Exploring The Impact Of Cognitive Awareness Scaffolding For Debugging In An Introductory Computer Science Class

Debugging is a significant part of programming. However, a lot of introductory pro- gramming classes tend to focus on writing and reading code than on debugging. They utilize programming assignments that are designed in ways such that students learn debugging by completing these assignments which makes debugging more of an im- plicit goal. In this thesis, we propose a cognitive awareness scaffolding in debugging to help students self-regulate their debugging process. We validate its effectiveness by conducting experiments with students in four sections of a Data Structures course, which is one of the introductory computer science classes at California Polytechnic State University, San Luis Obispo. In this form, students identified the debugging stage, described the bugs in their own words, and tracked their attempts to fix them. The exit survey responses that students filled out at the end of the quarter indi- cate that students seemed to find the debugging form helpful with self-regulation in debugging process. For further investigation, we attempt to measure students’ under- standing of the bugs explained on the form. Additionally, we also discuss potential improvements for the debugging form

Factors Affecting the Oxidative Stability of Foods-Interesterified Soybean Oil with High Intensity Ultrasound Treatment and Trona Mineral in Packaged Fresh Meats

Oxidation in oils and muscle foods has been studied for many years to understand its mechanism and furthermore to control and manage it. A series of different processing steps or different packaging techniques can alter oxidative stability. The objective of the current study was to examine oxidative stability of processed oil and to evaluate the effect of carbon dioxide generating mineral on quality of beef and chicken under different storage conditions. In Study 1 (Chapter 3), the effect of ultrasound on oxidative stability of interesterified soybean oil and soybean oil was examined. Sonication did not affect oxidation rate until the oils were highly oxidized. Sonicated interesterified soybean oil exhibited a slightly but significantly lower oxidation rate than non-sonicated oil during long-term storage, while sonication of non-interesterified soybean oil led to a significantly higher oxidation rate than in non-sonicated soybean oil after induction period. In Study 2 (Chapter 4), the feasibility of trona as a CO2 producing product in a model system and in modified atmosphere packaging of beef steaks was investigated. Trona was able to generate more carbon dioxide than sodium bicarbonate with salicylic acid in model systems. Steaks stored with trona/acid mixture had similar color stability and delayed lipid oxidation compared to those stored in high oxygen packaging. In Study 3 (Chapter 5), the effect of packets containing trona and acid placed in a simulated self serve retail case and closed butcher case on the quality of ground beef was studied. Mineral packets did not affect color, lipid oxidation, or microbial growth of ground beef since there was not a sufficient amount of moisture to generate CO2 effectively. In Study 4 (Chapter 6), the quality of chicken breast/thigh portions stored with mineral packets was compared to those without mineral packets during extended storage, and mineral packets had an antimicrobial effect of CO2 only on day 15. In conclusion, high intensity ultrasound did not affect the rate of oxidation of oil until the oil had already become noticeably rancid, and mineral packets containing trona and an acid with low water solubility can be used as CO2 generating sachet if sufficient moisture is given

The molecular-level characterization of the serum antibody repertoire to influenza

Vaccination is the most effective means to protect populations against infectious viruses by eliciting a diverse repertoire of antibodies. For influenza, a rapidly mutating virus posing a constant threat of a pandemic, seasonal vaccination is considered the best prophylactics, but it has limited efficacy and requires annual vaccination. To develop more effective influenza vaccine strategies, a comprehensive understanding of serum antibodies elicited by vaccination is essential, yet it has been confounded by the complexity of the antibody response. In this work, we used the high-resolution proteomics analysis of immunoglobulin (Ig-seq) coupled with the high-throughput sequencing of B cell receptor transcripts (BCR-seq) to quantitatively delineate the serum antibody repertoire to influenza. In Chapter 2, we analyzed the sera collected from four young adults before and after receiving trivalent seasonal influenza vaccine, which contains hemagglutinins from two influenza A virus strains (H1 and H3) and one influenza B strain. The serum repertoire comprised between 40-147 clonotypes specific to each of the three monovalent components of the vaccine, with ~60% of the vaccine response consisting of antibodies that were already present in serum before vaccination. We also observed a surprisingly high fraction of serum antibodies recognizing both the H1 and H3 monovalent vaccines (H1 + H3 cross-reactive antibodies). For subsequent analysis, in Chapter 3, we recombinantly expressed representative serum antibodies, and the H1 + H3 cross-reactive antibodies displayed a broad range of binding specificities to hemagglutinins from historic viral strains. We identified a group of antibodies recognizing the same conserved epitope in the hemagglutinin head domain that is only exposed in its monomeric form. These antibodies protected mice in challenging with H1N1 and H3N2 virus strains. In Chapter 4, we performed a longitudinal analysis of an individual’s serological repertoire specific to pandemic A/California/04/2009 (CA09pdm) viral strain across 6 years. Through multiple exposures to CA09pdm from infection and vaccination, our analysis revealed that the immediate antibody response to CA09pdm differed for each instance of exposure, but the antibody repertoire returned back to the pre-exposure state. This observation was due to the persistent antibodies comprising about half of the serum antibodies, while the intermittent antibodies were elicited following exposures but decayed soon after. The subsequent analysis on their binding specificities revealed that the persistent antibodies were likely to be targeting the stem region of the hemagglutinin while the intermittent antibodies tended to be head-specific. Collectively, our data provide unprecedented insights on the serological responses to influenza with direct implications for engineering a future influenza vaccine endowed with higher and broader protective efficacyChemical Engineerin

