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Luminescence and energy storage characteristics of coke-based graphite oxide
The substantial escalation in both energy consumption and ecological crisis prompts the utilization of conventional pollution-causing energy resources towards a proficient mode of energy production and storage. The most polluting fossil fuel like coal possesses a highly ordered sp2 carbon clusters, that can be easily tailored into graphene derivatives promising for energy-related applications. However, the impact of crystallinity and quality of the precursor coke on the physicochemical characteristics of extracted carbon nanostructures need to be identified. Herein, we have prepared graphite oxide structures (GrO) from high-quality coal, coke via Improved Hummers' method eliminating the need for toxic NaNO3. The inherent defect states own by coke are also of high significance as it performs the role of various photoluminescence emission centers. The sp2 domains and different surface defects promote excitation independent and dependent luminescence substantiating the distinct multi-emission property of GrO. The extent of functionalization during the oxidation process has also significantly affected the thermal stability of the carbogenic structure. The symmetric galvanostatic charge-discharge curves and lower internal resistance present superior stability and fatser ion transport of as-synthesized GrO. A specific capacitance of 193F/g was obtained at 0.2A/g with excellent capacitance retention over 2500 cycles. The versatile attributes of the coke derived GrO validate its realizable optoelectronics and energy storage applications. 2020 Elsevier B.V. -
PEVRM: Probabilistic Evolution Based Version Recommendation Model for Mobile Applications
Traditional recommendation approaches for the mobile Apps basically depend on the Apps related features. Now a days many users are in quench of Apps recommendation based on the version description. Earlier mobile Apps recommendation system do not handle the cold start problem and also lacks in time for recommending the related and latest version of Apps. To overcome this issues, a hybrid Apps recommendation framework which is considering the version of the mobile Apps is proposed. This novel framework named 'Probabilistic Evolution based Version Recommendation Model (PEVRM)' integrates the principles of Probabilistic Matrix Factorization (PMF) with Version Evolution Progress Model (VEPM). With the help this novel recommendation algorithm, the mobile users easily identify the specific Apps for particular task based on its version progression. At same time, this framework helps in resolving cold start problems of new users. Evaluations of this framework utilize a benchmark dataset, i.e., Apple's iTunes App Store3, for revealing its promising performance. 2013 IEEE. -
COVID-19: Trend analysis for market arrival of green gram in India
The unprecedented crisis hovering over the world due to Covid-19 pandemic has structurally impacted every sector of the world economy. This paper attempts to study the impact of ongoing pandemic in the agricultural sector specific to pulses in India. This paper finds that there is a significantly negative impact of Covid-19 on the pulses market. The market arrival of pulses has declined in the recent period while market demand for pulses has increased, therefore, there exists a supply side shortage for pulses in the domestic economy of India. This paper suggests that Government of India (Agriculture Department) urgently needs to deal with this shortage in supply of domestic pulses in "mandis" (agricultural markets). In this paper market arrival for pulses particularly green gram (Moong whole) has been forecasted for the next few months in Indian Agricultural Markets with the forecasting techniques such as Auto Regressive Integrated Moving Average (ARIMA) model and Artificial Neural Network (ANN) technique for pre and during pandemic period by using the arrival data in agriculture markets of India from Agmarknet. The results of this study using ARIMA models and ANNs have been compared to obtain the final conclusions with higher visibility in forecasting performance, which shows that there will be a sharp decline in market arrival with an average arrival of 494 quintals per day which can be maximized up to 623 quintals per day by stimulating the modal price at maximum possible point. Therefore, the urgent need of upscaling the technical efficiency of the farmers in different agro-climatic zones is needed to meet the domestic demand with domestic supply. 2020 DAV College. All rights reserved. -
Optical spectroscopy of Galactic field classical Be stars
