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Mentha spicata assisted AgCuO nanocomposite enables anti-diabetic and vitamin-C sensing activities
Diabetes mellitus (DM), a multifactorial chronic health condition, affects a sizable portion of the global population, and more people are expected to contract it in the future, according to the World Health Organisation (WHO). Diabetes mellitus can be treated with conventional drugs, but most of the medications have a variety of side effects. The use of nanocomposites (NCs) to treat diabetes has been prioritized in this scenario. In this study, AgCuO NCs were synthesized using a green method using Mentha spicata leaf extract and their physicochemical properties were investigated with a variety of analytical techniques. According to an extensive in vivo and in vitro analysis of the biological activities of as-synthesized AgCuO NCs, AgCuO NCs possess effective antibacterial, anti-diabetic, and anti-hyperlipidemic characteristics. When AgCuO NCs are administered to STZ-induced animals in a concentration-based manner, the blood levels of inflammatory and liver marker enzymes are reduced and antioxidant enzyme levels are increased. Besides, AgCuO NCs exhibit excellent sensing activity with a limit of detection of 86 nM against Vitamin-C. This study reveals that AgCuO NCs derived from Mentha spicata may, therefore, prove to be a very successful anti-diabetic and biosensor candidate in the future. 2024 Elsevier B.V. -
Exploring thermal and entropic behaviors in nanofluid stagnation point flow with nonlinear dynamics
This study investigates the optimization of heat and mass transfer in nanofluid stagnation point flow by analyzing entropy generation and its underlying physical mechanisms. Nanofluid technology, widely applied in thermal energy storage, and heat exchangers represents a significant advancement in modern thermal systems. While nanofluids enhance heat transfer rates, optimizing thermal conductivity through nanoparticle dispersion remains a key challenge. This work also incorporates the effects of a nonlinear chemical reaction to evaluate its impact on coupled heat and mass transport. The governing nonlinear partial differential equations, including momentum, energy, and concentration expressions, are reduced to a system of coupled ordinary differential equations using local similarity transformations. These equations are solved numerically using a Runge-Kutta scheme in MATLAB. The results, presented through tables and graphs, demonstrate how velocity, temperature, and concentration profiles vary with key physical parameters. Entropy generation is shown to increase with higher porosity, while reductions in slip and Williamson fluid parameters decrease it. Furthermore, the skin friction coefficient increases by approximately 7 % when the magnetic parameter M increases from 0 to 0.5, whereas the Nusselt number decreases by nearly 28.6 % as M increases from 0 to 1. Additionally, the local Sherwood number decreases by approximately 16.7 % when the permeability parameter Kp increases from 0 to 0.3. These findings provide practical insights into enhancing nanofluid based heat and mass transfer systems for engineering applications. 2025 The Authors. -
Experimental instigating a counter cultural film platform in Bangalore /
Moving Image Review & Art Journal (MIRAJ), Vol.7, Issue 2, pp.189-297, ISSN No: 2045-6298. -
Gaussian MutationSpider Monkey Optimization (GM-SMO) Model for Remote Sensing Scene Classification
Scene classification aims to classify various objects and land use classes such as farms, highways, rivers, and airplanes in the remote sensing images. In recent times, the Convolutional Neural Network (CNN) based models have been widely applied in scene classification, due to their efficiency in feature representation. The CNN based models have the limitation of overfitting problems, due to the generation of more features in the convolutional layer and imbalanced data problems. This study proposed Gaussian MutationSpider Monkey Optimization (GM-SMO) model for feature selection to solve overfitting and imbalanced data problems in scene classification. The Gaussian mutation changes the position of the solution after exploration to increase the exploitation in feature selection. The GM-SMO model maintains better tradeoff between exploration and exploitation to select relevant features for superior classification. The GM-SMO model selects unique features to overcome overfitting and imbalanced data problems. In this manuscript, the Generative Adversarial Network (GAN) is used for generating the augmented images, and the AlexNet and Visual Geometry Group (VGG) 19 models are applied to extract the features from the augmented images. Then, the GM-SMO model selects unique features, which are given to the Long Short-Term Memory (LSTM) network for classification. In the resulting phase, the GM-SMO model achieves 99.46% of accuracy, where the existing transformer-CNN has achieved only 98.76% on the UCM dataset. 2022 by the authors. -
Flexible and cost-effective cryptographic encryption algorithm for securing unencrypted database files at rest and in transit
