35688 research outputs found
Sort by
Ella\u27s Residence
No full textThis was a three-part project for an Interior Design class. We started with a basic floor plan. We inked that then moved on to creating the space by adding furniture details. The finishing touch was to add the electrical on top in red ink so the viewer can lift up one page and see the layers below.https://scholars.fhsu.edu/sacad_art/1013/thumbnail.jp
British Conduct is a Major Reason of Ireland Resist
Full text linkFor centuries, Ireland was under Britain\u27s rule, the struggle for Irish people’s independence, driven by a deep desire of self governance, economic justice, and cultural identity , has never stopped.
During the years of Britain domination, Irish people faced political repression, economic exploitation, and religious discrimination, specifically against its Catholic majority. These can be symbolled by The Great Famine (1845–1852) and British neglect further fueled resentment.
In 19th and 20th centuries, nationalism raised in Ireland, along with symbolic events like Easter Rising(1916) and the Irish War of Independence(1919–1921). These event solidified the demand of freedom and independence among Irish people.
This research focus on explore the historical, social, and political factors lead Irish people to struggle for freedom and independence form the rule of British Empire. Through analysis of existed research papers
22 Years Lost: The Story of Paul House
Full text linkThis project outlines the wrongful conviction of Paul House and his eventual exoneration after 22 years on death row in Tennessee. The murder of Carolyn Muncey occurred in 1985 in Luttrell, Tennessee and House was an immediate suspect with the finding of multiple pieces of forensic evidence. Namely, it was ABO serology testing that was used to tie House to the crime. Advancements in this form of testing showed that House was innocent. It was in 2006 that the Supreme Court ruled in favor of House’s exoneration due to the flaws in the original forensic evidence. This project exemplifies the important role that forensic evidence can play in convictions, the importance of evidence examination, and the importance of valid legal advocacy
Self-Defense or Served Justice? Kirstin Lobato Case
Full text linkKirstin Lobato spent 16 years in prison for the crimes of voluntary manslaughter and sexual penetration of a dead body. The Innocence Project took on her case and helped to exonerate her in 2017. After critical evidence that was withheld during the original trial was brought forward, Lobato was retried and had her conviction reversed. She was awarded $34 million in retribution for her time in prison due to her mistrial
Deception and Domination: A Comparative Analysis of Toxic Leadership in Scar: The Lion King and Mother Gothel: Tangled
Full text linkAbstract: This project revolves around toxic leadership through a comparative analysis of two infamous Disney villains: Scar from The Lion King and Mother Gothel from Tangled. Through the use of qualitative media analysis, this study examines how both characters manipulate power, as well as exploit followers, and exhibit various destructive leadership traits. Referencing Haslam et al.’s (2020) The New Psychology of Leadership, the analysis focuses on their particular leadership contexts, and their use of deception and control to create a sense of fear within those they lead. Scar’s leadership is characterized by manipulation, betrayal, and an obsession with power. In contrast, Mother Gothel employs psychological manipulation, gaslighting, and extreme emotional abuse to maintain control. Despite their different leadership styles; some being oppressive and the other being, controlling—both vividly demonstrate the dangers of toxic leadership. This project highlights the variety of ways that harmful leadership creates and the consequences it has on their followers and society. This project was completed for PSY 350/LDRS 490: Psychology of Leadership
A Geometric Morphometrics Analysis of the Limb Morphologies of Mosasaurs and Its Palaeoecological Implications
Full text linkMosasaurs were a major component of Late Cretaceous marine ecosystems, being the last Mesozoic radiation of secondarily aquatic tetrapods into diverse niches.
To shed light on the diversity and evolution of limb morphology in this clade, geometric morphometrics analyses were performed on seven skeletal elements from the forelimb and hindlimb. This study tested the hypothesis tested that mosasaurs living in similar environments would exhibit similar morphologies.
