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Perineuronal nets stabilize the grid cell network | 10.1038/s41467-020-20241-w | https://doi.org/10.1038/s41467-020-20241-w | Nature Communications | 2,021 | Christensen, A.; Lensjø, K.; Lepperød, M.; Dragly, S.; Sutterud, H. | Abstract
Grid cells are part of a widespread network which supports navigation and spatial memory. Stable grid patterns appear late in development, in concert with extracellular matrix aggregates termed perineuronal nets (PNNs) that condense around inhibitory neurons. It has been suggested that PNNs s... | CrossRef | DigiEnergy | Load Forecasting & Demand Management | Carbon Trading & New Business Models | Optimization & Control | |
A frequency-amplitude coordinator and its optimal energy consumption for biological oscillators | 10.1038/s41467-021-26182-2 | https://doi.org/10.1038/s41467-021-26182-2 | Nature Communications | 2,021 | Qin, B.; Zhao, L.; Lin, W. | AbstractBiorhythm including neuron firing and protein-mRNA interaction are fundamental activities with diffusive effect. Their well-balanced spatiotemporal dynamics are beneficial for healthy sustainability. Therefore, calibrating both anomalous frequency and amplitude of biorhythm prevents physiological dysfunctions o... | CrossRef | DigiEnergy | Load Forecasting & Demand Management | AI & Data Science for Urban Energy Systems | Demand Response & IoT | |
On-demand synthesis of phosphoramidites | 10.1038/s41467-021-22945-z | https://doi.org/10.1038/s41467-021-22945-z | Nature Communications | 2,021 | Sandahl, A.; Nguyen, T.; Hansen, R.; Johansen, M.; Skrydstrup, T. | Abstract
Automated chemical synthesis of oligonucleotides is of fundamental importance for the production of primers for the polymerase chain reaction (PCR), for oligonucleotide-based drugs, and for numerous other medical and biotechnological applications. The highly optimised automised chemical oligo... | CrossRef | DigiEnergy | Load Forecasting & Demand Management | AI & Data Science for Urban Energy Systems | Optimization & Control | |
Application of large-scale grid-connected solar photovoltaic system for voltage stability improvement of weak national grids | 10.1038/s41598-021-04300-w | https://doi.org/10.1038/s41598-021-04300-w | Scientific Reports | 2,021 | Adetokun, B.; Ojo, J.; Muriithi, C. | AbstractThis paper investigates the application of large-scale solar photovoltaic (SPV) system for voltage stability improvement of weak national grids. Large-scale SPV integration has been investigated on the Nigerian power system to enhance voltage stability and as a viable alternative to the aged shunt reactors curr... | CrossRef | CleanTech | Solar PV & Storage | Novel Low/Zero Carbon Technologies | Optimization & Control | |
Estimation of carbon dioxide emissions from the megafires of Australia in 2019–2020 | 10.1038/s41598-021-87721-x | https://doi.org/10.1038/s41598-021-87721-x | Scientific Reports | 2,021 | Shiraishi, T.; Hirata, R. | AbstractCatastrophic fires occurred in Australia between 2019 and 2020. These fires burned vast areas and caused extensive damage to the environment and wildlife. In this study, we estimated the carbon dioxide (CO2) emissions from these fires using a bottom-up method involving the improved burnt area approach and up-to... | CrossRef | DigiEnergy | Load Forecasting & Demand Management | Carbon Trading & New Business Models | Forecasting & Prediction | |
Controlled synthesis of various Fe2O3 morphologies as energy storage materials | 10.1038/s41598-021-84755-z | https://doi.org/10.1038/s41598-021-84755-z | Scientific Reports | 2,021 | Hang, B.; Anh, T. | AbstractAir pollution from vehicle emissions is a major problem in developing countries. Consequently, the use of iron-based rechargeable batteries, which is an effective method of reducing air pollution, have been extensively studied for electric vehicles. The structures and morphologies of iron particles significantl... | CrossRef | FLEXERGY | Electric Vehicles & Mobility | Demand Response & New Mobilities & Urban Planning | Optimization & Control | |
