The municipal solid waste compost consists of elements with a varied composition, including light and heavy metal elements. For MSW compost to act as a soil conditioner, and to ensure agricultural stakeholders to believe in its use for crops production, validation of elements is obligatory. The triangular membership function evaluates each element of a fuzzy set for both discrete and continuous values, and regression analysis estimates the relationship between values. In this paper, a triangular membership function (μf) is studied and used to characterize the effect of individual elements available in the compost sample. The characterization determines the variation in the composition of elements in the compost sample and accordingly calculates its scorei. Furthermore, a reinvestigation is done by applying multiple regression analysis, especially on heavy metals, to compare their composition with light mineral nutrients and other supplementary elements. A relationship between R=4.12 and R2=0.067498635 is derived to determine the predicted value and defines the composition of heavy metals as attributed to another mineral nutrients. Furthermore, a correlation (Co) is derived to find the performance of the compost sample todecide whether both light and supplementary mineral nutrients are capable of minimizing the effect of heavy metals. A gratuity score (Gsi) is added to each heavy metal depending on the correlation value to form a composti. The scorei=88.11 and composti = 9.12 obtained, was summated to derive Ci=97.23, stating that the increase in score value declares that the compost sample is mature enough to be used for agriculture and enhance crops productivity.

The work investigates the effect of indirect Gamification in Development and Operations (DevOps) Course teaching. The software development team consisting of multiple contributors coordinates in the collaborative work environment to achieve specific predefined goals in a regulated and controlled fashion. In DevOps Course, the Installation and configuration of the various software tools are useful in the context of application development. To teach DevOps Tools precisely to achieve the learning outcome is a skilled task for a trainer. From the perspective of the learner, the learning environment has to be encouraging and exciting. Pedagogical techniques and ICT tools play an essential role during the knowledge transfer process. The motivation and reward to the Student are essential in achieving the learning outcomes of the Course. The author used the third-party Competition as an indirect Gamification technique to achieve the learning outcomes of the Course. The author encouraged the students to participate in the Hacktoberfest Competition to use the practical skills learned in the Course. The Under Graduate (UG) and Post Graduate (PG) Students have gone through the regular sessions of DevOps. For the participation purpose, both groups of students communicated on one platform. The students who succeeded faster during the GitHub Pull Request (PR) submission shared their experiences with other participants. The PG students participated 68.75% higher than UG students. Minimum of four PR submissions on GitHub and acceptance by the repository maintainer are the task completion criteria. The active participation of the small number of UG students became a motivational factor for the PG students. The Gaussian distribution on the marks obtained by the experimental group shows the absence of outliers. The research shows that the effectiveness of indirect Gamification depends on the age group, level, course content, and learning environment. The participation of a faculty member in the Competition during the learning activity boosts the desire of the Student to complete the task. The experimental group of 15 Students has outperformed in terms of the marks obtained compared to the control group of 52 students.

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The scientist, engineers, and researchers highly need the high-performance computing (HPC) services for executing the energy, engineering, environmental sciences, weather, and life science simulations. The virtual machine (VM) or docker-enabled HPC Cloud service provides the advantages of consolidation and support for multiple users in public cloud environment. Adding the hypervisor on the top of bare metal hardware brings few challenges like the overhead of computation due to virtualization, especially in HPC environment. This chapter discusses the challenges, solutions, and opportunities due to input-output, VMM overheads, interconnection overheads, VM migration problems, and scalability problems in HPC Cloud. This chapter portrays HPC Cloud as highly complex distributed environment consisting of the heterogeneous types of architectures consisting of the different processor architectures, inter-connectivity techniques, the problems of the shared memory, distributed memory, and hybrid architectures in distributed computing like resilience, scalability, check-pointing, and fault tolerance.

This study shows an enhancement of IoT which gets sensor data and performs real-time face recognition to screen physical areas to find strange situations and send an alarm mail to the client to make remedial moves to avoid any potential misfortune in the environment. Sensor data is pushed onto the local system and GoDaddy Cloud, whenever the camera detects a person to optimize the Physical Location Monitoring System by reducing the bandwidth requirement and storage cost onto the Cloud using edge computation. The study reveals that Decision Tree (DT) and Random Forest give reasonably similar macro average f1-score to predict a person using sensor data. Experimental results show that DT is the most reliable predictive model for the Cloud datasets of three different physical locations to predict a person using timestamp with an accuracy of 83.99%, 88.92%, and 80.97%. This study also explains multivariate time series prediction using Vector Auto Regression that gives reasonably good Root Mean Squared Error to predict Temperature, Humidity, Light Dependent Resistor, and Gas time series.

The problem domain of lease processing in the infrastructureas-a-service (IaaS) Cloud with the uncertain requirements of the Consumer on the account of per-defined conditions of owner privileges is under consideration. The uncertain requirements against the owner conditions have been converted into the meaningful (definite) matrix values with help of possibility distributions. The Fuzzy Control Logic (FCL) is used to solve this problem, which will be integrated with Cloud Scheduler. The results of the same are illustrated in this work.

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