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    <edition/>
    <issue>2</issue>
    <volume>9</volume>
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    <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>
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    <language>eng</language>
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    <pageNumber>18</pageNumber>
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    <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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    <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>
    <collection role="institutes" number="19320">Institut für Innovative Mobilität (IIMo)</collection>
    <collection role="persons" number="26571">Endisch, Christian</collection>
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  <doc>
    <id>6797</id>
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    <publishedYear>2026</publishedYear>
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    <language>eng</language>
    <pageFirst>2047</pageFirst>
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    <publisherName>IEEE</publisherName>
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    <completedDate>2026-03-27</completedDate>
    <publishedDate>--</publishedDate>
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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>
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    <author>
      <first_name>Alexander</first_name>
      <last_name>Horn</last_name>
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      <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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    <id>6793</id>
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    <publishedYear>2025</publishedYear>
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    <pageNumber>14</pageNumber>
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    <issue>24</issue>
    <volume>27</volume>
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    <type>article</type>
    <publisherName>Wiley</publisherName>
    <publisherPlace>Weinheim</publisherPlace>
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    <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>
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    <id>6786</id>
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    <publishedYear>2026</publishedYear>
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    <pageLast/>
    <pageNumber>8</pageNumber>
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    <publisherName>IEEE</publisherName>
    <publisherPlace>Piscataway</publisherPlace>
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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>
      <last_name>Wei</last_name>
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    <author>
      <first_name>Yunruo</first_name>
      <last_name>Jiang</last_name>
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      <first_name>Pak Hung</first_name>
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      <first_name>Yiting</first_name>
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      <first_name>Valentina</first_name>
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    <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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    <author>
      <first_name>Robin</first_name>
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      <first_name>Thomas</first_name>
      <last_name>Tentrup</last_name>
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      <first_name>Hans-Georg</first_name>
      <last_name>Schweiger</last_name>
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    <collection role="persons" number="26111">Schweiger, Hans-Georg</collection>
    <thesisPublisher>Technische Hochschule Ingolstadt</thesisPublisher>
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    <completedDate>2026-03-17</completedDate>
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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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      <first_name>Heinz</first_name>
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      <first_name>Thomas Martin</first_name>
      <last_name>Deserno</last_name>
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    <author>
      <first_name>Viktoria</first_name>
      <last_name>Weiss</last_name>
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      <first_name>Andreas</first_name>
      <last_name>Maier</last_name>
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    <author>
      <first_name>Thomas</first_name>
      <last_name>Conrad</last_name>
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    <editor>
      <first_name>Klaus H.</first_name>
      <last_name>Maier-Hein</last_name>
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      <first_name>Christoph</first_name>
      <last_name>Stroblberger</last_name>
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    <pageLast/>
    <pageNumber>10</pageNumber>
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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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    <title language="eng">A Subphase-Labeled Mitotic Dataset for AI-powered Cell Division Analysis</title>
