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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. -
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. -
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 -
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. -
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. -
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. -
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. -
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. -
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. -
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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. -
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. -
Nanotechnology in the Food Industry
Food nanotechnology is a growing field that brings exciting new possibilities to the food industry, offering many opportunities for innovation. It can improve foods taste, health benefits, and nutritional value while leading to innovative products, packaging, and storage methods. Nanotechnology offers transformative potential in the food industry by designing nutrient delivery systems, nano-formulated agrochemicals, enhanced nutritional value, and novel bioactive encapsulation techniques. The food industry demands innovative technologies to maintain market leadership by producing fresh, authentic, convenient, and flavorful products. This chapter examines nanotechnologys role in food processing and packaging, emphasizing its potential for nanoscale control, personalized nutrition, enhanced product quality, and extended shelf life. It also provides insights into recent advances in industry-related R&D in food processing, focusing on innovations that improve efficiency and sustainability. This chapter further addresses food safety considerations and the regulatory measures necessary to manage health risks, such as nanoparticle accumulation and translocation in the body. 2026 selection and editorial matter, Reddicherla Umapathi, Naveen Kumar, and Rajat Singh; individual chapters, the contributors. -
Recent Trends and Progress in Corrosion Inhibitors and Electrochemical Evaluation
Science and engineering research studies are currently concentrating on synthesizing, designing, producing, and consuming ecologically benign chemical species to replace harmful chemicals. This is due to the increasing demands of conservation knowledge and strict ecological regulations. Numerous environmentally friendly substitutes produced from natural resources, including biopolymers, plant extracts, chemical pharmaceuticals (drugs), and so on, are now frequently used as inhibitors to replace dangerous corrosion inhibitors. Many compounds have been extensively used. A range of methods, including physisorption, chemisorption, barrier protection, thin-film growth, and electrochemical procedures, will be used to provide corrosion resistance. The various kinds of corrosion inhibitors (CIs), the mechanisms underlying inhibition, and the evaluation procedures have all been covered in-depth. This review provides an overview of the relevant literature in which researchers and scientists used different types of CIs, the effect of CIs on metals, and information about designs and mechanisms used to minimize corrosion in a variety of equipment composed of alloys or metals, along with electrochemical evaluation studies. This review will provide scholars with fresh insights to advance the discipline. 2023 by the authors. -
Experimental investigation of a biomass-derived nanofluid with enhanced thermal conductivity as a green, sustainable heat-transfer medium and qualitative comparison via mathematical modelling
In this study, bio-based carbon nanospheres (CNSs) were synthesized from lignocellulosic-rich groundnut skin (Arachis hypogaea) and tested for their practical application in nanofluids (NFs) for enhanced heat transfer. The CNSs were characterized using various techniques, including FESEM, EDS, XRD, Raman spectroscopy, zeta potential analysis, and FTIR. Thermal conductivity (TC) and viscosity measurements were conducted using transient plane source (TPS) technique with a Hot Disk thermal analyser and discovery hybrid rheometer, respectively. The nanoparticles (NPs) were dispersed in two base fluids: ethylene glycol (EG) and a 60 : 40 mixture of deionized water (DI) and EG. Optimization studies were performed by varying the stirring and measurement times to improve TC values. The results showed that when a power source of 40 mW was applied at a high concentration of nanoparticles (i.e., 0.1 wt%), there was a 91.9% increment in thermal conductivity (TC) compared to the base fluid EG. DI-EG-based nanofluids (NFs) exhibited enhancements of up to 45% compared to the base fluid DI-EG (60 : 40), with a heating power of 80 mW and concentration of 0.1 wt%. These results demonstrated significant TC improvements with NP incorporation. Further experiments were performed by varying the temperature in the range of 30-80 C with readings taken for every 10 C increase, which showed a direct relation with the TC values. At 80 C, EG-based NFs showed increments of 77%, 111.49%, 139.67% and 175% at 0.01, 0.02, 0.05 and 0.1 wt% concentrations of NPs, respectively. It was also found that with the increase in the concentration of NPs, viscosity increased, whereas an increase in the temperature led to a decrease in viscosity. The CNS nanofluid exhibited a Newtonian behaviour with the nanoparticle concentration and temperature, resulting in an approximately 114% enhancement compared to the base fluid when the concentration of CNSs was 0.1 wt% at 30 C but decreased by up to 18% when the temperature was increased to 90 C. Using appropriate mathematical models for assessing thermophysical quantities, it was discovered that the model values and experimental values correspond reasonably well. Our method thus validates our experimental results and deepens the understanding of the mechanisms behind enhancing thermal conductivity in biomass-derived nanofluids. In summary, our work advances sustainable nanomaterial synthesis, providing a new solution for boosting thermal conductivity while maintaining environmental integrity, thereby inspiring further research and innovation in this field. 2024 RSC. -
