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DGAA*: A Dynamically Repaired Double-Strand Break Genetic Algorithm with Pheromone Guided A*
No full textPath planning is an integral part of robotics that enhances their function: Mobile robots have an increased range of utility as compared to their stationary counterparts, but what good is their mobility if it is found to be inefficient? A* algorithm is a traversal method in which the immediate best position from the current spot is chosen without regard for the global configuration of the map, ensuring a fast path creation yet not the shortest path. Ant Colony Optimization (ACO) uses multiple agents, known as ants, to traverse the map to look for the best path, leaving pheromone trails whose influence depends on how many times said path is traveled. Genetic Algorithm (GA) is inspired from the biological processes of crossovers, mutations, and selection: an initial set of paths are produced, being subjected to mutations and combining with others to produce offspring with the hope the offspring is better than either of its parents. The best paths from the batch are then taken into the next generation, and this process repeats until either the maximum number of generations are met or the desired result is attained. This project introduces a method that combines the decisiveness of A*, the exploration factor of ACO, and the refinement of a more biologically accurate GA to procure a more structurally sound path planning method. DGAA*, the algorithm in question, is compared with the aforementioned three methods in environments of varying complexity to see if it is a viable alternative to other traversal algorithms. Results show that DGAA*, with the base of GA, both escapes the suboptimal routes A* gets trapped in and has a controlled global exploration factor, to reduce needless searches, guaranteeing that mobile robots using this technique travel efficiently
The Impact Strength of Parts Created Using Fused Deposition Modeling
No full textAdditive manufacturing (AM) has been increasingly popular in today’s industries, and a popular method within the AM sector is Fused Deposition Modeling (FDM) or commonly known as 3D printing. Despite many benefits offered through this process, issues with mechanical performance, optimization, and inability to predict failure of the manufactured parts are preventing FDM from being widely used. Raster angle and infill density are two of the most important variables in influencing the mechanical strength of components that are subjected to impact loads. This study aims to examine how the above two factors affect the impact of resistance of parts made of polylactic acid (PLA) and polyethylene terephthalate glycol (PETG). Specimens were produced with different infill densities of 25, 50, 75, and 100%, while the raster angle was varied across 0-90°, 30-60°, and 45-45°, resulting in a total of 12 unique arrangements. The impact strength of each sample was tested to analyze the relationship between infill density, raster angle, and material performance under impact conditions. The preliminary results indicate that an increase in infill density correlates with higher impact resistance, as specimens with higher infill density demonstrate better fracture behavior. Furthermore, among specimens with the same infill density, those manufactured with a raster angle of 30-60° exhibited better impact resistance. This indicates that optimizing the raster angle and choosing the right infill density could improve the energy absorption and stress distribution for parts produced using FDM. This result provides important understanding regarding the mechanical enhancement of FDM components, which may enhance their usability in sectors that demand durable, impact resistant materials. Future studies will investigate the fabrication and optimization of powder-based feedstocks for metal and ceramic additive manufacturing, focusing on compounding techniques, particle size distribution, and material characterization
Lord Chesterfield and the Licensing Act of 1737
No full textThe history of theatrical censorship in Britain is extensive and encompasses nearly 400 years of imperial, political, social, and cultural evolution. The shifting attitudes of authority toward artistic expression have shaped the development of theater, and at the center of this scholarship lies Robert Walpole. As Britain\u27s first Prime Minister, Walpole played a pivotal role in institutionalizing theatrical censorship, using his political influence to suppress satirical and oppositional works that directly criticized his government. Using the Licensing Act of 1737 to censor theater and curb the growing influence of political discourse in the theatrical arena, Walpole forced playwrights and theater managers to submit their works for approval before public performance. Philip D. Stanhope, the 4th Earl of Chesterfield, stood against the Act as the sole prominent Parliamentary opponent, denouncing it as a dangerous encroachment on civil liberties.
