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Through the Lens of Recession 2.0: Diversification Dynamics Between the Leading Asian Stock Markets
The focus of this article is to analyse the inter-linkages between eight leading stock markets in Asian continent from the period of July 2011 to February 2018. This period holds relevance as this was the time when Recession 2.0 set in, which adversely affected the developed economies; however, the developing economies withstood the crisis without much of an impact. Co-integration and Granger causality tests were conducted to probe the inter-linkages. Study revealed a positive impact on Asian stock market indices collectively on each of the indexes. The highest number of unidirectional causalities was to KOPSI and NIFTY from rest of the stock indices. Results confirmed that no co-integration relationship existed among the selected indices indicating favourable diversification opportunities. Thus, the study fosters global market participants and policymakers to consider the nitty-gritties of stock market integration so as to benefit from international stock market diversification in the Asian region. 2022 Management Development Institute. -
Phytogenic synthesis and antimicrobial activity of ZnO nano bow ties (ZnO NBTs): An experimental and computational study
Phytogenic synthesis is a sustainable and eco-friendly approach for producing nanoscale particles, using biological entities such as plants and their byproducts. In this study, Allium sativum extract was selected as a capping and reducing agent due to the presence of phytochemicals such as allicin, diallyl disulfide (DADS), vinyl dithiins, ajoene (E- and Z-ajoene), diallyl trisulfide (DATS), and thiol (sulfhydryl) groups. The resulting ZnO Nano Bow Ties (ZnO NBTs) were characterized using FE-SEM, XRD, EDX, DLS, zeta potential, FTIR, and UV-Vis spectroscopy to evaluate the size, morphology, and crystallinity. The obtained XRD, SEM, and DLS results suggested an average longitudinal length of ?372 nm with a maximum lateral width of ?64 nm and a Bow Tie shape. Gas Chromatography-Mass Spectroscopy (GC-MS) analysis was employed to elucidate the prominent phytochemical constituents of the Allium sativum extract. Preliminary antibacterial assays reveal significant inhibition zones and growth inhibition effects against gram-negative bacteria of both Klebsiella pneumoniae and Escherichia coli, suggesting the promising antimicrobial potential of these ZnO NBTs. Monte Carlo simulations revealed that the cone-shaped ZnO NBTs bind strongly to the active sites of the target proteins with binding affinities of ?36.20 and ?32.14 kcal/mol for Klebsiella pneumoniae and Escherichia coli respectively, which correlates with their activities. The ZnO NBTs complexes formed stronger hydrophobic interactions and hydrogen bonds with amino acid residues of Escherichia coli than with Klebsiella pneumoniae. This integrated experimental and computational study underscores the potential of the use of ZnO NBTs as a sustainable and effective strategy to combat bacterial pathogens. The findings of this study indicate that efficient morphology (shape) is a major contributor to the protein binding affinities of ZnO NBTs, with promising implications for the design of antibacterial drugs in nanomedicine. 2024 The Authors -
Construction of a waste-derived graphite electrode integrated IL/Ni-MOF flowers/Co3O4 NDs for specific enrichment and signal amplification to detect aspartame
A novel and cost-efficient electrochemical sensor was designed by immobilizing IL/Ni-MOF/Co3O4 nanodiamonds on the graphite (GE) electrode, marking the first application for the detection of aspartame. The graphite electrode was extracted and recycled from discharged batteries to serve as a working electrode. The nanocomposite features unique Co3O4 nanodiamonds, generated using Coriandrum sativum seed extract, alongside Ni-metal organic framework (MOF), which were synthesized through a solvothermal method. The conductivity and stability of the electrochemical sensor were enhanced through the incorporation of the ionic liquid (IL) ([BMIM][MeSO4]. The phytochemical profile of Coriandrum sativum seed extract, analyzed by GC-MS, identified key compounds involved in the synthesis of Co3O4 nanodiamonds. A comprehensive characterization of the nanocomposite was performed using UV-Vis, FTIR, DLS, Zeta potential, XRD, XPS, FE-SEM, TEM, optical profilometry, and AFM to confirm the structural and elemental modifications. Electrochemical