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Full-Scale Structural and Nonstructural System Damage Detection Using Wavelet-Based Fast Independent Component Analysis
No full textStructural Health Monitoring (SHM) has become an essential part of civil engineering, providing tools to evaluate the safety and performance of structures throughout their lifespan. Experience from past earthquakes shows that while complete structural failures are uncommon, one or more structural elements may sustain damage during moderate to severe shaking. In addition, nonstructural components such as ceilings, partitions, and mechanical or electrical equipment frequently experience failures that cause high financial losses, downtime, and safety risks. A persistent challenge in SHM is that accelerometers capture blended vibrations from multiple sources. Sudden damage changes a structure’s vibration characteristics, but these changes are not easily identified. Independent Component Analysis (ICA) offers a mathematical framework to separate mixed signals into statistically independent components, facilitating the detection of abnormal responses. Structural damage often appears as a change in stiffness, typically analyzed in the frequency domain. However, conventional tools such as the Fourier Transform (FT) and Fast Fourier Transform (FFT) cannot capture short-lived transients associated with cracking or spalling. Wavelet transforms overcome this limitation by providing simultaneous time–frequency resolution, making them effective for identifying sudden changes in vibration characteristics. This study combines wavelet-based preprocessing with FastICA to detect system-level damage using data from the Building Nonstructural Components and Systems (BNCS) test program at NEES@UCSD. Although the BNCS program primarily investigated nonstructural systems, the recorded acceleration responses reflect the integrated behavior of both structural and nonstructural elements. Because both contribute to mass and stiffness, the method identifies overall system damage without distinguishing between the two. A six-story reinforced concrete building was constructed, equipped with 24 accelerometers, and subjected to a series of earthquake simulations in both base-isolated and fixed-base configurations. The results indicate that sudden damage appears as spiky independent components, and the damage locations predicted by the proposed method align with observed damages reported in the BNCS study, demonstrating the promise of this approach for damage detection
ASSESSING URBAN FLOOD DYNAMICS USING DATA- DRIVEN AND PHYSICALLY BASED MODELLING FRAMEWORKS
No full textUrban floods pose a serious threat to public safety, infrastructural resilience, and sustainable urban development due to the rapid changes in land use and the growing consequences of climate change. In order to address this problem, the current study uses a dual-modeling framework to investigate hydrologic simulation and flood prediction using two approaches: (1) machine learning models applied to climate data, and (2) hydrologic models applied to an urban watershed environment using physical models.Four machine learning models—Support Vector Machine (SVM), Random Forest (RF), Artificial Neural Network (ANN), and Long Short-Term Memory (LSTM)—were employed in the first section of the study to forecast flood events in Sacramento, California, by examining past climate variables, such as temperature, precipitation, and soil moisture. The LSTM model performed far better than the others, with an accuracy of 89.9% in flood prediction. It demonstrated outstanding performance in capturing temporal dependencies and nonlinear interactions in sequential climate data. On the other hand, ANN and RF performed quite well but were limited by their static learning architectures, whilst SVM performed worse in classification.The second section evaluates the rainfall-runoff modeling performance of two widely used hydrologic simulation systems, PCSWMM and HEC-HMS, in the densely populated Briar Creek watershed of Charlotte, North Carolina. Although PCSWMM demonstrated better accuracy in capturing peak discharge and runoff volume, both models successfully simulated flood hydrographs using real storm events for model calibration and validation. During calibration, PCSWMM achieved a Nash-Sutcliffe Efficiency (NSE) of 0.949, a Percent Bias (PBIAS) of 1.4%, and an RSR of 0.2, placing it in the Very Good performance category. HEC-HMS also performed well (NSE of 0.937), but having a larger negative PBIAS (–12.53%), which indicates some underestimating of runoff volume.These studies show how effective data-driven and process-based approaches are for evaluating urban flooding. The findings also show that machine learning models, especially LSTM, can produce dependable early warning systems when trained on high-quality climate data, even though hydrologic models like PCSWMM and HEC-HMS are still essential tools for modeling watershed behavior and guiding infrastructure design. The comparative results of the study aid in the selection of modeling approaches based on project goals, watershed conditions, and data accessibility, ultimately enabling more sophisticated and adaptable urban flood management strategies
ADSORPTION-BASED ELECTROCHEMICAL ASSAY FOR NUCLEIC ACID DETECTION ON 2D NANOMATERIALS
