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Background
Post-COVID-19 syndrome (PCS) is a lingering disease with ongoing symptoms such as fatigue and cognitive impairment resulting in a high impact on the daily life of patients. Understanding the pathophysiology of PCS is a public health priority, as it still poses a diagnostic and treatment challenge for physicians.
Methods
In this prospective observational cohort study, we analyzed the retinal microcirculation using Retinal Vessel Analysis (RVA) in a cohort of patients with PCS and compared it to an age- and gender-matched healthy cohort (n = 41, matched out of n = 204).
Measurements and main results
PCS patients exhibit persistent endothelial dysfunction (ED), as indicated by significantly lower venular flicker-induced dilation (vFID; 3.42% ± 1.77% vs. 4.64% ± 2.59%; p = 0.02), narrower central retinal artery equivalent (CRAE; 178.1 [167.5–190.2] vs. 189.1 [179.4–197.2], p = 0.01) and lower arteriolar-venular ratio (AVR; (0.84 [0.8–0.9] vs. 0.88 [0.8–0.9], p = 0.007). When combining AVR and vFID, predicted scores reached good ability to discriminate groups (area under the curve: 0.75). Higher PCS severity scores correlated with lower AVR (R = − 0.37 p = 0.017). The association of microvascular changes with PCS severity were amplified in PCS patients exhibiting higher levels of inflammatory parameters.
Conclusion
Our results demonstrate that prolonged endothelial dysfunction is a hallmark of PCS, and impairments of the microcirculation seem to explain ongoing symptoms in patients. As potential therapies for PCS emerge, RVA parameters may become relevant as clinical biomarkers for diagnosis and therapy management.
Deammonification for nitrogen removal in municipal wastewater in temperate and cold climate zones is currently limited to the side stream of municipal wastewater treatment plants (MWWTP). This study developed a conceptual model of a mainstream deammonification plant, designed for 30,000 P.E., considering possible solutions corresponding to the challenging mainstream conditions in Germany. In addition, the energy-saving potential, nitrogen elimination performance and construction-related costs of mainstream deammonification were compared to a conventional plant model, having a single-stage activated sludge process with upstream denitrification. The results revealed that an additional treatment step by combining chemical precipitation and ultra-fine screening is advantageous prior the mainstream deammonification. Hereby chemical oxygen demand (COD) can be reduced by 80% so that the COD:N ratio can be reduced from 12 to 2.5. Laboratory experiments testing mainstream conditions of temperature (8–20°C), pH (6–9) and COD:N ratio (1–6) showed an achievable volumetric nitrogen removal rate (VNRR) of at least 50 gN/(m3∙d) for various deammonifying sludges from side stream deammonification systems in the state of North Rhine-Westphalia, Germany, where m3 denotes reactor volume. Assuming a retained Norganic content of 0.0035 kgNorg./(P.E.∙d) from the daily loads of N at carbon removal stage and a VNRR of 50 gN/(m3∙d) under mainstream conditions, a resident-specific reactor volume of 0.115 m3/(P.E.) is required for mainstream deammonification. This is in the same order of magnitude as the conventional activated sludge process, i.e., 0.173 m3/(P.E.) for an MWWTP of size class of 4. The conventional plant model yielded a total specific electricity demand of 35 kWh/(P.E.∙a) for the operation of the whole MWWTP and an energy recovery potential of 15.8 kWh/(P.E.∙a) through anaerobic digestion. In contrast, the developed mainstream deammonification model plant would require only a 21.5 kWh/(P.E.∙a) energy demand and result in 24 kWh/(P.E.∙a) energy recovery potential, enabling the mainstream deammonification model plant to be self-sufficient. The retrofitting costs for the implementation of mainstream deammonification in existing conventional MWWTPs are nearly negligible as the existing units like activated sludge reactors, aerators and monitoring technology are reusable. However, the mainstream deammonification must meet the performance requirement of VNRR of about 50 gN/(m3∙d) in this case.
