Digital Commons Network^™

Sea-Level Rise And Regional Economic Adjustment On The U.S. Gulf Coast, Md A M Hasif, Juhee Lee, Robin A. Choudhury

School of Earth, Environmental, & Marine Sciences Faculty Publications

Sea-level rise (SLR) poses escalating risks to the U.S. Gulf Coast, where critical industries, infrastructure, and populations are concentrated in low-lying areas. This study estimates the short- and long-run economic effects of SLR using a panel autoregressive distributed lag-pooled mean group framework applied to county-level data from 26 coastal counties across five Gulf Coast states from 2005 to 2021. Over this period, mean sea level increased by approximately 11 cm on average across counties, with cumulative changes ranging from about 4 cm to over 24 cm and substantial cross-county variation, providing a meaningful empirical setting for identifying economic adjustment. We …

Clinic-In-A-Box: A Portable, Software-Defined Cyber Range For Realistic, Scenario-Based Cybersecurity Training, Ethan Chumley, Aaron Nair, Royce Yaezenko, Joshua Payne, Veronika Kyles, Paul Wagner, Robert J. Honomichl, Ryan Straight, Shengjie Xu

Journal of Cybersecurity Education, Research and Practice

Realistic, hands-on cybersecurity training has traditionally depended on fixed infrastructure such as dedicated lab hardware, cloud subscriptions, or permanent network connectivity, limiting where and how often it can be delivered. This paper presents the design and implementation of a portable, scenario-based cybersecurity training platform housed in a single travel case and built from commodity hardware, type-1 hypervisor virtualization, containerized service orchestration, and software-defined networking. The platform clones, isolates, and resets complete lab environments on demand, allowing the same physical system to support repeated classroom, workshop, or field deployments with minimal manual reconfiguration. Training scenarios are grounded in generated organizational profiles …

Between Digital Transformation And Regulatory Vacuum: Cybersecurity Of Public Services In Mozambique, Faztudo Languisse Eng.

Journal of Cybersecurity Education, Research and Practice

The rapid expansion of digital public services in Mozambique—including e-government platforms, digital health systems, and electronic tax administration—has outpaced the development of a coherent legal framework for cybersecurity. While Law No. 3/2017 (Electronic Transactions Law) of 9 January 2017 introduced foundational data-protection principles, Mozambique long lacked a dedicated cybersecurity regulatory authority, mandatory security standards, and formal incident-notification mechanisms. This regulatory vacuum exposed critical public services to escalating cyber risks as digital transformation was actively promoted as a development priority. This article examines the legal and institutional gaps in Mozambique's cybersecurity governance framework prior to the 2026 Cybersecurity and Cybercrime Laws, …

Provincial Disparities In Adolescent Fertility In Indonesia: An Ecological Analysis Of Child Marriage, Median Age At First Marriage, And Family Development Index, Andi Nisa Fathimiyah Afidah, Milla Herdayati

Kesmas

Adolescent fertility remains a public health concern in Indonesia, with substantial variations across provinces. However, the evidence explaining provincial disparities using population-level indicators remains limited. This study aimed to examine provincial disparities in adolescent fertility and assess the association of child marriage, median age at first marriage among women, and the Family Development Index called iBangga with adolescent fertility across Indonesia. This ecological, cross-sectional study used aggregated data from 34 Indonesian provinces. Descriptive statistics, spatial visualization, Pearson’s correlation, and multiple linear regression analyses were performed. The mean adolescent fertility rate was 25.0 births per 1,000 female adolescents aged 15–19 years, …

The Association Between Rapid Growth In Children Under The Age Of Five And Adolescent Obesity, Ratu Ayu Dewi Sartika, Fadila Wirawan, Iche Andriyani Liberty, Nurul Husna Mohd Shukri, Siti Arifah Pujonarti, Edy Purwanto, Munaya Fauziah