Geomechanical simulation of energy storage in salt formations | 10.1038/s41598-021-99161-8 | https://doi.org/10.1038/s41598-021-99161-8 | Scientific Reports | 2,021 | Ramesh Kumar, K.; Makhmutov, A.; Spiers, C.; Hajibeygi, H. | AbstractA promising option for storing large-scale quantities of green gases (e.g., hydrogen) is in subsurface rock salt caverns. The mechanical performance of salt caverns utilized for long-term subsurface energy storage plays a significant role in long-term stability and serviceability. However, rock salt undergoes n... | CrossRef | DigiEnergy | Load Forecasting & Demand Management | AI & Data Science for Urban Energy Systems | Forecasting & Prediction | |
Energy budget and carbon footprint in a wheat and maize system under ridge furrow strategy in dry semi humid areas | 10.1038/s41598-021-88717-3 | https://doi.org/10.1038/s41598-021-88717-3 | Scientific Reports | 2,021 | Li, C.; Li, S. | AbstractThe well-irrigated planting strategy (WI) consumes a large amount of energy and exacerbates greenhouse gas emissions, endangering the sustainable agricultural production. This 2-year work aims to estimate the economic benefit, energy budget and carbon footprint of a wheat–maize double cropping system under conv... | CrossRef | DigiEnergy | Load Forecasting & Demand Management | Carbon Trading & New Business Models | Optimization & Control | |
Adaptive optimal allocation of water resources response to future water availability and water demand in the Han River basin, China | 10.1038/s41598-021-86961-1 | https://doi.org/10.1038/s41598-021-86961-1 | Scientific Reports | 2,021 | Tian, J.; Guo, S.; Deng, L.; Yin, J.; Pan, Z. | AbstractGlobal warming and anthropogenic changes can result in the heterogeneity of water availability in the spatiotemporal scale, which will further affect the allocation of water resources. A lot of researches have been devoted to examining the responses of water availability to global warming while neglected future... | CrossRef | DigiEnergy | Load Forecasting & Demand Management | AI & Data Science for Urban Energy Systems | Demand Response & IoT | |
Techno-economic analysis of long-duration energy storage and flexible power generation technologies to support high-variable renewable energy grids | 10.1016/j.joule.2021.06.018 | https://doi.org/10.1016/j.joule.2021.06.018 | Joule | 2,021 | Hunter, C.; Penev, M.; Reznicek, E.; Eichman, J.; Rustagi, N. | CrossRef | DigiEnergy | Load Forecasting & Demand Management | AI & Data Science for Urban Energy Systems | Optimization & Control | ||
Rwanda’s Off-Grid Solar Performance Targets | 10.1016/j.joule.2020.12.016 | https://doi.org/10.1016/j.joule.2020.12.016 | Joule | 2,021 | Asemota, G. | CrossRef | DigiEnergy | Load Forecasting & Demand Management | Novel Low/Zero Carbon Technologies | Optimization & Control | ||
Building and grid system benefits of demand flexibility and energy efficiency | 10.1016/j.joule.2021.08.001 | https://doi.org/10.1016/j.joule.2021.08.001 | Joule | 2,021 | Jackson, R.; Zhou, E.; Reyna, J. | CrossRef | DigiEnergy | Load Forecasting & Demand Management | AI & Data Science for Urban Energy Systems | Optimization & Control | ||
US building energy efficiency and flexibility as an electric grid resource | 10.1016/j.joule.2021.06.002 | https://doi.org/10.1016/j.joule.2021.06.002 | Joule | 2,021 | Langevin, J.; Harris, C.; Satre-Meloy, A.; Chandra-Putra, H.; Speake, A. | CrossRef | DigiEnergy | Load Forecasting & Demand Management | AI & Data Science for Urban Energy Systems | Optimization & Control | ||
