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On Efficient Position Dependent Computation of Radiation Near Accretion Disks
No full textAccretion onto super massive black holes (SMBH) plays a vital role in explaining our observations of active galactic nuclei (AGN). One key aspect of observations of AGN is the prevalence of massive outflows which transport matter and energy away from the galactic core. These outflows are characterized by highly blueshifted absorption lines as radiation from the accretion disk is absorbed by gas which is moving toward us. Radiation pressure on spectral lines is expected to play a vital role in launching and accelerating these winds. At the other end of the spectrum of black hole masses, accretion disks in X-ray binaries exhibit outflows likely driven by thermal pressure. Despite the difference in magnitude, both of these systems\u27 driving mechanisms share a strong dependence on the radiation field which permeates the gas above the accretion disk.While we have observations of accretion disks of all sizes throughout the universe, from X-ray binaries to AGN, they all share one limitation: accretion disks are far away from us. Especially in the case of AGN, each accretion disk is at such a distance from us that, over any reasonable timescale, we may only observe it from one distance and inclination angle. In contrast, along the outflow\u27s journey from the surface of the disk to the ISM, the radiation it sees from the accretion disk system changes drastically. We cannot use our observations from a great distance as a reliable proxy for the radiation field which is actually launching and accelerating the outflows. Accurately modeling the radiation at any position near the disk requires solving radiative transfer in all directions. This can be done to a fair extent utilizing radiation-hydrodynamic simulations in tandem with photoionization codes, but only at significant computational cost. This thesis works to develop tools which can quickly and accurately compute, as a function of position, properties of the radiation field, in order to alleviate some of the computational load. We motivate our work with an overview of outflows observed in AGN. We then review photoionization, its effect on heating and cooling of the gas, and line driving. Next, we extend prior position dependent theory to frequency dependent calculations, which allows us to compute spectral energy distributions (SEDs), ionizing intensities, mean photon energies, ionization parameters, line-driving force multipliers, and net cooling rates all in a position and direction dependent manner. We show verification of our methods using analytically tractable limits, followed by discussion of results at intermediate locations. Finally, we discuss future steps in the development of tools which will improve our ability to efficiently model radiative processes due to accretion disks
Influences On the Ecuadorian Flute Repertoire: Compositions by Sixto María Durán, Jacinto Freire, Gerardo Guevara, Luciano Carrera, Juan Carlos Urrutia, Blanca Layana, And Leonardo Cárdenas
No full textEcuadorian music is rich, diverse, and beautiful. It encompasses several genres, rhythms, histories, backgrounds, and influences. Ecuador is a small country situated in the northwest corner of South America atop by the equatorial line that divides the northern Hemisphere from the South, hence the name Ecuador. It borders to the north by Colombia, to the south and east border by Peru, and to the west, by the Pacific Ocean. The repertoire for flute from Ecuadorian composers is a very interesting topic that deserves further exploration and research. As additional repertoire is available; however, there are not many scholarly papers written about it. This fact encouraged me to explore this topic in more depth. Furthermore, there are no Ecuadorian flute repertoire pieces written that include extended techniques. As an Ecuadorian flutist myself, roots and culture are very important to me. Therefore, I intend to educate people about Ecuadorian flute music, a genre that music that far too often feels obscure to the general public. I feel a deep personal connection with this topic. Important pieces of music are unknown, lost or devalued, due to lack of publication, and other issues. I have chosen to bring to light the beautiful amazing world of Ecuadorian flute music through this document
Comparison of Gluteal Muscle Central Activation in Individuals with and without Patellofemoral Pain
No full textPurpose/Hypothesis: Individuals with patellofemoral pain (PFP) often present with knee valgus during weight-bearing activities. Weakness in the gluteal muscles is thought to contribute to this condition, but recent studies suggest that hip weakness might be a secondary problem in this population. Central nervous system adaptations have been noted in persons with PFP, which can lead to altered muscle function, faulty movement patterns, and poor function. Experimentally, central activation can be quantified by a central activation ratio (CAR) via superimposed burst (SIB) during a maximum voluntary isometric contraction (MVIC) where the ratio of volitionally activated motor units to total motor units of a single muscle can be determined. This study aims to compare the CAR of the gluteus medius (GMed) and gluteus maximus (GMax) between individuals with and without PFP and to assess the association between CAR of the gluteal musculature and the frontal plane projection angle (FPPA) of the trunk and lower extremity during weight-bearing activities using 2D motion analysis and functional assessment. We hypothesize that individuals with PFP would have a lower CAR of the GMed and GMax compared to controls, and this lower CAR would be associated with altered FPPA and diminished function.
