•This paper studies dynamic egocentric networks as the venue of knowledge creation.•An inverted U-shaped relation between network average tie strength and impact.•A skewed tie strength distribution ...contributes to higher citation impact.•Tie strength skewness moderates the effect of network average tie strength.
This paper studies the relationship between egocentric collaboration networks and knowledge creation at the individual level. For egocentric networks we focus on the characteristics of tie strength and tie configuration, and knowledge creation is assessed by the number of citations. Using a panel of 1042 American scientists in five disciplines and fixed effects models, we found an inverted U-shaped relationship between network average tie strength and citation impact, because an increase in tie strength on the one hand facilitates the collaborative knowledge creation process and on the other hand decreases cognitive diversity. In addition, when the network average tie strength is high, a more skewed network performs better because it still has a “healthy” mixture of weak and strong ties and a balance between exploration and exploitation. Furthermore, the tie strength skewness moderates the effect of network average tie strength: both the initial positive effect and the later negative effect of an increase in tie strength are smaller in a more skewed network than in a less skewed one.
This paper aims to inform choice of citation time window for research evaluation, by answering three questions: (1) How accurate is it to use citation counts in short time windows to approximate ...total citations? (2) How does citation ageing vary by research fields, document types, publication months, and total citations? (3) Can field normalization improve the accuracy of using short citation time windows? We investigate the 31-year life time non-self-citation processes of all Thomson Reuters Web of Science journal papers published in 1980. The correlation between non-self-citation counts in each time window and total non-self-citations in all 31 years is calculated, and it is lower for more highly cited papers than less highly cited ones. There are significant differences in citation ageing between different research fields, document types, total citation counts, and publication months. However, the within group differences are more striking; many papers in the slowest ageing field may still age faster than many papers in the fastest ageing field. Furthermore, field normalization cannot improve the accuracy of using short citation time windows. Implications and recommendations for choosing adequate citation time windows are discussed.
Angular momentum, a fundamental physical quantity, can be divided into spin angular momentum (SAM) and orbital angular momentum (OAM) in electromagnetic waves. Helically-phased or twisted light beams ...carrying OAM that exploit the spatial structure physical dimension of electromagnetic waves have benefited wide applications ranging from optical manipulation to quantum information processing. Using the two distinct properties of OAM, i.e., inherent orthogonality and unbounded states in principle, one can develop OAM modulation and OAM multiplexing techniques for twisted optical communications. OAM multiplexing is an alternative space-division multiplexing approach employing an orthogonal mode basis related to the spatial phase structure. In this paper, we review the recent progress in twisted optical communications using OAM in free space and fiber. The basic concept of momentum, angular momentum, SAM, OAM and OAM-carrying twisted optical communications, key techniques and devices of OAM generation/(de)multiplexing/detection, high-capacity spectrally-efficient free-space OAM links, fiber-based OAM links, and OAM processing functions are presented. Ultra-high spectral efficiency and petabit-scale freespace data links are achieved benefiting from OAM multiplexing. The key techniques and challenges of twisted optical communications are also discussed. Twisted optical communications using OAM are compatible with other existing physical dimensions such as frequency/wavelength, amplitude, phase, polarization and time, opening a possible way to facilitate continuous increase of the aggregate transmission capacity and spectral efficiency through
N
-dimensional multiplexing.
Batteries powering next‐generation flexible and wearable electronic devices require superior mechanical bendability and foldability. Herein, a self‐standing hybrid nanoarchitecture constructed by ...ultralong MnO2 nanowires and graphene nanosheets as an advanced and lightweight cathodes for flexible and foldable zinc‐ion batteries (ZIBs) is designed and fabricated. The new‐designed batteries exhibit not only a high energy density of 436 Wh kg−1 based on the total cathode mass but also good 2000‐cycling durability. More importantly, the shape‐deformable ZIBs can be operated without any capacity loss under both bent and folded circumstances. The foldable ZIBs with high energy density and long lifetime hold great promise for smart and wearable electronics.
