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Effect of Pre-adsorbed Species on High-Pressure Adsorption of Methane in Zeolite 5A Using Grand Canonical Monte Carlo (GCMC) Simulations
Full text linkNatural gas upgrading, which removes impurities from methane (CH4), is essential for industrial applications, including liquefied natural gas (LNG) production and power generation, as well as for residential use. Removing non-hydrocarbon impurities such as carbon dioxide (CO2), nitrogen (N2), and water vapor (H2O), among others, along with separating heavier hydrocarbon gases from raw natural gas, is required to achieve high- purity methane and prevent pipeline corrosion. Zeolite 5A is a microporous aluminosilicate material with a pore size of approximately 5 Å, containing sodium and calcium cations that balance the framework’s negative charge. Its structure offers high thermal stability and a large surface area, making it an effective adsorbent for gas separation and purification. Additionally, the presence of calcium cations enhances its effectiveness by significantly increasing the ion-adsorbate interactions, especially with molecules that have quadrupole moments, which is beneficial for removing impurities. Despite these advantages, equilibrium adsorption data on mixed gases in zeolites 5A at high pressure remain limited, which hinders the optimization of operating conditions for industrial applications. To address this, we conduct Grand Canonical Monte Carlo (GCMC) simulations to predict the effect of pre-adsorbed species (CO2, N2, and H2O) on methane adsorption in zeolite 5A. These simulations are performed at room temperature and pressures up to 100 bar. This study aims to provide molecular insights into determining competitive adsorption sites and the influence of pre-adsorbed species on CH4 adsorption, contributing to the development of improved adsorbent materials for natural gas upgrading.https://jagworks.southalabama.edu/southalabama-shgrf-posters/1014/thumbnail.jp
Out-of-band Anomaly Detection for Real Time Operating Systems
No full textReal Time Operating Systems (RTOS) are increasing present throughout the industrial, business, defense, and healthcare spaces. These lightweight and efficient operating systems are designed to run on embedded, resource constrained devices, often within cyber-physical systems (CFS). A defining characteristic of RTOSs is that they are deterministic. Tasks are scheduled to run on fixed timelines within guaranteed execution windows. To accomplish tasks on time, real time software must conform to worst case execution times (WCETs) as design parameters. WCET is the maximum time a particular task can take to complete. Exceeding the WCET could cause system failure and lead to damage, injury or even death. Thus, if the system exceeds its WCET estimate it could be assumed anomalous activity is occurring in the software. Unfortunately, many of the systems using RTOSs are extremely resource constrained because they have limited power, computing capacity, and memory. All these factors make security controls difficult since conventional security mechanisms put a burden on already strained resources.
This research aims to determine the viability of using out-of-system timing cues to detect timing anomalies in cyber-physical systems, which could indicate some form of attack. This approach would use physical manifestations of the beginning and end of execution of code regions and compare observed execution time to the expected WCET to detect timing anomalies. We will use existing processes to determine the WCET of the selected measurement regions and develop a prototype system to compare the actual execution time bounded by GPIO activity with the calculated WCET of the measurement regions. We will test the system by injecting additional code into the measurement regions to determine if the dynamically calculated WCET are exceeded.https://jagworks.southalabama.edu/southalabama-shgrf-posters/1011/thumbnail.jp
Forecasting Vehicle Driving Energy Consumption Based on User Driving Patterns
No full textResearch on driving energy has become more popular due to the high adoption of electric vehicles (EVs). The main concern of EV users is battery charging and uncertainty of when to charge. In addition, the increasing amount of EVs poses extra load to the electric grid that might not be prepared for it. Thus, predicting driving energy even from internal combustion energy (ICE) vehicles is necessary to estimate how much the load is expected to increase when the ICE drivers change their vehicles to EVs. This research created a comprehensive dataset from real-world driving data for one month in Mobile, AL. A vehicle\u27s CAN bus data logger was installed to collect dynamic data such as speed, acceleration, latitude, longitude, and altitude. The dataset containing about five thousand kilometers of driving distance and about one thousand individual trips was used to calculate the driving energy profile and present its time series. Statistical techniques such as the Augmented Dickey-Fuller (ADF) test, Auto-Correlation Function (ACF), and Partial Auto-Correlation Function (PACF) are implemented to evaluate stationarity, autocorrelation, and seasonality properties of the time series. The results show that the data is stationary. However, it has a seasonality of twelve hours, and energy consumption is higher at the beginning of every cycle than during the other cycle period. Then, machine learning algorithms such as Long Short-Term Memory (LSTM) are implemented to predict the vehicle\u27s energy consumption for the following day. The prediction helps EV users plan their vehicle charging times.https://jagworks.southalabama.edu/southalabama-shgrf-posters/1022/thumbnail.jp
Preliminary Assessment for Quality Improvement of Hypertension Management in Kenya