To prevent unauthorized access to the databases and to ensure that the data of the databases is protected from intruders and insiders, the data is being encrypted at the storage locations. The same goal is achieved with Transparent Data Encryption, a feature that can be found in almost all database products. However, it has been observed that the non-datafiles are being ignored and there is no standard encryption for them like there is for datafiles. Moreover, there was no standard algorithm to encrypt them without relying on third-party tools. Therefore, This study provides a robust algorithm to perform the encryption. This presentation also describes the importance of non-datafiles encryption, and how some non-datafiles can pose a threat to data and infrastructure without encryption. The practical implementation of the non-data file encryption algorithm shows the authentic results. Further, unlike existing algorithms, the proposed algorithm gives the file owner full control over the encryption logic. In the encryption process, two levels of encryption logics are combined with a passcode lock, while the same combination of two levels of reversing encryption and passcode is used in the decryption process to convert encoded data back into text format. 2022 The Author(s) -
Cloud databases: A resilient and robust framework to dissolve vendor lock-in
Vendor lock-in has become a major concern in cloud computing. The term vendor lock-in describes situations where the subscriber cannot move data or services to another cloud vendor. This is due to heavy data volumes, high network bandwidth costs, dependencies, or unacceptable downtime. The proposed vendor lock-in dissolution practice migrates the database effectively in noticeably less time, regardless of database size and with a nominal network bandwidth requirement. Through this new practice, databases can be migrated to very remote regions, even across continents. A real-time implementation of the proposed method presented in this paper. 2024 The Author(s) -
Assimilating sense into disaster recovery databases and judgement framing proceedings for the fastest recovery
The replication between the primary and secondary (standby) databases can be configured in either synchronous or asynchronous mode. It is referred to as out-of-sync in either mode if there is any lag between the primary and standby databases. In the previous research, the advantages of the asynchronous method were demonstrated over the synchronous method on highly transactional databases. The asynchronous method requires human intervention and a great deal of manual effort to configure disaster recovery database setups. Moreover, in existing setups there was no accurate calculation process for estimating the lag between the primary and standby databases in terms of sequences and time factors with intelligence. To address these research gaps, the current work has implemented a self-image looping database link process and provided decision-making capabilities at standby databases. Those decisions from standby are always in favor of selecting the most efficient data retrieval method and being in sync with the primary database. The purpose of this paper is to add intelligence and automation to the standby database to begin taking decisions based on the rate of concurrency in transactions at primary and out-of-sync status at standby. 2023 Institute of Advanced Engineering and Science. All rights reserved. -
A Compatible Hexadecimal Encryption-Booster Algorithm for Augmenting Security in the Advanced Encryption Standard
Among the most prominent encryption algorithms, Advanced Encryption Standard ranks first. Even so, many familiar characters can be seen when an AES encrypted file is opened. As of today, there have been very few contributions to research on suppressing known characters in AES encrypted files. It is possible to identify encrypted files not only by their name and content, but also by their size. As a result, hackers can identify files at source and target locations by comparing their sizes. In this paper, a methodology is presented to address these two research gaps. As a result of the proposed algorithm, almost all characters are transformed into an unintelligible format not only for humans, but also for computer interpreters. As an additional benefit, the proposed method makes the encrypted file appear smaller and conceals its actual size. The proposed Encryption Booster algorithm is also easily integrated with Advanced Encryption Standard. 2023 IEEE. -
Asynchronous Method of Oracle: A Cost-Effective and Reliable Model for Cloud Migration Using Incremental Backups
Cloud Computing has reached a new level in flexibility to provide infrastructure. The proper migration method should be chosen for better cost management and to avoid overpayments to unused resources. So, the migrations from On-Premises to cloud infrastructure is a challenge. The migration can be done in synchronous or asynchronous modes. The synchronous method is mostly used to minimize downtime while doing the cloud migrations. The asynchronous methods can do the migrations in offline mode and very consistently. This paper addresses various issues related to the synchronous mode of Oracle while doing highly transactional database migrations. The proposed methodology provides a solution with a combination of asynchronous and incremental backups for highly transactional databases. This proposed method will be a more cost-effective and reliable model without compromising consistency and integrity. 2021, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. -
Comparative Analysis of Disaster Recovery, Encryption, and Database Migration Methods in Cloud Environments
