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Synthesis of Graphene Oxide Nano Structures from Kerosene Soot and its Impedance Analysis
Graphene oxide was synthesized from kerosene soot, by adapting three different treatments. The properties of each sample were studied using X-ray diffraction, UV-visible spectroscopy, FTIR and impedance measurements. The XRD results showed that the structural parameters (layer spacing, number of layers) were in agreement with expected values, indicating the reliability of kerosene soot as a precursor for graphene. The grain size was found to be small (1 to 2 nm) confirming the nanostructure of kerosene soot. The UV-visible spectra revealed high band gap even while conductivity was appreciably high. Other characteristic measurements showed frequency-independent conductivity, low resistance and low capacitance. FTIR spectra of all the treated samples and the precursor show the differences brought about in functionalization, due to the different methods of treatment. These differences, however, does not appreciably affect parameters such as band gap, conductivity and dielectric loss in any drastic way. 2018 Chemical Publishing Co. All Rights Reserved. -
Isothermal autocatalysis of homogeneousheterogeneous chemical reaction in the nanofluid flowing in a diverging channel in the presence of bioconvection
The nonlinear differential equations play a prominent role in the mathematical description of many phenomena that occur in our world. A similar set of equations appear in this paper that govern the homogeneous and heterogeneous chemical reactions in the nanofluid flowing between two non-parallel walls. Since the concentration of the homogeneous species is substantially high, quartic autocatalysis is considered for the analysis. It is found to be more effective than the cubic autocatalysis. Further, to avoid the deposition of nanoparticles on the surface, self-propelled microorganisms called gyrotactic microorganisms are allowed to swim in the nanofluid. This movement of microorganisms constitutes a major phenomenon called bioconvection. The set of governing equations thus formed are made dimensionless and the resulting system of equations are solved by Differential Transformation Method (DTM) with the help of Padapproximant that reduces the power series into rational function. This transformation helps in achieving a better convergence rate. The fluid flow analysis is interpreted through graphs and tables where it is observed that the heat source enhances the temperature of the nanofluid. Further, the homogeneous and heterogeneous chemical reaction parameters have significant impacts on the concentration of the reactants. Also, the outcomes indicated that the reaction profiles and motile density profiles increase with the increase in Schmidt number. 2021 Informa UK Limited, trading as Taylor & Francis Group. -
Numerical simulation of unsteady MHD bio-convective flow of viscous nanofluid through a stretching surface
The current flow model is prepared to explore the characteristics of heat and mass transfer through a time-dependent bio-convection slip flow of viscous nanofluid moving over a porous radiative stretched surface model. The outset of bio-thermal convection in a suspension comprising gyrotactic microorganisms and nanoparticles is considered along with radiation and velocity slip. The presence of these nanoparticles and their motion within the nanofluid gives rise to thermophoresis as well as the Brownian motion phenomena and the consideration of these aspects in the model gives realistic results. Moreover, the present model includes the collective influence of the aligned magnetic field, heat source, and mass suction on the boundary. The similarity analysis has been carried out to transform the basic model equations into nonlinear dimensionless ordinary differential equations (ODEs) which are solved numerically using the bvp4c technique in MATLAB. Some reasonable values have been assigned to the flow parameters based on the above different conditions which provide various graphical results. Certain finding states that velocity and temperature respectively decrease and increase as the aligned magnetic field angle is scaled up, whereas the nano particles concentration strengthens with the amplifying values of convection diffusion and thermophoresis parameter and slumps with the rising values of Brownian motion parameter and Schmidt number respectively. Moreover, the concentration of microorganism and nano particles diminishes with the rising values of Schmidt number, as well as the improvement of convection diffusion parameter and Schmidt number magnifies the Sherwood number. The local density of motile microorganisms reduces with the improvement of stretching parameter and bio-convection Schmidt respectively. The obtained results have been validated by comparing them with the published literature. 2023 The Authors -
Heat transfer in a dissipative nanofluid passing by a convective stretching/shrinking cylinder near the stagnation point
This contemporary article examines the transfer of heat properties and the flow behavior of water-based nanofluid suspended with silver nanoparticles. These silver nanoparticles have a very huge thermal conductivity and hence it is presumed that the resulting nanofluid shall have enhanced thermal conductance. This article is more focused on the study of (Formula presented.) nanofluid flowing past a cylinder that is modeled mathematically using the cylindrical coordinate system. The initial modeling is designed using a system of partial derivatives while at a later stage, this system is transformed into a nonlinear group of ordinary differential equations (ODEs). The equations in this system are solved to obtain the dual solutions by implementing the RKF-45 method which has a greater rate of convergence and additionally, it is computationally very effective. The findings of the study are dealt by plotting graphs and the discussions are based on the appearance of graphs. It is further noticed that the critical point (Formula presented.) remains constant at (Formula presented.) for any changes made in the values of heat generation/absorption coefficient. Similarly, the critical value remains constant at (Formula presented.) for any change made in the values of the Eckert number. Meanwhile, it is also observed that the increase in the Eckert number increases the temperature absorbed by the nanofluid whereas it decreases the Nusselt number. Furthermore, the higher values of the velocity slip reduce the skin friction coefficient. 2023 Wiley-VCH GmbH. -
Numerical simulation and mathematical modeling for heat and mass transfer in MHD stagnation point flow of nanofluid consisting of entropy generation
The primary goal of this article is to explore the radiative stagnation point flow of nanofluid with cross-diffusion and entropy generation across a permeable curved surface. Moreover, the activation energy, Joule heating, slip condition, and viscous dissipation effects have been considered in order to achieve realistic results. The governing equations associated with the modeling of this research have been transformed into ordinary differential equations by utilizing appropriate transformation variable. The resulting system of equations was solved numerically by using Bvp4c built-in package in MATLAB. The impact of involved parameters have been graphically examined for the diverse features of velocity, temperature, and concentration profiles. Throughout the analysis, the volume fraction is assumed to be less than 5 % while the Prandtl number is set to be 6. In addition, the entropy generation, friction drag, Nusselt, and Sherwood numbers have been plotted for describing the diverse physical aspects of the underlying phenomena. The major outcomes reveal that the curvature parameter reduces the velocity profile and skin friction coefficient whereas the magnetic parameter, temperature difference parameter, and radiation parameter intensify the entropy generation. 2023, The Author(s). -
Analysis of Elliptic Curve Cryptography & RSA
In todays digital world, the Internet is an essential component of communication networks. It provides a platform for quickly exchanging information among communicating parties. There is a risk of unauthorized persons gaining access to our sensitive information while it is being transmitted. Cryptography is one of the most effective and efficient strategies for protecting our data and it are utilized all around the world. The efficiency of a cryptography algorithm is determined by a number of parameters, one of which is the length of the key. For cryptography, key (public/private) is an essential part. To provide robust security, RSA takes larger key size. If we use larger key size, the processing performance will be slowed. As a result, processing speed will decrease and memory consumption will increase. Due to this, cryptographic algorithms with smaller key size and higher security are becoming more popular. Out of the cryptographic algorithms, Elliptic Curve Cryptography (ECC) provides equivalent level of safety which RSA provides, but it takes smaller key size. On the basis of key size, our work focused on, studied, and compared the efficacy in terms of security among the well-known public key cryptography algorithms, namely ECC (Elliptic Curve Cryptography) and RSA (Rivets Shamir Adelman). 2023 River Publishers. -
A Novel CNN Approach for Condition Monitoring of Hydraulic Systems
In the dynamic landscape of Industry 4.0, the ascendancy of predictive analytics methods is a pivotal paradigm shift. The persistent challenge of machine failures poses a substantial hurdle to the seamless functioning of factories, compelling the need for strategic solutions. Traditional reactive maintenance checks, though effective, fall short in the face of contemporary demands. Forward-thinking leaders recognize the significance of integrating data-driven techniques to not only minimize disruptions but also enhance overall operational productivity while mitigating redundant costs. The innovative model proposed herein harnesses the robust capabilities of Convolutional Neural Networks (CNN) for predictive analytics. Distinctively, it selectively incorporates the most influential variables linked to each of the four target conditions, optimizing the model's predictive precision. The methodology involves a meticulous process of variable extraction based on a predetermined threshold, seamlessly integrated with the CNN framework. This nuanced and refined approach epitomizes a forward-looking strategy, empowering the model to discern intricate failure patterns with a high degree of accuracy. 2024 IEEE. -
Design requirements of a spectropolarimeter for solar extreme-ultraviolet observations and characterization of a K-mirror based on Brewster's angle
