PAPERCRETEPaper. Crete is essentially a type of industrial strength paper mach. There are many varieties of Paper. Crete possible. Essentially, the constituents when mixed in different proportions result in Paper. Manufacture Building a Mixer Mass Production Properties Strength. Home-Building Resources. Building with Papercrete and Paper Adobe Gordon Solberg. Dome Construction: Monolithic Domes. Generally, many easy handling sized bricks are made. Papercrete blocks are so versatile and easy to work with. Learn how to make your own. Next time: building with papercrete blocks. Papercrete is an incredible building material that is lightweight. How We Made Our Experimental Earthbag / Papercrete House by Kelly Hart. DVDs and PDF Technical. Buildings from waste paper. Papercrete is made from low-grade waste paper which would. The Building Department at ArchitectureWeek, the new magazine of design and building, serving architecture online like never. Building with Papercrete. Papercrete Tiny Houses. Green Home Building; Papercrete on Wikipedia; Related. January 20, 2009 at 2:01 am. Crete of varying properties. The basic constituents of Paper. Crete are: Paper: usually waste paper such as used newsprint or cardboard. Aggregate . The method of making Paper. Crete is very simple. The dry ingredients are mixed with water in a mixer to form slurry. The Truth about Papercrete by Kelly Hart. When I asked Harun Magnuson, who has been building a vaulted papercrete structure for several years. EPS Foam Shredder To make EPS-crete, it's necessary to have ground foam. Before you start building, here is something to consider. STRENGTH AND DURABILITY OF FLYASH BASED PAPERCRETE BUILDING. He has conducted extensive research on papercrete and built. T he Waiver of Liability exempts Living in Paper and its. Learn about using papercrete to build recycled houses. Sure, we'd heard the rumors about paper houses, but our initial reaction was largely incredulous. Intro: I Love Papercrete. That’s the response I always get when I’m describing my favorite building material. The slurry is cast into blocks or panels and allowed to dry in the sun. When it hardens up, Paper. Crete is lightweight (its 8. R 2. 8 per inch), holds its shape even when wet, and is remarkably strong (compressive strength of 2. And, since it contains paper fibers, it has considerable tensile strength as well as compressive strength (Solberg, 2. Paper. Crete is suitable for making low cost homes with limited longevity and durability. It is also suitable for making community rooms, sale booths, storage rooms and dwellings for livestock. Paper. Crete can also be used as a plaster. It can be sprayed on walls to give them good sound and heat insulating properties. Pros of Paper. Crete: Raw materials are very inexpensive and freely available. Equipment used is relatively low- tech and inexpensive. Has high compressive strength. It has excellent heat and sound insulating properties. Paper. Crete is light weight and can be used to make inexpensive roofing alternatives. Paper. Crete is very workable and can be formed into different shapes such as blocks, panels and sheets with ease. THIRTEEN YEARS LATER.Papercrete building is going strong with many educational YouTube video clips, an active chat group ([email protected]). How to Build with Papercrete How to Build with Papercrete. Using this technique for building a papercrete house is a good choice. Papercrete is a material made by pulping used paper. Vault building--free PDF for construction. It does not catch fire easily but smolders for hours. Limitations of Paper. Crete: Poor moisture resistance. Susceptible to termite and mould. Disintegrates when exposed to water for prolonged periods of time. Zach Rabon founded Mason Greenstar in Mason, Texas for the purpose of producing and selling a commercially viable papercrete block. His product, Blox Building System. PaperCrete is essentially a type of. Using PaperCrete as a wall filler is more likely to gain acceptance with building inspectors. One PaperCrete house has been. EVALUATION OF PAPERCRETE: An Innovative Building Material R.Selvaraj, R.Priyanka, M.Amirthavarshini, Dr.S.Prabavathy. Papercrete is said to be. It is widely believed that building with papercrete is safe. Houses Made From Papercrete and Fibrous Adobe. Mike's newest building, a laundry room in front with two restrooms behind.
Low longevity. Expands and contracts frequently leading to cracks and buckling. Poor tensile strength. Paper. Crete is a recent technology and its use is limited to experimental and recreation use. This has also resulted in the limited knowledge base about the technology. Paper. Crete when used in conjunction with more traditional building techniques has yielded positive and encouraging results. Paper. Crete Wall Panel. The code compliance of Paper. Crete remains sketchy simply because there are no codes for Paper. Crete. Many rural counties, particularly in the West, there are either no building codes or the existing codes are loosely enforced. In New Mexico one can apply for an experimental permit. This requires drawing up a set of plans and having an engineer sign off on them. Using Paper. Crete as a wall filler is more likely to gain acceptance with building inspectors. One Paper. Crete house has been built with a permit in Arizona. The building inspector insisted that it be built post- and- beam, with the Paper. Crete used only as infilling (Solberg, 2. A Paper. Crete and Glass Bottle House.
