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  • Rural Digital Europe
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  • image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/
    Authors: Anne-Sophie Christmann; Carlotta Crome; Valerie Graf-Drasch; Anna Maria Oberländer; +1 Authors

    AbstractComplex digitalization and sustainability challenges shape today’s management agendas. To date, the dedication of Information Systems research to both challenges has not been equal in terms of effort and reward. Building capabilities to leverage the synergetic potential of digital and sustainability transformation may enhance organizational performance and imply new value creation for the common good. To uncover such synergetic potential, this work conceptualizes the “twin transformation” construct as a value-adding reinforcing interplay between digital transformation and sustainability transformation efforts that improve an organization by leveraging digital technologies to enable sustainability and to guide digital progress by leveraging sustainability. The twin transformation conceptualization is complemented with a capability framework for twin transformation drawing from dynamic capability theory. This work contributes to descriptive knowledge of the interplay between digital transformation and sustainability transformation, setting a foundation for further theorizing on twin transformation and enabling organizations to twin transform.

    image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/ Fraunhofer-ePrintsarrow_drop_down
    image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/
    Fraunhofer-ePrints
    Article . 2024
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    Business & Information Systems Engineering
    Article . 2024 . Peer-reviewed
    License: CC BY
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      image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/ Fraunhofer-ePrintsarrow_drop_down
      image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/
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      Article . 2024
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      image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/
      Business & Information Systems Engineering
      Article . 2024 . Peer-reviewed
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  • image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/
    Authors: Rene-Pascal Fischer; Annika Volpert; Pablo Antonino; Theresa D. Ahrens;

    Digital twins are virtual models of physical artefacts that may or may not be synchronously connected, and that can be used to simulate their behavior. They are widely used in several domains such as manufacturing and automotive to enable achieving specific quality goals. In the health domain, so-called digital patient twins have been understood as virtual models of patients generated from population data and/or patient data, including, for example, real-time feedback from wearables. Along with the growing impact of data science technologies like artificial intelligence, novel health data ecosystems centered around digital patient twins could be developed. This paves the way for improved health monitoring and facilitation of personalized therapeutics based on management, analysis, and interpretation of medical data via digital patient twins. The utility and feasibility of digital patient twins in routine medical processes are still limited, despite practical endeavors to create digital twins of physiological functions, single organs, or holistic models. Moreover, reliable simulations for the prediction of individual drug responses are still missing. However, these simulations would be one important milestone for truly personalized therapeutics. Another prerequisite for this would be individualized pharmaceutical manufacturing with subsequent obstacles, such as low automation, scalability, and therefore high costs. Additionally, regulatory challenges must be met thus calling for more digitalization in this area. Therefore, this narrative mini-review provides a discussion on the potentials and limitations of digital patient twins, focusing on their potential bridging function for personalized therapeutics and an individualized pharmaceutical manufacturing while also looking at the regulatory impacts.

    image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/ Fraunhofer-ePrintsarrow_drop_down
    image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/
    Fraunhofer-ePrints
    Article . 2023
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    Frontiers in Digital Health
    Article . 2024 . Peer-reviewed
    License: CC BY
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    Frontiers in Digital Health
    Article . 2024
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      image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/ Fraunhofer-ePrintsarrow_drop_down
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      Fraunhofer-ePrints
      Article . 2023
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      image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/
      Frontiers in Digital Health
      Article . 2024 . Peer-reviewed
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      Frontiers in Digital Health
      Article . 2024
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  • Authors: Bastidas Cruz, Arturo; Jaya, T.; Thiele, Gregor; Krüger, Jörg;

    Human-robot collaboration (HRC) applications have been slowly making their path in the industry. Although the required hardware and the methods for the planning and development of collaborative robotic applications are mostly already developed, some industrial branches still struggle to implement HRC. This is the case in motorcycle production, where, unlike car production, the assembly line has been optimized for manual work. Based on the use case described above, this paper identifies new requirements of HRC for automated screwing assembly operations in flexible production environments. In order to compensate deviations in the position of the tool relative to the workpiece, a screwing strategy based on force control is proposed. Parameter sensitivity is considered and supported experimentally with a screwing task performed by a cobot, where a method for contact detection between the nutrunner and the screw head is analyzed. This paper brings a guideline for experts from the manufacturing system engineering to implement HRC in highly dynamic assembly environments.