    <abstract language="eng">Mitosis detection represents a critical task in digital pathology, as it plays an important role in the tumor grading and prognosis of patients. Manual determination is a labor-intensive task for practitioners with high interobserver variability, thus, automation is a priority. There has been substantial progress towards creating robust mitosis detection algorithms, primarily driven by the Mitosis Domain Generalization (MIDOG) challenges. Also, there has been growing interest in the molecular characterization of mitosis to achieve a more comprehensive understanding of its underlying mechanisms in a subphase-specific manner. We introduce a new mitotic figure dataset annotated with subphase information based on the MIDOG++ dataset as well as a previously unrepresented tumor domain to enhance the diversity and applicability. We envision a new perspective for domain generalization by improving model performance with subtyping mitosis, complemented with an atypical mitotic class. Our work has implications in two main areas: subtyping information can provide helpful information in mitosis detection, while also providing promising new directions in answering biological questions, such as molecular analysis of subphases.</abstract>
    <parentTitle language="eng">Scientific Data</parentTitle>
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    <pageFirst>126</pageFirst>
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    <issue>MELBA–BVM 2025 Special Issue</issue>
    <volume>3</volume>
    <articleNumber>2026:007</articleNumber>
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    <title language="eng">Stuck on Suggestions: Automation Bias, the Anchoring Effect, and the Factors That Shape Them in Computational Pathology</title>
    <abstract language="eng">Artificial intelligence (AI)-driven clinical decision support systems (CDSS) hold promise to improve diagnostic accuracy and efficiency in computational pathology. However, collaboration between human experts and AI may give rise to cognitive biases, such as automation and anchoring bias, wherein users may be inclined to blindly adopt system recommendations or be disproportionately influenced by the presence of AI predictions, even when they are inaccurate. These biases may be exacerbated under time pressure, pervasive in routine pathology diagnostics, or shaped by individual user characteristics. To investigate these effects, we conducted a web-based experiment in which trained pathology experts (n = 28) estimated tumor cell percentages twice: once independently and once with the aid of an AI. A subset of the estimates in each condition was performed under time constraints. Our findings indicate that AI integration generally enhances diagnostic performance. However, it also introduced a 7% automation bias rate, quantified as the number of accepted negative consultations, where a previously correct independent assessment gets overturned by inaccurate AI guidance. While time pressure did not increase the frequency of automation bias occurrence, it appeared to intensify its severity, as evidenced by a performance decline linked to increased automation reliance under cognitive load. A linear mixed-effects model (LMM) analysis, simulating weighted averaging, revealed a statistically significant positive coefficient for AI advice, indicating a moderate degree of anchoring on system output. This effect was further intensified under time pressure, suggesting that anchoring bias may become more pronounced when cognitive resources are limited. A secondary LMM evaluation assessing automation reliance, used as a proxy for both automation and anchoring bias, demonstrated that professional experience and self-efficacy were associated with reduced dependence on system support, whereas higher confidence during AI-assisted decision-making was linked to increased automation reliance. Together, these findings underscore the dual nature of AI integration in clinical workflows, offering performance benefits while also introducing risks of cognitive bias–driven diagnostic errors. As an initial investigation focused on a single medical specialty and diagnostic task, this study aims to lay the groundwork for future research to explore these phenomena across diverse clinical contexts, ultimately supporting the establishment of appropriate reliance on automated systems and the safe, effective integration of human–AI collaboration in medical decision-making.</abstract>
    <parentTitle language="eng">Machine Learning for Biomedical Imaging</parentTitle>
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    <author>
      <first_name>Emely</first_name>
      <last_name>Rosbach</last_name>
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      <first_name>Jonas</first_name>
      <last_name>Ammeling</last_name>
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    <author>
      <first_name>Jonathan</first_name>
      <last_name>Ganz</last_name>
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      <first_name>Christof</first_name>
      <last_name>Bertram</last_name>
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      <last_name>Conrad</last_name>
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    <title language="eng">Toxic gas emission in electric vehicles: What a battery fire means for occupant safety</title>