In this study, we analyse the emission lines of different species present in 118 Galactic field classical Be stars in the wavelength range of 3800-9000 We re-estimated the extinction parameter (AV) for our sample stars using the newly available data from Gaia DR2 and suggest that it is important to consider AV while measuring the Balmer decrement (i.e. D34 and D54) values in classical Be stars. Subsequently, we estimated the Balmer decrement values for 105 program stars and found that ?20 per cent of them show D34 ? 2.7, implying that their circumstellar disc are generally optically thick in nature. One program star, HD 60855 shows H? in absorption - indicative of disc-less phase. From our analysis, we found that in classical Be stars, H? emission equivalent width values are mostly lower than 40 which agrees with that present in literature. Moreover, we noticed that a threshold value of ?10of H? emission equivalent width is necessary for FeII emission to become visible. We also observed that emission line equivalent widths of H?, P14, FeII 5169, and OI 8446for our program stars tend to be more intense in earlier spectral types, peaking mostly near B1-B2. Furthermore, we explored various formation regions of Ca II emission lines around the circumstellar disc of classical Be stars. We suggest the possibility that Ca II triplet emission can originate either in the circumbinary disc or from the cooler outer regions of the disc, which might not be isothermal in nature. 2021 Oxford University Press. All rights reserved. -
A new stepwise method for selection of input and output variables in data envelopment analysis
Data envelopment analysis (DEA) is one of the widely accepted optimization technique uses to measure the relative efficiency of organizational units where multiple inputs and outputs are present. The significance of DEA results depends on the variables selected for DEA modelling. One of the main challenges in data envelopment analysis modelling is of identify the significant input and output variables for DEA modelling. In this study, we propose an enhanced stepwise method to identify the significant and insignificant input and output variable by reducing the iterations process in stepwise method. The statistical significance of the input and output variables evaluated using the statistical methods: Least significance difference (LSD), and Welchs statistics. The proposed method applied to the Indian banking sector and the results have shown that the proposed model significantly identified the significant and insignificant input and output variables with least loss of information. 2021 the author(s). -
Synthesis and Nuclear Magnetic Resonance Studies of 2-Thiophenecarboxaldehyde Nicotinic Hydrazone and 2-Thiophenecarboxaldehyde Benzhydrazone
Synthesis and NMR spectral studies of bidentate N and S heterocycles of 2-thiophenecarboxaldehyde nicotinic hydrazone and 2-thiophenecarboxaldehyde benzhydrazone have been carried out. The compounds, 2-thiophenecarboxaldehyde nicotinic hydrazone and 2-thiophenecarboxaldehyde benzhydrazone were synthesized by reacting stoichiometric quantities of nicotinic hydrazide and benzhydrazide with 2-thiophene carboxaldehyde in methanol in the presence of glacial acetic acid at refluxing temperature. Upon cooling the reaction mixture, the products were obtained as colorless solids. 1H, 13C, 1H-1H COSY, and 1H-13C HSQC experiments have been conducted to characterize the compounds. 2020 Malaysian Institute of Chemistry. All rights reserved. -
Green Bonds Driving Sustainable Transition in Asian Economies: The Case of India
On September 25, 2015, 193 countries of the United Nations (UN) General Assembly, signed the 2030 Agenda to work towards attaining 17 Sustainable Development Goals (SDGs) and its associated 169 targets and 232 indicators. With one of the largest renewable energy programs, India is well-poised to be a role model for low-carbon transformation to other Asian countries. However, bridging the financing gap is critical to ensure that the country meets its SDG targets. Though the SDGs identified by the UN are broad-based and interdependent, for ease of analysis we have grouped them into five themes people, planet, prosperity, peace, and partnership based on existing UN models. This paper investigates the financing gap for green' projects linked to planet-related SDG targets in India. It builds an argument for utilizing green bonds as an instrument to bridge the gap. After establishing the potential of green bonds in raising the finance to meet India's planet-related SDG targets, we look at the current policy landscape and suggest recommendations for successful execution. The paper concludes that deepening of the corporate fixed income securities market and firming up guidelines in line with India's climate action plans are inevitable before green bonds can be considered a viable financing option. 2021. This is an Open Access article distributed under the terms of the Creative Commons Attribution Non-Commercial License (https://creativecommons.org/licenses/by-nc/4.0/) which permits unrestricted non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. All rights reserved. -
