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Facial emotion recognition using convolutional neural networks
Emotional expressivity has always been a simple job for people, but computer programming is much harder to accomplish. Image emotions may be recognised by recent developments in computer vision and machine learning. In this article, we present a new method to detect face emotion. Use neural networks convolutionary (FERC). The FERC is based on a CNN network of two parts: the first portion removed the backdrop of the image, the second part removed the face vector. The expressional vector (EV) is utilised in the FERC model to detect the fve different kinds of regular facial expressions. The double-level CNN is continuous and the weights and exponent values of the final perception layer vary with each iteration. In that it improves accuracy, FERC varies from widely utilised CNN single-level technology. Moreover, EV generation prevents the development of a number of issues before a new background removal process is used (for example distance from the camera). 2021 -
Religions, Women and Discourse of Modernity in Colonial South India
Colonial education and missionary discourse of modernity intensified struggles for continuity and change among the followers of Hinduism and Christianity in nineteenth century India. While missionary modernity was characterised by an emphasis on sociocultural changes among the marginalized women through Christian norms of decency, orthodox Hindus used traditional cultural practices to confront missionary modernization endeavours. This article posits that the discourse of missionary modernity needs to be understood through the principles of Western secular modernity that impelled missionaries to employ decent clothing as a symbol of Christian femininity. It argues that missionary modernity not only emboldened the marginalized women to challenge their ascribed sociocultural standing but also solidified communitarian consciousness among the followers of Hinduism and Christianity substantially. Even though Travancore state defended the entrenched customary practices, including womens attire patterns, with all its potency through authoritative proclamations, it could not dissuade missionaries from converting the marginalized women to missionary modernity. 2022 by the author. -
Predicting Intention to Buy Organic Food during the COVID-19 Pandemic: A multi-group analysis based on the Health Belief Model
The ongoing COVID-19 pandemic has deeply affected physical and psychological health of people. It also had a huge impact on their dietary choices. This study specifically attempts to determine the impact of the constructs of health belief model on consumer purchase intention of organic food in the pandemic scenario. A survey was conducted among 413 Indian organic food consumers. The proposed hypotheses are tested by employing structural equation modeling. The findings highlight those perceived benefits is an important predictor of consumers behavioral intention to buy organic food, followed by cues to action and perceived threats. It is also found that consumers age moderates the impact of perceived threat and perceived barrier on consumers purchase intention, with a 22% difference in model prediction. In conclusion, the health belief model is found to be one of the most suitable models to predict consumer intention toward organic food purchase during the COVID-19 pandemic. 2022 Taylor & Francis Group, LLC. -
Iodine Mediated Oxidative Cross-Coupling of Benzo[d]Imidazo[2,1-b]Thiazoles with Ethylbenzene: An Unprecedented Approach of C3-Dicarbonylation
A versatile approach of iodine mediated C3-dicarbonylation of benzo[d]imidazo[2,1-b]thiazoles (IBTs) with ethylbenzene has been reported. The reaction conditions were optimized by screening in various solvents, catalysts, and oxidants. The reaction is compatible with various substrates and was successfully demonstrated to offer moderate to good yields. 2022 Taylor & Francis Group, LLC. -
Effect of alkyl chain length on the corrosion inhibition of mild steel in a simulated hydrochloric acid medium by a phosphonium based inhibitor
The corrosion inhibiting effect of three synthesised phosphonium containing ionic liquids of varying alkyl chain length, namely, butyltriphenyl phosphonium bromide (BTPPB), hexyltriphenyl phosphonium bromide (HTPPB) and hexadecyltriphenyl phosphonium bromide (HDTPPB) on mild steel, was evaluated in 1 M HCl medium. The corrosion inhibition performance was studied by gravimetric method, potentiodynamic polarization studies, electrochemical impedance spectroscopy and quantum chemical studies (DFT). However, the results of the SEM, AFM and contact angle tests confirmed that the protective layer formed on the mild steel. Furthermore, assessed the theoretical calculations for exploring the inhibition mechanism. A maximum of 95.77% inhibition efficiency was achieved using 250 ppm of HDTPPB. The obtained results showed that HDTPPB has greater inhibition ability than BTPPB and HTPPB. Adsorption studies obeyed the Langmuir adsorption isotherm. Moreover, the increased alkyl chain length of ionic liquids did increase their inhibition efficiency. 2021 Informa UK Limited, trading as Taylor & Francis Group. -
An empirical study on the clients perspective in decision making with regard to lawyer selection for trial courts: Does grey hair matter?
