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A Comprehensive Investigation of Blockchain Technology's Role in Cyber Security
In recent years, blockchain has become an extremely trending technology, capable of solving a variety of problems. One of these domains is cybersecurity, where blockchain technology has a huge scope. To dive deeper into this topic, we first need to understand the cybersecurity domain, the need for this field, and how it has become crucial to the current Information-Technology industry. Once we have a good understanding of the field of cybersecurity, we next focus on blockchain technology, its basic working process, and what makes it a trending infrastructural technology in today's world. The basic idea about the field of cybersecurity and blockchain technology can help us understand how the two different fields can be integrated to solve several problems in the cybersecurity domain. Eventually, we discuss the pros and cons of blockchain technology in cybersecurity and how the integration of the two different fields can make a difference. This study aims to explore various possibilities where blockchain technology can be utilized in several applications to solve a variety of problems in the field of cybersecurity. 2023 IEEE. -
Functional Foods: Exploring the Health Benefits of Bioactive Compounds from Plant and Animal Sources
"Let food be the medicine"(Hippocrates) is a historic quote that became the basis of food science and nutraceuticals. Due to their possible therapeutic advantages, extracts from food have attracted much interest in the medical community. These extracts are abundant in bioactive compounds, which are natural molecules that may be found in various foods and have been demonstrated to affect health positively. Food components have lots of bioactive components, including primary and secondary metabolites and nutritional components, for example, carbohydrates, proteins, vitamins, minerals, fatty acids, antioxidants, phenolics, and flavonoids. This study's primary focus is on the make-up and purpose of these bioactive components found in food extracts. This review aims to give readers a thorough grasp of the bioactive substances found in food extracts and their possible physiological uses. These bioactive substances' functional traits, such as their antioxidant, anti-inflammatory, antibacterial, anticancer, and neuroprotective actions, are also studied. Further research is required to create new functional foods, nutraceuticals, and dietary supplements with specific health advantages that can benefit from understanding these molecules' structure and function. 2023 Versha Dixit et al. -
Effect of heavy metal stress on biochemical and antioxidant efficacy of Chamaecostus cuspidatus
Chamaecostus cuspidatus, commonly known as insulin plant is medicinally important and a rich source of several secondary metabolites which exhibit pharmacological properties. In the present study, three different heavy metals (Pb, Cu and Cr) with different concentrations (Pb and Cr-50, 100, 150, 200 and 250 ppm and for Cu 25, 50, 75, 100 and 125 ppm) was used for heavy metal treatment and its impact on several biochemical and antioxidant parameters was measured of the test plant along with control. Current study mainly focuses on the biochemical and antioxidants estimation of root and rhizome of C. cuspidatus. Protein, proline and carbohydrate content was increased in the treated groups. Total phenol and total flavonoid content were also found to be increased in all the treated groups. Both enzymatic (SOD, CAT, APX) and nonenzymatic antioxidants (DPPH, FRAP and total antioxidant activity) was measured. Antioxidant activity was also high in the treated groups. Highest DPPH activity was found in Cu 25 treated rhizome 91.8030.157 and lowest was observed in Pb 50 treated root 4.5530.240. Highest reducing power activity (FRAP) was observed in Cr 100 treated rhizome 0.75860.0008 and least was found in control root 0.2090.0005. Heavy metals accumulation was also measured and maximum heavy metal accumulation was found in soil following by root and rhizome of all the treated groups. 2024, Indian journals. All rights reserved. -
Exploring the influence of immersive technologies on purchase behavior in the real estate sector: a cognition-affect-conation model approach
Purpose This article explores the role of immersive technologies and their influence on an individual's purchase behavior using cognition-affect-conation model. This article aims to investigate the role of virtual reality in the real estate sector to examine the effect on users' Investment Behavior unpinned by signaling theory. Design/methodology/approach The responses were recorded using a standardized instrument from 404 respondents. The responses were collected from the Delhi NCR region, where respondents recently visited the real estate offices and taken a virtual tour of their future dream house. Partial least squares-structural equation modeling (PLS-SEM) was applied to test the proposed hypotheses. Findings The findings of the study revealed a significant relationship between user immersiveness, virtual presence, user engagement, perceived realism and purchase intention. The moderating role of technological self-efficacy was also measured, and the relationship between perceived realism to purchase intention was significantly moderated. Surprisingly, there was no moderation of technological self-efficacy between user engagement and purchase intention. Practical implications The research article enables real estate companies to frame specific strategies and gain benefits from the information shared by the users. Real-time experience allows companies to understand the customers' needs and develop or customize their future houses accordingly. Originality/value Exploring the relationship between user immersiveness, virtual presence, user engagement, perceived realism, technological self-efficacy and purchase intention in the Indian real-estate sector is a relatively novel idea. Prior literature showed a dearth of research focused on technological self-efficacy's role through the signaling theory lens and underpinned through the CAC framework. These empirical findings help organizations to develop customized strategies. Emerald Publishing Limited -
