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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
Clinical Benefit Analysis of Modified Motion Management Protocols
No full textRadiation therapy is crucial for treating cancer, using ionizing radiation to target and destroy cancer cells. This radiation is produced by machines like linear accelerators or through natural radioactive decay. Radiation therapy treatment planning involves prescribing doses and contouring target areas. However, managing tumors that move with breathing, such as in the lungs, remains a major challenge. Tumor motion exceeding 5 mm, as outlined in the AAPM’s Task Group 76, can compromise treatment accuracy, risking underdosing the tumor and overdosing healthy tissue. This issue is especially critical in high dose treatments like stereotactic body radiation therapy (SBRT). TG-76 provides different motion management which include passive methods like 4-dimensional computed tomography (4DCT) imaging and internal target volume (ITV) contouring, which account for tumor motion during planning. Active techniques that directly restrict motion using methods like abdominal compression, breath-hold, or respiratory gating are also outlined in this report. At Utah Valley Hospital, passive methods such as 4DCT and ITV contouring are standard, alongside compression. This project will explore whether Utah Valley Hospital’s current motion management approach is enough to maintain tumor motion within the 5 mm threshold recommended by TG-76. By assessing current practices and exploring potential improvements, technology, and techniques, this project will aim to determine if investing in additional techniques could enhance clinical outcomes
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
There Is an Imposter Among Us: An Affective Constitutive Rhetorical Study on RPGS
No full textThis study explores how players verbally and affectively connect the emotional impact of playing as crewmates or imposters to the game design of Among Us. Through video analysis of gameplay sessions and follow-up focus group interviews, the research highlights how crewmates exhibit heightened affective and gestural responses when making accusations or facing eliminations, while imposters adopt a more controlled, neutral demeanor to maintain deception. The study identifies moments where players\u27 emotional and physical reactions peak, offering insights into how Among Us fosters complex emotional investments through its mechanics. These findings contribute to game studies by demonstrating how affect and role embodiment shape player interactions and decision-making in social deception games
Desert Slavery: How the Old Spanish Trail Sustained Captivity and Coerced Labor in the North American West
No full text“Desert Slavery: How the Old Spanish Trail Sustained Captivity and Coerced Labor in the North American West,” examines the Old Spanish Trail as a central corridor of enslavement in the North American West. Captive-taking and bondage regularly occurred along the Old Spanish Trail, which connected northern New Mexico to southern California. Indeed, the movement of enslaved people through the Great Basin helped maintain the Old Spanish Trail. The seventeenth and eighteenth centuries witnessed the development of coerced labor and enslavement as Spanish Mexican and Indigenous cultures intersected to create a system of slavery unique to New Mexico. However, as the Spanish empire expanded further north and west in the late eighteenth century, and then as Mexico achieved independence in the early nineteenth century, Euro-American traders, trappers, and settlers who entered the region adopted and spread these practices. The nineteenth century saw the introduction of Black chattel slavery to the area and the overlapping presence of these two systems of bondage along the trail. By the 1860s and 1870s, when the United States claimed the region, all forms of bondage were being curtailed, which helped bring an end to the use of the Old Spanish Trail. To the extent that previous historians have addressed bondage along the trail, they have focused on the Indigenous slave trade without highlighting the presence of Black chattel slavery. Yet the history of the trail is not complete without consideration of both systems of enslavement. This dissertation bridges the gap between the historiography of Black chattel slavery and Indigenous slavery along the Old Spanish Trail and the region it traversed
Gender Biases: The Interaction Between Children and Voice Assistants
No full textNowadays, children can access and interact with voice assistants (VAs) easily since the machines are more available in the family context. However, there is a gap in understanding how children conceptualize VAs when interacting with the machine. Specifically, this study aimed to examine whether children understand VAs as inanimate. Additionally, the study also compared how children and adults rate the VAs’ level of warmth and competence and the way they associate AI voices with feminine or masculine faces. Another goal was to explore how children and adults match the gender and competence/warmth of the VA with facial masculinity and femininity. The study employs the Computer as Social Actors (CASA) paradigm, where participants interact with a VA with different gender and competence/warmth conditions, while also using a Wizard of Oz design to simulate an autonomous VA. The study was a between-subjects design with children from 7- to -10 years old (n = 34) and adults from 18- to -35 years old (n = 61). The results indicated that children and adults rated the VA high on both warmth and competence, which are theoretically distinct dimensions. Interestingly, there were age differences in how people understood the VA through drawing. Adults were more likely to draw and describe the VA with human-like characteristics and children were more likely to draw and focus on the mechanical characteristics. This finding will help us design more suitable and beneficial AI systems for children’s learning by tailoring voice assistant interactions that align with children’s developmental understanding of technology. By recognizing how children conceptualize VAs in terms of warmth, competence, and gender, developers can create voice assistants that foster more effective, engaging, and age-appropriate educational experiences for children
El, A Navigational Assistant Based Upon Echolocation
No full textThis thesis investigates the integration of a parametric speaker with a microphone array to enhance the echolocation of objects. A parametric speaker focuses ultrasonic and audible waves in a specified direction. This endows the parametric speaker with the capacity to focus waves across a wide frequency spectrum enabling adaptation of the frequency to environmental considerations.Beam forming allows one to focus a microphone array in a specified direction. The thesis investigates different microphone array configurations for the purpose of enhancing the echolocation ability of an echolocation device. The primary goal of the thesis is the construction and testing of an echolocation device that uses a parametric speaker and a microphone array. The thesis describes construction parameters as well as potential improvements. Alongside the physical device are algorithms that interpret an echo’s signature that the microphone array captures. The thesis describes the algorithms that it employs. Testing includes measurement of the outgoing beam width, echolocation of a single object within the outgoing beam, and echolocation of multiple objects within the beam. Results indicate successes and failures of the device and associated algorithms. The failures point to improvements for further research