Measuring the linear polarization signal in extreme-ultraviolet (EUV) spectral lines, produced by the Hanle effect, offers a promising technique for studying magnetic fields in the solar corona. The required signal-to-noise ratio for detecting the Hanle polarization signals is on the order of 101 (off-limb) to 106 (disk center). Measuring such low signals in the photon starved observations demands highly efficient instruments. In this paper, we present the design of an instrument, SpectroPOLarimeter for Extreme-ultraviolet Observations (SPOLEO), which utilizes reflective components with suitable mirror coatings and thicknesses to minimize the throughput losses. We analyze the system performance within the spectral range from 740 to 800 The K-mirror-based polarimeter model provides a polarizing power of 20%40% in this wavelength range. Based on the system throughput and polarizing power, we discuss various possibilities for achieving the required signal-to-noise ratio, along with their limitations. Due to lack of facilities for fabrication and testing in the EUV, we have calibrated a prototype of the reflection-based polarimeter setup in the laboratory at the visible wavelength of 700 nm. 2024 Optica Publishing Group. -
Shell script to clone AODV routing protocol in network Simulator-2
Background and Objective: Most of the research that are carried in ad hoc routing protocol is through simulation. While working with a simulator, the codes are enclosed in a component that is accessible to all the developers. The difficulty arises as there is no enough documentation and users find it difficult to modify different C++ and TCL files. Even if one component is modified then the entire Network Simulator-2 (NS-2) suite must be reconfigured. Cloning the protocol manually takes a lot of time and prone to error. Our objective is to ease the work of developers and researchers by showing the procedure to clone the AODV protocol automatically using a script. Methodology: In this study, a shell script is developed that will clone the AODV protocol by modifying 18 C++ and TCL files of the protocol and NS-2 suite by automatically inserting the code in exact files at exact position. It also configures the NS-2 and installs the entire NS-2 suite along with setting the path in .bash files. Results: In this research work, a comparison of cloned protocol with AODV protocol is done based on throughput time and packet loss metrics and the results generated are exactly same for both the protocols. The results of the study reveal that the proposed script clones the AODV protocol successfully. Conclusion: This work proves that the proposed script can clone the AODV protocol faster with just one execution of shell script. This methodology will save the time and help the developers or research to focus more on their study on the protocol. 2018 Authors. -
Comparing machine learning and ensemble learning in the field of football
Football has been one of the most popular and loved sports since its birth on November 6th, 1869. The main reason for this is because it is highly unpredictable in nature. Predicting football matches results seems like the perfect problem for machine learning models. But there are various caveats such as picking the right features from an enormous number of available features. There have been many models which have been applied to various football-related datasets. This paper aims to compare Support Vector Machines a machine learning model and XGBoost an Ensemble learning model and how Ensemble Learning can greatly improve the accuracy of the predictions. Copyright 2019 Institute of Advanced Engineering and Science. All rights reserved. -
Simulation of IoT-based Smart City of Darwin: Leading Cyber Attacks and Prevention Techniques
The Rise of the Internet of Things (IoT) technology made the world smarter as it has embedded deeply in several application areas such as manufacturing, homes, cities, and health etc. In the developed cities, millions of IoT devices are deployed to enhance the lifestyle of citizens. IoT devices increases the efficiency and productivity with time and cost efficiency in smart cities, on the other hand, also set an attractive often easy targets for cybercriminals by exposing a wide variety of vulnerabilities. Cybersecurity risks, if ignored can results as very high cost to the citizens and management as well. In this research, simulated IoT network of Darwin CBD has been used with different IoT simulation tools. The treacherous effects of vulnerable IoT environment are demonstrated in this research followed by implementation of security measures to avoid the illustrated threats. 2023 IEEE. -
Marine macrolides as an efficient source of FMS-like tyrosine kinase 3 inhibitors: A comprehensive approach of in silico virtual screening
Marine organisms are a definitive source of antibiotics and kinase inhibitors which provide cues for discovering novel drug leads. Marine macrolides are getting much attraction due to their enzyme inhibitory potential. The present study comprehensively dealt with the virtual screening and structure-based prediction of macrolide compounds against FMS-like tyrosine kinase 3 receptors (FLT3). The FLT3 was chosen as a biological target against the 990 marine macrolides. Before the virtual screening of macrolide compounds, validation of molecular docking was carried out by re-docking of co-crystallized Gilteritinib within the FLT3. Among the selected 990 candidates of marine macrolides, 311 were failed due to the generation of insufficient conformers. Amongst the successful compounds, 22 compounds were also failed to dock within the receptor, while the remaining 657 marine macrolide entities elicited successful docking. The HYBRID Chemguass4 Score ranged from -10.17 to -0.02. This vast difference in the HYBRID ChemGuass4 score is attributed to the difference in binding potential with the receptor's binding pocket. The top ten compounds were selected based on the HYBRID ChemGuass4 Score lower than -8.0 against FLT3. The pharmacokinetics and ADME properties revealed the drug likeliness of the macrolides. 2022 SAAB -
Did Russia's Invasion of Ukraine Induce Herding Behavior in the Indian Stock Market?