characterization of the bare and modified electrodes was conducted through cyclic voltammetry (CV) and electrochemical impedance spectroscopy (EIS). The GE/IL/Ni-MOF/Co3O4 nanodiamonds modified electrode displayed enhanced electroanalytical performance for aspartame detection, characterized by signal amplification at +7.0 V. Quantitative analysis by Differential Pulse Voltammetry (DPV) and Square Wave Voltammetry (SWV) revealed a linear detection range of 315 M for aspartame. A comparison of SWV and DPV revealed superior analytical performance for SWV, with limit of detection (LOD) and limit of quantification (LOQ) values of 1.02 M and 3.1 M (R2 = 0.993) compared to 1.81 M and 5.5 M (R2 = 0.986) for DPV. This study reveals the excellent adsorption capabilities of Ni-MOF and Co3O4 nanodiamonds (Co3O4 NDs), attributed to their high porosity and large surface area, paving the way for the development of affordable sensing devices for artificial sweeteners. 2024 Elsevier B.V. -
Ni-Co MOF Flowers/ZnO NRs Mediated Electrochemical Sensor for Rapid and Ultrasensitive Detection of Neotame in Food Samples
This study focuses on the bioreduction of waste-derived graphite rods into reduced graphene oxide(rGO), followed by the fabrication with Ni-Co metal-organic flowers and Zinc oxide nanorods(ZnO NRs) using Nafion, for sensitive detection of neotame. The Ni-Co metal-organic flowers and ZnO NRs were synthesized using solvothermal synthesis and Azadirachta indica leaf extract, respectively. Additionally, Nafion polymer enhances the stability and conductivity of the nanocomposite. The nanocomposite was characterized using UVvis, Fourier transform infrared spectorscopy, X-ray diffraction, Raman spectroscopy, Dynamic light scattering, X-ray photoelectron spectroscopy, Field-emission scanning electron microscopy, Energy-dispersive X-ray analsysis, Transmission electron microscopy, and Atomic force microscopy. The electrochemical studies were carried out using Electrochemical impedance spectroscopy and Cyclic voltammetry. The modified electrode (rGO/Nafion/Ni-Co MOF/ZnO NRs) demonstrated improved electrochemical activity (34.01 ?A) for neotame with an enhanced peak current at +0.73 V. The LOD and LOQ values were calculated and found to be 0.32 and 0.99 ?M with a recovery (%) ranging from 94.50 to 101.34%. The outcome of this study identifies the morphological and electrochemical factors as major contributors to the adsorption affinities and catalytical activities, with promising possibilities for the design of electrochemical sensing of artificial sweeteners. 2024 The Electrochemical Society (ECS). Published on behalf of ECS by IOP Publishing Limited. All rights, including for text and data mining, AI training, and similar technologies, are reserved. -
Experimenting with scalability of Beacon controller in software defined network
In traditional network, a developer cannot develop software programs to control the behavior of the network switches due to closed vendor specific configuration scripts. In order to bring out innovations and to make the switches programmable a new network architecture must be developed. This led to a new concept of Software Defined Networking(SDN). In Software defined networking architecture, the control plane is detached from the data plane of a switch. The controller is implemented using the control plane which takes the heavy lift of all the requests of the network. Few of the controllers used in SDN are Floodlight, Ryu, Beacon, Open Daylight etc. In this paper, authors are evaluating the performance of Beacon controller using scalability parameter on network emulation tool Mininet and IPERF. The experiments are performed on multiple scenarios of topology size range from 50 to 1000 nodes and further analyzing the controller performance. BEIESP. -
A hybrid algorithm for face recognition using PCA, LDA and ANN
Face recognition is an evolving technique in the field of digital device security. The two procedures Principal Component Analysis and Linear Discriminant Analysis (LDA) are standard methodologies commonly used for feature extraction and dimension reduction techniques extensively used in the recognition of face system. This paper discourse, PCA trailed through a feed forward neural network (FFNN) called PCA-neural network and LDA trailed through feed forward neural network as LDA-neural network are considered for development of hybrid face recognition algorithm. In the current research work, a hybrid model of face recognition is presented with the integration of PCA, LDA, and FFNN. The proposed system experimental results indicate better performance compared to the state of the art literature methods. IAEME Publication. -
