Secure Cloud Data Storage System Using Hybrid Paillier–Blowfish Algorithm

Cloud computing utilizes enormous clusters of serviceable and manageable resources that can be virtually and dynamically reconfigured in order to deliver optimum resource utilization by exploiting the pay-per-use model. However, concerns around security have been an impediment in the extensive adoption of the cloud computing model. In this regard, advancements in cryptography, accelerated by the wide usage of the internet worldwide, has emerged as a key area in addressing some of these security concerns. In this document, a hybrid cryptographic protocol deploying Blowfish and Paillier encryption algorithms has been presented and its strength compared with the existing hybrid Advanced Encryption Standard (AES) and Rivest Shamir Adleman (RSA) techniques. Algorithms for secure data storage protocol in two phases have been presented. The proposed hybrid protocol endeavors to improve the power of cloud storage through a decrease in computation time and cipher-text size. Simulations have been carried out with Oracle Virtual Box and Fog server used on an Ubuntu 16.04 platform. This grouping of asymmetric and homomorphic procedures has demonstrated enhanced security. Compression usage has helped in decreasing the storage space and computation time. Performance analysis in terms of computation overhead and quality of service parameters like loads of parameters with and without attacks, throughput, and stream length for different modes of block cipher mode has been carried out. Security analysis has been carried out by utilizing the Hardening Index as an audit parameter using Lynis 2.7.1. Similarly, for halting the aforementioned approaches and for regulating traffic, firewall protection has been generated in the chosen hybrid algorithms. Finally, enhancements in the performance of the Paillier and Blowfish hybrid scheme with and without compression compared to the existing schemes using RSA and AES procedures have been demonstrated.     

Observing the effect of interprocess communication in auto controlled ant colony optimization‐based scheduling on computational grid

Computational grids allow the sharing of geographically distributed computational resources in an efficient, reliable, and secure manner. Grid is still in its infancy, and there are many problems associated with the computational grid, namely job scheduling, resource management, information service, information security, routing, fault tolerance, and many more. Scheduling of jobs on grid nodes is an NP‐class problem warranting for heuristic and meta‐heuristic solution approach. In the proposed work, a meta‐heuristic technique, auto controlled ant colony optimization, has been applied to solve this problem. The work observes the effect of interprocess communication in process to optimize turnaround time of the job. The proposed model has been simulated in Matlab. For the different scenarios in computational grid, results have been analyzed. Result of the proposed model is compared with another meta‐heuristic technique genetic algorithm that has been applied for the same purpose. It is found that auto controlled ant colony optimization not only gives better solution in comparison to genetic algorithm, but also converges faster because initial solution itself is good because of constructive and decision‐based policy adapted by the former. Concurrency and Computation: Practice and Experience, 2012.© 2012 Wiley Periodicals, Inc.     

Effects of precursor evaporation temperature on the properties of the yttrium oxide thin films deposited by microwave electron cyclotron resonance plasma assisted metal organic chemical vapor deposition

Yttrium oxide thin films are deposited using indigenously developed metal organic precursor (2,2,6,6-tetra methyl-3,5-hepitane dionate) yttrium, commonly known as Y(thd)₃ (synthesized by ultrasound method). Microwave electron cyclotron resonance plasma assisted metal organic chemical vapor deposition process was used for these depositions. Depositions were carried out at a substrate temperature of 350 °C with argon to oxygen gas flow rates fixed to 1 sccm and 10 sccm respectively throughout the experiments. The precursor evaporation temperature (precursor temperature) was varied over a range of 170-275 °C keeping all other parameters constant. The deposited coatings are characterized by X-ray photoelectron spectroscopy, glancing angle X-ray diffraction and infrared spectroscopy. Thickness and refractive index of the coatings are measured by the spectroscopic ellipsometry. Hardness and elastic modulus of the films are measured by load depth sensing nanoindentation technique.C-Y₂O₃ phase is deposited at lower precursor temperature (170 °C). At higher temperature (220 °C) cubic yttrium oxide is deposited with yttrium hydroxide carbonate as a minor phase. When the temperature of the precursor increased (275 °C) further, hexagonal Y₂O₃ with some multiphase structure including body centered cubic yttria and yttrium silicate is observed in the deposited film. The properties of the films drastically change with these structural transitions. These changes in the film properties are correlated here with the precursor evaporation characteristics obtained at low pressures.     