Leveraging Green Finance for Sustainable Development: An Empirical Analysis of Economic Growth and Environmental Sustainability of Asian OECD Economies
Present study investigates the impact of Green Finance and CO2 emissions on GDP per capita of four Asian OECDeconomies controlling for Expenditure on Education, and Foreign Direct Investment using panel data for the time-period 2015 to 2023, applying pooled OLS, Fixed Effects, and Random Effects Models, and ultimately selecting the Fixed Effects Model based on robust statistical tests (Hausman and Breusch-Pagan LM), revealing that Green Finance significantly enhances GDP per capita, Expenditure on Education unexpectedly hinders it in the short term, and both CO2 emissions and Foreign Direct Investment lack statistically significant effects within countries, thereby underscoring the importance of internal structural factors and advocating for tailored, sustainability-driven, and context-sensitive economic growth strategies. Copyright 2026, IGI Global Scientific Publishing. Copying or distributing in print or electronic forms without written permission of IGI Global Scientific Publishing is prohibited. Use of this chapter to train generative artificial intelligence (AI) technologies is expressly prohibited. The publisher reserves all rights to license its use for generative AI training and machine learning model development. -
Geographical Approaches to Global Sustainable Development Goals in BRICS Countries
Geographical factors play a key role in the realization of SDG's across all countries of the world. Present study is an endeavor in this direction and attempts to evaluate the impact of urbanization, agriculture, and water resource availability on the achievement of sustainable development of BRICS nations. The study employs Panel data analysis using Fixed Effects Model (FEM) and Random Effects Model (REM) along with the application of Hausman Test to determine the appropriate model selection. The results reveal that urbanization significantly enhances SDG progress, agriculture shows an insignificant effect, and water resource availability negatively impacts sustainability, indicating the urgent need for strategic urban planning, agricultural reforms, and efficient water management. The study also highlights that country-specific factors play a critical part in shaping sustainability outcomes, reinforcing the necessity for BRICS policymakers to adopt geographically tailored approaches that align economic growth with environmental sustainability. 2026, IGI Global Scientific Publishing. -
Economic Inequalities Amidst Social, Political, and Environmental Crises in BRICS Countries
This study investigates the complex interplay between economic inequalities and the intertwined social, political, and environmental crises within BRICS countries by empirically analyzing panel data from five nations to assess how women's income, political stability, and CO2 emissions influence wealth concentration among the top 10 percent, revealing through rigorous application of pooled OLS, random effects, and fixed effects models that environmental degradation measured by CO2 emissions is the sole significant and robust predictor positively associated with increased wealth disparity, while social and political variables show no statistically significant effects, thereby underscoring the urgent need for integrated policy frameworks in BRICS that prioritize sustainable environmental reforms alongside inclusive socio-political strategies to effectively mitigate growing economic inequalities and promote equitable and sustainable development in these rapidly evolving economies. 2026, IGI Global Scientific Publishing. All rights reserved. -
Human Resource Development and Economic Growth: Leveraging India's Youthful Population
This empirical investigation employs a multivariate time series framework utilizing the Vector Error Correction Model (VECM) to unravel the intricate long- run equilibrium and short- run dynamics among GDP, youth population, and youth literacy in India for the time- period 1990 to 2024, revealing through Johansen cointegration test the existence of two statistically significant cointegrating vectors, with normalized equations underscoring the pivotal elasticity- driven relationships between economic growth and demographic- literacy indicators, while adjustment coefficients demonstrate the system's endogenous correction mechanism toward long- run equilibrium, and the impulse response and variance decomposition analyses substantiate the dominance of literacy shocks in driving GDP variability over extended horizons, thereby yielding profound macroeconomic implications for policy formulation directed at enhancing human capital and ensuring sustainable. 2026 by IGI Global Scientific Publishing. All rights reserved. -
How deepfake technology impacts public trust in fiscal policies?
