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Fabrics of Power: Cutting Through the Noise in the Classroom
The hijab, purdah and veil though differently named constitute a continuum of meanings shaped by social, cultural and personal contexts. A womans decision to adopt or reject these garments situates her within a shifting spectrum of religious expression and secular alternatives. The volatility of these meanings renders the garments vulnerable to political appropriation, transforming them into contested symbols that are difficult to address pedagogically, therefore becoming a fabric of power. The hijab controversy that unfolded in Karnatakas educational institutions in early 2022 sharpened these complexities, prompting extensive public commentary on the purpose of education, the responsibilities of institutions, and the rhetorics of liberty, secularism, nation and religion. This article examines these commentarial responses ranging from editorials to columns in Kannada and the English media while reflecting on the parallel experience of teaching concepts such as liberty, dissent, secularism and religion during the period of unrest. In doing so, it foregrounds the paradox inherent in the politics of teaching literature, the framing of literature as political, and the pedagogical negotiations required when instruction unfolds within a charged and highly politicised atmosphere. 2025, Unisa Press. All rights reserved. -
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Computational investigation into the structure, effect of band gap energies, charge transfer, reactivity, thermal energies and NADPH inhibitory activity of a benzimidazole derivative
This work contains computational investigations of a benzimidazole derivative consisting of density functional theory, electronic structure and biological evaluation of a benzimidazole derivative. Density functional theory evaluation were conducted, starting from geometry optimisation, followed by the molecular electrostatic potential, spectral analyses, polarizability studies and thermodynamic analyses via the frequency calculations. Solvent frontier molecular orbital analyses, impact on the properties of the molecule were modelled with the IEFPCM model of solvation. Topological analyses helped to ascertain the molecule's electronic structure. Biological assessment included pharmacokinetic property evaluation and molecular docking. Pharmacokinetic descriptors were generated using online tools and the molecule was assessed for its efficacy as a drug molecule by comparing with the rules concerning drug-likeness and analysing the descriptors relating to absorption, distribution, metabolism, excretion and toxicity of the molecule. Docking of the molecule with the two targets, 7D3E and 3A1F, yielded a good binding energy of ?7.39 and ?5.81 kcal/mol respectively. 2024 Elsevier B.V. -
Quantum computational, solvation and in-silico biological studies of a potential anti-cancer thiophene derivative
Heterocyclic molecules display a wide spectrum of properties that span both material and biological domains. Material properties stem from their interactions in the bulk, where a large number of molecules of the same type get together resulting in an enhancement of properties. However, biological properties emanate from the interaction of a single or a few molecules with a biologically functional macromolecule. Computational tools offer a particularly useful way of theoretically studying molecules to arrive at a conclusion regarding such properties, even though they may vary when experimentally evaluated. This study concerns itself with the theoretical investigation comprising density functional theory calculations, topological analyses and in-silico biological evaluation of a thiophene compound, i.e. the title compound. Density functional theory was used to compute properties of the title molecule and their variations in unsolvated and solvated phases using Gaussian 09. The molecule in solvent phases encompassing organic polar protic, organic polar aprotic and inorganic polar protic nature have been subjected to theoretical investigations. The suitability of the molecule for deployment as a modern optical material is examined with positive results. Topological characteristics of the molecule were evaluated using Multiwfn 3.8 to examine electron density distribution and the possible resulting covalent, non-covalent and weak interactions because of such distribution. The potency of the molecule towards brain cancer was evaluated by molecular docking with Auto Dock Tools against two brain cancer protein targets 6ETJ and 6YPE with a good docking score of ?6.63 and ?6.21 kcal mol?1 respectively and the resulting interactions visualized and its pharmacokinetic properties obtained using online tools. 2024 Elsevier B.V. -
Analytical Results of Heart Attack Prediction Using Data Mining Techniques
