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Exploration of Thermophoresis and Brownian motion effect on the bio-convective flow of Newtonian fluid conveying tiny particles: Aspects of multi-layer model
This research deals with the analysis of bioconvection caused by the movement of gyrotactic microorganisms. The multi-layer immiscible Newtonian fluid flowing through the vertical channel conveying tiny particles is accounted. The immiscible fluids are arranged in the form of a sandwich where the middle layer has a different base fluid that does not mix with the base fluid of the adjacent fluid layer. This separation of the fluid layers gives rise to the interface boundary conditions. Such flows have found applications in electronic cooling and solar reactors processes. Buongiornos model has been incorporated to design the mathematical model that describes the three-layer flows of Newtonian fluid conveying tiny (metal/oxide) particles under thermophoretic force and Brownian motion. The model thus formed is in the form of the ordinary differential system of equations that are solved using the DTM-Pade approximant after non-dimensionalization. The limited results have an excellent comparison with the existing literature results. The results are discussed through graphs and tables. It is seen that thermophoresis enhances the temperature and particle concentration of the fluid whereas, the Brownian motion is found to enhance the temperature and decrease the concentration. The presence of bioconvection helps in achieving enhanced energy and mass transportation. Moreover, the heat transfer occurring between the different base fluids helps to maintain the optimum temperature in the systems. IMechE 2022. -
Heat amd Mass Transfer Analyses of Nanofluid in a Multilayer Model
The study offers an in-depth exploration into the dynamics and properties of multilayered nanofluids and hybrid nanofluid flow in newlinedifferent geometries. The in-vestigation ranges from sinusoidal channels with micropolar hybrid nanoliquids to concentric cylinders that exhibit electrokinetic effects and rotating disks. Also, the DarcyForchheimer model is introduced to assess non-Newtonian and Newtonian fluid interplay, emphasizing the role of asymmetric slip conditions which reduces the fluid flow. Moreover, the study on bioconvection obtained newlineby addition of gyrotac-tic microorganisms which enhances mass and heat transfer in multilayer Newtonian fluid channels. Studies explain the importance of interfacial regions in achieving optimal system temperature. The subsequent study examines the two-layer hybrid nanofluid (HNF) with magnetohydrodynamic properties between two newlineidentical ro-tating disks. The governing equations of the mathematical models are explained using PDE and solutions are attained using numerical and semi-analytical methods such as the DTM and Range Kutta method. Further, the obtained results have been explained with the help of tables and graphs. The study reveals that the immisci-bility of the base fluids forms an interfacial layer, revealing that the addition of two different fluids restricts the fluid motion nearer to the interfacial region, maintaining an optimum temperature in the system. Collectively, these findings pave the way for advanced applications in industries like solar, nuclear, biomedical, and electronic cooling, promising enhanced newlineperformance and efficiency. -
Building an Industry Standard Novel Language Model Using Named Entities
In every Industry, there is a significant amount of text used in their specific domains. As these are less prevalent in the testing set, anticipating entity names in a language model is a problem faced by the entire industry. In this research a unique and very effective strategy for creating exclusionary classification models that could map entity names based on entity type information is provided. A group of benchmark datasets based on Mortgage is presented, which we used to test the below-presented model. According to experimental findings, our model achieves a perplexity level that is 64% higher than that of the most advanced language models. 2022 IEEE. -
An Empirical Evaluation of the Relationship Between Economic Growth, Population and Solid Waste Generation in India
Municipal solid waste (MSW) poses a hazard to the environment, human health and well-being and economic growth, if not managed correctly.It is essential to study the determinants of municipal solid waste generation for efficient waste management planning and achieving sustainable growth.The main objective of this paper is to establish a relationship between economic growth, population and MSW generation.Secondly, it aims to verify whether the environmental Kuznets curve (EKC) hypothesis is valid in the Indian context with MSW generation as the proxy variable for environmental degradation.Panel regression has been run using statewise data of MSW generation, state net domestic product (SNDP) per capita and population for the years 2000-2019.The results show a significant positive relationship between the selected variables.Least square regression was applied to verify the validity of the environmental Kuznets curve (EKC) hypothesis in India using nationwide data for MSW generation and GDP per capita for the years mentioned above.The results depicted inverted U-shaped curve with MSW as the dependent variable and GDP per capita as the independent variable and confirmed the validity of the EKC hypothesis. 2022 Scientific Publishers. All rights reserved. -
