Shunt quantification in congenital heart disease based on two-dimensional speckle tracking

In this work we investigated how high frame rate speckle tracking based on plane wave imaging could be used to improve the quantification of peak velocities in shunt flows due to septal defects. Simulated jet flow was used to optimize acquisition and tracking parameters. In vivo, a packet based acquisition scheme was used where focused B-mode scans were interleaved high frame rate flow images (100 fps). Results showed that speckle tracking provides calibrated velocities in the shunt flow throughout the cardiac cycle, and improved estimates of peak velocities used for diagnosing shunt severity were acquired

A Full-Scale Study on Traffic Induced Vibrations of a Suspension Bridge

Master's thesis in Offshore Technology: Subsea and MarineThis thesis focuses on the traffic-induced vibrations of a suspension bridge, and the modal properties embedded in the traffic-induced response data. Traffic has been observed at Lysefjorden bridge for five different days with low wind speeds. Observations were synchronized with the acceleration and wind data continuously acquired on the bridge. The purpose of the work has been to study the impact load response from heavy vehicles and estimate the modal damping ratios of the bridge. In an experiment performed in cooperation with truck driver Anette Ravndal, a 50 tonne truck has crossed the bridge with different velocities from different directions. Findings from this experiment suggest that impact load response is present, but with different magnitude and cause for different vibration modes. For the first modes, response appears to be relatively similar for both exiting and entering the bridge at high velocities, while for higher modes with, frequencies over 1Hz, the impact effect for the vehicle entering the bridge at high velocity appears to be more significant. Eight modal damping ratios for the bridge have been estimated using viscous damping assumption. This is done by isolating the free decay of the bridge after heavy vehicles have excited and exited the bridge. The results have relatively large variations, but are reasonable compared to other methods used for estimating modal damping ratio for Lysefjorden bridge as well as similar structures in existing literature. These large variations can be explained by variations in wind and temperature, but are also likely due to inaccuracies in filtering of the data. A spectral analysis of the acceleration time series has been performed using Frequency Domain Decomposition. The findings agreewith results fromprevious studies which showthat response from vehicles consists of a combination of both low and higher frequencymodes. Also, all identified frequencies agree with those found previously both using analytical methods and the fullscale response measurements with very little deviation

Responses to Internal Displacement in Colombia: Guided by What Principles?

This article aims to explain the gap between IDP law and practice in Colombia. Colombia’s IDP legislation is con­sidered one of the world’s most advanced legal systems as it puts in practice the UN Guiding Principles on Internal Displacement. However, the reality of life for IDPs in Colombia does not match their legal rights. Especially the sections of the law related to preventing displacement and providing durable solutions for IDPs are poorly imple­mented. Following Ferguson’s work on depoliticization, we argue that displacement in Colombia is treated as a tech­nical rather than political problem, detaching it from root causes like landownership and structural class inequalities. This article provides an overview of the root causes and analyzes the different methods through which internal dis­placement is “depoliticized” in Colombia. In conclusion, we will discuss the wider implications of the Colombian case for understanding implementation challenges of the Guiding Principles.Cet article tente d’expliquer l’écart entre le droit des personnes déplacés internes et sa mise en application en Colombie. Le droit colombien en matière de déplacement interne est considéré comme l’un des systèmes juridiques les plus avancés au monde en ce qu’il met en pratique les Principes directeurs relatifs au déplacement de personnes à l’intérieur de leur propre pays des Nations Unies. Cependant, la réalité des personnes déplacées en Colombie ne correspond pas à leurs droits. En particulier, les sections de la loi relatives à la prévention des déplacements et à la mise en place de solutions durables pour les déplacés internes sont mal mises en œuvre. Suivant les travaux de Ferguson sur la dépolitisation, nous soutenons que le déplacement en Colombie est considéré comme un problème technique plutôt que politique, le détachant de ses causes premières telles la propriété foncière et les inégalités structurelles de classe. Nous donnons un aperçu des causes premières du déplacement et analysons les différentes méthodes par lesquelles le déplacement interne est « dépolitisé » en Colombie. En conclusion, nous discutons des implications plus larges du cas colombien pour la compréhension des défis de mise en œuvre des Principes directeurs

Using Machine Learning to Predict Port Congestion : A study of the port of Paranaguá

