation, managed services vendors are now investing heavily in remote-centric worker solutions, which can make the market highly resilient in the coming years, even as the world is currently rushing to achieve a COVID-19 breakthrough. Request a Free Sample @ https //www.marketresearchfuture.com/sample_request/6035 Segmentation The AI in construction market is differentiated by component, technology, organization size, deployment, stage, and application. On the basis of stage, the market is segmented into construction stage, pre-construction, and post-construction. Based on the component, the AI in construction market is bifurcated as solutions and services. The solution segment is categorized as demand forecasting, virtual assistant, revenue estimation, design planning, predictive maintenance, and others. The service sub-segment comprised implementation services, training consulting, and other support services. In terms of technology, the market is segregated into machine learning deep learning, neural networks, and natural learning programming (NLP). Based on the deployment, the market is divided into on-cloud and on-premises. Based on the organization size, the market is bifurcated into large enterprises, and small medium enterprise (SMEs). On the basis of application, the market is categorized as, project management, schedule management, risk management, equipment management, building information management, and supply chain management. Competitive Outlook The major market players operating in the global market as identified by MRFR are Oracle Corporation (U.S), IBM Corporation (U.S.), SAP SE (Germany), Alice Technologies.(U.S.), Microsoft Corporation (U.S.), Autodesk (U.S.), Aurora Computer Services(U.K), eSUB (U.S.), Smartvid.io(U.S.),and Building System Planning (U.S.). Some other market players who are involved in AI construction market are Jaroop, Deepomatic, Lili.Ai, Predii, Assignar, Coins Global, Beyond Limits, Doxel Askporter, Bentley Systems, Plangrid, and Renoworks Software Regional Analysis The geographical overview of the global market has been analyzed in four major regions, comprising the Asia Pacific, North America, Europe, and the rest of the world. On the building industry, North America is believed to have substantial growth in the AI, with the U.S. and Canada being the sector leading countries. Regional domination is due to increased investment by companies such as IBM Corporation, Oracle Corporation and many others, which invest directly in the advancement of technologies such as neural networks and machine learning in research and development. However, Asia Pacific is also expected to experience a strong market growth rate. The leading countries in this field are China, Japan, South Korea and India. The market growth is due to rise in demand by the region to improve smart city projects which require better facilities that boost the real estate sector. Table of Contents 1Executive Summary 2Scope of the Report 2.1Market Definition 2.2Scope of the Study 2.2.1Research objectives 2.2.2Assumptions Limitations 2.3Markets Structure Continued…. Browse Full Report Details @ https //www.marketresearchfuture.com/reports/ai-in-construction-market-6035 List of Tables Table1 Global AI In Construction Market By Region, 2020-2027 Table2 North America AI In Construction Market By Country, 2020-2027 Table3 Europe AI In Construction Market By Country, 2020-2027 Continued… List of Figures FIGURE 1 Global AI In Construction Software Market Segmentation FIGURE 2 Forecast Methodology FIGURE 3 Porter’s Five Forces Analysis of Global AI In Construction Software Market Continued… Trending #MRFR Report** https //ictmrfr.blogspot.com/2022/04/geofencing-market-companies-growth-with.html https //blogfreely.net/pranali004/telecom-expense-management-market-size-impressive-cagr-changing-business-scope https //postheaven.net/pranali004/financial-app-industry-impressive-cagr-changing-business-needs-scope-of https //market-research-future.tribe.so/post/openstack-service-market-research-impressive-cagr-changing-scope-of-current--6263de46791566c10c79891e https //www.scutify.com/articles/2022-04-24-infrastructure-as-a-service-industry-cagr-changing-business-scope-of-current-and-future-industry- About Market Research Future Market Research Future (MRFR) has created a niche in the world of market research. It is counted among the top market research companies that offer well-researched and updated market research reports and insights to businesses of all sizes. What sets us apart is our super-responsive team that offers quality work keeping clients abridged of the prospective challenges and opportunities in various markets. Our team is adept in their space as well as patiently listens to every client. The best part is they know their work inside out and possess the expertise to guide the client in the right direction and achieve results on a tight deadline. We are a one-stop solution for all your data research needs. Our team does not believe in the “one size fits all” approach to creating a report that is detailed and concise. We handle 13 industry verticals including Healthcare, Chemicals and Materials, Information and Communications Technology, Semiconductor and Electronics, Energy and Power, Food, Beverages Nutrition, Automobile, Consumer and Retail, Aerospace and Defense, Industrial Automation and Equipment, Packaging Transport, Construction, and Agriculture. With our unique approach for every market report, we aim to reach the zenith in qualitative business intelligence and syndicated market research. Contact Market Research Future (Part of Wantstats Research and Media Private Limited) 99 Hudson Street, 5Th Floor New York, NY 10013 United States of America 1 628 258 0071 (US) 44 2035 002 764 (UK) Email sales@marketresearchfuture.com Website https //www.marketresearchfuture.com
https://w.atwiki.jp/reshia/pages/12.html
HTMLの基本 基本はタグ。文章に記をつけていくところから。 はじめの一歩 文章に「しるし」をつけていくことからはじめる。 次のような文章があった場合 私のブログは、２００５年１月から始まりました。 もし「私のブログ」の部分をクリックしたときに 自分のブログに飛べるようにするには 次のように文章に「しるし」をつける。 a href="http //whoinside.blog3.fc2.com/" 私のブログ /a は、２００５年１月から始まりました。 この文章をウェブブラウザで見ると、次のようになる。 私のブログは、２００５年１月から始まりました。 さらに「２００５年１月」の部分に色を赤色にしたい場合は、つぎのようにする。 a href="http //whoinside.blog3.fc2.com/" 私のブログ /a は、 span style="color red" ２００５年１月 /span から始まりました。 タグ このように文章につける「しるし」のことを「タグ」と呼ぶ。 タグは、次のような名称を持っている。 基本は 要素名 要素の内容 /要素名 である。 要素名には、そのタグの種類を書く。 たとえば、リンクを貼りたいなら「a」、画像を貼り付けたいなら「img」 文字を装飾したいときなどは「span」となる。 また、「a」などのように、タグの種類を「要素名」として書くだけでは 機能として不十分なものがある（「a」はリンク先を示す必要がある）。 そんなときは、次のように「属性」を指定する。 要素名 属性名="属性値" 要素の内容 /要素名 また、属性に関しては「属性値」だけを持つものもある。 要素名 属性値 要素の内容 /要素名
https://w.atwiki.jp/220yearsafterlove/pages/46.html
ていうかそろそろ俺もネタ抜きに本気で彼女欲しいから『出会い系サイト』使うわ。そういや俺昔『出会い系サイトのサクラ』やってたなあー・・・。 http //20yearsafterlove.blog111.fc2.com/blog-entry-299.html