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Phase Transitions in an Ising Model of Agent Expectations in Financial Markets: Analytics and Numerical Results in One and Two-Dimensional Network Topologies
No full textWe cite correspondences between dynamics in competitive markets and information theory in the objective of recovering signal from noisy information sequences. In financial markets, this objective has been examined as recovering signal on phase transitions between ordered and disordered states of agents in the market. These transitions have been indicated to denote critical points in time series of market price. Although there is a noteworthy background in information theory in the study of the dynamics of the Ising model in this manuscript, we pursue a different modeling approach. Whereas phase transitions in a multicomponent model of market states have previously been studied with numerical methods, we provide an analytical demonstration that a multicomponent model as an Ising analogue can evidence phase transitions
Book Review: Dismantling Conspiracy Theories: Metaliteracy and Other Strategies for an Information-Disordered World, Katie Greer, Stephanie Beene
No full textPromoting Participation and Self-Efficacy Among Youth with Disabilities in Community-Based Vocational Education
No full textOccupational therapy, with its focus on participation, contextual adaptation, and strength-based practice, is uniquely suited to support the growth and inclusion of youth in transition (Angell et al., 2018; Cahill et al., 2020; Carroll et al., 2025). Research indicates that transitional-aged youth (TAY) with disabilities, ages 14-24, are less likely to graduate from high school, pursue postsecondary education, or live independently (BLS, 2024; Cheng & Shaewitz, 2022; Cook et al., 2015). These disparities are compounded by occupational deprivation, or limited opportunities for meaningful participation, which can negatively impact long-term self-efficacy and belonging (Arbesman & Logsdon, 2011; Cahill et al., 2020). With ongoing cuts to federally funded transitional education, these gaps are likely to widen.
The goal of this capstone is to assist a non-profit community organization which offers opportunities for animal assisted interventions, in expanding their vocational education program for TAY with disabilities with an emphasis on participation and self-efficacy. This project explores the question: How does engaging in community-based vocational education influence occupational participation and self-efficacy for TAY with disabilities?
Grounded in the Canadian Model of Occupational Participation (CanMOP), which centers occupational participation as the core focus of practice, this program was evaluated and expanded to emphasize autonomy, meaning, and engagement across levels of context (Egan & Restall, 2022). By incorporating an occupational therapy lens, the program aims to enhance goal-directed engagement, accessibility, and self-determined skill-building while promoting sustainability and inclusivity (Cook et al., 2015). Objectives include identifying evidence-based strategies to enhance participation and self-efficacy, creating and supporting staff training to build capacity, and applying the CanMOP to guide inclusive program development. Together, these objectives support the development of a sustainable, evidence-informed program model that advances participation and self-efficacy for TAY with disabilities
The Implementation of TransUNET Infrastructure for Gel Band Image Segmentation
No full textMolecular biology experiments traditionally produce nucleic acid or protein products. When an experiment produces these molecular products, the first step in determining the success is to validate the products. To accomplish this for nucleic acids, a charge separation-based technique called gel electrophoresis is used. The result is a gel that can be imaged and analyzed by the combination of specialized tools and scientific expertise. Given the potential expense and difficulty associated with this analysis, computer vision can be utilized to generate a more efficient pipeline. This project aims to adapt an image segmentation technology framework called TransUnet to validate and analyze the results of nucleic acid experiments. Specifically, the goals of this project are to identify bands in gel electrophoresis. The key components of this pipeline include data acquisition, pre-processing, data validation, establishing training/testing sets, and concentration calculation
Modelling Cognitive Brain Processes
No full textAgent based systems can simulate a variety of complex environments, social behavior andcognitive processes. Since the heart of these agents, Large Language Models (LLMs) are trainedon data generated by humans, they offer a route to seamless human cognition modelling. In thiswork, we build simulations of the visual cortex using the dorsal-ventral model of cognitive visionand further extend it to model V1–V6 visual processing subsystems, that perform functions likecontour detection, binocular disparity, motion perception and color perception that support visualprocessing in the human brain. To implement this, we developed a framework, Topology Mangerto enable deployment and management of LLM agents with customizable interaction patterns,memory management, and support for various LLM backends. We also finetuned a Phi-3.5-mini-instruct model on the ReClor logical reasoning dataset, achieving a prediction accuracy of 89%,which will later be used to maintain coherent reasoning across the simulated brain regions. Usingthese components, we construct a system that predicts human visual attention given an inputimage, achieving a precision score of 0.6 on a standard visual saliency dataset. This workdemonstrates the potential of multi-agent LLM systems in modeling brain processes andcontributes toward building cognitively grounded AI architectures