Synthesis of SnO2 hollow microspheres from ultrasonic atomization and their role in hydrogen sensing

Nanostructured SnO₂ hollow microspheres were synthesized using ultrasonic atomization technique. It is interesting that hollow microspheres could be prepared from ultrasonic atomization technique without any aid of template and surfactant. X-ray powder diffraction (XRD) confirmed the material to be SnO₂ having tetragonal structure. Average crystallite size calculated from X-ray diffractogram using Scherer's equation was found to be 8.45 nm. Scanning electron microscopy (SEM) and transmission electron microscopy (TEM) were used to study the microscopic study of fine powder particles. Powder consists of hollow microspheres of average diameter of 0.58 μm as well as nanoparticles of average diameter of 6 nm. The sensors fabricated from such powder show high hydrogen (1000 ppm) response (S = 2379) under the optimized experimental conditions. Sensor performance merits, such as, high hydrogen response, high hydrogen selectivity, short response time (2 s) and quick recovery time (15 s) may be due to both nanocrystallites and hollow microspheres associated in SnO₂ sensing material. The dramatic change in gas response was explained by the rapid diffusion of the target gas through the nano-porous structure of SnO₂ hollow microspheres.     

Influence of High Amplitude Mould Vibration on the Morphology of Silicon in the Al-Si Alloy (A308)

Silicon - Today’s casting industries strive to produce high-quality cast components in an efficient, cost-effective, eco-friendly, and sustainable way. To achieve this, with an indigenously...     

A Survey on Spot Pricing in Cloud Computing

Amazon offers spot instances to cloud customers using an auction-like mechanism. These instances are dynamically priced and offered at a lower price with less guarantee of availability. Observing the popularity of Amazon spot instances among the cloud users, research has intensified on defining the users’ and providers’ behavior in the spot market. This work presents an exhaustive survey of spot pricing in cloud ecosystem. An insight into the Amazon spot instances and its pricing mechanism has been presented for better understanding of the spot ecosystem. Spot pricing and resource provisioning problem, modeled as a market mechanism, is discussed from both computational and economics perspective. A significant amount of important research papers related to price prediction and modeling, spot resource provisioning, bidding strategy designing etc. are summarized and categorized to evaluate the state of the art in the context. All theoretical frameworks, developed for cloud spot market, are illustrated and compared in terms of the techniques and their findings. Finally, research gaps are identified and various economic and computational challenges in cloud spot ecosystem are discussed as a guide to the future research.     

Moving boundary approach to forecast tight oil production

A novel semianalytical production predictive tool for tight reservoirs based on the application of material balance on a transient linear flow system is developed in this paper. This method considers two important regions during transient production of oil reservoirs: the saturated region where gas evolves and flows with oil, and the undersaturated region where only oil flows. A zonal moving boundary approach is used to evolve the two regions as the reservoir pressure gradually decreases. A semianalytical method is used to calculate pressures in the various regions and volumetric expansions. For both black oil and volatile oil scenarios, calculations from this analytical framework are able to match reservoir pressures, oil and gas rates, and cumulative gas–oil ratios determined using a reservoir simulator. The model was also applied to wells in tight reservoirs around the United States such as the Bakken (ND) and the Eagle Ford (TX) with reasonable success.     

HaloTag-Based Reporters for Fluorescence Imaging and Biosensing

Visualizing the structure and dynamics of biomolecules is critical to understand biological function, and requires methods to fluorescently label targets of interest in their cellular context. Self-labelling proteins, which combine a genetically encoded tag with a small-molecule fluorophore, have attracted considerable attention for this purpose, as they can overcome limitations of fluorescent proteins. Among them, the HaloTag protein is the most broadly used, showing fast specific labelling with a small, easy to functionalize and cell-permeant ligand. Synthetic chemistry and protein engineering have provided a portfolio of powerful imaging tools exploiting HaloTag, along with general methods to optimize and adapt them to specific applications. Here, we provide an overview of fluorescent reporters based on the HaloTag protein for imaging and biosensing, highlighting engineering strategies and general applications.     

Natural tritium levels in tender and ripe coconut fruit (Cocos nucifera L.): a preliminary examination

Tissue Free Water Tritium concentrations (TFWT) were determined in tender coconut (Cocos nucifera L.) water, ripe coconut water, and kernel milk water, and compared with the same in groundwater collected from the vicinity of the trees. Samples with a very low tritium content were enriched by the alkaline electrolysis method. All of the samples were analyzed for tritium content in an ultra-low level liquid scintillation spectrometer. The TFWT in kernel milk water was found to be approximately 20-40 times higher than that in the groundwater. The tritium concentration in descending order in these samples is: kernel milk water, ripe coconut water, tender coconut water, and groundwater. The concentration of tritium increases as the fruit grows and the significantly high concentration in the kernel milk water suggests enrichment during the growth of the fruit.     

New Approach for Sediment Yield Forecasting with a Two-Phase Feedforward Neuron Network-Particle Swarm Optimization Model Integrated with the Gravitational Search Algorithm

Water Resources Management - Predicting sediment yield is an important task for decision-makers in environmental monitoring and water management since the benefits of applying non-linear,...