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Cons: Some parts of Gnome 2 were not migrated to MATE yet and a few aspects such as Bluetooth support might not work as well as they did with Gnome 2. Cinnamon 1. 4. Cinnamon was born for the same reasons as MATE. Gnome 2 was one of the core components of Linux Mint, and Gnome 3 lacked the features and design of a suitable alternative. Linux Mint started by developing the extra features it needed on top of Gnome 3 and eventually started to develop its own environment called Cinnamon. Both Linux Mint and the MATE project came up with really good alternatives to Gnome, but in completely different ways. Cinnamon is built on Clutter and Gnome 3. It's a modern- looking environment with a traditional and familiar desktop. It features compositing, animations and some unique innovations but it relies on 3. D acceleration, lacks some of the features found in Gnome 2 and isn't yet compatible with every graphics card available on the market. 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How to Replace Wheel Bearings on Cars by Contributing Writer; Updated June 1. A growling or humming noise when driving down the road is a sign of potential trouble. If you swerve the steering wheel and the noise changes, the culprit is probably a wheel bearing. Usually, this only occurs on one side. Determine which side has failed by lifting the front of the vehicle and spinning the wheels by hand. Often, you can hear the noise in the side that has failed. Mustang Install Videos provide installation advice and product overviews for 1994-2004 Ford Mustangs. Under The Hood: How to Replace Wheel Bearings on a Dodge Caravan; How To Replace Wheel Bearings On Dodge Vans; How to Replace the Wheel Bearings on a 2000 Sunfire. Mustangs For Sale *** NOTE - the Alberta Mustangs Auto Club is not responsible for the accuracy of any ad placed on the website. 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When finished lifting, place a safety stand in a position where it will support the vehicle if the jack fails. Use a wheel nut wrench to remove the five wheel nuts. Remove the tire and wheel assembly from the vehicle. You may want to enlist a helper to hold the brake pedal to make the removal process easier. Remove the cotter pin from the end of the axle shaft. A cotter pin is a thin piece of metal that looks like a sewing pin, with a rounded piece at the top. The bottom part is split and bent both ways to prevent it from coming out. Remove the nut lock and spring washer as well. While a helper holds the brake pedal, remove the axle shaft nut on the end of the axle shaft. Remove the brake caliper mounting bolts on the back side of the steering knuckle. Gently remove the brake caliper from the rotor, and support its weight with a coat hanger or bungee cord. If the brake rotor has never been off the vehicle, look for a small clip on one of the wheel studs. Remove this clip with pliers, and discard it. You will not need to re- use this- -it is for assembly- line purposes. Slide the brake rotor off the hub and bearing. If the rotor is stuck, apply penetrating lubricant to the area where the brake rotor meets the hub and bearing. Let it soak for 3. If it is still stuck, tap it gently with a hammer around the area where you sprayed penetrating lubricant. If the rotor still will not come off, you may tap the back of the rotor gently with a hammer to remove it. Push in on the axle shaft end, to disengage it from the splines that are part of the hub and bearing assembly. On the back of the steering knuckle, there are four hub and bearing mounting bolts. Using a socket wrench, remove these four bolts. Be careful not to come in contact with the tone wheel on the axle. The tone wheel is a round piece on the axle shaft with teeth cast in it. Remove the hub and bearing assembly from the steering knuckle. Installation. Clean the mounting surface on the steering knuckle carefully. Install the hub and bearing onto the splines of the axle shaft. Move the hub and bearing into the steering knuckle, until it is squarely seated on the knuckle. Install the four hub and bearing mounting bolts from the back of the steering knuckle. Tighten these bolts in a criss- cross pattern, to 4. Install the brake caliper and adapter assembly onto the brake rotor, pushing it down until the two bolt holes in the caliper align with the holes in the steering knuckle. Install the two bolts from the inside of the steering knuckle. Tighten these two bolts to 1. Use a helper to hold the brake pedal. Tighten the axle nut to 1. Install the spring washer and nut lock onto the axle nut. Install a new cotter pin through the hole