Once the model gets trained in a supervised environment by taking a large set of samples and data which is preprocessed for computation in a distributed Message Passing Interface (MPI) that is the "MPI standard distributed communication framework for scientific and commercial parallel distributed computing applications". It is used to adjust the parameters of the machine- or deep-learning model through a 'training' or optimization procedure. Each and every message communication channel in Denial of Distributed services through MPI library implies that the inefficacy of the large high computing hardware available for the processing of the TF/s (teraflop every second) data is insufficient. Also an organized framework of a network system containing numerous computational nodes with a low-latency high-transmission capacity will be insignificant if the MPI execution is not done in an efficient manner or if the performance of the MPI execution is not optimum.

In the first paper, "A Comparative Analysis of Requirement Gathering Techniques", by Babangida Zachariah and Ogwueleka Francisca Nonyelum, requirement elicitation as a concept of requirement engineering essentially describes the process of requirement gathering for the purpose of establishing end users' and stakeholders' system requirement. Effective gathering remains a fundamental prerequisite for developing and implementing successful systems. Though different requirement gathering techniques are available, no single technique serves for all peculiarities of (or parameters associated with) a system; therefore a combination of different techniques becomes a necessity. This paper presents possible combinations of techniques when considering system characteristics against system analyst's requirement elicitation experience level, analyst's system domain knowledge, stakeholders' articulacy level and stakeholders' availability, all of which are fundamental for effective requirement gathering and information against participant (analyst and stakeholders) characteristics in terms of experience and physical location. It was discovered that collaborative techniques of requirement gathering are mostly used and have the potential to be the most used technique in the future following the advances in technology. Therefore, analysts may consider developing these skills and become highly experienced with these techniques.

The second paper, "Message Passing Interface: An Overview", by Anshu Kumari and Ajit Singh, describes the MPI as a communication library specification for parallel computers and for the workstation network and signifies the role of MPI in parallel computing. The MPI specifications and the detailed view of the primary subject, including "evolution of MPI, modes of MPI, MPI features, communicator" etc., are ideal for the learner, new to parallel programming with MPI.

The last paper, "Efficient Memory Storage Based on DNA Computing", is authored by Agbaje Michael O, Akiode I, Ajike Uchenna and Agbaje Alice O. The biological composition of the human is so efficient in terms of storage of information using the DNA. Digital storing information in the DNA involves the process of encoding and decoding binary data from the synthesized DNA strand. The DNA molecule represents the genetic blueprints for living cells. The idea of using DNA to store data was first proposed in the mid-1990. Moore's Law is approaching its limit, but the ability to manipulate molecules is growing faster than ever. DNA could provide alternative substrates for computing and storage as the existing ones approach physical limits. The DNA fountain records a high capacity technique for storing information efficiently. This paper reviews the advances in DNA computing and how it affects efficient storage in memory elements.