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Beta carotene inhibiting HIV-1 reverse transcriptase, an in silico approach
Due to the outspread of various emerging diseases, research on the discovery of new drugs is being carried out extensively. Several phytochemicals with medicinal importance are now being used for this purpose due to its effectiveness and safety in comparison to the conventional synthetic ones. Computational docking is further being used for the fast and cost-effective screening. Reverse transcriptase is a key enzyme involved in the conversion of viral RNA sequence into complementary DNA (cDNA) sequence leading to various retroviral diseases like HIV/AIDS. Patchdock docking server was used in this study to perform in silico enzyme-inhibitor binding experiment between twenty phytocompounds and HIV-1 reverse transcriptase enzyme. Beta-carotene was found to have strongest binding potential in comparison to other phytochemicals. The results suggested that this compound can be used as therapeutics in the future as naturally occurring HIV-1 reverse transcriptase inhibitor. 2020 World Research Association. All rights reserved. -
In Silico Analysis of the Apoptotic and HPV Inhibitory Roles of Some Selected Phytochemicals Detected from the Rhizomes of Greater Cardamom
Occurrence of cervical cancer, caused due to persistent human papilloma virus (HPV) infection, is common in women of developing countries. As the conventional treatments are expensive and associated with severe side effects, there is a need to find safer alternatives, which is affordable and less toxic to the healthy human cells. Present study aimed to evaluate the anti-HPV and apoptotic potential of four compounds from the greater cardamom (Amomum subulatum Roxb. var. Golsey), namely rhein, phytosphingosine, n-hexadecenoic acid and coronarin E. Their anti-HPV and apoptotic potential were studied against viral E6, E7 and few anti-apoptotic proteins of host cell (BCL2, XIAP, LIVIN) by in silico docking technique. Phytochemicals from the plant extract were analysed and identified by LC/MS and GC/MS. Involvement of the target proteins in various biological pathways was determined through KEGG. Structural optimization of the three-dimensional structures of the ligands (four phytochemicals and control drug) was done by Avogadro1.1. Receptor protein models were built using ProMod3 and other advanced tools. Pharmacophore modelling of the selected phytochemicals was performed in ZINCPharmer. Swiss ADME studies were undertaken to determine drug likeness. The ligands and proteins were digitally docked in DockThor docking program. Protein flexibility-molecular dynamic simulation helped to study proteinligand stability in real time. Finally, the correlation of evaluated molecules was studied by the use of principal component analysis (PCA) based on the docking scores. All the ligands were found to possess apoptotic and anti-cancer activities and did not violate Lipinsky criteria. n-Hexadecanoic acid and its analogues showed maximum efficacy against the target proteins. All the proteinligand interactions were found to be stable. The uncommon phytochemicals identified from rhizomes of greater cardamom have anti-cancer, apoptotic and HPV inhibitory potentials as analysed by docking and other in silico studies, which can be utilized in drug development after proper experimental validation. 2022, The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature. -
An Integrated and Optimized Fog Computing enabled Framework to minimize Time Complexity in Smart Grids
A distributed computing paradigm known as 'cloud computing'works as a connection between IoT devices and cloud data centres. The environment system model in this work is on basis of clouds and fog and includes smart grids, which we explore. Prior to understanding the use of fog computing in smart grids we discuss about various features of cloud computing and talk about how to manage the connection between fog and cloud computing. Along with the usual performance of low latency, low cost, and high intelligence, the distinctive characteristics and service scenarios are also explored. Based on the outcome of the simulation, it appears that our suggested PSO-SA algorithm outperforms other optimization algorithms. It recorded a least mean response time of 3.86 seconds only. While the model build up delay was 4.6 seconds, the model execution delay was also found to be only 4.9 seconds with PSO-SA method. The improved efficiency of the technique can be credited to the best aspects of particle swarm optimisation (PSO) and a modified inertia weight obtained by simulated annealing. 2023 IEEE. -
Embedded Finance in Action: A Pathway to Sustainable Growth
