Physics Commons^™

Synthesis Of Gold Nanoparticles Using Hordeum Vulgare Leaf Extract And Their Antibacterial Activity, Mohamed M. Sehree, Shakir Ghazi Gergees, Pakhshan A. Hassan

Karbala International Journal of Modern Science

Plant extracts and gold nanoparticles are promising alternatives for combating antibiotic resistance in light of the increasing bacterial resistance. Leaf extract of barley was used to synthesize gold nanoparticles (AuNPs). Barley gold nanoparticles (BL-AuNPs) were produced by adjusting some reaction parameters. These BL-AuNPs were characterized through employing the UV-visible spectroscopy technique, the scanning electron microscope (SEM), Fourier transform infrared spectroscopy (FTIR), and energy dispersive X-ray spectroscopy (EDX). BL-AuNPs were tested for antibacterial efficacy against two strains of Gram-negative bacteria, clinically isolated and considered as multidrug-resistant pathogens, Acinetobacter baumannii and Salmonella typhi. The antimicrobial efficiency of the compounds was evaluated …

The Impact Of Microplastics On Water Quality, Heavy Metals, And Health Risks In Bioflocbased Tilapia Farming Systems, Dian Rizky Afriani, Deswati Deswati, Rahmiana Zein, Putri Ramadhani

Karbala International Journal of Modern Science

Along with microplastics, pollution of heavy metals, including iron (Fe), zinc (Zn), and copper (Cu), in freshwater ecosystems poses a serious environmental threat that can adversely affect human health. This study investigates the use of biofloc technology to reduce microplastic and heavy metal contamination while improving water quality. By utilizing microbial aggregates that capture microplastic and heavy metal particles through flocculation and biosorption processes, four experimental treatments were applied, i.e.: A (without biofloc and microplastics); B (with biofloc, without microplastics); C (with biofloc and low-density polyethylene microplastics); and D (with biofloc and high-density polyethylene microplastics). The results indicate that fish …

Deep Neural Networks For Particle Identification In Simulated Proton-Proton Collisions At Lhc And Rhic, Omar Mazhar Khalaf

Theses and Dissertations

Particle identification is an essential part of experimental high-energy physics, which allows the study of the most fundamental constituents of matter. This thesis explores the use of deep neural networks for identifying particles in simulated proton-proton collisions at the Large Hadron Collider (LHC) and the Relativistic Heavy Ion Collider (RHIC). The deep neural networks were trained on LHC datasets which have various momentum ranges including regions of high transverse momentum above 3 GeV/c. The key findings of thesis include achieving an accuracy of 99.99%, 98.3%, and 90.14% for 3-5 pt, 5-7 pt and above 7 pt regions respectively for the …

Basic Theory And Implementations Of Quantum Error Correction, Derek Rodriguez

Undergraduate Theses, Capstones, and Recitals

The introduction of quantum computing has presented algorithmic solutions to computationally difficult challenges that are far more efficient than those of classical computers. These algorithms leverage the properties of quantum mechanics to manipulate the quantum properties of subatomic particles, requiring immense precision and stability. Current quantum hardware, however, is too noisy and introduces too many errors for these algorithms to be useful in practice, necessitating the use of error correction algorithms. This field survey seeks to introduce various principles of quantum mechanics relevant to quantum computing and quantum error correction (QEC), detail the implementation and motivations of a basic QEC …

Transformer Decoder-Enhanced Swin Unetr For Multi-Organ Semantic Segmentation On Openkbp: Improving Radiotherapy Planning Accuracy, Zainab Adnan Jwad, Israa Hadi Ali

Karbala International Journal of Modern Science

Accurate segmentation of organs-at-risk (OARs) in head and neck CT scans is crucial for radiotherapy planning. The CNN-based decoder limitation of Swin UNETR hinders its capacity to process meaningful information from multiple organ positions essential for accurate medical segmentation. The proposed Transformer Decoder-enhanced Swin UNETR model targets the OpenKBP dataset multi-organ segmentation through its dedicated design for this purpose. The model utilizes transformers along with cross-attention approaches in its decoder to improve segmentation mask outputs through analysis of extensive global information. The model gets additional feature representation power through the addition of squeeze-and-excitation (SE) blocks linked with spatial attention mechanisms …

