Bapuji Institute of Engineering and Technology

Abstract This first in a series of articles characterized the different varieties of Indian silk for their macrostructural parameters, such as filament length, degumming loss, denier, cross section, moisture regain, and intrinsic viscosity, for example. The results of amino acid analysis using a reverse‐phase technique were also reported. Five Indian silk varieties—two mulberry (bivoltine and crossbreed) and three nonmulberry (tasar, muga, and eri)—were investigated. The differences existing between the different varieties and the extent of lengthwise variations within a cocoon in the dimensional and macrostructural parameters were discussed. It was observed that denier of the filament decreases considerably from the outer to the inner layers, whereas density showed an increasing trend in all the varieties. Both the mulberry silks demonstrated lower moisture regain. Electron micrographs of all the nonmulberry varieties showed microvoids in their cross section. Fraction studies showed the development of mushroom structure on the tips. In both types of mulberry silk, glycine, alanine, and serine constitute about 82% of the amino acids present. On the other hand, in nonmulberry silks, these constitute about 73% with a high proportion of alanine. The nonmulberry varieties showed a substantial proportion of amino acids with bulky side groups. Similarly, the higher hydrophilic to hydrophobic amino acid ratio (9.06–9.85) for nonmulberry silks, compared against that of the mulberry varieties (5.29–6.22), was shown to be responsible for the higher moisture content of nonmulberry silks. Cystine and methionine were present in all the varieties. The higher intrinsic viscosity of nonmulberry varieties suggested their higher molecular weight. Through amino acid analysis, it was shown that there is no difference in chemical architecture between the outer and the inner layers of cocoons. © 2004 Wiley Periodicals, Inc. J Appl Polym Sci 92: 1080–1097, 2004

Microbial cellulose, an exopolysaccharide produced by bacteria, has unique structural and mechanical properties and is highly pure compared to plant cellulose. Present study represents isolation, identification, and screening of cellulose producing bacteria and further process optimization. Isolation of thirty cellulose producers was carried out from natural sources like rotten fruits and rotten vegetables. The bacterial isolates obtained from rotten pomegranate, rotten sweet potato, and rotten potato were identified as Gluconacetobacter sp. RV28, Enterobacter sp. RV11, and Pseudomonas sp. RV14 through morphological and biochemical analysis. Optimization studies were conducted for process parameters like inoculum density, temperature, pH, agitation, and carbon and nitrogen sources using Gluconacetobacter sp. RV28. The strain produced 4.7 g/L of cellulose at optimum growth conditions of temperature (30°C), pH (6.0), sucrose (2%), peptone (0.5%), and inoculum density (5%). Characterization of microbial cellulose was done by scanning electron microscopy (SEM).

From last two to three decades, the world is facing the threat of finding treatment for Cancer. This disease is striking almost ten million people every year throughout the world. Anticancer drugs are those which are used to cure malignant disease i.e. Cancer. These anticancer drugs are available in different forms including alkalyting agents, hormones and anti metabolites. Various examinations reveals that, there will be a adjacent relationship between the characteristics of alkanes and the anticancer drugs viz. Boiling point, melting point, enthalpy etc. with their chemical structures. In this proposed work, various topological indices are defined on some anticancer drugs to help the researchers to know the physical characteristics and chemical reaction associated with them. We also discuss the QSPR analysis of thirteen degree based topological indices. Further, we showcase that the characteristics have good correlation with physico-chemical characteristics of anticancer drugs.

