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Computational investigation into structural, topological, electronic properties, and biological evaluation of spiro[1H-indole-3,2?-3H-1,3-benzothiazole]-2-one
The current work comprises theoretical studies on spiro[1H-indole-3,2?-3H-1,3-benzothiazole]-2-one employing density functional theory (DFT). The optimized structure and molecular geometry of the title compound were calculated. Topological studies were performed using Multiwfn 3.8, these include ELF, LOL and RDG studies to identify the main bonding regions and weak interactions in the molecule. Solvation effects were studied by taking different green solvents, using IEFPCM model. Solvation effects were investigated for electronic properties (HOMO-LUMO and UV), MEP, and NLO properties and some variation is observed in the behaviour of the title compound in gas and solvent phases. Natural bond orbital (NBO) calculations are performed to study the inter- and intra-molecular charge transfer and stability. Pharmacological evaluation comprising of drug-likeness, ADME, environmental toxicity properties using online tools such as SwissADME, Pre-ADMET, and GUSAR, to determine whether the molecule can be a potential drug candidate is performed. Finally, molecular docking against anti-melanoma targets whose Ramachandran plots have been depicted to determine the stability of the target proteins, with PyMOL, AutoDock Suite and Discovery Studio Visualizer, is carried out. 2022 Elsevier B.V. -
Computational investigation into the solvent effect, electron distribution, reactivity profile, pharmacokinetic properties and anti-cancer action of Hemimycalin C
This work consists of DFT studies and biological evaluation of the marine alkaloid Hemimycalin C. The DFT calculations include energy minimisation, reactivity analysis of the frontier molecular orbitals, electronic transition studies (UV spectra generation), molecular electrostatic potential colour map analysis (MEP), and natural bond orbitals (NBO) studies. Non-linear optical (NLO) properties estimation is also performed to obtain the first-order hyperpolarizability, mean polarizability and dipole moment of Hemimycalin C. The solvent methanol emerges as the most interesting among the polar solvents employed in this study, as it impacts the properties of Hemimycalin C to a significant extent. Multiwfn software is used for topological analyses, which include the calculation of Reduced Density Gradient (RDG), Localised Orbital Locator (LOL) maps), and Electron Localisation Function (ELF). The computed ADMET profile indicates that the molecule is a potent lead (drug candidate) as the medicinal chemistry parameters are mostly within the optimal range. The Ramachandran plots are also computed to show the stability and quality of the target proteins, by computation of the permitted psi and phi angles. The complexes of the ligand are docked using AutoDock Tools against blood cancer receptors to obtain good binding affinity values. 2025 Elsevier B.V. -
Computational investigation into the structure, effect of band gap energies, charge transfer, reactivity, thermal energies and NADPH inhibitory activity of a benzimidazole derivative
This work contains computational investigations of a benzimidazole derivative consisting of density functional theory, electronic structure and biological evaluation of a benzimidazole derivative. Density functional theory evaluation were conducted, starting from geometry optimisation, followed by the molecular electrostatic potential, spectral analyses, polarizability studies and thermodynamic analyses via the frequency calculations. Solvent frontier molecular orbital analyses, impact on the properties of the molecule were modelled with the IEFPCM model of solvation. Topological analyses helped to ascertain the molecule's electronic structure. Biological assessment included pharmacokinetic property evaluation and molecular docking. Pharmacokinetic descriptors were generated using online tools and the molecule was assessed for its efficacy as a drug molecule by comparing with the rules concerning drug-likeness and analysing the descriptors relating to absorption, distribution, metabolism, excretion and toxicity of the molecule. Docking of the molecule with the two targets, 7D3E and 3A1F, yielded a good binding energy of ?7.39 and ?5.81 kcal/mol respectively. 2024 Elsevier B.V. -
Computational Methods for Detection and Recognition of Coronary Artery Stenosis in Angiogram Images