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    Conference object . 2024
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    https://doi.org/10.1063/5.0189...
    Conference object . 2024 . Peer-reviewed
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      https://doi.org/10.1063/5.0189...
      Conference object . 2024 . Peer-reviewed
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  • Authors: Jarausch, Wolfgang; Thielert, Bonito Steffen; Michel, Markus; Menz, Patrick; +4 Authors

    The quarantine "flavescence dorée (FD)", associated with 16SrV-C and -D phytoplasmas, is threatening the wine growing areas of Germany which is so far regarded as FD-free. However, one single FD-infected grapevine plant has been detected in 2020 (Jarausch et al., 2021) which was immediately eradicated. This case as well as the new EU regulations highlighted the need for a large scale screening of FD. This is hampered by the widespread presence of "bois noir (BN)", associated with ‘Candidatus Phytoplasma solani’, which induces similar symptoms in grapevine like FD. Therefore, fast and reliable detection methods for FD monitoring in the field have to be developed. The concept of the PhenoTruckAI is based on three axes: large-scale screening of vineyards using remote sensing by drones (UASs), hyperspectral screening of leaf samples for phytoplasma infections and molecular identification of FD in a mobile laboratory. The mobile laboratory is a special vehicle with 4-wheel drive which allows autonomous laboratory work direct at the field. Drone image data will be automatically processed, and sample strategies developed. One compartment of the mobile laboratory is equipped with a dual hyperspectral camera system (VNIR+SWIR, wavelength range from 400 - 2500 nm). The spectra of leaf samples will be automatically analyzed for phytoplasma symptom presence. Ongoing research focus on the spectral discrimination of FD- and BN-infected leaves based on machine learning technologies. Rapid molecular identification of FD-infections will be achieved by LAMP assays. A case study with a first prototype of the forthcoming PhenoTruckAI was conducted in Trentino and South Tyrol in summer 2023. A total of 430 either FD- or BN-infected as well as asymptomatic leaf samples of the cultivars Chardonnay and Pinot Gris were analyzed with the hyperspectral camera system in a mobile laboratory. The same samples were extracted in the molecular compartment for identification of FD and BN by PCR. Later on, spectral data were processed and segmented leaf data were analyzed patch-wise by machine learning techniques with a leave-n-out cross validation. Phytoplasma infections were identified in VNIR spectra with 95% accuracy and in SWIR spectra with 98% accuracy compared to healthy leaves. Discrimination between FD- and BN-infected leaves was more challenging. Nevertheless, machine learning approaches achieved an accuracy of FD/BN distinction of about 80% in VNIR spectra. Further work is needed to improve the FD detection.

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  • Authors: Erdmann, Lorenz; Kimpeler, Simone; Gutknecht, Ralph; Cuhls, Kerstin; +1 Authors

    Digitalisation is profoundly changing our society and therefore also the way in which the environment is researched and governance is carried out. E-government is intended to make administration more efficient and bring it closer to citizens, while new ideas are constantly being implemented in environmental research, such as the ⁠CO2⁠ calculator, apps for sustainable consumption or big data-supported climate simulations. But are these measures already exploiting the full potential that digitalisation holds for the environment department and its environmental research and governance in the digital age? And how will the work of the environment department change when citizens, civil society, companies, science and politics, and therefore society as a whole, undergo major changes as a result of digitalisation?

