Advancement in treatment of wastewater: Fate of emerging contaminants

The scarcity of industrial and domestic use water has become an important issue as industrial operations and localized pollution have burgeoned over the last decade. Wastewater (WW) treatment for recycling and reuse is gaining importance as an alternate source of water supply to circumvent water shortages. Wastewater treatment requires thorough planning, design, construction, and management of treatment facilities in order to discharge the treated water to the aquatic environment or for recycling and reuse. One detrimental effect of growing populations and urbanization has been the release of many persistent emerging contaminants (ECs) to the environment, mainly detected in WW. The entry of these ECs to the aquatic environment through wastewater treatment plants (WWTPs) may cause different ecological risks such as reproductive toxicity, endocrine disruption, and microbial resistance. The quantification of ECs (in ng or pg level) in complex matrices, such as WW samples recognized through non‐target screening approaches, has played a key role in the planning and design of water treatment facilities. The purpose of this review is to provide information about advancements in wastewater treatment technologies such as constructed wetland (CW) and advanced oxidation processes (AOPs) and the fate of emerging contaminants during these treatments. Further, this review also reports the ecological effects of these contaminants and their by‐products formed during various advanced WW treatment processes. The review also discusses advancements in different analytical techniques for the analysis of ECs in WW.     

Electrochemical behavior of nanostructured graphene nickel phosphorus composite coating on copper

Journal of Applied Electrochemistry - The present work is focused on the effects of graphene addition on the electrochemical performance of environmentally benign nanostructured...     

Post-harvest Processing of Banana: Opportunities and Challenges

Banana has a special place in the daily diet of millions of people around the world for sustenance and nutrient enrichment. Some of the popular food uses of banana are chips, raw ripened fruit, cooked green banana, fermented and unfermented beverages, juice, puree, dried flour for bakery and infant formula food. Banana is also used as a starch source for various chemicals and packaging materials. The storability and functional properties of these products can be altered by the application of various innovative food processing technologies. This review article focuses on different banana products, their potential for non-conventional uses and associated prospective novel processing techniques for value addition and preservation.     

Structural,dielectric, and electrical properties of cerium-modified strontium manganite ceramics

Journal of Materials Science: Materials in Electronics - A solid-state reaction technique was used to make cerium (Ce) modified strontium manganite SrMn0.9Ce0.1O3 (SMCO). The Goldschmidt's...     

Evaluation of a commercial electronic nose system coupled with universal gas sensing chamber for sensing indicator compounds associated with meat safety

This study was conducted to evaluate the performance of a commercial electronic nose (Cyranose 320?) for sensing indicator compounds (ethanol and acetic acid) associated with spoiled beef. The present study reported the sensitivity of an array of 32 sensors to ethanol and acetic acid. Different vapor concentrations of ethanol (37, 100, 250 and 500 ppm) and acetic acid (38, 75, 100, 200 and 300 ppm) were tested to evaluate the performance of the commercial system. An in-house designed universal gas sensing and characterization system was coupled with the electronic nose system to generate the desired gas concentration. The raw smell print patterns were obtained and analyzed for individual and multiple detectors. Tukey’s multiple comparison technique was performed to analyze the response of individual detectors. Area above and below the baseline were selected as two features for pair wise comparison of the detectors. Different sensors showed different responses between various concentrations of gases. Analysis of multiple detectors was performed using linear and quadratic discriminant analysis (LDA and QDA) along with bootstrap. LDA along with bootstrap provided the highest total classification accuracies of 94.34 % between 100 and 200 ppm of acetic acid. QDA provided higher total classification accuracy of 89.69 % at lower concentration level of 38 and 75 ppm for acetic acid and 84.78 % between 37 and 100 ppm of ethanol. Hence, QDA was a better model of choice at lower concentrations. This study proved non-selective nature of sensors and showed that simultaneous use of multiple sensor information provided better classification accuracy for discriminating various gas concentrations as compared to using individual sensor output.     

