The accelerating transition toward electromobility necessitates effective strategies to reduce greenhouse gas emissions and secure access to critical raw materials. Lithium-ion batteries, as the dominant energy storage technology in electric vehicles, play a central role in this transition but pose substantial challenges at end-of-life. Establishing robust and local recycling structures can mitigate raw material dependencies, strengthen supply chain resilience, and lower the overall environmental footprint of battery electric vehicles. This study provides a comprehensive examination of the emerging battery recycling landscape in Germany, one of Europe’s most influential automotive markets and a country characterized by limited domestic raw material availability. To achieve this, a systematic literature analysis is combined with field research, enabling an integrated assessment of both the scientific discourse and the industrial ecosystem. Firstly, the results include a descriptive analytics component that maps publication trends and industry visibility. Secondly, the article offers a structured content analysis that identifies key technological, economic, regulatory, and ecological emphases within the literature. Thirdly, the study encompasses an in-depth company analysis that elucidates the technological characteristics, material recovery strategies, and company figures of recycling actors in Germany. Building on these findings, the study proposes a recommendation framework that outlines targeted actions for research, industry, and policymakers. This framework aims to support the strategic development and scaling of the German battery recycling industry in the coming years and can serve as a blueprint for analogous investigations in other European countries.
Traditional mathematical models for communication-system design often use simplifying assumptions and are difficult to extend to complex or rapidly changing channels. In recent years, deep learning, and in particular autoencoders (AEs), has enabled end-to-end optimization of transmitters and receivers and has shown strong performance in many communication scenarios. This paper provides a comprehensive review of AE-based communication systems across wireless, optical (fiber and wireless), semantic, and quantum domains. The review is based on recent works indexed in major digital libraries and is organized around four cross-cutting design themes: channel modeling and differentiability, complexity and scalability, generalization and model mismatch, and data scarcity and realism. For each theme, as well as for each application domain, we summarize representative AE architectures, training strategies, reported performance, and practical limitations. The paper also outlines how to quantify the computational complexity of AE-based transceivers using Big-O notation and discusses implications for deployment in resource-constrained environments. Finally, we identify open challenges, including robust learning over non-differentiable or partially known channels, scalable and hardware-aware architectures, and standardized evaluation protocols, and we provide guidelines for future research on AE-based designs for next-generation communication systems.
Generative artificial intelligence (AI) is increasingly used in higher education as an interactive tutoring partner rather than a passive information tool. While AI offers opportunities to support learning, concerns remain regarding cognitive offloading, reduced engagement, and unreflective use. Although instructional scaffolding is a well-established design principle for supporting complex learning, its role in shaping cognitive and metacognitive processes in AI-supported settings remains underexplored. This quasi-experimental pre–post study examined how varying levels of scaffolding influence learning outcomes and motivational, cognitive and metacognitive processes during AI-tutored learning. A total of 175 first-semester students from two faculties and diverse academic backgrounds completed the same academic task within a four-hour university session under one of three conditions: (1) full scaffolding, including a structured prompting template based on the Goal–Context–Constraints (GCC) strategy, iterative refinement, and reflective guidance; (2) light scaffolding, including the GCC prompting template; or (3) no scaffolding template as the control condition. Measures included knowledge gain, motivation, cognitive load, critical thinking, and reflective use. Data were analysed using ANOVAs, ANCOVAs, regression models, and PROCESS moderation and mediation analyses. Across the conditions, students showed significant gains in knowledge, critical thinking, and reflective use, while motivation remained stable and intrinsic and extraneous cognitive load decreased; no significant differences between scaffolding conditions were observed. The scaffolding conditions did not produce significant interaction effects, although descriptive trends suggested higher gains in higher-order knowledge under scaffolded conditions. Overall, the findings suggest that short-term learning gains in AI-supported settings may not depend on scaffolding intensity alone, but rather on how learners engage with AI during the learning process.
Polarization mode dispersion in optical fibers corrupts polarization-encoded quantum states through random, time- and frequency-varying rotations. Because quantum states cannot be directly measured and reused without disturbance, practical mitigation requires adaptive compensation that operates on a physically consistent state representation. This paper models the quantum polarization channel and compensator directly in the quaternion domain, where unit quaternions naturally represent SU(2) polarization rotations and preserve the unit-norm constraint of valid quantum states. Building on this model, we introduce a fading-memory quaternion multiplicative extended Kalman filter, which is a quaternion formulation augmented with a fading (forgetting) mechanism that selectively discounts outdated information so the estimator can rapidly re-track varying rotation and recover quickly after abrupt polarization changes. A comparative study against quaternion least mean squares, quaternion recursive least squares, and a baseline quaternion multiplicative extended Kalman filter shows that Kalman-based approaches achieve faster convergence and higher broadband spectral fidelity, while the proposed fading-memory multiplicative extended Kalman filter provides the strongest robustness in dynamic conditions by improving re-lock behavior and reducing outage events under stronger drift. These results indicate that quaternion-based channel modeling combined with fading-memory Kalman estimation is a strong approach for quantum-state-preserving polarization mode dispersion compensation in realistic fiber links.
Quantum satellite communication (QSC) is emerging as a strategic technology for secure global networking and long-distance quantum connectivity. This review prioritizes the major challenges that still hinder large-scale deployment, including atmospheric loss, beam pointing and tracking, payload constraints, synchronization, scalability, and integration with terrestrial infrastructure. To contextualize these issues, we provide only a concise overview of the core concepts and enabling technologies behind QSC, together with representative milestones such as the Micius mission. Building on this background, the paper surveys recent advances in protocols, hybrid space–terrestrial architectures, turbulence mitigation, and AI-assisted optimization. It then examines future directions, including quantum Internet integration, daylight operation, satellite-supported repeaters, and space-based quantum computing. By centering the discussion on open technical bottlenecks and emerging research trajectories, this review aims to support researchers and engineers working toward practical and resilient QSC systems.
