Circular business model innovation offers a path for the transformation of companies, enhancing resource productivity and efficiency, while also contributing to sustainable development. These ...fundamental changes in business are accompanied by a variety of challenges and barriers. To support companies on their journey, only a few studies have investigated the critical success factors for circular business model innovation through literature analysis. To contribute to this research, in this study, a methodological approach, mainly based on expert interviews, is proposed to gain in-depth insight into critical success factors for circular business model innovation. As a result, a framework covering critical success factors for circular business model innovation is developed, comprising nine top-codes and 37 sub-codes, and an analysis of each factor’s contribution to the UN’s Sustainable Development Goals is performed. The study thereby extends the theoretical basis for further research on circular business model innovation, as well as identifies their practical implications.
We describe a novel approach for experimental High-Energy Physics (HEP) data analyses that is centred around the declarative rather than imperative paradigm when describing analysis computational ...tasks. The analysis process can be structured in the form of a Directed Acyclic Graph (DAG), where each graph vertex represents a unit of computation with its inputs and outputs, and the graph edges describe the interconnection of various computational steps. We have developed REANA, a platform for reproducible data analyses, that supports several such DAG workflow specifications. The REANA platform parses the analysis workflow and dispatches its computational steps to various supported computing backends (Kubernetes, HTCondor, Slurm). The focus on declarative rather than imperative programming enables researchers to concentrate on the problem domain at hand without having to think about implementation details such as scalable job orchestration. The declarative programming approach is further exemplified by a multi-level job cascading paradigm that was implemented in the Yadage workflow specification language. We present two recent LHC particle physics analyses, ATLAS searches for dark matter and CMS jet energy correction pipelines, where the declarative approach was successfully applied. We argue that the declarative approach to data analyses, combined with recent advancements in container technology, facilitates the portability of computational data analyses to various compute backends, enhancing the reproducibility and the knowledge preservation behind particle physics data analyses.
Categorization represents one cognitive ability fundamental to animal behavior. Grouping of elements based on perceptual or semantic features helps to reduce processing resources and facilitates ...appropriate behavior. Corvids master complex categorization, yet the detailed categorization learning strategies are less well understood. We trained two jackdaws on a delayed match to category paradigm using a novel, artificial stimulus type, RUBubbles. Both birds learned to differentiate between two session-unique categories following two distinct learning protocols. Categories were either introduced via central category prototypes (low variability approach) or using a subset of diverse category exemplars from which diagnostic features had to be identified (high variability approach). In both versions, the stimulus similarity relative to a central category prototype explained categorization performance best. Jackdaws consistently used a central prototype to judge category membership, regardless of whether this prototype was used to introduce distinct categories or had to be inferred from multiple exemplars. Reliance on a category prototype occurred already after experiencing only a few trials with different category exemplars. High stimulus set variability prolonged initial learning but showed no consistent beneficial effect on later generalization performance. High numbers of stimuli, their perceptual similarity, and coherent category structure resulted in a prototype-based strategy, reflecting the most adaptive, efficient, and parsimonious way to represent RUBubble categories. Thus, our birds represent a valuable comparative animal model that permits further study of category representations throughout learning in different regions of a brain producing highly cognitive behavior.
Graphical abstract
Executive functions arise from multiple regions of the brain acting in concert. To facilitate such cross-regional computations, the brain is organized into distinct executive networks, like the ...frontoparietal network. Despite similar cognitive abilities across many domains, little is known about such executive networks in birds. Recent advances in avian fMRI have shown a possible subset of regions, including the nidopallium caudolaterale (NCL) and the lateral part of medial intermediate nidopallium (NIML), that may contribute to complex cognition, forming an action control system of pigeons. We investigated the neuronal activity of NCL and NIML. Single-cell recordings were obtained during the execution of a complex sequential motor task that required executive control to stop executing one behavior and continue with a different one. We compared the neuronal activity of NIML to NCL and found that both regions fully processed the ongoing sequential execution of the task. Differences arose from how behavioral outcome was processed. Our results indicate that NCL takes on a role in evaluating outcome, while NIML is more tightly associated with ongoing sequential steps. Importantly, both regions seem to contribute to overall behavioral output as parts of a possible avian executive network, crucial for behavioral flexibility and decision-making.
