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Statesmanship 101: Recommended Readings by Scholars and Diplomats
No full textThe Paul Simon Public Policy Institute asked leading diplomats and scholars this question: If you were teaching a Statesmanship 101 course to highly motivated undergraduates, what five or so books would you assign them to read? This book answers that question
Field Goals
No full textExternship faculty agree that goal setting is a key part of the externship learning experience, for example, helping students to become self-directed learners by deciding what they want to get out of a learning experience and then taking an active role in working toward that end. But while the literature advocating goal setting is based on years of experience and anecdotal evidence, one thing it is lacking is empirical support. In this paper, I report on my study of the pedagogical tool of student goal setting. This study included the review and coding of hundreds of student reflective journals in order to discover, based on the types of goals students select, where externship programs might turn more of their focus. I also examined hundreds more semester-end journals to understand whether students met their chosen goals during their externship semester, and if not, what can be done to better support future externs to achieve their goals. The study found that the vast majority of externs are meeting at least two of their three enumerated goals, primarily due to the guidance and feedback of their supervisors. The article concludes that more support could be given to externship students, particularly in the areas of remote and hybrid externships and those with less-than-optimal supervision, as well as allowing more credit hours to be worked, in order to help students achieve their externship goals
EDUCATIONAL DATA MINING TECHNIQUES TO ANALYZE STUDENT BEHAVIOR IN A LEARNING MANAGEMENT SYSTEM
No full textThis study identifies course-level patterns of LMS use in fully asynchronous graduate engineering management courses and examines the relationships between LMS use, course type, student satisfaction, and performance. Using the Community of Inquiry (CoI) framework and Moore’s interaction types, LMS metrics were mapped to learner–content, learner–learner, and learner–instructor interactions and clustered with k-means across nine courses. Profiles emerged—content-dominant, instructor-active, and balanced—showing that knowledge-based courses often emphasize content engagement, while application-based courses tend to be more balanced or instructor-driven. Satisfaction correlated most strongly with teaching/social-presence indicators (e.g., feedback cadence, discussion activity), whereas performance aligned with content-oriented engagement. The five strongest predictors of meeting the ≥70% competency threshold were content completion rate, total content views, time on content, quiz attempts, and assignment submissions. Results suggest aligning LMS use with course goals and assessment demands, balancing teaching/social presence for satisfaction with robust content engagement for performance, and providing practical guidance for design and continuous improvement in asynchronous programs
DOMENICO LOSURDO AND THE MARXIST-LENINIST CRITIQUE OF WESTERN MARXISM
No full textDomenico Losurdo’s work on Western Marxism pioneered a systematic critique of Western Marxism from the tradition of Marxism-Leninism, clarifying the points of demarcation between the two Marxisms and tracing the historical origins of the split. While indebted to his groundbreaking work, my dissertation argues that Losurdo’s project leaves certain crucial areas of critique underdeveloped, and others wholly absent. In this dissertation I elucidate what is at stake in understanding how the two Marxisms are to be distinguished. The key demarcation, I argue, is situated in the political positions each tradition takes on socialist and anti-imperialist states, and how such differences are rooted in important philosophical divergences. First, in how universals are to be thought of – either as abstract or concrete; second, in how analysis of the real world is carried out – either dialectically or through the purity fetish. In each instance, I argue that, although inroads are made, Losurdo’s framework of divergences in the “temporalities of communism” is insufficient in understanding the philosophical grounding of how each Marxism approaches key political questions on the state, economy, science and technology, the nation, and the tradition of twentieth and twenty-first century socialism. In addition, elaborating on the framework of comprehensive critique outlined by Georg Lukács in The Destruction of Reason, I demonstrate how Losurdo’s critique of Western Marxism also ignores the dimension of class and social function that, alongside a philosophical-immanent critique, is necessary to carry out a systematic Marxist-Leninist critique of another intellectual tradition. This dissertation thus seeks to refine and expand the project of critique of Western Marxism charted by Losurdo. While critical of its limitations, my project remains within and inspired by Losurdo’s tradition
MACHINE LEARNING-AIDED DESIGN OF HIGH-PERFORMANCE ALUMINUM ROTORS FOR ENHANCED REGENERATIVE BRAKING EFFICIENCY IN ELECTRIC VEHICLES
No full textThis thesis investigates automotive brake system improvements through computational fluid dynamics (CFD), experimental testing, additive manufacturing, and machine learning techniques. The primary objective is to develop realistic simulations and eco-friendly brake materials for enhanced performance and sustainability. Initially, a CFD model was developed using ANSYS CFX to analyze brake rotor interactions with aluminum NASA alloy material supplied by the company. Preliminary tribological assessment on a Universal Mechanical Tester (UMT) confirmed the material suitability. Multiple rotor geometries provided were modeled and simulated under FMVSS 135 hot stop conditions. Friction performance was validated through scaled-down UMT tests using commercial brake pads and an SUV baseline setup. Thermal properties and experimentally derived friction coefficients were integrated into simulations to rank rotor designs by airflow and heat dissipation efficiency. This approach achieved company approval, followed by full-scale die casting production and brake dynamometer testing.Second, 3D printing was utilized to prototype rotor designs for cost-effective evaluation of geometric optimizations informed by UMT and CFD results. Scaled-down experiments with printed rotors allowed assessment of heat dissipation effectiveness prior to committing to die manufacturing.Third, to promote sustainability, a design of experiments (DOE) was performed to optimize 3D printing parameters for recycling leftover rotor material. The objective was to match the tribological properties of virgin powder, enabling eco-friendly reuse.Finally, brake pad formulations containing over 60% recycled powder were developed for electric vehicles using a Taguchi L8 DOE combined with artificial neural network (ANN) and random forest (RF) modeling. A closed-loop process iteratively refined formulations and model predictions against physical testing on UMT. This methodology effectively integrates experimental data with predictive models to accelerate tribological material development
ELECTROCHEMICAL CHARACTERIZATION OF HIGH-ENTROPY ALLOYS FOR ENERGY STORAGE APPLICATIONS.