Participants: 12 participants without PFP (4M/8F, age=24.2±1.8 yrs, BMI=24.0±4.2) and 10 participants with PFP (4M/6F, age=22.4±2.8 yrs, BMI=23.5±3.9).
Materials and Methods: Participants performed a single-leg squat, single-leg hop, single-leg landing, forward step down, and lateral step down, analyzed using an iPhone 13 Pro Max. Frontal plane kinematics (lateral trunk lean (LTL), hip FPPA, knee FPPA, and dynamic valgus index (DVI)) were measured. CAR of the GMax and GMed was tested using the SIB protocol. CAR was calculated as the ratio of maximal torque output prior to and during SIB. PFP participants also completed the Anterior Knee Pain Scale (AKPS). Independent t-tests compared CAR between groups, and Pearson correlation coefficients evaluated the associations between CAR, frontal plane kinematics, and AKPS.
Results: There was no significant difference in CAR of the GMed and GMax between groups (p≥0.067). However, significant correlations were found between CAR of the GMax and AKPS (R=0.790, p=0.003), CAR of the GMed and AKPS (R=0.584, p=0.038), and CAR of the GMax and LTL during single-limb landing (R=0.533, p=0.006).
Conclusions: While no significant group differences were found, there was a trend towards lower GMax CAR in PFP participants (PFP 0.91; control=0.93; p ≥0.067). Higher CAR was associated with better function in participants with PFP. Lower GMax CAR may relate to ipsilateral trunk lean during single-limb weight-bearing, a strategy used to reduce the external hip torque.
Clinical Relevance: Lower central activation of gluteal muscles may be linked to poorer function and altered kinematics in patients with PFP. Future larger-scale studies should identify PFP subgroups with diminished gluteal central activation
An Edge Computing Device Optimized and Transfer Learning Enhanced Deep Learning Model for Detecting Wildfire Flame and Smoke
No full textThe integration of autonomous unmanned aerial vehicles (UAVs) with edge computing technology and deep learning (DL)-based object detection offers a groundbreaking solution for real-time wildfire detection, enabling rapid data processing directly on devices and minimizing response delays in critical scenarios. However, although showing early promise, performance is often constrained by limited training data and edge computing devices that lack graphics processing unit (GPU) acceleration. This thesis seeks to address these limitations in two stages.First, this work explores the transformative potential of Transfer Learning (TL) to enhance wildfire object detection model accuracy while also investigating TL’s impact, for DL-based object detection models, on edge computing performance metrics including inference speed, power consumption, and energy efficiency. Towards this end, we introduce the Aerial Fire and Smoke Essential (AFSE) dataset as a target dataset while utilizing the Flame and Smoke Detection Dataset (FASDD) and the general Microsoft Common Objects in Context (COCO) dataset as source datasets. By leveraging the AFSE, FASDD, COCO, and D-FIRE datasets, we also developed and tested a two-stage cascaded TL approach. The application of TL in a single stage significantly enhanced the detection accuracy of the You Only Look Once version 5 nano (YOLOv5n) model, achieving up to 79.2% mean Average Precision ([email protected]), while also reducing training time and increasing model generalizability across the AFSE dataset. Notably, cascaded TL showed no further improvement and TL alone did not enhance edge computing performance metrics. Secondly, this research develops a novel one-stage object detection algorithm based on the YOLOv5n architecture, optimized specifically for central processing unit (CPU)-based edge computing devices. YOLOv5n was selected for modification after demonstrating its superiority in edge computing device applications resulting from its speed and accuracy. Without hardware acceleration, an unmodified YOLOv5n model is shown to be able to inference images at nearly two-times the speed of YOLO11n, the latest in the YOLO family of object detectors. Architecture modifications include the use of MobileNetV3-Small as a backbone, Ghost Convolution modules, half the number of output channels in the neck, and the use of 3x3 kernels in the first convolution of all Bottleneck modules. After training, PyTorch weights are exported to two deployment optimized frameworks, Open Neural Network Exchange (ONNX) and Open Visual Inference and Neural Network Optimization (OpenVINO), to accelerate CPU-based inference. Compared to the original YOLOv5n, the modified model converted to OpenVINO demonstrates a 423% increase in inference speed - up to 31.9 frames per second (FPS) - along with an 11.4% reduction in power consumption on a CPU-based edge computing device. The experimental results confirm TL\u27s role in augmenting the accuracy of early-wildfire object detectors while also illustrating that the optimized architecture developed can significantly improve detection speed, power consumption, and overall energy efficiency for CPU-based edge computing devices
