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Awareness and perception of adolescent boys about menstruation: an exploratory study from rural India
Background/objectives: Menstruation, despite being a natural process, remains stigmatized in many patriarchal societies, where taboos and misinformation perpetuate silence and misconceptions. This study investigates adolescent boys awareness and perceptions of menstruation in rural India, examining the influence of cultural, educational, and social factors on their understanding. Methods: Employing an exploratory qualitative design, a focus group discussion (FGD) was conducted with eight 15-year-old boys from a rural secondary school in Kerala, India. The session was audio-recorded and supplemented with detailed field notes to capture verbal and non-verbal insights. Braun and Clarkes thematic analysis framework was used to identify key themes. Results: Three primary themes emerged: Levels of awareness (ranging from correct but insufficient to lack), Sources of knowledge (media, peers, cultural practices), and Desire for further knowledge. Findings indicate that fragmented or inaccurate knowledge, shaped by cultural and societal norms, reinforces stereotypes and stigma around menstruation. Conclusions: Findings underscore the need for inclusive and culturally sensitive educational interventions for boys, aimed at dispelling myths and fostering empathy. Such programs can contribute to improved awareness, reduced stigma, and greater support for menstrual hygiene management (MHM) practices. Future research should assess similar interventions across broader contexts to determine their impact on challenging menstrual taboos. The Author(s) 2025. -
Enhancing Lung Cancer Detection Accuracy: Implementing Smote for Balanced Learning
This research goal is to forecast lung cancer using machine learning, and addressing the dataset's class imbalance is a top priority. The data that was initially gathered was extremely unbalanced, with 87.38% of instances being of the minority class of lung cancer and only 12.62% being non-cancer cases. To address this imbalance, minority over-sampling through self-generated SMOTE (Synthetic Minority Over-sampling Technique) was implemented wherein there were 64.85% cases of lung cancer and 35.15% of non-lung cancer cases after deduplication. Logistic regression (LR), Gaussian naive Bayes, Support Vector Machine (SVM), Bernoulli naive Bayes, K nearest neighbors (KNN), Random Forest (RF), multi-layer perceptron, and extreme gradient boosting are among the machine learning methods that were tested. The best test performance was shown by the Random Forest and Extreme Gradient Boosting methods that achieved an accuracy of 97.3% followed by K Nearest Neighbors at 95.95%, and Multi-Layer Perceptron at 93.24%. This highlights the necessity of data balance and the ways in which these methods can improve the efficacy of predictive models for lung cancer. As such, this addition contributes to the dearly needed critical knowledge which may be a stepping stone for innovation within the domains of diagnosis and treatment medicine through machine learning. 2025 IEEE. -
Temperature-Tuned Nitrogen and Oxygen Self-Doped Carbonized Polymer Dots for Enhanced Supercapacitor Applications
A one-step hydrothermal method is used to synthesize nitrogen and oxygen self-doped carbonized polymer dots (N, O-CPDs) from o-phenylenediamine (o-PD) as the precursor. Detailed structural analysis shows that the evolution of defects is temperature-dependent, with the synthesis temperature being crucial in determining the level of carbonization and structural disorder. This process results in a complex carbon structure featuring sp2 graphitic domains mixed with controlled structural defects, essential for electrochemical activity. The N, O-CPDs demonstrate remarkable electrochemical performance when tested as electrode materials for supercapacitors. Notably, the sample synthesized at 220C achieves a high specific capacitance of 205 Fg?1 at 1 Ag?1 in a three-electrode setup and 58 Fg?1 in a two-electrode configuration. Additionally, it shows excellent cycling stability, maintaining 85% of its initial capacitance after 4500 cycles at 4 Ag?1. This impressive performance is attributed to the synergistic effects of nitrogen and oxygen doping, which create numerous active sites and enhance charge transfer efficiency. The combination of optimized structural disorder and heteroatom doping significantly improves the electrochemical properties of these N, O-CPDs, highlighting their potential as advanced materials for energy storage applications. 2025 Wiley-VCH GmbH. -
Fluorescent Carbonized Polymer Dots Derived from o-phenylenediamine and its Photonic Application
