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Machine Learning-Based Regression For Magnetic Field Prediction From Odmr Spectral Data, Jesse B. Hernandez

Electronic Theses, Projects, and Dissertations

Optically Detected Magnetic Resonance (ODMR) using nitrogen-vacancy (NV) centers in diamond enables sensitive, room-temperature magnetic field sensing, but real ODMR spectra are often noisy and difficult to analyze with traditional peak-fitting methods. This thesis investigates whether machine learning can reliably predict magnetic field strength directly from ODMR spectra, and compares four model families under a single regression task: a random forest, an artificial neural network (ANN), a one-dimensional convolutional neural network (1D-CNN), and a Transformer.

Training data were generated from an NV-ensemble simulation calibrated to real measurements provided by the Ulsan National Institute of Science and Technology (UNIST), spanning 0 …

Radiological And Chemical Safety Assessment Of Drinking Water From Treatment Plants And Rivers In Kut City, Iraq, Ahmed A. Alswaty, Hadi D. Alattabi

Karbala International Journal of Modern Science

Climate change and increasing anthropogenic activities, particularly wastewater discharge into rivers, have raised pollution levels in the water sources of Kut City, necessitating an assessment of the radiological and chemical safety of drinking water at the city's treatment plants. Thirteen water samples were collected, comprising ten treated and three raw water samples from the source rivers. An HPGe detector was used to measure the radionuclides ²¹⁴Bi, ²¹⁴Pb, ²¹²Bi, ²¹²Pb, ⁴⁰K, and ¹³⁷Cs. All measured radionuclides were below the minimum detectable activity (MDA). ICP-OES analysis supported these findings, as U concentrations in most samples were …

Noise Characterization And Mitigation In Intermediate-Scale Quantum Systems, Muhammad Qasim Khan

Dartmouth College Ph.D Dissertations

Current quantum processors, at the intermediate scale of tens to hundreds of qubits, remain error-limited. This thesis studies two related sources of error. The first is environmental noise, which may have temporal and spatial correlations and nonclassical components. The second is state-preparation and measurement (SPAM) error, which arises in the operations used to characterize this noise, a prerequisite for boosting operational fidelities. Neither can be characterized alone. Noise spectroscopy techniques use imperfect preparation and readout, while SPAM characterization is affected by qubit decoherence. Our methods vary measurement depth, drive duration, or sequence repetition so each source changes the measured signal …

The Application Of The Group Investigation (Gi) Model Of Cooperative Learning To Improve Students’ Understanding Of Biodiversity In Year 10 At Sman 2 Mori Atas, Nadia Rosmita Lumako, Mohammad Jamhari, Yulia Windarsih, Lilies Lilies, Rafiqa Rafiqa, Amalia Buntu

Jurnal Pendidikan Sains

Biology instruction at SMAN 2 Mori Atas was predominantly conducted through conventional teaching practices. Preliminary observations revealed that students had difficulty understanding biodiversity concepts, and several students did not meet the minimum passing criterion of 70. These conditions highlighted the need for learning activities that promote active investigation, discussion, and conceptual connections. This classroom action research examined the use of the Group Investigation (GI) cooperative learning model to improve Grade 10 students’ classroom engagement and conceptual understanding of biodiversity, as assessed through Higher-Order Thinking Skills (HOTS) questions based on the Structure of Observed Learning Outcomes (SOLO) taxonomy. The study involved …

The Effect Of The Problem-Based Learning Model On Biology Learning Outcomes In The 8th Grade Science Class At Smp Negeri 19 Palu, Delfi Srieviyani, Mursito S. Bialangi, Masrianih Masrianih, Abd Hakim Laenggeng, Vita Indri Febriani, Bustamin Bustamin