Kesmas

Early-life nutrition is a critical predictor of long-term health, yet the association between rapid early-childhood growth and adolescent obesity, particularly in relation to the “double burden of malnutrition,” remains under-researched in Indonesia. This study aimed to analyze the association between rapid growth and adolescent obesity. Data were obtained from the 1997, 2000, and 2014 waves of the Indonesian Family Life Survey (IFLS). This study included 641 children (aged 0–23 months at baseline) with complete anthropometric measurements across all three waves. Rapid growth was defined as an increase in z-scores of >0.67 in weight-for-age (WAZ), height-for-age (HAZ), or weight-for-height (WHZ) between …

Synthesizing Viscoelasticity: Living Polymers, Micro-Macro Relations, And Paths For Resolution, Adam M. Hasler

Graduate Masters Theses

This thesis investigates the relationship between macroscopic rheological signals and underlying microscopic dynamics in complex fluids, specifically focusing on ”living polymers” within the cetyltrimethylammonium bromide (CTAB) and sodium salicylate (NaSal) surfactant system. The research addresses the inverse parameterization problem, demonstrating how bulk measurements like zero-shear viscosity can mask fundamentally different physical topologies, such as purely cylindrical, reptating micelles versus highly branched networks. Through the successful synthesis of viscoelastically ”degenerate” samples, the study utilizes an array of characterization techniques including frequency sweeps, Large Amplitude Oscillatory Shear (LAOS) to resolve these unique underlying states. Furthermore, the work explores further avenues of study …

A Statistical Mechanics Approach To Reinforcement Learning, Jacob Adamczyk

Graduate Doctoral Dissertations

Reinforcement learning (RL), the study of optimal decision-making over long timescales in stochastic systems, has recently seen remarkable advances due in large part to the efforts of the deep learning community. RL has witnessed great success in solving problems in video games, robotics, biological control, and language modeling. However, a unified statistical mechanics framework to understand and develop the corresponding algorithms is lacking. To address this issue, we begin by showing that the reinforcement learning problem can be formulated and solved using the tools of statistical mechanics. Drawing on physical principles of free energy minimization and invariance, we address important …

The Grasshopper's Journey To The Bloch Sphere, David Llamas

Graduate Doctoral Dissertations

The Grasshopper Problem asks a simple geometric question. A grasshopper lands on a lawn of fixed area and jumps a fixed distance in a random direction. What shape of lawn maximizes the probability that the grasshopper remains on the lawn after jumping? The jump rule is rotationally symmetric, but the best lawns do not have to be. This dissertation studies how that symmetry breaking occurs, maps the continuum problem to a novel constrained spin system, and uses the spherical Grasshopper Problem to compare quantum singlet correlations with classical local models.

For planar lawns, boundary-integral and perturbative calculations explain why the …

Exposing And Addressing Machine Learning Brittleness Through Constraint Solving, Muyeed Ahmed

Dissertations

Machine Learning (ML) implementations are fundamentally brittle: nondeterministic, inconsistent, and prone to overfitting; however, constraint solving can be used to systematically expose, quantify, and address this brittleness.

This dissertation first establishes that widely-used implementations of popular ML algorithms are nondeterministic (producing different outputs on the same input, across different runs) and inconsistent (different implementations of the same algorithm producing different outputs on the same input). This is more prevalent in Unsupervised Learning (UL) implementations where, due to the lack of a ground truth, subtle execution errors can go unnoticed and are difficult to verify. Nondeterminism and inconsistency also introduce security …

Development Of Ultrafast Protein Digestion And Standards Free Quantitation Methods For Mass Spectrometric Analysis, Praneeth Ivan Joel Fnu

Dissertations

A wide variety of biologically important molecules, such as enzymes, antibodies, hormones, transporters and receptors are proteins by composition and they play key roles in biological functions such as cellular regulation, communication, metabolism and physiological function. Protein dysfunctions and abnormalities are associated with numerous diseases, making proteins critical targets for understanding disease mechanisms and developing therapeutic interventions. Protein-based therapeutics such as monoclonal antibodies, hormones and vaccines gained popularity due to their high specificity, efficacy, and ability to treat complex diseases that are often difficult to address with small-molecule drugs.