Cryptocurrency and blockchain are one of the most beautiful digital transformations occurring around the world. They have changed the orthodox meaning and working of currency as we know it. It is interesting to note how it excites and worries some. The main reason for the popularity of cryptocurrencies is tremendous returns in very little time. Social media platforms like twitter, provide a safe-place where individuals’ can share their thoughts as well as mindsets, which then can be heard and be reciprocated by others. This paper aims to draw a correlation between the hyped tweets and the prices of cryptocurrencies like Bitcoin - The Crypto King and Dogecoin - The Memecoin during those times. We also aim to predict the future price values of Bitcoin using its past values. By using cryptocurrencies’ financial data, twitter data, RAPIDS and cuml, a fine line can be drawn between the amount of impact tweets have on people as well as on the market. The tweets on cryptocurrency were segregated and price forecasting was done using augmented dickey fuller test and ARIMA models, 10 future values of bitcoin were predicted with 96% accuracy and 0.0395 average error.Besides, from the investigations above of the authentic cost of BTC, it is perfectly clear that there have been way more steep falls in the history of Cryptocurrencies even before Elon started tweeting about it. Thus, it can clearly be stated that no one person can control the utter volatile world of cryptocurrencies! And the decentralized system ledger of cryptocurrency remains unharmed.

Various antimicrobial textile materials are developed using a variety of active agents which include synthetic antimicrobial agents such as triclosan, metal and their salts, phenols, quaternary ammonium compounds, and organometallics. Although synthetic antimicrobial agents effectively inhibit the growth of microbes, most of them are toxic, can cause adverse effects on human health, and have environmental issues. Present studies prove that several plant extracts could be effective against both gram-positive and gram-negative bacteria depending on the type of components present in the plant extract. Hence, the research on eco-friendly antimicrobial agents and their application on various textile products gain worldwide importance. Natural antimicrobial compounds derived from plants such as neem, tea tree, azuki beans, aloe vera, tulsi leaves (Ocimum sanctum), clove oil, pomegranate rind, turmeric, eucalyptus oil, onion skin, and pulp extracts, are being used in the finishing of textiles. This paper highlights the possibilities of using these bioactive substances for imparting antimicrobial property to the textiles for developing health care products.

In this paper, mechanical properties of the fibers extracted from the areca fruit are determined and compared with the other known natural fiber coir. Further these Areca fibers were chemically treated and the effect of this treatment on fiber strength is studied. Areca fiber composite laminates were prepared using Phenol Formaldehyde with randomly distributed fibers. Composite laminates were prepared with different proportions of phenol formaldehyde and fibers. Other tests like adhesion tensile test, moisture absorption test, and biodegradable test of areca-reinforced phenol formaldehyde composite laminates were conducted and reported.

Green synthesis of Copper oxide nanoparticles (CuO-NPs) was achieved by using different parts of Mussaenda frondosa plant such as leaf, stem and leaf derived callus. Biofabricated CuO-NPs were characterized using Powder X- ray diffraction (XRD), Ultraviolet–visible spectroscopy (UV–Vis), Scanning Electron Microscopy (SEM), Transmission Electron Microscopy (TEM), Energy Dispersive Spectroscopy (EDS), Fourier Transform Infrared spectroscopy (FTIR) and Dynamic light scattering (DLS) analysis. The XRD spectra confirmed the formation of pure monoclinical crystalline nature of CuO-NPs with an average grain size in the range of 2–10 nm. An extremely strong Surface Plasmon Resonance (SPR) between 300 and 400 nm distinctly reveals the synthesis of CuO-NPs. SEM and TEM analysis revealed the formation of spherical shaped agglomerated structures amidst large surface area. EDS spectra proved the existence of copper and oxygen elements in nanomaterials. FTIR spectra explained the possible bioactive molecules liable for the reduction of copper ions. DLS analysis and Zeta potential values validated the stability of CuO-NPs. The pore width distribution by the BJH plot indicates the mixture of meso as well as macropores with large surface area confirmed by BET analysis. Furthermore, the biofabricated NPs were investigated for photocatalytic and biological applications. CuO-NPs were found to exhibit potent DPPH radical scavenging activity. The cytotoxicity study was evaluated by MTT assay against Human lung cancer cell line (A549) and affirms the potent anticancer activity of CuO-NPs. The results of photocatalytic activity of methylene blue dye under UV- light depict 97.36% degradation at 140 min of illumination. Our studies illustrate facile green synthesis of CuO-NPs and found to possess significant biomedical and industrial applications.