Structure-based virtual screening, pharmacokinetic prediction, molecular dynamics studies for the identification of novel EGFR inhibitors in breast cancer
Breast cancer is one of the most prevalent malignancy cancer types especially affecting women globally. EGFR is a proto onco gene as well as the first identified tyrosine kinase receptor. It plays a dynamic role in many biological tasks such as apoptosis, cell cycle progression, differentiation, development and transcription. Somatic mutation in the EGFR kinase domain derails the normal kinase activity and over expression leads to the progression of cancer especially breast cancer. EGFR is one of the well-known therapeutic targets for breast cancer. In this scenario, we attempt to identify novel potent inhibitors of EGFR. Initially, we performed structure-based virtual screening and identified four potential compounds effective against EGFR. Further, the compounds were subjected to ADME prediction as part of evaluation of the druggability and all the four compounds found to fall under satisfactory range with predicted pharmacokinetic properties. Eventually, the conformational stability of proteinligand complex was analyzed at different time scale by using Gromacs software. Molecular dynamics simulation run of 20 ns is carried out and results were analyzed using root mean square deviation (RMSD), root mean square fluctuation (RMSF) to signify the stability of proteinigand complex. The stability of the proteinligand complex is more stable throughout entire simulation. From the results obtained from in silico studies, we propose that these compounds are exceptionally useful for further lead optimization and drug development. Communicated by Ramaswamy H. Sarma. 2020 Informa UK Limited, trading as Taylor & Francis Group. -
An efficient nonlinear access policy based on quadratic residue for ciphertext policy attribute based encryption
Ciphertext Policy Attribute Based Encryption (CP-ABE) is an efficient encryption scheme as data owner is making decision about the attributes that can access his data and adding that attributes to access structure while encrypting that message. Most existing CP-ABE scheme are based traditional access structure such as linear secret sharing scheme which incur large ciphertext size and linearly increases according to the number of attributes. And those schemes have more computational overhead for calculating share for each attribute and when recalculating secret in data user side. In this paper, we propose a different secret sharing scheme that can be used in access policy for CP-ABE which will reduce the size of ciphertext and there by communication overhead. Furthermore, the proposed scheme reduced computational overhead of secret sharing scheme and improved overall efficiency of the scheme. 2021 Little Lion Scientific -
Machine Learning Techniques for Resource-Constrained Devices in IoT Applications with CP-ABE Scheme
Ciphertext-policy attribute-based encryption (CP-ABE) is one of the promising schemes which provides security and fine-grain access control for outsourced data. The emergence of cloud computing allows many organizations to store their data, even sensitive data, in cloud storage. This raises the concern of security and access control of stored data in a third-party service provider. To solve this problem, CP-ABE can be used. CP-ABE cannot only be used in cloud computing but can also be used in other areas such as machine learning (ML) and the Internet of things (IoT). In this paper, the main focus is discussing the use of the CP-ABE scheme in different areas mainly ML and IoT. In ML, data sets are trained, and they can be used for decision-making in the CP-ABE scheme in several scenarios. IoT devices are mostly resource-constrained and has to process huge amounts of data so these kinds of resource-constrained devices cannot use the CP-ABE scheme. So, some solutions for these problems are discussed in this paper. Two security schemes used in resource-constrained devices are discussed. 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. -
AN EFFICIENT ACCESS POLICY WITH MULTI-LINEAR SECRET-SHARING SCHEME IN CIPHERTEXT-POLICY ATTRIBUTE-BASED ENCRYPTION
