@article{DettmannHuittinenNicolasetal.2023, author = {Dettmann, Sophie and Huittinen, Nina Maria and Nicolas, Jahn and Kretzschmar, Jerome and Kumke, Michael and Kutyma, Tamara and Lohmann, Janik and Reich, Tobias and Schmeide, Katja and Azzam, Salim Shams Aldin and Spittler, Leon and Stietz, Janina}, title = {Influence of gluconate on the retention of Eu(III), Am(III), Th(IV), Pu(IV), and U(VI) by C-S-H (C/S = 0.8)}, series = {Frontiers in Nuclear Engineering}, volume = {2}, journal = {Frontiers in Nuclear Engineering}, publisher = {Frontiers Media}, address = {Lausanne}, issn = {2813-3412}, doi = {10.3389/fnuen.2023.1124856}, pages = {15}, year = {2023}, abstract = {The retention of actinides in different oxidation states (An(X), X = III, IV, VI) by a calcium-silicate-hydrate (C-S-H) phase with a Ca/Si (C/S) ratio of 0.8 was investigated in the presence of gluconate (GLU). The actinides considered were Am(III), Th(IV), Pu(IV), and U(VI). Eu(III) was investigated as chemical analogue for Am(III) and Cm(III). In addition to the ternary systems An(X)/GLU/C-S-H, also binary systems An(X)/C-S-H, GLU/C-S-H, and An(X)/GLU were studied. Complementary analytical techniques were applied to address the different specific aspects of the binary and ternary systems. Time-resolved laser-induced luminescence spectroscopy (TRLFS) was applied in combination with parallel factor analysis (PARAFAC) to identify retained species and to monitor species-selective sorption kinetics. ¹³C and ²⁹Si magic-angle-spinning (MAS) nuclear magnetic resonance (NMR) spectroscopy and X-ray photoelectron spectroscopy (XPS) were applied to determine the bulk structure and the composition of the C-S-H surface, respectively, in the absence and presence of GLU. The interaction of Th(IV) with GLU in different electrolytes was studied by capillary electrophoresis-inductively coupled plasma mass spectrometry (CE-ICP-MS). The influence of GLU on An(X) retention was investigated for a large concentration range up to 10⁻² M. The results showed that GLU had little to no effect on the overall An(X) retention by C-S-H with C/S of 0.8, regardless of the oxidation state of the actinides. For Eu(III), the TRLFS investigations additionally implied the formation of a Eu(III)-bearing precipitate with dissolved constituents of the C-S-H phase, which becomes structurally altered by the presence of GLU. For U(VI) sorption on the C-S-H phase, only a small influence of GLU could be established in the luminescence spectroscopic investigations, and no precipitation of U(VI)-containing secondary phases could be identified.}, language = {en} } @phdthesis{Lindinger2023, author = {Lindinger, Jakob}, title = {Variational inference for composite Gaussian process models}, doi = {10.25932/publishup-60444}, url = {http://nbn-resolving.de/urn:nbn:de:kobv:517-opus4-604441}, school = {Universit{\"a}t Potsdam}, pages = {xi, 122}, year = {2023}, abstract = {Most machine learning methods provide only point estimates when being queried to predict on new data. This is problematic when the data is corrupted by noise, e.g. from imperfect measurements, or when the queried data point is very different to the data that the machine learning model has been trained with. Probabilistic modelling in machine learning naturally equips predictions with corresponding uncertainty estimates which allows a practitioner to incorporate information about measurement noise into the modelling process and to know when not to trust the predictions. A well-understood, flexible probabilistic framework is provided by Gaussian processes that are ideal as building blocks of probabilistic models. They lend themself naturally to the problem of regression, i.e., being given a set of inputs and corresponding observations and then predicting likely observations for new unseen inputs, and can also be adapted to many more machine learning tasks. However, exactly inferring the optimal parameters of such a Gaussian process model (in a computationally tractable manner) is only possible for regression tasks in small data regimes. Otherwise, approximate inference methods are needed, the most prominent of which is variational inference. In this dissertation we study models that are composed of Gaussian processes embedded in other models in order to make those more flexible and/or probabilistic. The first example are deep Gaussian processes which can be thought of as a small network of Gaussian processes and which can be employed for flexible regression. The second model class that we study are Gaussian process state-space models. These can be used for time-series modelling, i.e., the task of being given a stream of data ordered by time and then predicting future observations. For both model classes the state-of-the-art approaches offer a trade-off between expressive models and computational properties (e.g. speed or convergence properties) and mostly employ variational inference. Our goal is to improve inference in both models by first getting a deep understanding of the existing methods and then, based on this, to design better inference methods. We achieve this by either exploring the existing trade-offs or by providing general improvements applicable to multiple methods. We first provide an extensive background, introducing Gaussian processes and their sparse (approximate and efficient) variants. We continue with a description of the models under consideration in this thesis, deep Gaussian processes and Gaussian process state-space models, including detailed derivations and a