Predicting battery end of life from solar off-grid system field data using machine learning | 10.1016/j.joule.2021.11.006 | https://doi.org/10.1016/j.joule.2021.11.006 | Joule | 2,021 | Aitio, A.; Howey, D. | CrossRef | DigiEnergy | Load Forecasting & Demand Management | Novel Low/Zero Carbon Technologies | Optimization & Control | ||
Toward carbon-neutral electricity and mobility: Is the grid infrastructure ready? | 10.1016/j.joule.2021.06.011 | https://doi.org/10.1016/j.joule.2021.06.011 | Joule | 2,021 | Xie, L.; Singh, C.; Mitter, S.; Dahleh, M.; Oren, S. | CrossRef | DigiEnergy | Load Forecasting & Demand Management | AI & Data Science for Urban Energy Systems | Optimization & Control | ||
Solar and wind grid system value in the United States: The effect of transmission congestion, generation profiles, and curtailment | 10.1016/j.joule.2021.05.009 | https://doi.org/10.1016/j.joule.2021.05.009 | Joule | 2,021 | Millstein, D.; Wiser, R.; Mills, A.; Bolinger, M.; Seel, J. | CrossRef | DigiEnergy | Load Forecasting & Demand Management | Novel Low/Zero Carbon Technologies | Optimization & Control | ||
From silos to systems: Enabling off-grid electrification of healthcare facilities, households, and businesses in sub-Saharan Africa | 10.1016/j.oneear.2021.10.021 | https://doi.org/10.1016/j.oneear.2021.10.021 | One Earth | 2,021 | Trotter, P. | CrossRef | DigiEnergy | Load Forecasting & Demand Management | AI & Data Science for Urban Energy Systems | Optimization & Control | ||
Cryo-EM grid optimization for membrane proteins | 10.1016/j.isci.2021.102139 | https://doi.org/10.1016/j.isci.2021.102139 | iScience | 2,021 | Kampjut, D.; Steiner, J.; Sazanov, L. | CrossRef | DigiEnergy | Load Forecasting & Demand Management | AI & Data Science for Urban Energy Systems | Optimization & Control | ||
Modularization of grid cells constrained by the pyramidal patch lattice | 10.1016/j.isci.2021.102301 | https://doi.org/10.1016/j.isci.2021.102301 | iScience | 2,021 | Wang, T.; Yang, F.; Wang, Z.; Zhang, B.; Wang, W. | CrossRef | DigiEnergy | Load Forecasting & Demand Management | AI & Data Science for Urban Energy Systems | Optimization & Control | ||
Comprehensive early warning strategies based on consistency deviation of thermal–electrical characteristics for energy storage grid | 10.1016/j.isci.2021.103058 | https://doi.org/10.1016/j.isci.2021.103058 | iScience | 2,021 | Wu, X.; Cui, Z.; Zhou, G.; Wen, T.; Hu, F. | CrossRef | DigiEnergy | Load Forecasting & Demand Management | AI & Data Science for Urban Energy Systems | Optimization & Control | ||
A high-performance triboelectric-electromagnetic hybrid wind energy harvester based on rotational tapered rollers aiming at outdoor IoT applications | 10.1016/j.isci.2021.102300 | https://doi.org/10.1016/j.isci.2021.102300 | iScience | 2,021 | Fang, Y.; Tang, T.; Li, Y.; Hou, C.; Wen, F. | CrossRef | DigiEnergy | Load Forecasting & Demand Management | AI & Data Science for Urban Energy Systems | Demand Response & IoT | ||
Machine learning toward advanced energy storage devices and systems | 10.1016/j.isci.2020.101936 | https://doi.org/10.1016/j.isci.2020.101936 | iScience | 2,021 | Gao, T.; Lu, W. | CrossRef | DigiEnergy | Load Forecasting & Demand Management | AI & Data Science for Urban Energy Systems | AI & Deep Learning | ||
China's vehicle electrification impacts on sales, fuel use, and battery material demand through 2050: Optimizing consumer and industry decisions | 10.1016/j.isci.2021.103375 | https://doi.org/10.1016/j.isci.2021.103375 | iScience | 2,021 | Ou, S.; Hsieh, I.; He, X.; Lin, Z.; Yu, R. | CrossRef | DigiEnergy | Load Forecasting & Demand Management | AI & Data Science for Urban Energy Systems | Demand Response & IoT | ||