Ciphertext-Policy Attribute-Based Encryption (CP-ABE) is a system in which attribute are used for user's identity and data owner determine the access policy to the data to be encrypt. Here access policy are attached with the ciphertext. In the form of a monotone Boolean formula monotone access structure, an access policy can be interpreted and a linear secret-sharing scheme (LSSS) can be implemented. In recent CP-ABE schemes, LSSS is a matrix whose row represent attributes and there exist a general algorithm which is proposed by Lewko and Waters it transforms a Boolean formula into corresponding LSSS matrix. But we may want to transform the monotone Boolean formula to an analogous but compressed formula first before applying the algorithm. This is a very complex procedure and require efficient optimization algorithm for obtaining equivalent but smaller size Boolean formula. So in this paper we are introducing an extended LSSS called multi-linear secret-sharing scheme where we can eliminate above optimization algorithm and directly convert any Boolean formula to multi-linear secret-sharing scheme. 2022 Little Lion Scientific. All rights reserved. -
A Revocable Multi-Authority Attribute Based Encryption Scheme Based On Nonlinear Access Policy
Due to the tremendous increase of data, currently individuals and organizations are increasingly opting to store their data with third-party providers as a solution to their storage issues. Ciphertext Policy Attribute Based Encryption facilitates data outsourcing by encrypting the data at the source and uploading it to a third-party storage provider with some restricted access which is mentioned using access policy. In classical Identity-based Encryption (IBE), when a data owner needs to transmit a message to a data user, they would send it together with the data user's specific identity, such as their email address. This ensures that only the intended recipient can access and read the message. The primary issue is that the data owner must possess knowledge of the identity of each user. Other than the traditional IBE, a data owner can utilize attribute-based encryption to deliver a message to a group of individuals who have the same attributes. Here, the data owner does not need to be aware of every user's identity; instead, he can send messages using the attributes and access policies that have been provided, such as which users can access this message. This research work primarily focuses on three CP-ABE aspects: access policy, number of attribute authority, and revocation. The current access policies are insecure due to their linear character, as they always calculate shares using the same linear equation. For this particular issue in this work, a non-linear secret sharing model that enhances the security of the model is proposed. For addressing the key escrow problem, a solution using multiauthority systems were introduced. These systems involve multiple attribute authorities, each responsible for holding a specific subset of attributes for each user. And access policy will be based on non-linear secret sharing scheme. In the third aspect related to revocation, this work has addressed both user and attribute revocation so that it will make this model a perfect implementation model in terms of improved security. Some of the existing approach for revocation are re-encryption, periodic updating of ciphertext instead this work used a polynomial called Lagrange polynomial which helps to address this problem in less complex and more efficient way. These features will make the proposed scheme a real model that is secure and can be implement in any organization. -
A Machine Learning Model for Augmenting the Media Accessibility for the Disabled People
In an era characterized by the proliferation of digital media, the need to efficiently use multimedia content has become paramount. This article discusses an innovative technique called 'Fast Captioning (FC)' to improve media accessibility, especially for people with disabilities and others with time restrictions. Modern Machine Learning (ML) algorithms are incorporated into the framework, which speeds up video consumption while maintaining content coherence. The procedure includes extracting complex features like Word2Vec embeddings, part-of-speech tags, named entities, and syntactic relationships. Using annotated data, a ML model is trained to forecast semantic similarity scores between words and frames. The predicted scores seamlessly integrate into equations that calculate similarity, thus enhancing content comprehension. Through this all-encompassing approach, the article offers a comprehensive solution that balances the requirements of contemporary media with the accessibility requirements of people with disabilities, producing a more inclusive digital environment. Machine Learning-based Media Augmentation (ML-MA) has achieved the highest accuracy of 96%, and the captioning is accurate. 2023 IEEE. -
Quality of Drinking Water and Sanitation in India
