Physics Commons^™

Novel In Situ Synthesis Of Copper Oxide Nanoparticles In Epoxy Network: Kinetics, Composite Mechanical And Dielectric Properties, Elena Bobina, Maxim Danilaev, Safaa.M.R.H. Hussein, Sergey Karandashov, Vladimir Kuklin, Ivan Lounev, Konstantin Faizullin

Karbala International Journal of Modern Science

>Mechanical properties of polymer composites with dispersed nanoparticles (CDNP) depend on interaction between the nanoparticles and the polymer matrix. Strength of polymer composites significantly decreases when there is no interaction between dispersed nanoparticles and the polymer. This limits the application of functional polymer composites with dispersed nanoparticles. In this study, CDNP based on ED-20 epoxy resin with dispersed copper oxide nanoparticles was obtained.These nanoparticles were synthesized in epoxy resin before curing: the nanoparticles were obtained by decomposition of copper hydroxide by heating its solution in ED-20 resin.The kinetics of copper oxide nanoparticles formation in CDNP samples were studied using two …

Elm And Lightgbm: A Hybrid Machine Learning Technique With Intelligent Iot To Predict The Cardiovascular Disease, Gorapalli Srinivasa Rao, G Muneeswari

Karbala International Journal of Modern Science

Cardiologists can more accurately classify a patient's condition by performing an accurate diagnostic and prognosis of cardiovascular disease (CVD). The clinical diagnosis, and therapies processes within the medical field have been substantially accelerated by ML-based approaches enabled by IoT-based systems. This structure is based on IoT-based system with enabled ML approach. This study investigates an approach known as ensemble categorization, which enhances the precision of weak algorithms by integrating multiple classifiers. For effective CVD classification, we utilized Ensemble learning machine (ELM) and Light GBM. The appropriate traits are chosen to speed up the categorization process using the Gorilla Troops Optimizer …

Analysing An Imbalanced Stroke Prediction Dataset Using Machine Learning Techniques, Viswapriya Subramaniyam Elangovan, Rajeswari Devarajan, Osamah I. Khalaf, Mhd Saeed Sharif, Wael Elmedany

Karbala International Journal of Modern Science

A stroke is a medical condition characterized by the rupture of blood vessels within the brain which can lead to brain damage. various symptoms may be exhibited when the brain's supply of blood and essential nutrients is disrupted. To forecast the possibility of brain stroke occurring at an early stage using Machine Learning and Deep Learning is the main objective of this study. Timely detection of the various warning signs of a stroke can significantly reduce its severity. This paper performed a comprehensive analysis of features to enhance stroke prediction effectiveness. A reliable dataset for stroke prediction is taken from …

Potential Enhancement Of Microbial Disinfection Using Oxygen Enriched Cold Atmospheric-Pressure Argon (Ar/O2) Plasma Jet, Waleed O. Younis, Mahmoud M. Berekaa, Mostafa A. Ellbban, Abdel-Sattar S. Gadallah, Jamal Q. Almarashi, Abdel-Aleam H. Mohamed

Karbala International Journal of Modern Science

Oxygen activated cold-atmospheric-pressure-argon plasma jet (APPJ) has gained prominence over the regular argon plasma especially in disinfection and decontamination. As an objective of the current research, an oxygen-enriched argon system was built, where plasma produced through a vessel metallic tube that is introduced into alumina one. A sinusoidal high voltage signal of 25 kHz was used to generate plasma jet. Potential impact of oxygen enriched APP jet (Ar/O₂) in decontamination of different microbial cells was observed. For examination, suspension of each tested microbe was placed in contact with plasma jet nearly 10 mm away from the jet nozzle …

Extraction Of Morphometric Features The Shape Of Mangrove Leaves Based On Digital Images And Classification Using The Support Vector Machine, Ishak Ariawan, Della Ayu Lestari, Luthfi Anzani, Tri Yanti, Cakra Rahardjo, M. Saleh, Sahril Angga Permana, Dea Aisyah Rusmawati