Being able to accurately predict future levels of port congestion is of great value to both port and ship operators. However, such a prediction tool is currently not available. In this thesis, a Long Short-Term Memory Recurrent Neural Network is built to fulfill this need. The prediction model uses information mined from Automatic Identification Systems (AIS) data, vessel characteristics, weather data, and commodity price data as input variables to predict the future level of congestion in the port of Paranaguå, Brazil. All data used in this study are publicly available. The predictions of the proposed model are shown to be promising with a satisfactory level of accuracy. The conclusion and evaluation of the presented model are that it serves its purpose and fulfills its objective within the constraints set by the authors and its inherent limitations.nhhma

Assessing the performance of ultrafast vector flow imaging in the neonatal heart via multiphysics modeling and In vitro experiments

Ultrafast vector flow imaging would benefit newborn patients with congenital heart disorders, but still requires thorough validation before translation to clinical practice. This paper investigates 2-D speckle tracking (ST) of intraventricular blood flow in neonates when transmitting diverging waves at ultrafast frame rate. Computational and in vitro studies enabled us to quantify the performance and identify artifacts related to the flow and the imaging sequence. First, synthetic ultrasound images of a neonate's left ventricular flow pattern were obtained with the ultrasound simulator Field II by propagating point scatterers according to 3-D intraventricular flow fields obtained with computational fluid dynamics (CFD). Noncompounded diverging waves (opening angle of 60 degrees) were transmitted at a pulse repetition frequency of 9 kHz. ST of the B-mode data provided 2-D flow estimates at 180 Hz, which were compared with the CFD flow field. We demonstrated that the diastolic inflow jet showed a strong bias in the lateral velocity estimates at the edges of the jet, as confirmed by additional in vitro tests on a jet flow phantom. Furthermore, ST performance was highly dependent on the cardiac phase with low flows (< 5 cm/s), high spatial flow gradients, and out-of-plane flow as deteriorating factors. Despite the observed artifacts, a good overall performance of 2-D ST was obtained with a median magnitude underestimation and angular deviation of, respectively, 28% and 13.5 degrees during systole and 16% and 10.5 degrees during diastole

Holistic management of a smart city thermal energy plant with sewage heat pumps, solar heating, and grey water recycling

This article introduces a modern thermal energy plant consisting of sewage heat pumps, a biogas boiler, thermal solar collectors, and grey water recycling. It further discusses advanced methods to achieve energy efficiency in the plant operation. The project is a collaboration between the industrial plant designer, the municipal plant owner, and the local academic institution. The article presents the framework for the collaboration. The overall target is to investigate how the experience and competence of the three partners can lead to improved operation using data-driven methods and optimization strategies. The industrial partner can closely follow up on its design and increase its knowledge of artificial intelligence and data-driven methods. The municipal partner is given a “free-of-charge” system review. New knowledge and reduced life cycle costs and emissions are possible outcomes. The academic partner gets access to a “living green laboratory,” a unique dataset, and the opportunity to validate developed models and optimization strategies. The plant represents the state-of-the-art for a medium scaled, local thermal energy production system in an existing building cluster. The design energy and emission targets are presented and compared to the operational results. Though the municipal partner can report good agreement between targets and results, an evaluation of the day-to-day operation identified practical examples of system conditions that Artificial Intelligence may improve. The article concludes with a description of plans for future work and a broader discussion of the impacts of introducing data-driven methods to real-life systems.publishedVersio

Using Artificial Neural Networks to Gather Intelligence on a Fully Operational Heat Pump System in an Existing Building Cluster

The use of heat pumps for heating and cooling of buildings is increasing, offering an efficient and eco-friendly thermal energy supply. However, their complexity and system integration require attention to detail, and minor design or operational errors can significantly impact a project’s success. Therefore, it is essential to have a thorough understanding of the system’s intricacies and demands, specifically detailed system knowledge and precise models. In this article, we propose a method using artificial neural networks to develop heat pump models from measured data. The investigation focuses on an operational heat pump plant for heating and cooling a cluster of municipal buildings in Stavanger, Norway. The work showcases that the network configurations can provide process insights and knowledge when detailed system information is unavailable. Model A predicts the heat pump response to temperature setpoint and inlet conditions. Except for some challenges during low-demand cooling mode, the model predicts outlet temperatures with Mean Absolute Percentage Error (MAPE) between 2 and 5% and energy production and consumption with MAPE below 10%. Summarizing the five-minute interval predictions, the model predicts the hourly energy production and consumption with MAPE at 3% or less. Model B predicts energy consumption and coefficient of performance (COP) from measured inlet and outlet conditions with MAPE below 5%. The model may serve as a tool to develop system-specific compressor maps for part-load conditions and for real-time performance monitoring.publishedVersio

