Research News Archives - Page 3 of 14 - Systems Engineering

Research: Switch to green wastewater infrastructure could reduce emissions, provide huge savings

A young man stands outside in a summer landscape.

From Childhood Curiosity to Combating Climate Change

A women researcher in a hardhat with a wired network at her feet and mountains in the background.

Researchers highlight potential cybersecurity threats to trucking industry, supply chain

A CSU-branded commercial truck sits next to a Tesla car.

Accounting for climate change in the water infrastructure design: evaluating approaches and recommending a hybrid framework

Image of a flood risk map with the following text overlayed: Adaptive Management Approaches, Inverse Climate Change Impact Methods, Machine Learning Methods, Adjusted Flood Frequency Analysis Methods, Soft Computing Methods.

A Geo-Spatial Method for Calculating BEV Charging Inconvenience Using Publicly Available Data

Image of an electric vehicle charging.

CSU gets Denver grant to fund sustainability ed for commissioners

A fancy new-age building.

Machine learning approaches to identify lithium concentration in petroleum produced waters

Alt-text: Images of maps of the U.S. South and Northeast. Text includes: Latest issue December 2023, Volume 36, Issue 4. “Machine learning approaches to identify lithium concentration in petroleum produced waters"

Federal award to CSU and CDOT will support planning needed to improve regional electric vehicle charging infrastructure

Car charging with wires going across a sidewalk.

Colorado State University key leader in $160M NSF-funded climate, community, and technology initiative

Clouds form and rain falls with burned trees in the foreground.

Predicting the Dynamics of Earned Value Creation in the Presence of Technical Debt

Image shows a ball and chain locked to a flowchart representing a system. On the ball is the term “Technical Debt.” Above this image there is a graph with the following caption: “FIGURE 10. Cumulative probabilities of completing the aircraft project under various technical debt and parallelism assumptions.” The variable Cumulative Probability on the Y axis, and the variable Project Duration (months) on the X axis. The lines showing “No Technical Debt” has the lowest time projection. The lines showing “High Technical Debt” has the most time projection. This is true when adjusting for task parallelism, though shifted down the X axis quite a lot.