Controlling electrochemical growth of metallic zinc electrodes: Toward affordable rechargeable energy storage systems | 10.1126/sciadv.abe0219 | https://doi.org/10.1126/sciadv.abe0219 | Science Advances | 2,021 | Zheng, J.; Archer, L. | Zinc anodes are a powerful platform for understanding metal deposition and for low-cost electrical energy storage. | CrossRef | DigiEnergy | Load Forecasting & Demand Management | AI & Data Science for Urban Energy Systems | Optimization & Control | |
Theta oscillations coordinate grid-like representations between ventromedial prefrontal and entorhinal cortex | 10.1126/sciadv.abj0200 | https://doi.org/10.1126/sciadv.abj0200 | Science Advances | 2,021 | Chen, D.; Kunz, L.; Lv, P.; Zhang, H.; Zhou, W. | Human iEEG reveals synchronous theta oscillations and coordinated grid-like representations between vmPFC and entorhinal cortex. | CrossRef | DigiEnergy | Load Forecasting & Demand Management | AI & Data Science for Urban Energy Systems | Optimization & Control | |
Optogenetic pacing of medial septum parvalbumin-positive cells disrupts temporal but not spatial firing in grid cells | 10.1126/sciadv.abd5684 | https://doi.org/10.1126/sciadv.abd5684 | Science Advances | 2,021 | Lepperød, M.; Christensen, A.; Lensjø, K.; Buccino, A.; Yu, J. | Spatial code of grid cells is independent of theta oscillations and phase precession. | CrossRef | DigiEnergy | Load Forecasting & Demand Management | AI & Data Science for Urban Energy Systems | Optimization & Control | |
A harmonised, high-coverage, open dataset of solar photovoltaic installations in the UK | 10.1038/s41597-020-00739-0 | https://doi.org/10.1038/s41597-020-00739-0 | Scientific Data | 2,020 | Stowell, D.; Kelly, J.; Tanner, D.; Taylor, J.; Jones, E. | AbstractSolar photovoltaic (PV) is an increasingly significant fraction of electricity generation. Efficient management, and innovations such as short-term forecasting and machine vision, demand high-resolution geographic datasets of PV installations. However, official and public sources have notable deficiencies: spat... | CrossRef | CleanTech | Solar PV & Storage | Novel Low/Zero Carbon Technologies | Forecasting & Prediction | |
A synthetic energy dataset for non-intrusive load monitoring in households | 10.1038/s41597-020-0434-6 | https://doi.org/10.1038/s41597-020-0434-6 | Scientific Data | 2,020 | Klemenjak, C.; Kovatsch, C.; Herold, M.; Elmenreich, W. | AbstractResearch on smart grid technologies is expected to result in effective climate change mitigation. Non-Intrusive Load Monitoring (NILM) is seen as a key technique for enabling innovative smart-grid services. By breaking down the energy consumption of households and industrial facilities into its components, NILM... | CrossRef | DigiEnergy | Load Forecasting & Demand Management | AI & Data Science for Urban Energy Systems | Forecasting & Prediction | |
Developing reliable hourly electricity demand data through screening and imputation | 10.1038/s41597-020-0483-x | https://doi.org/10.1038/s41597-020-0483-x | Scientific Data | 2,020 | Ruggles, T.; Farnham, D.; Tong, D.; Caldeira, K. | AbstractElectricity usage (demand) data are used by utilities, governments, and academics to model electric grids for a variety of planning (e.g., capacity expansion and system operation) purposes. The U.S. Energy Information Administration collects hourly demand data from all balancing authorities (BAs) in the contigu... | CrossRef | DigiEnergy | Load Forecasting & Demand Management | AI & Data Science for Urban Energy Systems | Optimization & Control | |
Version 4 of the CRU TS monthly high-resolution gridded multivariate climate dataset | 10.1038/s41597-020-0453-3 | https://doi.org/10.1038/s41597-020-0453-3 | Scientific Data | 2,020 | Harris, I.; Osborn, T.; Jones, P.; Lister, D. | Abstract