Advanced hybrid SVPWM techniques for two level VSI
This paper brings an advanced class of hybrid SVPWM techniques for medium voltage drive applications with two-level inverter which employs multiple division of active vector time (MDAVT) switching sequences to reduce total harmonic distortion (THD) and switching loss. The proposed hybrid SVPWM techniques are categorised based on the principle of bus-clamping strategies. Multiple division active vector time (MDAVT) switching sequences are used in the proposed strategies. The newly developed MDAVT switching strategies produce PWM waveform for all odd and even pulse number and maintain the symmetry of the voltage waveform. This work compares different MDAVT switching sequences based on modulation index and location of the clamping position (zero vector changing angle) of a phase in a line cycle. The proposed techniques lead to the reduction in weighted total harmonic distortion of line voltage (Vwthd) as well as switching loss. The results point to the superior order of performance of the developed MDAVT sequences in the various ranges of operation of modulation index and power factor values. The superior harmonic performance and switching loss characteristics of the MDAVT PWM techniques over the conventional SVPWM is experimentally verifiedona415 V, 2 hp induction motor drive. 2021 Informa UK Limited, trading as Taylor & Francis Group. -
Isolation of Plant Growth-Promoting Bacillus cereus from Soil and Its Use as a Microbial Inoculant
Modernization has introduced intensive agricultural practices wherein the pesticides play an important role both in stabilization and in increase of agricultural products. As a consequence, humans and members of other ecosystems are exposed to increased levels of compounds that have detrimental effects on their health, thereby signifying the importance of microbial inoculants. In order to achieve this goal 7 different bacterial species were initially screened for isolation of plant growth-promoting Bacillus sp. The isolate CUAMS116 was confirmed to be Bacillus cereus through biochemical and molecular characterization. The in vitro plant growth-promoting ability of the isolate was screened through standard tests. Different concentrations of bacterial inoculant (25%, 50%, 75%, 100%) were evaluated for its plant growth promotion ability using Phaseolus vulgaris L., under pot culture conditions. At the harvest stage, the mature control plants measured 16.53cm and mean treated plant height was measured to be 27.75cm, showing a maximum percentage increase in length of 67.87%. The results suggested that the B. cereus CUAMS116 isolated in this study can be extended as a PGPM through further field trials in other plants for improving crop yield and tolerance to biotic and abiotic stresses. 2020, King Fahd University of Petroleum & Minerals. -
Biosynthesized AG nanoparticles: A promising pathway for bandgap tailoring
The unrivaled features and prospective applications promote graphene as a potent contender for next-generation nanodevices. But the realization of a tunable bandgap structure for zero-bandgap graphene at all times persists as a dilemma. In this work, a green approach is adopted for the bandgap modulation of graphene oxide (GO). The biosynthesized silver nanoparticles (AgNPs) were introduced into the graphene matrix, and hence the bandgap was tailored for the formation of a semiconductor composite. The bare GO that has got a bandgap of 3.41 eV was tuned to 2.33 eV on the addition of AgNPs. The preparation of AgNPs using fruit extract of cyanococcus make the process greener, safer, and cost-effective. This paper intends to open a new venture towards the environment safe synthesis of semiconductor nanocomposite necessitate for optoelectronic and photovoltaic technologies. 2020 by the authors. -
Spider Monkey Crow Optimization Algorithm with Deep Learning for Sentiment Classification and Information Retrieval
The epidemic increase in online reviews' growth made the sentiment classification a fascinating domain in academic and industrial research. The reviews assist several domains, which is complicated to gather annotated training data. Several sentiment classification methodologies are devised for performing the sentiment analysis, but retrieval of information is not accurately performed, less effective, and less convergence speed. In this paper, we propose a sentiment paper proposes a sentiment classification model, namely Spider Monkey Crow Optimization algorithm (SMCA), for training the deep recurrent neural network (DeepRNN). In this method, the telecom review is employed to remove stop words and stemming to eliminate inappropriate data to minimize user's seeking time. Meanwhile, the feature extraction is performed using SentiWordNet to derive the sentiments from the reviews. The extracted SentiWordNet features and other features, like elongated words, punctuation, hashtag, and numerical values, are employed in the DeepRNN for classifying sentiments. To retrieve the required review, the Fuzzy K-Nearest neighbor (Fuzzy-KNN) is employed to retrieve the review based on a distance measure. With rigorous assessments and experimentation, it is observed that the proposed SMCA-based DeepRNN performs better in terms of accuracy of 97.7%, precision of 95.5%, recall of 94.6%, and F1-score 96.7%, respectively. 2013 IEEE. -
