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Surveying for Ophidiomyces ophidiicola, the causal agent of Snake Fungal Disease in South Dakota.
Get PDFFor the past decade there has been an emerging disease plaguing wild snakes across the Eastern United States and Europe. In 2006, researchers started investigating the decline of Timber rattlesnake populations in New Hampshire. They discovered a fungal infection killing off the young to mid-juvenal snakes, thus know as Snake Fungal Disease or Ophidiomycosis. In 2011, San Deigo State University, identified the pathogen that causes infection, the fungus Ophidiomyces ophiodiicola. O. ophiodiicola has now affected 30 different snakes from six families within at least 20 different states since it’s discovery. This study pertains to determining the prevalence of Snake Fungal Disease within South Dakota.https://scholar.dsu.edu/research-symposium/1028/thumbnail.jp
Natural LanguageProcessing,UnderstandingSlang and ColloquialSpeech
Get PDFWithin a culture, slang and colloquial speech act as transient elements within everyday vernacular. These phrases and sayings, while teachable to a generalized Natural Language Processing model, present certain issues concerning a model’s ability to keep up to date with this ever-changing language. This research project seeks to determine the cause of these issues and what potential solutions may exist
Assessing Security Flaws in Modern On-Board Precision Farming Systems
Get PDFPrecision farming equipment introduces interconnected technologies into the agricultural sector. The importance of agriculture to the industrial and national operations gives this technology a high value to individual and nation-state actors seeking to disrupt agricultural operations. This study aims to utilize a lab simulating the core components of an on-board precision farming machine to discover and analyze potential security vulnerabilities on these systems. Due to issues found during the project, no viable vulnerabilities were discovered or analyzed. A more robust lab should be utilized to find results that reflect an interconnected machine
Optimal Algorithm for Managing On-Campus Student Transportation
Get PDFThis study analyzed the transportation issues at the University of Bahrain Sakhir campus, where a bus system with an unorganized and fixed number of buses allocated each semester was in place. Data was collected through a survey, onsite observations, and student schedules to estimate the number of buses needed. The study was limited to students who require to move between buildings for academic purposes and not those who choose to ride buses for other reasons. An algorithm was designed to calculate the optimal number of buses for each time slot, and for each day. This solution could improve transportation efficiency, lower costs, enhance students’ mobility experience, and decrease CO2 emissions. A series of recommendations were provided to university officials including the need for further research to examine creating new routes and implementing express buses
Balancing Security and Correctness in Code Generation: An Empirical Study on Commercial Large Language Models PDF
No full textLarge language models (LLMs) continue to be adopted for a multitude of previously manual tasks, with code generation as a prominent use. Multiple commercial models have seen wide adoption due to the accessible nature of the interface. Simple prompts can lead to working solutions that save developers time. However, the generated code has a significant challenge with maintaining security. There are no guarantees on code safety, and LLM responses can readily include known weaknesses. To address this concern, our research examines different prompt types for shaping responses from code generation tasks to produce safer outputs. The top set of common weaknesses is generated through unconditioned prompts to create vulnerable code across multiple commercial LLMs. These inputs are then paired with different contexts, roles, and identification prompts intended to improve security. Our findings show that the inclusion of appropriate guidance reduces vulnerabilities in generated code, with the choice of model having the most significant effect. Additionally, timings are presented to demonstrate the efficiency of singular requests that limit the number of model interactions
In Response to Your Recent Article: How Letters to the Editor Challenge Historical Narratives
No full textConsidering the value of letters to the editor for expanding historical perspectives through the case study of Dr. Shaw’s 1972 NYT article
Academic Achievement among NCAA Division 2 Student-Athletes and Non-Athletes
Get PDFThere is a lack of published research on the evaluation of academic success among student-athletes in National Collegiate Athletic Association (NCAA) Division 2 (D2) institutions. Our study focused on comparing academic performance and career prospects between student-athletes and non-athletes (traditional students) at a D2 university. A survey measuring academic and career-related variables was administered to 170 participants, with 92 (54%) being student-athletes and 78 (46%) being non-athlete students. Our findings revealed no statistically significant differences between the two groups in terms of study hours, grade point average, and academic motivation. Moreover, there were no disparities in declared majors, expected graduation timelines, and career aspirations. The academic performance of student-athletes was found to be similar to that of their non-athlete counterparts. Most D2 student-athletes did not foresee pursuing professional sports careers, highlighting the importance of academic achievement in their overall career objectives
