Greenhouse gases like CO 2 are the drivers of climate change and global warming. In this project, we employ integral calculus and statistics-based methods to model and estimate the cumulative CO 2 emissions as a function of time. We study both global and county wise emissions, focusing on the largest emitters of CO 2 in the environment. We study the science of how CO 2 in the environment leads to global warming and implications for future if current emission trends are continued. We will discuss current advances for reducing CO 2 footprint that are being used for environmental sustainability including use of alternative sources of energy like solar and wind energy, planting more trees for a green environment, use of emission free electric vehicles for transit, developing mass transit to limit CO 2 emissions, etc. We will also discuss current research on CO 2 trapping in olivine minerals that scientists hope will help contain CO 2 and clean our environment in future. This project integrates Mathematical modeling with scientific research to study real world environmental sustainability problems of current interest.

Math - Edmonds College Mathematics: A Decade of Transformation

Presenters: Pat Averback (Math)

Supervising Faculty: N/A

Abstract/Description: Prior to 2013, the Direct Transfer Agreement (DTA) between the four-year colleges And the two-year colleges in Washington required all precollege pathways to funnel through Intermediate Algebra (Math 90) even though the non-STEM college level pathways required less symbolic algebraic skills than the STEM college level pathway. This single pathway to the college level created a barrier for many students to achieve their academic goals. This poster outlines the Math Departments efforts to revise the mathematics pathway and explores observed student outcomes.

Collision avoidance studies find important applications for motion planning of mobile robots for

deployment in outer space, nuclear waste management, mobiles used for process automation,

etc. Here, we integrate mobile robot simulations with mathematical modeling using python to

understand collision avoidance for mobile robotics. We used the open-source Pioneer code on

the Webots platform for simulations of mobile robots which employ Kinect based optical and IR

sensors and cameras for live-tracking of objects in the environment variable and has motion

controller Matlab software that provide the kinematic variables like position, velocity, and

acceleration of various objects in real time. We wrote a python code to digitize the image

matrices obtained from simulations and identified the pixels having objects that the mobile robot

must avoid for collision avoidance. We calculated the instantaneous distances between the

mobile robot and various objects to interpret and analyze the simulated trajectories. We used

jump collision avoidance models to estimate the mobile robot trajectories in the vicinity of

objects. The calculated object avoidance jump trajectory of the robot was smoothened using

Gaussian data convolution methods to obtain smooth trajectories. The simulations provide

attractive visualization and are useful for machine learning and testing algorithms for collision avoidance and motion planning.