Texas A&M University – Corpus Christi
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Development and validation of the Counseling Imposter Scale (CIS)
Get PDFA dissertation Submitted in Partial Fulfillment of the Requirements for the Degree of DOCTOR OF PHILOSOPHY in COUNSELOR EDUCATION from Texas A&M University-Corpus Christi in Corpus Christi, Texas.Impostor Phenomenon (IP), a psychological state in which people have self-doubt about their abilities and success despite the concrete evidence of their achievement, has been a widely studied construct across different fields. Most researchers found that IP negatively impacts the job performance and mental health of IP sufferers, such as lower job satisfaction, the risk for burnout, anxiety, emotional exhaustion, workaholism, and lower compassion fatigue. In the mental health field, IP negatively impacts counselors’ counseling self-efficacy, self-compassion, and compassion fatigue. However, research about IP among counselors has been minimal, and no instrument specifically measures IP in counseling practice among counselors. Thus, this study aimed to develop and validate a counseling impostor scale (CIS) to be used in clinical practice among counselors and examine the relationship between the counseling IP and clinical experience. Three hundred and ten responses from counselors and counselors-in-training were used for data analysis. The exploratory factor analysis (EFA) results indicated that the Counseling Impostor Scale (CIS) is a 38-item scale with promising psychometric properties. The bivariate correlation analysis revealed good convergent validity as evidenced by a strong correlation with Harvey’s Impostor Phenomenon Scale (HIPS) (r = .81) and a moderate correlation with the Burnout subscale of ProQOL (r = .44). The statistical analysis also revealed good internal consistency reliabilities of three subscales (? = .97, .95, .93) and the total scale (? = .98). The EFA revealed a three-factor structure explaining 58% of the variance. These factors are Counseling Self-Doubt, Fraud Counselor, and Praise Adverse. Overall, the CIS shows promise to be used to evaluate the counseling impostor phenomenon in clinical practice formally. Additional validation research is needed to establish consistent results.Counseling & Educational PsychologyCollege of Education and Human Developmen
Wait for it! The influence of delayed information about physical appearance on perceptions
No full textOur evaluations of others often occur implicitly (Ham & Van Den Bos, 2011, We can change our evaluations with new information. However, it is implicit thoughts and attitudes are more resistant to change (Wyer, 2010; Mann & Ferguson, 2015), People commonly create implicit judgements about physical appearance interactions (Palmer & Peterson, 2020), The halo effect phenomenon is a result of these implicit judgments interactions (Palmer & Peterson, 2020), Question: How does delaying information on physical appearance impact our evaluations, specifically when it comes to dating intentions and attraction
Increasing colorectal cancer knowledge, awareness, and intent to screen in an underserved region
Get PDFA dissertation submitted in partial fulfillment of the requirements for the degree of Doctor of Nursing Practice.Colorectal cancer (CRC) is a leading cause of cancer-related deaths in the United States that can be identified and prevented through early screening. Current screening rates do not meet existing recommendations, especially in medically underserved areas where there is reduced access to primary care services. A lack of CRC awareness and knowledge have been identified as two of the largest barriers to screening. An inflatable colon tour has been proven an effective intervention to address CRC knowledge and awareness deficits. This DNP project was designed as a community awareness initiative in an underserved area using a pre- and post-survey with the purpose of increasing colorectal cancer awareness, knowledge, and intent to discuss and complete CRC screening. This quasi-experimental study had a QI focus and used a convenience sample in a public setting who completed a pre-and post-survey assessing colorectal cancer awareness, knowledge, and intent to discuss and complete screening (n =185 persons screened with n =85 meeting inclusion criteria). Post-tour CRC awareness scores showed a statistically significant increase in mean scores at p <.001. Colorectal cancer knowledge scores showed a statistically significant increase in post-test scores at p <.001. Post-tour, there was an 82% increase in people who identified as "very likely" or "definitely" willing to discuss CRC screening with their healthcare provider and a 133% increase in people identifying as "very likely" or "definitely" likely to complete CRC screening in the next 6 months. This project is evidence that community events using inflatable models can successfully increase cancer awareness and knowledge in underserved populations.Nursing PracticeCollege of Nursing and Health Science
Towards intelligent and sustainable IOT system
