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Brand advocacy occurs when consumers who feel very passionately about a brand seek to promote it to others and defend it against its naysayers. These consumers are valuable to brands as information between consumers is more easily and rapidly shared than ever before, and as consumer distrust of brand-sponsored messages is high. As a result, brands are dependent upon their brand advocates to leverage the perceived reliability of peer recommendations to recruit new customers. However, thus far in the marketing literature, an adequate conceptualization of brand advocacy has failed to emerge. Instead, when discussed, brand advocacy is often measured using a proxy variable such as positive word-of-mouth. It is the supposition of the author that these two constructs are not synonymous and using one as a proxy for the other severely limits researchers’ understanding of the brand advocacy and its impact. The goal of this research is to define brand advocacy and develop a valid scale to measure it. Following Churchill’s (1979) paradigm for scale development, a series of four studies were undertaken to validate the new scale. The first two studies are qualitative in nature and help identify the domains of brand advocacy. Based on the results of the first study, a series of depth interviews, and the second study, an open-ended questionnaire, the following definition of brand advocacy is put forth: Brand advocacy is a combination of customer-motivated behaviors, including proactively recommending the brand and defending the brand against detractors, intended to maintain the customer’s relationship with the brand and promote it to others. The construct was determined to be a higher-order construct comprising two distinct sets of behaviors that address advocates’ need to not only defend the brand to naysayers but also to proactively spread positive brand communications to others. The third and fourth studies use quantitative data to complete the scale development process by proposing and validating a nine-item scale to measure the multi-dimensional construct of brand advocacy as well as provide evidence that it is a distinct construct from PWOM. The results of this research provide a definition and valid scale of brand advocacy.

Solving large-scale linear systems of equations (LSEs) is one of the most common and fundamental problems in big data. But such problems are often too expensive to solve for resource-limited users. Cloud computing has been proposed as an efficient and costeffective way of solving such tasks. Nevertheless, one critical concern in cloud computing is data privacy. Many previous works on secure outsourcing of LSEs have high computational complexity and share a common serious problem, i.e., a huge number of external memory I/O operations, which may render those outsourcing schemes impractical. We develop a practical secure outsourcing algorithm for solving large-scale LSEs, which has both low computational complexity and low memory I/O complexity and can protect clients privacy well. We implement our algorithm on a real-world cloud server and a laptop. We find that the proposed algorithm offers significant time savings for the client (up to 65%) compared to previous algorithms.

High Efficiency Particulate Air (HEPA) filters are defined as extended-medium, dry-type filters with: (1) a minimum particle removal efficiency of no less than 99.97 percent for 0.3 micrometer particles, (2) a maximum, clean resistance of 1.0 inch water column (in. WC) when operated at 1,000 cubic feet per minute (CFM), and (3) a rigid casing that extends the full depth of the medium. Specifically, ceramic media HEPA filters provide better performance at elevated temperatures, are moisture resistant and nonflammable, can perform their function if wetted and exposed to greater pressures, and can be cleaned and reused. This paper describes the modification and design of a large scale test stand which properly evaluates the filtration characteristics of a range of ceramic media filters challenged with a nuclear aerosol agent in order to develop Section FO of ASME AG-1.

