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Hourly Simulated Power Production Data with No Snow Loss Model at Queued Utility-Scale PV Sites Simulated as Single-Axis Tracking Systems in the U.S. Eastern Interconnection for Weather Year 2019
No full textUsing 2019 weather data, we ran PySAM power production simulations for utility-scale PV sites in the U.S. Eastern Interconnection queue. Site IDs, capacities, and locations (counties) were extracted from Lawrence Berkeley National Laboratory\u27s Queued Up: 2024 Edition dataset. No panel mount information was provided, so all sites were assumed to be the centers of the installation counties. See queued_site_metadata.csv file for individual site metadat
A New Model for Public Participatory Engagement in Social Sciences and Spatial Humanities Digital Mapping Projects
No full textScholars in the social sciences and spatial humanities have developed a robust body of scholarship touting the benefits for the public and researchers alike in engaging with mapping projects, especially using public participatory geographical information systems (PPGIS) approaches. Developing robust and sustainable engagement from community stakeholders has been a continual challenge, however. Scholars have promoted many different public outreach activities and programs that work to engage the public in mapping projects. Little work has been done, though, to develop a model that guides researchers on how to create and sustain public engagement in these projects. This article aims to fill this void by developing a sustainable public engagement model for digital mapping projects that integrates selected best practices from a wide range of fields including citizen science, social sciences and spatial humanities, public relations and communications, and public history and interpretation
Automated data processing for efficient development of multimodal machine learning models in tool wear detection
No full textThis study aims to develop an automated data processing (ADP) framework that automatically processes multimodal data for machine learning (ML) diagnostics of manufacturing processes. The objective is to contribute to the state-of-the-art automated ML (AutoML) domain by developing a novel all-in-one ML development framework that automatically processes data, trains ML models, and conducts evaluation based on prior process-based knowledge. For this purpose, the ADP framework was designed based on a higher-level decision-making unit and a lower-level machine learning unit to select the best set of data processing methods, ML models, and training strategies, defined as policies, for the machine tool monitoring. The ADP framework was tested with data collected from orthogonal tube turning experiments for cutting tool wear classification. The data were gathered using acceleration, acoustics, and temperature sensors mounted on a Computer Numerical Controller (CNC) lathe machine. The results demonstrate that the solutions selected by the ADP framework consistently exhibited similar performance as the global optimal policy with improved computational efficiency. Consequently, the proposed ADP framework was demonstrated to automatically conduct multiple steps within the ML development process without manual intervention for tool condition monitoring to facilitate democratization of ML development for manufacturers
Flood risk mapping in an urbanized tropical river basin in India using MCDA-AHP: a post-storm event evaluation
No full textFlooding is a persistent hazard in tropical regions of India, primarily driven by intense precipitation and further aggravated by anthropogenic activities. Despite ongoing efforts, a gap persists in the development of comprehensive risk models that integrate hazard, vulnerability, and exposure components at a watershed level. This research seeks to bridge that gap by implementing a multi-criteria decision analysis (MCDA) technique, specifically the Analytical Hierarchy Process (AHP), to generate a risk map for the tropical Meenachil River Basin (MRB), originating in the Western Ghats, southwest India. Nine conditioning factors (CFs) were evaluated to assess hazard, and the resulting hazard layer was integrated with vulnerability data and different exposure factors (EFs), such as built-up height, built-up surface, built-up volume, population, and total exposure, to produce a risk map. Validation of the hazard model utilizing the Receiver Operating Characteristic (ROC) curve achieved an excellent Area Under Curve (AUC) of 0.825, along with high accuracy (0.818), F1-score (0.802), precision (0.812), and recall (0.793). Approximately 11% of the MRB lies in a very high hazard zone and 1.51% in a very high risk zone. These results advocate for sustainable flood management by identifying key risk zones, thereby facilitating the implementation of focused site-specific mitigation strategies
KEWEENAW HEARTLANDS: BUSINESS CONSIDERATIONS IN RECREATION PLANNING
No full textIn 2022, The Nature Conservancy bought approximately 32,000 acres of working forestland in Keweenaw County located in Michigan’s Upper Peninsula, in order to save the land from potential development. Currently, The Nature Conservancy is working with the State of Michigan Department of Natural Resources (DNR), Keweenaw County community leaders, Tribal representatives, local advocacy groups, and the general public, to develop a long-lasting, community-based plan and model to care for these lands and waters for future generations. A new governance model is forming representing the will of the community. This report highlights potential opportunities for recreation revenue and community forest modeled recreation development
PERFORMANCE AND ENVIRONMENTAL IMPACT OF WASTE GLASS ASPHALT IN COLD REGIONS
No full textThe reuse of waste glass in asphalt mixtures has significant potential in sustainable pavement engineering, yet its performance under cold, freeze–thaw conditions remain underexplored. This dissertation presents a first-of-its-kind comprehensive evaluation of glass asphalt for cold regions, simultaneously assessing its low-temperature cracking resistance, moisture susceptibility, field performance, and life-cycle environmental impacts. A series of laboratory tests were performed on asphalt mixtures incorporating various proportions of crushed waste glass aggregate, including Disk-Shaped Compact Tension (DCT) and Indirect Tensile (IDT) tests for low-temperature fracture resistance, and Tensile Strength Ratio (TSR) and Hamburg Wheel Tracking (HWTT) tests for moisture damage and rutting susceptibility. Surface and chemical analyses such as Scanning Electron Microscopy (SEM) and Surface Free Energy (SFE) measurements were used to examine the asphalt–glass interfacial bonding and to evaluate the effects of glass surface treatments (acid and alkali washing). Optimal glass contents were identified for both the leveling course and the surface course. To validate the laboratory findings, a demonstration construction project was constructed, and field core samples were analyzed post-construction. Additionally, mechanistic–empirical pavement simulations (Pavement ME) were conducted to project long-term performance, and a life cycle assessment (LCA) was performed to quantify environmental benefits.
