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A novel approach to optically distinguish plastics based on fluorescence lifetime measurements
(2020)
In medical and biological research, fluorescence lifetime measurements and fluorescence lifetime imaging is already a part of the standardized analysis procedures. As first investigations have shown, polymers can be identified using fluorescence lifetime imaging and an evaluation algorithm. Thus, this contribution pursues a novel approach for the direct differentiation of four polymers with fluorescence lifetime imaging. Therefore, the evaluation algorithm is extended to compare several fluorescence lifetime images to prove that a distinction is possible.
Im Rahmen der Digitalisierung der industriellen Produktion mit Industrie 4.0 und des autonomen Fahrens erlangen möglichst detaillierte digitale Abbilder von Maschinen und Fahrzeugen für den vollständigen Zeitraum ihres Einsatzes eine zentrale Bedeutung. Sowohl durch geplante Veränderungen des Arbeitspunkts aber auch durch Abnutzung und Verschleiß kommt es zu Abweichungen des Systemverhaltens. In diesem Posterbeitrag werden die Entwicklung und die Anwendung eines Identifikations-Algorithmus für die automatisierte Modellierung eines dynamischen Systems basierend auf einem gemessenen Ein- und Ausgangssignal beschrieben. Der in MATLAB und Simulink programmierte Algorithmus läuft parallel zum realen System auf einer embedded Hardware ab, so dass eine Identifikation online und selbstständig ausgeführt wird. Die prozess- oder verschleißbedingten Veränderungen des Systemverhaltens werden damit unmittelbar bei der Modellierung während des Betriebs berücksichtigt. Im Anschluss an die Identifikation sind die Selbst-Optimierung, die Vorsteuerung und die Selbst-Konfiguration des Systems, z.B. die Anpassung von Reglern, basierend auf dem aktuellen Modell durchführbar.
Artificial Intelligence (AI) and Machine Learning (ML) promise to significantly enhance the capabilities of decision support systems in medicine. Yet, if these systems fail to providean understandable rationale of the decision making process the adoption of this powerful technology will be difficult. Hence, there is growing interest in Explainable Artificial Intelligence (XAI). Explanations that speak the language of the user are a cornerstone for Fair AI. In this position paper, we reflect on research about XAI and designing decision support in medicine. From there, we lay out an approach to use co-design methodology to explore how users perceive and interact with explanations of decision support systems.
Oncologic patients are regarded as the population most at risk of developing a severe course of COVID-19 due to the fact that malignant diseases and chemotherapy often weaken the immune system. In the face of the ongoing SARS-CoV-2 pandemic, how particular patients deal with this infection remains an important question. In the period between the 15 and 26 April 2020, a total of 1227 patients were tested in one of seven oncologic outpatient clinics for SARS-CoV-2, regardless of symptoms, employing RT-qPCR. Of 1227 patients, 78 (6.4%) were tested positive of SARS-CoV-2. Only one of the patients who tested positive developed a severe form of COVID-19 with pneumonia (CURB-65 score of 2), and two patients showed mild symptoms. Fourteen of 75 asymptomatic but positively tested patients received chemotherapy or chemo-immunotherapy according to their regular therapy algorithm (±4 weeks of SARS-CoV-2 test), and 48 of 78 (61.5%) positive-tested patients received glucocorticoids as co-medication. None of the asymptomatic infected patients showed unexpected complications due to the SARS-CoV-2 infection during the cancer treatment. These data clearly contrast the view that patients with an oncologic disease are particularly vulnerable to SARS-CoV-2 and suggest that compromising therapies could be continued or started despite the ongoing pandemic. Moreover the relatively low appearance of symptoms due to COVID-19 among patients on chemotherapy and other immunosuppressive co-medication like glucocorticoids indicate that suppressing the response capacity of the immune system reduces disease severity.
In this position paper, we lay out an approach to use participatory and co-design methodology to explore how users perceive and interact with explanations of artificially intelligent decision support systems. We describe how we intend to construct bottom-up participatory design spaces to systematically inform the design of interactive explanations in Human-AI interaction.
Artificial Intelligence (AI) is continuously moving into our surroundings. In its various forms, it has the potential to disrupt most aspects of human life. Yet, the discourse around AI has long been by experts and for experts. In this paper, we argue for a participatory approach towards designing human-AI interactions. We outline how we used design methodology to organise an interdisciplinary workshop with a diverse group of students – a workbook sprint with 45 participants from four different programs and 13 countries – to develop speculative design futures in five focus areas. We then provide insights into our findings and share our lessons learned regarding our workshop topic – AI and Space – our process, and our research. We learned that involving non-experts in complex technical discourses – such as AI – through the structural rigour of design methodology is a viable approach. We then conclude by laying out how others might use our findings and initiate their own workbook sprint to explore complex technologies in a human-centred way.
Design of a Low Multi-Loop Inductance Three Level Neutral Point Clamped Converter with GaN HEMTs
(2020)
This work shows a numerical and experimental analysis of a Neutral-Point-Clamp (NPC) three level inverter featuring an ultra low inductance printed circuit board (PCB) design in consideration of the mutual inductive and capacitive couplings. The commutation loops in this design are found to be strongly dependent on the vertical thickness of the used prepregs and the core. For vertical thicknesses ≤ 100 μm capacitive coupling must be taken into account in the switching cell design. Experimental measurements of a test set-up with a total PCB thickness of 400 μm results in commutation loop inductances from 1.4 nH up to 3.1 nH. In this set-up, switching tests without external gate resistor showed only a maximum voltage overshoot of 7% at 800 V. Based on a numerical analysis of the NPC cell we propose a further switching performance improvement with significant smaller parasitic inductance due to the application of novel printed circuit technologies such as the integration of bare dies into the printed circuit board or polyimide as an interlayer dielectric material.
Objective: Functioning is an important outcome for rheumatoid arthritis (RA) management. Heterogeneity of respective patient-reported outcome measures (PROMs) challenges direct comparisons between their results. This study aimed to standardize reporting of such PROMs measuring functioning in RA to facilitate comparability.
Methods: Common Item Non-Equivalent Groups Design (NEAT) with the Health Assessment Questionnaire (HAQ) as a common scale across data sets from various countries (incl. UK, Turkey and Germany) to establish a common metric. Other PROMs included are the Physical Function items of the Multidimensional Health Assessment Questionnaire (MDHAQ), Disabilities of Arm, Shoulder and Hand (DASH), Western Ontario and McMaster Universities Osteoarthritis Index (WOMAC), World Health Organization Disability Assessment Schedule Version 2.0 (WHODAS 2.0), and four short forms (20, 10, 6, and 4 physical function items) from the Patient-Reported Outcomes Measurement Information System (PROMIS). As the HAQ includes mobility, self-care and domestic life items, this study focuses on these three domains. PROMs were described using Standard Error of Measurement (SEM) and Smallest Detectable Difference (SDD). Rasch Measurement model was used to create the common metric.
Results: Range of SEM is 0.2 (MDHAQ) to 7.4 (SF36-PF). SDD revealed a range from 9.7 % (WOMAC-RAT) to 33.5 % (WHODAS-PF). PROMs co-calibration revealed fit to the Rasch measurement model. A transformation table was developed to allow exchange between PROMs scores.
Discussion: Scores between the Daily Activity PROMs commonly used in RA can now be compared. Factors such as SEM and SDD help determine choice of PROM in clinical practice and research.
Providing a subset of previously studied information as a retrieval cue can impair memory for the remaining information. Previous work with adults has shown that such part-list cuing impairment (PLCI) can be transient or lasting, depending on study condition. Here, we investigated the persistence of PLCI in children. Three age groups (7- and 8-year-olds, 9- and 10-year-olds, and 12- to 14-year-olds) learned a list of items, either through a single study trial (1-study condition) or through two study-test cycles (2-study-test condition). Subsequently, two recall tests were administered, with part-list cues being provided in the first (critical) test but not in the second (final) test. Of primary interest was whether the detrimental effect of part-list cuing induced in the critical test would persist to the uncued final test. In 12- to 14-year-olds, we found an adult-like pattern of results, with lasting impairment in the 1-study condition but transient impairment in the 2-study-test condition. In contrast, in the two younger age groups, we found PLCI to be lasting in both study conditions, suggesting age differences in the persistence of PLCI. The results are discussed in light of a recently proposed two-mechanism account of PLCI that attributes lasting impairment to retrieval inhibition and transient impairment to strategy disruption. Following this account, the results suggest that whereas 12- to 14-year-olds’ PLCI was caused by (lasting) retrieval inhibition in the 1-study condition and by (transient) strategy disruption in the 2-study-test condition, 7- and 8-year-olds’ and 9- and 10-year-olds’ PLCI was caused by (lasting) retrieval inhibition in both study conditions.
Background
Vertigo, dizziness and balance disorders (VDB) are among the most relevant contributors to the burden of disability among older adults living in the community and associated with immobility, limitations of activities of daily living and decreased participation. The aim of this study was to identify the quality of evidence of physical therapy interventions that address mobility and participation in older patients with VDB and to characterize the used primary and secondary outcomes.
Methods
A systematic search via MEDLINE (PubMed), Cochrane Library, CINAHL, PEDro, forward citation tracing and hand search was conducted initially in 11/2017 and updated in 7/2019. We included individual and cluster-randomized controlled trials and trials with quasi-experimental design, published between 2007 and 2017/2019 and including individuals ≥65 years with VDB. Physical therapy and related interventions were reviewed with no restrictions to outcome measurement. Screening of titles, abstracts and full texts, data extraction and critical appraisal was conducted by two independent researchers. The included studies were heterogeneous in terms of interventions and outcome measures. Therefore, a narrative synthesis was conducted.
Results
A total of 20 randomized and 2 non-randomized controlled trials with 1876 patients met the inclusion criteria. The included studies were heterogeneous in terms of complexity of interventions, outcome measures and methodological quality. Vestibular rehabilitation (VR) was examined in twelve studies, computer-assisted VR (CAVR) in five, Tai Chi as VR (TCVR) in three, canal repositioning manoeuvres (CRM) in one and manual therapy (MT) in one study. Mixed effects were found regarding body structure/function and activities/participation. Quality of life and/or falls were assessed, with no differences between groups. VR is with moderate quality of evidence superior to usual care to improve balance, mobility and symptoms.
Conclusion
To treat older individuals with VDB, VR in any variation and in addition to CRMs seems to be effective. High-quality randomized trials need to be conducted to inform clinical decision making.
Trial registration
PROSPERO 2017 CRD42017080291.
Knowing about the presence and number of people in a room can be of interest for precise control of heating, ventilation and air conditioning. To determine the number and presence of occupants cost-effectively, it is of interest to use already existing air condition sensors (temperature, humidity, CO2) of the building automation system. Different approaches and methods for determining presence have attracted attention in recent years. We propose an occupancy detection method based on a method of supervised machine learning. In an experiment, measurement data were recorded in a research apartment with controllable boundary conditions. The presence of people was simulated by artificial injection of water vapour, CO2 and heat dissipation. The variation of the number of artificial users, the duration of presence and the supply air volume flow of the ventilation resulted in a total of 720 combinations. By using artificial users, the boundary conditions were accurately defined, and different presence situations could be measured time-effectively. The data is evaluated with a method of supervised machine learning called random forest. The statistical model can determine precisely the number of people in over 93% of the cases in a disjoint test sample. The experiments took part in the Rosenheim Technical University of Applied Sciences laboratory.
The energy efficiency of the building HVAC systems can be improved when faults in the running system are known. To this day, there are no cost-efficient, automatic methods that detect faults of the building HVAC systems to a satisfactory degree. This study induces a new method for fault detection that can replace a graphical, user-subjective evaluation of a building data measured on site with an automatic, data-based approach. This method can be a step towards cost-effective monitoring. For this research, the data from a detailed simulation of a residential case study house was used to compare a faultless operation of a building with a faulty operation. We argue that one can detect faults by analysing the properties of residuals of the prediction to the actual data. A machine learning model and an ARX model predict the building operation, and the method employs various statistical tests such as the Sign Test, the Turning Point Test, the Box-Pierce Test and the Bartels-Rank Test. The results show that the amount of data, the type and density of system faults significantly affect the accuracy of the prediction of faults. It became apparent that the challenge is to find a decision rule for the best combination of statistical tests on residuals to predict a fault.