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  • image/svg+xml Jakob Voss, based on art designer at PLoS, modified by Wikipedia users Nina and Beao Closed Access logo, derived from PLoS Open Access logo. This version with transparent background. http://commons.wikimedia.org/wiki/File:Closed_Access_logo_transparent.svg Jakob Voss, based on art designer at PLoS, modified by Wikipedia users Nina and Beao
    Authors: J.I. Delgado-Centeno; P. Harder; V. Bickel; B. Moseley; +3 Authors

    Lunar exploration missions require detailed and accurate planning to ensure their safety. Remote sensing data, such as optical satellite imagery acquired by lunar orbiters, are key for the identification of future landing and mission sites. Here robot- and astronaut-scale obstacles are the most relevant to resolve; however, the spatial resolution of the available image data is often insufficient, particularly in the poorly illuminated polar regions of the moon, leading to uncertainty. This work shows how a novel single-image superresolution (SISR) application, the Adversarial Network for Uncertainty-Based Image SR (ANUBIS), can enhance lunar surface imagery by improving the resolution by a factor of two, outperforming other approaches and benchmarks. The enhanced images improve the reliability and detail of lunar traverse planning and topographic reconstruction, while providing an estimate of the uncertainty associated with the enhancement process, vital to ensure mission planning integrity. This work demonstrates how machine-learning-driven processing can enhance existing data products to maximize their value for science and the exploration of the moon and other celestial bodies.

    image/svg+xml Jakob Voss, based on art designer at PLoS, modified by Wikipedia users Nina and Beao Closed Access logo, derived from PLoS Open Access logo. This version with transparent background. http://commons.wikimedia.org/wiki/File:Closed_Access_logo_transparent.svg Jakob Voss, based on art designer at PLoS, modified by Wikipedia users Nina and Beao IEEE Robotics & Auto...arrow_drop_down
    image/svg+xml Jakob Voss, based on art designer at PLoS, modified by Wikipedia users Nina and Beao Closed Access logo, derived from PLoS Open Access logo. This version with transparent background. http://commons.wikimedia.org/wiki/File:Closed_Access_logo_transparent.svg Jakob Voss, based on art designer at PLoS, modified by Wikipedia users Nina and Beao
    IEEE Robotics & Automation Magazine
    Article . 2024 . Peer-reviewed
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    Article . 2023
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      image/svg+xml Jakob Voss, based on art designer at PLoS, modified by Wikipedia users Nina and Beao Closed Access logo, derived from PLoS Open Access logo. This version with transparent background. http://commons.wikimedia.org/wiki/File:Closed_Access_logo_transparent.svg Jakob Voss, based on art designer at PLoS, modified by Wikipedia users Nina and Beao IEEE Robotics & Auto...arrow_drop_down
      image/svg+xml Jakob Voss, based on art designer at PLoS, modified by Wikipedia users Nina and Beao Closed Access logo, derived from PLoS Open Access logo. This version with transparent background. http://commons.wikimedia.org/wiki/File:Closed_Access_logo_transparent.svg Jakob Voss, based on art designer at PLoS, modified by Wikipedia users Nina and Beao
      IEEE Robotics & Automation Magazine
      Article . 2024 . Peer-reviewed
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  • image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/
    Authors: Georg Maier; Robin Gruna; Thomas Längle; Jürgen Beyerer;

    Sensor-based sorting describes a family of systems that enable the removal of individual objects from a material stream. The technology is widely used in various industries such as agriculture, food, mining, and recycling. Examples of sorting tasks include the removal of fungus-infested grains, the enrichment of copper content in copper mining or the sorting of plastic waste according to the type of plastic. Sorting decisions are made based on information acquired by one or more sensors. A particular strength of the technology is the flexibility in sorting decisions, which is achieved by using various sensors and programming the data analysis. However, a comprehensive understanding of the process is necessary for the development of new sorting systems that can address previously unresolved tasks. This survey is aimed at innovative researchers and practitioners who are unfamiliar with sensor-based sorting or have only encountered certain aspects of the overall process. The references provided serve as starting points for further exploration of specific topics.

