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    <pageLast>345</pageLast>
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    <edition/>
    <issue>2</issue>
    <volume>9</volume>
    <articleNumber/>
    <type>article</type>
    <publisherName>Sage</publisherName>
    <publisherPlace>London</publisherPlace>
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    <completedDate>2026-04-02</completedDate>
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    <title language="eng">Using AI-Enabled Images and Comic Narrative to Enhance Entrepreneurship Education</title>
    <abstract language="eng">This learning innovation is an engaging technique to help students learn abstract concepts such as entrepreneurship theory, mindsets, and methods in a playful and exciting way. We explore how current AI tools can be used by entrepreneurship educators to enhance their course materials by creating memorable images and story-based, highly visual comic book content (“graphic novels”). We implemented these enhancements in an interactive learning platform based on Articulate Storyline and tested them in an introductory entrepreneurship course with over 2000 students per year on building an entrepreneurial mindset. In this article, we include sample copies of the images and graphic novel comics and highlight key methods, lessons, results, and design artifacts created during the course of our learning journey. Educators may add these free Open Educational Resource (OER) assets to their own courses by linking to the online version. We believe this approach to entrepreneurship education enhances students’ engagement, memory retention, and the ability to identify and name underlying concepts and entrepreneurship theories. We hope to spark the interest of other entrepreneurship educators to use similar approaches at their universities.</abstract>
    <parentTitle language="eng">Entrepreneurship Education and Pedagogy</parentTitle>
    <identifier type="issn">2515-1274</identifier>
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    <author>
      <first_name>Steven</first_name>
      <last_name>Gedeon</last_name>
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    <author>
      <first_name>Florian</first_name>
      <last_name>Huber</last_name>
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  <doc>
    <id>6805</id>
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    <publishedYear>2025</publishedYear>
    <thesisYearAccepted/>
    <language>eng</language>
    <pageFirst/>
    <pageLast/>
    <pageNumber>18</pageNumber>
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    <issue>1</issue>
    <volume>9</volume>
    <articleNumber>e202500349</articleNumber>
    <type>article</type>
    <publisherName>Wiley</publisherName>
    <publisherPlace>Weinheim</publisherPlace>
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    <belongsToBibliography>1</belongsToBibliography>
    <completedDate>2026-04-02</completedDate>
    <publishedDate>--</publishedDate>
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    <title language="eng">Comprehensive Analysis of Float Current Behavior and Calendar Aging Mechanisms in Lithium‐Ion Batteries</title>
    <abstract language="eng">Aiming to quantify degradation currents from solid electrolyte interphase formation (ISEIgrowth) and gain of active lithium due to cathode lithiation (ICL), resulting from electrolyte decomposition, the float current behavior of lithium-ion batteries is investigated with different cathode materials. The float current, IFloat , represents the recharge current required to maintain the cell at a fixed potential during calendar aging. This current arises as lithium is irreversibly consumed at the anode or inserted into the cathode, shifting the electrode potentials. To account for the asymmetric response of the electrodes, a voltage-dependent scaling factor, SF, is introduced, derived from the slopes of the electrode-specific voltage curves. Using this factor in combination with measured float currents and capacity loss rates from check-up tests, ISEIgrowth and ICL is quantified at 30 °C across various float voltages. Although the SF and capacity data are limited to 30 °C, the model is extended to a range of 5–50 °C using only float current measurements. The results show that using capacity loss rates alone underestimate ISEIgrowth and that ICL, contributes significantly to the observed float current at elevated voltages, indicating that cathode lithiation plays an increasingly important role in high-voltage calendar aging.</abstract>
    <parentTitle language="eng">Batteries &amp; Supercaps</parentTitle>
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    <licence>Creative Commons BY 4.0</licence>
    <author>
      <first_name>Mohamed</first_name>
      <last_name>Azzam</last_name>
    </author>
    <author>
      <first_name>Dirk Uwe</first_name>
      <last_name>Sauer</last_name>
    </author>
    <author>
      <first_name>Christian</first_name>
      <last_name>Endisch</last_name>
    </author>
    <author>
      <first_name>Meinert</first_name>
      <last_name>Lewerenz</last_name>
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    <collection role="institutes" number="19311">Fakultät Elektro- und Informationstechnik</collection>
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    <collection role="persons" number="26571">Endisch, Christian</collection>
    <thesisPublisher>Technische Hochschule Ingolstadt</thesisPublisher>
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    <id>6797</id>
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    <publishedYear>2026</publishedYear>
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    <language>eng</language>
    <pageFirst>2047</pageFirst>
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    <completedDate>2026-03-27</completedDate>
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    <title language="eng">A Benchmark Dataset for Bus Travel and Dwell Time Prediction</title>
    <parentTitle language="eng">2025 IEEE 28th International Conference on Intelligent Transportation Systems (ITSC)</parentTitle>
    <identifier type="isbn">979-8-3315-2418-0</identifier>
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    <author>
      <first_name>Alexander</first_name>
      <last_name>Horn</last_name>
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    <author>
      <first_name>Philip-Roman</first_name>
      <last_name>Adam</last_name>
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    <author>
