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Physical and Deep-Learning-Based Explorations of Microbe-Mediated Reactive Transport Processes in Porous Media Across Scales
No full textReactive transport (RT) simulators are important tools often used by researchers to gain insights into subsurface processes. These multi-physics simulations attempt to represent many hydro-biogeochemical phenomena, but they often fall short in terms of computational speed and physical accuracy. This dissertation provides several tools and conceptual advancements that can be used to improve the speed and accuracy of RT simulations and further our understanding of their outputs. Specifically, this work investigates microbial motility in porous microfluidic devices, a comparison of particle tracking methods in porous media, and an investigation of biomass growth and chromium reduction in the hyporheic zone. Furthermore, this dissertation details the development and performance-testing of deep-learning-based tools for the extraction of motion statistics from videos of particles and the upscaling of RT simulations. Overall, new tools and insights are provided to help improve environmental management strategies, such as bioremediation of contaminated groundwater or improved understanding of nutrient cycles in water systems.This dissertation advances our understanding of microbe-mediated reactive transport processes through a multi-scale approach that combines experimental observations, computational modeling, and innovative deep learning techniques. At the micro scale, experimental investigations reveal how different bacterial motility mechanisms respond to varying flow conditions, with peritrichous flagella enabling more resilient motility under higher flow rates compared to monotrichous flagella or pili. These findings provide crucial insights for developing more accurate models of microbial transport in subsurface environments.
To improve micro-scale investigations of bacterial transport, this dissertation gives a comparison of particle tracking (PT) methods and presents a novel deep-learning-based PT method. The comparison between PT methods provides guidance for future researchers in terms of appropriate particle tracking linking algorithms to use for dispersive particles in porous media, conditions for desirable particle tracking experimental setups, and the limitations of particle tracking as it relates to analysis of bacterial transport. The novel deep learning method, DeepTrackStat (DTS), provides a framework for extracting motion statistics from particle tracking videos, addressing fundamental limitations in traditional tracking methods while significantly reducing computational demands. DTS shows especially strong performance for high-speed particles, giving it a clear spot for application within the pantheon of PT methods.
In addition to the work at the micro scale, this dissertation also provides improvements to microbe-mediated reactive transport modeling at the Darcy scale. The integration of novel physical approaches enables comprehensive investigation of coupled hydro-biogeochemical processes in the hyporheic zone, particularly focusing on the interactions between fluid flow, biomass development, and chromium reduction. Through extensive sensitivity analyses, this work reveals that while abiotic reduction dominates in high-electron-donor environments, biotic processes crucially influence the spatial distribution of reduction hotspots. Furthermore, the research demonstrates that speed-based biomass decay significantly impacts biomass growth only under specific conditions of high fluid velocity or weak biofilm cohesion, providing important constraints for environmental management strategies. Expanding on the Darcy-scale microbe-mediated reactive transport modeling, this dissertation presents STAMNet, a neural network for upscaling reactive transport simulations that enables efficient prediction of large-scale transport phenomena while preserving the essential dynamics observed at smaller scales. STAMNet has a simple MLP structure with a spatiotemporal attention mechanism (STAM) that uses cross-dimensional residual connections to improve both spatial and temporal feature extraction.
