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A novel multi functional multi parameter concealed cluster based data aggregation scheme for wireless sensor networks (NMFMP-CDA)
Data aggregation is a promising solution for minimizing the communication overhead by merging redundant data thereby prolonging the lifetime of energy starving Wireless Sensor Network (WSN). Deployment of heterogeneous sensors for measuring different kinds of physical parameter requires the aggregator to combine diverse data in a smooth and secure manner. Supporting multi functional data aggregation can reduce the transmission cost wherein the base station can compute multiple statistical operations in one query. In this paper, we propose a novel secure energy efficient scheme for aggregating data of diverse parameters by representing sensed data as number of occurrences of different range value using binary encoded form thereby enabling the base station to compute multiple statistical functions over the obtained aggregate of each single parameter in one query. This also facilitates aggregation at every hop with less communication overhead and allows the network size to grow dynamically which in turn meets the need of large scale WSN. To support the recovery of parameter wise elaborated view from the multi parameter aggregate a novelty is employed in additive aggregation. End to end confidentiality of the data is secured by adopting elliptic curve based homomorphic encryption scheme. In addition, signature is attached with the cipher text to preserve the data integrity and authenticity of the node both at the base station and the aggregator which filters out false data at the earliest there by saving bandwidth. The efficiency of the proposed scheme is analyzed in terms of computation and communication overhead with respect to various schemes for various network sizes. This scheme is also validated against various attacks and proved to be efficient for aggregating more number of parameters. To the best of our understanding, our proposed scheme is the first to meet all of the above stated quality measures with a good performance. 2020, Springer Science+Business Media, LLC, part of Springer Nature. -
Identification of new classical Ae stars in the Galaxy using LAMOST DR5
We report the first systematic study to identify and characterize a sample of classical Ae stars in the Galaxy. The spectra of these stars were retrieved from the A-star catalogue using the Large sky Area Multi-Object fibre Spectroscopic Telescope (LAMOST) survey. We identified the emission-line stars in this catalogue from which 159 are confirmed as classical Ae stars. This increases the sample of known classical Ae stars by about nine times from the previously identified 21 stars. The evolutionary phase of classical Ae stars in this study is confirmed from the relatively small mid- and far-infrared excess and from their location in the optical colour-magnitude diagram. We estimated the spectral type using MILES spectral templates and identified classical Ae stars beyond A3, for the first time. The prominent emission lines in the spectra within the wavelength range 3700-9000 are identified and compared with the features present in classical Be stars. The H ? emission strength of the stars in our sample show a steady decrease from late-B type to Ae stars, suggesting that the disc size may be dependent on the spectral type. Interestingly, we noticed emission lines of Fe ii, O i, and Paschen series in the spectrum of some classical Ae stars. These lines are supposed to fade out by late B-type and should not be present in Ae stars. Further studies, including spectra with better resolution, is needed to correlate these results with the rotation rates of classical Ae stars. 2021 2020 The Author(s) Published by Oxford University Press on behalf of Royal Astronomical Society. -
A multi-scale and rotation-invariant phase pattern (MRIPP) and a stack of restricted Boltzmann machine (RBM) with preprocessing for facial expression classification
In facial expression recognition applications, the classification accuracy decreases because of the blur, illumination and localization problems in images. Therefore, a robust emotion recognition technique is needed. In this work, a Multi-scale and Rotation-Invariant Phase Pattern (MRIPP) is proposed. The MRIPP extracts the features from facial images, and the extracted patterns are blur-insensitive, rotation-invariant and robust. The performance of classification algorithms like Fisher faces, Support Vector Machine (SVM), Extreme Learning Machine (ELM), Convolutional Neural Network (CNN) and Deep Neural Network (DNN) are analyzed. In order to reduce the time for classification, an OPTICS-based pre-processing of the features is proposed that creates a non-redundant and compressed training set to classify the test set. Ten-fold cross validation is used in experimental analysis and the performance metric classification accuracy is used. The proposed approach has been evaluated with six datasets Japanese Female Facial Expression (JAFFE), Cohn Kanade (CK +), Multi- media Understanding Group (MUG), Static Facial Expressions in the Wild (SFEW), Oulu-Chinese Academy of Science, Institute of Automation (Oulu-CASIA) and ManMachine Interaction (MMI) datasets to meet a classification accuracy of 98.2%, 97.5%, 95.6%, 35.5%, 87.7% and 82.4% for seven class emotion detection using a stack of Restricted Boltzmann Machines(RBM), which is high when compared to other latest methods. 2020, Springer-Verlag GmbH Germany, part of Springer Nature. -
