Volume 14 | Issue 5 |

 Abstract

This paper explores the profound thematic and structural parallels between Masuji Ono in An Artist of the Floating World and Stevens in The Remains of the Day. Both protagonists function as high-level stewards of crumbling ideologies--specifically the imperialist fervour of Sh?wa-era Japan and the rigid class hierarchy of the British landed gentry. By examining their roles as unreliable narrators and "victims" of their own professional devotion, this study argues that both men are casualties of a "falling structural world." In this context, the moral frameworks they served have not only collapsed physically and politically but have been retroactively condemned by the very societies they aimed to improve. Their tragedy lies in the irreconcilable gap between their subjective sense of "dignity" and the objective historical reality of their complicity.

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 Keywords

Kazuo Ishiguro, Masuji Ono, Stevens, unreliable narration, moral ambiguity, historical complicity, social isolation, guilt, denial, emotional suppression

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 Abstract

With the rapid advancement of cloud computing and the widespread adoption of containerized applications, managing high-concurrency workloads efficiently has become a significant challenge in modern distributed systems. Traditional auto-scaling techniques, particularly reactive approaches such as threshold-based scaling, often fail to respond promptly to sudden and unpredictable workload variations, resulting in resource over-provisioning or under-provisioning and degraded system performance. To address these limitations, this paper proposes a trend detection-based auto-scaling method that integrates Long Short-Term Memory (LSTM)-based workload prediction with a trend detection mechanism. A cooldown strategy is also implemented to prevent frequent and unnecessary scaling operations, ensuring system stability. The proposed approach effectively combines prediction, trend awareness, and controlled execution to optimize resource utilization while maintaining performance under dynamic workload conditions in containerized environments.

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 Keywords

Auto-scaling, LSTM, Trend Detection, Kubernetes, Containers, High-Concurrency, Cloud Computing, Workload Prediction, Pod Allocation.

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 Abstract

In today's rapidly advancing technological world, efficient battery management is essential for applications such as electric vehicles, renewable energy systems, and portable electronics. A Battery Management System (BMS) ensures safe and reliable operation by monitoring key parameters like voltage, current, and temperature; however, existing systems often lack features such as user-defined charging limits, real-time State of Charge (SoC) and State of Health (SoH) monitoring, and effective fault protection. This paper presents the design and implementation of an improved BMS that incorporates a smart charging limiter, real-time monitoring, and MOSFET-based protection mechanisms. The system utilizes an analog front-end and a microcontroller to continuously monitor battery parameters and automatically protect against over-voltage, under-voltage, over-current, and overheating conditions. Additionally, the system provides monitoring of key parameters such as voltage, current, temperature, SoC, and SoH through a mobile application using the Blynk platform. The proposed system enhances battery safety, improves performance, and extends battery lifespan, making it suitable for modern energy storage applications.

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 Keywords

1. Battery Management System (BMS) 2. Lithium-Ion Battery 3. State of Charge (SoC) 4. State of Health (SoH) 5. Real-Time Monitoring 6. Smart Charging Limiter 7. Battery Safety 8. BQ76920 9. ESP32 Microcontroller 10. MOSFET Protection 11. CAN Communication 12. Blynk Platform 13. IoT-Based Monitoring 14. Cell Balancing 15. Protection Circuitry

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Creative Commons Attribution 4.0 and The Open Definition

 Abstract

The global digital economy is undergoing a structural transformation in which consumers are progressively transitioning from ownership rights to perpetual licensees paying recurring rents for digital goods and services. This paper explores the "Techno-Feudalism" framework advanced by Yanis Varoufakis, contrasting it against the "Platform Capitalism" counter-narrative, to examine how digital platforms in India extract cloud rent from consumers and businesses through algorithmically governed ecosystems. Employing a mixed-methods design combining secondary data analysis of global subscription economy trends with an exploratory pilot survey of urban Indian digital consumers (n=100), the study investigates the economic drivers compelling the transition from ownership to subscription models, the theoretical validity of digital rentier frameworks, and the psychological impact of subscription proliferation. Key findings reveal that 86% of the primarily urban, Gen-Z respondents prefer perpetual ownership over subscription access, yet 86% currently pay for at least one subscription service. This reflects a structural preference-reality dissonance driven by ecosystem lock-in, choice architecture, and the systematic elimination of buy-once alternatives. Platform entrapment is widespread, with 86% of respondents feeling trapped within digital ecosystems, and the average perceived ownership score standing at only 2.43 out of 5. The paper situates these findings within the theoretical debates between Technofeudalism and late-stage capitalism, while examining India's stalled Digital Competition Bill 2024 and institutional capacity constraints at the Competition Commission of India as of early 2026. The research concludes that without robust ex-ante legislative intervention and institutional capacity building, the Indian digital economy risks consolidating into a rentier system structurally constraining consumer economic agency.

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 Keywords

Techno-feudalism, platform capitalism, subscription economy, digital ownership, cloud capital, consumer behaviour, Digital Competition Bill India, digital fatigue.