Wide disparity exists in access to drinking water across social groups in rural and urban India. This article shows that the economically weaker sections or the lower quintile class does not have access to water within the premises both in rural and urban areas. This indicates that low income or wealth would mean poor access to basic amenities for households. Similarly, access to toilets and incidence of open defaecation reflect social disparities. The regression results show that an increase in the household income increases the predicted probability of maintaining an exclusive latrine. Further, compared to the General Category, the Scheduled Castes and Other Backward Classes have a lower probability of constructing an exclusive latrine facility, in the rural and urban areas. 2021 Institute for Human Development. -
Changes in Wage Trends and Earnings Differences in Kerala
In this article, the weekly earnings gap between men and women in Kerala is examined by a number of inequality indices such as the percentile ratio and the Gini coefficient. The entropy measures of inequality are used to decompose wage inequality into within-group and between-group inequalities. The earnings inequality between men and women has been increasing, even though their wage grows faster than mens wage. The indices of inequality suggest the growing wage disparity in the regular and casual labour market. The result reveals that the levels of education and earnings are positively correlated, but women with the same level of education earn much less than men in regular salaried work. The rising wage inequality of men and women during 20042009 were associated with the growth rate of wages in the same period. That is, the wage rates of both regular and casual workers have increased more than four per cent during the period that experienced the highest inequality. 2019, Indian Society of Labour Economics. -
Education Inequality in India: An Empirical Analysis Using National Sample Survey Data
This research examines the ruralurban differences in educational inequality of major states in India. Using National Sample Survey Office (NSSO) data and decomposition methods, this study finds that overall educational inequality has come down but still very high in rural areas. We found that factors such as limited access to higher education, financial constraints and social factors are responsible for the high inequality in rural areas. This study highlights the need for government intervention to enhance educational access by increasing institutions and providing financial aid. It also notes that non-financial barriers like English proficiency further exclude lower socio-economic groups. Hence, we argue for inclusive education policies to improve the existing situation. 2024 Institute for Human Development. -
Investment Decisions : Behavioral Biases in Selected Less Volatile Asset Classes
This study investigates the behavioral biases in selected less volatile asset classes and their influence on investment decisions(IDs). This study compares and contrasts demographic factors(DF) that influence behavioral biases(BB), examines the relationship between behavioral biases(BB) and risk-taking newlinebehaviors (RTB), determines whether BBs can be used to predict RTB and IDs, and looks at covariance patterns between factors that influence BBs, RTB, and IDs.A comprehensive analysis was conducted, considering various DF such as age, gender, education, annual income, marital status, total annual savings newlinepercentage, and the number of dependents in the family. The findings revealed no statistically significant interaction effects between these demographic variables and the combined dependent variables. Additionally, no significant main effects of age, gender, annual income, education, marital status, or paying tax were observed on the combined dependent variables. The study identified several correlations among the behavioral biases examined, including overconfidence(OC), representativeness(R), anchoring(A), herding(H), mental accounting(MA), and conservatism bias(CB). Positive correlations were found between OC and R, A and OC, A and R, H and OC, H newlineand R, MA and OC, MA and R, CB and OC, CB and R, CB and A, CB and H, CB and MA, risk-taking behaviors and overconfidence, risk-taking behaviors and representativeness, risk-taking behaviors and anchoring, risk-taking newlinebehaviors and herding, risk-taking behaviors and mental accounting, and risktaking behaviors and conservatism bias. Furthermore, herding and conservatism bias was significantly associated with risk-taking behaviors, while anchoring, herding, mental accounting, and conservatism bias were associated considerably with IDs. As part of the assessment techniques utilized in this study, seven characteristics or latent constructs were examined using various observable variables or scale items. -
Does the green finance initiatives transform the world into a green economy? A study of green bond issuing countries