This chapter examines how embedded finance reshapes sustainable investment through AI, machine learning, and DeFi integration with ESG practices. These technologies can address behavioral biases such as loss aversion, yet major challenges remain. ESG data is fragmented, with rating providers often showing correlations below 0.6, weakening automated screening. Greenwashing risks rise as automation favors convenience over impact checks, while regulation lags behind and leaves fiduciary duties unclear. The discussion links these issues to finance theory, applying CAPM to test whether ESG factors improve risk- adjusted returns and using behavioral insights to assess how embedded systems may correct cognitive limits. Case studies show that while embedded finance reduces transaction friction, turning convenience into measurable sustainability outcomes is still uncertain. The chapter contributes by connecting finance theory with emerging technologies to evaluate real versus promised effects on sustainable investment. 2026, IGI Global Scientific Publishing. -
Carbon Nanotubes as Carriers in Plant Science
Carbon nanotubes have emerged as promising nanomaterials in plant sciences, offering innovative solutions for enhancing sustainable agricultural production. This chapter provides a comprehensive analysis of the structural variations of CNTs, including single-walled, stacked-cup, and multi-walled nanotubes, and their applications in gene delivery, biosensing, and development of nano-pesticides and nano-insecticides. CNTs facilitate improved nutrient uptake, promote plant growth, and enhance stress tolerance through their unique physicochemical properties. Additionally, their interactions with plant-associated microbes and soil microbial communities, plant health and soil fertility are discussed. However, challenges related to CNT toxicity, environmental persistence, and their effects on plant physiology necessitate further research. The chapter also explores the integration of machine learning, artificial intelligence, and multi omics approaches in optimizing CNT applications for plant breeding, precision agriculture, and crop improvement. 2025 by IGI Global Scientific Publishing. All rights reserved. -
Biogenically forged ZnO nanoparticles using Salvia hispanica L. microgreens for their potential antimicrobial activity towards food-borne pathogens
Microgreens have been extensively researched because of their dense nutritional content and high concentration of health-promoting and therapeutic bioactive compounds. Simultaneously, green synthesis of nanoparticles has emerged as a biogenic and sustainable approach for nanomaterial preparation using plant extracts as reducing and stabilizing agents. In the current study, zinc oxide nanoparticles (ZnO NPs) were synthesized using phytochemically enriched extracts of chia (Salvia hispanica) microgreens. The synthesis of ZnO NPs was systematically optimized, and the resulting nanomaterials were characterized using UV-Visible spectroscopy, XRD, FTIR, SEM, DLS, and TEM to confirm their structural, morphological, and physicochemical properties. The characterization results confirmed the successful formation of ZnO NPs with a crystalline size of 79.4 nm and a zeta potential of ?42.2 0.29 mV, indicating good stability and uniformity. To further explore the bioactivity, in silico molecular docking was performed to investigate the interactions between chia-derived phytochemicals and key receptors of food-borne pathogens Aeromonas caviae and Staphylococcus pasteuri isolated from chicken meat. Based on these insights, the antimicrobial activity of MG-ZnO NPs (Microgreen-derived zinc oxide nanoparticles) was evaluated. The nanoparticles exhibited notable antibacterial activity, with greater effectiveness against S. pasteuri. MIC values for S. pasteuri and A. caviae were found to be 62.5 ?g mL?1 and 250 ?g mL?1, respectively, while the corresponding MBC values were 125 ?g mL?1 and 500 ?g mL?1. The MBC/MIC ratios confirmed the bactericidal nature of MG-ZnO NPs against both strains. These findings highlight the potential of chia microgreen-derived ZnO nanoparticles as promising antimicrobial agents for combating foodborne pathogens. 2025 The Royal Society of Chemistry. -
Robot-Assisted Children-Centric Strategies in the Hotel Industry: Enhancing Parental Attraction and Sales Growth