Die Auswahl der passenden Geschäftsprozesse für eine Automatisierung mittels Robotic Process Automation (RPA) ist für den Erfolg von RPA-Projekten entscheidend. Das vorliegende Kapitel liefert dafür Selektionskriterien, die aus einer qualitativen Studie mit elf interviewten RPA-Experten aus dem Versicherungsumfeld resultieren. Das Ergebnis umfasst eine gewichtete Liste von sieben Dimensionen und 51 Prozesskriterien, welche die Automatisierung mit Softwarerobotern begünstigen beziehungsweise deren Nichterfüllung eine Umsetzung erschweren oder sogar verhindern. Die drei wichtigsten Kriterien zur Auswahl von Geschäftsprozessen für die Automatisierung mittels RPA umfassen die Entlastung der an dem Prozess mitwirkenden Mitarbeiter (Arbeitnehmerentlastung), die Ausführbarkeit des Prozesses mittels Regeln (Regelbasierte Prozessteuerung) sowie ein positiver Kosten-Nutzen-Vergleich. Auf diesen Ergebnissen aufbauend wird ein Vergleich mit den bereits bekannten Selektionskriterien aus der Literatur erstellt und diskutiert. Praktiker können die Ergebnisse verwenden, um eine systematische Auswahl von RPA-relevanten Prozessen vorzunehmen. Aus wissenschaftlicher Perspektive stellen die Ergebnisse eine Grundlage zur Erklärung des Erfolgs und Misserfolgs von RPA-Projekten dar.
Atmen tut jeder, automatisch. Es wird nicht auf die Ausführung geachtet. Doch was, wenn nur ein wenig Feinschliff an unserer Atmung bereits Großes für unsere Gesundheit bewirkt?
Von etlichen unterschiedlichen Studien wurde bezeugt, dass Atmung und Empfinden eins sind. Das persönliche Empfinden ist unser Portal zur Außenwelt. Die Art wie wir auf äußerliche Reize reagieren, wie achtsam wir im Tun und Denken sind, spiegelt unsere Innenwelt und körperliches Wohlbefinden wieder.
FLOWCEAN QI dient dazu, Stress- und Angststörungen im Alltagsleben für Berufstätige mit hohem Stressfaktor zu reduzieren. Vor allem, um das Gesundheitssystem zu entlasten und die Psyche der Menschen gesund wieder aufzubauen. Sind Berufstätige viel gelassener, steigen auch Leistung und Produktivität. Es ist immer wichtig, die Kernursache von Problemen zu finden und zu lösen. So können auch vielerlei andere auf die Psyche zurückzuführende Probleme der Gesellschaft gelöst werden.
FLOWCEAN QI agiert durch modernste Technologie aktiv mit dem Nutzer. Die KI-Assistenz gestaltet das Lernen der Atemtechniken spaßiger, was wiederum den Lerneffekt verbessert. Der Beamer wird im Innenbereich platziert und der Tracker begleitet einen unterwegs und zeichnet die Datenanalyse auf. Für mehr Datenschutz ist das Armband ein reines offline Produkt. Förmlich sollte es dem Nutzer nah sein, naturverbunden, vertrauenswürdig und beruhigend wirken.
FLOWCEAN QI basiert gestalterisch auf eine antike japanische Philosophie namens „Kintsugi“. Nach der japanischen Philosophie Kintsugi werden zerbrochene Teegläser wiederzusammengeklebt statt weggeworfen. Die in Teile getrennten Elemente werden glatt und geschmeidig wieder zusammengefügt. Sie formen ein neues Ganzes, dass die Schönheit des Originals meist übertrifft. Die Ästhetik hinter „Kintsugi“ nennt man „Wabi-Sabi“. Es bedeutet, die Schönheit im Vergänglichen, Alten oder Fehlerhaften zu verstehen. Die Philosophie dahinter wird metaphorisch auf das Design abgebildet und auf unsere Gesundheit übertragen. Statt letztere zu ignorieren, schenken wir ihr unsere volle Aufmerksamkeit. Ziel der Produkte ist, uns stets an sie zu erinnern, sodass wir täglich an unserem Wohlbefinden arbeiten können.
