| 1. |
- Regmi, P., et al.
(författare)
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The future of WRRF modelling - Outlook and challenges
- 2019
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Ingår i: Water Science and Technology. - : IWA Publishing. - 0273-1223 .- 1996-9732. ; 79:1, s. 3-14
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Tidskriftsartikel (refereegranskat)abstract
- The wastewater industry is currently facing dramatic changes, shifting away from energy-intensive wastewater treatment towards low-energy, sustainable technologies capable of achieving energy positive operation and resource recovery. The latter will shift the focus of the wastewater industry to how one could manage and extract resources from the wastewater, as opposed to the conventional paradigm of treatment. Debatable questions arise: Can the more complex models be calibrated, or will additional unknowns be introduced? After almost 30 years using well-known International Water Association (IWA) models, should the community move to other components, processes, or model structures like 'black box' models, computational fluid dynamics techniques, etc.? Can new data sources - e.g. on-line sensor data, chemical and molecular analyses, new analytical techniques, off-gas analysis - keep up with the increasing process complexity? Are different methods for data management, data reconciliation, and fault detection mature enough for coping with such a large amount of information? Are the available calibration techniques able to cope with such complex models? This paper describes the thoughts and opinions collected during the closing session of the 6th IWA/WEF Water Resource Recovery Modelling Seminar 2018. It presents a concerted and collective effort by individuals from many different sectors of the wastewater industry to offer past and present insights, as well as an outlook into the future of wastewater modelling.
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| 2. |
- Michel, M., et al.
(författare)
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Small-molecule activation of OGG1 increases oxidative DNA damage repair by gaining a new function
- 2022
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Ingår i: Science. - Stockholm : American Association for the Advancement of Science. - 0036-8075 .- 1095-9203. ; 376:6600, s. 1471-1476
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Tidskriftsartikel (refereegranskat)abstract
- Oxidative DNA damage is recognized by 8-oxoguanine (8-oxoG) DNA glycosylase 1 (OGG1), which excises 8-oxoG, leaving a substrate for apurinic endonuclease 1 (APE1) and initiating repair. Here, we describe a small molecule (TH10785) that interacts with the phenylalanine-319 and glycine-42 amino acids of OGG1, increases the enzyme activity 10-fold, and generates a previously undescribed b,d-lyase enzymatic function. TH10785 controls the catalytic activity mediated by a nitrogen base within its molecular structure. In cells, TH10785 increases OGG1 recruitment to and repair of oxidative DNA damage. This alters the repair process, which no longer requires APE1 but instead is dependent on polynucleotide kinase phosphatase (PNKP1) activity. The increased repair of oxidative DNA lesions with a small molecule may have therapeutic applications in various diseases and aging. © 2022 The Authors, some rights reserved; exclusive licensee American Association for the Advancement of Science. No claim to original U.S. Government Works
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| 3. |
- Tapia-Ruiz, Nuria, et al.
(författare)
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2021 roadmap for sodium-ion batteries
- 2021
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Ingår i: Journal of Physics. - : Institute of Physics Publishing (IOPP). - 2515-7655. ; 3:3
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Tidskriftsartikel (refereegranskat)abstract
- Increasing concerns regarding the sustainability of lithium sources, due to their limited availability and consequent expected price increase, have raised awareness of the importance of developing alternative energy-storage candidates that can sustain the ever-growing energy demand. Furthermore, limitations on the availability of the transition metals used in the manufacturing of cathode materials, together with questionable mining practices, are driving development towards more sustainable elements. Given the uniformly high abundance and cost-effectiveness of sodium, as well as its very suitable redox potential (close to that of lithium), sodium-ion battery technology offers tremendous potential to be a counterpart to lithium-ion batteries (LIBs) in different application scenarios, such as stationary energy storage and low-cost vehicles. This potential is reflected by the major investments that are being made by industry in a wide variety of markets and in diverse material combinations. Despite the associated advantages of being a drop-in replacement for LIBs, there are remarkable differences in the physicochemical properties between sodium and lithium that give rise to different behaviours, for example, different coordination preferences in compounds, desolvation energies, or solubility of the solid-electrolyte interphase inorganic salt components. This demands a more detailed study of the underlying physical and chemical processes occurring in sodium-ion batteries and allows great scope for groundbreaking advances in the field, from lab-scale to scale-up. This roadmap provides an extensive review by experts in academia and industry of the current state of the art in 2021 and the different research directions and strategies currently underway to improve the performance of sodium-ion batteries. The aim is to provide an opinion with respect to the current challenges and opportunities, from the fundamental properties to the practical applications of this technology.
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| 4. |
- Weidema, B. P, et al.
(författare)
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Impacts from Resource Use : A common position paper
- 2005
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Ingår i: The International Journal of Life Cycle Assessment. - : Springer Science and Business Media LLC. - 0948-3349 .- 1614-7502. ; 10:6, s. 382-382
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Tidskriftsartikel (övrigt vetenskapligt/konstnärligt)
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| 5. |
- Mirus, F., et al.
(författare)
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An investigation of vehicle behavior prediction using a vector power representation to encode spatial positions of multiple objects and neural networks
- 2019
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Ingår i: Frontiers in Neurorobotics. - : Frontiers Media SA. - 1662-5218. ; 13
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Tidskriftsartikel (refereegranskat)abstract
- Predicting future behavior and positions of other traffic participants from observations is a key problem that needs to be solved by human drivers and automated vehicles alike to safely navigate their environment and to reach their desired goal. In this paper, we expand on previous work on an automotive environment model based on vector symbolic architectures (VSAs). We investigate a vector-representation to encapsulate spatial information of multiple objects based on a convolutive power encoding. Assuming that future positions of vehicles are influenced not only by their own past positions and dynamics (e.g., velocity and acceleration) but also by the behavior of the other traffic participants in the vehicle's surroundings, our motivation is 3-fold: we hypothesize that our structured vector-representation will be able to capture these relations and mutual influence between multiple traffic participants. Furthermore, the dimension of the encoding vectors remains fixed while being independent of the number of other vehicles encoded in addition to the target vehicle. Finally, a VSA-based encoding allows us to combine symbol-like processing with the advantages of neural network learning. In this work, we use our vector representation as input for a long short-term memory (LSTM) network for sequence to sequence prediction of vehicle positions. In an extensive evaluation, we compare this approach to other LSTM-based benchmark systems using alternative data encoding schemes, simple feed-forward neural networks as well as a simple linear prediction model for reference. We analyze advantages and drawbacks of the presented methods and identify specific driving situations where our approach performs best. We use characteristics specifying such situations as a foundation for an online-learning mixture-of-experts prototype, which chooses at run time between several available predictors depending on the current driving situation to achieve the best possible forecast.
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| 6. |
- Mirus, F., et al.
(författare)
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Predicting vehicle behaviour using LSTMs and a vector power representation for spatial positions
- 2019
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Ingår i: ESANN 2019 - Proceedings, 27th European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning. - : ESANN (i6doc.com). - 9782875870650 ; , s. 113-118
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Konferensbidrag (refereegranskat)abstract
- Predicting future vehicle behaviour is an essential task to enable safe and situation-aware automated driving. In this paper, we propose to encapsulate spatial information of multiple objects in a semantic vector-representation. Assuming that future vehicle motion is influenced not only by past positions but also by the behaviour of other traffic participants, we use this representation as input for a Long Short-Term Memory (LSTM) network for sequence to sequence prediction of vehicle positions. We train and evaluate our system on real-world driving data collected mainly on highways in southern Germany and compare it to other models for reference.
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