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A comparative analysis of unsupervised machine-learning methods in PSG-related phenotyping
(2025-06) Ghorvei, Mohammadreza; Karhu, Tuomas; Hietakoste, Salla; Ferreira-Santos, Daniela; Hrubos-Strøm, Harald; Islind, Anna Sigridur; Biedebach, Luka; Nikkonen, Sami; Leppänen, Timo; Rusanen, Matias; Department of Computer Science; Department of Engineering
Obstructive sleep apnea is a heterogeneous sleep disorder with varying phenotypes. Several studies have already performed cluster analyses to discover various obstructive sleep apnea phenotypic clusters. However, the selection of the clustering method might affect the outputs. Consequently, it is unclear whether similar obstructive sleep apnea clusters can be reproduced using different clustering methods. In this study, we applied four well-known clustering methods: Agglomerative Hierarchical Clustering; K-means; Fuzzy C-means; and Gaussian Mixture Model to a population of 865 suspected obstructive sleep apnea patients. By creating five clusters with each method, we examined the effect of clustering methods on forming obstructive sleep apnea clusters and the differences in their physiological characteristics. We utilized a visualization technique to indicate the cluster formations, Cohen's kappa statistics to find the similarity and agreement between clustering methods, and performance evaluation to compare the clustering performance. As a result, two out of five clusters were distinctly different with all four methods, while three other clusters exhibited overlapping features across all methods. In terms of agreement, Fuzzy C-means and K-means had the strongest (κ = 0.87), and Agglomerative hierarchical clustering and Gaussian Mixture Model had the weakest agreement (κ = 0.51) between each other. The K-means showed the best clustering performance, followed by the Fuzzy C-means in most evaluation criteria. Moreover, Fuzzy C-means showed the greatest potential in handling overlapping clusters compared with other methods. In conclusion, we revealed a direct impact of clustering method selection on the formation and physiological characteristics of obstructive sleep apnea clusters. In addition, we highlighted the capability of soft clustering methods, particularly Fuzzy C-means, in the application of obstructive sleep apnea phenotyping.
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Aluminium recycling in single-and multiple-capillary laboratory electrolysis cells
(2021-07) Yasinskiy, Andrey; Padamata, Sai Krishna; Moiseenko, Ilya; Stopic, Srecko; Feldhaus, Dominic; Friedrich, Bernd; Polyakov, Peter; Department of Engineering
This work is a contribution to the approach for Al purification and extraction from scrap using the thin-layer multiple-capillary molten salt electrochemical system. The single-and multiple-capillary cells were designed and used to study the kinetics of aluminium reduction in LiF–AlF3 and equimolar NaCl–KCl with 10 wt.% AlF3 addition at 720–850◦C. The cathodic process on the vertical liquid aluminium electrode in NaCl–KCl (+10 wt.% AlF3) in the 2.5 mm length capillary had mixed kinetics with signs of both diffusion and chemical reaction control. The apparent mass transport coefficient changed from 5.6·10−3 cm.s−1 to 13.1·10−3 cm.s−1 in the mentioned temperature range. The dependence between the mass transport coefficient and temperature follows an Arrhenius-type behaviour with an activation energy equal to 60.5 J.mol−1. In the multiple-capillary laboratory electrolysis cell, galvanostatic electrolysis in a 64LiF–36AlF3 melt showed that the electrochemical refinery can be performed at a current density of 1 A.cm−2 or higher with a total voltage drop of around 2.0 V and specific energy consumption of about 6–7 kW.kg−1 . The resistance fluctuated between 0.9 and 1.4 Ω during the electrolysis depending on the current density. Thin-layer aluminium recycling and refinery seems to be a promising approach capable of producing high-purity aluminium with low specific energy consumption.
