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Unpacking Performance Factors of Innovation Systems and Studying Germany’s Attempt to Foster the Role of the Patient Through a Market Access Pathway for Digital Health Applications (DiGAs): Exploratory Mixed Methods Study
No full textBackground: Health care innovation faces significant challenges, including system inertia and diverse stakeholders, making regulated market access pathways essential for facilitating the adoption of new technologies. The German Digital Healthcare Act, introduced in 2019, offers a model by enabling digital health applications (DiGAs) to be reimbursed by statutory health insurance, improving market access and patient empowerment. However, the factors influencing the success of these pathways in driving innovation remain unclear. Objective: This study aims to identify the key performance factors of the innovation system shaped by the patient-relevant
structural and procedural improvement (pSVV) pathway within the DiGA model. By examining how this pathway supports the entry of innovative digital health technologies, we seek to uncover the systemic dynamics that influence its effectiveness in fostering patient-centered digital health solutions. Methods: This study, conducted from May 2023 to November 2024, used a mixed methods approach. A descriptive analysis assessed how DiGA manufacturers use positive health care effects, giving a market overview of the pSVV technology. A qualitative analysis using grounded theory and Gioia methodology provided insights into stakeholder perspectives, focusing on manufacturers and regulatory bodies. A functional-structural analysis examined how components of the innovation system, such as actors, institutions, interactions, and infrastructure, interact and impact the effectiveness of the pathway. Results: The descriptive analysis showed that only 11 (20%) of the 56 DiGAs available in Germany used the pSVV pathway, with only 1 (2%) provisionally listed DiGA using pSVV as a primary end point; 6 of 9 (67%) pSVV key areas were used. The qualitative analysis revealed that manufacturers prioritize demonstrating medical benefits over pSVV due to evidence requirements and uncertainties around pSVV cceptance. Operational barriers hindered the adoption of pSVV , despite a positive reception among stakeholders. The systemic analysis identified key issues, including a lack of entrepreneurial focus on pSVV , limited
regulatory experience, inadequate measurement methods, and entrenched practices prioritizing medical benefits, that hinder market formation and legitimacy. Conclusions: This study identifies key factors for effectively implementing innovation systems through regulated market access pathways, including content and format security, clearer framework specification, active innovation process management, and market formation stimulation. Addressing these factors can reduce uncertainties and promote wider adoption of digital health technologies. The findings highlight the need for future research on patient empowerment and the development of methodologies beyond traditional therapeutic outcomes
Automatic Segmentation of Vestibular Schwannoma From MRI Using Two Cascaded Deep Learning Networks
No full textObjective: Automatic segmentation and detection of vestibular schwannoma (VS) in MRI by deep learning is an upcoming topic. However, deep learning faces generalization challenges due to tumor variability even though measurements and segmentation of VS are essential for growth monitoring and treatment planning. Therefore, we introduce a novel model combining two Convolutional Neural Network (CNN) models for the detection of VS by deep learning aiming to improve performance of automatic segmentation. Methods: Deep learning techniques have been employed for automatic VS tumor segmentation, including 2D, 2.5D, and 3D UNet-like architectures, which is a specific CNN designed to improve automatic segmentation for medical imaging. Specifically, we introduce a sequential connection where the first UNet's predicted segmentation map is passed to a second complementary network for refinement. Additionally, spatial attention mechanisms are utilized to further guide refinement in the second network. Results: We conducted experiments on both public and private datasets containing contrast-enhanced T1 and high-resolution T2-weighted magnetic resonance imaging (MRI). Across the public dataset, we observed consistent improvements in Dice scores for all variants of 2D, 2.5D, and 3D CNN methods, with a notable enhancement of 8.86% for the 2D UNet variant on T1. In our private dataset, a 3.75% improvement was reported for 2D T1. Moreover, we found that T1 images generally outperformed T2 in VS segmentation. Conclusion: We demonstrate that sequential connection of UNets combined with spatial attention mechanisms enhances VS segmentation performance across state-of-the-art 2D, 2.5D, and 3D deep learning methods. Level of Evidence: 3 Laryngoscope, 2024
Ultra-low cycle fatigue of ship hull structure: an alternately-cyclically loaded four-point bending test of a large box girder