Computational Approach: 3d-Qsar, Molecular Docking, Molecular Dynamics Simulation Investigations, Drug- Like-Ness, And Dft Score Evaluation Of A Potential Novel And Retrosynthesis Of Some Tr-H Derivatives As Streptococcus Pneumoniae Drug., Belaidi Mustapha, Tchouar Noureddine, Djelilate Mohammed, Saleh Bufarwa, Dalal K. Thbayh

Karbala International Journal of Modern Science

Streptococcus pneumoniae is the main source of hospital-acquired pneumonia and meningococcal pneumonia in children, adults, and the elderly, especially the immunocompromised. Recently, it has attracted the attention of many research studies around the world as a potential target for remediation. TR-H are considered SP antibacterial agents, as they can inhibit. To describe their mode of action and to identify new antibacterial drugs against Streptococcus pneumoniae exhibiting the TR-H scaffold, the present work included 3D-QSAR, in- silico pharmacokinetic evaluation and molecular simulation modelling of TR-H. Using an atom-based method for quantitative structure-activity relationship (QSAR) study, a comprehensive 3D-QSAR model …

Simulating 3d Humanoid Ragdoll Physics Using Velocity Verlet Integration, Pin Constraints, And Rigid Body Collision Systems, Son D. Nguyen

Programming Theses and Dissertations

Ragdoll physics simulates realistic character collapse with physical realism by responding to environmental forces rather than using predefined animations.

Data Encoding, Compilation, And Algorithms For Quantum Machine Learning, Aviraj Sinha

Computer Science and Engineering Theses and Dissertations

Quantum computing enables new approaches to data processing, especially in quantum machine learning. Unlike classical systems, quantum data must be synthesized through operations and can exist in superposition. Encoding choices affect efficiency, noise resilience, and trainability—key factors in quantum machine learning models. This dissertation enhances quantum data encodings by extending quantum read-only memory (QROM) beyond binary representations, improving efficiency and parallelism. It introduces new compilation methods for quantum random number generators (QRNGs), supporting non-parametric distributions for post-quantum cryptography. Additionally, it explores Cayley graph-based encodings to extract spectral features for quantum machine learning.

Noise-Embedded Image Processing Based On Quantum Data Encodings, Yayu Mo

Multidisciplinary Studies Theses and Dissertations

Advancements in quantum information have significantly impacted the field of image processing, although challenges remain. Especially in the edge detection and image encoding area, distorted feature and noises would affect the further classification or super resolution tasks. In our work, we conduct researches on two stages to both evaluate the potential of Quantum-based Convolutional Structure in extracting distorted feature and further explore the effects of quantum noise channels on quantum image encodings.

In the first stage, we propose a method to extract distorted edge features by applying shallow layers in quantum convolutional neural networks (QCNN). By combining the advantages of …

Computational Design Of Potent Sirna For Braf Oncogene Silencing For Enhancing Cancer Therapy, Muhammad Hermawan Widyananda, Ricadonna Raissa

Karbala International Journal of Modern Science

The discovery of oncogenic BRAF mutations has prompted the development of inhibitors, yet resistance remains widespread. A more effective strategy involves targeting BRAF mRNA with siRNA to overcome resistance to BRAF inhibitors. This study aims to design potent siRNA for BRAF oncogene silencing using a computational approach. The full coding sequence of BRAF was retrieved from the NCBI database and potential siRNAs were predicted using the Ui-Tei, Reynolds, and Amarzguioui rules. Identified siRNAs were further analyzed using various prediction systems and parameters, including their interaction with the hAgo2 protein. The results identified that seven siRNAs (siRNA 23, siRNA 24, siRNA …

Unpaired Virtual Histological Staining Of Tissue From Autofluorescence Using Regularized Cycle-Consistent Adversarial Networks, Zhesi Wen

Theses and Dissertations

We present a regularized CycleGAN with a Dense Residual U-Net to virtually stain autofluorescence images of tissue into H&E-like images. Our method outperforms standard architectures, reduces artifacts, and achieves superior FID scores, enabling efficient, label-free, and accurate digital pathology for unpaired datasets using multi-channel fluorescence inputs.

