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Exploration of activation energy and binary chemical reaction effects on nano Casson fluid flow with thermal and exponential space-based heat source
Purpose: The purpose of this paper is to explore the effects of binary chemical reaction and activation energy on nano Casson liquid flow past a stretched plate with non-linear radiative heat, and also, the effect of a novel exponential space-dependent heat source (ESHS) aspect along with thermal-dependent heat source (THS) effect in the analysis of heat transfer in nanofluid. Comparative analysis is carried out between the flows with linear radiative heat process and non-linear radiative heat process. Design/methodology/approach: A similarity transformation technique is utilised to access the ODEs from the governed PDEs. The manipulation of subsequent non-linear equations is carried out by a well-known numerical approach called RungeKuttaFehlberg scheme. Obtained solutions are briefly discussed with the help of graphical and tabular illustrations. Findings: The effects of various physical parameters on temperature, nanoparticles volume fraction and velocity fields within the boundary layer are discussed for two different flow situations, namely, flow with linear radiative heat and flow with non-linear radiative heat. It is found that an irregular heat source/sink (ESHS and THS) and non-linear solar radiation play a vital role in the enhancement of the temperature distributions. Originality/value: The problem is relatively original to study the effects of activation energy and binary chemical reaction along with a novel exponential space-based heat source on laminar boundary flow past a stretched plate in the presence of non-linear Rosseland radiative heat. 2019, Emerald Publishing Limited. -
Exploration of aldazine Schiff bases as promising bioactive agents: A synergistic approach using DFT, ADME, antibacterial and cytotoxicity analysis
A straightforward method for synthesizing four new asymmetric Aldazine Schiff base derivatives using aromatic aldehydes and hydrazine precursors was successfully demonstrated under moderate conditions. These compound are designated as follows: 1-((E)-(((E)-2-ethoxy benzylidene) hydrazineylidene) methyl)naphthalene-2-ol (2-EHMN) (L1), 1-((4-ethoxy benzylidene) hydrazineylidene) methyl) naphthalene-2-ol (4-EHMN) (L2), 1-((2?hydroxy-4-methoxybenzylidene) hydrazineylidene) methyl) naphthalene-2-ol (HMHMN) (L3), and 1-((2?chloro-6-hydroxyybenzylidene) hydrazineylidene) methyl) naphthalene-2-ol (CHHMN) (L4). The compounds obtained were analyzed via FT-IR, 1H-/13CNMR spectroscopy, HRMS spectrometry techniques, and elemental analysis. Infrared (IR) spectroscopy, UVVis spectroscopy, and accurate melting point determination all contribute to the improved study of synthesised compounds. A comprehensive solubility analysis was conducted for all synthesized compounds, demonstrating their solubility in dichloromethane (DCM), tetrahydrofuran (THF), and dimethylformamide (DMF). Thermoanalytical studies of all the ligands were also examined and compared. Furthermore, a single-crystal X-ray diffraction (SCXRD) analysis of L1 was conducted using a single-crystal diffractometer, with unit cell calculations and data collection performed using MoK? radiation (? = 0.7107 . Density functional theory (DFT) computations were used to optimise the structures of molecules and assess reactivity, durability, and electronic characteristics of the developed ligands. Molecular docking of L1, L2 and L3 has been done in different proteins, which gives precise results to show the activity for cytotoxicity and antibacterial studies. In silico, the ADME process calculations showed that the synthesised compounds have favourable drug-like features. In vitro antibacterial (L2 and L3) and cytotoxicity (L1) tests were also performed to assess their efficacy as therapeutic agents. 2025 Elsevier B.V. -
Exploration of carbon nano dots in hydro carbon soot and carbon black
Hydrocarbon soot, a prime component of particulate matter pollution, poses a great threat to the environment. In this study, we put forth a novel way of harnessing carbon nanodots from the soot particulates thereby converting an environmentally perilous component to an innocuous entity suitable for many applications such as biomedical tracers, gas detectors etc. Large scale production of pure carbon nanodots (PCN) was achieved via direct catalyst free thermal decomposition of kerosene and diesel. Nanostructure of carbon black and graphite is also investigated for comparative studies. In UV-Vis spectra, absorptions at 233, 232 and 229 nm are attributed to ?-?? transition of the C=C bonding. XRD of the samples shows a highly intense peak at ?24 and a slightly broadened peak around 42 due to (002) and (010) reflections of graphitic planes respectively. In IR spectra, peaks at 3431 and 1047 cm-1 were assigned to O-H and C-O stretching vibrations respectively. The band observed at 1619 cm-1 manifests the skeletal vibrations from graphitic domains and hence indicates the presence of crystalline graphitic carbon. The absorption bands at 2920 and 2850 cm-1 arise because of the existence of aliphatic groups in the soot sample. 2017, International Congress of Chemistry and Environment. All rights reserved. -