    <abstract language="eng">This work investigates the ability of occupants to escape from a battery electric vehicle during a thermal runaway of the traction battery initiated by nail penetration. Such events generate intense fires and large amounts of toxic gases, rapidly reducing the time available for safe evacuation. In controlled full-scale outdoor experiments on two identical mid-range battery electric vehicles, the smoke gas composition inside the cabin is examined. Using FTIR spectrometers and an oxygen analyzer, temporal and spatial concentrations of organic carbonates, hydrocarbons, hydrogen fluoride, hydrogen cyanide, acetylene, and oxygen are measured. Sampling locations include the driver's breathing zone, the right rear passenger's breathing zone, and the exterior right rear wheel housing. The study further evaluates occupant's escape capability using the fractional effective dose (FED) model in accordance with ISO 13571. Results show that toxic gases can reach harmful concentrations within minutes after smoke enters the cabin, though smoke entry times vary widely with fire progression. Differences in fire development and smoke dispersion strongly affect FED values and thus the time available for self-rescue. Overall, the findings provide an important basis for assessing occupant safety during battery electric vehicle fires and highlight the need for improved mitigation strategies.</abstract>
    <parentTitle language="eng">Fire Safety Journal</parentTitle>
    <identifier type="issn">1873-7226</identifier>
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    <author>
      <first_name>Tim</first_name>
      <last_name>Rappsilber</last_name>
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    <author>
      <first_name>Simone</first_name>
      <last_name>Krüger</last_name>
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      <first_name>Tina</first_name>
      <last_name>Raspe</last_name>
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      <first_name>Rudolf</first_name>
      <last_name>Reclo</last_name>
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      <first_name>Hans-Georg</first_name>
      <last_name>Schweiger</last_name>
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    <collection role="persons" number="26111">Schweiger, Hans-Georg</collection>
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    <publisherPlace>Cham</publisherPlace>
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    <title language="eng">The Search for Meaning at Work: A Randomized Controlled Trial of a Cognitive Crafting Intervention</title>
    <abstract language="eng">Many employees struggle to experience a sense of meaning in their work, posing a challenge to workplace flourishing. Cognitive crafting—the process of reframing how one interprets their work—can enhance meaningful work and well-being. However, strategies to effectively promote it remain unclear. Building on positive psychology interventions, self-determination theory, and job crafting theory, we developed a brief online intervention using meaning-based reflection and cognitive reframing. Its effectiveness was evaluated in a pre-registered randomized controlled trial ( N = 260) with an active control group, assessing outcomes post-intervention and at one-month follow-up. Compared to the control group, the intervention group reported significantly higher levels of meaningful work, work engagement, and job satisfaction. However, these differences were driven by lower scores in the control group compared to baseline, while the intervention group remained stable. These findings highlight the complexity of designing effective cognitive crafting interventions and underscore the need for further theory-driven research to identify strategies that reliably enhance positive work evaluations and work-related well-being.</abstract>
    <parentTitle language="eng">International Journal of Applied Positive Psychology</parentTitle>
    <identifier type="issn">2364-5059</identifier>
    <identifier type="urn">urn:nbn:de:bvb:573-67739</identifier>
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    <author>
      <first_name>Saskia</first_name>
      <last_name>Glaas</last_name>
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    <author>
      <first_name>Huong</first_name>
      <last_name>Pham</last_name>
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    <author>
      <first_name>Hannes</first_name>
      <last_name>Münstermann</last_name>
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    <author>
      <first_name>Dieter</first_name>
      <last_name>Frey</last_name>
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    <id>6755</id>
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    <publishedYear>2026</publishedYear>
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    <language>eng</language>
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    <pageLast/>
    <pageNumber>16</pageNumber>
    <edition/>
    <issue>264</issue>
    <volume>2026</volume>
    <articleNumber>115770</articleNumber>
    <type>article</type>
    <publisherName>Elsevier</publisherName>
    <publisherPlace>Amsterdam</publisherPlace>
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    <completedDate>2026-03-09</completedDate>
