We theoretically investigate the crossed spin conductance (CSC) of a graphene-based heterostructure consists of ferromagnet, Rashba spin-orbit and superconductor regions. Using Dirac Bogoliubov-de Gennes formalism in the ballistic regime, we show that in the presence of Rashba spin-orbit coupling there are an anomalous crossed Andreev reection and spin-ipped co-tunneling in the process of quantum transport. We demonstrate that the CSC can be reversed with respect to charge conductance by tuning the Rashba spin-orbit coupling which experimentally can be adjusted by the applied perpendicular electric field on the graphene sheet. This feature in addition to a long spin relaxation time of Dirac fermions in graphene proposes designing a device with a non-local spin switch which is crucial for spintronics circuits. A wide variety of interesting phenomena such as topological superconductivity2 Majorana fermions5, topological quantum computation6 require superconductivity. Also, a major part of growing field of spintronics needs superconductivity7. More than 15 years, scientist have been tried to find a way for combining superconductivity and spintronics to make a long-range spin-polarized super-currents. Long spin relaxation time of Dirac fermions in graphene made it an important candidate for using in such a device9. Graphene not only has a simple structure, but also can be easily prepared in experiments with different properties. Superconductivity (S) and ferromagnetism (F) can be induced into graphene by means of proximity effect14. Designing hybrid structures involving S and F regions make graphene a very good host to explore the fascinating phenomena such as Klein tunneling16, supercurrent π-junction17 and spin-triplet correlation18. In contrast ot ordinary superconductors, the superconducting junction of graphene has the capability of reflecting an incident electron as a hole with a specular path. This process is known as Andreev reflection and the missing charge of 2e enters the superconductor as a Cooper pair20. The low-energy excitations of graphene near the K and K′ points of the Brillouin zone are governed by a 2D massless Dirac Hamiltonian22. The linear conduction and valence bands cross the Fermi level at these points. Because of these unique properties, graphene attracts huge attentions of scientists from fundamental to the technical point of view23. The Fermi level in graphene in contrast to ordinary conductors or semiconductors, can be also tuned by a gate voltage24.Hybrid structures of superconductivity have a suitable potential to use in the future technology28. The first one which is known as Dresselhaus spin-orbit (DSO) interaction29 comes from the spin dependent second neighbor hopping. The last one which is known as Rashba spin-orbit (RSO) interaction30, arises from a perpendicular electric field or interactions with substrate31. The RSO interaction which is induced by proximity to a tungsten disulphide substrate28 confirmed experimentally to be ~17 meV. The DSO interaction which is responsible to create a small gap in the band structure, predicted to be quite small in comparison with the larger effects like RSO interaction32. So, in this paper we ignore the DSO interaction and just focus on the RSO interaction.Spin-Orbit interaction has intrinsic or extrinsic origin in the graphene33, maximum spin Seebeck effect34, and tunable magneto-resistance35. In our previous work36, we show that the RSO interaction in the interfaces of F-S-F graphene junction generates the anomalous CAR and spin-flipped CT. These effects are the direct signature of breaking translational symmetry at the interfaces due to the RSO interaction and penetrating equal-spin triplet correlation into ferromagnetic region. Using gate voltage and tuning Fermi level, the sign of charge conductance in the ferromagnetic drain lead (F2) can be negative  competes with elastic co-tunneling (CT). The graphene-based junctions contain RSO interaction in their interfaces predicted to show odd-frequency triplet correlations37 and Blonder-Tinkham-Klapwijk38 formalism to derive two formulas for the CCC and CSC in the drain lead. We also show that the sign of the spin conductance can be switch from positive to negative by changing RSO interaction which is experimentally possible by tuning perpendicular electric field. This property in addition to the long spin relaxation time of Dirac fermions makes this set up very suitable for spintronics circuits. It is known that the typically p-wave superconductors are rare in nature. Normal impurities can easily break the Cooper pairs in p–wave and d–wave superconductors. But, the s-wave superconductors are robust against impurities and much more common in nature. So we consider an s-wave superconductor in the junction.In this letter, we proposed an experimental accessible device to generate negative crossed spin conductance (CSC) and tunable crossed charge conductance (CCC). We extend the Landauerx − y plane. We suppose that ferromagnetism, singlet superconductivity and RSO interaction are applied on the graphene by proximity. As shown in Fig. 36 because in a typical experimental situation, ballistic propagations can be spoiled by edge disorders.We consider a sheet of graphene in the The low-energy of quasi-particles of such a device describes by Dirac Bogoliubov-de Gennes DBdG equation19, and μi. Also, 0 is the superconducting gap in zero temperature and 39 in which The original DBdG Hamiltonian is a 16 × 16 matrix in the presence of an arbitrary direction of magnetization and spin-orbit interaction. However, graphene has another degrees of freedom, valley degeneracy which is responsible to electron-hole conversion. Due to the valley degeneracy in graphene, the Hamiltonian reduces to a 8 × 8 matrix. Nonetheless, one can use different basis sets that consequently change the form of Hamiltonian without affecting final physical results and conclusions. In our representation, the hole part is time-reversed of the electron part which denoted it by l and Fr) represents by hl and hr, respectively. Their directions fixed along the z axis without loss of generality. This choice makes the spin-dependent analyses of scattering process clear. λ is the strength of RSO interaction32. U0 is an electrostatic potential which is necessary to prepare enough density of states for induced superconductivity. So we assume heavily doped approximation, 39. The length of RSO and S regions are LRSO and LS, respectively. The eigenvalues of each region are obtained by diagonalizing Eq.  process or as a hole which is called crossed Andreev reflection (CAR). So, in the scattering process into drain lead we have four probabilities: spin-preserved CT 33. In the RSO region, the wave function is split into electron and hole sector, and