@misc{12896, abstract = {{Poly-3-hydroxybutyrate (P3HB) is a promising alternative to persistent conventional plastics, capable of biodegrading within months. However, its microbial-driven degradation raises concerns about nutrient immobilization and impacts on plant growth. The biodegradation process occurs in multiple stages, during which shifts in the microbial community can alter soil properties and influence utilization of both intrinsic and polymer-derived organic matter. This study employs a novel approach to investigate how nutrient dynamics during the late stage of P3HB biodegradation affect lettuce (Lactuca sativa var. capitata cv. Brilliant) growth. Soil-to-sand mixtures (100_0, 80_20, 60_40, 40_60, 20_80, and 0_100 ratios) were spiked with P3HB, allowed to biodegrade for eight weeks, and then planted with sprouted lettuce seeds, which were cultivated for another eight weeks. P3HB addition inhibited plant growth and root development in all soil-sand mixtures. However, increasing the sand proportion enhanced plants' nitrogen content by 13-45 % compared to 100 % soil + P3HB. Depending on the sand-to-soil ratio, P3HB stimulated most enzymes involved in carbon, nitrogen and phosphorus acquisition. Basal and substrate-induced respirations were 9-209 % higher under P3HB addition compared to P3HB-free soil, likely due to an increase in the stabilized soil organic matter fraction. Residual P3HB analysis revealed that diluting soil with 20 % sand accelerated biodegradation, despite a decrease in bacterial abundance. In the 80_20 variant, the microbial community shifted toward higher fungal abundance, 19 % more than in 100_0 soil. While microbial proliferation was observed, it effect was outweighed by negative impacts on dry aboveground and root biomass. The highest P3HB biodegradation rate occurred in the 80_20 variant, underscoring soil texture as a critical factor in P3HB biodegradation. While microbial communities can degrade bioplastics, this process may compromise plant nutrient availability and hinder plant growth. }}, author = {{Brtnicky, Martin and Mustafa, Adnan and Holatko, Jiri and Gunina, Anna and Ondrasek, Gabrijel and Naveed, Muhammad and Hammerschmiedt, Tereza and Kamenikova, Eliska and Alamri, Saud and Siddiqui, Manzer H. and Kintl, Antonin and Baltazar, Tivadar and Malicek, Ondrej and Kucerik, Jiri}}, booktitle = {{Environmental Research}}, issn = {{1096-0953}}, keywords = {{Bioplastics, Nutrient acquisition, Plant growth reduction, Soil microbes, Soil texture.}}, publisher = {{Elsevier BV}}, title = {{{Soil texture-driven modulation of poly-3-hydroxybutyrate (P3HB) biodegradation: Microbial shifts, and trade-offs between nutrient availability and lettuce growth}}}, doi = {{10.1016/j.envres.2025.121618}}, volume = {{278}}, year = {{2025}}, } @misc{12215, abstract = {{Water-level reduction frequently occurs in deep reservoirs, but its effect on dissolved oxygen concentration is not well understood. In this study we used a well-established water quality model to illustrate effects of water level dynamics on oxygen concentration in Rappbode Reservoir, Germany. We then systematically elucidated the potential of selective withdrawal to control hypoxia under changing water levels. Our results documented a gradual decrease of hypolimnetic oxygen concentration under decreasing water level, and hypoxia occurred when the initial level was lower than 410 m a.s.l (71 m relative to the reservoir bottom). We also suggested that changes of hypoxic region, under increasing hypolimnetic withdrawal discharge, followed a unimodal trajectory with the maximum hypoxic area projected under the discharge between 3 m3/sec and 4 m3/sec. Besides, our results illustrated the extent of hypoxia was most effectively inhibited if the withdrawal strategy was applied at the end of stratification with the outlet elevation at the deepest part of the reservoir. Moreover, hypoxia can be totally avoided under a hybrid elevation withdrawal strategy using surface withdrawal during early and mid stratification, and deep withdrawal at the end of stratification. We further confirmed the decisive role of thermal structure in the formation of hypoxia under water-level reduction and withdrawal strategies. We believe the conclusions from this study can be applied to many deep waters in the temperate zone, and the results should guide stakeholders to mitigate negative impacts of hypoxia on aquatic ecosystems.