Exploration of Chemical Reaction Effects on Entropy Generation in Heat and Mass Transfer of Magneto-Jeffery Liquid
In many chemical engineering processes, a chemical reaction between a foreign mass and the fluid does occur. These processes find relevance in polymer production, oxidation of solid materials, ceramics or glassware manufacturing, tubular reactors, food processing, and synthesis of ceramic materials. Therefore, an exploration of homogeneous first-order chemical reaction effects on heat and mass transfer along with entropy analysis of Jeffrey liquid flow towards a stretched isothermal porous sheet is performed. Fluid is conducting electrically in the company of transverse magnetic field. Variations in heat and mass transfer mechanisms are accounted in the presence of viscous dissipation, heat source/sink and cross-diffusion aspects. The partial differential equations system governing the heat transfer of Jeffery liquid is reformed to the ordinary differential system through relevant transformations. Numerical solutions based on Runge-Kutta shooting method are obtained for the subsequent nonlinear problem. A parametric exploration is conducted to reveal the tendency of the solutions. The present study reveals that the Lorentz force due to magnetism can be used as a key parameter to control the flow fields. Entropy number is larger for higher values of Deborah and Brinkman numbers. It is also established that the concentration species field and its layer thickness of the Jeffery liquid decreases for a stronger chemical reaction aspect. To comprehend the legitimacy of numerical results a comparison with the existing results is made in this exploration and alleged an admirable agreement. 2018 Walter de Gruyter GmbH, Berlin/Boston 2018. -
Exploration of digital image tampering detection using CNN with modified particle swarm optimization in deep learning
The field of image processing is crucial for many different applications, including forensic evidence, insurance claims, medical imaging, bio-informatics, artifact collection and more. In many sectors nowadays, digital photographs are regarded as a trustworthy source of information. The manipulation of such photographs leads to a variety of issues. The study presents a method using convolutional neural networks (CNN) combined with modified particle swarm optimization (MPSO) to improve the accuracy of tampering detection. This advancement contributes to improved reliability in fields requiring image authenticity verification, such as forensics and media. The design includes the collection of a dataset comprising both original and tampered images for training and testing the model. A dataset, such as the Media Integration and Communication Center (MICC) dataset, is utilized, which includes various images that have been altered through different tampering techniques. This dataset serves as the foundation for training the CNN and evaluating its performance The findings indicate that the proposed MPSO_CNN method outperforms traditional techniques in terms of precision, accuracy, recall, and F-measure, demonstrating its effectiveness in identifying tampered images. The results highlight the significance of using advanced deep learning techniques for reliable image authenticity verification. 2025, Institute of Advanced Engineering and Science. All rights reserved. -
Exploration of low heat rejection engine characteristics powered with carbon nanotubes-added waste plastic pyrolysis oil