    image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/ IEEE Accessarrow_drop_down
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    IEEE Access
    Article . 2024 . Peer-reviewed
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      IEEE Access
      Article . 2024 . Peer-reviewed
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  • image/svg+xml Jakob Voss, based on art designer at PLoS, modified by Wikipedia users Nina and Beao Closed Access logo, derived from PLoS Open Access logo. This version with transparent background. http://commons.wikimedia.org/wiki/File:Closed_Access_logo_transparent.svg Jakob Voss, based on art designer at PLoS, modified by Wikipedia users Nina and Beao
    Authors: Schuh, Günther; Cassel, Leonard; Uedelhoven, Marc;

    In the course of the advancing digitalization of industrial production, many enterprises have already laid the foundations for a more comprehensive end-to-end recording and accessibility of production related data. Machine learning (ML), implemented in specific industrial use cases, offers the possibility of automated analysis of these large volumes of data with considerably reduced manual effort. In industrial practice, however, the selection of use cases with an economic and long-lasting strategic impact poses challenges, since much of the decision-relevant information of individual use cases is mostly discovered during the actual implementation phase. Additionally, as the datasets required for a successful application are often not sufficiently known prior to this phase, a previous assessment regarding the data basis for individual use cases is also needed. To address these challenges, this paper presents a concept constructed in the research process for applied sciences according to ULRICH for a-priori evaluation and prioritization of use cases for machine learning in industrial production. In particular, the potential benefits, implementation efforts, and the technical feasibility are considered as evaluation dimensions.

    image/svg+xml Jakob Voss, based on art designer at PLoS, modified by Wikipedia users Nina and Beao Closed Access logo, derived from PLoS Open Access logo. This version with transparent background. http://commons.wikimedia.org/wiki/File:Closed_Access_logo_transparent.svg Jakob Voss, based on art designer at PLoS, modified by Wikipedia users Nina and Beao https://doi.org/10.1...arrow_drop_down
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    https://doi.org/10.1109/ieem58...
    Conference object . 2023 . Peer-reviewed
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      image/svg+xml Jakob Voss, based on art designer at PLoS, modified by Wikipedia users Nina and Beao Closed Access logo, derived from PLoS Open Access logo. This version with transparent background. http://commons.wikimedia.org/wiki/File:Closed_Access_logo_transparent.svg Jakob Voss, based on art designer at PLoS, modified by Wikipedia users Nina and Beao https://doi.org/10.1...arrow_drop_down
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      https://doi.org/10.1109/ieem58...
      Conference object . 2023 . Peer-reviewed
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  • image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/
    Authors: Kleebauer, Maximilian; Marz, Christopher; Reudenbach, Christoph; Braun, Martin;

    In the realm of solar photovoltaic system image segmentation, existing deep learning networks focus almost exclusively on single image sources both in terms of sensors used and image resolution. This often prevents the wide deployment of such networks. Our research introduces a novel approach to train a network on a diverse range of image data, spanning UAV, aerial, and satellite imagery at both native and aggregated resolutions of 0.1 m, 0.2 m, 0.3 m, 0.8 m, 1.6 m, and 3.2 m. Using extensive hyperparameter tuning, we first determined the best possible parameter combinations for the network based on the DeepLabV3 ResNet101 architecture. We then trained a model using the wide range of different image sources. The final network offers several advantages. It outperforms networks trained with single image sources in multiple test applications as measured by the F1-Score (95.27%) and IoU (91.04%). The network is also able to work with a variety of target imagery due to the fact that a diverse range of image data was used to train it. The model is made freely available for further applications.

