Healthcare users and providers increasingly utilize social media to interact with one another. For a future understanding of when and how these interactions supplement or replace offline doctor-patient interactions, it is essential to understand who interacts, about what, and how these interactions can be categorized in a taxonomy. We draw on affordance theory and employ a mixed-methods approach to study social media interactions among healthcare users and providers. We first engage in qualitative content analysis, which is followed by cluster analysis. We identify five archetypal interactions and categorize these in a taxonomy that adds to current literature on how social media is utilized in the healthcare context. We also provide a clear and systematic overview of the interactions in different social media categories that can stimulate future research regarding doctor-patient interactions. Furthermore, we identify a new and distinct type of social media enabled interaction in healthcare, namely lifestyle support, focusing on prevention.

Abstract In recent years, significant achievements have been made with respect to the development of SF6-free gas insulated substations (GIS). In parallel, the interest in installing SF6-free GIS by utilities increased steadily and tenders for new substations or upgrades, which regularly also include alternative technologies. The excellent performance of SF6 was unequivocally accepted by all vendors and users so that the community became used to single technology solutions. This is no longer the case with alternative gas mixtures, and multiple technological solutions are available. However, from the present body of literature it is not possible to make a full and comparative evaluation of different alternative gas switchgear, i.e. circuit-breakers and disconnectors. Thus, the High-Voltage Laboratory of ETH Zürich started investigations and measurements of basic experiments that allow an unbiased comparison of properties of alternative gas mixtures relevant for switching. The two main purposes of these investigations are to define a set of measurements that allow an estimation and comparison of switching performance with different gas mixtures, independent of a specific interruption nozzle geometry, drive system, electrostatic design, and other design specific features, and to perform (some of) these measurements comparing pure SF6, with air, pure CO2, CO2/O2 mixture, and further specific gas mixtures that are currently proposed by manufacturers for SF6 replacement. The basic analysis behind the definition of measurements will be given in detail and the design principles of the chosen test devices and the derived test currents and diagnostics will be introduced. Test results themselves will not be given, rather they will be the subject of separate future publications.

Abstract The importance of the high voltage circuit breaker for the power system’s safe and reliable operation is paramount. This research aims to analyse and provide the most significant high voltage circuit breaker health state indices based on the non-invasive vibration fingerprint measurement method. Results obtained and presented in this paper are validated on the data set acquired from the vacuum circuit breaker.

Abstract Pneumonia is a leading cause of mortality in limited resource settings (LRS), which are common in low- and middle-income countries (LMICs). Accurate referrals can reduce the devastating impact of pneumonia, especially in LRS. Discriminating pneumonia from other respiratory conditions based only on symptoms is a major challenge. Machine learning has shown promise in overcoming the diagnostic difficulties of pneumonia (i.e., low specificity of symptoms, lack of accessible diagnostic tests and varied clinical presentation). Many scientific papers are now focusing on deep-learning methods applied to clinical images, which is unaffordable for initial patient referral in LMICs. The current study used a dataset of 4500 patients (1500 patients affected by bronchitis, 3000 by pneumonia) from a middle-income country, containing information on subject population characteristics, symptoms and laboratory test results. Manual feature selection was performed, focusing on clinical symptoms that are easily measurable in LRS and in community settings. Three common machine learning methods were tested and compared: logistic regression; decision tree and support vector machine. Models were developed through a holdout process of training-validation and testing. We focused on six clinically relevant, easily interpreted patient symptoms as best indicators for pneumonia. Our final model was a decision tree, achieving an AUC of 93%, with the advantage of being fully intelligible and easily interpreted. The performance achieved suggested that intelligible machine learning models can enhance symptom-based referral of pneumonia in LRS and in community settings.

Alcoholic liver cirrhosis (ALC) is the most common indication for liver transplantation (LT) in Croatia and presents a risk factor for the development of hepatocellular carcinoma (HCC). However, genetic susceptibility has not yet been systematically studied. We aimed to investigate the contribution of the risk polymorphisms PNPLA3 rs738409, EGF rs4444903, TM6SF2 rs58542926, MTHFR rs1801133, previously identified in other populations and, additionally, the contribution of Notch-related polymorphisms (NOTCH1 rs3124591, NOTCH3 rs1043996 and rs1044116, NOTCH4 rs422951). The study included 401 patients. The ALC group consisted of 260 LT candidates, 128 of whom had histopathologically confirmed HCC, and 132 of whom were without HCC. The control group included 141 patients without liver disease. Genotyping was performed by PCR using Taqman assays. The patients’ susceptibility to ALC was significantly associated with PNPLA3 rs738409, TM6SF2 rs58542926, and NOTCH3 rs1043996 polymorphisms. These polymorphisms remained significantly associated with ALC occurrence in a logistic regression model, even after additional model adjustment for sex and age. Cirrhotic patients with the PNPLA3 GG genotype demonstrated higher activity of ALT aminotransferases than patients with CC or CG genotypes. The susceptibility to the development of HCC in ALC was significantly associated with PNPLA3 rs738409 and EGF rs4444903 polymorphisms, and logistic regression confirmed these polymorphisms as independent predictors.

Aims: We studied the syntaxonomic position, biodiversity, ecological features, nature conservation value and current status of dry grasslands investigated by Josias Braun-Blanquet more than 60 years ago. Study area: Inner-alpine valleys of Austria. Methods: We sampled 67 plots of 10 m2, following the standardized EDGG methodology. We subjected our plots to an unsupervised classification with the modified TWINSPAN algorithm and interpreted the branches of the dendrogram syntaxonomically. Biodiversity, structural and ecological characteristics of the resulting vegetation units at association and order level were compared by ANOVAs. Results: All the examined grasslands belong to the class Festuco-Brometea. From ten distinguished clusters, we could assign four clusters to validly published associations, while the remaining six clusters were named tentatively. We classified them into three orders: Stipo-Festucetalia pallentis (Armerio elongatae-Potentilletum arenariae, Phleo phleoidis-Pulsatilletum nigricantis, Medicago minima-Melica ciliata community, Koelerio pyramidatae-Teucrietum montani), Festucetalia valesiacae (Sempervivum tectorum-Festuca valesiaca community); Brachypodietalia pinnati (Astragalo onobrychidis-Brometum erecti, Agrostis capillaris-Avenula adsurgens community, Anthericum ramosum-Brachypodium pinnatum community, Ranunculus bulbosus-Festuca rubra community, Carduus defloratus-Brachypodium pinnatum community). Conclusions: The ten distinguished dry grassland communities of the Austrian inner-alpine valleys differ in their ecological affinities as well as their vascular plant, bryophyte and lichen diversity. We point out their high nature conservation importance, as each of them presents a unique habitat of high value. Taxonomic reference: Names of vascular plants, bryophytes and lichens follow Fischer et al. (2008), Frahm and Frey (2004) and Nimis et al. (2018), respectively. Syntaxonomic reference: Names of orders and classes follow Mucina et al. (2016), references for associations and alliances are given in the text. Abbreviations: ANOVA = analysis of variance; DCA: detrended correspondence analyses; EDGG: Eurasian Dry Grassland Group; EIV: ecological indicator value; FL: Fließ; GR: Griffen; GU: Gulsen; KA: Kaunerberg; LA: Laudegg castle in Ladis; MA: Marin; NM: Neumarkt in der Steiermark; OM: Obermauern; PÖ: Pöls; PU: Puxer Loch; TWINSPAN = Two-way indicator species analysis; ZS: Zinizachspitze.

Architectural design decisions, such as service deployment and composition, plant layout synthesis, or production planning, are an indispensable and overarching part of an industrial manufacturing system design. In the fourth industrial revolution (Industry 4.0), frequent production changes trigger their synthesis, and preferably optimization. Yet, knowledge on architecture synthesis and optimization has been scattered around other domains, such as generic software engineering. We take a step towards synthesizing current knowledge on architectural design decisions in Industry 4.0. We developed a taxonomy describing architectural models, design decisions, and optimization possibilities. The developed taxonomy serves as a guideline for comparing different possibilities (e.g., application of different optimization algorithms) and selecting appropriate ones for a given context. Furthermore, we reviewed and mapped 30 relevant research works to the taxonomy, identifying research trends and gaps. We discuss interesting, and yet uncovered topics that emerged from our review.