Compression ignition (CI)-powered alternative energy sources are currently the main focus due to the constantly rising worldwide demand for energy and the growing industrialization of the automotive sector. Due to their difficulty of disposal, non-degradable plastics contribute significantly to solid waste and pollution. The waste plastics were simply dropped into the sea, wasting no energy in the process. Attempts have been made to convert plastic waste into usable energy through recycling. Waste plastic oil (WPO) is produced by pyrolyzing waste plastic to produce a fuel that is comparable to diesel. Initially, a standard CI engine was utilized for testing with diesel and WPO20 (20% WPO+80% diesel). When compared to conventional fuel, the brake thermal efficiency (BTE) of WPO20 dropped by 3.2%, although smoke, carbon monoxide (CO), and hydrocarbon (HC) emissions were reasonably reduced. As a result, nitrogen oxide (NOx) emissions decreased while HC and CO emissions marginally increased in subsequent studies utilizing WPO20 with the addition of 5% water. When combined with WPO20 emulsion, nanoadditives have the potential to significantly cut HC and CO emissions without impacting performance. The possibility of incorporating nanoparticles into fuel to improve performance and lower NOx emissions should also be explored. In order to reduce heat loss through the coolant, prevent heat transfer into the cylinder liner, and increase combustion efficiency, the thermal barrier coating (TBC) material is also coated inside the combustion chamber surface. In this work, low heat rejection (LHR) engines powered by emulsion WPO20 containing varying percentages of carbon nanotubes (CNT) are explored. The LHR engine was operated with a combination of 10 ppm, 20 ppm, and 30 ppm CNT mixed with WPO20. It was shown that while using 20 ppm of CNT with WPO20, smoke, hydrocarbons, and carbon monoxide emissions were reduced by 11.9%, 21.8%, and 22.7%, respectively, when compared to diesel operating in normal mode. The LHR engine achieved the greatest BTE of 31.7% as a result of the improved emulsification and vaporization induced by CNT-doped WPO20. According to the study's findings, WPO20 with 20 ppm CNT is the most promising low-polluting fuel for CI engines. 2023 The Institution of Chemical Engineers -
Exploration of non-linear thermal radiation and suspended nanoparticles effects on mixed convection boundary layer flow of nanoliquids on a melting vertical surface
In this paper, the significance of increasing nonlinear thermal radiation on boundary layer flow of some nanofluids is deliberated upon. The effects of magnetic field, melting and viscous dissipation are also considered. The numerical results are obtained for governing flow equations and compared with the previously published results for a special case and found to be in excellent agreement. The effects of various physical parameters such as melting parameter, thermal radiation parameter, temperature ratio parameter and Eckert number on velocity and temperature profiles are analyzed through several plots. The numerical results of physical quantities of engineering interest such as skin friction coefficient and local Nusselt number are presented and discussed in detail. It is found that the nonlinear thermal radiation effect is favourable for heating processes than linear thermal radiation effect. Additionally, the moving parameter and melting parameter can be used to reduce the friction or drag forces. 2018 by American Scientific Publishers All rights reserved. -
Exploration of the dual fuel combustion mode on a direct injection diesel engine powered with hydrogen as gaseous fuel in port injection and diesel-diethyl ether blend as liquid fuel
The present study explores the possibilities of the use of diesel-diethyl ether (DDEE) blends as pilot fuel, and hydrogen (H2) as inducted gaseous fuel in a diesel engine operated on dual fuel mode (DFM). DEE was added to diesel in ratios of 525% in increasing steps of 5%, to prepare the DDEE5, DDEE10, DDEE15, DDEE20, and DDEE25 blends that were used as pilot fuel. In this current study, for hydrogen gas generation, a hydrogen production kit was fabricated which was powered by solar energy. The hydrogen gas was produced from the electrolysis of water-KOH solution. During the experiment, hydrogen was inducted through the engine intake port employing an electronic gas injector. The quantity of hydrogen injection was set constant of 0.2 lpm for all the test cases. DDEE-hydrogen (DDEE+H2) blends accomplished overall good results compared to diesel. DDEE20+H2 furnished optimal results compared to diesel and other DDEE+H2 blends. Peak cylinder pressure for DDEE20+H2 was 66.91 bar at 5.2oCA aTDC, and the maximum HRR was 32.75 J/deg.CA. Compared to diesel, the BTE of engine for DDEE20+H2 was augmented by about 0.6% and the BSFC was diminished by about 3.7%, at maximum load conditions. A decline in CO and HC emissions of 29.6%, and 35% were observed for DDEE20+H2 at maximum load condition, but the NO and CO2 emanation was observed to be higher by around 29.4%, and 17.4% in comparison to diesel respectively. 2023 Hydrogen Energy Publications LLC -
Exploration of the effects of anisotropy and rotation on RayleighBard convection of nanoliquid-saturated porous medium using general boundary conditions
