Daily Archives: July 12, 2025

Authors: Amit Sharma

Abstract: As the printing industry transitions into the era of Industry 4.0, traditional quality assurance methods—centered on manual inspection and reactive defect handling—are increasingly inadequate for the speed, complexity, and customization demands of modern pressrooms. This paper explores the transformative potential of Artificial Intelligence (AI) and Machine Learning (ML) in real-time monitoring and quality assurance (QA) across print production workflows. Leveraging technologies such as computer vision, IoT sensor networks, and predictive analytics, AI-enabled systems enable proactive defect detection, automated correction, and dynamic process optimization. Applications include in-line visual inspection, root cause analysis, intelligent alerting, and traceable compliance logging. Case studies demonstrate significant gains in defect reduction, throughput, and client satisfaction. However, adoption remains hindered by challenges such as legacy equipment integration, data infrastructure gaps, workforce readiness, and cybersecurity concerns. Future directions emphasize the role of digital twins, federated learning, cloud-based QA hubs, and sustainability-aware defect prevention. Ultimately, AI transforms quality assurance from a reactive function into a strategic enabler—advancing efficiency, brand protection, and environmental responsibility in next-generation print operations.

DOI: https://doi.org/10.5281/zenodo.15868325

 

Published by: vikaspatanker

Authors: Rupsa Sarkar

Abstract: This work introduces a new energy-based model based on cross product torque analysis for the generalization of the tipping condition of rigid bodies of general shape. Classical mechanics employs torque to find rotational balance, but my method introduces the percent contact area (PCA) to define the extent to which the object is supported on a surface and how this influences tipping. The article presents a formula for computing the minimum amount of external energy to cause tipping by considering torque through the cross product and accounting for geometric distribution and weight. The PyBullet simulations yield high correlation, affirming the model's ability to make predictions.

DOI: https://doi.org/10.5281/zenodo.15867962

 

Published by: vikaspatanker

Authors: Dr. Sanjeev Puri, Sandeep Keshav

Abstract: The evolution of Edge-AI technologies has created new opportunities in pediatric healthcare, allowing for real-time monitoring of infants while maintaining privacy. This research introduces an innovative multi-modal Edge-AI framework that combines video, audio, and physiological data to anticipate potential mental health issues in infants. The proposed system processes information locally on edge devices, minimizing latency, enhancing privacy, and enabling continuous monitoring in both clinical and home settings. By employing lightweight AI models for on-device processing, the system promotes early identification of neurodevelopmental challenges and encourages timely interventions. This approach aims to shift healthcare from a reactive stance to a preventive one, ultimately aiming to foster long-term enhancements in mental health. The paper outlines the system's architecture, techniques for optimizing AI models, and prospective applications in pediatric healthcare environments.

DOI: https://doi.org/10.5281/zenodo.15867669

 

Published by: vikaspatanker