Daily Archives: April 24, 2026

Authors: N.Santosh Kumar, V.Kavya, M.Harshitha, P.SudheerKumar, V.Karthik

Abstract: Train collisions are one of the critical safety concerns in railways, which could result from human error, signal failure, communication delay, or reduced line of sight owing to complex terrain. This paper proposes a low-cost intelligent TCPS designed using ESP32 microcontrollers, ultrasonic sensors, ESP-NOW wireless communication, and an automatic servo-based braking mechanism. The trackside unit constantly monitors the movement of the train through two ultrasonic sensors and calculates real-time distances for potential head-on collision detection. When the system identifies a threat, it issues an instantaneous emergency braking signal to the onboard units, which trigger the servo-driven brake assembly. Further, the proposed system is integrated with regenerative braking to recover the kinetic energy for recharging the lithium-ion battery supply present in the system. Experimental testing on a prototype railway track has shown high detection accuracy, quick wireless communication, and reliable automatic braking. The proposed system gives a scalable, modular, and energy-efficient alternative to conventional railway safety mechanisms that can be integrated with state-of-the-art signalling and AI-based prediction in future applications.

Published by: vikaspatanker

Authors: Riya Srivastava, Dr. Shilpi Aggarwal

Abstract: This study delves into the diverse perceptions of mental health, with a particular focus onSchizophrenia, across different generational cohorts. By examining how perceptions have evolved over decades, from the "Gen Z" cohort to older generations, this research aims to broaden our understanding of the disorder and its impact on individuals and society. The study encompasses an extensive analysis of Schizophrenia, covering its historical evolution, contemporary awareness, and societal attitudes. Through a comparative lens, it investigates how perceptions of Schizophrenia and the resulting social distance vary among individuals of diverse ages. Employing a mixed-methods approach, primarily utilizing an online survey, this research captures a comprehensive picture of mental health literacy and stigma across these generations. This work contributes valuable, actionable data to the field of mental health advocacy and education. Ultimately, it advocates for a more inclusive and supportive society, where mental health is understood, accepted, and supported across all generations.

DOI: https://doi.org/10.5281/zenodo.19728938

Published by: vikaspatanker

Authors: Yaminideavi A, Elakkiya N S

Abstract: The rapid expansion of urban populations has significantly intensified waste generation, straining the efficiency of traditional collection methods that rely on static, fixed schedules. Such conventional systems often result in overflowing bins, inefficient collection routes, and escalated operational costs. This paper proposes a comprehensive Long Range Wide Area Network (LoRaWAN) infrastructure designed to modernize Smart City waste management. Unlike existing single-task architectures, the proposed framework integrates a multi-tiered hierarchy of LoRaWAN device classes to manage services of varying complexity. At the foundational level, smart bins utilize ultrasonic sensors and low-power microcontrollers to monitor fill levels and environmental conditions. Higher-level smart drop-off containers facilitate user interaction and support asynchronous downlink queries for real-time data exchange. Data is transmitted via LoRa gateways to a centralized cloud-based dashboard, enabling municipal authorities to monitor bin status and dynamically optimize collection routes. Experimental results suggest that this scalable, energy-efficient IoT paradigm not only prevents bin overflow through automated threshold alerts but also reduces fuel consumption and environmental impact. The integration of diverse LoRaWAN node classes provides a robust, cost-effective solution for real-time urban process control within the Smart City ecosystem.

Published by: vikaspatanker

Authors: Dr. Dhanalakshmi S, B. Sasi Prabha

Abstract: In an era where financial transactions are increasingly digital, the threat of cyber fraud has become a growing concern for both institutions and individuals. With every swipe, click, or transfer, there's a risk that sensitive data could be exploited by attackers using sophisticated techniques. As fraudsters become smarter, our defenses must evolve too. This chapter presents a practical approach to fraud detection using MATLAB, focusing on a simple, transparent, and explainable rule-based system. Rather than relying on complex machine learning models that can act as "black boxes," this method uses intuitive rules based on transaction amount, time, and location to flag potentially fraudulent activity. The system is built with ease of implementation in mind, making it ideal for financial institutions looking for an interpretable starting point or a lightweight solution for early warning detection. The model is demonstrated on simulated transaction data, and its results are visualized clearly to show the difference between normal and suspicious behavior. By the end of this chapter, readers will not only understand how to build a basic fraud detection system in MATLAB, but also appreciate the importance of balancing technical rigor with real-world usability in cybersecurity efforts.

DOI: https://doi.org/10.5281/zenodo.19718898

Published by: vikaspatanker

Authors: Kriti.R. Shukla

Abstract: As of 2026, the biotechnology sector has undergone a fundamental paradigm shift from a traditional "wet-lab first" experimental model to an "in silico first" computational framework. This evolution is driven by the maturation of generative artificial intelligence (AI), geometric deep learning, and multi-modal foundational models. This article explores the current state of AI in biotechnology, focusing on protein engineering, generative chemistry, genomic interpretation, and bioprocess optimization. We examine how the integration of Large Language Models (LLMs) and diffusion-based generative models has accelerated the drug discovery pipeline, reduced R&D costs, and enabled the design of de novo biological systems with unprecedented precision.

Published by: vikaspatanker