Jurnal Pendidikan Sains

Research background: The low student achievement in biology at SMP Negeri 19 Palu specifically regarding the human respiratory system is attributed to the conventional teaching methods still employed by teachers. Purpose and scope of the article: This study examines the effect of the Problem-Based Learning (PBL) model on students’ learning outcomes in biology, specifically regarding the human respiratory system. Method: This study used a quasi-experimental design with a quantitative approach. The research design employed was a nonequivalent control group design using two groups: an experimental group (Class VIII D) consisting of 25 students who received the PBL model, and a …

A Systematic Approach For Controlling Tem Sample Thicknesses, Monte Kozell

Physics Faculty Publications and Presentations

Historically, TEM prep has been an artisan craft without a systematic workflow that ensures quantified control over TEM sample thickness. Over- and under-thinning is a significant problem in the TEM prep process. A direct measurement process was demonstrated to control the final thickness of the TEM lamella. Final lamella thickness was controlled by directly measuring lamella thickness in real time using a 10–15 kV SEM while performing secondary electron imaging at the mill position, allowing for (human-mediated) closed-loop processing. We demonstrated the utility of this technique by systematically thinning five TEM samples to discretely target thicknesses ranging from 100 nm …

K 1-6 Is A Photoionised Interstellar Medium Nebula Shaped By A Fast-Moving Hot White Dwarf In A Triple System, Jaroslav Merc, David Jones, Ana Escorza, Henri M. J. Boffin, Nicole Reindl, Michael Abdul-Masih, Thomas Masseron, Paulina Sowicka, Jan Kára, Jorge Jorge García-Rojas

Physics & Astronomy Faculty Publications

Aims. K 1-6 has long been classified as a planetary nebula (PN) hosting a binary central star; however, it has remained poorly studied due to its faintness. The central star exhibits pronounced photometric variability whose origin is still unclear. We present a comprehensive characterisation of the K 1-6 system, including the physical properties of its stellar components and the nature of the surrounding nebulosity.

Methods. We conducted a multi-wavelength analysis combining optical and UV spectroscopy obtained with the Gran Telescopio Canarias, the Telescopio Nazionale Galileo, the Nordic Optical Telescope, and the Hubble Space Telescope. We also present long-term multi-band ground- …

A Calibrated And Conformal Deep Learning Framework For Trustworthy Antinuclear Antibody Pattern Recognition With Selective Referral To Experts, Hussein Ali Hussein Al Naffakh, Ahmed Dheyaa Radhi, Raghdah Maytham Hameed, Muntaha Abdullah Reishaan, Fouad A. Majeed, Rozaida Ghazali

Karbala International Journal of Modern Science

Reading antinuclear antibody patterns on human epithelial cells by indirect immunofluorescence is the reference screen for systemic autoimmune rheumatic diseases, but it is slow, subjective, and variable between observers. Deep learning reaches high accuracy on this task, yet most systems return a single prediction without stating how reliable it is, which is unsafe in a diagnostic workflow. This paper presents an intelligent decision support framework built around a single calibrated uncertainty signal. That signal is the control variable for four reliability modules: confidence calibration, conformal prediction, error detection, and selective referral. A feature space out of distribution detector serves as …

Gc-Ms Profiling And Antibacterial Activity Of Cold-Macerated Garlic Extracts Against Multidrug-Resistant Uropathogens, Fatima A. Khalaf, Saeed A. Fayadh

Karbala International Journal of Modern Science

The growing number of cases of multidrug-resistant (MDR) urinary tract infections has made the clinical management of urinary tract diseases a significant challenge; therefore, the need for effective adjunctive therapies cannot be overemphasized. This study evaluated the bioactive components and antibacterial efficacy of cold-macerated garlic extracts obtained using distilled water, 70% ethanol and hexane solvents against antimicrobial-resistant urinary isolates. Out of 250 urine samples that were analyzed, 124 (49.6%) showed significant microbial growth, with Escherichia coli being the most frequently isolated organism. The results of the phytochemical screening and Gas Chromatography-Mass Spectrometry (GC-MS) analysis showed that the ethanolic extract had …