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Review of Snow Data Assimilation Methods for Hydrological, Land Surface, Meteorological and Climate Models: Results from a COST HarmoSnow Survey
(2018-12-14) Helmert, Jürgen; Şensoy Şorman, Aynur; Alvarado Montero, Rodolfo; De Michele, Carlo; de Rosnay, Patricia; Dumont, Marie; Finger, David C.; Lange, Martin; Picard, Ghislain; Potopová, Vera; Pullen, Samantha; Vikhamar-Schuler, Dagrun; Arslan, Ali; Department of Engineering
The European Cooperation in Science and Technology (COST) Action ES1404 HarmoSnow, entitled, A European network for a harmonized monitoring of snow for the benefit of climate change scenarios, hydrology and numerical weather prediction (2014-2018) aims to coordinate efforts in Europe to harmonize approaches to validation, and methodologies of snow measurement practices, instrumentation, algorithms and data assimilation (DA) techniques. One of the key objectives of the action was Advance the application of snow DA in numerical weather prediction (NWP) and hydrological models and show its benefit for weather and hydrological forecasting as well as other applications. This paper reviews approaches used for assimilation of snow measurements such as remotely sensed and in situ observations into hydrological, land surface, meteorological and climate models based on a COST HarmoSnow survey exploring the common practices on the use of snow observation data in different modeling environments. The aim is to assess the current situation and understand the diversity of usage of snow observations in DA, forcing, monitoring, validation, or verification within NWP, hydrology, snow and climate models. Based on the responses from the community to the questionnaire and on literature review the status and requirements for the future evolution of conventional snow observations from national networks and satellite products, for data assimilation and model validation are derived and suggestions are formulated towards standardized and improved usage of snow observation data in snow DA. Results of the conducted survey showed that there is a fit between the snow macro-physical variables required for snow DA and those provided by the measurement networks, instruments, and techniques. Data availability and resources to integrate the data in the model environment are identified as the current barriers and limitations for the use of new or upcoming snow data sources. Broadening resources to integrate enhanced snow data would promote the future plans to make use of them in all model environments.
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Do We Agree on What an “Audit” Is? Toward Standardized Smart Contract Audit Reporting
(Association for Computing Machinery, Inc, 2026-07-31) Qasse, Ilham Ahmed; Hjálmtýsson, Gísli; Hamdaqa, Mohammad; Department of Computer Science
Smart contract security audits are essential for trust in decentralized finance (DeFi), yet audit reports from different firms vary widely in scope definition, severity labels, fix verification, and report structure. These differences make it hard for developers, users, and other stakeholders to assess risk. In this paper, we address these issues by empirically analyzing 160 audit reports from 26 leading auditing firms to uncover patterns and gaps in current practices. Using qualitative content analysis, we extract a taxonomy of 19 common properties that audit reports include (or omit). We then apply Formal Concept Analysis (FCA) to identify five distinct “report style families” used by auditors, and perform a temporal trend analysis to see if the industry is converging on certain best practices. Finally, we synthesize a feature model that specifies a minimal defensible baseline for audit reports, distinguishing mandatory sections from optional extensions to support traceability and consistent interpretation across reports. This model enables reproducible comparisons across auditors, strengthens accountability for scope definition and fix verification, and provides an evidence base to improve the quality and uniformity of smart contract audit reporting.