Effectiveness of Transfer Learning with Light-weight Architecture for Covid-19 Imaging
Get PDFTransfer learning has emerged as a pivotal technique in deep learning, allowing pre-trained models to be fine-tuned for novel tasks. This has often led to enhanced performance and reduced training time. Lightweight architectures known for their efficiency and speed, without compromising accuracy complement this technique. These two techniques combined address the issues of limited availability of training data and the requirement of high computational resources. Numerous researchers delved into these methods to address the challenges posed by the Covid-19 pandemic. Within this framework, our study sought to evaluate the multi-stage transfer learning method across several dataset sizes and truncated versions of the lightweight MobileNetV2 architecture for medical image classification. Initially, an exhaustive literature review was conducted to present the latest advancements in transfer learning and lightweight architecture applications for COVID-19 image classification. Additionally, this study explores the trade-offs between finetuning and dataset size in a multi-stage transfer learning system using a lightweight architecture. Lastly, in this study, we aim to analyze the performance of truncated lightweight models against different training dataset sizes in a multi-stage transfer learning framework. The results of this study significantly enhance the knowledge in this specialized research area. The most popular lightweight architectures were identified, and we found that standard CNNs can be truncated without losing performance. The study also established that mid-sized datasets and freezing 90 to 95 layers yield the best performance on the target task. Furthermore, the study highlights a key trade-off in stage one transfer learning: smaller datasets require more extensive layer training, while larger datasets need fewer re-trained layers for optimal performance. Also, beyond a certain dataset size, deeper fine-tuning does not lead to improved accuracy. Finally, we established that complex models tolerate more frozen layers, maintaining adequate learning capacity. The findings support the idea that while more complex models demonstrate higher accuracy, simpler models perform competitively. Moreover, the impact of model complexity is reduced on target performance with small-size datasets. These insights underscore the efficiency of multi-stage transfer learning with lightweight models, especially when pre-trained models are applied to new datasets. The present research adds to the improvement of the effectiveness of medical expert systems while also reducing the burden on healthcare professionals
Something for Every Kind of Learner: Students’ Perceptions of an Educational Recommender Study Tool
Get PDFThe field of education has the potential to better facilitate student learning by employing educational recommender systems that adapt the learning process to the needs of individual learners. There is a lack of research that ties educational theory to the design and implementation of these systems. In this research, the design science methodology is employed to advocate for an educational recommender framework with a theoretical base in self-regulated learning. This paper focuses on the qualitative evaluation of this approach to gain insights on students’ perceptions of the resulting recommender when deployed to assist students when studying for an upcoming exam. Student perceptions are analyzed to obtain design themes that serve to aid future researchers and practitioners in the design of these systems
UAV Drone Security of Control for Increased Safety-of-flight
Get PDFThis research explores the security vulnerabilities of unmanned aircraft vehicles (UAVs), or drones, which are increasingly prevalent in agriculture, delivery, public safety, recreation, photography, and emergency management. The Federal Aviation Administration (FAA) classifies UAVs as legal aircraft yet encounters notable control and data security challenges. The study conducts comprehensive wireless vulnerability scans on commercially available, small, licensable-size drones to identify and analyze communication and data vulnerabilities and integrate them with flight safety risk management. The research method involves demonstrating and documenting these vulnerabilities practically. A custom-built drone with a reliable and full-featured open-source flight control software package, serving as a comparison baseline. The research team also performs similar tests on commercially available drones to ensure consistency and repeatability.
Moreover, strategies were implemented to validate best practices for drones against unauthorized takeovers and deliberate interference, which can result in unsafe flight conditions. This aspect is crucial for the safe operation and longevity of UAVs. The recommendations are provided to address and mitigate common vulnerabilities, enhancing these systems\u27 safety, security, and reliability in their diverse applications