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Ethics, the compass for academic leadership
But again, with a multitude of leadership styles available, choosing the appropriate type that may best be suitable to the organisation becomes tricky -
Ethnic Food: A Solution With Sustainable Food Resources A Study On Consumer Awareness Of Ethnic Food And Its Impact On Consumption Attitude
Food has seen numerous transformations over the centuries and has been a focus of study pertaining to culture and evolution. Besides being a celebration of diversity and a marker of human adaptations, food is also a broad knowledge domain that represents various geographic, cultural and lifestyle outlooks. Ethnic food relates to a heritage or the culture of an ethnic group with them incorporating the local produce and animal sources into their diet. Ethnic food also has a sustainability aspect to it in terms of food miles and carbon emissions since more transportation involved means higher level of GHG released, economic aspect such as with composition changes and food security, and community relationship. This paper finds that when consumer awareness of ethnic food increases, the consumption attitude towards it does, too. This could be of importance in policy implementation and identifying sustainability systems. A connection with the land and a community relationship involving food could help represent more ethnic food, to increase awareness on a global level and also allow more people to experience these vast cultural diversities. If understood well and implemented, ethnic food could be of use as a tourism brochure, sustainability driver, economical promoter and community supporter. The Electrochemical Society -
Ethnic Food: The Food Way Forward
In the context of food security, two things are significant. To ensure availability, affordability and accessibility of adequate food to people throughout the country. Also, to promote entrepreneurship for sustainable food production and supply. This paper highlights differences between food security and food insecurity. The global population in 2050 is predicted at 9 billion in which case the output must double considering the dwindling and degrading resources. This may be a challenge for agronomists and policy-makers. Considering that food security must be achieved at individual, household, district, national and global levels, India may need an Integrated Farming System (IFS) to take agriculture further. There are numerous challenges besides the environment that must be considered for this. It is important to ensure that the dignity of the farmer is not compromised while strategizing food security. Currently, private-public partnerships are being introduced in some places as a potential model. However, all stakeholders in food security have their task cut out (1). This paper is a review of existing literature to understand the level of information we have documented. It tries to highlight ways in which consumption of ethnic food could be a way forward in terms of food security and sustainability. The Electrochemical Society -
Ethnographic research on primary education of tribals: a scoping review
Ethnographic research offers comprehensive learning outcomes by examining the socio-emotional, economic and cultural components crucial for comprehending marginalized groups experiences. This study aims to examine the methodologies used in studies and the gaps in the literature on the primary education of tribal communities, highlighting the limitations of the current research approaches. Using the preferred reporting items for systematic reviews and meta-analyses (PRISMA) of Arksey and OMalleys six-step framework, the scoping review has considered 19 studies of 406 research articles published from 2015 to 2024 across the databases Scopus, JSTOR, and ERIC. The review highlights that most of these studies used descriptive survey design, mixed-method research design, and ethnographic research design. While the first two document barriers, the ethnographic studies provide richer cultural in-depth also. However, gaps in the literature include a lack of interventions for specific tribes, such as the Mannan community in Kerala, India, and the integration of indigenous knowledge, which is only possible through cultural inclusiveness. The findings suggest that future research should prioritize interdisciplinary collaboration and teacher training in multilingual education (MLE) through ethnographic methods for developing culturally sensitive interventions. These recommendations aim to contribute to developing more culturally inclusive educational practices and policies in the primary education curricula. 2026, Intelektual Pustaka Media Utama. All rights reserved. -
Ethylenediamine modified carbon nanospheres from biomass for selective membrane filtration
The present work investigates the antifouling properties dye and antibiotic removal efficiency of PVDF/E-CNS membranes. Carbon nanospheres (CNS) derived from rice husk (RH) were pyrolyzed at 800 C. Further, ethylenediamine functionalized carbon nanospheres (E-CNS) were obtained via in situ decoration of ethylenediamine on acid-functionalized carbon nanospheres (O-CNS). The synthesized E-CNS were characterized by techniques such as XRD, FESEM, Raman spectroscopy, FTIR and BET. The membranes were fabricated by integrating E-CNS at varying loadings (0.10.7 wt%) via a non-solvent induced phase separation (NIPS) technique. The membrane properties were assessed through FESEM, water contact angle measurements, pure water flux, antifouling studies and membrane rejections. In comparison to the other developed membranes, PVDF-2 with 0.3 wt% E-CNS loading displayed optimal performance, pure water flux (PWF) of ?318.90 L m?2 h?1, flux recovery ratio (FRR) > 90% up to three cycles, improved contact angle (80.24 to 68.44) and reduced roughness. Furthermore, PVDF-2 achieved dye rejection of methyl orange (MO 93.2%) and rhodamine B (RB 94.6%), and antibiotic rejection of amoxicillin (AM 93.8%) and tetracycline (TC 94.1%), respectively. These findings demonstrate the integration of E-CNS derived from a bio-source, making them a promising additive to improve PVDF membrane performance. This journal is The Royal Society of Chemistry -