theoretical comparison of existing methods. Then we start analysing deep Gaussian processes more closely: Trading off the properties (good optimisation versus expressivity) of state-of-the-art methods in this field, we propose a new variational inference based approach. We then demonstrate experimentally that our new algorithm leads to better calibrated uncertainty estimates than existing methods. Next, we turn our attention to Gaussian process state-space models, where we closely analyse the theoretical properties of existing methods.The understanding gained in this process leads us to propose a new inference scheme for general Gaussian process state-space models that incorporates effects on multiple time scales. This method is more efficient than previous approaches for long timeseries and outperforms its comparison partners on data sets in which effects on multiple time scales (fast and slowly varying dynamics) are present. Finally, we propose a new inference approach for Gaussian process state-space models that trades off the properties of state-of-the-art methods in this field. By combining variational inference with another approximate inference method, the Laplace approximation, we design an efficient algorithm that outperforms its comparison partners since it achieves better calibrated uncertainties.}, language = {en} } @article{SchladebachBarsan2023, author = {Schladebach, Marcus and B{\^a}rsan, Catinca}, title = {Der Mondbergbau als v{\"o}lkerrechtliche Herausforderung}, series = {Zeitschrift f{\"u}r Bergrecht}, volume = {164}, journal = {Zeitschrift f{\"u}r Bergrecht}, number = {2}, publisher = {Carl Heymanns Verlag}, address = {K{\"o}ln}, issn = {0340-3939}, pages = {97 -- 107}, year = {2023}, language = {de} } @article{BaumAbramovaMeissneretal.2023, author = {Baum, Katharina and Abramova, Olga and Meißner, Stefan and Krasnova, Hanna}, title = {The effects of targeted political advertising on user privacy concerns and digital product acceptance}, series = {Electronic markets}, volume = {33}, journal = {Electronic markets}, number = {46}, publisher = {Springer}, address = {Heidelberg}, issn = {1019-6781}, doi = {10.1007/s12525-023-00656-1}, pages = {17}, year = {2023}, abstract = {Online businesses are increasingly relying on targeted advertisements as a revenue stream, which might lead to privacy concerns and hinder product adoption. Therefore, it is crucial for online companies to understand which types of targeted advertisements consumers will accept. In recent years, users have been increasingly targeted by political advertisements, which has caused adverse reactions in media and society. Nonetheless, few studies experimentally investigate user privacy concerns and their role in acceptance decisions in response to targeted political advertisements. To fill this gap, we explore the magnitude of privacy concerns towards targeted political ads compared to "traditional" targeting in the product context. Surprisingly, we find no notable differences in privacy concerns between these data use purposes. In the next step, user preferences over ad types are elicited with the help of a discrete choice experiment in the mobile app adoption context. Our findings suggest that while targeted political advertising is somewhat less desirable than targeted product advertising, the odds of choosing an app are statistically insignificant between two data use purposes. Together, these results contribute to a better understanding of users' privacy concerns and preferences in the context of targeted political advertising online.}, language = {en} } @article{HagemannAbramova2023, author = {Hagemann, Linus and Abramova, Olga}, title = {Sentiment, we-talk and engagement on social media}, series = {Internet research}, volume = {33}, journal = {Internet research}, number = {6}, publisher = {Emeral}, address = {Bingley}, issn = {1066-2243}, doi = {10.1108/INTR-12-2021-0885}, pages = {2058 -- 2085}, year = {2023}, abstract = {Purpose Given inconsistent results in prior studies, this paper applies the dual process theory to investigate what social media messages yield audience engagement during a political event. It tests how affective cues (emotional valence, intensity and collective self-representation) and cognitive cues (insight, causation, certainty and discrepancy) contribute to public engagement. Design/methodology/approach The authors created a dataset of more than three million tweets during the 2020 United States (US) presidential elections. Affective and cognitive cues were assessed via sentiment analysis. The hypotheses were tested in negative binomial regressions. The authors also scrutinized a subsample of far-famed Twitter users. The final dataset, scraping code, preprocessing and analysis are available in an open repository. Findings The authors found the prominence of both affective and cognitive cues. For the overall sample, negativity bias was registered, and the tweet's emotionality was negatively related to engagement. In contrast, in the sub-sample of tweets from famous users, emotionally charged content produced higher engagement. The role of sentiment decreases when the number of followers grows and ultimately becomes insignificant for Twitter participants with many followers. Collective self-representation ("we-talk") is consistently associated with more likes, comments and retweets in the overall sample and subsamples. Originality/value The authors expand the dominating one-sided perspective to social media message processing focused on the peripheral route and hence affective cues. Leaning on the dual process theory, the authors shed light on the effectiveness of both affective (peripheral route) and cognitive (central route) cues on information appeal and dissemination on Twitter during a political event. The popularity of the tweet's author moderates these relationships.