bluuebunny/crossref_metadata_2025_split huggingface dataset:
Effects of metallic electrodes on the thermoelectric properties of zigzag graphene nanoribbons with periodic vacancies
Authors: Kuo David M T 
Published: April 2023

Abstract
We theoretically analyze the thermoelectric properties of graphene quantum dot arrays (GQDAs) with line- or surface-contacted metal electrodes. Such GQDAs are realized as zigzag graphene nanoribbons (ZGNRs) with periodic vacancies. Gaps and minibands are formed in these GQDAs, which can have metallic and semiconducting phases. The electronic states of the first conduction (valence) miniband with nonlinear dispersion may have long coherent lengths along the zigzag edge direction. With line-contacted metal electrodes, the GQDAs have the characteristics of serially coupled quantum dots (SCQDs) if the armchair edge atoms of the ZGNRs are coupled to the electrodes. By contrast, the GQDAs have the characteristics of parallel quantum dots if the zigzag edge atoms are coupled to the electrodes. The maximum thermoelectric power factors of SCQDs with line-contacted electrodes of Cu, Au, Pt, Pd, or Ti at room temperature were similar or greater than 0.186 nW K−1; their figures of merit were greater than three. GQDAs with line-contacted metal electrodes have much better thermoelectric performance than surface contacted metal electrodes.
Localized magnetic moment induced by boron adatoms chemisorbed on graphene
Authors: Li Chong , Wang Fei , Cui Bin , Pan Zhifeng , Jia Yu 
Published: April 2023

Abstract
Inducing local spin-polarization in pristine graphene is highly desirable and recent experiment shows that boron adatom chemical attachment to graphene exhibits local high spin state. Using hybrid exchange-correlation functional, we show that boron (B) monomer chemisorbed on the bridge site of graphene is energically favorable, and indeed induces a weak local spin-polarization ∼0.56 μB. The localized magnetic moment can be attributed to the charge transfer from boron atom to graphene, resulting in local spin charge dominantly surrounding to the adsorbed B and neighboring carbon (C) atoms. We also surprisingly find that boron dimer can even much more stable upright anchor the same site of graphene, giving rise to sizable spin magnetic moment 2.00 μB. Although the apparent spin state remains mainly contributed by B p and C p orbitals as the case of boron monomer, the delicate and substantial charge transfer of the intra-dimer plays a fundamental role in producing such sizable local spin-polarization. We employed various van der Waals corrections to check and confirm the validity of appeared local spin-polarization. In terms of the almost identical simulated scanning tunneling microscope between boron monomer and dimer, we might tend to support the fact that boron dimer can also be chemisorbed on graphene with much larger and stable localized spin magnetic moment.
Randomized channel subsampling method for efficient ultrafast ultrasound imaging
Authors: Yu Junjin , Guo Xingyi , Yan Shaoyuan , Le Qiumin , Hingot Vincent , Ta Dean , Couture Olivier , Xu Kailiang 
Published: April 2023

Abstract
Emerging as a novel modality with high spatiotemporal sensitivity, ultrafast ultrasound imaging has been well established and incorporated into clinical apparatus. However, such a mode typically requires large data acquisitions, with more than thousands of frames per second within a relatively long period, which poses high demands on real-time storage and processing hardware. Herein, an image reconstruction approach based on randomized channel subsampling is presented, which significantly reduces the amount of data acquisition while maintaining imaging performance. In vivo datasets from rats were used to evaluate the performance of the proposed method in B-mode imaging, ultrafast Doppler imaging, and super-resolution ultrasound localization microscopy (ULM) under different subsampling conditions. Following a thorough comparison of the contrast-to-noise ratio, signal-to-noise ratio, and visibility of the ultrafast Doppler based small-vessel imaging, the resolution and saturation of ULM imaging were also investigated under different subsampling conditions. The feasibility of the proposed method was demonstrated in task-based functional ultrasound (fUS) imaging with whisker stimulation. A comparison between the results of fUS based on introduced randomized channel subsampling and conventional fUS was also conducted to evaluate the subsampling effects on reconstruction accuracy and cerebral change detection sensitivity.
Tritium-titanium target degradation due to deuterium irradiation for DT neutron production
Authors: Rajput M. , Swami H.L. , Vala S. , Abhangi M. , Kumar Ratnesh , Kumar R. 
Published: April 2023