Identification and structure-activity relationship studies of small molecule inhibitors of the human cathepsin D
Cathepsin D, an aspartyl protease, is an attractive therapeutic target for various diseases, primarily cancer and osteoarthritis. However, despite several small molecule cathepsin D inhibitors being developed, that are highly potent, most of them show poor microsomal stability, which in turn limits their clinical translation. Herein, we describe the design, optimization and evaluation of a series of novel non-peptidic acylguanidine based small molecule inhibitors of cathepsin D. Optimization of our hit compound 1a (IC50 = 29 nM) led to the highly potent mono sulphonamide analogue 4b (IC50 = 4 nM), however with poor microsomal stability (HLM: 177 and MLM: 177 ?l/min/mg). To further improve the microsomal stability while retaining the potency, we carried out an extensive structureactivity relationship screen which led to the identification of our optimised lead 24e (IC50 = 45 nM), with an improved microsomal stability (HLM: 59.1 and MLM: 86.8 ?l/min/mg). Our efforts reveal that 24e could be a good starting point or potential candidate for further preclinical studies against diseases where Cathepsin D plays an important role. 2020 Elsevier Ltd -
Flow of nanoliquid past a vertical plate with novel quadratic thermal radiation and quadratic Boussinesq approximation: Sensitivity analysis
The effects of quadratic thermal radiation and quadratic Boussinesq approximation are investigated on the heat transport of a 36 nm Al2O3 ? H2O nanofluid over a vertical plate. The modified Buongiorno model is used in the analysis that includes the effectual thermophysical properties of the nanofluid and the key slip mechanisms. Experimentally verified correlations are used for the thermophysical properties. The reduced nonlinear differential problem is solved numerically by the Finite Difference Method (FDM). Flow profiles are displayed and analyzed for changes in dimensionless parameters. Further, the heat transfer flux at the wall is analyzed for interactive impacts of the buoyancy ratio, Brownian random motion, and thermophoresis parameters using the face-centered Central Composite Design (CCD) of the Response Surface Methodology (RSM). A sensitivity analysis is carried out for the heat transfer flux of the nanoliquid. Quadratic thermal radiation was found to improve the temperature profile. Furthermore, the mechanisms of Brownian random motion and thermophoresis have a negative sensitivity towards the rate of heat transfer. In various thermal applications like solar collectors, the density variation in terms of temperature differences is significantly high. Such phenomena can be accurately modeled by utilizing the quadratic Boussinesq approximation and the novel quadratic thermal radiation aspect. 2020 Elsevier Ltd -
BSSA: Binary Salp Swarm Algorithm with Hybrid Data Transformation for Feature Selection
Feature selection is a technique commonly used in Data Mining and Machine Learning. Traditional feature selection methods, when applied to large datasets, generate a large number of feature subsets. Selecting optimal features within this high dimensional data space is time-consuming and negatively affects the system's performance. This paper proposes a new binary Salp Swarm Algorithm (bSSA) for selecting the best feature set from transformed datasets. The proposed feature selection method first transforms the original data-set using Principal Component Analysis (PCA) and fast Independent Component Analysis (fastICA) based hybrid data transformation methods; next, a binary Salp Swarm optimizer is used for finding the best features. The proposed feature selection approach improves accuracy and eliminates the selection of irrelevant features. We validate our technique on fifteen different benchmark data sets. We conduct an extensive study to measure the performance and feature selection accuracy of the proposed technique. The proposed bSSA is compared to Binary Genetic Algorithm (bGA), Binary Binomial Cuckoo Search (bBCS), Binary Grey Wolf Optimizer (bGWO), Binary Competitive Swarm Optimizer (bCSO), and Binary Crow Search Algorithm (bCSA). The proposed method attains a mean accuracy of 95.26% with 7.78% features on PCA-fastICA transformed datasets. The results show that bSSA outperforms the existing methods for the majority of the performance measures. 2013 IEEE. -
A new meta-heuristic pathfinder algorithm for solving optimal allocation of solar photovoltaic system in multi-lateral distribution system for improving resilience