A freestanding MnO2/graphene hybrid membrane is fabricated to construct highly flexible and foldable zinc‐ion batteries that delivers an unprecedented high energy density of 436 Wh kg−1 based on the total cathode mass and long lifetime over 2000 cycles.
Support recovery of sparse signals from compressed linear measurements is a fundamental problem in compressed sensing (CS). In this article, we study the orthogonal matching pursuit (OMP) algorithm ...for the recovery of support under noise. We consider two signal-to-noise ratio (SNR) settings: 1) the SNR depends on the sparsity level K of input signals, and 2) the SNR is an absolute constant independent of K. For the first setting, we establish necessary and sufficient conditions for the exact support recovery with OMP, expressed as lower bounds on the SNR. Our results indicate that in order to ensure the exact support recovery of all K-sparse signals with the OMP algorithm, the SNR must at least scale linearly with the sparsity level K. In the second setting, since the necessary condition on the SNR is not fulfilled, the exact support recovery with OMP is impossible. However, our analysis shows that recovery with an arbitrarily small but constant fraction of errors is possible with the OMP algorithm. This result may be useful for some practical applications where obtaining some large fraction of support positions is adequate.
Necrosis is one of the main forms of cardiomyocyte death in heart disease. Recent studies have demonstrated that certain types of necrosis are regulated and programmed dependent on the activation of ...receptor-interacting serine/threonine-protein kinase (RIPK) 1 and 3 which may be negatively regulated by Fas-associated protein with death domain (FADD). In addition, microRNAs and long noncoding RNAs have been shown to play important roles in various biological processes recently.
The purpose of this study was to test the hypothesis that microRNA-103/107 and H19 can participate in the regulation of RIPK1- and RIPK3-dependent necrosis in fetal cardiomyocyte-derived H9c2 cells and myocardial infarction through targeting FADD.
Our results show that FADD participates in H2O2-induced necrosis by influencing the formation of RIPK1 and RIPK3 complexes in H9c2 cells. We further demonstrate that miR-103/107 target FADD directly. Knockdown of miR-103/107 antagonizes necrosis in the cellular model and also myocardial infarction in a mouse ischemia/reperfusion model. The miR-103/107-FADD pathway does not participate in tumor necrosis factor-α-induced necrosis. In exploring the molecular mechanism by which miR-103/107 are regulated, we show that long noncoding RNA H19 directly binds to miR-103/107 and regulates FADD expression and necrosis.
Our results reveal a novel myocardial necrosis regulation model, which is composed of H19, miR-103/107, and FADD. Modulation of their levels may provide a new approach for preventing myocardial necrosis.
The intrinsic zinc dendrite growth aggravated by the uneven electric field at the Zn anode surface and the water‐induced parasitic reactions have largely impeded rechargeable aqueous zinc‐ion ...batteries for the practical applications in large‐scale energy storage. Here, an effective strategy is proposed to manipulate Zn deposition and simultaneously prevent the generation of insulating by‐products (Zn4SO4(OH)6·xH2O) for improved plating/stripping on Zn anodes by the addition of a nontoxic electrolyte additive, β‐cyclodextrin (β‐CD). The simulation results indicate that β‐CD molecules prefer to adsorb horizontally on Zn (002) plane, regulating the diffusion pathways and deposition sites of Zn2+ for the preferred Zn deposition along (002) plane without dendrite formation and inhibiting the H2 generation and the formation of Zn4SO4(OH)6·xH2O by facilitating desolvation of Zn(H2O)62+. Consequently, an ultra‐long stable cycling up to 1700 h at a high current density of 4 mA cm−2 can be achieved by the addition of β‐CD, 17 times that of the pure ZnSO4 electrolyte and the remarkable stability is also maintained under harsh test condition (40 mA cm−2, 20 mAh cm−2). This study highlights the important role of β‐CD in engineering the interfacial stability during Zn plating/stripping for high‐performing aqueous batteries.