Karbala International Journal of Modern Science

At present, several botanists still rely on the use of manual estimating methods to assess the carbon content in mangrove. However, these methods have been reported to be extremely time-consuming, showing the need to develop a system for prediction. An effective solution lies in the creation of an artificial intelligence application, which can provide rapid and cost-effective results. In constructing this application, careful consideration must be given to the selection of parameters or attributes. Species is an essential parameter for the assessment of carbon content, but its determination has proven to be challenging due to the similarities of mangrove. The …

Removal Of Phenol From Oilfield Produced Water Using Non-Conventional Adsorbent Medium By An Eco-Friendly Approach, Salem Jawad Alhamd, Mohammed Nsaif Abbas, Hassan Jameel Jawad Al-Fatlawy, Thekra Atta Ibrahim, Zaid Nsaif Abbas

Karbala International Journal of Modern Science

Petroleum extraction generates substantial quantities of produced water, a challenge compounded by water scarcity in oil-producing regions, notably the Middle East. Leveraging produced water effectively, adhering to environmental standards, can offer a viable solution to the issue of water scarcity. This study explores the potential of mandarin peels as an available, cost-effective adsorbent for treating synthetic aqueous solution simulated to oil-field produced water, specifically targeting phenol, a dangerous pollutant. Employing a batch-mode adsorption unit, six operational factors—phenol concentration, acidity, agitation speed, contact time, adsorbent dose, and temperature—were investigated. Results revealed an inverse relationship between phenol removal and pH, concentration, and …

Evaluation Of An End-To-End Radiotherapy Treatment Planning Pipeline For Prostate Cancer, Mohammad Daniel El Basha, Court Laurence, Carlos Eduardo Cardenas, Julianne Pollard-Larkin, Steven Frank, David T. Fuentes, Falk Poenisch, Zhiqian H. Yu

Dissertations & Theses (Open Access)

Radiation treatment planning is a crucial and time-intensive process in radiation therapy. This planning involves carefully designing a treatment regimen tailored to a patient’s specific condition, including the type, location, and size of the tumor with reference to surrounding healthy tissues. For prostate cancer, this tumor may be either local, locally advanced with extracapsular involvement, or extend into the pelvic lymph node chain. Automating essential parts of this process would allow for the rapid development of effective treatment plans and better plan optimization to enhance tumor control for better outcomes.

The first objective of this work, to automate the treatment …

Improving The Scalability Of Neural Network Surface Code Decoders, Kevin Wu

Undergraduate Honors Theses

Quantum computers have recently gained significant recognition due to their ability to solve problems intractable to classical computers. However, due to difficulties in building actual quantum computers, they have large error rates. Thus, advancements in quantum error correction are urgently needed to improve both their reliability and scalability. Here, we first present a type of topological quantum error correction code called the surface code, and we discuss recent developments and challenges of creating neural network decoders for surface codes. In particular, the amount of training data needed to reach the performance of algorithmic decoders grows exponentially with the size of …

Modeling The Neutral Densities Of Sparc Using A Python Version Of Kn1d, Gwendolyn R. Galleher

Undergraduate Honors Theses

Currently, neutral recycling is a crucial contributor to fueling the plasma within tokamaks. However, Commonwealth Fusion System’s SPARC Tokamak is expected to be more opaque to neutrals. Thus, we anticipate that the role of neutral recycling in fueling will decrease. Since SPARC is predicted to have a groundbreaking fusion power gain ratio of Q ≈ 10, we must have a concrete understanding of the opacity
and whether or not alternative fueling practices must be included. To develop said understanding, we produced neutral density profiles via KN1DPy, a 1D kinetic neutral transport code for atomic and molecular hydrogen in an ionizing …

Proof-Of-Concept For Converging Beam Small Animal Irradiator, Benjamin Insley

Dissertations & Theses (Open Access)

The Monte Carlo particle simulator TOPAS, the multiphysics solver COMSOL., and

several analytical radiation transport methods were employed to perform an in-depth proof-ofconcept

for a high dose rate, high precision converging beam small animal irradiation platform.

