Strain engineering and photocatalytic application of single-layer ReS2_2

We present a theoretical study on the electronic, dynamical, and photocatalytic properties of single-layer ReS$_2$ under uniaxial and shear strains. The single-layer ReS$_2$ shows strong anisotropic responses to straining. It remains dynamically stable for a wide range of $x$-axial strain, but becomes unstable for 2\% $y$-axial compressive strain. The single-layer ReS$_2$ is calculated to be an indirect bandgap semiconductor, and there is an indirect$-$direct bandgap transition under 1$-$5\% $x$-axial tensile straining. The single-layer ReS$_2$ is predicted incapable of catalyzing the water oxidation reaction. However, 1$-$5\% $y$-axial tensile strain can enable the single-layer ReS$_2$ for overall photocatalytic water splitting. Besides, the single-layer ReS$_2$ can also catalyze the overall water splitting and be most efficient under acidic water solutions with pH=3.8

Asymptotic coverage probabilities of bootstrap percentile confidence intervals for constrained parameters

The asymptotic behaviour of the commonly used bootstrap percentile confidence interval is investigated when the parameters are subject to linear inequality constraints. We concentrate on the important one- and two-sample problems with data generated from general parametric distributions in the natural exponential family. The focus of this paper is on quantifying the coverage probabilities of the parametric bootstrap percentile confidence intervals, in particular their limiting behaviour near boundaries. We propose a local asymptotic framework to study this subtle coverage behaviour. Under this framework, we discover that when the true parameters are on, or close to, the restriction boundary, the asymptotic coverage probabilities can always exceed the nominal level in the one-sample case; however, they can be, remarkably, both under and over the nominal level in the two-sample case. Using illustrative examples, we show that the results provide theoretical justification and guidance on applying the bootstrap percentile method to constrained inference problems.Comment: 22 pages, 6 figure

Simultaneous Optimization of Application Utility and Consumed Energy in Mobile Grid

Mobile grid computing is aimed at making grid services available and accessible anytime anywhere from mobile device; at the same time, grid users can exploit the limited resources of mobile devices. This paper proposes simultaneous optimization of application utility and consumed energy in mobile grid. The paper provides a comprehensive utility function, which optimizes both the application level satisfaction such as execution success ratio and the system level requirements such as high resource utilization. The utility function models various aspects of job, application and system. The goal of maximizing the utility is achieved by decomposing the problem into a sequence of sub-problems that are then solved using the NUM optimization framework. The proposed price-based iterative algorithms enable the sub-problems to be processed in parallel. The simulations and analysis are given to study the performance of the algorithm

Market Mechanism for Dynamic Resource Management in Computational Grid

This paper presents a market mechanism for dynamic resource allocation in computational grid. Grid market is described that consists of two economic agent types; it allows agents representing various grid resources to coordinate their resource allocation decisions without assuming a priori cooperation. The grid task agents buy resources to complete tasks. Grid resource agents charge the task agents for the amount of resource capacity allocated. Grid resource allocation problem is presented as grid user utility optimization. Given grid resource agent's pricing policy, the task agent optimization problem is to complete its job as quickly as possible when spending the least possible amount of money. This paper provides a resource allocation and pricing algorithm. Experiments are made to compare the performance of the price-directed resource allocation with conventional Round-Robin allocation

A Distributed Iterative Algorithm for Optimal Scheduling in Grid Computing

The paper studies a distributed iterative algorithm for optimal scheduling in grid computing. Grid user's requirements are formulated as dimensions in a quality of service problem expressed as a market game played by grid resource agents and grid task agents. User benefits resulting from taking decisions regarding each Quality of Service dimension are described by separate utility functions. The total system quality of service utility is defined as a linear combination of the discrete form utility functions. The paper presents distributed algorithms to iteratively optimize task agents and resource agents functioning as sub-problems of the grid resource QoS scheduling optimization. Such constructed resource scheduling algorithm finds a multiple quality of service solution optimal for grid users, which fulfils some specified user preferences. The proposed pricing based distributed iterative algorithm has been evaluated by studying the effect of QoS factors on benefits of grid user utility, revenue of grid resource provider and execution success ratio

Appropriate Machine Learning Algorithm for Big Data Processing

MLlib is Spark’s library of machine learning functions developed to operate in parallel on clusters. MLlib comprises of different types of learning algorithms and is available from all of Spark’s programming languages. Machine Learning is important to data scientists with a machine learning background considering using Spark, as well as engineers working with a machine learning professionals. A lot of algorithms in MLlib function better in terms of forecasting precision with regularization when that choice is accessible. Again, a lot of the SGDbased algorithms demand around 100 iterations to obtain good outcome. The paper presents the types of algorithms on distributed data sets, indicating all data as RDDs and recommends one which is more appropriate and effective for huge data processing. An assessment will be made based on their strength and weakness on the number of machine learning algorithms and come out with one which is effective for big data processing. The appropriate and effective machine learning algorithm is HashingTF as it takes the hash code of each word modulo a desired vector size, S, and thus maps each word to a number between 0 and S–1. This always provides an S-dimensional vector, and in practice is quite robust even if multiple words map to the same hash code. The MLlib inventors recommend setting S between 2 HashingTF can run either on one document at a time or on a whole RDD. It demands each “document” to be represented as an iterable order of objects for example, a list in Python or a Collection in Java

Big Data Processing with Apache Spark in Tertiary Institutions: Spark Streaming

In tertiary institutions, different set of information are derived from the various department and other functional sections. Individual departments and other functional sections in the institutions manage their data separately. This situation has resulted in huge number of different set of data across the various departments in tertiary institutions. There is no centralized data centre where data/information can be retrieved for the management committee when the need arises. In academic institution data captured is restricted to the institution which collected it but centralisation of the various data in the various functional sections does not exist. This makes it difficult for the management committee to take decisions based on relevant information needed. In order to address this problem, we proposed Spark Streaming. Spark Streaming is an element which facilitates processing of live flows of data. Spark streaming will able to capture data in real time, process it and make it available to the management committee when the need arises Keywords: Spark, Streaming, Big data, Processing, Tertiary, Institutio