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Analysis of Modeled 3D Solar Magnetic Field during 30 X/M-class Solar Flares
Get PDFUsing non-linear force free field (NLFFF) extrapolation, 3D magnetic fields were modeled from the 12-min cadence Solar Dynamics Observatory Helioseismic and Magnetic Imager (HMI) photospheric vector magnetograms, spanning a time period of 1 hour before through 1 hour after the start of 18 X-class and 12 M-class solar flares. Several magnetic field parameters were calculated from the modeled fields directly, as well as from the power spectrum of surface maps generated by summing the fields along the vertical axis, for two different regions: areas with photospheric |Bz|≥ 300 G (active region—AR) and areas above the photosphere with the magnitude of the non-potential field (BNP) greater than three standard deviations above |B-NP| of the AR field and either the unsigned twist number |Tw| ≥ 1 turn or the shear angle Ψ ≥ 80° (non-potential region—NPR). Superposed epoch (SPE) plots of the magnetic field parameters were analyzed to investigate the evolution of the 3D solar field during the solar flare events and discern consistent trends across all solar flare events in the dataset, as well as across subsets of flare events categorized by their magnetic and sunspot classifications. The relationship between different flare properties and the magnetic field parameters was quantitatively described by the Spearman ranking correlation coefficient, rs. The parameters that showed the most consistent and discernable trends among the flare events, particularly for the hour leading up to the eruption, were the total unsigned flux ϕ), free magnetic energy (EFree), total unsigned magnetic twist (τTot), and total unsigned free magnetic twist (ρTot). Strong (|rs| ∈ [0.6, 0.8)) to very strong (|rs| ∈ [0.8, 1.0]) correlations were found between the magnetic field parameters and the following flare properties: peak X-ray flux, duration, rise time, decay time, impulsiveness, and integrated flux; the strongest correlation coefficient calculated for each flare property was 0.62, 0.85, 0.73, 0.82, −0.81, and 0.82, respectively
Pursuit-Evasion on a Sphere and When It Can Be Considered Flat
No full textIn classical works on a planar differential pursuit-evasion game with a faster pursuer, the intercept point resulting from the equilibrium strategies lies on the Apollonius circle. This property was exploited for the construction of the equilibrium strategies for two faster pursuers against one evader. Extensions for planar multiple-pursuer single-evader scenarios have been considered. We study a pursuit-evasion game on a sphere and the relation of the equilibrium intercept point to the Apollonius domain on the sphere. The domain is a generalization of the planar Apollonius circle set. We find a condition resulting in the intercept point belonging to the Apollonius domain, which is the characteristic of the planar game solution. Finally, we use this characteristic to discuss pursuit and evasion strategies in the context of two pursuers and a single slower evader on the sphere and illustrate it using numerical simulations
The Development of a Feature-Driven Analytical Approach for Gamma-Ray Spectral Analysis
No full textGamma-ray spectroscopy is an essential tool in nuclear science, nuclear security, and environmental monitoring. However, challenges arise in interpreting spectral data due to the presence of low counts, multiple sources, and dynamic backgrounds. To address these issues, a novel feature-driven analytical approach for gamma-ray spectral analysis using machine-learning techniques is developed. The method utilizes a series of random forest models for in-distribution (ID) multi-label classification, and the model-derived feature importance values to guide the out-of-distribution (OOD) detection task. The performance of this approach is quantitatively evaluated across various spectral parameters, including acquisition time, number of sources, energy of an OOD source, and background composition. Increasing the acquisition time from 1 s to 100 s leads to improved performance for multi-label classification, with 22 sources achieving F1-scores ≥ 0.9 after 50 s acquisitions for a CLLBC handheld detector and a standoff distance of 30 cm. The feature-driven analytical approach also demonstrates robustness when handling complex source mixtures. Furthermore, it provides contextual energetic information for OOD detection. The results presented here highlight the interpretability of the approach, establishing clear links between the spectral features and underlying physics. Moreover, the approach effectively distinguishes overlapping spectral signatures of different ID gamma-ray sources, enhancing human reliability in machine learning-based gamma-ray spectral analysis. The feature-driven analytical approach offers a promising solution to automate gamma-ray spectral analysis by addressing existing limitations and providing insights into performance across diverse spectral parameters
Recent Advances in Technologies for Phosphate Removal and Recovery: A Review
No full textPhosphorus is a nonrenewable resource, yet an essential nutrient in crop fertilizers that helps meet growing agricultural and food demands. As a limiting nutrient for primary producers, an excess amount of phosphorus entering water sources through agricultural runoff can lead to eutrophication events downstream. Therefore, to address global issues associated with the depletion of phosphate rock reserves and minimize the eutrophication of water bodies, numerous studies have investigated the removal and recovery of phosphates in usable forms using various chemical, physical, and biological methods. This review provides a comprehensive and critical evaluation of the literature, focusing on the widely employed adsorption and chemical precipitation for phosphate recovery from various wastewaters. Several experimental performance parameters including temperature, pH, coexisting ions (e.g., NO3–, HCO3–, Cl–, SO42–), surface area, porosity, and calcination are highlighted for their importance in optimizing adsorption capacity and struvite crystallization/precipitation. Furthermore, the morphological and structural characterization of various selected adsorbents and precipitated struvite crystals is discussed
Data Supporting Research on Personalized Learning Paths
