Supply Chain Competence Center (Germany)

We determine the composition dependence of the below-bandgap anisotropic refractive indices and dielectric functions of β-(AlxGa1−x)2O3 for x up to 25% Al. We use Mueller matrix generalized spectroscopic ellipsometry and investigate a set of high quality single crystalline bulk (x=0%,5%,10%,15%,20%,25%) and thin film (4.6%, 9.7%, 12%, 15%, 16.3%, 21%) samples grown by the Czochralski method and by plasma-assisted molecular beam epitaxy, respectively. Bulk samples with 0.2% silicon doping are cut from ingots with (100) surface and the unintentionally doped thin films samples are grown pseudomorphically on (010) β-Ga2O3. The combination of ellipsometry data from both sample sets permits to fully determine the optical properties of the transparent alloys. We employ a quasi-orthorhombic approach which ignores the very small shear components induced by the monoclinic crystal structure. We also ignore the possibly small effects due to strain within the pseudomorphically grown epitaxial samples. We report indices and dielectric functions for polarization along lattice vectors a, b, and reciprocal lattice vector c⋆. All indices reduce with increasing incorporation of aluminum while the spectral range of transparency increases due to the increase in bandgap energy with composition. We use our results to predict Bragg reflector multilayer structures on b-plane β-(Al0.20Ga0.80)2O3, for use in polarization sensitive narrow-band optical filters in the short wavelength region.

This paper presents a case study of an artificial intelligence (AI)-supported media monitoring system implemented in a European commercial banking group to enhance early detection of emerging risks. The pipeline processes millions of media articles daily using a scalable Databricks-based architecture and large language models (LLMs) for relevance scoring, novelty detection and summarisation. It demonstrates how AI-based text analysis supports credit, liquidity and geopolitical risk monitoring by transforming unstructured news into risk-relevant intelligence delivered through automated e-mail briefings, dashboards and early warning system (EWS) integration. The case study further illustrates how such AI-supported media monitoring can be governed and deployed within the regulatory and supervisory framework of the European banking sector while strengthening risk awareness and decision making and offering additional value for compliance, operational risk and communication functions. This article is also included in The Business & Management Collection which can be accessed at https://hstalks.com/business/.

Despite therapeutic achievements in haemophilia care, there is still the need to monitor and define personal treatment outcomes and document results to achieve the best possible care. Hence, a need for unbiased, timely and comprehensive real-world information exists to support informed shared decision-making regarding treatment and care. To describe a medical device for people living with haemophilia (PLWH) supporting an active involvement to achieve a near to normal life. Florio HAEMO was developed as haemophilia monitoring platform to support PLWH and their care teams in documenting, interpreting and analysing personal reported outcomes. The tool was created partnering closely with PLWH and healthcare professionals to address previously unmet needs compared to existing applications. Florio HAEMO was launched in March 2020. Currently, it is available in 25 countries and 24 languages; 1558 PLWH (86% with haemophilia A) are registered users in 121 treatment centres across 20 countries. All users included are on a prophylactic treatment regimen. Florio HAEMO allows the collection of contemporaneous data to monitor treatment, like factor level, adherence and consumption as well as monitoring treatment outcomes, including pain, bleeds, wellbeing and levels of physical activity to support self-management, shared decision-making and to enable better care for PLWH. Data collected over time may help to show the impact of individualised prophylaxis and may support the definition of factor levels required for good bleed and joint protection in a real world setting from daily life to physical activities.