A new meta-heuristic Pathfinder Algorithm (PFA) is adopted in this paper for optimal allocation and simultaneous integration of a solar photovoltaic system among multi-laterals, called interline-photovoltaic (I-PV) system. At first, the performance of PFA is evaluated by solving the optimal allocation of distribution generation problem in IEEE 33- and 69-bus systems for loss minimization. The obtained results show that the performance of proposed PFA is superior to PSO, TLBO, CSA, and GOA and other approaches cited in literature. The comparison of different performance measures of 50 independent trail runs predominantly shows the effectiveness of PFA and its efficiency for global optima. Subsequently, PFA is implemented for determining the optimal I-PV configuration considering the resilience without compromising the various operational and radiality constraints. Different case studies are simulated and the impact of the I-PV system is analyzed in terms of voltage profile and voltage stability. The proposed optimal I-PV configuration resulted in loss reduction of 77.87% and 98.33% in IEEE 33- and 69-bus systems, respectively. Further, the reduced average voltage deviation index and increased voltage stability index result in an improved voltage profile and enhanced voltage stability margin in radial distribution systems and its suitability for practical applications. 2021, The Author(s). -
Analysis of unsteady flow of blood conveying iron oxide nanoparticles on melting surface due to free convection using Casson model
Iron oxide nanoparticles have great importance in future biomedical applications because of their intrinsic properties, such as low toxicity, colloidal stability, and surface engineering capability. So, blood containing iron oxide nanoparticles are used in biomedical sciences as contrast agents following intravenous administration. The current problem deals with an analysis of the melting heat transfer of blood consisting iron nanoparticles in the existence of free convection. The principal equations of the problem are extremely nonlinear partial differential equations which transmute into a set of nonlinear ordinary differential equations by applying proper similarity transformations. The acquired similarity equalities are then solved numerically by Runge-Kutta Felhsberg 45th-order method. The results acquired are on the same level with past available results. Some noteworthy findings of the study are: the rate of heat transfer increases as the Casson parameter increases and also found that the temperature of the blood can be controlled by increasing or decreasing the Prandtl number. Hence, we conclude that flow and heat transfer of blood have significant clinical importance during the stages where the blood flow needs to be checked (surgery) and the heat transfer rate must be controlled (therapy). 2020 Wiley Periodicals LLC -
Auto-diagnosis of covid-19 using lung ct images with semi-supervised shallow learning network
In the current world pandemic situation, the contagious Novel Coronavirus Disease 2019 (COVID-19) has raised a real threat to human lives owing to infection on lung cells and human respiratory systems. It is a daunting task for the researchers to find suitable infection patterns on lung CT images for automated diagnosis of COVID-19. A novel integrated semi-supervised shallow neural network framework comprising a Parallel Quantum-Inspired Self-supervised Network (PQIS-Net) for automatic segmentation of lung CT images followed by Fully Connected (FC) layers, is proposed in this article. The proposed PQIS-Net model is aimed at providing fully automated segmentation of lung CT slices without incorporating pre-trained convolutional neural network based models. A parallel trinity of layered structure of quantum bits are interconnected using an N -connected second order neighborhood-based topology in the suggested PQIS-Net architecture for segmentation of lung CT slices with wide variations of local intensities. A random patch-based classification on PQIS-Net segmented slices is incorporated at the classification layers of the suggested semi-supervised shallow neural network framework. Intensive experiments have been conducted using three publicly available data sets, one for purely segmentation task and the other two for classification (COVID-19 diagnosis). The experimental outcome on segmentation of CT slices using self-supervised PQIS-Net and the diagnosis efficiency (Accuracy, Precision and AUC) of the integrated semi-supervised shallow framework is found to be promising. The proposed model is also found to be superior than the best state-of-the-art techniques and pre-trained convolutional neural network-based models, specially in COVID-19 and Mycoplasma Pneumonia (MP) screening. 2013 IEEE. -
Strength Development of Geopolymer Composites Made from Red Mud-Fly Ash as a Subgrade Material in Road Construction