Ultrahigh Power Factors in Ultrawide-Band-Gap GaB3N4and AlB3N4for High-Temperature Thermoelectric Applications
With recent thermoelectric studies concentrating too much on low- and mid-temperature applications, an interesting question is, "are there any materials suitable for high-temperature thermoelectric operations?"To answer this, we have demonstrated in this work the viability of the ternary ultrawide-band-gap materials GaB3N4 and AlB3N4 for high-temperature thermoelectric applications using the first-principles calculation method. Our accurate transport calculations, considering both elastic and inelastic scattering mechanisms, reveal the ultrahigh power factors as high as 1821 ?W m-1 K-2 in GaB3N4 and 1876 ?W m-1 K-2 in AlB3N4 at 2000 K. The power factors are calculated from the Seebeck coefficients and electrical conductivities for both electron and hole carrier concentrations between 1018 and 1021 cm-3. For the figure-of-merit (ZT) calculation, the obtained power factors along with the electronic thermal conductivities determined from the definite Lorenz numbers and the lattice thermal conductivities from the phonon vibrations were used. The calculated ZT values seem to be appreciable for high-temperature applications considering the materials' stability factor and the temperature range within the optimum electron carrier concentration of 1021 cm-3. Although the lattice thermal conductivities are higher, which decrease the values of ZT, considering the ultrahigh power factors instead of the ZT factor could be the right choice for high-temperature thermoelectric applications. -
Skincare Products as Sources of Mutagenic Exposure to Infants: An Imperative Study Using a Battery of Microbial Bioassays
Infant skin is highly absorptive and sensitive to exposure from external agents (microbes, toxicants, heat, cold, etc.). Many specialized infant skincare products are currently commercially available. Although the manufacturers claim that their products are mild enough to suit the infant skin, these products need to be studied for their safety. Using animal models to examine the safety of the ever-increasing number of skincare products is not economically or logistically feasible. To overcome this problem, we suggest using a battery of microbial bioassays as a robust system for monitoring the mutagenic potential of skincare products. We picked popular infant skincare products from the Indian market and assessed them by using a battery of three microbial mutagenicity bioassays. Most of them showed significant and reproducible mutagenic potential. Our study results raise concerns about regular use of infant products and emphasize the need to enforce strict regulations for the manufacturing and safety assessment of infant products. 2021, The Author(s), under exclusive licence to Springer Science+Business Media, LLC part of Springer Nature. -
An effective face recognition system based on Cloud based IoT with a deep learning model
As of late, the Internet of Things (IoT) innovation has been utilized in applications, for example, transportation, medical care, video observation, and so on. The quick appropriation and development of IoT in these segments are producing an enormous measure of information. For instance, IoT gadgets, for example, cameras produce various pictures when utilized in medical clinic reconnaissance sees. Here, face acknowledgement is one of the most significant instruments that can be utilized for clinic affirmations, enthusiastic discovery, and identification of patients, location of fake gadgets. patient, and test clinic models. Programmed and shrewd face acknowledgement frameworks are profoundly precise in an overseen climate; notwithstanding, they are less exact in an unmanaged climate. Additionally, frameworks must keep on running on numerous occasions in different applications, for example, insightful wellbeing. This work presents a tree-based profound framework for programmed face acknowledgement in a cloud climate. The inside and out pattern have been proposed to cost less for the PC without focusing on unwavering quality. In the model, the additional size is isolated into a few sections, and a stick is made for each part. The tree is characterized by its branch area and stature. The branches are spoken to by a leftover capacity, which comprises of a twofold layer, a stack game plan, and a non-direct capacity. The proposed technique is assessed in an assortment of generally accessible information bases. An examination of the method is likewise finished with top to bottom craftsmanship models for the eye to eye connection. The aftereffects of the tests indicated that the example was considered to have accomplished a precision of 98.65%, 99.19%, and 95.84%. 2020 -