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    Remote Sensing
    Article . 2023
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    ZENODO; Remote Sensing
    Article . 2023 . Peer-reviewed
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    https://doi.org/10.17170/kobra...
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      Remote Sensing
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      ZENODO; Remote Sensing
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    Authors: S. Kunkel; P. Neuhäusler; M. Matthess; M.F. Dachrodt;

    Digitalisation in manufacturing (or "industry 4.0") is expected to improve energy efficiency and thus reduce energy intensity in manufacturing, but studies show that it may also increase energy consumption. In this article, we investigate to what extent the degree of industry 4.0 is linked to energy consumption and energy intensity in ten Chinese manufacturing sectors between 2006 and 2019. We approximate the degree of industry 4.0 by combining data on a) patent intensity of industry 4.0-related technologies and b) industrial robot intensity. Our results indicate that there is no significant overall relationship between the degree of industry 4.0 and energy consumption or energy intensity, in contrast to some earlier studies in the Chinese context which find energy intensity reducing effects of digitalisation. We argue that industry 4.0 in China might have fewer energy related benefits than expected by politics and industry. Growth-inducing effects and outsourcing of energy-intensive manufacturing tasks, for instance, may counteract efficiency-related savings. To decarbonise manufacturing in line with China's proclaimed objective of carbon neutrality by 2060, policy makers and industry should identify specific opportunities and take seriously risks of industry 4.0. The focus should be on reducing absolute energy consumption as opposed to energy intensity, which may disguise digital rebound effects; and on integrating renewable energies, particularly in the most energy-intensive sectors (metals, chemicals, non-metallic minerals).

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    IASSpublic
    Article . 2023
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    Renewable and Sustainable Energy Reviews
    Article . 2023 . Peer-reviewed
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      Renewable and Sustainable Energy Reviews
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  • image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/
    Authors: Anne-Sophie Christmann; Carlotta Crome; Valerie Graf-Drasch; Anna Maria Oberländer; +1 Authors

    AbstractComplex digitalization and sustainability challenges shape today’s management agendas. To date, the dedication of Information Systems research to both challenges has not been equal in terms of effort and reward. Building capabilities to leverage the synergetic potential of digital and sustainability transformation may enhance organizational performance and imply new value creation for the common good. To uncover such synergetic potential, this work conceptualizes the “twin transformation” construct as a value-adding reinforcing interplay between digital transformation and sustainability transformation efforts that improve an organization by leveraging digital technologies to enable sustainability and to guide digital progress by leveraging sustainability. The twin transformation conceptualization is complemented with a capability framework for twin transformation drawing from dynamic capability theory. This work contributes to descriptive knowledge of the interplay between digital transformation and sustainability transformation, setting a foundation for further theorizing on twin transformation and enabling organizations to twin transform.

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    Fraunhofer-ePrints
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    Business & Information Systems Engineering
    Article . 2024 . Peer-reviewed
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      Business & Information Systems Engineering
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    Authors: Rene-Pascal Fischer; Annika Volpert; Pablo Antonino; Theresa D. Ahrens;

    Digital twins are virtual models of physical artefacts that may or may not be synchronously connected, and that can be used to simulate their behavior. They are widely used in several domains such as manufacturing and automotive to enable achieving specific quality goals. In the health domain, so-called digital patient twins have been understood as virtual models of patients generated from population data and/or patient data, including, for example, real-time feedback from wearables. Along with the growing impact of data science technologies like artificial intelligence, novel health data ecosystems centered around digital patient twins could be developed. This paves the way for improved health monitoring and facilitation of personalized therapeutics based on management, analysis, and interpretation of medical data via digital patient twins. The utility and feasibility of digital patient twins in routine medical processes are still limited, despite practical endeavors to create digital twins of physiological functions, single organs, or holistic models. Moreover, reliable simulations for the prediction of individual drug responses are still missing. However, these simulations would be one important milestone for truly personalized therapeutics. Another prerequisite for this would be individualized pharmaceutical manufacturing with subsequent obstacles, such as low automation, scalability, and therefore high costs. Additionally, regulatory challenges must be met thus calling for more digitalization in this area. Therefore, this narrative mini-review provides a discussion on the potentials and limitations of digital patient twins, focusing on their potential bridging function for personalized therapeutics and an individualized pharmaceutical manufacturing while also looking at the regulatory impacts.