Results indicate that incorporating waste glass into asphalt can significantly improve low-temperature cracking resistance, effectively mitigating thermal cracking in freeze–thaw environments. While untreated glass slightly increased moisture susceptibility, the application of glass surface treatments and the use of anti-stripping additives markedly improved moisture durability, as evidenced by higher TSR values and reduced stripping in HWTT. Field performance of the glass asphalt section, supported by Pavement ME predictions, confirmed that using glass in the leveling course beneath a rubber-modified asphalt surface layer is a viable design for cold regions, achieving comparable or better resistance to cracking and rutting than conventional pavements. LCA results further show that glass asphalt can reduce environmental impacts through decreased virgin material usage and lower greenhouse gas emissions, underscoring its sustainability advantages. This comprehensive study provides a foundation for the practical adoption of glass asphalt, offering evidence-based insights that can inform transportation agency specifications and policies. The findings demonstrate that with proper mixture design and treatment, glass asphalt is a durable, eco-friendly alternative, bridging the gap between laboratory research and field application in pursuit of sustainable infrastructure
IN-SITU REDUCTIVE BIOLEACHING OF MANGANESE ORES
No full textManganese is critical in the steel and battery industries, but the United States has not produced it since 1974. While potential manganese ore deposits are available in the US, mining them with the current technology does not result in a competitive product. This dissertation explores the development and evaluation of a novel in-situ bioleaching process that uses anaerobic organisms and biomass-derived reagents to extract manganese from low-grade ore bodies without the use of purchased toxic chemicals.
The research begins with a literature review of in-situ manganese bioleaching, addressing biological dissolution mechanisms, manganese-reducing organisms, nutrient sources, manganese precipitation methods, and challenges in field-scale implementation. It then extends previous work at Michigan Technological University that demonstrated that microorganisms could dissolve manganese at laboratory scale.
A pilot plant study demonstrated successful manganese extraction using a mixed-culture of manganese-reducing organisms and fen with organics produced by decomposition of biomass from Typha latifolia, which was fermented to generate organic acids. The resulting manganese-rich leachate was treated using electrolytic oxidation for the recovery of high-purity manganese oxide. This approach achieved over 90% product purity with minimum iron contamination. In addition to manganese, the same anaerobic organisms were used to evaluate their potential for extracting iron from iron ore tailings, when manganese is not present.
Direct leaching of manganese by microorganisms leads to loss of manganese as manganese carbonate. To overcome the challenge of manganese carbonate formation, a two-stage bioleaching strategy was developed using bio-solubilized iron as a reductant. In this approach, metal-reducing organisms were first used to dissolve iron, producing a Fe2+ rich solution. This iron-bearing solution was then used to reduce and solubilize manganese oxides. The two-stage method significantly enhanced manganese dissolution while effectively preventing manganese carbonate precipitation.
A techno-economic analysis based on pilot-scale investigations demonstrated that the in-situ bioleaching process is both technically scalable and economically competitive with conventional extraction methods. Low capital and operating costs, along with strong financial performance metrics, demonstrate that the process is well-suited for industrial-scale implementation. Overall, this research demonstrates that in-situ bioleaching is a technically feasible, economically viable, and scalable method for extracting manganese from low-grade ores
METHODS IN STATISTICS, MACHINE LEARNING, AND DEEP LEARNING FOR COMBINING MULTI-OMICS DATASET
No full textTranscriptome-wide association studies (TWAS) have emerged as a powerful strategy to bridge genome-wide association studies (GWAS) with gene regulatory mechanisms by integrating genotypic data with gene expression data. While early TWAS methods typically rely on linear models and single-tissue expression references, recent advances underscore the need for flexible, multi-tissue approaches that can capture heterogeneous regulatory architectures and tissue-specific expression patterns. This dissertation introduces a three‑part research project that advances multi‑tissue transcriptome‑wide association studies (TWAS) along complementary axes of methodology, statistical power, and modelling flexibility.
