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Unlocking efficiency: experimental and theoretical insights into biomass-derived carbon nanofluids with enhanced thermal conductivity
The study presents an experimental investigation, supported by theoretical analysis, into the effects of nanoparticle (NPs) concentration, particle size, and shape on the thermal conductivity (TC) of carbon nanosphere (CNS)-based nanofluids (NF). CNS was synthesized from garlic peels (Allium sativum) via pyrolysis at varying temperatures and characterized using X-ray diffraction (XRD), field emission scanning electron microscopy (FESEM), transmission electron microscopy (TEM), and other techniques. The NFs were prepared using a two-step method with different CNS concentrations in propylene glycol (PG) and deionized water (DI)/PG mixtures. Particle size distribution and colloidal stability were evaluated using dynamic light scattering (DLS) and zeta potential analysis. The TC of the NFs was measured across various temperatures, revealing a significant dependency on both particle size and concentration. All NFs exhibited enhanced thermal conductivity to the base fluid (BF), with increases of 52.60%, 101.28%, 108.51%, 114.60%, and 122.64% at 80 C for CNS synthesized at 500 C (AS500), 600 C (AS600), 700 C (AS700), 800 C (AS800), and 900 C (AS900), respectively. Rheological analysis showed a linear increase in dynamic viscosity (V) with rising CNS concentration within the dilute limits (0.01 to 0.1 wt%) and a strong correlation between particle size and thermal conductivity enhancement. These findings emphasize the critical role of CNS particle size in optimizing thermal performance, with potential applications in heat transfer systems. The study culminates with an exercise aimed towards presenting thermal conductivity and dynamic viscosity as surface plots. These plots provide behavioral trends for understanding the dependence of TC and V on nanoparticle size and temperature. 2025 The Royal Society of Chemistry. -
Effects of Dispersion on Thermal Conductivity and Viscosity in Biomass-Based Nano Systems
Ensuring the long-term stability of nanofluids (NFs) remains a challenge due to nanoparticle aggregation, precipitation, and poor dispersion. Zeta potential (ZP) plays a crucial role in preventing agglomeration and enhancing stability. This study investigates, for the first time, the combined effect of stability and thermal conductivity (TC) enhancement in nanofluids based on biomass-derived carbon nanospheres (CNSs). CNSs synthesized from eight different biowaste sources exhibited ZP values ranging from ?17.0 to ?45.6 mV, influencing dispersion and fluid behavior. These NFs demonstrated exceptional stability for up to 40 days without surfactants and achieved a TC enhancement of up to 111.8%. The research also explores the influence of ZP on TC, dynamic viscosity (V), and thermal diffusivity. The NFs displayed shear-thinning, non-Newtonian behavior, with viscosity values depending on CNS concentration, reaching 0.0000000302 Pas. The effect of pH (312) on stability and TC revealed maximum performance at pH 8, while optimal TC enhancement was achieved at 0.1 wt% CNS concentration. This study bridges the gap between laboratory research and industrial applications, offering sustainable, low-cost, and high-efficiency coolant solutions for the automotive sector. It supports seven Sustainable Development Goals (SDGs) through an innovative waste-to-wealth approach. 2025 Wiley-VCH GmbH. -
A hybrid level set based approach for surface water delineation using landsat-8 multispectral images
The detection and delineation of surface water is a crucial step in change detection studies on water bodies using satellite images. Single band methods, spectral index methods, classification using machine learning and spectral un-mixing methods are the widely used strategies for surface water mapping from multi-spectral images. Level set theory based algorithms have been successfully employed in image segmentation problems and are proven to be effective. This study presents a hybrid level set theory based segmentation algorithm which is a combination of edge based and region based approaches to detect and delineate surface water bodies in Landsat 8 images. Level set algorithms were applied in combination with Modified Normalized Difference Water Index (MNDWI) to further improve the delineation accuracy. Robustness of the proposed approach was established by successfully applying the algorithm to delineate water bodies of different sizes, ranging from 0.5 km2 to 298 km2 in surface area. The proposed algorithm was also compared with established machine learning based delineation methods and found to be faster than the algorithms those produced comparable delineation outputs. As the ground truth was not available for accuracy measurement, the output image of the proposed method was compared with the outputs of the machine learning algorithms using Pearsons correlation co-efficient, Structural Similarity Index (SSIM) and Dice Similarity Index. The proposed algorithm was subsequently applied to multi-temporal Landsat data for water body change detection and analysis. 2021, International Association of Engineers. All rights reserved. -
Surface water detection and delineation using remote sensing images: a review of methods and algorithms