In the first aim of this work, a novel carbon nanotube-based compact X-ray tube optimized for

high output and high directionality was designed and characterized. In the second aim, an

optimization algorithm was developed to customize a collimator geometry for this unique Xray

source to simultaneously maximize the irradiator’s intensity and precision. Then, a full

converging beam irradiator apparatus was fit with a multitude …

A Comparative Analysis Of Field Electron Emission From Carbon Black Embedded Within Insulated Copper Hollowed Wires And Glass Tubes, Hatem A. Al-Braikat, Ahmad M D Jaber, Adel M. Abuamr, Mazen A. Madanat, Aseel A. Al-Jbarart, M-Ali H. Al-Akhras, Marwan S. Mousa

Karbala International Journal of Modern Science

In this study, two different methods are used to investigate carbon black as a cold field electron emitter. The first method is to incorporate carbon black into a specially designed insulated copper hollowed wire. The wire has a cup-shaped structure created by electrochemical etching. The second method involves the incorporation of carbon black into narrow glass tubes. A Comparative analyses is carried out to evaluate the effectiveness of each method. To evaluate the performance of the samples, the current-voltage characteristics will be examined using field electron microscopes. This analysis will provide an understanding of the emission of the carbon black …

Implementation Of Python Based High Voltage Tests For Gem Detectors, John Paul Hernandez

Aerospace, Physics, and Space Science Student Publications

The Compact Muon Solenoid, CMS, and other detectors at LHC are in the process of being upgraded for the HL-LHC (High-Luminosity Large Hadron Collider) which will produce more than 5 times the particle interactions than of the current LHC. One upgrade to CMS is the introduction of new GEM detectors (Gaseous Electron Multiplier), GE2/1 and ME0 shown at right are new detectors to CMS and therefore must be tested thoroughly prior to being installed.

Predicting Biomolecular Properties And Interactions Using Numerical, Statistical And Machine Learning Methods, Elyssa Sliheet

Mathematics Theses and Dissertations

We investigate machine learning and electrostatic methods to predict biophysical properties of proteins, such as solvation energy and protein ligand binding affinity, for the purpose of drug discovery/development. We focus on the Poisson-Boltzmann model and various high performance computing considerations such as parallelization schemes.

Mechanistic Investigation Of C—C Bond Activation Of Phosphaalkynes With Pt(0) Complexes, Roberto M. Escobar, Abdurrahman C. Ateşin, Christian Müller, William D. Jones, Tülay Ateşin

Research Symposium

Carbon–carbon (C–C) bond activation has gained increased attention as a direct method for the synthesis of pharmaceuticals. Due to the thermodynamic stability and kinetic inaccessibility of the C–C bonds, however, activation of C–C bonds by homogeneous transition-metal catalysts under mild homogeneous conditions is still a challenge. Most of the systems in which the activation occurs either have aromatization or relief of ring strain as the primary driving force. The activation of unstrained C–C bonds of phosphaalkynes does not have this advantage. This study employs Density Functional Theory (DFT) calculations to elucidate Pt(0)-mediated C–CP bond activation mechanisms in phosphaalkynes. Investigating the …

Spectralomics – Towards A Holistic Adaptation Of Label Free Spectroscopy, Hugh Byrne

Articles

Vibrational spectroscopy, largely based on infrared absorption and Raman scattering techniques, is much vaunted as a label free approach, delivering a high content, holistic characterisation of a sample, with demonstrable applications in a broad range of fields, from process analytical technologies and preclinical drug screening, to disease diagnostics, therapeutics, prognostics and personalised medicine. However, in the analysis of such complex systems, a trend has emerged in which spectral analysis is reduced to the identification of individual peaks, based on reference tables of assignments derived from literature, which are then interpreted as biomarkers. More sophisticated analysis attempts to unmix the spectrum …

Investigations Of The Eutectic Formation And Skin Rejuvenation By Hyaluronan - Kojic Acid Dipalmitate System, Syed Waqar Hussain Shah, Sumbal Imran, Iram Bibi, Kashif Ali, Nadia Bashir

