Academic Articles & Research Papers

Independent Acidic pH Reactivity of Non-Iron-Fenton Reaction Catalyzed by Copper-Based Nanoparticles for Fluorescent Dye Oxidation

The process of hydrogen peroxide decomposition, facilitated by copper oxide nanoparticles, produces reactive oxidants that possess the ability to oxidize multiple pollutants. CuO/Cu2O hybrid nanoparticles were successfully synthesized throu...

Interpretable and Explainable Machine Learning for Materials Science and Chemistry

High Resolution Image Download MS PowerPoint Slide Conspectus Machine learning has become a common and powerful tool in materials research. As more data become available, with the use of high-performance computing and high-throughput experi...

Investigating Convergence of the ICD-11 Personality Disorder Model and the DSM-5's Alternative Model of Personality Disorders.

Phase prediction, microstructure and high hardness of novel light-weight high entropy alloys

Guided by CALPHAD modeling, low-density and multiphase three novel High Entropy Alloys (HEAs), Al40Cu15Cr15Fe15Si15, Al65Cu5Cr5Si15Mn5Ti5 and Al60Cu10Fe10Cr5Mn5Ni5Mg5 were produced by large scale vacuum die casting. A mixture of simple and ...

Fundamentals of Materials Science and Engineering : An Integrated Approach

Chapter 1 - Introduction. 1.1 Historical Perspective 1.2 Materials Science and Engineering 1.3 Why Study Materials Science and Engineering? 1.4 Classification of Materials Materials of Importance-Carbonated Beverage Containers 1.5 Advanced ...

On the Challenges of Quantum Circuit Encoding Using Deep and Reinforcement Learning

Quantum computers may solve some computing tasks more efficiently than classical com- puters, but to do so requires the design of appropriate quantum circuits. However, it is hard to design even simple quantum programs. Recent results have ...

QC-MM: A Metadata and Schema Model for Traceable Quantum-Circuit Experiments

Context: Modern quantum-computing experimentation generates heterogeneous, context-dependent execution data whose scientific value depends on preserving calibration state, compilation decisions, and run outcomes in a traceable and repositor...

Expressibility and trainability of parametrized analog quantum systems for machine learning applications

Parametrized quantum evolution is the main ingredient in variational quantum algorithms for near-term quantum devices. In digital quantum computing, it has been shown that random parametrized quantum circuits are able to express complex dis...

Collective dynamics in holographic fractonic solids

Abstract Fractonic phases of matter, a class of states in which collective excitations with constrained mobility exist, were originally discovered in the study of quantum error-correcting codes in solvable lattice spin models such as Haah’s...

Bridging Deep Learning and Quantum Computing for EEG-Driven P300 Targeted ASD Response Detection

Autism Spectrum Disorder (ASD) is a neurodevelopmental condition whose diagnosis relies heavily on subjective behavioral assessments. Electroencephalography (EEG) provides an inexpensive and non-invasive method of objective biomarker detect...

A Survey of Universal Quantum von Neumann Architecture

The existence of universal quantum computers has been theoretically well established. However, building up a real quantum computer system not only relies on the theory of universality, but also needs methods to satisfy requirements on other...

An introduction to Bayesian simulation-based inference for quantum machine learning with examples

Simulation is an indispensable tool in both engineering and the sciences. In simulation-based modeling, a parametric simulator is adopted as a mechanistic model of a physical system. The problem of designing algorithms that optimize the sim...

ℤ₂ × ℤ₂ Equivariant Quantum Neural Networks: Benchmarking against Classical Neural Networks

This paper presents a comparative analysis of the performance of Equivariant Quantum Neural Networks (EQNNs) and Quantum Neural Networks (QNNs), juxtaposed against their classical counterparts: Equivariant Neural Networks (ENNs) and Deep Ne...

Rarefied Reactive Gas Flows over Simple and Complex Geometries Using an Open-Source DSMC Solver

During atmospheric reentry, a significant number of chemical reactions are produced inside the high-temperature shock wave formed upstream of the spacecraft. Chemical reactions can significantly alter the flowfield structure surrounding the...

A quantum fuzzy logic-enhanced evolutionary framework for energy-latency optimization in 6G Edge-IioT

Abstract The transition toward 6G-enabled Industrial Internet of Things (IIoT) requires strict Ultra-Reliable Low-Latency Communication (URLLC), creating a fundamental trade-off with energy consumption. Orchestrating resources in dense, hyp...

Canonical quantization of a memristive leaky integrate-and-fire neuron circuit

We present a theoretical framework for a quantized memristive Leaky Integrate-and-Fire (LIF) neuron, uniting principles from neuromorphic engineering and open quantum systems. Starting from a classical memristive LIF circuit, we apply canon...

Stability of thermal equilibrium in long-range quantum systems

Experimental realizations of spin models are irremediably prone to errors, which can propagate through the system corrupting experimental signals. We study how such errors affect the measurement of local observables in systems with long-ran...

Quantum computing: a gentle introduction

The combination of two of the twentieth centurys most influential and revolutionary scientific theories, information theory and quantum mechanics, gave rise to a radically new view of computing and information. Quantum information processin...

Coalition of explainable artificial intelligence and quantum computing in precision medicine

This survey examines the convergence of Explainable Artificial Intelligence (XAI) and Quantum Computing (QC) toward precision medicine. We review developments from 2018 to 2025, summarizing quantum algorithms, quantum-machine-learning model...

Performance prediction and optimization of a high-efficiency tessellated diamond fractal MIMO antenna for terahertz 6G communication using machine learning approaches

Abstract This paper introduces the design and exploration of a compact, high-performance multiple-input multiple-output (MIMO) antenna for 6G applications operating in the terahertz (THz) frequency range. Leveraging a meta learner-based sta...