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288 articles for “High-performance computing”
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Cutting-Edge Developments and Innovations in Amazon Web Services (AWS)
Abstract: In 2025, Amazon Web Services (AWS) continues to dominate the cloud computing industry through groundbreaking innovations in artificial intelligence (AI), strategic partnerships, data center advancements, and expansion into new markets. These initiatives help AWS maintain its position as a top provider of secure, scalable, and efficient cloud services, adapting to the ever-changing demands of businesses across the globe. One of AWS’s most significant advancements is its AI-driven cloud services, which …
Published in Recent Trends in Parallel Computing · Vol. 12, Issue 2, 2025 · pp. 01–08 Read article
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Computational Analysis of Leaf Spring System with Functionally Graded Materials Using ANSYS
Abstract: Leaf springs are important parts of a vehicle’s suspension system, and their main function is to absorb shocks, improve stability, and make the ride more comfortable. Traditionally, ASTM A36 steel is used because it is strong, long-lasting, affordable, and easy to get. However, this type of steel is very dense, which makes vehicles heavier. This extra weight makes the vehicle less efficient and increases emissions. Because of these issues, there …
Published in Journal of Polymer & Composites · Vol. 14, Issue 2, 2026 · pp. 380–397 Read article
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Optimized Receivers for Underwater Visible Light Communication
Abstract: For uses like ocean exploration, environmental monitoring, and underwater data transfer, wireless communication under water is crucial. Conventional acoustic and radio frequency communication methods suffer from low bandwidth, high latency, and severe signal attenuation in underwater environments. With its high data rate and low propagation delay, Visible Light Communication (VLC) provides a promising alternative. In this work, an underwater VLC system is implemented using Light Emitting Diodes (LEDs) with intensity …
Published in Trends in Opto-electro & Optical Communication · Vol. 16, Issue 1, 2026 · pp. 22–33 Read article
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Algorithmic Strategies for Complex Data Handling: Optimizing Data Structures for Enhanced Computational Performance
Abstract: We live in an age of big data and processing very large often complicated datasets can be crucial to efficient algorithmic performance. This paper discusses different algorithmic techniques when working with difficult data and how to arrange your information structures correctly for better functionality in large-scale methods. It checks the impact of different algorithms like sorting, searching, and hashing in boosting its processing speed as well as memory use. This …
Published in International Journal of Data Structure Studies · Vol. 2, Issue 2, 2024 · pp. 1–10 Read article
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Optimization of Structural Health Monitoring Using Artificial Neural Network and Comparison with Traditional Method: A Comprehensive Review
Abstract: Structural health monitoring (SHM) has a critical role in ensuring civil infrastructure safety, reliability, and durability through real-time, condition-based monitoring. Traditional SHM systems employ hundreds of sensors such as accelerometers, strain gauges, and displacement transducers for monitoring vast amounts of data for structural inspection, but do not effectively manage complicated nonlinear data. This research paper, “Optimization of Structural Health Monitoring Using Artificial Neural Network and Comparison with Traditional Methods,” investigates …
Published in Journal of Structural Engineering and Management · Vol. 13, Issue 1, 2026 · pp. 23–33 Read article
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Aerodynamic Optimization of UAV Wings Using Machine Learning
Abstract: Unmanned Aerial Vehicles (UAVs) are increasingly deployed across defense, transportation, agriculture, and environmental monitoring, demanding improved aerodynamic efficiency to enhance endurance, stability, and payload capacity. Traditional aerodynamic optimization approaches, relying on computational fluid dynamics (CFD) simulations and wind tunnel experiments, are often time-consuming and computationally expensive. This study proposes a machine learning (ML)-driven framework for the aerodynamic optimization of UAV wing geometries, aiming to significantly reduce design cycles while improving …
Published in International Journal on Drones · Vol. 2, Issue 1, 2026 · pp. 1–7 Read article
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Parallel Privacy-Preserving Adaptive Federated Learning on GPU-Enabled Multi-Core Architectures
Abstract: The increasing deployment of parallel and distributed intelligent systems has intensified the need for privacy-preserving learning frameworks that can exploit multi-core and GPU-based architectures without centralizing sensitive data. This work proposes a parallel Adaptive Federated Learning (AFL) framework that integrates Differential Privacy and Secure Aggregation over heterogeneous multi-core and GPU platforms to enhance both data confidentiality and convergence efficiency. The framework dynamically adjusts client participation, learning rates, and aggregation weights …
