From Idea to Solution for (int i = 0; i < list.length; i++) { select the smallest element in list[i..listSize-1]; swap the smallest with list[i], if necessary; // list[i] is in its correct position. // The next iteration apply on list[i..listSize-1] } list[0] list[1] list[2] list[3] ... list[10] list[0] list[1] list[2] list[3] ... list[10] list[0] list[1] list[2] list[3] ... list[10] list[0] list[1] list[2] list[3] ... list[10] list[0] list[1] list[2] list[3] ... list[10] ... list[0] list[1] list[2] list[3] ... list[10]
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A list of the different modes of opening a file: wb+ Opens a file for both writing and reading in binary format. Overwrites the existing file if the file exists. If the file does not exist, creates a new file for reading and writing. a Opens a file for appending. The file pointer is at the end of the file if the file exists. That is, the file is in the append mode. If the file does not exist, it creates a new file for writing. ab Opens a file for appending in binary format. The file pointer is at the end of the file if the file exists. That is, the file is in the append mode. If the file does not exist, it creates a new file for writing. a+ Opens a file for both appending and reading. The file pointer is at the end of the file if the file exists. The file opens in the append mode. If the file does not exist, it creates a new file for reading and writing. ab+ Opens a file for both appending and reading in binary format. The file pointer is at the end of the file if the file exists. The file opens in the append mode. If the file does not exist, it creates a new file for reading and writing.
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HDFS (Hadoop Distributed File System) is a distr file sys for commodity hdwr. Differences from other distr file sys are few but significant. HDFS is highly fault-tolerant and is designed to be deployed on low-cost hardware. HDFS provides hi thruput access to app data and is suitable for apps that have large data sets. HDFS relaxes a few POSIX requirements to enable streaming access to file system data. HDFS originally was infrastructure for Apache Nutch web search engine project, is part of Apache Hadoop Core http://hadoop.apache.org/core/ 2.1. Hardware Failure Hardware failure is the normal. An HDFS may consist of hundreds or thousands of server machines, each storing part of the file system’s data. There are many components and each component has a non-trivial prob of failure means that some component of HDFS is always non-functional. Detection of faults and quick, automatic recovery from them is core arch goal of HDFS. 2.2. Streaming Data Access Applications that run on HDFS need streaming access to their data sets. They are not general purpose applications that typically run on general purpose file systems. HDFS is designed more for batch processing rather than interactive use by users. The emphasis is on high throughput of data access rather than low latency of data access. POSIX imposes many hard requirements not needed for applications that are targeted for HDFS. POSIX semantics in a few key areas has been traded to increase data throughput rates. 2.3. Large Data Sets Apps on HDFS have large data sets, typically gigabytes to terabytes in size. Thus, HDFS is tuned to support large files. It provides high aggregate data bandwidth and scale to hundreds of nodes in a single cluster. It supports ~10 million files in a single instance. 2.4. Simple Coherency Model: HDFS apps need a write-once-read-many access model for files. A file once created, written, and closed need not be changed. This assumption simplifies data coherency issues and enables high throughput data access. A Map/Reduce application or a web crawler application fits perfectly with this model. There is a plan to support appending-writes to files in future [write once read many at file level] 2.5. “Moving Computation is Cheaper than Moving Data” A computation requested by an application is much more efficient if it is executed near the data it operates on. This is especially true when the size of the data set is huge. This minimizes network congestion and increases the overall throughput of the system. The assumption is that it is often better to migrate the computation closer to where the data is located rather than moving the data to where the app is running. HDFS provides interfaces for applications to move themselves closer to where the data is located. 2.6. Portability Across Heterogeneous Hardware and Software Platforms: HDFS has been designed to be easily portable from one platform to another. This facilitates widespread adoption of HDFS as a platform of choice for a large set of applications. 