It is difficult for users to submit jobs to distributed computers and to retrieve calculation data from them in scientific computings. In this paper, we discuss and develop a robust job execution service system in a closed distributed computer system. The job execution service system consists of a dynamic system management servers, execution servers and data servers. The dynamic system management server is duplicated in order to keep the system robust, and has an assistant management server. The dynamic system management server has a function of the job execution system management, including software deployment, program compilation, job execution, job status retrieval and computing data retrieval. This system does not require special middleware such as Globus or UNICORE or GLite or so. Users access the web page on the dynamic system management server, and the clients submit jobs. After the submitted job finishes, the dynamic system management server collects the information from other distributed computers. The dynamic management server and its assistant server move dynamically to new servers, if the present servers become busy. The dynamic system management server also demands the execution server to transfer the result data to the optimal data server. The dynamic system management server copies the computing data and sends the compressed computing data to another optimal data server in order for a robust data storage system. The clients can deploy their programs, execute jobs and retrieve the result data by accessing only the web page in the job execution service system. This job execution management server also has a function of automatic system construction, so that the users can manage the setup of the job execution management system easily on their closed distributed computers.

In computing sciences (e-Sciences) we need computer power, excellent algorithms and programming power in order to solve scientific problems leading to discoveries and development of innovative new products. So far, the computer power and the computing algorithms have been developed incredibly, and have provided enormous contributions to e-Sciences and e-Productions. Problem solving environments (PSE) support the programming power in e-Sciences and e-Productions, and have been studied actively for a few decades. In this paper a distributed PSE, named D-NCAS is presented, and a computer-assisted mathematical modeling support is also discussed in D-NCAS, which helps users solve partial differential equation (PDE) based problems in scientific computing. The D-NCAS PSE inputs a problem description and outputs a program flow, a C-language source code for the problem and also a document for the program. Each PSE module is distributed on networklinked distributed computers. The PSE holds all the information of the problem: PDEs, discretization scheme, mesh information, equation manipulation results, designed program structure, variable and constant definitions and program itself. The PSE concept has been opening a new direction for the computer-assisted programming or development of scientific simulation programs or CAEs.

A distributed problem solving environment (PSE) is proposed to help users solve partial differential equation (PDE) based problems in scientific computing. The system inputs a problem description and outputs a program flow, a C-language source code for the problem and also a document for the program. Each module is distributed on distributed computers. The PSE contains all the information of the problem, PDEs, discretization scheme, mesh information, equation manipulation results, designed program structure, variable and constant definitions and program itself. Therefore the documentation support module generates a document for the generated program and the problem itself in the PSE. The module liaison module generates an adapter module among the distributed PSE modules. The job execution service module deploys programs generated or prepared on distributed computer resources and helps users run the programs on the distributed computers. The concept of the distributed PSE extends the potential of conventional PSE systems

Codes for correcting and detecting unidirectional byte errors are investigated. The relations between distances and the error-correcting and -detecting capabilities of such codes are derived. It is shown that a two-fold unidirectional b-bit-byte error-correcting (2-UbEC) code requires at least three check bytes and that a one-fold unidirectional b-bit-byte error-correcting/2-fold unidirectional b-bit-byte error-detecting (1-UbEC/2-UbED) code requires at least two check bytes. Construction methods for 2-UbEC codes with three check bytes and 1-UbEC/2-UbEd codes with two check bytes are proposed.>

Codes are considered for correction and detection of unidirectional byte errors. A code construction based on the generalized concatenated code construction is proposed. This construction gives a large number of efficient codes. For example, from this construction, a 72-input-bit encoder of the triple unidirectional 8-bit-byte error-correcting and fourfold unidirectional 8-bit-byte error-detecting code with a length of 112 bits and rate 9/14 is obtained, whereas the ordinary triple 8-bit-byte error-correcting and fourfold 8-bit-byte error-detecting code of the same length has only 56 information bits and is of rate 1/2. The proposed construction is generalized to one that gives efficient short-length codes.>

Codes correcting/detecting unidirectional byte errors are investigated. It is shown that such codes have the capability of correcting combinations of bidirectional and unidirectional byte errors and code constructions. These are derived from the combination of two codes. One is a code for encoding the Hamming weights of data bytes and is used to estimate error locations. The other is a byte-error-correcting code for error evaluation. Moreover, the authors describe a decoding procedure for the codes, correcting combinations of bidirectional and unidirectional byte errors. The constructions provide many efficient codes of short length. For example, when a byte consists of 32 bits, a systematic code can be constructed with 61 information bytes and 3 check bytes that has the capability of correcting a single bidirectional byte error for double unidirectional byte errors, whereas the best known double-byte-error-correcting code requires 4 check bytes.>

The authors give a tabulation of the numbers of codewords in new binary codes with the asymmetric/unidirectional error-correcting capabilities of 3, 4, 5, 6 for lengths 14, 15, . . . , 23. The new codes have greater sizes than the known codes for 14>