17179 research outputs found
Sort by
Sodium dichloroisocyanurate: A promising candidate for the disinfection of resilient drain biofilm
No full textBiofilms are complex multicellular communities of microorganisms which can lead to a variety of human diseases and infections. Biofilms consist of microbes which are contained within a protective extra polymeric substance layer, resulting in increased resistance to disinfectants. From an economic perspective, it is estimated that potentially deadly biofilms, which cause 1.7 million hospital acquired infections (HAIs) each year in the US, can result in costs of $11.5 billion in treatment.
Biofilms thrive in a variety of diverse environments, including hospital sinks and drains, which can pose a significant threat to patient safety. Sink drains maintain one of the highest burdens of antibiotic-resistant bacteria amongst all other surfaces, with Pseudomonas aeruginosa consistently being the most prominent species found within ICU sink drains. Studies have shown that by completely removing sinks from patient rooms, there is a significant reduction in the colonization of gram-negative bacteria, a leading cause of HAIs.
Health care facilities serve as havens for patients seeking treatment for disease and injury. However, they can also be home to a hidden world of microbes, lurking in places and devices that lead to life threatening infections. According to the U.S. Centers for Disease Control and Prevention (CDC), 1 in 31 patients will acquire at least 1 health care-associated infection (HAI)—including infections with antibiotic-resistant organisms—while being treated for something unrelated.Medical Laboratory Scienc
An agent-based model to assess interventions for continuous care of cardiovascular diseases after natural disasters
No full textNo abstract prepared.Engineerin
Deportable Adoptees: Citizenship, Statelessness, and Adoption in the United States
No full textThis dissertation explores the intersection of intercountry adoption, U.S. immigration policy, and the criminalization of adoptees, with a focus on how the criminalization of the U.S. immigration system has produced undocumented (and deportable) adoptees. Drawing on feminist geopolitical and indigenous studies frameworks, this research critically examines how national security discourses, racial hierarchies, and gendered power dynamics have shaped the precarious legal status of non-Western adoptees. Through comprehensive archival analysis, case law review, and legislative study, it traces the historical and contemporary impacts of exclusionary immigration laws, such as the Immigration Act of 1924 and the Antiterrorism and Effective Death Penalty Act (AEDPA) and the Illegal Immigration Reform and Immigrant Responsibility Act (IIRIRA) of 1996, revealing how these laws continue to create racialized pathways of exclusion for stateless adoptees.
This research emphasizes how the misalignment between adoption and immigration systems has perpetuated statelessness, leaving many adoptees without legal protection or citizenship. Despite the humanitarian narratives surrounding adoption, bureaucratic oversights and gaps in U.S. immigration and adoption laws have left many in legal limbo, vulnerable to deportation. While the Child Citizenship Act (CCA) of 2000 sought to address these issues, it failed to retroactively grant citizenship to impacted adult adoptees, leaving them at risk of deportation. The commodification of adoptees under U.S. adoption laws reflects broader colonial logics, framing adoptees as "rescued" assets while neglecting their long-term legal identities.
The research demonstrates that the production of undocumented adoptees is deeply intertwined with American foreign policy, legislative oversights, and the racialized nature of the U.S. deportability regime. It calls for comprehensive reforms, including granting automatic citizenship to all adoptees upon adoption, to address the systemic failures in adoption and immigration processes. Until these reforms are enacted, impacted adoptees will continue to face the devastating consequences of statelessness and exclusion in the only country they know as home.Geography and Environmental Studie
Evaluating the Effectiveness of Plantar Pressure Sensors for Fall Detection in Sloped Surfaces
No full textFalls are a major safety concern in physically demanding occupations such as roofing, where workers operate on inclined surfaces under unstable postures. While inertial measurement units (IMUs) are widely used in wearable fall detection systems, they often fail to capture early indicators of instability related to foot–ground interactions. This study evaluates the effectiveness of plantar pressure sensors, alone and combined with IMUs, for fall detection on sloped surfaces. We collected data in a controlled laboratory environment using a custom-built roof mockup with incline angles of 0°, 15°, and 30°. Participants performed roofing-relevant activities, including standing, walking, stooping, kneeling, and simulated fall events. Statistical features were extracted from synchronized IMU and plantar pressure data, and multiple machine learning models were trained and evaluated, including traditional classifiers and deep learning architectures, such as MLP and CNN. Our results show that integrating plantar pressure sensors significantly improves fall detection. A CNN using just three IMUs and two plantar pressure sensors achieved the highest F1 score of 0.88, outperforming the full 17-sensor IMU setup. These findings support the use of multimodal sensor fusion for developing efficient and accurate wearable systems for fall detection and physical health monitoring.Engineering TechnologyMaterials Science, Engineering, and CommercializationComputer Scienc
Online Analysis of N-Nitrosodimethylamine in Purified Reclaimed Water for Direct Potable Reuse
No full textIncreasing water scarcity has led to a rising focus on water reuse as a practical solution, with direct potable reuse (DPR) offering a resilient solution. DPR converts municipal wastewater into drinking water through advanced treatment. However, DPR faces water quality challenges like pathogen removal and formation of disinfection byproducts (DBPs). N-nitrosodimethylamine (NDMA), a probable human carcinogen, is a DBP that has been detected in chloraminated and ozonated drinking waters and wastewaters. NDMA can form at levels exceeding the California notification level (10 ng/L) during the chloramination and ozonation process, with advanced water purification (AWP) facilities offering limited removal. For example, NDMA is not effectively removed by RO (around 10-70%) due to its small molecular size, requiring a very high UV dose for degradation. Therefore, continuous online monitoring is critical to ensure the safety and reliability of DPR operations.
