Inleiding in de gezondheidszorg

Burgt, Marieke van der; Mechelen-Gevers, E. van

doi:10.1007/978-90-313-9166-0

Cited by 3 publications

(4 citation statements)

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“…1 Now, with over 60 monoclonal antibody drugs approved by the Food and Drug Administration (FDA), 2 both pharmaceutical companies and academicians have rapidly developed targeted workflows for antibodies and antibody drug conjugates that provide information on composition, disulfide bonding, glycosylation, degradation, biotransformation, and more. [3][4][5][6][7][8][9][10][11][12][13][14][15][16][17][18] The combination of these measurements affords the level of precision required by regulatory agencies worldwide to confidently assess the safety and efficacy of these drugs.…”

Section: Notesmentioning

confidence: 99%

Novel Interface for High-Throughput Analysis of Biotherapeutics by Electrospray Mass Spectrometry

et al. 2019

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With the rapid rise of therapeutic antibodies and antibody-drug conjugates, significant investments have been made in developing workflows that utilize mass spectrometry to detect these intact molecules, the large fragments generated by their selective digestion, and the peptides generated by traditional proteomics workflows. The resultant data is used to gain insight into a wide range of parameters, including primary sequence, disulfide bonding, glycosylation patterns, biotransformation, and more. However, many of the technologies utilized to couple these workflows to mass spectrometers have significant limitations that force nonoptimal modifications to upstream sample preparation steps, limit the throughput of high-volume workflows, and prevent the harmonization of diverse experiments onto a single hardware platform. Here, we describe a new analytical platform that enables direct and high-throughput coupling to electrospray ionization mass spectrometry. The SampleStream platform is compatible with both native and denaturing electrospray, operates with a throughput of up to 15 s/sample, provides extensive concentration of dilute samples, and affords similar sensitivity to comparable liquid chromatographic methods.

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Section: Notesmentioning

confidence: 99%

Novel Interface for High-Throughput Analysis of Biotherapeutics by Electrospray Mass Spectrometry

et al. 2019

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“…Overall, this work aims to provide a deeper understanding on the effect of processing conditions on the crystalline morphology and crystalline phases on nanocomposites and PBNANO's which would allow designing materials with desired properties for each application. and (040) diffraction peaks [26]. The degree of crystallinity is obtained by calculating the ratio between the integrated intensity below the amorphous halo and the total pattern, respectively.…”

Section: Introductionmentioning

confidence: 99%

Effect of shear rate and pressure on the crystallization of PP nanocomposites and PP/PET polymer blend nanocomposites

Sangroniz

Drongelen

Cardinaels

et al. 2020

Polymer

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The effect of shear flow on the crystallization of homopolymers has been widely studied, however more complex systems such as nanocomposites or polymer blends nanocomposites (PBNANOs) has received less attention. In this work, the effect of pressure and shear flow on the crystallization process of polypropylene-based nanocomposites and polypropylene/polyethylene terephthalate PBNANOs has been studied employing dilatometry (PVT), X-ray, rheology, and DSC. The material systems used allow studying the effect of shear in the presence of nanoparticles that do not have a nucleating effect, as well as the combined effect of shear and nucleating nanoparticles. Nucleating nanoparticles increase the crystallization temperature more significantly because of the combined effect of nucleation and flow. For carbon nanotubes, nucleation is more pronounced due to stronger interactions with PP chains, as demonstrated by rheological measurements. For PBNANOs the effect of shear on crystallization is slightly more significant due to the higher amount of available surfaces.

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“…El esquema de control propuesto en [35][36][37][38][39][40][41][42][43][44][45][46][47] se conoce como control directo del par (DTC).…”

Section: Sistemas De Control Directo Del Par (Dtc)unclassified

“…-La referencia [44] propone un modelo de tensión/corriente con lazo de realimentación interna el cual permite obtener un buen comportamiento del accionamiento por campo orientado en todo el rango de la velocidad, excepto para un área pequeña cerca al cero de la velocidad del flujo.…”

Section: -9unclassified

Aplicación de las redes neuronales en los sistemas de control vectorial de los motores de inducción