Get PDFA dissertation Submitted in Partial Fulfillment of the Requirements for the Degree of DOCTOR OF PHILOSOPHY in GEOSPATIAL COMPUTING SCIENCE from Texas A&M University-Corpus Christi in Corpus Christi, Texas.The rapid integration of Artificial Intelligence into the Internet of Everything (AIoE) has led to the ubiquitous presence of embedded devices, playing crucial roles in various aspects of our lives. However, the limited battery life of these devices poses a significant challenge as they are expected to deliver an increasing number of services and applications. Consequently, the sustainability of embedded devices has become a paramount concern for both academia and industry. In response, Energy Harvesting (EH) has emerged as a promising solution, enabling devices to harvest energy from the surrounding environment, such as radio frequency and thermal energy, to power them- selves perpetually. While EH has extended device lifetimes, effectively utilizing EH-powered de- vices in large-scale deployments remains a challenge. The transient nature of energy harvesting ne- cessitates that EH devices alternate between active (discharging) and dormant (recharging) states, resulting in frequent interruptions during sensing and communication activities. Traditional strate- gies for sensing, communication, and energy allocation are ill-suited for EH devices, as those tradi- tional strategies assume devices can be activated at any time, contrary to the characteristics of EH devices. Although prior research has focused on EH-aware sensing and communication strategies, most existing studies have predominantly approached optimization from an individual perspective. To address these challenges, this dissertation proposes a sustainable and intelligent IoT system leveraging emerging technologies, such as deep reinforcement learning and compressed sensing. The proposed framework encompasses three key components: 1) A comprehensive sparsity-aware spatiotemporal data sensing framework for EH IoT systems, aiming to optimize data collection by selectively sensing and processing relevant data based on sparsity characteristics with the consid- eration of the intermittency of EH IoT devices. 2) Environment adaptive multi-hop routing and energy allocation for EH IoT networks, considering the intermittent nature of EH devices. This component incorporates dynamic changes in energy harvesting and jointly optimizes routing poli- cies and energy allocation for efficient data routing. 3) A unmanned aerial vehicles (UAVs)-assisted EH IoT framework that assists ground EH devices in completing data collection tasks such as environment monitoring. By leveraging the capabilities of UAVs, this integrated approach enhances data collection efficiency and extends the reach of EH-powered IoT systems. Through these contributions, this dissertation addresses the challenges associated with EH IoT systems, emphasizing energy efficiency, sustainable operation, and intelligent decision-making. The proposed framework integrates deep reinforcement learning and compressed sensing tech- niques, fostering the development of a resilient and efficient IoT ecosystem.Computing SciencesCollege of Engineerin
Analysis of microalgal density estimation by using LASSO and image texture features
Get PDFMonitoring and estimating the density of microalgae in a closed cultivation system is a critical task in culturing algae since it allows growers to optimally control both nutrients and cultivating conditions. Among the estimation techniques proposed so far, image-based methods, which are less invasive, nondestructive, and more biosecure, are practically preferred. Nevertheless, the premise behind most of those approaches is simply averaging the pixel values of images as inputs of a regression model to predict density values, which may not provide rich information of the microalgae presenting in the images. In this work, we propose to exploit more advanced texture features extracted from captured images, including confidence intervals of means of pixel values, powers of spatial frequencies presenting in images, and entropies accounting for pixel distribution. These diverse features can provide more information of microalgae, which can lead to more accurate estimation results. More importantly, we propose to use the texture features as inputs of a data-driven model based on L1 regularization, called least absolute shrinkage and selection operator (LASSO), where their coefficients are optimized in a manner that prioritizes more informative features. The LASSO model was then employed to efficiently estimate the density of microalgae presenting in a new image. The proposed approach was validated in real-world experiments monitoring the Chlorella vulgaris microalgae strain, where the obtained results demonstrate its outperformance compared with other methods. More specifically, the average error in the estimation obtained by the proposed approach is 1.54, whereas those obtained by the Gaussian process and gray-scale-based methods are 2.16 and 3.68, respectively.Monitoring and estimating the density of microalgae in a closed cultivation system is a critical task in culturing algae since it allows growers to optimally control both nutrients and cultivating conditions. Among the estimation techniques proposed so far, image-based methods, which are less invasive, nondestructive, and more biosecure, are practically preferred. Nevertheless, the premise behind most of those approaches is simply averaging the pixel values of images as inputs of a regression model to predict density values, which may not provide rich information of the microalgae presenting in the images. In this work, we propose to exploit more advanced texture features extracted from captured images, including confidence intervals of means of pixel values, powers of spatial frequencies presenting in images, and entropies accounting for pixel distribution. These diverse features can provide more information of microalgae, which can lead to more accurate estimation results. More importantly, we propose to use the texture features as inputs of a data-driven model based on L1 regularization, called least absolute shrinkage and selection operator (LASSO), where their coefficients are optimized in a manner that prioritizes more informative features. The LASSO model was then employed to efficiently estimate the density of microalgae presenting in a new image. The proposed approach was validated in real-world experiments monitoring the Chlorella vulgaris microalgae strain, where the obtained results demonstrate its outperformance compared with other methods. More specifically, the average error in the estimation obtained by the proposed approach is 1.54, whereas those obtained by the Gaussian process and gray-scale-based methods are 2.16 and 3.68, respectively
