Analysis of the power balance In the cells of a multilevel cascaded H-Bridge converter

Multilevel cascaded H-Bridge converters (CHB) have been presented as a good solution for high power applications. In this way, several control and modulation techniques have been proposed for this power converter topology. In this paper the steady state power balance in the cells of the single phase two cell CHB is studied. The capability to be supplied with active power from the grid or to deliver active power to the grid in each cell is analyzed according to the dc-link voltages and the desired ac output voltage value. Limits of the maximum and minimum input active power for stable operation of the CHB are addressed. Simulation results are shown to validate the presented analysis

Cell line derived xenograft mouse models are a suitable in vivo model for studying tumor budding in colorectal cancer

Tumor budding (TB) is an important prognostic parameter in colorectal cancer (CRC) and associated with metastasis. However, the mechanisms of TB have not been fully elucidated and a major limitation is the absence of in vivo models. Here, we determine the suitability of human cell line derived xenografts (CDX) as models of TB in CRC. Pan-cytokeratin (CK)-stained next-generation Tissue Microarrays (ngTMA) of two CDX models (HT-29, n = 12 and HCT-8, n = 8) and human CRC (n = 27 high-grade and 25 low-grade budding tumors, each) were evaluated for TB. Immunohistochemistry for E-cadherin, β-catenin, Ki-67, ZEB1, and TWIST1 was performed. HT-29 and HCT-8 were predominantly high-grade and no/low-grade TB tumors, respectively. TB counts in the tumor center (intratumoral budding, ITB) were significantly higher in HT-29 CDX tumors compared to human CRC (p = 0.0099). No difference was found in TB counts at the invasion front (peritumoral budding, PTB; p=0.07). ITB and PTB were strongly correlated (r = 0.438 and r = 0.62 in CDX and human CRC, respectively). Immunohistochemistry profiles were comparable in CDX and human CRC tissues. TB in the CDX mouse models is phenotypically similar to human CRCs and highlights comparable protein profiles. The HT-29 CDX could be a suitable model for the in vivo assessment of TB.SCOPUS: ar.jinfo:eu-repo/semantics/publishe

Feed-forward Space Vector Modulation for Single-Phase Multilevel Cascade Converters with any DC voltage ratio

Modulation techniques for multilevel converters can create distorted output voltages and currents if the DC link voltages are unbalanced. This situation can be avoided if the instantaneous DC voltage error is not taken into account in the modulation process. This paper proposes a feed-forward space vector modulation method for a single-phase multilevel cascade converter. Using this modulation technique, the modulated output voltage of the power converter always generates the reference determined by the controller even in worst case voltage unbalance conditions. In addition the possibility of optimizing the DC voltage ratio between the H-bridges of the power converter is introduced. Experimental results from a 5kVA prototype are presented in order to validate the proposed modulation technique

An Evaluation of Inflow Profiles for CFD Modeling of Neutral ABL and Turbulent Airflow over a Hill Model

The implementation of the wind turbine is a major issue in the wind engineering sector. However, the presence of wind turbines in the lower part of the atmospheric boundary layer (ABL) requires an appropriate study for the simulation of turbulent airflow in the wind farm situated on hilly terrain. The use of precise Computational Fluid Dynamics (CFD) simulations for the ABL flow is vital for numerous applications, such as wind energy, building, urban planning, etc. To achieve accurate results, it is imperative that the inlet boundary conditions produce vertical profiles that keep a uniform horizontal distribution (with no streamwise gradients) in the upstream region of the computational domain for all important parameters. A development approach is proposed herein, focused on the imposition of two different inlet profiles when used in combination with the rough z0-type scalable wall function. The horizontal homogeneity of these profiles has been verified by 2D Reynolds averaged Navier-Stokes (RANS) through the examination of a neutral ABL in an empty computational domain using the k-ε turbulence model. The findings indicate that the use of this modeling approach can yield relatively consistent homogeneity of neutral ABL for both inlet boundary conditions. Subsequently, sensitivity analyses were performed on the inflow profiles to forecast the evolution of the bottom half of an idealized truly-neutral ABL and to accurately capture the complex dynamics of atmospheric flows over hilly terrain. This study compares the results with the CRIACIV (Inter-University Research Centre on Building Aerodynamics and Wind Engineering) boundary layer wind tunnel experimental data, showing that the inflow profiles and the presence of topographic complex have a significant impact on air velocity, turbulent kinetic energy and turbulence intensity in the x-direction. The results obtained are in goo

Nested Socio-Ecological Maps as a Spatial Planning Instrument for Estuary Conservation and Ecosystem-Based Management