Abstract
In the present article, we have investigated tritium removal from tritium-titanium targets during fusion neutron production and the impact of tritium degradation on neutron production. The removal of tritium from the target is predicted for deuterium ion irradiation with the SDTrimSp code. We adopt the binary collision approximation method to simulate the recoils and projectile trajectories and concentration of constituents in the target. We have modelled four phenomena in our simulations; ion exchange, sputtering, outgassing of tritium, and thermal diffusion of hydrogen isotopes in the target caused by deuterium irradiation. Insignificant contributors such as burn-up of tritium in neutron production and loss of tritium due to radioactive decay are not included in our model. This tritium removal results in the nonuniform distribution of tritium in the target. A Python-based script is developed to investigate the effects of tritium removal on neutron production with these pristine and irradiated targets. This script uses the layered composition of the constituents’ atoms, DT reaction cross-section, and stopping power of deuterium ions in the target. The script is validated with the NeuSdesc code for the pristine target. Using the layered composition of tritium atoms in the target obtained from the SDTrimSp simulations, the script predicts the degradation in neutron production for different irradiation scenarios.
A quasi-isodynamic configuration with good confinement of fast ions at low plasma β
Authors: Sánchez E. , Velasco J.L. , Calvo I. , Mulas S. 
Published: April 2023

Abstract
A new quasi-isodynamic (QI) stellarator configuration optimized for the confinement of energetic ions at low plasma β is obtained. The numerical optimization is carried out using the STELLOPT suite of codes. New proxies to measure closeness to quasi-isodynamicity and quality of fast ion confinement have been included. The new configuration has poloidally closed contours of magnetic field strength, low magnetic shear and a rotational transform profile allowing an island divertor. It shows ideal and ballooning magnetohydrodynamic stability up to

β
=
5
%

, reduced effective ripple, with

ϵ

e
f
f

<
0.5
%

in the plasma core. Even at low β, the configuration approximately satisfies the maximum-J property, and the confinement of fast ions is good at

β
∼
1.5
%

and becomes excellent at reactor values,

β
∼
4
%

. An evaluation of the D
31 neoclassical mono-energetic coefficient supports the expectation of a reduced bootstrap current for plasmas confined in QI configurations. A set of filamentary coils that preserve the good confinement of fast ions in the core is presented.
Optimal scheduling strategy of the hydrogen-electric coupled system considering source-load uncertainty
Authors: Chen Jianhao , Xie Changjun , Ye Hao , Li Shenwei , Li Yingzhu 
Published: April 2023

Abstract
Hybrid energy storage systems are increasingly envisaged to be used in the construction of microgrids to alleviate the intermittency of renewable energy output and achieve large-scale penetration into the power grid. The hydrogen-electric coupled system (HECS) combines long-term hydrogen storage with short-term battery storage and realizes the conversion of multiple energy flows through power to gas and gas to power. However, the optimal scheduling of such a hybrid energy storage system is more complex than a single one. Faced with this issue, this paper proposed a HECS scheduling method based on the storage degradation cost to solve the power distribution problem. Furthermore, to reduce the impact of source-load uncertainty, the improved k-medoids method is used to extract representatively typical and extreme scenarios from the annual data to evaluate the economy and reliability of the system operation. According to the simulation result, we can conclude that the proposed method achieves cost-optimal scheduling by maximizing hydrogen production in the on-grid mode and reducing the battery cycle cost in the off-grid mode. Compared with the state machine control strategy, the proposed method has lowered the operation cost in all simulation scenarios and shows its effectiveness. Finally, the results show that the operation cost can be reduced by applying a demand response program, especially in the off-grid mode.
Application of Fast-Charging Graphite-Anode Lithium-Ion Batteries in Black Start
Authors: Chen Yixing , Zuo Jian , He Xiangzhen , Zhang Shuiping , Bao Bo 
Published: April 2023