Abel Inversion Comparison Of Geant4 Simulation And Ozone Production Using Cavity Ringdown Spectroscopy In Nitrogen/Oxygen Mixtures In The Presence Of Alpha Radiation, Sidney John Gautrau

Dissertations

The effects of radioactive materials on atmospheric gases have been a topic of interest for years. Radioactive materials ionize the surrounding air, and subsequent reactions lead to molecules such as ozone and nitrogen oxides. The presence of these species above background levels can be used as a marker for radioactive materials which has desirable defense applications like remote detection of radioactive materials. The molecules created in the presence of radioactive materials have been quantified in literature using G-values, which is the number of molecules of a product produced per 100 eV of deposited energy. In this work, Cavity Ringdown Spectroscopy …

Physics Embedded Neural Network: A Novel Data-Free Numerical Method For Solving Computational Physics Problems, Pawan Gaire

Dissertations and Doctoral Documents, University of Nebraska-Lincoln, 2023–

A novel approach for solving partial differential equations (PDEs) using neural networks for scientific computing is introduced. The proposed approach, referred to as physics-embedded neural network (PENN), features a unique architecture that incorporates the PDE and boundary conditions information directly within the final fully-connected layer of the feed-forward neural network (NN). The key aspect of PENN is the parallel numerical embedding of a differential equation associated with physical problems within the activation function of the network’s final layer. This integration leads to a new class of computational solvers competitive with classical methods like the Finite Element Method (FEM) and capable …

Divergence-Free Smoothed Particle Hydrodynamics In A Stream Digital Twin, Austin Hartley

All Theses

Digital Twins (DT) are being explored by the South Carolina (SC) water community to simulate how SC streams will flow at various water levels. Currently, a DT called Gilligan simulates these streams utilizing weakly-incompressible Smoothed Particle Hydrodynamics (SPH). This method does not strictly enforce incompressibility, which leads to unrealistic water flows and unwanted visual artifacts that require post-processing effects to hide. To address these problems and simulate more realistic water flows, the Gilligan stream logic is updated and a state-of-the-art SPH method that enforces incompressibility—Divergence-Free SPH (DFSPH)—is implemented within the Gilligan framework. DFSPH is able to make use of two …

A System And Method For Measuring Spatially Varying Surface Appearances With A Study Of Feathers, Jessica Baron-Lis

All Dissertations

Real-world materials, particularly biological structures such as feathers exhibit complex appearances that vary spatially across their surfaces. The field of computer graphics provides a means of understanding such surfaces through material modeling which uses both analytical models and data acquired from light-surface interactions. There are many efforts within the past decade in measuring materials for graphics, but common limitations in these works include not accounting for spatially varying properties and reliance on neural networks and synthetic datasets.

Feathers from modern birds present diverse appearances due to how light interacts with their unique hierarchical microstructures. Variations in those structures lead to …

Predicting Healthcare Service Quality Based On A Kalman-Optimized Bi-Lstm-Inspired Deep Learning Model, Mohammed K. Al-Khafaji, Eman S. Al-Shamery

Karbala International Journal of Modern Science

Health is one of the most important aspects of human well-being, and access to high-quality healthcare is essential for a good quality of life. Providing top-level health services at all times is crucial. However, the research in healthcare poses significant challenges due to the diversity and variations of medical practices across different hospitals. This paper aims to tackle the challenge of data missing and scattering during data collection. Then, the quality of services (QoS) offered by healthcare facilities will be analyzed and predicted from the patient's perspective. The model begins preprocessing data by data cleaning, handling missing values, and scattering …

Volume 16, Maggie Duncan, Madeline Little, Alicia Hoffman, Megan Livesay, Gabrielle Quaresma, Serenity Allen, Laina Pfountz, Ainslie Allred, Sabrina Robles, Nicholas J. Duellman, Trinity L. Deguzman, Melissa H. Savage, Margaret Dudley, Jocelyn Escobar, Olivia Hildreth, Olivia Hopkins, Benjamin Gettier, Lee Kassay, Jade Riddle, Ashley Seiders

Incite: The Journal of Undergraduate Scholarship

Introduction Dr. Amorette Barber, Director, Office of Student Research

From the Editor Dr. Hannah Dudley-Shotwell

Artist’s Statement Maggie Duncan

On Mentoring Dr. Lee Millar Bidwell

The Hujum Campaign in Uzbekistan and its Consequences by Madeline Little

Wet Cupping Compared to Dry Needling for Treatment of Patients with Low Back Pain: A Critically Appraised Topic by Alicia Hoffman and Megan Livesay