This work's multi-scale, multi-method approach provides a foundation for improving predictions of reactive transport in heterogeneous porous media while offering practical tools for environmental monitoring and remediation. The findings and methodologies presented here advance our ability to bridge scales in reactive transport modeling, from individual bacterial behavior to field-scale predictions, while the developed deep learning tools offer new possibilities for efficient analysis and upscaling of complex environmental processes. These contributions support more informed decision-making in environmental management and provide a framework for future investigations of coupled biological, chemical, and physical processes in porous media systems
Real-Time Allocation of Mobile Resources in a Naval Defense Strategy
No full textThis thesis focuses on the naval domain and presents a real-time approach for responding to threats posed by groups of enemy agents. In addition, extensions are made to an existing naval simulator to enable the development of complex naval scenarios. Naval threats are considered dynamic as they can change over the course of a scenario. Such threats impose some level of vulnerability on friendly vessels. Determining the optimal response to such threats is a computationally demanding problem, and many solutions are not viable in real-time. To address this problem, a greedy optimization strategy was developed that allocates mobile resources into a defense maneuver that minimizes a set of vulnerability attributes of high-value assets. Specifically, enemies that enter certain zones around a high-value asset increase the vulnerability of said asset, and therefore an optimal response minimizes enemy time within those zones. 7 different scenarios were designed, and the real-time greedy defense strategy was tested on each scenario in the simulator. The strategy proved successful in repelling enemies away from a high value asset across the set of varying scenarios. Moreover, the behaviors of the defenders during the response are realistic and could be applied in a real-world naval event
Cyberinfrastructure for IoT-Based Environmental Monitoring: Advancements with LoRaWAN
No full textEnvironmental research increasingly relies on advanced technological systems to handle data collection, processing, and application at scale. However, issues like device interoperability, system scalability, and accessibility often limit adoption. This thesis presents a LoRaWAN-based environmental monitoring system developed as part of the National Science Foundation's Cyberinfrastructure for Sustained Scientific Innovation (CSSI) initiative. LoRaWAN’s energy-efficient and long-range communication capabilities make it ideal for applications such as environmental monitoring, precision agriculture, and industrial operations. The system integrates cutting-edge hardware and powerful open-source software frameworks to ensure seamless data collection and management. Comprehensive assessments of the system and its underlying technology demonstrate its reliability, energy efficiency, and scalability in varied operational scenarios. By addressing key technical challenges, this work provides a foundation for highly scalable, user-friendly environmental monitoring systems that meet the diverse needs of researchers, industries, and the general public
Geographic Analysis of Electric Vehicle Adoption in California, USA and Shanghai, China
No full textThis dissertation presents geographic analysis of adoption and consideration of electric vehicles (EV) in California, United States (U.S.), and Shanghai, China, focusing on the socio-economic factors, policy frameworks, and infrastructural elements that drive or inhibit EV uptake. The research uses detailed secondary GIS data for California and primary survey data for Shanghai to provide a comprehensive understanding of the spatial and temporal trends in EV adoption within these two distinct geographic areas. In California, the study characterizes EV adoption statewide and within its major cities, revealing variation in how adoption rates have evolved before, during, and after the COVID-19 pandemic. Key factors influencing EV adoption include the availability of charging infrastructure, income levels, and educational attainment, while disadvantaged communities have yet to see much uptake, despite policy aims to do so. In Shanghai, primary survey data is used to assess residents' awareness, attitudes towards EVs, and how it compares by residential location. This detailed analysis highlights the importance of a well-developed public charging infrastructure and identifies gaps in infrastructure planning, especially in peripheral urban areas. The study also examines shifts in EV interest and adoption patterns over recent years, including during the COVID-19 pandemic. An analysis of how EV policies and incentives are evaluated and used by Shanghai residents, focusing on regional variations within Shanghai and California., emphasizes the necessity of tailored policy approaches that consider geographic variation. The study finds that both centralized policies and decentralized strategies have influenced EV adoption awareness and interest in Shanghai, providing valuable observations for policymakers in similar regions. This research contributes to the broader understanding of EV adoption by providing detailed insights into the factors influencing adoption in two major global markets. The findings offer actionable recommendations for policymakers, urban planners, and researchers, supporting the global transition to sustainable transportation systems and contributing to broader environmental and societal goals
Nutrient Deprivation and Wildfire Smoke Inhalation as Environmental Stressors on the Epigenetic Regulation of Liver Biological Age and Blood Proteome of Cattle