No full textUltra-low cycle fatigue (ULCF) refers to material failure at small number of loading cycles. For large complex structures like ships, the damage from ULCF can bring hazardous consequences. In this study, an alternately-cyclically loaded four-point bending test of a large box girder is introduced as the specimen to represent the ULCF of ship hull structure. In every load during the test, large deformation is applied to the specimen even after reaching its ultimate hull girder strength (UHGS), thus extensive plastic deformation and obvious fracture can occur in the specimen. The severely damaged specimen is further tested until 1.5 cycles of bending are finished, thus the test of post-damage box girder is realized. Moreover, the box girder is divided into 3 sub-sections, which show different but still interacting structural behavior. The result of the test shows the structural behavior of a large complex structure suffering severe damage during alternate hogging and sagging after reaching its UHGS, which corresponds to the consequence of ULCF. The presented ULCF test also provides experiences for investigations of large complex structures with existing damages or after accidental loads. Considering the number of cycles in the test, this study can bridge the gap between monotonic overload and ultra-low cycle fatigue
Electrifying distillation − Optimization-based evaluation of internally heat-integrated distillation columns
No full textImproving the energy efficiency of distillation processes is essential for reducing the chemical industry's substantial energy demand and environmental footprint. The use of mechanical heat pumps with compressors is an important asset in this transformation process, as it not only enables the recovery of heat rejected at low temperature, reducing external energy requirements, but also facilitates the electrification of chemical processes and distillation in specific. The necessary temperature lift dictates the required compression rate for the compressor and is therefore of considerable importance for the applicability of mechanical heat pumps. By operating the rectifying and stripping sections of a column at different pressures and enabling heat exchange between the respective sections, temperature lift and compression ratio can be reduced for the so-called Internally Heat-Integrated Distillation Columns compared to mechanical vapor recompression. In order to enable a quick problem specific evaluation of the possible benefits of this concept we propose two novel superstructure models for optimal design, that allow for heat exchange between stages at the same height or arbitrary stages in the rectifying and stripping section, provided a minimum temperature difference is maintained. The respective optimization problems are solved as a series of successively relaxed mixed-integer nonlinear programming problems in GAMS. An automatic stepwise initialization and optimization strategy provides a computationally efficient approach for the determination of optimized designs
The strong effect of flexible capacities on achievable WIP levels and throughput times : a simple model and its implications
No full textSetting WIP levels at the workstations of a production is an important task: WIP levels affect throughput times as well as utilisation rates. Consequently, they also impact the delivery times to customers and the productivity. Queueing theory provides profound knowledge regarding the factors for determining suitable WIP levels. However, the existing models have been neglecting the important influence of capacity flexibility. This paper suggests an extension of the well-known Kingman equation to model the impact of flexible capacities on the required WIP levels. Simulation experiments have shown that the model is able to accurately forecast the utilisation level of a workstation for different factor levels of capacity flexibility, WIP and load variance. Companies can use the model to set consistent target levels for these factors
Near-field scattering phenomena in monostatic radar applications derived from physical optics
No full textIn the context of radar, this article examines phenomena that occur in conditions where the physical extent of the target cannot be considered negligible compared to the range and, thus, far-field assumptions are violated. Based on physical optics principles, a transfer function of the radar channel between the antenna input and output power wave is derived for a monostatic configuration. The effects are studied using a circular metallic disk as an exemplary target. Exact and approximative expressions for the transfer function are derived incorporating the influence of the antenna radiation characteristic in addition. Measurements with targets of different sizes performed with horn as well as open-ended waveguide antennas at 24 and 61 GHz validate the phenomena of receive power level fading and nonlinear phase distortion
Morphological Search for Near-Field Equivalent Infinitesimal Dipole Models