This study empirically examines the herding behavior of the Indian stock market investors during the heightened geopolitical tensions between Russia and Ukraine in 2022. An intensified Russia-Ukraine geopolitical event window was constructed, and the high-frequency trading data (intraday) of the Nifty index was analyzed using Multifractal Detrended Fluctuation Analysis (MFDFA) to compute the 5th-order Hurst exponent (Hq (5)) that detects herding behavior. The study's empirical results revealed the presence of profound herding behavior during the intensified Russia-Ukraine geopolitical event window. The study contributes to the existing literature on herding behavior by examining the impact of a geopolitical event on the Indian stock market. Additionally, the study utilizes MFDFA to compute Hurst exponents, a relatively new approach to detecting herding behavior in financial markets. The findings of this study may assist investors and policymakers in understanding the impact of geopolitical events on financial markets and the potential for herding behavior among investors during times of heightened uncertainty. The study's results demonstrate the interconnectedness of global events and financial markets, highlighting the need for policymakers to consider the potential social and economic consequences of geopolitical events. 2023 The Author(s). -
Do all shocks produce embedded herding and bubble? An empirical observation of the Indian stock market
Herding has a history of igniting large, irrational market ups and downs, usually based on a lack of fundamental support. Intuitively, most herds start with an external shock. This empirical study seeks to detect shock-induced herding and the creation of nascent bubbles in the Indian stock market. Initially, the multifractal form of the detrended fluctuation analysis was applied. Then the Reformulated Hurst exponent for the Bombay stock exchange (BSE) was determined using Kantelhardt's calibration. The investigation found evidence of high-level herding and a bubble in 2012, with a high value of Hurst Exponent (0.7349). The other years of the research period (2011, 2013, 2016, 2018, 2020-2021) observed mild to significant herding with comparatively lower Hurst values. The results confirm that herding behavior occurs during a crisis and harsh situations emitting shocks. The study concludes that shock-based herding is prevalent in all six shocks: the economic meltdown, commodities and currency devaluation, geo-political problems, the Central Bank's decision on liquidity management, and the Pandemic. Additionally, the years following the Financial Crisis and the years of the Pandemic are when herding and bubble are prominent. Tabassum Khan, Suresh G., 2022. -
ENHANCING FAKE NEWS DETECTION ON SOCIAL MEDIA THROUGH ADVANCED MACHINE LEARNING AND USER PROFILE ANALYSIS
Social media news consumption is growing in popularity. Users find social media appealing because it's inexpensive, easy to use, and information spreads quickly. Social media does, however, also contribute to the spread of false information. The detection of fake news has gained more attention due to the negative effects it has on society. However, since fake news is created to seem like real news, the detection performance when relying solely on news contents is typically unsatisfactory. Therefore, a thorough understanding of the connection between fake news and social media user profiles is required. In order to detect fake news, this research paper investigates the use of machine learning techniques, covering important topics like feature integration, user profiles, and dataset analysis. To generate extensive feature sets, the study integrates User Profile Features (UPF), Linguistic Inquiry and Word Count (LIWC) features, and Rhetorical Structure Theory (RST) features. Principal Component Analysis (PCA) is used to reduce dimensionality and lessen the difficulties presented by high-dimensional datasets. The study entails a comprehensive assessment of multiple machine learning models using datasets from "Politifact" and "Gossipofact," which cover a range of data processing methods. The evaluation of the XGBoost classification model is further enhanced by the analysis of Receiver Operating Characteristic (ROC) curves. The results demonstrate the effectiveness of particular combinations of features and models, with XGBoost outperforming other models on the suggested unified feature set (ALL). 2023 Little Lion Scientific. -
Fake News Detection Using TF-IDF Weighted with Word2Vec: An Ensemble Approach
Social media platforms' utilization for news consumption is steadily growing due to their accessibility, affordability, appeal, and ability to propagate misinformation. False information, whether intentionally or unintentionally created, is being disseminated across the internet. Certain individuals spread inaccurate information on social media to gain attention, financial benefits, or political advantage. This has a detrimental impact on a substantial portion of society that is heavily influenced by technology. It is imperative for us to develop better discernment in distinguishing between fake and genuine news. In this research paper, we present an ensemble approach for detecting fake news by using TF-IDF Weighted Vector with Word2Vec. The extracted features capture specific textual characteristics, which are converted into numerical representations for training the models and balanced dataset with the Random over Sampling technique. The implementation of our proposed framework utilized the ensemble approach with majority voting which combines 2 machine learning models like Random Forest and Decision Tree. The proposed strategy was adopted empirically evaluated against contemporary techniques and basic classifiers, including Gaussian Nae Bayes, Logistic Regression, Multilayer Perceptron, and XGBoost Classifier. The effectiveness of our approach is validated through the evaluation of the accuracy, F1-Score, Precision, Recall, and Auc curve, yielding an impressive accuracy score of 94.24% on the FakeNewsNet dataset. 2023, Ismail Saritas. All rights reserved. -