in the end of the axle shaft, and split the cotter pin at the end, bending the ends apart, to secure the axle nut. Install tire and wheel assembly. Tighten the wheel nuts to 1. Remove the safety stand, and lower the vehicle. Items you will need. Floor jack. Jack stands. Wheel nut wrench. Basic set of hand tools. New hub and bearing assembly. Helper. Coat hanger or bungee cord. Torque wrench. How To Replace Wheel Bearings On Dodge Vans. Jack the van up leaving clearance between the ground and the bottom of the tire. Remove the front wheel. Locate and remove the two bolts holding the caliper in place. They will be in back of the caliper. Remove the dust cap in the center of the spindle. This can be done by prying it out with a screwdriver. Pry the cotter pin with a pair of pliers. You may need to straighten the ends of the pin out. Loosen the nut and unscrew it off the spindle. The washer will come out behind it. Pull the rotor off the spindle. You may need to tap it with a hammer if it does not come out. Empty the old bearings and oil seal from the inside of the rotor if they have not fallen out during removal. The wheel seal will pry out with a flathead screwdriver. Replacing the bearings on a Dodge Van. Pack the bearings in white lithium grease. Place the bearings in the tub of grease to ensure grease gets properly circulated in the bearings. Clean the spindle off with a clean rag. Clean the rotor out of any debris if your using the old one. Pull the larger bearing out of the tub of grease. Place the bearing and the race in the back end of the rotor. Place the oil seal on the back end of the rotor. You can tap it in with a hammer. Make sure it is flush with the rotor. Fill the remaining space with grease to make sure the bearing is properly lubricated. This will ensure the bearing is properly packed as well. Slide the rotor on the assembly. It will slide to the back of the spindle. If the rotor does not go all the way you will need to pull the rotor off to inspect for blockage. Place the smaller bearing and race in the spindle on the front end. The bearing and race will slide to the back of the front half of the rotor. Pack the remaining portion of space with grease to ensure the bearing is packed. This will keep the bearing lubricated. Slide the washer on the spindle. Follow it with the lock nut. Tighten the nut until it is snug. When the nut is snug, turn the nut back half a turn. Slip the cotter pin into the hole on the lock nut. Separate the bottom of the cotter pin to ensure it does not slip out. Mount the wheel back on the van. Repeat steps on the other front wheel. Items you will need. New bearings. New races. Flathead screwdriver. Socket set. Ratchet. White lithium grease. Clean rags. Jack. Chock. How to Replace the Wheel Bearings on a 2. Sunfire. Place wheel chocks behind both rear tires. Remove the wheel covers or center caps from both front wheels. Loosen but do not remove the axle nuts on both CV axles with a 1/2- inch drive breaker bar and an M2. Loosen the front lug nuts with a tire iron. Raise the front of the Sunfire with a jack. Place jack stands beneath the front subframe members on each side of the engine. Lower the car onto the jack stands. Remove the front lug nuts, then remove the both front wheels from the Sunfire. Remove the caliper mounting bolts with a 3/8- inch- drive ratchet and a hex head socket. Remove the caliper from the brake assembly, using a small pry bar if needed. Hang the front caliper on the front strut coil spring, using a metal clothes hanger or metal hook. Remove the front rotor and place the rotor out of your work area. Rotate the axle nut until the outer lip of the nut is flush with the end of the axle. Strike the end of the axle on the nut with a rubber mallet, until the axle pops loose from inside the hub assembly. Do not use a metal or ball peen hammer to strike the axle nut. Push the axle inward with the palm of your hand, to see if it is moving inside the hub assembly. Insert a T5. 