Eicosapentaenoic acid (EPA) and docosahexaenoic acid (DHA) are essential omega-3 polyunsaturated fatty acids (n-3 PUFAs) with well-established health benefits. They occur primarily in marine resources, while their quantitative distribution within the glycerolipidome is rarely analyzed. Therefore, we investigated major commercial sources, including 12 microalgal species, the protist Schizochytrium sp., four fish species, and nine commercial n-3 supplements (fish, krill and Schizochytrium-derived “algal” oils) by high-performance thin-layer chromatography–gas chromatography–mass spectrometry (HPTLC–GC–MS). The class-resolved mapping of EPA and DHA revealed signature lipid profiles across all sources. In microalgae, 60–80% of EPA was localized in glycolipids, whereas in Schizochytrium and fish, >90% of DHA occurred in triacylglycerols. Krill oils exhibited phospholipid-rich profiles with ~70% of phosphatidylcholine-bound DHA. Nutritional indices also highlighted major differences: fish and fish oils showed favorable PUFA-to-saturated FA ratios (>0.45) and hypocholesterolemic-to-hypercholesterolemic ratios (>1), while Schizochytrium-based “algal” oils even surpassed these values. The microalgae Nannochloropsis granulata contained the highest EPA content in biomass form, combined with favorable nutritional indices. Beyond total n-3 content in relation to recommended daily intake values, the lipid-class distribution and nutritional indices should be considered decisive metrics for evaluating the health relevance of n-3 resources in the human diet.

Domestic violence services (DV services) have faced a number of organizational challenges with the onset of the COVID-19 pandemic. Qualified staff and additional financial resources were quickly needed to meet digitalization requirements; hygiene measures had to be implemented. The aim of this study is to analyze the relation between staff structure and the stress level of employees in the context of the pandemic by controlling for further organizational factors. Survey data was collected online among professionals of German DV service institutions. The sample includes 232 participants. A negative binomial regression model was used to analyze the impact of organizational challenges on employees’ stress level. Data provide evidence that a poor human resource structure increases the stress level of employees. The influence of additional financial requirements however seems negligible. The more the hygiene concept could be adapted to the individual situation of the facility, the less burden was placed on employees. In order to be able to strategically respond to challenges in environmental conditions, it is essential to examine the influence of organizational factors in social services. The adaptation of those factors helps to improve working conditions for professionals during environmental change.

The production process chain of lithium-ion battery (LiB) cells consists of several interconnected process steps with many parameters that can influence the final properties and quality of the cells. In the development of cylindrical LiB cells, steps such as winding, cell assembly, filling, and formation play an important role in determining the properties of the intermediate product and the final performance of the battery cell. Continuous and low scrap production in the experimental stages of LiB battery cell production is challenging due to the limited understanding of cause-and-effect relationships. For example, the machine parameters that need to be configured depending on the properties of the input material. As a result, individual tuning of production machine settings for different materials is required. This is particularly critical in research pilot production environments, such as the BMW Battery Cell Competence Centre, where different materials are tested and experimental approaches are required to optimize the manufacturing processes. This paper presents an approach for utilizing data analytics and machine learning with Open Platform Communications Unified Architecture (OPC UA) and Internet of Things (IoT) data pipeline to better understand the relationships between process parameters, input material, intermediate product features and final quality in the production of cylindrical LiB battery cells. In this study, different machine learning algorithms were tested to predict the initial setpoints of the routing path and the gripper values from the parameters of the winding process and the characteristics of the input material in order to obtain an intermediate product with the desired quality. The best results were obtained using Random Forest Regression with an R<sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> of 0.976.

ABSTRACTGPR39, a zinc‐sensing receptor, is essential for bone homeostasis in male mice through regulation of osteoblast function and matrix composition. This study examined the effects of GPR39 deficiency in female mice using both global and osteoblast lineage‐specific GPR39 knockout models (Gpr39Ob−/Ob−). In vivo, GPR39‐deficient female mice exhibited reduced bone mass, increased mineralization rates, and significantly lower and more variable serum levels of pro‐collagen type I N‐propeptide (PINP), indicating impaired collagen synthesis and matrix remodeling. OVX models further demonstrated that GPR39 deficiency exacerbates estrogen‐deficiency‐induced bone loss, highlighting its protective role in postmenopausal‐like states. Osteoblast lineage‐specific GPR39 deletion replicated the skeletal abnormalities observed in global knockouts, revealing that GPR39 activity in the osteoblast lineage is indispensable for proper collagen deposition and mineralization. Western blot analysis of Gpr39Ob−/Ob− osteoblasts confirmed reduced extracellular collagen levels, while quantitative mRNA analysis of Col1a2 revealed zinc signaling through GPR39 as a key regulator of collagen production. Zinc‐induced Col1a2 expression, dependent on GPR39 and mediated via Gαq signaling, was abolished in GPR39‐deficient osteoblasts. These findings provide insights into how zinc signaling via GPR39 regulates osteoblast function and collagen synthesis, emphasizing its role in maintaining matrix composition. Targeting GPR39 may offer novel therapeutic strategies for osteoporosis and other bone disorders characterized by impaired matrix remodeling.