The application of industrial waste in construction reduces the dependency on natural resources. The materials, including red mud (RM) and fly ash (FA), proved to be favorable materials. However, the materials potential together as a geopolymer composite for road applications has rarely been explored. This study will examine the possibility of the replacement of natural materials in subgrade applications. To achieve this, the geopolymer compositions will be prepared by replacing RM with FA at replacement rates of 10%, 20%, and 30% by dry weight basis. The alkaline activator solution of 8 M will be prepared using sodium hydroxide (NaOH) and sodium silicate to develop geopolymer composites. The strength properties will be studied using the California Bearing Ratio (CBR) and unconfined compression strength (UCS) and validated with microstructural analysis using scanning electron microscopy (SEM), X-ray diffraction (XRD), and Fourier-transform infrared spectroscopy (FTIR). The results reveal that geopolymer composites could achieve a maximum CBR value of 12% and UCS of 2,700 kPa. The microstructural analysis revealed that the formation of dense calcium aluminate hydrate (C-A-H) and calcium silicate hydrate (C-S-H) are the reason for strength improvement. The leaching studies show that the toxic elements were within the permissible limits. Overall, the test results confirmed that the geopolymer composites meet the required strength and could be used as a subgrade material in road construction. 2020 American Society of Civil Engineers. -
Significance of quadratic thermal radiation and quadratic convection on boundary layer two-phase flow of a dusty nanoliquid past a vertical plate
Boundary layer two-phase flow of particulate Al2O3-H2O nanoliquid over a vertical flat plate is studied numerically subjected to the aspects of quadratic thermal convection and quadratic thermal radiation. The Khanafer-Vafai-Lightstone monophasic nanofluid model (KVL model) and Saffman's dusty fluid model are used for the equations governing the flow of dusty nanoliquids. The quadratic Boussinesq approximation is used together with the Prandtl's boundary layer approximation. The non-linear problem is treated with the finite difference method. Surface plots and streamlines are presented to visualize the results. A comparison of linear thermal radiation, quadratic thermal radiation, and nonlinear thermal radiation is performed. Among the three types of radiation, the greatest heat transfer is observed in nonlinear thermal radiation followed by quadratic thermal radiation and linear thermal radiation. Also, in the presence of quadratic convection, the heat transport, and velocity field get enhanced. It is found that the presence of Al2O3 nanoparticles of 3% volume concentration in particulate water effectively advances the heat transport of the system. However, heat transport gets reduced by increasing the mass fraction of dust particles. Furthermore, in the presence of a transverse magnetic field, the velocity of the dusty nanoliquid gets reduced. 2020 -
Collaborative intrusion detection system in cognitive smart city network (CSC-Net)
Smart environment is about incorporating smart thinking in the environment and implementing the technical intervention that improvise the city's environment. Artificial intelligence (AI) provides solutions in huge technological issues in various aspects of day-to-day life such as autonomous transportation, governance, healthcare, agriculture, maintenance, logistics, and education that are automated, managed, controlled, and accessed remotely with the aid of smart devices. Cognitive computing is denoted as a next-generation AI-dependent method that gives human-computer interactions with personalized services that replicate manual behavior. Simultaneously, massive data is generated from the applications of the smart city like smart transportation, retail industry, healthcare, and governance. It is necessary to obtain a reliable, sustainable, continuous, and secure framework in the cloud centralized infrastructure. In this research article, the authors proposed the architecture of cognitive smart city network (CSC-Net) that defines how data are collected from applications of smart city and scrutinized by cognitive computing. This research article predicts the mobile edge computing solution (MEC) that permits node collaboration between internet of things (IoT) devices for providing secure and reliable communication among smart devices and fog layer, conversely fog layer and cloud layer. This proposed work helps to reduce the excessive traffic flow in smart environment with the support of node to node communication protocols. Collaborative-dependent intrusion detection system (C-IDS) is proposed to solve the data security issues in fog and cloud layers. Copyright 2021, IGI Global. Copying or distributing in print or electronic forms without written permission of IGI Global is prohibited.