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In Bewegung kommen
(2023)
Armut im Blick?
(2023)
Power plant operators increasingly rely on predictive models to diagnose and monitor their systems. Data-driven prediction models are generally simple and can have high precision, making them superior to physics-based or knowledge-based models, especially for complex systems like thermal power plants. However, the accuracy of data-driven predictions depends on (1) the quality of the dataset, (2) a suitable selection of sensor signals, and (3) an appropriate selection of the training period. In some instances, redundancies and irrelevant sensors may even reduce the prediction quality.
We investigate ideal configurations for predicting the live steam production of a solid fuel-burning thermal power plant in the pulp and paper industry for different modes of operation. To this end, we benchmark four machine learning algorithms on two feature sets and two training sets to predict steam production. Our results indicate that with the best possible configuration, a coefficient of determination of R^2 = 0.95 and a mean absolute error of MAE=1.2 t/h with an average steam production of 35.1 t/h is reached. On average, using a dynamic dataset for training lowers MAE by 32% compared to a static dataset for training. A feature set based on expert knowledge lowers MAE by an additional 32 %, compared to a simple feature set representing the fuel inputs. We can conclude that based on the static training set and the basic feature set, machine learning algorithms can identify long-term changes. When using a dynamic dataset the performance parameters of thermal power plants are predicted with high accuracy and allow for detecting short-term problems.
Highly-sensitive single-step sensing of levodopa by swellable microneedle-mounted nanogap sensors
(2023)
Microneedle (MN) sensing of biomarkers in interstitial fluid (ISF) can overcome the challenges of self-diagnosis of diseases by a patient, such as blood sampling, handling, and measurement analysis. However, the MN sensing technologies still suffer from poor measurement accuracy due to the small amount of target molecules present in ISF, and require multiple steps of ISF extraction, ISF isolation from MN, and measurement with additional equipment. Here, we present a swellable MN-mounted nanogap sensor that can be inserted into the skin tissue, absorb ISF rapidly, and measure biomarkers in situ by amplifying the measurement signals by redox cycling in nanogap electrodes. We demonstrate that the MN-nanogap sensor measures levodopa (LDA), medication for Parkinson disease, down to 100 nM in an aqueous solution, and 1 μM in both the skin-mimicked gelatin phantom and porcine skin.
Organic acidurias (OAs), urea-cycle disorders (UCDs), and maple syrup urine disease (MSUD) belong to the category of intoxication-type inborn errors of metabolism (IT-IEM). Liver transplantation (LTx) is increasingly utilized in IT-IEM. However, its impact has been mainly focused on clinical outcome measures and rarely on health-related quality of life (HRQoL). Aim of the study was to investigate the impact of LTx on HrQoL in IT-IEMs. This single center prospective study involved 32 patients (15 OA, 11 UCD, 6 MSUD; median age at LTx 3.0 years, range 0.8–26.0). HRQoL was assessed pre/post transplantation by PedsQL-General Module 4.0 and by MetabQoL 1.0, a specifically designed tool for IT-IEM. PedsQL highlighted significant post-LTx improvements in total and physical functioning in both patients' and parents' scores. According to age at transplantation (≤3 vs. >3 years), younger patients showed higher post-LTx scores on Physical (p = 0.03), Social (p < 0.001), and Total (p =0.007) functioning. MetabQoL confirmed significant post-LTx changes in Total and Physical functioning in both patients and parents scores (p ≤ 0.009). Differently from PedsQL, MetabQoL Mental (patients p = 0.013, parents p = 0.03) and Social scores (patients p = 0.02, parents p = 0.012) were significantly higher post-LTx. Significant improvements (p = 0.001–0.04) were also detected both in self- and proxy-reports for almost all MetabQoL subscales. This study shows the importance of assessing the impact of transplantation on HrQoL, a meaningful outcome reflecting patients' wellbeing. LTx is associated with significant improvements of HrQol in both self- and parentreports. The comparison between PedsQL-GM and MetabQoL highlighted that MetabQoL demonstrated higher sensitivity in the assessment of diseasespecific domains than the generic PedsQL tool.
Measuring what matters
(2023)
Patient reported outcomes (PROs) are generally defined as ‘any report of the status of a patient's health condition that comes directly from the patient, without interpretation of the patient's response by a clinician or anyone else’. A broader definition of PRO also includes ‘any information on the outcomes of health care obtained directly from patients without modification by clinicians or other health care professionals’. Following this approach, PROs encompass subjective perceptions of patients on how they function or feel not only in relation to a health condition but also to its treatment as well as concepts such as health-related quality of life (HrQoL), information on the functional status of a patient, signs and symptoms and symptom burden. PRO measurement instruments (PROMs) are mostly questionnaires and inform about what patients can do and how they feel. PROs and PROMs have not yet found unconditional acceptance and wide use in the field of inborn errors of metabolism. This review summarises the importance and usefulness of PROs in research, drug legislation and clinical care and informs about quality standards, development, and potential methodological shortfalls of PROMs. Inclusion of PROs measured with high-quality, well-selected PROMs into clinical care, drug legislation, and research helps to identify unmet needs, improve quality of care, and define outcomes that are meaningful to patients. The field of IEM should open to new methodological approaches such as the definition of core sets of variables including PROs to be systematically assessed in specific metabolic conditions and new collaborations with PRO experts, such as psychologists to facilitate the systematic collection of meaningful data.
Zu Beginn des Forschungsprojektes „Neue Museumswelten“ wollten wir von Studierenden der Studiengänge Bachelor und Master InterMedia an der Fachhochschule Vorarlberg wissen, was sie mit dem Begriff „Museum“ verbinden. „Museum? Da gehe ich nur im Urlaub hin. Aber hier in Vorarlberg nicht“, war eine geläufige Aussage, die auf ein bestimmtes mentales Modell von Museum verweist. Viele Studierenden gehören also der Gruppe der Gelegenheits- oder Nichtbesucher:innen an. Wie sich in weiterer Folge herausstellte, handelt es sich dabei um eine durchaus heterogene Gruppe, die jedoch auch Gemeinsamkeiten aufweist: Alle Menschen, mit denen wir bisher im Zuge des Forschungsprojekts im Stadtraum, auf Events und im Museum sprachen, spielten und Zukunftsvisionen entwickelten, brachten ihre eigenen Bilder, Vorstellungen und Ideen zu Museen und möglichen Museumszukünften mit. Der Untersuchung genau dieser Bilder und Vorstellungen – der mentalen Modelle von Museum also –, widmet sich das von Interreg geförderte Forschungsprojekt unter Einbindung vielfältiger Perspektiven und Methoden aus Sozialforschung und Design. Es verfolgt den innovativen Weg, „Designerly Ways of Knowing“0 von Beginn an konsequent in den Forschungsprozess zu integrieren, um anstrebenswerte Museumszukünfte gemeinsam mit den Menschen auszuloten...
A step change is needed in the deployment of renewable energy if the triple challenge of ensuring climate change mitigation, energy security, and energy affordability is to be met. Yet, social acceptance of infrastructure projects and policies remains a key concern. While there has been decades of fruitful research on the social acceptance of wind energy and other renewables, much of the extant research is cross-sectional in nature, failing to capture the important dynamic processes that can make or break renewable energy projects. This paper introduces a Special Issue of Energy Policy which focuses on the neglected topic of the dynamics of social acceptance of renewable energy, drawing on contributions made at an international research conference held in St. Gallen (Switzerland) in June 2022. In addition to introducing these papers and drawing out common themes, we also seek to offer some conceptual clarity on the issue of dynamics in social acceptance, taking into account the influence of time, power, and scale in shaping decision-making processes. We conclude by highlighting a number of avenues of potential future research.
X-ray microtomography is a nondestructive, three-dimensional inspection technique applied across a vast range of fields and disciplines, ranging from research to industrial, encompassing engineering, biology, and medical research. Phasecontrast imaging extends the domain of application of x-ray microtomography to classes of samples that exhibit weak attenuation, thus appearing with poor contrast in standard x-ray imaging. Notable examples are low-atomic-number materials, like carbon-fiber composites, soft matter, and biological soft tissues.We report on a compact and cost-effective system for x-ray phase-contrast microtomography. The system features high sensitivity to phase gradients and high resolution, requires a low-power sealed x-ray tube, a single optical element, and fits in a small footprint. It is compatible with standard x-ray detector technologies: in our experiments, we have observed that single-photon counting offered higher angular sensitivity, whereas flat panels provided a larger field of view. The system is benchmarked against knownmaterial phantoms, and its potential for soft-tissue three-dimensional imaging is demonstrated on small-animal organs: a piglet esophagus and a rat heart.We believe that the simplicity of the setupwe are proposing, combined with its robustness and sensitivity, will facilitate accessing quantitative x-ray phase-contrast microtomography as a research tool across disciplines, including tissue engineering, materials science, and nondestructive testing in general.
Parametric anti-resonance is a phenomenon that occurs in systems with at least two degrees of freedom; this can be achieved by periodically exciting some parameters of the system. The effect of this properly tuned periodicity is to increase the dissipation in the system, which leads to a raising in the effective damping of vibrations. This contribution presents the design of an open-loop control to reduce the settling time using the anti-resonance concept. The control signal consists of a quasi-periodic signal capable of transferring the system’s oscillations from one mode to another mode of the system. The general averaging technique is used to characterize the dynamics, particularly the so-called slow dynamics of motion. With this analysis, the control signal is designed for the potential application of a microelectromechanical sensor arrangement; for this specific example, up to 96.8% reduction of settling time is achieved.
In this work, parametric excitation is introduced in a fully balanced flexible rotor mounted on two identical active gas foil bearings. The active gas foil bearings change the top foil shape harmonically with a specific amplitude and frequency. The deformable foil shape is approximated by an analytical function, while the gas pressure distribution is evaluated by the numerical solution of the Reynolds equation for compressible flow. The harmonic variation of the foil shape generates a respective variation in the bearings’ stiffness and damping properties and the system experiences parametric resonances and antiresonances in specific excitation frequencies. The nonlinear gas bearing forces generate bifurcations in the solutions of the system at certain rotating speeds and excitation frequencies; period doubling and Neimark-Sacker bifurcations are noticed in the examined system, and their progress is evaluated as the two bifurcation parameters (rotating speed and parametric excitation frequency) are changed, though a codimension-2 numerical continuation of limit cycles. It is found that at specific range of excitation frequency there are parametric anti-resonances and the bifurcations collide and vanish. Therefore, a bifurcation-free operating range is established and the system can operate stable at a wide speed range.
Il presente capitolo è incentrato su uno dei nuovi paradigmi di produzione e consumo, l’economia circolare – i.e., “un’economia in cui gli scarti e l’inquinamento sono minimizzati grazie al design consapevole di prodotti, processi e servizi, il valore delle risorse è mantenuto il più a lungo possibile e i sistemi naturali vengono rigenerati” (Gusmerotti et al., 2020, p. 9). L’elaborato si focalizza in particolare su uno dei modelli di business considerati “circolari” (CBM) (Linder e Williander, 2017), il product-as-a-service (PaaS da qui in poi) (Lacy et al., 2016). Alla base di questo modello di business vi è l’idea che i clienti non acquisiscono la proprietà dei beni, bensì li utilizzano al pari dei servizi, a fronte di un pagamento, come riporta la letteratura concernente i product-service systems (PSS) (Lacy et al., 2016; Mont, 2002; Tukker e Tischner, 2006; Tukker, 2015). Questo approccio al consumo rientra nel più complesso fenomeno della servitizzazione, la quale comporta “l’innovazione nelle capacità e nei processi di un’impresa, in modo che essa possa meglio creare valore – per il cliente e l’impresa stessa – passando dalla vendita di prodotti alla vendita di sistemi di prodotto-servizio” (Neely, 2009, p. 10).
Digitalization is changing business models and operational processes. At the same time, improved data availability and powerful analytical methods are influencing controlling and increasingly require the use of statistical and information technology skills and knowledge. Using a case study from marketing controlling, the article shows the use of business analytics methods and addresses the tasks of controlling in the digital age.
Statistische Methodenkompetenz wird im Controlling zunehmend bedeutender, so ist die evidenzbasierte Prognose durch algorithmische Analyse von Datenbeständen ein Schwerpunkt von Controlling Analytics. Die Fallstudie durchleuchtet daher anhand von Datenmaterial des Schweizer Bundesamts für Statistik das Vorgehen bei einer Datenanalyse, insbesondere beim Einsatz der Zeitregression für Prognosezwecke, und geht dabei auf methodische Besonderheiten, Caveats und Einsatzmöglichkeiten in Microsoft Excel ein. Die Fallbeschreibung und Aufgaben sind im WiSt-Heft Nr. 4/23 zu finden.
Controlling Analytics: Einsatz für Prognosen im Controlling - Teil 1: Fallbeschreibung und Aufgaben
(2023)
Statistische Methodenkompetenz wird im Controlling zunehmend bedeutender, so ist die evidenzbasierte Prognose durch algorithmische Analyse von Datenbeständen ein Schwerpunkt von Controlling Analytics. Die Fallstudie durchleuchtet daher anhand von Datenmaterial des Schweizer Bundesamts für Statistik das Vorgehen bei einer Datenanalyse, insbesondere beim Einsatz der Zeitregression für Prognosezwecke, und geht dabei auf methodische Besonderheiten, Caveats und Einsatzmöglichkeiten in Microsoft Excel ein.