Beim Planen, Bauen und Betreiben von Verkehrswasserbauwerken sind Bestandspläne von großer Bedeutung. Gerade für Instandsetzungen oder Ersatzneubauten, die im Wasserbau einen Großteil der ...Baumaßnahmen darstellen, beinhalten sie zentrale Informationen u. a. zur Konstruktion der bestehenden Bauwerke. Die Wasserstraßen‐ und Schifffahrtsverwaltung des Bundes (WSV) hat Anfang des Jahrtausends daher alle Pläne mit hohem Aufwand digitalisiert. In dem Digitalisierungsprozess wurden aus technischen Gründen etwa 35.000 Dokumente in Teilaufnahmen (TA) zerstückelt, was deren Übergabe und Nutzung erheblich stört. Diese, im Rahmen eines FuE‐Projekts der Bundesanstalt für Wasserbau (BAW) entstandene, Arbeit zeigt, dass mit Image Stitching, einer Methode aus dem Bereich des computerbasierten Sehens (Computer Vision), die Pläne zum Großteil automatisiert zusammengeführt werden können. Mit einem merkmalsbasierten Ansatz werden dafür die Zusammenhänge zwischen den einzelnen TA modelliert und diese in einer gemeinsamen Bildebene aneinander ausgerichtet. Beim Zusammenfügen zu einem Gesamtplan wird mit einer nahtbasierten Methode sichergestellt, dass die Elemente im Plan möglichst am Stück erhalten bleiben. Es wird gezeigt, wie mit innovativen Methoden die Gesamtqualität des Baubestandswerks der WSV automatisiert verbessert werden kann und diskutiert, wie weitere Techniken aus dem Bereich der Computer Vision eingesetzt werden können, um die Ergebnisse noch weiter zu verbessern.
Automatic stitching of fragmented construction plans of hydraulic structures
Inventory plans are of great importance during planning, construction and operation of hydraulic structures. Particularly for repairs or replacements, which represent a large part of the construction measures in hydraulic engineering, they contain central information on the construction of the existing structures. At the beginning of the millennium, the German Federal Waterways and Shipping Administration (WSV) therefore digitized all plans at great expense. In the digitization process, about 35,000 documents were fragmented into partial images for technical reasons, which significantly disturb their transfer and use. This work, which is a result of a R&D‐project of the Federal Waterways Engineering and Research Institute (BAW), shows that with image stitching, a method from the field of computer vision, the plans can be stitched automatically for the most part. Using a feature‐based approach, the relationships between the individual fragments are modeled and aligned in a common image plane. When merging to an overall plan, a seam‐based method ensures that the elements in the plan are preserved. It is shown how innovative methods can be used to improve the overall quality of the WSV's inventory documents in an automated manner, and it is discussed how further techniques from the field of computer vision can be used to improve the results even further.
Abstract
Beim Planen, Bauen und Betreiben von Verkehrswasserbauwerken sind Bestandspläne von großer Bedeutung. Gerade für Instandsetzungen oder Ersatzneubauten, die im Wasserbau einen Großteil der ...Baumaßnahmen darstellen, beinhalten sie zentrale Informationen u. a. zur Konstruktion der bestehenden Bauwerke. Die Wasserstraßen‐ und Schifffahrtsverwaltung des Bundes (WSV) hat Anfang des Jahrtausends daher alle Pläne mit hohem Aufwand digitalisiert. In dem Digitalisierungsprozess wurden aus technischen Gründen etwa 35.000 Dokumente in Teilaufnahmen (TA) zerstückelt, was deren Übergabe und Nutzung erheblich stört. Diese, im Rahmen eines FuE‐Projekts der Bundesanstalt für Wasserbau (BAW) entstandene, Arbeit zeigt, dass mit Image Stitching, einer Methode aus dem Bereich des computerbasierten Sehens (Computer Vision), die Pläne zum Großteil automatisiert zusammengeführt werden können. Mit einem merkmalsbasierten Ansatz werden dafür die Zusammenhänge zwischen den einzelnen TA modelliert und diese in einer gemeinsamen Bildebene aneinander ausgerichtet. Beim Zusammenfügen zu einem Gesamtplan wird mit einer nahtbasierten Methode sichergestellt, dass die Elemente im Plan möglichst am Stück erhalten bleiben. Es wird gezeigt, wie mit innovativen Methoden die Gesamtqualität des Baubestandswerks der WSV automatisiert verbessert werden kann und diskutiert, wie weitere Techniken aus dem Bereich der Computer Vision eingesetzt werden können, um die Ergebnisse noch weiter zu verbessern.
Abstract
Automatic stitching of fragmented construction plans of hydraulic structures
Inventory plans are of great importance during planning, construction and operation of hydraulic structures. Particularly for repairs or replacements, which represent a large part of the construction measures in hydraulic engineering, they contain central information on the construction of the existing structures. At the beginning of the millennium, the German Federal Waterways and Shipping Administration (WSV) therefore digitized all plans at great expense. In the digitization process, about 35,000 documents were fragmented into partial images for technical reasons, which significantly disturb their transfer and use. This work, which is a result of a R&D‐project of the Federal Waterways Engineering and Research Institute (BAW), shows that with image stitching, a method from the field of computer vision, the plans can be stitched automatically for the most part. Using a feature‐based approach, the relationships between the individual fragments are modeled and aligned in a common image plane. When merging to an overall plan, a seam‐based method ensures that the elements in the plan are preserved. It is shown how innovative methods can be used to improve the overall quality of the WSV's inventory documents in an automated manner, and it is discussed how further techniques from the field of computer vision can be used to improve the results even further.