Coronary Artery Disease (CAD) is caused by stenosis of the coronary artery's lumen. This heart disease is one of the reasons for the highest mortality worldwide. This illness manifests as stenosis or plaque in the coronary arteries and causes atherosclerosis. It damages or clogs the heart arteries, causing a lack of blood flow to the heart muscles and leading to a heart attack. There are different medical modalities to diagnose the heart artery disease. A standard method used by the cardiologist to diagnose the severity of this disease is coronary angiography. An X-ray machine is used to capture the angiogram image at various angles during cardiac catheterization. Experts examine the data and offers different opinions. owever, most of the angiogram videos consist of unclear images with artifacts, and because of the complex structure of the arteries, medical experts fail to get accurate information about the damages and blockages in arteries. Based on the cardiologist's suggestions, a computational model is proposed as a secondary method to detect and recognize the stenosis level from the coronary angiogram images. The proposed model is Coronary Artery Stenosis Detection Using Digital Image Processing (CASDDIP). The proposed research model/framework can identify the stenosis in the cardiogram image with good accuracy of 98.06% precision. This proposed research experimentation can be compared with existing literature methods which outperforms compared to other methods using real time dataset. A dataset, such as angiogram videos and images of patients under varying age groups, is used to train the model. These videos are acquired from the healthcare center with due consent. The proposed CASDDIP model consists of four modules: Keyframe extraction and preprocessing Coronary Artery Segmentation Feature extraction and stenosis detection Initially, a novel keyframe extraction method is proposed to find the keyframe from the angiogram video. Followed by a hybrid segmentation method is presented in this research to extract the coronary artery region from the image. Further a method is proposed to detect the stenosis by extracting and fusing different features. Detected stenosis is categorized using the proposed stenosis level classification method. This CASDDIP model is a supporting tool to help the cardiologist during diagnosis. -
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Computational Methods to Predict Suicide Ideation among Adolescents
Suicide has been a prominent cause of death worldwide, regardless of age, sex, geography, and so on, and predominantly suicide among teens, increased as the years have passed. Suicide ideation, suicide risk, suicide attempts have been studied extensively, and the most common cause has been identified as depression, followed by familial concerns, hereditary factors, stress, avoidance fear, and a variety of other variables. When visited by a doctor, most adolescents are unaware of their mental state and hence do not take action on their own or are not assisted by family or peer members to overcome their fear of social stigma or the treatment they must undergo. According to popular belief, early treatment and detection are the most effective ways to reduce the risk of suicide. As a result, the focus of this study is to illustrate some of the computational strategies utilized in deep learning and machine learning fields to detect kids at risk of suicide 2022 IEEE. -
Computational Model for Hybrid Job Scheduling in Grid Computing
Grid computing the job scheduling is the major issue that needs to be addressed prior to the development of a grid system or architecture. Scheduling is the users job to apropos resources in the grid environment. Grid computing has got a very wide domain in its application and thus induces various research opportunities that are generally spread over many areas of distributed computing and computer science. The cardinal point of scheduling is being attaining apex attainable performance and to satisfy the application requirements with computing resources at exposure. This paper posits techniques of using different scheduling techniques for increasing the efficacy of the grid system. This hybrid scheduler could enable the grid system to reduce the execution time. This paper also proposes an architecture which could be implemented ensuring the optimal results in the grid environment. This adaptive scheduler would possibly combine the pros of two scheduling strategies to produce a hybrid scheduling strategy which could cater the ever changing workload encountered by the gird system. The main objective of the proposed system is to reduce to overall job execution time and processor utilization time. 2020, Springer Nature Switzerland AG. -
Computational modeling of heat transfer in magneto-non-Newtonian material in a circular tube with viscous and Joule heating