This paper presents an analysis of RayleighBard convection (RBC) of a Newtonian-nanoliquid-saturated anisotropic porous medium in the presence of rotation (RayleighBardTaylor convection). The investigation is performed using non-classical boundary conditions. The effect of various parameters on the onset of convection is presented graphically. The system sees stabilisation due to an increase in the rotation rate and thermal anisotropy parameter whereas the system destabilises due to an increase in the mechanical anisotropy parameter. The results of 82 limiting cases can be extracted from the current work. The results of free-free, rigid-free and rigid-rigid isothermal/adiabatic boundaries are obtained from the present study by considering appropriate limits. The results of the limiting cases of the present study are in excellent agreement with those observed in earlier investigations. 2024 Informa UK Limited, trading as Taylor & Francis Group. -
Exploration of Thermophoresis and Brownian motion effect on the bio-convective flow of Newtonian fluid conveying tiny particles: Aspects of multi-layer model
This research deals with the analysis of bioconvection caused by the movement of gyrotactic microorganisms. The multi-layer immiscible Newtonian fluid flowing through the vertical channel conveying tiny particles is accounted. The immiscible fluids are arranged in the form of a sandwich where the middle layer has a different base fluid that does not mix with the base fluid of the adjacent fluid layer. This separation of the fluid layers gives rise to the interface boundary conditions. Such flows have found applications in electronic cooling and solar reactors processes. Buongiornos model has been incorporated to design the mathematical model that describes the three-layer flows of Newtonian fluid conveying tiny (metal/oxide) particles under thermophoretic force and Brownian motion. The model thus formed is in the form of the ordinary differential system of equations that are solved using the DTM-Pade approximant after non-dimensionalization. The limited results have an excellent comparison with the existing literature results. The results are discussed through graphs and tables. It is seen that thermophoresis enhances the temperature and particle concentration of the fluid whereas, the Brownian motion is found to enhance the temperature and decrease the concentration. The presence of bioconvection helps in achieving enhanced energy and mass transportation. Moreover, the heat transfer occurring between the different base fluids helps to maintain the optimum temperature in the systems. IMechE 2022. -
Explorations of the links between multiculturalism and religious diversity
This chapter explores the complex intersections of multiculturalism and religious diversity in educational settings. It examines the religious landscape in the context of education and how religious diversity is addressed in educational policies and procedures. It discusses the role of faith in education, highlighting its importance and potential limitations. Furthermore, it explores the interplay between multiculturalism and religious diversity, identifying potential challenges and opportunities. Strategies for addressing these challenges and leveraging the opportunities are discussed, including intercultural dialogue, curriculum integration, and parent and community engagement. The chapter presents case studies that illustrate the complexities of multiculturalism and religious diversity in educational practices, analyzing their successes and challenges. Lessons learned from these case studies and implications for future practice are discussed, emphasizing the need for policy development, curriculum design, teacher training, and community engagement. 2023 by IGI Global. All rights reserved. -
Exploratory Analysis and Pattern Recognition in Energy Production and Demand: A Data-Driven Approach Using Multi-Source Energy Metrics
Hydropower is a leading renewable energy source due to its high efficiency and low operational costs. However, it still faces significant environmental, operational, and forecasting challenges. This paper explores the use of machine learning (ML) models such as SARIMA, Random Forest (RF), and Neural Basis Expansion Analysis for Time Series (NBEATS) to optimize hydropower operations. By analyzing diverse data sets, including hydrometeorological data, plant operations, sensor inputs, and other energy production and demand metrics such as solar, wind, coal, nuclear, and storage, ML enhances decision-making in areas such as inflow forecasting, predictive maintenance, and environmental sustainability. The paper presents an exploratory analysis of 48 -hour energy production and demand patterns across multiple sources (Hydro, Coal, Solar, Wind, Nuclear, and Storage), offering insights into interdependencies and system behavior. It also reviews current ML applications in hydropower, highlights challenges such as data quality and model interpretability, and discusses emerging technologies such as reinforcement learning, explainable AI (XAI), and digital twins as promising future directions. 2025 IEEE. -