Corrosion has a significant impact on the global economy, leading to high costs for preventing damage, repairing deterioration, and replacing defective components. Industry 4.0 brings computational vision as a key role for visual inspection of assets ensuring quality and reducing the downtime. This study conducts a comparative analysis of computer vision techniques for detecting corrosion, with an emphasis on segmentation neural networks built upon U-Net architectures, focusing on the trade-off between segmentation accuracy and computational efficiency. We benchmark four U-Net-based segmentation models using pretrained backbones: MobileNetV2, ResNet152, InceptionV4 and VGG16 on a publicly available data set of corroded metallic surfaces. All models are trained using the full dataset and evaluated based on per-image F1-score and training time. Our results show that InceptionV4 achieves the highest F1-score (0.895), while MobileNetV2 offers a comparable score (0.892) with nearly half the training time. These findings suggest that lightweight backbones can deliver accurate segmentation with significantly reduced computational cost, making them promising candidates for near real-time industrial inspection systems under resource constraints.

This paper presents the design, implementation, and validation of a distributed measurement system secured by Quantum Key Distribution (QKD). Based on a client-server architecture, the system allows a client, connected to a node of a network, to encrypt and send instructions to a remote server for controlling automatic test equipment. The proposed architecture was designed to ensure secure real-time data exchange by leveraging both quantum and classical communication channels.A key management architecture was proposed to allow flexibility and adaptability in a variety of scenarios. The developed system was implemented using the LabVIEW development environment, using as a case study the Quantum Key Distribution Platforms provided by ThinkQuantum S.r.l. As the research work is in its starting phase, attention has been paid to a single client-server link, establishing the foundational architecture and demonstrating feasibility.Experimental results show the possibility of applying QKD to secure distributed measurement systems, which can handle real-time data exchange with key retrieval times in the order of seconds. The system is designed to be adaptable to various QKD technologies, laying the groundwork for future real-world applications in secure distributed measurement systems.

This study aimed to perform microstructural characterization of the increased fragility of human bone in type 2 diabetes mellitus (T2DM) by exploring the matrix mineralization and osteocyte lacunar density at the superolateral femoral neck-the typical fracture-initiating site. Postmortem specimens of the full-length superolateral femoral neck from 16 elderly men with T2DM and age-matched non-DM controls were examined using backscattered-electron microscopy in terms of mineralization parameters and parameters of osteocyte lacunar density. The T2DM and control groups did not differ in age and body mass index (p > 0.05). In the endocortical region, T2DM was associated with a lower degree of mineralization (lower CaMean: p = 0.04), a higher proportion of extremely low-mineralized areas (higher CaLow: p = 0.027), and greater mineralization heterogeneity (higher CaWidth: p = 0.003) relative to controls. However, there were no significant intergroup differences in mineralization parameters in the periosteal region. In the endocortical region, T2DM showed lower unmineralized (p = 0.006) and total osteocyte lacunar number (Lc.N) per bone area (B.Ar) (p = 0.018) coupled with a higher percentage of mineralized lacunae (%Mn.Lc) relative to controls (p = 0.05). In the periosteal region, only Lc.N/B.Ar was lower in T2DM (p = 0.004). As for the trabecular compartment, T2DM was associated with lower trabecular CaMean (p = 0.048) and higher trabecular CaLow and CaWidth (p = 0.005, p = 0.007). Altered pattern of mineralization in the cortical (especially in the endocortical region) and trabecular compartments of the superolateral femoral neck and reduced cortical osteocyte lacunar density are structural hallmarks of bone fragility in T2DM.