One Monad to Prove Them All
Dylus, S., Christiansen, J., & Teegen, F. (2019). One Monad to Prove Them All. The Art, Science, and Engineering of Programming, 3. http://doi.org/10.22152/programming-journal.org/2019/3/8
Publikationen
2019
Usage, retention, and abandonment of agile practices: a survey and interviews results
Nurdiani, I., Börstler, J., Fricker, S., & Petersen, K. (2019). Usage, retention, and abandonment of agile practices: a survey and interviews results. e-Informatica Software Engineering Journal, 13.Exportformate
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Simultaneous Inertia Contribution and Optimal Grid Utilization with Wind Turbines
Jauch, C., & Gloe, A. (2019). Simultaneous Inertia Contribution and Optimal Grid Utilization with Wind Turbines. Energies, 3013. http://doi.org/10.3390/en12153013Exportformate
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Load Analysis of Hydraulic-Pneumatic Flywheel Configurations Integrated in a Wind Turbine Rotor
Jauch, C., & Hippel, S. (2019). Load Analysis of Hydraulic-Pneumatic Flywheel Configurations Integrated in a Wind Turbine Rotor. Wind Energy, 22, 13. http://doi.org/10.1002/we.2349Exportformate
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Doppelbesteuerung beim Übergang zur nachgelagerten Besteuerung gesetzlicher Renten? Steuersystematische Überlegungen und empirische Ergebnisse
Chirvi, M., & Maiterth, R. (2019). Doppelbesteuerung beim Übergang zur nachgelagerten Besteuerung gesetzlicher Renten? Steuersystematische Überlegungen und empirische Ergebnisse. Steuer und Wirtschaft, 96(2). http://doi.org/10.9785/stuw-2019-960205Abstract
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2018
Still wrong use of pairings in cryptography
Uzunkol, O., & Kiraz, M. S. (2018). Still wrong use of pairings in cryptography. Applied Mathematics and Computation, 333, 467–479. http://doi.org/10.1016/j.amc.2018.03.062 (Original work published 2026)Exportformate
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The Open Energy Modelling Framework (oemof) - A new approach to facilitate open science in energy system modelling
Hilpert, S., Kaldemeyer, C., Krien, U., Günther, S., Wingenbach, C., & Plessmann, G. (2018). The Open Energy Modelling Framework (oemof) - A new approach to facilitate open science in energy system modelling. Energy Strategy Reviews, 22, 16–25. http://doi.org/10.1016/j.esr.2018.07.001 (Original work published 2026)Exportformate
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Analysis of control-oriented wake modeling tools using lidar field results
Annoni, J., Fleming, P., Scholbrock, A., Roadman, J., Dana, S., Adcock, C., … Schlipf, D. (2018). Analysis of control-oriented wake modeling tools using lidar field results. Wind Energy Science, 3, 819–831. http://doi.org/10.5194/wes-3-819-2018 (Original work published 2026)Exportformate
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IEA wind task 32: wind lidar identifying and mitigating barriers to the adoption of wind lidar
Clifton, A., Clive, P., Gottschall, J., Schlipf, D., Simley, E., Simmons, L., … Würth, I. (2018). IEA wind task 32: wind lidar identifying and mitigating barriers to the adoption of wind lidar. Remote Sensing, 10, 406. http://doi.org/10.3390/rs10030406 (Original work published 2026)Exportformate
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Optimizing lidars for wind turbine control applications results from the IEA wind task 32 workshop
Simley, E., Fürst, H., Haizmann, F., & Schlipf, D. (2018). Optimizing lidars for wind turbine control applications results from the IEA wind task 32 workshop. Remote Sensing, 10, 863. http://doi.org/10.3390/rs10060863 (Original work published 2026)Exportformate
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