Full text linkHypertension is a leading contributor to cardiovascular disease (CVD) morbidity and mortality worldwide, with a disproportionate burden in low- and middle-income countries (LMICs). Despite relatively low rates of common behavioral risk factors, the Kasigau region of Kenya experiences high prevalence of hypertension and hypertension-related complications. This study explores self-efficacy, health beliefs, and management behaviors among adults in Kasigau to provide pilot quality improvement data for providers and future intervention planning. A cross-sectional, randomized survey of 45 adults was conducted at the Bughuta Clinic in southeastern Kenya. Participants completed a five-question hypertension-specific, self-efficacy assessment and a health belief model-based survey, with blood pressure measurements recorded during their clinic visit. Analyses examined overall trends, as well as differences by sex and medication management status. Significance testing (Student t-tests and Mann-Whitney rank sum analysis) revealed that managed patients reported significantly higher confidence in four out of five self-efficacy areas (p \u3c 0.05) and stronger beliefs in treatment benefits, yet these factors alone were not associated with improved blood pressure control. Regression analysis also found that older age predicted lower confidence in self-managing hypertension (β = -6.661, p = 0.001). While most participants reported moderate to high self-efficacy and recognition of hypertension’s seriousness, clinical measurements showed poor blood pressure control in nearly all participants — only one individual met the criteria for normal BP. These results highlight the gap between confidence and outcomes and may point to structural and cultural barriers that limit effective hypertension management. These findings support the need for holistic, community-based interventions that go beyond patient education to address systemic health inequities in rural Kenyan communities
Composition and Rich/Lean Loading-Dependent Density Measurements of a Series of Aqueous Ionic Amines for CO2 Capture
Full text linkAs climate change worsens worldwide, there is a drive to develop more efficient and sustainable methods to mitigate its impact through carbon capture methods like carbon dioxide scrubbing. Carbon scrubbing directly captures carbon dioxide (CO2) emissions from industries or power plants before they are released into the environment. The CO2 captured can be utilized for various applications or can be stored underground. However, this method faces numerous challenges, such as the degradation of the solvent or corrosion of equipment. As a result, research has expanded to find a better medium to overcome these issues. In particular, aqueous ionic amines (AIA) are novel salts that are nonvolatile and highly thermally stable. Not much is known about the compounds studied in this research, so preliminary data measurements such as density, solubility, viscosity, and surface tensions are needed to model these compounds in an industrial setting. The focus of this research is the collection of density measurements for these compounds with and without CO2. The density measurements were collected over a temperature range of 20 to 80 °C at mass percentages ranging from 15-55%. The property measurements altogether will aid in future design and optimization purposes for industries. Additionally, understanding these properties will lay the groundwork for developing better and more efficient solvents to be used for carbon capture
Joan Browning Pre-presentation Welcome Photo 2
No full textPhoto of Joan Browning and Lorene Flanders, USA Libraries Director, before presentation.https://jagworks.southalabama.edu/freedom-rider-browning_photos/1001/thumbnail.jp
Joan Browning Presentation Reception Photo 1
No full textReception at the McCall Archive Library for Ms. Joan Browning after her presentation at the University of South Alabama.https://jagworks.southalabama.edu/freedom-rider-browning_photos/1010/thumbnail.jp
Joan Browning Presentation Reception Photo 3
No full textReception at the McCall Archive Library for Ms. Joan Browning after her presentation at the University of South Alabama.https://jagworks.southalabama.edu/freedom-rider-browning_photos/1012/thumbnail.jp
Funded to Win? Super PACs and Electoral Advantage in the Southeast
Full text linkIn 2010, two key pieces of legislation were passed: Citizens United v. FEC and Speechnow.org v. FEC. The results of these cases led to the creation of a new type of organization known as the super PAC. After their inception, the amount of super PACs exploded going from 83 in 2010 to 2,393 in 2016. Since then, the number of super PACs has remained relatively constant with a total of 2,458 super PACs in 2024. While Super PACs constitute a new method of civic participation in politics, there are concerns regarding their potential influence. The results of this research support the claim that super PACs have influence on the success of local, congressional candidates.https://jagworks.southalabama.edu/honors_college_posters/1047/thumbnail.jp
Connecting the Dots: Teacher Self-Efficacy, Student-Teacher Relationships, and Behavior Management in Early Childhood
Full text linkThis study was designed to explore the relationships between teacher self-efficacy, student-teacher relationships, and use of behavior management strategies in early childhood education. Previous research suggests that effective early childhood education improves a child’s academic and social outcomes. This study sought to explore factors contributing to teacher effectiveness in preschool teachers. The hypotheses were that (1) teacher self-efficacy will directly affect student teacher relationships, (2) that studentteacher relationships will directly affect teacher’s frequency, confidence, and success of their behavior management strategies, and (3) that student-teacher relationships will act as a mediating variable between teacher self-efficacy and the frequency, confidence, and success of teacher’s behavior management strategies. Data were collected from 52 preschool teachers in Mobile and Baldwin County through a survey using multiple adapted scales. An exploratory factor analysis was conducted due to wording changes in the scales and led to the exclusion of certain items. Then, a confirmatory factor analysis was conducted and determined that all the models ranged from good to excellent fit. Structural equation modeling was then used to test the models of the hypotheses. All hypotheses were found to be insignificant. These findings suggest that more research needs to be conducted to determine the complex relationship structure between teacher self-efficacy, student-teacher relationships, and behavior management strategies