Potential Energy Landscape Formalism For Quantum Liquids, Yang Zhou

Dissertations, Theses, and Capstone Projects

Atomic delocalization due to nuclear quantum effects (NQE) remains poorly understood in low-temperature liquids near the glass state and during vitrification. Many liquids can be described accurately by treating their nuclei as classical particles, but this approximation fails for light elements such as He and H₂, small hydrogen-containing molecules such as water, and systems in which zero-point motion or isotope-substitution effects are important. Developing a general thermodynamic and statistical-mechanical description of such liquids has been challenging. This dissertation extends the potential energy landscape (PEL) formalism, originally developed for classical liquids and glasses, to liquids that obey quantum mechanics and exhibit …

Shadowless In A World Of Shadows: Diminishing The Refraction Of Light Via Nanoconfinement, Vuk Uskoković

Administration and Staff Articles and Research

This paper presents a physicochemical study that merges science and art in the context of exploring the refractive index of water under nanoconfinement. Using a simple and economical experimental approach, it is shown that confinement within the micropores of a zeolitic structure, characterized using electron microscopy and X-ray diffraction, paradoxically reduces the refractive index of water, bringing confined water droplets closer to a shadowless state. Counterintuitively, water under confinement, as indicated by vibrational spectroscopic analyses performed in the transmittance mode in the IR range and reflectance mode in the near-IR range, exhibits greater structural freedom than in its bulk or …

Karhunen-Loève Modes For Coherent Arrays, Jack E. Mccrae, Santasri R. Bose-Pillai, Steven T. Fiorino

Faculty Publications

The optimal modes for correcting atmospheric turbulence on coherent arrays are determined. These Karhunen-Loève modes are eigenvectors of a covariance matrix. Creating this covariance matrix requires knowledge of the power spectrum of the turbulence, the aperture geometry, and a basis set for the matrix. The Kolmogorov power law is ordinarily chosen here for the turbulence spectrum. By choosing the phase on each array subaperture element minus the phase averaged over all subapertures as this basis, infinite values in the variances and covariances can be avoided. The piston mode of the whole array, which would otherwise also be infinite, is thus …

Symmetry Breaking And Restoration In The Grasshopper Ising Model, Aradh Bisarya

Graduate Masters Theses

Distance-selective interactions in Ising systems give rise to a range of rich and unexpected phenomena. A notable example is the so-called Grasshopper Ising model, a conserved spin Ising model with interactions only between spins at a fixed distance. This model is the discrete version of the following mathematical problem: A grasshopper lands at a random point on a planar lawn of area one. It then makes one jump of fixed distance in a random direction. What lawn shape maximizes the probability that the grasshopper lands on the lawn after the jump? The solution to this problem, corresponding to the ground …

A Variational Algorithm For Preparing Superoptimal Thermal States For Quantum Thermometric Measurement Adaptable To Nisq Hardware, Nicholas Donatelli

Graduate Masters Theses

The conditional thermal state (CTS) is a probe-specific quantum thermal state that is known to outperform the Gibbs state in quantum thermometric measurements at sufficiently low temperatures. This thesis introduces a variational quantum algorithm designed to prepare the CTS on quantum computing hardware which optimizes the angular parameters of the quantum gates that comprise the ansatz circuit and, for the one-qubit case, minimizes relative infidelity of the output state with the known CTS. Using noiseless numerical simulations (SPSA with Qiskit), attempting to prepare the CTS yielded relative infidelities with the known CTS on the order of $10^{-5}$ for most parameter …

Synthesizing Viscoelasticity: Living Polymers, Micro-Macro Relations, And Paths For Resolution, Adam M. Hasler