The hotel industry is undergoing a profound transformation, shaped by shifting consumer preferences and technological innovations. An intriguing trend within this transformation is the growing adoption of robotic technology to elevate the guest experience. Of particular interest is the emergence of child-centric robots designed to engage and educate young guests. These robots have the potential to significantly influence parental attraction to hotels, a factor that bears substantial implications for hotel sales and profitability. This research delves into the phenomenon of Robot-Assisted Child-Centric Strategies in the Hotel Industry and its impact on increasing parental attraction and, in turn, driving sales growth. It explores how hotels strategically integrate child-centric robots to create distinctive and engaging experiences for families. These robots offer interactive concierge services, in-room companions, educational support, and entertainment, enriching childrens stays and allowing parents to relax. The study investigates the technological innovations and capabilities of these robots, the strategies hotels employ to seamlessly incorporate them, and their impact on parental attraction. Employing a mixed-methods approach, including surveys, interviews, and data analysis, it uncovers the drivers behind the adoption of child-centric strategies and their correlation with sales growth. Ultimately, this research reveals how child-centric robots can revolutionize the hotel industry by attracting families and enhancing guest experiences. It provides valuable insights for hoteliers seeking to leverage this trend, helping them find the delicate balance between guest satisfaction and profitability in a competitive market. Child-centric robots offer a promising avenue for hotels, providing unforgettable stays for families while boosting sales and profitability. The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025. -
Edible Innovation: How Youth-Driven Trends Are Shaping the Future of Food Marketing
The food market is undergoing changes, and consumers 2020 to 2024 are shaping a market segment with unique eating behaviours. Nowhere is the impact of transformative trends that are reshaping the present and near future of markets and populations so pronounced than in food marketing and consumption. Dates throughout 2020-2024 are being pondered in this chapter to explain what makes this current generation tick. It also contains an overview of new innovations and modern changes in products and process, promotional, and marketing activities. This chapter looks at how the strong will, character, and ambition of the new generation are paving new paths in the food industry, leaving outdated practices behind and hitting the reset button on standards of health and sustainability. 2025 by IGI Global Scientific Publishing. All rights reserved. -
Edible Innovation: How Youth-Driven Trends Are Shaping the Future of Food Marketing
The food market is undergoing changes, and consumers 2020 to 2024 are shaping a market segment with unique eating behaviours. Nowhere is the impact of transformative trends that are reshaping the present and near future of markets and populations so pronounced than in food marketing and consumption. Dates throughout 2020-2024 are being pondered in this chapter to explain what makes this current generation tick. It also contains an overview of new innovations and modern changes in products and process, promotional, and marketing activities. This chapter looks at how the strong will, character, and ambition of the new generation are paving new paths in the food industry, leaving outdated practices behind and hitting the reset button on standards of health and sustainability. 2025 by IGI Global Scientific Publishing. All rights reserved. -
Prediction of Material Removal Rate and Surface Roughness in Hot Air Assisted Hybrid Machining on Soda-Lime-Silica Glass using Regression Analysis and Artificial Neural Network
Hybrid machining is a combination of conventional with the non-conventional process or two non-conventional processes. In the present work, an attempt has been made to combine hot air with a conventional cutting tool to form a novel Hot Air Assisted Hybrid Machining (HAAHM) for the machining of soda-lime-silica glass. The mathematical model for the Material Removal Rate (MRR) and Surface Roughness (Ra) using Regression Analysis (RA) and the Artificial Neural Network (ANN) models has been developed for the grooving process. The deviation of 8.24% and 7.70% were found in the prediction of MRR and Ra by regression analysis and the deviation of 1.89% and 1.70% for MRR and Ra using an artificial neural network model. The deviation between the predicted and the experimental results of both the models are found to be within the permissible limit. Higher predictive capabilities were observed in ANN model than the regression model. However, both models demonstrated good agreement with the MRR of soda-lime-silica glass by this hybrid machining process. 2020, Springer Nature B.V. -
Parametric optimization on hot air assisted hybrid machining of soda-lime glass using Taguchi based grey relational analysis