Abstract
Industrial and commercial energy storage are widely used. Large industrial users can adjust the peak and valley power consumption during the summer peak power consumption to reduce the burden on transformers and to gain certain benefits from the peak and valley power difference. For users with important loads, the energy storage system is a alternative power supply to ensure the continuity of power supply for important loads. This paper systematically introduced the research progress based on the optimization of electrolyte and charging strategy in the field of improving the rapid charging of graphite anodes in recent years, which provided a reference for promoting the commercial application of rapid charging technology and the high-quality development of domestic energy storage technology. The high-power energy storage battery, in particular, can recover power supply as soon as possible after large-scale power system shutdown accidents or even power outages, reducing economic losses and social impact.
Quantum scrambling via accessible tripartite information
Authors: Lo Monaco Gabriele , Innocenti Luca , Cilluffo Dario , Chisholm Diana A , Lorenzo Salvatore , Palma G Massimo 
Published: April 2023

Abstract
Quantum information scrambling (QIS), from the perspective of quantum information theory, is generally understood as local non-retrievability of information evolved through some dynamical process, and is often quantified via entropic quantities such as the tripartite information. We argue that this approach comes with a number of issues, in large part due to its reliance on quantum mutual informations, which do not faithfully quantify correlations directly retrievable via measurements, and in part due to the specific methodology used to compute tripartite informations of the studied dynamics. We show that these issues can be overcome by using accessible mutual informations, defining corresponding ‘accessible tripartite informations’, and provide explicit examples of dynamics whose scrambling properties are not properly quantified by the standard tripartite information. Our results lay the groundwork for a more profound understanding of what QIS represents, and reveal a number of promising, as of yet unexplored, venues for further research.
A study of intention to recycle pesticide packaging among rural farmers in China based on the logistic model
Authors: Li Ye , Wang Ying , Han Xinran , Wang Haodong , Fu Jia 
Published: April 2023

Abstract
A consequence of the improvement in China’s agricultural production capacity has been increased production of pesticide packaging waste, which is a major obstacle to the sustainable development of rural areas. Although the Chinese government has made great efforts to control pollution by waste pesticide packaging, a unified recycling system has not yet been established. Therefore, determining whether farmers are willing to participate in recycling of pesticide packaging is of great relevance. This study conducted a random questionnaire survey in which 459 responses were obtained in rural areas in Liaoning, Henan, Guizhou, Anhui, Gansu, and Hunan provinces. The disorderly classification logistic model was used to analyze the individual characteristics of farmers, pesticide container sizes, perception of risk, conditions of local facilities, and awareness. The result showed that the individual characteristics of farmers and awareness had no influence of their willingness to participate in recycling, whereas container size, perception of risk, and condition of local facilities did have an influence. Container specifications have the strongest correlation with farmers’ willingness to recycle, and attempts can be made to promote pesticide packaging of about 500 ml; raising farmers’ awareness of environmental safety and possible health hazards, all of which can increase farmers’ participation in the work. Using these results, this study proposed suggestions for pesticide container waste disposal. Attempts can be made to establish a suitable recycling model and management mechanism through the establishment of additional recycling points. Refine pesticide packaging waste recycling, collection and storage, disposal and other measures to purify the environment of origin, to achieve green agricultural development, and promote the construction of ecological civilization.
Dimensions, Ratios, Volumes, and Weights of the Fatty Parts of the Vulva (Mons Pubis and Labia Majora)
Authors: Seleem Mostafa , Osman Omneya M , Kashmar Sanaa G , Lotfy Rehab 
Published: April 2023

Abstract

Background
The anatomy of the fatty parts of the vulva, specifically the mons pubis and labia majora, has not been extensively studied.

Objectives
This study aimed to provide a comprehensive 3-dimensional picture of the mons pubis and labia majora by defining and measuring their dimensions. Furthermore, the study sought to calculate the different ratios, volumes, and weights of these structures.

