Effect of sonication on the bioactive,quality and rheological characteristics of fruit smoothies

Power ultrasound was investigated as a nonthermal processing technique for fruit smoothies. Smoothies were sonicated at different amplitude levels (24.4–61.0 μm) for processing times (3–10 min). Total antioxidant capacity (TAC), total phenolics (TP), colour parameters, particle size and rheological characteristics were assessed. Sensory evaluation was conducted on selected samples for acceptability. Highest reductions in TAC (P < 0.001) and TP (P < 0.001) were observed at the maximum amplitude level (61.0 μm). Sonication reduced colour and rheological parameters and shortened particle size compared with fresh and thermal smoothies. However, sonicated samples received satisfactory (>3) sensory acceptability scores. Response surface methodology was applied to determine the effect of amplitude level and processing time on TP, TAC and colour parameters. A second‐order polynomial model fitted well to TP (R² = 0.74) and TAC (0.79) experimental data. Lower values were obtained for colour parameters, possibly because of processing limitations. Alternatives to overcome these limitations are discussed.     

Extraction of aluminum and iron from bauxite: A unique closed-loop ore refining process utilizing oxalate chemistry

The Bayer process holds an exclusive status for alumina extraction, but a massive amount of caustic “red mud” waste is generated. In this work, three oxalate reagents: potassium hydrogen oxalate (KHC₂O₄), potassium tetraoxalate (KHC₂O₄·H₂C₂O₄), and oxalic acid (H₂C₂O₄) were investigated for the Al and Fe extraction process from NIST SRM 600 Australian-Darling range bauxite ore. More than 90% of Al and Fe was extracted into the aqueous phase in less than 2 h with 0.50 M for all three reagents. The Fe and Al can be selectively precipitated by hydrolyzing the aqueous phase. By acidifying the Al and Fe free filtrate, 80% of the can be precipitated as KHC₂O₄·H₂C₂O₄. Greater than 90% of the aqueous acid can also be recycled using a cation-exchange resin. The proposed closed-loop process is an energy-efficient, cost-effective, environmentally–friendly route for extracting Al and Fe from bauxite ore.     

Detection of Copy-Move Forgery in Digital Image Using Multi-scale,Multi-stage Deep Learning Model

Neural Processing Letters - Images are an important source of information and copy-move forgery (CMF) is one of the vicious forgery attacks. Its objective is to conceal sensitive information from...     

Traveling Wire Electrochemical Discharge Machining (TW-ECDM) of Quartz Using Zinc Coated Brass Wire: Investigations on Material Removal Rate and Kerf Width Characteristics

Silicon - In this era, advanced non-conducting materials are gaining importance due to their superior properties. However, it is difficult to micro-machine these materials inefficiency and also...     

Experimental study of hydrous ethanol gasoline blend (E10) in a four stroke port fuel‐injected spark ignition engine

Performance, emissions and combustion characteristics of a port‐injected engine fuelled with hydrous ethanol gasoline blend (E10 ‐ 10% of hydrous ethanol by volume in gasoline) were compared with gasoline operation. Hydrous ethanol blend produced higher power output with lean mixtures at part throttle condition. Higher flame velocity and wider flammability limits of the blend resulted in lower cycle‐by‐cycle variations in indicated mean effective pressure as compared to gasoline. Hydro carbon emission was also lower due to the oxygen available in the fuel (E10), which enhanced the combustion rate. Higher latent heat of evaporation of the ethanol blend and water present in it resulted in lower in‐cylinder temperature, which in turn led to lesser NOx emissions. Thermal efficiency with the blend was higher in the leaner operating conditions than gasoline. Not much difference in performance, emission and combustion characteristics between neat gasoline and E10 were observed at full throttle operation. On the whole, hydrous ethanol blends can be used as a fuel with good performance and low emissions at part load condition. Copyright © 2012 John Wiley & Sons, Ltd.     

