"TEAMGROUP T-FORCE 250GB DELTA Phantom Gaming RGB SSD 2.5"" SATA III Solid State DriveTEAMGROUP T-FORCE once again joins forces with motherboard leader ASRock and releases the T-FORCE DELTA Phantom Gaming RGB SSD(5V) which is certified and strictly tested by ASRock Phantom Gaming. The appearance is designed with the unique style of Phantom Gaming. Consumers not only can have RGB SSD’s most gorgeous lighting effects, but also can experience its extreme performance. T-FORCE DELTA Phantom Gaming RGB SSD(5V) is definitely your best choice when it comes to stomping enemies and winning games!Features- Certified by ASRock Phantom Gaming- 5:3 ratio luminous area. The largest in the industry- 16.8 million RGB color display- Support synchronization with motherboard’s lighting effects- Read/write speed is 4 times faster than traditional hard drive. Read speed up to 560MB/s- 2.5-inch hard drive with 9.5mm in height- Support S.M.A.R.T. technology – monitoring hard drive status efficiently- Support TRIM – bring out its best performance on the compatible operating systemPhantom Gaming design elementsWith creativity and ingenuity, TEAMGROUP design team adds Phantom Gaming’s elements on the ASRock co-branded high speed SSD, making it unique and attractive in a cool way. The creatively built T-FORCE DELTA Phantom Gaming RGB SSD(5V) is definitely top gamers’ best choice!Fully upgraded, extreme performanceThe maximum read speed of the T-FORCE DELTA Phantom Gaming RGB SSD(5V) is up to 560MB per second and booting or loading games will only take a few seconds. T-FORCE DELTA Phantom Gaming RGB SSD(5V) is using SATA III 6Gbps interface and capacities available in 250GB、500GB、1TB, etc. for gamers to choose from. The industry-standard 2.5” form factor with only 9.5mm in height, it meets the standard size of high speed SSD on the market. The upgrade can be done effortlessly.Tough protectionT-FORCE DELTA Phantom Gaming RGB SSD(5V) is using 3D NAND flash memory chip, which is durable, shockproof, drop resistant and offers a total protection for the data. Even if you accidently drop your computer or it landed on a hard surface, it can still prevent damages and data corruption from accidents.Durable and reliableT-FORCE DELTA Phantom Gaming RGB SSD(5V)’s built-in smart algorithm management mechanism has functions such as garbage collection which is able to ensure operation efficiency. The powerful Wear-Leveling technology and ECC (Error Correction Code) function enhance the reliability of data transfer. It supports Windows TRIM optimization command which is able to release free blocks, allowing operating system to use them later immediately when writing data. The optimized access control technology of NCQ can speed up the transfer and write performance of the high speed SSD, and effectively reduces performance degradation and wear and tear of the drive. This can prolong the service life of the SSD perfectly.Synchronization with motherboard’s lighting effectsThrough activating ASRock Polychrome Sync lighting effect software, T-FORCE DELTA Phantom Gaming RGB SSD (5V) can archive synchronization with motherboard’s lighting effects.3/4 pin to USB Micro B cableT-FORCE DELTA Phantom Gaming RGB SSD(5V) is compatible with motherboards that have 5V ADD headers(As shown). It provides a variety of changing lighting effects. Not only a consistent of surface glow can be achieved, but it can also present a magnificent mixed color effect with water flowing lighting to maximize the variability of color. Through connecting motherboard’s 5V ADD Header, the motherboard is able to control and achieve a more holistic lighting effect.T-FORCET-FORCE is TEAM force. The red ""T"" on the logo of ""TF"" represents TEAMGROUP's passion for the storage products. The black ""F"" represents TEAMGROUP's over 18 years of promotion of storage products. The visual design of the perfect combination elegantly symbolizes a pair of flying wings. They represent that the high quality and extreme performance gaming products from TEAMGROUP are capable of allowing all gamers to break the speed limit and enjoy the ever-changing world of gaming."
Garbage in. Garbage out. In a world ruled by Big Data and BigPharma, society's only hope falls into the hands of neophyte data jockeys known as Somnambulous. While the human race was jacking in and sucking down a few ones and zeros to ride the Lament, Somnambulous discovered something big and dark on the horizon. Big Data had Big Pharma. Big Pharma had Lament. Lament had everyone. On the heels of their discovery, Somnambulous sends their strongest coder into Ocular Reality to recruit the one construct that could put an end to both Big Data and Big Pharma. 1. Language: English. Narrator: Jack Wallen. Audio sample: http://samples.audible.de/bk/acx0/085654/bk_acx0_085654_sample.mp3. Digital audiobook in aax.
Organizations invest incredible amounts of time and money in obtaining and then storing big data in stores called data lakes. But how many of these organizations can actually get the data back out in a useable form? Very few can turn a data lake into an information gold mine. Most wind up with garbage dumps. Data Lake Architecture will explain how to build a useful data lake where data scientists and data analysts can solve business challenges and identify new business opportunities. Learn how to structure data lakes as well as analog, application, and text-based data ponds to provide maximum business value. Understand the role of the raw data pond and when to use an archival data pond. Leverage the four key ingredients for data lake success: metadata, integration mapping, context, and metaprocess. Bill Inmon opened our eyes to the architecture and benefits of a data warehouse, and now he takes us to the next level of data lake architecture. 1. Language: English. Narrator: Mark Shumka. Audio sample: http://samples.audible.de/bk/acx0/062307/bk_acx0_062307_sample.mp3. Digital audiobook in aax.