Published in Recent Trends in Parallel Computing · Vol. 13, Issue 1, 2026 Read article
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Integrated Optimization of Solar Photovoltaic Systems Using Taguchi Method and Computational Fluid Dynamics for Enhanced Efficiency
Abstract: The transition to renewable energy demands efficient and reliable photovoltaic (PV) systems to meet rising global energy needs. This study presents an integrated optimization framework combining the Taguchi method and Computational Fluid Dynamics (CFD) to improve the thermal and electrical performance of solar PV systems. A structured experimental design using an L9 orthogonal array evaluates the influence of three key parameters—material type, panel thickness, and cooling mechanism—on system efficiency. Analysis …
Published in Recent Trends in Fluid Mechanics · Vol. 12, Issue 3, 2025 · pp. 10–25 Read article
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Hybrid Graceful QoS Degradation in Distributed Operating Systems
Abstract: Maintaining Quality of Service (QoS) in distributed operating systems is a critical challenge, especially in dynamic and resource-constrained environments. Traditional QoS mechanisms often fail to adapt effectively to unforeseen failures or load spikes, leading to abrupt service disruptions. This study reviews the concept of hybrid graceful QoS degradation, a paradigm that combines multiple strategies to ensure continuous, albeit potentially reduced, service availability. By intelligently integrating techniques like resource reservation, priority-based …
Published in Journal of Operating Systems Development & Trends · Vol. 12, Issue 2, 2025 · pp. 01–07 Read article
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Review on to Design High Speed and Area Three Oprend Binary Adder Using MDCLCG Architecture
Abstract: The three operands binary adder is a basic function used in the creation of modular arithmetic in various algorithms, such as the pseudorandom bit generator and cryptography. The CS3A carry save adder is commonly used to perform this operation. Nevertheless, the operation's outcome delayed the transmission of O(n) because of the ripple carry step. A dual-optoic adder for parallel prefix computation, such as the Han-Carlson method, can be used to …
Published in Journal of VLSI Design Tools and Technology · Vol. 14, Issue 1, 2024 · pp. 14–22 Read article
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AI-Powered Emotion Recognition in Dog
Abstract: Understanding animal emotions is important for improving veterinary care, human animal interaction, and overall pet well-being. Inspired by previous research that utilized a modified EfficientNetB5 model for emotion classification in cats and dogs, our study builds upon this foundation with a focus on real-time emotion recognition in dogs. While earlier approaches achieved high accuracy using Dense Residual and Squeeze-and-Excitation blocks, they often lacked real-time applicability and were not optimized for …
Published in International Journal of Machine Systems and Manufacturing Technology · Vol. 4, Issue 1, 2026 · pp. 20–32 Read article
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Power and Area - Aware Recursive Multiplier Architecture Utilizing Polymer Composites for Neural Network Acceleration
Abstract: Approximate computing is widely applied in error - tolerant systems as an effective technique to enhance circuit performance by deliberately allowing occasional inaccuracies instead of strictly ensuring precise results for every computation. Among the fundamental building blocks of digital systems, multipliers play a crucial role in signal processing, control systems, and machine learning applications; however, they demand significant power, silicon area, and timing resources. Leveraging error - tolerant approximate multipliers …
Published in Journal of Polymer & Composites · Vol. 14, Issue 1, 2026 · pp. 1320–1337 Read article
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Cloud-based Application Development and Optimization
Abstract: As cloud computing powers today’s applications, optimizing cloud-based development is crucial to achieve performance, cost effectiveness, and scalability. This research focuses on enhancing the design, deployment, and maintenance of cloud applications, tackling challenges in resource management, scalability, and resilience. We specifically explore dynamic resource allocation algorithms that use predictive analytics for auto-scaling based on workload variations, aiming to cut costs while preserving high performance. The study also investigates cross-cloud optimization …
Published in Journal of Open Source Developments · Vol. 12, Issue 1, 2025 · pp. 37–42 Read article
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Investigating Temperature Effects on Thermal Analysis of Composite Heat Pipes with Diverse Geometrical Configurations