3. NameNode and DataNodes: HDFS has a master/slave architecture. An HDFS cluster consists of a single NameNode, a master server that manages the file system namespace and regulates access to files by clients. In addition, there are a number of DataNodes, usually one per node in the cluster, which manage storage attached to the nodes that they run on. HDFS exposes a file system namespace and allows user data to be stored in files. Internally, a file is 1 blocks stored in a set of DataNodes. The NameNode executes file system namespace operations like opening, closing, and renaming files and directories. It also determines the mapping of blocks to DataNodes. The DataNodes are responsible for serving read and write requests from the file system’s clients. The DataNodes also perform block creation, deletion, and replication upon instruction The NameNode and DataNode are pieces of software designed to run on commodity machines, typically run GNU/Linux operating system (OS). HDFS is built using the Java language; any machine that supports Java can run the NameNode or the DataNode software. Usage of the highly portable Java language means that HDFS can be deployed on a wide range of machines. A typical deployment has a dedicated machine that runs only the NameNode software. Each of the other machines in the cluster runs one instance of the DataNode software. The architecture does not preclude running multiple DataNodes on the same machine but in a real deployment that is rarely the case. The existence of a single NameNode in a cluster greatly simplifies the architecture of the system. The NameNode is the arbitrator and repository for all HDFS metadata. The system is designed in such a way that user data never flows through the NameNode. 4. The File System Namespace: HDFS supports a traditional hierarchical file organization. A user or an application can create directories and store files inside these directories. The file system namespace hierarchy is similar to most other existing file systems; one can create and remove files, move a file from one directory to another, or rename a file. HDFS does not yet implement user quotas or access permissions. HDFS does not support hard links or soft links. However, the HDFS architecture does not preclude implementing these features. The NameNode maintains the file system namespace. Any change to the file system namespace or its properties is recorded by the NameNode. An application can specify the number of replicas of a file that should be maintained by HDFS. The number of copies of a file is called the replication factor of that file. This info is stored by NameNode. 5. Data Replication: HDFS is designed to reliably store very large files across machines in a large cluster. It stores each file as a sequence of blocks; all blocks in a file except the last block are the same size. The blocks of a file are replicated for fault tolerance. The block size and replication factor are configurable per file. An application can specify the number of replicas of a file. The replication factor can be specified at file creation time and can be changed later. Files in HDFS are write-once and have strictly one writer at any time. The NameNode makes all decisions regarding replication of blocks. It periodically receives a Heartbeat and a Blockreport from each of the DataNodes in the cluster. Receipt of a Heartbeat implies that the DataNode is functioning properly. A Blockreport contains a list of all blocks on a DataNode
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See complete file on webpage for a list of 24 common items resulting in book-tax differences. T0-Chp-14-5-Schedule-MItems
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A list of the different modes of opening a file: Modes Description r Opens a file for reading only. The file pointer is placed at the beginning of the file. This is the default mode. rb Opens a file for reading only in binary format. The file pointer is placed at the beginning of the file. This is the default mode. r+ Opens a file for both reading and writing. The file pointer will be at the beginning of the file. rb+ Opens a file for both reading and writing in binary format. The file pointer will be at the beginning of the file. w Opens a file for writing only. Overwrites the file if the file exists. If the file does not exist, creates a new file for writing. wb Opens a file for writing only in binary format. Overwrites the file if the file exists. If the file does not exist, creates a new file for writing. w+ Opens a file for both writing and reading. Overwrites the existing file if the file exists. If the file does not exist, creates a new file for reading and writing.