Discovery of an M-type companion to the Herbig Ae Star V1787 Ori
The intermediate-mass Herbig Ae star V1787 Ori is a member of the L1641 star-forming region in the Orion A molecular cloud. We report the detection of an M-type companion to V1787 Ori at a projected separation of 6.66 arcsec (corresponding to 2577 au), from the analysis of VLT/NACO adaptive optics Ks-band image. Using astrometric data from Gaia DR2, we show that V1787 Ori A and B share similar distance (d ?387 pc) and proper motion, indicating that they are physically associated. We estimate the spectral type of V1787 Ori B to be M5 2 from colour-spectral type calibration tables and template matching using SpeX spectral library. By fitting PARSEC models in the Pan-STARRS colour-magnitude diagram, we find that V1787 Ori B has an age of 8.1$^{+1.7}_{-1.5}$ Myr and a mass of 0.39$^{+0.02}_{-0.05}$ M. We show that V1787 Ori is a pre-main-sequence wide binary system with a mass ratio of 0.23. Such a low-mass ratio system is rarely identified in Herbig Ae/Be binary systems. We conclude this work with a discussion on possible mechanisms for the formation of V1787 Ori wide binary system. 2020 The Author(s) Published by Oxford University Press on behalf of Royal Astronomical Society. -
Linear and non-linear analyses of double-diffusive-Chandrasekhar convection coupled with cross-diffusion in micropolar fluid over saturated porous medium
Purpose: The problem aims to find the effects of coupled cross-diffusion in micropolar fluid oversaturated porous medium, subjected to Double-Diffusive Chandrasekhar convection. Design/methodology/approach: Normal mode and perturbation technique have been employed to determine the critical Rayleigh number. Non-linear analysis is carried out by deriving the Lorenz equations using truncated Fourier series representation. Heat and Mass transport are quantified by Nusselt and Sherwood numbers, respectively. Findings: Analysis related to the effects of various parameters is plotted, and the results for the same are interpreted. It is observed from the results that the Dufour parameter and Soret parameter have an opposite influence on the system of cross-diffusion. Originality/value: The effect of the magnetic field on the onset of double-diffusive convection in a porous medium coupled with cross-diffusion in a micropolar fluid is studied for the first time. 2020, Emerald Publishing Limited. -
Structure and biological properties of exopolysaccharide isolated from Citrobacter freundii
This study aimed to investigate the molecular characterization, antioxidant activity in vitro, cytotoxicity study of an exopolysaccharide isolated from Citrobacter freundii. Firstly, the culture conditions were standardized by the Design of experiments (DoE) based approach, and the final yield of thecrude exopolysaccharide was optimized at 2568 169 mg L?1. One large fraction of exopolysaccharide was obtained from the culture filtrate by size exclusion chromatography and molecular characteristics were studied. A new mannose rich exopolysaccharide (Fraction-I) with average molecular weight ~ 1.34 105 Da was isolated. The sugar analysis showed the presence of mannose and glucose in a molar ratio of nearly 7:2 respectively. The structure of the repeating unit in the exopolysaccharide was determined through chemical and 1D/2D- NMR experiments as:[Formula presented] Finally, the antioxidant activity, and the cytotoxicity of the exopolysaccharide were investigated and the relationship with molecular properties was discussed as well. 2020 Elsevier B.V. -
Socio-economic development of Darjeeling Himalayas: Categorical principal component analysis (CATPCA) and ordinal logistic regression (OLR)
The measurement of regional development plays a crucial role in improving the quality of life of local communities. However, the process of analyzing the regional progress was challenging as regional development was presented as a multidimensional concept. Nonetheless, the study's primary objective was to understand the indicators that genuinely reflect the development process's various dimensions in the northernmost district of West Bengal, Darjeeling Himalayas. Seven dimensions of development, namely psychological well-being, health, education, governance, safety and crime, energy and environment and standard of living were identified for analyzing the socio-economic development of the Darjeeling Himalaya. A questionnaire was framed and circulated in the region for the collection of data. By applying Categorical Principal Component Analysis (CATPCA), the data collected was aggregated into the above mentioned seven dimensions of development and analyzed the relationship between these development indicators through the Ordinal Logistic Regression model (OLR). The results showed that education and governance indicators had a significant impact on psychological wellbeing. Governance was affected by psychological wellbeing, while the standard of living was affected by psychological wellbeing and health indicators in the region. 2021 The Society of Economics and Development, except certain content provided by third parties. -
