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AI in Failure Management - Data Quality for LLM-based Information Retrieval
No full text889896Die Qualität textueller Daten im Produktionskontext stellt einen noch unzureichend betrachteten Stellhebel zur Etablierung LLM-basierter Informationsbereitstellungssysteme dar. Dieser Beitrag zeigt Herausforderungen in der LLM-basierten Informationsverarbeitung hinsichtlich der Datenqualität auf und leitet Handlungsempfehlungen für die Gestaltung textueller Dokumente im Produktionskontext ab.The quality of textual data in a production context represents an influencing factor for establishing LLM based information retrieval systems that has not yet been sufficiently considered. This article outlines the limitations of LLM-based information processing regarding data quality and provides recommendations for the design of textual documents in a production context.11511-1
Fatigue life of wheels regarding design and test constraints
No full text299310Wheels both for passenger cars and commercial vehicles are one of the most safety critical parts on vehicles. Knowing this, in the 1970’s automotive industry started to rethink the design and also the lifetime assessment of wheels with respect to their different use case scenarios. Detailed measurements revealed the load influences from on- and off-road usage also including special and extreme load events. At that time, the BiAx or ZWARP test technology for rotating components of wheel ends were developed using these real-life measurements to achieve a realistic fatigue assessment for any kind of wheel or hub in a lab environment. In the last years, the demands for the fatigue assessment have developed both with the variety of wheels and the usage itself. Especially, with regard to wheel and tire sizes as well as wheel test technology new demands from the customer side open up new research demands to assess the wheel’s strength. To detect the influences of fatigue test methods, the wheel and also the tire size intense fatigue investigations at Fraunhofer LBF were conducted. The aim was to derive the significances influencing the fatigue result and the parameters driving the fatigue assessment of passenger car and truck wheels. The investigations showed that these parameters have significant influence on the local loads on the wheel and thus on the local fatigue strength. This needs to be taken into account for the lifetime assessment and the fatigue validation procedure to ensure a safe and reliable product.7
A Deep Learning Framework for Automated Collection and Analysis of Traffic Data based on Identifying and Classifying Delivery Vehicles in Logistics
No full text254263In urban logistics, analyzing urban traffic data plays an important role in achieving higher schedule reliability and delivery time efficiency. To increase the diversity of urban traffic data, we developed a solution for the automated collection and analysis of different types of traffic data. These are needed to optimize and control the flow of traffic. The use of traditional on-road sensors (e.g., inductive loops) for collecting data is necessary but not currently sufficient because it cannot draw any conclusions about the type of goods being transported. In this paper, we propose a framework in which different classes of delivery vehicles and types of goods being shipped are identified in road videos by deep-learning-based image recognition method. Video sequences are automatically evaluated according to the following criteria: (i) distinguish between individual and commercial vehicles, (ii) identify the category of commercial vehicles, for example, van, box trucks, small trucks, etc. (iii) identify the special features of the vehicle body (such as the name of the carrier) to classify commercial transportation of food, general goods or package services, etc. Using this method, logistics throughput of a designated city or region and the peak time of goods transportation can be obtained. This provides the carrier with better pre-advice and potential actions to improve transportation efficiency. For the evaluation of our framework, we collected real street videos at different time points in the main traffic arteries of Heilbronn, Germany. In particular, the difference between traffic flow of logistics services before and during the COVID-19 epidemic was compared. The results of implementation and testing demonstrated a high-precision, low-latency performance of the framework for obtaining urban logistics data
Sustainability and Resilience in Alliance-Driven Manufacturing Ecosystems: A Strategic Conceptual Modeling Perspective
No full text36533662The challenge of sustainability rests on the ability of organizations to change their practices to meet the needs of current and future generations. To date, most research on organizational change has focused on how to change within a single organization. However, an increasing number of sustainability challenges require changes across multiple organizations. In this paper, we summarize strategic challenges faced in such a setting and outline a conceptual modeling approach for strategic analysis of alliance-driven solutions. We illustrate our ideas with a case study in digital agriculture, a field particularly relevant to sustainability, and end with the identification of issues for further research
Development of an efficient oxygen separation process Entwicklung eines effizienten Sauerstoffabtrennungs-Verfahrens
No full text8286Ziele von HeatToO2 sind die Entwicklung, Skalierung und Anwendung eines Verfahrens zur lokalen, effizienten O2-Erzeugung. Es beruht auf Keramik-Materialien mit optimierter Zusammensetzung, die als OSM-Pellets (Oxygen Storage Material) reversibel O2 speichern. Dabei wird O2 bei t > 750 °C aus der Luftströmung adsorbiert und beim Wechsel zur Dampfspülung wieder desorbiert. Das Verfahren vereint wesentliche Vorteile: hohe Reinheit (> 98 [%]), Vermeidung von Fremdgasen und geringen Elektroenergiebedarf.1213-
Assembling silk into nanomedicines