In higher courts, statutory interpretation and persuasion of the court with new perspectives on legal principles are critical. Whereas, the main focus of the subordinate courts is on evidential underpinnings. Hence, the lawyers in all courts are an indispensable part of the judicial process and play a seminal role in the dispensation of justice. The existing literature demonstrates that engaging a senior lawyer increases the chances of winning cases. However, young lawyers today make their mark by establishing their own offices, succeeding in the profession, while some remain the dark horses of their incumbent seniors. Against this backdrop, the authors explore the clients perception of the lawyer selection process and the significance of lawyers age using a mixed methods approach. The data for this study was gathered from trial court clients using a convenient sampling method. The qualitative data were collected by conducting semi-structured interviews with clients. Interviews were analysed at a thematic level and broad themes were identified and used as constructs for a quantitative survey questionnaire. The quantitative data were analysed through Pearson correlation, regression analysis, and factor analysis. The study determined that lawyers efficiency is a key factor considered by trial court clients in selecting their lawyers. The results also revealed that there exists a significant positive correlation between the age of the lawyer and the clients decision making when choosing their lawyers. In the end, the implications of the findings are discussed. 2022, The Author(s), under exclusive licence to O.P. Jindal Global University (JGU). -
Covert Conditioning for Persistent Aggressive Behaviors: A Case Illustration
In psychotherapy practice and training, single case study design plays an indispensable role by effectively articulating the application of textbook knowledge, thereby bridging the gap between theory and practice. This article, on similar lines, illustrates one such successful example of the application of the classical behavioral technique of covert conditioning modified with a component of verbal challenging. A woman in her late-thirties reported with long-standing seemingly-resistant-to-treat symptoms of aggressive behavior of beating children. The client had a total of 10 daily sessions of 6090 minutes each. By the end of one week, she reported not beating children in this period. She felt extremely relieved because it had happened for the first time in 10 years. The intensity of anger had decreased drastically, and she was not shouting any longer. She had to discontinue sessions abruptly due to unavoidable circumstances. Although she was suggested to follow up the intensive sessions again, she was not able to do it due to feasibility issues. The improvement was maintained on follow-up visits after two weeks, four weeks, and three months. 2021 The Author(s). -
Synthesis, DFT and In Silico Anti-COVID Evaluation of Novel Tetrazole Analogues
A new series of 3-aryl/heteroaryl-2-(1H-tetrazol-5-yl) acrylamides have been synthesized through catalyst-free, one-pot cascade reactions, utilizing click chemistry approach and evaluated for their anti-COVID activities against two proteins in silico. The structural properties of the synthesized molecules were evaluated based on DFT calculations. Total energy of the synthesized tetrazole compounds were obtained through computational analysis which indicate the high stability of the synthesized compounds. The Frontier Molecular Orbitals (FMO) and associated energies and molecular electrostatic potential (MEP) surfaces were generated for the compounds. Spectral analysis by DFT gave additional evidence to the structural properties of the synthesized molecules. All tetrazole analogues come under good ADMET data as they followed the standard value for ADMET parameters. Docking studies offered evidence of the molecules displaying excellent biological properties as an anti-Covid drug. Compound 4 g exhibited excellent anti-COVID-19 properties with four hydrogen binding interactions with amino acids GLN 2.486 GLN 2.436 THR 2.186 and HSD 2.468 with good full-fitness score (1189.12) and DeltaG (7.19). Similarly, compound 4d shown potent activity against anti-COVID-19 mutant protein (PDB: 3K7H) with three hydrogen binding interactions, i.e., SER 2.274 GLU 1.758 and GLU 1.853 with full-fitness score of 786.60) and DeltaG (6.85). The result of these studies revealed that the compounds have the potential to become lead molecules in the drug discovery process. 2022 Taylor & Francis Group, LLC. -
Enhancing the energy efficiency for prolonging the network life time in multi-conditional multi-sensor based wireless sensor network