No full textWe investigated the impacts of environmental stressors, specifically nutrient deprivation and wildfire smoke exposure, on the epigenetic aging and proteomic composition of the liver and blood in cattle. Using a controlled stress-induced model, we examined how substantial fluctuations in nutrient intake and exposure to smoke particulate matter affect biological aging markers and resilience in breeding bulls. The study was conducted through a feed restriction phase, followed by compensatory growth, to mimic conditions in extensive production systems. DNA methylation analysis was employed to assess liver epigenetic age, revealing distinct aging patterns across animals. Some bulls exhibited accelerated liver aging during nutrient stress, while others showed signs of epigenetic rejuvenation post-recovery, indicating differential responses to metabolic recovery.We monitored a group of cannulated steers during the 2022 fire season while animals were naturally exposed to wildfire smoke aerosols and performed blood proteomic profiling before, during and post exposure to evaluate immune, inflammatory, and metabolic responses. Proteomics revealed significant changes in protein expression associated with inflammation and immune modulation, highlighting adaptive responses to oxidative stress and airborne particulate matter. These findings provide insights into cattle resilience, emphasizing the importance of understanding epigenetic and proteomic adaptations for animals living under environmental stressors so we can improve animal welfare, health and productive performance in increasingly variable climates. In parallel, to investigate the specific impacts of different types of smoke inhalation, we designed two novel, cost-effective controlled smoke exposure system. The system includes a mask with an adaptable configuration that allows precise regulation of smoke and particulate exposure. Constructed from PVC and 3D-printed components, the mask enables modular attachments, including valves, filters, and mixing chambers for varied particulate and oxidative radical exposure. This system permits controlled, timed smoke exposure intervals, enabling access to physiological and performance effects of smoke inhalation by ruminants in a controlled setting. By providing a platform for studying the health impacts of smoke and particulate matter on livestock, this work contributes to strategic knowledge for producers and policymakers facing seasonal fire risks and air quality reductions, ultimately supporting proactive measures for the development of sustainable ruminant production systems
Recent Developments in the Heap Leaching of Chalcopyrite Ores
No full textThis paper was presented at the Heap Leach Solutions Conference, October 19-21, 2025, Sparks, Nevada.The global copper industry is facing increasing pressure to develop economically viable and environmentally responsible methods for extraction of copper from low-grade and refractory ores. This has led to a substantial growth in the number of heap leach operations over the past decades. However, chalcopyrite, an abundant copper source, is currently not amenable to heap leaching at ambient conditions due to passivation of the mineral surface. Over the past decade, several technologies have been developed to overcome this challenge. These include microbial-assisted heap leaching with internal heat generation or external heating. Additionally, heap leaching processes utilising acidic chloride medium have also been developed for both secondary and primary copper sulphide ores. Despite these advancements, several challenges remain, limiting commercial application to date. This paper provides a review of existing processes and presents a new Mintek-developed process that combines an acidic chloride medium with external heating. The innovative approach overcomes obstacles commonly associated with high copper inventories and high heating costs. It also allows reduction in acid consumption. For example, whereas existing processes recycle 5 g/L Cu over the heaps, the new process only requires 0.5 g/L Cu. Also the solar heating capital cost can be reduced from USD 18 million to USD 1.5 million for a heap processing 2 million tonnes per annum
Reading Between the Strands: Deciphering Double Stranded RNA-Based Immune Responses in Mosquito-Virus Interactions
No full textMosquitoes are responsible for transmitting several medically important viruses that cause widespread illness and pose a major public health concern. Among them, Culex quinquefasciatus is a globally distributed vector species that contributes to the spread of multiple arthropod-borne viruses. Despite their significance, the molecular mechanisms by which Culex species mosquitoes recognize and respond to viral infection remain poorly understood. Antiviral immunity in mosquitoes is initiated by the recognition of viral pathogen-associated molecular patterns, with double-stranded RNA (dsRNA), a replication intermediate of many viruses, serving as a key immunostimulatory signal. While RNA interference (RNAi) remains the most well-characterized antiviral pathway triggered by dsRNA, accumulating evidence suggests that mosquitoes also employ RNAi-independent mechanisms that detect and respond to viral dsRNA.This dissertation examines these alternative responses in Cx. quinquefasciatus, focusing on immune pathways and proteins operating outside of RNAi. We first provide initial evidence for an RNAi-independent, dsRNA-triggered antiviral response. We also characterize Cx. quinquefasciatus Toll6, a previously unstudied Toll-like receptor in this species, as an antiviral gene and potential dsRNA sensor, alongside a broader survey of Toll gene phylogeny and inducibility.
