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Biocontrol of Aedes aegypti using Talaromyces islandicus Synthesized Silver Nanoparticles
Aedes aegypti is the vector that spreads the dengue virus, causing dengue fever and dengue hemorrhagic fever. With more than half the worlds population at the risk of acquiring this infection, controlling the Aedes mosquitoes is the only path to limit the spread of the fatal disease. The emergence of insect resistance in mosquitoes raised the need for developing novel insecticides. Present research is focused on using fungus (Talaromyces islandicus) as the biosystem in the synthesis of nanoparticles. Myco-synthesized silver nanoparticles were characterized using UV-visible spectrometry that exhibited a peak at 429 nm. The XRD spectral peaks were in the range of 27.83, 32.27, 38.23and 65.01. The FTIR spectrum showed peaks corresponding to O-H, N-O, S=O, etc. representing the silver nanoparticles. SEM and EDAX represent the formation of silver ions that are spherical in shape with a size range of 23 to 26 nm. The antioxidant activity of silver nanoparticles and the extract of Talaromyces islandicus were assessed by DPPH assay, reducing power assay and hydrogen peroxide assay. The nanoparticles studied for its bio efficacy against the larval stages of Aedes aegypti indicated the LC50 value of 352.03, 389.86, 397.72 and 443.50 when tested against first, second, third and fourth instar larvae. respectively. The LC50 value of 540.41 was determined against the pupae of Aedes. The predatory efficiency of P. reticulata indicated the positive feeding behaviour of the fish when exposed to the silver nanoparticles. The cell toxicity assay was conducted against C6/36 insect cell lines and the cell viability inhibition was calculated. A toxic free, environmentally acceptable approach for controlling the mosquito vector by utilizing fungal nanoparticles was assessed and their efficacy in vector control was analyzed in this study. 2022 Chemical Publishing Co.. All rights reserved. -
Extraction, characterization, and fabrication of cellulose biopolymer sheets from Pistia stratiotes as a biodegradative coating material: an unique strategy for the conversion of invasive weeds into value-added products
This study explores the possibility of using Water lettuce (Pistia stratiotes) as a cost-effective substrate for the commercial extraction of cellulose biopolymer using a wide variety of physicochemical treatment methods to compare their efficiency in cellulose extraction. The extraction of cellulose from water lettuce, although promising due to their high cellulose content, was less explored as per the available literature. In this study, functional properties like bulk density-packed density, hydrated density, water retention capacity, oil retention capacity, emulsifying activity and setting volume of the extracted cellulose were studied. The cellulose content from water lettuce was found to be 38.94 0.10% by anthrone method. Preliminary confirmation of cellulose biopolymer was done using the study of functional groups using Fourier Transform Infrared (FT-IR) analysis. Further characterization studies like Scanning Electron Microscopy (SEM), X- Ray Diffraction (XRD), Differential Scanning Calorimetry (DSC) and thermogravimetric analysis (TGA) were conducted to understand the molecular architecture and purity of the cellulose extracted. Fabrication of cellulose sheets was carried out using starch as the plasticizer. Biodegradation studies were conducted in garden soil for four weeks and a high degradation rate of 78.22 0.71% was observed in the fourth week of soil burial. 2022, The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature. -
Gaussian MutationSpider Monkey Optimization (GM-SMO) Model for Remote Sensing Scene Classification
Scene classification aims to classify various objects and land use classes such as farms, highways, rivers, and airplanes in the remote sensing images. In recent times, the Convolutional Neural Network (CNN) based models have been widely applied in scene classification, due to their efficiency in feature representation. The CNN based models have the limitation of overfitting problems, due to the generation of more features in the convolutional layer and imbalanced data problems. This study proposed Gaussian MutationSpider Monkey Optimization (GM-SMO) model for feature selection to solve overfitting and imbalanced data problems in scene classification. The Gaussian mutation changes the position of the solution after exploration to increase the exploitation in feature selection. The GM-SMO model maintains better tradeoff between exploration and exploitation to select relevant features for superior classification. The GM-SMO model selects unique features to overcome overfitting and imbalanced data problems. In this manuscript, the Generative Adversarial Network (GAN) is used for generating the augmented images, and the AlexNet and Visual Geometry Group (VGG) 19 models are applied to extract the features from the augmented images. Then, the GM-SMO model selects unique features, which are given to the Long Short-Term Memory (LSTM) network for classification. In the resulting phase, the GM-SMO model achieves 99.46% of accuracy, where the existing transformer-CNN has achieved only 98.76% on the UCM dataset. 2022 by the authors. -
Potassium tert-Butoxide-Mediated Synthesis of 2-Aminoquinolines from Alkylnitriles and 2-Aminobenzaldehyde Derivatives