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Answer #include struct Book { char title[50]; char author[50]; char subject[100]; int book_id; }; void printBook( struct Book *book ) { /* function declaration */ void printBook( struct Book *book ); printf( printf( printf( printf( int main( ) { struct Book Book1; struct Book Book2; /* Declare Book1 of type Book */ /* Declare Book2 of type Book */ "Book "Book "Book "Book title : %s\n", book->title); author : %s\n", book->author); subject : %s\n", book->subject); book_id : %d\n\n", book->book_id); } /* book 1 specification */ strcpy( Book1.title, "C Programming"); strcpy( Book1.author, "Nuha Ali"); strcpy( Book1.subject, "C Programming Tutorial"); Book1.book_id = 6495407; /* book 2 specification */ strcpy( Book2.title, "Telecom Billing"); strcpy( Book2.author, "Zara Ali"); strcpy( Book2.subject, "Telecom Billing Tutorial"); Book2.book_id = 6495700; /* print Book1 info by passing address of Book1 */ printBook( &Book1 ); /* print Book2 info by passing address of Book2 */ printBook( &Book2 ); return 0; } Copyright © 2017 by Jones & Bartlett Learning, LLC an Ascend Learning Company
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6. The Persistence of File System Metadata: The HDFS namespace is stored by the NameNode. The NameNode uses a transaction log called the EditLog to persistently record every change that occurs to file system metadata. For example, creating a new file in HDFS causes the NameNode to insert a record into the EditLog indicating this. Similarly, changing the replication factor of a file causes a new record to be inserted into the EditLog. The NameNode uses a file in its local host OS file system to store the EditLog. The entire file system namespace, including the mapping of blocks to files and file system properties, is stored in a file called the FsImage. The FsImage is stored as a file in the NameNode’s local file system too. The NameNode keeps an image of the entire file system namespace and file Blockmap in memory. This key metadata item is designed to be compact, such that a NameNode with 4 GB of RAM is plenty to support a huge number of files and directories. When the NameNode starts up, it reads the FsImage and EditLog from disk, applies all the transactions from the EditLog to the in-memory representation of the FsImage, and flushes out this new version into a new FsImage on disk. It can then truncate the old EditLog because its transactions have been applied to the persistent FsImage. This process is called a checkpoint. In the current implementation, a checkpoint only occurs when the NameNode starts up. Work is in progress to support periodic checkpointing in the near future. The DataNode stores HDFS data in files in its local file system. The DataNode has no knowledge about HDFS files. It stores each block of HDFS data in a separate file in its local file system. The DataNode does not create all files in the same directory. Instead, it uses a heuristic to determine the optimal number of files per directory and creates subdirectories appropriately. It is not optimal to create all local files in the same directory because the local file system might not be able to efficiently support a huge number of files in a single directory. When a DataNode starts up, it scans through its local file system, generates a list of all HDFS data blocks that correspond to each of these local files and sends this report to the NameNode: this is the Blockreport. 7. The Communication Protocols: All HDFS communication protocols are layered on top of the TCP/IP protocol. A client establishes a connection to a configurable TCP port on the NameNode machine. It talks the ClientProtocol with the NameNode. The DataNodes talk to the NameNode using the DataNode Protocol. A Remote Procedure Call (RPC) abstraction wraps both the Client Protocol and the DataNode Protocol. By design, the NameNode never initiates any RPCs. Instead, it only responds to RPC requests issued by DataNodes or clients. 8. Robustness: The primary objective of HDFS is to store data reliably even in the presence of failures. The three common types of failures are NameNode failures, DataNode failures and network partitions. 8.1. Data Disk Failure, Heartbeats and Re-Replication: Each DataNode sends a Heartbeat message to the NameNode periodically. A network partition can cause a subset of DataNodes to lose connectivity with the NameNode. The NameNode detects this condition by the absence of a Heartbeat message. The NameNode marks DataNodes without recent Heartbeats as dead and does not forward any new IO requests to them. Any data that was registered to a dead DataNode is not available to HDFS any more. DataNode death may cause the replication factor of some blocks to fall below their specified value. The NameNode constantly tracks which blocks need to be replicated and initiates replication whenever necessary. The necessity for re-replication may arise due to many reasons: a DataNode may become unavailable, a replica may become corrupted, a hard disk on a DataNode may fail, or the replication factor of a file may be increased. 8.2. Cluster Rebalancing: HDFS arch is compatible with data rebalancing . A scheme might automatically move data from 1 DataNode to another if the free space on a DataNode falls below a certain threshold. In the event of a sudden high demand for a particular file, a scheme might dynamically create additional replicas and rebalance other data in the cluster. These types of data rebalancing schemes are not yet implemented. 8.3. Data Integrity: It is possible that a block of data fetched from a DataNode arrives corrupted. This corruption can occur because of faults in a storage device, network faults, or buggy software. The HDFS client software implements checksum checking on the contents of HDFS files. When a client creates an HDFS file, it computes a checksum of each block of the file and stores these checksums in a separate hidden file in the same HDFS namespace. When a client retrieves file contents it verifies that the data it received from each DataNode matches the checksum stored in the associated checksum file. If not, then the client can opt to retrieve that block from another DataNode that has a replica of that block.