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Data Structures/Hash Tables - Wikibooks, open books for an open world. Hash Tables. The primary operation it supports efficiently is a lookup: given a key (e. It works by transforming the key using a hash function into a hash, a number that the hash table uses to locate the desired value. This hash maps directly to a bucket in the array of key/value pairs, hence the name hash map. The mapping method lets us directly access the storage location for any key/value pair. Hash table< Element> Operationsmake- hash- table(integer n): Hash. Table. Create a hash table with n buckets. Hash. Table h, Comparable key): Element. An Extensive Examination of Data Structures Using. The Dictionary class is a. The default hash is a template class that is not defined for the general case. You are implement a simple spell checker using a hash. The program should read the dictionary and insert the. Dictionaries implement the associative array. Multiplicative hashing is a simple type of hash function often used by teachers. Returns the value of the element for the given key. The key must be some comparable type. Hash. Table h, Comparable key, Element new- value)Sets the element of the array for the given key to be equal to new- value. The key must be some comparable type. Hash. Table h, Comparable key)Remove the element for the given key from the hash table. The key must be some comparable type. Like arrays, hash tables provide constant- time O(1) lookup on average, regardless of the number of items in the table. The (hopefully rare) worst- case lookup time in most hash table schemes is O(n). Hash tables may also be adopted for use with persistent data structures; database indexes commonly use disk- based data structures based on hash tables. Hash tables are also used to speed- up string searching in many implementations of data compression. In computer chess, a hash table can be used to implement the transposition table. Choosing a good hash function. A poor choice of hash function is likely to lead to clustering behavior, in which the probability of keys mapping to the same hash bucket (i. A nonzero probability of collisions is inevitable in any hash implementation, but the number of operations to resolve collisions usually scales linearly with the number of keys mapping to the same bucket, so excess collisions will degrade performance significantly. In addition, some hash functions are computationally expensive, so the amount of time (and, in some cases, memory) taken to compute the hash may be burdensome. Choosing a good hash function is tricky. The literature is replete with poor choices, at least when measured by modern standards. For example, the very popular multiplicative hash advocated by Knuth in The Art of Computer Programming (see reference below) has particularly poor clustering behavior. However, since poor hashing merely degrades hash table performance for particular input key distributions, such problems go undetected far too often. The literature is also sparse on the criteria for choosing a hash function. Unlike most other fundamental algorithms and data structures, there is no universal consensus on what makes a . The remainder of this section is organized by three criteria: simplicity, speed, and strength, and will survey algorithms known to perform well by these criteria. Simplicity and speed are readily measured objectively (by number of lines of code and CPU benchmarks, for example), but strength is a more slippery concept. Obviously, a cryptographic hash function such as SHA- 1 would satisfy the relatively lax strength requirements needed for hash tables, but their slowness and complexity makes them unappealing. In fact, even a cryptographic hash does not provide protection against an adversary who wishes to degrade hash table performance by choosing keys all hashing to the same bucket. For these specialized cases, a universal hash function should be used instead of any one static hash, no matter how sophisticated. In the absence of a standard measure for hash function strength, the current state of the art is to employ a battery of statistical tests to measure whether the hash function can be readily distinguished from a random function. Arguably the most important such test is to determine whether the hash function displays the avalanche effect, which essentially states that any single- bit change in the input key should affect on average half the bits in the output. Bret Mulvey advocates testing the strict avalanche condition in particular, which states