We then further characterized non-canonical functions of Dicer-2 (Dcr-2), a key RNAi component known to bind viral dsRNA. Using a CRISPR-generated Dcr-2 knockout cell line, coupled with poly(I:C)-based proteomics and transcriptomic profiling, we demonstrate that Dcr-2 regulates immune gene expression and restricts virus replication independently of its RNAi activity. These analyses also revealed additional dsRNA-binding proteins and candidate Dcr-2 binding partners, helping to refine our understanding of dsRNA sensing and Dcr-2’s downstream signaling network.
Finally, we investigated adenosine deaminase acting on RNA (ADAR), a conserved dsRNA-binding protein. Using both knockdown and overexpression approaches, we found that each reduced virus replication and dampened immune gene expression, suggesting that ADAR levels must be tightly regulated to balance antiviral defense and host immune activity. These effects occurred independently of Dcr-2, indicating that ADAR modulates virus infection and immune signaling through a Dcr-2-independent mechanism, potentially involving its RNA editing activity.
Together, this work advances our understanding of dsRNA-based immunity in Cx. quinquefasciatus, highlights novel roles for conserved immune factors, and provides tools and insights for probing antiviral defenses in mosquitoes. These findings may ultimately inform strategies for controlling mosquito-borne diseases by targeting immune sensing pathways
AIEI Newsletter, October 2025
No full textFederal Highway AdministrationUnited States Department of Transportatio
Publish or perish? A content analysis of scholarship criteria in R1 academic libraries' promotion and tenure documentation
No full textThis study sought to understand how R1 libraries define scholarship and creative activities and how they address quality of scholarship through a content analysis of promotion and tenure documentation. Peer review is the most common indicator of quality mentioned in the documents, followed by the geographical reach of scholarship and originality of the research. Other common scholarship criteria included the need to demonstrate sustained scholarship activity, while discussions of open access research were rare. Academic libraries that offer promotion and tenure should evaluate their documentation to ensure they provide clarity to candidates and are up to date
Gender Representation in Kenyan Journalism: Analyzing Gender-Sensitive Reporting Pedagogy and Faculty-Student Perspectives in Journalism Programs
No full textThis study investigates the integration of gender-sensitive reporting (GSR) approaches in journalism education across different Kenyan public and private universities and colleges. There’s a broad corpus on gender inclusion in the media; however, little research has examined structural issues in integrating gender-sensitive reporting concepts in the journalism curricula. In response to persistent gender-based violence and media misrepresentation of women, this survey research study incorporates mixed-methods to examine pedagogical approaches that promote gender awareness in journalism programs. This study surveyed (N=67) journalism students and faculty members. It used structured online questionnaires to explore curriculum design, teaching practices, institutional support, and the influence of socio-cultural norms on GSR approaches. A thematic analysis that involved open coding of the data was preferred to identify recurring themes and patterns. This survey research revealed three significant findings: (1) Kenyan journalism programs lack standardized curriculum approaches to GSR education; (2) there is a lack of both faculty and institutional preparedness for GSR; and (3) persistent social-cultural norms (the dominant limiting factor) that influence the perception of gender issues among students and faculty members. It also highlighted a critical gap between conceptual understanding and practical implementation of GSR. Several challenges emerged, including outdated curricula, insufficient faculty training, socio-cultural resistance, and over-reliance on Western teaching models. While most participants recognize the importance of gender-sensitive reporting, inconsistencies in curriculum reviews and a lack of standardization limit its effectiveness. This study contributes to a nascent area of research, offering new perspectives and underscoring the critical need for curricular reform, faculty development, and institutional support to bridge these gaps