The present research underlines the development of a hybrid method for the machining of soda-lime glass known as the hot air assisted hybrid machining. It is a combination of conventional machining assisted with the jet of hot air. The influence of process variables such as feed of the cutting tool, flow of hot air, depth of cut, and the air temperature on the material removal rate (MRR) and surface roughness (Ra) applied to the grooving operation have been investigated. The Taguchi orthogonal array L27 was considered to reduce the number of experiments. The ANOVA was used to recognize the major influencing process parameters for the MRR and Ra. The results of ANOVA indicate that the air temperature is the most significant parameter for the objective of maximum MRR and minimum Ra with contributions ratios of 56.91% and 52.68% respectively for the grooving operation on soda-lime glass. The optimal machining parameters for the maximum MRR and minimum Ra were found to be A1B1C3D3 and A1B1C1D3 respectively. The multi-objective optimization was performed using the Taguchi based grey relational analysis (GRA). The optimal level of parameters based on GRA for maximum MRR and minimum Ra was found to be A1B1C3D3. In addition, the material removal process was explained with the help of SEM micrographs. 2021, Springer Nature Switzerland AG. -
Comparative Analysis of Classification Models Using Various Feature Sets
Feature selection is a fundamental step in Machine Learning (ML) that involves choosing some input data that would enhance the model performance. The model is able to run faster using lesser computational resources while giving reasonable results. Hence, feature selection as important as selection of a good model. In this chapter the aim is to analyze how the performance of different multiclass classification algorithms is affected on different features. The algorithms used are K-Nearest Neighbor (KNN), Support Vector Machines (SVM), Linear Discriminant Analysis (LDA), and Convolutional Neural Network (CNN) on the CIFAR-10 dataset. To obtain the new dataset with modified features, we use dimension reduction methods on the original dataset. The new dataset is at least 500x smaller, and we have noticed that in the best case scenarios reducing dimensions reduces the accuracy score only marginally. The SVM is the most consistent among the experimented models. The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025. -
Smart Wound Guard
Traditional wound healing processes suffer from a lack of real-time monitoring, often relying on subjective assessments, leading to missed opportunities for timely treatment adjustments. This idea proposal is particularly concerning for patients with chronic wounds, as traditional dressings fail to provide essential information on wound conditions. To address this gap, wearable electronics are emerging as a promising solution. This context proposes the development of a flexible plaster embedded with electronics capable of sensing various wound-related factors such as pH, temperature, glucose, uric acid, moisture, oxygen, and protein levels in both the wound and its surrounding environment. By continuously monitoring these factors and providing alerts, this innovative solution aims to facilitate faster wound healing. Moreover, it enables automatic drug delivery, enhancing treatment efficacy and patient outcomes. This approach represents a significant advancement in wound care, offering real-time insights and interventions that can revolutionize how wounds are managed, ultimately improving patient care and reducing healthcare costs. -
Influence of heat treatment and reinforcements on tensile characteristics of aluminium aa 5083/silicon carbide/fly ash composites
The effect of reinforcements and thermal exposure on the tensile properties of aluminium AA 5083silicon carbide (SiC)fly ash composites were studied in the present work. The specimens were fabricated with varying wt.% of fly ash and silicon carbide and subjected to T6 thermal cycle conditions to enhance the properties through precipitation hardening. The analyses of the microstructure and the elemental distribution were carried out using scanning electron microscopic (SEM) images and energy dispersive spectroscopy (EDS). The composite specimens thus subjected to thermal treatment exhibit uniform distribution of the reinforcements, and the energy dispersive spectrum exhibit the presence of Al, Si, Mg, O elements, along with the traces of few other elements. The effects of reinforcements and heat treatment on the tensile properties were investigated through a set of scientifically designed experimental trials. From the investigations, it is observed that the tensile and yield strength increases up to 