KOtBu mediates the reaction between 2-amino arylcarbaldehydes and benzyl/alkyl cyanides toward the expeditious formation of 2-aminoquinolines under transition-metal-free conditions. The described transformation proceeds through in-situ generated enimine intermediate from benzyl/alkyl cyanides under KOtBu-mediated reaction conditions. The substituted 2-aminoquinolines were realized in excellent yields at room temperature and shorter reaction time. The designed process exhibits operational simplicity and broad functional group tolerance in delivering the products of high significance. 2022 Wiley-VCH GmbH. -
Evidence and Predictors of Resilience among Young Adults Exposed to Traumatic Events of the Armed Conflict in Kashmir
No study to date exists regarding resilience in the context of armed conflict in Kashmir, India. Therefore, this study intended to understand the factors that predict resilience among young adults affected by the violence of the protracted conflict in Kashmir. The data were collected from 656 students, who experienced stress, related to the conflict. Findings showed that more than a quarter of the respondents (35.8%) were exposed, from 7 to 10, less than a quarter (16.6%) of participants reported 26, and almost half of the respondents (47.6%) were exposed to 11 or more stressful events related to the conflict in Kashmir. Multiple hierarchical regression analysis was used to examine the role of conflict exposure, social support, and demographic variables in predicting resilience. The results of the final regression model revealed that exposure to armed conflict, social support, level of education, monthly family income and gender, emerged as significant predictors of resilience. The study recommends the formulation of programs to sensitize people living in the areas affected by the armed conflicts, regarding the importance of social support and resilience, to help them withstand various adverse life experiences. 2021 Taylor & Francis Group, LLC. -
Ganga and Yamuna Rivers: Through the Lens of the National Green Tribunal
Despite the country's extensive environmental jurisprudence and many historic rulings in which the courts have rescued worsening environmental situations, river (Ganga and Yamuna) water does not match the mandated minimum "bathing quality." Rivers like the Ganga and Yamuna, which flow through numerous states and towns, would be in a different situation. Without strict monitoring and enforcement of the measures, no action plan can work. Punishment of defaulters can serve as deterrence while also instilling fear in other non-compliant enterprises. In comparison to environmental legislation, the NGT Act allows for substantially harsher fines and penalties. River rejuvenation plans must be carefully monitored to ensure that they do not suffer the same fate. Making action plans will not improve river water quality unless they are implemented with sincerity and consistency, as well as continuous monitoring and severe enforcement. 2022 Technoscience Publications. All rights reserved. -
FAMILY OF CONGRUENCES FOR (2, ?)?REGULAR BIPARTITION TRIPLES
Though congruences have their limitations, they have significant importance in the field of number theory and helps in proving many interesting results. Thus, this article has adopted the technique and properties of congruences to identify and prove a set of congruent properties for integer partition. The partition of a positive integer is a way of expressing the number as a sum of positive integers. One such partitions known as regular bipartition triple are discussed in this article. New congruences modulo even integers and modulo prime (p ? 5) powers are derived for (2, ?)?regular bipartition triples. Also infinite families of congruences modulo 2 for some (2, ?)?regular bipartition triples are derived. The theorems stated in this article are proved using the q?series notation and some of the prominent results such as Eulers pentagonal number theorem and Jacobis triple product identities. There are certain lemmas which are derived using these results that help in proving the major results of this article. 2022, RAMANUJAN SOCIETY OF MATHEMATICS AND MATHEMATICAL SCIENCES. All rights reserved. -
IM-EDRD from Retinal Fundus Images Using Multi-Level Classification Techniques
In recent years, there has been a significant increase in the number of people suffering from eye illnesses, which should be treated as soon as possible in order to avoid blindness. Retinal Fundus images are employed for this purpose, as well as for analysing eye abnormalities and diagnosing eye illnesses. Exudates can be recognised as bright lesions in fundus pictures, which can be the first indi-cator of diabetic retinopathy. With that in mind, the purpose of this work is to cre-ate an Integrated Model for Exudate and Diabetic Retinopathy Diagnosis (IM-EDRD) with multi-level classifications. The model uses Support Vector Machine (SVM)-based classification to separate normal and abnormal fundus images at the first level. The input pictures for SVM are pre-processed with Green Channel Extraction and the retrieved features are based on Gray Level Co-occurrence Matrix (GLCM). Furthermore, the presence of Exudate and Diabetic Retinopathy (DR) in fundus images is detected using the Adaptive Neuro Fuzzy Inference System (ANFIS) classifier at the second level of classification. Exudate detection, blood vessel extraction, and Optic Disc (OD) detection are all processed to achieve suitable results. Furthermore, the second level processing comprises Morphological Component Analysis (MCA) based image enhancement and object segmentation processes, as well as feature extraction for training the ANFIS clas-sifier, to reliably diagnose DR. Furthermore, the findings reveal that the proposed model surpasses existing models in terms of accuracy, time efficiency, and precision rate with the lowest possible error rate. 2023, Tech Science Press. All rights reserved. -
