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Quantification of Blood Flow in the Carotid Bifurcation of Healthy Subjects
Get PDFLocally disturbed blood flow patterns are known to create an atherogenic environment, particularly in the presence of other cardiovascular risk factors. Given the geometry of a healthy carotid artery, complex flow patterns are expected to be present. This study aims to characterize (complex) blood flow patterns and estimate flow-derived parameters in the carotid bifurcation of healthy subjects. Ultrasound-based velocity vector imaging (US-VVI) was acquired in the carotid bifurcation of 20 healthy subjects. Hemodynamic parameters, including temporal velocity profile, vector complexity (VC), vortex presence, and wall shear stress (WSS), were derived and compared between two age groups (20-30 and 65-75 years). Lower velocities and higher VC values were observed in the older age group for all timepoints. The highest presence of vortices was observed during the systolic deceleration, which was more exposed in younger subjects (5 out of 10) compared to older subjects (3 out of 9). A quick build-up and consequent resolving of the vortices was reflected by the relatively short vortex duration, with a vortex presence of 11.4% (7.9-15.6) and 13.1% (5.9-18.6) as a percentage of the cardiac cycle in younger and older subjects, respectively. Larger WSS estimates, represented as median along the complete vessel wall, were found in the younger subjects at all timestamps, except at systolic deceleration. In conclusion, the presence of complex flow patterns was confirmed in healthy subjects and multiple flow-derived hemodynamic parameters were evaluated in two age groups, providing an insight into age-related differences in hemodynamics. Aging seemed to result in higher vector complexities, whereas the presence of recirculating flow is less in older subjects.</p
Effect of magnetic field angle, cabling deformation and transverse loading on ITER Nb<sub>3</sub>Sn strand hysteresis loss
Get PDFFor the ITER superconducting magnets, evaluation of their AC losses with computer models is essential for the preparation of the ITER magnet system commissioning and operation. A benchmarking effort on the recently tested CS Module 2 and 6 cold test AC loss data, revealed a significant discrepancy between model and experiment. For better understanding, the magnetization is measured on virgin strands and compared with AC loss of full-size CS CICCs in virgin condition and after being subjected to transverse load cycling. Eleven different ITER Nb3Sn strand types are tested in a Vibrating Sample Magnetometer (VSM) for different angles between strand axis and magnetic field orientation. In addition, few longer strand samples with different winding density were tested in the Twente magnetometer for comparison to short VSM sample results. The comparisons serve to evaluate the potential impact of cabling, transverse load strand damage, demagnetization and field angle. It was found that these effects were not large enough to explain the discrepancy between short sample measurements and model at one side and CS module loss at the other side.</p
Impact of grass retroreflection on bifacial solar panel electricity yield in agrivoltaics
Get PDFThe efficiency of bifacial photovoltaic modules, which can capture light from both sides, is significantly influenced by the reflectivity of the surrounding environment, for example in agrivoltaic settings. We investigate the retroreflective properties of grass and their impact on the energy yield of bifacial solar panels. By combining spectro-angular reflection measurements with computational modeling, we quantify the contribution of grass reflection to overall solar electricity production and evaluate the inaccuracies associated with the assumption that grass behaves as a diffuse reflector. Our results show that this assumption can lead to an overestimation of energy yield by up to 10%, due to the actual retroreflective behavior of grass. This effect is particularly pronounced for long grass configurations. The research underscores the importance of considering the detailed reflectance properties of vegetation in optimizing the placement and performance of bifacial photovoltaics in agricultural environments, with implications for improving the efficiency and sustainability of solar energy generation
Thermal-hydrodynamic modeling and design for microchannel cold plates subjected to multiple heat sources
Get PDFWith advancing electronics, effective thermal management is crucial to maintain optimal performance and prevent overheating. Addressing the challenge of efficient cooling solutions has become a crucial area of research in modern thermal management. This paper applies and validates the Thermal-Hydrodynamic Model to bridge the knowledge gap on how straight, manifold, and serpentine microchannel configurations meet industry standards. The model predicts critical parameters, including electronic package temperatures, temperature differences across packages, thermal resistances, and pressure drops. Findings underscore the effectiveness of the model in accurately estimating thermal resistances and pressure drops within acceptable error margins compared to numerical simulations. Pressure drop estimates for straight channels consistently remain within a 10% error margin. For serpentine microchannels, the error is within 10% when the Dean number is at maximum 40. Manifold configurations, however, do not meet the 10% criterion. For manifold predictions within a 15% error margin, an Inlet Ratio of at most 0.13, a Velocity Ratio of unity, and low Reynolds numbers are necessary. Furthermore, for thermal resistance estimations, a number of grooves of at least 23 is required to maintain 10% validity. Additionally, a case study demonstrates the model's potential as a practical alternative to simulation-based methods for identifying the optimal cold plate configuration, achieving cooling power requirements at least twice as low as other configurations within the design space.</p
The Bakers and Millers Game with Restricted Locations