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Summary 7. Every tax consists of a tax base, which defines what is taxed, and a tax structure, which specifies how the tax depends on the tax base. Different tax bases give rise to different taxes— the income tax, payroll tax , sales tax, profits tax , property tax, and wealth tax. 8. A tax is progressive if higher-income people pay a higher percentage of their income in taxes than lower-income people and regressive if they pay a lower percentage. Progressive taxes are often justified by the ability-to-pay principle. However, a highly progressive tax system significantly distorts incentives because it leads to a high marginal tax rate, the percentage of an increase in income that is taxed away, on high earners. The U.S. tax system is progressive overall, although it contains a mixture of progressive and regressive taxes. BA 210 Lesson I.8 Taxes 39
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Long-term liabilities  Long-term debt is any loan or debt obligation with a maturity of more than one year.  Capital leases are long-term lease contracts that obligate the firm to make regular payments in exchange for the use of an asset.  Deferred taxes are taxes that are owed but not yet paid. Firms keep two sets of books – one for financial reporting and one for tax purposes. Deferred tax liabilities arise when the firm’s financial (accounting) income exceeds its income for tax purposes. If a firm depreciates assets faster for tax purposes than for reporting purposes, its tax paid will be less than tax due according to reported income. Hence it will look as if the firm has not paid taxes that it owes.  Over time, the discrepancy will disappear and the tax due will be “paid.” Hence deferred tax is recorded as a liability.  For example, if reported depreciation is $200, while actual depreciation for tax purposes is $300. Then if the tax rate is 20%, actual tax paid (based on the higher depreciation) will be 20%(100) or $20 less. Hence this will create a tax liability, i.e. an obligation to pay the IRS $20 in the future. However, in an subsequent period, if reported depreciation is $300, while actual depreciation for tax purposes is $200; then the actual tax paid (based on the lower depreciation of $200 will be 20%(100) or $20 more than the tax required on the reported income. This will reduce the tax liability to zero. P.V. Viswanath 8
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Comparison: Retrieving a List an array Using a linked list fromUsing a File void GetList(int List[50], int &ListSize &ListSize) ) { ifstream file; char fileName[50]; int val; void GetList(nodePtr &List) { ifstream file; char fileName[50]; int val; nodePtr ptr; cout << "Enter the name " << "of the file: "; cin >> fileName; file.open(fileName); assert(!file.fail()); cout << "Enter the name " << “of the file: "; cin >> fileName; file.open(fileName); assert(!file.fail()); ListSize = 0; file >> val; while ((!file.eof()) && (ListSize < 50) 50)) ) { List[ListSize] = val; ListSize++; file >> val; } file.close(); List = NULL; file >> val; while (!file.eof()) { ptr = new node; ptr->value = val; ptr->next = List; List = ptr; file >> val; } file.close(); } Extra concern: Exceeding array’s size } Extra concern: Allocating new Chapter 9 Abstract Data Types and Algorithms Page 11
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Tax Shields • Tax savings/costs due to reporting expenses that have no cash flow effect • Four common tax shields – Depreciation tax shield • Depreciation expense × tax rate – Amortization tax shield • Amortization expense × tax rate – Loss tax shield • Loss on disposal × tax rate – Gain negative tax shield • Gain on disposal × tax rate Tax savings due to reporting an expense that does not use cash Additional tax cost due to reporting a revenue that does not generate cash Depreciation tax shield = Depreciation expense x income tax rate
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The Tax System Some important taxes and their tax bases are as follows: • Income tax: a tax that depends on the income of an individual or a family from wages and investments • Payroll tax: a tax that depends on the earnings an employer pays to an employee • Sales tax: a tax that depends on the value of goods sold (also known as an excise tax) • Profits tax: a tax that depends on a firm’s profits • Property tax: a tax that depends on the value of property, such as the value of a home • Wealth tax: a tax that depends on an individual’s wealth BA 210 Lesson I.8 Taxes 26