No full textNear-field modeling techniques have proven to be very useful for electromagnetic compatibility evaluation. In this article, a new method for building infinitesimal dipolemodels using near-field phaseless scans is presented. The proposed approach enables the identification of optimal dipole source locations by comparing the pattern of an infinitesimal dipole with the measured field maps. Finally, the dipole moments are fitted by using the Levenberg–Marquardt algorithm. This method is validated on two different virtual devices and on measurements from a real device. These test cases cover a range of frequencies (156, 381, 514, and 1 GHz) and scan dimensions (9.4 cm × 8.4 cm and 10.6 cm × 14 cm), demonstrating the robustness and versatility of the proposed method
From waste to value: extraction of protease enzymes from Brewer’s spent yeast
No full textThis study investigates the potential of additive-free extraction techniques to produce a proteolytically active yeast extract for use in the food industry. Brewer’s spent yeast, a by-product of the brewing industry, is utilized as a feedstock, and thus a new route for its valorization is proposed. Four methods of releasing these components while maintaining their intrinsic bioactivity are investigated: thermal autolysis, ultrasonication, cell milling and high-pressure homogenization. Thermal yeast autolysis resulted in the highest release of protease activity, with 2.45 ± 0.05 U/gdm after 3 h incubation at 45 °C. However, autolysis poses challenges for automation, and thus a stop criterion, due to the lack of in-line enzyme activity assays,. While glass bead treatment gave the highest reproducibility, ultrasonication and high-pressure homogenization resulted in comparably high protease activities in the BSY extracts produced. Both methods, in the form of a cell mill and high-pressure homogenizer, are cell disruption methods that are already employed on an industrial scale. It has now been demonstrated that these methods can be used to produce proteolytically active yeast extracts from a previously considered waste stream
Redox biocatalysis in lidocaine-based hydrophobic deep eutectic solvents: non-conventional media outperform aqueous conditions
No full textRedox biocatalysis is an essential pillar of the chemical industry. Yet, the enzymes’ nature restricts most reactions to aqueous conditions, where the limited substrate solubility leads to unsustainable diluted biotranformations. Non-aqueous media represent a strategic solution to conduct intensified biocatalytic routes. Deep eutectic solvents (DESs) are designable solvents that can be customized to meet specific application needs. Within the large design space of combining DES components (and ratios), hydrophobic DESs hold the potential to be both enzyme-compatible – keeping the enzymes’ hydration –, and solubilizers for hydrophobic reactants. We explored two hydrophobic DESs, lidocaine/oleic acid, and lidocaine/decanoic acid, as reaction media for carbonyl reduction catalyzed by horse liver alcohol dehydrogenase, focusing on the effect of water contents and on maximizing substrate loadings. Enzymes remained highly active and stable in the DESs with 20 wt % buffer, whereas the reaction performance in DESs outperformed the pure buffer system with hydrophobic substrates (e. g., cinnamaldehyde to form the industrially relevant cinnamyl alcohol), with a 3-fold specific activity. Notably, the cinnamaldehyde reduction was for the first time performed at 800 mM (~100 g L−1) with full conversion, which opens up new avenues to industrial applications of hydrophobic DESs for enzyme catalysis
Simulation-based analysis of thermal and electrical degradation in high-temperature PEM fuel cells for hydrogen aircraft applications
No full textThe integration of high-temperature proton exchange membrane fuel cells into hydrogenpowered regional aircraft is evaluated in the scope of this paper, focusing on a concept aircraft with ten propulsion units, each containing a hybrid fuel cell system. High-temperature PEM fuel cells, operating at temperatures between 140 ◦C and 200 ◦C, offer potential advantages in cooling performance compared to low-temperature PEM fuel cells, which operate between 60 ◦C and 80 ◦C and require large and complex cooling systems due to their limited temperature difference with the environment. As part of early system design, an empirical degradation model for high-temperature PEM fuel cells is developed and applied to assess degradation effects, cooling performance, and hybridization requirements. Key findings include the identification of temperature as a critical factor in high-temperature PEM fuel cells degradation, the lifespan increase due to hybridization and oversizing of the fuel cell system, and the determination that air cooling systems are infeasible at operating temperatures below 330 ◦C. Despite their anticipated advantages, high-temperature PEM fuel cells have a shorter operational lifespan of approximately 3610 h in the presented use case, compared to about 8610 h for low-temperature PEM fuel cells, primarily due to their currently lower level of technological maturity