Etiology and Advanced Detection Techniques for Fetal Brain Abnormalities: A Comprehensive Study
In the modern world, women's health deserves greater focus, particularly in a country like India and particularly during pregnancy. The health of mother's as well as baby's is important throw-out this process. According to a survey, three cases out of a thousand have abnormalities in fetal brain. The likelihood of survival of mother and child significantly increased by early identification of these diseases. Several procedures, including fetal ultrasound, MRI, fetal echocardiography etc. must be carried out during pregnancy in order to monitor fetal development. Pregnancyrelated MRI-scans always practiced to identify and treat fetal brain disorders in former stages. It is possible to identify, examin fetal brain problems early on by doing a prenatal MRI examination. The diagnosis of issues with fetal brain MRI imaging involves several crucial procedures. Among these are image segmentation; image analysis, which comprises extracting characteristic characteristics, improving image quality, identifying relevant patterns, and categorising images based on predefined standards. Classification determines if an anomaly is present or not. Analysing pictures can be difficult due to the wide range of shapes, spatial arrangements, and intensities that are present. The primary subjects of this work are the review and comparison of various fetal brain malformations, as well as their causes and commonalities. 2025 IEEE. -
ETL and Business Analytics Correlation Mapping with Software Engineering
Large information approach can't be effectively accomplished utilizing customary information investigation strategies. Rather, unstructured information requires specific information demonstrating methods, apparatuses, and frameworks to separate experiences and data varying by associations. Information science is a logical methodology that applies scientific and measurable thoughts and PC instruments for preparing large information. At present, we all are seeing an exceptional development of data created worldwide and on the web to bring about the idea of large information. Information science is a significant testing zone because of the complexities engaged with consolidating and applying various strategies, calculations, and complex programming procedures to perform insightful investigation in huge volumes of information. Thus, the field of information science has developed from enormous information, or huge information and information science are indistinguishable. In this article we have tried to create bridge between ETL and software engineering. 2021, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. -
Euclid: Early Release Observations of ram-pressure stripping in the Perseus cluster: Detection of parsec-scale star formation within the low surface brightness stripped tails of UGC 2665 and MCG +07-07-070
Euclid is delivering optical and near-infrared imaging data over 14 000 deg2 on the sky at spatial resolution and surface brightness levels that can be used to understand the morphological transformation of galaxies within groups and clusters. Using the Early Release Observations (ERO) of the Perseus cluster, we demonstrate the capability offered by Euclid in studying the nature of perturbations for galaxies in clusters. Filamentary structures are observed along the discs of two spiral galaxies, UGC 2665 and MCG +07-07-070, with no extended diffuse emission expected from tidal interactions at surface brightness levels of a30 mag arcseca 2. The detected features exhibit a good correspondence in morphology between optical and near-infrared wavelengths, with a surface brightness of a25 mag arcseca 2, and the knots within the features have sizes of a 100 pc, as observed through IE imaging. Using the Euclid, CFHT, UVIT, and LOFAR 144 MHz radio continuum observations, we conducted a detailed analysis to understand the origin of the detected features. We constructed the Euclid IEaYE, YEaHE, and CFHT u ar, g ai colour-colour plane and show that these features contain recent star formation events, which are also indicated by their H? and NUV emissions. Euclid colours alone are insufficient for studying stellar population ages in unresolved star-forming regions, which require multi-wavelength optical imaging data. There are features with red colours that can be explained by dust being stripped along with the gas in these regions. The morphological shape, orientation, and mean age of the stellar population, combined with the presence of extended radio continuum cometary tails can be consistently explained if these features formed during a recent ram-pressure stripping event. This result further confirms the exceptional qualities of Euclid in the study of galaxy evolution in dense environments. 2025 EDP Sciences. All rights reserved. -
EUI: A Novel Underwater Image Enhancement Network