Early diagnosis of COVID-19 patients using deep learning-based deep forest model
Coronavirus disease-19 (COVID-19) has rapidly spread all over the world. It is found that the low sensitivity of reverse transcription-polymerase chain reaction (RT-PCR) examinations during the early stage of COVID-19 disease. Thus, efficient models are desirable for early-stage testing of COVID-19 infected patients. Chest X-ray (CXR) images of COVID-19 infected patients have shown some bilateral changes. In this paper, deep transfer learning and a deep forest-based model are proposed to diagnose COVID-19 infection from CXR images. Initially, features of X-ray images are extracted using the well-known deep transfer learning model (i.e., ResNet101), which does not require tuning many parameters compared to the deep convolutional neural network (CNN). After that, the deep forest model is utilised to predict COVID-19 infected patients. The deep forest is based upon ensemble learning and requires a small number of hyper-parameters. Additionally, the proposed model is trained on a multi-class dataset that contains four different classes as COVID-19 (+), pneumonia, tuberculosis, and healthy patients. The comparisons are drawn among the proposed deep transfer learning and deep forest-based models, the competitive models. The obtained results show that the proposed model effectively diagnoses COVID-19 infection with an accuracy of 99.4%. 2022 Informa UK Limited, trading as Taylor & Francis Group. -
Flat Unglazed Transpired Solar Collector: Performance Probability Prediction Approach Using Monte Carlo Simulation Technique
Engineering applications including food processing, wastewater treatment, home heating, commercial heating, and institutional heating successfully use unglazed transpired solar collectors (UTCs). Trapping of solar energy is the prime goal of developing an unglazed transpired solar collector. The UTC is usually developed in and around the walls of the building and absorbs the solar energy to heat the air. One of the key challenges faced by the UTC designer is the prediction of performance and its warranty under uncertain operating conditions of flow variables. Some of the flow features are the velocity distribution, plate temperature, exit temperature and perforation location. The objective of the present study was to establish correlations among these flow features and demonstrate a method of predicting the performance of the UTC. Hence, a correlation matrix was generated from the dataset prepared after solving the airflow over a perforated flat UTC. Further, both strong and weak correlations of flow features were captured through Pearsons correlation coefficient. A comparison between the outcomes from a linear regression model and that of computational simulation was showcased. The performance probability for the UTC was interlinked with correlation matrix data. The Monte Carlo simulation was used to predict the performance from random values of the flow parameters. The study showed that the difference between the free stream value of temperature and the value of temperature inside the UTCs chamber varied between 15 and 20 C. The probability of achieving system efficiency greater than 35% was 55.2%. This has raised the hope of recommending the UTC for drying and heating where the required temperature differential is within 20 C. 2022 by the authors. -
Presence or absence of Dunning-Kruger effect: Differences in narcissism, general self-efficacy and decision-making styles in young adults
The Dunning-Kruger effect is a cognitive bias in which individuals who are unskilled in certain domains overestimate their ability and are unaware of it. Past studies have focused on establishing the effect but have not looked into associated factors. This study aimed to see if the Dunning-Kruger effect has any influence on an individuals narcissism, general self-efficacy and decision making styles especially in young adults in the Indian population. The Dunning- Kruger effect was established using scores from the Cognitive Reflection Task and the Rationality scale from Rational Experiential Inventory, keeping the Unskilled and Unaware phrase under consideration, while establishing cut-offs. The participants were also divided into three groups - the group that was able to estimate their performance, the group that over-estimated their performance and the group that underestimated their performance. The dependent variables were measured using the NPI-16, General Self-Efficacy Scale and Flinders Decision-Making Styles Questionnaire. The Kruskal-Wallis H results showed that there is a significant difference between the group with Dunning-Kruger effect, without Dunning-Kruger effect and the group that underestimated their performance with reference to Narcissism, General Self-Efficacy, Vigilance and Hypervigilance decision-making styles. The Mann-Whitney U results further indicated a significant difference in Narcissism and Vigilance, between the groups that overestimated their performance and the group that accurately estimated their performance. However, there was no correlation between the CRT discrepancy scores of the individuals with Dunning-Kruger effect and the dependent variables. 2021, The Author(s), under exclusive licence to Springer Science+Business Media, LLC part of Springer Nature. -