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    Fraunhofer-ePrints
    Article . 2023
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    Frontiers in Digital Health
    Article . 2024 . Peer-reviewed
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    Frontiers in Digital Health
    Article . 2024
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      Frontiers in Digital Health
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      Frontiers in Digital Health
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  • Authors: Bastidas Cruz, Arturo; Jaya, T.; Thiele, Gregor; Krüger, Jörg;

    Human-robot collaboration (HRC) applications have been slowly making their path in the industry. Although the required hardware and the methods for the planning and development of collaborative robotic applications are mostly already developed, some industrial branches still struggle to implement HRC. This is the case in motorcycle production, where, unlike car production, the assembly line has been optimized for manual work. Based on the use case described above, this paper identifies new requirements of HRC for automated screwing assembly operations in flexible production environments. In order to compensate deviations in the position of the tool relative to the workpiece, a screwing strategy based on force control is proposed. Parameter sensitivity is considered and supported experimentally with a screwing task performed by a cobot, where a method for contact detection between the nutrunner and the screw head is analyzed. This paper brings a guideline for experts from the manufacturing system engineering to implement HRC in highly dynamic assembly environments.

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    https://doi.org/10.1063/5.0189...
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  • Authors: Jarausch, Wolfgang; Thielert, Bonito Steffen; Michel, Markus; Menz, Patrick; +4 Authors

    The quarantine "flavescence dorée (FD)", associated with 16SrV-C and -D phytoplasmas, is threatening the wine growing areas of Germany which is so far regarded as FD-free. However, one single FD-infected grapevine plant has been detected in 2020 (Jarausch et al., 2021) which was immediately eradicated. This case as well as the new EU regulations highlighted the need for a large scale screening of FD. This is hampered by the widespread presence of "bois noir (BN)", associated with ‘Candidatus Phytoplasma solani’, which induces similar symptoms in grapevine like FD. Therefore, fast and reliable detection methods for FD monitoring in the field have to be developed. The concept of the PhenoTruckAI is based on three axes: large-scale screening of vineyards using remote sensing by drones (UASs), hyperspectral screening of leaf samples for phytoplasma infections and molecular identification of FD in a mobile laboratory. The mobile laboratory is a special vehicle with 4-wheel drive which allows autonomous laboratory work direct at the field. Drone image data will be automatically processed, and sample strategies developed. One compartment of the mobile laboratory is equipped with a dual hyperspectral camera system (VNIR+SWIR, wavelength range from 400 - 2500 nm). The spectra of leaf samples will be automatically analyzed for phytoplasma symptom presence. Ongoing research focus on the spectral discrimination of FD- and BN-infected leaves based on machine learning technologies. Rapid molecular identification of FD-infections will be achieved by LAMP assays. A case study with a first prototype of the forthcoming PhenoTruckAI was conducted in Trentino and South Tyrol in summer 2023. A total of 430 either FD- or BN-infected as well as asymptomatic leaf samples of the cultivars Chardonnay and Pinot Gris were analyzed with the hyperspectral camera system in a mobile laboratory. The same samples were extracted in the molecular compartment for identification of FD and BN by PCR. Later on, spectral data were processed and segmented leaf data were analyzed patch-wise by machine learning techniques with a leave-n-out cross validation. Phytoplasma infections were identified in VNIR spectra with 95% accuracy and in SWIR spectra with 98% accuracy compared to healthy leaves. Discrimination between FD- and BN-infected leaves was more challenging. Nevertheless, machine learning approaches achieved an accuracy of FD/BN distinction of about 80% in VNIR spectra. Further work is needed to improve the FD detection.

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  • Authors: Erdmann, Lorenz; Kimpeler, Simone; Gutknecht, Ralph; Cuhls, Kerstin; +1 Authors

    Digitalisation is profoundly changing our society and therefore also the way in which the environment is researched and governance is carried out. E-government is intended to make administration more efficient and bring it closer to citizens, while new ideas are constantly being implemented in environmental research, such as the ⁠CO2⁠ calculator, apps for sustainable consumption or big data-supported climate simulations. But are these measures already exploiting the full potential that digitalisation holds for the environment department and its environmental research and governance in the digital age? And how will the work of the environment department change when citizens, civil society, companies, science and politics, and therefore society as a whole, undergo major changes as a result of digitalisation?