Karbala International Journal of Modern Science

Eutectic phenomenon has been investigated in binary system based on biopolymer hyaluronan (HN) and kojic acid dipalmitate (KAD). Solid-liquid phase diagram showed a significant dependence of melting points on weight fraction of KAD up to KAD < 0.5. A negligible regain to melting temperature of pure KAD occurred later. Simulations of molecular mechanics using a four-unit segment of HN and KAD revealed the interaction between carbonyl of KAD with 4-OH on N-acetylglucosamine unit of oligomer. Infrared vibrational spectroscopy also endorsed the existence of a weakly interacting system. Such behavior was expected due to steric hinderance and rigidity of biopolymer. The thermal decomposition temperature of HN (i.e., 215 °C) was increased to 322 °C in HK50 having HN and KAD in 1:50 w/w. Bioelectric impedance analysis revealed that these green materials could promote skin health in humans.

Synthesis And Characterization Of Renewable Heterogeneous Catalyst Zno Supported Biogenic Silica From Pineapple Leaves Ash For Sustainable Biodiesel Conversion, Nadila Pratiwi, Suriati Eka Putri, Yulia Shinta, Arya Ibnu Batara, Diana Eka Pratiwi, Abd Rahman, Nur Ahmad, Heryanto Heryanto

Karbala International Journal of Modern Science

This study reports on the first case of the low-cost and environmentally friendly ZnO/SiO2 heterogeneous catalyst from pineapple leaves ash (PLA). Catalyst shows excellent performance in catalyzing the transesterification of waste cooking oil (WCO) with methanol for biodiesel conversion. This study focuses on assessing the influence of Zn content on physicochemical characteristics, using XRD, FTIR, SEM, and N2 adsorption-desorption methods. In addition, three different Zn content levels (20, 25, and 30 %wt) were applied. The results showed that all ZnO/SiO2 samples exhibited characteristics suitable for use as catalyst with an average crystallite size of 31.83-34.15 nm, and a surface area …

Anomaly Detection On Small Wind Turbine Blades Using Deep Learning Algorithms, Bridger Altice, Edwin Nazario, Mason Davis, Mohammad Shekaramiz, Todd K. Moon, Mohammad A. S. Masoum

Electrical and Computer Engineering Faculty Publications

Wind turbine blade maintenance is expensive, dangerous, time-consuming, and prone to misdiagnosis. A potential solution to aid preventative maintenance is using deep learning and drones for inspection and early fault detection. In this research, five base deep learning architectures are investigated for anomaly detection on wind turbine blades, including Xception, Resnet-50, AlexNet, and VGG-19, along with a custom convolutional neural network. For further analysis, transfer learning approaches were also proposed and developed, utilizing these architectures as the feature extraction layers. In order to investigate model performance, a new dataset containing 6000 RGB images was created, making use of indoor and …

Butterworth Filter To Reduce Reactivity Fluctuations, Daniel Suescún-Díaz, Geraldyne Ule-Duque, Luis E. Cardoso-Páez

Karbala International Journal of Modern Science

In this study, we introduce the calculation of reactivity in nuclear reactors. The proposed method uses the Euler-Maclaurin series to approximate the integral in the inverse equation of point kinetics. The approximation is done with the first three terms, the first term represents the approximation of a zero-order sum, the second term the trapezoidal rule and the third term the first Bernoulli number. These three terms improve the approximation, along with an estimate of the neutron density using the prompt jump approximation. To reduce neutron density fluctuations, a second-order Butterworth filter for the reactivity calculation was implemented, which offers the …

Machine Learning Model And Molecular Docking For Screening Medicinal Plants As Hiv-1 Reverse Transcriptase Inhibitors, Muthia Rahayu Iresha, Firdayani Firdayani, Agam Wira Sani, Nihayatul Karimah, Shelvi Listiana, Irfansyah Yudhi Tanasa, Arief Sartono, Ayu Masyita

