Due to the increase in online retail in recent decades, distribution centers in the supply chains of large e-commerce companies in particular are increasingly operating according to the “parts-to-picker” paradigm: storage bins (e.g., shelves) containing requested items are automatically transported to picking stations. This eliminates unproductive walking distances for warehouse employees, as is the case in conventional warehouses (“picker-to-parts”). Instead, workers remain at the stations to focus exclusively on picking and packing of customer orders. The storage bins are transported either by autonomous mobile robots (usually floor-bound vehicles) or the picking stations are directly connected to an automatic storage and retrieval system. To optimize picking in such a warehouse, incoming storage bins must be coordinated (i.e., synchronized) with outgoing customer orders at the picking stations in order to increase throughput and to reduce the load on the automatic storage and retrieval system. Depending on the (technical) equipment of the picking stations, the storage policy, and the composition of the customer orders, a number of slightly different synchronization problems arise. From an operations research perspective, these synchronization problems are characterized by the combination of elements of known combinatorial optimization problems: set covering, optimal order batching, and sequencing (of storage bins and orders). We analyze the computational complexity of various synchronization problems, develop and evaluate suitable exact and heuristic solution approaches for optimizing order picking (with a focus on application in robotic mobile fulfillment systems), and present business insights for practitioners.

Values don’t just fall from the sky. They are shaped by media, infrastructure, and social practices. With new protocols and media objects – such as smart contracts, cryptocurrencies, and NFTs – Web3 not only extends platform capitalism but also redefines value, labor, and community. While these technologies reinforce proprietary markets and corporate governance structures, they simultaneously open up alternative new ways of organizing life, challenging traditional economic and social models. This issue builds on the hybrid workshop “Digital Biedermeier – or Radical Democratic Utopia? NFTs as Interfaces of Cryptocurrencies”, organized by Johannes Bennke at the Humboldt University Berlin and Mirjam Schaub at University of Applied Sciences (HAW) Hamburg in October 2023. The issue brings together eight contributions by media scholars, artists, and curators who examine different media cultures of value – exploring protocols, infrastructures, labor, NFTs, art, and political stakes of Web3 governance.

Pacemakers are an integral component of cardiovascular therapy. They are employed when a patient’s cardiac rhythm is insufficient to meet the individual demands. Given the considerable patient-specific variability over a day and throughout the lifespan, this long-term implant has to adapt to the individual patient’s changing needs and regulate the appropriate heart rate. In order to avoid potential risks to the patient, several factors have to be considered when pacing the heart. Stimulation at an inappropriate temporal phase can result in potential life-threatening adverse effects. Similarly, uncoordinated contraction among the heart chambers can lead to complications such as exercise intolerance, significantly impacting the patient’s quality of life. The growing complexity of pacemakers is driven by the need to accommodate the unique needs of patients. This has led to an increased reliance on pacemakers to perform their functions with greater autonomy, necessitating comprehensive testing to ensure their reliability in diverse scenarios. The complexity of this process is further compounded by the multitude of factors that affect the pacemaker. Consequently, it is essential to develop a comprehensive testing and verification environment that can reflect the diverse range of influencing factors and borderline cases the pacemaker may encounter over its lifetime. In this thesis, a heart model is presented that is capable of mirroring the Electrical Conduction System of the Heart (ECSH), the chambers and the valves of the heart. The model is capable of including the status of the chambers and valves as a function of the stimulation interval and the Action Potential Duration (APD). This enables the investigation of diverse scenarios, including pacing in the vulnerable phase, the coordination of the chambers, the number of contractions for various diseases concerning the ECSH and the heart chambers. The model is demonstrated to be capable of modeling 14 diseases concerning the ECSH, the generation of pulses within the heart and the heart chambers, such as various atrioventricular blockages, bradycardia, premature contractions, flutter, and variances in the QT interval due to the menstrual cycle. The objective is to demonstrate the value of formal verification and Hardware-in-the-Loop (HIL) techniques for the development and improvement of increasingly complex medical devices in the future. The proposed heart model is transformed into a formal verification model and a HIL model. The latter is then evaluated in interaction with a real pacemaker. A formal verification model of a pacemaker is constructed to assess the interaction between the formal verification model of the heart and a pacemaker. A particular challenge is to incorporate the various impact factors while keeping the model executable, especially since it is essential that the heart model interacts with the pacemaker in real time for HIL testing. The HIL tests indicate the potential for life-threatening conditions concerning the number of contractions per minute, pacing during the vulnerable phase, and the coordination of the chambers of the heart. The results also show the impact on the patient’s safety of different diseases, of the chamber monitored by the pacemaker, of the frequency of sinoatrial node (SA node) self-stimulation and of the replacement rhythms. Our pacemaker model for formal verification shows promising results for the double chamber pacemaker. The single chamber model, however, is not yet suitable for all diseases. To the best of our knowledge, the pacing during the vulnerable phase and the chamber coordination have not been evaluated before.