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8.4. Metadata Disk Failure: The FsImage and EditLog are central data structures. A corruption of these files can cause the HDFS instance to be non-functional. For this reason, the NameNode can be configured to support maintaining multiple copies of the FsImage and EditLog. Any update to either the FsImage or EditLog causes each of the FsImages and EditLogs to get updated synchronously. This synchronous updating of multiple copies of the FsImage and EditLog may degrade the rate of namespace transactions per second that a NameNode can support. However, this degradation is acceptable because even though HDFS applications are very data intensive in nature, they are not metadata intensive. When a NameNode restarts, it selects the latest consistent FsImage and EditLog to use. The NameNode machine is a single point of failure for an HDFS cluster. If the NameNode machine fails, manual intervention is necessary. Currently, automatic restart and failover of the NameNode software to another machine is not supported. 8.5. Snapshots: Snapshots support storing a copy of data at a particular instant of time. One usage of the snapshot feature may be to roll back a corrupted HDFS instance to a previously known good point in time. HDFS does not currently support snapshots but will in a future release. 9. Data Organization 9.1. Data Blocks: HDFS is designed to support very large files. Applications that are compatible with HDFS are those that deal with large data sets. These applications write their data only once but they read it one or more times and require these reads to be satisfied at streaming speeds. HDFS supports write-once-read-many semantics on files. A typical block size used by HDFS is 64 MB. Thus, an HDFS file is chopped up into 64 MB chunks, and if possible, each chunk will reside on a different DataNode. 9.2. Staging: A client request to create a file does not reach the NameNode immediately. In fact, initially the HDFS client caches the file data into a temporary local file. Application writes are transparently redirected to this temporary local file. When the local file accumulates data worth over one HDFS block size, the client contacts the NameNode. The NameNode inserts the file name into the file system hierarchy and allocates a data block for it. The NameNode responds to the client request with the identity of the DataNode and the destination data block. Then the client flushes the block of data from the local temporary file to the specified DataNode. When a file is closed, the remaining un-flushed data in the temporary local file is transferred to the DataNode. The client then tells the NameNode that the file is closed. At this point, the NameNode commits the file creation operation into a persistent store. If the NameNode dies before the file is closed, the file is lost. The above approach has been adopted after careful consideration of target applications that run on HDFS. These applications need streaming writes to files. If a client writes to a remote file directly without any client side buffering, the network speed and the congestion in the network impacts throughput considerably. This approach is not without precedent. Earlier distributed file systems, e.g. AFS, have used client side caching to improve performance. A POSIX requirement has been relaxed to achieve higher performance of data uploads. 9.3. Replication Pipelining: When a client is writing data to an HDFS file, its data is first written to a local file as explained in the previous section. Suppose the HDFS file has a replication factor of three. When the local file accumulates a full block of user data, the client retrieves a list of DataNodes from the NameNode. This list contains the DataNodes that will host a replica of that block. The client then flushes the data block to the first DataNode. The first DataNode starts receiving the data in small portions (4 KB), writes each portion to its local repository and transfers that portion to the second DataNode in the list. The second DataNode, in turn starts receiving each portion of the data block, writes that portion to its repository and then flushes that portion to the third DataNode. Finally, the third DataNode writes the data to its local repository. Thus, a DataNode can be receiving data from the previous one in the pipeline and at the same time forwarding data to the next one in the pipeline. Thus, the data is pipelined from one DataNode to the next.
