Graphene Aerogel-Directed Fabrication of Phase Change Composites

Although phase change materials have been extensively used for thermal energy storage, various shortcomings such as low thermal conductivity, leakage during work, and shortage of multiple driving ways greatly hinder their practical applications. Among the new materials that can overcome these problems, graphene aerogel has attracted special interest owing to its 3D conductive network and extraordinary capillary force. In this chapter, we review recent progress of graphene-aerogel-based phase change composites (PCCs) and provide a brief introduction on the following topics: 1) why graphene aerogels can be used for PCCs, 2) the sol-gel transition synthesis of graphene aerogels, 3) the fabrication of graphene-aerogel-based PCCs, and 4) their applications in thermal energy storage, electric-thermal conversion and storage, solar-thermal conversion and storage, and thermal buffer. Finally, we also discuss the limitation and future development of these graphene-based materials

Distributed Logistic Regression for Massive Data with Rare Events

Large-scale rare events data are commonly encountered in practice. To tackle the massive rare events data, we propose a novel distributed estimation method for logistic regression in a distributed system. For a distributed framework, we face the following two challenges. The first challenge is how to distribute the data. In this regard, two different distribution strategies (i.e., the RANDOM strategy and the COPY strategy) are investigated. The second challenge is how to select an appropriate type of objective function so that the best asymptotic efficiency can be achieved. Then, the under-sampled (US) and inverse probability weighted (IPW) types of objective functions are considered. Our results suggest that the COPY strategy together with the IPW objective function is the best solution for distributed logistic regression with rare events. The finite sample performance of the distributed methods is demonstrated by simulation studies and a real-world Sweden Traffic Sign dataset

Subnetwork Estimation for Spatial Autoregressive Models in Large-scale Networks

Large-scale networks are commonly encountered in practice (e.g., Facebook and Twitter) by researchers. In order to study the network interaction between different nodes of large-scale networks, the spatial autoregressive (SAR) model has been popularly employed. Despite its popularity, the estimation of a SAR model on large-scale networks remains very challenging. On the one hand, due to policy limitations or high collection costs, it is often impossible for independent researchers to observe or collect all network information. On the other hand, even if the entire network is accessible, estimating the SAR model using the quasi-maximum likelihood estimator (QMLE) could be computationally infeasible due to its high computational cost. To address these challenges, we propose here a subnetwork estimation method based on QMLE for the SAR model. By using appropriate sampling methods, a subnetwork, consisting of a much-reduced number of nodes, can be constructed. Subsequently, the standard QMLE can be computed by treating the sampled subnetwork as if it were the entire network. This leads to a significant reduction in information collection and model computation costs, which increases the practical feasibility of the effort. Theoretically, we show that the subnetwork-based QMLE is consistent and asymptotically normal under appropriate regularity conditions. Extensive simulation studies, based on both simulated and real network structures, are presented

Trajectories of the Hippocampal Subfields Atrophy in the Alzheimer’s Disease: A Structural Imaging Study

BackgroundThe hippocampus and hippocampal subfields have been found to be diversely affected in Alzheimer’s Disease (AD) and early stages of Alzheimer’s disease by neuroimaging studies. However, our knowledge is still lacking about the trajectories of the hippocampus and hippocampal subfields atrophy with the progression of Alzheimer’s disease.ObjectiveTo identify which subfields of the hippocampus differ in the trajectories of Alzheimer’s disease by magnetic resonance imaging (MRI) and to determine whether individual differences on memory could be explained by structural volumes of hippocampal subfields.MethodsFour groups of participants including 41 AD patients, 43 amnestic mild cognitive impairment (aMCI) patients, 35 subjective cognitive decline (SCD) patients and 42 normal controls (NC) received their structural MRI brain scans. Structural MR images were processed by the FreeSurfer 6.0 image analysis suite to extract the hippocampus and its subfields. Furthermore, we investigated relationships between hippocampal subfield volumes and memory test variables (AVLT-immediate recall, AVLT-delayed recall, AVLT-recognition) and the regression model analyses were controlled for age, gender, education and eTIV.ResultsCA1, subiculum, presubiculum, molecular layer and fimbria showed the trend toward significant volume reduction among four groups with the progression of Alzheimer’s disease. Volume of left subiculum was most strongly and actively correlated with performance across AVLT measures.ConclusionThe trend changes in the hippocampus subfields and further illustrates that SCD is the preclinical stage of AD earlier than aMCI. Future studies should aim to associate the atrophy of the hippocampal subfields in SCD with possible conversion to aMCI or AD with longitudinal design