A Big Data Analysis Using Fuzzy Deep Convolution Network Based Model for Heart Disease Classification
Heart disease is a serious disease that causes sudden death among 80% of the people around the world. The traditional models performed predictive analytics using machine learning techniques to make a better decision. For better decision making in heart disease prediction, the big data analysis shows the great opportunities to predict the future health status from health parameters and provide best outcomes. However, the traditional decision making models had traffic data or contained noise and uncertainty was unpredictable as the data ambiguity emerged. In order to overcome such an issue, the big data is used to ensure the medical service which is mostly needed in a timely manner and for accurate diagnosis. The pre-processing of the medical data acquired from Cleveland heart disease UCI datasets has a vast number of attributes which helps to predict the heart disease. The data are contaminated with the noise and some of the data are missing, so the pre-processing using Min max Normalization is performed to remove contaminated noise acquired in the data which is taken from the UCI repository dataset. The proposed Fuzzy Deep Convolution Network (FDCN) permits the input features for fuzzification process that uses transformed features. The fuzzification process eliminates the redundant or irrelevant fuzzified features and overcomes the system complexity problems. The proposed FDCN obtains accuracy of 95.56 % and 92 % of F-score shows better results when compared with the existing KNN-DT, Naive Bayes, and Random Forest algorithms. 2021,International Journal of Intelligent Engineering and Systems. All Rights Reserved. -
A prediction technique for heart disease based on long short term memory recurrent neural network
In recent years, heart disease is one of the leading cause of death for both women and men. So, heart disease prediction is considered as a significant part in the clinical data analysis. Standard data mining techniques like Support Vector Machine (SVM), Naive Bayes and other machine learning techniques used in the earlier research for heart disease prediction. These methods are not sufficient for effective heart disease prediction due to insufficient test data. In this research, Bi-directional Long Short Term Memory with Conditional Random Field (BiLSTM-CRF) has been proposed to increase the efficiency of heart disease prediction. The input medical data were analyzed in a bidirectional manner for effective analysis, and CRF provided the linear relationship between the features. The BiLSTMCRF method has been tested on the Cleveland dataset to analyze the performance and compared with existing methods. The results showed that the proposed BiLSTM-CRF outperformed the existing methods in heart disease prediction. The average accuracy of the proposed BiLSTM-CRF is 90.04%, which is higher than the existing methods. 2020 by the authors. -
A stochastic propagation model to the energy dependent rapid temporal behaviour of Cygnus X-1 as observed by AstroSat in the hard state
We report the results from analysis of six observations of Cygnus X-1 by Large Area X-ray Proportional Counter (LAXPC) and Soft X-ray Telescope (SXT) onboard AstroSat, when the source was in the hard spectral state as revealed by the broad-band spectra. The spectra obtained from all the observations can be described by a single-temperature Comptonizing region with disc and reflection components. The event mode data from LAXPC provides unprecedented energy dependent fractional root mean square (rms) and time-lag at different frequencies which we fit with empirical functions.We invoke a fluctuation propagation model for a simple geometry of a truncated disc with a hot inner region. Unlike other propagation models, the hard X-ray emission (>4 keV) is assumed to be from the hot inner disc by a single-temperature thermal Comptonization process. The fluctuations first cause a variation in the temperature of the truncated disc and then the temperature of the inner disc after a frequency dependent time delay.We find that the model can explain the energy dependent rms and time-lag at different frequencies. 2019 The Author(s) Published by Oxford University Press on behalf of the Royal Astronomical Society. -