Maternal Stress, Sleep, and Well-being in Mothers of Middle Age Children with Developmental Disabilities

Problem: Mothers of children with developmental disabilities (DDs) experience high levels of stress, such as parenting stress and caregiving burden, due to their children with DDs’ life-long care needs. The high levels of stress results in impaired sleep as well as poor well-being among mothers raising children with DDs. Mothers’ sleep may be an important mediator in linking maternal stress to health-related well-being. The purpose of this study was to examine the mediating effect of mothers’ sleep between maternal stress and well-being after controlling for child behavior problems in a community sample of mothers of middle age children (ages 6-12) with DDs. This study used the integrated model, which proposes both the individual differences to the stress response and the cumulative effects of stress on health. Methods: A cross-sectional, correlational design was used. Forty mothers of middle age children with DDs (M = 8.8 ± 2.2 years) from various community settings volunteered and completed a set of questionnaires and a 5-day sleep diary. Instruments measured parenting stress, caregiving burden, perceived sleep quality, child behavior problems, depressive symptoms, and physical as well as mental well-being. A series of regression analyses were used to test the mediation. Results: Mothers were in early 40’s (M = 42.1 ± 5.3 years), married (75%), White (70%), well-educated (88% with college degree), and with a high income (73% were $75,000 or greater). Children were mostly boys (74%) and diagnosed with autism, Down syndrome, or cerebral palsy. Although the mothers’ physical well-being scores fell around the U.S. norm scores, their mental well-being scores were almost 1 SD below the general population. Mothers also reported on average poor sleep quality (PSQI \u3e 5, M =7.9 ± 4.8), high parenting stress, moderate to severe caregiving burden, and high levels of depressive symptoms (CES-D \u3e 16, 53%). Mothers’ perceived sleep quality only mediated in the relationship between caregiving burden and depressive symptoms. Conclusions: The study results call for close monitoring of mothers’ sleep and provide a direction for interventions designed to improve sleep and well-being in mothers of children with DDs

A Preliminary Study of Machine-Learning-Based Ranging with LTE Channel Impulse Response in Multipath Environment

Alternative navigation technology to global navigation satellite systems (GNSSs) is required for unmanned ground vehicles (UGVs) in multipath environments (such as urban areas). In urban areas, long-term evolution (LTE) signals can be received ubiquitously at high power without any additional infrastructure. We present a machine learning approach to estimate the range between the LTE base station and UGV based on the LTE channel impulse response (CIR). The CIR, which includes information of signal attenuation from the channel, was extracted from the LTE physical layer using a software-defined radio (SDR). We designed a convolutional neural network (CNN) that estimates ranges with the CIR as input. The proposed method demonstrated better ranging performance than a received signal strength indicator (RSSI)-based method during our field test.Comment: Submitted to IEEE/IEIE ICCE-Asia 202

Performance Comparison of Numerical Optimization Algorithms for RSS-TOA-Based Target Localization

The maximum likelihood (ML) estimator can be applied to localize a target mobile device using the RSS and TOA. However, the ML estimator for the RSS-TOA-based target localization problem is nonconvex and nonlinear, having no analytical solution. Therefore, the ML estimator should be solved numerically, unless it is relaxed into a convex or linear form. This study investigates the target localization performance and computational complexity of numerical methods for solving an ML estimator. The three widely used numerical methods are: grid search, gradient descent, and particle swarm optimization. In the experimental evaluation, the grid search yielded the lowest target localization root-mean-squared error; however, the 95th percentile error of the grid search was larger than those of the other two algorithms. The average code computation time of the grid search was extremely large compared with those of the other two algorithms, and gradient descent exhibited the lowest computation time.Comment: Submitted to the 2023 IEEE 97th Vehicular Technology Conference (VTC2023-Spring