@article{FayyaziSardarThomasetal.2023,
  author    = {Fayyazi, Mojgan and Sardar, Paramjotsingh and Thomas, Sumit Infent and Daghigh, Roonak and Jamali, Ali and Esch, Thomas and Kemper, Hans and Langari, Reza and Khayyam, Hamid},
  title     = {Artificial intelligence/machine learning in energy management systems, control, and optimization of hydrogen fuel cell vehicles},
  volume    = {15},
  number    = {6},
  publisher = {MDPI},
  address   = {Basel},
  doi       = {10.3390/su15065249},
  pages     = {38},
  year      = {2023},
  abstract  = {Environmental emissions, global warming, and energy-related concerns have accelerated the advancements in conventional vehicles that primarily use internal combustion engines. Among the existing technologies, hydrogen fuel cell electric vehicles and fuel cell hybrid electric vehicles may have minimal contributions to greenhouse gas emissions and thus are the prime choices for environmental concerns. However, energy management in fuel cell electric vehicles and fuel cell hybrid electric vehicles is a major challenge. Appropriate control strategies should be used for effective energy management in these vehicles. On the other hand, there has been significant progress in artificial intelligence, machine learning, and designing data-driven intelligent controllers. These techniques have found much attention within the community, and state-of-the-art energy management technologies have been developed based on them. This manuscript reviews the application of machine learning and intelligent controllers for prediction, control, energy management, and vehicle to everything (V2X) in hydrogen fuel cell vehicles. The effectiveness of data-driven control and optimization systems are investigated to evolve, classify, and compare, and future trends and directions for sustainability are discussed.},
  language  = {en}
}
@incollection{CroonCzarnecki2021,
  author    = {Croon, Philipp and Czarnecki, Christian},
  title     = {Liability for loss or damages caused by RPA},
  series = {Robotic process automation : Management, technology, applications},
  booktitle = {Robotic process automation : Management, technology, applications},
  editor    = {Czarnecki, Christian and Fettke, Peter},
  publisher = {De Gruyter},
  address   = {Oldenbourg},
  isbn      = {9783110676778},
  doi       = {10.1515/9783110676693-202},
  pages     = {135 -- 151},
  year      = {2021},
  abstract  = {Intelligent autonomous software robots replacing human activities and performing administrative processes are reality in today's corporate world. This includes, for example, decisions about invoice payments, identification of customers for a marketing campaign, and answering customer complaints. What happens if such a software robot causes a damage? Due to the complete absence of human activities, the question is not trivial. It could even happen that no one is liable for a damage towards a third party, which could create an uncalculatable legal risk for business partners. Furthermore, the implementation and operation of those software robots involves various stakeholders, which result in the unsolvable endeavor of identifying the originator of a damage. Overall it is advisable to all involved parties to carefully consider the legal situation. This chapter discusses the liability of software robots from an interdisciplinary perspective. Based on different technical scenarios the legal aspects of liability are discussed.},
  language  = {en}
}