The proliferation of Deepfake technology, with its unparalleled ability to fabricate hyper-realistic audiovisual content, poses a profound and multifaceted threat to the integrity of public discourse and the perceived veracity of governmental communications, particularly in the domain of fiscal policy, as it engenders a pernicious erosion of trust, fuels scepticism regarding the authenticity of official statements, and amplifies the vulnerability of the populace to disinformation campaigns, thus undermining the foundational tenets of democratic governance, exacerbating the opacity of fiscal decision-making processes, and destabilizing the delicate equilibrium between transparency, accountability, and citizen confidence in state-managed economic stewardship. Keeping this in mind, the present study endeavours to assess the impact of deepfake technology on public trust in economic policies by taking a sample of 134 respondents from the Mumbai region of India. The results indicate a significant negative relationship between exposure to Deepfake technology and public trust in fiscal policies implying that Deepfake technology leads to an erosion of public trust in fiscal policies of the government calling out for appropriate steps that must be taken to counteract the derogatory effects of Deepfake technology. 2025 Nova Science Publishers, Inc. All rights reserved. -
A Case Study on Zonal Analysis of Cybercrimes Over a Decade in India
Human intelligence has transformed the world through various innovative technologies. One such transformative technology is the internet. The world of the internet, known as cyberspace, though powerful, is also where most crimes occur. Cybercrime is one of the significant factors in cybersecurity, which plays a vital role in information technology and needs to be addressed with high priority. This chapter is a case study where we analyze cybercrimes in India. The data collected from NCRB for 2010 to 2020 are a primary source for the analysis. A detailed analysis of cybercrime across India is done by dividing locations into seven zones: central, east, west, north, south, northeast, and union territories. Cybercrimes reported in each zone are examined to identify which zone requires immediate measures to be taken to provide security. The work also identifies the top ten states which rank high in cybercrime. The main aim of this chapter is to provide a detailed analysis of crimes that occurred and the measures taken to curb them. Along with the primary data, secondary data from CERT-In are also used to provide an analysis of measures taken for handling cybercrime over a decade. The outcome facilitates various stakeholders to better bridge the gap in handling cybercrime incidences, thus helping in incidence prevention and response services as well as security quality management services. 2023 selection and editorial matter, Narasimha Rao Vajjhala and Kenneth David Strang; individual chapters, the contributors. -
Harnessing Machine Learning toOptimize Social Media Marketing
Machine learning (ML) has transformed the way digital advertising is done and analyzed by social media data. This paper explores ML in targeted advertising, including techniques such as supervised learning, neural networks, and NLP. While ML improves campaign precision and consumer engagement, it also presents challenges such as algorithmic bias, data privacy concerns, and computational scalability. This study is a synthesis of existing research and explores real-world applications, providing a critical analysis of MLs capacity to optimize social media advertising. It argues that while ML may provide exceptional possibilities for customization and engagement, its success can only be ensured through appropriate ethical practices, transparency, and innovation in technology. The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2026. -
Child mental health: The role of different attributional styles
Background: High prevalence of mental health issues in the twenty-first century accounts for a lion share in the worldwide burden of disease. There is an alarming decrease in the onset of half of the mental health problems. Hence, it is necessary to explore the current situation and figure out the causes and preventive measures as well as the appropriate mental health enhancement measures. Individual characteristics, such as thinking patterns and perception, have an impact on the mental health. Attributional style is one source of cognitive vulnerability which influences mental health disorders. Therefore, the present study examines whether there are any variations in the mental health of children with different attributional styles. Methods: The current research adopted a cross-sectional research design and selected 150 school going students [74 males and 76 females] between 10-13 years of age as participants. The Child Attributional Style Questionnaire [CASQ], Satisfaction with Life Scale-Children [SWLS-C], Brief Resilience Scale, and Revised Child Anxiety and Depression Scale [RCADS] are used to gather information. Results: The results indicated that children with a pessimistic attributional style experienced more depression and generalized anxiety than children with other two attributional styles. In terms of gender differences in mental health, female students with pessimistic attributional style significantly differed from their counterparts on depression [?2 [2] = 10.131, p = 0.006] and separation anxiety [?2 [2] = 6.456, p = 0.040]. Conclusion: Attributional style seems to have a significant role in depression and anxiety in female children. Although male children did not show any statistically significant results, they were more likely to be pessimistic in terms of their attributional style, which makes them vulnerable to mental health issues. 2020, Indian Association for Child and Adolescent Mental Health. All rights reserved. -