After analyzing datasets containing 29 taxa, the observed patterns did not support the hypothesis that habitat was the primary variable influencing morphology. The recurrence of a pattern, the plotting of plioplatecarpine mosasaurs closer to mosasaurine mosasaurs than tylosaurine mosasaurs, which is contrary to phylogenetic expectations in multiple datasets does suggest that relatedness between clades alone does not explain the disparity. In addition, after observing multiple allometric trends for the skeletal elements, a pattern emerged of larger specimens having proportionally less surface area for muscle attachment. It is hypothesized that this is a consequence of differing styles of locomotion
Deep Learning-Based Ensemble Two-Step Classification of Medical Images Using CNN Architectures and Ensemble Methods
Full text linkBreast cancer remains one of the most common cancers amongst women globally. Early detection is crucial for improving survival rates. While mammography is widely used and an effective imaging technique, it can sometimes yield false positive or false negatives. Mammogram interpretation is highly operator-dependent, introducing variability and the potential for diagnostic errors. Additionally, mammographic images have limitations, such as low contrast in breast tissue and overlapping structures that can obscure lesions or mimic abnormalities. These limitations can lead to unnecessary biopsies or delayed diagnosis. These challenges highlight the needs for advanced and data driven diagnostic tools to support and enhance current screening practices.
In recent years, machine learning has emerged as a transformative force in the health care industry offering innovative solution to complex medical challenges. One of the most promising applications of this technology is in the early and accurate detection of breast cancer particularly through the mammography image analysis. Machine learning algorithms, especially deep learning models, have shown significant potential in identifying patterns in mammography that may indicate abnormal cases. This study utilizes the power of deep learning particularly Convolutional Neural Networks (CNNs) to enhance the classification of breast ultrasound images.
This research proposes a novel two-level classification approach. At the first level classification, the system distinguishes normal from abnormal cases. Next, the second level classification, it further classifies abnormal cases into benign or malignant tumors. To address challenges such as limited and imbalanced datasets, the study incorporates comprehensive preprocessing techniques including contrast enhancement via Contrast Limited Adaptive Histogram Equalization (CLAHE) and region of interest (ROI). The preprocessing step were evaluated separately and in combination to assess their individual contributions. Additionally, data augmentation is utilized to improve model generalization. To further enhance performance and robustness, multiple CNN architectures are combined using an ensemble mechanism. Soft voting and hard voting leverage the strengths of diverse CNNs reducing individual model biases and improving overall accuracy.
The main contributions of this research are: (1) the design of a two-level classification system (2) the utilization of multiple preprocessing techniques to improve image quality, (3) the application of voting ensemble learning to improve predictive stability and accuracy, and (4) an extensive evaluation on the BUSI dataset. This breaks down the complex diagnostic task into smaller, manageable steps to allow for more accurate comparisons, analysis, and improvements at each stage.
The first preprocessing step involved the application of CLAHE. In the first level classification (normal vs abnormal), performance of various models has been evaluated. The models achieved an 82.05% accuracy for AlexNet, 84.62% for DenseNet, 85.26% for Inception, 82.69% for MobileNet, 82.05% for ResNet and 82.69% for VGG16. The accuracy of soft voting is 96.20% and accuracy of hard voting 92.09%. In the second level classification (benign vs malignant), the models achieved an 75.00% accuracy for AlexNet, 80.47% for DenseNet, 35.94% for Inception, 41.41% for MobileNet, 35.94% for ResNet and 82.81% for VGG16. The accuracy of soft voting is 90.84% and hard voting is 85.76%.
The second preprocessing step involved the ROI was implemented. This step helped isolate the regions containing potential abnormalities, such as tumors, while filtering out irrelevant areas. After the application of ROI (normal vs abnormal), the system evaluated the performance of various models. In the first level classification (normal vs abnormal), the models achieved an 82.69% accuracy for AlexNet, 82.69% for DenseNet, 83.97% for Inception, 87.18% for MobileNet, 82.69% for ResNet and 82.05% for VGG16. The accuracy of soft voting is 100.00% and hard voting is 98.08%. In the second level classification (benign vs malignant), the system evaluated the performance of various models. The models achieved an 75.00% accuracy for AlexNet, 80.47% for DenseNet, 35.94% for Inception, 41.41% for MobileNet, 35.94% for ResNet and 82.81% for VGG16. Soft voting achieved 89.15% and hard voting achieved 80.95%.
The final step in the preprocessing stage combines both CLAHE and ROI. The combination led to an improvement in classification performance. In the first level classification (normal vs abnormal), the models achieved an 91.77% accuracy for AlexNet, 92.72% for DenseNet, 95.57% for Inception, 96.52% for MobileNet, 76.90% for ResNet and 91.14% for VGG16. The accuracy of soft voting is 90.88% and hard voting is 93.99%. In the second level classification (benign vs malignant), the system evaluated the performance of various models. The models achieved an 87.69% accuracy for AlexNet, 99.23% for DenseNet, 96.92% for Inception, 93.08% for MobileNet, 94.62% for ResNet and 97.69% for VGG16. Soft voting achieved 96.92% and hard voting achieved 97.69%.