Optimizing the optoelectronic characteristics of low-dimensional carbon dots (CDs) through surface modifications and doping has proven instrumental in tailoring them for diverse applications. This study explores a facile and economical hydrothermal synthesis method for generating Carbonized Polymer Dots using o-phenylenediamine at different temperatures. The resulting materials exhibit structural and morphological variations linked to the synthesis temperature. A transition from carbon dots (CDs) embedded in reduced graphene oxide (rGO)-like sheet structures at low temperatures to the core-shell structure at the highest temperature is observed in HR-TEM, implying the formation of CPDs. X-ray photoelectron spectroscopy (XPS) corroborates these findings, showing an augmented degree of graphitization in alignment with HR-TEM results. The photoluminescence spectra of CPDs synthesized at the lowest temperature exhibit multiple emission peaks, resulting in a yellowish-orange color. Utilizing these CPDs to fabricate light-emitting diodes (LEDs) produces a vivid bright-green emission with CIE coordinates (0.378, 0.522). Moreover, the CPDs demonstrate solvatochromism across diverse solvents of varying polarity, covering the entire visible spectrum. This intriguing solvatochromic effect positions the CPDs as promising materials for polarity probing applications. The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2024. -
Bioactive Compounds and Biological Activities of Mangrove-Associated Bacteria
Mangroves are used by folklore in indigenous medicine for the treatment of diseases. They contain an array of pharmacologically significant bioactive compounds. The endophytes of the mangroves have the capability of producing biologically active compounds which may be similar to their host plant. They are also able to produce novel and unique bioactive compounds which can be used in therapeutics. Bacillus and Streptomyces are the major genera of bacterial endophytes found in the mangroves. The major groups of bioactive compounds produced by the bacterial endophytes of mangroves include terpenoids, alkaloids, polyketides, etc. The bioactive compounds produced by the endophytes possesses biological activities such as antibacterial, cytotoxic, antioxidant activity, etc. These compounds have profound applications in the discovery of drugs. The present chapter focuses on the bacterial endophytes found in the mangroves, the bioactive molecules produced by them, and the pharmacological activities associated with these endophytes. Springer Nature Switzerland AG 2025. -
Bioactive Compounds and Biological Activities of Mangrove-Associated Bacteria
Mangroves are used by folklore in indigenous medicine for the treatment of diseases. They contain an array of pharmacologically significant bioactive compounds. The endophytes of the mangroves have the capability of producing biologically active compounds which may be similar to their host plant. They are also able to produce novel and unique bioactive compounds which can be used in therapeutics. Bacillus and Streptomyces are the major genera of bacterial endophytes found in the mangroves. The major groups of bioactive compounds produced by the bacterial endophytes of mangroves include terpenoids, alkaloids, polyketides, etc. The bioactive compounds produced by the endophytes possesses biological activities, such as antibacterial, cytotoxic, antioxidant activity, etc. These compounds have profound applications in the discovery of drugs. The present chapter focuses on the bacterial endophytes found in the mangroves, the bioactive molecules produced by them, and the pharmacological activities associated with these endophytes. Springer Nature Switzerland AG 2026. -
Precision Farming on Sugarcane: Drone-Based Disease Detection Using YOLOv8 Neural Models
Precision agriculture is being revolutionized by the use of UAVs and AI, enabling more efficient and sustainable crop monitoring. This study presents a drone-based solution for real-time detection of sugarcane diseases such as Rust, Red Rot, Mosaic, and Yellow Leaf. A custom quadcopter, outfitted with a high-resolution camera and Raspberry Pi 4, is used to capture aerial imagery. The onboard YOLOv8 model processes images in real time, with data stored locally on an SD card for further evaluation. The paper covers the complete system setup, including hardware components, neural network deployment, and the end-to-end workflowfrom image capture to decision support. This integrated approach supports early intervention, better yield outcomes, and cost-effective disease management in sugarcane farming. 2025 IEEE. -
A Legal Analysis on Navigating Facial Recognition Technology in Indias State Surveillance Framework
The evolution of the state surveillance apparatus in India, particularly through the integration of novel technologies such as Facial Recognition Technology (FRT) and Artificial Intelligence (AI), has significantly transformed national security and law enforcement strategies. While these advancements enhance the states capacity for counter- terrorism and crime prevention, they also raise critical privacy and human rights concerns. The paper analyses the existing legal framework governing surveillance in India, focusing on the implications of AI- driven FRT. Key concerns are categorised into three areas: (a) security vulnerabilities, (b) inaccuracies, biases, and lack of transparency in FRT systems, and (c) the potential misuse of surveillance powers by the state. The paper is not technical in nature; instead, it critically analyses existing laws and proposes policy recommendations and regulatory mechanisms to balance national security imperatives with the protection of individual privacy rights in a democratic society. 2026 by IGI Global Scientific Publishing. -