Abstract: As the demand for efficient heat dissipation technologies continues to surge, understanding the intricate interplay between temperature variations and the thermal performance of heat pipes assumes paramount importance. This study investigates the impact of temperature fluctuations on the thermal behaviour of heat pipes with diverse geometric configurations. A comprehensive analysis is conducted utilizing advanced computational simulations coupled with experimental validation techniques. Heat pipes are an innovative heat transfer device with …
Published in Journal of Polymer & Composites · Vol. 12, Issue 3, 2024 · pp. 20–34 Read article
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Thermo–Electrical Performance Enhancement of a Lightweight Polymer–Metal Hybrid Electrostatic Precipitator Using Epoxy-Based Composite Housing for Industrial Particulate Control
Abstract: Airborne particulate emissions, particularly PM₁₀ and PM₂.₅ produced by industrial activities and combustion processes, they continue to pose a significant threat to both the environment and public health. Electrostatic precipitators (ESPs) are widely recognized for their ability to achieve high collection efficiencies; however, conventional metallic constructions often lead to increased system weight, higher fabrication costs, and long-term corrosion-related challenges. In this study, a lightweight hybrid material approach is proposed by …
Published in Journal of Polymer & Composites · Vol. 14, Issue 2, 2026 · pp. 652–668 Read article
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Ai-Driven Healthcare System for Enhanced Diagnosis and Patient Interaction
Abstract: Deep learning techniques are used in an AI-driven healthcare system to improve disease identification and medical picture analysis. Data collection, preprocessing, model training, and evaluation are all part of the system's systematic workflow. Various deep learning architectures, such as ResNet50, VGG-16, and U-Net, are employed for precise classification and segmentation of medical images. The approach incorporates advanced techniques such as optimization, transfer learning, and data augmentation to significantly enhance the …
Published in Research and Reviews: A Journal of Health Professions · Vol. 15, Issue 2, 2025 Read article
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Real-time Operating Systems in the Era of IoT: Challenges and Solutions for Time-Critical Applications
Abstract: Real-time operating systems (RTOS) are essential in the Internet of Things (IoT), as they ensure timely responses to events, which is critical for the performance and reliability of connected devices. This paper delves into the unique challenges faced by RTOS in IoT environments, highlighting issues such as limited computational resources, strict latency requirements, and the increasing need for robust security mechanisms. The resource constraints inherent in many IoT devices, which …
Published in Journal of Operating Systems Development & Trends · Vol. 11, Issue 3, 2024 · pp. 13–24 Read article
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Next-Generation Catalysts: Enhancing Efficiency and Selectivity in Chemical Reactions
Abstract: This study investigates the fundamental principles of coatings and their rejuvenation mechanisms, focusing on the development of advanced coatings that not only protect but also restore the performance of degraded surfaces. The article delves into the various types of coatings, including organic, inorganic, and hybrid formulations, emphasizing their distinct characteristics and applications. A significant portion of the study is dedicated to the mechanisms of rejuvenation, where we analyze how specific …
Published in Journal of Catalyst & Catalysis · Vol. 11, Issue 3, 2024 · pp. 24–28 Read article
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HCI: A Systematic Review of Trends, Challenges, and Future Directions
Abstract: The field of human-computer interaction (HCI), which connects people and technology, is becoming increasingly important and developing quickly. By emphasizing intuitive interaction, user-focused design, and overall system usability, it contributes to the development of more intelligent, responsive, and human-centered systems. This thorough analysis explores the many uses of HCI, such as mobile and multi-screen platforms that demand smooth, cross-device interactions and immersive settings like the Metaverse, where users interact within …
Published in Journal of Multimedia Technology & Recent Advancements · Vol. 12, Issue 3, 2025 · pp. 10–16 Read article
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Understanding Sentiment Trends Through Zero-Shot and Few-Shot Learning Models
Abstract: The requirement for large, manually labeled datasets is one of the main barriers to applying sentiment analysis algorithms in specialized or rapidly evolving disciplines in the present natural language processing (NLP) landscape. This work investigates a paradigm shift from traditional fully supervised learning to data-efficient methods, specifically zero-shot learning (ZSL) and few-shot learning (FSL). This study uses the advanced capabilities of instruction-tuned large language models (LLMs), like GPT-4, to assess …
Published in International Journal of Computer Science Languages · Vol. 4, Issue 1, 2026 · pp. 01–08 Read article