5 Torx socket onto the end of a 3/8- inch- drive extension. Insert the extension through the holes in the hub face to access the hub bearing mounting bolts. Line the Torx bit up with the mounting bolt, then gently tap the outer end of the extension to make sure the bit is properly seated in the head of the bolt. Install a ratchet onto the end of the extension and remove the mounting bolt counterclockwise. Repmove all three mounting bolts. Remove the hub assembly from the front of the steering knuckle, using a 3- to 5- pound mini- sledge hammer from behind. Hammer the hub bearing from several different angles to separate it from the steering knuckle. Pull the hub off by hand when it is free of the knuckle. Thoroughly clean the rust from the inside flange of the steering knuckle and the face of the steering knuckle that the hub assembly mounts on. Use a wire brush or 8. Add anti- seize to the face of the knuckle, iwhere the hub is going to be mounted. Add anti- seize to the inside of the knuckle flange that the hub bearing goes through. Coat the inner splines of your new hub bearing with anti- seize to ease the process of inserting the axle through the new hub. Coat the outer splines of the axle with anti- seize as well. Insert the new hub bearing assembly onto the Sunfire, and hold it in place with one hand. Push the CV axle gently through the new hub bearing from the behind the steering knuckle. Install the axle nut washer and the axle nut with your hand. Add a 1/2- inch- drive to 3/8- inch- drive adapter onto your 1/2- inch- drive torque wrench. Install the T5. 5 Torx and extension onto one of the mounting bolts. Tighten the mounting bolts to between 6. Install the brake rotor onto the new hub assembly. Turn a single lug nut onto one of the lugs, and spin it until it presses against the face of the rotor. This will hold the rotor during your brake installation. Wrap a towel around the end of your small pry bar. Insert the pry bar end between the two brake pads on the front caliper. Gently pry the inboard brake pad inward to compress the caliper piston slightly. Install the caliper over the rotor and tighten the mounting bolts to 4. Repeat steps 3 through 1. Sunfire, if applicable. Install the front wheels and snug the lug nuts with a tire iron, in a star pattern. Distributed Computing - Active Projectsclimateprediction. Earth's climate 5. It outlines the challenges of running a large- scale. CPDN with other. volunteer computing projects, and shows how using BOINC has really helped. ![]()
![]() Online Courses, Classes, Training, Tutorials. I've been out of college for about two years, Lynda. Taking the foundational courses in marketing has helped me launch my career.–Jenny, Associate Marketing. From your Windows 10 desktop, hit Start, then Settings (the gear in the bottom left of your task bar). From there, hit System > Notifications & Actions to change. Simple data mining examples and datasets. Data Mining: Know it All. This excerpt from Data Mining: Know It All includes examples that show how data mining algorithms and datasets.. By submitting your personal information, you agree that Tech. Target and its partners may contact you regarding relevant content, products and special offers. Learn how companies can make data- related decisions based on set rules. Simple Examples: The Weather Problem and Others. We use a lot of examples in this book, which seems particularly appropriate considering that the book is all about learning from examples! There are several standard datasets that we will come back to repeatedly. Different datasets tend to expose new issues and challenges, and it is interesting and instructive to have in mind a variety of problems when considering learning methods. In fact, the need to work with different datasets is so important that a corpus containing around 1. Whatever you do with a web browser, iMacros can automate it from web automation/web scripting, to data extraction, to web testing, and much, much more. Use the Data Mining Model Designer in SQL Server Data Tools (SSDT) and the data mining add-ins for Excel 2013 to mine data in SQL Server. ![]() The illustrations used here are all unrealistically simple. Serious application of data mining involves thousands, hundreds of thousands, or even millions of individual cases. But when explaining what algorithms do and how they work, we need simple examples that capture the essence of the problem but are small enough to be comprehensible in every detail. This definition explains the meaning of data mining and how enterprises can use it to sort through information to make better business decisions.The illustrations we will be working with are intended to be . Some actual fielded applications of learning techniques are discussed in Section 1. Further Reading section at the end of the chapter. Another problem with actual real- life datasets is that they are often proprietary. No corporation is going to share its customer and product choice database with you so that you can understand the details of its data mining application and how it works. Corporate data is a valuable asset, one whose value has increased enormously with the development of data mining techniques such as those described in this book.