AI-driven surveillance in India: Reconciling privacy, national security and legal oversight
Artificial intelligence (AI) is having a significant impact on how the surveillance apparatus in India operates. Along with the numerous possibilities, the indoctrination of AI in surveillance mechanisms poses serious privacy concerns. The conflict between state surveillance and the fundamental right of privacy is apparent even at the conceptual level. On the one hand, the rise of advanced surveillance mechanisms has been an abetting factor in this conflict, while on the other hand, many theorists have been at work to find a harmonisation between them. Throughout Indian history, surveillance apparatus has helped thwart threats to national security and maintain the nations integrity. The apparent disadvantage of surveillance can be its intrusion into citizens right to privacy, which poses several legal challenges. This paper explores how incorporating AI in surveillance mechanisms enhances Indias surveillance apparatus and influences the conflict between national security and privacy rights. The paper examines how revolutionary AI technologies such as predictive policing, facial recognition (FRT) and AI-enhanced monitoring systems aggravate the apparent conflict between national security interests and the fundamental right to privacy, as adjudged in the Puttaswamy judgment. The paper critically analyses the existing legal architecture, which consists of the Telecommunications Act and the IT Act, and highlights its shortcomings. Further, the paper traverses how legal frameworks of other jurisdictions such as the European Union (EU) AI Act, the Canadian AI and Data Act (AIDA) and the US regulatory guidelines could guide India in determining a well-rounded regulatory approach. Additionally, the paper proposes adopting a context-based or risk-based approach to AI regulation and the practical challenges therewith in an attempt to harmonise the state security imperative with citizens privacy rights without obstructing technological advancement. The comparative analysis of different regulatory guidelines and legislations and the potential regulations would provide practical insights for the legislature, law enforcement and other stakeholders. The paper ultimately argues that there is an exigence for a comprehensive regulatory framework to conciliate national security and privacy rights in the AI-powered digital landscape. This article is also included in The Business and Management Collection which can be accessed at https://hstalks.com/business/. Henry Stewart Publications. -
Right to Privacy and State Surveillance: An Analysis of the Legal and Ethical Challenges Concerning State Surveillance
The conflict between state surveillance and the fundamental right of privacy is apparent even at the conceptual level. The rise of advanced surveillance mechanisms has been another abetting factor to this conflict while on the other hand many theorists have been at work to find a harmonisation between them. Surveillance techniques and mechanisms have indeed been helpful in thwarting the threats to national security and keeping the integrity of a nation. The chapter analyses whether the collective good that upholds state surveillance tends to override the individual interest that advocates the right to privacy. It considers the criteria or procedures followed to arrive at such a decision and whether the authorities could have been advised to find a better solution through harmonization rather than neglecting one in favour of the other. It explores the morality behind surveillance, including the panopticon model by Bentham, which was an example of promoting moral behaviour through surveillance. 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. -
Increasing usage of indirect advertisements and its effects /
Advertising is not at all a new term for the contemporary world. Brands have been using advertisements for the promotion and popularity. As an industry which needs a lot of creativity, advertising industry had undergone a lot of changes. According to the time and trend advertising industry found various changes and practised it. Here the researcher is trying to find out the latest trend in the advertising industry. -
AI Based Seamless Vehicle License Plate Recognition Using Raspberry Pi Technology
This research presents the implementation of an innovative Vehicle Management System designed specifically for the Christ University Project 'CampusWheels.' The system incorporates cutting-edge technologies, including YOLOv8 and Tesseract OCR, for robust license plate recognition. Addressing the unique challenges faced by Christ University in managing and securing vehicular movements within the campus, this project becomes crucial as the number of vehicles on campuses continues to grow. It not only provides an effective solution to these challenges but also introduces innovative methodologies, marking a significant departure from conventional campus management practices. The paramount importance of this project lies in its ability to enhance campus security through real-time vehicle monitoring and identification. The utilization of YOLOv8 for vehicle detection and Tesseract OCR for license plate recognition ensures a high level of accuracy in identifying and tracking vehicles entering and leaving the campus. This precision significantly contributes to the prevention of unauthorized vehicle access, a common security concern on educational campuses. Moreover, the system's ability to streamline traffic flow and improve efficiency in parking and access control addresses practical issues faced by campus administrators and security personnel. 2024 IEEE. -