Multispectral and hyperspectral images captured by remote sensing satellites or airborne sensors contain abundant information that can be used to study and analyze objects of interest on the surface of earth and their properties. The potential of remotely sensed images for studying natural resources like water has been studied by researchers over the past many years. As water is an important natural resource that needs to be conserved, such studies have been of great interest to the scientific community. By employing appropriate digital image processing techniques on images taken from remote sensing satellites or airborne sensors, an effective system can be developed to study the quantitative and qualitative changes happening to surface water bodies over a period of time. Surface water detection and mapping is a crucial and necessary step in such studies and different automated and semi-automated methods have been developed over the years for mapping water in remotely sensed images. Remote sensing sensors capture images at multiple bands corresponding to different wavelength ranges in the EM spectrum. Digital image processing based techniques for water mapping falls predominantly into four categories; (i) single band based methods, (ii) spectral index based methods, (iii) machine learning based methods and (iv) spectral mixture analysis based methods. This paper presents a review of techniques, methods, algorithms and the sensors/satellites that have been developed and experimented with to perform surface water body detection and delineation from remote sensing images. 2020, Springer Nature Switzerland AG. -
Evaluation of machine learning algorithms for surface water delineation using landsat 8 images
Surface water detection and delineation is an important and necessary step in change detection studies on water bodies using multispectral images. Commonly used techniques for surface water delineation from multispectral images are single band density slicing, spectral index based, machine learning based classification and spectral un mixing based methods. This paper presents a comparative study of commonly used machine learning algorithms viz. ANN, SVM, Decision Tree, Random Forest and K-means clustering for their suitability and effectiveness when applied on Landsat 8 images for surface water detection and delineation. The algorithms are compared for their classification accuracy and execution time. While all the above mentioned algorithms exhibited their usefulness in water detection, Decision Tree and Random Forest algorithms were found be faster in both training phase and testing phase and also yielded better accuracy with fewer miss-classifications. Though K-means clustering with more than four clusters yielded results comparable to that of supervised classification methods, it requires appropriate post-processing to obtain the output image with only two clusters; corresponding to water pixels and non-water pixels. Pierson's correlation co-efficient and Structural similarity Index (SSIM) are computed to compare the correlation and similarity of the output images yielded by the algorithms being studied. 2020, Institute of Advanced Scientific Research, Inc. All rights reserved. -
Advancing Brain Tumor Recurrence Prediction: Integrating AI andAdvanced Imaging Technologies forEnhanced Prognosis
Integrating artificial intelligence (AI) and advanced imaging technologies in medical diagnostics is revolutionizing brain tumor recurrence prediction. This study aims to develop a precise prognosis model following Gamma Knife radiation therapy by utilizing state-of-the-art architectures such as EfficientNetV2 and Vision Transformers (ViTs), alongside transfer learning. The research identifies complex patterns and features in brain tumor images by leveraging pre-trained models on large-scale image datasets, enabling more accurate and reliable recurrence predictions. EfficientNetV2 and Vision Transformers (ViTs) produced prediction accuracy of 98.1% and 94.85%, respectively. The studys comprehensive development lifecycle includes dataset collection, preparation, model training, and evaluation, with rigorous testing to ensure performance and clinical relevance. Successful implementation of the proposed model will significantly enhance clinical decision-making, providing critical insights into patient prognosis and treatment strategies. By improving the prediction of tumor recurrence, this research advances neuro-oncology, enhances patient outcomes, and personalizes treatment plans. This approach enhances training efficiency and generalization to unseen data, ultimately increasing the clinical utility of the predictive model in real-world healthcare settings. The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2026. -
Exploring Applications, Datasets, Algorithms, and Technologies in Satellite Image Processing
Amidst an era filled with complex local and global problems, satellite data presents itself as a revolutionary tool with unmatched potential to tackle practical problems in a variety of fields. This article investigates how satellite imagery, which is available through open data programs and repositories, is a valuable tool for applications including wildlife conservation, urban planning, precision agriculture, and disaster management. It highlights the unique perspective that satellite data offers. Various sources for data acquisition, the applications that are suitable for a chosen satellite data and commonly used algorithms and techniques are discussed. Through case studies, the paper demonstrates how quick and reliable data provided by satellites can be used to solve complex real-world problems. The benefits of satellite data are emphasized, including its affordability, ability to monitor in real-time, and ability to support sustainable behaviours and policy-making. The study explores cutting-edge technologies, highlighting cloud computing and GIS integration as well as machine learning algorithms to build robust solutions using satellite data. The immense potential of satellite data is accompanied by challenges, including data integration, computational complexity, and ethical considerations. These challenges underscore the need for standardization and continuous efforts to fully realize the potential of satellite data in sustainable development and informed decision-making. 2025 Bijeesh TV, Bejoy BJ, Michael Moses Thiruthuvanthan and Raju G. -