Numerous industrial and engineering systems, like, heat exchangers, chemical action reactors, geothermic systems, geological setups, and many others, involve convective heat transfer through a porous medium. The diffusion rate, drag force, and mechanical phenomenon are dealt with in the DarcyForchheimer model, and hence this model is vital to study the fluid flow and heat transport analysis. Therefore, numerical simulation of the DarcyForchheimer dynamics of a Casson material in a circular tube subjected to the energy losses due to the viscous heating and Joule dissipation mechanisms is performed. The novelty of the present investigation is to scrutinize the convective heat transport characteristics in a circular tube saturated with DarcyForchheimer porous matrix by utilizing the non-Newtonian Casson fluid. The flow occurs due to the elongation of the surface of a tube with a uniform heat-based source/sink. The similarity solution of the nonlinear problem was obtained using dimensionless similarity variables. The effects of operating parameters related to the flow phenomena are analyzed. Further, the friction factor and Nusselt number are also analyzed in detail. The present flow model ensures no flow reversal and acts as a coolant of the heated cylindrical surface; the existence of the magnetic field, as well as an inertial coefficient,acts as the momentum-breaking forces, whereas Casson fluidity buildsit. The Joule heating phenomenon enhances the magnitude of temperature. The thermal field of the Casson fluid is higher at the surface of the circular pipe due to convective thermal conditions. 2021 Wiley Periodicals LLC. -
Computational Modelling of Complex Systems for Democratizing Higher Education: A Tutorial on SAR Simulation
Engineering systems like Synthetic Aperture Radar (SAR) are complex systems and require multi-domain knowledge to understand. Teaching and learning SAR processing is intensive in terms of time and resources. It also requires software tools and computational power for preprocessing and image analysis. Extensive literature exists on computational models of SAR in MATLAB and other commercial platforms. Availability of computational models in open-source reproducible platforms like Python kernel in Jupyter notebooks running on Google Colaboratory democratizes such difficult topics and facilitates student learning. The model, discussed here, generates SAR data for a point scatterer using SAR geometry, antenna pattern, and range equation and processes the data in range and azimuth with an aim to generate SAR image. The model demonstrates the generation of synthetic aperture and the echo signal qualities as also how the pulse-to-pulse fluctuating range of a target requires resampling to align the energy with a regular grid. The model allows for changing parameters to alter for resolution, squint, geometry, radar elements such as antenna dimensions, and other factors. A successful learning outcome would be to understand where parameters need to be changed, to affect the model in a specific way. Factors affecting Range Doppler processing are demonstrated. Use of the discussed model nullifies use of commercial software and democratizes SAR topic in higher education. 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. -
Computational screening of natural compounds from Salvia plebeia R. Br. for inhibition of SARS-CoV-2 main protease
The novel Severe Acute Respiratory Syndrome Coronavirus (SARS-CoV-2) has emerged to be the reason behind the COVID-19 pandemic. It was discovered in Wuhan, China and then began spreading around the world, impacting the health of millions. Efforts for treatment have been hampered as there are no antiviral drugs that are effective against this virus. In the present study, we have explored the phytochemical constituents of Salvia plebeia R. Br., in terms of its binding affinity by targeting COVID-19 main protease (Mpro) using computational analysis. Molecular docking analysis was performed using PyRx software. The ADMET and drug-likeness properties of the top 10 compounds showing binding affinity greater than or equal to ? 8.0kcal/mol were analysed using pkCSM and DruLiTo, respectively. Based on the docking studies, it was confirmed that Rutin and Plebeiosides B were the most potent inhibitors of the main protease of SARS-CoV-2 with the best binding affinities of ? 9.1kcal/mol and ? 8.9kcal/mol, respectively. Further, the two compounds were analysed by studying their biological activity using the PASS webserver. Molecular dynamics simulation analysis was performed for the selected proteinligand complexes to confirm their stability at 300ns. MM-PBSA provided the basis for analyzing the affinity of the phytochemicals towards Mpro by calculating the binding energy, and secondary structure analysis indicated the stability of protease structure when it is bound to Rutin and Plebeiosides B. Altogether, the study identifies Rutin and Plebeiosides B to be potent Mpro inhibitors of SARS-CoV-2. Graphic abstract: [Figure not available: see fulltext.] 2021, Society for Plant Research. -
Computational simulation of surface tension and gravitation-induced convective flow of a nanoliquid with cross-diffusion: An optimization procedure
