259890 research outputs found
Sort by
The Cost of Quantum Wars: Small State Standpoint
No full textAs an emerging technology, quantum technology claims to have a tremendous impact on all spheres of social activity, as well as to significantly change the perception of war and peace, diversify the nature of security threats, opening up new, previously unknown layers in them. The development of quantum technologies is of exceptional importance for small states in terms of defense and security, because due to some important features (efficiency, speed, accuracy, dataflow security) it can offset the scarcity of resources (financial, material and human) typical of small countries, enabling the opportunity to address the issues of low efficiency due to the lack of scale effect, and serve as a factor of achieving an asymmetric advantage over potential adversaries. The article presents general considerations on the benefits of this revolutionary technology, map the possibilities of its military application, and outline the prospects for small states that quantum technologies may make possible
Policing the Favelas of Rio de Janeiro: Cosmologies of War and the Far-Right. By Tomas Salem. Cham, Switzerland: Palgrave Macmillan, 2024.
No full textAlice McKay and Crichton McKay Oral History Interview
No full textCrichton and Alice McKay discuss their lives and contributions to the Tampa area. Crichton discusses his family\u27s history, including that of his ancestor James McKay, who came from Scotland and owned a shipping business and his great uncle Donald McKay who was once the mayor of Tampa. Alice discusses her time as a switchboard operator in the Hillsborough County Police Department and her career touring as a country musician. Crichton discusses the evolution of race relations in Tampa, the formation of Ambulance Incorporated of Florida, and the impact of segregation on the services provided
Unveiling Suicide Risk in Bipolar Disorder Communities: Static Graph Analysis and Comparative Modeling on Reddit Networks
No full textSuicide risk detection in online mental health communities poses significant challenges, particularly within bipolar disorder support groups where user interactions are diverse and complex. This study presents a foundational investigation using graph neural networks (GNNs) to model Reddit-based communities through both homogeneous and heterogeneous static graphs. In our homogeneous graph, where all users and interactions are treated uniformly, we extract key centrality measures—degree, closeness, betweenness, eigenvector, and PageRank—to identify influential users and understand the network structure. To capture the richer semantics of the heterogeneous graph, we derive meta-path-based centrality measures that leverage predefined meta-paths to uncover deeper relational patterns among different types of nodes and edges. We generate node embeddings by combining structural features from the graph with textual representations extracted using Sentence-BERT to evaluate baseline GNN architectures for downstream node classification. Our findings reveal distinct structural signals in each graph type and offer insights into risk-related patterns within the community. This work establishes a basis for future extensions incorporating temporal and dynamic graph modeling and emphasizes responsible AI practices, including user privacy and ethical data usage, throughout the research process
Harnessing Molecular Docking for Drug Discovery in the Galectin-3 Protein
No full textMolecular docking is a fundamental technique in drug discovery, used to predict how a ligand interacts with a target protein or enzyme. This research focuses on the Galectin-3 (G-3) protein, a key factor in excessive scar tissue formation, which is prevalent in pulmonary fibrosis patients. One of the greatest challenges in drug discovery is accurately calculating the binding energy between a potential drug candidate and its target. By constructing drug-protein complexes, binding affinities can be determined using Amber. The selection of the best drug binding affinity corrects the malfunction of the G-3 protein
Evaluating Extreme Weather and Climate Adaptation Concerns for Corn and Soybean Farmers
No full textCorn and soybeans are the most economically valuable crops in Minnesota and represent significant land usage in the state with farmers reporting 14.5 million acres of corn and 7 million acres of soybeans planted in 2024. Continued warming and fluctuations between extreme weather events driven by climate change are expected to increase pest pressure, flood damage, and risk of heat stress for crops and farmworkers. Six climate-conscious commercial corn and soybean farmers located predominantly in southern Minnesota were interviewed to understand their perceived extreme weather and climate impacts, farm management responses to these conditions, and risk management concerns for their farm’s future. These semi structured interviews were recorded, transcribed, and underwent a preliminary analysis of key concerns to ground the development of an interactive decision-making tool. The goal of this research is to help farmers and their agricultural advisors assess their agricultural risks due to climate change and the actions they can take to prepare for Minnesota’s extreme weather and climate impacts. We expect that findings from our research will inform our ability to advise on best climate preparedness practices for short term (in season), mid-term (1-3 years), and long term (10-15 year) time horizons
Machine Learning Insights into Multilingual Children\u27s English Reading Achievement: Evaluating the Predictive Power of Kindergarten Factors on Elementary English Reading Achievement
No full textExisting machine learning research that examines factors influencing student’s academic achievement largely focuses on monolingual rather than multilingual students. To address this gap, we employ machine learning to analyze the Early Childhood Longitudinal Study, Kindergarten Class of 2010-11 (ECLS-K:2011) data of 3,542 nationally representative multilingual children from 970 U.S. schools, who were followed from kindergarten (Fall 2010) to 5th grade (Spring 2016). Our study models and compares the predictive power of six key factors in kindergarten (early reading ability, cognitive and language skills, socio-emotional skills, child characteristics, home literacy environment, and school/classroom characteristics) on English reading achievement across primary years. Using Random Forest and Elastic Net, we will predict English reading achievement from 1st to 5th grade based on the kindergarten predictors and analyze how the predictors’ importance evolves over time. Preliminary results indicate strong correlations within cognitive and language skills but weak correlations across other predictor factors. We are currently running machine learning analyses to answer our research questions. We hypothesize that initial English reading achievement will be the strongest predictor but diminishes over time, with other factors such as cognitive and language skills taking dominance in later elementary school. The findings of this study will suggest the significance of each key factor in multilingual children\u27s long-term English reading achievement across primary years
