Showing 521 - 530 of 2039 Items
Identity Formation in the Lebanese-American Christian Diaspora
Date: 2024-01-01
Creator: Matthew Cesar Audi
Access: Open access
- Since the late 1800s, people have immigrated to the United states from Lebanon and Syria, and the community’s racial and ethnic position within the United States has been contested ever since. Previous research emphasizes that while people from the Middle East and North Africa (MENA) are legally classified as “white” on the U.S. Census. However, many people from the region do not identify as white, and they often face discrimination or threats of violence. For people of Arab and Christian backgrounds this is further complicated because they are a part of the majority through their religion, but part of a minority through their ethnic background. In addition, media depictions of MENAs tend to be homogenizing and stereotypical. This thesis attempts to fill a gap in literature on Christian Lebanese American identities by conducting ethnographic interviews with Lebanese-Americans from a variety of generations. It pulls from theories of diaspora and race, emphasizing the importance of context and migration trajectories when understanding Lebanese American identities. My findings demonstrate wide-ranging diversity in how Christian Lebanese-Americans understand and articulate identity due to three major factors: divergent migrant pathways in multiple countries, generational difference given changing racial politics in the U.S., and generational difference given the impacts of U.S. foreign policy in the Middle East upon young Lebanese-Americans.
The combinatorial effects of temperature and salinity on the nervous system of the American lobster, Homarus americanus
Date: 2024-01-01
Creator: Katrina Carrier
Access: Open access
- The ability of nervous systems to maintain function when exposed to global perturbations in temperature and salinity is a non-trivial task. The nervous system of the American lobster (H. americanus), a marine osmoconformer and poikilotherm, must be robust to these stressors, as they frequently experience fluctuations in both. I characterized the effects of temperature on the output of the pyloric circuit, a central pattern generator in the stomatogastric nervous system (STNS) that controls food filtration and established the maximum temperature that neurons in this circuit can withstand without “crashing” (ceasing to function but recovering when returned to normal conditions). I established a range of saline concentrations that did not cause the system to crash, and then determined whether combinatorial changes in temperature and salinity concentrations alter the maximum temperature the system tolerated. Even as burst frequency increased as temperature increased, phase constancy was observed. Interestingly, the system crashed at higher temperatures upon exposure to lower saline concentrations and lower temperatures in higher saline concentrations. I also established the range of saline concentrations that the lobster’s whole heart and cardiac ganglion (CG), the nervous system that controls the lobster’s heartbeat, can withstand. Then, I examined whether exposure to altered salinity and elevated temperature alters the crash temperature of the whole heart and CG. The CG crashed at higher temperatures than the whole heart in each saline concentration. Like the STNS, the whole heart and CG both crashed at higher temperatures in lower saline concentrations and higher temperatures in lower saline concentrations.
Mathematical Notions of Resilience: The Effects of DisturbanceI in One-Dimensional Nonlinear Systems
Date: 2015-05-01
Creator: Stephen Ligtenberg
Access: Open access
Descriptive Catalogue of the Bowdoin College Art Collections
Date: 1895-01-01
Creator: Henry Johnson
Access: Open access
- Includes index.
The Best and the Brightest?: Race, Class, and Merit in America's Elite Colleges
Date: 2017-05-01
Creator: Walter Chacon
Access: Open access
The Body Negotiating Unprecedented Movement
Date: 2024-01-01
Creator: Mei Bock
Access: Open access
- A collection of poems exploring threads including the Lower East Side, immigration, stray animals, art, and Chinese-American identity.