Background: Predicting hypoglycemia in diabetes management remains a substantial challenge, especially for individuals with Type 1 Diabetes (T1D), advanced Type 2 Diabetes (T2D), and prediabetes. Existing prediction systems are largely dependent on continuous glucose monitoring (CGM) data and offer limited accuracy for extended prediction horizons. These systems often fail to account for the complex, individualized physiological variations inherent to each patient. Traditional monitoring methods are primarily invasive and lack the foresight needed for timely intervention. This limitation results in a heightened risk of severe complications and a significant decline in the quality of life (QoL) of patients with diabetes. Therefore, addressing these limitations requires an innovative, personalized, and multimodal approach to enhance the efficacy of hypoglycemia prediction and empower proactive diabetes management. Objectives: The core aim of this research is to develop and validate advanced predictive models for personalized hypoglycemia prediction through three primary domains: (i.) Methodological Development focused on advanced algorithm development, temporal modeling, and the creation of semantic frameworks to capture complex physiological interactions. (ii.) Data Integration and Analysis emphasizes multimodal data integration, the use of non-invasive monitoring approaches, and advanced pattern recognition to enhance the predictive power of the models. (iii.) the Implementation Framework aims at establishing personalization strategies, assessing clinical implementation, and optimizing technological solutions for embedding predictive models into wearable devices. Collectively, these objectives work towards an innovative, personalized, and practical approach to managing hypoglycemia in individuals with diabetes. Methods: This cumulative thesis synthesized findings from five peer-reviewed publications, utilizing data from three complementary datasets: D1NAMO (n=7, Type 1 Diabetes (T1D) patients), BIG IDEAs Lab (n=16, prediabetic individuals), and MIMIC-III (glucose-insulin paired data from 9 518 patients). Key methodologies included shapelet-based feature extraction to identify distinctive physiological patterns indicative of hypoglycemia and semantic integration using ontologies and knowledge graphs for enhanced data context. Both traditional machine learning (ML) and Deep Learning (DL) models, such as Fully Convolutional Network (FCN) and Residual Network (ResNet), were evaluated for their predictive capabilities. Model validation implemented holdout and leave-one-person-out cross-validation. This approach emphasized personalized performance, temporal alignment, and the integration of multimodal physiological signals to ensure robust, individualized hypoglycemia prediction. Results: This research resulted in several key advancements in predictive modeling for hypoglycemia: (1) The FCN achieved 97% accuracy in predicting the time-to-hypoglycemia, extending prediction horizons up to 48 hours; while the ResNet model achieved 94% accuracy, emphasizing the role of model architecture in optimizing prediction capabilities. (2) Temporal analysis revealed critical glucose normalization patterns within a 1–4 hour timeframe before hypoglycemic episodes, underscoring opportunities for preventive interventions. (3) Shapelet-based analysis revealed varying model performances: the three-layered Convolutional Neural Network (CNN) achieved 76% accuracy with heart rate data, while the two-layered CNN model reached 67% accuracy. In comparison, traditional machine learning (ML) approaches showed complementary strengths – Random Forest Classifier (RFC) demonstrated 73% accuracy with heart rate and 69% with breathing rate data, and Support Vector Machine (SVM) achieved 56% accuracy with heart rate and 65% with breathing rate data. These differences in performance demonstrated that advanced architecture optimization is vital for capturing personalized physiological responses. (4) Correlation analyses demonstrated substantial inter-individual variability in glucose-heart rate relationships, with correlation coefficients ranging from -0.4087 to 0.1882, thus highlighting the necessity for tailored modeling approaches. (5) Integration of a semantic framework, utilizing ontologies and knowledge graphs, uncovered previously undetectable patterns through structured representations of patient-specific factors. This structured knowledge representation contributed to improve interpretability and prediction capabilities. (6) Classification models with temporal pattern modeliing, adapted to patient-specific glucose fluctuations achieved accuracy rates ranging from 84% to 99% for different individuals, thus, highlighting the importance of personalization in predictive modeling. Conclusion: This research demonstrated that integrating multimodal physiological data, advanced temporal modeling, and semantic knowledge frameworks significantly enhances the prediction of hypoglycemic events. Also, by employing personalized modeling approaches, predictive accuracy per patient can be improved, enabling timely and patient-specific interventions. These advancements pave the way for transforming hypoglycemia prediction into a proactive and individualized system, ultimately contributing to better clinical outcomes and improved QoL for patients with diabetes.