Effect of filler material on three-body abrasive wear behaviour of Glass – Epoxy composites has been investigated. The abrading distance, applied load and sliding speed are the parameters used for the study. A L9 orthogonal array and ANOVA were used to identify the contribution of individual parameters. The result shows that the weight loss increases with increase in load, sliding speed and abrading distance. The abrading distance has more effect on the wear compared to other parameters. The filler material (SiC) contributes a significant wear resistance of the G–E composites.

The utilization of Maxwell fluid with nanoparticle suspension exhibits promising prospects in enhancing the efficacy of energy conversion and storage mechanisms. They have the potential to be utilized in sophisticated cooling systems for power generation facilities, thereby augmenting the overall energy efficacy. Keeping this in mind, the current research examines the Maxwell nanofluid flow over a rotating disk with the impact of a heat source/sink. The present study centers on the examination of flow characteristics in the existence of a uniform magnetic field. The conversion of governing equations into ordinary differential equations is achieved using appropriate similarity variables. To derive the Nusselt number (Nu) and skin friction (SF) model related to the flow and temperature parameters, the suggested back-propagation artificial neural networking (ANN) technique is used. The Runge-Kutta-Fehlberg fourth-fifth order (RKF-45) method is used to solve the reduced equations and produce the necessary data to create the Nu and SF model. Both the Nu and SF models require 1000 data for training the network, respectively. Graphs are utilized to communicate numerical outcomes. The results concluded that the upsurge in magnetic parameter drops the velocity profile but advances the heat transport. Rise in the thermal conductivity parameter, increases the heat transport.

A Glauert type laminar wall jet issuing into a stationary liquid medium lying above a wall has technical uses in wall cooling and flow control. It plays a vital role in industrial applications like cooling/heating by impingement of jet, turbine blades, film cooling, mass and heat transfer phenomena. In this regard, a steady incompressible two-dimensional laminar Glauert kind wall jet is scrutinized in this study by considering nanoparticles suspension in the base liquid sodium alginate (NaAlg) with suction and wall slip boundary conditions. Further, a comparative study is done by considering aluminum alloy(AA7075) and single-walled carbon nanotube (SWCNT) as nanoparticles. The reduced ordinary differential equations (ODEs) are numerically solved by applying Runge–Kutta–Fehlberg fourth fifth-order (RKF-45) technique along with the shooting method. Results reveal that NaAlg−SWCNT Casson nanofluid shows enhanced heat transfer than NaAlg−AA7075 Casson nanoliquid for increased values of radiation parameter. The rising values of the Casson parameter deteriorate the heat transfer rate of both nanoliquids but an inverse trend is seen for improved values of radiation parameter.

The current study explores a three-dimensional swirling flow of titania–ethylene glycol-based nanofluid over a stretchable cylinder with torsional motion. The heat transfer process is explored subject to heat source/sink. Here, titania–ethylene glycol–water-based nanofluid is used. The Maxwell–Bruggeman models for thermal conductivity and modified Krieger–Dougherty models for viscosity are employed to scrutinize the impact of nanoparticle aggregation. A mathematical model based on partial differential equations (PDEs) is developed to solve the flow problem. Following that, a similarity transformation is performed to reduce the equations to ordinary differential equations (ODEs), which are then solved using the finite element method. It has been proven that nanoparticle aggregation significantly increases the temperature field. The results reveal that the rise in Reynolds number improves the heat transport rate, whereas an increase in the heat source/sink parameter value declines the heat transport rate. Swirling flows are commonly found in many industrial processes such as combustion, mixing, and fluidized bed reactors. Studying the behavior of nanofluids in these flows can lead to the development of more efficient and effective industrial processes.

Metal matrix composite of Aluminium alloy (Al 6061) matrix reinforced with titanium carbide (TiC) particulates was fabricated by stir casting technique. Aluminium alloy is selected as the matrix material and titanium carbide particulates were incorporated with varying proportions of 3wt%, 5wt% and 7%. Stirring was done to achieve uniform distribution of reinforcement particulates and rectangular shaped castings were made by pouring the composite mixture into the steel mould. The composite was then studied with respect to its microstructure and mechanical properties. Tensile specimens as per ASTM standards were machined to find out the mechanical properties of the composite. Comparative study for all the said composites is done with respect to Modulus of elasticity, yield stress, percentage elongation and microhardness. Scanning Electron Microscope (SEM) is used to observe the distribution of particulates and to understand the nature of fractured surface.

