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The best fully automated analysis process achieves even better classification results than the established manual process. The best algorithms for the three analysis steps are (i) SGLTR (Savitzky-Golay Laplace operator filter thresholding regions) and LM (Local Maxima) for automated peak identification, (ii) EM clustering (Expectation Maximization) and DBSCAN (Density-Based Spatial Clustering of Applications with Noise) for the clustering step and (iii) RF (Random Forest) for multivariate classification. Thus, automated methods can replace the manual steps in the analysis process to enable an unbiased high throughput use of the technology.
Propofol is an intravenous anesthetic. Currently, it is not possible to routinely measure blood concentration of the drug in real time. However, multi-capillary column ion-mobility spectrometry of exhaled gas can estimate blood propofol concentration.Unfortunately, adhesion of volatile propofol on plastic materials complicates measurements. Therefore, it is necessary to consider the extent to which volatile propofol adheres to various plastics used in sampling tubing. Perfluoralkoxy (PFA), polytetrafluorethylene (PTFE), polyurethane (PUR), silicone, and Tygon tubing were investigated in an experimental setting using a calibration gas generator (HovaCAL). Propofol gas was measured for one hour at 26 °C, 50 °C, and 90 °C tubing temperature. Test tubing segments were then flushed with N2 to quantify desorption. PUR and Tygon sample tubing absorbed all volatile propofol. The silicone tubing reached the maximum propofol concentration after 119 min which was 29 min after propofol gas exposure stopped. The use of PFAor PTFE tubing produced comparable and reasonably accurate propofol measurements. The desaturation time for the PFA was 10 min shorter at 26 °C than for PTFE. PFA tubing thus seems most suitable for measurement of volatile propofol,with PTFE as an alternative.
Propofol in exhaled breath can be measured and may provide a real-time estimate of plasma concentration. However, propofol is absorbed in plastic tubing, thus estimates may fail to reflect lung/blood concentration if expired gas is not extracted directly from the endotracheal tube.We evaluated exhaled propofol in five ventilated ICU patients who were sedated with propofol. Exhaled propofol was measured once per minute using ion mobility spectrometry. Exhaled air was sampled directly from the endotracheal tube and at the ventilator end of the expiratory side of the anesthetic circuit. The circuit was disconnected from the patient and propofol was washed out with a separate clean ventilator. Propofol molecules, which discharged from the expiratory portion of the breathing circuit, were measured for up to 60 h.We also determined whether propofol passes through the plastic of breathing circuits. A total of 984 data pairs (presented as median values, with 95% confidence interval), consisting of both concentrations were collected. The concentration of propofol sampled near the patient was always substantially higher, at 10.4 [10.25–10.55] versus 5.73 [5.66–5.88] ppb (p<0.001). The reduction in concentration over the breathing circuit tubing was 4.58 [4.48–4.68] ppb, 3.46 [3.21–3.73] in the first hour, 4.05 [3.77–4.34] in the second hour, and 4.01 [3.36–4.40] in the third hour. Out-gassing propofol from the breathing circuit remained at 2.8 ppb after 60 h of washing out. Diffusion through the plastic was not observed. Volatile propofol binds or adsorbs to the plastic of a breathing circuit with saturation kinetics. The bond is reversible so propofol can be washed out from the plastic. Our data confirm earlier findings that accurate measurements of volatile propofol require exhaled air to be sampled as close as possible to the patient.
LDMOS transistors in integrated power technologies are often subject to thermo-mechanical stress, which degrades the on-chip metallization and eventually leads to a short. This paper investigates small sense lines embedded in the LDMOS metallization. It will be shown that their resistance depends strongly on the stress cycle number. Thus, they can be used as aging sensors and predict impending failures. Different test structures have been investigated to identify promising layout configurations. Such sensors are key components for resilient systems that adaptively reduce stress to allow aggressive LDMOS scaling without increasing the risk of failure.
Pokémon Go was the first mobile augmented reality (AR) game to reach the top of the download charts of mobile applications. However, little is known about this new generation of mobile online AR games. Existing theories provide limited applicability for user understanding. Against this background, this research provides a comprehensive framework based on uses and gratification theory, technology risk research, and flow theory. The proposed framework aims to explain the drivers of attitudinal and intentional reactions, such as continuance in gaming or willingness to invest money in in-app purchases. A survey among 642 Pokémon Go players provides insights into the psychological drivers of mobile AR games. The results show that hedonic, emotional, and social benefits and social norms drive consumer reactions while physical risks (but not data privacy risks) hinder consumer reactions. However, the importance of these drivers differs depending on the form of user behavior.
Decreasing batch sizes in production in line with Industrie 4.0 will lead to tremendous changes of the control of logistic processes in future production systems. Intelligent bins are crucial enablers to establish decentrally controlled material flow systems in value chain networks as well as at the intralogistics level. These intelligent bins have to be integrated into an overall decentralized monitoring and control approach and have to interact with humans and other entities just like other cyber-physical systems (CPS) within the cyber-physical production system (CPPS). To realize a decentralized material supply following the overall aim of a decentralized control of all production and logistics processes, an intelligent bin system is currently developed at the ESB Logistics Learning Factory. This intelligent bin system will be integrated into the self developed, cloud-based and event-oriented SES system (so-called “Self Execution System”) which goes beyond the common functionalities and capabilities of traditional manufacturing execution systems (MES).
To ensure a holistic integration of the intelligent bin for different material types into the SES framework, the required hard- and software components for the decentrally controlled bin system will be split into a common and an adaptable component. The common component represents the localization and network layer which is common for every bin, whereas the flexible component will be customizable to different requirements, like to the specific characteristics of the parts.
Purpose: Medical processes can be modeled using different methods and notations.Currently used modeling systems like Business Process Model and Notation (BPMN) are not capable of describing the highly flexible and variable medical processes in sufficient detail.
Methods: We combined two modeling systems, Business Process Management (BPM) and Adaptive Case Management (ACM), to be able to model non-deterministic medical processes. We used the new Standards Case Management Model and Notation (CMMN) and Decision Management Notation (DMN).
Results: First, we explain how CMMN, DMN and BPMN could be used to model non-deterministic medical processes. We applied this methodology to model 79 cataract operations provided by University Hospital Leipzig, Germany, and four cataract operations provided by University Eye Hospital Tuebingen, Germany. Our model consists of 85 tasks and about 20 decisions in BPMN. We were able to expand the system with more complex situations that might appear during an intervention.
Conclusion: An effective modeling of the cataract intervention is possible using the combination of BPM and ACM. The combination gives the possibility to depict complex processes with complex decisions. This combination allows a significant advantage for modeling perioperative processes.
In this paper a method for the generation of gSPM with ontology-based generalization was presented. The resulting gSPM was modeled with BPMN/BPMNsix in an efficient way and could be executed with BPMN workflow engines. In the next step the implementation of resource concepts, anatomical structures, and transition probabilities for workflow execution will be realized.
Electric freight vehicles have the potential to mitigate local urban road freight transport emissions, but their numbers are still insignificant. Logistics companies often consider electric vehicles as too costly compared to vehicles powered by combustion engines. Research within the body of the current literature suggests that increasing the driven mileage can enhance the competitiveness of electric freight vehicles. In this paper we develop a numeric simulation approach to analyze the cost-optimal balance between a high utilization of medium-duty electric vehicles – which often have low operational costs – and the common requirement that their batteries will need expensive replacements. Our work relies on empirical findings of the real-world energy consumption from a large German field test with medium-duty electric vehicles. Our results suggest that increasing the range to the technical maximum by intermediate (quick) charging and multi-shift usage is not the most cost-efficient strategy in every case. A low daily mileage is more cost-efficient at high energy prices or consumptions, relative to diesel prices or consumptions, or if the battery is not safeguarded by a long warranty. In practical applications our model may help companies to choose the most suitable electric vehicle for the application purpose or the optimal trip length from a given set of options. For policymakers, our analysis provides insights on the relevant parameters that may either reduce the cost gap at lower daily mileages, or increase the utilization of medium-duty electric vehicles, in order to abate the negative impact of urban road freight transport on the environment.
It is assumed that more education leads to better understanding of complex systems. Some researchers, however, find indications that simple mechanisms like stocks and flows are not well understood even by people who have passed higher education. In this paper, we test people’s understanding of complex systems with the widely studied stock-and-flow (SF) tasks. SF tasks assess people’s understanding of the interplay between stocks and flows. We investigate SF failure of domain experts and novices in different knowledge domains. In particular, we compare performance on the original study’s bathtub task with the square wave pattern with two alternative cover stories from the engineering and business domains on different groups of business and engineering students from different semesters. Further, we show that, while engineering students perform better than business students, with progressing in higher education, students may lose the capability of dealing with simple SF tasks. We thus find hints on déformation professionelle in higher education.