This contribution discusses the possibilities to increase the efficiency of large Discrete Element Method (DEM) simulations. Simulations were conducted to test particle upscaling, decreasing shear ...modulus and using GPU instead of CPU for the computation. The conducted simulations modelled a simple ore extraction process from a defined outlet of a stope in a cave mine. The analysis is based on the influence of the approaches on the computation speed-up and the accuracy of the result. It was found that the real shear modulus could be decreased in the simulation by a factor of 10
3
without interfering strongly with the result, provided that the decreased shear modulus is not smaller than 10
8
Pa. However, a reduction of 10
2
was found to bring the highest speed-up to the present application.
For the particle upscaling in this contribution without parameter calibration, the upscaling factor should not exceed 3. Higher upscaling factors affect the results significantly. However, the flow dynamics was already differing for an upscaling factor of 2, even though the flow zones were comparable. Using a GPU instead of a CPU is only recommended if the simulation contains a high number of particles. The speed-up for a simulation with almost 250,000 particles was over 8, but the advantage diminished for less particles. Furthermore, differences in the results between CPU and GPU computation could be found, which could be a starting point for future work. In summary, this research significantly aids in the development of more efficient DEM simulations for large-scale applications, such as cave mining.
Complex cognition requires coordinated neuronal activity at the network level. In mammals, this coordination results in distinct dynamics of local field potentials (LFP) central to many models of ...higher cognition. These models often implicitly assume a cortical organization. Higher associative regions of the brains of birds do not have cortical layering, yet single-cell correlates of higher cognition are very similar to those found in mammals. We recorded LFP in the avian equivalent of prefrontal cortex while crows performed a highly controlled and cognitively demanding working memory task. We found signatures in local field potentials, modulated by working memory. Frequencies of a narrow gamma and the beta band contained information about the location of target items and were modulated by working memory load. This indicates a critical involvement of these bands in ongoing cognitive processing. We also observed bursts in the beta and gamma frequencies, similar to those that play a vital part in ‘activity silent’ models of working memory. Thus, despite the lack of a cortical organization the avian associative pallium can create LFP signatures reminiscent of those observed in primates. This points towards a critical cognitive function of oscillatory dynamics evolved through convergence in species capable of complex cognition.
Display omitted
•Despite different brain organization, crows and monkeys share oscillatory fingerprints.•Neural networks required for higher cognition evolved in parallel.•Contemporary mammalian models of working memory are applicable to birds.
Deep Riemannian Networks for EEG Decoding Wilson, Daniel; Robin Tibor Schirrmeister; Lukas Alexander Wilhelm Gemein ...
arXiv (Cornell University),
08/2023
Paper, Journal Article
Odprti dostop
State-of-the-art performance in electroencephalography (EEG) decoding tasks is currently often achieved with either Deep-Learning (DL) or Riemannian-Geometry-based decoders (RBDs). Recently, there is ...growing interest in Deep Riemannian Networks (DRNs) possibly combining the advantages of both previous classes of methods. However, there are still a range of topics where additional insight is needed to pave the way for a more widespread application of DRNs in EEG. These include architecture design questions such as network size and end-to-end ability.How these factors affect model performance has not been explored. Additionally, it is not clear how the data within these networks is transformed, and whether this would correlate with traditional EEG decoding. Our study aims to lay the groundwork in the area of these topics through the analysis of DRNs for EEG with a wide range of hyperparameters. Networks were tested on two public EEG datasets and compared with state-of-the-art ConvNets. Here we propose end-to-end EEG SPDNet (EE(G)-SPDNet), and we show that this wide, end-to-end DRN can outperform the ConvNets, and in doing so use physiologically plausible frequency regions. We also show that the end-to-end approach learns more complex filters than traditional band-pass filters targeting the classical alpha, beta, and gamma frequency bands of the EEG, and that performance can benefit from channel specific filtering approaches. Additionally, architectural analysis revealed areas for further improvement due to the possible loss of Riemannian specific information throughout the network. Our study thus shows how to design and train DRNs to infer task-related information from the raw EEG without the need of handcrafted filterbanks and highlights the potential of end-to-end DRNs such as EE(G)-SPDNet for high-performance EEG decoding.
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