Get PDFWe study strategic location choice by customers and sellers, termed the Bakers and Millers Game in the literature. In our generalized setting, each miller can freely choose any location for setting up a mill, while each baker is restricted in the choice of location for setting up a bakery. For optimal bargaining power, a baker would like to select a location with many millers to buy flour from and with little competition from other bakers. Likewise, a miller aims for a location with many bakers and few competing millers. Thus, both types of agents choose locations to optimize the ratio of agents of opposite type divided by agents of the same type at their chosen location. Originally raised in the context of Fractional Hedonic Games, the Bakers and Millers Game has applications that range from commerce to product design. We study the impact of location restrictions on the properties of the game. While pure Nash equilibria trivially exist in the setting without location restrictions, we show via a sophisticated, efficient algorithm that even the more challenging restricted setting admits equilibria. Moreover, the computed equilibrium approximates the optimal social welfare by a factor of at most . Furthermore, we give tight bounds on the price of anarchy/stability. On the conceptual side, the location choice feature adds a new layer to the standard setting of Hedonic Games, in the sense that agents that select the same location form a coalition. This allows to naturally restrict the possible coalitions that can be formed. With this, our model generalizes simple symmetric Fractional Hedonic Games on complete bipartite valuation graphs and also Hedonic Diversity Games with utilities single-peaked at 0. We believe that this generalization is also a very interesting direction for other types of Hedonic Games
Globally scalable glacier mapping by deep learning matches expert delineation accuracy
Get PDFAccurate global glacier mapping is critical for understanding climate change impacts. Despite its importance, automated glacier mapping at a global scale remains largely unexplored. Here we address this gap and propose Glacier-VisionTransformer-U-Net (GlaViTU), a convolutional-transformer deep learning model, and five strategies for multitemporal global-scale glacier mapping using open satellite imagery. Assessing the spatial, temporal and cross-sensor generalisation shows that our best strategy achieves intersection over union >0.85 on previously unobserved images in most cases, which drops to >0.75 for debris-rich areas such as High-Mountain Asia and increases to >0.90 for regions dominated by clean ice. A comparative validation against human expert uncertainties in terms of area and distance deviations underscores GlaViTU performance, approaching or matching expert-level delineation. Adding synthetic aperture radar data, namely, backscatter and interferometric coherence, increases the accuracy in all regions where available. The calibrated confidence for glacier extents is reported making the predictions more reliable and interpretable. We also release a benchmark dataset that covers 9% of glaciers worldwide. Our results support efforts towards automated multitemporal and global glacier mapping.</p
Assessing the riverine flood forecast skill of GloFAS and Google Flood Hub with impact data and river flow observations to support early actions in Mali
Get PDFRiverine floods are among the most destructive and frequent natural hazards in Mali. To mitigate their impacts, the Mali Red Cross has implemented an anticipatory action mechanism that activates early responses when predefined triggers are met. Currently, the Early Action Protocol (EAP) relies on real-time water level observations from the National Directorate of Hydraulics (DNH) of Mali. Triggers are activated when upstream water levels exceed thresholds, which are extrapolated downstream along the river network using estimated propagation times as the lead time. The current EAP’s trigger model lacks meteorological inputs, limiting skilful lead times to less than four days. Recent advancements in global operational flood forecasting systems present opportunities to enhance Mali's EAP by leveraging increasingly skilful medium-range weather forecasts as inputs of both physically-based models, as in the Copernicus Emergency Management Service's Global Flood Awareness System (GloFAS), and artificial intelligence-based models, like in Google Flood Hub. Incorporating forecasts from these models in Mali’s EAP could improve flood anticipation. This study evaluates the performance of the latest version of GloFAS (version 4) and Google Flood Hub alongside Mali’s current trigger model for the Niger and Senegal river basins in Mali. We evaluated hindcasted triggers aggregated to administrative units, using river flow observations and flood impact data, sourced from OCHA, EMDAT, DesInventar, DRPC Mali, DGPC Mali, CatNat, Relief, and a text-mining algorithm applied to newspaper articles. Model performance was assessed using Probability of Detection (POD) and False Alarm Ratio (FAR) for different lead times and discharge return period thresholds. GloFAS and Google Flood Hub demonstrated similar skill in frequently flooded regions, suggesting that lead times can be extended beyond the four-day window. However, performance assessments are limited by the quality of impact data. This study highlights the potential and challenges of enhancing flood forecasting and anticipatory action in Mali. In the future, incorporating flood extent mapping may improve forecast value by pinpointing affected communities, and impact databases can be improved using satellite imagery, enhancing forecast assessments for early actions
Statistical mapping and modelling of urban flood using social media data