No full textElectrochemical biosensing based on the adsorption of nucleic acids onto two-dimensional (2D) nanomaterials offers distinct advantages over conventional techniques, including enhanced sensitivity, selectivity, and simplified probe immobilization. Significant efforts have been directed toward developing sensing platforms that exploit the high surface area and unique physicochemical properties of materials such as graphene oxide (GO) and molybdenum disulfide (MoS₂) to achieve efficient signal transduction. This adsorption-based approach eliminates the need for complex surface modifications or labeling, as nucleic acids interact with 2D surfaces through π–π stacking, van der Waals forces, hydrogen bonding, electrostatic interactions, and ion bridging.The efficiency of nucleic acid immobilization depends on factors such as ionic strength, pH, incubation time, and probe concentration. Under neutral conditions, the negative charges on both the nanomaterials and nucleic acids lead to electrostatic repulsion. To overcome this, metal ions are introduced to screen the charges, thereby enhancing probe adsorption onto the nanomaterial surface. Traditional electrochemical chips are generally limited to detecting a single analyte. In contrast, multiplexed platforms enable the simultaneous detection of multiple targets from a single sample. Wax-on-plastic multiplex devices—fabricated using low-cost and accessible techniques such as inkjet and wax printing—offer additional advantages, including ease of fabrication, fast response, and mechanical flexibility
THE IMPACT OF ADVERSE CHILDHOOD EXPERIENCES (ACEs) AND BENEVOLENT CHILDHOOD EXPERIENCES (BCEs) ON THE PHYSICAL AND MENTAL HEALTH OF AFRICAN AMERICAN VETERANS
No full textThis dissertation examines the impact of Adverse Childhood Experiences (ACEs) and Benevolent Childhood Experiences (BCEs) on the physical and mental health of African American veterans. Although a large body of research has documented the long-term health consequences of early adversity, there remains limited empirical work focusing specifically on African American veterans and the potentially protective role of BCEs. This study addresses that gap by investigating whether ACEs and BCEs are predictive of physical health conditions, behavioral health patterns, and mental health outcomes in this population.The research utilized data from the 2021–2023 Behavioral Risk Factor Surveillance System (BRFSS), focusing on a subsample of 133 African American veterans who responded to the optional ACE and BCE modules. Confirmatory factor analyses (CFA) were used to validate the psychometric properties of the ACE and BCE scales. Logistic regression models were applied to assess associations between childhood experience scores and key health indicators, including obesity, cardiovascular disease, physical activity, smoking, binge drinking, depression, and psychological distress. Among the most frequently reported adverse experiences in this sample was childhood sexual abuse, a form of trauma consistently linked to long-term psychological distress. Veterans with higher ACE scores, including those exposed to sexual trauma, demonstrated significantly greater risks of depression and more frequent poor mental health days. ACEs were also significantly associated with decreased physical activity. In contrast, BCEs were not significantly protective against poor mental health outcomes but were inversely associated with binge drinking. The limited effects of BCEs may reflect measurement constraints rather than an absence of impact. This dissertation highlights the importance of trauma-informed, culturally responsive health care interventions tailored to the lived experiences of African American veterans. It emphasizes the need for improved screening of childhood experiences, particularly sexual trauma, within clinical and public health settings. The findings also support the development of comprehensive, culturally grounded tools to measure resilience-building factors, such as BCEs. Ultimately, healing and health promotion must begin with a deeper understanding of each veteran’s whole life story, including the adversities and affirmations that shaped them long before military service began
THE COLLEGE TRANSITION AND EXPERIENCE: SWIFT CHANGES FOR AFRICAN AMERICAN FIRST-GENERATION STUDENTS IN HIGHER EDUCATION
No full textThere is limited research available that focuses specifically on the lived experiences of African American, female, traditional first-generation college students (FGCS) who have attended rural Predominantly White Institutions (PWIs). Therefore, researchers need to conduct further studies to better understand the barriers faced by this population and how African American first-generation college students at PWIs navigate, experience, and cope with their college transition. A few barriers that African American first-generation college students that will be discussed throughout this research includes academic unpreparedness, campus climate, institution type, mental health, parental transition/education status, potential lack of guidance and knowledge of institutionally offered student services, racial/ethnic discrimination, sense of belonging and student engagement and socioeconomic status.
The first purpose of this study is to examine how these lived experiences on a college campus determine the transition, cognitive thinking, and overall success of African American, female, traditional, first-generation college students (FGCS) within postsecondary education who are enrolled at a rural Predominantly White Institution (PWI). It is imperative to gain knowledge on factors that motivate or inhibit them to continue through their education despite the uprising challenges.
The second purpose of this study is to acknowledge how African American, female, traditional FGCS interpret these potential barriers and their college experience as a FGCS at a rural PWI. This study aims to explore and gain insight into how these students navigate their college transition, internalize their experiences, and respond to them from their own perspective. This study also focuses on understanding the barriers endured during their time on a college campus and the motivating factors that assisted these students in carrying on achieving overall success within their rural PWI.