Yet we are concerned here with understanding how the methods used for data mining work and understanding the details of these methods so that we can trace their operation on actual data. That is why our illustrations are simple ones. But they are not simplistic: they exhibit the features of real datasets. An Overview of Data Mining Techniques. Excerpted from the book Building Data Mining Applications for CRM by Alex Berson, Stephen Smith, and Kurt Thearling. Multivariate Data Analysis are powerful statistical techniques for analyzing data with many variables simultaneously to identify patterns & relationships. The Weather Problem. The weather problem is a tiny dataset that we will use repeatedly to illustrate machine learning methods. Entirely fictitious, it supposedly concerns the conditions that are suitable for playing some unspecified game. In general, instances in a dataset are characterized by the values of features, or attributes, that measure different aspects of the instance. In this case there are four attributes: outlook, temperature, humidity, and windy. The outcome is whether or not to play. In its simplest form, shown in Table 1. Outlook can be sunny, overcast, or rainy; temperature can be hot, mild, or cool; humidity can be high or normal; and windy can be true or false. This creates 3. 6 possible combinations (3 X . Interpreted as a decision list, the rules correctly classify all of the examples in the table, whereas taken individually, out of context, some of the rules are incorrect. For example, the rule if humidity = normal, then play = . The meaning of a set of rules depends on how it is interpreted - - not surprisingly! In the slightly more complex form shown in Table 1. This means that any learning method must create inequalities involving these attributes rather than simple equality tests, as in the former case. This is called a numeric- attribute problem - - in this case, a mixed- attribute problem because not all attributes are numeric. Now the first rule given earlier might take the following form: If outlook = . It is equally possible to disregard the classification and just look for any rules that strongly associate different attribute values. These are called association rules. Many association rules can be derived from the weather data in Table 1. Some good ones are as follows: If temperature = coolthen humidity = normal. If humidity = normal and windy = falsethen play = yes. If outlook = sunny and play = nothen humidity = high. If windy = false and play = nothen outlook = sunnyand humidity = high. Table 1. 3 Weather Data with Some Numeric Attribute. Outlook. Temperature. Humidity. Windy. Play. Sunny. 85. 85. False. No. Sunny. 80. 90. True. No. Overcast. False. Yes. Rainy. False. Yes. Rainy. False. Yes. Rainy. True. No. Overcast. True. Yes. Sunny. False. No. Sunny. False. Yes. Rainy. False. Yes. Sunny. True. Yes. Overcast. True. Yes. Overcast. False. Yes. Rainy. True. No. All these rules are 1. The first two apply to four examples in the dataset, the third to three examples, and the fourth to two examples. There are many other rules: in fact, nearly 6. If you look for rules that are less than 1. There are so many because unlike classification rules, association rules can . For example, the fourth rule predicts both that outlook will be sunny and that humidity will be high. Contact Lenses: An Idealized Problem. The contact lens data introduced earlier tells you the kind of contact lens to prescribe, given certain information about a patient. Note that this example is intended for illustration only: it grossly oversimplifies the problem and should certainly not be used for diagnostic purposes! The first column of Table 1. In case you're wondering, presbyopia is a form of longsightedness that accompanies the onset of middle age. The second gives the spectacle prescription: myope means shortsighted and hypermetrope means longsighted. The third shows whether the patient is astigmatic, and the fourth relates to the rate of tear production, which is important in this context because tears lubricate contact lenses. The final column shows which kind of lenses to prescribe: hard, soft, or none. All possible combinations of the attribute values are represented in the table. A sample set of rules learned from this information is shown in Figure 1. This is a large set of rules, but they do correctly classify all the examples. These rules are complete and deterministic: they give a unique prescription for every conceivable example. Generally, this is not the case. Sometimes there are situations in which no rule applies; other times more than one rule may apply, resulting in conflicting recommendations. Sometimes probabilities or weights may be associated with the rules themselves to indicate