}}, author = {{Mi, Chenxi and Rinke, Karsten and Shatwell, Tom}}, booktitle = {{Journal of Environmental Sciences}}, issn = {{1878-7320}}, keywords = {{Hypoxia, Water-level reduction, Hypolimnetic water withdrawal, Stratification phenology, Water quality simulation, Sediment oxygen demand}}, number = {{12}}, pages = {{127--139}}, publisher = {{Elsevier BV}}, title = {{{Optimizing selective withdrawal strategies to mitigate hypoxia under water-level reduction in Germany's largest drinking water reservoir}}}, doi = {{10.1016/j.jes.2023.06.025}}, volume = {{146}}, year = {{2024}}, } @misc{13224, abstract = {{This paper presents a robust methodology for optimizing CO2 emissions and electricity costs in industrial applications, with the aim of developing a flexible and dynamic energy management strategy that balances sustainability and cost-efficiency. Addressing the growing need for sustainable and economically viable energy solutions amidst the global urgency of climate change mitigation, the proposed approach is based on dynamic energy management techniques that minimize dependence on grid electricity, which can fluctuate between energy import and export. A flexible cost function is developed to simultaneously account for CO2 emissions and electricity prices, enabling a balance between environmental impact and operational costs. The optimization framework employs Mixed-Integer Linear Programming (MILP) to derive the optimal energy management strategy, showcasing significant potential for reducing both CO2 emissions and electricity costs. Although the methodology is demonstrated in a specific industrial setting, its flexible design ensures applicability across various energy profiles and operational scenarios, making it relevant for a wide range of industrial applications.}}, author = {{Mousavi, Seyed Davood and Griese, Martin and Schulte, Thomas}}, booktitle = {{2024 International Conference on Electrical and Computer Engineering Researches (ICECER)}}, keywords = {{CO2 Reduction, Electricity Cost Minimization, Life Cycle Assessment, MILP, Smart-E-Factory, Dynamic Energy Management}}, location = {{Gaborone, Botswana }}, publisher = {{IEEE}}, title = {{{Dynamic Optimization of CO2 Emissions and Electricity Costs in Smart Factories}}}, doi = {{10.1109/icecer62944.2024.10920418}}, year = {{2024}}, } @misc{12804, abstract = {{Data in many applications follows systems of Ordinary Differential Equations (ODEs). This paper presents a novel algorithmic and symbolic construction for covariance functions of Gaussian Processes (GPs) with realizations strictly following a system of linear homogeneous ODEs with constant coefficients, which we call LODE-GPs. Introducing this strong inductive bias into a GP improves modelling of such data. Using smith normal form algorithms, a symbolic technique, we overcome two current restrictions in the state of the art: (1) the need for certain uniqueness conditions in the set of solutions, typically assumed in classical ODE solvers and their probabilistic counterparts, and (2) the restriction to controllable systems, typically assumed when encoding differential equations in covariance functions. We show the effectiveness of LODE-GPs in a number of experiments, for example learning physically interpretable parameters by maximizing the likelihood.}}, author = {{Besginow, Andreas and Lange-Hegermann, Markus}}, booktitle = {{36th Conference on Neural Information Processing Systems (NeurIPS 2022) }}, editor = {{Koyejo, S. and Mohamed, S. and Agarwal, A. and Belgrave, D. and Cho, K. and Oh, A.}}, isbn = {{978-1-7138-7108-8 }}, issn = {{1049-5258}}, keywords = {{SMITH NORMAL-FORM, ALGORITHMS, REDUCTION}}, location = {{New Orleans, La.; Online}}, pages = {{29386 -- 29399}}, publisher = {{Curran Associates, Inc.