A molecular docking study of SARS-CoV-2 main protease against phytochemicals of Boerhavia diffusa Linn. for novel COVID-19 drug discovery
SARS-CoV-2, the causative virus of the Corona virus disease that was first recorded in 2019 (COVID-19), has already affected over 110 million people across the world with no clear targeted drug therapy that can be efficiently administered to the wide spread victims. This study tries to discover a novel potential inhibitor to the main protease of the virus, by computer aided drug discovery where various major active phytochemicals of the plant Boerhavia diffusa Linn. namely 2-3-4 beta-Ecdysone, Bioquercetin, Biorobin, Boeravinone J, Boerhavisterol, kaempferol, Liriodendrin, quercetin and trans-caftaric acid were docked to SAR-CoV-2 Main Protease using Molecular docking server. The ligands that showed the least binding energy were Biorobin with ? 8.17kcal/mol, Bioquercetin with ? 7.97kcal/mol and Boerhavisterol with ? 6.77kcal/mol. These binding energies were found to be favorable for an efficient docking and resultant inhibition of the viral main protease. The graphical illustrations and visualizations of the docking were obtained along with inhibition constant, intermolecular energy (total and degenerate), interaction surfaces and HB Plot for all the successfully docked conditions of all the 9 ligands mentioned. Additionally the druglikeness of the top 3 hits namely Bioquercetin, Biorobin and Boeravisterol were tested by ADME studies and Boeravisterol was found to be a suitable candidate obeying the Lipinskys rule. Since the main protease of SARS has been reported to possess structural similarity with the main protease of MERS, comparative docking of these ligands were also carried out on the MERS Mpro, however the binding energies for this target was found to be unfavorable for spontaneous binding. From these results, it was concluded that Boerhavia diffusa possess potential therapeutic properties against COVID-19. 2021, Indian Virological Society. -
On m-quasi Einstein almost Kenmotsu manifolds
In this article, we consider m-quasi Einstein structures on two class of almost Kenmotsu manifolds. Firstly, we study a closed m-quasi Einstein metric on a Kenmotsu manifold. Next, we proved that if a Kenmotsu manifold M admits an m-quasi Einstein metric with conformal vector field V, then M is Einstein. Finally, we prove that a non-Kenmotsu almost Kenmotsu (?,?)' -manifold admitting a closed m-quasi Einstein metric is locally isometric to the Riemannian product Hn+1Rn, provided that ?-?(2n+m)/2m = 1. 2021 Universita degli Studi di Parma. All rights reserved. -
SAARC Regional Disaster Law: Need for Progressive Development
[No abstract available] -
Mutual Information Pre-processing Based Broken-stick Linear Regression Technique for Web User Behaviour Pattern Mining
Web usage behaviour mining is a substantial research problem to be resolved as it identifies different user's behaviour pattern by analysing web log files. But, accuracy of finding the usage behaviour of users frequently accessed web patterns was limited and also it requires more time. Mutual Information Pre-processing based Broken-Stick Linear Regression (MIP-BSLR) technique is proposed for refining the performance of web user behaviour pattern mining with higher accuracy. Initially, web log files from Apache web log dataset and NASA dataset are considered as input. Then, Mutual Information based Pre-processing (MI-P) method is applied to compute mutual dependence between the two web patterns. Based on the computed value, web access patterns which relevant are taken for further processing and irrelevant patterns are removed. After that, Broken-Stick Linear Regression analysis (BLRA) is performed in MIP-BSLR for Web User Behaviour analysis. By applying the BLRA, the frequently visited web patterns are identified. With the identification of frequently visited web patterns, MIP-BSLR technique exactly predicts the usage behaviour of web users, and also increases the performance of web usage behaviour mining. Experimental evaluation of MIP-BSLR method is conducted on factors such as pattern mining accuracy, false positives, time requirements and space requirements with respect to number of web patterns. Outcomes show that the proposed technique improves the pattern mining accuracy by 14%, and reduces the false positive rate by 52%, time requirement by 19% and space complexity by 21% using Apache web log dataset as compared to conventional methods. Similarly, the pattern mining accuracy of NASA dataset is increased by 16% with the reduction of false positive rate by 47%, time requirement by 20% and space complexity by 22% as compared to conventional methods. 2020. All Rights Reserved. -