ABSTRACT: Estuaries are socio-ecological systems that can be represented as a holistic combination of biotic and abiotic conditions in spatially explicit units defined by: (i) the ecotope, as the integration of the physiotope (abiotic-homogeneous units) and the biotope (biotic-homogeneous units), and (ii) the anthrotope, synthesizing data on human drivers of ecological change. Nested physiotopes were identified in an estuary using a hierarchical approach that integrates information about eight abiotic, and biologically meaningful, variables. The biotope of Zostera noltei was delimited using a potential distribution model of species and overlapped with the physiotope map to characterize the ecotopes. The anthrotope was estimated as the cumulative impacts of anthropic activities over the ecotopes. The diversity of Z. noltei ecotopes was compared with the anthrotope map to estimate the potential impacts of human pressures on this species. The hierarchical methodology and resulting maps provide flexible and interdisciplinary tools for conservation, management, education and research.This research was part of the ECOTOPO project (RTI2018-096409-B-I00) financially supported by the Spanish Ministry of Science and Innovation through the National Plan for Scientific Research

Biomoléculas en nanotecnología

Las propiedades de reconocimiento molecular y autoensamble intrínseco de las biomoléculas han fascinado a la nanotecnología. Su incursión está revolucionando la forma de fabricar nanomateriales. En este trabajo revisa- mos un número de publicaciones que emplean biomoléculas en la fabricación de Nanomateriales

Selective Harmonic Mitigation Technique for Cascaded H-Bridge Converters With Nonequal DC Link Voltages

Multilevel converters have received increased interest recently as a result of their ability to generate high quality output waveforms with a low switching frequency. This makes them very attractive for high power applications. A Cascaded HBridge converter is a multilevel topology which is formed from the series connection of H-Bridge cells. Optimized pulse width modulation techniques such as Selective Harmonic Elimination (SHE-PWM) or Selective Harmonic Mitigation (SHM-PWM) are capable of pre-programming the harmonic profile of the output waveform over a range of modulation indices. Such modulation methods may however not perform optimally if the DC links of the Cascaded H-Bridge Converter are not balanced. This paper presents a new SHM-PWM control strategy which is capable of meeting grid codes even under non-equal DC link voltages. The method is based on the interpolation of different sets of angles obtained for specific situations of imbalance. Both simulation and experimental results are presented to validate the proposed control method

Feature Selection Using Genetic Algorithms for the Generation of a Recognition and Classification of Children Activities Model Using Environmental Sound

In the area of recognition and classification of children activities, numerous works have been proposed that make use of different data sources. In most of them, sensors embedded in children’s garments are used. In this work, the use of environmental sound data is proposed to generate a recognition and classification of children activities model through automatic learning techniques, optimized for application on mobile devices. Initially, the use of a genetic algorithm for a feature selection is presented, reducing the original size of the dataset used, an important aspect when working with the limited resources of a mobile device. For the evaluation of this process, five different classification methods are applied, k-nearest neighbor (k-NN), nearest centroid (NC), artificial neural networks (ANNs), random forest (RF), and recursive partitioning trees (Rpart). Finally, a comparison of the models obtained, based on the accuracy, is performed, in order to identify the classification method that presents the best performance in the development of a model that allows the identification of children activity based on audio signals. According to the results, the best performance is presented by the five-feature model developed through RF, obtaining an accuracy of 0.92, which allows to conclude that it is possible to automatically classify children activity based on a reduced set of features with significant accuracy.In the area of recognition and classification of children activities, numerous works have been proposed that make use of different data sources. In most of them, sensors embedded in children’s garments are used. In this work, the use of environmental sound data is proposed to generate a recognition and classification of children activities model through automatic learning techniques, optimized for application on mobile devices. Initially, the use of a genetic algorithm for a feature selection is presented, reducing the original size of the dataset used, an important aspect when working with the limited resources of a mobile device. For the evaluation of this process, five different classification methods are applied, k-nearest neighbor (k-NN), nearest centroid (NC), artificial neural networks (ANNs), random forest (RF), and recursive partitioning trees (Rpart). Finally, a comparison of the models obtained, based on the accuracy, is performed, in order to identify the classification method that presents the best performance in the development of a model that allows the identification of children activity based on audio signals. According to the results, the best performance is presented by the five-feature model developed through RF, obtaining an accuracy of 0.92, which allows to conclude that it is possible to automatically classify children activity based on a reduced set of features with significant accuracy

Machines vs Malaria: A Flow-Based Preparation of the Drug Candidate OZ439.

An efficient preparation of the antimalarial drug candidate OZ439, which was obtained by integrating a machine-assisted approach with batch processes, is reported. This approach allows a rapid and cost-effective production of the key intermediates that were readily elaborated into the target molecule.We are grateful to Croucher Foundation and Cambridge Trust (SHL), MEC-Spain (FPU-predoctoral grants, AG), Pfizer World-wide Research and Development (CB), the Xunta de Galicia Gov-ernment (JAS), and the EPSRC (SVL, grant n° EP/K0099494/1 and EP/K039520/1) for financial support.This is the final version. It first appeared at http://pubs.acs.org/doi/abs/10.1021/acs.orglett.5b01307