Dynamic Time-Dependent Route Planning in Road Networks with User Preferences

There has been tremendous progress in algorithmic methods for computing driving directions on road networks. Most of that work focuses on time-independent route planning, where it is assumed that the cost on each arc is constant per query. In practice, the current traffic situation significantly influences the travel time on large parts of the road network, and it changes over the day. One can distinguish between traffic congestion that can be predicted using historical traffic data, and congestion due to unpredictable events, e.g., accidents. In this work, we study the \emph{dynamic and time-dependent} route planning problem, which takes both prediction (based on historical data) and live traffic into account. To this end, we propose a practical algorithm that, while robust to user preferences, is able to integrate global changes of the time-dependent metric~(e.g., due to traffic updates or user restrictions) faster than previous approaches, while allowing subsequent queries that enable interactive applications

Novel miscanthus germplasm-based value chains : A Life Cycle Assessment

The OPTIMISC project received funding from the European Union Seventh Framework Programme (FP7/2007–2013) under grant agreement No. 289159. In addition, the study was partly supported by a grant from the Ministry of Science, Research and the Arts of Baden-Württemberg (funding code: 7533-10-5-70) as part of the BBW ForWerts Graduate Programme. We are grateful to Nicole Gaudet for editing the manuscript.Peer reviewedPublisher PD

Consumption Profiles in Route Planning for Electric Vehicles: Theory and Applications

In route planning for electric vehicles (EVs), consumption profiles are a functional representation of optimal energy consumption between two locations, subject to initial state of charge. Efficient computation of profiles is a relevant problem on its own, but also a fundamental ingredient to many route planning approaches for EVs. In this work, we show that the complexity of a profile is at most linear in the graph size. Based on this insight, we derive a polynomial-time algorithm for the problem of finding an energy-optimal path between two locations that allows stops at charging stations. Exploiting efficient profile search, our approach also allows partial recharging at charging stations to save energy. In a sense, our results close the gap between efficient techniques for energy-optimal routes (based on simpler models) and NP-hard time-constrained problems involving charging stops for EVs. We propose a practical implementation, which we carefully integrate with Contraction Hierarchies and A* search. Even though the practical variant formally drops correctness, a comprehensive experimental study on a realistic, large-scale road network reveals that it always finds the optimal solution in our tests and computes even long-distance routes with charging stops in less than 300 ms

Modeling and Engineering Constrained Shortest Path Algorithms for Battery Electric Vehicles

We study the problem of computing constrained shortest paths for battery electric vehicles. Since battery capacities are limited, fastest routes are often infeasible. Instead, users are interested in fast routes where the energy consumption does not exceed the battery capacity. For that, drivers can deliberately reduce speed to save energy. Hence, route planning should provide both path and speed recommendations. To tackle the resulting NP-hard optimization problem, previous work trades correctness or accuracy of the underlying model for practical running times. In this work, we present a novel framework to compute optimal constrained shortest paths for electric vehicles that uses more realistic physical models, while taking speed adaptation into account. Careful algorithm engineering makes the approach practical even on large, realistic road networks: We compute optimal solutions in less than a second for typical battery capacities, matching performance of previous inexact methods. For even faster performance, the approach can easily be extended with heuristics that provide high quality solutions within milliseconds

Preservice Physical Education Teachers’ Digital Literacy

Introduction and theoretical framework Teachers play a central role in the process of digitalizing education (Wohlfart & Wagner, 2022). However, international studies confirm that teachers in Germany have low digital skills and rarely use digital tools in teaching (Eickelmann et al., 2019). Therefore, this study investigates how digitally competent trainee teachers in the subject of physical ed-ucation assess themselves and analyzes the interdependency of digital literacy, role modeling by university lecturers, and intended integration of ICT in future teaching. The Technological Pedagogical Content Knowledge (TPACK) framework guided the study (Mishra & Koehler, 2006). Method A 50-item questionnaire based on Schmidt et al.’s (2009) validated TPACK questionnaire was administered to a subject-specific sample of all 185 pre-service physical education teachers in Baden-Württemberg, Germany. The survey included questions on de-mographics, education-specific information, content- and context-related information as-sociated with the TPACK model (scale from 1=disagree to 5=agree fully), and role model-ling of ICT integration in higher education and in respondents’ own intended practice. Results The results show that the pre-service physical education teachers feel better prepared in terms of content knowledge (CK; m=4,25, SD=0,53) above pedagogical knowledge (PK; m=3,81, SD=0,55) for an increasingly digital educational environment. While they feel confident in conveying content knowledge in a pedagogic manner (PCK; m=3,96, SD=0,59), the integration of technology highlighted both lower values and more variance in the self-assessment (TK, TPK, TCK, TPACK; m=3,30-3,57; 0,63-0,76). The correlation analysis of the knowledge domains shows significant correlations between the seven knowledge domains with moderate to large effect. Finally, the results of the regression models show a positive effect of CK and PK on overall intent to integrate ICT in teaching (R2 = 0.178, adjusted R2 = 0.163, p < .05) and the results further indicate that the effects of role modeling depend on the choice of ICT. Discussion PK and CK (rather than self-assessment within the technology-specific dimensions) had a positive impact on the intent to integrate ICT in PE. While the consideration of ICT in the classroom still represents a barrier, specific support for the pedagogical implementation of ICT (TPK) may have a (subject-specific) positive influence on TPACK. Further, the results confirmed the positive effect of teacher educators\u27 role modeling on digital literacy and general ICT integration intent, including specific types of ICT. We discuss the potential implications of inadequate role modeling on pre-service teachers and PE instruction. References Eickelmann, B., Bos, W., Gerick, J., Goldhammer, F., Schaumburg, H., Schwippert, K. & Vahrenhold, J. (2019). ICILS 2018# Deutschland. Computer-und informationsbezogene Kompetenzen von Schülerinnen und Schülern im zweiten internationalen Vergleich und Kompetenzen im Bereich Computational Thinking. Münster. Mishra, P. & Koehler, M. (2006). Technological Pedagogical Content Knowledge: A Framework for Teacher Knowledge. Teachers College Record, 108, 1017-1054. Schmidt, D. A., Baran, E., Thompson, A. D., Mishra, P., Koehler, M. J., & Shin, T. S. (2009). Technological Pedagogical Content Knowledge (TPACK). Journal of Research on Technology in Education, 42(2), 123–149. https://doi.org/https://doi.org/10.1080/15391523.2009.10782544 Wohlfart, O., & Wagner, I. (2022). Teachers\u27 role in digitalizing education: An umbrella review. Educational Technology Research and Development. https://doi.org/10.1007/s11423-022-10166-