Perceived Personalization of Luxury Hotel Brands: An Empirical Examination Using Structural Equation Modeling and Machine Learning
No full textThis study explores the outcomes of perceived personalization in the context of luxury hotels through the theoretical lenses of Stimulus-Organism-Response (SOR) Theory, Personalization-Privacy Paradox Theory, and Privacy Calculus Theory. The study incorporates multiple latent constructs, such as brand engagement, brand experience, self-brand connection, consumer-brand identification, privacy concerns, behavioral loyalty intentions, and willingness to disclose information, to understand how perceived personalization influences customer perceptions and behaviors. Understanding both data (e.g., willingness to disclose information) and marketing (e.g., behavioral loyalty intentions) constructs is crucial in the era of data-driven marketing, as it helps address the complexities of personalization.A sample of 476 U.S. luxury hotel consumers was collected using a Qualtrics panel. Structural Equation Modeling (SEM) (i.e., explanation) and Machine Learning (ML) using SHapley Additive exPlanations (SHAP) values (i.e., prediction) were integrated for comprehensive analysis. The measurement model revealed the need to establish two second-order constructs, “brand engagement experience” (combining brand engagement and brand experience) and “brand identification connection” (combining self-brand connection and consumer-brand identification), to better represent the underlying relationships. The structural model revealed that perceived personalization positively influences brand engagement experience and brand identification connection. Brand engagement experience partially mediated the relationship between perceived personalization and behavioral loyalty intentions, while brand identification connection fully mediated the relationship between perceived personalization and willingness to disclose personal information. Privacy concerns did not significantly moderate the relationship between personalization and the brand-related constructs. Additionally, brand engagement experience did not significantly lead to willingness to disclose information, and brand identification connection did not significantly lead to behavioral loyalty intentions. The performance of six ML models was compared for each outcome variable. Deep neural network was the best method for predicting willingness to disclose information, and k-nearest neighbors was the best for predicting behavioral loyalty intentions. A comparative analysis between the SEM direct effects and the SHAP mean scores for the top-performing ML models demonstrated convergence for willingness to disclose information but divergence for behavioral loyalty intentions. These findings offer theoretical, practical, and methodological insights within the context of luxury hotel personalization
The Impact of Business-Consumer and Consumer-Consumer Interaction Via Social Media Posts on Consumer Behavioral Intentions
No full textSocial media posts have evolved into a powerful tool for consumers to explore and evaluate products, services, and businesses due to the increased accessibility of the Internet and rapidly developing real-time, mobile digital technologies. Based on stimulus-organism-response (SOR) theory, involvement theory, and benign violation theory, this study examines how interactions between food and beverage businesses, customers, and commenters via social media posts about negative online reviews influence the attitudes and purchase intentions of observers.Two major studies with eight scenarios in total were designed and conducted. The negative reviews were classified into two categories: unreasonable negative reviews and negative reviews that included comparisons with the business\u27s competitor. Both studies implemented a 2 (social media post creator: pro-business vs. anti-business) x 2 (social media post commenter: pro-business vs. anti-business) between-subjects design. In Study 1, 643 individuals, and in Study 2, 640 individuals were randomly distributed and assigned to each scenario, where food and beverage businesses responded to negative online reviews with aggressive humor, including other consumers\u27 comments on social media posts. Both studies constructed structural equation models to analyze the results. Findings showed that consumer-inferred negative motives of the businesses behind aggressive humor and their appreciation of humor positively influence their attitudes toward the business, in turn serving as a significant determinant of their purchase intentions. Specifically, while attitudes toward social media posts did not directly impact purchase behavior, attitudes toward the business consistently were found to mediate the relationship between consumers’ impression of the business\u27s response to negative online reviews and purchase intention, suggesting a more complex consumer decision-making process from a theoretical perspective. This study supported the application of SOR theory, expanded involvement theory, and benign violation theory by demonstrating that consumers could promote businesses that employ aggressive humor aimed at individuals who have posted negative reviews. Furthermore, the study revealed that increased customer involvement with social media content could correspond to more cognitive processes, which may lead to a more unfavorable perception of the associated business if customers perceive it as potentially harmful to them. The research also provided practical recommendations for hospitality businesses on how to address negative online reviews strategically with an understanding of consumers\u27 perceptions of humor and inferred negative intentions to effectively defend the business and attract consumers simultaneously
School-Based Agricultural Educators’ Educational Technology Integration Self-Efficacy and Technological Pedagogical Content Knowledge: A Relational Study
No full textThe purpose of this study was to assess the relationship between school-based agricultural education (SBAE) teacher Technological Content Knowledge, Pedagogical Content Knowledge, and Technological Pedagogical Knowledge and teachers’ technology integration self-efficacy through the use of TPACK and the Intrapersonal Technology Integration Scale (ITIS). In-service SBAE teachers from four upper-Midwest states were surveyed to assess their current self-efficacy related to the integration and utilization of the educational technology available in their classroom and curriculum. According to the findings of this study, SBAE teachers are using educational technology in their classrooms and curriculum daily but are only slightly confident in their ability to use it effectively. Additionally, the findings identified an association between the amount of professional development and higher levels of technology integration self-efficacy, as well as a significant positive correlation between technology integration self-efficacy and technological content knowledge, technological pedagogical knowledge, and pedagogical content knowledge. Thus, to increase the utilization of instructional technology in SBAE classrooms, we recommend teachers participate in professional development which is focused on not only how to use educational technology, but also on how to teach agriculture content using the educational technology specific to their accessible devices