Optimization of eDNA Air Sampling Via 3D Printed Fan by Gabrielle Quaresma

Beyond the Classroom: A Qualitative Study of Teacher Attrition and Retention by Serenity Allen and Laina Pfountz

The Treatment of Subacromial Impingement Syndrome with Platelet-Rich plasma Injections Verses …

32 - Nested Two Level Decomposition For Quantum Computing, Andrew Maciejunes, John Stenger, Dan Gunlycke, Nikos Chrisochoides

Undergraduate Research Symposium

Abstract—We present a two-level decomposition strategy for solving the Vehicle Routing Problem (VRP) using the Quantum Approximate Optimization Algorithm (QAOA). A Problem-Level Decomposition (PLD) partitions a 9-node (72-qubit) VRP into smaller Traveling Salesman Problem (TSP) instances. Each TSP is then further simplified via Circuit-Level Decomposition (CLD), enabling execution on near-term quantum devices. Our approach achieves up to 90% reductions in circuit depth and qubit count. These results demonstrate the feasibility of solving VRPs previously too complex for quantum simulators and provide early evidence of potential quantum utility.

Highly Regenerable Magnetic Sulfonated Chitosan Crosslinked With Glutaraldehyde Composite Beads (Iron Sand/Naso3-Chi-G) For Aqueous Mercury Removal, Fathurrahmi Fathurrahmi, Rahmi Rahmi, Lelifajri Lelifajri, Anggun Sixthia Wulan Ayu, Muhammad Iqhrammullah

Karbala International Journal of Modern Science

We developed a novel adsorbent from sulfonated, glutaraldehyde-crosslinked chitosan embedded with magnetic iron sand. The adsorbent was synthesized through a two-step process: (1) sulfonation with N(SO₃Na)₃ to introduce sulfonate groups, and (2) crosslinking with glutaraldehyde to enhance structural stability. The optimal formulation, containing 43.5% iron sand and crosslinked with 0.17 M glutaraldehyde, exhibited the highest Hg²⁺ adsorption capacity (30.74 mg/g) at pH 3. The adsorbents were characterized using Scanning Electron Microscopy (SEM), Fourier transform infra-red spectroscopic (FT-IR), and X-ray diffraction (XRD) techniques. Adsorption equilibrium was achieved within 60 minutes, and isotherm modeling showed that the process followed the Freundlich model …

Replicating And Testing The First Point-Contact Transistor, Lucas Ethington, Lana Herkenhoff, Braden Stillmaker, Punit Turlapati

Miners Solving for Tomorrow Research Conference

No abstract provided.

Robust Spacecraft Autonomy For Deep Space Exploration In Special Euclidean Group Se(3), Matthew Wittal

Doctoral Dissertations and Master's Theses

Over the past half-century, humanity has gained extensive experience conducting manned spaceflight near Earth. Arguably, "near Earth" could even include the Moon — the most distant destination humans have reached. However, "near" in this work primarily refers low Earth orbit (LEO). One could argue that we have not truly left Earth since the Apollo, as spacecraft in some LEOs remain subject to atmospheric drag thus emphasizing their continued connection to Earth's immediate environment. Reflecting on this, it becomes clear that humanity has largely remained bound to Earth’s immediate vicinity since the Apollo missions reached the Moon. However, that is set …

Energy-Aware Clustering Using Intelligent Scheme For Heterogeneous Wireless Sensor Networks, Enaam A. Al-Hussain, Ghaida A. Al-Suhail

Karbala International Journal of Modern Science

Heterogeneous Wireless Sensor Networks (WSNs) involve nodes with varying capabilities, such as different energy levels, sensing ranges, and computational abilities, which enable them to execute different tasks professionally. Clustering techniques play a crucial role in improving energy efficiency and reliability in WSNs. The evolution of cluster based WSNs from homogeneous into heterogeneous techniques allowed the deployment of smart devices capable of performing complex operations in in diverse environments. However, the heterogeneity of nodes necessitates more sophisticated and adaptive algorithms to fully exploit these capabilities. This paper proposes a new protocol, referred to as IT2F-HLEACH, which integrates Interval Type-2 Fuzzy Logic …