Analysis of Incident Duration and Real-Time Incident Delay Estimation Using Big Data Analytics and Machine Learning Techniques
No full textTraffic incidents have a significant impact on freeway operations, leading to severe delays, congestion, fuel wastage, and economic losses for commuters. Each year, billions of gallons of fuel are wasted, and drivers incur thousands of dollars in lost time due to incident-related delays. To mitigate these effects, Traffic Management Centers (TMCs) implement incident management strategies aimed at reducing both the frequency and severity of incidents while ensuring their prompt and safe clearance.
Dynamic Message Signs (DMSs) are key tools used by TMCs to communicate travel times and incident information to commuters. Under normal conditions, default travel times are displayed on DMSs. However, when an incident occurs, these messages are often replaced with generic warnings that may be vague, difficult to interpret, and provide little actionable information. While displaying estimated travel times could help drivers make more informed route decisions, TMCs currently lack the capability to predict Incident Duration and estimate incident-induced delays in real time. This limitation makes it challenging to provide drivers with accurate travel time updates precisely when they are needed most.
To address this challenge, this study focuses on three key objectives:1. Collecting and processing reliable traffic data related to incidents and freeway conditions. 2. Developing machine learning models to predict Incident Duration accurately. 3. Estimating real-time delays and integrating them into DMS messages to assist drivers before reaching an incident.
Multiple datasets are utilized to achieve the study\u27s objectives. The first key source is the Incident Database (IDB) provided by FAST, which contains records of all reported incidents on the Las Vegas freeway system. This study focuses on incidents occurring in both directions of I-15 from St. Rose Parkway to the Las Vegas Motor Speedway between September 1, 2014, and August 31, 2015, from 5:00 AM to 8:00 PM, with their impacts measured until 10:00 PM.
Another crucial dataset, the One-Minute Traffic Characteristics Database (OMDB), also provided by FAST, contains traffic data recorded at one-minute intervals for Nevada’s freeway system. This extensive database comprises 525,600 XML files, each containing 17 columns and approximately 1,600 rows, totaling 14.29 billion data points. Big data analytics plays a pivotal role in this research, utilizing statistical analysis techniques such as clustering and regression to identify patterns, trends, and correlations within the data.
As part of this study, a Video Snapshots Dataset (VSDS) was created using 15-second video snapshots of incidents. This dataset visually documents incident characteristics for 272 of the 643 recorded incidents. Based on these observations, several analyses were conducted, including calculating total blockage duration, average blockage duration, and other key incident attributes.
To further analyze incident impacts, Incident Impact Heat Map Datasets (IHDS) were generated to assess both the spatial and temporal extent of each incident. By combining these two dimensions, each incident\u27s impact was represented as a box , covering the affected time and location. The IHDS provides a comprehensive visualization of how incidents influenced traffic conditions across different locations and time periods throughout the study year.
Using the IHDS, impact boxes were projected for all incidents recorded in the IDB, enabling a direct comparison between incidents and traffic conditions captured in the OMDB. This process resulted in the creation of the Incident Condition Dataset (ICDS) at one-minute intervals, providing a detailed and time-specific representation of incident-affected traffic conditions. Additionally, a Non-Incident Condition Dataset (NICDS) was generated to serve as a baseline for normal traffic conditions. The NICDS enables comparisons between incident-induced disruptions and typical traffic flow, improving the accuracy of delay estimations and impact analyses.
