Mini Baja CVT Optimization

The goal of this project is to design and optimize an eCVT setup for The University of Akron’s Zips Baja SAE car. The current CVT is a centrifugal CVT which changes its gear ratio through a series of weights and springs, while an eCVT uses a motor to change the gear ratio.The advantage of an eCVT is that the motor can be programmed to adjust in real-time based on the engine rpm. A centrifugal CVT can only be tuned ahead of time. It is necessary to compare the new acceleration performance to a tuned centrifugal CVT to determine if it would be an improvement over the Baja team’s current CVT design

Inferring cellular and molecular processes in single-cell data with non-negative matrix factorization using Python, R and GenePattern Notebook implementations of CoGAPS

Non-negative matrix factorization (NMF) is an unsupervised learning method well suited to high-throughput biology. However, inferring biological processes from an NMF result still requires additional post hoc statistics and annotation for interpretation of learned features. Here, we introduce a suite of computational tools that implement NMF and provide methods for accurate and clear biological interpretation and analysis. A generalized discussion of NMF covering its benefits, limitations and open questions is followed by four procedures for the Bayesian NMF algorithm Coordinated Gene Activity across Pattern Subsets (CoGAPS). Each procedure will demonstrate NMF analysis to quantify cell state transitions in a public domain single-cell RNA-sequencing dataset. The first demonstrates PyCoGAPS, our new Python implementation that enhances runtime for large datasets, and the second allows its deployment in Docker. The third procedure steps through the same single-cell NMF analysis using our R CoGAPS interface. The fourth introduces a beginner-friendly CoGAPS platform using GenePattern Notebook, aimed at users with a working conceptual knowledge of data analysis but without a basic proficiency in the R or Python programming language. We also constructed a user-facing website to serve as a central repository for information and instructional materials about CoGAPS and its application programming interfaces. The expected timing to setup the packages and conduct a test run is around 15 min, and an additional 30 min to conduct analyses on a precomputed result. The expected runtime on the user's desired dataset can vary from hours to days depending on factors such as dataset size or input parameters

Author Correction: Community-wide hackathons to identify central themes in single-cell multi-omics

Correction to "Community-wide hackathons to identify central themes in single-cell multi-omics

Community-wide hackathons to identify central themes in single-cell multi-omics

Biological systems are fundamentally multi-scale, with mostly uncharacterized molecular pathways, cellular actions, and cellular communities that collectively give rise to their function. While one high-throughput measurement technology can resolve specific biological molecules, comprehensive characterization of biological systems can only be achieved by integration of multi-modal data types across molecular, cellular, spatial, and population scales. The integration of heterogeneous and complementary assays from multi-omics can reveal interactions between modalities that drive biological systems and processes. Recent advances in single-cell multi-omics technologies provide unprecedented opportunities for such multi-scale characterization but interpreting biological processes from these data requires parallel advances in novel computational techniques

Facilitating the Development and Integration of Low-Carbon Energy Technologies

<p>Climate change mitigation will require extensive decarbonization of the electricity sector. This thesis addresses both large-scale wind integration (Papers 1-3) and development of new energy technologies (Paper 4) in service of this goal.</p> <p>Compressed air energy storage (CAES) could be paired with a wind farm to provide firm, dispatchable baseload power, or serve as a peaking plant and capture upswings in electricity prices. Paper 1 presents a firm-level engineering-economic analysis of a wind/CAES system with a wind farm in central Texas, load in either Dallas or Houston, and a CAES plant whose location is profit-optimized. Of a range of market scenarios considered, the CAES plant is found to be profitable only given the existence of large and infrequent price spikes. Social benefits of wind/CAES include avoided construction of new generation capacity, improved air quality during peak demand, and increased economic surplus, but may not outweigh the private cost of the CAES system nor justify a subsidy.</p> <p>Like CAES, pumped hydropower storage (PHS) ramps quickly enough to smooth wind power and could profit from arbitrage on short-term price fluctuations exacerbated by large-scale wind. Germany has aggressive plans for wind power expansion, and Paper 2 analyzes an investment opportunity in a PHS facility in Norway that practices arbitrage in the German spot market. Price forecasts given increased wind capacity are used to calculate profit-maximizing production schedules and annual revenue streams. Real options theory is used to value the investment opportunity, since unlike net present value, it accounts for uncertainty and intertemporal choice. Results show that the optimal investment strategy under the base scenario is to wait approximately eight years then invest in the largest available plant.</p> <p>Paper 3 examines long-distance interconnection as an alternate method of wind power smoothing. Frequency-domain analysis indicates that interconnection of aggregate regional wind plants across much of the western and mid-western U.S. would not result in significantly greater smoothing than interconnection within a single region. Time-domain analysis shows that interconnection across regions reduces the magnitude of low-probability step changes and doubles firm power output (capacity available at least 92 % of the time) compared with a single region. An approximate cost analysis indicates that despite these benefits, balancing wind and providing firm power with local natural gas turbines would be more cost-effective than with transmission interconnection.</p> <p>Papers 1 and 3 demonstrate the need for further RD&D (research, development, and deployment) of low-carbon energy technologies. Energy technology development is highly uncertain but most often modeled as deterministic, which neglects the ability both to adapt RD&D strategy to changing conditions and to invest in initially high-cost technologies with small breakthrough probabilities. Paper 4 develops an analytical stochastic dynamic programming framework in which RD&D spending decreases the expected value of the stochastic cost of a technology. Results for a two-factor cost model (which separates RD&D into R&D and learning-by-doing) applied to carbon capture and sequestration (CCS) indicate that given 15 years until large-scale deployment, investment in the RD&D program is optimal over a very broad range of initial mitigation costs ($10-$380/tCO2). While the NPV of the program is zero if initial mitigation cost is $100/tCO2, under uncertainty the program is worth about$7 billion. If initial mitigation cost is high, the program is worth most if cost reductions exogenous to the program (e.g. due to private sector activity) are also high. Factors that promote R&D spending over learning-by-doing include more imminent deployment, high initial cost, lower exogenous cost reductions, and lower program funds available.</p