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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. -
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. -
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 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 Personal Identity Among Individuals with Multiple Inter-state Migration Experiences
Migration is an increasingly common phenomenon for various reasons like economic betterment and educational purposes. Migration is also considered a life-event causing psychological distress. Individuals who migrate multiple times, are faced with a challenge of adapting to a new environment multiple times, thus having to give up and incorporate certain elements from the environment into the self, in turn altering their personal identity. This research is focused on exploring the personal identity of individuals who have undergone multiple interstate migrations within India. Life histories of 12 individuals were taken and analysed using thematic analysis. The findings indicate that there are changes in various components of personal identity like certain changes within the family, development of a multicultural perspective, certain cognitive elements like divergent thinking and development of certain personal traits like acceptance. These individuals are highly adaptable to different kinds of environments. They do not have strong attachments with peers. Keywords: personal identity, multiple interstate migrations -
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 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 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 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 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 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 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 and Analysis of Seizure Spikes Through Spectral Domain Transformation
Seizure detection is the most crucial area of investigation when it comes to understanding brain disorders. This proposed research study embarked on an automated model for epileptic seizure diagnosis by means of different kinds of Spectral transformation using EEG inputs from seizure sufferers and healthy subjects. This automated model accommodates non-invasive brain electrical activity monitoring. This method aims to facilitate the analysis and identification of epileptic seizure states since, monitoring and diagnosing such brain electrical activity is a complex task due to its numerous divisions and underlying features. The primary objective of this research study is to distinguish between EEG-based seizures and healthy individuals. To achieve this goal, a combination of spectral transformation and EEG analysis techniques is utilized. These techniques include examining the frequency spectrum, magnitude spectrum, correlation, and T-Distributed Stochastic Neighboring Embedding (T-SNE) analysis. This analysis yields valuable insights from EEG data, refining the input data and making it more suitable for prediction and identification. The models performance is evaluated using two distinct datasets: real-time EEG data from individuals experiencing epileptic seizures and EEG data from healthy subjects. These datasets are sourced from the Bangalore EEG Epilepsy Dataset (BEED), India and the BONN epilepsy dataset from the UCI repository. In a comparative study of spectral transformation methods, including Complex Fast Fourier Transform (CFFT) and Real-Valued Fast Fourier Transform (RFFT), it is discovered that reducing the data dimension by using feature extraction is not the optimal approach. This simplification leads to the loss of valuable information. Therefore, preserving the full spectrum of EEG characteristics is crucial for gaining valuable insights into brain neuronal functions, ultimately enabling more accurate seizure prediction. The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024. -
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Explanations for anomalies in semi strong from of efficient market hypithesis
The tradition notion of efficient market described in the academic literature is quite strong and probably is unrealistic because of following factors: short selling, short covering, fresh buying and profit booking. After all, science is just a human idea about phenomena which can change over time. In real markets, traders can cause markets to be inefficient. According to verifiability newlinetheory of meaning, most of statements are meaningless such as prices reflect earning .in order to make a statement meaningful that statements have to be tested with all ideas related to it. Researcher is testing EMH under various conditions to make meaningful. In this study, researcher is testing so called semi strong under market condition and firm size which is based upon market capitalization. National stock exchange provides classification of companies based newlineupon market capitalization. This is an event study to study stock return to earning newlineannouncements in recession and post recession periods. In other words, testing so called semi strong under market conditions and firm size of market capitalization such as large cap, mid cap and small cap. Researcher uses AARs, CAARS and T-test to study the impact of earning announcements on stock returns. Result of this study shows its onerous to accept semi strong so Fama received the Nobel prize in economics for what? -
Explaining the intention to uptake COVID-19 vaccination using the behavioral and social drivers of vaccination (BeSD) model