}}, title = {{{Constraining Gaussian Processes to Systems of Linear Ordinary Differential Equations}}}, volume = {{35}}, year = {{2022}}, } @misc{12803, abstract = {{The increasing amount of alarms and information for an operator in a modern plant becomes a significant safety risk. Although the notifications are a valuable support, they also lead to the curse of overloading with information for the operator. Due to the huge amount of alarms it is almost impossible to separate the crucial information from the insignificant ones. Therefore, new procedures are required to reduce these alarm floods and support the operator to minimize the safety risk. One approach is based on learning a causal model that represents the relationships between the alarms. This allows alarm sequences that are causally implied to be reduced to the root cause alarm. Fundamental element of this approach is the causal model. Therefore in this work, different probabilistic graphical models are considered and evaluated on the basis of appropriate criteria. A real use case of a bottle filling module serves as a benchmark for how well they are suitable as a causal model for the application in alarm flood reduction.}}, author = {{Wunderlich, Paul and Hranisavljevic, Nemanja}}, booktitle = {{2019 IEEE 17th International Conference on Industrial Informatics (INDIN)}}, isbn = {{978-1-7281-2928-0}}, keywords = {{probabilistic graphical model, causal model, alarm flood reduction, Bayesian network, Markov chain, restricted boltzmann machine, automata}}, location = {{Helsinki, Finland }}, pages = {{1285--1290}}, publisher = {{IEEE}}, title = {{{Comparison of Different Probabilistic Graphical Models as Causal Models in Alarm Flood Reduction}}}, doi = {{10.1109/indin41052.2019.8972251}}, year = {{2019}}, } @inproceedings{2005, abstract = {{We present a method for the fast and robust linear classification of badly conditioned data. In our considerations, badly conditioned data are such data which are numerically difficult to handle. Due to, e.g. a large number of features or a large number of objects representing classes as well as noise, outliers or incompleteness, the common software computation of the discriminating linear combination of features between classes fails or is extremely time consuming. The theoretical foundations of our approach are based on the single feature ranking, which allows fast calculation of the approximative initial classification boundary. For the increasing of classification accuracy of this boundary, the refinement is performed in the lower dimensional space. Our approach is tested on several datasets from UCI Reposi-tiory. Experimental results indicate high classification accuracy of the approach. For the modern real industrial applications such a method is especially suitable in the Cyber-Physical-System environments and provides a part of the workflow for the automated classifier design}}, author = {{Dörksen, Helene and Lohweg, Volker}}, booktitle = {{23rd IEEE International Conference on Emerging Technologies and Factory Automation (ETFA)}}, keywords = {{Task analysis, Software, Linear discriminant analysis, Dimensionality reduction, Mathematical model, Covariance matrices, Measurement}}, location = {{ Turin, Italy }}, title = {{{Linear Classification of Badly Conditioned Data. }}}, doi = {{10.1109/ETFA.2018.8502485}}, year = {{2018}}, } @inproceedings{4254, abstract = {{The current trend of integrating machines and factories into cyber-physical systems (CPS) creates an enormous complexity for operators of such systems. Especially the search for the root cause of cascading failures becomes highly time-consuming. Within this paper, we address the question on how to help human users to better and faster understand root causes of such situations. We propose a concept of interactive alarm flood reduction and present the implementation of a first vertical prototype for such a system. We consider this prototype as a first artifact to be discussed by the research community and aim towards an incremental further development of the system in order to support humans in complex error situations.}}, author = {{Büttner, Sebastian and Wunderlich, Paul and Heinz, Mario and Niggemann, Oliver and Röcker, Carsten}}, booktitle = {{ Machine Learning and Knowledge Extraction : First IFIP TC 5, WG 8.4, 8.9, 12.9 International Cross-Domain Conference, CD-MAKE 2017, Reggio, Italy, August 29 – September 1, 2017, Proceedings}}, editor = {{Holzinger, Andreas}}, isbn = {{978-3-319-66807-9}}, keywords = {{Alarm flood reduction, Machine learning, Assistive system}}, location = {{Reggio, Italy}}, pages = {{69--82}}, publisher = {{Springer}}, title = {{{Managing Complexity: Towards Intelligent Error-Handling Assistance Trough Interactive Alarm Flood Reduction}}}, volume = {{10410}}, year = {{2017}}, } @inproceedings{328, abstract = {{In this paper, concepts for an extended DC network for the main