that, for any single- bit change, each of the output bits should change with probability one- half, independent of the other bits in the key. Purely additive hash functions such as CRC fail this stronger condition miserably. Clearly, a strong hash function should have a uniform distribution of hash values. Bret Mulvey proposes the use of a chi- squared test for uniformity, based on power of two hash table sizes ranging from 2. This test is considerably more sensitive than many others proposed for measuring hash functions, and finds problems in many popular hash functions. Fortunately, there are good hash functions that satisfy all these criteria. The simplest class all consume one byte of the input key per iteration of the inner loop. Within this class, simplicity and speed are closely related, as fast algorithms simply don't have time to perform complex calculations. Of these, one that performs particularly well is the Jenkins One- at- a- time hash, adapted here from an article by Bob Jenkins, its creator. The image was made using Bret Mulvey's Avalanche. Test in his Hash. Each row corresponds to a single bit in the input, and each column to a bit in the output. A green square indicates good mixing behavior, a yellow square weak mixing behavior, and red would indicate no mixing. Only a few bits in the last byte are weakly mixed, a performance vastly better than a number of widely used hash functions. Many commonly used hash functions perform poorly when subjected to such rigorous avalanche testing. The widely favored FNV hash, for example, shows many bits with no mixing at all, especially for short keys. See the evaluation of FNV by Bret Mulvey for a more thorough analysis. If speed is more important than simplicity, then the class of hash functions which consume multibyte chunks per iteration may be of interest. One of the most sophisticated is . Note, though, that any speed improvement from the use of this hash is only likely to be useful for large keys, and that the increased complexity may also have speed consequences such as preventing an optimizing compiler from inlining the hash function. Bret Mulvey analyzed an earlier version, lookup. One desirable property of a hash function is that conversion from the hash value (typically 3. This property enables the technique of incremental doubling of the size of the hash table - each bucket in the old table maps to only two in the new table. Because of its use of XOR- folding, the FNV hash does not have this property. Some older hashes are even worse, requiring table sizes to be a prime number rather than a power of two, again computing the bucket index as the hash value modulo the table size. In general, such a requirement is a sign of a fundamentally weak function; using a prime table size is a poor substitute for using a stronger function. Collision resolution. So, if it's already occupied, we must find another location to store the new record, and do it so that we can find it when we look it up later on. To give an idea of the importance of a good collision resolution strategy, consider the following result, derived using the birthday paradox. Even if we assume that our hash function outputs random indices uniformly distributed over the array, and even for an array with 1 million entries, there is a 9. There are a number of collision resolution techniques, but the most popular are chaining and open addressing. Chaining. Insertion requires finding the correct slot, and appending to either end of the list in that slot; deletion requires searching the list and removal. Chained hash tables have advantages over open addressed hash tables in that the removal operation is simple and resizing the table can be postponed for a much longer time because performance degrades more gracefully even when every slot is used. The Queue, Stack, and Hashtable. As we'll see, the Queue and Stack are specialized Lists, providing storage for a variable number of objects, but restricting the order in which the items may be accessed. The Hashtable provides an array- like abstraction with greater indexing flexibility. Whereas an array requires that its elements be indexed by an ordinal value, Hashtables allow items to be indexed by any type of object, such as a string. In January 2. 00. NET Framework version 2. C# 2. 