Video-based visual feedback to enhance motor learning in physical education—a systematic review [Videobasiertes visuelles Feedback zur Verbesserung des motorischen Lernens im Sportunterricht – ein systematisches Review]

While studies have indicated that visual feedback promotes skill acquisition and motor learning in controlled settings and for various sports, less is known about its feasibility in physical education, which has specific needs and conditions. For this reason, a systematic literature review was conducted regarding video-based visual feedback in physical education. Out of 2030 initially examined studies, 11 matched the selection and quality criteria. The goal was to determine whether visual feedback can be effective regarding motor learning in physical education in primary and secondary schools, and to investigate whether different visual feedback variants (expert modeling and self-modeling), supported by verbal feedback, are more effective than verbal feedback alone. Subsequently, the different conditions (e.g., age, group size, duration) of the included studies were evaluated for their suitability for everyday applications. Video-based visual feedback seems to be effective to enhance motor learning in physical education and seems to be more effective than solely verbal feedback. However, the results show that the specific conditions (class size, scheduled lessons, available time, technical equipment, the digital literacy of teachers, and data protection) of a school environment must be considered before implementing visual video feedback in daily practice

Information and communication technologies in physical education: Exploring the association between role modeling and digital literacy

Teacher educators should serve as role models in terms of information and communication technologies (ICTs) use to promote digital literacy of future teachers. To analyze the association between role modeling by teacher educators and preservice teachers’ digital literacy and ICT integration intention in their classrooms, 185 physical education (PE) preservice teachers in the German federal state of Baden-Württemberg completed an online questionnaire of self-assessed technological, pedagogical, and content knowledge (TPACK) and ICT integration. The results of regression models revealed a positive association between content knowledge and pedagogical knowledge (PK) and overall intent to integrate ICT into teaching. The results further indicated that the impact of role modeling on preservice teachers varies depending on the chosen ICT. In this paper, we discuss the implications of these findings for higher education in general and for PE in particular

Group-wise Sparse and Explainable Adversarial Attacks

Sparse adversarial attacks fool deep neural networks (DNNs) through minimal pixel perturbations, typically regularized by the $\ell_0$ norm. Recent efforts have replaced this norm with a structural sparsity regularizer, such as the nuclear group norm, to craft group-wise sparse adversarial attacks. The resulting perturbations are thus explainable and hold significant practical relevance, shedding light on an even greater vulnerability of DNNs than previously anticipated. However, crafting such attacks poses an optimization challenge, as it involves computing norms for groups of pixels within a non-convex objective. In this paper, we tackle this challenge by presenting an algorithm that simultaneously generates group-wise sparse attacks within semantically meaningful areas of an image. In each iteration, the core operation of our algorithm involves the optimization of a quasinorm adversarial loss. This optimization is achieved by employing the $1/2$-quasinorm proximal operator for some iterations, a method tailored for nonconvex programming. Subsequently, the algorithm transitions to a projected Nesterov's accelerated gradient descent with $2$-norm regularization applied to perturbation magnitudes. We rigorously evaluate the efficacy of our novel attack in both targeted and non-targeted attack scenarios, on CIFAR-10 and ImageNet datasets. When compared to state-of-the-art methods, our attack consistently results in a remarkable increase in group-wise sparsity, e.g., an increase of $48.12\%$ on CIFAR-10 and $40.78\%$ on ImageNet (average case, targeted attack), all while maintaining lower perturbation magnitudes. Notably, this performance is complemented by a significantly faster computation time and a $100\%$ attack success rate