power supply of components from various manufacturers in industrial production are presented. In the first part, detailed requirements for such a network are given from the viewpoint of a customer. Based on those, different concepts for AC/DC conversion and energy management are discussed. As far as AC/DC conversion is concerned, the advantages and drawbacks of several rectifier topologies are listed, as they have a significant impact on the system behavior and EMC properties. An intelligent energy management can improve the energy efficiency and reduce downtimes of a plant, which are major requirements from a customer’s viewpoint. }}, author = {{Borcherding, Holger and Austermann, Johann and Kuhlmann, Timm and Weis, Benno and Leonide, Andre}}, booktitle = {{2017 IEEE Second International Conference on DC Microgrids (ICDCM)}}, keywords = {{AC-DC power convertors, electromagnetic compatibility, energy conservation, energy management systems, rectifiers, main power supply, industrial production, DC network, AC-DC conversion, rectifier topologies, EMC properties, intelligent energy management, energy efficiency improvement, downtime reduction, Rectifiers, Switches, Voltage control, Topology, Network topology, Production, Grounding, industrial DC grid, SMART Grid}}, location = {{Nürnberg}}, number = {{1}}, pages = {{227--234}}, title = {{{Concepts for a DC Network in Industrial Production}}}, doi = {{10.1109/ICDCM.2017.8001049}}, year = {{2017}}, } @inproceedings{273, abstract = {{This paper introduces an efficient modular solution kit for intralogistic drives, which reduces the total energy consumption of all drives in an automated warehouse by more than 15%. The reduction of energy consumption results from the interaction of optimized components (motors, control techniques and regeneration units), which are described in detail in this paper. Different motor concepts like the induction motor, the synchronous reluctance motor and the permanent magnet synchronous machine are compared according to the special requirements for intralogistics applications. Different control techniques are presented in order to achieve sensorless and efficient-optimal operation of these motors. The sensorless control technique uses signal injection to detect the rotor position sufficiently exact also in case of speed near standstill. Efficient-optimal operation is achieved by reducing the motor current with regard to the torque (MTPA-control). Furthermore this paper introduces a regeneration unit that can be connected between the DC link of frequency inverters and the mains to feed back regenerative energy. The regeneration unit consisting of a buck converter, a synchronous inverter and a line-filter can work in parallel to commonly used uncontrolled rectifiers. Its functioning is shown with the help of measurement results of a 1kW laboratory prototype. The last section shows a demonstrator in which a conventional conveyer system (with induction motor and braking resistor) is compared with an optimized one using the presented components. Power versus time measurements show specific energy savings resulting from the interaction of the optimized components.}}, author = {{Austermann, Johann and Borcherding, Holger and Stichweh, H. and Grabs, Volker}}, booktitle = {{2016 18th European Conference on Power Electronics and Applications (EPE'16 ECCE Europe)}}, isbn = {{978-3-8007-4186-1}}, keywords = {{electric current control, induction motors, invertors, permanent magnet motors, reluctance motors, sensorless machine control, torque control, modular solution kit, intralogistic drives, automated warehouse, energy consumption reduction, optimized components interaction, induction motor, synchronous reluctance motor, permanent magnet synchronous machine, sensorless control technique, signal injection, rotor position, motor current, MTPA-control, regeneration unit, DC link, frequency inverters, buck converter, synchronous inverter, line-filter, Induction motors, Reluctance motors, Permanent magnet motors, Rotors, Torque, Inverters, intralogistics, reluctance motor, sensorless control, regeneration unit, braking energy}}, location = {{Karlsruhe}}, pages = {{1639--1646}}, publisher = {{VDE Verlag}}, title = {{{High Efficient Modular Drive System - An Ideal Approach for Green Intralogistics Applications}}}, doi = {{10.1109/EPE.2016.7695687}}, year = {{2016}}, }