Brain Development During the Preschool Years

The preschool years represent a time of expansive psychological growth, with the initial expression of many psychological abilities that will continue to be refined into young adulthood. Likewise, brain development during this age is characterized by its “blossoming” nature, showing some of its most dynamic and elaborative anatomical and physiological changes. In this article, we review human brain development during the preschool years, sampling scientific evidence from a variety of sources. First, we cover neurobiological foundations of early postnatal development, explaining some of the primary mechanisms seen at a larger scale within neuroimaging studies. Next, we review evidence from both structural and functional imaging studies, which now accounts for a large portion of our current understanding of typical brain development. Within anatomical imaging, we focus on studies of developing brain morphology and tissue properties, including diffusivity of white matter fiber tracts. We also present new data on changes during the preschool years in cortical area, thickness, and volume. Physiological brain development is then reviewed, touching on influential results from several different functional imaging and recording modalities in the preschool and early school-age years, including positron emission tomography (PET), electroencephalography (EEG) and event-related potentials (ERP), functional magnetic resonance imaging (fMRI), magnetoencephalography (MEG), and near-infrared spectroscopy (NIRS). Here, more space is devoted to explaining some of the key methodological factors that are required for interpretation. We end with a section on multimodal and multidimensional imaging approaches, which we believe will be critical for increasing our understanding of brain development and its relationship to cognitive and behavioral growth in the preschool years and beyond

First principles and integrated modelling achievements towards trustful fusion power predictions for JET and ITER

Predictability of burning plasmas is a key issue for designing and building credible future fusion devices. In this context, an important effort of physics understanding and guidance is being carried out in parallel to JET experimental campaigns in H and D by performing analyses and modelling towards an improvement of the understanding of DT physics for the optimization of the JET-DT neutron yield and fusion born alpha particle physics. Extrapolations to JET-DT from recent experiments using the maximum power available have been performed including some of the most sophisticated codes and a broad selection of models. There is a general agreement that 11-15 MW of fusion power can be expected in DT for the hybrid and baseline scenarios. On the other hand, in high beta, torque and fast ion fraction conditions, isotope effects could be favourable leading to higher fusion yield. It is shown that alpha particles related physics, such as TAE destabilization or fusion power electron heating, could be studied in ITER relevant JET-DT plasmas

Role of fast ion pressure in the isotope effect in JET L-mode plasmas

This paper presents results of JET ITER-like wall L-mode experiments in hydrogen and deuterium (D) plasmas, dedicated to the study of the isotope dependence of ion heat transport by determination of the ion critical gradient and stiffness by varying the ion cyclotron resonance heating power deposition. When no strong role of fast ions in the plasma core is expected, the main difference between the two isotope plasmas is determined by the plasma edge and the core behavior is consistent with a gyro-Bohm scaling. When the heating power (and the fast ion pressure) is increased, in addition to the difference in the edge region, also the plasma core shows substantial changes. The stabilization of ion heat transport by fast ions, clearly visible in D plasmas, appears to be weaker in H plasmas, resulting in a higher ion heat flux in H with apparent anti-gyro-Bohm mass scaling. The difference is found to be caused by the different fast ion pressure between H and D plasmas, related to the heating power settings and to the different fast ion slowing down time, and is completely accounted for in non-linear gyrokinetic simulations. The application of the TGLF quasi-linear model to this set of data is also discussed

Ion cyclotron resonance heating scenarios for DEMO

The present paper offers an overview of the potential of ion cyclotron resonance heating (ICRH) or radio frequency heating for the DEMO machine. It is found that various suitable heating schemes are available. Similar to ITER and in view of the limited bandwidth of about 10 MHz that can be achieved to ensure optimal functioning of the launcher, it is proposed to make core second harmonic tritium heating the key ion heating scheme, assisted by fundamental cyclotron heating He-3 in the early phase of the discharge; for the present design of DEMO-with a static magnetic field strength of B-o = 5.855 T-that places the T and 3He layers in the core for f = 60 MHz and suggests centering the bandwidth around that main operating frequency. In line with earlier studies for hot, dense plasmas in large-size magnetic confinement machines, it is shown that good single pass absorption is achieved but that the size as well as the operating density and temperature of the machine cause the electrons to absorb a non-negligible fraction of the power away from the core when core ion heating is aimed at. Current drive and alternative heating options are briefly discussed and a dedicated computation is done for the traveling wave antenna, proposed for DEMO in view of its compatibility with substantial antenna-plasma distances. The various tasks that ICRH can fulfill are briefly listed. Finally, the impact of transport and the sensitivity of the obtained results to changes in the machine parameters is commented on

A new mechanism for increasing density peaking in tokamaks: improvement of the inward particle pinch with edge E x B shearing

Developing successful tokamak operation scenarios, as well as confident extrapolation of present-day knowledge requires a rigorous understanding of plasma turbulence, which largely determines the quality of the confinement. In particular, accurate particle transport predictions are essential due to the strong dependence of fusion power or bootstrap current on the particle density details. Here, gyrokinetic turbulence simulations are performed with physics inputs taken from a JET power scan, for which a relatively weak degradation of energy confinement and a significant density peaking is obtained with increasing input power. This way physics parameters that lead to such increase in the density peaking shall be elucidated. While well-known candidates, such as the collisionality, previously found in other studies are also recovered in this study, it is furthermore found that edge E x B shearing may adopt a crucial role by enhancing the inward pinch. These results may indicate that a plasma with rotational shear could develop a stronger density peaking as compared to a non-rotating one, because its inward convection is increased compared to the outward diffusive particle flux as long as this rotation has a significant on E x B flow shear stabilization. The possibly significant implications for future devices, which will exhibit much less torque compared to present day experiments, are discussed

Deep neural networks for plasma tomography with applications to JET and COMPASS

Convolutional neural networks (CNNs) have found applications in many image processing tasks, such as feature extraction, image classification, and object recognition. It has also been shown that the inverse of CNNs, so-called deconvolutional neural networks, can be used for inverse problems such as plasma tomography. In essence, plasma tomography consists in reconstructing the 2D plasma profile on a poloidal cross-section of a fusion device, based on line-integrated measurements from multiple radiation detectors. Since the reconstruction process is computationally intensive, a deconvolutional neural network trained to produce the same results will yield a significant computational speedup, at the expense of a small error which can be assessed using different metrics. In this work, we discuss the design principles behind such networks, including the use of multiple layers, how they can be stacked, and how their dimensions can be tuned according to the number of detectors and the desired tomographic resolution for a given fusion device. We describe the application of such networks at JET and COMPASS, where at JET we use the bolometer system, and at COMPASS we use the soft X-ray diagnostic based on photodiode arrays

Synthetic diagnostic for the JET scintillator probe lost alpha measurements

A synthetic diagnostic has been developed for the JET lost alpha scintillator probe, based on the ASCOT fast ion orbit following code and the AFSI fusion source code. The synthetic diagnostic models the velocity space distribution of lost fusion products in the scintillator probe. Validation with experimental measurements is presented, where the synthetic diagnostic is shown to predict the gyroradius and pitch angle of lost DD protons and tritons. Additionally, the synthetic diagnostic reproduces relative differences in total loss rates in multiple phases of the discharge, which can be used as a basis for total loss rate predictions