This study examines the patterns and determinants of solid waste disposal practices adopted by various households in Dar es Salaam city, Tanzania, using household budget survey data. The study uses a Multinomial Logit (MNL) model to examine the underlying determinants of choosing ways to dispose garbage, i.e., rubbish pit inside compound, rubbish pit outside compound, rubbish bin, thrown outside and other. The descriptive results reveal that about 35% of the respondents used rubbish bin, and approximately 24% were using throwing out option. This is against the use of rubbish pit outside compound, rubbish pit inside compound and other. Estimation results of a MNL suggest that household choices of these practices are determined by a certain combination of factors such as age, education and occupation of the household head, distance to the main road, home ownership, proportions of females and family members above 45 years, expenditure per adult equivalent and municipality location. This study provides useful insights into sustainable Solid Waste Management practices in Dar es Salaam city.
Theoretical analysis is interesting, however, the brain washing is inevitable in addition. Due to irregularities and inhomogenities in the actual structure the garbage in, garbage out phenomenon appears as dominant option in numerical analysis. Without databank of all experimental results obtained in tests and summed up in corresponding database used as input data, is useless to start sophisticated analysis in order to obtain the output data required. Calculations are to be based on theoretical approaches and be confronted with experimental results in order to develop the virtual models for the assessment of the problem. With such models are made all assessments and ultimate virtual tests of the bridge studied.
Many real-world phenomena, in particular in economics, are modelled as constrained optimization problems. The usefulness of such models depends on the values of their parameters garbage in, garbage out. Traditional statistical methods generally lack the ability to make efficient use of the multiple data sources upon which such models depend and to provide estimates that are consistent with a model structure that may be both non-linear and inequality-constrained. This is book proposes and demonstrates methods for econometric specification of parameters of constrained optimization models, with special attention to issues that arise when (i) inequality constraints are involved and/or (ii) when the estimation problem is ill-posed (underdetermined) or data come from diverse sources. The general approach followed here is to directly estimate the optimality conditions of the optimization model, together with additional equations for including prior information. The book blends theoretical analyses with didactic as well as full-scale empirical applications, and should prove useful to applied modellers in various areas.
Algorithm. Analysis of algorithms, Profiling (computer programming), Program optimization, List of algorithms, Complexity class, Abstract machine, Algorithm characterizations, Algorithm examples, Algorithmic composition, Garbage In, Garbage Out, High-level synthesis, Algorithmic trading, Introduction to Algorithms, List of algorithm general topics, List of terms relating to algorithms and data structures, Randomized algorithm, Quantum algorithm, False cognate, Decidability (logic), Axiom, Imperative programming, Termination analysis
Focus on the most important and most often overlooked factor in a successful Tableau project-data. Without a reliable data source, you will not achieve the results you hope for in Tableau. This book does more than teach the mechanics of data preparation. It teaches you: how to look at data in a new way, to recognize the most common issues that hinder analytics, and how to mitigate those factors one by one.Tableau can change the course of business, but the old adage of "garbage in, garbage out" is the hard truth that hides behind every Tableau sales pitch. That amazing sales demo does not work as well with bad data. The unfortunate reality is that almost all data starts out in a less-than-perfect state. Data prep is hard.Traditionally, we were forced into the world of the database where complex ETL (Extract, Transform, Load) operations created by the data team did all the heavy lifting for us. Fortunately, we have moved past those days. With the introduction of the Tableau Data Prep tool you can now handle most of the common Data Prep and cleanup tasks on your own, at your desk, and without the help of the data team. This essential book will guide you through:The layout and important parts of the Tableau Data Prep toolConnecting to dataData quality and consistencyThe shape of the data. Is the data oriented in columns or rows? How to decide? Why does it matter?What is the level of detail in the source data? Why is that important?Combining source data to bring in more fields and rowsSaving the data flow and the results of our data prep workCommon cleanup and setup tasks in Tableau DesktopWhat You Will LearnRecognize data sources that are good candidates for analytics in TableauConnect to local, server, and cloud-based data sourcesProfile data to better understand its content and structureRename fields, adjust data types, group data points, and aggregate numeric dataPivot dataJoin data from local, server, and cloud-based sources for unified analyticsReview the steps and results of each phase of the Data Prep processOutput new data sources that can be reviewed in Tableau or any other analytics toolWho This Book Is ForTableau Desktop users who want to: connect to data, profile the data to identify common issues, clean up those issues, join to additional data sources, and save the newly cleaned, joined data so that it can be used more effectively in Tableau
'Data acquisition' is concerned with taking one or more analogue signals and converting them to digital form with sufficient accu racy and speed to be ready for processing by a computer. The increasing use of computers makes this an expanding field, and it is important that the conversion process is done correctly because information lost at this stage can never be regained, no matter how good the computation. The old saying - garbage in, garbage out - is very relevant to data acquisition, and so every part of the book contains a discussion of errors: where do they come from, how large are they, and what can be done to reduce them? The book aims to treat the data acquisition process in depth with less detailed chapters on the fundamental principles of measure ment, sensors and signal conditioning. There is also a chapter on software packages, which are becoming increasingly popular. This is such a rapidly changing topic that any review of available pro grams is bound to be out of date before the book reaches the read ers. For this reason, I have described the data handling which is available in various types of program and left it to the reader to select from whatever is on the market at the time.