Methods
The study included 214 women, who were divided into 2 groups based on their BMI. The researchers defined 27 dimensions to describe the width, height, and length of the mons pubis and labia majora, and specified the measuring methodology.

Results
The height of the mons pubis was approximately three-quarters of its length and half its width. The labia majora were wider and taller anteriorly than posteriorly, and their ratios decreased as they went posteriorly, from 4 to 2. The ratio of monal height to maximum labial height and to labial length was 3 and 1, respectively, whereas the ratio of monal width to the sum of maximum labial widths was nearly 1.5. The calculated volumes and weights of the mons pubis and labium majus were 200 mL, 30 mL, 180 g, and 27 g in Group 1, and 300 mL, 40 mL, 270 g, and 36 g in Group 2, respectively.

Conclusions
The dimensions, ratios, weights, and volumes defined and measured in this study can serve as the basis for any aesthetic measures involving the mons pubis and labia majora. Moreover, BMI and the different ratios should be considered in such measures.
Transformer-based anti-noise models for CRISPR-Cas9 off-target activities prediction
Authors: Guan Zengrui , Jiang Zhenran 
Published: April 2023

Abstract
The off-target effect occurring in the CRISPR-Cas9 system has been a challenging problem for the practical application of this gene editing technology. In recent years, various prediction models have been proposed to predict potential off-target activities. However, most of the existing prediction methods do not fully exploit guide RNA (gRNA) and DNA sequence pair information effectively. In addition, available prediction methods usually ignore the noise effect in original off-target datasets. To address these issues, we design a novel coding scheme, which considers the key features of mismatch type, mismatch location and the gRNA-DNA sequence pair information. Furthermore, a transformer-based anti-noise model called CrisprDNT is developed to solve the noise problem that exists in the off-target data. Experimental results of eight existing datasets demonstrate that the method with the inclusion of the anti-noise loss functions is superior to available state-of-the-art prediction methods. CrisprDNT is available at https://github.com/gzrgzx/CrisprDNT.
SmartGate is a spatial metabolomics tool for resolving tissue structures
Authors: Xiao Kaixuan , Wang Yu , Dong Kangning , Zhang Shihua 
Published: April 2023

Abstract
Imaging mass spectrometry (IMS) is one of the powerful tools in spatial metabolomics for obtaining metabolite data and probing the internal microenvironment of organisms. It has dramatically advanced the understanding of the structure of biological tissues and the drug treatment of diseases. However, the complexity of IMS data hinders the further acquisition of biomarkers and the study of certain specific activities of organisms. To this end, we introduce an artificial intelligence tool, SmartGate, to enable automatic peak selection and spatial structure identification in an iterative manner. SmartGate selects discriminative m/z features from the previous iteration by differential analysis and employs a graph attention autoencoder model to perform spatial clustering for tissue segmentation using the selected features. We applied SmartGate to diverse IMS data at multicellular or subcellular spatial resolutions and compared it with four competing methods to demonstrate its effectiveness. SmartGate can significantly improve the accuracy of spatial segmentation and identify biomarker metabolites based on tissue structure–guided differential analysis. For multiple consecutive IMS data, SmartGate can effectively identify structures with spatial heterogeneity by introducing three-dimensional spatial neighbor information.
Potential of dissimilarity measure-based computation of protein thermal stability data for determining protein interactions
Authors: Teitz Joshua , Sander Joerg , Sarker Hassan , Fernandez-Patron Carlos 
Published: April 2023