Comprehensive Review On The Application Of Bio-Immunoinformatics In The Development Of Highly Ef-Fective New Candidate Vaccines Against Tuberculosis, Ahyar Ahmad, Andriansjah Rukmana, Miski A. Khairinisa, Dian A. E. Pitaloka, Rosana Agus, Rusdina B. Ladju, Tarwadi Ahmad, Astutiati Nurhasanah, Carina C. D. Joe, Muhammad N. Massi, Harningsih Karim, Irda Handayani, Siti Roszilawati Binti Ramli

Karbala International Journal of Modern Science

Tuberculosis (TB) remains a significant public health challenge worldwide. Currently, Bacillus Calmette-Guerin (BCG) is the only vaccine available for TB prophylaxis. However, the efficacy of the BCG vaccine against adult pulmonary TB is considered inconsistent. This condition encourages researchers to look for more effective options, such as subunit vaccines. This condition requires the development of a more effective subunit vaccine to protect active TB in productive and adult ages. There is an urgent need for more effective vaccines, as the Bacillus Calmette-Guérin (BCG) vaccine currently available has inconsistent efficacy and is only partially effective in adults. Bio-immunoinformatics, an interdisciplinary field …

Evaluating The New Nd: Yag Laser Method In Phytosynthesizing Silver Nanoparticles And Assessing Their Medical Applications., Arshad Mahdi Hamad, Qanat Mahmood Atiya

Karbala International Journal of Modern Science

Silver nanoparticles (AgNPs) were synthesized via an innovative green synthesis method using amygdalin (Am) as a reducing agent and the Nd: YAG laser as a catalyst. We studied the properties of the nanoparticles using X-ray diffraction (XRD), field emission scanning electron microscopy (FESEM), energy dispersive X-ray spectroscopy (EDX), atomic force microscopy (AFM), ultraviolet-visible spectroscopy (UV), and Fourier transform infrared spectroscopy (FTIR) techniques. All the results of the examination demonstrate excellent structural and optical properties. In addition, the molecular docking of the complex composed of amygdalin and AgNPs was tested on three proteins concerned with the virulence of Pseudomonas aeruginosa and …

Characterization Of 1,8-Cineole (Eucalyptol) From Myrtle And Its Potential Antibacterial And Antioxidant Activities*, Humera Khan

Karbala International Journal of Modern Science

1,8-Cineole is a naturally occurring chemical molecule predominantly found in fragrant plants, particularly Myrtle. Its aroma is distinctive and has been the subject of numerous investigations due to its various biological actions. This study examines the characterization of 1,8-Cineole derived from Myrtle and investigates its antibacterial and antioxidant properties. This study seeks to compare 1,8-Cineole with antibiotics like Amoxicillin and Tetracycline, and moreover, to investigate its antioxidant capabilities against diverse bacterial strains (both Gram-positive and Gram-negative). 1,8-Cineole exhibits the most effective antibacterial properties, demonstrating an inhibition zone of 12.00 mm against Staphylococcus aureus and 10.00 mm against Pseudomonas aeruginosa. …

An Effective Secure Multi-Objective Task Scheduling Algorithm In Multi-Cloud Environment, V K S K Sai Vadapalli, Ramesh Babu Gurujukota, Phaneendra Varma Chintalapati, Satyanarayana Murty, G. Sai Chaitanya Kumar, Satish Kumar Kode

Karbala International Journal of Modern Science

In cloud environments, task scheduling is essential for improving performance. Nevertheless, the existence of several heterogeneous clouds makes scheduling extremely difficult, requiring increasingly advanced algorithms to manage these environments' diversity and dynamic nature. To solve this, numerous authors have created a variety of task schedulers utilizing heuristic and metaheuristic techniques. Nevertheless, it remains dynamic and challenging because task scheduling is an NP-hard issue. Furthermore, in many complicated situations, it is still problematic to guarantee security throughout the task’s execution. Therefore, this paper introduces a multi-objective security-aware task scheduler using the Crayfish Mud Ring Optimization Algorithm for a multi-cloud environment. This …