These results indicate that every preprocessing step contributed to an improvement in classification accuracy across all models. This study demonstrates that a two-level, ensemble-based deep learning system can significantly improve the diagnostic accuracy of breast ultrasound images, offering a promising tool to assist radiologists and support early detection efforts, particularly in resource-constrained healthcare environments
Assessing Awareness and Use of Library Services within Chinese Partner Academic Programs: A Case Study Developing and Analyzing Bilingual Surveys
Full text linkA library at a regional comprehensive university created a Global Access Committee to provide support for students and faculty at two international partner campuses in China. In 2022, the committee sent out a survey to these students to assess their awareness, usage, and satisfaction with library resources and services. Students reported high awareness, usage, and satisfaction with online library resources, instructional materials such as guides and tutorials, and the library’s Ask A Librarian service. However, there was a discrepancy between students’ self-reported usage and usage statistics collected by the library. The responses may have been influenced by cultural differences and by the translation of the questions and responses into and out of Mandarin Chinese. However, the library was able to make positive changes based on the survey results.
https://pal-ojs-tamu.tdl.org/pal/article/view/720
Eisenhower, Wilson, and Professional Baseball in Kansas, Revised
Full text linkEver since General Dwight David Eisenhower mentioned in 1945 that he had played professional baseball under the pseudonym Wilson sometime after his 1909 graduation from Abilene High School, there have been attempts to document this assertion. Yet, he offered little detail for researchers to follow, not even the team or year. If true, however, it has been speculated this would have made him ineligible for intercollegiate competition in 1911–1915 while he attended the US Military Academy at West Point, New York, where he played on the football team. Thus, interest in the story has persisted. Newspaper accounts of baseball mention a professional ballplayer named Wilson, who was a member of the minor league teams in Abilene and other towns in the region from 1909 through 1914. It was during 1909–1911 that the recently graduated Eisenhower waited in Abilene, seeking opportunities to earn money that would help pay for a college education. Superficial research has led to speculation that this ballplayer named Wilson, at least in some instances, was actually Dwight Eisenhower. However, it is impractical to try making sense of Eisenhower’s sparse comments about his baseball career without a thorough consideration of the historical context. This study examines Eisenhower’s experiences in baseball, his statements about playing professional baseball, and the contemporary intercollegiate eligibility rules. A biographical summary for the ballplayer named Wilson associated with baseball in Abilene is also provided. This essay was originally published in 2017 and has undergone revisions and corrections for its release in 2025 as part of the five-volume anthology Peeking through the Knothole. The open-access, digital version of this essay is available through the “Download” button on this webpage. The print-on-demand version is available through the “Buy this Book” button for volume two of the anthology (Baseball Biographies with Kansas Connections).https://scholars.fhsu.edu/all_monographs/1000/thumbnail.jp
Who’s on First? Kansas City’s Female Baseball Stars, 1899–1929, Revised
Full text linkAlthough female players were typically excluded from formal baseball teams, teams consisting entirely or partly of female players were organized across the country as early as the mid-1800s. The first female baseball club in Kansas and adjacent states was organized in Wichita in 1873. These early teams predated the arrival of the barnstorming teams with female players and usually one or more male players, who were sometimes disguised as women. Female players on most of these early traveling teams wore bloomers, and the teams were referred to as “bloomer girls.” Women on later teams wore traditional baseball uniforms and objected to the name. Some of these professional female ballplayers of the late 1800s and early 1900s, such as Maud Nelson of Chicago and Lizzie Murphy of New England, became well known. Two of the prominent players lived in Kansas City. This is the story of the professional careers in baseball—not softball—of Mae Arbaugh from Kansas City, Kansas and Ruth Egan from Kansas City, Missouri, both of whom played first base from 1899 to 1929, earning the respect of fans and male players. This essay was originally published in 2019 and has undergone revisions and corrections for its release in 2025 as part of the five-volume anthology Peeking through the Knothole. The open-access, digital version of this essay is available through the “Download” button on this webpage. The print-on-demand version is available through the “Buy this Book” button for volume five of the anthology (Essays on Baseball from Various Viewpoints, 1856–1940).https://scholars.fhsu.edu/all_monographs/1009/thumbnail.jp