This study has two primary objectives: (1) to evaluate the treatment performance of two schemes of a pilot-scale AWP system and (2) to incorporate and evaluate an online NDMA monitoring analyzer within the AWP system. The first scheme in the AWP system employed ultrafiltration (UF), reverse osmosis (RO), and ultraviolet-hydrogen peroxide advanced oxidation process (UV/H2O2 AOP). The second scheme incorporated ozonation (O3) followed by biologically activated carbon (BAC) filtration. The UF-RO-UV/H2O2 AOP system consistently met all tested primary and secondary drinking water standards, including removal of major ions, metals, disinfection byproducts, and per- and polyfluoroalkyl substances. In contrast, the O3-BAC system had elevated levels of nitrate and total dissolved solids in the treated effluent, indicating a need for additional treatment to meet DPR treatment goals. Both schemes achieved effective microbial removal, with 3 to 4-log reductions in Escherichia coli and total coliform were observed. The online analyzer, which utilized high-performance liquid chromatography (HPLC), anion exchange module (AEM), photochemical reactor (PR), and chemiluminescence (CL) detection, was integrated into the AWP system for NDMA monitoring. To ensure accurate NDMA quantification, a dedicated quenching unit was developed and installed to remove interfering oxidizing agents continuously. In the UF-RO-UV/H2O2 AOP system, NDMA concentrations in the RO permeate ranged from 10.2 to 20.3 ng/L and were effectively reduced to 0.3-0.5 ng/L following the UV/H2O2. In the O3-BAC system, NDMA had a significant increase (up to 70.5 ng/L) downstream of ozonation at a high dose (>10 mg/L), though partially mitigated by BAC treatment (around 60-65%). NDMA formation could be mitigated by optimizing and reducing the applied ozone dose (< 3 mg/L) during treatment. Subsequently, a 72-hour continuous online monitoring experiment was conducted under variable operational conditions in the UF-RO-UV/H2O2 AOP system using the HPLC-AEM-PR-CL method. During intentional disturbances, such as increasing the monochloramine feed, the analyzer detected a rapid NDMA increase (up to 21.5 ng/L). These results demonstrate the importance of continuous NDMA monitoring, enabling real-time operational decision-making in potable reuse systems. Overall, this approach will advance process optimization and operational control within DPR facilities.Engineerin
Oral history interview: Josiah Vandertulip
No full textTranscript file (.pdf) and closed captioning available.Interview with Blake Holbrook. Blake describes his relationship with Josiah Vandertulip and the emotional impact of losing a battle buddy under his command. Vandertulip's bravery and commitment are remembered with deep respect. Vandertulip courageously volunteered for a dangerous mission and was tragically killed by a sniper
Agency Nursing Staff Utilization and Turnover in Nursing Homes: A Longitudinal Analysis
No full textBackground/Objectives: Nursing staff turnover can adversely affect nursing home (NH) performance. To address staffing shortages, NHs are increasingly turning to agency nursing staff as a solution. This study examined the relationship between the use of agency nursing staff and turnover rates among NH permanent nursing staff. Methods: This retrospective, observational study used secondary data from several sources, including the Payroll-Based Journal, the Care Compare: Five Star Quality Rating System, and Area Health Resource Files (n: =35,200, years: 2021–2023). The dependent variable was turnover rates among registered nurses (RNs), licensed practical nurses (LPNs), and certified nursing assistants (CNAs). The independent variable was the classification of NHs based on their level of agency nursing staff utilization. Facilities were classified as “high utilizers” (the top 25% in agency nursing staff use) and “low utilizers” (the remaining 75%). This classification was informed by prior research indicating that the impact of agency nursing staff on NH performance is most pronounced at higher levels of utilization. A two-way fixed-effects regression model (facility and year) was used, with appropriate control variables. Results: NHs identified as high utilizers had significantly higher turnover rates among permanent RNs (7%) and CNAs (1.9%) compared to facilities that had low utilization of agency nurses (p < 0.001). No significant association was found between agency LPN utilization and LPN turnover. Conclusions: Greater reliance on agency nursing staff was associated with increased turnover, with the strongest effect observed for RNs. NH administrators should consider strategies to balance agency staff utilization with efforts to retain permanent staff, emphasizing long-term workforce stability.Health AdministrationNursin