Classification of terrestrial lidar data directly from digitized echo waveforms
Get PDFInformation derived from full-waveform (FW) data collected by FW laser scanning systems has already been shown to be relevant for point cloud analysis tasks. Relevant waveform attributes to populate the corresponding point’s feature vector are typically provided through a post-processing FW analysis (FWA) technique based on fitting the echo waveform with a parametric function describing the shape and location of the echo pulse in the waveform. Samples of the digitized echo are the primary source for any waveform analysis using parametric functions. On the other hand, for some FW laser scanning systems, describing the complex system response model using a simple parametric function seems challenging or impractical. Earlier studies have shown the potential of waveform’s digital samples as relevant waveform attributes, for point cloud classification. The main goal of this study is to extend earlier experiments on direct exploitation of returned waveform signals collected by a FW terrestrial laser scanning (TLS) system in a built environment for point cloud classification, to multi-return waveform signals. Furthermore, the classification performance on feature vectors containing calibrated waveform attributes, derived from a waveform processing approach performed in real-time by the FW TLS system, is evaluated on multiple-echo waveforms and compared with the classification performance derived from the proposed FW data classification technique. Classification performance derived through the proposed technique demonstrates high information content of raw digitized waveform samples. Results show that feature vectors containing samples of digitized echoes carry more information about physical properties of the target than those containing calibrated waveform attributes.Information derived from full-waveform (FW) data collected by FW laser scanning systems has already been shown to be relevant for point cloud analysis tasks. Relevant waveform attributes to populate the corresponding point’s feature vector are typically provided through a post-processing FW analysis (FWA) technique based on fitting the echo waveform with a parametric function describing the shape and location of the echo pulse in the waveform. Samples of the digitized echo are the primary source for any waveform analysis using parametric functions. On the other hand, for some FW laser scanning systems, describing the complex system response model using a simple parametric function seems challenging or impractical. Earlier studies have shown the potential of waveform’s digital samples as relevant waveform attributes, for point cloud classification. The main goal of this study is to extend earlier experiments on direct exploitation of returned waveform signals collected by a FW terrestrial laser scanning (TLS) system in a built environment for point cloud classification, to multi-return waveform signals. Furthermore, the classification performance on feature vectors containing calibrated waveform attributes, derived from a waveform processing approach performed in real-time by the FW TLS system, is evaluated on multiple-echo waveforms and compared with the classification performance derived from the proposed FW data classification technique. Classification performance derived through the proposed technique demonstrates high information content of raw digitized waveform samples. Results show that feature vectors containing samples of digitized echoes carry more information about physical properties of the target than those containing calibrated waveform attributes.This work was supported in part by the National Oceanic and Atmospheric Administration (NOAA), U.S. Department of Commerce under Award NA18NOS4000198 and in part by the National Science Foundation (NSF) under Award 2112631.This work was supported in part by the National Oceanic and Atmospheric Administration (NOAA), U.S. Department of Commerce under Award NA18NOS4000198 and in part by the National Science Foundation (NSF) under Award 2112631
03 Cognitive Psychology: Module 6
No full textModule 6: Sensation Versus Perception
Perry's finger is still feeling a bit sensitive from touching his hot cup of cocoa. He feels a small jolt of pain each time he presses a computer key. Why does my finger feel like that? he wonders. He knows that nerves in his peripheral nervous system are sending signals to his central nervous system, but why does he feel the pain in his finger? He looks away from his bright computer monitor to ponder this question, and he notices his room appears dimmer than it should. After a few moments of looking around the room, he begins to see more details of the objects in his room. The lighting hasn't changed, but what Perry can see has changed.
The topics of sensation and perception are among the oldest and most important in all of psychology. People are equipped with senses such as sight, hearing and taste that help us to take in the world around us. Amazingly, our senses have the ability to convert real-world information into electrical information that can be processed by the brain. The way we interpret this information—our perceptions—is what leads to our experiences of the world