Ideas and perspectives: climate-relevant marine biologically driven mechanisms in Earth system models

The current generation of marine biogeochemical modules in Earth system models (ESMs) considers mainly the effect of marine biota on the carbon cycle. We propose to also implement other biologically driven mechanisms in ESMs so that more climate-relevant feedbacks are captured. We classify these mechanisms in three categories according to their functional role in the Earth system: (1) "biogeochemical pumps", which affect the carbon cycling; (2) "biological gas and particle shuttles", which affect the atmospheric composition; and (3) "biogeophysical mechanisms", which affect the thermal, optical, and mechanical properties of the ocean. To resolve mechanisms from all three classes, we find it sufficient to include five functional groups: bulk phyto- and zooplankton, calcifiers, and coastal gas and surface mat producers. We strongly suggest to account for a larger mechanism diversity in ESMs in the future to improve the quality of climate projections

Mass Loss and Displacement Modeling for Multi-Axis Milling

During the cutting process, material of the workpiece is continuously being removed by the cutting tool, which results in a reduction of mass as well as a displacement in the center of the workpiece mass. When using workpiece sided force sensors, such as table dynamometers, the total mass and the displacement of the center of mass affects the force measurement due to gravitational and inertial effects. The high flexibility of the milling process leads to a complex change of volume and mass and necessitates the consideration of the engagement conditions between tool and workpiece along the tool path in order to estimate changes in mass and center of mass. This paper proposes a method for estimating the mass loss and the displacement of the center of mass during multi-axis milling processes. In this method the tool gets numerically sliced along the tool axis and the workpiece removal for each slice along an arbitrary tool path gets calculated. To validate the mass loss model, experiments in both three-axis milling as well as multi-axis milling processes have been conducted. Since it is difficult to measure the center of mass, validation for the displacement of the center of mass was done by comparison with data extracted from CAD. The results show good agreement between the simulated and measured mass loss using the proposed approach

Clinical relevance and utility of cetuximab-related changes in magnesium and calcium serum levels

Hypomagnesemia and hypocalcemia are common adverse events during cetuximab treatment. The influence of the chemotherapeutic combination on serum levels is unknown and the predictive value is currently under discussion. This analysis investigated 79 patients who had received cetuximab for at least 6 weeks in the day clinic of the Comprehensive Cancer Center, University of Munich. Calcium and magnesium serum levels were analyzed weekly; tumor response and adverse events were followed. Thirty-eight patients had metastatic colorectal cancer (mCRC) and the predictive value of hypomagnesemia was tested in these patients. During therapy, calcium serum levels decreased to about 97% of the baseline levels and were maintained for the duration of treatment. Magnesium levels showed a significant time-dependent decrease. Serum levels of magnesium were lower when cetuximab was combined with a platinum derivative. After a treatment duration of 12 weeks, magnesium levels decreased to 70% in platinum-treated patients, whereas they decreased to only 90% of baseline in patients who did not receive platinum therapy. In patients treated for mCRC, a decrease of serum magnesium below 95% of the baseline levels 14 days after initiating treatment separated patients significantly in terms of survival times. Magnesium levels decrease in a time-dependent manner during cetuximab therapy. As hypomagnesemia was more prominent in patients receiving platinum agents, magnesium measurements may be advised in these patients. In mCRC patients treated with cetuximab, day-14 magnesium serum levels correlated with treatment efficacy

Outcome Prediction in Patients with Severe COVID-19 Requiring Extracorporeal Membrane Oxygenation—A Retrospective International Multicenter Study