In the modern era of living a fast lifestyle, people are not more conscious of their food eating and lifestyle. Due to these reasons, the chances of having a cardiac-related disease have risen drastically. This paper has studied the various supervised and unsupervised machine learning algorithms in comparative methods with best accuracy. Models like classification algorithms, regression algorithms, and clustering algorithms have been used for this paper. This research paper majorly focuses on patients with certain medical attributes that indicate a higher risk of heart disease. The model almost gives a good accuracy for all the regression and classification models when compared to the clustering models. Among all the algorithms, random forest and decision tree gives better accuracy 2023 IEEE. -
Sentiment Analysis on Live Webscraped YouTube Comments Using VADER Sentiment Analyzer
After the covid disease came in the beginning of 2020s, the amount of people using social medias has increased dramatically. So as an effect of that, the viewers and engagement in one of the worlds largest platform by google called YouTube also increased. So many new content creators also born during these times. So this project is getting the sentiment from the audience or user to the content creators by which they can improve their content quality. This research holds promise in harnessing the power of sentiment analysis to enhance the overall YouTube experience and inform content creators and platform administrators in their decision-making processes. Understanding these trends is vital for content creators, as it can offer invaluable insights into viewer engagement and preferences. By gaining a deeper understanding of how viewers react to content, creators can refine their strategies, tailor their content to their audience, and enhance the overall quality of videos. By incorporating sentiment information into recommendations, the platform can suggest videos that resonate more effectively with users, thereby increasing engagement and satisfaction. The identification of negative sentiment and harmful comments enables YouTubes content moderation systems to proactively address issues such as hate speech, harassment, and toxicity. This, in turn, contributes to a safer and more welcoming space for users to share their thoughts and opinions. The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024. -
Dynamic Financial Portfolio Optimization Using Temporal Convolutional Networks and Real-Time Data Analysis
This paper presents an integrated framework for AI-driven portfolio optimization combining temporal convolutional networks (TCNs) with conditional value-at-risk (CVaR) minimization. Our system processes real-time market data through an automated pipeline implementing volatility-adjusted feature engineering and walk-forward validation. The architecture employs dilated causal convolutions for temporal pattern extraction combined with Ledoit-Wolf shrinkage covariance estimation for robust portfolio optimization. Experimental results demonstrate an 18.7% annualized return with 22.3% volatility, outperforming traditional mean-variance optimization by 14.2% in risk-adjusted returns. The implementation addresses key challenges in numerical stability and computational efficiency through eigenvalue clamping and gradient checkpointing. 2025 IEEE. -
Theoretical investigation of a thiazole carboxamide derivative and its interactions with tribbles pseudokinase
In this work, we present the computational investigations of a thiazole carboxamide derivative encompassing density functional theory, topological analyses and in-silico biological studies. Beginning with geometry optimization, DFT studies included frontier molecular orbital and theoretical electronic spectra analyses, polarizability and hyperpolarizability studies and thermodynamic studies via frequency calculations. All calculations were modelled in five solvents using IEFPCM solvation model. Detailed insights into the electronic structure of the molecule were obtained by topological analyses. Biological assessment included generation of drug-likeness and pharmacokinetic descriptors using online tools and molecular docking. Docking of the molecule in Tribbles pseudokinase targets 5CEK and 5CEM revealed a binding energy of ?6.93 kcal/mol and ?6.46 kcal/mol respectively with a corresponding inhibition constant of 8.26 and 18.50 ?M. 2025 Elsevier B.V. -
Power Efficient e-Bike with Terrain Adaptive Intelligence
Electric bicycles or e-bikes are gaining momentum in the market as they are offering a smooth, noiseless and pollution free option for individual transportation in cities as well as in countryside. E-bikes are usually with a battery powered electric motor drive with an additional option for pedaling. In this work a low cost e-bike was designed and developed with a brushless DC hub motor with controllers. For smart control, smartphone was used a console and the e-bike can be controlled using a mobile application which was connected to the e-bike through Bluetooth. The controller will pick the gradient of the terrain and will control the power of the motor, which results in energy saving. Predicted range of the e-bike, speed, acceleration and total distance covered were displayed in the console along with the geographical position on the map and throttle control options. The bike with the proposed control tested and the results were giving a reduction in current drawn from the battery. 2019 IEEE. -
