Nanostructured MIEC Ba0.5Sr0.5Co0.6Fe0.4O3−δ (BSCF5564) cathode for IT-SOFC by nitric acid aided EDTA–citric acid complexing process (NECC)

Ba_0.5Sr_0.5Co_0.6Fe_0.4O_3−δ(BSCF5564) was synthesized by nitric acid aided EDTA–citric acid complexing sol-gel method (NECC). Both, the phase formation temperature and time of BSCF5564 synthesized NECC were found to be low i.e. single perovskite phase formation temperature is 200 °C less as compared to the conventional method of synthesis. The orthorhombic perovskite structure has been formed after calcination at 800 °C for 5 h. Scanning electron microscopy reveals the formation of porous material constituting nano-sized and irregularly shaped rod-like structure with particle size approximately ranges from 90 to 160 nm. The total weight loss of the BSCF5564 sample comes out to be 6.6%, indicating that quadrivalence state Co⁴⁺ and Fe⁴⁺ in the sample have been completely reduced to the trivalent state Co³⁺ and Fe³⁺ due to thermal analysis. The value of E_a for BSCF5564 prepared by NECC was 0.2288 eV. The electrical conductivity of BSCF5564 synthesized by NECC is observed to be steady at high temperature (above 700 °C).     

Analysis of surface wave behavior in corrugated piezomagnetic layer resting on inhomogeneous half-space

This paper deals with propagation of Love type waves in a Piezomagnetic layer with corrugated boundaries overlying an inhomogeneous half-space. Inhomogeneity of elastic half-space is caused due to exponential variations in elastic parameters. Dispersion relations are obtained for magnetically open and short cases. Prominent effects of inhomogeneity, layer's width and corrugation on the phase velocity of considered wave are illustrated through graphs. Some particular cases are derived and exhibited through graphs. Also the influence of undulation parameter, elastic parameter, and piezomagnetic coefficient on phase velocity of considered wave has been marked separately. The present study finds its applications in designing and optimization of Love wave sensors and Seismic Acoustic Wave (SAW) devices. Findings may also be used for analytical study of wave propagation in piezomagnetic coupled structures.     

Focal Flow: Velocity and Depth from Differential Defocus Through Motion

We present the focal flow sensor. It is an unactuated, monocular camera that simultaneously exploits defocus and differential motion to measure a depth map and a 3D scene velocity field. It does this using an optical-flow-like, per-pixel linear constraint that relates image derivatives to depth and velocity. We derive this constraint, prove its invariance to scene texture, and prove that it is exactly satisfied only when the sensor’s blur kernels are Gaussian. We analyze the inherent sensitivity of the focal flow cue, and we build and test a prototype. Experiments produce useful depth and velocity information for a broader set of aperture configurations, including a simple lens with a pillbox aperture.     

First-principles studies of the superconductivity and vibrational properties of transition-metal nitrides TMN (TM = Ti,V, and Cr)

The present paper reports the structural, electronic, phonon and thermodynamical properties of some transition-metal nitrides (TMN: TiN, VN and CrN) by means of first-principles calculations. The computed equilibrium lattice constant and bulk modulus agree well with the available experimental and theoretical data. The electronic band structure and density of states calculations show metallic nature. The phonon frequencies are positive throughout the Brillouin zone for these compounds in rocksalt structure indicating dynamical stability. The calculated electron–phonon coupling constant λ and superconducting transition temperature agree reasonably well with the available experimental data. These compounds behave as a conventional phonon-mediated superconductor. Within the GGA and quasi-harmonic approximation, thermodynamical properties are also investigated.     

A grey approach for forecasting in a supply chain during intermittentdisruptions

The supply chains today have become vulnerable to frequent disruptions, and with continuing emphasis on efficiency, lacks robustness to deal with them. A part of the solution lies in forecasting the disruption beforehand and the other part in knowing which policies will suit such disrupted conditions best. Accurate and immediate forecasts are a must in a supply chain and hence play a huge role in stabilizing. This study compares the performance of three established forecasting methods (moving average, weighted moving average and exponential smoothing) as well as grey prediction method, during disruptions and stable situations. The experiments are performed in the form of discrete event simulation, on a four stage beer game settings. The results show that moving average and weighted moving average methods become incompetent during disruptions, and are useful only during stable times, when the demand hovers around a predefined mean value. Exponential smoothing and grey method seems to give better results during disruptions and also during stable times in upstream tiers. Grey prediction method in particular is the best method when the disruption frequency is high and also when the disruption impact is gradual rather than sudden.     

A multi-label ensemble predicting model to service recommendation from social media contents

The Journal of Supercomputing - Consumer sentiment is one of the essential measures of predictive recommendations in travel and tourism. Nowadays, a massive amount of data is available on the...     

Neural network based non-invasive method to detect anemia from images of eye conjunctiva

Detection of anemia can be done by examining the hemoglobin concentration level in the blood using complete blood count, which is an invasive, time-consuming, and costly technique. Preliminary methods for detecting anemia include examining the color of the palpebral conjunctiva, which is a non-invasive method, but color perception may vary from person to person. This study aims to develop a computerized non-invasive technique for anemia detection. We propose a novel machine learning model using the artificial neural network to detect anemic patients from the images of eye conjunctiva. Since limited and small dataset has been used in the earlier approaches, this may cause over fitting of the model. We have improved the number of available training images using image augmentation techniques. To standardize a non-invasive method, we have used computer vision algorithms for preprocessing and feature extraction. This article derives the backpropagation rules mathematically for adjusting the weights for the proposed neural network model. After hyper parameter tuning and using the mathematically derived backpropagation rules, the model was able to achieve the best accuracy of 97.00% with sensitivity 99.21% and specificity 95.42% on the created dataset.