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 Abstract

Anomaly detection in network traffic plays a critical role in ensuring network security, as unusual patterns may indicate cyberattacks, intrusions, or system failures. With the rapid growth of modern networks, the volume and complexity of traffic data have increased significantly, making traditional rule-based and statistical techniques less effective in identifying abnormal behaviour. This paper explores the use of machine learning techniques for efficient and accurate anomaly detection in network traffic. The proposed approach focuses on analysing network traffic characteristics and leveraging data-driven models to automatically learn normal and abnormal patterns. Machine learning algorithms including Isolation Forest, Naive Bayes, XGBoost, LightGBM, and Support Vector Machine (SVM) are employed to process large-scale datasets, enabling the detection of subtle and complex anomalies that are difficult to identify using conventional methods. The system incorporates data preprocessing using the KDDCup99 dataset, feature extraction via Principal Component Analysis (PCA), and class balancing through the Synthetic Minority Over-sampling Technique (SMOTE). The effectiveness of the proposed method is evaluated using standard performance metrics such as accuracy, precision, recall, and F1-score. The results demonstrate that machine learning-based approaches provide a scalable and reliable solution for real-time anomaly detection in modern network environments.

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 Keywords

Anomaly Detection, Network Traffic, Machine Learning, KDDCup99, PCA, SMOTE, XGBoost, LightGBM, Isolation Forest, Intrusion Detection.

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 Abstract

In today's digital era, the secure storage and transmission of sensitive documents is a significant challenge. Manual record-keeping and traditional password-based systems are prone to threats such as unauthorized access, theft, and data manipulation. To resolve these concerns, this research proposes a secure Digital Locker System that enables users to store, upload, encrypt, and manage personal documents using AES-256 encryption with Fernet implementation and OTP-based authentication through Twilio. The system is developed using the Flask framework and provides a user-friendly interface resembling a physical locker. The Digital Locker ensures confidentiality, integrity, and high security of sensitive documents such as ID proofs, certificates, medical reports, and legal papers. This project demonstrates a scalable, secure, and user-centric digital document vault for individuals and organizations.

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 Keywords

Digital Locker, Encryption, AES-256, OTP Verification, Flask, Secure Storage, Authentication

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Creative Commons Attribution 4.0 and The Open Definition

 Abstract

The rapid development of artificial intelligence has led in the creation of manipulated digital media, referred to as "deepfakes," which pose major challenges to cybersecurity, public trust, and information authenticity. In today's digital environment, detecting these highly realistic synthetic images and videos is critical since they can be used maliciously for identity impersonation, misinformation, and other purposes. This research proposes a deep learning method that employs convolutional neural networks (CNNs) to detect corrupted media. By detecting microscopic imperfections that are difficult to detect by hand, the system is designed to accurately evaluate images, videos, and audios. Real-time media analysis and effective user interaction are made possible by a combination of a high-performance backend driven by FastAPI with an accessible web frontend created with React. The model uses transfer learning based on the ResNet architecture to improve detection performance, which enables it to extract complex visual features and achieve high classification accuracy even with a limited amount of training data. To increase robustness and generalization, preprocessing methods like normalization, expanding, and data augmentation are also used. Users can submit media files and get immediate predictions on their accuracy because to the scalable and user-friendly suggested architecture. The system consistently distinguishes between authentic and altered content, according to experimental results. All things considered, the framework offers a quick, automated, and efficient media verification solution that significantly reduces the need for manual inspection.

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 Keywords

Deepfake Detection, Convolutional Neural Networks (CNN), Transfer Learning, ResNet, Media Authentication, Image and Video Forensics, Artificial Intelligence, FastAPI, React

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Creative Commons Attribution 4.0 and The Open Definition

 Abstract

Artificial Intelligence (AI) is no longer a peripheral innovation in higher education; it is rapidly becoming part of everyday teaching, learning, and academic decision-making. From generative AI tools that assist students in writing and problem-solving to algorithmic systems that shape assessment and institutional governance, AI is reshaping how knowledge is produced and evaluated. While these developments offer genuine opportunities to enhance learning and inclusion, they also raise pressing questions about human agency, ethical responsibility, and the future role of educators and students. This paper argues that higher education must move beyond viewing AI as a purely technical solution and instead approach it as a socio-ethical challenge. Drawing on interdisciplinary research and global policy frameworks, the paper proposes a human-centered reframing of AI integration--one that preserves human judgment, strengthens ethical accountability, and aligns technological innovation with education's public mission.

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 Keywords

Artificial Intelligence; Higher Education; Human Agency; Ethical Responsibility; Generative AI; Human-Centered AI; Ethical Accountability; Educational Innovation.

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Creative Commons Attribution 4.0 and The Open Definition

 Abstract

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 Keywords

Method development, Validation, Lobeglitazone, spectroscopy, green analytical approach

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 Abstract

The Vedas serve as the ancient foundational source of cosmic truth, with Ayurveda emerging as a vital Upaveda of the Atharvaveda. To understand the origins of Ayurveda, this study explores two primary conceptual frameworks: the mythological narrative presented in the Vishnu Purana and the Ayurveda Avatarana as described in the classical Ayurveda Samhitas. Rather than viewing these accounts as mere folklore, this article analyses the scientific rationality embedded within their characters and events. By evaluative symbolic elements such as Samudra, Manthana, and Kurma etc from the Puranic tradition alongside the historical-symbolic roles of Indra, Bharadwaja, and the Ashwini Kumaras etc from the Samhitas, the research reveals a systematic logic of medical evolution. The study concludes that these narratives are not merely stories but represent a structured scientific basis for the emergence of Ayurvedic knowledge. Hence in the present work the effort is made to interpret the evolution of Ayurveda by comparing with Samudra Manthana and Ayurveda Avatarana.

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 Keywords

Samudra, Manthana, Ayurveda Avatarana, Upaveda, Bharadwaja, Indra, Amrutha.

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