The control of heat transfer in the hydromagnetic semiconductor crystal involves Marangoni convection with buoyancy forces. In this study, the conventional thermo-solutal Marangoni mixed flow model is modified by incorporating the solutal buoyancy effects that are significant in the flow phenomenon. The heat and mass transfer (HMT) characteristics of the Marangoni convective flow of a Cu ? H2O nanofluid subjected to the assisting/resisting buoyancy forces and cross-diffusion are numerically studied. The homogeneous single-phase nanoliquid model is used in conjunction with experimental data of dynamic viscosity and thermal conductivity. The Dufour and Soret effects are considered. Governing equations are solved using the finite difference-based algorithm. The problem is analyzed in a unified way considering the cases of buoyancy-assisted flow and buoyancy-opposed flow. The response surface methodology (RSM) based on the face-centered composite design (CCD) is used to optimize the heat and mass transfer rates. A multivariate regression model is proposed and authenticated prior to optimization. Additionally, sensitivity analysis is performed using the full quadratic regression model. The increase in the temperature profile is more significant due to the radiative heat flux than the inclined magnetic field. Heat transfer has a high sensitivity to the appearance of thermal radiation, while mass transfer has a high sensitivity to the Soret effect. Simultaneous optimization of HMT rates is achieved with the high level of thermal radiation and low levels of the cross-diffusion aspects. 2022 -
Computational statistics of data science for secured software engineering
The chapter focuses on exploring the work done for applying data science for software engineering, focusing on secured software systems development. With requirements management being the first stage of the life cycle, all the approaches that can help security mindset right at the beginning are explored. By exploring the work done in this area, various key themes of security and its data sources are explored, which will mark the setup of base for advanced exploration of the better approaches to make software systems mature. Based on the assessments of some of the work done in this area, possible prospects are explored. This exploration also helps to emphasize the key challenges that are causing trouble for the software development community. The work also explores the possible collaboration across machine learning, deep learning, and natural language processing approaches. The work helps to throw light on critical dimensions of software development where security plays a key role. 2021, IGI Global. -
Computational studies into the chemical nature, thermal behaviour, solvent role, reactivity and biological evaluation of Rigidin E A marine alkaloid with potent liver cancer inhibition
The current work includes theoretical studies of Rigidin E (marine alkaloid) molecule with the DFT technique and evaluation of its biological properties in silico. DFT calculations in different media were performed for the title molecule. Gradual changes were noticed in the properties of the title compound when subjected to solvation in polar solvents. Electron density distribution, interaction and excitation were demonstrated using topological studies (ELF, LOL, RDG, and charge transfer) done using Multiwfn software. From FMO analysis, methanol is the solvent in which the title compound has the highest band gap value (3.8972 eV) compared to other solvents, and in the gas phase it has a band gap value of 3.6886 eV. Theoretical UV studies show that n ->?* and n ->?* electronic transitions are significant in Rigidin E. In water, the title molecule has a first-order hyperpolarizability about 100 times that of the reference substance urea, indicating its powerful NLO potential in aqueous medium. ADMET profile was generated using online tools (ADMET lab 2.0, PreADMET, and SwissADME). For the title molecule, docking was done against select liver cancer targets using AutoDock Tools and the lowest binding affinity was obtained ?4.62 kcal/mol against 4H6J protein. 2023 Elsevier B.V. -
Computational study of charge transfer iso-surface in first three excited states, electron-hole transition effects, chemical nature and bond order analysis investigations of chrysogine
This work presents the theoretical DFT (Density Functional Theory) studies and the biological application of chrysogine, a marine alkaloid. Energy minimisation and additional DFT evaluations were performed for vacuum and solvent media. It has been observed that solvation with polar solvents has resulted in a slight variation in the molecule's properties. The Multiwfn software was employed to carry out various topological analyses. Among these, the charge transfer studies show that the second and third excited states are the most significant. From the reactivity analysis, the least energy gap (4.9624 eV) is obtained in water, indicating that chrysogine is most reactive in aqueous media. Theoretical UV studies show that the trends in ?max values correspond to n >?* and n >?* electronic transitions within the molecule. An increase in medium polarity has demonstrated in the MEP (Molecular Electrostatic Potential) maps an increase in the potential range from ?6.619 10?2 a.u. to 6.619 10?2 a.u. in the gas phase, to a sharp rise to ?8.036 10?2 a.u. to 8.036 10?2 a.u. in ethanol, ?8.098 10?2 a.u. to 8.098 10?2 a.u. in methanol, ?8.130 10?2 a.u. to 8.130 10?2 a.u. in DMSO, and ?8.127 10-2 a.u. to 8.127 10?2 a.u. in water. The most significant transition contributing to molecular stability from NBO (Natural Bond Orbital) analysis is: (O2-C9) ?* ? ?* (C7-C8) with the energy of 258.13 kcal mol?1. The ADMET profile for the molecule was assimilated with the help of online servers. The molecule was docked against lung cancer target proteins (PDB ID: 1NTK, 3QFB) using software such as AutoDock Tools and PyMOL. The respective illustrations and data were visualised using Discovery Studio Visualizer. Good binding affinities (?5.69 kcal mol?1 for 1NTK and ?6.64 kcal mol?1 for 3QFB proteins) and interactions were achieved with the selected targets. 2024 Elsevier B.V. -