Modulation of the crustacean cardiac neuromuscular system by the SLY neuropeptide family
Date: 2024-01-01
Creator: Grant Griesman
Access: Open access
- Central pattern generators (CPGs) are neuronal networks that produce rhythmic motor output in the absence of sensory stimuli. Invertebrate CPGs are valuable models of neural circuit dynamics and neuromodulation because they continue to generate fictive activity in vitro. For example, the cardiac ganglion (CG) of the Jonah crab (Cancer borealis) and American lobster (Homarus americanus) contains nine electrochemically coupled neurons that fire bursts of action potentials to trigger a heartbeat. The CG is modulated by neuropeptides, amines, small molecule transmitters, gases, and mechanosensory feedback pathways that enable flexibility and constrain output. One such modulator, the SLY neuropeptide family, was previously shown to be expressed in hormonal release sites and within the CG itself and has unusual processing features. However, its physiological effect was unknown. Here, I performed dose-response experiments in the crab and lobster whole heart and isolated CG to determine the threshold concentration of SLY neuropeptides to which these systems respond. The crab isoform had strong, excitatory effects in the crab whole heart and weakly modulated the crab CG. The lobster isoform weakly modulated the lobster whole heart and CG. Surprisingly, the crab isoform exerted large, variable effects on the lobster system, which suggests that SLY neuropeptides, their receptors, and their signaling pathways may be evolutionarily conserved across these two species. This research contributes to our understanding of how neural circuits can generate flexible output in response to modulation. It may also offer insight into processes influenced by peptidergic neurotransmission in the nervous systems of other animals, including mammals.
Basins of Attraction and Metaoptimization for Particle Swarm Optimization Methods
Date: 2024-01-01
Creator: David Ma
Access: Open access
- Particle swarm optimization (PSO) is a metaheuristic optimization method that finds near- optima by spawning particles which explore within a given search space while exploiting the best candidate solutions of the swarm. PSO algorithms emulate the behavior of, say, a flock of birds or a school of fish, and encapsulate the randomness that is present in natural processes. In this paper, we discuss different initialization schemes and meta-optimizations for PSO, its performances on various multi-minima functions, and the unique intricacies and obstacles that the method faces when attempting to produce images for basins of attraction, which are the sets of initial points that are mapped to the same minima by the method. This project compares the relative strengths and weaknesses of the Particle Swarm with other optimization methods, namely gradient-descent, in the context of basin mapping and other metrics. It was found that with proper parameterization, PSO can amply explore the search space regardless of initialization. For all functions, the swarm was capable of finding, within some tolerance, the global minimum or minima in fewer than 60 iterations by having sufficiently well chosen parameters and parameterization schemes. The shortcomings of the Particle Swarm method, however, are that its parameters often require fine-tuning for different search spaces to most efficiently optimize and that the swarm cannot produce the analytical minimum. Overall, the PSO is a highly adaptive and computationally efficient method with few initial restraints that can be readily used as the first step of any optimization task.
A Machine Learning Approach to Sector Based Market Efficiency
Date: 2023-01-01
Creator: Angus Zuklie
Access: Open access
- In economic circles, there is an idea that the increasing prevalence of algorithmic trading is improving the information efficiency of electronic stock markets. This project sought to test the above theory computationally. If an algorithm can accurately forecast near-term equity prices using historical data, there must be predictive information present in the data. Changes in the predictive accuracy of such algorithms should correlate with increasing or decreasing market efficiency. By using advanced machine learning approaches, including dense neural networks, LSTM, and CNN models, I modified intra day predictive precision to act as a proxy for market efficiency. Allowing for the basic comparisons of the weak form efficiency of four sectors over the same time period: utilities, healthcare, technology and energy. Finally, Within these sectors, I was able to detect inefficiencies in the stock market up to four years closer to modern day than previous studies.
Real-Time Object Recognition using a Multi-Framed Temporal Approach
Date: 2018-05-01
Creator: Corinne Alini
Access: Open access
- Computer Vision involves the extraction of data from images that are analyzed in order to provide information crucial to many modern technologies. Object recognition has proven to be a difficult task and programming reliable object recognition remains elusive. Image processing is computationally intensive and this issue is amplified on mobile platforms with processor restrictions. The real-time constraints demanded by robotic soccer in RoboCup competition serve as an ideal format to test programming that seeks to overcome these challenges. This paper presents a method for ball recognition by analyzing the movement of the ball. Major findings include enhanced ball discrimination by replacing the analysis of static images with absolute change in brightness in conjunction with the classification of apparent motion change.