Anticorrosive studies of Chitosan/TiO2/g-C3N4 composite on mild steel in saline and acidic conditions
This work focuses on the synthesis of a nanocomposite coating that enhances the anticorrosive property of the metal. The nanocomposite under study is a synergistic blend of chitosan, titanium dioxide (TiO2), and graphitic carbon nitride (g-C3N4), effectively challenging the corrosion problem faced by various industries. The environment-friendly and natural properties of chitosan, the photocatalytic activity of TiO2 nanoparticles, and the efficient electrical conductivity of g-C3N4 make the composite an ideal material for studying anticorrosion activity. Experimental techniques like XPS, XRD, HR-TEM, FE-SEM, TGA, BET surface area, and FTIR analysis have been employed to characterize the nanocomposite. Weight loss studies indicate the efficacy of the nanocomposite on mild steel in 3.5 % NaCl and 1 M HCl. The corrosion behavior of the nanocomposite is examined by Tafel curves and electrochemical impedance analysis. The results indicate that the inhibition efficiency of chitosan/TiO2/g-C3N4 nanocomposite is 99 % with a charge transfer resistance value of 152.43 ?, which is more effective in the corrosion inhibition of mild steel than chitosan, TiO2, and g-C3N4 when taken separately. The anticorrosive coating prepared using this composite can be applied on different surfaces under various environmental conditions to reduce corrosion. 2025 Elsevier B.V. -
Grey Wolf Optimization Guided Non-Local Means Denoising for Localizing and Extracting Bone Regions from X-Ray Images
The key focus of the current study is implementation of an automated semantic segmentation model to localize and extract bone regions from digital X-ray images. Methods: The proposed segmentation framework uses a pre-processing stage which follows convolutional neural network (CNN) obtained segmentation stage to extract the bone region from X-ray images, mainly for diagnosing critical conditions such as osteoporosis. Since the presence of noise is critical in image analysis, the X-ray images are initially processed with a grey wolf optimization (GWO) guided non-local means (NLM) denoising. The segmentation stage uses a Multi-Res U-Net architecture with attention modules. Findings: The proposed methodology shows superior results while segmenting bone regions from real X-ray images. The experiments include an ablation study that substantiates the need for the proposed denoising approach. Several standard segmentation benchmarks such as precision, recall, Dice-score, specificity, Intersection over Union (IOU), and total accuracy have been used for a comprehensive study. The proposed architectural has good impact compared to the state-of-the-art bone segmentation models and is compared both quantitatively and qualitatively. Novelty: The denoising using GWO-NLM adaptively chose the denoising parameters based on the required conditions and can be reused in other medical image analysis domains with minimal finetuning. The design of the proposed CNN model also aims at better performance on the target datasets. 2023 Biomedical & Pharmacology Journal. -
Transfer Learning-Based Osteoporosis Classification Using Simple Radiographs
Osteoporosis is a condition that affects the entire skeletal system, resulting in a decreased density of bone mass and the weakening of bone tissues micro-architecture. This leads to weaker bones that are more susceptible to fractures. Detecting and measuring bone mineral density has always been a critical area of focus for researchers in the diagnosis of bone diseases such as osteoporosis. However, existing algorithms used for osteoporosis diagnosis encounter challenges in obtaining accurate results due to X-ray image noise and variations in bone shapes, especially in low-contrast conditions. Therefore, the development of efficient algorithms that can mitigate these challenges and improve the accuracy of osteoporosis diagnosis is essential. In this research paper, a comparative analysis was conducted Assessing the accuracy and efficiency of the latest deep learning CNN model, such as VGG16, VGG19, DenseNet121, Resnet50, and InceptionV3 in detecting to Classify Normal and Osteoporosis cases. The study employed 830 X-ray images of the Spine, Hand, Leg, Knee, and Hip, comprising Normal (420) and Osteoporosis (410) cases. Various performance metrics were utilized to evaluate each model. The findings indicate that DenseNet121 exhibited superior performance with an accuracy rate of 93.4% Achieving an error rate of 0.07 and a validation loss of only 0.57 compared with other models considered in this study. 2023, International journal of online and biomedical engineering. All Rights Reserved. -
Diagnosis of Osteoporosis from X-ray Images using Automated Techniques
Osteoporosis is Bone Disease most commonly seen in aged people due to various food habits and life style habits. The bone becomes so brittle and weak which may break just from a fall. So, it is required to attend this Issue as there are various challenges faced by medical domain to identify and treat Osteoporosis. In this paper we focus on identifying and detecting osteoporosis using X-ray images using modified U-net Architecture using Residual Block and skip connections and done comparison study with existing models, as per state-of-art our model outcomes issues in existing model and obtain better accuracy. 2022 IEEE. -
