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Cerebrospinal fluid (CSF) α-synuclein (α-syn) seed amplification assays (SAAs) can detect Lewy body pathology (LBP) with high accuracy but are invasive and costly. To address these challenges, this study evaluated a two-step workflow combining prescreening via smell-function testing with confirmatory CSF α-syn SAA testing only in individuals with reduced smell, for predicting postmortem LBP status

The influence of individual psychological and cognitive characteristics on preferences for value versus growth stocks (VSvGS) is not well understood. This study examines the influence of personality traits, financial self-efficacy (FSE), and versatile cognitive styles (VCS) on the choice between VSvGS. Specifically, it examines both the direct effects of personality traits on individual preference

Vindkraften spelar en avgörande roll i den gröna omställningen. Men vad händer när vindkraftverken nått slutet av sin livslängd? Tornen som består av enorma mängder stål återbrukas i vissa fall efter renovering men efter två–tre gånger sätter de strukturella förutsättningar stopp för återbruk och torndelarna i stål smälts oftast om till ”nytt stål”. En process som kräver mycket energi och resulterClimate change is a fact, new innovations and solutions need to be applied to mitigate the effects around the world. Greenhouse gases from burning fossil fuels are the primary cause of climate change, one of them being carbon dioxide. There are many attempts to make a turn-around regarding the use of fossil fuels and one of them is wind power. However, due to extreme loads their service life is ju

This study is concerned with examining the interrelated themes of power, resistance and exile in a Burmese context. It outlines concepts and exercise of power in Burma as well as the background of present-day leftist articulations within the anti-military movement. This study tentatively explores the context in which leftist dissidents are displaced in Thailand, and involve an investigating of the

MushroomRL is an open-source Python library developed to simplify the process of im- plementing and running Reinforcement Learning (RL) experiments. Compared to other available libraries, MushroomRL has been created with the purpose of providing a com- prehensive and exible framework to minimize the effort in implementing and testing novel RL methodologies. The architecture of MushroomRL is built

Highly dynamic robotic tasks require high-speed and reactive robots. These tasks are particularly challenging due to the physical constraints, hardware limitations, and the high uncertainty of dynamics and sensor measures. To face these issues, it's crucial to design robotics agents that generate precise and fast trajectories and react immediately to environmental changes. Air hockey is an example

Reinforcement learning in robotics is extremely challenging due to many practical issues, including safety, mechanical constraints, and wear and tear. Typically, these issues are not considered in the machine learning literature. One crucial problem in applying reinforcement learning in the real world is Safe Exploration, which requires physical and safety constraints satisfaction throughout the l

This thesis investigates the role of trust in the context of decentralised finance (DeFi). Representing a new paradigm shift in finance, DeFi emerge from the technological developments that we have witnessing in the last decades, particularly decentralised ledger technologies (DLTs) of which Blockchain is perhaps the most popular. DeFi is changing the way that firms raise financial capital and a c

This paper examines how the concept ‘nature-positive’ as a means to enhance biodiversity is defined and used, viewed through the lens of power. Building on a three-dimensional conceptualization of power, we elaborate on i) how ‘nature-positive’ enters and remains on business and policy agendas, ii) different interpretations from both ecological and business perspectives, and iii) the governance of

Transferring patients from the ward of their specialty or consultant is described as boarding. 1 Boarding patients is becoming increasingly prevalent due to greater pressure on hospital capacity. This practice compromises patient safety through delayed investigations, prolonged hospital stays, and increased risk of hospital-acquired infections. 1 2 We evaluated how regularly boarded patients were

Reinforcement learning methods for robotics are increasingly successful due to the constant development of better policy gradient techniques. A precise (low variance) and accurate (low bias) gradient estimator is crucial to face increasingly complex tasks. Traditional policy gradient algorithms use the likelihood-ratio trick, which is known to produce unbiased but high variance estimates. More mod

We report an unusual case of a patient presenting with Cushing’s syndrome caused by a phaeochromocytoma secreting adrenocorticotropic hormone (ACTH). The patient had a history of treatment-resistant hypertension, secondary amenorrhoea and tendency towards hypokalaemia. She had multiple signs of Cushing’s syndrome, such as swelling, bruising, abdominal striae and proximal myopathy. Hypokalaemia is

Global environmental challenges, coupled with growing material resource consumption, are exacerbated by an increasingly hostile geopolitical environment and the securitisation of politics. The circular economy (CE) proposes a solution to address these challenges by transforming production and consumption systems, which has sparked significant interest in the concept among scholars, policymakers, a

This study protocol outlines the test evaluation of the design of a comprehensive Nordic hospital at home (HaH) digital education program entitled “the Nordic Digital Health and Education” program (NorDigHE). The NorDigHE program aims to prepare healthcare professionals for healthcare provision in a HaH context. The program, developed by a Nordic consortium, spans five modules over 3.5 weeks of fu

We study the benefit of sharing representations among tasks to enable the effective use of deep neural networks in Multi-Task Reinforcement Learning. We leverage the assumption that learning from different tasks, sharing common properties, is helpful to generalize the knowledge of them resulting in a more effective feature extraction compared to learning a single task. Intuitively, the resulting s

We introduce ImitationFlow, a novel Deep generative model that allows learning complex globally stable, stochastic, nonlinear dynamics. Our approach extends the Normalizing Flows framework to learn stable Stochastic Differential Equations. We prove the Lyapunov stability for a class of Stochastic Differential Equations and we propose a learning algorithm to learn them from a set of demonstrated tr

There is an increasing interest in Reinforcement Learning to solve new and more challenging problems, as those emerging in robotics and unmanned autonomous vehicles. To face these complex systems, a hierarchical and multi-scale representation is crucial. This has brought the interest on Hierarchical Deep Reinforcement learning systems. Despite their successful application, Deep Reinforcement Learn