Ferroelectrics are promising candidate materials for electrocaloric refrigeration. Materials with a large electrocaloric effect (ECE) near room temperature and a broad working temperature range are getting closer to practical applications. However, the enhanced ECE is always achieved under high electric field, which limits their wide cooling applications. In this paper, the phase diagram of lead-free BaHf x Ti 1-x O 3 (BHT) ferroelectric ceramics was established. A large ECE under relatively low electric field (ΔE=10 kV/cm) is firstly reported in BHT ferroelectric ceramics. The direct temperature change (ΔT=0.35 °C under 10 kV/cm) in BHT ceramics is comparable with those reported in the literature under high electric fields. Meanwhile, the electrocaloric efficiency (ΔT/ΔE=0.35 K mm kV-1 under 10 kV/cm) is thirteen percent higher than the best value reported previously under high electric field (ΔE=145 kV/cm). We demonstrate that the ECE can be greatly enhanced by tuning the composition of the lead-free BHT ceramics to its first-order phase transition (FPT), invariant critical point (ICP) or diffuse phase transition (DPT). It is shown that the enhancement in ECE is strongly dependent on the nature of structural phase transition and electric field coupling effect, which has been confirmed by both the indirect and direct ECE measurements. A phenomenological explanation based on Landau model was also proposed to understand this phenomenon. Our findings in this work may provide a better understanding and design methodology for developing more practically useful electrocaloric materials.

Bayesian network (BN) modeling has recently been introduced as a tool for determining the dependencies between brain regions from functional-magnetic-resonance-imaging (fMRI) data. However, studies to date have yet to explore the optimum way for meaningfully combining individually determined BN models to make group inferences. We contrasted the results from three broad approaches: the "virtual-typical-subject" (VTS) approach which pools or averages group data as if they are sampled from a single, hypothetical virtual typical subject; the "individual-structure" (IS) approach that learns a separate BN for each subject, and then finds commonality across the individual structures, and the "common-structure" (CS) approach that imposes the same network structure on the BN of every subject, but allows the parameters to differ across subjects. To explore the effects of these three approaches, we applied them to an fMRI study exploring the motor effect of L-dopa medication on ten subjects with Parkinson's disease (PD), as the profound clinical effects of this medication suggest that fMRI activation in PD subjects after medication should start approaching that of age-matched controls. We found that none of these approaches is generally superior over the others, according to Bayesian-information-criterion (BIC) scores, and that they led to considerably different group-level results. The IS approach was more sensitive to the normalization effect of the L-dopa medication on brain connectivity. However, for the more homogeneous control population, the VTS approach was superior. Group-analysis approaches should be selected carefully with consideration of both statistical and biomedical evidence.

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During the second trimester, the human fetal brain undergoes numerous changes that lead to substantial variation in the neonatal in terms of its morphology and tissue types. As fetal MRI is more and more widely used for studying the human brain development during this period, a spatiotemporal atlas becomes necessary for characterizing the dynamic structural changes. In this study, 34 postmortem human fetal brains with gestational ages ranging from 15 to 22 weeks were scanned using 7.0 T MR. We used automated morphometrics, tensor-based morphometry and surface modeling techniques to analyze the data. Spatiotemporal atlases of each week and the overall atlas covering the whole period with high resolution and contrast were created. These atlases were used for the analysis of age-specific shape changes during this period, including development of the cerebral wall, lateral ventricles, Sylvian fissure, and growth direction based on local surface measurements. Our findings indicate that growth of the subplate zone is especially striking and is the main cause for the lamination pattern changes. Changes in the cortex around Sylvian fissure demonstrate that cortical growth may be one of the mechanisms for gyration. Surface deformation mapping, revealed by local shape analysis, indicates that there is global anterior–posterior growth pattern, with frontal and temporal lobes developing relatively quickly during this period. Our results are valuable for understanding the normal brain development trajectories and anatomical characteristics. These week-by-week fetal brain atlases can be used as reference in in vivo studies, and may facilitate the quantification of fetal brain development across space and time.