Supervised machine learning and deep learning require a large amount of labeled data, which data scientists obtain in a manual, and time-consuming annotation process. To mitigate this challenge, Active Learning (AL) proposes promising data points to annotators they annotate next instead of a subsequent or random sample. This method is supposed to save annotation effort while maintaining model performance.
However, practitioners face many AL strategies for different tasks and need an empirical basis to choose between them. Surveys categorize AL strategies into taxonomies without performance indications. Presentations of novel AL strategies compare the performance to a small subset of strategies. Our contribution addresses the empirical basis by introducing a reproducible active learning evaluation (ALE) framework for the comparative evaluation of AL strategies in NLP.
The framework allows the implementation of AL strategies with low effort and a fair data-driven comparison through defining and tracking experiment parameters (e.g., initial dataset size, number of data points per query step, and the budget). ALE helps practitioners to make more informed decisions, and researchers can focus on developing new, effective AL strategies and deriving best practices for specific use cases. With best practices, practitioners can lower their annotation costs. We present a case study to illustrate how to use the framework.
The work in modern open-pit and underground mines requires the transportation of large amounts of resources between fixed points. The navigation to these fixed points is a repetitive task that can be automated. The challenge in automating the navigation of vehicles commonly used in mines is the systemic properties of such vehicles. Many mining vehicles, such as the one we have used in the research for this paper, use steering systems with an articulated joint bending the vehicle’s drive axis to change its course and a hydraulic drive system to actuate axial drive components or the movements of tippers if available. To address the difficulties of controlling such a vehicle, we present a model-predictive approach for controlling the vehicle. While the control optimisation based on a parallel error minimisation of the predicted state has already been established in the past, we provide insight into the design and implementation of an MPC for an articulated mining vehicle and show the results of real-world experiments in an open-pit mine environment.
Extracting workflow nets from textual descriptions can be used to simplify guidelines or formalize textual descriptions of formal processes like business processes and algorithms. The task of manually extracting processes, however, requires domain expertise and effort. While automatic process model extraction is desirable, annotating texts with formalized process models is expensive. Therefore, there are only a few machine-learning-based extraction approaches. Rule-based approaches, in turn, require domain specificity to work well and can rarely distinguish relevant and irrelevant information in textual descriptions. In this paper, we present GUIDO, a hybrid approach to the process model extraction task that first, classifies sentences regarding their relevance to the process model, using a BERT-based sentence classifier, and second, extracts a process model from the sentences classified as relevant, using dependency parsing. The presented approach achieves significantly better resul ts than a pure rule-based approach. GUIDO achieves an average behavioral similarity score of 0.93. Still, in comparison to purely machine-learning-based approaches, the annotation costs stay low.
In recent years, the development of large pretrained language models, such as BERT and GPT, significantly improved information extraction systems on various tasks, including relation classification. State-of-the-art systems are highly accurate on scientific benchmarks. A lack of explainability is currently a complicating factor in many real-world applications. Comprehensible systems are necessary to prevent biased, counterintuitive, or harmful decisions.
We introduce semantic extents, a concept to analyze decision patterns for the relation classification task. Semantic extents are the most influential parts of texts concerning classification decisions. Our definition allows similar procedures to determine semantic extents for humans and models. We provide an annotation tool and a software framework to determine semantic extents for humans and models conveniently and reproducibly. Comparing both reveals that models tend to learn shortcut patterns from data. These patterns are hard to detect with current interpretability methods, such as input reductions. Our approach can help detect and eliminate spurious decision patterns during model development. Semantic extents can increase the reliability and security of natural language processing systems. Semantic extents are an essential step in enabling applications in critical areas like healthcare or finance. Moreover, our work opens new research directions for developing methods to explain deep learning models.