Enhanced efficiency of crystalline Si solar cells based on kerfless-thin wafers with nanohole arrays

Several techniques have been proposed for kerfless wafering of thin Si wafers, which is one of the most essential techniques for reducing Si material loss in conventional wafering methods to lower cell cost. Proton induced exfoliation is one of promising kerfless techniques due to the simplicity of the process of implantation and cleaving. However, for application to high efficiency solar cells, it is necessary to cope with some problems such as implantation damage removal and texturing of (111) oriented wafers. This study analyzes the end-of-range defects at both kerfless and donor wafers and ion cutting sites. Thermal treatment and isotropic etching processes allow nearly complete removal of implantation damages in the cleaved-thin wafers. Combining laser interference lithography and a reactive ion etch process, a facile nanoscale texturing process for the kerfless thin wafers of a (111) crystal orientation has been developed. We demonstrate that the introduction of nanohole array textures with an optimal design and complete damage removal lead to an improved efficiency of 15.2% based on the kerfless wafer of a 48 mu m thickness using the standard architecture of the Al back surface field

Computed tomographic evaluation of abdominal fat in minipigs

Computed tomography (CT) exams were conducted to determine the distribution of abdominal fat identified based on the CT number measured in Hounsfield Units (HU) and to measure the volume of the abdominal visceral and subcutaneous fat in minipigs. The relationship between the CT-based fat volumes of several vertebral levels and the entire abdomen and anthropometric data including the sagittal abdominal diameter and waist circumference were evaluated. Moreover, the total fat volumes at the T11, T13, L3, and L5 levels were compared with the total fat volume of the entire abdomen to define the landmark of abdominal fat distribution. Using a single-detector CT, six 6-month-old male minipigs were scanned under general anesthesia. Three radiologists then assessed the HU value of visceral and subcutaneous abdominal fat by drawing the region of interest manually at the T11, T13, L1, L3, and L5 levels. The CT number and abdominal fat determined in this way by the three radiologists was found to be correlated (intra-class coefficient = 0.9). The overall HU ranges for the visceral and subcutaneous fat depots were -147.47 to -83.46 and -131.62 to -90.97, respectively. The total fat volume of the entire abdomen was highly correlated with the volume of abdominal fat at the T13 level (r = 0.97, p < 0.0001). These findings demonstrate that the volume of abdominal adipose tissue measured at the T13 level using CT is a strong and reliable predictor of total abdominal adipose volume

Calsyntenin-3 Interacts With Both α- And β-Neurexins in the Regulation of Excitatory Synaptic Innervation in Specific Schaffer Collateral Pathways

Calsyntenin-3 (Clstn3) is a postsynaptic adhesion molecule that induces presynaptic differentiation via presynaptic neurexins (Nrxns), but whether Nrxns directly bind to Clstn3 has been a matter of debate. Here, using LC-MS/MS-based protein analysis, confocal microscopy, RNAscope assays, and electrophysiological recordings, we show that β-Nrxns directly interact via their LNS domain with Clstn3 and Clstn3 cadherin domains. Expression of splice site 4 (SS4) insert-positive β-Nrxn variants, but not insert-negative variants, reversed the impaired Clstn3 synaptogenic activity observed in Nrxn-deficient neurons. Consistently, Clstn3 selectively formed complexes with SS4-positive Nrxns in vivo Neuron-specific Clstn3 deletion caused significant reductions in number of excitatory synaptic inputs. Moreover, expression of Clstn3 cadherin domains in CA1 neurons of Clstn3 conditional knockout mice rescued structural deficits in excitatory synapses, especially within the stratum radiatum layer. Collectively, our results suggest that Clstn3 links to SS4-positive Nrxns to induce presynaptic differentiation and orchestrate excitatory synapse development in specific hippocampal neural circuits, including Schaffer collateral afferents. © 2020 Kim et al.1

LRRTM3 Regulates Excitatory Synapse Development through Alternative Splicing and Neurexin Binding

