Spark

G asoline direct injection (GDI) engines are well known for their ability to operate at the stratified fuel-air mixture, and thereby they are highly efficient than port fuel injection (PFI) engines. However, the stratification of the in-cylinder mixture leads to higher nitrogen oxides (NO x) and soot emissions with lower hydrocarbon (HC) emissions. The PFI works under a homogeneous mixture, which leads to lower NO x and soot emissions with compensation of HC emissions. By combining the advantages of GDI and PFI modes, it is possible to achieve higher fuel efficiency with lower emissions. Therefore, in the present study, four different injection strategies, namely, pure GDI, gasoline-direct multipleinjection (GDMI), combined GDI with PFI (GDI-PFI), and pure PFI are investigated under various load conditions using computational fluid dynamics (CFD) analysis. The effect of these strategies on mixture formation, indicated mean effective pressure (IMEP), and emissions are evaluated. For the analysis, the equivalence ratio (ER) is varied from 0.5 to 0.9 under naturally aspirated conditions. From the results, it is found that in the GDI-PFI mode, at the split fraction of 0.6 and ER of 0.9, the NO x and soot emissions are at the minimum levels because of good partial stratification. The IMEP improved by about 10.6% and the HC emissions reduced by about 82.8% than that of the corresponding GDMI mode. However, the NO x and soot emissions increased by about 39.4% and 9.24%, respectively compared to that of the corresponding GDMI modes. Also, the average flame speed for the GDMI mode with the split fraction of 0.6 and ER of 0.9 is higher by about 3% than that of the GDI-PFI mode.

In the phase of association rule mining, frequent itemset mining is an important move. Conventional methods to mine frequent itemsets in the big data epoch pose major obstacles as there is insufficient processing capacity and memory space. This work presents a novel frequent itemset mining technique that utilizes both matrix-based pruning method and Spark. The presented Spark based distributed (SBD) frequent itemset mining technique can efficiently reduce the number of campaign itemsets and also boost the performance of iterative computing. The experimental analysis is carried out using typical benchmark datasets to compare the presented SBD algorithm with the previous algorithm such as parallel FP-growth and distributed frequent itemset mining. The numerical results obtained from the experimental analysis indicate that SBD algorithm has better scalability and efficiency. Furthermore, a case study is accomplished to verify the efficacy of the presented SBD frequent itemset mining technique

Children with autism spectrum disorder (ASD) experience problems in motor skills which can affect other abilities such as academic and daily life skills. In this regard, the cost of interventions and the long distance from the rehabilitation centers put pressure on the family of children with ASD. Previous studies have shown the potential for using telerehabilitation as a more affordable way to provide interventions for children with ASD. Therefore, the present study aimed to investigate the non-attendant family-centered Sports, Play and Active Recreation for Kids (SPARK) motor program intervention to develop gross motor skills in children with a high-function ASD. Materials and Methods: The research is a quasi-experimental study with a pretest-posttest design. The participants were 9 families of children with high-functioning ASD referring to the rehabilitation centers in Tehran, Isfahan, Quds, and Kermanshah provinces, Iran. Telerehabilitation of SPARK motor program involves exercising and playing performed in 24 sessions of 45 minutes each (3 sessions per week) for 8 weeks applied for parents. The test of gross motor development-2 (TGMD-2) was used for data collection. The collected data were analyzed by paired t-test. Results: The results showed that family-based telerehabilitation of the SPARK program significantly improved gross motor skills in children with high-functioning ASD (P<0.05). Conclusion: It seems that the telerehabilitation of SPARK can be helpful for families who do not have access to rehabilitation centers.

The proliferation of information technology produces a huge amount of data called big data that cannot be processed by traditional database systems. These Various types of data come from different sources. However, stragglers are a major bottleneck in big data processing, and hence the early detection and accurate identification of stragglers can have important impacts on the performance of big data processing. This work aims to assess five stragglers identification methods: Hadoop native scheduler, LATE Scheduler, Mantri, MonTool, and Dolly. The performance of these techniques was evaluated based on three benchmarked methods: Sort, Grep and WordCount. The results show that the LATE Scheduler performs the best and it would be efficient to obtain better results for stragglers identification.

The Big data is the name used ubiquitously now a day in distributed paradigm on the web. As the name point out it is the collection of sets of very large amounts of data in pet bytes, Exabyte etc. related systems as well as the algorithms used to analyze this enormous data. Hadoop technology as a big data processing technology has proven to be the go to solution for processing enormous data sets. MapReduce is a conspicuous solution for computations, which requirement one-pass to complete, but not exact efficient for use cases that need multi-pass for computations and algorithms. The Job output data between every stage has to be stored in the file system before the next stage can begin. Consequently, this method is slow, disk Input/output operations and due to replication. Additionally, Hadoop ecosystem doesn't have every component to ending a big data use case. Suppose we want to do an iterative job, you would have to stitch together a sequence of MapReduce jobs and execute them in sequence. Every this job has high-latency, and each depends upon the completion of the previous stage. Apache Spark is one of the most widely used open source processing engines for big data, with wealthy language-integrated APIs and an extensive range of libraries. Apache Spark is a usual framework for distributed computing that offers high performance for both batch and interactive processing. In this paper, we aimed to demonstrate a close-up view about Apache Spark and its features and working with Spark using Hadoop. We are in a nutshell discussing about the Resilient Distributed Datasets (RDD), RDD operations, features, and limitation. Spark can be used along with MapReduce in the same Hadoop cluster or can be used lonely as a processing framework. In the last comparative analysis between Spark and Hadoop and MapReduce in this paper.

In this paper we report on our experiments in using the currently popular Smt (Sat Modulo Theories) solvers Yices [10] and Cvc3 [1] and the Simplify theorem prover [9] to discharge verification conditions (VCs) from programs written in the Spark language [5]. Spark is a subset of Ada used primarily in high-integrity systems in the aerospace, defence, rail and security

Three properties of matrices: the spark, the mutual incoherence and the restricted isometry property have recently been introduced in the context of compressed sensing. We study these properties for matrices that are Kronecker products and show how these properties relate to those of the factors. For the mutual incoherence we also discuss results for sums of Kronecker products.

Business is important in development of any country as it contributes into the country's GDP. India has 29% of GDP and 28% of employment. Its nominal output ranks 16th in the world and the service industry ranks 15th. This project is example of business process improvement or re-engineering. The proposed project applies machine learning concepts and determines the appropriate change in tread for any business firm. Using Principle Component Analysis as a dimensionality reduction technique we have reduced number of feature to minimum. This feature reduction technique helps the model to execute efficiently and provide better results.