Adsorption of anionic dye on eco-friendly synthesised reduced graphene oxide anchored with lanthanum aluminate: Isotherms,kinetics and statistical error analysis

In the present study we made an effort to deploy eco-friendly synthesized reduced graphene oxide/Lanthanum Alluminate nanocomposites (RGO-LaAlO₃) and Lanthanum Alluminate (LaAlO₃) as adsorbents to remove dye from the synthetic media. XRD, SEM, BET surface area and EDX have been used to characterize the above-mentioned adsorbents. The impacts of different factors like adsorbent dosage, the concentration of adsorbate and PH on adsorption were studied. The best fit linear and nonlinear equations for the adsorption isotherms and kinetic models had been examined. The sum of the normalized errors and the coefficient of determination were used to determine the best fit model. The experimental data were more aptly fitted for nonlinear forms of isotherms and kinetic equations. Pseudo-second-order and Freundlich isotherm model fits the equilibrium data satisfactorily. Methyl orange (MO) has been used as model dye pollutant and maximum adsorption capacity was found to be 469.7 and 702.2 mg g^?1 for LaAlO₃ and RGO-LaAlO₃, respectively.     

Large eddy simulation of flow over square cylinder arrays in an octagonal configuration at subcritical Reynolds numbers

In this study, fluid flow over an array of eight, 0.029 m × 0.029 m, square cross‐section cylinders in an octagonal configuration is studied numerically. The mean force coefficients (drag and lift) and the vortex formation characteristics of the array are calculated numerically by utilizing a three‐dimensional large eddy simulation mathematical model for turbulence. The numerical simulation is performed with commercial software ANSYS Fluent 19R1. To investigate the parametric influences, three spacings between the cylinders (0.07, 0.14, and 0.2 m), two array attack angles (0° and 15°), and two Reynolds numbers (4060 and 45 800) are considered. The results comprise flow patterns and force coefficients' variations with Reynolds numbers. The lift force of the downstream cylinder reaches its maximum at α = 15°, and the drag force of the upstream cylinders finds its peak at α = 0°. It is observed through velocity and viscosity contour plots that vortex formation length near the cylinder increases at higher Reynolds number. Velocity vector plots are also presented to show fluid flow behavior near the cylinder. Furthermore, the predicted mean forces on the cylinders are slightly different for different Reynolds numbers, spacings, and angles of attack.     

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.     

The sociotechnical constitution of cognitive work analysis: roles,affordances and malfunctions

Analysis of everyday work practices in sociotechnical systems for eliciting design/intervention requirements involves appropriate work analysis frameworks. The current article provides an extension to one such framework — Cognitive Work Analysis (CWA) — by scrutinising its sociotechnical basis. CWA's forte depends on its ‘design for adaptation’, system related operations, and operators. In contrast, sociotechnical work systems require not only operators and adaptation, but also a significant emphasis on ‘users’ and ‘appropriation’. The current article extends CWA (based on Rasmussen's original concepts) for users; subsequently allowing for system flexibility and possibilities of ‘appropriation’ within acceptable boundaries of the system's correct functioning. To this end, the first phase of Work Domain Analysis is extended by adding a new layer to the abstraction hierarchy (AH), based on Rasmussen's original insights.     

Experimental investigation of Co and Fe-Doped CuO nanostructured electrode material for remarkable electrochemical performance

The current research work presents a facile and cost–effective co-precipitation method to prepare doped (Co & Fe) CuO and undoped CuO nanostructures without usage of any type of surfactant or capping agents. The structural analysis reveals monoclinic crystal structure of synthesized pure CuO and doped-CuO nanostructures. The effect of different morphologies on the performance of supercapacitors has been found in CV (cyclic voltammetry) and GCD (galvanic charge discharge) investigations. The specific capacitances have been obtained 156 (±5) Fg^?1, 168(±5) Fg^?1 and 186 (±5) Fg^?1 for CuO, Co-doped CuO and Fe-doped CuO electrodes, respectively at scan rate of 5 mVs^?1, while it is found to be 114 (±5) Fg^?1, 136 (±5) Fg^?1 and 170 (±5) Fg^?1 for CuO, Co–CuO and Fe–CuO, respectively at 0.5 Ag^-1 as calculated from the GCD. The super capacitive performance of the Fe–CuO nanorods is mainly attributed to the synergism that evolves between CuO and Fe metal ion. The Fe-doped CuO with its nanorods like morphology provides superior specific capacitance value and excellent cyclic stability among all studied nanostructured electrodes. Consequently, it motivates to the use of Fe-doped CuO nanostructures as electrode material in the next generation energy storage devices.     

Identifying crop water stress using deep learning models

Neural Computing and Applications - The identification of water stress is a major challenge for timely and effective irrigation to ensure global food security and sustainable agriculture. Several...     

A Novel Self-Organizing Approach to Automatic Traffic Light Management System for Road Traffic Network

Wireless Personal Communications - Traffic network is basically a “network of networks” consisting of mainly two types of networks: road network and a travel network. Due to drastic...     

Interplay of reactive oxygen species (ROS) and tissue engineering: a review on clinical aspects of ROS-responsive biomaterials

Reactive oxygen species (ROS) refers to the reactive molecules and free radicals of oxygen generated as the by-products of aerobic respiration. Historically, ROS are known as stress markers that are linked to the response of immune cell against microbial invasion, but recent discoveries suggest their role as secondary messengers in signal transduction and cell cycle. Tissue engineering (TE) techniques have the capabilities to harness such properties of ROS for the effective regeneration of damaged tissues. TE employs stem cells and biomaterial matrix, to heal and regenerate injured tissue and organ. During regeneration, one of the constraints is the unavailability of oxygen as proper vasculature is absent at the injured site. This creates hypoxic conditions at the site of regeneration. Hence, effective response against the stresses like hypoxia spurs the regeneration process. Contrary, hyperoxic condition may increase the risk of ROS stress at the site. TE tries to overcome these limitations with the new class of biomaterials that can sense such stresses and respond accordingly. This review endeavors to explain the role of ROS in stem cell proliferation and differentiation, which is a key component in regeneration. This compilation also highlights the new class of biomaterials that can overcome the hypoxic conditions during tissue regeneration along with emphasis on the ROS-responsive biomaterials and their clinical applications. Incorporating these biomaterials in scaffolds development holds huge potential in tissue or organ regeneration and even in drug delivery.

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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.     

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.