No full textHigh-entropy alloys (HEAs) based materials have been investigated in this thesis to assess their viability as electrode materials for Electrochemical Double Layer Capacitors (EDLCs). Two different types and morphologies of HEAs were used as electrode material: one of them was FeCoNiCrAg (FCNCA) in the bulk form, and the other was FeCoNiCrMn (FCNCM) in powder form.To investigate the electrochemical performance of these HEAs, EDLC devices were fabricated using an aqueous 6 M KOH electrolyte in a two-electrode system, employing Cyclic Voltammetry (CV), Galvanostatic Charge-Discharge (GCD), and Electrochemical Impedance Spectroscopy (EIS) techniques. One EDLC device was assembled with two bulk pieces of FCNCA as electrodes, which demonstrated good capacitive behavior with ~5.64mFcm^(-2) areal capacitance at 10mVs^(-1). Another device was assembled with electrodes, fabricated using a composite of Multiwall Carbon Nanotube (MWNT) and FCNCM powder. The fabrication of the MWNT–FCNCM composites was necessary since it was difficult to fabricate free-standing electrodes from the as-received FCNCM powder. The areal capacitance was found to be 29.57 mFcm^(-2) at 10mVs^(-1)scan rate. A single device was also prepared only with MWNT electrodes in order to compare the capacitive behavior with the composite device. The MWNT device provides an areal capacitance of ~41.18mFcm^(-2) at 50mVs^(-1)scan rate. The specific energy of the composite device was 3.3×10^(-3) Wh〖kg〗^(-1) and specific power of 1.19 W〖kg〗^(-1) at 0.01 mA current. Overall, these findings show that HEAs can be utilized as the electrode material for EDLCs; however, further refinement of the materials and/or the structure of the electrode needs to be investigated in order to have higher values of capacitance. While the HEA-MWNT composite electrodes\u27 performance was not better than the MWNTs electrodes, the study was informative in demonstrating how careful design of HEA composition, morphology, and integration methods will influence EDLC performances. This research emphasizes the importance of additional HEA optimizations in the future to enable next-generation supercapacitor materials
Synthetic Consciousness: A Global Workspace and Predictive Processing Approach to Autonomous Cognitive Agents
No full textThis case study presents a novel cognitive architecture that integrates Global Workspace Theory (GWT) and Predictive Processing (PP) to model synthetic consciousness in a reinforcement learning (RL) agent. Implemented within the MiniGrid environment, the proposed agent learns to navigate and perform goal-directed tasks under partial observability using minimal supervision. The system incorporates specialized modules for sensation, prediction, emotion, memory, and active policy, unified through a global workspace that enables attentional broadcasting and adaptive decision-making. Learning occurs through continuous prediction-error minimization and free-energy reduction, allowing the agent to form internal representations, update beliefs, and maintain homeostatic balance across drives such as energy, threat, and curiosity. Empirical results demonstrate that the agent exhibits emergent cognitive dynamics including stable prediction convergence, periodic workspace activation, and emotionally modulated behavior, indicative of adaptive and self-regulating intelligence. This work contributes a computational framework for exploring conscious-like learning mechanisms in artificial agents and provides insights into the intersection of cognitive neuroscience and machine learning
EMBEDDED SENSOR DESIGN FOR NON-ROBUST DELAY TESTING
No full textCurrent delay testing methodologies for integrated circuits face critical limitations, asrobust tests remain unachievable for most paths, forcing reliance on Non-Robust (NR) tests that suffer from unrealistic single-fault assumption, the path under test non being sensitized, and vulnerability to hazard-induced errors. This thesis propose a built-in sen- sor to detect delayed hazards and a sensor to diagnose circuits in the presence of those hazards resulting reliable detection. This thesis intends to propose 2 sensors for non-robust test-based monitoring and test- ing. The first sensor discussed is for IC testing with NR tests. This sensor is capable of detecting any full-swing transient that occurs right after the capture clock. The second sensor allows a test point to be sampled multiple times per test vector with high resolu- tion to extract a sequence of events it undergoes during the sensor’s sampling interval. These data will be used to diagnose IC defects and for monitoring ICs with deep learn- ing. The proposed sensors can effectively address current challenges in IC testing, diagnos- ing, and monitoring, leading to improved product quality and reliability. The introduced IC testing and monitoring approaches significantly enhance reliability, reducing the test escapes, surpassing the limitations of conventional statistical methods