Exploratory Analysis of Anthropometric and Demographic Factors Influencing PCOS: A Study on BMI, Weight, and Waist Ratio
Polycystic Ovary Syndrome (PCOS) is a prevalent endocrine disorder affecting women of reproductive age, often leading to hormonal imbalances, obesity, and an increased risk of metabolic and cardiovascular diseases. This study examines the impact of PCOS on anthropometric and demographic variables such as Body Mass Index (BMI), weight, height, and waist ratio, using a comprehensive dataset. By comparing these factors between individuals with and without PCOS, the study aims to identify significant differences and correlations, thereby contributing to a deeper understanding of PCOS and informing clinical management strategies. The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2026. -
Exploratory analysis of legal case citation data using node embedding
Legal case citation network is primary tool to understand mutable landscape of the legal domain. These networks are also used to study legal knowledge transfer, similar precedents and inter-relationship among laws of a judiciary. These networks are often very huge and complex due to the multidimensional texture of this domain. In recent years, network embedding using deep learning emerges as a promising breakthrough for analyzing networks. This paper presents a novel approach of learning vector representation for a legal case based on its citation context in the network using node2vec algorithm. These vector embedding are further used in understanding similarities between cases. Paper highlights that the tSNE reduced representation of the obtained vectors facilitates visual exploration and provides insights into the complex citation network. Suitability of node embedding for application of machine learning algorithm is demonstrated by clustering the node vectors for finding similar cases. ICIC International 2019. -
Exploratory Architectures Analysis of Various Pre-trained Image Classification Models for Deep Learning
The image classification is one of the significant applications in the area of Deep Learning (DL) with respective to various sectors. Different types of neural network architectures are available to perform the image classification and each of which produces the different accuracy. The dataset and the features used are influence the outcome of the model. The research community is working towards the generalized model at least to the domain specific. On this gesture the contemporary survey of various Deep Learning models is identified using knowledge information management methods to move further to provide optimal architecture and also to generalized Deep Learning model to classify images narrow down to the sector specific. The study systematically presents the different types of architecture, its variants, layers and parameters used for each version of Deep Learning model. Domain specific applications and limitations of the type of architecture are detailed. It helps the researchers to select appropriate Deep Learning architecture for specific sector. 2024 by the authors. -
Exploring a GE/Nafion/Co-MOF nanosheets/CuO NPs/GOx powered electrochemical biosensor for ultrasensitive detection of rebaudioside A
Rebaudioside A (Reb A) is a natural, non-nutritive sweetener highly prevalent in the global sweetener market and widely preferred by consumers. In this study, an advanced electrochemical biosensor was developed for sensing Reb A, using a modified graphite rod electrode extracted from discharged ZnC batteries. The electrode was fabricated using a layer-by-layer strategy with Nafion, Co-MOF nanosheets, CuO NPs, and glucose oxidase (GOx) enzyme. The nanomaterials were characterized by UV-vis, FTIR, DLS, zeta potential measurements, XRD, Raman, SEM, TEM, EDS, and XPS techniques. Electrochemical characterization via Cyclic Voltammetry (CV) and Electrochemical Impedance Spectroscopy (EIS) revealed a significant enhancement in electrical conductivity and increased electroactive surface area. The designed biosensor exhibited a sharp oxidation peak at 0.16 V due to ester bond cleavage in Reb A, which was further amplified in the presence of GOx, resulting from hydroxyl oxidation and hydrogen peroxide generation. Differential pulse voltammetry (DPV) demonstrated a linear response over a concentration range of 2.014 M (R2 = 0.993) with a limit of detection (LOD) of 0.23 M. The sensor displayed excellent analytical performance, with repeatability, reproducibility (RSD = 3.9%), and stability. Additionally, recovery studies confirmed its accuracy, ranging from 97% to 98.17%. Further, the molecular docking studies confirmed strong Reb AGOx interactions (?7.26 kcal mol?1), supporting the biosensor's specificity. The developed biosensor demonstrates excellent analytical performance, making it highly suitable for routine laboratory analysis of sweeteners in complex food matrices. This journal is The Royal Society of Chemistry, 2026 -