Comparative Study of Clustering Algorithms for Device Grouping in Wi-Fi HaLow Networks
No full textIEEE 802.11ah, also known as Wi-Fi HaLow, is a wireless networking standard that is an expansion of the IEEE 802.11 protocol family, popularly known as Wi-Fi. One of the primary features of this standard is the Restricted Access Window (RAW) mechanism. This is a key technique to handle lots of devices when they contend for the channel to transmit their data. This mechanism limits this contention by grouping the stations into RAW groups and then further divided into RAW slots. In IEEE 802.11ah, a critical challenge emerges when many devices contend for the same transmission channel, leading to congestion and inefficiency. To tackle this, stations need to be efficiently grouped in a way that reduces interference and ensures optimal data transmission. Grouping stations is essential for managing the contention and making the most efficient use of the available resources, particularly through dynamic assignment of identifiers (AID). Recently many machine learning algorithms have been applied to address this problem. One of the techniques used in this RAW mechanism is grouping of stations with dynamic AID assignment by the means of K-Means algorithm. Nevertheless, there are other clustering algorithms that can also be taken into consideration when doing this dynamic AID assignment for grouping the stations. This proposal will explore the possibility of implementing clustering algorithms such as KMeans clustering, Density-Based Spatial Clustering of Applications with Noise (DBSCAN) to group the stations so that to observe which algorithms are suited for which certain conditions. Finding a flexible way to determine the best number of groups (K) is an important research goal. Moreover, after the simulations, we will analyze the network metrics such as fairness, throughput etc. with all these grouping schemes compared to random grouping over different network scenarios. Furthermore, to simulate realistic wireless conditions, the effect of fading is incorporated into the evaluation framework. This includes analyzing the behavior and robustness of clustering-based grouping under various fading intensities. The influence of multipath propagation on the performance of different grouping algorithms is examined through Rician fading models. This extended analysis helps assess the adaptability of clustering schemes under fluctuating channel environments, providing deeper insight into their applicability in practical deployments.Engineerin
Detection of Flexible Pavement Surface Cracks in Coastal Regions Using Deep Learning and 2D/3D Images
No full textPavement surface distresses are analyzed by transportation agencies to determine section performance across their pavement networks. To efficiently collect and evaluate thousands of lane-miles, automated processes utilizing image-capturing techniques and detection algorithms are applied to perform these tasks. However, the precision of this novel technology often leads to inaccuracies that must be verified by pavement engineers. Developments in artificial intelligence and machine learning (AI/ML) can aid in the progress of more robust and precise detection algorithms. Deep learning models are efficient for visual distress identification of pavement. With the use of 2D/3D pavement images, surface distress analysis can help train models to efficiently detect and classify surface distresses that may be caused by traffic loading, weather, aging, and other environmental factors. The formation of these distresses is developing at a higher rate in coastal regions, where extreme weather phenomena are more frequent and intensive. This study aims to develop a YOLOv5 model with 2D/3D images collected in the states of Louisiana, Mississippi, and Texas in the U.S. to establish a library of data on pavement sections near the Gulf of Mexico. Images with a resolution of 4096 × 2048 are annotated by utilizing bounding boxes based on a class list of nine distress and non-distress objects. Along with emphasis on efforts to detect cracks in the presence of background noise on asphalt pavements, six scenarios for augmentation were made to evaluate the model’s performance based on flip probability in the horizontal and vertical directions. The YOLOv5 models are able to detect defined distresses consistently, with the highest mAP50 scores ranging from 0.437 to 0.462 throughout the training scenarios.Engineerin