Split keyword fuzzy and synonym search over encrypted cloud data

Multimedia Tools and Applications - A substitute solution for various organizations of data owners to store their data in the cloud using storage as a service(SaaS). The outsourced sensitive data...     

Thermo-Mechanical Properties and Abrasive Wear Behavior of Silicon Carbide Filled Woven Glass Fiber Composites

A study was done to determine the effect of physical, mechanical, thermal and three body abrasive wear response of Silicon Carbide (SiC) filled Glass Fiber Reinforced Epoxy (GFRE) composites. The main purpose was to study the influence of different weight percentages (wt.%) of SiC filler in addition to that of glass fiber. A three body abrasive wear analysis was conducted by varying different factors such as fiber/filler reinforcement, abrasive particle size, normal load, sliding distance and sliding velocity. An attempt was made to find out the dominant factor and the effect of each factor on specific wear rate analysis. Physical and mechanical properties, i.e. density, hardness, tensile strength, flexural strength, inter laminar shear strength and impact strength, were determined for each weight percent of filler reinforcement to determine the behavior of mechanical properties with varying SiC filler loading. Thermo – mechanical properties of the material, i.e. storage modulus, loss modulus and tan delta with temperature were measured using a Dynamic Mechanical Analyzer (DMA). The result shows the increasing / decreasing trend and critical points of each analysis. The trend and major factors responsible for reducing the specific wear rate were determined. Mechanical properties, i.e. hardness and impact strength, increase with the increase in SiC content, whereas tensile strength, flexural strength and inter laminar shear strength decrease. Worn surfaces were studied using scanning electron microscopy (SEM) to give an insight into the wear mechanisms.     

Intelligent Models of the Quantitative Behavior of Microbial Systems

Under different environmental conditions, microbial systems display complex behavioral patterns that are difficult to express quantitatively by mechanistic methods. Therefore, two alternate approaches based on different forms of intelligence have emerged. One approach uses methods of artificial intelligence (AI) such as neural networks, expert systems, and genetic algorithms to describe cellular behavior. The second methodology, which leads to the class of cybernetic models, relies on intelligence postulated to be possessed by the cells themselves. While both AI and cybernetic methods have been effective in many applications where mechanistic models are inadequate, all three methods have strengths and weaknesses. This recognition has led to the development of hybrid systems that combine two or more approaches for different aspects of a microbial system. However, the optimum design of hybrid systems still remains heuristic. The rationale and the developments from mechanistic to hybrid models are discussed here, and it is suggested that eventually a truly intelligent system should be self-evolving to maintain itself at the optimum configuration at all times. IMTECH communication no.017/2008     

Performance prediction of solar air heater having roughened duct provided with transverse and inclined ribs as artificial roughness

An experimental study has been carried out to investigate the effective efficiency of a solar air heater duct provided with transverse and inclined ribs as artificial roughness elements on the absorber plate. The range of parameters considered for the present investigation; Reynolds number (Re) 2000–14,000, relative roughness pitch (p/e) 3–8 and a fixed value of relative roughness height (e/D) of 0.030. The effective efficiency has been computed based on the experimentally determined values for the range of parameters considered. Further an attempt has also been made to optimize the thermal efficiency for the same system under similar conditions by Taguchi method.     

Preliminary Evaluations on Development of Recycled Porcelain Reinforced LM-26/Al-Si10Cu3Mg1 Alloy for Piston Materials

Silicon - The current work deals with an endeavor to synthesis, metal composites by taking aluminum alloy (i.e. LM 26) as matrix material and porcelain (powder form) as reinforcement. The metal...     