Graduate Masters Theses

This thesis investigates the relationship between macroscopic rheological signals and underlying microscopic dynamics in complex fluids, specifically focusing on ”living polymers” within the cetyltrimethylammonium bromide (CTAB) and sodium salicylate (NaSal) surfactant system. The research addresses the inverse parameterization problem, demonstrating how bulk measurements like zero-shear viscosity can mask fundamentally different physical topologies, such as purely cylindrical, reptating micelles versus highly branched networks. Through the successful synthesis of viscoelastically ”degenerate” samples, the study utilizes an array of characterization techniques including frequency sweeps, Large Amplitude Oscillatory Shear (LAOS) to resolve these unique underlying states. Furthermore, the work explores further avenues of study …

A Statistical Mechanics Approach To Reinforcement Learning, Jacob Adamczyk

Graduate Doctoral Dissertations

Reinforcement learning (RL), the study of optimal decision-making over long timescales in stochastic systems, has recently seen remarkable advances due in large part to the efforts of the deep learning community. RL has witnessed great success in solving problems in video games, robotics, biological control, and language modeling. However, a unified statistical mechanics framework to understand and develop the corresponding algorithms is lacking. To address this issue, we begin by showing that the reinforcement learning problem can be formulated and solved using the tools of statistical mechanics. Drawing on physical principles of free energy minimization and invariance, we address important …

Modeling Kinetics Of 5-Aminolevulinic Acid-Induced Protoporphyrin Ix Accumulation For Optimal Photodynamic Therapy, Priscilla Melebor

Graduate Masters Theses

Photodynamic therapy (PDT) is a minimally invasive treatment that uses light-activated photosensitizers to selectively destroy diseased tissue through the generation of reactive oxygen species. However, conventional in vitro models often fail to accurately reproduce the pharmacokinetics of photosensitizer accumulation and clearance observed in vivo. This study investigated protoporphyrin IX (PpIX) accumulation following 5-aminolevulinic acid (ALA) exposure under physiologically relevant conditions using two-dimensional (2D) monolayers and three-dimensional (3D) spheroids. By comparing continuous ALA exposure with transient exposure followed by ALA washout, this work provides a more biologically relevant framework for studying PpIX kinetics and optimizing photodynamic therapy.

The Grasshopper's Journey To The Bloch Sphere, David Llamas

Graduate Doctoral Dissertations

The Grasshopper Problem asks a simple geometric question. A grasshopper lands on a lawn of fixed area and jumps a fixed distance in a random direction. What shape of lawn maximizes the probability that the grasshopper remains on the lawn after jumping? The jump rule is rotationally symmetric, but the best lawns do not have to be. This dissertation studies how that symmetry breaking occurs, maps the continuum problem to a novel constrained spin system, and uses the spherical Grasshopper Problem to compare quantum singlet correlations with classical local models.

For planar lawns, boundary-integral and perturbative calculations explain why the …

Fabrication Of Objectives For Imaging Ultracold Lithium, Danelle Akanova

Dartmouth College Master’s Theses

This thesis develops a documented, reproducible procedure for building diffraction-limited microscope objectives entirely from catalog singlets, for imaging ultracold 6Li atoms in a ring-trap experiment. The objectives must resolve micron-scale features through a 5 mm fused-silica vacuum window, operate at multiple wavelengths, fit inside a 48 mm magnet bore, and contain no conductive or magnetic material, because the surrounding coils switch 0.1 T fields on microsecond timescales. Commercial long-working-distance objectives are universally housed in metal, which the eddy-current constraint rules out, and custom fabrication of a suitable non-conductive matched pair is estimated at nearly $200k.

The procedure is developed and …

Development Of Quantum Cryptographic Algorithm For Next Generation Secure Systems, Gururaja Ts Mr

Theses and Dissertations

The rapid advancement of digital communication and quantum technologies are developing at an accelerated pace makes cybersecurity and data protection a big challenge. The traditional cryptographic methods are increasingly under attack by phishing, brute force, cryptanalytic attack and threats of quantum computing. This research presents a quantum-secure communication based on Quantum Random Number Generator (QRNG), Quantum Key Distribution (QKD), Quantum Image Encryption (QIE) and secure quantum key access using a dongle-based hardware.

