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SKYDEIDRÆT SOM REHABILITERENDE INDSATS FOR PERSONER MED EN ERHVERVET HJERNESKADE– ET KVALITATIVT STUDIE MED FOKUS PÅ BETYDNING AF DELTAGELSE I ALMENE FÆLLESSKABER
No full textPersoner med erhvervet hjerneskade (EH) kan opleve at stå uden for fællesskaber og mangle et aktivt fritidsliv, som de havde, før skaden indtraf. Det kan have betydning for trivsel og livskvalitet, både for den enkelte, men også for familie, netværk og andre relationer. Erfaringer fra praksis viser, at skydeidræt som foreningsfællesskab kan skabe mening at deltage i for voksne med EH. Derudover peger forskning på, at børn med ADHD træner deres koncentration og oplever ro i kroppen, når de går til skydning i skoletiden i skytteforeninger i Danmark. Med afsæt i dette blev indsatsen Hjerne-FOKUS udviklet i samarbejde mellem Aalborg Kommune, Taleinstituttet og Hjerneskadecenter Nordjylland og DGI Nordjylland. I fire udvalgte skytteforeninger går voksne med EH til skydning i fritiden. Nærværende studie kobler sig på idrætsprojektet og belyser, hvilken betydning skydeidræt har for syv personer med EH. Studiet er designet som en kvalitativ interviewundersøgelse med en fænomenologisk-hermeneutisk tilgang funderet i IPA. Der er fremanalyseret fire temaer: skydeidræt som frirum; at være social og indgå i foreningsfællesskabet på lige fod med alle andre; at opleve mental ro; samt at føle sig normal. Studiet peger på, at deltagelse i skydeidræt kan have en positiv betydning for informanterne i forhold til deres fysiske, psykiske og sociale liv, men at der mangler yderligere forskning, bl.a. i forhold til at belyse eventuelle ekskluderende forhold samt yderligere viden ift. skydeidrættens mulige effekt på kognitive og fysiologiske faktorer
Estimation of Wave Kinematics of Nonlinear Multidirectional Waves using Multiple Surface Elevation Measurements
Get PDFTo correctly assess the wave loading on coastal and offshore structures in physical model testing, it is necessary to relate the wave forces to the wave kinematics. In the model, reflection may be present and, if neglected, could result in an incorrect evaluation of the load coefficients. Thus, measurement of the wave kinematics in physical model testing of such structures is needed to accurately estimate the load coefficients in the model. However, such measurements can be an expensive and cumbersome task, especially because measurement of the total acceleration, including the convective terms is difficult. Thus, the particle velocities and accelerations are often estimated by a mathematical model established from measurements of the surface elevation. The present work describes how the results from a NL-SORS wave decomposition of nonlinear, short-crested waves measured in physical models can be used for the estimation of the particle velocities and accelerations of such waves. The overall finding is that the wave particle velocities may be accurately estimated in the presence of nonlinear interactions, directional spreading and reflected waves, as opposed to existing methods that estimate the particle velocities assuming that all energy propagate in the same direction. The approach is demonstrated using data of increasing complexity, ranging from simple trichromatic synthetically generated wave fields to numerical, and finally, experimental data
Machine learning for blockchain data analysis:Progress and opportunities
No full textBlockchain technology has rapidly emerged to mainstream attention. At the same time, its publicly accessible, heterogeneous, massive-volume, and temporal data are reminiscent of the complex dynamics encountered during the last decade of big data. Unlike any prior data source, blockchain datasets encompass multiple layers of interactions across real-world entities, e.g., human users, autonomous programs, and smart contracts. Furthermore, blockchain’s integration with cryptocurrencies has introduced financial aspects of unprecedented scale and complexity, such as decentralized finance, stablecoins, non-fungible tokens, and central bank digital currencies. These unique characteristics present opportunities and challenges for machine learning on blockchain data.On the one hand, we examine the state-of-the-art solutions, applications, and future directions associated with leveraging machine learning for blockchain data analysis critical for improving blockchain technology, such as e-crime detection and trends prediction. On the other hand, we shed light on blockchain’s pivotal role by providing vast datasets and tools that can catalyze the growth of the evolving machine learning ecosystem. This paper is a comprehensive resource for researchers, practitioners, and policymakers, offering a roadmap for navigating this dynamic and transformative field.<br/