My research explores why Lord Chesterfield (Philip Dormer Stanhope, 4th Earl of Chesterfield) was the only major opponent of the Licensing Act of 1737. Through analysis of primary sources such as pamphlets, newspapers, legislation, Chesterfield’s literary works, speeches, and periodical essays from the period, my research seeks to uncover the political, ideological, and personal motivations behind Chesterfield’s opposition. Secondary scholarship on the topic will also help bolster my argument, and by examining the works of individuals such as Vincent Liesenfeld, Leonard W. Connolly, P.J. Crean, and Julia Swindells, I aim to contextualize the reasons behind Chesterfield’s opposition to the Licensing Act of 1737 within the broader framework of eighteenth-century British politics and society
Evasive Steering: Left vs. Right Directional Preference in Automated Vehicles
No full textSince its first debut in the 20th century, self-driving technology has seen an increase in demand. Accessibility to control has proven to be a principal influence on driver’s comfortability in self-driving vehicles. In response to this need for control, our study focused on the steering direction exhibited by drivers during takeover for self-driving vehicles approaching a potential risk of crashing. More specifically, we investigated which direction—left or right—people are greater likely to steer when they do not have their hands on the wheel prior to taking control. We expected that more people will steer the vehicle right than left when approaching an obstacle, due to driving rules and regulations in the United States, such as right–side driving of the road and the stability of the right–hand turn. To test this hypothesis, participants watched a series of prerecorded driving simulation videos that portrayed various instances in which the driver would need to take over control to avoid a collision. We are currently collecting the data
Uncovering Genetic Patterns in Salmonella enterica Using K-Means Clustering
No full textClustering techniques play a crucial role in genomic data analysis by uncovering hidden patterns and relationships within large datasets. This study applies k-means clustering to Salmonella Enterica genomic data to classify and analyze genetic variations among different strains. Salmonella Enterica is a significant pathogen responsible for foodborne illnesses worldwide, and understanding its genetic diversity can aid in tracking outbreaks and improving public health responses. Our approach involves preprocessing genomic sequence data, extracting relevant features, and applying k-means clustering to group similar strains based on genetic similarity. The results reveal distinct groupings that may correspond to variations in virulence, antibiotic resistance, or geographic origin. These insights contribute to a deeper understanding of Salmonella Enterica population structures and could enhance epidemiological surveillance efforts. This mentored research leverages unsupervised machine learning to generate new knowledge in bacterial genomics. By applying computational clustering methods to pathogen data, this study provides an innovative approach to classifying Salmonella Enterica strains, which may have implications for public health monitoring and outbreak prevention
KWAD III- KSU all Weather Autonomous Drone
No full textThe KSU all Weather Autonomous Drone III or “KWAD III” is created as part of a research project to design and develop a water-resistant Unmanned Aircraft System (UAS). It is the third rendition of the Kennesaw all Weather Drone projects. This research is in collaboration with Ultool, LLC and contracted by the Department of Defense. The capabilities of KWAD III allow it to travel up to a range of 15km with the use of GPS navigation while carrying a payload of 2kg. The UAS’s eight powerful motors and sturdy frame have allowed it to carry the Federal Aviation Administration’s payload limit of over 22kg. It’s intended flight is a straight line beyond the line of sight. In order to increase efficiency and range, KWAD III has a water-resistant aerodynamic housing nicknamed “Aeroshell.” Team members have conducted several Computational Fluid Dynamics (CFD) analyses to continue to optimize the Aeroshell and reduce drag. The Aeroshell along with KWAD III component mounts utilize 3D printed parts to test and finalize these parts. The customizations made to the frame of KWAD III allow it to fit components in the most optimal manner to minimize weight and drag. The simulation and flight tests of this UAS have proved that it will be capable of its mission and more
Lagged Functional Connectivity Reflects the Cognitive Impairment of MCI
No full textThe dorsolateral prefrontal cortex is an important part of the frontal cortex in mild cognitive impairment and Alzheimer’s disease. In our study, we measured the electroencephalogram and neuropsychological tests in 60 older adults with mild cognitive impairment and 63 age-matched controls. Functional connectivity was then estimated between Brodmann areas 9 and 46 and several other brain regions using Low Resolution Electromagnetic Tomography (LORETA). Functional connectivity was estimated between 10 brain regions, including hippocampus and dorsolateral prefrontal cortex. We then identified the 10 most promising measures of MCI status based on differences between groups and entered them into a logistic regression. Five of 10 were statistically significant predictors, with the strongest predictors being the lagged phase coherence between left and right hippocampus and the lagged connectivity between left visual area MT and right Brodmann Area 9, a region of the dorsolateral prefrontal cortex. The classification accuracy of the model was 75.6%
Class and Molly Culture in Eighteenth-Century London
No full textThis research focuses on the connections between eighteenth century “molly” culture and the burgeoning working class. In the eighteenth century, “molly” houses were a haven for men who would be considered homosexual or queer today. Throughout the many primary sources I found that mention molly houses, I have noted that most if not all the men in these houses were laborers, common people, and small business owners. This is despite men in the upper echelons also seeking homosexual interactions. The research process includes an extensive review of primary sources including trials that are about molly culture. The process also includes a historiography of molly culture and how it connects to the class culture of eighteenth century London. The expected results are likely to be that their working class “mollies” of London found acceptance of their queer culture in “molly” houses, in contrast to a place where upper class men with the same feelings would attend
Automating Misinformation Detection: A Neural Network Approach to News Classification
No full textThe rapid spread of misinformation presents a significant challenge in the digital age, influencing public opinion and shaping societal narratives. This research aims to develop a machine learning model leveraging Natural Language Processing (NLP) to automatically classify news articles based on their misinformation type. The approach consists of three key steps: 1) collecting and utilizing publicly available datasets containing labeled misinformation articles, such as the Fake News Challenge dataset, the LIAR dataset, and Kaggle’s Fake and Real News dataset; 2) fine-tuning pre-trained transformer models like BERT and BART to detect and classify news articles into categories including Clickbait, Satire/Parody, Conspiracy Theories, Biased News, and Objective News; 3) implementing the classification model using TensorFlow and PyTorch to ensure scalability and efficiency. The anticipated outcome is a robust and automated misinformation detection system that enhances users\u27 ability to critically assess news content. This research contributes to ongoing efforts in combating misinformation, with future iterations expanding detection capabilities to multiple languages and social media platforms
SHQIPTARI: THE ORIGINS AND DISTRIBUTION OF POPULATION IN ALBANIA
Get PDFFrom a hearthland in the eastern Caucasus, the Albanians migrated by two routes and three waves. The Gheg moved north of the Black Sea to the Illyrian culture area, and the Tosc through Anatolia. Modern censuses of the People\u27s Republic of Albania are improving in content and accuracy and population movement over the decade 1960-1969 shows marked trends towards urbanization concentrated in the rrathe of Vlore, Berat, Lushnje, Durres and Tirane. The old are left in the highlands, and despite wartime losses, males still outnumber females