Fault diagnosis in wireless sensor network using clonal selection principle and probabilistic neural network approach

The fault diagnosis in wireless sensor networks is one of the most important topics in the recent years of research work. The problem of fault diagnosis in wireless sensor network can be resembled with artificial immune system in many different ways. In this paper, a detection algorithm has been proposed to identify faulty sensor nodes using clonal selection principle of artificial immune system, and then the faults are classified into permanent, intermittent, and transient fault using the probabilistic neural network approach. After the actual fault status is detected, the faulty nodes are isolated in the isolation phase. The performance metrics such as detection accuracy, false alarm rate, false‐positive rate, fault classification accuracy, false classification rate, diagnosis latency, and energy consumption are used to evaluate the performance of the proposed algorithm. The simulation results show that the proposed algorithm gives superior results as compared with existing algorithms in terms of the performance metrics. The fault classification performance is measured by fault classification accuracy and false classification rate. It has also seen that the proposed algorithm provides less diagnosis latency and consumes less energy than that of the existing algorithms proposed by Mohapatra et al, Panda et al, and Elhadef et al for wireless sensor network.     

Curvelet Transform Based on Edge Preserving Filter for Retinal Blood Vessel Segmentation

Segmentation of vessel in retinal fundus images is a primary step for the clinical identification for specific eye diseases. Effective diagnosis of vascular pathologies from angiographic images is thus a vital aspect and generally depends on segmentation of vascular structure. Although various approaches for retinal vessel segmentation are extensively utilized, however, the responses are lower at vessel's edges. The curvelet transform signifies edges better than wavelets, and hence convenient for multiscale edge enhancement. The bilateral filter is a nonlinear filter that is capable of providing effective smoothing while preserving strong edges. Fast bilateral filter is an advanced version of bilateral filter that regulates the contrast while preserving the edges. Therefore, in this paper a fusion algorithm is recommended by fusing fast bilateral filter that can effectively preserve the edge details and curvelet transform that has better capability to detect the edge direction feature and better investigation and tracking of significant characteristics of the image. Afterwards C mean thresholding is used for the extraction of vessel. The recommended fusion approach is assessed on DRIVE dataset. Experimental results illustrate that the fusion algorithm preserved the advantages of the both and provides better result. The results demonstrate that the recommended method outperforms the traditional approaches.     

Thermal evolution of morphological,optical, and photocatalytic properties of Au–Cu2O–CuO nanocomposite thin film

Plasmonic nanocomposite thin films find exciting applications in environmental remediation and photovoltaics. We report on thermal annealing driven development of morphology, structure and photocatalytic performance of Au–Cu₂O–CuO nanocomposite thin film. Nanocomposite thin film coatings of Au–Cu₂O–CuO, prepared by radio frequency (RF) magnetron co-sputtering, were annealed at different temperatures. Thermal annealing driven evolution of morphology of Au–Cu₂O–CuO nanocomposite was studied by field emission scanning electron microscopy (FESEM), which revealed significant growth in size of nanostructures from 10 nm to 69 nm upon annealing. X-ray diffraction (XRD) together with Raman studies confirmed the nanocomposite nature of Au–Cu₂O–CuO film. UV-visible diffuse reflectance spectroscopy (UV-vis-DRS) studies showed band gap variation from 2.44 eV to 1.8 eV upon annealing at 250 °C. Nanocomposite thin film annealed at 250 °C exhibited superior photocatalytic activity for organic pollutants [methylene blue (MB) and methyl orange (MO)] decomposition. The origins of thermal transformation of morphological, optical and photocatalytic behaviour of the Au–Cu₂O–CuO nanocomposite coating are discussed.

     

Fault diagnosis in wireless sensor network using negative selection algorithm and support vector machine

In this article, an improved negative selection algorithm (INSA) has been proposed to identify faulty sensor nodes in wireless sensor network (WSN) and then the faults are classified into soft permanent, soft intermittent, and soft transient fault using the support vector machine technique. The performance metrics such as fault detection accuracy, false alarm rate, false positive rate, diagnosis latency (DL), energy consumption, fault classification accuracy (FCA), and false classification rate (FCR) are used to evaluate the performance of the proposed INSA. The simulation result shows that the INSA gives better result as compared to the existing algorithms in terms of performance metrics. The fault classification performance is measured by FCA and FCR. It has also seen that the proposed algorithm gives less DL and consumes less energy than that of existing algorithms proposed by Mohapatra et al, Zhang et al, and Panda et al for WSN.     

SEEMAKA: Secured Energy-Efficient Mutual Authentication and Key Agreement Scheme for Wireless Body Area Networks

Wireless Personal Communications - Sparse code multiple access (SCMA) is one of the algorithms in non-orthogonality multiple access. The algorithm utilizes advanced multi-dimensional codebook to...