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Narrating Trauma as Victims of Human Trafficking in China: A Study on Select North Korean Memoirs
The memoirs titled In Order to Live; A North Korean Girl's Journey, to Freedom and; A Thousand Miles to Freedom: My Escape from North Korea are written by Yeonmi Park and Eunsun Kim two women who managed to escape from North Korea. They went through an experience of being forced into labour in China as victims of trafficking. In their memoirs these authors vividly depict the pain that comes with being exploited. The main aim of this study is to analyse how memoirs can effectively address the issue of trafficking. These remarkable women skilfully use the memoir genre to make a personal plea for action. They strategically make choices appeal to readers emotions openly share their distressing experiences and support their stories with research and evidence that connect their experiences with the broader problem of human trafficking in China. This study clearly shows that both these memoirs emphasize the importance of the memoir genre in advocating for rights. It also highlights how survivor memoirs have the potential to inspire advocacy and involvement, in combating trafficking. 2025 Sciedu Press. All rights reserved. -
Inpatient complaining behaviour: A study on the overt and covert behaviour of inpatients in Indian hospitals
Consumer dissatisfaction and complaining behaviour have always been a topic of discussion in educational institutes and industries alike. Whereas dissatisfaction with product purchases and subsequent returns or associated consumer responses is very common, the same in the service sector has been quite different. In India, it is not only the patient who decides, which healthcare service to opt for, because Indians are culturally embedded in a system of collective consumption where other family members or relatives or friends also influence their decision-making. This paper is an exploratory study done to comprehend the chosen behavioural responses of dissatisfied inpatients in India through a questionnaire survey. The survey followed a retrospective recall technique in which the recall window was fixed at six months. The sampling technique followed was probability sampling. The data collection tool was structured and self-administered questionnaire administered in the sampled nine districts of Kerala. A good number of respondents attributed their overt complaining behaviour to lack of cordiality of doctors, nurses or the attending staff and lack of proper care and concern from doctors or nurses. Post complaining, service recovery was found to be satisfactory for most of the complainers. 2020, Kamala-Raj Enterprises. All rights reserved. -
Blowing Your Own Trumpet: How to Increase the Online Visibility of Your Publication?
After seeing ones manuscript in the print form in a journal, the author feels a sense of elation which is indescribable. However, if one really want peers and other researchers to take note of the work, some more effort is needed. With the massive increase in the number of biomedical journals in print supplemented by another large chunk onlinequite a few published papers remain unread by majority of the readers. The availability of social sites, persistent identifiers, and manuscript-sharing sites has simplified the job of increasing the impact of an article. We herein share some of these tricks-of-the-trade. 2018, Indian Academy of Pediatrics. -
An internet of health things-driven deep learning framework for detection and classification of skin cancer using transfer learning
As specified by World Health Organization, the occurrence of skin cancer has been growing over the past decades. At present, 2 to 3 million nonmelanoma skin cancers and 132 000 melanoma skin cancers arise worldwide annually. The detection and classification of skin cancer in early stage of development allow patients to have proper diagnosis and treatment. The goal of this article is to present a novel deep learning internet of health and things (IoHT) driven framework for skin lesion classification in skin images using the concept of transfer learning. In proposed framework, automatic features are extracted from images using different pretrained architectures like VGG19, Inception V3, ResNet50, and SqueezeNet, which are fed into fully connected layer of convolutional neural network for classification of skin benign and malignant cells using dense and max pooling operation. In addition, the proposed system is fully integrated with an IoHT framework and can be used remotely to assist medical specialists in the diagnosis and treatment of skin cancer. It has been observed that performance metric evaluation of proposed framework outperformed other pretrained architectures in term of precision, recall, and accuracy in detection and classification of skin cancer from skin lesion images. 2020 John Wiley & Sons, Ltd. -
Predictive analytics in cryptocurrency using neural networks: A comparative study
This paper is concerned with assessing different neural network based predictive models. Each of these predictive models has one goal and that is to predict the price of a cryptocurrency, Bitcoin is the cryptocurrency taken into consideration. The models will be focusing on predicting the USD equivalent value of bitcoin using historical data and live data. The neural network models being assessed are a Convolutional Neural Network, and two variations of the Recurrent Neural Network that are Long Short Term Memory (LSTM) and Gated Recurrent Unit (GRU). The goal is to observe the validation loss of each model and also the time it takes to train or epoch for each training set which basically just determine its efficiency and performance. The results that are achieved are almost what was expected as LSTM outperforms CNN but the when we take a look at GRU, it is at par with LSTM. However, CNN is quicker at training or creating epochs and the validation loss is acceptable and not too high but it looks so when it is compared with the Recurrent Neural Networks such as Long Short Term Memory (LSTM) and Gated Recurrent Unit (GRU). BEIESP. -
Integrating deep learning in an IoT model to build smart applications for sustainable cities
These days, many CS experts focus their efforts on IoT. IoT is an emerging & cutting-edge technology that enables many items, including vehicles and home appliances, to connect and cooperate via mechanisms like machine to machine communication, big data, and AI. It has found use in a wide range of settings, from smart homes and cities, to healthcare and agriculture, to factory automation. Smart cities are becoming smarter, cars are getting more features, and health and fitness devices are getting more sophisticated thanks to the internet of things. Many problems that are directly relevant to the IoT's development have yet to be resolved. The exponential development of IoT has given birth to new problems, including concerns about personal data and security. There is need of a comprehensive approach that tackles the scalability, security, efficiency, and privacy concerns raised by the widespread deployment of IoT. 2023, IGI Global. -
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).