Characterization of the Binding of Hydroxyindole,Indoleacetic acid,and Morpholinoaniline to the Salmonella Type III Secretion System Proteins SipD and SipB

Many Gram‐negative bacteria require the type III secretion system (T3SS) to cause infectious diseases in humans. A looming public health problem is that all bacterial pathogens that require the T3SS to cause infectious diseases in humans have developed multidrug resistance to current antibiotics. The T3SS is an attractive target for the development of new antibiotics because of its critical role in virulence. An initial step in developing anti‐T3SS‐based therapeutics is the identification of small molecules that can bind to T3SS proteins. Currently, the only small molecules that are known to bind to the Salmonella T3SS proteins SipD and SipB are bile salts (to SipD) and sphingolipids and cholesterol (to SipB). Herein we report the results of a surface plasmon resonance screen of 288 compounds wherein the binding of 4‐morpholinoaniline to SipD, 3‐indoleacetic acid to SipB, and 5‐hydroxyindole to both SipD and SipB were identified. We also identified by NMR the SipD surfaces involved in binding. These three compounds represent a new class of molecules that can bind to T3SS tip (SipD) and translocon (SipB) proteins that could find use in future drug design.     

Thermal interface tailoring in composite materials

The thermal transport in heterogeneous materials systems, such as in composites, is essentially controlled by the phonon scattering phenomena at the materials interface due to the interface materials property mismatch. Such phenomena are also prevalent in joints or component interfaces. The thermal property mismatch at the materials interface, in the molecular scale, is primarily dictated by the phonon density of state across the interface. In this paper, the interface materials configuration for tailoring the thermal properties of composite materials with nano constituents is presented. The materials modeling using both the finite element analysis (FEM) and molecular dynamics (MD) simulations is performed to identify the effect of materials constituent scale as well as the nano constituent surface functionalization on the interface thermal transport phenomena. It is observed that the effect of surface functionalization towards establishing covalent bonding between the nano constituent surface the matrix (such as polymers) is extremely important in enhancing the interface thermal conductance.     

Effect of electrical properties on gas sensitivity of polypyrrole/cds nanocomposites

In the present work, polypyrrole (PPy) nanocomposites were synthesized using ferric chloride (FeCl₃) as an oxidant by in situ polymerization at room temperature. Cadmium sulfide (CdS) nanoparticles were synthesized by ultrasonication technique with size ranging between 60 and 110 nm. The PPy/CdS nanocomposites were prepared by taking 1–3 wt % loading of CdS to measure the electrical conductivity. The PPy nanocomposites were characterized by using FTIR, X‐ray diffraction, UV, and SEM. Furthermore, these PPy/CdS nanocomposites were investigated to study their effect of electrical properties on gas sensitivity of ammonia and LPG. The nanocomposites showed improvement in conductivity and sensing response toward 250 ppm NH₃ was found to be maximum (4.2) compared with 100 and 500 ppm NH₃ gas, whereas in the case of LPG, it showed sensitive response. © 2015 Wiley Periodicals, Inc. J. Appl. Polym. Sci. 2015 , 132, 42379.     

Design Considerations in Hybrid Neural Optimization of Fed-Batch Fermentation for PHB Production by <Emphasis Type="Italic">Ralstonia eutropha</Emphasis>

Mechanistic models are often inadequate for non-ideal fermentation processes. This has been a major limitation in the commercial success of fed-batch fermentations for poly-β-hydroxybutyrate (PHB), a biopolymer that is potentially better than many synthetic polymers. A previous study with Ralstonia eutropha has shown that optimization through neural networks in place of mechanistic models can help to enhance PHB production substantially. Hybrid models combine the strengths of mechanistic and neural models and minimize their weaknesses. However, there is no unique hybrid neural model for a given application. Therefore, three fundamental hybrid designs have been compared with the neural and mechanistic optimizations done earlier for a fed-batch bioreactor for PHB production. Non-ideal features were introduced by adding Gaussian noise in the two feed streams—glucose and NH₄Cl—and incorporating the optimum finite dispersion determined previously. All three hybrid designs were superior to the neural and mechanistic approaches. The best hybrid system, using a weighted combination of mechanistic and neural kinetic rates, generated 140% more of biomass and 330% more of PHB than conventional mechanistic optimization. This was achieved with reduced consumption of the two primary substrates. The supremacy of this hybrid system underlines the complexity of the PHB synthesis network and the merit in combining mechanistic and neural kinetics, and the performance enhancement achieved indicates the feasibility of such a system for an economically viable large-scale PHB fermentation.