      <first_name>Stefanie</first_name>
      <last_name>Schmidtner</last_name>
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    <collection role="institutes" number="19311">Fakultät Elektro- und Informationstechnik</collection>
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    <id>6794</id>
    <completedYear/>
    <publishedYear>2018</publishedYear>
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    <language>eng</language>
    <pageFirst>1050</pageFirst>
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    <edition/>
    <issue>5</issue>
    <volume>22</volume>
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    <publisherName>Wiley</publisherName>
    <publisherPlace>Oxford</publisherPlace>
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    <contributingCorporation/>
    <belongsToBibliography>0</belongsToBibliography>
    <completedDate>2026-03-25</completedDate>
    <publishedDate>--</publishedDate>
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    <title language="eng">Transdisciplinary Development of a Life Cycle–Based Approach to Measure and Communicate Waste Prevention Effects in Local Authorities</title>
    <parentTitle language="eng">Journal of Industrial Ecology</parentTitle>
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      <first_name>Petra</first_name>
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      <first_name>Christoph</first_name>
      <last_name>Helbig</last_name>
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      <first_name>Dennis</first_name>
      <last_name>Stindt</last_name>
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      <first_name>Andrea</first_name>
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      <first_name>Axel</first_name>
      <last_name>Tuma</last_name>
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  </doc>
  <doc>
    <id>6793</id>
    <completedYear/>
    <publishedYear>2025</publishedYear>
    <thesisYearAccepted/>
    <language>eng</language>
    <pageFirst/>
    <pageLast/>
    <pageNumber>14</pageNumber>
    <edition/>
    <issue>24</issue>
    <volume>27</volume>
    <articleNumber>2500306</articleNumber>
    <type>article</type>
    <publisherName>Wiley</publisherName>
    <publisherPlace>Weinheim</publisherPlace>
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    <belongsToBibliography>1</belongsToBibliography>
    <completedDate>2026-03-24</completedDate>
    <publishedDate>--</publishedDate>
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    <title language="eng">Influence of a Standard Heat Treatment on the Microstructure and Properties of Inconel X‐750 Produced by Laser Powder Bed Fusion</title>
    <abstract language="eng">The production of nickel‐base superalloys through powder bed fusion using a laser beam (PBF‐LB) offers a promising method for reducing the cost of aeronautical components, especially those with complex geometries. However, when these materials are processed using PBF‐LB, the standard heat treatments developed for conventional manufacturing routes do not consistently yield predictable microstructures or mechanical properties. Thus, this study analyzes how PBF‐LB affects the microstructure and mechanical behavior of Inconel alloy X‐750 when compared to a hot‐rolled counterpart after undergoing a standard precipitation‐hardening heat treatment (solution annealing, stabilization, and precipitation). Microstructure is examined using analytical electron microscopy and high‐energy X‐ray diffraction, while mechanical behavior is assessed through microhardness. Significant differences between both manufacturing routes are detected, so that PBF‐LB processing led to an increase in grain size, anisotropy, fraction of low‐angle grain boundaries, mean angle of misorientation, and dislocation density. Moreover, PBF‐LB promoted the formation of γ′‐Ni3(Al,Ti) and η‐Ni3Ti phases during solution annealing and stabilization, respectively. Notably, the formation of γ′ contributes to an anomalous microhardness increase observed between these steps. These findings suggest that PBF‐LB may inhibit recrystallization and facilitate precipitation during heat treatment, thereby affecting the performance of components produced using this technology.</abstract>
    <parentTitle language="eng">Advanced Engineering Materials</parentTitle>
    <identifier type="issn">1527-2648</identifier>
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    <author>
      <first_name>Guilherme</first_name>
      <last_name>Volpato</last_name>
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    <author>
      <first_name>Adriano</first_name>
      <last_name>de Souza Pinto Pereira</last_name>
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    <author>
      <first_name>Steffen</first_name>
      <last_name>Neumeier</last_name>
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    <author>
      <first_name>Dachamir</first_name>
      <last_name>Hotza</last_name>
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    <author>
      <first_name>Mathias</first_name>
      <last_name>Göken</last_name>
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    <author>
      <first_name>Ulrich</first_name>
      <last_name>Tetzlaff</last_name>
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    <author>
      <first_name>Márcio Celso</first_name>
      <last_name>Fredel</last_name>
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    <collection role="institutes" number="15985">Fakultät Maschinenbau</collection>
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    <collection role="persons" number="26484">Tetzlaff, Ulrich</collection>
    <thesisPublisher>Technische Hochschule Ingolstadt</thesisPublisher>
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    <id>6786</id>
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    <pageLast/>
    <pageNumber>8</pageNumber>
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    <belongsToBibliography>1</belongsToBibliography>
    <completedDate>2026-03-18</completedDate>
    <publishedDate>--</publishedDate>
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    <title language="eng">Darwick: A Paired Dataset in Low-Light Driving Scenarios for Advanced Perceptual Enhancement and Benchmarking Assessment</title>
    <parentTitle language="eng">2024 IEEE 27th International Conference on Intelligent Transportation Systems (ITSC)</parentTitle>
    <identifier type="isbn">979-8-3315-0592-9</identifier>
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    <author>