Green finance initiatives have received global support in modern times, relatively in response to safeguard the environment and preserve natural resources through channelizing the investments to create a green economy. This paper attempts to evaluate and compare the green finance growth in green bond issuing nations across the world. This study also assesses the effect of green finance growth on the dependence of non-renewable energy resources especially fossil fuels that have been creating several environmental issues for the past years. This study develops a pressure-state-response framework to evaluate the comprehensive system of green finance growth that depicts the interaction of sub-aspects. We employ the entropy technique to calculate the weights at each level within the evaluation system. We also constructed empirical models to assess the relationship between green finance growth and dependence on fossil fuel consumption and found that there exists a negative relationship between the two. The results convey that proliferation of green finance instruments can reduce the dependence on fossil fuels and smoothen the transition towards a carbon negative world. 2023, The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature. -
Malicious URL Detection Using Machine Learning Techniques
Cyber security is a very important requirement for users. With the rise in Internet usage in recent years, cyber security has become a serious concern for computer systems. When a user accesses a malicious Web site, it initiates a malicious behavior that has been pre-programmed. As a result, there are numerous methods for locating potentially hazardous URLs on the Internet. Traditionally, detection was based heavily on the usage of blacklists. Blacklists, on the other hand, are not exhaustive and cannot detect newly created harmful URLs. Recently, machine learning methods have received a lot of importance as a way to improve the majority of malicious URL detectors. The main goal of this research is to compile a list of significant features that can be utilized to detect and classify the majority of malicious URLs. To increase the effectiveness of classifiers for detecting malicious URLs, this study recommends utilizing host-based and lexical aspects of the URLs. Malicious and benign URLs were classified using machine learning classifiers such as AdaBoost and Random Forest algorithms. The experiment shows that Random Forest performs really well when checked using voting classifier on AdaBoost and Random Forest Algorithms. The Random Forest achieves about 99% accuracy. 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. -
Synthesis of ZnO and NiO nano ceramics composite high-performance supercapacitor and its catalytic capabilities
NiO and ZnO mixed nanocomposites were manufactured using the solution combustion process. As-prepared samples were analyzed using XRD. The XRD shows an average crystallite size of 3540 nm. The elemental composition determined by EDS indicates a nearly equal proportion of Ni and Zn, with an atomic ratio of Ni/Zn = 0.96. The specific capacitances of NiO is 295.5 Fg-1, ZnO is 117.3 Fg-1 and ZnO/NiO nanocomposites is 561.75 Fg-1 which are more than NiO and ZnO alone. This study shows that constructing binary oxide nanocomposites is an approach for developing high-performance supercapacitor electrode materials. Experimental observations on catalytic activity revealed that NiO/ZnO increased catalytic activity. Furthermore, adding NiO to ZnO in the composite increased the overall amount of oxygen vacancies in the samples. Our research lays the door for a simple, inexpensive, nontoxic, and quick technique to synthesize binary transition metal oxide-based electrode materials for high-performance supercapacitors. 2024 Elsevier Ltd and Techna Group S.r.l. -
Enhanced supercapacitors and LPG sensing performance of reduced graphene oxide/cobalt chromate pigments for energy storage applications
It is imperative that an initial inquiry be conducted as soon as possible since the production of monolayer of carbon atoms (rGO) composites is the root cause of their poor performance in supercapacitor and LPG sensors. Here, an effort is undertaken to construct a cobalt chromate pigments-reduced graphene oxide (CoCr2O4/rGO) by solution combustion method for the supercapacitor and LPG sensor. The proposed method is efficient and easy in terms of its application to the production of CoCr2O4/rGO polycrystalline composite on a wide scale. Within the scope of this work is an investigation into the improved supercapacitor and LPG sensing behaviour of CoCr2O4/rGO polycrystalline composite. We have implemented a simple method that has been identified for mass-producing reduced graphene oxide. The Solution combustion technique was used, and it was successful in achieving this goal for the very first time. X-ray diffraction technique is used analyse crystallinity, phase, and structural investigation. The nature of gas sensing behaviour with a step function of LPG gas at 500 ppb was studied at room temperature for rGO, The CoCr2O4 pigments and 0.5CoCr2O4+0.5rGo polycrystalline composite samples. The gas response is maximum for 0.5CoCr2O4+0.5rGo polycrystalline composite in the order of 97% in compare with the reduced graphene oxide sample which shows the lowest sensitivity in the order of 26% on exposure of liquified petroleum gas (LPG). The recorded response and recovery times of 0.5CoCr2O4+0.5rGo polycrystalline composite is found to be 40 s and 52 s respectively in comparison to the rGO sample about 58 and 74 s respectively. By adding rGo to the material, the cyclic voltammetry (CV) findings demonstrate improved current density and area of CV loop with increased scan rate. In three-electrode reveals the system, a CoCr2O4-rGo material exhibits a specific capacitance of 226 F/g. Thus, the results reveals that rGo is contributing significantly to the enhancement of a supercapacitor's performance of CoCr2O4. 2023 Elsevier Ltd and Techna Group S.r.l. -