The four members of the LRRTM family (LRRTM1-4) are postsynaptic adhesion molecules essential for excitatory synapse development. They have also been implicated in neuropsychiatric diseases. Here, we focus on LRRTM3, showing that two distinct LRRTM3 variants generated by alternative splicing regulate LRRTM3 interaction with PSD-95, but not its excitatory synapse-promoting activity. Overexpression of either LRRTM3 variant increased excitatory synapse density in dentate gyrus (DG) granule neurons, whereas LRRTM3 knockdown decreased it. LRRTM3 also controlled activity-regulated AMPA receptor surface expression in an alternative splicing-dependent manner. Furthermore, Lrrtm3-knockout mice displayed specific alterations in excitatory synapse density, excitatory synaptic transmission and excitability in DG granule neurons but not in CA1 pyramidal neurons. Lastly, LRRTM3 required only specific splice variants of presynaptic neurexins for their synaptogenic activity. Collectively, our data highlight alternative splicing and differential presynaptic ligand utilization in the regulation of LRRTMs, revealing key regulatory mechanisms for excitatory synapse development.Peer reviewe

Alexander Rosenblatt's Piano Sonata No. 2 and Its Influences: The Blending of Classical Techniques and Jazz Elements

Alexander Rosenblatt (b. 1956), a Russian composer, pianist and conductor, is an outstanding contemporary composer. He has composed more than forty works in diverse genres, with almost half of his output devoted to piano compositions, including piano solo, duets, concerti, and chamber music. In this study, I discuss Rosenblatt's background as a musician and composer and provide an analysis of his compositional styles as found in Piano Sonata No. 2 (1988). Rosenblatt's music integrates classical form with a variety of other influences creating a style that brings together diverse elements from those of precedent composers, such as Chopin, Rachmaninoff, and Prokofiev and their compositional techniques, to the stylistic trends of jazz, including quartal harmony, syncopation, suspended chords, swing, and improvisational style. With his fusion of classical and jazz compositional techniques, Rosenblatt occupies an important place in contemporary piano literature. My research sheds light on Rosenblatt's compositional techniques, contributing to the scholarship on contemporary piano literature in general and providing insight for performers and scholars with specific interest in Rosenblatt. It is my hope that my work will stimulate further research on Rosenblatt and his contemporaries, and that his compositions may assume a prominent place in the standard repertory

Selective synthesis of Bernal-stacked and 30o-twisted bilayer graphene on Cu(111) catalyst by chemical vapor deposition

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Synthesis of bilayer graphene with controlled stacking order on Cu catalyst by chemical vapor deposition

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Deep Learning-Based Prediction of Resource Block Usage Rate for Spectrum Saturation Diagnosis

Strict restrictions on spectrum utilization and the rapid increases in mobile users have brought fundamental challenges for mobile network operators in securing sufficient spectrum resources. In designing reliable cellular networks, it is essential to predict spectrum saturation events in the future by analyzing the past behavior of base stations, especially their frequency resource block (RB) utilization states. This paper investigates a deep learning-based forecasting strategy of the future RB usage rate (RBUR) status of hundreds of LTE base stations deployed in Seoul, South Korea. The dataset consists of real measurement RBUR samples with a randomly varying number of base stations at each measurement time. This poses a difficulty in handling variable-length RBUR data vectors, which is not trivial for state-of-the-art deep learning estimation models, e.g., recurrent neural networks (RNNs), developed for handling fixed-length inputs. To this end, we propose a two-step RBUR estimation approach. In the first step, we extract a useful feature of the RBUR dataset that accurately approximates the behavior of the top quantile base stations. The feature parameters are carefully designed to be fixed-length vectors regardless of the dimensions of the raw RBUR samples. The fixed-length feature parameter vectors are readily exploited as the training dataset of RNN-based prediction models. Thus, in the second step, we propose a feature estimation strategy where the RNN is trained to predict the future RBUR from the input feature parameter sequences. With the estimated RBUR at hand, we can easily predict the spectrum saturation of the future LTE systems by examining the resource utilization states of the top quantile base stations. Numerical results demonstrate the performance of the proposed RBUR estimation methods with the real measurement dataset