The use of optical imaging cameras on underwater vehicles has been increasingly common in recent years. These cameras are increasingly being utilized for the purpose of assisting with the search for aquatic items and the gathering of images. The past ten years have seen the publication of a number of techniques that have been utilized to enhance underwater images. These techniques include enhancing the signal-to-noise ratio and decreasing the amount of backscattered noise at the receiving end. Nevertheless, the development of these algorithms was driven by the need to find a solution to the problem of improving photographs of underwater environments when they are exposed to daylight. The accuracy of these, on the other hand, will not be known until they have been tested on photographs taken underwater in low light conditions. As a result of the fact that dark underwater scene photos typically have an exceptionally low quality and the presence of a great deal of noise, it is simple for artifacts to arise during the process of improvement. In order to fill this need, we conduct an in-depth analysis of the most recent deep learning-based algorithms for the enhancement of underwater images. A novel underwater image augmentation network that is capable of handling the severe decrease in underwater image quality that is induced by low illumination is that which we propose as our last suggestion. Our approach allows for the possibility of ULPs avoiding both low-light damage and scattering at the same time. Additionally, the results of our tests suggest that our method continues to be trustworthy even when exposed to different levels of illumination, which has allowed it to be applied to a wider range of applications. When compared to some of the most cutting-edge strategies for improving underwater pictures that are already in use, as well as techniques for improving low-light images, our method has shown to be superior in terms of performance in a variety of low-light underwater situations. 2025 IEEE. -
EUI: A Novel Underwater Image Enhancement Network
The use of optical imaging cameras on underwater vehicles has been increasingly common in recent years. These cameras are increasingly being utilized for the purpose of assisting with the search for aquatic items and the gathering of images. The past ten years have seen the publication of a number of techniques that have been utilized to enhance underwater images. These techniques include enhancing the signal-to-noise ratio and decreasing the amount of backscattered noise at the receiving end. Nevertheless, the development of these algorithms was driven by the need to find a solution to the problem of improving photographs of underwater environments when they are exposed to daylight. The accuracy of these, on the other hand, will not be known until they have been tested on photographs taken underwater in low light conditions. As a result of the fact that dark underwater scene photos typically have an exceptionally low quality and the presence of a great deal of noise, it is simple for artifacts to arise during the process of improvement. In order to fill this need, we conduct an in-depth analysis of the most recent deep learning-based algorithms for the enhancement of underwater images. A novel underwater image augmentation network that is capable of handling the severe decrease in underwater image quality that is induced by low illumination is that which we propose as our last suggestion. Our approach allows for the possibility of ULPs avoiding both low-light damage and scattering at the same time. Additionally, the results of our tests suggest that our method continues to be trustworthy even when exposed to different levels of illumination, which has allowed it to be applied to a wider range of applications. When compared to some of the most cutting-edge strategies for improving underwater pictures that are already in use, as well as techniques for improving low-light images, our method has shown to be superior in terms of performance in a variety of low-light underwater situations. 2025 IEEE. -
European VLBI Network observations of the peculiar radio source 4C 35.06 overlapping with a compact group of nine galaxies
Context. According to the hierarchical structure formation model, brightest cluster galaxies (BCGs) evolve into the most luminous and massive galaxies in the Universe through multiple merger events. The peculiar radio source 4C 35.06 is located at the core of the galaxy cluster Abell 407, overlapping with a compact group of nine galaxies. Low-frequency radio observations have revealed a helical, steep-spectrum, kiloparsec-scale jet structure and inner lobes with less steep spectra, compatible with a recurring active galactic nucleus (AGN) activity scenario. However, the host galaxy of the AGN responsible for the detected radio emission remained unclear. Aims. We aim to identify the host of 4C 35.06 by studying the object at high angular resolution and thereby confirm the recurrent AGN activity scenario. Methods. To reveal the host of the radio source, we carried out very long baseline interferometry (VLBI) observations with the European VLBI Network of the nine galaxies in the group at 1.7 and 4.9 GHz. Results. We detected compact radio emission from an AGN located between the two inner lobes at both observing frequencies. In addition, we detected another galaxy at 1.7 GHz, whose position appears more consistent with the principal jet axis and is located closer to the low-frequency radio peak of 4C 35.06. The presence of another radio-loud AGN in the nonet sheds new light on the BCG formation and provides an alternative scenario in which not just one but two AGNs are responsible for the complex large-scale radio structure. The Authors 2024. -