Sensitivity analysis of nonlinear radiated heat transport of hybrid nanoliquid in an annulus subjected to the nonlinear Boussinesq approximation
The main emphasis of the current study is to analyze the novel feature of the quadratic convective and nonlinear radiative flow of MHD hybrid nanoliquid (CuAl2O3H2O) in an annulus with sensitivity analysis. The significance of exponential space-related heat source, movement of annuli and a new radiation parameter corresponding to an asymptotic nature are also comprehended in the existing study. The dimensionless governing nonlinear equations are treated numerically by employing shooting technique. Impact of effective parameters on the flow and heat transport features has been scrutinized. The optimization procedure is implemented to analyze the influence of three effective parameters (1.5?Rf?5.5,1?QE?3and1%??Cu?3%) on skin friction and Nusselt number by utilizing response surface methodology and sensitivity analysis. The obtained results portray that the nonlinear convection parameter is more favorable for the skin friction coefficient. Further, a comparison of sensitivity depicts that the skin friction coefficient is more sensitive to Rf and QE, whereas Nusselt number is more sensitive to ?Cu. 2020, Akadiai Kiad Budapest, Hungary. -
A Study on Decision Paralysis in Customers with Special Reference to Placing Order in Restaurant
Owing to the vast number of choices open to customers, they can often feel paralysed in their decision-making. Offering a wide range of options can activate the effect of Decision Paralysis, which delays the client's final decision. The impact of Decision Paralysis can prevail in restaurants. This study reveals the existence of decision paralysis among customers in restaurants when placing an order. The aim is to investigate the prevalence of Decision Paralysis among customers, with particular reference to placing an order in a restaurant and the influence on consumers purchase decisions. A survey questionnaire was rolled out using Google forms to customers who have experienced dining in a restaurant. A total of 416 survey responses were collected for data analysis through the convenience sampling method. It was found that, customer purchase decision has been affected by the decision paralysis effect. It was also found that customers experience a dilemma due to tremendous options or choices in the food sector by the service providers. This study was limited to restaurants and in terms of cuisine, with hotels not being considered. Hence, the main limitation is not being able to generalise the findings of this study to the whole of the food catering sector. The study will benefit both scholars and marketing practitioners in understanding the difficulty a customer faces during purchase decision-making. 2021 Transnational Press London -
Sensitivity analysis of Marangoni convection in TiO2EG nanoliquid with nanoparticle aggregation and temperature-dependent surface tension
The sensitivity analysis of the magnetohydrodynamic thermal Marangoni convection of ethylene glycol (EG)-based titania (TiO2) nanoliquid is carried out by considering the effect of nanoparticle aggregation. The rate of heat transfer is explored by utilizing response surface methodology and estimating the sensitivity of the heat transfer rate toward the effective parameters: radiation parameter (1 ? R ? 3), magnetic parameter (1 ? M ? 3) and nanoparticle volume fraction (1 % ? ?? 5 %). The heat transfer phenomenon is scrutinized with thermal radiation and variable temperature at the surface. The effective thermal conductivity and viscosity with aggregation are modeled by using the MaxwellBruggeman and KriegerDougherty models. The governing equations are solved by using the apposite similarity transformations. It is found that when the effect of aggregation is considered, the velocity profile is lower. A positive sensitivity of the Nusselt number toward thermal radiation is observed. Further, a negative sensitivity of the heat transfer rate is observed toward the magnetic field and nanoparticle volume fraction. 2020, Akadiai Kiad Budapest, Hungary. -
Comparative optimization studies (Isp 4 vs isp 3 vs isp 2 media) of mangrovian streptomyces pluripotens anukcjv1 for its ?-amylase production and geographical correlation of mangrovian actinomycetes strains