After processing, sorting, and restructuring the data, Incident Duration was modeled using machine learning methods. Three models were tested:1. Multiple Linear Regression 2. Lasso Regression with 10-fold Cross-Validation 3. Ridge Regression with 10-fold Cross-Validation
Among these, Ridge Regression demonstrated the best performance in predicting Incident Duration and was selected as the final model.
After Incident Duration predictions were in place, delay estimations were developed to support real-time incident management. This study introduces the Real-Time Incident Delay Estimation (RIDE) methodology, a dynamic, computationally efficient approach that estimates delay in 10-minute intervals—or less—to provide real-time insights into incident impacts.
Unlike traditional methods that rely on static Total Delay calculations, RIDE continuously updates Incident Duration predictions using machine learning techniques and recalculates delay estimates based on evolving conditions at the incident scene. The methodology incorporates:• Time-Interval Delay (TID): Capturing incremental delays across multiple time segments. • Average Time-Interval Delay (AvgTID): Quantifying the per-vehicle delay within each time interval.
This study also introduces a Ratio-Based Approach (RBA) to dynamically distribute Total Delays while addressing real-world complexities such as lane closures and reopenings, responder activities, and fluctuating traffic conditions.
Results indicate that RIDE is computationally efficient, replicable by Traffic Management Centers, and enables Dynamic Message Signs to display real-time, high-precision delay estimates, ultimately providing drivers with actionable travel time information.
Through the integration of granular delay estimations, spatial-temporal impact analysis, and real-time adaptability, this research provides a robust, data-driven framework for freeway incident management, helping to reduce congestion, improve traffic flow, and enhance traveler decision-making
Within and Between-Person Variability in Glycemic and Lipidemic Responses to High-Fat and High-Carbohydrate Meals
No full textBackground: Understanding postprandial metabolic responses to nutritional inputs is important for developing effective dietary recommendations and preventing chronic diseases. While between-person variability in postprandial glucose and lipid responses is well studied, within-person variability is not fully understood. Lifestyle factors such as dietary quality, physical activity, sleep, and stress, may contribute to this variability. This study aimed to investigate within- and between-person variability in postprandial glycemic and lipidemic responses to high-carbohydrate and high-fat meals, and to explore associations between lifestyle behaviors and postprandial responses. Methods: Twenty participants were randomized to a high-carbohydrate or high-fat meal condition, where the assigned meal type was consumed at three separate timepoints. Eleven participants completed both conditions in a crossover design. Dietary quality was assessed using the Healthy Eating Index based on three 24-hour dietary recalls collected via the Automated Self-Administered 24-Hour (ASA-24®) Dietary Tool. Physical activity was assessed using Activities Completed over Time in 24 Hours (ACT-24) recalls and International Physical Activity Questionnaires (IPAQ), while sleep quality was evaluated through the Pittsburgh Sleep Quality Index (PSQI) and stress levels using the Depression, Anxiety, and Stress Scale-21 (DASS-21). Physical activity was also objectively measured using accelerometry (Fibion SENS). Continuous glucose monitors (Dexcom G7) recorded postprandial glucose at 0, 15, 30, 60, 90, and 120 minutes, while blood lipids were assessed via fingersticks at 0, 120, and 240 minutes. Incremental area under the curve (iAUC) was calculated using the trapezoidal rule. Intraclass correlation coefficients (ICCs) were used to compare within- and between-participant variability. Results: The ICCs revealed substantial within-person variability in metabolic responses, particularly after high-carbohydrate meals. For the high-carbohydrate meal condition, lower and negative ICC values (-0.171 for single measures and -0.412 for average measures) with 95% confidence intervals ranging from -0.603 to 0.339 and -3.040 to 0.507 respectively, suggested that individual responses were highly inconsistent across different time points, with between-person differences contributing minimally to the overall variance. This was further supported by the non-significant F-test for reliability (F = 0.708, p = 0.744). Following the high-fat meal, glucose iAUC values ranged from 0.0 to 4507.5 mg/dL × 2 h and TG iAUC values from 340.0 ± 588.9 to 12,960.0 ± 4817.9 mg/dL × 4 h. There was significant between-person variability in glucose (p = 0.002), but not TG (p = 0.68) following the high-fat meal. Poorer sleep quality was associated with lower glucose responses (rho = -0.509, p = 0.004). Conclusions: Findings highlight the importance of considering within-person variability as compared to the current popular focus on between-person variability. The observed heterogeneity in metabolic responses suggests the need for repeated measurements to improve individualized dietary recommendations