Assesment of bone mineral density in X-ray images using image processing
X-ray application in medical fields has given rise to various research challenges related to bone, due to its wide usage in finding out the disease related to human anatomy. It has lot of research challenges to solve using available wide application of medical imaging techniques and inspired by this, a novel X-ray images based survey was conducted to understand the role of Xray images in medical field. Bone mass density identification is the standard procedure to monitor the risk of fracture in bone using DEXA. Lot of research has been carried out to calculate BMD using X-ray images and it provided prominent results. Since Xray is economically affordable and very economical compared to DEXA, we have decided to work on X-ray images. This paper explains us about various current advancements and disadvantages with respect to X-ray image in medical sector and various techniques related to BMD calculation. X-ray images characteristics and its fundamentals in the medical field for identifying bone related diseases are also discussed. 2021 Bharati Vidyapeeth, New Delhi. Copy Right in Bulk will be transferred to IEEE by Bharati Vidyapeeth. -
Novel Approach for Osteoporosis Classification Using X-ray Images
This research delves into the technical advancements of image segmentation and classification models, specifically the refined Pix2Pix and Vision Transformer (ViT) architectures, for the crucial task of osteoporosis detection using X-ray images. The improved Pix2Pix model demonstrates noteworthy strides in image segmentation, achieving a specificity of 97.24% and excelling in the reduction of false positives. Simultaneously, the modified ViT models, especially the MViT-B/16 variant, exhibit superior accuracy at 96.01% in classifying osteoporosis cases, showcasing their proficiency in identifying critical medical conditions. These models are poised to revolutionize osteoporosis diagnosis, providing clinicians with accurate tools for early detection and intervention. The synergies between the Pix2Pix and ViT models open avenues for nuanced approaches in automated diagnostic systems, with the potential to significantly improve clinical results and contribute to the broader landscape of medical image analysis. As osteoporosis remains a prevalent and often undiagnosed condition, the technical insights from this study hold substantial importance in advancing the field, emphasizing the critical role of accurate diagnostic tools in improving patient care and health outcomes. 2025 Oriental Scientific Publishing Company. All rights reserved. -
Phytofabricated Silver Nanoparticles Derived from Leea crispa Leaf Extract: Antituberculosis and Anticancer Activities
Aqueous extracts of Leea crispa used for producing silver nanoparticles (AgNPs) with the supplementation of the external capping substance, were determined by UV-Visible spectroscopy. The synthesized nanoparticles were examined for their antituberculosis and anticancer activities. The presence of phytoconstituents available for reducing silver ions and to form the AgNPs was confirmed using FTIR analysis. The XRD and TEM examination validated the AgNPs spherical particle shape and size of 15 to 85 nm and their face-centered cubic crystal form. Additionally, the FTIR spectrum revealed variation in the band values in the range 1384.0 to 3419.4 cm-1, respectively, and the EDX noted a strong band at 3 keV induced the presence of metallic silver. The AgNPs exhibited comparatively potential anti-tuberculosis activity (0.2 to 100 g/mL) respectively. Alternatively, various doses of AgNPs, 12.5 to 400 g/mL documented considerable activity towards the human breast cancer cell lines. The percentage of cell viability increased at 12.5g/mL and declined at 400 ng/mL concentrations of AgNPs solution. The AgNPs synthesized from L. crispa exhibited potential activity against life-threatening tuberculosis and cancer cells. 2025, North Carolina State University. All rights reserved. -
Effectiveness of Learning Management System (LMS) in Sustainable Learning and Development among Bank Employees
Learning Management Systems in the form of E-Learning platform is currently an evolving scenario for the primary means of delivering various courses across educational, business, industries and vocational learning environments in the form of Learning and Development activities in all the sectors. LMS is a challenging and resource-intensive task requires demanding substantial knowledge, time, and effort. Consequently, there emerged a necessity in both research and practical applications to establish the personalized usage process of an LMS. Despite its significant impact on the outcomes of such an Information System (IS), the usage process has to be analysed. The researcher developed a conceptual model to delineate with set of factors to influence LMS course in Learning and Development Practices in industry context. Researcher revealed specific set of factors such as interface design, content presentation format, transfer of learning, and feedback mechanisms significantly impact learner satisfaction among Bank employees in their Learning and Development activities. Moreover, learner satisfaction depends on the application platform and content. The findings offer a valuable insight to design a corporate education system, with the quality content delivery and practical delivery. By considering these results, designers can develop more integrated and effective LMS to cater the needs and satisfaction of Learning and Development activities among Bank employees. 2024, Creative Publishing House. All rights reserved.