Effect of nano additives (titanium and zirconium oxides) and diethyl ether on biodiesel-ethanol fuelled CI engine

The present work is dedicated to the comparative experimental study of biodiesel-ethanol blends in a compression ignition engine using TiO₂ (Titanium oxide) nanoparticle, ZrO₂ (Zirconium oxide) nanoparticle and DEE (Diethyl ether) additives. The test fuels used are a blend of biodiesel (80%) -ethanol (20%) (denoted as BE), a blend of BE with 25 ppm Titanium oxide nanoparticle (denoted as BE-Ti), a blend of BE with 25 ppm Zirconium oxide nanoparticle (denoted as BE-Zr) and a blend of BE with 50 ml Diethyl ether (denoted as BE-DEE). Addition of nanoparticles increases the oxidation rate, reduces the light-off temperature and creates large contact surface area with the base fuel thereby enhancing the combustion with minimal emissions. Experimental results shown that addition of Titanium nanoparticles increased NOx, HC and smoke with lowered BSFC and CO. Whereas addition of Zirconium nanoparticles increases BSFC and HC emissions with lowered CO, CO₂ and smoke emissions in comparison with BE blends. DEE addition to BE blends improved the heat release rate and increased HC, CO emissions were observed with lowered BSFC, NOx and smoke. Simultaneous reduction of NOx and smoke indicates the effect of DEE on Low temperature combustion (LTC).

     

Influence of Acid Modification on Selective Phenol Hydrogenation Over Pd/Activated Carbon Catalysts

The influence of the acid treatment on cyclohexanone selectivity of phenol hydrogenation over Pd on active carbon was studied in liquid phase reaction and by temperature-programmed desorption. Acid treatment of activated carbon led to an increased cyclohexanone/cyclohexanol ratio. Acid modification of the carbon support enriched the electron density of Pd, and enhanced the desorption of the phenoxy species, which resulted in improved cyclohexanone selectivity in phenol hydrogenation.     

Hydrogen evolution reaction: The role of arsenene nanosheet and dopant

The state-of-the-art density functional theory (DFT) is employed to study the catalytic activity of arsenene for hydrogen evolution reaction (HER) and oxygen evolution reaction (OER). We have included dispersion correction to get accurate adsorption energy on the individual catalytic surface (top site). Using binding energy calculation, arsenene is shown to be a potential candidate for HER. Here we investigate the stability and electronic properties of the honeycomb structure of the arsenene system using first-principles calculation to find the effect of different dopants on the fundamental band gap, which is one of the primary parameters in the photocatalytic water splitting. Further, we sieved the dopant for better HER catalytic activity by substituting one of the arsenene (As) atoms by B, N, O, Ge, Ga and Se atoms to make arsenene a better candidate for HER. Our studies depict that HER activity is increased by 82% for O-doped arsenene and OER activity by 87% for B-doped arsenene as compared to pristine arsenene.     

Relative immunogenicity of commonly allergenic foods versus rarely allergenic and nonallergenic foods in mice

Food allergies affect 6 to 8% of children and 2% of adults in the United States. For reasons that are not clear, eight types of food account for a vast majority (approximately 90%) of food-induced hypersensitivity reactions. In this study, C57Bl/6 mice were used to test the hypothesis that commonly allergenic foods are intrinsically more immunogenic than rarely allergenic or nonallergenic foods in allergy-susceptible hosts. Groups of mice (n = 4 to 5) were injected intraperitoneally with the protein extracts (plus alum as an adjuvant) from chicken eggs, peanuts, almonds, filberts-hazelnuts, walnuts, soybeans, and wheat (commonly allergenic foods) and coffee, sweet potatoes, carrots, white potatoes, cherries, lettuce, and spinach (rarely allergenic and nonallergenic foods). Primary and secondary immune responses (as measured by specific IgG1 antibody serum levels) were measured by an enzyme-linked immunosorbent assay. Proteins from peanuts, almonds, filberts, sweet potatoes, cherries, and spinach elicited robust primary and/or secondary immune responses. Proteins from eggs, walnuts, and lettuce elicited poor primary responses but significant secondary responses. In contrast, wheat, soybeans, coffee, carrots, and white potatoes elicited barely detectable to poor primary and secondary immune responses. The order of the immunogenicity levels of these foods in mice is as follows: almonds = filberts > spinach (Rubisco) > peanuts > or = sweet potatoes > cherries > lettuce > walnuts > chicken eggs > carrots > or = white potatoes > wheat = coffee = soybeans. In summary, these data demonstrate for the first time that: (i) foods vary widely with regard to their relative immunogenicity in allergy-susceptible hosts and (ii) intrinsic immunogenicity in mice does not distinguish commonly allergenic foods from rarely allergenic or nonallergenic foods.     