This research conducts comparative analysis and performance evaluation on disaster recovery approaches, encryption strategies, and database migration methods in cloud environments. The study highlights deeper technical insights encryption techniques and demonstrates superior performance compared to the other encryption methods in securing non-data files. This approach enhances protection against insider threats while avoiding reliance on existing Oracle wallet features, ultimately leading to a reduction in licensing expenses. This study also evaluates various database migration solutions, specifically AWS DMS, Google DMS, Azure DMS, and IBMS. Notably, IBMS stands out for its proficiency in producing cross-region data copies while achieving a 75% reduction in infrastructure costs. A comparative analysis was conducted on various disaster recovery strategies, including Standard DR, Pilot Light, Warm Standby, Hot Standby, Semi Replication, and DDI. Among these, the DDI is being observed as noteworthy since it excels in decision making capabilities and auto replication role switching advantages of standby databases. The Author(s), under exclusive license to Springer Nature Switzerland AG 2026. -
Exchange rate, stock price and trade volume in US-China trade war during COVID-19: An empirical study
This article aims to examine the influence of international trade wars on the majority of stock market operations, both directly and indirectly affected. The impact of the trade war on the exchange rates of the participating countries was similarly negative. This article seeks to trace the conversion standards' footprints in the United States, China, and India using several indexes such as the Shanghai Composite Index, Dow Jones index, and Nifty 50. The cost of closing down various indices on a daily basis, as well as the conversion standard upsides of the participating currencies, are all examined in this study. Furthermore, utilizing the OLS and GARCH models, this work provides insights into measuring the uncertainties about the impact of exchanging scale on financial exchange. According to the findings of OLS, changes in the swapping scale have had a minor impact on the daily closing costs of stock records in the individual countries. The conversion standard, on the other hand, has a major impact on trade volumes in all three stock markets. When compared to the SSE and DJI equities, the GARCH model predicts that the contingent shift will be less shocking, resulting in a smaller impact on Nifty trade volume. To replicate the impact of trade wars during the Covid-19 crisis, the final results imply that data from domestic and international financial transactions must include securities market transactions. Author This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License (CC BY-NC-ND 4.0). -
Effects of Peer Monitoring on Student Stress Level of College Students Based on Multi-Layer Perceptron Approach
The classroom is just one of many places where the proposed approach encounter stress. Previous studies have shown that college students experience high rates of stress. It is not known if the Student Stress Inventory-Stress Manifestations (SSI-SM) is useful in identifying stressors and evaluating stress manifestations among college students. To this end, it was created a college-specific version of the Student Stress Inventory-Stress Manifestations (SSI-SM) and administered it to students to determine its validity and reliability. These procedures comprise the proposed technique and include preprocessing, feature selection, and model training. It uses Normalization as a preprocessing approach. The term' normalization' refers to the procedure of rescaling or modifying data so that all categories have the same variance. The proposed approach employed linear discriminant analysis as a means of selecting features. The models are then trained using MLP after information gain has been used to choose relevant features. The proposed approach achieves better results than the two leading alternatives, CNN and RNN. 2024 IEEE. -
Feminism in Practice: Learning from the Barefoot Solar Mamas
The Barefoot College (India) is an NGO working in the fields of education, skills development, health, drinking water, and solar power mainly to train older, rural women who are determined to challenge restrictive gender roles in their respective communities. Since its inception, the NGO has trained over 2,000 rural women as solar engineers across 93 countries worldwide and has brought electricity to over 18,000 homes. Barefoot trainers employ non-normative methods of sharing knowledge such as color coding, sign language, and practical experience. This paper conducts a critical assessment of the Barefoot College Solar Electrification Programme to explore how it empowers illiterate and semi-literate women from remote rural areas around the world to become solar engineers (or Solar Mamas). It utilizes qualitative research methods to analyze this women's empowerment project as a landmark practical application of decolonial feminist theory. The paper contends that the Barefoot approach both challenges and conforms to the Women in Development and Gender and Development approaches of the past. The research is grounded methodologically in feminist praxis and also borrows from the conceptual frameworks of Feminist Political Ecology and Women and the Politics of Place. Stories and personal experiences from Solar Mamas have been highlighted to understand the real world impact of the program. The main findings indicate that the Barefoot College's innovative approach to empower marginalized communities and educate older women is achieved through decentralizing control and demystifying technology. (2024), (Bridgewater State College). All Rights Reserved. -
DOES COVID-19 AFFECT SHARIAH COMPLIANT STOCK? EVIDENCE FROM SELECTED OIC COUNTRIES