Get PDFPersonalized Learning Paths (PLPs) are a key application of Artificial Intelligence in E-Learning. In contrast to regular Learning Paths, they return a unique sequence of learning materials identified as meeting the individual needs of the students. In the literature, PLPs are often created from knowledge graphs, which assist with ordering topics and their associated learning materials. Knowledge graphs are typically directed and acyclic, to capture prerequisite relationships between topics, though they can also have bidirectional edges when these prerequisite relationships are not necessary. This data package provides a primarily un-directed knowledge graph, with associated repository of open-source learning materials that provide AI education, along with student profile data. These data are intended to support the automatic creation of PLPs for students and to enable the comparison of PLP design approaches. This technical data package is associated with the paper listed below
Technical Data Package for SysMLv2 Vignettes
Get PDFThis record has been moved to a new Student Publications collection. https://scholar.afit.edu/studentpub/1
Adversarial Risk Analysis for Automated Lane-Changing in Heterogeneous Traffic
No full textThe global transition from manned to automated vehicles is anticipated to occur incrementally. As such, interactions between automated driving systems (ADS) and manned vehicles motivate related decision-support research. This manuscript develops a novel modeling framework based on adversarial risk analysis focusing on lane-changing maneuvers. An empirical evaluation is provided within a simulated environment serving to validate the modeling approach and solution methodology under a specified traffic scene. Additional model extensions to alternative traffic scenes and different driver-rationality assumptions are provided. In so doing, we showcase the potential for decision theory to manage ADS behavior in heterogeneous traffic. This research also highlights the need for an ADS to prudently balance computational resources between perception and decision tasks. Abstract © SpringerNatur
Global Empirical Model of Sporadic-E Occurrence Rates
Get PDFIntense ionization enhancements in the Earth’s ionosphere, known as sporadic-E (Es), can degrade and severely disrupt the propagation of radio signals. Although many previous studies have analyzed the characteristics and morphologies of sporadic-E, few efforts have attempted to model global Es occurrence rates (ORs) at high time resolutions. This study develops a global empirical model of blanketing sporadic-E occurrence rates using a Karhunen–Loéve Expansion (KLE) of a global OR climatology built with Global Navigation Satellite System radio occultation (GNSS-RO) and ionosonde observations. Using an fbE ≥ threshold of 3 MHz, the model outputs a blanketing sporadic-E OR for a given geomagnetic latitude, longitude, day of year, and local solar time. The model outputs are compared to digisonde observations at four sites with varying geomagnetic latitudes, resulting in correlation coefficients ranging from 0.5 to 0.9 for monthly averaged observations and an uncertainty of 11%. Furthermore, the average uncertainty is estimated to be 12%. This Global Empirical Model of Sporadic-E Occurrence Rates (GEMSOR) is capable of providing blanketing sporadic-E OR estimates for global radio frequency (RF) operations
City Climate Action Plans through the Lens of the Food-energy-water Nexus
Get PDFThe concepts of interdependent resource management have roots in the mid-20th century, and, more recently, the term ‘nexus’ has been used to describe the interconnected relationships among various resources, including food, energy, water, climate, and land. United States and European science foundations have shown a growing interest in the food-energy-water (FEW) nexus, leading to increased research on their joint management. Concurrently, in response to the looming threats of climate change, many cities in the United States have addressed climate governance by developing climate action plans (CAPs) for both mitigation and adaptation. However, one major criticism of the FEW nexus is the limited translation of the research into practical policies and implementation, such as CAPs. To assess the incorporation of FEW nexus principles into climate planning, we systematically evaluate 100 CAPs from large United States cities (population over 100 000). We identified primary themes and objectives for each resource, examining explicit or implicit connections within the CAPs. Our findings show that the energy sector is a central focus in nearly every CAP (98%), followed by water (75%), and food (66%). Within the food sector, we observed a significant emphasis on food waste reduction and composting (about 80%) compared to other food-related topics. Among water-related matters, drinking water receives the most attention, compared to wastewater and stormwater. Notably, the most discussed food-energy-water (FEW) links are those that involve energy, particularly the water-for-energy and food-for-energy connections, found in over half of the documents (56%). Our analysis promotes the integration of the FEW nexus into CAPs while discussing the barriers to its effective implementation
Systematic Review of Supply Chain Control Tower Critical Success Factors and Resilience Effects
No full textSupply chain control towers (SCCTs) are emerging as vital components of modern supply chain management (SCM). However, research on SCCTs is limited and disjointed. This paper explores critical success factors (CSFs) necessary for high-performing SCCTs and examines their impact on supply chain resilience (SCRES). Through a comprehensive systematic literature review, this research inductively identifies and classifies 14 CSFs into a framework consisting of organizational, process, and technological dimensions. We further map complex relationships among these CSFs, revealing how SCCT performance relies on CSFs interacting synergistically as dynamic capabilities. Moreover, this study extends the theory related to SCRES under dynamic and disruptive market conditions by discretely connecting SCCT outcomes to supply chain readiness, responsiveness, recovery, and renewal. By integrating both theoretical and practical perspectives, this study contributes to existing SCM knowledge by providing both a structured approach to identifying and implementing CSFs for SCCTs and explaining the role of SCCTs in advancing SCRES. This paper, therefore, serves as a valuable resource for both researchers and industry practitioners in understanding SCCT operations and outcomes in achieving efficient, robust, and resilient supply chains