Treexpan instantiation of xpattern framework
Most of the data generated from social media, Internet of Things, etc. are semi-structured or unstructured. XML is a leading semi-structured data commonly used over cross-platforms. XML clustering is an active research area. Because of the complexity of XML clustering, it remains a challenging area in data analytics, especially when Big Data is considered. In this paper, we focus on clustering of XML based on structure. A novel method for representing XML documents, Compressed Representation of XML Tree, is proposed following the concept of frequent pattern tree structure. From the proposed structure, clustering is carried out with a new algorithm, TreeXP, which follows the XPattern framework. The performances of the proposed representation and clustering algorithm are compared with a well-established PathXP algorithm and found to give the same performance, but require very less time. The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd 2021. -
It is their duty: a commentary on filial discrepancy and perceived neglect among older adults
Filial piety, a traditional value that dominates the family structure in many societies, plays a significant role in the well-being of the geriatric population. The gravity of a healthy filial relation is weighed out in the existing literature, by highlighting correlations with the onset of depression, loneliness, low life-satisfaction and suicidal tendencies. Grounded in the expectations of respect, care and obedience, these values have been challenged by the changing family dynamics of the modern world. This commentary explores how such discrepancies could instill a perception of neglect and abuse among the older population, thereby affecting their quality of life. 2025 Taylor & Francis Group, LLC. -
Coming out of the desi closet: disclosure of same-sex sexuality in metropolitan-India
Coming out of the closet is a psychosocial process that entails the disclosure of ones non- heteronormative sexual orientation to family, peers, and the wider publica phenomenon that is necessitated by the prevalence of societal heteronormativity. With the recent legal decriminalization of consensual same-sex sexual relationships in India, there is renewed interest in and emergent necessity to expand upon the existing academic discourse on the lives, rights, health and well-being of same-sex attracted individuals in India. The present study accumulates detailed narratives of disclosure of sexual orientation of five male and five female young-adults of same-sex sexuality from ages 18 to 25 in metropolitan cities of India. Thematic narrative analysis is used to gain insight into the factors of being in the closet, those underlying coming out of the closet, and the expectations from and impact of coming out to ones family. Five major themes have emergedthree restraint factors and two propulsion factors influencing sexual identity disclosure. Restraint factors are those that reduce the probability of coming out and these arean incessant pressure to conceal, perceived lack of stability and support, and anticipated disintegration of long-standing familial tradition. Propulsion factors act as catalysts of disclosure and these are target congeniality i.e. approachability of the target of disclosure, and parental validationwhich, when attained, enables the individual to come out more easily to others. The findings have been critically compared and contrasted with the existing body of literature in the domain, which sets the agenda for further inquiry. 2021 Taylor & Francis Group, LLC. -
Dynamics of fractional model of biological pest control in tea plants with beddingtondeangelis functional response
In this study, we depicted the spread of pests in tea plants and their control by biological enemies in the frame of a fractional-order model, and its dynamics are surveyed in terms of boundedness, uniqueness, and the existence of the solutions. To reduce the harm to the tea plant, a harvesting term is introduced into the equation that estimates the growth of tea leaves. We analyzed various points of equilibrium of the projected model and derived the conditions for the stability of these equilibrium points. The complex nature is examined by changing the values of various parameters and fractional derivatives. Numerical computations are conducted to strengthen the theoretical findings. 2021 by the authors. Licensee MDPI, Basel, Switzerland. -
A Neural Network Based Customer Churn Prediction Algorithm for Telecom Sector