A generic framework for forecasting lake surface area dynamics using level set segmentation and double exponential smoothing
Water has been a crucial element for the sustenance of civilization throughout history and civilizations have sprung up around a body of water in one form or another. It becomes imperative to address the pressing issue of water shortage and the shrinking size of urban water bodies, which is particularly relevant in Indian cities like Bangalore. The effective management and preservation of these invaluable resources depend on the development of accurate and automated tools to monitor them. The proposed framework introduces a novel approach, combining a level set-based segmentation algorithm with double exponential smoothing to monitor water bodies using multispectral satellite images. In-depth review of nine lakes within Bangalore was carried out using a Landsat time series data set spanning 1987 to 2020. The resulting forecasting model, employing a univariate smoothing methodology, showcased exceptional performance metrics. Notably, it yielded an average error of 0.072 and exhibited a robust correlation coefficient of 0.94 when cross-referenced with proven results. The proposed framework holds great potential for practical implementation in the domain of long-term water body analysis, effectively catering to the requirements of administrative and decision-making entities. Moreover, the adaptability of this framework for the incorporation of additional external factors, as well as its potential to analyze seasonal dynamics, offers exciting avenues for further exploration. The dataset of delineated lake images prepared in this study presents an opportunity for the advancement of image-to-image regression networks, enabling the prediction of both area and shape variations for lakes, thereby enhancing predictive accuracy and insights. The Author(s), under exclusive licence to Springer Nature Switzerland AG 2025. -
Enzyme immobilization on biomass-derived carbon materials as a sustainable approach towards environmental applications
Enzymes with their environment-friendly nature and versatility have become highly important green tools with a wide range of applications. Enzyme immobilization has further increased the utility and efficiency of these enzymes by improving their stability, reusability, and recyclability. Biomass-derived matrices when used for enzyme immobilization offer a sustainable solution to environmental pollution and fuel depletion at low costs. Biochar and other biomass-derived carbon materials obtained are suitable for the immobilization of enzymes through different immobilization strategies. Environmental pollution has become an utmost topic of research interest due to an ever-increasing trend being observed in anthropogenic activities. This has widely contributed to the release of various toxic effluents into the environment in their native or metabolized forms. Therefore, more focus is being directed toward the utilization of immobilized enzymes in the bioremediation of water and soil, biofuel production, and other environmental applications. In this review, up-to-date literature concerning the immobilization and potential uses of enzymes immobilized on biomass-derived carbon materials has been presented. 2022 Elsevier Ltd -
Social capital in the form of self-help groups in India: a powerful resilient solution to reduce household financial vulnerability
Due to the COVID-19 pandemic and the economys general situation, many households are now financially vulnerable. It is like a vicious cycle: once a household is caught, it will remain in the trap until and unless it competently manages its finances. These problems experienced by households have drawn attention to social capital. Self-help groups (SHGs) originated in India to pull out low-income households from poverty and are now recognized as social capital, which can be defined as the action of a group cooperating to enhance all its members benefits. This article aims to explain how SHGs have contributed to reducing various factors or determinants of household financial vulnerability through a review of several other publications, theses, newspaper articles, and reports. It was discovered that SHGs now provide much more benefits than just alleviating poverty. They have helped to reduce bad loans or non-performing assets, reduced the dependence on informal sources of finance, made households more resilient toward crises such as COVID-19, and enabled households to save money and manage their finances accurately. Organizing themselves into SHGs is the only way for rural households to overcome financial difficulties. 2023 Taylor & Francis Group, LLC. -
Financial Vulnerability in Households: Dissecting the Roots of Financial Instability
The phenomenon of household financial vulnerability, defined by unexpected shocksin income and expenditures, carries major implications for both individual households and the overall economy of a nation. For a considerable time, household debt has been widely acknowledged as the primary determinant of household financial vulnerability. This study aims to extend the analysis beyond the scope of household debt. Middle-income households may experience financial difficulties when faced with unexpected changes in income and expenses. These challenges can arise from several circumstances, including the inability to engage in discretionary activities such as dining out or vacations. For a very long time, it has been posited that low-income households exclusively experience financial vulnerability. Hence, it is imperative to thoroughly examine the concept of household financial vulnerability and its underlying factors to enhance households' ability to withstand adversities and better clarify the matter. In light of the prevailing economic recession triggered by the global pandemic and the ongoing confrontation between Russia and Ukraine, the significance of the matter is further underscored. This study aims to comprehensively define household financial vulnerability and examine its relationship with financial capability, digitalized payments, financial stress, and financial socialization. The current study anticipates establishing a foundational framework for future research endeavors in this specific field. Moreover, this paper also explores potential avenues for future research. The Author(s), under exclusive license to Springer Nature Switzerland AG 2025. -