CRU TS (Climatic Research Unit gridded Time Series) is a widely used climate dataset on a 0.5° latitude by 0.5° longitude grid over all land domains of the world except Antarctica. It is derived by the interpolation of monthly climate anomalies from extensive networks of weather station obser... | CrossRef | DigiEnergy | Load Forecasting & Demand Management | AI & Data Science for Urban Energy Systems | Forecasting & Prediction | |
Machine learning model to project the impact of COVID-19 on US motor gasoline demand | 10.1038/s41560-020-0662-1 | https://doi.org/10.1038/s41560-020-0662-1 | Nature Energy | 2,020 | Ou, S.; He, X.; Ji, W.; Chen, W.; Sui, L. | CrossRef | DigiEnergy | Load Forecasting & Demand Management | AI & Data Science for Urban Energy Systems | AI & Deep Learning | ||
Author Correction: Machine learning model to project the impact of COVID-19 on US motor gasoline demand | 10.1038/s41560-020-00711-7 | https://doi.org/10.1038/s41560-020-00711-7 | Nature Energy | 2,020 | Ou, S.; He, X.; Ji, W.; Chen, W.; Sui, L. | CrossRef | DigiEnergy | Load Forecasting & Demand Management | AI & Data Science for Urban Energy Systems | AI & Deep Learning | ||
Cleaning the grid | 10.1038/s41893-020-00663-6 | https://doi.org/10.1038/s41893-020-00663-6 | Nature Sustainability | 2,020 | Millstein, D. | CrossRef | DigiEnergy | Load Forecasting & Demand Management | AI & Data Science for Urban Energy Systems | Optimization & Control | ||
Re-evaluating effectiveness of vehicle emission control programmes targeting high-emitters | 10.1038/s41893-020-0573-y | https://doi.org/10.1038/s41893-020-0573-y | Nature Sustainability | 2,020 | Huang, Y.; Surawski, N.; Yam, Y.; Lee, C.; Zhou, J. | CrossRef | DigiEnergy | Load Forecasting & Demand Management | AI & Data Science for Urban Energy Systems | Optimization & Control | ||
Electricity-powered artificial root nodule | 10.1038/s41467-020-15314-9 | https://doi.org/10.1038/s41467-020-15314-9 | Nature Communications | 2,020 | Lu, S.; Guan, X.; Liu, C. | AbstractRoot nodules are agricultural-important symbiotic plant-microbe composites in which microorganisms receive energy from plants and reduce dinitrogen (N2) into fertilizers. Mimicking root nodules using artificial devices can enable renewable energy-driven fertilizer production. This task is challenging due to the... | CrossRef | DigiEnergy | Load Forecasting & Demand Management | AI & Data Science for Urban Energy Systems | Demand Response & IoT | |
Anomalous supply shortages from dynamic pricing in on-demand mobility | 10.1038/s41467-020-18370-3 | https://doi.org/10.1038/s41467-020-18370-3 | Nature Communications | 2,020 | Schröder, M.; Storch, D.; Marszal, P.; Timme, M. | AbstractDynamic pricing schemes are increasingly employed across industries to maintain a self-organized balance of demand and supply. However, throughout complex dynamical systems, unintended collective states exist that may compromise their function. Here we reveal how dynamic pricing may induce demand-supply imbalan... | CrossRef | DigiEnergy | Load Forecasting & Demand Management | Demand Response & New Mobilities & Urban Planning | Forecasting & Prediction | |
Grid cells are modulated by local head direction | 10.1038/s41467-020-17500-1 | https://doi.org/10.1038/s41467-020-17500-1 | Nature Communications | 2,020 | Gerlei, K.; Passlack, J.; Hawes, I.; Vandrey, B.; Stevens, H. | AbstractGrid and head direction codes represent cognitive spaces for navigation and memory. Pure grid cells generate grid codes that have been assumed to be independent of head direction, whereas conjunctive cells generate grid representations that are tuned to a single head direction. Here, we demonstrate that pure gr... | CrossRef | DigiEnergy | Load Forecasting & Demand Management | AI & Data Science for Urban Energy Systems | Optimization & Control | |