Given the important role of proteins in a variety of biological …

Nonlinear System Identification Based On Fuzzy Radial Basis Neural Network With Multi-Connected Weight Connections, Kabul Khudaybergenov

Chemical Technology, Control and Management

This paper builds on our earlier radial basis function network with multiple connections (RBFMC) by placing it within a fuzzy inference framework for nonlinear system identification. The idea is inspired by the diversity of neurotransmitters found in biological neurons: instead of a single hidden-to-output weight, RBFMC gives each hidden unit a multi-dimensional connection whose components act as independent filters. Once fuzzy logic is added, each hidden neuron becomes a fuzzy rule, and its antecedent is built from several Gaussian membership functions, one per connection. The resulting Fuzzy RBFMC produces an interpretable, multi-filter description of local regions of the input space …

Re: Comment Letter For Butte Priority Soils Operable Unit (Bpsou) Draft Diggings East Dewatering Treatability Study Pre-Design Investigation Work Plan (Pdiwp) (Dated August 24, 2026) And The 2026 Draft Diggings East Dewatering Treatability Study Quality Assurance Project Plan (Qapp) (Dated August 24, 2026), Emma Rott

Silver Bow Creek/Butte Area Superfund Site

No abstract provided.

Motor Imagery Eeg Decoding For Brain-Computer Interfaces: Structured Representation, Transfer, And Drift, Yiming Shen

Graduate Doctoral Dissertations

Motor imagery EEG decoding is often summarized by the accuracy of a final classifier, but the classifier is only the last stage of the pipeline. Before classification, the signal has already been shaped by preprocessing, feature extraction, source-session organization, and adaptation. This dissertation studies how feature representation, source-session transfer, and drift shape reliable MI-EEG decoding for brain-computer interfaces.

It first studies within-session decoding on public MI-EEG datasets using nested validation that keeps preprocessing, feature fitting, and model selection inside the training folds. This analysis separates gains from feature representation from gains due to nonlinear classification, and relates both comparisons to …

Redesigning Quantum Theory, Matthew Weiss

Graduate Doctoral Dissertations

QBism understands quantum mechanics to be probability theory supplemented by additional nonclassical coherence conditions. In this dissertation, we develop these nonclassical coherence conditions from first principles, emphasizing the role of a well chosen reference measurement. After treating standard probability on subjective Bayesian lines, we demonstrate an equivalence between the QBist approach and the existing framework of generalized probabilistic theories. We show that the fundamental nonclassical coherence relation may almost always be taken to be a gentle modification of the law of total probability, and give a coherentist account of when an experimental scenario has a classical explanation. Finally, we show …

Toward A Post-Modern Daoist Pedagogy: Bridging Complexity And Dao Through Inner Cultivation And Autopoietic Currere, Jie Yu, Jingyu Liu

Journal of Contemplative and Holistic Education

This article first critically examines the call to reenchant the world as it resonates within a middle space between science and mysticism and then connects key ideas from complexity theory and systems thinking on complex adaptive systems with two seminal Daoist texts: the Neiye (《內業》, Inner Cultivation), a proto-Daoist chapter from the Guanzi anthology (4th–3rd century B.C.E.), and the Huangting Jing (《黃庭經》, Yellow Court Scripture), a later Daoist classic (3rd–4th century C.E.). Through this dialogue, the article advocates a post-modern Daoist pedagogy through inner cultivation and autopoietic currere—an autopoietic process wherein curriculum emerges organically through participatory dialogue …

Neural Network Driven By Electrochemical Performance Data For Predicting The Discharge Termination Time Of Seawater Electrolyte-Based Metal-Air Batteries, Peng-Peng Shen, Yi-Chi Pan, Yu-Rong Liu, Lu-Dan Zhang, Ning Niu, Guan-Jun Wang, De-Kun Yang, Xin-Long Tian, Peng Rao