The path to resilience: Exploring household financial vulnerability
Household financial vulnerability represents a significant financial challenge, predominantly impacting low and middle-income households when faced with sudden changes in income or expenses. At the household level, this vulnerability might arise as short-term liquidity issues or long-term solvency concerns. While household debt is a primary factor contributing to this vulnerability, elements like financial capability and the use of digital payments also play roles. The repercussions of household financial vulnerability encompass financial stress and potential bankruptcy, underscoring the critical need to comprehend its dynamics. Thus, this chapter aims to extensively explore household financial vulnerability, including its determinants, theoretical frameworks, assessment methodologies, and strategies for mitigation. 2024 by IGI Global. All rights reserved. -
The Need for Universal Design for Learning in Higher Education for the Specially-AbledAn Essay
Educators at any grade level or subject area can apply Universal Design for Learning (UDL), which is a set of principles for curriculum development that attempts to give all students an equal opportunity to learn. The provision of instructional alignment between objectives, instructional design, methods of delivery and assessment of learning outcomes, which could be individualized and which works for all is blueprinted in a UDL framework. The approaches and methods for instruction in UDL are adaptable and not the same for all the learners or it is not one size fits all approach according to the National Center for Universal Design for Learning (Harper, 2018). The guiding principles of UDL include acceptance and practice of various means of equivalent representation or acquiring information, various means of equivalent expression or demonstrating the learning and various means of equivalent engagement to enhance learning. Given the multiple potentials of specially-abled (SA) students, inclusive learning through UDL provides an environment of diversity and unison. The key attempt is to provide instructional delivery of the same topic to different learners with different learning abilities and approaches in the same course, resulting in comparable outcomes. This chapter highlights the various strategies of UDL that may be extended to assist SA students transition through the pandemic, some of which include customizing learning contents with assured accessibility, individualizing learning goals as per student potential, flexible/customized assessments, and qualitative grading. The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023, corrected publication 2024. -
Future Inclusive Education
The United Nations (UN) Sustainable Development Goals (SDGs) ensure inclusive and equitable quality education for promoting lifelong learning. Inclusive education fosters an environment for access to quality education by addressing diversity and barriers that can cause exclusion. COVID-19 has reimagined Higher Education with new challenges and opportunities for the present and future. Digital divide, gender inequality, addressing specially-abled students, and a non-inclusive learning environment are the major barriers to inclusive education. Inclusive education ensures that no one leaves behind, and higher education institutes can enhance their capacity building to promote inclusivity for the common good. Employability is one of the key concepts in higher education that builds the workforce and contributes to nation-building. With COVID-19, nature of work has seen radical changes; hence, graduate attributes have evolved with the 21st-century skills. The chapter emphasizes the role of inclusive education and reimagining higher education with suggestions to using existing strategies in life-long and futuristic inclusive learning. The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023, corrected publication 2024. -
Study on Spray Dried Yttria Stabilized Zirconia Dental Implants
Medical implants are devices, tissues or supports that are positioned in a suitable manner on any defective part of the human body to facilitate its smooth functioning again. Known as 'prosthetics', they may be used to offer support to a specific organ or tissues, distribute medication, or observe the body condition. While many of the implants are made from skin, bone or other tissues removed from the body itself, the artificial ones are made from engineering materials which could be any of the compatible metals, plastics, ceramics or even composites. The high end technologically advanced implant material is expected to withstand severe barriers and compatibility issues when in contact with the human body. One such application is dental implants, where, the materials must possess superior mechanical properties, exhibit good hydro-chemical and low thermal degradation characteristics. They are also required to possess characteristics such as low friction, strong wear resistance, good wettability and biocompatibility, when placed in the mouth. The only materials that come close to meeting the needs are ceramics, limited by the associated high fracture rate. Stabilized zirconia (stabilized with yttria, ceria etc.) has provided potential solution. Among the two stabilizers, ceria stabilized zirconia may be a better alternative to yttria stabilized zirconia. Other alternatives are alumina, apatites: but their use are constrained based upon technological and cost considerations. Implant product is a highly demanding technology. Spray drying is a suitable process methodology to obtain free flowing powders with uniform morphology and chemical composition, essential for an implant production. This paper presents (i) results from spray drying 8% Y2O3-stabilized ZrO2 and (ii) a review of published literature pertaining to dental implant materials, the various processing methodologies, with special reference to stabilized zirconia and spray drying. Published under licence by IOP Publishing Ltd. -