India’s potential for integrating solar and on- and offshore wind power into its energy system | 10.1038/s41467-020-18318-7 | https://doi.org/10.1038/s41467-020-18318-7 | Nature Communications | 2,020 | Lu, T.; Sherman, P.; Chen, X.; Chen, S.; Lu, X. | AbstractThis paper considers options for a future Indian power economy in which renewables, wind and solar, could meet 80% of anticipated 2040 power demand supplanting the country’s current reliance on coal. Using a cost optimization model, here we show that renewables could provide a source of power cheaper or at leas... | CrossRef | DigiEnergy | Renewable Energy Resource Mapping | Novel Low/Zero Carbon Technologies | Optimization & Control | |
A wind-albedo-wind feedback driven by landscape evolution | 10.1038/s41467-019-13661-w | https://doi.org/10.1038/s41467-019-13661-w | Nature Communications | 2,020 | Abell, J.; Pullen, A.; Lebo, Z.; Kapp, P.; Gloege, L. | AbstractThe accurate characterization of near-surface winds is critical to our understanding of past and modern climate. Dust lofted by these winds has the potential to modify surface and atmospheric conditions as well as ocean biogeochemistry. Stony deserts, low dust emitting regions today, represent expansive areas w... | CrossRef | DigiEnergy | Load Forecasting & Demand Management | AI & Data Science for Urban Energy Systems | Forecasting & Prediction | |
Vicinal difunctionalization of carbon–carbon double bond for the platform synthesis of trifluoroalkyl amines | 10.1038/s41467-020-19748-z | https://doi.org/10.1038/s41467-020-19748-z | Nature Communications | 2,020 | Béke, F.; Mészáros, Á.; Tóth, Á.; Botlik, B.; Novák, Z. | AbstractRegioselective vicinal diamination of carbon–carbon double bonds with two different amines is a synthetic challenge under transition metal-free conditions, especially for the synthesis of trifluoromethylated amines. However, the synthesis of ethylene diamines and fluorinated amine compounds is demanded, especia... | CrossRef | DigiEnergy | Load Forecasting & Demand Management | AI & Data Science for Urban Energy Systems | Optimization & Control | |
Prefrontal reinstatement of contextual task demand is predicted by separable hippocampal patterns | 10.1038/s41467-020-15928-z | https://doi.org/10.1038/s41467-020-15928-z | Nature Communications | 2,020 | Jiang, J.; Wang, S.; Guo, W.; Fernandez, C.; Wagner, A. | AbstractGoal-directed behavior requires the representation of a task-set that defines the task-relevance of stimuli and guides stimulus-action mappings. Past experience provides one source of knowledge about likely task demands in the present, with learning enabling future predictions about anticipated demands. We exam... | CrossRef | DigiEnergy | Load Forecasting & Demand Management | AI & Data Science for Urban Energy Systems | Forecasting & Prediction | |
Cost of wind energy generation should include energy storage allowance | 10.1038/s41598-020-59936-x | https://doi.org/10.1038/s41598-020-59936-x | Scientific Reports | 2,020 | Boretti, A.; Castelletto, S. | AbstractThe statistic of wind energy in the US is presently based on annual average capacity factors, and construction cost (CAPEX). This approach suffers from one major downfall, as it does not include any parameter describing the variability of the wind energy generation. As a grid wind and solar only requires signif... | CrossRef | DigiEnergy | Load Forecasting & Demand Management | Novel Low/Zero Carbon Technologies | Optimization & Control | |
Mitigating Curtailment and Carbon Emissions through Load Migration between Data Centers | 10.1016/j.joule.2020.08.001 | https://doi.org/10.1016/j.joule.2020.08.001 | Joule | 2,020 | Zheng, J.; Chien, A.; Suh, S. | CrossRef | DigiEnergy | Load Forecasting & Demand Management | AI & Data Science for Urban Energy Systems | Forecasting & Prediction | ||
Reducing Emissions and Costs with Vehicle-to-Grid | 10.1016/j.joule.2020.08.003 | https://doi.org/10.1016/j.joule.2020.08.003 | Joule | 2,020 | Sutherland, B. | CrossRef | FLEXERGY | Electric Vehicles & Mobility | Demand Response & New Mobilities & Urban Planning | Optimization & Control | ||