Pyrophosphate sensing by a fluorescent Zn2+ bound triazole linked imino-thiophenyl conjugate of calix[4]arene in HEPES buffer medium: spectroscopy, microscopy, and cellular studies

An in situ prepared Zn(2+) complex of triazole linked imino-thiophenyl conjugate of calix[4]arene, [ZnL], was demonstrated to be highly fluorescent in HEPES buffer solution. [ZnL] has been used as a chemo-sensing ensemble for the recognition of phosphates in general and pyrophosphates in particular among the eighteen different anions studied. The chemo-sensing behavior of the [ZnL] has been demonstrated through fluorescence, absorption, visual fluorescent color changes, ESI MS, and (1)H NMR titrations. Variations in the microstructural features of L, its zinc complex and the complex upon addition of PPi have been demonstrated through atomic force microscopy and transmission electron microscopy. Such studies have been extended to see the permeability of the conjugate into the HeLa cells by fluorescence microscopy. In accession, a reversible "write-read-erase-read" logic gate property of L has been demonstrated through a feedback loop in the presence of Zn(2+) and PPi.     

Directed Assembly of Liquid Metal–Elastomer Conductors for Stretchable and Self-Healing Electronics

Growing interest in soft robotics, stretchable electronics, and electronic skins has created demand for soft, compliant, and stretchable electrodes and interconnects. Here, dielectrophoresis (DEP) is used to assemble, align, and sinter eutectic gallium indium (EGaIn) microdroplets in uncured poly(dimethylsiloxane) (PDMS) to form electrically conducting microwires. There are several noteworthy aspects of this approach. 1) Generally, EGaIn droplets in silicone at loadings approaching 90 wt% remain insulating and form a conductive network only when subjected to sintering. Here, DEP facilitates assembly of EGaIn droplets into conductive microwires at loadings as low as 10 wt%. 2) DEP is done in silicone for the first time, enabling the microwires to be cured in a stretchable matrix. 3) Liquid EGaIn droplets sinter during DEP to form a stretchable metallic microwire that retains its shape after curing the silicone. 4) Use of liquid metal eliminates the issue of compliance mismatch observed in soft polymers with solid fillers. 5) The silicone–EGaIn “ink” can be assembled by DEP within the crevices of severely damaged wires to create stretchable interconnects that heal the damage mechanically and electrically. The DEP process of this unique set of materials is characterized and the interesting attributes enabled by such liquid microwires are demonstrated.     

Deep convolutional neural networks for eigenvalue problems in mechanics

We show that deep convolutional neural networks (CNNs) can massively outperform traditional densely connected neural networks (NNs) (both deep or shallow) in predicting eigenvalue problems in mechanics. In this sense, we strike out in a new direction in mechanics computations with strongly predictive NNs whose success depends not only on architectures being deep but also being fundamentally different from the widely used to date. We consider a model problem: predicting the eigenvalues of one-dimensional (1D) and two-dimensional (2D) phononic crystals. For the 1D case, the optimal CNN architecture reaches 98% accuracy level on unseen data when trained with just 20 000 samples, compared to 85% accuracy even with 100 000 samples for the typical network of choice in mechanics research. We show that, with relatively high data efficiency, CNNs have the capability to generalize well and automatically learn deep symmetry operations, easily extending to higher dimensions and our 2D case. Most importantly, we show how CNNs can naturally represent mechanical material tensors, with its convolution kernels serving as local receptive fields, which is a natural representation of mechanical response. Strategies proposed are applicable to other mechanics' problems and may, in the future, be used to sidestep cumbersome algorithms with purely data-driven approaches based upon modern deep architectures.     

A fast algorithm for the elastic fields due to a single fiber break in a periodic fiber-reinforced composite

The stress state in a shear-lag model of a unidirectional fiber composite with an arbitrary configuration of fiber breaks is obtained by the weighted superposition of the stress state due to a single broken fiber. In a periodic patch comprised of N fibers located at the points of a regular lattice, a method to determine the stress state due to a single break was proposed by Landis et al. (J Mech Phys Solids 48(3):621–648, 2000). This method entails the determination of the eigenspace of an \(N\times N\) matrix, at a computational cost of \(O(N^3)\). In the present work, an alternative algorithm is proposed. This algorithm exploits the circulant structure of the matrix describing the inter-fiber interactions. The asymptotic computational complexity of the present algorithm equals that of the discrete Fourier transform: \({O}(N \log N)\). Run times of the present method with the eigensolution based method are compared, and shown to be very favorable for the present method, even for small N. Power-law scaling of the overloads due to a single break to much larger distances than previously possible has been verified using the present method.