Sowilam¹

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Últimamente se ha incrementado considerablemente la utilización de la inteligencia artificial en los campos de la electrónica de potencia y de los sistemas de control de la velocidad y del posicionamiento. Estos métodos, basados en sistemas expertos, la lógica difusa y las redes neuronales artificiales, necesitan una gran base de datos o conocimientos que describan el funcionamiento del sistema junto a un análisis lógico, en lugar del análisis matemático, para controlar el sistema y facilitar la toma de decisiones. Uno de estos métodos corresponde a las redes neuronales artificiales. En esta tesis doctoral se presenta la aplicación de redes neuronales artificiales en los sistemas de control vectorial y control directo del par para una máquina de inducción. Esta tecnología, relativamente nueva, sirve para poder reemplazar eficientemente algunos equipos analógicos ya existentes. Los datos necesarios para entrenar las redes neuronales artificiales se obtienen, en primer lugar, de los resultados de las simulaciones del sistema formado por el motor y su control, y en segundo lugar, a partir de los resultados experimentales. A tal fin se implementa un modelo del motor de inducción que se prueba con motores de diferentes valores nominales y se controla con diferentes sistema de control: control vectorial por el método indirecto con y sin realimentación de velocidad, control vectorial por el método directo con realimentación de velocidad y control directo del par. Para el caso de los sistemas del control vectorial por el método indirecto, con y sin realimentación de velocidad se han utilizado redes neuronales artificiales de respuesta a impulso finito (FIRANN) para emular algunos bloques de los sistemas del control vectorial tales como la estimación de referencia de corriente estator. Cada red neuronal artificial se ha diseñado, entrenado y probado como una parte del conjunto del sistema de control. Con respecto al control vectorial por el método directo y con realimentación de velocidad, se estima la referencia de la corriente del estator y también se realiza la mayor parte del control vectorial con redes neuronales artificiales FIRANN. Cada red neuronal artificial se ha diseñado, entrenado y probado como una parte del conjunto del sistema de control. Para el sistema de control directo del par se han utilizado redes neuronales artificiales de propagación hacia adelante (MFANN) que emulan la tabla de conmutación del ondulador, la transformación de las tensiones en el sistema de referencia "abc" al sistema de referencia fijado al estator "dq", la transformación de las corrientes de estator en el sistema de referencia "abc" al sistema de referencia fijado al estator "dq" y por último, un estimador de par. Estas cuatro redes neuronales se probaron en línea tanto de forma independiente como conjuntamente. Su comportamiento ha sido bueno en comparación con el sistema sin redes neuronales. Además, se realizó la implementación física del equipo para el control en tiempo real de un motor de inducción por el método directo clásico, y usando redes neuronales mediante una tarjeta dSPACE "DS1103". Este montaje ha permitido hacer las medidas necesarias para poder comparar el comportamiento del sistema bajo las alternativas propuestas. Recently, the use of artificial intelligence has been increased in the fields of power electronics, and position and speed control systems. These methods, based on expert systems, fuzzy logic and artificial neural networks, require a large data base or knowledge to describe the operation of the system, besides to the logic analysis instead of the mathematical analysis in order to control the system and facilitate the decision tasks. One of these methods are the artificial neural networks. This doctoral thesis shows the aplication of artificial neural networks to the vector control systems and direct torque control of induction machines. This technology is useful to replace some existing analogical equipment in an efficient way. The data needed to train the artificial neural networks is obtained in the first place from the simulation results of the system, and in the second place from the experimental results. For that purpose, it was implemented an induction motor model which is tested with different machine characteristics, and it is controlled with different control systems: indirect method vector control with and without speed feedback, direct method vector control with speed feedback and direct torque control. For indirect method vector control with and without speed feedback, it has been used finite impulse response artificial neural networks (FIRANN) to emulate some blocks of the vector control systems such as the estimation of the stator current. Each artificial neural network has been designed, trained and tested as a part of the control system set. For the case of direct method vector control with speed feedback, the stator current reference and the majority of the vector control is realized also with artificial neural networks FIRANN. Each artificial neural network has been designed, trained and tested as a part of the control system set. In the direct torque control, it has been used feed-forward artificial neural networks (MFANN) to emulate the commutation table of the inverter, to develop the transformation of the voltages from the "abc" reference frame to the "dq" reference frame, and the transformation of the stator currents from the "abc" reference frame to the "dq" reference frame, and finally, to estimate the torque. These four neural networks have been tested online in an independent way and also as a whole. Its performance has been good in comparison with the system without neural networks. Finally, it was realized a physical implementation of the equipment for real time control of an induction motor by the classical method and by artificial neural networks using a dSPACE "DS1103" card. This implementation has allowed realize the measurements to be able to compare the performance of the system under the different proposed alternatives.

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Inleiding in de gezondheidszorg

Cited by 3 publications

References 0 publications

Novel Interface for High-Throughput Analysis of Biotherapeutics by Electrospray Mass Spectrometry

Novel Interface for High-Throughput Analysis of Biotherapeutics by Electrospray Mass Spectrometry

Effect of shear rate and pressure on the crystallization of PP nanocomposites and PP/PET polymer blend nanocomposites

Aplicación de las redes neuronales en los sistemas de control vectorial de los motores de inducción

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