160?C, beyond which there is a slight reduction in the tensile and yield strength with an increase in temperature (i.e., 200?C). Additionally, the % elongation of the composites decreases substantially with the inclusion of the reinforcements and thermal exposure, leading to an increase in stiffness and elastic modulus of the specimens. The improvement in the strength and elastic modulus of the composites is attributed to a number of factors, i.e., the diffusion mechanism, composition of the reinforcements, heat treatment temperatures, and grain refinement. Further, the optimisation studies and ANN modelling validated the experimental outcomes and provided the training models for the test data with the correlation coefficients for interpolating the results for different sets of parameters, thereby facilitating the fabrication of hybrid composite components for various automotive and aerospace applications. 2021 by the authors. -
Wear and Friction Behaviour of Aluminium Metal Matrix Composite Reinforced with Graphite Nano Particles for Vehicle Structures
In the current research work, AA7050 a marine aluminium alloy was reinforced with the nano graphite particles processed through stir casting technique. The scanning electron microstructure reveals that the nano particles were uniformly distributed over the matrix material and the hardness of the composites increase with raise in weight percentage of Gr particles owing to the Hall-Petch effect. The wear experiments were conducted by varying reinforcement, load, velocity, distance and temperature. The experimental runs were designed using the L25 orthogonal array in which wear, coefficient of friction and worn surface hardness were recorded as response. The wear resistance of the composites increases with raise in the graphite content attributed to the formation of mechanical mixed layer, the wear rate transfer from mild to severe when there swift in temperature from 100C to 150C. The worn surface hardness of the composites was higher than the as cast composites owing to the presence of Fe on the surface confirmed through the EDAX mapping. The composites were optimized using the modified PROMETHEE optimization technique and results revealed that AA7050 reinforced with 8% Gr particles showed best result and recommended for the marine sector. 2024. Carbon Magics Ltd. -
WEAR AND FRICTION BEHAVIOUR OF ALUMINIUM METAL MATRIX COMPOSITE REINFORCED WITH GRAPHITE NANOPARTICLES
In the current research work, AA7050 a marine aluminium alloy was reinforced with the nano-graphite particles, processed through the stir casting technique. The scanning electron microstructure reveals, that the nanoparticles were uniformly distributed over the matrix material and the hardness of the composites increased with a rise in the weight percentage of Gr particles owing to the Hall patch effect. The wear experiments were conducted by varying reinforcement, load, velocity, distance, and temperature, and the experimental runs were designed using the L25 orthogonal array, in which wear, coefficient of friction and worn surface hardness were recorded as a response. The wear resistance of the composites increases with a rise in the graphite content attributed to the formation of a mechanically mixed layer, the wear rate transfers from mild to severe, when there is shift in temperature from 100C to 150C. The worn surface hardness of the composites was higher than those of as-cast composites owing to the presence of Fe on the surface confirmed through the EDAX mapping. The composites were optimized using the modified PROMETHEE optimization technique and the results revealed that AA7050 reinforced with 8% Gr particles showed the best result and was recommended for the marine sector. 2024, Scibulcom Ltd.. All rights reserved. -
Fabrication and Characterization of AA7050 Nano Composites by Enhancing Directional Properties for High Impact Load Applications
The demand for materials with superior strength and impact resistance has driven the exploration of innovative composite materials. In this research, Al 7050 is chosen as the matrix material due to its excellent mechanical properties, whereas SiC and graphene nanoparticles are incorporated to tailor its directional strength characteristics. The fabrication process involves the synthesis of Al7050 nanocomposites through a meticulous blending of nanoparticles with the matrix material. The characterization phase encompasses a comprehensive analysis of various techniques, including scanning electron microscopy, X-ray diffraction, and mechanical testing. The results shows that the directional strength improvements achieved through SiC and graphene nanoparticle reinforcement with Al7050. The tensile strength of the aluminum in the AA7050-7.5g composite rose from 185.3 to 256.1MPa upon the addition of SiC and graphene. The findings of this study contribute to the evolving field of nanocomposite materials, offering insights into the design and development of advanced materials tailored for specific directional strength requirements. The Institution of Engineers (India) 2024. -