A Simple and Efficient Ligand-Free Copper-Catalyzed C-N Bond Formation of Aryl (Hetero) Halides and N-Heteroaryl Amines
In this protocol, we report a simple, inexpensive, and user-friendly conventional method for C-N cross coupling between aryl/heteroaryl halides and hetero aryl amines using copper iodide as a catalyst in DMSO as a solvent to prepare pyrimidines and pyrazines derivatives. The reaction conditions were optimized by screening in various copper catalysts and bases. The substrate scope of the reaction was also carried out to prepare novel functionalized N-arylated compounds in good yields. 2021 Taylor & Francis Group, LLC. -
Stability Testing and Restoration of a DEIG-Based Wind Power Plant with Indirect Grid Control Strategies
In the current scenario, because of government policies, environmental factors, and technological improvements, there is a rapid growth in renewable energy sector. The emphasis is to obtain better system performance by effective resource utilization and providing security and reliability. This paper discusses the design and implementation of indirect grid control of a wind power plant by controlling the parameters in both grid and rotor side converters. The proposed system consists of Doubly Excited Induction Generator (DEIG) with Wind turbine system (WTS) and Mechanical and Electrical Power Controlling Systems (MPCS-EPCS). Various transmission line faults (symmetrical and asymmetrical faults) incur power imbalances in power grid. The developed MPCS and EPCS are helpful to perform grid monitoring and controlling under different types of faulty conditions. The MPCS monitors the effective source utilization and EPCS helpful for matching the grid energy levels under normal and faulty conditions. Modification in the converter topologies to minimize the impact of adverse effects of faults on the DEIG-WTS and to improve resiliency in the power grid is also discussed. To improve the stability and enhancing resource utilization to improve the efficiency of the overall system with the enhancement of fault voltage ride-through capability in DEIG-WTS under fault conditions are also considered. The stability of the system is tested under steady-state and dynamic-state conditions by applying faulty conditions in MATLAB/Simulink environment. 2023 IETE. -
Study of multilayer flow of a bi-viscous Bingham fluid sandwiched between hybrid nanofluid in a vertical slab with nonlinear Boussinesq approximation
Bi-viscosity Bingham plastic fluids are used to understand the rheological characteristics of pigment-oil suspensions, polymeric gels, emulsions, heavy oil, etc. In many industrial and engineering problems involving high-temperature situation, a linear density-temperature variation is inadequate to describe the convective heat transport. Therefore, the characteristics of the nonlinear convective flow of a bi-viscous Bingham fluid (BVBF) through three layers in a vertical slab are studied. The two outer layers of the oil-based hybrid nanofluid and the intermediate layer of BVBF are considered. The thermal buoyancy force is governed by the nonlinear Boussinesq approximation. Continuity of heat flux, velocity, shear stress, and temperature are imposed on the interfaces. The governing equations are derived from the Navier-Stokes equation, conservation of energy, and conservation of mass for three layers. The nonlinear multi-point (four-point) boundary value problem is solved using the differential transform method (DTM). Converging DTM solutions are obtained, and they are validated. The entropy equation and Bejan number were also derived and analyzed. It is established that the nonlinear density-temperature variation leads to a significant improvement in the magnitude of the velocity and temperature profiles due to the increased buoyancy force, and as a result, the drag force on the walls gets reduced. The drag force on the slab gets reduced by decreasing the volume fraction of nanoparticles. Furthermore, nonlinear convection and mixed convection give rise to an advanced rate of heat transport on the walls and thereby to an enhanced heat transport situation. 2022 Author(s). -
Effect of face sheet on the flexural and tensile characteristics in GLARE laminates
The present study is carried out to study Glass Fibre Reinforced Aluminium Laminate (GLARE) structures and to evaluate their flexural and tensile properties. The GLARE specimens were fabricated using hand layup with vacuum bag moulding process wherein the aluminium sheets and E-Glass fibre woven mats of fixed thickness are bonded together by application of epoxy resins. Three different thicknesses of aluminium alloy (0.2 mm, 0.3 mm and 0.4 mm) Al-2024 T3 are used for the purpose of the study. The aluminium sheets are stacked together by application of epoxy resin between the sheets and are cured under a compression moulding machine under constant pressure. The overall thickness of the specimen is maintained constant for 2 mm. The samples were subjected to a three-point bending and tensile test as per ASTM D790 and ASTM D3039 standards, respectively, to evaluate their mechanical properties. The results indicate that the tensile strength of the composites is maximum for the specimen with aluminium 2024 T3 face sheet with a minimum thickness of 0.2 mm; however, with the increase in the thickness, the tensile strength is found to be decreasing. 2021 Engineers Australia. -
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.