AUTOMATED CORAL DETECTION USING MASK R-CNN FOR RESTORATION MONITORING AT THE FLORIDA AQUARIUM
No full textCoral reef ecosystems have been drastically declining due to environmental stressors like climate change, diseases, and pollution, creating a need for restoration efforts. In the United States of America, the Florida Aquarium has been making a great effort to restore coral reefs through its coral propagation programs. This project demonstrates the potential to make the Florida Aquarium restoration efforts easier and faster by using Mask R-CNN for consistent, scalable, and automated coral identification. Deep Learning Models, such as Mask R-CNN and other segmentation frameworks, are effective tools for coral recognition as well as recording coral’s morphological changes. In this project, using the Coral Vision platform, a Mask R-CNN model was trained to detect coral fragments in images that came from a coral nursery at the Florida Aquarium. Out of 360 provided coral images, 30 were manually masked for training. The model showed high detection accuracy for the majority of the images, with confidence scores ranging between 97% to 100%. Some images, however, showed segmentation inconsistencies, indicating room for model refinement
The (Birthweight) Gains From Trade: Chinese Imports and Infant Health in Sub-Saharan Africa
No full textA growing literature has documented broad negative impacts of Chinese imports to advanced economies, mainly due to the competition of these imports with local production. However, for countries with a smaller manufacturing sector and which have not experienced a structural transformation away from agriculture, Chinese imports could have positive effects. We find evidence supporting this claim using a sample of over 350,000 births from 25 countries in Sub-Sahara Africa. Our identification compares the birthweight of biological siblings born at different levels of Chinese imports to their country. We find that an increase in Chinese imports of $100 (constant USD) is associated with an increase in birthweight by almost 14 g. Gains are larger for female children, children born to lesser-educated mothers and from imports of health- and food-related goods
AI Test Modeling for Computer Vision System—A Case Study
No full textThis paper presents an intelligent AI test modeling framework for computer vision systems, focused on image-based systems. A three-dimensional (3D) model using decision tables enables model-based function testing, automated test data generation, and comprehensive coverage analysis. A case study using the Seek by iNaturalist application demonstrates the framework’s applicability to real-world CV tasks. It effectively identifies species and non-species under varying image conditions such as distance, blur, brightness, and grayscale. This study contributes a structured methodology that advances our academic understanding of model-based CV testing while offering practical tools for improving the robustness and reliability of AI-driven vision applications
(De)centralized Water Futures: Key Dimensions of Infrastructure, Governance, and Operations
No full textWater system centralization and decentralization have variously been promoted as key to achieving household water security and Sustainable Development Goal 6.1. We argue that the lack of specificity with which scholars and practitioners use the terms centralization and decentralization limits our understanding of different water system configurations and their impacts. In this Primer, we provide a framework for thinking about levels of (de)centralization across three linked system dimensions: infrastructure, governance, and operations and maintenance. We encourage those analyzing water systems to characterize (de)centralization with respect to these multiple dimensions, as well as the system\u27s broader political-economic and hydro-climatic contexts. Emphasizing the importance of delineating the scale of analysis, we highlight distinct system configurations and the prevalence of hybridity. Increased specificity about dimensions and scale can clarify how the character of, or changes to, a given system impact users, which is critical to assessing their implications for water security, sustainability, and equity. We conclude with recommendations for future research to analyze the opportunities and challenges associated with different water system configurations. This article is categorized under: Human Water \u3e Water Governance
Accelerating the fusion workforce in the USA
No full textThe fusion energy research and development landscape has seen significant advances in recent years, with important scientific and technological breakthroughs and a rapid rise of investment in the private sector. The workforce needs of the nascent fusion industry are growing at a rate that academic workforce development programs are not currently able to match. This paper presents the findings of the Workforce Accelerator for Fusion Energy Development Conference held in Hampton, Virginia, United States of America (USA), on 29-30 May 2024, which was funded by the National Science Foundation of the USA. A major goal of the conference was to focus on bringing public and private stakeholders together to identify opportunities for partnership in fusion research and education with the goal of meeting the needs for a talented and diverse workforce. Representatives from industry, academia, and national laboratories participated in the conference through the preparation of white papers, presentations, and group discussions, and the production of recommendations to address the challenges facing the fusion workforce in the USA