Fintech Innovations in the Financial Service Industry
Digital transformation underscored by the fourth industrial revolution has led to the emergence of sophisticated technology-enabled financial services known as fintech, that has swiftly altered traditional financial services space. Global adoption of fintech is rapidly increasing due to its disruptive nature and is largely embraced by participants who are underserved by traditional financial service providers. Global investments in fintech are growing rapidly year by year owing to increased interconnectivity with the digital revolution. Fintech is expansive, engulfing a plethora of innovative applications in various services including payments, financing, asset management, insurance, etc. There exists a gap in the literature and visualization research on impact and future pathway of fintech innovations in payments and financial services and role of financial regulations. This study aims to enrich the understanding of fintech innovations in payments and financing and investigate the correlation and significance of regulatory framework in maintaining a fair ecosystem. With this objective, an extant systematic review was performed using research articles published in peer-reviewed journals for the period 20142022 when there has been a burgeoning of interest in fintech globally. The findings of this study contribute to the theoretical constructs of fintech innovations in the financial services industry and show that such innovations play a crucial role in shaping the nature of future of business. The results of this study have implications for researchers who could deploy this research as a reference point to get a holistic insight and a detailed mapping of innovations in fintech. 2022 by the authors. Licensee MDPI, Basel, Switzerland. -
Intelligent Optimized Delay Algorithm for Improved Quality of Service in Healthcare Social Internet of Things
Internet of Things (IoT) interconnects billions of devices by establishing a network that adheres to International Organization of Standardization (ISO) standards. These devices communicate with each other by sharing data regulated by the application. This is performed to accomplish a task or service that the application demands. The social or human-like behaviors are adapted in the IoT environment forming the Social IoT (SIoT). The SIoT integrates social networks in IoT-connected devices, making them unique and identifiable. Recent advancements in networking, intelligent network management, battery management, remote sensing, sensors, and other related technologies convinced users and designers to adopt IoT even for large-scale applications where the data involved is enormous. Leveraging the advancements in medical IoT, which focuses on healthcare to patients, can improve its service by removing redundant manual processes, long wait times, and providing other automated services. The advancements in real-time healthcare IoT devices and wearables make a strong case for implementing SIoT in the healthcare domain. SIoT in the healthcare domain has the potential to benefit users on a large scale. This chapter comprehends the challenges and solutions of using SIoT in medical and healthcare solutions from a networking quality of service (QoS) perspective. In addition, this chapter compares the intelligent algorithm, which can be used to improve the QoS of SIoT. Achieving higher QoS is necessary for healthcare services, especially while handling data from emergency and intensive care units. These data cannot tolerate errors and delays. Intelligent network management has become unavoidable in the health and medical services to achieve a higher degree of QoS system, which indirectly decreases data transfer time. The data from the sensor devices sent across the network leads to data loss and delay in data transmission due to congestion in the network and gateway devices. The optimized algorithms incorporated with the delay-based algorithm improves the QoS predominantly and reduces the delay in data transfer. Similarly, the particle swarm optimization algorithm allocates resources over the network and dynamically makes the network adapt to increased and reduced data flow, which reduces the delay and improves the QoS. Intelligent optimized delay algorithm (IODA) is proposed to improve the network performance by reducing the delay and using available bandwidth for data transfer in SIoT. 2023 selection and editorial matter, Gururaj H L, Pramod H B, and Gowtham M; individual chapters, the contributors.