QRNG models were designed in the quantum simulator and hardware. The sequences produce high entropy and they passed the statistical randomness tests with p-values higher than 0.01 indicates …

Temperature Predictions With Zncuins/Zns Quantum Dots, Reu/Fri Program Physics And Astronomy, Amber Banks

Student Works

For hundreds of years, humans have built thermometers based on the principles of thermal expansion and contraction. In the modern world, there is a need to scale down our thermometers to monitor cellular reactions, both within the human body and within artificial organ chips. Lewis et al. (2020) introduces a nanoscale thermometer based on the thermal expansion of CdTe quantum dots. When ex panded, these dots exhibit systematic changes in photoluminescence (PL) which are recognized by a neural network as an increase in temperature. Our research builds upon the framework of Lewis et al. (2020), testing its feasibility with ZnCuInS/ZnS …

Feedback Indices To Evaluate Llm Responses To Rebuttals For Multiple Choice Type Questions, Justin C. Dunlap, Anne-Simone Parent, Ralf Widenhorn

Physics Faculty Publications and Presentations

We present a set of indices designed to characterize Large Language Model (LLM) responses when challenged with rebuttals during a chat. Assessing how LLMs respond to user dissent is crucial for understanding their reliability and behavior patterns, yet the complexity of human-LLM interactions makes systematic evaluation challenging. Our approach employs a fictitious-response rebuttal method that quantifies LLM behavior when presented with multiple-choice questions followed by deliberate challenges to their fictitious previous response. The indices are specifically designed to detect and measure what could be characterized as sycophantic behavior (excessive agreement with user challenges) or stubborn responses (rigid adherence to the …

Evaluating A Dual-Beacon Hartmann Turbulence Profiling Technique Using Wave Optics Simulations, Benjamin C. Wilson, Matthew Kalensky, Santasri Bose-Pillai, Jack E. Mccrae

Faculty Publications

Resolving how optical turbulence varies along a propagation path remains a key challenge for designers of free-space optical propagation systems. Instruments such as scintillometers and differential image motion monitors are commonly used, but only provide path-integrated turbulence estimates. Point sensors provide localized estimates of turbulence strength and can be used to generate path-resolved profiles when an array of point sensors are distributed along the optical path. However, this approach can be costly and complex to deploy in certain environments. Alternatively, a single point sensor can be mounted on a mobile platform that collects data while traversing the optical path, although …

Electronic Structure Discretization And Compression Using Diagonal Basis Sets, Casey Lee Dowdle

Dartmouth College Ph.D Dissertations

Numerically solving the electronic structure problem is a fundamentally difficult problem due to the exponential growth in the dimension of the Hilbert space as the system size increases. In order to solve problems at a chemically relevant accuracy, both the choice of basis set and numerical method are important factors that are intrinsically connected.

In this thesis, we study the discretization and resulting compression of electronic Hamiltonians using diagonal basis sets. A diagonal basis set approximately diagonalizes the matrix and tensor representations of the one- and two-body potentials. This can reduce storage, simplify matrix-vector products, and lower the complexity of …

Development Of Robust Ratio Linear Fitting Method Of Temperature And Emissivity Separation For High-Temperature Data, Mitchell Manzardo, Michael L. Dexter, Shannon R. Young, John Bowlan, Anthony L. Franz

Faculty Publications

Accurate temperature and emissivity separation from thermal infrared radiance is essential for characterizing materials under high-temperature laboratory conditions. Existing temperature and emissivity separation methods have largely been developed for multispectral remote sensing applications, where long atmospheric path lengths require extensive atmospheric compensation. In contrast, the current work considers hyperspectral laboratory measurements acquired over a short optical path, where atmospheric effects are comparatively small but increased measurement uncertainty remains within portions of the measured spectrum. The ABB MR304 FTIR spectrometer used in this study exhibits reduced optical transmission below approximately 2.5 μm, producing increased measurement uncertainty within the spectral region containing …

Palmnet: Confidence-Calibrated Edge-Cloud Ai For Field Diagnosis Of Date Palm Diseases, Muntadher Kareem, Raed Majeed

Karbala International Journal of Modern Science

Date palm (Phoenix dactylifera L.) is a cornerstone crop for Iraq and the wider MENA region, yet reliable in-field diagnosis of leaf disorders remains slow, labour-intensive, and constrained by a limited pool of agronomists. This paper presents PalmNet, a full-stack diagnostic system that classifies nine leaf conditions through a calibrated edge-cloud framework. The system is developed and evaluated on a public dataset of 3,089 field images spanning the nine classes, using a 70/15/15 stratified split. A ShuffleNetV2 student network, distilled from a ConvNeXt-Tiny teacher, is deployed on two complementary edge endpoints: a Raspberry Pi Zero 2 W field station …

Scattering Phase Shift In Quantum Mechanics On Quantum Computers: Non-Hermitian Systems And Imaginary-Time Simulations, Peng Guo, Paul Levan, Frank Lee, Yong Zhao

Research & Publications

To overcome the fast oscillatory behavior of correlation functions for extracting scattering phase shift in real-time quantum simulations encountered in the work of Guo et al. [Phys. Rev. D 113, 054512 (2026)], we propose and test two solutions in the present work. One is to simulate Hermitian systems in imaginary time, and the other is to simulate non-Hermitian systems in real time. We demonstrate that both approaches lead to the problem of nonunitary quantum evolution that can be solved by combining two quantum algorithms: block encoding and Hadamard test. The combined quantum algorithm does not require midcircuit …

Machine Learning Applications To Physical Processes, William Charles

Arts & Sciences Graduate Student Theses and Dissertations

This thesis demonstrates how machine learning techniques can solve computationally challenging problems across diverse areas of physics, from high-energy astrophysics to condensed matter systems, accelerating traditional computation. My first contribution addresses the computational expense of Monte Carlo calculations for radiative processes in relativistic plasmas. I develop a neural network sampling method that enables fast sampling from an arbitrary probability density, and demonstrate the method on inverse Compton scattering, achieving a speedup of up to an order of magnitude beyond standard methods. My second contribution addresses the structure and radiation of neutron star magnetospheres. I use physics-informed neural networks to model …

X-Ray Emission From V1674 Her (Nova Her 2021) And Characterization Of Cebr3 Detectors For Gamma-Ray Spectrometry, Tekeba Olbemo

Arts & Sciences Graduate Student Theses and Dissertations

Novae are thermonuclear explosions on the surface of the white dwarf in a close binary system. They are multi-wavelength transients emitting across the electromagnetic spectrum from radio to gamma-rays. This thesis primarily focuses on the X-ray emission from one particular nova, V1674 Her. V1674 Her (Nova Her 2021) is known for its ultra-fast decline time of ��2 ∼ 1 day. This under normal circumstances implies massive white dwarf potentially approaching the Chandrasekhar limit. We test this for V1674 Her by measuring its mass via X-ray spectroscopy method. The method calculates X-ray emission from physically motivated model of post-shock accretion column …

A Feedback Loop Control To Automate Cold Atmospheric Pressure Plasma Based Additive Manufacturing, Arineh Shahbazi

Discovery Day - Daytona Beach

Currently, nanoparticle annealing based additive manufacturing process requires high temperatures, making them unsuitable for a broad range of applications. By lowering the temperature and developing a process to use Cold Atmospheric Pressure Plasma (CAPP) for annealing, many more doors are opened to a wide range of materials, films, and environments for space, communication, and microelectronics. CAPP-based additive manufacturing technology is continually evolving, making projects like nanoparticle annealing more feasible. In this work, we designed a CAPP jet printer assembly consisting of a 0.25-inch outer diameter glass tube connected to a 3D printing nozzle. The flow of Argon gas in the …