Decolonizing Higher Education: Rationales and Implementations from the Subject of Music History

In addition to ongoing territorial and material re-organization of power as a result of 19th-century European colonialism, there has been an increasing focus on decolonializing knowledge practices in higher education. Research communities are discussing what it could mean to decolonize thinking practices, conditions of knowledge creation, and access to higher education. Since the arts are a powerful tool for change, we want to contribute to the ongoing scholarly discussion by introducing examples of decolonial practices used in the Bachelor program in music performance. By presenting three cases from the subject of music history at two Norwegian universities, we provide insight into why and how we can teach differently, what kind of resistance we meet, and how we can make use of discomfort to decolonize knowledge practices. Our empirical material is composed of our own experiences, student course evaluations, conversations with students and teachers, as well as module descriptions. In the basis of our findings, we propose a strategic canonism to mediate in an increasingly polarized field

Optimizing micro gas turbine operation in a microgrid system with natural gas and hydrogen fuel: an AI-based approach

In the coming years, decentralized power generation systems with renewables are expected to take a leading role, and micro gas turbines will serve as backup sources to compensate for times of low inputs from other sources. In order to deal with the unpredictable energy inputs from renewables, the micro gas turbine must be capable of running under varying load conditions and making fast transitions between them. The operation of a micro gas turbine in an integrated microgrid has the potential to reduce operational costs and ensure the delivery of demanded heat and power to consumers. This paper investigates the operation of a micro gas turbine in a microgrid, serving as a supplementary power source for a municipal building. The building's required energy is initially provided by wind turbine power, and the micro gas turbine serves as a backup source during times of wind power deficiency. The micro gas turbine can operate using a natural gas/hydrogen fuel blend ranging from zero to 100% hydrogen. Furthermore, a water electrolyzer with a hydrogen tank is available to operate as a storage system within the microgrid. The study's results demonstrate the economic and environmental benefits of using hydrogen storage and optimizing operational planning in the microgrid. The primary objective of the paper is to highlight the feasibility and benefits of employing micro gas turbines and hydrogen storage systems within a microgrid as a renewable energy backup power source

Oral health related quality of life and reasons for non-dental attendance among patients with substance use disorders in withdrawal rehabilitation

No study has assessed the socio-behavioral distribution of oral health related quality of life (OHRQoL) among patients with substance use disorders receiving medically assisted rehabilitation therapy (MAR) in Norway. Objectives To examine the prevalence of oral impacts on daily performances (OIDP) and its distribution among MAR patients in western Norway. We also examined whether oral impacts discriminate with different reasons for non-dental attendance. Material and Methods A cross-sectional study focusing OHRQoL was nested to the INTRO-HCV study and implemented in six rehabilitation clinics for people with substance use disorders. A total of 167 MAR patients completed personal interviews and oral clinical examination upon entering the clinic for their MAR medication. Results The prevalence of oral impacts (OIDP > 0) was 61%. Logistic regression, adjusted for sex and age presented with odds ratios (OR) with 95% confidence intervals (CI) revealed that less than 20 remaining teeth (OR = 5.3 95% CI: 1.6–23.3) and dissatisfaction with dental care (OR = 5.1 95% CI: 1.3–19.0) increased the odds of having OIDP > 0. OIDP > 0 was also associated with insufficient dental follow-up due to dental anxiety and poor experiences with perceived attitudes of dental workers. Means OIDP among people with negative experiences with attitudes of dental care workers were 3.1 (SD 0.8) compared to 1.4 (SD 0.7) among those without negative experiences, and 2.8 (SD) for those with dental anxiety compared to 1.8 (SD) among those without. Conclusion OHRQoL among MAR patients was generally poor. To reach those with a need for dental care, modification of the existing rehabilitation approach toward closer collaboration between dental health care workers and others in contact with drug users might be necessary.publishedVersio