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    4170 research outputs found

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    Assigning Calendar Aging during Open Circuit and Float Current Analysis to Anode and Cathode Aging Using Three-Electrode Cells

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    Analyzing calendar aging by float current analysis, instead of solely repeating capacity tests, gains more insights into aging. However, the underlying mechanisms of the recharge current required to keep the voltage constant are not yet fully understood. Therefore, this study performs float current and additional open circuit voltage (OCV) tests on 3-electrode cells for four different voltages at 30 °C. By monitoring the potentials of the anode and the cathode over time individually, the aging rate on both electrodes and the present capacity can be calculated based on adjusting the electrode-specific OCV curves according to the voltage slippery theory. The electrode-specific aging and the capacity prediction show high agreement among both methods and the capacity tests at the end of the experiment. As a result, the capacity loss appears linear-like and is associated with the loss of active lithium mainly due to SEI formation on the anode. Additionally, a significant cathode voltage decay is observed especially for higher cell voltage. According to the literature, this is associated with cathode lithiation where the conductive salt is decomposed by inserting active lithium into the cathode masking SEI losses. Finally, OCV and float current analysis return comparable results and support present 2-electrode evaluation approaches

    Abbildung und Integration von Elementarfaktoren in SAP S/4HANA

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    Thriving or Withdrawing: How Job Crafting and Disengagement Shape Responses to Daily Laissez-faire Leadership

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    Although laissez-faire leadership is common in organizations and has been linked to detrimental effects on employees, little is known about its daily effects and how employees cope with this type of behavior. Drawing on the job demands-resources model and using a daily diary design, we examine the daily effects of laissez-faire leadership depending on followers’ coping styles, capturing both adaptive and maladaptive responses. Specifically, we argue that the negative effect of laissez-faire leadership on next-day performance via evening job satisfaction is mitigated on days that followers engage in job crafting, while it is amplified on days that followers engage in disengagement coping. We collected data twice a day over one working week in an experience sampling study with 127 employees (i.e., after work and before bedtime; 359 data points). Our findings revealed no direct effect of laissez-faire leadership. However, there was a positive indirect effect of laissez-faire leadership on next-day performance via evening job satisfaction on days when employees engaged in high levels of job crafting, supporting its hypothesized beneficial effect. Conversely, as hypothesized, a negative indirect effect was observed on days when disengagement coping was high. By identifying the mixed effects of laissez-faire leadership, this study offers a fresh perspective that challenges the dominant view of its uniformly negative consequences, revealing how its downstream effects vary within individuals depending on their daily coping strategies

    Vehicle crash simulation models for reinforcement learning driven crash-detection algorithm calibration

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    The development of finite element vehicle models for crash simulations is a highly complex task. The main aim of these models is to simulate a variety of crash scenarios and assess all the safety systems for their respective performances. These vehicle models possess a substantial amount of data pertaining to the vehicle’s geometry, structure, materials, etc., and are used to estimate a large set of system and component level characteristics using crash simulations. It is understood that even the most well-developed simulation models are prone to deviations in estimation when compared to real-world physical test results. This is generally due to our inability to model the chaos and uncertainties introduced in the real world. Such unavoidable deviations render the use of virtual simulations ineffective for the calibration process of the algorithms that activate the restraint systems in the event of a crash (crash-detection algorithm). In the scope of this research, authors hypothesize the possibility of accounting for such variations introduced in the real world by creating a feedback loop between real-world crash tests and crash simulations. To accomplish this, a Reinforcement Learning (RL) compatible virtual surrogate model is used, which is adapted from crash simulation models. Hence, a conceptual methodology is illustrated in this paper for developing an RL-compatible model that can be trained using the results of crash simulations and crash tests. As the calibration of the crash-detection algorithm is fundamentally dependent upon the crash pulses, the scope of the expected output is limited to advancing the ability to estimate crash pulses. Furthermore, the real-time implementation of the methodology is illustrated using an actual vehicle model

    Barrier Analysis of Flexibilization of Cooling Supply Systems

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    The present study examines the barriers that prevent cooling system flexibility from being optimized. In the context of an increasing reliance on renewable energy sources, the necessity for flexible energy utilization is becoming increasingly apparent. A survey and discussion groups were conducted with various stakeholders within the cooling value chain to obtain their experiences and insights regarding barriers to flexibilization. The findings point out that economic, technological, and regulatory barriers are the primary factors impeding the implementation of flexible solutions. In particular, high investment costs, complex technical implementation, a lack of information, and a complicated legal framework were identified as significant impediments. To enhance the flexibility of cooling systems, coordinated efforts are necessary to address these barriers. Practical examples, training, and the standardization and digitalization of processes could facilitate the widespread implementation of flexible cooling systems

    A Systematic Mapping Study on Contract-based Software Design for Dependable Systems

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    Background: Contract-based Design (CbD) is a valuable methodology for software design that allows annotation of code and architectural components with contracts, thereby enhancing clarity and reliability in software development. It establishes rules that outline the behaviour of software components and their interfaces and interactions. This modular approach enables the design process to be segmented into smaller, independently developed, tested, and verified system components, ultimately leading to more robust and dependable software. Aim: Despite the significance and well-established theoretical background of CbD, there is a need for a comprehensive systematic mapping study for reliable software systems. Our study provides an evidence-based overview of a method and demonstrates its practical feasibility. Method: To conduct this study, we systematically searched three different databases using specially formulated queries, which initially yielded 1,221 primary studies. After voting, we focused on 288 primary studies for more detailed analysis. Finally, a collaborative review allowed us to gather relevant evidence and information to address our research questions. Results: Our findings suggest potential avenues for future research trajectories in CbD, emphasising its role in improving the dependability of software systems. We highlight maturity levels across different domains and identify areas that may benefit from further research. Conclusion: Although CbD is a well-established software design approach, a more comprehensive literature review is needed to clarify its theoretical state about dependable systems. Our study addresses this gap by providing a detailed overview of CbD from various perspectives, identifying key gaps, and suggesting future research directions

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