No full text689708Biopolymer silk has a long tradition of use in human health. However, our ability to unspin the silk fiber, in combination with our emerging understanding of the silk self-assembly process, now enables us to exploit silk for new applications, including silk nanomedicines. While the nanomedicine field is rapidly coming of age, a need remains to learn from past lessons to inform current and future silk nanomedicine research. This chapter provides a brief background on the use of polymers in nanomedicines. This information subsequently sets the stage for a selected but critical examination of silk processing, silk nanoparticle manufacture, and assessment
Baseload power plants are not essential for future power systems
No full textThe transition to decarbonized energy systems has fueled a controversial debate over the necessity of traditional "baseload" power. Skepticism remains regarding the reliability and economic feasibility of power systems relying mainly on cheap variable renewable energy (VRE) sources. Addressing this, the German Academies' project "Energy Systems of the Future" (ESYS) analyzed the role of baseload power plants within a decarbonized, continental-scale energy system. Their findings indicate that a secure, net-zero European electricity system is technically robust and economically viable when based on VRE paired with extensive flexibility, storage, and grid interconnections, without requiring new baseload capacity. The integration of new low-carbon baseload technologies, such as nuclear fission or fusion, natural gas with carbon capture and storage (CCS), or geothermal energy, has a marginal impact on overall system costs. While low-cost baseload technologies could be efficiently integrated to achieve high utilization, their future role is contingent on achieving cost reductions beyond current realities.7
Modeling and simulation of mechanical effects in two-photon lithography
No full textTwo-photon lithography (TPL) has emerged as a pivotal technique for fabricating highly intricate micro- and nanoscale structures with diverse applications, ranging from photonics and microfluidics to biomedical engineering. Despite its significant advantages, the manufacturing process is often compromised by defects such as structural collapse and deformation. These issues predominantly arise due to capillary forces exerted by solvents during the development stage, significantly affecting the structural integrity, quality, and throughput of the fabricated components. To address these challenges, this study systematically investigates the mechanical phenomena underpinning these defects, leveraging multiphysics simulations to identify their origins and propose effective modeling strategies. A dual-framework approach was employed to achieve these objectives. First, a structural analysis was performed to determine the critical aspect ratio that would help predict a collapse. This model was validated via finite element simulations. Second, a rheological flow model was formulated to simulate the polymer melt behavior. The models exhibit good agreement with the experimental data and provide useful pointers to the fabricators
Blood compatibility of silk nanoparticles: impact of size and dose
No full textThe increasing application of nanomedicine requires deeper understanding of the interaction of the carrier with the circulatory system, as this system often serves as the gateway for carrier distribution in the body. As foreign materials, nanoparticles can activate blood coagulation and inflammatory systems. However, whether these activation processes respond linearly to the particle count, the interface area with blood, or the total mass remains uncertain. This study incubated 115, 240 or 450 nm diameter silk nanoparticles in vitro in flowing whole human blood at concentrations matching the total mass, total surface area of ideal spherical particles, and particle count and then analyzed the activation of the coagulation cascade, blood platelets, complement cascade and granulocytes. The immunocell association of nanoparticles was highly modulated by plasma proteins, which had a passivating effect. The activation of the humoral cascades mainly depended on the applied mass concentration, whereas granulocyte activation tended to show linear dependence mainly on the particle count, suggesting a direct interaction effect. Additional factors, such as curvature and area restriction for the assembly of enzyme complexes, superposed these effects on basic geometric parameters. Thus, the design of silk nanoparticles for drug delivery has to prioritize either low cell activation or low activation of plasmatic pathways.69
Active Learning of Ordinal Embeddings: A User Study on Football Data
No full textHumans innately measure the distance between instances in an unlabeled dataset using an unknown similarity function. Distance metrics can only serve as a proxy for similarity in information retrieval of similar instances. Learning a good similarity function from human annotations improves the quality of retrievals. This work uses deep metric learning to learn these user-defined similarity functions from few annotations for a large football trajectory dataset. We adapt an entropy-based active learning method with recent work from triplet mining to collect easy-to-answer but still informative annotations from human participants and use them to train a deep convolutional network that generalizes to unseen samples. Our user study shows that our approach improves the quality of the information retrieval compared to a previous deep metric learning approach that relies on a Siamese network. Specifically, we shed light on the strengths and weaknesses of passive sampling heuristics and active learners alike by analyzing the participants’ response efficacy. To this end, we collect accuracy, algorithmic time complexity, the participants’ fatigue, time-to-response, qualitative self-assessment and statements, as well as the effects of mixed-expertise annota-tors and their consistency on model performance and transfer learning.202