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    <title language="eng">The columnar to bimodal transition of Al–Cr–Mo–Sc–Zr alloys during laser powder bed fusion</title>
    <abstract language="eng">Aluminium alloys processed via Powder Bed Fusion using a Laser Beam (PBF-LB) enable complex and lightweight&#13;
components. Especially high-strength and heat-resistant Al alloys are in high demand for aerospace and motorsports applications, but the processability of these alloys remains critical due to hot-cracking along columnar grains. Therefore, the microstructural control of PBF-LB alloys is under increasing consideration. Al-Cr alloys have shown promising high-temperature properties and good processability in previous studies despite having a coarse and fully columnar grain structure. In this study, Al-Cr-Mo-Sc-Zr alloys with different compositions are subjected to phase analysis to understand the transition from fully columnar to bimodal grain structures. Synchrotron diffraction, electron dispersive spectroscopy, and atom probe tomography results have shown that three phases are present, irrespective of the alloys’ composition: 𝛼-Al, Al45Cr7, and Al3(Sc,Zr). The introduction of Cr increases the lattice mismatch between 𝛼-Al and Al3(Sc,Zr) and is suspected to hinder grain refinement by&#13;
Sc and Zr. Various thermodynamic simulations revealed the effect of grain refining elements (Sc+Zr) and Cr on the solidification path. The combination of simulation and experimental investigations led to three hypotheses on the columnar to bimodal transition in Al-Cr alloys. These results will help to further enable the microstructural control of PBF-LB processed alloys.</abstract>
    <parentTitle language="eng">Materials &amp; Design</parentTitle>
    <identifier type="issn">0264-1275</identifier>
    <identifier type="urn">urn:nbn:de:bvb:573-67559</identifier>
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    <author>
      <first_name>Barbara</first_name>
      <last_name>Agricola</last_name>
    </author>
    <author>
      <first_name>Marko</first_name>
      <last_name>Bärtl</last_name>
    </author>
    <author>
      <first_name>Martin</first_name>
      <last_name>Landesberger</last_name>
    </author>
    <author>
      <first_name>Huan</first_name>
      <last_name>Zhao</last_name>
    </author>
    <author>
      <first_name>Frank</first_name>
      <last_name>Palm</last_name>
    </author>
    <author>
      <first_name>Annegret</first_name>
      <last_name>Kerschenlohr</last_name>
    </author>
    <author>
      <first_name>Eric A.</first_name>
      <last_name>Jägle</last_name>
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    <collection role="persons" number="23385">Kerschenlohr, Annegret</collection>
    <collection role="persons" number="51201">Landesberger, Martin</collection>
    <thesisPublisher>Technische Hochschule Ingolstadt</thesisPublisher>
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  </doc>
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    <id>6747</id>
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    <publishedYear>2026</publishedYear>
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    <pageFirst/>
    <pageLast/>
    <pageNumber>20</pageNumber>
    <edition/>
    <issue>5</issue>
    <volume>19</volume>
    <articleNumber>1303</articleNumber>
    <type>article</type>
    <publisherName>MDPI</publisherName>
    <publisherPlace>Basel</publisherPlace>
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    <belongsToBibliography>1</belongsToBibliography>
    <completedDate>2026-03-09</completedDate>
    <publishedDate>--</publishedDate>
    <thesisDateAccepted>--</thesisDateAccepted>
    <title language="eng">Evaluating Feedstock Pretreatment Methods to Unlock Co-Digestion in Small-Scale Biogas Systems: A Multi-Criteria Approach to System Integration and Decision Making</title>
    <abstract language="eng">Small-scale biogas plants in developing countries present a viable alternative to traditional polluting energy sources, particularly in rural and underserved communities. These systems typically rely on locally sourced livestock manure; however, inconsistent supply often results in underfeeding, reduced biogas production, and, in many cases, system abandonment. Co-digestion with crop residues presents a promising strategy to enhance feedstock availability and system resilience. However, the recalcitrant nature of lignocellulosic biomass and limited access to suitable pretreatment technologies have constrained its adoption. This paper evaluates feasible pretreatment methods for integrating crop residues, especially straw, into small-scale biogas systems. Using the Analytic Hierarchy Process (AHP), pretreatment methods are assessed based on five criteria: (i) technology simplicity, (ii) energy requirements, (iii) capital and operational costs, (iv) effectiveness, and (v) environmental impact. The analysis identifies microbial pretreatment using the liquid fraction of digestate, combined with mechanical size reduction, as the most suitable approach for small-scale implementation, utilizing low-cost, simplified mechanical devices adaptable to various crop residues with minimal energy input. A conceptual design of a demonstration plant is proposed to validate this integrated pretreatment approach and assess its impact on biogas yield, system performance, and technology adoption. The design incorporates an on-site digestate separation unit to supply microbial inoculum and emphasizes simplicity and cost-effectiveness in material handling and energy use. Pilot trials are proposed to evaluate key performance indicators, including specific methane yield (LCH4/gVS added), volatile solids reduction (%), and methane content increase (%), ensuring evidence-based adoption and practical applicability of the design.</abstract>