A wireless sensor network is one of the networks that is highly demanding by various real-time networking applications nowadays. A huge amount of sensor nodes is deployed in the network randomly and distributed. Most of the applications using wireless sensor network (WSN) are surveillance monitoring applications like a forest, home, healthcare, environment, and remote monitoring systems. Based on the application usage, the type of sensor, a number of sensor nodes are deployed in such a manner where the sensors can be used effectively. But the sensor nodes are restricted in the battery and sensing region. Thus, the battery of the sensor nodes is decreased based on the nodes function. The energy level of the sensor nodes highly affects the network lifetime. Improving the energy efficiency in WSN is one of the most important challenging tasks. Most of the earlier research works have proposed various methods, techniques, and routing protocols, but they are application dependent and as a common method. So, this paper is motivated to propose a Multi-Conditional Network Analysis (MCNA) framework for saving the energy level of the sensor nodes by reducing energy consumption. The MCNA framework involves two different clustering processes with cluster head selection, choosing the best nodes based on the signal strength, and the best route for data transmission. The data transmission is done by cluster based on source-destination based. The simulation results proved that the proposed MCNA framework outperforms the other existing methods. 2022 Northeastern University, China. -
Heat transfer enhancement using temperature-dependent effective properties of alumina-water nanoliquid with thermo-solutal Marangoni convection: A sensitivity analysis
The sensitivity of the heat transport rate in the thermo-solutal Marangoni convection of Al 2O 3- H 2O nanoliquid at 300K is analyzed. The nanoliquid is modeled using the modified Buongiorno model which incorporates the Brownian motion, effective nanoliquid properties, and thermophoresis effects. The thermophysical models proposed by Khanafer and Vafai are chosen in this analysis as these correlations are in good agreement with the experimental values. External constraining factors like thermal radiation and variable magnetic field are also considered. The basic equations are solved using apposite transformation variables and Finite Difference Method (FDM). The impacts of the effectual parameters on all the profiles are analyzed. Furthermore, the heat transport is analyzed by executing a Response Surface Methodology (RSM) model with the Brownian motion parameter (0.1 ? Nb ? 0.5), thermophoretic parameter (0.1 ? Nt ? 0.5), and nanoparticle volume fraction (1 % ? ?? 3 %). The modified Buongiorno model yields lower temperature and concentration profiles when compared to the conventional Buongiorno model. The heat transfer rate is the most sensitive to the Brownian motion parameter than thermophoresis and nanoparticle (NP) volume fraction parameters. The results of this study would be instrumental in improving the efficiency of the welding process, crystal growth, and coating technologies. 2021, King Abdulaziz City for Science and Technology. -
P-ROCK: A Sustainable Clustering Algorithm for Large Categorical Datasets
Data clustering is crucial when it comes to data processing and analytics. The new clustering method overcomes the challenge of evaluating and extracting data from big data. Numerical or categorical data can be grouped. Existing clustering methods favor numerical data clustering and ignore categorical data clustering. Until recently, the only way to cluster categorical data was to convert it to a numeric representation and then cluster it using current numeric clustering methods. However, these algorithms could not use the concept of categorical data for clustering. Following that, suggestions for expanding traditional categorical data processing methods were made. In addition to expansions, several new clustering methods and extensions have been proposed in recent years. ROCK is an adaptable and straightforward algorithm for calculating the similarity between data sets to cluster them. This paper aims to modify the algorithm by creating a parameterized version that takes specific algorithm parameters as input and outputs satisfactory cluster structures. The parameterized ROCK algorithm is the name given to the modified algorithm (P-ROCK). The proposed modification makes the original algorithm more flexible by using user-defined parameters. A detailed hypothesis was developed later validated with experimental results on real-world datasets using our proposed P-ROCK algorithm. A comparison with the original ROCK algorithm is also provided. Experiment results show that the proposed algorithm is on par with the original ROCK algorithm with an accuracy of 97.9%. The proposed P-ROCK algorithm has improved the runtime and is more flexible and scalable. 2023, Tech Science Press. All rights reserved. -
Kitchen Waste Derived Porous Nanocarbon Spheres for Metal Free Degradation of Azo Dyes: An Environmental Friendly, Cost Effective Method