      <first_name>Zixiang</first_name>
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      <first_name>Yunruo</first_name>
      <last_name>Jiang</last_name>
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      <first_name>Pak Hung</first_name>
      <last_name>Chan</last_name>
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      <first_name>Yiting</first_name>
      <last_name>Wang</last_name>
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      <first_name>Yuri</first_name>
      <last_name>Poledna</last_name>
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    <author>
      <first_name>Valentina</first_name>
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    <collection role="institutes" number="19317">CARISSMA Institute of Automated Driving  (C-IAD)</collection>
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    <belongsToBibliography>1</belongsToBibliography>
    <completedDate>2026-03-17</completedDate>
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    <title language="eng">A Vehicle-in-the-Loop Approach for Front Camera Verification Using Adaptive High Beam</title>
    <abstract language="eng">As automated driving functions based on environmental sensors become increasingly deployed, ensuring reliable performance over the vehicle lifetime is essential. Currently, verification is carried out through internal self-diagnostics, which do not always operate correctly, and periodic technical inspection, which assesses only the test criteria installation and condition. Test criteria for function and efficiency of environmental sensors are neither standardized nor routinely assessed, creating the need for new testing approaches. Previous low-cost research approaches defined a method and conducted experiments to verify a vehicle’s front camera by displaying visual stimuli and evaluating the high beam assist response. Whereas the camera’s function could be verified through a basic qualitative check, the approach did not enable a quantitative evaluation of its performance. The aim of this work was therefore to advance this approach and investigate the added value of a Vehicle-in-the-Loop test bench for front camera verification. Three tests were conducted. A supporting method was introduced to reproducibly detect and define the position of the headlight cutoff line, enabling consistent evaluation of the vehicle’s reaction. With static camera stimuli (Test I), the function of the front camera could be verified, and the influence of the vehicle geometry on the reaction was assessed. Dynamic stimuli (Test II) additionally enabled an efficiency evaluation, allowing quantitative comparison between vehicles. However, transferring the stimuli into a reproducible virtual simulation (Test III) remained challenging, as the vehicles under test did not respond consistently. Further research is required to refine and simplify the method toward a standardized periodic technical inspection procedure.</abstract>
    <parentTitle language="ces">IEEE Open Journal of Intelligent Transportation Systems</parentTitle>
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    <title language="eng">Enabling Fast and Mobile Histopathology Image Annotation through Swipeable Interfaces SWAN</title>
    <parentTitle language="eng">Bildverarbeitung für die Medizin 2026: Proceedings, German Conference on Medical Image Computing, Lübeck, March 15–17, 2026</parentTitle>
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      <last_name>Conrad</last_name>
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      <first_name>Klaus H.</first_name>
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    <publisherName>Taylor &amp; Francis</publisherName>
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    <title language="eng">Thermal imaging for the detection of driver impairment: evidence from a high-fidelity driving simulator study</title>
    <abstract language="eng">Objectives&#13;
Driving under the influence of alcohol (DUI) remains a major contributor to fatal traffic crashes worldwide. With increasing regulatory pressure, such as requirements by Euro NCAP for in-vehicle impairment detection, there is a growing need for reliable, real-time monitoring solutions. While traditional DUI detection approaches focus on driving behavior or eye movement analysis, this study explores the potential of thermal imaging as a noninvasive alternative for detecting alcohol impairment.&#13;
&#13;
Methods&#13;
We conducted a large-scale experimental study with 120 participants in a high-fidelity driving simulator, capturing thermal facial data under both non-impaired and alcohol-impaired conditions. A novel temperature extraction method was developed based on facial landmarks, incorporating multiple frames to reduce noise and improve temporal stability. Ambient cabin temperature was also recorded to normalize facial temperature readings and control for environmental influences. Several machine learning classifiers, including Logistic Regression, Random Forest, Support Vector Machine (SVM), and Gradient-Boosting Models, were trained using five facial temperature features (cheek, temple, ear, forehead, and nasal tip) and evaluated via five-fold subject-wise cross-validation.&#13;
&#13;
Results&#13;
Significant temperature changes were observed in specific facial regions (particularly the cheek, ear, temple, and nasal tip) under alcohol influence. Among the evaluated models, Logistic Regression achieved the highest average classification accuracy (62%), while SVM demonstrated the most stable performance across folds. The model showed a slight conservative bias toward predicting the baseline (non-impaired) class, thereby reducing the risk of false positive classifications. Environmental conditions, including cabin temperature, were verified to be stable across both driving sessions, ensuring the validity of the physiological measurements.&#13;
&#13;
Conclusions&#13;
This study demonstrates the feasibility of using thermal imaging for in-vehicle DUI detection under realistic conditions. Our contributions include the development of a robust facial temperature processing pipeline, the creation of a unique dataset collected under ecologically valid conditions, and a comprehensive comparison of seven state-of-the-art classification models. Thermal imaging represents a promising complementary modality for future driver monitoring systems focused on safety and impairment detection.</abstract>
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