In chapter One, TWAS‑CTL introduces a two‑stage cross‑tissue learner that trains any user‑chosen single‑tissue imputers (STLs) and then fuses their predictions with an empirical utility weight function that down‑weights poorly transferring tissues. Extensive simulations show that TWAS‑CTL can control the type I error rates and exceeds unified test for molecular signatures (UTMOST), one of the leading methods in this field, in power while cutting computational time by more than half. In the analysis of a GWAS cohort, it recovers more trait‑relevant genes than some existing benchmark works like PrediXcan (a foundational approach in the development of TWAS) and UTMOST.
In Chapter Two, GWAS-boosted cross-tissue learner (G‑Boost‑CTL) extends this framework by re‑weighting STLs with genotypic information extracted directly from the GWAS cohort—e.g., the cross‑sample variability of the imputed expression- so that tissues that carry stronger association signals are automatically emphasized. The dual weighting scheme preserves appropriate type I error rates yet delivers marked power gains over linear-penalized and covariance‑based tools across a wide spectrum of tissue‑sharing scenarios. G-Boost-CTL outperforms existing multi-tissue TWAS approaches in the analysis of a real data set as well by uncovering more statistically significant and biologically plausible disease loci.
In Chapter Three, we explore and replace the linear imputers that dominate TWAS with two non‑linear engines- gradient‑boosted trees and deep learning. Using data from the genotype-tissue expression project (GTEx) of 49 tissues, we show, through large‑scale simulation and real‑data analysis, that these learners maintain appropriate type I error rates but boost discovery, with improved powers for gradient-boosted trees and deep learning methods (e.g., deep neural networks) revealing more complementary, tissue‑specific signals.
Collectively, these studies demonstrate that (i) adaptive, cross‑tissue weighting, (ii) incorporation of GWAS‑derived information, and (iii) non‑linear advanced machine learning and deep learning imputers each confer substantial and largely orthogonal benefits. Taken together, they outline a scalable, modular blueprint for advanced multi‑tissue TWAS that more faithfully captures the complex, heterogeneous architecture of gene regulation and unlocks deeper insights into the molecular basis of human complex disease
Creativity and Curb Cuts: Experiences in Our First Offering of a Front End Development and Accessibility Focused CS Course
No full textStudents learn an abundance of technical skills while obtaining a computer science degree. The ability to develop meaningful front end user interfaces is often considered the domain of only \u27\u27more artistic\u27\u27 CS students. However, for users to effectively engage with any piece of software, functional user interfaces are critical. Moreover, even among students who have front end skills, semantic and accessible design is all too often less considered. The first author piloted a \u27\u27Front End Development and Accessibility\u27\u27 course this past Fall. This course teaches basic skills of front end with web and leverages key accessibility standards via WCAG. This experience report highlights challenges, triumphs, and takeaways from the first course offering. Three students - all with different backgrounds relating to the course content - share their experiences as part of this report as well
Gene regulatory network prediction using machine learning, deep learning, and hybrid approaches
No full textConstruction of gene regulatory networks (GRNs) is essential for elucidating the regulatory mechanisms underlying metabolic pathways, biological processes, and complex traits. In this study, we developed and evaluated machine learning, deep learning, and hybrid approaches for constructing GRNs by integrating prior knowledge and large-scale transcriptomic data from Arabidopsis thaliana, poplar, and maize. Among these, hybrid models that combined convolutional neural networks and machine learning consistently outperformed traditional machine learning and statistical methods, achieving over 95% accuracy on the holdout test datasets. These models not only identified a greater number of known transcription factors regulating the lignin biosynthesis pathway but also demonstrated higher precision in ranking key master regulators such as MYB46 and MYB83, as well as many upstream regulators, including members of the VND, NST, and SND families, at the top of candidate lists. To address the challenge of limited training data in non-model species, we implemented transfer learning, enabling cross-species GRN inference by applying models trained on well-characterized and data-rich species to another species with limited data. This strategy enhanced model performance and demonstrated the feasibility of knowledge transfer across species. Overall, our findings underscore the effectiveness of hybrid and transfer learning approaches in GRN prediction, offering a scalable framework for elucidating regulatory mechanisms in both model and non-model plant systems