Effect of 100 KeV Ar+ ion beam irradiation on ZnO thin films – Influence of morphology vis-a-vis electrode/electrolyte interface and its impact on photoelectrochemical water splitting

The present study attempts quantitative determination of changes in the morphological surface features viz. fractal dimension, lower and upper cut off length scale through Power Spectral Density analysis prior to and after irradiation of 100 KeV Ar⁺ ion beam at incidence angles of 0°, 40° and 60° on ZnO thin films. All the unirradiated and irradiated samples are subjected to photoelectrochemical characterization and a correlation between photoelectrochemical performance and morphological parameters is established. Sample irradiated at 40° angle at the fluence of 5 × 10¹⁶ ions/cm² is found to possess maximum fractal dimension of 2.72, lower and upper cut off length scale of 3.16 nm and 63.00 nm respectively. This sample exhibits maximum photocurrent density of 3.19 mA/cm² and applied bias photon-to-current efficiency of 1.12% at 1.23 V/RHE. Hydrogen gas collected for duration of 1 h for the same sample was ~4.83 mLcm^?2.     

Enhanced green upconversion luminescence properties of Er3+/Yb3+ co-doped strontium gadolinium silicate oxyapatite phosphor

Upconversion Sr₂(Gd_.98-xEr_.02Yb_x)₈Si₆O₂₆ (SGSO:2Er³⁺/xYb³⁺) phosphor materials were synthesized using a citrate sol-gel process. X-ray diffraction patterns confirmed their hexagonal structure. Field emission scanning electron microscopy images of SGSO:2Er³⁺/xYb³⁺ phosphors depicted submicron particles. The enhanced upconversion luminescence properties of SGSO:2Er³⁺/xYb³⁺ phosphors were analysed as a function of Yb³⁺ ion concentration and laser power. The energy transfer induced enhanced emission of the Er³⁺/ Yb³⁺ ions co-doped SGSO phosphors was ascribed to multi-phonon relaxation. The calculated chromaticity coordinates of the SGSO:2Er³⁺/xYb³⁺ phosphors showed emissions could be tuned by changing Yb³⁺ ion concentration. Optimized sample exhibited the chromaticity coordinate values near to the ultra-high definition television standard green emission coordinates.     

Deep autoencoders for feature learning with embeddings for recommendations: a novel recommender system solution

Neural Computing and Applications - We propose “Deep Autoencoders for Feature Learning in Recommender Systems,” a novel discriminative model based on the incorporation of features from...     

Electrical conductivity of poly(vinyl alcohol)/carbon nanotube multilayer thin films: Influence of sodium polystyrene sulfonate mediated carbon nanotube dispersion

This investigation was aimed to enhance the dispersibility of multi-walled carbon nanotubes (MWCNT) using sodium polystyrene sulfonate (Na-PSS) polyelectrolyte. Subsequently, electrically conducting, multi-layer thin films are prepared utilizing layer by layer assembly method with poly(vinyl alcohol) as a host matrix. The highest extent of MWCNT dispersion was observed in MWCNT:Na-PSS ratio of 1:9 (wt/wt), which was estimated from UV-Vis spectroscopic analysis. Zeta potential measurements of Na-PSS modified MWCNT dispersion showed large negative potentials ranging from −52 to −64 mV in the most stable pH range of 4 to 10, suggesting the colloidal stability is due to the long-range repulsive nature of electrostatic interactions from negatively charged sulfonate groups. Complementary molecular dynamics simulations showed that adsorption of Na-PSS imparts a large negative potential to the carbon nanotube surface, which increases with an increase in Na-PSS concentration. The multi-layer thin film of (1:9) MWCNT:Na-PSS exhibited a DC electrical conductivity of 2.96 × 10² S/m.     

Modified transform-based gamma correction for MRI tumor image denoising and segmentation by optimized histon-based elephant herding algorithm

Medical image processing is typically performed to diagnose a patient's brain tumor prior to surgery. In this study, a technique in denoising and segmentation was developed to improve medical image processing. The proposed approach employs multiple modules. In the first module, the noisy brain tumor image is transformed into multiple low- and high-pass tetrolet coefficients. In the second module, multiple low-pass tetrolet coefficients are applied through a modified transform-based gamma correction method. Generalized cross-validation is used on multiple high-pass tetrolet coefficients to obtain the best threshold value. In the third module, all enhanced coefficients are applied to the partial differential equation method. In the final module, the denoised image is applied to Atanassov's intuitionistic fuzzy set histon-based fuzzy clustering method with centroid optimization using an elephant herding method. Accordingly, the tumor part is segmented from the nontumor part in the magnetic resonance imaging brain images. The method was assessed in terms of peak signal-to-noise ratio, mean square error, specificity, sensitivity, and accuracy. The experimental results showed that the suggested method is superior to traditional methods.     

MRMR Based Feature Vector Design for Efficient Citrus Disease Detection

In recent times, the images and videos have emerged as one of the most important information source depicting the real time scenarios. Digital images nowadays serve as input for many applications and replacing the manual methods due to their capabilities of 3D scene representation in 2D plane. The capabilities of digital images along with utilization of machine learning methodologies are showing promising accuracies in many applications of prediction and pattern recognition. One of the application fields pertains to detection of diseases occurring in the plants, which are destroying the widespread fields. Traditionally the disease detection process was done by a domain expert using manual examination and laboratory tests. This is a tedious and time consuming process and does not suffice the accuracy levels. This creates a room for the research in developing automation based methods where the images captured through sensors and cameras will be used for detection of disease and control its spreading. The digital images captured from the field's forms the dataset which trains the machine learning models to predict the nature of the disease. The accuracy of these models is greatly affected by the amount of noise and ailments present in the input images, appropriate segmentation methodology, feature vector development and the choice of machine learning algorithm. To ensure the high rated performance of the designed system the research is moving in a direction to fine tune each and every stage separately considering their dependencies on subsequent stages. Therefore the most optimum solution can be obtained by considering the image processing methodologies for improving the quality of image and then applying statistical methods for feature extraction and selection. The training vector thus developed is capable of presenting the relationship between the feature values and the target class. In this article, a highly accurate system model for detecting the diseases occurring in citrus fruits using a hybrid feature development approach is proposed. The overall improvement in terms of accuracy is measured and depicted.     

Muddling between science and engineering: an epistemic strategy for developing human factors and ergonomics as a hybrid discipline

Human Factors and Ergonomics (HFE) recognises itself as a design-driven, systemic and scientific discipline geared towards well-being and performance. Being a scientific discipline and design-oriented requires that the epistemic basis of science and design/engineering be fully comprehended. In interdisciplinary research where these two viewpoints meet, there are often dilemmas posed in terms of knowledge construction and labelling of activity. Therefore, this article scrutinises these two orientations and addresses the differences and commonalities, using case studies from engineering and psychological science (both constituents of HFE). Based on these insights, a way forward is suggested in terms of (1) a reflexive engagement with epistemic concepts and methods; (2) finding a conceptual space for balancing and bridging the science-engineering divide; (3) comprehending ‘design-thinking/design knowledge’ and not treating it as an application of science; (4) providing emphasis on problem formulation and practices of HFE focusing on developing them in systemic terms.