}, language = {en} } @article{ApergiZimmermannWekoetal.2023, author = {Apergi, Maria and Zimmermann, Eva and Weko, Silvia and Lilliestam, Johan}, title = {Is renewable energy technology trade more or less conflictive than other trade?}, series = {Energy policy}, volume = {177}, journal = {Energy policy}, publisher = {Elsevier}, address = {Amsterdam}, issn = {0301-4215}, doi = {10.1016/j.enpol.2023.113538}, year = {2023}, abstract = {Renewable energy changes the geopolitics of energy: whereas access to fossil fuel resources were key in the past, control over technology and industry will be key in the future. Consequently, different scholars have predicted that a growing focus on renewables will increase or decrease conflict in the energy sector, with no consensus on which is most likely. Here, we investigate the degree of conflict in renewable energy technology (RET) trade by analyzing data on 7041 trade conflicts 1995-2020, guided by two sets of theory-driven hypotheses. We show that RET trade is associated with more, longer, and more intense trade conflicts than other trade conflicts for 1995-2016. This supports the neorealist, geo-economic view of countries being willing to risk conflict to increase their share of a market rather than avoiding conflicts to increase the overall market size. It also contradicts the view that renewables will reduce conflict: at least in the past and regarding trade, it has increased rather than decreased conflict. For 2017-2020, this trend is reversed and RET trade became significantly less conflictive than other trade. Our findings imply that improved conflict-resolution institutions for RET are needed. We also suggest establishing specific institutions to govern trade in immature technologies.}, language = {en} } @article{ArguellodeSouzaSamprognaMohorGuzmanAriasetal.2023, author = {Arguello de Souza, Felipe Augusto and Samprogna Mohor, Guilherme and Guzman Arias, Diego Alejandro and Sarmento Buarque, Ana Carolina and Taffarello, Denise and Mendiondo, Eduardo Mario}, title = {Droughts in S{\~a}o Paulo}, series = {Urban water journal}, volume = {20}, journal = {Urban water journal}, number = {10}, publisher = {Taylor \& Francis}, address = {London [u.a.]}, issn = {1573-062X}, doi = {10.1080/1573062X.2022.2047735}, pages = {1682 -- 1694}, year = {2023}, abstract = {Literature has suggested that droughts and societies are mutually shaped and, therefore, both require a better understanding of their coevolution on risk reduction and water adaptation. Although the Sao Paulo Metropolitan Region drew attention because of the 2013-2015 drought, this was not the first event. This paper revisits this event and the 1985-1986 drought to compare the evolution of drought risk management aspects. Documents and hydrological records are analyzed to evaluate the hazard intensity, preparedness, exposure, vulnerability, responses, and mitigation aspects of both events. Although the hazard intensity and exposure of the latter event were larger than the former one, the policy implementation delay and the dependency of service areas in a single reservoir exposed the region to higher vulnerability. In addition to the structural and non-structural tools implemented just after the events, this work raises the possibility of rainwater reuse for reducing the stress in reservoirs.}, language = {en} } @misc{PanzerBenderGronau2023, author = {Panzer, Marcel and Bender, Benedict and Gronau, Norbert}, title = {A deep reinforcement learning based hyper-heuristic for modular production control}, series = {Zweitver{\"o}ffentlichungen der Universit{\"a}t Potsdam : Wirtschafts- und Sozialwissenschaftliche Reihe}, journal = {Zweitver{\"o}ffentlichungen der Universit{\"a}t Potsdam : Wirtschafts- und Sozialwissenschaftliche Reihe}, issn = {1867-5808}, doi = {10.25932/publishup-60564}, url = {http://nbn-resolving.de/urn:nbn:de:kobv:517-opus4-605642}, pages = {24}, year = {2023}, abstract = {In nowadays production, fluctuations in demand, shortening product life-cycles, and highly configurable products require an adaptive and robust control approach to maintain competitiveness. This approach must not only optimise desired production objectives but also cope with unforeseen machine failures, rush orders, and changes in short-term demand. Previous control approaches were often implemented using a single operations layer and a standalone deep learning approach, which may not adequately address the complex organisational demands of modern manufacturing systems. To address this challenge, we propose a hyper-heuristics control model within a semi-heterarchical production system, in which multiple manufacturing and distribution agents are spread across pre-defined modules. The agents employ a deep reinforcement learning algorithm to learn a policy for selecting low-level heuristics in a situation-specific manner, thereby leveraging system performance and adaptability. We tested our approach in simulation and transferred it to a hybrid production environment. By that, we were able to demonstrate its multi-objective optimisation capabilities compared to conventional approaches in terms of mean throughput time, tardiness, and processing of prioritised orders in a multi-layered production system. The modular design is promising in reducing the overall system complexity and facilitates a quick and seamless integration into other scenarios.