Education suffering within structural inequalities: A Critical Discourse Analysis of a policy framework
Education acts as an important catalyst for socioeconomic and democratic evolution in society and is a critical tool for building an equitable system. In our paper, we have historicized one of the most important educational policies, viz. Samagra Shiksha Abhiyan (SAMSA) in India that carries large expectations to minimize the educational divide. We have studied the policy through the lens of Political Economy and have further critiqued it through the framework of Critical Discourse Analysis. We find in our paper that the budget allocated to SAMSA was revised in 2022, from its preceding years with a 28 per cent slash. We critically reflect on the principles mentioned in the policy and find that although there has been an attempt to mitigate the hazards of banking education the Public-Private Partnership initiative reinforces struggles for equitable education, and further, the privatization sets the government free from any accountability. Moreover, a constitutional right like the Right to Education (RTE) is not sufficient enough to meet the goals of universalisation of education. Besides, we analyse the principles such as Education for All, Equity, Equal Opportunity, Access, Gender Concern, Centrality of teacher, Moral Compulsion, and Convergent and integrated system of education management, and argue that although some of the facets of societal structural inequalities are addressed, however, there exists hardly a proper roadmap that could be monitoring the process of creating an inclusive educational paradigm. 2023, Institute for Education Policy Studies. All rights reserved. -
Machine Learning Methods leveraging ADFA-LD Dataset for Anomaly Detection in Linux Host Systems
Advancement in network technology and revolution in the global internet transformed the overall Information Technology (IT) infrastructure and its usage. In the era of the Internet of Things (IoT) and the Internet of Everything (IoE), most everyday gadgets and electronic devices are IT-enabled and can be connected over the internet. With the advancements in IT technologies, operating systems also evolved to leverage these advancements. Today's operating systems are more user-friendly and feature-rich to support current IT requirements and provide sophisticated functionalities. On the one hand, these features enabled operating systems accomplish all current requirements, but on the other hand, these modern operating systems increased their attack surface considerably. Intrusion detection systems play a significant role in providing security against the broad spectrum of attacks on host systems. Intrusion detection systems based on anomaly detection have become a prominent research area among diverse areas of cyber security. The traditional approaches for anomaly detection are inadequate to discover the operating system level anomalies. The advancement and research in Machine Learning (ML) based anomaly detection open new opportunities to tackle this challenge. The dataset plays a significant role in ML-based system efficacy. The Australian Defence Force Academy Linux Dataset (ADFA-LD) comprises thousands of normal and attack processes system call traces for the Linux platform. It is the benchmark dataset used for dynamic approach-based anomaly detection. This paper provided a comprehensive and structured study of various research works based on the ADFA-LD for host-based anomaly detection and presented a comparative analysis. 2022 IEEE. -
Eco-friendly innovations in food packaging: A sustainable revolution
Packaging is crucial in ensuring the quality and safety of food, protecting it from various contaminants, and extending its shelf life. Materials used for packaging food must be economical, durable, and possess good barrier properties. One of the major challenges faced by the food industry is developing an eco-friendly, economical, and sustainable packaging system. The conventional materials, which majorly depend on petroleum-derived polymers, are associated with several significant problems, such as environmental pollution, depletion of resources, generation of single-use wastes, leakage of chemicals into food products, limited recycling, and so on. As the food sector focuses on reducing its environmental impact, by encouraging revolutionary changes for an effective sustainable food packaging approach. The core objective of industrial packaging was to innovate a biodegradable material, especially derived from renewable biomass resources as eco-friendly alternatives in the food industry. One of the significant trends involves production of bioplastics, which are derived from renewable polymers such as corn starch, sugarcane, or algae. These materials offer a viable alternative to traditional petroleum-based plastics, as they are often compostable or biodegradable. The development of advanced bioplastics with improved barrier properties and durability is gaining traction, addressing environmental and health concerns and functionalizing a packaging material. The present review discusses the limitations of conventional packaging materials used in the food industry and focuses on the various polymers derived from natural sources, their physio-chemical properties, and their potential application as a sustainable material that reduce carbon emission, and enhance preservation of food and ensure food safety. 2024 Elsevier B.V.