The role of veno-venous extracorporeal membrane oxygenation therapy (V-V ECMO) in severe COVID-19 acute respiratory distress syndrome (ARDS) is still under debate and conclusive data from large cohorts are scarce. Furthermore, criteria for the selection of patients that benefit most from this highly invasive and resource-demanding therapy are yet to be defined. In this study, we assess survival in an international multicenter cohort of COVID-19 patients treated with V-V ECMO and evaluate the performance of several clinical scores to predict 30-day survival. Methods: This is an investigator-initiated retrospective non-interventional international multicenter registry study (NCT04405973, first registered 28 May 2020). In 127 patients treated with V-V ECMO at 15 centers in Germany, Switzerland, Italy, Belgium, and the United States, we calculated the Sequential Organ Failure Assessment (SOFA) Score, Simplified Acute Physiology Score II (SAPS II), Acute Physiology And Chronic Health Evaluation II (APACHE II) Score, Respiratory Extracorporeal Membrane Oxygenation Survival Prediction (RESP) Score, Predicting Death for Severe ARDS on V-V ECMO (PRESERVE) Score, and 30-day survival. Results: In our study cohort which enrolled 127 patients, overall 30-day survival was 54%. Median SOFA, SAPS II, APACHE II, RESP, and PRESERVE were 9, 36, 17, 1, and 4, respectively. The prognostic accuracy for all these scores (area under the receiver operating characteristic—AUROC) ranged between 0.548 and 0.605. Conclusions: The use of scores for the prediction of mortality cannot be recommended for treatment decisions in severe COVID-19 ARDS undergoing V-V ECMO; nevertheless, scoring results below or above a specific cut-off value may be considered as an additional tool in the evaluation of prognosis. Survival rates in this cohort of COVID-19 patients treated with V-V ECMO were slightly lower than those reported in non-COVID-19 ARDS patients treated with V-V ECMO

Intelligente Regelungsstrategien als Schlüsseltechnologie selbstoptimierender Fertigungssysteme

The settings of manufacturing systems are often based on the experiences of the machine operator, previously identified technology tables or various simulation tools. Machine oriented control loops ensure the reproducibility of the machine behavior. However, changing material properties and environmental conditions lead to a varying process behavior, which generally results in deviations in the quality of the manufactured parts. To establish process and quality control loops, the quality measurement of the workpiece is necessary, though it can usually be determined only after the manufacturing process. Therefore, this thesis targets the concept of model-based self-optimization of manufacturing systems. An essential element of this concept is the quality model which describes the relationship between the considered quality variables and the relevant process variables. On this basis, an optimal trajectory for process variables can be determined, which are controlled by process control loops. This thesis aims at investigating intelligent controllers for process and quality control loops in manufacturing technology. Suitable modeling approaches are first presented, which can explicitly be used in the control scheme. Furthermore, the concept of state estimation is introduced to predict non-measurable state variables. Apart from this, a model-based predictive controller as well as an iterative learning controller are introduced, which allow the explicit consideration of model knowledge, constraints and arbitrary control objectives. The mentioned concept of model-based self-optimization is applied to roughing in milling and plastic injection molding. For plastic injection molding, a quality control approach is developed which aims to control the weight of the manufactured workpieces. Considering the desired weight of the workpiece, the pvT-optimization determines an optimal trajectory for the cavity pressure. A model-based, predictive control as well as an iterative learning control scheme are examined regarding their ability to control the nonlinear process behavior. Additionally, it is shown that the accuracy of the weight can significantly be increased by the combination of the mentioned control approaches and the pvT-optimization. In milling, a model-based predictive force controller is developed which aims at decreasing the manufacturing time. The controller predicts the future cutting force which occurs on the milling cutter by a force model and adjusts the feed velocity online. The explicit consideration of a machine model in the controller enables the definition of constraints for a maximum desired cutting force and thus the feed velocity. Exceeding cutting forces lead to increased tool wear or in worst case to tool damage. In order to increase the accuracy of the cutting force model, the parameters of the force model are identified at runtime. The feed rate as well as the cutting force can be high-dynamically controlled by the mentioned controllers. Furthermore, variable process conditions such as tool wear ormaterial fluctuations are compensated by the online identification of the force model. The presented results show that novel process control loops can be realized by the establishment of intelligent controllers. These are in turn prerequisites for the implementation of the model-based self-optimization in manufacturing. The controller is parameterized using the process models. Thus, the mentioned approaches can be applied to other manufacturing process by the adaption of the process model