Journal of Electrochemistry

Seawater electrolyte-based metal-air batteries exhibit great promise for marine energy supply systems. However, conventional statistical analysis methods, though applicable to seawater metal-air battery lifetime prediction, have inherent limitations of insufficient prediction accuracy and large error. Herein, a deep time-series regression framework based on InceptionTime and incorporating prior-biased attention pooling is proposed to construct a nonlinear mapping between electrochemical performance sequences and the discharge termination time of catalysts. Specifically, chronoamperometric profiles are employed to extract long-term stability features, while prior knowledge derived from linear sweep voltammetry is introduced to strengthen the attention weighting over critical potential regions. Under a nested leave-one-catalyst-out …

A Path To Creating Global Citizens: Education For Sustainable Development In Small Liberal Arts Colleges, Shari Bissoondatt (Boochay), Timea Varga, Antonella Regueiro, Amy An

Faculty and Staff Publications & Presentations

Higher Education Institutions (HEIs) play a critical role in advancing the United Nations Sustainable Development Goals (UN SDGs) by preparing students to address complex social, environmental, and economic challenges. In the literature, less attention has been paid to scalable curricular models implemented in small liberal arts colleges. This paper presents a case study of Lynn University’s Impact Series, an interdisciplinary and co-curricular initiative, intentionally integrated into the undergraduate curriculum to advance Education for Sustainable Development (ESD). Drawing on institutional reports, program documentation, and attendance data from Fall 2022 through Spring 2025, the study examines how the Impact Series promotes short-term …

Ai, The Liberal Arts, And Indigenous Languages: Forming Code Into Language, Christina Graebner

Summer Research Showcase

During the Summer, Spanish Professor Adam Coon and I worked on creating an annotated biography on AI and Indigenous languages for the Digital Well at the UMN Morris Library. Through this project, we have dived into conversations and research focusing on using AI as a translator. In recent years, the conversation around AI has created a surge of studies and research around the relationship between Indigenous languages and artificial intelligence. AI will only continue to expand, and it creates new ways to open communication but creates new ethical guidelines needed to be followed. Our project gathers research articles, podcasts, and …

Evolution Of Global Sea‐Level Rise Projections And Their Uncertainty, Andra J. Garner, Amy Appollina, Jessica Slotter, Gregory G. Garner, Aimée B. A. Slangen, Robert E. Kopp, Benjamin P. Horton

School of Earth, Environment, and Society Faculty Publications and Presentations

For more than 40 years, scientists have projected future sea‐level change. Documenting how sea‐level projections have evolved is vital for tracking progress, uncertainties, and future research needs. Here, we update and analyze a database of global‐mean sea level (GMSL) projections dating from 1982 to 2025, identifying five key findings. First, GMSL projection generation has been concentrated in a small number of developed countries, with 95% of projections produced in the United States, United Kingdom, European Union, or Australia. Second, while GMSL projections for 2050 and 2100 have been published regularly since the early 1980s, only 30 of 103 studies have …

Self Efficacy And Instructional Support Predict Cyber Deception Acceptance In Ics And Ot Cybersecurity, Daniel Ward

Journal of Cybersecurity Education, Research and Practice

Cyber deception can produce high-confidence evidence of unauthorized activity in industrial control systems (ICS) and operational technology (OT), but practitioners must consider the technology useful, safe, understandable, and supported before they will use it. This study reports a secondary quantitative analysis of a deidentified survey of United States-based ICS and OT professionals to determine whether psychological and instructional factors predict adoption readiness and effective utilization beyond education, experience, and sector. Hierarchical ordinary least squares regression with HC3 robust standard errors was conducted on 262 complete cases. The demographics-only model was not significant and explained 2.8 percent of outcome variance. Adding …

Citizen Science As A Long-Term Environmental Baseline: Assessing Impacts Of A Small Dam Removal In Montana, Usa, Bethany Blakey, Natalie Bursztyn