Counselling and psychological wellbeing of people living with HIV in Kerala
There is a dearth in the documentation of the benefits of HIV-counseiling in India. This article deals with how HIV-counselling facilitates the psychological wellbeing of Persons Living with HIV (PLHIV) in Kerala, India. About 269 PLHIV participated in the study. Meaning in Life Questionnaire, Illness Perception Questionnaire and Psychological Wellbeing Scale were used. It was noticed that counselling did not impact the scores on subscales such as Timeline, Emotional Representation and Consequences, while the scores on Self-Acceptance and Autonomy did not differ even with counselling. Findings call for a reconsideration of the way HIV-counselling is provided. -
Understanding stigma and burnout among HIV/ AIDS health care workers Implications for counselling
The article examines the association between burnout and stigma among Health Care Workers (HCWs) and highlights the need for counselling services in the care of the HCWs. Stereotypes of HIV/AIDS and burnout in HCWs caring for people living with HIV/AIDS (PLHIV) were assessed using self-report methods. Stereotypes about AIDS Scale (SAAS) and Maslach Burnout Inventory MBI were completed by 120 staff from 8 community care centres for PLHIV across south India. Results of SAAS showed that about 33 percent respondents manifested high level of stigma while 35 percent exhibited moderate levels. The results of MBI showed high level of burnout in about 31 percent and moderate in 35 percent respondents. -
From Text to Action: NLP Techniques for Washing Machine Manual Processing
This scientific research study focuses on the advancements in Natural Language Processing (NLP) driven by large-scale parallel corpora and presents a comprehensive methodology for creating a parallel, multilingual corpus using NLP techniques and semantic technologies, with a particular focus on washing machine manuals. The study highlights the significant progress made in NLP through the utilization of large-scale parallel corpora and advanced NLP techniques. The successful creation of a parallel, multilingual corpus for washing machine manuals, coupled with the integration of semantic technologies and ontology modeling, demonstrates the broad applicability and potential of NLP in diverse domains.The research covers various aspects, including text extraction, segmentation, and the development of specialized pipelines for question-answering, translation, and text summarization tailored for washing machine manuals. Translation experiments using fine-tuned models demonstrated the feasibility of providing washing machine manuals in local languages, expanding accessibility and understanding for users worldwide. Additionally, the study explored text summarization using a powerful transformer-based model, which exhibited remarkable proficiency in generating concise and coherent summaries from complex input texts. The implementation of a question-answering pipeline showcased the effectiveness of various language models in handling question-answering tasks with high accuracy and effectiveness.Additionally, the article discusses the processes of data collection, information preparation, ontology creation, alignment strategies, and text analytics. Furthermore, the study addresses the challenges and potential future developments in this field, offering insights into the promising applications of NLP in the context of washing machine manuals. 2024 Elsevier B.V.. All rights reserved. -
A multilevel analysis of hiv1-miR-H1 miRNA using KPCA, K-means, Random Forest and online target tools
The goal of this study was to propose a workflow using machine learning to identify and predict the miRNA targets of Human Immunodeficiency virus 1. miRNAs which is ~21 nt long are attained from larger hairpin RNA precursors and is maintained in the secondary structure of their precursor relatively than in primary chain of successions. The proposition approach for identification and prediction of miRNA targets in hiv1-miR-H1is based on secondary structure and E-value through machine learning. Data Linearity of Length and e-value for sequence match with hiv1-mir-H1 is verified using Kernel PCA. miRNA targets were grouped into clusters thereby indicating similar targets using K-means algorithm. Classification model using Random Forest was implemented regards to each secondary features variable considering feature relevance. A learning methodology is put forward that assimilate and integrate the score returned by various machine learning algorithms to predict cellular hiv1-miR-H1 targets. Gene targets results using TargetScan, miRanda, PITA, DIANA microT and RNAhybrid are also explored for multiple parameters. 2021 Inderscience Enterprises Ltd. -
Data linearity using Kernel PCA with Performance Evaluation of Random Forest for training data: A machine learning approach
In this study, Kernel Principal Component Analysis is applied to understand and visualize non-linear variation patterns by inverse mapping the projected data from a high-dimensional feature space back to the original input space. Performance Evaluation of Random Forest on various data sets has been compared to understand accuracy and various statistical measures of interest. 2016 IEEE.