0. The original articles are still available at http: //msdn. In addition to reviewing the basics of data structures and their analysis, we also looked at the most commonly used data structure, the array. The array holds a set of homogeneous elements indexed by ordinal value. The actual contents of an array are laid out as a contiguous block, thereby making reading from or writing to a specific array element very fast. In addition to the standard array, the . NET Framework Base Class Library offers the List class. Like the array, the List is a collection of homogeneous data items. With a List, you don't need to worry about resizing or capacity limits, and there are numerous List methods for searching, sorting, and modifying the List's data. As discussed in the previous article, the List class uses Generics to provide a type- safe, reusable collection data structure. In this second installment of the article series, we'll continue our examination of array- like data structures by first examining the Queue and Stack. These two data structures are similar in some aspects to the List. The Queue and Stack differ from the List class in that there are limitations on how the Queue and Stack data can be accessed. Following our look at the Queue and Stack, we'll spend the rest of this article digging into the Hashtable data structure. A Hashtable, which is sometimes referred to as an associative array, stores a collection of elements, but indexes these elements by an arbitrary object (such as a string), as opposed to an ordinal index. Providing First Come, First Served Job Processing. If you are creating any kind of computer service. The two most common approaches used are. First come, first served. Priority- based processing. First come, first served is the job- scheduling task you'll find at your grocery store, the bank, and licensing departments. Those waiting for service stand in a line. The people in front of you will be served before you while the people behind you will be served after. Priority- based processing serves those with a higher priority before those with a lesser priority. For example, a hospital emergency room uses this strategy, opting to help someone with a potentially fatal wound before someone with a less threatening wound, regardless of who arrived first. Imagine that you need to build a computer service and that you want to handle requests in the order in which they were received. Because the number of incoming requests might happen quicker than you can process them, you'll need to place the requests in some sort of buffer that can preserve the order in which they arrived. One option is to use a List and an integer variable called next. Job. Pos to indicate the position of the next job to be completed. When each new job request comes in, simply use the List's Add() method to add it to the end of the List. Whenever you are ready to process a job in the buffer, grab the job at the next. Job. Pos position in the List and increment next. Job. Pos. The following simple program illustrates this algorithm: public class Job. Processing. . For starters, the List will continue to grow unabated with each job that's added to the buffer, even if the jobs are processed immediately after being added to the buffer. Consider the case where every second a new job is added to the buffer and a job is removed from the buffer. This means that once a second the Add. Job() method is called, which calls the List's Add() method. As the Add() method is continually called, the List's internal array's size is continually redoubled as needed. After five minutes (3. List's internal array will be dimensioned for 5. This trend, of course, will continue so long as the program continues to run and the jobs continue to come in. The reason the List grows in such ridiculous proportions is because the buffer locations used for old jobs are not reclaimed. That is, when the first job is added to the buffer, and then processed, clearly the first spot in the List is ready to be reused again. Consider the job schedule presented in the previous code sample. After the first two lines. The Array. List after the first two lines of code. Note that there are 1. List at this point because the List was initialized with a capacity of 1. Next, the Get. Next. Job() method is invoked, which removes the first job, resulting in Fiure 2. Figure 2. Program after the Get. Next. Job() method is invoked. When Add. Job(. Clearly the first List element (index 0) is available for reuse. Initially it might make sense to put the third job in the 0 index. However, this approach can be eliminated by considering what would happen if after Add. Job(. If we placed the third job in the 0 index and then the fourth job in the 2 index, we'd have something like the problem displayed in Figure 3. Figure 3. Issue created by placing jobs in the O index. Now, when Get. Next. Job() was called, the second job would be removed from the buffer, and next. Job. Pos would be incremented to point to index 2. Therefore, when Get. Next. Job() was called again, the fourth job would be removed and processed prior to the third job, thereby violating the first come, first served order we need to maintain. The crux of this problem arises because the List represents the list of jobs in a linear ordering. That is, we need to keep adding the new jobs to the right of the old jobs to guarantee that the correct processing order is maintained. Whenever we hit the end of the List, the List is doubled, even if there are unused List elements due to calls to Get. Next. Job(). To fix this problem, we need to make our List circular. A circular array is one that has no definite start or end. Rather, we have to use variables to remember the beginning and end positions of the array. A graphical representation of a circular array is shown in Figure 4. Example of a circular array. With a circular array, the Add. Job() method adds the new job in index end. Pos and then . The Get. Next. Job() method plucks the job from start. Pos, sets the element at the start. Pos index to null, and . I put the word increments in quotation marks because here incrementing is a trifle more complex than simply adding one to the variable's current value. To see why we can't just add 1, consider the case when end. Pos equals 1. 