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Pain is a complex neuro-psychosocial experience that is internal and private making it difficult to assess in both humans and animals. In research approximately 95% of animal models use rodents, with rats being among the most common for pain studies [3]. However, traditional assessments of the pain response struggle to demonstrate that the behaviors are a direct measurement of pain. The rat grimace scale (RGS) was developed based on facial action coding systems (FACS) which have known utility in non-verbal humans [6, 9]. The RGS measures facial action units of orbital tightening, ear changes, nose flattening, and whisker changes in an attempt to quantify the pain behaviors of the rat. These action units are measured on frontal images of rats with their face in clear view on a scale of 0-2, then summed together. The total score is then averaged to find a final value for RGS between 0-2. Currently, the software program Rodent Face Finder® can extract frontal face images. However, the RGS scores are still manually recorded which is a labor-intensive process, requiring hours of training. Furthermore, the scoring can be subjective, with differences existing between researchers and lab groups. The primary aim of this study is to develop an automated system that can detect action unit regions and generate a RGS score for each image. To accomplish this objective, a YOLOv5 object detector and Vision Transformers (ViT) for classification were trained on a dataset of frontal-facing images extracted using Rodent Face Finder®. Subsequently, the model was then validated using a RGS test for blast traumatic brain injury (bTBI). The validation dataset consisted of 40 control images of uninjured rats, 40 images from the bTBI study on the day of injury, and 40 images 1-month post-injury. All 120 images in the validation set were then manually graded for RGS and tested using the automated RGS system. The results indicated that the automated RGS system accurately and efficiently graded the images with minimal variation in results compared to human graders in just 1/14th of the time. This system provides a fast and reliable method to extract meaningful information of rats' internal pain state. Furthermore, the study presents an avenue for future research into real-time pain monitoring. / Master of Science / Pain is a difficult experience to measure, both in humans and animals. It can be a subjective experience that is largely based on individual perception and interpretation. Furthermore, in animals, pain is even more challenging to assess because they cannot communicate their experience through language. Nonetheless, animal research plays an important role in understanding and treating the underlying mechanisms of pain. In animal research, rats are commonly used to study pain. However, traditional methods of assessing pain behaviors are not meant to observe the pain experience, but instead analyze a response to an external stimulus. The rat grimace scale (RGS) was developed as a direct measurement of the pain experience by analyzing the facial features. Currently, RGS scores are manually recorded by trained researchers which is time-consuming and can be subjective. This study aimed to develop an automated system to identify pain related facial expressions and generate a RGS score for frontal-images of rats. The system was trained using a dataset of frontal-facing rat images with varying levels of RGS scores and validated using images of rats from a traumatic brain injury study. The results showed that the automated RGS system accurately identified RGS pain level differences between recently injured rats, uninjured rats, and rats which were allowed to recover for 1-month. Furthermore, the system provided a fast and reliable method for measuring rat pain behavior when compared to manual grading. With this system, researchers will be able to efficiently perform RGS test. Additionally, this study presents an opportunity for future automation of other grimace scales as well as research into real-time pain monitoring.

Better understanding difficulties in emotion regulation may help integrate a conceptualization for the etiology of a number of emotional disorders, such as depression or anxiety, and personality disorders. However, one deficit in extant literature has been in identifying a widely accepted measure in assessing problems with emotion regulation. A number of emotion regulation measures are currently used in the literature. Because each measure addresses regulation from a different perspective, the definition of emotion regulation remains unclear. Gratz and Roemer (2004) provide an integrated conceptualization for emotion regulation that accounts for the many ways in which emotions may affect the expression of psychological disorders. Gratz and Roemer constructed the Difficulties in Emotion Regulation Scale (DERS) to assess various areas in which one might experiences problems with emotion regulation. The DERS is a 36-item scale that has six factors related to emotion regulation: Awareness, Non-acceptance, Clarity, Impulsivity, Strategies, and Goals. The purpose of this study was to perform a confirmatory factor analysis on the DERS, and to examine its predictive validity for disorders that are associated with difficulties in emotion regulation. The data for this study was collected from part of a larger study on emotion regulation. Participants consisted of undergraduates at a rural mid-western university who completed a survey packet including the DERS, the Trait Meta-Mood Scale (TMMS) the Center for Epidemiological Studies Depression Scale (CESDS), and the Posttraumatic Stress Disorder Checklist (PCL). It hypothesized that a six factor solution would be found, validating the findings of Gratz and Roemer (2004). It was also expected that total DERS scores will be associated with CESDS and PCL scores, suggesting that participants who scored higher on these measures of depression and PTSD would also report difficulties with emotion regulation. The DERS factors were expected to be related to corresponding factors on the TMMS. Results indicated that although a 6-factor solution was supported, several modifications were needed to the original model in order to achieve acceptable goodness of fit index values. Additionally, a number of other suggestions for changes to the measure, including re-wording several of the items or the possible removal of the Awareness factor, are discussed.

Research involving attribution theories typically surrounds attributions of responsibility for actions in general. However, people also regularly attribute responsibility to themselves, others, or environmental circumstances for emotions. This research aims to develop a measure of a person's proclivity to attribute responsibility to others for their emotions—the Attributions of Responsibility for Emotions Scale (ARES). The research involves two studies, the first designed to develop items for inclusion in the ARES, and the second designed to validate and determine the reliability of the ARES. Participants in Study 1 included 71 (30 male and 41 female) undergraduate students from Brigham Young University. These participants took part either in focus groups or in responding to a preliminary 24-item version of the ARES online. Participants in Study 2 included 306 undergraduate students from Brigham Young University. These participants responded to several scales, including a 21-item version of the ARES, which all measured constructs similar to responsibility for emotions. Results suggested that a 10-item version of the ARES was the most valid and reliable measure of persons' proclivity for attributing responsibility to others for their emotions.