    <parentTitle language="eng">Energies</parentTitle>
    <identifier type="issn">1996-1073</identifier>
    <identifier type="urn">urn:nbn:de:bvb:573-67479</identifier>
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    <licence>Creative Commons BY 4.0</licence>
    <author>
      <first_name>Joshua Kiplagat</first_name>
      <last_name>Ngetuny</last_name>
    </author>
    <author>
      <first_name>Tobias</first_name>
      <last_name>Baldauf</last_name>
    </author>
    <author>
      <first_name>Wilfried</first_name>
      <last_name>Zörner</last_name>
    </author>
    <collection role="institutes" number="15985">Fakultät Maschinenbau</collection>
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    <collection role="persons" number="26227">Zörner, Wilfried</collection>
    <collection role="institutes" number="19321">Institut für neue Energie-Systeme (InES)</collection>
    <thesisPublisher>Technische Hochschule Ingolstadt</thesisPublisher>
    <file>https://opus4.kobv.de/opus4-haw/files/6747/energies-19-01303.pdf</file>
  </doc>
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    <id>6709</id>
    <completedYear/>
    <publishedYear>2024</publishedYear>
    <thesisYearAccepted/>
    <language>eng</language>
    <pageFirst/>
    <pageLast/>
    <pageNumber>11</pageNumber>
    <edition/>
    <issue>235</issue>
    <volume>2024</volume>
    <articleNumber>110607</articleNumber>
    <type>article</type>
    <publisherName>Elsevier</publisherName>
    <publisherPlace>Amsterdam</publisherPlace>
    <creatingCorporation/>
    <contributingCorporation/>
    <belongsToBibliography>1</belongsToBibliography>
    <completedDate>2026-02-26</completedDate>
    <publishedDate>--</publishedDate>
    <thesisDateAccepted>--</thesisDateAccepted>
    <title language="eng">Sample-by-sample Power Quality Disturbance classification based on Sliding Window Recursive Discrete Fourier Transform</title>
    <parentTitle language="eng">Electric Power Systems Research</parentTitle>
    <identifier type="issn">1873-2046</identifier>
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    <author>
      <first_name>Luiz Fernando Alves</first_name>
      <last_name>Rodrigues</last_name>
    </author>
    <author>
      <first_name>Henrique L.M.</first_name>
      <last_name>Monteiro</last_name>
    </author>
    <author>
      <first_name>Danton</first_name>
      <last_name>Ferreira</last_name>
    </author>
    <author>
      <first_name>Bruno H. G.</first_name>
      <last_name>Barbosa</last_name>
    </author>
    <author>
      <first_name>Carlos Antônio</first_name>
      <last_name>Rufino Júnior</last_name>
    </author>
    <author>
      <first_name>Carlos</first_name>
      <last_name>Duque</last_name>
    </author>
    <collection role="institutes" number="19319">CARISSMA Institute of Electric, Connected and Secure Mobility (C-ECOS)</collection>
  </doc>
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    <id>6708</id>
    <completedYear/>
    <publishedYear>2024</publishedYear>
    <thesisYearAccepted/>
    <language>eng</language>
    <pageFirst/>
    <pageLast/>
    <pageNumber>12</pageNumber>
    <edition/>
    <issue>2</issue>
    <volume>2</volume>
    <articleNumber>100097</articleNumber>
    <type>article</type>
    <publisherName>Elsevier</publisherName>
    <publisherPlace>Amsterdam</publisherPlace>
    <creatingCorporation/>
    <contributingCorporation/>
    <belongsToBibliography>1</belongsToBibliography>
    <completedDate>2026-02-26</completedDate>
    <publishedDate>--</publishedDate>
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    <title language="eng">When own interest stands against the “greater good” – Decision randomization in ethical dilemmas of autonomous systems that involve their user’s self-interest</title>
    <abstract language="eng">Autonomous systems (ASs) decide upon ethical dilemmas and their artificial intelligence as well as situational settings become more and more complex. However, to study common-sense morality concerning ASs abstracted dilemmas on autonomous vehicle (AV) accidents are a common tool. A special case of ethical dilemmas is when the AS’s users are affected. Many people want AVs to adhere to utilitarian programming (e.g., to save the larger group), or egalitarian programming (i.e., to treat every person equally). However, they want their own AV to protect them instead of the “greater good”. That people reject utilitarian programming as an AS’s user while supporting the idea from an impartial perspective has been termed the “social dilemma of AVs”. Meanwhile, preferences for another technical capability, which would implement egalitarian programming, have not been elicited for dilemmas involving self-interest: decision randomization. This paper investigates normative and descriptive preferences for a self-protective, self-sacrificial, or randomized choice by an AS in a dilemma where people are the sole passenger of an AV, and their survival stands against the survival of several others. Results suggest that randomization may mitigate the “social dilemma of AVs” by bridging between a societally accepted programming and the urge of ASs’ users for self-protection.</abstract>