that some are more important, or more reliable, than others. You might be wondering whether there is a smaller rule set that performs as well. If so, would you be better off using the smaller rule set and, if so, why? These are exactly the kinds of questions that will occupy us in this book. Because the examples form a complete set for the problem space, the rules do no more than summarize all the information that is given, expressing it in a different and more concise way. Even though it involves no generalization, this is often a useful thing to do! People frequently use machine learning techniques to gain insight into the structure of their data rather than to make predictions for new cases. In fact, a prominent and successful line of research in machine learning began as an attempt to compress a huge database of possible chess endgames and their outcomes into a data structure of reasonable size. The data structure chosen for this enterprise was not a set of rules, but a decision tree. Figure 1. 1. Rules for the contact lenses data. If tear production rate = reduced then recommendation = none. If age = young and astigmatic = no andtear production rate = normal then recommendation = soft. If age = pre- presbyopic and astigmatic = no andtear production rate = normal then recommendation = soft. If age = presbyopic and spectacle prescription = myope andastigmatic = no then recommendation = none. If spectacle prescription = hypermetrope and astigmatic = no andtear production rate = normal then recommendation = soft. If spectacle prescription = myope and astigmatic = yes andtear production rate = normal then recommendation = hard. If age = young and astigmatic = yes andtear production rate = normal then recommendation = hard. If age = pre- presbyopic andspectacle prescription = hypermetrope and astigmatic = yesthen recommendation = none. If age = presbyopic and spectacle prescription = hypermetropeand astigmatic = yes then recommendation = none. Figure 1. 2. Decision tree for the contact lenses data. Figure 1. 2 presents a structural description for the contact lens data in the form of a decision tree, which for many purposes is a more concise and perspicuous representation of the rules and has the advantage that it can be visualized more easily. If tear production rate is reduced (the left branch), the outcome is none. If it is normal (the right branch), a second test is made, this time on astigmatism. Eventually, whatever the outcome of the tests, a leaf of the tree is reached that dictates the contact lens recommendation for that case. Irises: A Classic Numeric Dataset. The iris dataset, which dates back to seminal work by the eminent statistician R. Fisher in the mid- 1. Iris setosa, Iris versicolor, and Iris virginica. It is excerpted in Table 1. There are four attributes: sepal length, sepal width, petal length, and petal width (all measured in centimeters). Unlike previous datasets, all attributes have numeric values. Table 1. 4 The Iris Data Sepal Length (cm)Sepal Width (cm)Petal Length (cm)Petal Width (cm)Type. Iris setosa. 24. 9. Iris setosa. 34. 7. Iris setosa. 44. 6. Iris setosa. 55. 0. Iris setosa. Table 1. It concerns the relative performance of computer processing power on the basis of a number of relevant attributes; each row represents 1 of 2. The classic way of dealing with continuous prediction is to write the outcome as a linear sum of the attribute values with appropriate weights, for example. PRP - 5. 5. 9 + 0. MYCT + 0. 0. 15. 3 MMIN + 0. MMAX + 0. 6. 41. 0 CACH. CHMIN + 1. 4. 80 CHMAX. Table 1. 5 The CPU Performance Data Cycle Time (ns) MYCTMain Memory (KB)Cache (KB) CACHChannels. Download a fast & free security scan for your PC to check for computer viruses & online security threats with real-time results from recognized experts. Windows Vista - Wikipedia. Windows Vista (codenamed Longhorn. Development was completed on 8 November 2. On 3. 0 January 2. It was succeeded by Windows 7, which was released to manufacturing on 2. July 2. 00. 9 and released worldwide for retail on 2. October 2. 00. 