Karbala International Journal of Modern Science

The human immunodeficiency virus type 1 reverse transcriptase (HIV-1 RT) plays a significant role in viral replication and is one of the targets for anti-HIV. However, a mutation in viral strains rapidly developed the resistance of the com-pounds to the protein, reducing the effectiveness of the inhibitors. This work seeks to utilize machine learning-based quantitative structure-activity relationship (QSAR) analysis in combination with molecular docking simulations to forecast the presence of active compounds derived from medicinal plants. Specifically, the objective is to identify com-pounds that have the potential to operate as inhibitors of HIV-1 reverse transcriptase (RT), encompassing both wild-type and …

Multi-Agent System For Portfolio Profit Optimization For Future Stock Trading, Usha Devi, Mohan R

Karbala International Journal of Modern Science

Stock trading highly contributes to the economic growth of the country. The stock trading objective is to earn profits with buy/sell/hold decisions on the set of stocks in the portfolio. The portfolio optimization problem is finding the decision sequence that leads to higher profit and lower risk. Portfolio optimization is challenging due to complex price history patterns and an uncertain environment. Incorrect decisions in stock trading lead to massive losses. The proposed Multi-Agent System for Portfolio Profit Optimization (MASPPO) aims to optimize trading profit and reduce risk with accurate predictions. The proposed model integrates the Fuzzy c-means with the Deep …

Genome-Wide Profiling Of Novel Conserved Zea Mays Micrornas Along With Their Key Biological, Molecular And Cellular Targets And Validation Using An Rt-Pcr Platform, Abdul Baqi, Sami Ullah, Muhammad Ayub, Muhammad Zafar Saleem, Ghulam Mustafa Khan, Asad Ullah

Karbala International Journal of Modern Science

MicroRNAs (miRNAs), which are typically non-coding RNAs that start off as endogenous molecules and regulate post-transcriptional levels of gene expression by mRNA degradation or translational repression. They are 18–26 nucleotides long, evolutionarily conserved and essential for predicting novel miRNAs in a variety of plants. Maize (Zea mays) is a significant food and forage crop in the globe today. In the present study, many maize miRNAs have been found to be associated with both plant development and responses to stress. In this study, 66 unique conserved maize miRNAs from 65 different miRNA families were predicted using several genomics-based methods …

The Role Of Cu (0-0.03) And Zn (0.02) Substitution On The Structural, Optical And Magnetic Properties Of Mgo Nanoparticles, S. Naseem Shah, Atif Dawar, Yasmeen Bibi, Abid Ali, M. Asif Siddiqui

Karbala International Journal of Modern Science

The co-precipitation method was employed to prepared Cu (0-0.03) and Zn (0.02) dual doped MgO nanoparticles. The secondary phases of CuO and Cu₂O were observed along with the cubical phase of MgO. The doping induced effect was noticed for the crystallite size variations (14.39-19.89 nm). The morphological transformation from spherical to rice-like shape were observed. The estimated values of optical bandgap (4.66-4.45 eV) were well correlated with the crystallite size and dopant concentrations. The ferromagnetic ordering was observed at room temperature and the enchantment in the coercivity (142.27 Oe) with Zn (0.02) doping was noticed. Such type of …

Applications Of Independent And Identically Distributed (Iid) Random Processes In Polarimetry And Climatology, Dan Kestner

Dissertations, Master's Theses and Master's Reports

The unifying theme of this thesis is the characterization of “perfect randomness,” i.e., independent and identically distributed (IID) stochastic processes as these are applied in physical science. Two specific and mathematically distinct applications are chosen: (i) Radar and optical polarimetry; (ii) Analysis of time series in meteorology. In (i), IID process of a special kind, namely, with a distribution defined by symmetry, is used to link its multivariate Gaussian density to uniformity on the Poincaré sphere. This “statistical ellipsometry” approach is then used to relate polarimetric mismatches or imbalances to ellipsometric variables and suitably chosen cross-correlation measures. In (ii), recently …

Accelerating Markov Chain Monte Carlo Sampling With Diffusion Models, N. T. Hunt-Smith, W. Melnitchouk, F. Ringer, N. Sato, A. W. Thomas, M. J. White