Computational Investigation Of The Unveils Nsd2 Inhibition Potential Of Berberis Vulgaris, Sambucus Nigra, And Morus Alba Through Virtual Screening, Molecular Docking, Md Simulation, And Dft Analyses, Supriyo Saha, Vanshita Gupta, Ahad Hossain, Prinsa Prinsa, Jannatul Ferdous, Kiran Bharat Lokhande, Vikash Jakhmola, Sarkar M. A. Kawsar

Karbala International Journal of Modern Science

Nuclear receptor binding set domain protein 2 (NSD2) plays a key role in chromatin regulation and is associated with different cancers and other developmental problems. Berries are rich in major secondary metabolites with anticancer properties. Here, we virtually screened 145 berry phytochemicals as putative NSD2 inhibitors via structure-based virtual screening, molecular docking, MD simulations, and DFT and ADMET analyses. Among them, α-carotene had the maximum docking score of -9.9 kcal/mol, followed by sulfuretin and β-amyrin. MD simulation analysis revealed that the dynamic behavior of the ligand‒NSD2 complexes was within the limit, indicating no significant changes in the structural integrity of …

Evaluation Of Green Tea Yoghurt Enriched With Lacticaseibacillus Paracasei E1 Microcapsules On Macrophage M1 Profile In High Fat-Fructose Diet Mice, Esha Ardiansyah, Nur Alfi Maghfirotus Sa’Adah, Rahmi Izati, Belinda Nabiila Al Faizah, Dawama Nur Fadlilah, Septhyanti Aprilia Kavitarna, Mochammad Fitri Atho’Illah, Siti Nur Arifah, Yoga Dwi Jatmiko, Muhaimin Rifa’I

Karbala International Journal of Modern Science

Obesity is caused by an energy imbalance that increases chronic low-grade inflammation, including macrophage cell infiltration. Adipose tissue macrophages are polarized into pro-inflammatory macrophage type 1 (M1), secrete large amounts of pro-inflammatory cytokines, and activate transcription factors. Yoghurt with probiotics is popular at all ages for its health benefits and must be protected by microencapsulation. Green tea (Camellia sinensis L.) fortification provides yoghurt nutrients while improving its functional qualities and bioactivity. This study aimed to evaluate the effect of microencapsulation of Lacticaseibacillus paracasei E1 in green tea yoghurt (GTY) on the profile of M1 macrophages in mice fed a …

Development And Characterization Of Sodium Alginate-Based Active Edible Films Functionalized With Olive Mill Wastewater Extract, Nassima Hadri, Mohamed Didi Ould El-Hadj, Zineb Mahcene, Fatih Bozkurt, Rusen Metin Yildirim, Youcef Rahmani, Aicha Tedjani, Muhammet Arici

Karbala International Journal of Modern Science

Phenolic compounds from olive mill wastewater (PCO) of Algerian origin were used to produce sodium alginate-based active films using the casting method. The effects of adding various concentrations of PCO (0%, 0.1%, and 0.2% w/v) were evaluated regarding the molecular, morphological, thermal, physicochemical, optical, barrier, biodegradability, antimicrobial, and antioxidant properties of the alginate films. The FTIR and SEM results elucidated the development of a coherent cross-linked structure attributed to hydrogen bonding interactions between PCO and alginate chains. Consequently, the films exhibited enhanced crystallinity and thermal stability, as revealed by DSC analysis. Moreover, PCO addition positively influenced several film properties, including …

Ai For Nuclear Physics: The Exclaim Project, S. Liuti, D. Adams, M. Boër, G. W. Chern, M. Cuic, M. Engelhardt, G. R. Goldstein, B. Kriesten, Y. Li, H. W. Lin, M. Sievert, D. Sivers

Computer Science Faculty Publications

An overview of the recent activity of the newly funded EXCLusives with AI and Machine learning (EXCLAIM) collaboration is presented. The main goal of the collaboration is to develop a framework to implement AI and machine learning techniques in problems emerging from the phenomenology of high energy exclusive scattering processes from nucleons and nuclei, maximizing the information that can be extracted from various sets of experimental data, while implementing theoretical constraints from lattice QCD. A specific perspective embraced by EXCLAIM is to use the methods of theoretical physics to understand the working of ML, beyond its standardized applications to physics …