This study aims to examine the movements of Islamic stock markets in ten selected OIC (Organization of Islamic Cooperation) countries in relation to Covid-19 cases, providing a comprehensive analysis of market behavior during the pandemic. The countries-Saudi Arabia, Pakistan, Bangladesh, Turkey, Indonesia, Oman, Qatar, UAE, Kuwait, and Bahrain-were chosen based on their large Muslim populations. Data was collected over a one-year period from January 1, 2020, to January 31, 2021, analyzing the relationship between Covid-19 cases and Islamic stock market indices. The study employed co-integration tests to identify long-term relationships and the Vector Error Correction Model (VECM) to explore short-run dynamics. The co-integration test results show a significant long-run relationship between Covid-19 cases and Islamic stock markets in most of the selected OIC countries. Specifically, the Shariah indices in Pakistan, Bangladesh, Turkey, Qatar, UAE, Kuwait, and Bahrain have a positive and significant relationship with Covid-19 cases. Conversely, Saudi Arabia, Indonesia, and Oman exhibit a negative long-term relationship with Covid-19 cases, suggesting a different market response. These results suggest that countries with diversified economies, particularly those relying on natural resources such as oil and agriculture, were more resilient during the pandemic. This study provides novel insights into the unique responses of Islamic stock markets in OIC countries during the pandemic, highlighting regional differences in market behavior and recovery. It suggests that despite the global economic downturn, OIC countries present attractive investment opportunities, particularly due to their swift recovery and resource-based economies, offering a robust portfolio for investors during crises. 2024 by the author(s). -
Unlocking the potential of AI for efficient governance: Innovative approaches of Bahrain
The rapid development and implementation of artificial intelligence (AI) technologies will have significant economic, social, and ethical impacts. Efficient governance is essential to maximize AI's benefits while minimizing its risks. Bahrain is positioning itself as a fintech hub, with AI playing a central role in this transformation. Bahrain's smart governance efforts will be strengthened by integrating AI into public services. E-government efforts will use AI to streamline processes, improve citizen experience, and build a more responsive and efficient public administration. The study provides an overview of how artificial intelligence (AI) is transforming various sectors in Bahrain with innovative approaches to boost productivity, better decision-making, and improve the general quality of services that may also impact the Bahraini economy. Bahrain continues to drive digital innovation, paving the way for a better and more prosperous future and sustainable development. Bahrain's digital transformation has been largely successful thanks to strong government measures. 2024, IGI Global. All rights reserved. -
Pangenomics for developing salinity stress-tolerant plants
Soil salinity is a critical agricultural challenge that significantly reduces crop productivity and threatens global food security. With approximately 20% of irrigated land affected by salinity, innovative strategies are essential to develop salinity stress-tolerant crops. The field of pangenomics, a comprehensive approach to studying the genetic diversity within species, has immense potential to address this issue. Pangenomics includes core genomes, spanning the entire genus, and accessory genomes, which are species-specific, thus capturing the full spectrum of genetic variation. This approach enables the identification of novel genes and alleles associated with salinity tolerance, providing a robust foundation for genetic improvement programs. Salinity stress has a profound molecular and physiological impact on plants with multiple phenotypic manifestations, such as stunted growth, lesser crop yield, and reduced reproductive success. To solve these issues, advanced sequencing technologies and bioinformatics tools used in constructing and analyzing pangenomes play a crucial role. This chapter goes into detail about techniques such as comparative genomics and genome-wide association studies (GWAS), which are important for their effectiveness in identifying salinity tolerance genes. Functional validation methods, including CRISPR/Cas9 and RNA interference (RNAi), have also been discussed. This chapter highlights case studies on crops like rice and wheat to demonstrate the practical applications of pangenomics in developing salinity-tolerant varieties. Furthermore, by addressing the challenges and future directions in the field, one can emphasize the need for integrating multiomics data and refining analytical methods. Such an approach can help guide future research and breeding efforts toward sustainable agricultural practices and enhanced global food security. 2025 Elsevier Inc. All rights reserved. -
Integrating Advanced Metabolomics with Plant Functional Genomics
Metabolomics encompasses the entire suite of small-molecule compounds or metabolites synthesized by an or ganism, whereas functional genomics refers to the gene-level functioning of an organism. The genome of a plant will dictate its metabolome, but the link between the two omics data may not always be clearly visible or properly explored. This chapter delves into the integration of advanced metabolomics with plant functional genomics, highlighting its pivotal role in advancing our understanding of plant biology and its applications in agriculture. Metabolomics provides a comprehensive analysis of small molecules, bridging the gap between genotype and phenotype by elucidating the dynamic interactions within plant systems. Key techniques such as mass spectrom etry and nuclear magnetic resonance are explored, emphasizing their importance in high-throughput and high-resolution metabolite profiling. The chapter further discusses the synergy between metabolomics and other omics technologies, including genomics, transcriptomics, and proteomics, underscoring its significance in iden tifying gene functions and metabolic pathways linked to complex traits such as stress tolerance. Applications in plant breeding are also highlighted, showcasing how metabolomics can drive the development of crops with en hanced stress resilience, yield, and nutritional quality. The chapter concludes by emphasizing the transformative potential of this integrated approach in shaping future agricultural practices and improving food security. CAB International 2025. All rights reserved. -