Physics Faculty Publications

Global fits of physics models require efficient methods for exploring high-dimensional and/or multimodal posterior functions. We introduce a novel method for accelerating Markov Chain Monte Carlo (MCMC) sampling by pairing a Metropolis-Hastings algorithm with a diffusion model that can draw global samples with the aim of approximating the posterior. We briefly review diffusion models in the context of image synthesis before providing a streamlined diffusion model tailored towards low-dimensional data arrays. We then present our adapted Metropolis-Hastings algorithm which combines local proposals with global proposals taken from a diffusion model that is regularly trained on the samples produced during the …

Effective Drag Coefficient Prediction On Single-View 2d Images Of Snowflakes, Cameron Hudson

Graduate College Dissertations and Theses

The drag coefficient of snowflakes is an crucial particle descriptor that can quantify the relationships with the mass, shape, size, and fall speed of snowflake particles. Previous studies has relied on estimating and improving empirical correlations for the drag coefficient of particles, utilizing 3D images from the Multi-Angled Snowflake Camera Database (MASCDB) to estimate snowflake properties such as mass, geometry, shape classification, and rimming degree. However, predictions of the drag coefficient with single-view 2D images of snowflakes has proven to be a challenging problem, primarily due to the lack of data and time-consuming, expensive methods used to estimate snowflake shape …

Robust Interventions In Network Epidemiology, Erik Weis

Graduate College Dissertations and Theses

Which individual should we vaccinate to minimize the spread of a disease? Designing optimal interventions of this kind can be formalized as an optimization problem on networks, in which we have to select a budgeted number of dynamically important nodes to receive treatment that optimizes a dynamical outcome. Describing this optimization problem requires specifying the network, a model of the dynamics, and an objective for the outcome of the dynamics. In real-world contexts, these inputs are vulnerable to misspecification---the network and dynamics must be inferred from data, and the decision-maker must operationalize some (potentially abstract) goal into a mathematical objective …

Cross-Layer Design Of Highly Scalable And Energy-Efficient Ai Accelerator Systems Using Photonic Integrated Circuits, Sairam Sri Vatsavai

Theses and Dissertations--Electrical and Computer Engineering

Artificial Intelligence (AI) has experienced remarkable success in recent years, solving complex computational problems across various domains, including computer vision, natural language processing, and pattern recognition. Much of this success can be attributed to the advancements in deep learning algorithms and models, particularly Artificial Neural Networks (ANNs). In recent times, deep ANNs have achieved unprecedented levels of accuracy, surpassing human capabilities in some cases. However, these deep ANN models come at a significant computational cost, with billions to trillions of parameters. Recent trends indicate that the number of parameters per ANN model will continue to grow exponentially in the foreseeable …

Reducing Food Scarcity: The Benefits Of Urban Farming, S.A. Claudell, Emilio Mejia

Journal of Nonprofit Innovation

Urban farming can enhance the lives of communities and help reduce food scarcity. This paper presents a conceptual prototype of an efficient urban farming community that can be scaled for a single apartment building or an entire community across all global geoeconomics regions, including densely populated cities and rural, developing towns and communities. When deployed in coordination with smart crop choices, local farm support, and efficient transportation then the result isn’t just sustainability, but also increasing fresh produce accessibility, optimizing nutritional value, eliminating the use of ‘forever chemicals’, reducing transportation costs, and fostering global environmental benefits.

Imagine Doris, who is …

High-Performance Computing In Covariant Loop Quantum Gravity, Pietropaolo Frisoni

Electronic Thesis and Dissertation Repository

This Ph.D. thesis presents a compilation of the scientific papers I published over the last three years during my Ph.D. in loop quantum gravity (LQG). First, we comprehensively introduce spinfoam calculations with a practical pedagogical paper. We highlight LQG's unique features and mathematical formalism and emphasize the computational complexities associated with its calculations. The subsequent articles delve into specific aspects of employing high-performance computing (HPC) in LQG research. We discuss the results obtained by applying numerical methods to studying spinfoams' infrared divergences, or ``bubbles''. This research direction is crucial to define the continuum limit of LQG properly. We investigate the …