EV Service Stations for Future Smart Cities
The market for electric vehicles (EVs) has been growing at a fast pace in recent years. It is expected to continue growing at a much faster pace in the coming decades. The emerging EV technology is increasingly gaining a high demand for continued good transport connections in smart cities. Most of the Smart Cities' charging infrastructure and future growth revolve around its public transport network, especially an EV service station. New technologies, therefore, need to be complemented with new and versatile charging options to cater to different types of charging options available for charging Li-ion Batteries with newer materials and charging capacity. Building an EV service station in the ongoing scenario anticipates smart engineering knowledge to complement innovative charging methods. An EV service station needs hardware, software, and test equipment before charging, during charge, and post-charge states. It is expected to inform the user of available options to choose and select from. This paper investigates the challenges and suggests solutions to meet the EV service station support for EV vehicles in present and future smart cities. It also highlights the demand for a skilled workforce to maintain these service stations, including updating their skills. Examples of a few smart cities in developed as well as developing countries have been quoted. These developments will contribute to the transport infrastructure needed for future smart cities. The paper paves the way for future research in this area. The Institution of Engineering & Technology 2023. -
Evaluate and design the mini-hexagon-shaped monopole antenna controller to minimize losses in the unit
Main Aim: Hexagon-shaped mono-pole transmitters are developed, computed, and evaluated in a range of applications. Their whole performance is being compared. Methods: Various hexagon-shaped mono-pole transmitters are built and modeled using the HFSS. These transmitters are built with Defective Ground Structure (DGS) but include openings in the patch antenna for High-Frequency Spread Spectrum (HFSS), also on the surface, but also. That influence including its position including its slot upon this radiation pattern is examined. Evaluate the modeling, the controller was designed for the broadcast subsystems and respective reflectivity and VSWR have been found. Findings: The specifications of the antenna is return losses, VSWR, amplification and switching frequency, among other things are assessed as are usually uncertain and VSWR for the manufactured device. The transmissions are continuously monitored. Another most unclear wavelength is around 10 dB among a large bandwidth and that they are less than 10 dB over a specific frequency range. The value of VSWR is less than 2. Applications: These transmitters may be utilized for wirelessly and interior activities via UWB technology. 2021, SciTechnol, All Rights Reserved. -
Evaluate Machine Learning Techniques for Early Disease of Cardiovascular Disease
Cardiovascular diseases are one of the major causes of death around the world, and their early detection is critical for effective intervention. The paper presents a systematic review of machine learning techniques used for the early prediction of cardiovascular diseases, focusing on studies carried out between 2019 and 2024. Widely used models considered in the review include Logistic Regression, Support Vector Machines, Random Forest, K-Nearest Neighbors, Gradient Boosting, and hybrid ensemble methods with the aim of ascertaining predictive accuracy, interpretability, and clinical relevance. In most of the reviewed studies, ensemble and Random Forest models attained the highest accuracies of 90% - 98%, while Gradient Boosting and SVMs were mostly above 90% in balanced datasets. Logistic Regression had a moderate accuracy of 85%-91% but remained the most interpretable, while KNN established the lowest performance of 80%-86%. Despite the promising strides, there are a number of limitations, such as imbalance in datasets, limited external validation, and small benchmark datasets, that are limiting general application in health. This systematic review highlights strengths and weaknesses of the contemporary machine learning approaches and makes it evident that clinically validated, interpretable, and generalizable models should be developed in order to assist real-world medical decision-making. 2025 IEEE. -
Evaluating Allocations of Opportunities
This paper provides a robust criterion for comparing lists of probability distributionsinterpreted as allocations of opportunitiesfaced by different social groups. We axiomatically argue in favor of comparing those lists of probability distributions on the basis of a uniformamong groupsvaluation of their expected utility. We identify an empirically implementable criterion for comparing allocations of opportunities that coincides with the unanimity of all such uniform valuations of expected utility that exhibit aversion to inequality of opportunity. We illustrate our criterion by evaluating allocations of educational opportunities among castes and genders in 14 Indian states. 2025 The Author(s). International Economic Review published by Wiley Periodicals LLC on behalf of The Economics Department of the University of Pennsylvania and the University of Osaka Institute of Social and Economic Research Association. -
Evaluating Building Damage Classification Accuracy: A Benchmarking Study of UNet