The music that breaks the gramophone: opening service design education to perspectives beyond the present paradigm.:A conversation among Nicola Morelli, Amalia de Götzen, and Luca Simeone with Grazia Concilio, Francesca Cognetti, and Ni Minqing
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Decentralized Reinforcement Learning for Adaptive Power Sharing in Hybrid DC Microgrids
No full text This paper proposes a decentralized voltage control strategy for islanded DC microgrids that replaces conventional droop control with a reinforcement learning (RL)-based approach. Using a Deep Deterministic Policy Gradient (DDPG) agent, the controller learns to generate real-time voltage references based solely on local measurements, eliminating the need for inter-unit communication. Compared to droop control, the proposed method reduces power sharing error from +30% to +8% and halves bus voltage deviation under high line impedance scenarios. The framework adapts to dynamic load and network conditions, offering a scalable and resilient control solution for next-generation microgrids.</p
Techno-economic evaluation of maximizing minimum liquid discharge from seawater desalination for the fertilizer industry
Get PDFThe Middle East and North Africa region faces critical water scarcity and food security challenges that threaten economic development. Fertilizer use supports food self-sufficiency, but its production is highly water intensive. Supplying desalinated water to a decarbonized fertilizer plant offers an environmentally sustainable pathway.This study investigates co-locating a decarbonized fertilizer plant with a seawater desalination facility, optionally implementing minimum liquid discharge (MLD) to generate additional revenue through recovery of magnesium hydroxide and sodium chloride (NaCl). Three configurations were modeled: a conventional seawater reverse osmosis (SWRO)-based plant; and two MLD configurations using high-pressure RO (HPRO), osmotically-assisted RO (OARO), and crystallizers. Financial performance was assessed using a novel discounted and allocated levelized cost (DALC) method, internal rate of return (IRR), and net present value (NPV).In a Moroccan case study, the conventional configuration achieved the lowest DALC and energy consumption (0.70 USD/m3water and 3.8 KWhel/m3), with an IRR of 23.9 %. The first MLD configuration had higher costs (0.94 USD/m3water, 12.0 KWhel/m3) and a lower IRR (14.5 %), with water recovery limited to 71.4 % due to nonuse of magnesium crystallizer effluent (60.4 % in the conventional setup). Reusing this effluent in the second MLD configuration increased water recovery to 96.7 %, yet higher impurities at the NaCl crystallizer feed reduced the IRR to 9.7 %, which could be improved through financing strategies such as lowering capital costs to endorse the MLD-maximizing option.The findings emphasize advancing impurity removal methods and exploring innovative project financing strategies to enable financially and environmentally sustainable seawater desalination for decarbonized fertilizer production
Retention in care and viral suppression in pregnant/postpartum vs. nonpregnant/nonpostpartum women with HIV
No full textOBJECTIVE: To investigate retention in care, viral suppression, and virological failure in pregnant and postpartum women with HIV compared to nonpregnant/nonpostpartum women with HIV in Denmark, and to explore factors associated with adverse HIV care outcomes.DESIGN: A nationwide registry-based cohort study.METHODS: All women with HIV, who delivered in Denmark from 2000 to 2019, alongside a comparison group of nonpregnant/nonpostpartum women with HIV were included from the Danish HIV Birth Cohort and the Danish HIV Cohort Study and linked to national health registries. We assessed outcomes: retention in care (two HIV RNA or CD4+ measurements ≥90 days apart within a year), viral suppression (HIV RNA <200 copies/ml at the latest measurement), and virological failure (two consecutive HIV RNA measurements >200 copies/ml or one >1000 copies/ml). Incidence rate ratios evaluated group differences, and logistic regression analyzed factors linked to adverse outcomes.RESULTS: We included 564 pregnant and 1705 nonpregnant/nonpostpartum women with HIV. Retention in care was significantly lower during pregnancy, especially for deliveries before 2014, and in the second postpartum year. No significant differences in viral suppression were found between groups after stratification by delivery year or in women with more than 1 year since HIV diagnosis. Pregnant women had higher rates of virological failure, while postpartum women had lower rates, significant only in the second postpartum year for women delivering before 2010.CONCLUSION: Based on CD4+/HIV RNA measurements, retention in care was lower in pregnant and postpartum women, particularly in the second year. Reassuringly, viral suppression and virological failure rates were comparable.</p