BSLnO: Multi-agent based distributed intrusion detection system using Bat Sea Lion Optimization-based hybrid deep learning approach
Intrusion detection system (IDS) is a robust model that plays an essential role in dealing with intrusion detection, especially in detecting abnormal anomalies and unknown attacks. The major challenges faced by IDS are the computation time required for analysis, and the exchange of a huge amount of data from one division of the network to another. For the sake of tackling such limitations, this probe proposes a multi-agent enabled IDS for detecting intrusions using the Bat Sea Lion Optimization (BSLnO) algorithm. The proposed strategy consists of five phases, namely pre-processor agent, reducer agent, augmentation agent, classifier agent, and decision agent. Initially, input data is subjected to pre-processor agent, where pre-processing is carried out using data normalization and missing value imputation. Thereafter, the pre-processed result is fed up to the reducer agent, where dimension reduction is carried out using mutual information. The third step is data augmentation in which the dimensionality of data is enhanced. After that, the augmented result is subjected to classifier agent to classify intrusions or malicious activities present in the network based on hybrid deep learning strategies, namely deep maxout network and deep residual network. A developed BSLnO is implemented by incorporating Bat Algorithm (BA) and Sea Lion Optimization (SLnO) algorithm to train the hybrid classifier. The proposed scheme has acquired a higher precision of 0.936, recall of 0.904, and F-measure of 0.920. 2022 John Wiley & Sons Ltd. -
Load shedding using GA and ACO in smart gird environment
Increasing pressure on the utilities to accommodate energy efficiency, load management and progress in advanced technology has led to transformations for existing grid into a smarter grid. Creating awareness among the end-users to participate in load management programs instead of capacity addition is the best solution for maintaining the stability in the grid. Load shedding is a strategy under load management in which load connected to the smart grid is individually controlled via two- way communication. In this paper, a Smart Load shedding approach is developed based on load prioritization. The required amount of load to be shed under lack of sufficient generation level is optimized by Genetic Algorithm (GA) and Ant Colony Optimization (ACO) algorithms. The proposed approach is implemented using a real time feeder data from the substation, India. The results reflect the effectiveness of proposed algorithms taken into practical applications. -
Intelligent load shedding using ant colony algorithm in smart grid environment
For every country which is expecting a large growth in power demand in the near future or facing a power crisis, an effective load control and power distribution strategy is a necessity. Load shedding is done whenever power demand is more than power generation in order to sustain power system stability. The current load shedding strategies fails to shed exact amount of load as per the system requirement and does not prioritize loads which are being shed. Given the dimension of the problem, it would not be feasible computationally, to use regular optimization techniques to solve the problem. The problem is typically suited for application of meta-heuristic algorithms. This paper proposes a new scheme for optimizing load shedding using ant colony algorithm in a smart grid platform considering loads at utility level. The algorithm developed considers each electrical connection from Distribution Company as one lumped load and provides an effective methodology to control the load based on various constrains such as importance of load and time of load shedding. Springer India 2015. -
Molecularly imprinted PEDOT on carbon fiber paper electrode for the electrochemical determination of 2,4-dichlorophenol
