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Intraosseous infusion (IO) is the process of injecting medication, fluids, or blood products directly into the bone marrow; [1] this provides a non-collapsible entry point into the systemic venous system. [2] The intraosseous infusion technique is used to provide fluids and medication when intravenous access is not
Data loading, or simply loading, is a part of data processing where data is moved between two systems so that it ends up in a staging area on the target system. With the traditional extract, transform and load (ETL) method, the load job is the last step, and the data that is loaded has already been transformed.
As long as = in the underlying process which generates the data, the appropriate use of the IV estimator will identify this parameter. This works because IV solves for the unique parameter that satisfies Z T e = 0 {\displaystyle Z^{\mathrm {T} }e=0} , and therefore hones in on the true underlying parameter as the sample size grows.
The first use of channel I/O was with the IBM 709 [2] vacuum tube mainframe in 1957, whose Model 766 Data Synchronizer was the first channel controller. The 709's transistorized successor, the IBM 7090, [3] had two to eight 6-bit channels (the 7607) and a channel multiplexor (the 7606) which could control up to eight channels.
Memory-mapped I/O is preferred in IA-32 and x86-64 based architectures because the instructions that perform port-based I/O are limited to one register: EAX, AX, and AL are the only registers that data can be moved into or out of, and either a byte-sized immediate value in the instruction or a value in register DX determines which port is the source or destination port of the transfer.
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identifying data: infectious disease: infectious dose: intellectual disability: I&D: incision and drainage (how to treat an abscess) IDA: iron deficiency anemia: IDC: idiopathic dilated cardiomyopathy: indwelling catheter: infiltrating ductal carcinoma: IDDM: insulin-dependent diabetes mellitus (now called diabetes mellitus type 1) IDL ...
Extract, load, transform (ELT) is an alternative to extract, transform, load (ETL) used with data lake implementations. In contrast to ETL, in ELT models the data is not transformed on entry to the data lake, but stored in its original raw format.