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Overview of Lecture Notes  Introduction to Game Theory: Lecture 1, book 1  Non-cooperative Games: Lecture 1, Chapter 3, book 1  Bayesian Games: Lecture 2, Chapter 4, book 1  Differential Games: Lecture 3, Chapter 5, book 1  Evolutionary Games: Lecture 4, Chapter 6, book 1  Cooperative Games: Lecture 5, Chapter 7, book 1  Auction Theory: Lecture 6, Chapter 8, book 1  Matching Game: Lecture 7, Chapter 2, book 2  Contract Theory, Lecture 8, Chapter 3, book 2  Learning in Game, Lecture 9, Chapter 6, book 2  Stochastic Game, Lecture 10, Chapter 4, book 2  Game with Bounded Rationality, Lecture 11, Chapter 5, book 2  Equilibrium Programming with Equilibrium Constraint, Lecture 12, Chapter 7, book 2  Zero Determinant Strategy, Lecture 13, Chapter 8, book 2  Mean Field Game, Lecture 14, UCLA course, book 2  Network Economy, Lecture 15, Dr. Jianwei Huang, book 2 [2]
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03/18/19 18:01 9 Example 0 0849350808 0 0 0 Bhavani Thuraisingham 0 CRC Press 0 2001 0 0849300371 0 0
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Overview of Lecture Notes  Introduction to Game Theory: Lecture 1, book 1  Non-cooperative Games: Lecture 1, Chapter 3, book 1  Bayesian Games: Lecture 2, Chapter 4, book 1  Differential Games: Lecture 3, Chapter 5, book 1  Evolutionary Games: Lecture 4, Chapter 6, book 1  Cooperative Games: Lecture 5, Chapter 7, book 1  Auction Theory: Lecture 6, Chapter 8, book 1  Matching Game: Lecture 7, Chapter 2, book 2  Contract Theory, Lecture 8, Chapter 3, book 2  Learning in Game, Lecture 9, Chapter 6, book 2  Stochastic Game, Lecture 10, Chapter 4, book 2  Game with Bounded Rationality, Lecture 11, Chapter 5, book 2  Equilibrium Programming with Equilibrium Constraint, Lecture 12, Chapter 7, book 2  Zero Determinant Strategy, Lecture 13, Chapter 8, book 2  Mean Field Game, Lecture 14, UCLA course, book 2  Network Economy, Lecture 15, Dr. Jianwei Huang, book 2 [2]
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Question - books.c • Write a program to define a structure called Book. The Book structure should have a title, author, subject and a book id as attributes. • Write a function printBook(struct Books *book) that print book details: • E.g Book title : C Programming Book author : Sam Anders Book subject : C Programming [email protected] 117 ~/CS310/ch02]$ ./books Book book_id : 6495407 Book title : C Programming • In theBook main function declare and initialize 2 books and print author : Sam Anders Book subject : C Programming Tutorial their details: Book book_id : 6495407 Book Book Book Book title : Telecom Billing author : Sara Smith subject : Telecom Billing Tutorial book_id : 6495700 Copyright © 2017 by Jones & Bartlett Learning, LLC an Ascend Learning Company
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Event Reconstruction - Reconstruction of deleted files.  In most file systems file deletion does not erase the information stored in the file. Instead, the file entry and the data blocks used by the file are marked as unallocated, so that they can be reused later for another file. Thus, unless the data blocks and the deleted file entry have been reallocated to another file, the deleted file can usually be recovered by restoring its file entry and data blocks to active status.  Even if the file entry and some of the data blocks have been re-allocated, it may still be possible to reconstruct parts of the file. The lazarus tool for example, uses several heuristics to find and piece together blocks that (could have) once belonged to a file. Lazarus uses heuristics about file systems and common file formats.  In most file systems, a file begins at the beginning of a disk block; Most file systems write file into contiguous blocks, if possible; Most file formats have a distinguishing pattern of bytes near the beginning of the le; For most file formats, same type of data is stored in all blocks of a file.