Abstract The current paper explores the three-dimensional flow of an Oldroyd-B liquid with the impact of a magnetic dipole that occurred by stretching a flat surface placed in the plane with a linear velocity variation in two directions containing motile gyrotactic microorganisms. Using proper similarity transformations, the governing equations are reduced into nonlinear coupled ordinary differential equations (ODEs). The ODEs are then solved using Runge–Kutta-Fehlberg (RKF) method. The training, testing, and validation processes are carried out in parallel to adapt neural networks and calculate an approximate solution for the considered model. This helps to reduce the mean square error (MSE) function by Levenberg–Marquardt backpropagation. The efficiency of the suggested backpropagated neural networks methodology has been demonstrated by utilizing outcomes such as MSE, error histograms, correlation and regression. Results reveal that the heat transport augments for increased Biot number values. The mass transport declines for improved chemical reaction rate parameter values. A higher Peclet number will result in a lower motile diffusivity and result in a decline in the micro-organism’s density profile. For the least value of Mu and gradient, better convergence of the findings can be achieved with better network testing and training.

Abstract The consequence of exothermic/endothermic chemical reactions and Arrhenius activation on the heat and mass transport of the liquid flow past a cylinder in the incidence of a magnetic dipole is considered in the current investigation. Magnetic dipoles are used in medical applications such as magnotherapy and spectroscopy, to produce static magnetic fields. Scientists and engineers can improve the effectiveness of chemical reactions or heat transfer operations by analyzing the impact of reactions on flow and building systems with optimized flows. The modelled equations are converted into non-dimensional ordinary differential equations (ODEs) by using similarity variables. The resultant equations are solved by employing the physics-informed neural network (PINN) technique. Additionally, the comparison of PINN with the numerical method Runge–Kutta Fehlberg’s fourth-fifth order (RKF-45) is studied. The effects of different parameters on the temperature, concentration, and velocity profiles for endothermic/exothermic instances are shown graphically. The thermal, velocity, and concentration profiles get stronger as the curvature parameter values increase for both endothermic and exothermic cases. The influence of activation energy parameters, chemical reaction parameters, and endothermic/exothermic reaction parameters on the thermal and concentration is also depicted.

The use of topological descriptors is the key method, regardless of great advances taking place in the field of drug design. Descriptors portray the chemical characteristic of a molecule in numerical form, that is used for QSAR/QSPR models. The numerical values related with chemical constitutions that correlate the chemical structure with the physical properties refer to topological indices. The study of chemical structure with chemical reactivity or biological activity is termed quantitative structure activity relationship, in which topological index plays a significant role. Chemical graph theory is one such significant branch of science which plays a key role in QSAR/QSPR/QSTR studies. This work is focused on computing various degree-based topological indices and regression model of nine anti-malaria drugs. Regression models are fitted for computed indices values with 6 physicochemical properties of the anti-malaria drugs are studied. Based on the results obtained, an analysis is carried out for various statistical parameters for which conclusions are drawn.

The purpose of the proposed research work is to explore the heat source or sink impact on the unsteady three-dimensional flow of ternary-hybrid nanofluid through a rotating disk. The magnetohydrodynamic flow of ternary-hybrid nanofluid under the impact of radiative heat transfer and uniform suction is also discussed in this study. The partial differential equations of the flow problem are reduced into ordinary differential equations by employing apt similarity transformation and solved numerically using the Runge–Kutta Fehlberg fourth–fifth order method. The various nondimensional parameters' effects on velocity and thermal profiles are illustrated using graphs. In addition, a Levenberg Marquardt backpropagated neural network is employed for determining the Nusselt number and skin friction model. The outcomes of the developed Levenberg Marquardt backpropagated neural network models are indicated through the performance metrics. Result reveals that a rise in the suction parameter decreases the velocity profiles. The thermal profile increases with higher values of thermal radiation and heat source/sink parameters. In addition, the presented Levenberg Marquardt backpropagated neural network models' scheme is found to be a perfect tool for estimating heat transfer and surface drag force models.