Background: The World Health Organization (WHO) has proposed a tool to measure behavioral and social drivers (BeSD) of vaccination uptake intentions of people across all countries. This study tests BeSD model to predict people's intentions to uptake COVID-19 vaccination in rural India. Methods: An online cross-sectional survey was developed for the purpose based on the components of the BeSD model, i.e., confidence, motivation, and behavioral intention. A convenient sampling technique was used to collect samples, amounting to a total of 625, from rural Bengaluru, in the Karnataka state of India. Structural equation modelling (SEM) was applied to examine the proposed model. All respondents for the survey were in the age category of 1868 years with a mean age of 35 years. Findings: The results showed that 85% of COVID-19 vaccine uptake intentions can directly or indirectly be attributed to the government's vaccine communication strategy, perceived threats about the vaccine, and their trust in the healthcare sector. The dimensions of the vaccine acceptance scale (motivation factors) act as a mediator between these factors and COVID-19 vaccination uptake (the behavioral factor). Conclusion: The study demonstrates that the BeSD framework is an efficient model for predicting the COVID-19 vaccination uptake in India. 2022 The Authors -
Explaining Autism Diagnosis Model Through Local Interpretability Techniques - A Post-hoc Approach
In this era of machine learning and deep learning algorithms dominating the Artificial Intelligence (AI) world, the trustworthiness of these black box models is still questionable. Life-caring sectors like healthcare and banking make use of these black box models as assistance in critical decision-making processes, but the degree of reliability of these decisions is still uncertain. This is because these black box models will not reveal the causation of the predicted outcome. However, creating an interpretable model that can explain the internal workings of these black box models can provide some reliable insights and trustable justifications for the predicted outcome. This study aimed to create an interpretable model for autism diagnosis which can give some trustable explanations for its predicted outcome. Using local interpretability methods such as LIME, SHAP, and Anchors the predicted outcome for each instance is explained well with some standard visual representations. As a result, this study developed an interpretable autism diagnosis model with an accuracy rate of 91.37% and with good local model explanations. 2023 IEEE. -
Explainable Temporal Knowledge Graph Reasoning for Geopolitical Risk Assessment
The paper examines the application of the Graph Neural Networks (GNNs) to the ICEWS14 temporal knowledge graph to predict geopolitical events and relationship. We suggest a new architecture that involves negative sampling and temporal encoding trick that enhances performance on temporal link prediction. Our model combines a central temporal-conscious attention GNN with numerous domain-specific GNN sub-models that are taught on economics trends, political choices, and business interrelations and it additionally incorporates knowledge graphs, sentiment analysis of the people, and market tendencies data to provide comprehensive consulting assistance. The framework has an AUCROC of 0.9234, test set, indicating that the framework is more likely to detect both positive and negative links. Exploring the high-confidence predictions, we identify the regularities of the political enforcement action, whereas the low-confidence ones assist in suspecting the improbable or counterfeit connections. Such explanations are useful in designing early warning systems, risk-assessment monitors, and decision-support frameworks in foreign affairs and think tank consultancy. The Author(s), under exclusive license to Springer Nature Switzerland AG 2026. -
Explainable IoT Forensics: Investigation on Digital Evidence
This research examines the relevance of digital forensics in the field of Internet of Things and describes how different forensics tools and software are used to investigate cybercrimes. It emphasizes the importance of IoT Forensics and how it's used to tackle cybercrimes. It also discusses on the challenges faced by IoT forensics and gives an insight into the recent advancements in the field. It gives a walkthrough about how digital forensics investigation is done in 'data stolen' or 'data deleted' scenario. An outline of research potential and problems in IoT forensics is given in this chapter. The main details of IoT forensics are described. In all stages of a forensic investigation, issues linked to IoT are highlighted along with the potential that IoT presents for forensics. An illustration of an IoT forensics case is given with appropriate analytics. A brief research overview is provided, with information on the important research directions and a review of relevant articles. Future research proposals are included in the chapter's conclusion. 2023 IEEE. -
Explainable Intrusion Detection System for Internet of Things-explainability with reliability
Explainable Artificial Intelligence (XAI) based Intrusion Detection System (IDS) (X-IDS) has transformed the traditional IDS into interpretable and transparent system with the goal of providing interpretable justification for IDS models. XAI is now being used to extract more appropriate features for specific cyber-attacks. The black-box model of ML based IDS is not capable of giving reason for false positive to the cyber defense personnel. XAI tools reduces this abstraction by locally interpreting the model's behaviour at some datapoints along with global interpretability. This article proposes an explainable IDS by using XAI tools. We used SHAP (SHapley Additive exPlanations) to identify the variations in feature importance of selected ML based IDSs and explain the variations of their detection accuracies. Also, we have shown that with same dataset, feature importance varies differently with different ML models. This leads us to the conclusion that specific set of features are required for specific ML models while other can be discarded. The explainability proposed in this study also help to select less set of features to overcome time of execution and cost. 2025 IEEE.