Switching Pattern Improvement for One-Cycle Zero-Integral-Error Current Controller

[EN] The one-cycle current control is a non-linear technique based on the cycle-by-cycle calculation of the ON time of the power converter switches. Its application is not common in tracking fast-changing reference currents, due to the necessity of fast and accurate measurements, and high-speed computing. In a previous study, a one-cycle digital current controller based on the minimization of the integral error of the current was developed and applied to the control of a three-phase shunt active power filter. In the present work, the one-cycle controller has been improved by proposing a new switching pattern. It allows an easy implementation that reduces the critical computational cost and avoids the main drawbacks of the previous implementation. The controller has been applied in a three-leg four-wire shunt active power filter, including a stability analysis considering the proposed switching pattern. Simulated and experimental results are presented to validate the proposed controller.Orts-Grau, S.; Balaguer-Herrero, P.; Alfonso-Gil, JC.; Martínez-Márquez, CI.; Martínez-Navarro, G.; Gimeno Sales, FJ.; Segui-Chilet, S. (2022). Switching Pattern Improvement for One-Cycle Zero-Integral-Error Current Controller. IEEE Access. 10:158-167. https://doi.org/10.1109/ACCESS.2021.31377581581671

Objective comparison of methods to decode anomalous diffusion

Deviations from Brownian motion leading to anomalous diffusion are found in transport dynamics from quantum physics to life sciences. The characterization of anomalous diffusion from the measurement of an individual trajectory is a challenging task, which traditionally relies on calculating the trajectory mean squared displacement. However, this approach breaks down for cases of practical interest, e.g., short or noisy trajectories, heterogeneous behaviour, or non-ergodic processes. Recently, several new approaches have been proposed, mostly building on the ongoing machine-learning revolution. To perform an objective comparison of methods, we gathered the community and organized an open competition, the Anomalous Diffusion challenge (AnDi). Participating teams applied their algorithms to a commonly-defined dataset including diverse conditions. Although no single method performed best across all scenarios, machine-learning-based approaches achieved superior performance for all tasks. The discussion of the challenge results provides practical advice for users and a benchmark for developers. Deviations from Brownian motion leading to anomalous diffusion are ubiquitously found in transport dynamics but often difficult to characterize. Here the authors compare approaches for single trajectory analysis through an open competition, showing that machine learning methods outperform classical approaches

Objective comparison of methods to decode anomalous diffusion

Deviations from Brownian motion leading to anomalous diffusion are found in transport dynamics from quantum physics to life sciences. The characterization of anomalous diffusion from the measurement of an individual trajectory is a challenging task, which traditionally relies on calculating the trajectory mean squared displacement. However, this approach breaks down for cases of practical interest, e.g., short or noisy trajectories, heterogeneous behaviour, or non-ergodic processes. Recently, several new approaches have been proposed, mostly building on the ongoing machine-learning revolution. To perform an objective comparison of methods, we gathered the community and organized an open competition, the Anomalous Diffusion challenge (AnDi). Participating teams applied their algorithms to a commonly-defined dataset including diverse conditions. Although no single method performed best across all scenarios, machine-learning-based approaches achieved superior performance for all tasks. The discussion of the challenge results provides practical advice for users and a benchmark for developers

Imaging early endothelial inflammation following stroke by core shell silica superparamagnetic glyconanoparticles that target selectin

Activation of the endothelium is a pivotal first step for leukocyte migration into the diseased brain. Consequently, imaging this activation process is highly desirable. We synthesized carbohydrate-functionalized magnetic nanoparticles that bind specifically to the endothelial transmembrane inflammatory proteins E and P selectin. Magnetic resonance imaging revealed that the targeted nanoparticles accumulated in the brain vasculature following acute administration into a clinically relevant animal model of stroke, though increases in selectin expression were observed in both brain hemispheres. Nonfunctionalized naked particles also appear to be a plausible agent to target the ischemic vasculature. The importance of these findings is discussed regarding the potential for translation into the clinic

X chromosome inactivation does not necessarily determine the severity of the phenotype in Rett syndrome patients

Rett syndrome (RTT) is a severe neurological disorder usually caused by mutations in the MECP2 gene. Since the MECP2 gene is located on the X chromosome, X chromosome inactivation (XCI) could play a role in the wide range of phenotypic variation of RTT patients; however, classical methylation-based protocols to evaluate XCI could not determine whether the preferentially inactivated X chromosome carried the mutant or the wild-type allele. Therefore, we developed an allele-specific methylation-based assay to evaluate methylation at the loci of several recurrent MECP2 mutations. We analyzed the XCI patterns in the blood of 174 RTT patients, but we did not find a clear correlation between XCI and the clinical presentation. We also compared XCI in blood and brain cortex samples of two patients and found differences between XCI patterns in these tissues. However, RTT mainly being a neurological disease complicates the establishment of a correlation between the XCI in blood and the clinical presentation of the patients. Furthermore, we analyzed MECP2 transcript levels and found differences from the expected levels according to XCI. Many factors other than XCI could affect the RTT phenotype, which in combination could influence the clinical presentation of RTT patients to a greater extent than slight variations in the XCI pattern

Flow-based surface decoration of microparticles with titania and other transition metal oxide nanoparticles

We present a rapid approach for forming monodisperse silica microcapsules decorated with metal oxide nanoparticles; the silica–metal oxide composites have a hierarchical architecture and a range of compositions. The details of the method were defined using titania precursors. Silica capsules containing low concentrations of titania (<1 wt. %) were produced via an interfacial reaction using a simple mesofluidic T-junction droplet generator. Increasing the titania content of the capsules was achieved using two related, flow-based postsynthetic approaches. In the first approach, a precursor solution containing titanium alkoxides was flowed through a packed-bed of capsules. The second approach provided the highest concentration of titania (3.5 wt. %) and was achieved by evaporating titanium precursor solutions onto a capsule packed-bed using air flow to accelerate evaporation. Decorated capsules, regardless of the method, were annealed to improve the titania crystallinity and analyzed by optical microscopy, scanning electron microscopy (SEM), transmission electron microscopy (TEM), energy dispersive X-ray spectroscopy (EDX), powder X-ray diffraction (PXRD), and Fourier transform infrared (FT-IR) spectroscopy. The photocatalytic properties were then compared to a commercial nanoparticulate titania, which the microcapsule-supported titania outperformed in terms of rate of degradation of an organic dye and recyclability. Finally, the generality of the flow-based surface decoration procedures was demonstrated by synthesizing several composite transition metal oxide–silica microparticle materials