To achieve this, I conducted a qualitative collective instrumental study with eight African American, female, traditional, FGCS who have attended a PWI in rural Midwestern area and successfully completed at least one full year of their academic career in higher education. For the purpose of this study, the term “successful” illustrates African American first-generation college students who were in good academic standing within the institution during their undergraduate career, specifically with a minimum of a 2.5 grade point average (GPA). The researcher used purposeful sampling as the criteria when selecting participants. The selection criterion was based on specific characteristics, criteria, and experiences related to the research questions. Thematic coding was used to analyze the data. Data analysis results revealed three major themes: (1) Navigating barriers as first-generation students, including academic unpreparedness, limited familial guidance, and social isolation; (2) Internal and external motivators for persistence, such as personal goals, family encouragement, and campus involvement; and (3) Support systems that promote belonging and success, highlighting the importance of culturally responsive institutional resources. These findings emphasize the intersecting challenges and strengths of African American, female, traditional-aged (18-24) FGCS. Recommendations were provided to help prepare higher education professionals for this underrepresented population of students and increase their chances of academic success through practice, policy and future research. Furthermore, assisting higher education professionals in gaining knowledge on how they can better serve this population both professionally and personally throughout their college career to give them the tools and resources needed to begin a successful career
A STUDY ON COVID-19 OUTBREAK IN COOK COUNTY OF ILLINOIS
No full textThis study presents a mathematical model developed to better understand the transmission dynamics of COVID-19 and evaluate the effectiveness of different public health strategies. The model, called SEQIRD, includes of the disease such as exposure, quarantine, infection, recovery, and death. Using real case and death data from Cook County, Illinois, the model closely fits observed trends and captures both pre and post-lockdown dynamics with high accuracy.
Our results show that early intervention significantly reduces total infections, with lockdown imposed even a few days earlier preventing thousands of cases. Through sensitivity analysis, we also identify that parameters such as transmission rate, quarantine effectiveness, and isolation compliance have the largest impact on both epidemic size and basic reproduction number, leading to better disease control results.
Overall, this study offers a new perspective on epidemic modeling by introducing a regionally adaptive control strategy. The findings provide practical guidance for public health officials and policymakers, showing that timely, targeted, and region-specific actions can control disease spread more effectively while minimizing the economic disruption caused by state-wide lockdown
THE EFFECTS OF TOKEN ECONOMIES ON RESPONDING IN CHILDREN WITH AUTISM SPECTRUM DISORDER
No full textResearch demonstrating the effectiveness of token economies as a behavioral intervention spans several decades. Behavior analytic research specifically utilizing token economies peaked in the 1970’s, with sporadic development since (Doll et al., 2013; Hackenberg 2018). Despite the long history of research surrounding token economies, there is little research focused on the token itself. Much of the research that underscores the token focuses more on the pairing of the token with backup reinforcers. Yet, by using objects of obsession or perseverative interest items as tokens, both Charlop-Christy and Haymes (2014) and Carnett et al. (2014) saw higher levels of on-task behavior than when using neutral or arbitrary tokens were used. Therefore, this paper will focus on the effect tokens have on increasing on task behavior while decreasing challenging behaviors for children with autism spectrum disorder
Institutional Aspects of Assessing Surface Water Availability
Get PDFWater resources of river and reservoir systems are shared by many people for diverse, sometimes complimentary but often competing purposes. Effective water management requires a thorough understanding of water availability assessed from a reliability or frequency perspective. The great variability inherent in river system hydrology and the complexities of managing constructed facilities are important considerations in assessing capabilities for meeting water needs. The following two institutional dimensions highlighted in this article are also crucial in water availability modeling
The Regulatory Void of PFAS and Contaminated Sites
Get PDFPer- and polyfluoroalkyl substances (PFAS), often referred to as “forever chemicals,” are widely used due to their unique properties. The adverse health impacts of PFAS have been available for the last two decades, but the persistence of inadequate and poorly enforced regulations has led to pervasive environmental contamination. Recent regulatory changes by the U.S. Environmental Protection Agency address PFAS in drinking water under the Safe Drinking Water Act and address PFAS in contaminated sites under the Comprehensive Environmental Response, Compensation, and Liability Act. However, regulatory gaps persist, particularly regarding acceptable risks from sites contaminated with thousands of PFAS. This paper contextualizes PFAS contamination as a “wicked problem,” a multifaceted challenge with no straightforward or obvious solution. In the context of PFAS, we explore failures in current regulatory frameworks and identify strategies for addressing these shortcomings. Based on interviews with experts and our own policy analysis, we conclude that there is an implicit need for policies that account for the diverse and interconnected pathways of PFAS contamination, including groundwater, soil, and food products. A holistic approach to PFAS regulation must emphasize the importance of federal leadership, accountability, and robust research and innovation. This will mitigate the long-term risks to human health and the environment by allowing policymakers to develop more inclusive strategies for remediation and prevention