A porous nanocarbon spheres (PNCSs) were prepared from kitchen waste and successfully used for the metal and oxidant free degradation of azo compounds. The PNCSs obtained by the pyrolysis of onion peel, at 1000C, were found to be effective catalysts for the reductive degradation of azo dyes in presence of hydrazine hydrate. The reductive cleavage of azo bonds (N=N) was achieved under microwave irradiation. The degradation process was completed in a span of 1040min; the process was monitored by ultravioletvisible spectroscopy. Fourier transform infrared spectroscopy was also used for the illustration of azo degradation. Interestingly, the reductive degradation of azo dyes produced corresponding amines and they were successfully reused for the preparation of fresh azo compounds. The work, therefore, highlights the valorization of largely produced kitchen-wastes to the sustainable PNCSs and it also provides a platform to demonstrate their applicability as highly cost-effective catalysts for bulk scale chemical transformations. Graphical Abstract: [Figure not available: see fulltext.]. 2021, The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature. -
Drivers of Mandatory and Non-Mandatory Internet Corporate Reporting in Public and Private Sector Indian Companies
The papers objective was to measure the drivers of mandatory and non-mandatory internet corporate reporting by public and private sector companies following the internet disclosure compliance of listing and obligation requirement of SEBI under Clause 46. Several drivers, namely firm size, profitability, leverage, liquidity, board size, independence of board, and CEO duality, were used to measure the effectiveness of mandatory and non-mandatory disclosure. A multiple regression model was applied to test the present papers hypotheses. The results of multiple regression revealed that the firms size was exceptionally important for both sectors. In contrast, public sector disclosure was largely impacted by leverage, liquidity, board size, and board independence. In comparison, the private sector disclosure scores were mainly impacted by leverage and board size, although there is no relationship between ICR and firm profitability and CEO duality. In performing separate multivariate regression between the two sectors, many disparities emerged. This disparity showed that public and private sector corporations had quite different firm and governance characteristics of the disclosure. As the first exploratory research to assess the mandate internet disclosure of public and private sector companies in India, it is very informational, specifically for those working on Indian companies regulation, compliance, and research. 2022, Associated Management Consultants Pvt. Ltd. All rights reserved. -
Reinventing Coffee: Pandemic Lessons from Sleepy Owl Coffee
[No abstract available] -
National cinema in India: exploring myths and realities
[No abstract available] -
QSPR ANALYSIS OF CERTAIN ANTI-HIV DRUGS
A broad spectrum of advanced medications appears yearly following the accelerated evolution of the chemical and pharmaceutical industry. In this paper, various degree-based and neighborhood degree sum-based topological indices of some anti-HIV drugs are explored applying the M-polynomial and NM-polynomial formulations. Moreover, QSPR analysis is carried out for the topological indices with regard to the physico-chemical properties of the anti-HIV drugs. The activity of nucleoside and non-nucleoside reverse transcriptase inhibitors is implemented as in drug configuration to manifest the significance of topological indices in the medicinal world. The procured outcomes affirm that topological indices being stud- ied reflect effective correlation in accordance to physical and chemical properties of the anti-HIV drugs and consequently can assist in development of advanced and promising pharmaceutical for HIV medication. 2022, RAMANUJAN SOCIETY OF MATHEMATICS AND MATHEMATICAL SCIENCES. All rights reserved. -
Users Perception and Barriers to Using Self-Driven Rental Bikes
The research study has two objectives. The first objective of this paper was to find users' perception towards self-drive rental bikes. The second objective was to identify the factors that act as barriers to users using self-drive rental bikes. The research was a formal and structured conclusive research type and used quantitative data analysis techniques. The study had a representative sample of 350 respondents. The population selected for this study were people of various demographics in Bangalore. We used judgemental sampling to decide on the right sample. In achieving both objectives, factor analysis was used to arrive at a minimum number of factors or dimensions. The major perception factors are: Economical Choice, Environmental Consciousness, Alternative Source of Transport, Rationality, and Convenience. The major barriers to using self-drive rental bikes are Safety Issues, Conservative Nature of Users, the Expensive Nature of Service, and the Difficulty in Using Mobile Applications. 2022, Associated Management Consultants Pvt. Ltd.. All rights reserved. -