9. New features of Windows Vista include an updated graphical user interface and visual style dubbed Aero, a new search component called Windows Search, redesigned networking, audio, print and display sub- systems, and new multimedia tools such as Windows DVD Maker. Vista aimed to increase the level of communication between machines on a home network, using peer- to- peer technology to simplify sharing files and media between computers and devices. Windows Vista included version 3. NET Framework, allowing software developers to write applications without traditional Windows APIs. Don’t let your system get infected. Detect and remove viruses, worms, spyware, and other malicious threats for free. Start your free scan – it’s easy. ![]() ![]() Microsoft's primary stated objective with Windows Vista was to improve the state of security in the Windows operating system. In light of this, Microsoft chairman Bill Gates announced in early 2. Criticism of Windows Vista has targeted its high system requirements, its more restrictive licensing terms, the inclusion of a number of, then, new DRM technologies aimed at restricting the copying of protected digital media, lack of compatibility with some pre- Vista hardware and software, longer boot time, and the number of authorization prompts for User Account Control. As a result of these and other issues, Windows Vista had seen initial adoption and satisfaction rates lower than Windows XP. It was originally expected to ship sometime late in 2. Windows XP and Blackcomb, which was planned to be the company's next major operating system release. Gradually, . In some builds of Longhorn, their license agreement said . Many of Microsoft's developers were also re- tasked to build updates to Windows XP and Windows Server 2. Faced with ongoing delays and concerns about feature creep, Microsoft announced on 2. August 2. 00. 4, that it had revised its plans. For this reason, Longhorn was reset to start work on componentizing the Windows Server 2. Service Pack 1 codebase, and over time re- incorporating the features that would be intended for an actual operating system release. However, some previously announced features such as Win. FS were dropped or postponed, and a new software development methodology called the Security Development Lifecycle was incorporated in an effort to address concerns with the security of the Windows codebase, which is programmed in C, C++ and assembly. WinHlp32.exe is required to display 32-bit Help files that have the ".hlp" file name extension. To view.hlp files on Windows Vista, you need to install this application.Longhorn became known as Vista in 2. During this period, Microsoft was fairly quiet about what was being worked on, as their marketing and public relations focus was more strongly focused on Windows XP, and Windows Server 2. April 2. 00. 3. Occasional builds of Longhorn were leaked onto popular file sharing networks such as IRC, Bit. Torrent, e. Donkey and various newsgroups, and so most of what is known about builds prior to the first sanctioned development release of Longhorn in May 2. After several months of relatively little news or activity from Microsoft with Longhorn, Microsoft released Build 4. Internet around 2. February 2. 00. 3. As an evolutionary release over build 3. An optional . The incorporation of the Plex theme made blue the dominant color of the entire application. The Windows XP- style task pane was almost completely replaced with a large horizontal pane that appeared under the toolbars. A new search interface allowed for filtering of results, searching of Windows help, and natural- language queries that would be used to integrate with Win. FS. The animated search characters were also removed. File metadata was also made more visible and more easily editable, with more active encouragement to fill out missing pieces of information. Also of note was the conversion of Windows Explorer to being a . NET application. Most builds of Longhorn and Vista were identified by a label that was always displayed in the bottom- right corner of the desktop. A typical build label would look like . Higher build numbers did not automatically mean that the latest features from every development team at Microsoft was included. Typically, a team working on a certain feature or subsystem would generate their own working builds which developers would test with, and when the code was deemed stable, all the changes would be incorporated back into the main development tree at once. At Microsoft, a number of . The name of the lab in which any given build originated is shown as part of the build label, and the date and time of the build follows that. Some builds (such as Beta 1 and Beta 2) only display the build label in the version information dialog (Winver). The icons used in these builds are from Windows XP. At the Windows Hardware Engineering Conference (Win. HEC) in May 2. 