Computational Study of MHD Nanofluid Flow with Effects of Variable Viscosity and Non-uniform Heat Generation
The thermodynamic study of an unsteady two-dimensional nanofluid flow through a permeable stretched sheet is looked at. Water is used as the primary fluid, along with four different nanoparticles, including copper (Cu), titanium dioxide (TiO2), copper oxide (CuO), and aluminium oxide (Al2O3). The heat transfer phenomenon is explained by an outside source. Additionally considered are the impacts of heat generation and absorption. A similarity transformation is used to convert the considered set of mathematical equations into a system of ODEs. The BVP4C method is then mathematically applied, coupled with shooting fashion. The results are given for cases involving copper nanoparticles. The effects of various physical parameters on the profiles of the dimensionless flow field, temperature, average entropy generation function, skin friction, and the Nusselt number are examined with illustrations and thorough explanations. As exceptional circumstances of the current inquiry, there is a strong agreement between the current conclusion and the findings of the other researchers. The average entropy generation number, temperature, and velocity profiles are shown to be strongly influenced by regulating factors. The authors conclude that the average entropy production number decreased in the existence of a temperature- and space-dependent heat source/sink, but it increased with increasing the viscosity parameter. 2023, The Author(s), under exclusive licence to Springer Nature India Private Limited. -
Computational techniques for sustainable green procurement and production
Computational techniques are used to generate, solve, analyze, explain, or manage any simple or complex task. The use of environmentally responsible techniques to meet demand for resources, commodities, utilities, and services is known as green procurement. Computational technique in green procurement and production is one of the components of sustainable procurement, along with a commitment to social responsibility and good corporate behavior. Some solutions for this kind of issue are low-maintenance, energy-efficient, and long-lasting. Several experts and researchers provided their findings on the environmental impact of ICT with the use of computational techniques. Also, the importance of energy-efficient information technology for environmentally conscious and feasible information technology is a hot topic because a computer faces environmental challenges at every stage of its life, from development to use to disposal. Due to changing environmental conditions, corporations have prioritized carbon emissions in procurement and transportation, which have the highest carbon impact. To encourage potential suppliers to adopt environmentally friendly practices, green criteria should be introduced into public procurement. Environmentally friendly corporate practices and environmental conservation are considered significant tools through public procurement. Techniques for green procurement and production procedures have recently been correlated with the concept of computational techniques of green procurement and production, owing to the increased emphasis on the concept of computational approaches. For eco-friendly procurement and production operations, computational approaches are inculcated and presented in the same way that they are for green procurement and manufacturing. From this perspective, this chapter presents a methodology for merging computational techniques into green procurement and production in public procurement in the form of green computing. 2024 by Elsevier Inc. All rights reserved, including those for text and data mining, AI training, and similar technologies. -
Computational techniques to study the dynamics of generalized unstable nonlinear Schringer equation