Abstract
Determining the interacting proteins in multiprotein complexes can be technically challenging. An emerging biochemical approach to this end is based on the ‘thermal proximity co-aggregation’ (TPCA) phenomenon. Accordingly, when two or more proteins interact to form a complex, they tend to co-aggregate when subjected to heat-induced denaturation and thus exhibit similar melting curves. Here, we explore the potential of leveraging TPCA for determining protein interactions. We demonstrate that dissimilarity measure-based information retrieval applied to melting curves tends to rank a protein-of-interest’s interactors higher than its non-interactors, as shown in the context of pull-down assay results. Consequently, such rankings can reduce the number of confirmatory biochemical experiments needed to find bona fide protein–protein interactions. In general, rankings based on dissimilarity measures generated through metric learning further reduce the required number of experiments compared to those based on standard dissimilarity measures such as Euclidean distance. When a protein mixture’s melting curves are obtained in two conditions, we propose a scoring function that uses melting curve data to inform how likely a protein pair is to interact in one condition but not another. We show that ranking protein pairs by their scores is an effective approach for determining condition-specific protein–protein interactions. By contrast, clustering melting curve data generally does not inform about the interacting proteins in multiprotein complexes. In conclusion, we report improved methods for dissimilarity measure-based computation of melting curves data that can greatly enhance the determination of interacting proteins in multiprotein complexes.
Self-supervised semantic segmentation of retinal pigment epithelium cells in flatmount fluorescent microscopy images
Authors: Yu Hanyi , Wang Fusheng , Teodoro George , Chen Fan , Guo Xiaoyuan , Nickerson John M , Kong Jun 
Published: April 2023

Abstract

Motivation
Morphological analyses with flatmount fluorescent images are essential to retinal pigment epithelial (RPE) aging studies and thus require accurate RPE cell segmentation. Although rapid technology advances in deep learning semantic segmentation have achieved great success in many biomedical research, the performance of these supervised learning methods for RPE cell segmentation is still limited by inadequate training data with high-quality annotations.

Results
To address this problem, we develop a Self-Supervised Semantic Segmentation (S4) method that utilizes a self-supervised learning strategy to train a semantic segmentation network with an encoder–decoder architecture. We employ a reconstruction and a pairwise representation loss to make the encoder extract structural information, while we create a morphology loss to produce the segmentation map. In addition, we develop a novel image augmentation algorithm (AugCut) to produce multiple views for self-supervised learning and enhance the network training performance. To validate the efficacy of our method, we applied our developed S4 method for RPE cell segmentation to a large set of flatmount fluorescent microscopy images, we compare our developed method for RPE cell segmentation with other state-of-the-art deep learning approaches. Compared with other state-of-the-art deep learning approaches, our method demonstrates better performance in both qualitative and quantitative evaluations, suggesting its promising potential to support large-scale cell morphological analyses in RPE aging investigations.

Availability and implementation
The codes and the documentation are available at: https://github.com/jkonglab/S4_RPE.
POIROT: a powerful test for parent-of-origin effects in unrelated samples leveraging multiple phenotypes
Authors: Head S Taylor , Leslie Elizabeth J , Cutler David J , Epstein Michael P 
Published: April 2023

Abstract

Motivation
There is widespread interest in identifying genetic variants that exhibit parent-of-origin effects (POEs) wherein the effect of an allele on phenotype expression depends on its parental origin. POEs can arise from different phenomena including genomic imprinting and have been documented for many complex traits. Traditional tests for POEs require family data to determine parental origins of transmitted alleles. As most genome-wide association studies (GWAS) sample unrelated individuals (where allelic parental origin is unknown), the study of POEs in such datasets requires sophisticated statistical methods that exploit genetic patterns we anticipate observing when POEs exist. We propose a method to improve discovery of POE variants in large-scale GWAS samples that leverages potential pleiotropy among multiple correlated traits often collected in such studies. Our method compares the phenotypic covariance matrix of heterozygotes to homozygotes based on a Robust Omnibus Test. We refer to our method as the Parent of Origin Inference using Robust Omnibus Test (POIROT) of multiple quantitative traits.

Results
Through simulation studies, we compared POIROT to a competing univariate variance-based method which considers separate analysis of each phenotype. We observed POIROT to be well-calibrated with improved power to detect POEs compared to univariate methods. POIROT is robust to non-normality of phenotypes and can adjust for population stratification and other confounders. Finally, we applied POIROT to GWAS data from the UK Biobank using BMI and two cholesterol phenotypes. We identified 338 genome-wide significant loci for follow-up investigation.

Availability and implementation
The code for this method is available at https://github.com/staylorhead/POIROT-POE.