Get PDFUrban flooding poses significant challenges to cities worldwide, leading to substantial economic losses, infrastructure damage, and loss of life. Rapid urbanization and climate change have intensified these issues, highlighting the urgent need for effective flood mapping and management strategies. This thesis develops a comprehensive framework for urban flood mapping and management. It addresses flood susceptibility, prediction of their intensity, coupling of 1D-2D flooding and simulation models, and targeted mitigation measures across different scales. The methods, applied to the cities of Chengdu and Haining in China, integrate novel data sources, social media data with advanced machine learning models, spatial statistical methods, and hydrodynamic simulations. They offer new insights into urban flooding in rapidly urbanizing regions. The first chapter is an introduction to the research and offers its background. The second chapter explores the potential of social media data as a novel, low-cost, and real-time source for urban flood mapping. Using environmental factors, a naïve Bayes model was developed to assess flood susceptibility, achieving high accuracy (0.95) and identifying high flood-susceptibility areas in Chengdu. The findings highlight the importance of vegetation density in flood resilience while social media data can effectively fill the gaps in traditional flood monitoring systems. The third chapter presents a Log-Gaussian Cox Process (LGCP) model that predicts urban flood intensity. It incorporates fixed environmental effects and spatial random effects. This spatial statistical model captures unexplained spatial variability, offering more accurate predictions than traditional deterministic models. Applied to Chengdu, the results identified the central region as a flood hotspot with significant spatial random effects, emphasizing the value of spatial statistical models for understanding the complex spatial dynamics of urban flooding. The fourth chapter presents a 1D-2D coupled model to simulate urban inundation, combining the 1D Storm Water Management Model (SWMM) with a 2D diffusion-based physical model. This coupling addresses the limitations of traditional 1D models by predicting more detailed spatial inundation distributions and depths. Applied to Haining the model identified severe inundation areas and provided actionable insights for early warning and management. By combining hydrodynamic models with geostatistical models, the results demonstrate the advantages of integrating physical and statistical models to address uncertainties in flood simulations. The fifth chapter analyzes the influence of landscape patterns and topographic factors on urban flooding using a multi-scale stepwise regression model. Key drivers, such as impervious surface coverage and elevation gradients, were identified, highlighting the need for spatially heterogeneous and scale-specific planning approaches. These insights inform the design of targeted flood mitigation strategies that align with urban development goals. The last chapter summarizes the key findings and places them in a broader scientific and societal context to highlight future directions. In summary, this thesis demonstrates the potential of integrating social media data with advanced spatial statistical and hydrodynamic models to improve urban flood mapping and management. It offers scalable solutions for cities facing similar challenges globally and has a broad application potential for addressing other natural hazards, such as landslides, earthquakes, and wildfires
Suitability of just-in-time adaptive intervention in post-COVID-19-related symptoms:A systematic scoping review
Get PDFPatients with post-COVID-19-related symptoms require active and timely support in self-management. Just-in-time adaptive interventions (JITAI) seem promising in meeting these needs, as they aim to provide tailored interventions based on patient-centred measures. This systematic scoping review explores the suitability and examines key components of a potential JITAI in post-COVID-19 syndrome. Databases (PsycINFO, PubMed, and Scopus) were searched using terms related to post-COVID-19-related symptom clusters (fatigue and pain; respiratory problems; cognitive dysfunction; psychological problems) and to JITAI. Studies were summarised to identify potential components (interventions options, tailoring variables and decision rules), feasibility and effectiveness, and potential barriers. Out of the 341 screened records, 11 papers were included (five single-armed pilot or feasibility studies, three two-armed randomised controlled trial studies, and three observational studies). Two articles addressed fatigue or pain-related complaints, and nine addressed psychological problems. No articles about JITAI for respiratory problems or cognitive dysfunction clusters were found. Most interventions provided monitoring, education or reinforcement support, using mostly ecological momentary assessments or smartphone-based sensing. JITAIs were found to be acceptable and feasible, and seemingly effective, although evidence is limited. Given these findings, a JITAI for post-COVID-19 syndrome is promising, but needs to fit the complex, multifaceted nature of its symptoms. Future studies should assess the feasibility of machine learning to accurately predict when to execute timely interventions.</p
Digital health interventions for spinal surgery patients:A systematic scoping review
Get PDFIntroduction: The potential of digital health interventions to optimize healthcare is promising also in the context of spinal surgery. However, a systematic review assessing the quality of digital health interventions for spinal surgery patients and the potential effects on these patients is lacking.Method: The objective of the current scoping review was to provide a systematic overview of digital health interventions for spinal surgery patients described in scientific literature. The focus was on describing the current digital health interventions, assessing the quality of these descriptions, reviewing the reported effects and assessing the methodological quality of the included studies.Results: A total of 14 full-text articles, describing 11 digital health interventions were included in the final analysis. These digital health interventions ranged from a website and app to a mobile phone messaging system and mobile phone interface. Most digital health interventions aim to improve adherence to rehabilitation guidelines and physical health. The included studies were generally of moderate to high quality and showed significant effects on physical health. Vital aspects of digital interventions such as “working mechanism theory” and “prompts and reminders” were often absent in the description of interventions.Conclusion: The study of digital interventions for spinal surgery patient is emerging and promising. However, there is a scarcity of studies using a rigorous design. A more systematic and comprehensive framework for developing and describing digital interventions for spinal surgery patients is highly recommended.</p