ENHANCING HEALTHCARE SECURITY WITH BLOCKCHAIN-POWERED SMART CONTRACTS
The rationale behind this research stems from the increasing frequency of data breaches in healthcare and the inadequacy of centralized systems to ensure privacy, interoperability, and regulatory compliance. The Present study emphasizes the importance of applying security in healthcare. This model was prepared by utilizing Smart Contracts. It has been noted that there are some emerging concerns about data security and privacy as well as interoperability within healthcare organizations. The focus of a research paper is on the deployment of Smart Contracts along with blockchain technologies. The fundamental vision is to improve healthcare infrastructures security. Blockchain is transforming healthcare systems for the better by eliminating inefficiencies caused by fraud and outdated technologies, allowing for the efficient, transparent, and secure issuance of Smart Contracts. The challenges of confidentiality, data security, and access to relevant patient information for medical professionals have been a problem in the healthcare sector. Most of the existing EHR systems do not have adequate mechanisms for enforcing security access controls, which hampers cooperation between healthcare institutions. These security concerns pose risks for patients privacy and cripple the adoption of modern information technology within the health sector. Simulation works shows that Transaction processing time in case of proposed model is below 1.5 second where as it is 2.5 in case of conventional model. Security breach probability of proposed model has been reduced to 0.05 that was 0.35 in case of conventional model. Data integrity verification time in case of proposed model is below 1.0 that is above 1.75 in case of conventional model. While with the existing Electronic Health Record (EHR) systems face limitations in security, privacy enforcement, and interoperability, this study addresses the lack of automated, decentralized access control mechanisms. It proposes a blockchain-powered Smart Contract model to fill these gaps and enhance healthcare data governance and trust. Little Lion Scientific -
'Angry Young Women': Evolving Forms of Female Resistance in Contemporary Bollywood Cinema
This paper explores the representation of select female characters from two contemporary Hindi filmsLipstick Under My Burkha (2016) and Gully Boy (2019)to trace the emergence of the angry young woman archetype within the broader framework of contemporary Indian cinema. The protagonists in these films confront their marginalisation across multiple intersecting axesclass, religion, and gender. Through nuanced roles, they navigate their personal and political struggles, breaking traditional norms by expressing anger, both violent and silent, as forms of resistance. This shift marks a departure from the stereotypical portrayals of women in earlier Bollywood films, where they were relegated to roles of victims, damsels in distress, or moral custodians of Indian values. The paper focuses on how female characters in recent Bollywood films deploy anger not only to challenge or subvert patriarchal structures but also as a tool for asserting new forms of agency and autonomy. This evolving depiction of anger signals a broader reconfiguration of female empowerment, where rage becomes a means of self-expression, identity formation, and personal liberation. 2025 Australian National University, Dept. of Gender, Media and Cultural Studies. All rights reserved. -
Valuation of the Capital Assets Pricing Model on the Islamic Retails Banks in Bahrain
The Gulf countries are rapidly changing after the coronavirus pandemic. It has many notable impacts on the banking sectors especially in the Islamic retail banks. This study has focused on the valuation of the Islamic retail banks in Bahrain. There are only six Islamic retail banks that follow Shariah rules and regulations. This paper consists of the monthly data from July 2016 to June 2021. The CAPM model has been applied for the valuation of Islamic retail banks and the multiple regression method run for the impact of Islamic retail banks on the Bahrain Bank index during the period. The outcomes of CAPM have identified overvalued BISB, Baraka, Salam, KHCB, and ITHMR except for the KFH, which is undervalued. The significant results have found the relationship between the six retail Islamic banks and the Bahrain banks index in Bahrain. The interpretation of t-statistics shows a substantial difference between the CAPM and actual returns of Islamic retail banks in Bahrain. The banking system will be a modern economic world which helps to create the nation. The nation will develop if society will be aware about financial literacy and analytics. This paper will help the industrialist, practitioner, brokers, promoters, and investors. The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.