Abstract This second in a series of articles deals with studies on the structure and physical properties of five varieties of Indian silk: two mulberry (bivoltine and crossbreed) and three nonmulberry (tasar, muga, and eri). A detailed analysis of the microstructural parameters and mechanical properties was reported. Significant differences between and within the varieties with respect to microstructural parameters (crystallinity, density, birefringence, dichroic ratio, sonic modulus, etc.), as well as the effect of microstructural parameters on mechanical properties, were discussed. Some of the observations made on the inverse stress relaxation behavior of the different silk varieties were also reported. The extent of variation of these morphological parameters was found to correlate well with the mechanical properties. © 2004 Wiley Periodicals, Inc. J Appl Polym Sci 92: 1098–1115, 2004

Abstract Arecanut disease identification is a challenging problem in the field of image processing. In this work, we present a new combination of multi‐gradient‐direction and deep convolutional neural networks for arecanut disease identification, namely, rot, split and rot‐split. Due to the effect of the disease, there are chances of losing vital details in the images. To enhance the fine details in the images affected by diseases, we explore multi‐Sobel directional masks for convolving with the input image, which results in enhanced images. The proposed method extracts arecanut as foreground from the enhanced images using Otsu thresholding. Further, the features are extracted for foreground information for disease identification by exploring the ResNet architecture. The advantage of the proposed approach is that it identifies the diseased images from the healthy arecanut images. Experimental results on the dataset of four classes (healthy, rot, split and rot‐split) show that the proposed model is superior in terms of classification rate.

The time-dependent Maxwell nanofluid flow with thermophoretic particle deposition is examined in this study by considering the solid–liquid interfacial layer and nanoparticle diameter. The governing partial differential equations are reduced to ordinary differential equations using suitable similarity transformations. Later, these reduced equations are solved using Runge–Kutta–Fehlberg’s fourth and fifth-order method via a shooting approach. An artificial neural network serves as a surrogate model, making quick and precise predictions about the behaviour of nanofluid flow for various input parameters. The impact of dimensionless parameters on flow, heat, and mass transport is determined via graphs. The results reveal that the velocity profile drops with an upsurge in unsteadiness parameter values and Deborah number values. The rise in space and temperature-dependent heat source/sink parameters value increases the temperature. The concentration profile decreases as the thermophoretic parameter upsurges. Finally, the method’s correctness and stability are confirmed by the fact that the maximum number of values is near the zero-line error. The zero error is attained near the values 2.68×10−6, 2.14×10−9, and 8.5×10−7 for the velocity, thermal, and concentration profiles, respectively.

This paper investigates the impact of homogeneous-heterogeneous reactions on the flow between the coaxial cylinders with heat generation/absorption. From an engineering perspective, in energy absorber design for the shrinking and expanding of a circular metal tube in the production of extruding materials by stretching cylinders designing with great care is necessary to regulate skin friction and heat transfer rate. Studying processes in confined geometries enhances the basic knowledge of chemical kinetics and fluid dynamics. Researchers may increase the chemical processes efficiency/heat transfer activities by designing systems with an optimized flow and analysing the effects of reactions on flow. Both cost reductions and higher-quality products may result from this optimization. This research may yield insights with far-reaching implications across multiple disciplines. The modelled equations are transformed into ordinary differential equations by employing apt similarity transformations and obtained equations are solved using the collocation method with Probabilists’ Hermite polynomial. Graphs illustrating the variation of velocity, thermal, and concentration profiles have been plotted using the influence of various parameters. The increase in curvature parameter causes the velocity and thermal profile upsurge. The rise in heat source/sink parameter improves the heat transport. As the heterogeneous and homogeneous parameters upsurge, the concentration profile decreases.