Physical aging of biopolymers and their nanocomposites
With growing environmental concerns, particularly around the widespread use of conventional plastics, there is an urgent need to develop sustainable alternatives like bioplastics and biocomposites. These eco-friendly materials offer the potential to significantly reduce the carbon footprint while enhancing product performance and durability in various applications. This chapter aims to expand scientific understanding of biocomposites, focusing on their behavior under different aging conditions. A comprehensive analysis is provided on aging processes, aging mechanisms, and strategies to improve the longevity and performance of biocomposites. Special emphasis is placed on future research directions and the adoption of innovative aging techniques to optimize the performance of biopolymers. This review explores both the advantages and challenges of using biocomposites as replacements for traditional petroleum-based plastics, with a particular focus on their degradation behavior over time. The insights presented here are essential for driving further research and development in bio-based and biodegradable polymers, highlighting their potential for both academic inquiry and industrial application. By addressing key aspects of biocomposite aging, this chapter aims to guide researchers in overcoming existing challenges and advancing the field toward a more sustainable future. 2026 Elsevier Ltd. All rights reserved. -
Fabrication and Characterization of AA7050 Nano Composites by Enhancing Directional Properties for High Impact Load Applications
The demand for materials with superior strength and impact resistance has driven the exploration of innovative composite materials. In this research, Al 7050 is chosen as the matrix material due to its excellent mechanical properties, whereas SiC and graphene nanoparticles are incorporated to tailor its directional strength characteristics. The fabrication process involves the synthesis of Al7050 nanocomposites through a meticulous blending of nanoparticles with the matrix material. The characterization phase encompasses a comprehensive analysis of various techniques, including scanning electron microscopy, X-ray diffraction, and mechanical testing. The results shows that the directional strength improvements achieved through SiC and graphene nanoparticle reinforcement with Al7050. The tensile strength of the aluminum in the AA7050-7.5g composite rose from 185.3 to 256.1MPa upon the addition of SiC and graphene. The findings of this study contribute to the evolving field of nanocomposite materials, offering insights into the design and development of advanced materials tailored for specific directional strength requirements. The Institution of Engineers (India) 2024. -
A Novel Edge-Based Trust Management System for the Smart City Environment Using Eigenvector Analysis
The proposed Edge-based Trust Management System (E-TMS) uses an Eigenvector-based approach for eliminating the security threats present in the Internet of Things (IoT) enabled smart city environment. In most existing trust management systems, the trust aggregation process completely depends on the direct trust ratings obtained from both legitimate and malicious neighboring IoT devices. E-TMS possesses an edge-assisted two-level trust computation approach for ensuring the malicious free trust evaluation of IoT devices. The E-TMS aims at removing the false contribution on aggregated trust data. It utilizes the properties of the Eigenvector for identifying compromised IoT devices. The Eigenvector Analysis also helps to avoid false detection. The analysis involves a comparison of all the contributed trust data about every single connected device. A spectral matrix will be generated corresponding to the contributions and the received trust will be scaled based on the obtained spectral values. The absolute sum of obtained values will contain only true contributions. The accurate identification of false data will remove the effect of malicious contributions from the final trust value of a connected IoT device. Since the final trust value calculated by the edge node contains only the trustworthy data, the prediction about the malicious nodes will be accurate. Eventually, the performance of E-TMS has been validated. Throughput and network resilience are higher than the existing system. 2022 G. Nagarajan et al.