Fluorescent PVDF dots: from synthesis to biocidal activity
Infection by microorganisms is a serious concern in food storage, water purification, drugs, and particularly in biomedical devices. Long-term use of permanent implants often leads to its contamination due to pathogens. Timely tracking of bacterial activity and its interaction with antibodies are crucial for overcoming these infections. In this work, fluorescent polymeric biocides are obtained from a non-conjugated polymer polyvinylidene fluoride (PVDF), which is neither emissive nor known for its antibacterial activity. PVDF dot was synthesized via hydrothermal treatment eliminating the need for complicated and toxic preparation strategies. PVDF-based dot exhibits high fluorescence aroused from the carbogenic core due to the carbonization of the hydrocarbon chain. It is found that the dots were semiconducting contrary to the bulk form of PVDF. The photoluminescent polymer dots also exhibited an excellent antibacterial activity toward Escherichia coli (E.coli) and Streptococcus bacteria. This luminescence and biocidal activity of PVDF-derived dots have attractive applications in the field of fluorescent diagnostics and therapeutics. Graphical abstract: [Figure not available: see fulltext.] 2022, The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature. -
Sinking houseboats and swaying home stays: community resilience and local impacts of COVID-19 in managing tourism crisis in Kerala
Purpose: The tourism sector of the state of Kerala in India is highly vulnerable and has been extensively impacted by the global pandemic disaster. This paper aims to analyze the impact of COVID-19 (Corona virus pandemic) on houseboat operators and homestay managers. Design/methodology/approach: This paper indicates a multi-stakeholder assessment method to examine various pandemic disaster facets through a structured discussion with different destination stakeholders. This study examines qualitative data collected through semi-structured interviews from homestay owners, houseboat operators and government designators in Kerala. This study proposes a conceptual community resilience competency framework that could facilitate speedy crisis management responses. In this study, the sample comprises of nine respondents who play a pivotal role in the travel business, comprising the public sector, private sector, NGO's and community leaders. Findings: The qualitative findings identify Indias and the state of Kerala's roles in handling crisis management scenarios over internal strategies and strategy formulation. The results indicate that the supplementary industry practitioners explore tactical and strategic management initiatives to sustain their businesses. The dynamics of stakeholder engagement adopted by the state is given prominence. Originality/value: This study suggests mechanisms to re-establish the brand image and the possible strategies and suggestions that could help in the survival of the Kerala tourism industry in the post-disaster period. The new normal has been substantiated in the study by incorporating strategies and precautionary methods adopted by the homestay and houseboat operators so as to address the guests' safety concerns. 2021, International Tourism Studies Association. -
An efficient clustering approach for optimized path selection and route maintenance in mobile ad hoc networks
Mobile ad hoc network (MANET) is arranged with multiple nodes that communicate wirelessly. However, MANET communication suffers from various issues such as inadequate security, low stability, high power consumption, and a lack of specific infrastructure of the network. Moreover, the route failure happened in the network due to the unrestricted node movement, which has increased energy utilization, delay, and reduced lifetime of the nodes. To overcome these issues, the novel Eagle Based Density Clustering (EBDC) approach is developed in this research that predicts the link failure and increased the lifetime of the nodes. Here, the developed EBDC approach is utilized for clustering and route maintenance in MANET for that it creates the nodes using the star topology. Initially, the developed approach selects the Cluster Head and transmits the message through the created path. Subsequently, the link failure is detected by the EBDC model, and it creates a new reference layer to replace the exhausted layer. Hence, the developed EBDC model has enhanced the network lifetime and reduced energy utilization. Furthermore, this model is implemented using Network Simulator 2, and the parameters like accuracy, energy consumption, Packet Delivery Ratio, network lifetime, end-to-end delay, and throughput are calculated. Additionally, the attained outcomes are compared with prevailing methods for evaluating the efficiency of the developed approach. 2021, The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature.