Physical Sciences and Mathematics Commons^™

Streams Guidelines: Standards For Technical Reporting In Environmental And Host-Associated Microbiome Studies, Julia M. Kelliher, Chloe Mirzayi, Sarah R. Bordenstein, Aaron Oliver, Christina A. Kellogg, Eneida L. Hatcher, Maureen Berg, Petr Baldrian, Mashael Aljumaah, Cassandra Maria Luz Miller, Christopher Mungall, Vlastimil Novak, Alexis Palucki, Ethan Smith, Nazifa Tabassum, Gregory Bonito, J. Rodney Brister, Patrick S. Chain, Mingfei Chen, Samuel Degregori, Xuefeng Peng, Et. Al.

Faculty Publications

The interdisciplinary nature of microbiome research, coupled with the generation of complex multi-omics data, makes knowledge sharing challenging. The Strengthening the Organization and Reporting of Microbiome Studies (STORMS) guidelines provide a checklist for the reporting of study information, experimental design and analytical methods within a scientific manuscript on human microbiome research. Here, in this Consensus Statement, we present the standards for technical reporting in environmental and host-associated microbiome studies (STREAMS) guidelines. The guidelines expand on STORMS and include 67 items to support the reporting and review of environmental (for example, terrestrial, aquatic, atmospheric and engineered), synthetic and non-human host-associated microbiome …

Field Validation Of Multiple Species Distribution Models Shows Variation In Performance For Predicting Aedes Albopictus Distributions At The Invasion Edge, Anna V. Shattuck, Brandon D. Hollingsworth Ph.D., Jared Skrotzki, Scott R. Campbell, Christopher L. Romano, Courtney C. Murdock

Faculty Publications

Background

Climate and land use changes have resulted in range expansion of many species. In this shifting disease landscape, it is important to leverage tools that can predict the distributions of invading vectors to target surveillance and control efforts and identify at-risk populations. Species distribution models (SDMs) are used to predict ranges of invasive species; however, invasive species often violate assumptions of equilibrium and niche conservatism. Moreover, these studies are rarely validated using independent data.

Methods 

We use long-term surveillance data for Aedes albopictus, a highly invasive mosquito capable of transmitting several arboviruses, at its range edge to evaluate a …

Constraints On Inner Core Composition From Seismicanisotropy: The Importance Of Light Elements And Nickel, Jada Bollmeyer, Daniel A. Frost, Prajna Paramita Das

Faculty Publications

The inner core is seismically anisotropic, with PKIKP waves traversing the inner core parallel to the rotation axis faster than those in the equatorial plane. This anisotropy increases with depth into the inner core and may result from alignment of iron crystals deformed during inner core growth. Using previously calculated elastic properties of iron, we seek to determine the most likely iron-light element alloy (FeC, FeO, FeS, or FeSi). For each FeX alloy, we interpolate elastic tensors across the pressure and temperature range of the inner core and model the anisotropy resulting from flow during core growth. Lastly, we compare …

Chronosort: Revealing Hidden Dynamics In Alphafold3 Structure Predictions, Matthew J. Argyle, William P. Heaps, Corbyn Kubalek, Spencer Gardiner, Bradley C. Bundy, Dennis Della Corte

Faculty Publications

Protein function emerges from dynamic conformational changes, yet structure prediction methods provide only static snapshots. While AlphaFold3 (AF3) predicts protein structures, the potential for extracting dynamic information from its ensemble predictions has remained underexplored. Here, we demonstrate that AF3 structural ensembles contain substantial dynamic information that correlates remarkably well with molecular dynamics simulations (MD). We developed ChronoSort, a novel algorithm that organizes static structure predictions into temporally coherent trajectories by minimizing structural differences between neighboring frames. Through systematic analysis of four diverse protein targets, we show that root-mean-square fluctuations derived from AF3 ensembles can correlate strongly with those from MD …

User Interface And Watchstation Improvements Required For Multi-Vehicle Usv Operations, Val Schmidt, Joshua Bergeron

Faculty Publications

In October 2024, the University of New Hampshire and NOAA’s Uncrewed Systems Office embarked on a mapping mission in the Gulf of Maine, simultaneously operating two DriX Un-crewed Surface Vehicles. Goals of the project were focused on testing hypotheses related to concepts of operation, including the safety of operations, cognitive loading of operators, management of vehicle endurance, vehicle logistics, maintenance and field support, refueling and a host of others.

Interpretable Machine Learning For Cardiovascular Risk Prediction: Insights From Nhanes Dietary And Health Data, Md Ahiduzzaman, Md Nahid Hasan

Faculty Publications

Background: Cardiovascular diseases (CVD) are one of the leading global causes of death, which requires an accurate early prediction. This study aimed to develop transparent machine learning (ML) models using National Health and Nutrition Examination Survey (NHANES) data from 2017–2023 to predict CVD risk based on dietary and health factors.

Methods: We analyzed data from 12,382 adults (aged 18 and older) from NHANES 2017–2023, including 41 dietary, anthropometric, clinical, and demographic variables. Recursive Feature Elimination (RFE) was used to select an optimal subset of 30 predictors. To address substantial class imbalance in the outcome, we applied the Random Over-Sampling Examples …

Circulating Levels Of Insulin-Like Growth Factor I (Igf-I) And Risk Ofmultiple Myeloma: An Observational And Mendelian Randomisation Study, Yolanda Benavente, Sara Hermosa, Nikos Papadimitriou, Alyssa I. Clay-Gilmour Ph.D., Elizabeth E. Brown, Jonathan N. Hofmann, Nathaniel Rothman, Qing Lan, Sonja I. Berndt, Demetrius Albanes, Mark Purdue, Mitchell J. Machiela, Stephen J. Chanock, Parveen Bhatti, Wendy Cozen, Aaron Norman, Susan L. Slager, James R. Cerhan, Vincent Rajkumar, Shaji J. Kumar, Et. Al.

Faculty Publications

Evidence for an association between insulin-like growth factors (IGF) and multiple myeloma (MM) is inconsistent. We examined total IGF-I concentrations and risk of MM by combining baseline serological data among UK Biobank participants (n = 444 187; 732 incident MM) with a two-sample Mendelian randomisation (MR) analysis using identified genetic variants associated with circulating total IGF-I and IGF-binding protein 3 (IGFBP-3) in the InterLymph consortium (2434 MM and their 2567 controls). Finally, additional lymphoid neoplasm (LN) subtypes were included for comparison with the main hypothesis. Circulating IGF-I level was positively associated with MM risk Hazard ratio-HR-per one standard deviation-SD-increase …

Electrochemical Breath Sensors In Medical Diagnostics: Emerging Trends And Future Directions, Natalie E. Strom, Courtney J. Weber, Olja Simoska

Faculty Publications

With the rising prevalence of metabolic diseases, infections, and mental health disorders, there is a growing demand for noninvasive diagnostic tools that enable early detection and continuous health monitoring. In this context, exhaled breath biomarkers provide insight into physiological and pathological processes. Electrochemical breath sensors (EBSs) have emerged as a promising platform for rapid, real-time, and cost-effective disease tracking via the detection of volatile breath biomarkers, such as NH₃, NO, and CO₂. Recent advancements in electrode materials, biological recognition elements, and sensor architectures—spanning nanomaterials, enzymes, aptamers, and molecularly imprinted polymers—have enhanced the analytical performance of EBSs. …

Electrochemical Breath Sensors In Medical Diagnostics: Emerging Trends And Future Directions, Natalie E. Strom, Courtney J. Weber, Olja Simoska

Faculty Publications

With the rising prevalence of metabolic diseases, infections, and mental health disorders, there is a growing demand for noninvasive diagnostic tools that enable early detection and continuous health monitoring. In this context, exhaled breath biomarkers provide insight into physiological and pathological processes. Electrochemical breath sensors (EBSs) have emerged as a promising platform for rapid, real-time, and cost-effective disease tracking via the detection of volatile breath biomarkers, such as NH₃, NO, and CO₂. Recent advancements in electrode materials, biological recognition elements, and sensor architectures—spanning nanomaterials, enzymes, aptamers, and molecularly imprinted polymers—have enhanced the analytical performance of EBSs. …

Flow Structure And Mixing Near A Small River Plume Front: Winyah Bay, Sc, Usa, Christopher Papageorgiou, George Voulgaris, Alexander E. Yankovsky, Diane Bennett Fribance

Faculty Publications

This study presents Eulerian data from Winyah Bay, SC, USA collected during the passage of a tidal plume. The data captured the evolution and structure of the plume and include high-resolution velocity and temperature time series, supplemented by temperature – salinity profiles from a MicroCTD profiler. The observations identified a pre-existing plume extending to 4 m, with a water density of 1023.6 kg m⁻³, laying above denser ambient waters. Upon arrival, the newly discharged tidal plume introduced a fresher layer (1020.7 kg m⁻³) extending to 2.6 m, gradually thinning due to radial spreading. The plume's frontal …

Applying Machine Learning Methods To Laser Acceleration Of Protons: Synthetic Data For Exploring The High Repetition Rate Regime, John J. Felice, Ronak Desai, Nathaniel Tamminga, Joseph R. Smith, Alona Kryshchenko, Christopher M. Orban, Michael L. Dexter, Anil K. Patnaik

Faculty Publications

Advances in ultra‐intense laser technology have increased repetition rates and average power for chirped‐pulse laser systems, which offer a promising solution for many applications including energetic proton sources. An important challenge is the need to optimize and control the proton source by varying some of the many degrees of freedom inherent to the laser‐plasma interactions. Machine learning can play an important role in this task, as our work examines. Building on our earlier work in Desai et al. 2024, we generate a large ∼1.5 million data point synthetic data set for proton acceleration using a physics‐informed analytic model that we …

Flow Structure And Mixing Near A Small River Plume Front: Winyah Bay, Sc, Usa, Christopher Papageorgiou, George Voulgaris, Alexander E. Yankovsky, Diane Bennett Fribance

Faculty Publications

This study presents Eulerian data from Winyah Bay, SC, USA collected during the passage of a tidal plume. The data captured the evolution and structure of the plume and include high-resolution velocity and temperature time series, supplemented by temperature – salinity profiles from a MicroCTD profiler. The observations identified a pre-existing plume extending to 4 m, with a water density of 1023.6 kg m⁻³, laying above denser ambient waters. Upon arrival, the newly discharged tidal plume introduced a fresher layer (1020.7 kg m⁻³) extending to 2.6 m, gradually thinning due to radial spreading. The plume's frontal …

Interactions And Community Structure Of Fungi And Prokaryotes In Salt And Brackish Marsh Ecosystems, Madeleine A. Thompso, Xuefeng Peng

Faculty Publications

Microbial communities play a fundamental role in biogeochemical cycling within salt and brackish marsh ecosystems, yet fungal-prokaryotic interactions in these environments remain poorly understood. This study employed metabarcoding of the 16S and 28S rRNA genes to investigate prokaryotic and fungal communities across four locations in sediments and surface waters of the North Inlet salt marsh and Winyah Bay brackish marsh (South Carolina, USA) over four time points from 2020 to 2021. Co-occurrence network analyses were used to identify potential microbial interactions and their ecological implications. Distinct fungal and prokaryotic communities were observed between the two marsh types. From the 16S …

Deep Learning Based Contactless Fingerprint Identification, Mohammad Alsmirat, M. Moneb Khaled, Aghyad A.L. Sayadi

Faculty Publications

Biometric authentication systems, particularly contactless fingerprint methods, offer enhanced security and convenience across various domains like access control, law enforcement, and finance. Despite these advantages, contactless systems face significant challenges related to image quality, finger orientation, and environmental factors. To address this, our paper presents the first extensive deep learning-based study on contactless fingerprint recognition using a large dataset of 2,143 images from 175 individuals. Our proposed approach integrates state-of-the-art preprocessing techniques with deep learning models to boost identification performance. After studying various transfer learning models, we achieved a high accuracy of 93.5%. We also conducted two further studies on …

A New Low-Rate Stable Hydrogel Cathode For Aqueous Zn-Ion Batteries, Roya Rajabi, Shichen Sun, Jamil A. Khan, Morgan Stefik, Kevin Huang

Faculty Publications

Aqueous Zn-ion batteries (ZIBs) are attractive candidates for large-scale energy storage owing to the abundance, low cost, and intrinsic safety of Zn metal. However, their practical application is hindered by poor cycle stability, especially at low current densities, due to cathode dissolution and limited electrochemically active sites (EAS). Herein, a hydrogel-based cathode comprising ammonium vanadate, carbon black, and a Zn-ion-conducting carboxymethyl chitosan–acrylamide hydrogel matrix doped with Zn(ClO4)2 is reported. This design establishes a continuous Zn-ion-conducting network, thereby maximizing EAS density throughout the electrode volume. The ZIB with the hydrogel cathode exhibits outstanding cycling stability, with 77% capacity retention after 2000 …

Joint Neutrino Oscillation Analysis From The T2k And Nova Experiments, S. Abubakar, M. A. Acero, B. Acharya, P. Adamson, N. Anfimov, A. Antoshkin, E. Arrieta-Diaz, L. Asquith, A. Aurisano, D. Azevedo, A. Back, N. Balashov, P. Baldi, A. Bambah, E. F. Barenboim, A. Bashyal, A. Basti, K. Bromberg, N. Buchanan, Roberto Petti

Faculty Publications

The landmark discovery that neutrinos have mass and can change type (or flavour) as they propagate—a process called neutrino oscillation^1,2,3,4,5,6—has opened up a rich array of theoretical and experimental questions being actively pursued today. Neutrino oscillation remains the most powerful experimental tool for addressing many of these questions, including whether neutrinos violate charge-parity (CP) symmetry, which has possible connections to the unexplained preponderance of matter over antimatter in the Universe^7,8,9,10,11. Oscillation measurements also probe the mass-squared differences between the different neutrino mass states (Δm²), whether there are two light states and a heavier …

Intrinsic Defects (Vacancies And Antisites) In Neutron Irradiated Cdsip2 Crystals, Timothy D. Gustafson, Elizabeth M. Scherrer, Nancy C. Giles, Kevin T. Zawilski, Peter G. Schunemann, Jonathan E. Slagle, Kent L. Averett, Larry E. Halliburton

Faculty Publications

Cadmium silicon phosphide (CdSiP₂) is a nonlinear optical material widely used in optical parametric oscillators. Intrinsic defects (vacancies and antisites) are responsible for unwanted broad optical absorption bands in these crystals that degrade the performance of the devices. In the present work, optical absorption and electron paramagnetic resonance (EPR) spectra are acquired (at room temperature and 12 K, respectively) from a neutron-irradiated CdSiP₂ crystal. After the irradiation, the crystal is highly absorbing from the band edge near 600 nm to beyond 1.3 μm because of overlapping defect-related absorption bands. Heating to 550 °C removes nearly all the …

Pathlength, Altitude And Angle Of Incidence Dependence Of Remote Water Raman Scattering, Whitney E. Schuler, Paige K. Williams, Zechariah B. Kitzhaber, Caitlyn M. English, Tammi L. Richardson, Nikos Vitzilaios, Michael L. Myrick

Faculty Publications

A small remote Raman sensor was used to measure the Raman scattering signal from clear, still water as a function of water depth (12 cm and 396 cm depth), sensor distance above the water surface (20–300 cm), and angle of incidence (0–80°) to the normal of the water surface. Under thick- and thin-sample conditions, the signal depends on either the inverse, or the inverse square, of sensor distance from the water surface, respectively. A model is derived that fits data for different sensor distances, water depths, and angles of incidence. Fits to the measured data are consistent with the known …

Detecting Polar Ring Galaxies Via Deep Learning, Fawad Kirmani, Anathavishnu S. Unnii, Varsha P. Kulkarni, Kyle Lackey, John R. Rose

Faculty Publications

Polar ring galaxies (PRGs) are peculiar galaxies that show a ring of stars, gas, and dust oriented roughly over the poles of the central ‘host’ galaxy (i.e. roughly orthogonal to the disc of the host galaxy). The formation models for these rings involve mergers or tidal interactions of the host galaxy with another galaxy. Although the identified PRGs look different from each other, they all have a ring that is not in the same plane as the disc of the host galaxy. Unlike in galaxies such as our Milky Way, where stars form in spiral arms, the rings exemplify an …

Adipose Tissue Estrogen Receptor-Alpha Overexpression Ameliorates High-Fat Diet–Induced Adipose Tissue Inflammation, Marion C. Hope, Christian A. Unger, M. Chase Kettering, Cassidy E. Socia, Ahmed K. Aladhami, Barton C. Rice, Darya S. Niamira, Ben P. Wiznitzer, Diego Altomare Ph.D., William E. Cotham, Reilly T. Enos

Faculty Publications

Context

We created an innovative mouse model that enables inducible overexpression of estrogen receptor-alpha (ERα), specifically in adipose tissue (Adipo-ERα↑). Objective

We aimed to investigate how elevated Adipo-ERα↑ influences the development of high-fat diet (HFD)-induced obesity in both male and female mice. Methods

Male and female Adipo-ERα↑ mice and littermate controls were fed a low-fat diet (LFD) or HFD for 13 weeks. Adipo-ERα↑ was induced at the initiation of dietary treatment. Body morphology and composition, hepatic lipid accumulation, glucose tolerance, fasting insulin concentrations, and adipose tissue mRNA profiling were assessed. Liquid chromatography–mass spectrometry was used to determine circulating and adipose …

Pentanary And Hexanary Alkali Lanthanide Germanates Grown From Lanthanide Rich Flux Growth Reactions, Gregory Morrison, K. Pilar Zamorano, Virginia G. Jones, Ethan N. Adams, Hans Conrad Zur Loye

Faculty Publications

Single crystals of five compounds of a new structure type, the K3Nd52Ge12O100F7 type, were grown from lanthanide rich reactions using a KF/KCl flux: K3Ln52Ge12O100F7 (Ln = Nd, Eu), K3Ln52Ge12O100+δF7-δ (Ln = Pr, Tb), and K3Ca0.872Nd51.128Ge12O99.128F7.872. The five compounds crystallize in the cubic space group I-43d with lattice parameter 22.0350(2) Å for K3Nd52Ge12O100F7. The structure consists of a 3D checkerboard of LnX8, LnX7, LnX6, and GeO4 polyhedra, where X = O/F, and can be conceptually derived from the fluorite structure. K3Eu52Ge12O100F7 exhibits the fluorescence typical of Eu³⁺.

Better Digital Contracts With Prosocial Friction-In-Design, Brett Frischmann, Moshe Y. Vardi

Faculty Publications

Contract law is supposed to enable people to reach genuine agreements and cooperate. If this ideal was ever a reality, the rise of mass market contracts and boil­erplate rendered it pure fiction. Modern consumer contracts are incomprehensible to most people. No one reads them anyway.

Digital contracting involves design features that amplify traditional boilerplate harms and create others. For example, digital contracting is too cheap; low marginal costs lead to overexpansion in scale and scope. To make matters worse, the loss of autonomy from repeat engagement with digital contracting systems is pernicious. People become increasingly predictable and programmable as digital …

Glucodensity Functional Profiles Outperform Traditional Continuous Glucose Monitoring Metrics, Marcos Matabuena, Rahul Ghosal Ph.D., Javier Enrique Aguilar, Ayya Keshet, Robert Wagner, Carmen Fernández Merino, Juan Sánchez Castro, Vadim Zipunnikov, Jukka-Pekka Onnela, Francisco Gude

Faculty Publications

Continuous glucose monitoring (CGM) data have revolutionized the management of type 1 diabetes, particularly when integrated with insulin pumps to mitigate clinical events such as hypoglycemia. Recently, there has been growing interest in utilizing CGM devices in clinical studies involving healthy and diabetic populations. However, efficiently exploiting the high temporal resolution of CGM profiles remains a significant challenge. Numerous indices—such as time–in–range metrics and glucose variability measures–have been proposed, but evidence suggests these metrics overlook critical aspects of dynamic glucose homeostasis. As an alternative method, this paper explores the clinical value of glucodensity metrics in capturing glucose dynamics—specifically the speed …

Polymorphism Crystal Structure Prediction With Adaptive Space Group Diversity Control, Sadman Saadeed Omee, Lai Wei, Jianjun Hu

Faculty Publications

Crystalline materials can form different structural arrangements (i.e., polymorphs) with the same chemical composition, exhibiting distinct physical properties depending on how they are synthesized or the conditions under which they operate. For example, carbon can exist as graphite (soft, conductive) or diamond (hard, insulating). Computational methods that can predict these polymorphs are vital in materials science, which help understand stability relationships, guide synthesis efforts, and discover new materials with desired properties without extensive trial-and-error experimentation. However, effective crystal structure prediction (CSP) algorithms for inorganic polymorph structures remain limited. ParetoCSP2 is proposed, a multi-objective genetic algorithm for polymorphism CSP that incorporates …

Effects Of Climate Change On River And Groundwater Nutrient Inputs To The Coastal Ocean, Christina M. Richardson, Bernhard Peucker-Ehrenbrink, Shea Wyatt, Annie Bourbonnais, Vanessa Hatje, Claudia Frey, Tina Sanders, Diana E. Varela, Adina Paytan

Faculty Publications

Rivers and groundwater are major sources of nutrients to the global coastal ocean. Climate change is expected to impact nutrient fluxes from river basins and coastal aquifers through alterations to both hydrological and nutrient cycling processes. In this Review, we identify and summarize how climate change impacts, such as changes in precipitation, increased cryosphere melt, and sea level rise, will affect water discharge and nutrient concentrations in rivers and coastal groundwater, which ultimately control nutrient inputs to the coastal ocean. We also document key limitations in the current understanding of climate-related changes to nutrient fluxes, especially in coastal groundwater basins. …

Microarchitectural Malware Detection Via Translation Lookaside Buffer (Tlb) Events, Cristian Agredo, Daniel F. Koranek, Christine M. Schubert Kabban, Jose R. Gutierrez Del Arroyo, Scott R. Graham

Faculty Publications

Prior work has shown that Translation Lookaside Buffer (TLB) data contains valuable behavioral information. Many existing methodologies rely on timing features or focus solely on workload classification. In this study, we propose a novel approach to malware classification using only TLB-related Hardware Performance Counters (HPCs), explicitly excluding any dependence on timing features such as task execution duration or memory access timing. Our methodology evaluates whether TLB data alone, without any timing information, can effectively distinguish between malicious and benign programs. We test this across three classification scenarios: (1) A binary classification problem involving distinguishing malicious from benign tasks, (2) a …

Effect Of Vitamin D Supplementation During Pregnancy And Lactation On The Development Of Infants Born To Tanzanian Women Living With Hiv: A Secondary Analysis Of A Randomised Controlled Trial, Temiwunmi Shobanke, Alfa Muhihi, Nandita Perumal Phd, Nzovu Ulenga, Fadhlun M. Alwy Al-Beity, Christopher P. Duggan, Wafaie W. Fawzi, Karim P. Manji, Christopher R. Sudfeld

Faculty Publications

Background Infants born to pregnant women living with HIV (WLHIV) are at greater risk for morbidity and mortality and may also have poorer developmental outcomes as compared with infants who are not exposed to HIV. Nutrition interventions in pregnancy may affect developmental outcomes.

Objectives This study evaluated the effect of maternal vitamin D supplementation on infant development outcomes.

Design We conducted a secondary analysis of a randomised, triple-blind, placebo-controlled trial of maternal vitamin D supplementation from June 2015 to October 2019.

Setting Antenatal care clinics in Dar es Salaam, Tanzania.

Participants Pregnant WLHIV and their offspring.

Interventions Daily 3000 IU …

Wave Intensity Analysis With Exercise Identifies Impairments In Pulmonary Hypertension, Christopher G. Lechuga, Farhan Raza, Mitchel J. Colebank, Claudia E. Korcarz, Jens C. Eickhoff, Naomi C. Chesler

Faculty Publications

Wave intensity analysis provides a novel approach to understanding the dynamic interactions between the right ventricle and pulmonary vasculature, particularly in pulmonary hypertension, a condition characterized by elevated pulmonary arterial pressures and vascular remodeling. This prospective study used wave intensity analysis to evaluate right ventricular and pulmonary vascular mechanics in 22 participants with pulmonary hypertension (including precapillary, isolated postcapillary, and combined pre/postcapillary pulmonary hypertension), and three without pulmonary hypertension. Forward and backward compression and decompression waves were quantified at rest and during incremental exercise (25, 50, and 75 W). Relationships between metrics of wave intensity analysis, hemodynamics, right ventricular function, …

Exact And Approximate Conformal Inference For Multi-Output Regression, Chancellor Johnstone, Eugene Ndiaye

Faculty Publications

It is common in machine learning to estimate a response y given covariate information x . However, these predictions alone do not quantify any uncertainty associated with said predictions. One way to overcome this deficiency is with conformal inference methods, which construct a set containing the unobserved response with a prescribed probability. Unfortunately, even with a one-dimensional response, conformal inference is computationally expensive despite recent encouraging advances. In this paper, we explore multi-output regression, delivering exact derivations of conformal inference p-values when the predictive model can be described as a linear function of y . Additionally, we introduce a multivariate …

Comparative Analysis Of Different Magnetic Anomaly Datasets Using Navigation Performance With Flight Test Data, Aaron P. Nielsen, Brandon M. Blakely, Patrick Duff

Faculty Publications

Magnetic Anomaly Navigation (MagNav) is a map-based method of navigation which relies on accurately obtaining the anomaly field to a high level of precision to achieve good navigation results. This requires utilizing high quality sensors, accurately modeling disturbance fields from the aircraft & other sources, and creating high-fidelity maps. Aeromagnetic survey data or marine track survey data must be processed into a product that can be used as a reference for a magnetic navigator and a variety of techniques can be utilized for this processing. The data collection for different survey types reflects choices typically made to study the underlying …