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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...
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.