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  • image/svg+xml Jakob Voss, based on art designer at PLoS, modified by Wikipedia users Nina and Beao Closed Access logo, derived from PLoS Open Access logo. This version with transparent background. http://commons.wikimedia.org/wiki/File:Closed_Access_logo_transparent.svg Jakob Voss, based on art designer at PLoS, modified by Wikipedia users Nina and Beao
    Authors: J.I. Delgado-Centeno; P. Harder; V. Bickel; B. Moseley; +3 Authors

    Lunar exploration missions require detailed and accurate planning to ensure their safety. Remote sensing data, such as optical satellite imagery acquired by lunar orbiters, are key for the identification of future landing and mission sites. Here robot- and astronaut-scale obstacles are the most relevant to resolve; however, the spatial resolution of the available image data is often insufficient, particularly in the poorly illuminated polar regions of the moon, leading to uncertainty. This work shows how a novel single-image superresolution (SISR) application, the Adversarial Network for Uncertainty-Based Image SR (ANUBIS), can enhance lunar surface imagery by improving the resolution by a factor of two, outperforming other approaches and benchmarks. The enhanced images improve the reliability and detail of lunar traverse planning and topographic reconstruction, while providing an estimate of the uncertainty associated with the enhancement process, vital to ensure mission planning integrity. This work demonstrates how machine-learning-driven processing can enhance existing data products to maximize their value for science and the exploration of the moon and other celestial bodies.

    image/svg+xml Jakob Voss, based on art designer at PLoS, modified by Wikipedia users Nina and Beao Closed Access logo, derived from PLoS Open Access logo. This version with transparent background. http://commons.wikimedia.org/wiki/File:Closed_Access_logo_transparent.svg Jakob Voss, based on art designer at PLoS, modified by Wikipedia users Nina and Beao IEEE Robotics & Auto...arrow_drop_down
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    IEEE Robotics & Automation Magazine
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      image/svg+xml Jakob Voss, based on art designer at PLoS, modified by Wikipedia users Nina and Beao Closed Access logo, derived from PLoS Open Access logo. This version with transparent background. http://commons.wikimedia.org/wiki/File:Closed_Access_logo_transparent.svg Jakob Voss, based on art designer at PLoS, modified by Wikipedia users Nina and Beao IEEE Robotics & Auto...arrow_drop_down
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    Authors: Georg Maier; Robin Gruna; Thomas Längle; Jürgen Beyerer;

    Sensor-based sorting describes a family of systems that enable the removal of individual objects from a material stream. The technology is widely used in various industries such as agriculture, food, mining, and recycling. Examples of sorting tasks include the removal of fungus-infested grains, the enrichment of copper content in copper mining or the sorting of plastic waste according to the type of plastic. Sorting decisions are made based on information acquired by one or more sensors. A particular strength of the technology is the flexibility in sorting decisions, which is achieved by using various sensors and programming the data analysis. However, a comprehensive understanding of the process is necessary for the development of new sorting systems that can address previously unresolved tasks. This survey is aimed at innovative researchers and practitioners who are unfamiliar with sensor-based sorting or have only encountered certain aspects of the overall process. The references provided serve as starting points for further exploration of specific topics.

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    IEEE Access
    Article . 2024 . Peer-reviewed
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    IEEE Access
    Article . 2024
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      IEEE Access
      Article . 2024 . Peer-reviewed
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      IEEE Access
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  • image/svg+xml Jakob Voss, based on art designer at PLoS, modified by Wikipedia users Nina and Beao Closed Access logo, derived from PLoS Open Access logo. This version with transparent background. http://commons.wikimedia.org/wiki/File:Closed_Access_logo_transparent.svg Jakob Voss, based on art designer at PLoS, modified by Wikipedia users Nina and Beao
    Authors: Schuh, Günther; Cassel, Leonard; Uedelhoven, Marc;