Surface water and groundwater interactions within the riparian wetlands of the Mississippi River Dogtooth Bend Southern Illinois
No full textRiparian wetlands serve as transition areas connecting terrestrial and aquatic ecosystems, providing vital functions in maintaining water quality and habitat for diverse array of organisms. A comprehensive understanding of the interaction between surface water (SW) and groundwater (GW) in riparian wetlands is crucial for the management of these ecologically significant habitats. However, the interactions between SW and GW are often only partially constrained particularly with respect to their effects on water chemistry and nutrient cycling.In this study I utilized geochemical and hydrological approaches to examine the dynamics of surface water and groundwater interactions within the riparian wetlands of the Dogtooth Bend, a region along the Mississippi River in Alexander County, Illinois. The study involved the collection of surface water and groundwater from six riparian wetlands namely Grand Lake (GL), Bayou Stony (BS), Lake Milligan (LM), Big Cypress (BC), Central Bend (CB) and Santa Fe (SF), from the August 2022 to November 2023. Additional surface water samples were collected from the Mississippi River at Thebes Illinois and the Horseshoe Lake (HSL), located northwest to the studied wetlands in southern Illinois. To evaluate the spatial and temporal hydrological and geochemical trends, various parameters were measured both in the field and in the laboratory. In the field, I assessed the presence of surface water and groundwater and when the water was present, I measured the water level elevation in the wells. Collected water samples were assessed for physico-chemical attributes, including pH, temperature, dissolved oxygen (DO), oxidation reduction potential (ORP) using the YSI 626904 ProDSS instrument sensor. In the laboratory, I measured the concentration of ammonia (NH3), hydrogen sulfide (H2S), ferrous iron (Fe2+), and alkalinity using Hach® methodologies. The concentration of chloride (Cl-), sulfate (SO42-), and nitrate (NO3- ) was determine using Thermo Fisher Integrion HPIC system in unison with a Dionex AS-DV auto sampler. Quantification of total organic carbon (TOC), total nitrogen (TN) and total phosphorus (TP) concentrations was carried out at the J.F. Costello Confluence Field Station Environmental Chemistry Laboratory at the National Great Rivers Research and Education Center located in Alton Illinois. The TOC values were determined by analyzing the unfiltered water samples via a VarioTOC select combustion analysis while the TP and TN measurements were made utilizing the methods described in the National Environmental Methods Index ID I-4650-03. The stable isotopes of water (δ2HVSMOW and δ18OVSMOW)) and dissolved nitrate (δ15NNO3- and δ18ONO3- ) were determined by the UC Davis Stable Isotope Facility using standard methods. To provide a more robust data set from which to inform my interpretations of the hydrological and geochemical trends in the riparian wetlands at Dogtooth Bend, I added to my data previously collected from 2021 to 2022 at the same locations by Genz (2023). The isotopic signature of water samples from the SR-SW, CB-SW and SF-SW are similar to those of local precipitation suggesting a meteoric origin. Nitrate (NO₃⁻) concentrations show varying correlation patterns across Dogtooth Bend, indicating site-specific nutrient transfer pathways. Strong correlations (SR-GW and SR-SW, r = 0.89) suggest active surface–groundwater interaction, while negative correlations (e.g., LM-GW and LM-SW, r = -0.44) imply limited connectivity and distinct nitrate sources. The isotope data analysis for δ15NNO3- and δ18ONO3- shows that the sources of nitrate were fertilizers, precipitation and soil organic N. TN and TP concentrations varied significantly among sites, with correlation analyses indicating possible links to mineralization, redox cycling, and Fe²⁺ interactions. Notably, strong correlations between TP and Fe²⁺ (r = 0.82) highlight redox-driven phosphorus mobilization, while TN correlations varied widely, reflecting localized biotic and abiotic nitrogen cycling. Cl⁻ concentrations shows high statistical significance between surface and groundwater at sites such as Tbebes vs. GL-GW (P = 0.00) and THEBES vs. SR-GW (P = 0.00), while showing no significant differences with CB-GW (P = 0.89), suggesting localized recharge or mixing patterns. Similarly, SO₄²⁻ showed strong differences across some sites (GL-GW and BC-GW, r = 0.45), with weak positive correlations indicative of redox-driven transformations. This overall complex interplay of external influences, direct hydrological pathways, and active biogeochemical processes, including coupled Fe2+/TP cycling ultimately drives the observed geochemical variations and similarities in GW and SW across the Dogtooth Bend wetlands. These findings highlight the importance of integrated surface water and groundwater management in wetland conservation, as site-specific processes can influence nutrient transport, water quality, and ecological function
