Applicant: Jennifer Chaytor
Title: Synthesis of C-Linked Carbohydrates with Potential Anti-Hyperglycemic Activity
Period of Support: May 1, 2024 – April 30, 2025
Abstract: The project “Synthesis of C-Linked Carbohydrates with Potential Anti-Hyperglycemic Activity will be conducted under the mentorship of Dr. Jennifer Chaytor. During this project, undergraduate students will conduct organic chemistry multi-step reactions to synthesize novel carbohydrates. The students will conduct all synthetic experiments and confirm the identity of all intermediates and target compounds using spectroscopic methods. The structures of the target compounds are similar to known compounds that exhibit anti-hyperglycemic activity. Following completion of the synthesis, the synthesized compounds can be tested and evaluated for anti-hyperglycemic activity. Compounds that display anti-hyperglycemic activity could be used as novel treatments for Type II diabetes.
Applicant: Mohammad Khan
Title: System design to explore sustainable energy subsequent to solar energy harvesting through solar cage
Period of Support: June 1, 2024 – April 30, 2025
Abstract: Renewable energy is of great focus to reduce carbon emission and climate change. As one result, solar energy harvesting is gaining traction throughout the globe. However, how much energy harvesting is sustainable? The project aims to answer the question indirectly. The study focuses on designing and building a structure, called solar cage, and designing a circuit to harvest solar energy through connected photovoltaic cells, PV cells, and modeling the energy remaining in the solar cage. The varying light intensity outside the cage will be utilized to model the remaining energy inside. According to the proposed model, the structure and circuit will be designed, simulated, built, and tested. The final outcomes will be numerical values for the harvested and remaining energy and can indicate the sustainability of a healthy biome.
Applicant: Kenneth Luzynski
Title: Mosquito Monitoring in Mid-Michigan
Period of Support: January 3, 2024 – December 20, 2024
Abstract: Background: Within the Saginaw Bay Area mosquitos are vectors for pathogens that cause diseases like West Nile Virus and multiple nervous system disorders. The low, flat wetlands of the Saginaw Bay region of Michigan provide ample habitat for mosquitos to breed, making it important to monitor the local mosquito populations for pathogens. Understanding the prevalence, range, and climate effects on the changing distribution of mosquito populations and their associated pathogens can help public health officials monitor risks and target interventions.
Project Goals: The goal of this project is for SVSU students to partner with local mosquito control districts to collect mosquito specimens from the Saginaw Bay Area. Students will then conduct trapping and genetic testing to identify any human pathogens present in the mosquitos. The results of these analyses will be presented to mosquito control agencies for inclusion in their annual reports and to inform their abatement plans.
Student Training: Undergraduate students interested in biology, public health, and related disciplines will be recruited and trained in proper field collection techniques of adult mosquitos. In the lab, students will learn to extract genetic material from specimens and use DNA sequencing and bioinformatics to identify pathogens. Proper data collection and skill with statistical programs will also be developed.
Anticipated Results: Students will collect mosquito specimens from a variety of habitats around the Saginaw Bay region and determine the presence and identity of specific mosquito-borne pathogens. This project will provide the students with valuable research skills while generating important public health data for the region. The pathogen testing results will be shared with local mosquito control commissions for inclusion in their annual report. Furthermore, students can present results at several regional meetings organized by pest management, public health, and bioinformatic research societies.
Applicant: Aos Mulahuwaish
Tittle: Emotional Analytics in Cybersecurity: Deciphering Threat Sentiment from Social Media Dynamics using Machine Learning
Period of Support: May 10, 2024 – April 30, 2025
Abstract: The emotional undertones of social media content, especially on platforms like Twitter, have often been overlooked in the domain of cybersecurity. Before a cyber threat materializes, there's a buzz, a change in sentiment and emotion within the cybersecurity community and its observers. This research aims to delve into the intricate tapestry of emotions and sentiments expressed in tweets related to potential cyber threats, drawing correlations between these sentiments and the actual escalation of these threats in the real world. By employing advanced sentiment analysis algorithms, our framework will decipher the emotional intensity and nuances behind tweets. We theorize that heightened negative sentiments or drastic shifts in emotions could act as precursors to significant cyber threats or attacks. A dataset comprising tweets from influential cybersecurity accounts, enthusiasts, and even potential threat actors, spanning over 18 months, will be the foundation of this research. We'll utilize machine learning models optimized for sentiment analysis, such as BERT and Transformer-based models, to dissect and understand these emotions. Ensuring student participation remains a keystone of this research. Students will immerse themselves in the world of emotional analytics, bridging the gap between human psychology and cybersecurity. This unique blend promises to offer them a fresh perspective, potentially inspiring them to undertake further explorations in this interdisciplinary realm. This project underscores SVSU's pioneering spirit in promoting undergraduate research that merges traditional boundaries.