In this paper, a more general form of unstable nonlinear Schringer equation which describe the time evolution of disturbances in marginally stable or unstable media is studied. A new modification of the Sardar sub-equation method is discussed and employed to retrieve solitons and other solutions of the suggested nonlinear model. A variety of solutions, including bright solitons, dark solitons, singular solitons, combo bright-singular solitons, periodic, exponential, and rational solutions are provided with considerable physical perspective. Using the q-homotopy analysis algorithm in combination with the Laplace transform, we present the approximate solutions of the bright and dark solitons, including the physical nature of the attained solutions. The computation complexity and results indicate that the given techniques are simple, effective, uncomplicated, and that they may be used to a wide range of unstable and stable nonlinear evolution equations encountered in mathematics, mathematical physics, and other applied disciplines. 2022 -
Computationally Efficient Machine Learning Methodology for Indian Nobel Laureate Classification
A computationally efficient methodology for Indian Nobel Laureate classification is proposed in this study, emphasizing the optimization of image categorization through supervised learning techniques. Leveraging advancements in Convolutional Neural Networks (CNNs), the research aims to enhance the efficiency and precision of image classification tasks. The study utilizes Logistic Regression for dataset analysis, initially employing browser extensions for mass downloading categorized image data. Haar cascade classifiers are then used for data wrangling, focusing on facial, nose, and mouth recognition. Following this, feature engineering through wavelet transformation reduces image dimensionality, preparing the dataset for the chosen ML model, Logistic Regression. The primary focus is to simplify technology for improved image categorization. Support Vector Machines (SVM), Random Forest, and Logistic Regression are examined, with Logistic Regression emerging as the most effective model, achieving an accuracy rate of 87.5%. A thorough evaluation using Confusion Matrices reveals Logistic Regression's superior performance in classifying images of Indian Nobel laureates. A strategic up-sampling approach is implemented to address dataset inconsistencies, ensuring balanced representation across classes. The Haar wavelet transform is then applied for feature extraction, optimizing the dataset for ML models. The dataset is split into training and testing sets (80-20), and the three models are trained and evaluated for accuracy. Logistic Regression proves to be the best performer, offering insights into prominent leaders' identification. The research offers a detailed pipeline for data preprocessing, feature engineering, and model assessment, culminating in a robust image categorization system. Logistic Regression emerges as a reliable method for biographical picture identification, demonstrating superior accuracy over SVM and Random Forest. This research underscores the importance of efficient and accurate image classification methodologies for practical applications in real-world scenarios, particularly in recognizing influential leaders. 2024 IEEE. -
Computationally efficient wavelet domain solver for florescence diffuse optical tomography
Estrogen induced proliferation of mutant cells is a growth signal hallmark of breast cancer. Fluorescent molecule that can tag Estrogen Receptor (ER) can be effectively used for detecting cancerous tissue at an early stage. A novel targetspecific NIRf dye conjugate aimed at measuring ER status was synthesized by ester formation between 17-? estradiol and a hydrophilic derivative of ICG, cyanine dye, bis-1,1-(4-sulfobutyl) indotricarbocyanine-5-carboxylic acid, sodium salt. In-vitro studies provided specific binding on ER+ve [MCF-7] cells clearly indicating nuclear localization of the dye for ER+ve as compared to plasma level staining for MDAMB-231. Furthermore, cancer prone cells showed 4.5-fold increase in fluorescence signal intensity compared to control.; A model of breast phantom was simulated to study the in-vivo efficiency of dye with the parameters of dye obtained from photo-physical and in-vitro studies. The excitation (754 nm) and emission (787 nm) equation are solved independently using parallel processing strategies. The results were obtained by carrying out wavelet transformation on forward and the inverse data sets. An improvisation of the Information content of system matrix was suggested in wavelet domain. The inverse problem was addressed using LevenbergMarquardt (LM) procedure with the minimization of objective function using Tikhonov approach. The multi resolution property of wavelet transform was explored in reducing error and increasing computational efficiency. Our results were compared with the single resolution approach on various parameters like computational time, error function, and Normalized Root Mean Square (NRMS) error. A model with background absorption coefficient of 0.01 mm-1 with anomalies of 0.02 mm-1 with constant reduced scattering of 2.0 mm for different concentration of dye was compared in the result. The reconstructed optical properties were in concurrence with the tissue property at 787 nm. We intend our future plans on in-vivo study on developing a complete instrumentation for imaging a target specific lipophilic dye. Springer International Publishing Switzerland 2014.