Assessing Land Use Transformation in Kanhangad Town: A Special Emphasis on Wetland Ecosystems
Kerala, renowned for its lush landscapes, is facing environmental challenges due to rapid urbanization, particularly in Kanhangad. This area, notable for its unique wetland ecosystem crucial for biodiversity and human livelihoods, is experiencing a conflict between residential development and wetland conservation. A comprehensive study in Kanhangad, employing diverse data sources such as open-source data, Google Earth Satellite Imagery, OpenStreetMap, and tools like ArcGIS, provides a detailed analysis of land use and its environmental impacts. The study combines digital data analysis with physical surveys to understand the ecological and developmental status comprehensively. The study reveals a dominant trend in Kanhangad's land use, with residential areas comprising 52% of the total land, mostly large, detached single-family homes. This reflects a societal shift towards viewing homes as status symbols, contributing to natural resource depletion. The research underscores the need for sustainable, low-cost housing, suggesting vertical housing as a potential solution to balance residential demands with environmental conservation. Kanhangad's wetlands, essential for local biodiversity and livelihoods, face threats from urban development and infrastructural expansion. The study shows a drastic reduction in wetland area, from 12.9 km in 2004-05 to just 1.66 km by 2020-21, indicating severe ecological degradation. Despite the Kerala Conservation of Paddy land and Wetland Act of 2008, which aims to protect these ecosystems, its limited effectiveness is evident from the ongoing depletion of wetlands. This situation calls for stricter enforcement of environmental regulations and greater public involvement in conservation efforts. Furthermore, the research examines the Kerala Paddy and Wetland Conservation Act-2008, analysing its role and effectiveness in local environmental governance. The Act, focusing on prohibiting wetland and paddy land conversion, is vital for regional conservation. However, gaps in its implementation are highlighted, especially considering the exacerbation of the 2018 and 2019 Kerala floods due to land conversion practices. The study emphasizes the urgent need for more robust environmental protection measures. 2024 by authors. All rights reserved. -
Impact of ESG Index on the Stock Return: Empirical Evidence from CRIP Sector
In modern times, investment decisions are significantly influenced by a range of metrics. One widely embraced investment strategy in both developed and developing economies is investment through analysing Environmental, Social Responsibility, and Governance (ESG) factors. Investors rely on ESG scores as a valuable resource to pinpoint companies that are more likely to maintain their growth trajectory while reducing the possibility of encountering negative occurrences such as legal complications, controversies, and unfavourable public attention. This, in turn, facilitates more effective risk management and enhances returns on investment. However, the influence of ESG factors on stock returns within the Construction, Real Estate, Infrastructure, and Project (CRIP) sector is relatively limited. Consequently, the main aim of this study is to assess how ESG aspects influence the returns of stocks in companies operating in the CRIP sector. To conduct this analysis, we employed the Crisil ESG database, which provides comprehensive data on ESG metrics and stock returns. A sample containing 35 companies from the CRIP industry was meticulously chosen for investigation. To quantify the influence of ESG aspects on stock returns within the CRIP sector, a Fixed Effect Panel Regression Model was applied. The study results suggest a favourable and considerable relationship of ESG ratings on the closing stock price. Furthermore, the analysis demonstrates a large and beneficial influence of ESG ratings on stock returns. These results contain substantial implications for investors and stakeholders having a vested interest in making well-informed investment choices within the CRIP industry. The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025. -
Continuum Mechanical Study of Love-Wave Propagation in Fibre-Reinforced Plate Over an Orthotropic Substrate with Interfacial Imperfection