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Example: Define Dependencies (after 1 of 2) Changed one association to a dependency relationship. (This change discussed on ‘next’ slide) Here, during a registration session, the Registration Controller works with a single Student, the registrant, and one Schedule, the current Schedule for the Student. <> These instances need to be accessed by ICourseCatalogSystem more than one of the Registration Controller’s, (from External System Interfaces) operations so Field Visibility is chosen + getCourseOfferings(forSemester : Semester) : CourseOfferingList from Registration Controller Global visibility to Student and from Registration Controller <> <> Schedule to Schedule. RegistrationController (from University Artifacts) (from Registration) Thus relationships currentSchedule - semester remain + // submit schedule() 0..1+ submit() 0..1 + // save schedule() associations. + // save() + // create schedule with offerings() # any conflicts?() (more ‘permanent’) + // getCourseOfferings(forSemester) : CourseOfferingList Field + // create with offerings() A Student manages his/her own Schedules, so Field visibility is chosen from Student to Schedule – and relation remains aggregation. Again, more ‘permanent.’ 0..* 0..1 registrant - name - address - StudentID : int Field visibility 1 0..1 <> Student (from University Artifacts) see Schedule as parameter below + addSchedule(theSchedule : Schedule, forSemester : Semester) + getSchedule(forSemester : Semester) : Schedule + hasPrerequisites(forCourseOffering : CourseOffering) : boolean # passed(theCourseOffering : CourseOffering) : boolean 35 More  0..* alternateCourses 0..2 0..* primaryCourses 0..4 <> CourseOffering (from University Artifacts) - number : String = "100" - startTime : Time - endTime : Time - days : Enum + addStudent(studentSchedule : Schedule) + removeStudent(studentSchedule : Schedule) + new() + setData() 36 Parameter visibility see Course Offering in Student as parameter
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The hypotheses to be tested Null: no significant differences Hypothesis Burial Type No significant differences will Populations are be found in socially biological undifferentiated markers of diet, in terms of diet, inter-life health status, and mobility, place of origin pathology rates, throughout the or childhood Late Neolithic and health between Copper Age burials. Time Period No significant differences will be found in biological markers of diet, interlife mobility, pathology rates, or childhood health across time periods Alternative 1: Significant differences between burials. Time not important Alternative Hypothesis 1 Social Populations are differentiation is biological evident in marked homogeneous (in differences in diet, terms of the health status and selected markers) place of origin, within burials and burial but differ across These differences are location burials temporally consistent Alternative 2: Significant differences between burials. Increase Alternative over time Hypothesis 2 Social differentiation is evident in marked Populations are differences in diet, biological health status and homogeneous (in place of origin, terms of the and burial selected markers) location. These within burials but differences differ across increase overtime burials Alternative Hypothesis 4 The pattern of differentiation is more evident in later burials, less pronounced or nonexistent in earlier burials Social differentiation is evident in marked differences in diet, and place of Populations are origin between biological individuals. heterogeneous (in Burials spaces are terms of the undifferentiated, selected markers) Later burials are more but differences within some heterogenous than increase over time burials. earlier burials Alternative Hypothesis 3 Social differentiation is evident in marked differences in diet, and place of origin between individuals. Burials spaces are undifferentiated. Populations are biological heterogeneou s (in terms of the selected markers) These differences within are temporally burials. consistent Alternative 4: Significant differences within burials. Increases over time Alternative 3: Significant differences within burials. Time unimportant
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Required Information in Incurred Cost Submission FAR 52.2167(d)(2)(iii) (referenced to ICE model) Schedule A Summary of Indirect Expense Rates Schedule B, C, D Indirect Cost Pools Schedule E Claimed Allocation Bases Schedule F Cost of Money Schedule G Booked and Claimed Direct Costs Schedule H Direct Costs by Contract at Claimed Rates Schedule H-1 Government Participation by Pool Schedule I Cumulative Allowable Cost Worksheet Schedule J Subcontract Information Schedule K Hours and Amounts on T&M Contracts Schedule L Payroll Reconciliation Schedule M Accounting/Organization Changes Schedule N Certificate of Indirect Costs Schedule O Contract Closing Information 105
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