RECRUITMENT APP – A CUSTOM APPLICATION ON SALESFORCE.COM

Cloud Computing is one of the upcoming Internet based technology. It is been considered as the next generation computing model for its advantages. It is the latest computational model after distributed computing, parallel processing and grid computing. To be effective they need to tap all available sources of supply, both internal and external. The system has facilities where prospective candidates can upload their CV’s and other academic achievements. Earlier recruitment was done manually and it was all at a time-consuming work. Now it is all possible in a fraction of second. Better recruitment and selection strategies result in improved organizational outcomes. With reference to this context, the research paper entitled Recruitment and Selection has been prepared to put a light on Recruitment and Selection process.

Parallel Processing Strategies for Geospatial Data in a Cloud Computing Infrastructure

This paper is on the optimization of computing resources to process geospatial image data in a cloud computing infrastructure. Parallelization was tested by combining two different strategies: image tiling and multi-threading. The objective here was to get insight on the optimal use of available processing resources in order to minimize the processing time. Maximum speedup was obtained when combining tiling and multi-threading techniques. Both techniques are complementary, but a trade-off also exists. Speedup is improved with tiling, as parts of the image can run in parallel. But reading part of the image introduces an overhead and increases the relative part of the program that can only run in serial. This limits speedup that can be achieved via multi-threading. The optimal strategy of tiling and multi-threading that maximizes speedup depends on the scale of the application (global or local processing area), the implementation of the algorithm (processing libraries), and on the available computing resources (amount of memory and cores). A medium-sized virtual server that has been obtained from a cloud service provider has rather limited computing resources. Tiling will not only improve speedup but can be necessary to reduce the memory footprint. However, a tiling scheme with many small tiles increases overhead and can introduce extra latency due to queued tiles that are waiting to be processed. In a high-throughput computing cluster with hundreds of physical processing cores, more tiles can be processed in parallel, and the optimal strategy will be different. A quantitative assessment of the speedup was performed in this study, based on a number of experiments for different computing environments. The potential and limitations of parallel processing by tiling and multi-threading were hereby assessed. Experiments were based on an implementation that relies on an application programming interface (API) abstracting any platform-specific details, such as those related to data access.

15 Paired Electrolysis

While paired electrochemical reactions have a history that can be traced back to the 19th century and have been very effectively used for the production of commercial products, the larger synthetic community has only recently started to embrace the opportunities this approach offers to maximize the overall energy and atom efficiency of electrochemical processes. In this review, a summary of these efforts is presented in the context of four classes of paired electrochemical reactions. These classes of reaction involve parallel processing of products at the anode and cathode, divergent reactions that use a single starting material in different ways, convergent reactions that combine products made at the anode and cathode, and sequential reactions that pass a substrate between the electrodes.

Development, Implementation and Testing of Algorithm to Detect Ionopause-Like Structure Using Parallel Processing in the Martian Atmosphere

This study assesses the Martian ionopause using MAVEN datasets between periapsis and 150-600 km. Ionopause is an abrupt reduction of the electron density with increasing altitude. It is also required to verify the simultaneous increase of the electron temperature and variability below 400 km. To address this issue, we have adopted a computational approach in determining the ionopause-like density structure of the ionospheric profile. From computing thermal & magnetic pressures, radial magnetic field components, ionopause-like density gradient are detected and stored. The ionopause (theoretically) is formed where the total ionospheric pressure equals solar wind dynamic pressure. The present algorithm consists of a comprehensive set of conditions to be performed on the dataset sequentially. These include datasets from various instruments simultaneously observed. The primary objective of the present study is to describe the implementation and testing of this algorithm for big datasets of the Martian ionosphere and extract ionopause-like density gradient using automation. Keywords: Ionopause, Mars, Remote sensing, MAVEN dataset, Parallel-processing