    In the course of the advancing digitalization of industrial production, many enterprises have already laid the foundations for a more comprehensive end-to-end recording and accessibility of production related data. Machine learning (ML), implemented in specific industrial use cases, offers the possibility of automated analysis of these large volumes of data with considerably reduced manual effort. In industrial practice, however, the selection of use cases with an economic and long-lasting strategic impact poses challenges, since much of the decision-relevant information of individual use cases is mostly discovered during the actual implementation phase. Additionally, as the datasets required for a successful application are often not sufficiently known prior to this phase, a previous assessment regarding the data basis for individual use cases is also needed. To address these challenges, this paper presents a concept constructed in the research process for applied sciences according to ULRICH for a-priori evaluation and prioritization of use cases for machine learning in industrial production. In particular, the potential benefits, implementation efforts, and the technical feasibility are considered as evaluation dimensions.

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    https://doi.org/10.1109/ieem58...
    Conference object . 2023 . Peer-reviewed
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      https://doi.org/10.1109/ieem58...
      Conference object . 2023 . Peer-reviewed
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    Authors: Kleebauer, Maximilian; Marz, Christopher; Reudenbach, Christoph; Braun, Martin;

    In the realm of solar photovoltaic system image segmentation, existing deep learning networks focus almost exclusively on single image sources both in terms of sensors used and image resolution. This often prevents the wide deployment of such networks. Our research introduces a novel approach to train a network on a diverse range of image data, spanning UAV, aerial, and satellite imagery at both native and aggregated resolutions of 0.1 m, 0.2 m, 0.3 m, 0.8 m, 1.6 m, and 3.2 m. Using extensive hyperparameter tuning, we first determined the best possible parameter combinations for the network based on the DeepLabV3 ResNet101 architecture. We then trained a model using the wide range of different image sources. The final network offers several advantages. It outperforms networks trained with single image sources in multiple test applications as measured by the F1-Score (95.27%) and IoU (91.04%). The network is also able to work with a variety of target imagery due to the fact that a diverse range of image data was used to train it. The model is made freely available for further applications.

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    Remote Sensing
    Article . 2023
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    ZENODO; Remote Sensing
    Article . 2023 . Peer-reviewed
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    https://doi.org/10.17170/kobra...
    Article . 2024
    License: CC BY
    Data sources: Datacite
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      ZENODO; Remote Sensing
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    Authors: S. Kunkel; P. Neuhäusler; M. Matthess; M.F. Dachrodt;

    Digitalisation in manufacturing (or "industry 4.0") is expected to improve energy efficiency and thus reduce energy intensity in manufacturing, but studies show that it may also increase energy consumption. In this article, we investigate to what extent the degree of industry 4.0 is linked to energy consumption and energy intensity in ten Chinese manufacturing sectors between 2006 and 2019. We approximate the degree of industry 4.0 by combining data on a) patent intensity of industry 4.0-related technologies and b) industrial robot intensity. Our results indicate that there is no significant overall relationship between the degree of industry 4.0 and energy consumption or energy intensity, in contrast to some earlier studies in the Chinese context which find energy intensity reducing effects of digitalisation. We argue that industry 4.0 in China might have fewer energy related benefits than expected by politics and industry. Growth-inducing effects and outsourcing of energy-intensive manufacturing tasks, for instance, may counteract efficiency-related savings. To decarbonise manufacturing in line with China's proclaimed objective of carbon neutrality by 2060, policy makers and industry should identify specific opportunities and take seriously risks of industry 4.0. The focus should be on reducing absolute energy consumption as opposed to energy intensity, which may disguise digital rebound effects; and on integrating renewable energies, particularly in the most energy-intensive sectors (metals, chemicals, non-metallic minerals).

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    IASSpublic
    Article . 2023
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    Renewable and Sustainable Energy Reviews
    Article . 2023 . Peer-reviewed
    License: CC BY
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      Renewable and Sustainable Energy Reviews
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