Watershed Sciences Student Research

As dam removals increase in frequency across Europe and North America, most research has focused on the impact of larger dam removals, despite the removal of small dams being much more common. There are hundreds of small dams in Montana, USA, and this research investigates impacts on stream ecology and morphology using citizen science data collected over eight years spanning before and after a 2020 small dam removal in Rattlesnake Creek. We analyzed pebble count grain size distributions and aquatic macroinvertebrate biotic indices from 2017 to 2024 to assess changes in sediment transport and macroinvertebrate population as well as evaluate …

Artificial Intelligence And Social Equities: Navigating The Intersectionalities In A Digital Age (Editorial), Daisuke Akiba, Julie Albright

Publications and Research

This editorial article introduces and synthesizes the Special Issue, “Artificial intelligence and social equities: navigating the intersectionalities in a digital age,” which examines how AI systems intersect with race, ethnicity, and interconnected identity dimensions across global contexts. The eight contributions span healthcare, digital media, higher education, organizational communication, and speculative futures, addressing anti-racist psychiatric algorithms, AI-generated visual disinformation, epistemic injustice between the Global North and South, algorithmically mediated rural–urban divides, culturally untranslated technology transfer, accessibility auditing across the AI lifecycle, and the tension between mechanical objectivity and empathic understanding. Read together, they show that AI is neither inherently …

A Vision For The Future Of Academic Publishing In Sports Analytics, Ryan Elmore, B. Baumer, Brian Macdonald, Gregory J. Matthews, Michael E. Schuckers

Statistical and Data Sciences: Faculty Publications

This article introduces the Journal of Statistics and Data Science in Sports (JSDSS), a Diamond Open Access, peer-reviewed journal. The journal is founded on three core principles. First, our commitment to open access is absolute. Second, reproducibility is critical and fundamental to the journal. Third, we believe sport is a rich and underutilized laboratory for statistical and data science innovation. The aim of the Journal of Statistics and Data Science in Sports is to provide an outlet for original, rigorous, practical, state-of-the-art, reproducible, and peer-reviewed analysis of sports data as well as the data science tools (software, applications, data, etc.) …

Personal Authenticity For Engagement And Transfer In Introductory Cybersecurity Education, Daniel T. Hickey, Ronald J. Kantor

Journal of Cybersecurity Education, Research and Practice

Abstract—This conceptual/theoretical paper explores how personal authenticity might promote generative learning in introductory cybersecurity courses. Generative learning transfers confidently to future educational, professional, personal, and testing situations. This cycle of design-based research addresses the concern that more typical professionally authentic contexts (e.g., hospitals, banks, etc.) may be alien and overwhelming to many students, particularly those in introductory courses and/or from non-professional families and communities. If so, this leads to “inert” knowledge that does not transfer. Personal authenticity is rooted in expansive framing, a modern theory of learning transfer. We reframe expansive framing as personal authenticity to make it …

2026 Final Bpsou Backfill And Cover Material Quality Assurance Project Plan (Qapp), Pioneer Technical Services, Inc.

Silver Bow Creek/Butte Area Superfund Site

This Butte Priority Soils Operable Unit (BPSOU) Backfill and Cover Material Quality Assurance Project Plan (QAPP) provides the procedures and protocols necessary to conduct sampling and geotechnical characterization activities on import borrow and other externally sourced materials that will be used in backfill, cover soil, or engineered caps for upcoming BPSOU remediation activities described in the BPSOU Consent Decree (CD) (referred to as BPSOU CD; EPA, 2020a) Further Remedial Elements Scope of Work (FRESOW) (Attachment C of Appendix D to the BPSOU CD), the 2020 Residential Metals Abatement Program (RMAP) Unilateral Administrative Order (UAO) Amendment (EPA, 2020a), and ongoing maintenance …

Key Technologies And Their Application Of Vertical Large Models For Geological Guarantee In Coal Mining, Liu Zaibin, Fan Tao, Liu Borui, Chen Changyuan, Li Guihong, Li Wei, Jing Xiaotian, Li Xiping, Du Yiming