Stronger Together: A Mixed-Methods Examination of Identity, Efficacy, And Emotions in Young Adults’ Collective Action
No full textYoung adults are increasingly engaging in collective action for social change, yet we lack a clear understanding of the factors driving their participation in social movements. This mixed-methods study uses a social psychological framework to examine key quantitative factors related to young adults’ intentions to engage in collective action for racial justice, as well as their reasoning around their involvement. A survey of 373 racially and ethnically diverse young adults (18-35 years old) assessed the impact of racial justice ally identity, emotions (anger and contempt), and dimensions of efficacy on action intentions. Path analysis revealed that young adults’ identification as allies or supporters for racial justice and anger significantly contributed to greater normative action intentions (e.g., donating, signing petitions), while stronger ally identity and lower political efficacy was significantly associated with greater willingness to engage in non-normative actions (e.g., disruptive protest). Participants also described their prior experiences with collective action and responded to open-ended questions about their motivations or barriers to participation. Thematic analysis identified five core themes: action motivated by a belief in the effectiveness of collective action and expressing solidarity and shared values and inaction themes focused on participation barriers, ambivalence, or low perceived efficacy and a psychological distancing from social movements (i.e., disidentification). Multinomial logistic regression showed that prior experience significantly predicted action-oriented themes, while stronger nonnormative intentions decreased the likelihood of the disidentification theme. These findings shed light on diverse pathways through which young adults engage in various social change actions and underscore the importance of building efficacy among young aspiring allies without prior collective action experience
Ethnoracial Characterization of Cognitive Function and Activities of Daily Living in Neurodegenerative Diseases
No full textObjective: Neurodegenerative diseases, such as Alzheimer\u27s disease (AD), lead to progressive cognitive and functional decline, which significantly affects individuals’ ability to perform their everyday Activities of Daily Living (ADLs). Occupational Therapist (OTs) assess cognitive impairments and their impact on daily function by utilizing tools like the Montreal Cognitive Assessment (MoCA) to guide intervention planning. However, research on the MoCA’s effectiveness across ethnoracial groups is limited, raising potential biases in the accuracy of cognitive assessments. This study examines the relationship between cognitive function, measured by the MoCA, and functional impairment, assessed using the Functional Activities Questionnaire (FAQ), among ethnoracial older adults with neurodegenerative diseases. This research aims to investigate potential disparities in those with differing levels of cognition and the overall effectiveness of cognitive screening tools. Methods: A stratified random sample was identified to analyze data from 600 participants drawn from the National Alzheimer’s Coordinating Center (NACC) Uniform Data Set (UDS). Participants included community-dwelling older adults aged 55 and above, representing three ethnoracial groups: Non-Hispanic White, Hispanic White, and Black Non-Hispanic. Cognitive performance was assessed using the Montreal Cognitive Assessment (MoCA), while functional abilities were measured through the Functional Assessment Questionnaire (FAQ). Statistical analyses included Pearson Correlation Coefficient to examine the relationship between cognitive and functional performance and one-way ANOVA to identify potential differences in MoCA and FAQ scores across ethnoracial groups. Robust methods were applied to account for statistical assumption violations, ensuring accurate results interpretation. Results: Analyses included 600 participants divided into three ethnoracial groups: Non-Hispanic White (n = 200), Hispanic White (n = 200), and Black Non-Hispanic (n = 200). A one-way ANOVA revealed no statistically significant differences in MoCA scores among the groups, F(2, 597) = 2.11, p = .123. However, Levene’s test indicated unequal variances (p \u3c .001), and robust tests (Welch’s and Brown-Forsythe) confirmed the non-significant findings. For FAQ scores, a significant group difference was observed, F(2, 597) = 9.15, p \u3c .001, and robust tests supported this result (Welch’s F = 10.92; Brown-Forsythe F = 9.15, both p \u3c .001). NHW participants had the highest mean