00. Microsoft gave their first public demonstrations of the new Desktop Window Manager and Aero. The demonstrations were done on a revised build 4. A number of sessions for developers and hardware engineers at the conference focused on these new features, as well as the Next- Generation Secure Computing Base (previously known as . Also at this conference, Microsoft reiterated their roadmap for delivering Longhorn, pointing to an . Internally, some Microsoft employees were describing the Longhorn project as . It offered only a limited subset of features planned for Longhorn, in particular fast file searching and integrated graphics and sound processing, but appeared to have impressive reliability and performance compared to contemporary Longhorn builds. Allchin went on to explain how in December 2. Brian Valentine and Amitabh Srivastava, the former being experienced with shipping software at Microsoft, most notably Windows Server 2. Future Longhorn builds would start from Windows Server 2. Service Pack 1 and continue from there. This change, announced internally to Microsoft employees on 2. August 2. 00. 4, began in earnest in September, though it would take several more months before the new development process and build methodology would be used by all of the development teams. A number of complaints came from individual developers, and Bill Gates himself, that the new development process was going to be prohibitively difficult to work within. As Windows Vista. By approximately November 2. In the end, Microsoft chose Windows Vista as confirmed on 2. July 2. 00. 5, believing it to be a . That's what Windows Vista is all about: . In September of that year, Microsoft started releasing regular Community Technology Previews (CTP) to beta testers from July 2. February 2. 00. 6. The first of these was distributed at the 2. Microsoft Professional Developers Conference, and was subsequently released to beta testers and Microsoft Developer Network subscribers. The builds that followed incorporated most of the planned features for the final product, as well as a number of changes to the user interface, based largely on feedback from beta testers. Windows Vista was deemed feature- complete with the release of the . Beta 2, released in late May, was the first build to be made available to the general public through Microsoft's Customer Preview Program. It was downloaded by over five million people. Two release candidates followed in September and October, both of which were made available to a large number of users. The UEFI 2. 0 specification (which replaces EFI 1. Microsoft's announcement, no firmware manufacturers had completed a production implementation which could be used for testing. As a result, the decision was made to postpone the introduction of UEFI support to Windows; support for UEFI on 6. Vista Service Pack 1 and Windows Server 2. UEFI would not be supported, as Microsoft does not expect many such systems to be built as the market moves to 6. Because a release to manufacturing (RTM) build is the final version of code shipped to retailers and other distributors, the purpose of a pre- RTM build is to eliminate any last . Thus, it is unlikely that any major new features would be introduced; instead, work would focus on Vista's . In just a few days, developers had managed to drop Vista's bug count from over 2. September to just over 1. RC2 shipped in early October. However, they still had a way to go before Vista was ready to RTM. Microsoft's internal processes required Vista's bug count to drop to 5. RTM. During a demonstration of the speech recognition feature new to Windows Vista at Microsoft's Financial Analyst Meeting on 2. July 2. 00. 6, the software recognized the phrase . After several failed attempts to correct the error, the sentence eventually became . On 1. 6 November 2. Microsoft made the final build available to MSDN and Technet Plus subscribers. Laptop users report, however, that enabling Aero shortens battery life. A search box appears in every Explorer window. The address bar has been replaced with a breadcrumb navigation bar. Icons of certain file types in Windows Explorer are . The preview pane allows users to see thumbnails of various files and view the contents of documents. The details pane shows information such as file size and type, and allows viewing and editing of embedded tags in supported file formats. The Start menu has changed as well; incorporating an instant search box, and the All Programs list uses a horizontal scroll bar instead of the cascading flyout menu seen in Windows XP. Gadgets can also be placed on the desktop. IE7 in Windows Vista runs in isolation from other applications in the operating system (protected mode); exploits and malicious software are restricted from writing to any location beyond Temporary Internet Files without explicit user consent. Windows Media Player 1. Microsoft's program for playing and organizing music and video. New features in this version include word wheeling (incremental search or . |