This innovative practice paper presents the development and progress of a unique pathway to an academic degree. The pathway begins at a technical college that grants technical competency certificates and extends through degrees from a public research university. Recent reports confirm that while technical and community colleges are commonly a less costly educational alternative to four-year universities, for those who want to continue their education toward a bachelor's degree or beyond, students often encounter unexpected hurdles. Transferring courses not just for bulk credit hours toward graduation, but also to satisfy major course requirements, often goes differently than students would wish. For various reasons, transfer credit is disallowed towards degree requirements. In such a case, student work cannot effectively be applied toward a university degree unless provisions are made. This has been the case for Utah's public technical colleges. These eight geographically dispersed schools work closely with nearby high schools and with nontraditional students in their communities to provide vocational training in many fields, including Information Technology, Cybersecurity, Manufacturing and Robotics. Their vocational orientation has resulted in schools emphasizing competency-based certificates instead of hours in the classroom. Students emerging from these programs are well prepared to enter the technical workforce, but have not had an easy path for further education. About a decade ago employers expressed a desire for these technical professionals to be able to improve their capabilities beyond entry level jobs into more senior positions. The state legislature responded to these demands, mandating that a pathway be provided for students in select areas of study at the technical colleges to allow them to continue academic studies at Utah State University (USU), a public, research-intensive institution. In addition to the roles commonly associated with such a university, as a land-grant school, Utah State prioritizes statewide outreach into underserved areas. Since these are some of the same communities served by the state technical colleges, when needs for education beyond technical college certificates were recognized, USU was able to step into this role. Notably, it reaches underserved populations including first-generation students, rural residents, and Native Americans. As a result, USU has instituted a bachelor's degree in Technology Systems with various emphases designed to use certificates from technical colleges as lower-division technical prerequisites.

MOTIVATION: Lipid nanoparticles (LNPs) are the most widely used vehicles for mRNA vaccine delivery. The structure of the lipids composing the LNPs can have a major impact on the effectiveness of the mRNA payload. Several properties should be optimized to improve delivery and expression including biodegradability, synthetic accessibility, and transfection efficiency. RESULTS: To optimize LNPs, we developed and tested models that enable the virtual screening of LNPs with high transfection efficiency. Our best method uses the lipid Simplified Molecular-Input Line-Entry System (SMILES) as inputs to a large language model. Large language model-generated embeddings are then used by a downstream gradient-boosting classifier. As we show, our method can more accurately predict lipid properties, which could lead to higher efficiency and reduced experimental time and costs. AVAILABILITY AND IMPLEMENTATION: Code and data links available at: https://github.com/Sanofi-Public/LipoBART.

Identifying the same internet user across devices or over time is often infeasible. This presents a problem for online experiments, as it precludes person-level randomization. Randomization must instead be done using imperfect proxies for people, like cookies, email addresses, or device identifiers. Users may be partially treated and partially untreated as some of their cookies are assigned to the test group and some to the control group, complicating statistical inference. We show that the estimated treatment effect in a cookie-level experiment converges to a weighted average of the marginal effects of treating more of a user's cookies. If the marginal effects of cookie treatment exposure are positive and constant, it underestimates the true person-level effect by a factor equal to the number of cookies per person. Using two separate datasets---cookie assignment data from Atlas and advertising exposure and purchase data from Facebook---we empirically quantify the differences between cookie and person-level advertising effectiveness experiments. The effects are substantial: cookie tests underestimate the true person-level effects by a factor of about three, and require two to three times the number of people to achieve the same power as a test with perfect treatment assignment.

This paper presents the design and results of autonomous behaviors for tightly-coupled cooperation in heterogeneous robot teams, specifically for the task of navigation assistance. These cooperative behaviors enable capable, sensor rich ("leader") robots to assist in the navigation of sensor-limited ("simple") robots that have no onboard capabilities for obstacle avoidance or localization, and only minimal capabilities for kin recognition. The simple robots must be dispersed throughout a known, indoor environment to serve as a sensor network. However, because of their navigation limitations, they are unable to autonomously disperse themselves or move to planned sensor deployment positions independently. To address this challenge, we present cooperative behaviors for heterogeneous robots that enable the successful deployment of sensor-limited robots by assistance from more capable leader robots. These heterogeneous cooperative behaviors are quite complex, and involve the combination of several behavior components, including vision-based marker detection, autonomous teleoperation, color marker following in robot chains, laser-based localization, map-based path planning, and ad hoc mobile networking. We present the results of the implementation and extensive testing of these behaviors for deployment in a rigorous test environment. To our knowledge, this is the most complex heterogeneous robot team cooperative task ever attempted on physical robots. We consider it a significant success to have achieved such a high degree of system effectiveness, given the complexity of the overall heterogeneous system.