β‐Cyclodextrin (β‐CD) additive with a special cavity structure is developed to regulate the deposition orientation of zinc ions and inhibit the parasitic reaction at the same time, resulting in highly reversible and stable Zn anode. Herein, the Zn//Zn cells with β‐CD display remarkable stability at different current densities ranging from 4 to 40 mA cm−2, much better than that in pure ZnSO4 electrolyte. This study demonstrates the remarkable effect of β‐CD on stabilizing the Zn anodes and provides insight into the design of versatile electrolytes for aqueous ion batteries.
Human infections with zoonotic coronaviruses (CoVs), including severe acute respiratory syndrome (SARS)-CoV and Middle East respiratory syndrome (MERS)-CoV, have raised great public health concern ...globally. Here, we report a novel bat-origin CoV causing severe and fatal pneumonia in humans.
We collected clinical data and bronchoalveolar lavage (BAL) specimens from five patients with severe pneumonia from Wuhan Jinyintan Hospital, Hubei province, China. Nucleic acids of the BAL were extracted and subjected to next-generation sequencing. Virus isolation was carried out, and maximum-likelihood phylogenetic trees were constructed.
Five patients hospitalized from December 18 to December 29, 2019 presented with fever, cough, and dyspnea accompanied by complications of acute respiratory distress syndrome. Chest radiography revealed diffuse opacities and consolidation. One of these patients died. Sequence results revealed the presence of a previously unknown β-CoV strain in all five patients, with 99.8% to 99.9% nucleotide identities among the isolates. These isolates showed 79.0% nucleotide identity with the sequence of SARS-CoV (GenBank NC_004718) and 51.8% identity with the sequence of MERS-CoV (GenBank NC_019843). The virus is phylogenetically closest to a bat SARS-like CoV (SL-ZC45, GenBank MG772933) with 87.6% to 87.7% nucleotide identity, but is in a separate clade. Moreover, these viruses have a single intact open reading frame gene 8, as a further indicator of bat-origin CoVs. However, the amino acid sequence of the tentative receptor-binding domain resembles that of SARS-CoV, indicating that these viruses might use the same receptor.
A novel bat-borne CoV was identified that is associated with severe and fatal respiratory disease in humans.
•Enable CNN-based physics-informed deep learning for PDEs on irregular domain.•The proposed network can be trained without any labeled data.•Boundary conditions are strictly encoded in a hard ...manner.•Investigated complex parametric PDEs, e.g., Naiver-Stokes with varying geometries.•Shows improvements of efficiency and accuracy over FC-NN formulations.
Recently, the advent of deep learning has spurred interest in the development of physics-informed neural networks (PINN) for efficiently solving partial differential equations (PDEs), particularly in a parametric setting. Among all different classes of deep neural networks, the convolutional neural network (CNN) has attracted increasing attention in the scientific machine learning community, since the parameter-sharing feature in CNN enables efficient learning for problems with large-scale spatiotemporal fields. However, one of the biggest challenges is that CNN only can handle regular geometries with image-like format (i.e., rectangular domains with uniform grids). In this paper, we propose a novel physics-constrained CNN learning architecture, aiming to learn solutions of parametric PDEs on irregular domains without any labeled data. In order to leverage powerful classic CNN backbones, elliptic coordinate mapping is introduced to enable coordinate transforms between the irregular physical domain and regular reference domain. The proposed method has been assessed by solving a number of steady-state PDEs on irregular domains, including heat equations, Navier-Stokes equations, and Poisson equations with parameterized boundary conditions, varying geometries, and spatially-varying source fields. Moreover, the proposed method has also been compared against the state-of-the-art PINN with fully-connected neural network (FC-NN) formulation. The numerical results demonstrate the effectiveness of the proposed approach and exhibit notable superiority over the FC-NN based PINN in terms of efficiency and accuracy.
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