}, language = {en} } @article{PanzerBenderGronau2023, author = {Panzer, Marcel and Bender, Benedict and Gronau, Norbert}, title = {A deep reinforcement learning based hyper-heuristic for modular production control}, series = {International journal of production research}, journal = {International journal of production research}, publisher = {Taylor \& Francis}, address = {London}, issn = {0020-7543}, doi = {10.1080/00207543.2023.2233641}, pages = {1 -- 22}, year = {2023}, abstract = {In nowadays production, fluctuations in demand, shortening product life-cycles, and highly configurable products require an adaptive and robust control approach to maintain competitiveness. This approach must not only optimise desired production objectives but also cope with unforeseen machine failures, rush orders, and changes in short-term demand. Previous control approaches were often implemented using a single operations layer and a standalone deep learning approach, which may not adequately address the complex organisational demands of modern manufacturing systems. To address this challenge, we propose a hyper-heuristics control model within a semi-heterarchical production system, in which multiple manufacturing and distribution agents are spread across pre-defined modules. The agents employ a deep reinforcement learning algorithm to learn a policy for selecting low-level heuristics in a situation-specific manner, thereby leveraging system performance and adaptability. We tested our approach in simulation and transferred it to a hybrid production environment. By that, we were able to demonstrate its multi-objective optimisation capabilities compared to conventional approaches in terms of mean throughput time, tardiness, and processing of prioritised orders in a multi-layered production system. The modular design is promising in reducing the overall system complexity and facilitates a quick and seamless integration into other scenarios.}, language = {en} } @article{ErlerRiebeBeitzetal.2023, author = {Erler, Alexander and Riebe, Daniel and Beitz, Toralf and L{\"o}hmannsr{\"o}ben, Hans-Gerd and Leenen, Mathias and P{\"a}tzold, Stefan and Ostermann, Markus and W{\´o}jcik, Michał}, title = {Mobile laser-induced breakdown spectroscopy for future application in precision agriculture}, series = {Sensors}, volume = {23}, journal = {Sensors}, number = {16}, publisher = {MDPI}, address = {Basel}, issn = {1424-8220}, doi = {10.3390/s23167178}, pages = {17}, year = {2023}, abstract = {In precision agriculture, the estimation of soil parameters via sensors and the creation of nutrient maps are a prerequisite for farmers to take targeted measures such as spatially resolved fertilization. In this work, 68 soil samples uniformly distributed over a field near Bonn are investigated using laser-induced breakdown spectroscopy (LIBS). These investigations include the determination of the total contents of macro- and micronutrients as well as further soil parameters such as soil pH, soil organic matter (SOM) content, and soil texture. The applied LIBS instruments are a handheld and a platform spectrometer, which potentially allows for the single-point measurement and scanning of whole fields, respectively. Their results are compared with a high-resolution lab spectrometer. The prediction of soil parameters was based on multivariate methods. Different feature selection methods and regression methods like PLS, PCR, SVM, Lasso, and Gaussian processes were tested and compared. While good predictions were obtained for Ca, Mg, P, Mn, Cu, and silt content, excellent predictions were obtained for K, Fe, and clay content. The comparison of the three different spectrometers showed that although the lab spectrometer gives the best results, measurements with both field spectrometers also yield good results. This allows for a method transfer to the in-field measurements.}, language = {en} } @article{KapidzicFreyNeubergeretal.2023, author = {Kapidzic, Sanja and Frey, Felix and Neuberger, Christoph and Stieglitz, Stefan and Mirbabaie, Milad}, title = {Crisis communication on Twitter}, series = {International journal of communication}, volume = {17}, journal = {International journal of communication}, publisher = {The Annenberg Center for Communication}, address = {Los Angeles, Calif.}, issn = {1932-8036}, pages = {735 -- 754}, year = {2023}, abstract = {The study explores differences between three user types in the top tweets about the 2015 "refugee crisis" in Germany and presents the results of a quantitative content analysis. All tweets with the keyword "Fl{\"u}chtlinge" posted for a monthlong period following September 13, 2015, the day Germany decided to implement border controls, were collected (N = 763,752). The top 2,495 tweets according to number of retweets were selected for analysis. Differences between news media, public and private actor tweets in topics, tweet characteristics such as tone and opinion expression, links, and specific sentiments toward refugees were analyzed. We found strong differences between the tweets. Public actor tweets were the main source of positive sentiment toward refugees and the main information source on refugee support. News media tweets mostly reflected traditional journalistic norms of impartiality and objectivity, whereas private actor tweets were more diverse in sentiments toward refugees.}, language = {en} }