    <parentTitle language="eng">Computers in Human Behavior: Artificial Humans</parentTitle>
    <identifier type="issn">2949-8821</identifier>
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    <author>
      <first_name>Anja</first_name>
      <last_name>Bodenschatz</last_name>
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    <id>6691</id>
    <completedYear/>
    <publishedYear>2026</publishedYear>
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    <language>eng</language>
    <pageFirst/>
    <pageLast/>
    <pageNumber>15</pageNumber>
    <edition/>
    <issue>1</issue>
    <volume>16</volume>
    <articleNumber>7387</articleNumber>
    <type>article</type>
    <publisherName>Springer Nature</publisherName>
    <publisherPlace>London</publisherPlace>
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    <belongsToBibliography>1</belongsToBibliography>
    <completedDate>2026-02-23</completedDate>
    <publishedDate>--</publishedDate>
    <thesisDateAccepted>--</thesisDateAccepted>
    <title language="eng">Quantitative Kernel estimation from traffic signs using slanted edge spatial frequency response as a sharpness metric</title>
    <abstract language="eng">Sharpness is a critical optical property of automotive cameras, measured by the spatial frequency response (SFR) within the end-of-line (EOL) test after manufacturing. This work presents a method to estimate the blurring kernel of an automotive camera, which could be the first step toward state monitoring of automotive cameras. To achieve this, Principal Component Analysis (PCA) was performed, using synthetic kernels generated by Zemax. The PCA model was built with approximately 1300 base kernels representing spatially variant point spread functions (PSFs). This model generates kernel samples during the estimation process. Synthetic images were created by convolving the synthetic kernels with reference traffic sign images and compared with real-life data captured by an automotive camera. These synthetic data were utilized for algorithm development, and later on, validation was performed on real-life data. The algorithm extracts two pixels regions of interest (ROIs) containing slanted edges from the blurred image and crops matching ROIs from a reference sharp image. Each candidate kernel was used to blur the reference ROIs, and the resulting SFR was compared with the blurred ROIs’ SFR. Differential evolution optimization minimizes the SFR difference, selecting the kernel that best matches the observed blur. The final kernel was evaluated against the true kernel for accuracy. The structural similarity index measure (SSIM) between the original and estimated blurred ROIs ranges from 0.808 to 0.945. For true vs. estimated kernels, SSIM varies from 0.92 to 0.98. Pearson correlation coefficients range from 0.84 to 0.99, Cosine similarity from 0.86 to 0.99, and mean squared error (MSE) from to . Validation on real-life camera images showed that the SSIM between the estimated and blurred ROI was &gt;0.82, showing promising accuracy in kernel estimation, which could be used towards in-field monitoring of camera sharpness degradation.</abstract>
    <parentTitle language="eng">Scientific Reports</parentTitle>
    <identifier type="issn">2045-2322</identifier>
    <identifier type="urn">urn:nbn:de:bvb:573-66915</identifier>
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    <licence>Creative Commons BY 4.0</licence>
    <author>
      <first_name>Amit</first_name>
      <last_name>Pandey</last_name>
    </author>
    <author>
      <first_name>Zubair Akhtar</first_name>
      <last_name>Mohd</last_name>
    </author>
    <author>
      <first_name>Nandana Kappuva</first_name>
      <last_name>Veettil</last_name>
    </author>
    <author>
      <first_name>Bernhard</first_name>
      <last_name>Wunderle</last_name>
    </author>
    <author>
      <first_name>Gordon</first_name>
      <last_name>Elger</last_name>
    </author>
    <collection role="open_access" number="">open_access</collection>
    <collection role="institutes" number="19311">Fakultät Elektro- und Informationstechnik</collection>
    <collection role="institutes" number="19320">Institut für Innovative Mobilität (IIMo)</collection>
    <collection role="persons" number="26589">Elger, Gordon</collection>
    <collection role="Import" number="deepgreen">DeepGreen</collection>
    <thesisPublisher>Technische Hochschule Ingolstadt</thesisPublisher>
    <file>https://opus4.kobv.de/opus4-haw/files/6691/41598_2026_Article_40556.pdf</file>
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    <id>6687</id>
    <completedYear/>
    <publishedYear>2024</publishedYear>
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    <language>eng</language>
    <pageFirst/>
    <pageLast/>
    <pageNumber>15</pageNumber>
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    <publisherName>TIB Open Publishing</publisherName>
    <publisherPlace>Hannover</publisherPlace>
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    <belongsToBibliography>1</belongsToBibliography>
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