Coal Geology & Exploration

Background General-purpose large language models (LLMs) have remarkable capabilities in natural language understanding and complex-task reasoning. However, when applied to geological guarantee in coal mining, these models still suffer from several inherent limitations, including insufficient professional geological knowledge, limited insights into industrial terminology, and inadequate integration of engineering logic and rules. Consequently, they face challenges in accurately capturing domain-specific knowledge and reasoning mechanisms required for geological interpretation, early warning of disasters, and decision-making for disaster prevention and control. These issues lead to limited applicability and reliability of general-purpose LLMs. On the other hand, geological guarantee in coal mining involves multi-source …

Selection And Verification Of Optimal Key Parameters Of Ultra-Wideband Radars For Life Detection In Coal-Rock Shielding Environments In Coal Mines, Huang Yuan, Zheng Xuezhao, Wen Hu, Jin Feiyang, Xu Chengyu, Liu Yin, Ding Wen

Coal Geology & Exploration

Objective and Methods Ultra-wideband (UWB) radars can transmit electromagnetic waves that penetrate coal and rock media, thereby addressing the challenge of detecting and localizing personnel trapped in shielding environments during mine collapse. However, key parameters for UWB radar detection are difficult to determine due to complex mine environments, diverse media, and unclear dispersion characteristics. To deal with this issue, this study determined the frequency-dependent dielectric properties of various coal and rock media, as well as human tissues, through experiments and fitting. In combination with actual rescue scenarios, this study established the electromagnetic equivalent model of shielding environments in coal mines. …

Factors Influencing The Build-Up Rate Of Small-Diameter Rotary Steerable Systems In Underground Coal Mines, Yang Dongdong, Li Quanxin, Zhang Youzhen, Chen Long, Chu Zhiwei, Jiang Bici, Wang Bo, Yan Wenchao

Coal Geology & Exploration

Objective Intelligent drilling represents an important part of intelligent coal mine construction. However, measurement-while-drilling (MWD) directional drilling, the core of underground intelligent drilling operations, currently suffers from several limitations, including the high dogleg severity of wellbore trajectories, low drilling efficiency, and poor hard-rock penetration. Rotary steerable system (RSS) technology has emerged as a key approach to addressing these limitations owing to its high-precision control on drilling trajectories. Methods This study systematically analyzed the factors influencing the build-up rate of small-diameter, push-the-bit RSSs using a modified 3-point circle method and the longitudinal beam bending method. Through theoretical modeling, the coupled impacts …

Machine Learning-Based Diagnosis Of Fracture-Induced Lost Circulation And Intelligent Selection Of Lost Circulation Control Fluids, Sun Huan, Zhu Mingming, Wang Jinshu, Yao Lu, Zhou Fangfang, Tan Xuebin, Sun Yan, Qu Yanping, Chen Ning

Coal Geology & Exploration

Objective and Method Drilling operations in the Changqing area face several challenges in lost circulation, including complex types, significantly different characteristics, and difficulty in designing plugging formulations. To address these issues, this study proposes a DSX-Hybrid coupled intelligent decision-making framework, which serves as an integrated scheme to identify lost circulation channels, perform intelligent selection of plugging formulations, and offer recommendations on construction process. In this framework, given the scarcity of labeled log data, the density-based spatial clustering of applications with noise (DBSCAN) is employed to mine latent geological characteristics of unlabeled data. Then, the self-attention convolutional neural network (SACNN) is …

Fast-Track Ode: A Student-Centered, Game-Based Summer Differential Equations Course, Chamila D. Malagoda Gamage

CODEE Journal

This article describes a student-centered and game-based redesign of an Elementary Differential Equations course taught during a six-week summer session. In this compressed setting, students had to move through the standard course content quickly while still developing procedural fluency, conceptual understanding, and confidence with applications. To support these goals, the course used real-time feedback tools, collaborative problem solving, applications connected to students' fields, and mathematical games. The paper describes the course context, major activities, implementation details, and student feedback. Student responses suggest that the activities were well received and helped students stay engaged, participate regularly, prepare for exams, and see …