Building damage classification must be done accurately and quickly in order to support disaster response and recovery activities. Deep learning models, particularly U-Net, have demonstrated strong potential in automating damage assessment from satellite and aerial imagery. This study benchmarks the accuracy of U-Net in classifying building damage across multiple datasets, evaluating its performance against ground truth labels. Key factors such as data preprocessing, augmentation techniques, and model variations are analyzed to determine their impact on classification accuracy. The results provide insights into the strengths and limitations of variations in U-Net for damage assessment, highlighting areas for improvement and future research directions 2025 IEEE. -
Evaluating Energy Consumption Patterns in a Smart Grid with Data Analytics Models
With the rapid pace of technological advancement, it is a well established fact that in todays era, economical and industrial development go hand in hand with the growth in technology. Today, massive amounts of data are generated everyday and are only growing exponentially. The collected data, whether structured or unstructured, could prove to be very beneficial in terms of improving operational efficiency by analyzing and extracting valuable information to find patterns to optimize asset utilization and improve asset intelligence. Big data analytics can very well contribute to the evolution of the digital electrical power industry. The objective of this paper is to explore how smart grid technology can be enhanced by leveraging big data analytics. Different predictive models are used for the purpose. Among them, decision tree model outperformed others recording a training and tetsing accuracy of 94.4% and 92.7% respectively while noting a least execution latency of only 4.3 seconds. 2023 IEEE. -
Evaluating forces associated with sentient drivers over the purchase intention of organic food products
The study proposes to find out the factors which influence awareness among the consumers towards purchasing organic food product. The study is based on primary data by using tools Chi-square test, Cronbach alpha, KMO, and Bartlett's test, ANOVA, regression, correlation, and cross-tabulation. The study found that awareness driver's nutritional information, price, certification, brand name, and logos have an essential influence on the purchase intention of the product of organic food. However, labeling and food standards do not show a noteworthy rapport between labeling and organic food products' purchase plans. The core commitment and flow to explore are to analyze purchasers with respect to organic guarantee systems (accreditation, guidelines, logo, imprints, and confirmation) so we can distinguish the genuine organic products. The independent factors of awareness like organic buying preference and buying frequency, have a significant influence on the purchase intention of organic food. The research provided evidence of consumer awareness and purchase intention of organic food that would help the organic food industry to promote their products according to the attribute of customers. 2020 Asian Economic and Social Society. All rights reserved. -
Evaluating Generalization and Robustness of U-Net Based Image Steganography
This paper investigates the effectiveness and generalization ability of U-Net based image steganography models across multiple datasets, with comparisons to the classical Least Significant Bit (LSB) substitution method. Models were trained on STL-10, CIFAR-10, and Stanford Cars datasets and evaluated both in-distribution and on out-of-distribution internet images. Results show that the STL-10 model consistently achieved the best trade-off between imperceptibility and recovery quality, while the CIFAR-10 model failed to generalize due to its low resolution and limited diversity. Baseline experiments confirmed that LSB achieves extremely high PSNR and SSIM at low payloads, but suffers sharp increases in Bit Error Rate (BER) under higher payloads or even mild distortions such as JPEG compression and Gaussian noise. By contrast, the U-Net model provided more stable recovery and greater robustness, highlighting the advantages of learned feature embeddings over handcrafted substitution. These findings underscore the importance of dataset diversity and robustness testing in developing practical steganographic systems for real-world deployment. 2025 IEEE. -
Evaluating prolonged corrosion inhibition performance of benzyltributylammonium tetrachloroaluminate ionic liquid using electrochemical analysis and Monte Carlo simulation
Corrosion inhibition performance of a newly synthesized ionic liquid Benzyltributylammonium tetrachloroaluminate [BTBA]+[AlCl4]?on carbon steel has been studied using electrochemical impedance and noise analysis in 2 N HCl medium. The synthesized product was characterized by ATR-FTIR and1H NMR spectroscopic studies. The investigation revealed that the synthesized ionic liquid, [BTBA]+[AlCl4]?showed a remarkable noise and charge transfer resistance against corrosion. The adsorption behaviour of [BTBA]+[AlCl4]- on metal surface was found to follow Langmuir adsorption isotherm. The inhibition efficiency is measured as a function of immersion time and exhibited prolonged protection against acidic corrosion. Results derived from UVVis spectra explained the complex formation between the metal surface and ionic liquid in acid medium. SEM/EDAX has been used to examine the surface protection offered by the ionic liquid. [BTBA]+[AlCl4]?ionic liquid exhibited good corrosion inhibitor property with an efficiency of 97% at the optimum concentration. Quantum chemical analysis and molecular simulation studies were performed to support the experimental data. 2019 Elsevier B.V.