Forensic applications of graphene oxide
Forensic analysis is an enormous field comprising the detection of various types of samples. The objective of forensic evidence examination is to provide a cohesive, transparent, and unbiased judgment of the evidence's significance to the investigators. Graphene oxide (GO) is an abundant substance that comprises carbon, hydrogen, and oxygen in a single layer making it highly economical. Therefore, the utilization of GO is highly considered for achieving the detection and analysis of various substrates. This can be justified by its facile and economical preparation that contributes to improving its significance and applicability. Forensic samples include trace elements that can be pre-concentrated with the help of a sustainable medium provided by GO. This book chapter aims to provide exciting insights into the synthesis, properties, and applications of GO in the detection of various forensic samples of GO. 2024 -
Comprehensive Review on CdTe Crystals: Growth, Properties, and Photovoltaic Application
Abstract: Despite the deep interest of materials scientists in cadmium telluride (CdTe) crystal growth, there is no single source to which the researchers can turn towards for comprehensive knowledge of CdTe compound semiconductor synthesis protocols, physical properties and performance. Considering this, the present review work focuses to bridge this shortcoming. The direct band gap (Eg) CdTe crystals have been in limelight in photovoltaic application (PV) since the optoelectronic properties such as Eg (1.49 eV), absorption coefficient (~105 cm1), p-type conductivity, carrier concentration (6 1016 cm3) and mobility (1040 cm2/(V s)) at the room temperature are reported that optimum for solar cells. Additionally, Cd-based compounds such as CdTe and CdZnTe have also been widely studied in the field of ? and ?-ray radiation detector, because of their extraordinary advantages like large atomic number, low weight, high mechanical hardness, flexibility, and the availability of the constituent materials. CdTe has demerits like toxicity and high melting temperature, which will complicate the growth of stoichiometric cadmium telluride crystals at high temperatures. In this regard, the review work focused the periodic evolution of the growth protocols until now. The different synthesis methods, characterization, and recent progress in the field of crystalline CdTe were discussed briefly. Important optical and electrical characteristics are presented in the tables and remaining issues have discussed, this could be looked into for further research. The applications of CdTe crystals for photovoltaic fields are also discussed in this review paper. Pleiades Publishing, Ltd. 2023. ISSN 0031-918X, Physics of Metals and Metallography, 2023, Vol. 124, No. 14, pp. 17951812. Pleiades Publishing, Ltd., 2023. ISSN 0031-918X, Physics of Metals and Metallography, 2023. Pleiades Publishing, Ltd., 2023. -
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Vapor growth and optimization of supersaturation for tailoring the physical properties of stoichiometric Sb2Se3 crystalline habits
The evolution of different morphologies (fibers, whiskers, needles, and spherulites) of antimony selenide (Sb2Se3), devoid of foreign chemical elements, was explored by the physical vapor deposition (PVD) method, employing an indigenously assembled tubular furnace, which showed layer growth mode as per the metallurgical and scanning electron micrographs. Supersaturation for crystallization was optimized by precisely controlling the difference in temperatures of nutrient and growth zones, ?T = TN ? TG, where ?T = 125 to 350C. The strain and dislocation density of the crystals were evaluated from the crystallographic data. Monophase nature has been confirmed by Rietveld refinement analysis of the PXRD findings, using Full Proof software. UVVis-NIR and PL spectra of the morphologies revealed band gap, Eg in the range, 1.151.18eV. Among these habits, good-quality whiskers bearing flat faces of appreciable crystallinity, stoichiometry, thermal stability and mechanical strength were produced due to the periodic deposition of atoms associated with the progression of smooth vaporsolid (v?) interface as evident from PXRD, EDAX, XPS, TGA and microindentation analyses. Hall effect measurements resulted in obtaining appreciable values of electrical parameters, ? = 145.36 ? cm and n = 7.39 1018cm?3 for PV applications. Moreover, optical studies justified direct transition with adequate photon absorption which promises the suitability of whiskers as absorbers in the energy conversion process. 2022, The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature. -
Physical vapor deposition and enhancement of optoelectronic properties of SnSe2 platelets
Stoichiometric tin diselenide (SnSe2) platelet crystals have been prepared by physical vapor deposition (PVD) method under high vacuum (~ 106mbar) using a homemade dual-zone furnace. The driving force for growth was optimized in terms of temperature difference (?T = 270 to 420C) of nutrient and growth zones. Good quality platelets, devoid of any screw dislocations, hillocks, defects etc. were crystallized at ?T = 400C by layer growth mode as per the 3D optical profiler and electron microscopic images. Rietveld refinement of the PXRD data using FullProf suite software justified hexagonal crystal structure with a = 3.811 c = 6.137and the computed density (5.967g/cm3) is in agreement with that obtained based on Archimedes principle. Chemical homogeneity of these samples was probed by EDAX, XPS and Raman analysis. The thermal and mechanical behavior was investigated by TGA as well as Vickers microhardness experiments. The values of optical band gap (1.20eV), absorption coefficient (7.25 105cm?1), resistivity (2.70 ? cm), mobility (32.70 cm2V?1s?1) and carrier concentration (3.08 1016cm?3) have been evaluated using UVVis-NIR, photoluminescence, and Hall effect measurements. The enhancement of optoelectronic parameters of the as-grown SnSe2 platelets free of polytypism, throws light on their potential for photovoltaic applications. 2022, The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature.