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With a radar working in the 24 GHz ISM-band in a frequency modulated continuous wave mode the major vital signs heartbeat and respiration rate are monitored. The observation is hereby contactless with the patient sitting straight up in a distance of 1–2 m to the radar. Radar and sampling platform are components developed internally in the university institution. The communication with the radar is handled with MATLAB via TCP/IP. The signal processing and real-time visualization is developed in MATLAB, too. Cornerstone of this publication are the wavelet packet transformation and a spectral frequency estimation for vital sign calculation. The wavelet transformation allows a fine tuning of frequency subspaces, separating the heartbeat signal from the respiration and more important from noise and other movement. Heartbeat and respiration are monitored independently and compared to parallel recorded ECG-data.
The aim of this work was to develop and evaluate the reinforcement learning algorithm VentAI, which is able to suggest a dynamically optimized mechanical ventilation regime for critically-ill patients. We built, validated and tested its performance on 11,943 events of volume-controlled mechanical ventilation derived from 61,532 distinct ICU admissions and tested it on an independent, secondary dataset (200,859 ICU stays; 25,086 mechanical ventilation events). A patient “data fingerprint” of 44 features was extracted as multidimensional time series in 4-hour time steps. We used a Markov decision process, including a reward system and a Q-learning approach, to find the optimized settings for positive end-expiratory pressure (PEEP), fraction of inspired oxygen (FiO2) and ideal body weight-adjusted tidal volume (Vt). The observed outcome was in-hospital or 90-day mortality. VentAI reached a significantly increased estimated performance return of 83.3 (primary dataset) and 84.1 (secondary dataset) compared to physicians’ standard clinical care (51.1). The number of recommended action changes per mechanically ventilated patient constantly exceeded those of the clinicians. VentAI chose 202.9% more frequently ventilation regimes with lower Vt (5–7.5 mL/kg), but 50.8% less for regimes with higher Vt (7.5–10 mL/kg). VentAI recommended 29.3% more frequently PEEP levels of 5–7 cm H2O and 53.6% more frequently PEEP levels of 7–9 cmH2O. VentAI avoided high (>55%) FiO2 values (59.8% decrease), while preferring the range of 50–55% (140.3% increase). In conclusion, VentAI provides reproducible high performance by dynamically choosing an optimized, individualized ventilation strategy and thus might be of benefit for critically ill patients.
Laboratory protocols using magnetic beads have gained importance in the purification of mRNA for vaccines. Here, the produced mRNA hybridizes specifically to oligo(dT)-functionalized magnetic beads after cell lysis. The mRNA-loaded magnetic beads can be selectively separated using a magnet. Subsequently, impurities are removed by washing steps and the mRNA is eluted. Magnetic separation is utilized in each step, using different buffers such as the lysis/binding buffer. To reduce the time required for purification of larger amounts of mRNA vaccine for clinical trials, high-gradient magnetic separation (HGMS) is suitable. Thereby, magnetic beads are selectively retained in a flow-through separation chamber. To meet the requirements of biopharmaceutical production, a disposable HGMS separation chamber with a certified material (United States Pharmacopeia Class VI) was developed which can be manufactured using 3D printing. Due to the special design, the filter matrix itself is not in contact with the product. The separation chamber was tested with suspensions of oligo(dT)-functionalized Dynabeads MyOne loaded with synthetic mRNA. At a concentration of cB = 1.6–2.1 g·L–1 in lysis/binding buffer, these 1 μm magnetic particles are retained to more than 99.39% at volumetric flows of up to 150 mL·min–1 with the developed SU-HGMS separation chamber. When using the separation chamber with volumetric flow rates below 50 mL·min–1, the retained particle mass is even more than 99.99%.
E-commerce live streaming - An emerging industry in China and a potential future trend in the world
(2021)
With the widespread use of the Internet, many industries have developed rapidly. The economy based on the Internet poses a significant threat to the traditional economy. Live streaming plus e-commerce, which is acknowledged as the current global economic status, is the result of combing live streaming and various industries through the Internet. E-commerce live streaming is one of the most essential types of online live streaming. In this article, it is defined as the live streaming of the e-commerce platform used by Key Opinion Leaders or product sellers through the built-in live streaming function of the platform to propagate goods, brands, events, etc. to achieve goals of brand exposure and product sales. Compared with the traditional economic model, the combined model of e-commerce and live streaming has its advantages and characteristics. This kind of marketing tool is now prevalent. However, there are many deficiencies in e-commerce live streaming that need to be improved since the development of e-commerce is immature and supervision of Internet use is ongoing.
Vibroarthrography measures joint sounds caused by sliding of the joint surfaces over each other. and can be affected by joint health, load and type of movement. Since both warm-up and muscle fatigue lead to local changes in the knee joint (e.g., temperature increase, lubrication of the joint, muscle activation), these may impact knee joint sounds. Therefore, this study investigates the effects of warm-up and muscle fatiguing exercise on knee joint sounds during an activity of daily living. Seventeen healthy, physically active volunteers (25.7 ± 2 years, 7 males) performed a control and an intervention session with a wash-out phase of one week. The control session consisted of sitting on a chair, while the intervention session contained a warm-up (walking on a treadmill) followed by a fatiguing exercise (modified sit-to-stand) protocol. Knee sounds were recorded by vibroarthrography (at the medial tibia plateau and at the patella) at three time points in each session during a sit-to-stand movement. The primary outcome was the mean signal amplitude (MSA, dB). Differences between sessions were determined by repeated measures ANOVA with intra-individual pre-post differences for the warm-up and for the muscle fatigue effect. We found a significant difference for MSA at the medial tibia plateau (intervention: mean 1.51 dB, standard deviation 2.51 dB; control: mean -1.28 dB, SD 2.61 dB; F = 9.5; p = .007; η2 = .37) during extension (from sit to stand) after the warm-up. There was no significant difference for any parameter after the muscle fatiguing exercise (p > .05). The increase in MSA may mostly be explained by an increase in internal knee load and joint friction. However, neuromuscular changes may also have played a role. It appears that the muscle fatiguing exercise has no impact on knee joint sounds in young, active, symptom-free participants during sit to stand.
Modeling and executing knowledge-intensive processes (KiPs) are challenging with state-of-the-art approaches, and the specific demands of KiPs are the subject of ongoing research. In this context, little attention has been paid to the ontology-driven combination of data-centric and semantic business process modeling, which finds additional motivation by enabling the division of labor between humans and artificial intelligence. Such approaches have characteristics that could allow support for KiPs based on the inferencing capabilities of reasoners. We confirm this as we show that reasoners can infer the executability of tasks based on a currently researched ontology- and data-driven business process model (ODD-BP model). Further support for KiPs by the proposed inference mechanism results from its ability to infer the relevance of tasks, depending on the extent to which their execution would contribute to process progress. Besides these contributions along with the execution perspective (start-to-end direction), we will also show how our approach can help to reach specific process goals by inferring the relevance of process elements regarding their support to achieve such goals (end-to-start direction). The elements with the most valuable process progress can be identified in the intersection of both, the execution and goal perspective. This paper will introduce this new approach and verifies its practicability with an evaluation of a KiP in the field of emergency call centers.
Intraspecific diet specialization, usually driven by resource availability, competition and predation, is common in natural populations. However, the role of parasites on diet specialization of their hosts has rarely been studied. Eye flukes can impair vision ability of their hosts and have been associated with alterations of fish feeding behavior. Here it was assessed whether European perch (Perca fluviatilis) alter their diet composition as a consequence of infection with eye flukes. Young-of-the-year (YOY) perch from temperate Lake Müggelsee (Berlin, Germany) were sampled in two years, eye flukes counted and fish diet was evaluated using both stomach content and stable isotope analyses. Perch diet was dominated by zooplankton and benthic macroinvertebrates. Both methods indicated that with increasing eye fluke infection intensity fish had a more selective diet, feeding mainly on the benthic macroinvertebrate Dikerogammarus villosus, while less intensively infected fish appeared to be generalist feeders showing no preference for any particular prey type. Our results show that infection with eye flukes can indirectly affect interaction of the host with lower trophic levels by altering the diet composition and highlight the underestimated role of parasites in food web studies.
Agility and digital trends go hand in hand, but the advantages of digitalization perform a high pressure on the established automotive companies. For years now, automotive groups have no longer been innovation drivers in the industry. This status is reserved for radical companies like Tesla. But is there any chance that conservative companies will reinvent themselves, establish leaner structures and thus regain market dominance and innovation?
This text will explain which role “Green Bonds” play in financing projects and how the green factor is weighted. It will be discussed on how the term “green” can change the price of the bond, if there is a “green premium” and for which group of investors this type of bond is interesting. We will discuss ways to reduce their cost of capital, also considering the risks and on ways on how to improve their conditions. The sustainable and eco-friendly aspects are also highlighted in this text and they might become crucial in future investing, which gives the bond an interesting role.
Big Data is now poised to mutate decision-making systems. Indeed, the decision is no longer based solely on the structured information that was hitherto collected and stored by the organization, but also on all data not structured outside the corporate straitjacket. The cloud and the information it contains impacts decisions and the industry is witnessing the emergence of business intelligence 3.0. With the growth of the internet, social networks, connected objects and communication information are now more abundant than ever before, along with rapid and substantial growth in their production. In 2012, 2.5 exabytes of data (one exabyte representing a million gigabytes of data) came every day to swell the ranks of big data (McAfee et al., 2012), which should weigh more than 40 zettabytes from 2020 (Valduriez, 2014) for 30 billion connected devices (The Internet Of Nothings, 2014) and 50 billion sensors (Davenport & Soulard, 2014). One of the most critical aspects of all of this information flow is the impact these will have on the way decisions are made. Indeed, in the part of an environment in which data was scarce and difficult to obtain, it was logical to let decision-making be conditioned by the intuition of the experienced decision-maker (Klein, Phillips, Rall, & Peluso, 2007). However, since information and knowledge are now available to everyone, the role of experts and decision-makers is gradually changing. Big data, in particular, makes it possible for analytical and decision-making systems to base their decision-making on global models. However, considering all the dimensions of the situations encountered, it was not until now that these systems were not within the reach of man, but were rationally limited (Simon & Newell, 1971). Big data and however, the processing of unstructured data requires modifying the architecture of decision support systems (DSS) of organizations. This paper is an inventory of developments undergone by aid systems decision-making, under the pressure of big data. Finally, it opens the debate on ethical questions raised by these new technologies, and it is observed that now, data analysis of personal data has become more debatable than in the past.