Announcements

Title: Phospholipid Behavior and Dynamics in Curved Biological Membranes

Author: Haoyuan Jing

Date/Time: November 6, 2020 11:00am-1:00pm

Zoom Link: https://umd.zoom.us/j/2663589029

Committee Members
Dr. Siddhartha Das (Chair)
Dr. Silvina Matysiak (Dean’s Represenative)
Dr. Doron Levy
Dr. Amir Riaz
Dr. Peter Chung
Dr. Kumaran Ramamurthi (Special Member)

Abstract: Curvature in biological membranes defines the morphology of cells and organelles and serves key roles in maintaining a variety of cellular functions, enabling trafficking, recruiting and localizing shape-responsive proteins. For example, the bacterial protein SpoVM is a small amphipathic alpha-helical protein that localizes to the outer surface of a forespore, the only convex surface in the mother bacteria. Understanding several of these membrane curvature dependent events rely on a thorough understanding of the properties, energetics, and interactions of the constituent lipid molecules in presence of curvatures.  

In this dissertation, we have used molecular dynamics (MD) simulations to explore how the curvature of the lipid bilayer (LBL), a simplified mimic of the cell membrane, affects the packing fraction and diffusivity of lipid molecules in the LBL, energetics of lipid flip flop in the LBL, and lipid desorption from the LBLs. We have also investigated the interaction between LBLs and a small bacterial protein, SpoVM, which was previously shown to preferentially embed in positively curved membranes. Our work started with simulating convex surface, represented by the nanoparticle supported lipid bilayers (NPSLBLs) in MD. We first quantified the self-assembly, structure, and properties of a NPSLBL with a diameter of 20 nm and showed how the type of the nanoparticle (NP) affects the properties of the NPSLBLs. Second, we studied the energetics of lipid flip flop and desorption from LBLs for the cases of planar substrate supported lipid bilayer (PSSLBL) and NPSLBL. Finally, we investigated the energetics of SpoVM desorption from the PSSLBL and the NPSLBL providing clues to the fundamental driving forces dictating the curvature sensing of SpoVM. 

Title: CERTIFYING AN AUTONOMOUS SYSTEM TO COMPLETE TASKS CURRENTLY RESERVED FOR QUALIFIED PILOTS

Author: Donald “Bucket” Costello

Date/Time: Oct 29, 2020 07:00 PM Eastern Time (US and Canada)

Examining Committee:

• Asst. Prof. Huan Xu, Chair/Advisor
• Prof. Adam Porter, Dean’s Representative
• Prof. Jeffrey Herrmann
• Prof. Miao Yu
• Asst. Prof. Sarah Bergbreiter

Abstract: When naval certification officials issue a safety of flight clearance, they are certifying that when the vehicle is used by a qualified pilot they can safety accomplish their mission. The pilot is ultimately responsible for the vehicle. While the naval safety of flight clearance process is an engineering based risk mitigation process, the qualification process for military pilots is largely a trust process. When a commanding officer designates a pilot as being fully qualified, they are placing their trust in the pilot’s decision making abilities during off nominal conditions. The advent of autonomous systems will shift this established paradigm as there will no longer be a human in the loop who is responsible for the vehicle. Yet, a method for certifying an autonomous vehicle to make decisions currently reserved for qualified pilots does not exist. We propose and exercise a methodology for certifying an autonomous system to complete tasks currently reserved for qualified pilots. First, we decompose the steps currently taken by qualified pilots to the basic requirements. We then develop a specification which defines the envelope where a system can exhibit autonomous behavior. Following a formal methods approach to analyzing the specification, we developed a protocol that software developers can use to ensure the vehicle will remain within the clearance envelope when operating autonomously. Second, we analyze flight test data of an autonomous system completing a task currently re-served for qualified pilots while focusing on legacy test and evaluation methods to determine suitability for obtaining a certification. We found that the system could complete the task under controlled conditions. However, when faced with conditions that were not anticipated (situations where a pilot uses their judgment) the vehicle was unable to complete the task. Third, we highlight an issue with the use of onboard sensors to build the situational awareness of an autonomous system. As those sensors degrade, a point exists where the situational awareness provided is insufficient for sound aeronautical decisions. We demonstrate (through modeling and simulation) an objective measure for adequate situational awareness (subjective end) to complete a task currently reserved for qualified pilots.

Author: John McGahagan

Advisory Committee:
Professor Michel Cukier, Chair
Professor Jennifer Golbeck, Dean’s Representative
Associate Professor Katrina Groth
Professor Jeffrey Herrmann
Professor Mohammad Modarres 

Date/Time: October 23rd 1pm-3pm ET

Abstract: Although the internet enables many important functions of modern life, it is also a ground for nefarious activity by malicious actors and cybercriminals. For example, malicious websites facilitate phishing attacks, malware infections, data theft, and disruption. A major component of cybersecurity is to detect and mitigate attacks enabled by malicious websites. Although prior researchers have presented promising results – specifically in the use of website features to detect malicious websites – malicious website detection continues to pose major challenges. This dissertation presents an investigation into feature-based malicious website detection. We conducted six studies on malicious website detection, with a focus on discovering new features for malicious website detection, challenging assumptions of features from prior research, comparing the importance of the features for malicious website detection, building and evaluating detection models over various scenarios, and evaluating malicious website detection models across different datasets and over time. We evaluated this approach on various datasets, including: a dataset composed of several threats from industry; a dataset derived from the Alexa top one million domains and supplemented with open source threat intelligence information; and a dataset consisting of websites gathered repeatedly over time. Results led us to postulate that new, unstudied, features could be incorporated to improve malicious website detection models, since, in many cases, models built with new features outperformed models built from features used in prior research and did so with fewer features. We also found that features discovered using feature selection could be applied to other datasets with minor adjustments. In addition: we demonstrated that the performance of detection models decreased over time; we measured the change of websites in relation to our detection model; and we demonstrated the benefit of re-training in various scenarios.