A highly selective electrochemical sensor has been developed for the determination of the pesticide molecule, 2,4-dichlorophenol (2,4-DCP) using molecularly imprinted conducting polymer. 2,4-dichlorophenol imprinted polymer films were prepared by electropolymerising 3,4-ethylenedioxythiophene (EDOT) on surface of carbon fiber paper electrode (CFP) in presence of 2,4-dichlorophenol. Electrochemical over-oxidation was carried out for the controlled release of 2,4-DCP templates and to generate definite imprinting sites. Surface morphology of the imprinted electrode was analysed by Scanning Electron Microscopy-Energy Dispersive X-ray Spectrometry, Fourier Transform Infrared and Raman spectroscopy. In optimized conditions, the voltammetric sensor gave a linear response in the range of 0.21 nM 300 nM. The significantly low detection limit (0.07 nM) demonstrates the ultra-low sensitivity of the method. The imprinted sensor displayed higher affinity and selectivity towards the target 2,4-DCP over similar structural analogical interference than the non-imprinted sensor. MIP sensor was efficaciously employed for the selective determination of 2,4-DCP in real samples of water. 2020 Elsevier B.V. -
Facile synthesis of nickel nanoparticles and its efficient dye degradation
The present investigation involves the synthesis of nickel (Ni) nanoparticles (NPs) by the chemical reduction of nickel chloride using hydrazine hydrate without the need for an inert atmosphere from an external source. The photocatalytic activity, structure and morphology of the NPs were studied by employing UV-Visible (UV-Vis) spectroscopy, powder X-ray diffraction (PXRD) and transmission electron microscopy (TEM). Degradation of methylene blue(MB) and rhodamine B(Rh-B) dyes using Ni NPs was investigated to see the feasibility in degrading these dyes from polluted water at low cost. Ni NPs showed a good photocatalytic activity of 84.1% under visible light for the degradation of MB when compared to Rh-B which showed an efficiency of 47.3 %. 2020 World Research Association. All rights reserved. -
Feature extraction of optical character recognition: Survey
Optical Character Recognition is still prevailing even after many decades of implementation. The challenges faced here are increasing day by day so as its applications. From Punched cards to Handwritten Text, from images to video, from uniform font to universal font, from English text to Global language, from researchers to visually handicapped are the transformations obtained from an era of the 1980s to 2010. This paper has covered the advancement of acknowledging the characters, how are features are extracted, various methodologies used and more importantly what is the use of OCR. Research India Publications. -
Computational and experimental investigation on biological and photophysical properties of high yielded novel aryl-substituted pyrazolone analogue
A series of new aryl-substituted pyrazolone derivatives 5(a-h) were synthesised via the Baylis-Hillman acetate reaction with pyrazolones and tested for antifungal, antibacterial, and antioxidant properties in vitro. Among the tested molecules 5d and 5e show good in vitro antifungal and antibacterial activities due to the presence of fluorine, which enhances the absorption rate by increasing lipid solubility and improves the pharmacological activity. It is also evident from the results obtained from structure-activity relationship (SAR) studies. Further, the photophysical properties of synthesized compounds were theoretically estimated using the ab-intio technique. The ground state optimization and HOMOLUMO energy levels are calculated using the DFT-B3LYP-6-311 basis set. Using the theoretically estimated HOMOLUMO value, global chemical reactivity descriptor parameters are estimated, and the result shows that compounds 5d and 5e have a higher electronegative and electrophilicity index than other molecules. Overall results suggest that, fluorine substituted pyrazolone derivatives show good photophysical, SAR, and biological properties. 2022 Elsevier B.V. -
Photophysical and In Vitro-In Silico Studies on Newly Synthesized Ethyl 3-((3-Methyl-1-phenyl-1H-pyrazol-5-yl)oxy)-2-methyleneheptanoate
Abstract: In the present work, the aryl-substituted pyrazolone derivative ethyl 3-((3-methyl-1-phenyl-1H-pyrazol-5-yl)oxy)-2-methyleneheptanoate (ETT) has been synthesized by the reaction of Baylis-Hillman acetate with pyrazolones and screened for their in vitro antifungal, antibacterial, and antioxidant properties. The molecule shows good in vitro antifungal and antibacterial activities due to the presence of pentane, which enhances the absorption rate by its increased lipid solubility and improves the pharmacological activity. It is also evident from the results obtained from structure-activity relationship (SAR) studies. In silico studies were conducted on the synthesized molecule, examining its interactions with DNA Gyrase, Lanosterol14 alpha demethylase, and KEAP1-NRF2 proteins. The results revealed strong binding interactions at specific sites. Further, the photophysical properties of synthesized