SiN-on-SOI Optical Phased Array LiDAR for Ultra-Wide Field of View and 4D Sensing

Three-dimensional (3D) imaging techniques are facilitating the autonomous vehicles to build intelligent system. Optical phased arrays (OPAs) featured by all solid-state configurations are becoming a promising solution for 3D imaging. However, majority of state-of-art OPAs commonly suffer from severe power degradation at the edge of field of view (FoV), resulting in limited effective FoV and deteriorating 3D imaging quality. Here, we synergize chained grating antenna and vernier concept to design a novel OPA for realizing a record wide 160{\deg}-FoV 3D imaging. By virtue of the chained antenna, the OPA exhibits less than 3-dB beam power variation within the 160{\deg} FoV. In addition, two OPAs with different pitch are integrated monolithically to form a quasi-coaxial Vernier OPA transceiver. With the aid of flat beam power profile provided by the chained antennas, the OPA exhibits uniform beam quality at an arbitrary steering angle. The superior beam steering performance enables the OPA to accomplish 160{\deg} wide-FoV 3D imaging based on the frequency-modulated continuous-wave (FMCW) LiDAR scheme. The ranging accuracy is 5.5-mm. Moreover, the OPA is also applied to velocity measurement for 4D sensing. To our best knowledge, it is the first experimental implementation of a Vernier OPA LiDAR on 3D imaging to achieve a remarkable FoV.Comment: 18 pages with 13 figure

Assembling Hollow Carbon Sphere-Graphene Polylithic Aerogels for Thermoelectric Cells

Aerogels are highly porous bulk materials assembled chemically or physically with various nanoscale building blocks and thus hold promise for numerous applications including energy storage and conversion. Assembling of hollow or porous particles with the diameter larger than 100 nm into hierarchically porous aerogels is efficient but challenging for achieving a high specific surface of aerogel. In this regard, submicron-sized carbon spheres with hollow cores and microporous shells are assembled into bulk aerogels, for the first time, in the presence of two-dimensional graphene sheets as special cross-linkers. The resulting bead-to-sheet polylithic aerogels show ultra-low density (51–67 mg cm−3), high conductivity (263–695 S m−1) and high specific surface area (569–609 m2 g−1). An application of thermocells is demonstrated with maximum output power of 1.05 W m−2 and maximum energy conversion efficiency of 1.4% relative to Carnot engine, outperforming the current simple U-shaped thermocells reported elsewhere

Quantitative structural analysis of hemifacial microsomia mandibles in different age groups

IntroductionThis study aims to quantitively analyze mandibular ramus and body deformities, assessing the asymmetry and progression in different components.MethodsThis is a retrospective study on hemifacial microsomia children. They were divided into mild/severe groups by Pruzansky-Kaban classification and into three age groups (<1 year,1–5 years, 6–12 years old). Linear and volumetric measurements of the ramus and the body were collected via their preoperative imaging data to compare between the different sides and severities, using independent and paired tests, respectively. The progression of asymmetry was assessed by changes in affected/contralateral ratios with age using multi-group comparisons.ResultsTwo hundred and ten unilateral cases were studied. Generally, the affected ramus and body were significantly smaller than those on the contralateral side. Linear measurements on the affected side were shorter in the severe group. Regarding affected/contralateral ratios, the body was less affected than the ramus. Progressively decreased affected/contralateral ratios of body length, dentate segment volume, and hemimandible volume were found.DiscussionThere were asymmetries in mandibular ramus and body regions, which involved the ramus more. A significant contribution to progressive asymmetry from the body suggests treatment focus in this region

ARID1A Deficiency Impairs the DNA Damage Checkpoint and Sensitizes Cells to PARP Inhibitors

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