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Updated trends in the global prevalence and burden of mental disorders, 1990–2023 : a systematic analysis for the Global Burden of Disease Study 2023
(2026-05-23) A.J.FerrariAlize Jd.santomauro@uq.edu.auGBD 2023 Mental Disorder Collaborators; Department of Psychology
Background The 2023 iteration of the Global Burden of Diseases, Injuries, and Risk Factors Study (GBD) estimated prevalence, incidence, and health burden for 375 diseases and injuries, including 12 mental disorders. We assess past, current, and emerging trends in the prevalence and burden of mental disorders across sexes and age groups, for 21 regions, 204 countries and territories, and by Socio-demographic Index (SDI) quintile, from 1990 to 2023. Methods Mental disorders included in GBD 2023 were anxiety disorders, major depressive disorder, dysthymia, bipolar disorder, schizophrenia, autism spectrum disorders, conduct disorder, attention-deficit hyperactivity disorder, anorexia nervosa, bulimia nervosa, idiopathic developmental intellectual disability, and a residual category of other mental disorders. A literature review identified epidemiological data for each disorder. These were analysed via a Bayesian meta-regression to estimate prevalence by disorder, sex, age, location, and year. Disorder-specific prevalence was multiplied by disability weights representing the severity of health loss associated with each disorder to estimate years lived with disability (YLDs). Deaths due to anorexia nervosa were assessed with a Cause of Death Ensemble modelling strategy to estimate deaths by sex, age, location, and year, and then multiplied by the standard life expectancy at age of death to estimate years of life lost (YLLs). YLDs equalled disability-adjusted life-years (DALYs) for all mental disorders except anorexia nervosa (the only mental disorder considered as an underlying cause of death in GBD), for which DALYs represented the sum of YLDs and YLLs. We presented prevalence, deaths, YLDs, YLLs, and DALYs as counts, age-specific rates per 100 000 population, and age-standardised rates per 100 000 population. Findings We estimated 1·17 billion (95% uncertainty interval 1·06–1·31) prevalent cases of mental disorders globally in 2023, equivalent to an age-standardised prevalence rate of 14 210·7 cases (12 849·5–15 940·1) per 100 000 population. These estimates represented a 95·5% (75·0–121·2) increase in prevalent cases and 24·2% (11·4–41·4) increase in age-standardised prevalence rate between 1990 and 2023. All mental disorders showed increases in prevalent cases between 1990 and 2023, while notable increases were seen in age-standardised prevalence rates for anxiety disorders, major depressive disorder, dysthymia, anorexia nervosa, bulimia nervosa, schizophrenia, and conduct disorder. There were an estimated 171 million (127–228) DALYs due to mental disorders globally across sex and age in 2023, equivalent to an age-standardised DALY rate of 2070·5 DALYs (1519·1–2750·5) per 100 000 population. Mental disorders contributed to 6·1% (4·8–7·6) of all-cause DALYs in 2023, making them the fifth leading cause of global DALYs (up from 12th in 1990). DALYs were almost entirely composed of YLDs. Mental disorders were the leading cause of YLDs in 2023 (up from second in 1990), explaining 17·3% (14·8–20·6) of all-cause global YLDs. Leading causes of mental disorder DALYs were anxiety disorders (ranked 11th among the 304 diseases and injuries at Level 4 of the GBD cause hierarchy), major depressive disorder (15th), and schizophrenia (41st). Globally in 2023, mental disorder age-standardised DALY rates were higher among females (2239·6 [1643·7–3014·1] per 100 000) than among males (1900·2 [1399·8–2510·8] per 100 000), and peaked in the 15–19 years age group (2617·3 [1850·6–3696·8] per 100 000). All locations showed increased mental disorder DALY rates in 2023 compared with 1990, ranging across countries and territories from 1302·4 (952·7–1683·7) per 100 000 in Viet Nam to 3555·8 (2661·9–4715·0) per 100 000 in the Netherlands. Across SDI quintiles, DALY rates ranged from 1853·0 (1352·1–2469·3) per 100 000 for middle SDI to 2184·1 (1606·1–2890·3) per 100 000 for high SDI. Interpretation A significant health burden was imposed by mental disorders in all countries and territories in 2023, irrespective of the health resources available. In some instances, this burden has increased over time and is unevenly distributed across populations. Stronger surveillance systems, particularly in low-income and middle-income countries, are required. Additionally, we need more coordinated and inclusive policies to reduce the burden through early treatment and prevention, tailored to sex and age differences across locations. Responding to the mental health needs of our global population, especially those most vulnerable, is an obligation, not a choice. Funding Gates Foundation, Queensland Health, and University of Queensland.

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