Exploring advancements in space object detection through computer vision
[No abstract available] -
Exploring Advances in Machine Learning and Deep Learning for Anticipating Air Quality Index and Forecasting Ambient Air Pollutants: A Comprehensive Review with Trend Analysis
India and the rest of the world are growing more and more worried about polluted atmosphere on a daily basis. A comprehensive prevision and prognostication of air quality parameters is vital due to the major harm that air pollution causes to both the environment and public health, causing concern on a global scale. In-depth analyses of the methods for predicting ambient air pollutants, like carbon monoxide (CO), sulfur dioxide (SO2), nitrogen dioxide (NO2), particulate matter with diameters less than 10? (PM10) and less than 2.5? (PM2.5), and ozone (O3), are provided in this work in tandem with the modeling of the Air Quality Index (AQI).To further enhance the anticipated precision and applicability of these models, the assessment additionally employs trend analysis to determine precedents and new trends in air quality. This paper offers insights into recent advances in algorithms using deep learning and machine learning for anticipating AQI and forecasting pollutant concentrations by combining current research in this topic. In order to inform policy decisions and measures aimed at reducing air pollution and its adverse effects on public health, trend analysis integration affords a more thorough comprehension of the dynamics of air quality. 2024 IEEE. -
Exploring AI and ML Strategies for Crop Health Monitoring and Management
This chapter offers a thorough examination of machine learning (ML) and artificial intelligence (AI) approaches designed especially for agricultural crop health monitoring. The story starts with a basic introduction to AI and ML ideas and then covers supervised and unsupervised learning approaches, the fundamentals of reinforcement learning, and the significance of high-quality data preparation in agricultural settings. This chapter explores the use of deep learning architectures and neural networks, explaining how they can be used to simulate human brain activity and how they can be used in picture identification to identify crop diseases. A detailed analysis is conducted of the practical aspects of ML for agriculture, encompassing feature engineering and model assessment methodologies. Additionally, the chapter highlights the ethical issues involved in the proper application of AI/ML models in agricultural contexts. These kinds of applications. In conclusion, the chapter discusses obstacles, offers predictions for future developments, and discusses new lines of inquiry for AI and ML research related to crop health monitoring. Through this thorough research, the chapter seeks to offer insightful information on the transformative potential of AI/ML approaches in supporting efficient and sustainable agriculture practices for improved crop health management. (Publisher name) (publishing year) all right reserved. -
Exploring AI-Driven Accessibility Solutions: A Comprehensive Study on Assistive Tools for the Visually Impaired
Artificial intelligence (AI) has significant access to visually impaired persons, which enables more freedom in digital interactions. This article examines the role of AI-operated equipment, including chatbots, speech recognition, and natural language processing (NLP) models, to improve communication, education, and navigation for blind users. It undergoes the largest progress of AI-driven accessibility solutions, especially in examination and interactive virtual scriptures. Despite the remarkable progress, challenges in speech remain accreditation accuracy, user interface targeted, and real -time treatment efficiency. The study highlights the ongoing research trends, identifies significant intervals, and emphasizes the need for better training data, adaptive AI interfaces, and improved user experience to promote more inclusion in education and the professional environment. 2026 IEEE.