Electricity Load Implications of Space Heating Decarbonization Pathways | 10.1016/j.joule.2019.11.011 | https://doi.org/10.1016/j.joule.2019.11.011 | Joule | 2,020 | Waite, M.; Modi, V. | CrossRef | DigiEnergy | Load Forecasting & Demand Management | AI & Data Science for Urban Energy Systems | Forecasting & Prediction | ||
Securing Smart Grids with Machine Learning | 10.1016/j.joule.2020.02.013 | https://doi.org/10.1016/j.joule.2020.02.013 | Joule | 2,020 | Sutherland, B. | CrossRef | DigiEnergy | Load Forecasting & Demand Management | AI & Data Science for Urban Energy Systems | Optimization & Control | ||
Toward Controlled Thermal Energy Storage and Release in Organic Phase Change Materials | 10.1016/j.joule.2020.07.011 | https://doi.org/10.1016/j.joule.2020.07.011 | Joule | 2,020 | Gerkman, M.; Han, G. | CrossRef | CleanTech | Building Energy Materials | Novel Low/Zero Carbon Technologies | Optimization & Control | ||
Ecosystem services at risk: integrating spatiotemporal dynamics of supply and demand to promote long-term provision | 10.1016/j.oneear.2020.11.003 | https://doi.org/10.1016/j.oneear.2020.11.003 | One Earth | 2,020 | Boesing, A.; Prist, P.; Barreto, J.; Hohlenwerger, C.; Maron, M. | CrossRef | DigiEnergy | Load Forecasting & Demand Management | AI & Data Science for Urban Energy Systems | Demand Response & IoT | ||
Impacts of Green New Deal Energy Plans on Grid Stability, Costs, Jobs, Health, and Climate in 143 Countries | 10.1016/j.oneear.2020.01.007 | https://doi.org/10.1016/j.oneear.2020.01.007 | One Earth | 2,020 | Jacobson, M.; Delucchi, M.; Cameron, M.; Coughlin, S.; Hay, C. | CrossRef | DigiEnergy | Load Forecasting & Demand Management | AI & Data Science for Urban Energy Systems | Optimization & Control | ||
Chemical anti-corrosion strategy for stable inverted perovskite solar cells | 10.1126/sciadv.abd1580 | https://doi.org/10.1126/sciadv.abd1580 | Science Advances | 2,020 | Li, X.; Fu, S.; Zhang, W.; Ke, S.; Song, W. | Chemical anticorrosion of metal electrode with benzotriazole inhibitor enhances the stability of perovskite solar cells. | CrossRef | CleanTech | Solar PV & Storage | Novel Low/Zero Carbon Technologies | Optimization & Control | |
Controlling colloidal crystals via morphing energy landscapes and reinforcement learning | 10.1126/sciadv.abd6716 | https://doi.org/10.1126/sciadv.abd6716 | Science Advances | 2,020 | Zhang, J.; Yang, J.; Zhang, Y.; Bevan, M. | A feedback control scheme is developed for rapid assembly of perfect target structures on morphing energy landscapes. | CrossRef | DigiEnergy | Load Forecasting & Demand Management | AI & Data Science for Urban Energy Systems | Optimization & Control | |
Voltage controlled on-demand magnonic nanochannels | 10.1126/sciadv.aba5457 | https://doi.org/10.1126/sciadv.aba5457 | Science Advances | 2,020 | Choudhury, S.; Chaurasiya, A.; Mondal, A.; Rana, B.; Miura, K. | On-demand magnonic nanochannels are achieved by modulation of voltage-controlled magnetic anisotropy in CoFeB/MgO heterostructure. | CrossRef | DigiEnergy | Load Forecasting & Demand Management | AI & Data Science for Urban Energy Systems | Optimization & Control | |
Grid diagrams as tools to investigate knot spaces and topoisomerase-mediated simplification of DNA topology | 10.1126/sciadv.aay1458 | https://doi.org/10.1126/sciadv.aay1458 | Science Advances | 2,020 | Barbensi, A.; Celoria, D.; Harrington, H.; Stasiak, A.; Buck, D. | Grid diagrams grasp the principle of DNA topology simplification by DNA topoisomerases. | CrossRef | DigiEnergy | Load Forecasting & Demand Management | AI & Data Science for Urban Energy Systems | Optimization & Control |
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