5. If we increment end. Pos by adding 1, end. Pos will equal 1. In the next Add. Job() call, the index 1. Index. Out. Of. Range. Exception. Rather, when end. Pos equals 1. 5, we want to increment end. Pos by resetting it to 0. This can either be done by creating an increment(variable) function that checks to see if the passed- in variable equals the array's size and, if so, reset it to 0. Alternatively, the variable can have its value incremented by 1 and then mod- ed by the size of the array. In such a case, the code for increment() would look like: int increment(int variable). The remainder will always be between 0 and y ? Like with the List's Add() method, we'll need to resize the circular array appropriately by, say, doubling the size of the array. The System. Collections. Generic. Queue Class. The functionality we have just described. The . NET Framework Base Class Library provides the System. Collections. Generic. Queue class, which uses Generics to provide a type- safe Queue implementation. Whereas our earlier code provided Add. Job() and Get. Next. Job() methods, the Queue class provides identical functionality with its Enqueue(item) and Dequeue() methods, respectively. Classical Sanskrit became fixed with. This presentation file will explain some very useful tools for learning indian languages. More than 19 million people use GitHub to discover. Sanskrit Tools developed by University of Hyderabad and. You can't perform that action at this time. Assistant Professor Sanskrit Jobs In India. Full-time (8) Employer/Recruiter. Latest sanskrit in schools News, Photos, Blogposts, Videos and Wallpapers. Explore sanskrit in schools profile at Times of India. The seminal texts of knowledge over a period of time tend to (i) grow. Department of Sanskrit Studies, University of Hyderabad. Indian Language Tools. Robot Check. Enter the characters you see below. Sorry, we just need to make sure you're not a robot. For best results, please make sure your browser is accepting cookies. LUCKNOW: When it comes to teaching an ancient language, digital tools still lag behind. Instead, teaching kits like empty nailpaint bottle, a broken. Sanskrit Morphological Analyser: Some Issues Amba Kulkarni and Devanand Shukl Department of Sanskrit Studies, University of Hyderabad, Hyderabad [email protected]. Traditionally Panchanga is used for finding Muhurta (right time for doing something) and for general evaluation of auspiciousness of any moment in time. The Institute offers challenging academic programmes. Sanskrit scholars and the. Interested people can enroll in the P.G. Consortium Leader for the project on Developmet of Sanskrit Computational Tools and Sanskrit-Hindi. Sanskrit Studies, University of Hyderabad. Sanskrit and Computational Linguistics. Akshar Bharati, Amba Kulkarni Department of Sanskrit Studies University of Hyderabad Hyderabad [email protected]. But at the same time, it was the fervent desire to help needy and poor students in their education. Amba Kulkarni's Sanskrit analysis tools, U. Sanskrit and its reflection in ancient science found resonance at the Indian Science Congress on Sunday. For the first time in a history of more than 100 years of the. The aim of the workshop is to introduce Sanskrit teachers to various available tools so that they can use these tools effectively as a teaching aid. This site provides tools for Sanskrit processing: dictionary search, morphology generation and analysis, segmentation, tagging and parsing. Even as the BJP government seeks to promote Sanskrit in Kendriya Vidyalayas, Sanskrit Bharati — considered an organisation inspired by the Sangh — has been. We propose to demonstrate a collection of tools for Sanskrit Computational Linguistics developed by cooperating teams in the general setting of Web services. Hyderabad Bengaluru Videos. At no point of time has there been a conflict between these. Google introduces tools for small. Launching FREE online Sanskrit Courses Find number of Part Time Jobs in Medchal, hyderabad at QuikrJobs. Create free resume now. Sanskrit dictionary Online Sanskrit Dictionaries The online Sanskrit dictionary is intended to build a repository of Sanskrit words/meanings in a simple. Buy Sanskrit: A Complete Course for Beginners (Teach Yourself Books) on Amazon.com FREE SHIPPING on qualified orders. Discover ayurveda books. It is one of the rare works of Ayurveda which are not available today in any other language other than Sanskrit. 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