compounds were theoretically estimated using the ab-intio technique. The ground state optimization, dipole moment, and HOMOLUMO energy levels are calculated using the DFT-B3LYP-6-31G(d) basis set. Using the theoretically estimated HOMOLUMO value, global chemical reactivity descriptor parameters are estimated, and the result shows the synthesised molecule has a highly electronegative and electrophilic index. NBO analysis proved the presence of intermolecular ON.H hydrogen bonds caused by the interaction of the lone pair of oxygen with the anti-bonding orbital. The results suggest that pentane-substituted pyrazolone derivatives show good photophysical and biological applications. Pleiades Publishing, Ltd. 2024. -
NSS-ML: a Novel spectrum sensing framework using machine learning for cognitive radio IoT networks
A key component of cognitive radio systems is spectrum sensing, which reduces coexistence problems and maximises spectrum efficiency. However, the introduction of multiple situations with distinct characteristics brought about by 5G communication presents problems for spectrum sensing to support a wide range of applications with high performance and flexible implementation. Inspired by these difficulties, a new method with a multi-layer extreme learning machine optimised for bats is presented in this study. This technique makes use of a variety of input vectors, such as channel ID, energy, distance, and received signal intensity, to enhance user categorization and sensing capabilities. Moreover, we compare the proposed method with the state-of-the-art spectrum sensing approaches in order to evaluate its effectiveness in 5G situations, especially in healthcare applications. Evaluation metrics including channel detection probability, sensitivity, and selectivity are carefully examined. The findings unequivocally prove the suggested spectrum sensing approachs superiority over current methods and highlight its potential for smooth incorporation into a variety of 5G applications. Bharati Vidyapeeth's Institute of Computer Applications and Management 2024. -
Unfolding the aggression and locus of control paradigm in sportspersons and non-sportspersons
The present study investigated Aggression and Locus of Control on Combat Sports Persons, Non-Combat Sports Persons, and Non-Sports Persons. In this study, a sample of 240 individuals (80 Combat sports, 80 Non-Combat Sports & 80 Non-Sportspersons) was used through purposive sampling. The tools administered were the Buss and Perry Aggression Questionnaire by Arnold H. Buss and Mark Perry and Rotters Locus of Control Scale by Julian Rotter respectively. The objective of the study was to investigate Aggression and Locus of Control in males and females from Combat, Non-Combat, and Non-Sports persons. This research also aims to explore the relationship between Aggression and Locus of Control. Mean, t-test, F-value (ANOVA), and correlation have been computed over SPSS-16. Results suggest that males from Combat have higher Aggression than people from non-sports and non-combat sports. There is also a significant difference between non-sports persons and sports people over the Locus of Control, sports persons showed internal locus of control compared to non-sports persons who were higher on external locus of control. The result also indicates a significant relationship between the anger dimension of the Aggression and Locus of Control. 2025 ARD Asociaci Espala. -
A self-cooperative trust scheme against black hole attacks in vehicular ad hoc networks
The main objective of the Vehicular Adhoc NETwork (VANET) is to provide secure communications for the vehicles in the network without fixed infrastructures. It inherits all the properties of the MANET. Achieving reliable routing to avoid various routing attacks is the major concern in the vehicular network. Routing attacks degrade the performance of the network. Black hole attack is one of the routing attacks, which drops the data packets without forwarding them to the destination vehicle. Different routing schemes are proposed to provide security against these attacks, which still have security issues. Hence a new self-cooperative trust scheme is proposed in this paper, to detect single as well as collaborative black hole attackers in the network. Two processes: self-detection and cooperative detection, are used to detect attackers in the network. Results show that the proposed scheme has better performance in terms of throughput, PDR and delay. Copyright 2021 Inderscience Enterprises Ltd.