Enhancing biometric recognition with spatio-temporal reasoning in smart environments

We discuss smart environments that identify and track their occupants using unobtrusive recognition modalities such as face, gait, and voice. In order to alleviate the inherent limitations of recognition, we propose spatio-temporal reasoning techniques based upon an analysis of the occupant tracks. The key idea underlying our approach is to determine the identity of a person based upon information from a track of events rather than a single event. We abstract a smart environment by a probabilistic state transition system in which each state records a set of individuals who are present in various zones of the smart environment. An event abstracts a recognition step, and the transition function defines the mapping between states upon the occurrence of an event. We express two forms of spatio-temporal reasoning in the form of transition functions: a track-based transition function and an error-correcting transition function. We also define the concepts of ‘precision’ and ‘recall’ to quantify the performance of the smart environment and provide experimental results to clarify the performance improvements from spatio-temporal reasoning. Our conclusion is that the state transition system is an effective abstraction of a smart environment and the application of spatial-temporal reasoning enhances the overall performance of a biometric recognition system.     

Strength characteristics of stone masonry

This paper deals with an experimental investigation on the strength of stone and stone masonry. Granitoidgneiss is commonly used for masonry construction in India. The compressive strength of stone has been determined through 80 mm size cubes. It has been found that the compressive strength of granitoid-gneiss is greater when the load is parallel to the mineral bands. The compressive strength of stone masonry was studied through masonry prisms using 1∶4 and 1∶8 cement mortars. These tests have revealed that masonry strength is higher when the load applied is parallel to the mineral bands. The flexural bond strength of stone masonry walls was studied through full-scale tests. Flexural bond strength appears to play a major role in the failure of stone masonry walls.     

Logical form generation as abduction: Part I. Representation of linguistic concepts

For some time, researchers have become increasingly aware that some aspects of natural language processing can be viewed as abductive inference. This article describes knowledge representation in dual-route parsimonious covering theory, based on an existing diagnostic abductive inference model, extended to address issues specific to logic form generation. the two routes of covering deal with syntactic and semantic aspects of language, and are integrated by attributing both syntactic and semantic facets to each “open class” concept. Such extensions reflect some fundamental differences between the two task domains. the syntactic aspect of covering is described to show the differences, and some interesting properties are established. the semantic associations are characterized in terms of how they can be used in an abductive model. A major significance of this work is that it paves the way for a nondeductive inference method for word sense disambiguation and logical form generation, exploiting the associative linguistic knowledge. This approach sharply contrasts with others, where knowledge has usually been laboriously encoded into pattern-action rules that are hard to modify. Further, this work represents yet another application for the general principle of parsimonious covering. © 1994 John Wiley & Sons, Inc.     

In vivo Targeting of DNA Vaccines to Dendritic Cells via the Mannose Receptor Induces Long-Lasting Immunity against Melanoma

Herein, we report effective, C-type lectin mannose receptor (MR)-selective, in vivo dendritic cell (DC)-targeting lipid nanoparticles (LNPs) of a novel lipid-containing mannose-mimicking di-shikimoyl- and guanidine head group and two n-hexadecyl hydrophobic tails (DSG). Subcutaneous administration of LNPs of the DSG/p-CMV-GFP complex showed a significant expression of green fluorescence protein in the CD11c⁺ DCs of the neighboring lymph nodes compared to the control LNPs of the BBG/p-CMV-GFP complex. Mannose receptor-facilitated in vivo DC-targeted vaccination (s.c.) with the electrostatic complex of LNPs of DSG/pCMV-MART1 stimulated long-lasting (270 days post B16F10 tumor challenge) antimelanoma immunity under prophylactic conditions. Remarkably, under therapeutic settings, vaccination (s.c.) with LNPs of the DSG/pCMV-MART1 complex significantly delayed melanoma growth and improved the survival of mice with melanoma. These findings demonstrate that this nonviral delivery system offers a resilient and potential approach to deliver DNA vaccines encoding tumor antigens to DCs in vivo with high efficacy.     

The optimisation of the grinding of silicon carbide with diamond wheels using genetic algorithms

Modelling and optimisation are necessary for the control of any process to achieve improved product quality, high productivity and low cost. The grinding of silicon carbide is difficult because of its low fracture toughness, making it very sensitive to cracking. The efficient grinding of high performance ceramics involves the selection of operating parameters to maximise the MRR while maintaining the required surface finish and limiting surface damage. In the present work, experimental studies have been carried out to obtain optimum conditions for silicon carbide grinding. The effect of wheel grit size and grinding parameters such as wheel depth of cut and work feed rate on the surface roughness and damage are investigated. The significance of these parameters, on the surface roughness and the number of flaws, has been established using the analysis of variance. Mathematical models have also been developed for estimating the surface roughness and the number of flaws on the basis of experimental results. The optimisation of silicon carbide grinding has been carried out using genetic algorithms to obtain a maximum MRR with reference to surface finish and damage.Nomenclature C constant in mathematical model - C₁ constant in surface roughness model - C₂ constant in the number of flaws model - d depth of cut, m - dof degrees of freedom - f table feed rate, mm/min - M grit size (mesh) - MRR material removal rate, mm³/mm width-min - N_c number of flaws measured - R_a surface roughness measured, m - Y machining response - depth of cut exponent in mathematical model - ₁ depth of cut exponent in surface roughness model - ₂ depth of cut exponent in number of flaws model - feed rate exponent in mathematical model - ₁ feed rate exponent in surface roughness model - ₂ feed rate exponent in number of flaws model - grit size exponent in mathematical model - ₁ grit size exponent in surface roughness model - ₂ grit size exponent in number of flaws model