FAQ score, indicating greater functional impairment. A Pearson correlation revealed a strong negative relationship between MoCA and FAQ scores in the overall sample (r = -0.623, p \u3c .001), with similarly significant negative correlations within each group. Fisher’s r-to-z transformation indicated that the strength of the correlation between cognitive and functional scores differed significantly between NHW and both Black Non-Hispanic (z = 2.156, p \u3c .05) and Hispanic White participants. Conclusion: The results indicate that MoCA total scores are largely consistent across Non-Hispanic White (NHW), Hispanic White, and Black Non-Hispanic older adults, with no significant differences in the overall analysis. However, Fisher’s r-to-z transformation revealed significant differences in the relationship between cognitive performance and functional abilities when comparing NHW participants to Hispanic White and Black Non-Hispanic participants. These findings suggest that the MoCA remains a reliable tool for cognitive screening across ethnoracial populations. Further research is needed to explore additional factors, including cultural and contextual influences, that may shape cognitive performance and ratings of functional outcome
Reimagining Learning, Integrity, and Creativity in the Age of AI: A Discussion with Summit Contributors
No full textAs AI technologies–including independently acting AI agents–become more sophisticated in research and writing, solving complex problems, and even evaluating student work, higher education faces a time of disruptive shifts. The very tools that promise to enhance learning personalization and increase teaching task efficiency are unsettling our most fundamental assumptions about the processes and goals of education, developed over centuries in academic traditions. We are compelled to reconsider what teaching and learning mean now, in the age of AI.
Bringing together educators, researchers, and practitioners, this session confronts rapidly emerging new realities to reimagine teaching and learning in an era of intelligent automation. Together, we will discuss how universities can ensure that the use of AI technologies in higher education enhances learning and knowledge creation, and that academic integrity and human creativity and discernment remain at the heart of academic practice.
This culminating discussion draws on insights from across the sessions. The presentations in this summit reveal the profoundly shifting landscape in higher education, and span a wide range of perspectives and practices—from reimagining academic integrity in the keynote The Opposite of Cheating to personalizing learning through Build Your Own GPT: Tailoring AI to Your Academic Needs.
Faculty across disciplines share innovative approaches in Process over Product: Rethinking Assignments in the Age of AI, in Reflections Through AI: Visualizing Learning, Emotion, and Mindset, in AI Across the Disciplines: Bridging Perspectives in STEM and the Humanities and in Using NotebookLM in the Undergraduate Classroom. Other sessions examine design and pedagogy in specialized fields, including Teaching, Building, Learning: AI in Nursing Education, AI-assisted Avenues for Linguistic Assessment and Intervention Methods in Speech-Language Pathology, AI in Medical Practice and Education: A Work in Progress.
Presentations also engage with timely conversations about assessment and equity in Grading in the Age of AI: Equity, Transparency, and the Role of Human Feedback, explore alternative modes of demonstrating learning in From Paper to Podium: AI Proof Delivery, and experiment with engagement strategies in Gamifying Generative AI to Facilitate Students’ Development of GenAI Fluency.
The Summit also highlights innovation in course material creation with Design as Dialogue: Working with AI in Course Creation and Artificial Intelligence, Accessibility, and Inclusion, examines social implications in Robots as Social and Behavioral Change Agents in the Classroom, and explores creativity and critical thinking in Prompting Creativity: Using Generative AI in the Humanities Classroom.
Collectively, these presentations offer a multifaceted view of how AI is reshaping teaching, learning, and integrity across the academy—challenging educators to rethink what authentic human learning means in the age of intelligent machines
Local Journalist Equivalents (LJEs) in the Mountain West, 2025
No full textThis fact sheet presents 2025 data on Local Journalist Equivalents (LJEs) for the Mountain West states of Arizona, Colorado, Nevada, New Mexico, and Utah. The data are derived from the Muck Rack report “Local Journalist Index 2025,” which includes data on LJEs for states and counties across the U.S. This fact sheet highlights state-level LJE data among Mountain West states, as well as LJE data for the following 17 Nevada counties: Carson City, Churchill, Clark, Douglas, Elko, Esmeralda, Eureka, Humboldt, Lander, Lincoln, Lyon, Mineral, Nye, Pershing, Storey, Washoe, and White Pine