Abstract: This paper aims to analyse the propagation of Love-type waves in a fibre-reinforced composite (FRC) over a pre-stressed orthotropic substrate. The upper fibre-reinforced layer is imperfectly bonded to the lower orthotropic half-space. The structure considered has a mechanical imperfection (MI). The purpose is to investigate the integrated effect of fibre reinforcement, initial stress, and flawed interface on the dispersion and attenuation of Love-type waves. The dispersion relation is derived analytically using the boundary condition. The phase velocity of the Love-type wave has been discussed in its typical cases: (i) in the infusion of reinforcement and absence of initial stress, (ii) in the presence of reinforcement subjected to initial stress, considering scenarios with and without the flawed coupling. The numerical computation of the analytical finding was performed and plotted using MATLAB, showing the impact of the parameters considered. It is observed that the interfacial imperfection positively influences the phase velocity of the Love-type wave in the model considered. The increase in the reinforcement parameter and the decrease in lead to attenuation in phase velocity. In engineering, fibres are considered to be oriented in two or more directions, and the study assumes only bidirectional fibres. The results highlight the combined influence of mechanical anisotropy, pre-stress, and interface quality on guided wave characteristics, offering valuable insights for the design and optimisation of layered composites, acoustic sensors, and structural health monitoring systems. Pleiades Publishing, Ltd. 2025. -
The effect of non-thermal argon plasma treatment on material properties and photo-catalytic behavior of TiO2 nanoparticles
In this paper, a brief study on the effect of non-thermal plasma generated with argon carrier on material properties and photo-catalytic reduction behavior of TiO2 is presented. Commercially available TiO2 nanoparticles (20 nm size) were subjected to Ar cold plasma at different time durations. Then the plasma treated materials were explored for chemical reduction of carbon dioxide (CO2) into methane (CH4) using sunlight as photo-irradiation source. The results show that the non-thermal plasma affects the material properties of TiO2 such as UV-visible absorption, XRD patterns and Raman scattering significantly and also the enhancement of CH4 yields in CO2photo-chemical reduction. 2020 American Institute of Physics Inc.. All rights reserved. -
Real-Time Fabric Defect Detection Using a Lightweight YOLOv8 Model on Edge Devices
The detection of defects in fabric is a critical process for maintaining quality standards and reducing economic losses in the textile industry. Traditional inspection methods, which rely on human operators, are often slow, inconsistent, and susceptible to error. This research introduces an innovative solution that harnesses Edge AI and deep learning to facilitate real-time, on-site defect detection. We developed a highly efficient and lightweight model based on the YOLOv8 architecture, specifically tailored for deployment on resource-constrained edge devices like NVIDIA Jetson Nano or Raspberry Pi. Through a process of comprehensive literature analysis and domain expertise, a compact, high-precision model was trained on diverse fabric defect datasets. To ensure optimal performance on edge hardware, we employed advanced optimization techniques like quantization and pruning. The primary offering of the work are threefold: the making of a streamlined YOLOv8-based model for fabric defect detection, a comparative analysis of various edge inference strategies, and a proposed system architecture for real-time embedded deployment. This study effectively demonstrates the practical application of advanced AI to solve longstanding challenges in textile quality control. Future efforts will be directed towards extensive real-world operational testing and exploring localized Model Training with Federated Learning enhancement. 2025 IEEE. -
Exploring The Multifaceted Benefits Of Strobilanthes Jomyi P. Biju, Josekutty, Rekha & J. R. I. Wood : A Comprehensive Pharmacognostic Investigation On Its Medicinal And Insecticidal Properties
Plant-based medication, is an established practice in Indian medicine, initially newlineinvolvedin the direct use of raw plant parts for treating various health conditions. Later, valuable components are identified, isolated, and utilized to treat diseases. The newlineStrobilanthes Blume genus has a rich therapeutic history around the globe, especially in countries such as India, China, Myanmar, and Thailand. Strobilanthes jomyi, a recently identified species found in Kerala, India is still in wide use by tribal communities in the Kasaragod district for wound healing. This study aimed to evaluate the microscopic, macroscopic, organoleptic, fluorescent, physicochemical, mineral composition, phytochemical, Gas Chromatography Mass Spectrometry, antioxidant, anthelmintic, insecticidal, antimicrobial, and cytotoxicity activities of S. jomyi leaves, stem, and root. The different vegetative parts were subjected to Soxhlet extraction using methanol as a newlinesolvent and analysed using standard Protocols. Macroscopic andmicroscopic examinations