Streptomyces pluripotens ANUKCJV1 was isolated from Coringa Mangroves which was located along the South Indian Delta. The Current work which was in continuation to our previously reported work which suggests that Streptomyces pluripotens ANUKCJV1 was the potential strain and the same has been subjected to comparative optimization studies in the current work by employing three media: ISP 4; ISP 3; ISP 2 media for enhanced ?-Amylase Production. Physico-Chemical variables viz Incubation period, PH, Temperature, Carbon and Nitrogen sources with respect to three different media (ISP 4, ISP 3 and ISP 2) were tested and cumulative analysis of three different media for differential bioactivity of ?-Amylase was done. Results suggest that ISP 4 found to be the best medium with cumulative value of 24.2 U/mL, where as the cumulative value of ISP 3 and ISP 2 were 19.3 U/mL and 19.4 U/mL respectively. Peptone as Nitrogen source of ISP 4 found to be the favourite Individual variable among all with production value of 8.0 U/mL. Geographical correlation with respect to number of Actinomycetes strains and ?-Amylase Bioactivity depicts that Distant geographical soil samples from the shore found to be favourable for higher number of Actinomycetes strains: A1 soil samples (~ 500 m)-33 %; A2 samples (~ 400 m)-22 %. With regard to ?-Amylase Bioactivity, A5 samples (~ 100 m) analysed to be the potential geographical bioactive zone for ?-Amylase Production. From the study it can be concluded that since ISP 4 found to be the favourite medium of the potential strain, by employing the same large scale exploration of the Streptomyces pluripotens ANUKCJV1 of the Coringa Mangroves may be done to tap the industrial benefits of ?-Amylase. EM International. -
The realist approach for evaluation of computational intelligence in software engineering
Secured software development must employ a security mindset across software engineering practices. Software security must be considered during the requirements phase so that it is included throughout the development phase. Do the requirements gathering team get the proper input from the technical team? This paper unearths some of the data sources buried within software development phases and describes the potential approaches to understand them. Concepts such as machine learning and deep learning are explored to understand the data sources and explore how these learnings can be provided to the requirements gathering team. This knowledge system will help bring objectivity in the conversations between the requirements gathering team and the customer's business team. A literature review is also done to secure requirements management and identify the possible gaps in providing future research direction to enhance our understanding. Feature engineering in the landscape of software development is explored to understand the data sources. Experts offer their insight on the root cause of the lack of security focus in requirements gathering practices. The core theme is statistical modeling of all the software artifacts that hold information related to the software development life cycle. Strengthening of some traditional methods like threat modeling is also a key area explored. Subjectivity involved in these approaches can be made more objective. 2021, The Author(s), under exclusive licence to Springer-Verlag London Ltd. part of Springer Nature. -
Spatial analysis of CO poisoning in high temperature polymer electrolyte membrane fuel cells
The improved tolerance of the High Temperature-Polymer Electrolyte Membrane Fuel Cell (HT-PEMFC) to CO allows the use of reformate as an anode feed. However, the presence of several per cent of CO in the reformate, which is inevitable particularly in on-board reformation in automobiles, which otherwise demands complex systems to keep the CO level very low, will significantly lower the cell performance, especially when the HT-PEMFC is operated at 160 C or below. In this study, a three-dimensional, non-isothermal numerical model is developed and applied to a single straight-channel HT-PEMFC geometry. The model is validated against the experimental data for a broad range of current densities at different CO concentration and operating temperatures. A significant spatial variation in current density distribution is observed in the membrane because the CO sorption is a spatially non-homogeneous process depending on local operating conditions and dilution of the H2 stream. To investigate the local spatial effects on HT-PEMFC operation, the model is applied to a real cell of size 49.4 cm2 with an 8-pass serpentine flow-field at the anode and the cathode. The membrane and anode catalyst layer are segmented into 5 array to investigate the spatial resolution of the polarization curves, H2 concentration, current density, and anode polarization loss. The simulation results show that the presence of CO in the anode feed reduces cell performance, however, the results reveal that uniformity in current density distribution in the membrane improves when the cell is operated in potentiostatic mode. The results are discussed in detail with the help of several line plots and multi-dimensional contours. The study also emphasizes on the importance of optimizing the reformate anode feed rate to improve cell performance. 2020 Hydrogen Energy Publications LLC