Signal quality monitoring aspects in GNSS signals affected by evil waveforms

Evil waveforms (EWF) are anomalies in the GNSS transmitted signals that can degrade significantly the accuracy of the PVT solution. The cross-correlation function of the incoming signal disturbed by EWF distortion and the locally-generated code signal is obtained analytically for threat models TM-A, TM-B and TM-C. These results are useful to evaluate efficiently the performance of EWF detectors, namely the detectability and hazard regions.info:eu-repo/semantics/publishedVersio

Metamodels’ Development for High Pressure Die Casting of Aluminum Alloy

Simulation is a very useful tool in the design of the part and process conditions of high pressure die casting (HPDC), due to the intrinsic complexity of this manufacturing process. Usually, physics-based models solved by finite element or finite volume methods are used, but their main drawback is the long calculation time. In order to apply optimization strategies in the design process or to implement online predictive systems, faster models are required. One solution is the use of surrogate models, also called metamodels or grey-box models. The novelty of the work presented here lies in the development of several metamodels for the HPDC process. These metamodels are based on a gradient boosting regressor technique and derived from a physics-based finite element model. The results show that the developed metamodels are able to predict with high accuracy the secondary dendrite arm spacing (SDAS) of the cast parts and, with good accuracy, the misrun risk and the shrinkage level. Results obtained in the predictions of microporosity and macroporosity, eutectic percentage, and grain density were less accurate. The metamodels were very fast (less than 1 s); therefore, they can be used for optimization activities or be integrated into online prediction systems for the HPDC industry. The case study corresponds to several parts of aluminum cast alloys, used in the automotive industry, manufactured by high-pressure die casting in a multicavity mold.This work was supported by projects OASIS and SMAPRO. The OASIS project has received funding from the European Union’s Horizon 2020 Research and Innovation Programme under Grant Agreement No 814581. The SMAPRO project has received funding from the Basque Government under the ELKARTEK Program (KK-2017/00021)

A slag prediction model in an electric arc furnace process for special steel production

In the steel industry, there are some parameters that are difficult to measure online due to technical difficulties. In these scenarios, soft-sensors, which are online tools that aim forecasting of certain variables, play an indispensable role for quality control. In this investigation, different soft sensors are developed to address the problem of predicting the slag quantity and composition in an electric arc furnace process. The results provide evidence that the models perform better for simulated data than for real data. They also reveal higher accuracy in predicting the composition of the slag than the measured quantity of the slag.The project leading to this research work has received funding from the European Unions Horizon 2020 research and innovation programme under grant agreement No 820670

Data Driven Performance Prediction in Steel Making

This work presents three data-driven models based on process data, to estimate different indicators related to process performance in a steel production process. The generated models allow the optimization of the process parameters to achieve optimal performance and quality levels. A new approach based on ensembles has been developed with feature selection methods and four state-of-the-art regression approximations (random forest, gradient boosting, xgboost and neural networks). The results show that the proposed approach makes the prediction more stable reducing the variance for all cases, even in one case, slightly reducing the bias. Furthermore, from the four machine learning paradigms presented, random forest is the one with the best results in a quantitative way, obtaining a coefficient of determination of 0.98 as a maximum, depending on the target sub-process.This research is supported by the European Union’s Horizon 2020 Research and Innovation Framework Programme [grant agreement No 723661; COCOP; http://www.cocop-spire.eu (accessed on 6 January 2022)]. The authors want to acknowledge the work of the whole COCOP consortium

Derivados del tamoxifeno, procedimiento de obtención y sus aplicaciones

La invención se refiere a derivados del tamoxifeno, y sus usos como composición farmacéutica en el tratamiento de enfermedades que cursan con activación de la señalización por estrógenos o como herramienta para la identificación dianas moleculares del tamoxifeno o como emisor laser molecular. Además, la presente invención se refiere al procedimiento de obtención de estos compuestos y a composiciones que los comprenden.Peer reviewedUniversidad de la Laguna, Consejo Superior de Investigaciones Científicas (España)A1 Solicitud de patente con informe sobre el estado de la técnic

Endotoxin increase after fat overload is related to postprandial hypertriglyceridemia in morbidly obese patients.

The low-grade inflammation observed in obesity has been associated with a high-fat diet, though this relation is not fully understood. Bacterial endotoxin, produced by gut microbiota, may be the linking factor. However, this has not been confirmed in obese patients. To study the relationship between a high-fat diet and bacterial endotoxin, we analyzed postprandial endotoxemia in morbidly obese patients after a fat overload. The endotoxin levels were determined in serum and the chylomicron fraction at baseline and 3 h after a fat overload in 40 morbidly obese patients and their levels related with the degree of insulin resistance and postprandial hypertriglyceridemia. The morbidly obese patients with the highest postprandial hypertriglyceridemia showed a significant increase in lipopolysaccharide (LPS) levels in serum and the chylomicron fraction after the fat overload. Postprandial chylomicron LPS levels correlated positively with the difference between postprandial triglycerides and baseline triglycerides. There were no significant correlations between C-reactive protein (CRP) and LPS levels. The main variables contributing to serum LPS levels after fat overload were baseline and postprandial triglyceride levels but not glucose or insulin resistance. Additionally, superoxide dismutase activity decreased significantly after the fat overload. Postprandial LPS increase after a fat overload is related to postprandial hypertriglyceridemia but not to degree of insulin resistance in morbidly obese patients