Journal of Computer Science Implications

Optimizing Cloud Infrastructure on AWS: Strategies for Production and Development Environments

Carlos Alvarez, Priya Nair — Sun, 05 Jan 2025 00:00:00 +0000

The advent of cloud computing, particularly Amazon Web Services (AWS), has revolutionized how organizations deploy and manage their digital infrastructure. However, effectively governing distinct environments—such as production and development—within AWS presents unique challenges related to consistency, security, scalability, and cost efficiency. This article outlines a comprehensive approach to managing these critical cloud environments, emphasizing key strategies including Infrastructure as Code (IaC), robust security protocols, continuous monitoring, and the adoption of modern architectural patterns like serverless computing. By adhering to these methodologies, organizations can achieve greater operational agility, reduce deployment risks, and optimize resource utilization across their AWS footprint.

The Evolving Landscape of Privacy Engineering: Practitioner Perspectives, Organizational Dynamics, and Current Methodologies

Dr. Ibrahim H. Al-Mutairi — Fri, 11 Apr 2025 00:00:00 +0000

The proliferation of personal data and the increasing stringency of global privacy regulations have elevated privacy from a mere legal compliance concern to a fundamental engineering challenge. Privacy engineering, a nascent yet critical discipline, focuses on embedding privacy protections directly into the design and operation of information systems. This article synthesizes existing research to explore the multifaceted realities of privacy engineering as experienced by practitioners in real-world settings. Specifically, it delves into the mindsets of privacy engineers and software developers, examines the organizational factors that influence privacy integration, and reviews the current methodologies and practices employed. Through a qualitative synthesis of relevant literature, we highlight the significant gaps between regulatory expectations and practical implementation, driven by varied practitioner understandings, diverse organizational cultures and climates, and the inherent complexities of translating abstract privacy principles into concrete technical solutions. The findings underscore the socio-technical nature of privacy engineering, emphasizing that effective privacy protection requires not only robust technical tools but also a strong organizational commitment and a pervasive privacy-aware mindset among all stakeholders.

Real-Time Digital Twin for Stewart Platform Control and Trajectory Synthesis

Dr. Julian Blackwood, Professor Elara Croft — Mon, 24 Feb 2025 00:00:00 +0000

This article presents the design, implementation, and validation of a real-time digital twin for a Stewart platform, integrated with advanced trajectory computation capabilities. Stewart platforms, known for their high precision and multi-degree-of-freedom motion, are widely used in applications such as flight simulators, medical devices, and manufacturing systems. The integration of a digital twin allows for real-time monitoring, predictive analysis, and enhanced control, thereby improving the system's operational efficiency and resilience. This work details the architectural framework of the digital twin, the methods for real-time data synchronization, the algorithms for dynamic trajectory generation, and the validation of the virtual model against the physical Stewart platform. The proposed system demonstrates significant potential for improving the control, diagnostics, and overall performance of complex robotic manipulators through a comprehensive virtual representation.

Towards Eco-Friendly Additive Manufacturing: An AI-Powered Model for Filament Waste Reduction through Failure Prediction

Prof. Ethan Walker, Dr. Wang Lijuan — Thu, 22 May 2025 00:00:00 +0000

The rapid growth of 3D printing (additive manufacturing) has revolutionized various industries, offering unprecedented capabilities for rapid prototyping, customized production, and complex geometries. However, this transformative technology is not without its environmental footprint, particularly concerning material waste generated from failed prints and support structures. Traditional quality control methods are often reactive, leading to significant filament waste and increased production costs. This article presents the development of an Artificial Intelligence (AI) based failure predictor model designed to proactively identify and mitigate print defects, thereby reducing filament waste and enhancing the sustainability of the 3D printing process. The methodology involves leveraging diverse sensor data, including computer vision and vibrational signals, to train advanced machine learning algorithms for real-time defect detection and prediction. Hypothetical results demonstrate the model's high accuracy in identifying common print failures such as warping, stringing, and spaghetti defects, enabling automated print intervention. The findings underscore the critical role of AI in improving print quality, optimizing material utilization, and fostering more sustainable additive manufacturing practices, paving the way for a greener future in industrial production.

Sustainable IT Operations: Strategies and Techniques for Green Computing

Dr. Radhika Iyer — Wed, 19 Mar 2025 00:00:00 +0000

In an era marked by rapid technological advancement and growing environmental concerns, sustainable IT operations have become a critical focus for organizations worldwide. The increasing demand for digital services has led to a significant rise in energy consumption, electronic waste, and carbon emissions from data centers, enterprise infrastructures, and end-user devices. As a response, green computing—the practice of designing, using, and disposing of IT resources in an environmentally responsible way—has emerged as a strategic imperative. This paper explores the core principles, strategies, and technological innovations driving sustainable IT operations. It examines energy-efficient hardware, virtualization, cloud optimization, e-waste recycling, and renewable energy integration as key enablers of green computing. Additionally, the paper highlights best practices in data center design, workload management, and IT procurement that align with sustainability goals. The role of automation, AI, and monitoring tools in reducing Its environmental footprint is also analyzed. Through case studies and industry benchmarks, the study demonstrates how organizations can reduce operational costs while achieving environmental compliance and corporate social responsibility (CSR) objectives. Ultimately, this paper underscores the importance of embedding sustainability into IT strategy to promote ecological balance, long-term efficiency, and responsible innovation in the digital age.