For telecommunication service providers, a key method for decreasing costs and making revenue is to focus on retaining existing subscribers rather than obtaining new customers. To support this strategy, it is significant to understand customer concerns as early as possible to avoid churn. When customers switch to another competitive service provider, it results in the instant loss of business. This work focuses on building a classification model for predicting customer churn. Four different deep learning models are designed by applying different activation functions on different layers for classifying the customers into two different categories. A comparison of the performance of the different models is done by using various performance measures such as accuracy, precision, recall, and area under the curve (AUC) to determine the best activation function for the model among tanh, ReLU, ELU, and SELU. 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. -
Securing International Law Against Cyber Attacks through Blockchain Integration
Cyber-attacks have become a growing concern for governments, organizations, and individuals worldwide. In this paper, we explore the use of blockchain technology to secure international law against cyber-attacks. We discuss the advantages of blockchain technology in providing secure and transparent data storage and transmission, and how it can enhance the security of international law. We also review the current state of international law regarding cyber-attacks and the need for a robust and effective legal framework to address cyber threats. The study proposes a blockchain-based approach to secure international law against cyber-attacks. We examine the potential of blockchain technology in providing a decentralized and tamper-proof database that can record and track the implementation of international laws related to cyber-attacks. We also discuss how smart contracts can be utilized to automate compliance with international laws and regulations related to cybersecurity. The study also discusses the challenges and limitations of using blockchain technology to secure international law against cyber-attacks. These include the need for interoperability between different blockchain networks, the high energy consumption of blockchain technology, and the need for international cooperation in implementing and enforcing international laws related to cybersecurity. Overall, this study provides a comprehensive overview of the potential of blockchain technology in securing international law against cyber-attacks. It highlights the need for a robust legal framework to address cyber threats and emphasizes the importance of international cooperation in implementing and enforcing international laws related to cybersecurity. 2023 IEEE. -
An Innovative Method for Election Prediction using Hybrid A-BiCNN-RNN Approach
Sentiment, volumetric, and social network analyses, as well as other methods, are examined for their ability to predict key outcomes using data collected from social media. Different points of view are essential for making significant discoveries. Social media have been used by individuals all over the world to communicate and share ideas for decades. Sentiment analysis, often known as opinion mining, is a technique used to glean insights about how the public feels and thinks. By gauging how people feel about a candidate on social media, they can utilize sentiment analysis to predict who will win an upcoming election. There are three main steps in the proposed approach, and they are preprocessing, feature extraction, and model training. Negation handling often requires preprocessing. Natural Language Processing makes use of feature extraction. Following the feature selection process, the models are trained using BiCNN-RNN. The proposed method is superiorto the widely usedBiCNN and RNN methods. 2023 IEEE. -
Nonlinear Dynamics in Distributed Ledger Blockchain and analysis using Statistical Perspective
More and more in healthcare is blockchain technology applied for safe and open data storage. Still, it is understudied how deeply regression analysis combined with nonlinear dynamics into distributed ledger systems performs. This kind of approach may help to increase data transfer efficiency and help storage management in blockchain systems. Data speed and storage efficiency restrictions make current blockchain systems difficult to handle for large amounts of healthcare data. Conventional methods find poor data retrieval and transfer due to the great complexity and nonlinear characteristics of healthcare data. Combining nonlinear dynamics with deep regression analysis, this paper proposes a fresh approach for maximizing data transfer and storage in blockchain systems. Inspired by nonlinear dynamics ideas, a deep regression model aimed at maximizing block storage and forecast data transmission requirements was assessed on a simulated healthcare dataset using a distributed ledger system with 1,000 blocks and a 500 GB total dataset size. Performance criteria covered transmission efficiency and storage consumption. The proposed technique improved data transmission efficiency by thirty percent over current techniques. Another clear improvement was using storage; block size needs fell 25%. The best model, according to numerical research, lowered an average transmission time from 120 to 84 minutes and storage overhead from 200 to 150 GB. 2024, International Publications. All rights reserved.