revealed non-glandular trichomes and paracytic stomata in the leaves, raphides in the stem and petiole, and tannin cells in the root. Cystoliths were observed only in the petiole. Powder analysis exhibited the presence of fibres, trichomes, palisade cells, spiral xylem vessels, bordered pit vessels, and raphides. The leaves contained higher phenolics, flavonoids, carbohydrate, protein, proline, and chlorophyll content compared to the root and stem. The methanolic extract of leaves showed higher antioxidant activities than the root and stem. GC-MS analysis identified bioactive compounds such as 2,4-di-tert-butyl phenol, phytol,squalene, phenol, neophytadiene, and lupeol in the root, stem, and leaves. All vegetative partsof S. jomyi exhibited excellent anthelmintic activity, with the highest newlineobserved in the leaves, followed by the root and stem. Insecticidal activity was only newlineobserved in the leaf extract. Anti-microbial and anti-cancerous activities were remarkable newlineacross all vegetative parts. -
An Approach for Detecting Frauds in E-Commerce Transactions using Machine Learning Techniques
This paper is primarily focused on E-commerce fraud detection using machine learning techniques. There are many different ways to detect E-commerce fraud using machine learning approach. In this work, comparison study is conducted between various available machine learning algorithms to detect the online frauds. During the comparative study, focus is underlined on comparison of all the algorithms to identify the fraud transactions. When compared to other algorithms, such as support vector machine, Decision Tree, K-nearest neighbour and Random Forest, it has been observed that Logistic regression gives better result among all machine learning algorithms. 2021 IEEE. -
Recommendations from teachers on schools' roles in identifying problems and building awareness among students
Students develop skills, gain knowledge, and achieve greater wellbeing by creating a positive school environment. Through the years, schools have realized the importance of mental health services for adolescents. Research on the role of schools in mental health awareness building and preventing mental health problems is meager, and focuses on students in the western context. This chapter focuses on the recommendations given by teachers on what role schools can play in identifying, preventing, and building awareness among adolescents. These recommendations are based on the themes obtained through semi-structured interviews with 24 teachers teaching 10th, 11th, and 12th graders in private high schools and colleges in Bangalore. Consequently, it aims to provide an overview of incorporating techniques and strategies to enhance mental health among school students in the Indian Scenario. 2024, IGI Global. All rights reserved. -
A Pathway to Better EMI Shielding Performance in Natural Rubber Through Ternary Carbonaceous Filler Systems
In the present study, we fabricated and characterized ternary hybrid fillers of conductive carbon black (CCB), carbon nanotubes (CNT), and reduced graphene oxide (RGO) reinforced natural rubber (NR) composites. The ternary filler system exhibited good filler-polymer interaction as observed from the cure characteristics and mechanical properties. We used impedance analysis to study the dielectric permittivity and associated polarization mechanisms, and the AC conductivity was fitted using the Jonsher Power law. The presence of functional groups on the ternary nanofiller surfaces caused increased filler-filler interactions, leading to the formation of an excellent conductive network. Mechanical and viscoelastic studies revealed the reinforcing effect of the CCB, CNT, and RGO fillers. The theoretical models, such as Nicolais-Narkis and Turcsanyi, were employed to predict the tensile strength. Morphological analysis confirms the homogeneous dispersion of filler in the matrix. The present system also demonstrated excellent electromagnetic interference (EMI) shielding performance, with the highest shielding effectiveness (SE) values of 37.4 and 35.3 dB at 12 GHz for the ternary composites, satisfying commercial requirements. 2026 John Wiley & Sons Ltd. -
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Inverted LPDA for Broadband Radio Astronomy Observation between 150 and 800 Mhz
Radio transients are celestial objects that vary their brightness in time. The brightness can vary from a few milliseconds to a few hours and exhibit emissions across Radio waves to X-rays and even in Gamma rays. Sophisticated search techniques such as single pulse search, clustering, advanced AI, and digital signal processing are used to detect the radio signals emitted from these transient sources. A study of the signals from the transient sources helps to understand their origin and nature. This paper describes the details of a new antenna designed to detect radio transients at low frequencies between 150 MHz and 800 MHz at RRI Gauribidanur Observatory. 2025 IEEE.