@article{YeZhangWarbyetal.2022,
  author    = {Ye, Fangyuan and Zhang, Shuo and Warby, Jonathan and Wu, Jiawei and Gutierrez-Partida, Emilio and Lang, Felix and Shah, Sahil and Saglamkaya, Elifnaz and Sun, Bowen and Zu, Fengshuo and Shoai, Safa and Wang, Haifeng and Stiller, Burkhard and Neher, Dieter and Zhu, Wei-Hong and Stolterfoht, Martin and Wu, Yongzhen},
  title     = {Overcoming C₆₀-induced interfacial recombination in inverted perovskite solar cells by electron-transporting carborane},
  series = {Nature Communications},
  volume    = {13},
  journal   = {Nature Communications},
  publisher = {Springer Nature},
  address   = {London},
  issn      = {2041-1723},
  doi       = {10.1038/s41467-022-34203-x},
  pages     = {12},
  year      = {2022},
  abstract  = {Inverted perovskite solar cells still suffer from significant non-radiative recombination losses at the perovskite surface and across the perovskite/C₆₀ interface, limiting the future development of perovskite-based single- and multi-junction photovoltaics. Therefore, more effective inter- or transport layers are urgently required. To tackle these recombination losses, we introduce ortho-carborane as an interlayer material that has a spherical molecular structure and a three-dimensional aromaticity. Based on a variety of experimental techniques, we show that ortho-carborane decorated with phenylamino groups effectively passivates the perovskite surface and essentially eliminates the non-radiative recombination loss across the perovskite/C₆₀ interface with high thermal stability. We further demonstrate the potential of carborane as an electron transport material, facilitating electron extraction while blocking holes from the interface. The resulting inverted perovskite solar cells deliver a power conversion efficiency of over 23\% with a low non-radiative voltage loss of 110 mV, and retain >97\% of the initial efficiency after 400 h of maximum power point tracking. Overall, the designed carborane based interlayer simultaneously enables passivation, electron-transport and hole-blocking and paves the way toward more efficient and stable perovskite solar cells.},
  language  = {en}
}
@article{ZieglerPfitznerSchulzetal.2022,
  author    = {Ziegler, Joceline and Pfitzner, Bjarne and Schulz, Heinrich and Saalbach, Axel and Arnrich, Bert},
  title     = {Defending against Reconstruction Attacks through Differentially Private Federated Learning for Classification of Heterogeneous Chest X-ray Data},
  series = {Sensors},
  volume    = {22},
  journal   = {Sensors},
  edition   = {14},
  publisher = {MDPI},
  address   = {Basel, Schweiz},
  issn      = {1424-8220},
  doi       = {10.3390/s22145195},
  pages     = {25},
  year      = {2022},
  abstract  = {Privacy regulations and the physical distribution of heterogeneous data are often primary concerns for the development of deep learning models in a medical context. This paper evaluates the feasibility of differentially private federated learning for chest X-ray classification as a defense against data privacy attacks. To the best of our knowledge, we are the first to directly compare the impact of differentially private training on two different neural network architectures, DenseNet121 and ResNet50. Extending the federated learning environments previously analyzed in terms of privacy, we simulated a heterogeneous and imbalanced federated setting by distributing images from the public CheXpert and Mendeley chest X-ray datasets unevenly among 36 clients. Both non-private baseline models achieved an area under the receiver operating characteristic curve (AUC) of 0.940.94 on the binary classification task of detecting the presence of a medical finding. We demonstrate that both model architectures are vulnerable to privacy violation by applying image reconstruction attacks to local model updates from individual clients. The attack was particularly successful during later training stages. To mitigate the risk of a privacy breach, we integrated R{\´e}nyi differential privacy with a Gaussian noise mechanism into local model training. We evaluate model performance and attack vulnerability for privacy budgets ε∈{1,3,6,10}�∈{1,3,6,10}. The DenseNet121 achieved the best utility-privacy trade-off with an AUC of 0.940.94 for ε=6�=6. Model performance deteriorated slightly for individual clients compared to the non-private baseline. The ResNet50 only reached an AUC of 0.760.76 in the same privacy setting. Its performance was inferior to that of the DenseNet121 for all considered privacy constraints, suggesting that the DenseNet121 architecture is more robust to differentially private training.},
  language  = {en}
}
@article{ZurellKoenigMalchowetal.2022,
  author    = {Zurell, Damaris and K{\"o}nig, Christian and Malchow, Anne-Kathleen and Kapitza, Simon and Bocedi, Greta and Travis, Justin M. J. and Fandos, Guillermo},
  title     = {Spatially explicit models for decision-making in animal conservation and restoration},
  series = {Ecography : pattern and diversity in ecology / Nordic Ecologic Society Oikos},
  journal   = {Ecography : pattern and diversity in ecology / Nordic Ecologic Society Oikos},
  number    = {4},
  publisher = {Wiley-Blackwell},
  address   = {Oxford},
  issn      = {1600-0587},
  doi       = {10.1111/ecog.05787},
  pages     = {1 -- 16},
  year      = {2022},
  abstract  = {Models are useful tools for understanding and predicting ecological patterns and processes. Under ongoing climate and biodiversity change, they can greatly facilitate decision-making in conservation and restoration and help designing adequate management strategies for an uncertain future. Here, we review the use of spatially explicit models for decision support and to identify key gaps in current modelling in conservation and restoration. Of 650 reviewed publications, 217 publications had a clear management application and were included in our quantitative analyses. Overall, modelling studies were biased towards static models (79\%), towards the species and population level (80\%) and towards conservation (rather than restoration) applications (71\%). Correlative niche models were the most widely used model type. Dynamic models as well as the gene-to-individual level and the community-to-ecosystem level were underrepresented, and explicit cost optimisation approaches were only used in 10\% of the studies. We present a new model typology for selecting models for animal conservation and restoration, characterising model types according to organisational levels, biological processes of interest and desired management applications. This typology will help to more closely link models to management goals. Additionally, future efforts need to overcome important challenges related to data integration, model integration and decision-making. We conclude with five key recommendations, suggesting that wider usage of spatially explicit models for decision support can be achieved by 1) developing a toolbox with multiple, easier-to-use methods, 2) improving calibration and validation of dynamic modelling approaches and 3) developing best-practise guidelines for applying these models. Further, more robust decision-making can be achieved by 4) combining multiple modelling approaches to assess uncertainty, and 5) placing models at the core of adaptive management. These efforts must be accompanied by long-term funding for modelling and monitoring, and improved communication between research and practise to ensure optimal conservation and restoration outcomes.},
  language  = {en}
}