GURLS: a Toolbox for Regularized Least Squares Learning

We present GURLS, a toolbox for supervised learning based on the regularized least squares algorithm. The toolbox takes advantage of all the favorable properties of least squares and is tailored to deal in particular with multi-category/multi-label problems. One of the main advantages of GURLS is that it allows training and tuning a multi-category classifier at essentially the same cost of one single binary classifier. The toolbox provides a set of basic functionalities including different training strategies and routines to handle computations with very large matrices by means of both memory-mapped storage and distributed task execution. The system is modular and can serve as a basis for easily prototyping new algorithms. The toolbox is available for download, easy to set-up and use

Decarbonization of aviation via hydrogen propulsion: technology performance targets and energy system impacts

The aviation sector is challenging to decarbonize since aircraft require high power and energy per unit of weight. Liquid hydrogen is an interesting solution due to its high gravimetric energy density, minimal warming impact, and low-carbon production potential. We quantify the performance targets for fuel cell systems and on-board storage to enable hydrogen-powered regional aviation. We then explore the energy infrastructure impacts of meeting this additional H2 demand in the European context under deep decarbonization scenarios. We find that minimal payload reduction would be needed for powering regional aviation up to 1000 nmi if fuel cell system specific power of 2 kW/kg and tank gravimetric index of 50% can be achieved. The energy systems analysis highlights the importance of utilizing multiple technology options: such as nuclear expansion and natural gas reforming with CCS for hydrogen production. Levelized cost of liquid hydrogen as low as 3.5 Euros/kg demonstrates pathways for Europe to achieve cost-competitive production.Comment: 25 pages, 6 figures. (38 pages with SI, 7 SI figures

Sector coupling via hydrogen to lower the cost of energy system decarbonization

There is growing interest in hydrogen (H$_2$) use for long-duration energy storage in a future electric grid dominated by variable renewable energy (VRE) resources. Modelling the role of H$_2$ as grid-scale energy storage, often referred as "power-to-gas-to-power (P2G2P)" overlooks the cost-sharing and emission benefits from using the deployed H$_2$ production and storage assets to also supply H$_2$ for decarbonizing other end-use sectors where direct electrification may be challenged. Here, we develop a generalized modelling framework for co-optimizing energy infrastructure investment and operation across power and transportation sectors and the supply chains of electricity and H$_2$, while accounting for spatio-temporal variations in energy demand and supply. Applying this sector-coupling framework to the U.S. Northeast under a range of technology cost and carbon price scenarios, we find a greater value of power-to-H$_2$ (P2G) versus P2G2P routes. P2G provides flexible demand response, while the extra cost and efficiency penalties of P2G2P routes make the solution less attractive for grid balancing. The effects of sector-coupling are significant, boosting VRE generation by 12-55% with both increased capacities and reduced curtailments and reducing the total system cost (or levelized costs of energy) by 6-14% under 96% decarbonization scenarios. Both the cost savings and emission reductions from sector coupling increase with H$_2$ demand for other end-uses, more than doubling for a 96% decarbonization scenario as H$_2$ demand quadraples. Moreover, we found that the deployment of carbon capture and storage is more cost-effective in the H$_2$ sector because of the lower cost and higher utilization rate. These findings highlight the importance of using an integrated multi-sector energy system framework with multiple energy vectors in planning energy system decarbonization pathways.Comment: 19 pages, 7 figure

Integrating Hydrogen in Single-Price Electricity Systems: The Effects of Spatial Economic Signals

Hydrogen can contribute substantially to the reduction of carbon emissions in industry and transportation. However, the production of hydrogen through electrolysis creates interdependencies between hydrogen supply chains and electricity systems. Therefore, as governments worldwide are planning considerable financial subsidies and new regulation to promote hydrogen infrastructure investments in the next years, energy policy research is needed to guide such policies with holistic analyses. In this study, we link a electrolytic hydrogen supply chain model with an electricity system dispatch model, for a cross-sectoral case study of Germany in 2030. We find that hydrogen infrastructure investments and their effects on the electricity system are strongly influenced by electricity prices. Given current uniform prices, hydrogen production increases congestion costs in the electricity grid by 17%. In contrast, passing spatially resolved electricity price signals leads to electrolyzers being placed at low-cost grid nodes and further away from consumption centers. This causes lower end-use costs for hydrogen. Moreover, congestion management costs decrease substantially, by up to 20% compared to the benchmark case without hydrogen. These savings could be transferred into according subsidies for hydrogen production. Thus, our study demonstrates the benefits of differentiating economic signals for hydrogen production based on spatial criteria

Soluble factors from neocortical astrocytes enhance neuronal differentiation of neural progenitor cells from adult rat hippocampus on micropatterned polymer substrates

Rat adult hippocampal progenitor cells (AHPCs) are self-renewing, multipotent neural progenitors that have the ability to differentiate into neurons and glia. Previously, we demonstrated that coculture of AHPCs with postnatal day two, type 1 cortical astrocytes on laminin-coated micropatterned polymer substrates facilitates selective neuronal differentiation of the AHPCs 1. Under this condition, multi-dimensional cell-cell and/or cell-extracellular matrix interactions, as well as possible soluble factors released from astrocytes provided spatial and temporal control selectively enhancing neuronal differentiation and neurite alignment on topographically different regions of the same substrate. To investigate the potential role of astrocyte-derived soluble factors as cues involved in neuronal differentiation, a non-contact co-culture system was used. Under control conditions, approximately 14% of the AHPCs were immunoreactive (IR) for the neuronal marker, class III β-tubulin (TUJ1-IR). When co-cultured in physical contact with astrocytes, neuronal differentiation increased significantly to about 25%, consistent with our previous results. Moreover, under non-contact co-culture conditions using Transwell insert cultures, neuronal differentiation was dramatically increased to approximately 64%. Furthermore, neurite outgrowth from neuronal cell bodies was considerably greater on the patterned substrate, compared to the non-patterned planar substrate under non-contact co-culture conditions. Taken together, our results demonstrate that astrocyte-derived soluble factors provide cues for specific neuronal differentiation of AHPCs cultured on micropatterned substrates. In addition, a suppressive influence on neuronal differentiation appears to be mediated by contact with co-cultured astrocytes. These results provide important insights into mechanisms for controlling neural progenitor/stem cell differentiation and facilitate development of strategies for CNS repair