these two are not coupled. When an incoming electron is entering the RSO region and reaching the RSO/S interface, it is converted into a hole that travels back into the ferromagnetic contact. All of the possibilities and scattering processes are explained in previous works in details33. All wave functions should satisfy the boundary conditions as follow, to calculate the probability amplitudes.Furthermore, the total wave function in RSO . At the point where these two types convert to each other, the mentioned discontinuity would happen.The application of RSO interaction can completely suppress the values of junction’s conductance, leading to negative CSC and positive CCC simultaneously. When the current approaches the right ferromagnetic lead in the junction, it passes through with no reflection. This is a manifestation of the CCC. The passed carriers have a down-spin or up-spin direction. Negative CSC means that all carriers have similar spin directions. As the Interestingly, changing the value of RSO term alters the sign of CSC from positive to negative values and vice versa, as shown in Fig. Motivated by recent achievements in the induction of RSO interaction into a graphene sheet, we have theoretically studied quantum transport properties of a junction consists of ferromagnets, singlet superconductors and RSO interaction. As known, it is possible to change the RSO term non-locally in graphene by applying electric field. This situation provides a spin-switch device which has a capability to block the current with the specific spin direction. Our results demonstrate that in the presence of RSO interaction, there are new types of reflection and transmission probabilities. Among them, those who live in drain lead are our interest because of their non-local nature. Spin-flipped CT and anomalous CAR have negative roles in CSC. Due to these interesting capabilities which can be very crucial in spintronics, graphene junctions may, in principle, be used for spin-current control or spin orbit sensing applications in superconducting spintronics.


This article distinguishes between clique family subgroups and communities in a crisis response network. Then, we examine the way organizations interacted to achieve a common goal by employing community analysis of an epidemic response network in Korea in 2015. The results indicate that the network split into two groups: core response communities in one group and supportive functional communities in the other. The core response communities include organizations across government jurisdictions, sectors, and geographic locations. Other communities are confined geographically, homogenous functionally, or both. We also find that whenever intergovernmental relations were present in communities, the member connectivity was low, even if intersectoral relations appeared together within them. Many countries have struggled with coordinating efforts among diverse organizations in response to crises attributable to disasters and emergencies . Disasten-cliques and k-cores) focus on internal edges, while the community is a concept based on the distinction between internal edges and the outside. We argue that community analysis can provide useful insights about the interrelations among diverse organizations in the ERN.In this article, we distinguish between clique family subgroups and communities in ERNs. Conceptually, a clique is “…a group [in which] all members of which are in contact with each other or are friends, know each other, etc.” which generally refers to a social circle , p. 161,We have not yet found any studies that have investigated cohesive subgroups in large multilevel, multisectoral ERNs through a community lens. With limited guidance from the literature on ERNs, we lack specific expectations or hypotheses about what the community structure in the network may look like. Therefore, our study focuses on identifying and analyzing communities in the 2015 Middle East Respiratory Syndrome Coronavirus (MERS) response in South Korea as a case study. We address the following research questions: (1) In what way were distinctive communities divided in the ERN? and (2) How did the interorganizational relations relate to the internal characteristics of the communities? By detecting and analyzing the community structure in an ERN, we offer insights for future empirical studies on ERNs.1 in which a small number of nodes had a large number of edges and a large number of nodes had a small number of edges . The n-cliques can include nodes that are not in the clique but are accessible. Similarly, k-cores refer to maximal subgraphs with a minimum degree of at least k. Many cliques were identified in the Katrina response network, in which federal and state agencies appeared frequently , and intersectoral relations involve the interaction among organizations across different sectors . Recent studies have suggested that both intergovernmental and intersectoral relations shape ERNs , but fewn-cliques and k-cores) demand an answer to the question: “What is the n- or k-?” The clique and clique-like approaches have a limited ability to define and identify cohesive subgroups in a task-oriented network because they do not clearly explain why the subgroups need to be defined and identified in such a manner. We proposed a different way of thinking about and finding subsets of organizations in ERNs: community.The analysis of cliques is a very strict way of operationalizing cohesive subgroups from a social network perspective , and theWhen a network consists of subsets of nodes with many edges that connect nodes of the same subset, but few that lay between subsets, the network is said to have a community structure . NetworkQ), which shows the quality of partitions, is measured and assessed quantitatively:Modularity  through concerted efforts on the part of those with different capabilities . OrganizIt is equally difficult to set expectations about communities’ internal structure in ERNs. We can expect that, regardless of their types, sectors, and locations, some organizations work and interact closely—perhaps even more so in such a disruptive situation. Emergent needs for coordination, communication, and collaboration also can trigger organizational interactions that extend beyond the usual or planned structure. Thus, the relations among organizations become dense and evolve into the community in which every member is connected. On the other hand, a community in the task network may not require all of the organizations within it to interact. For example, if a presumed structure is strongly established, organizations are more likely to interact with others within the planned structure following the chain of command and control. Even without such a structure, government organizations may coordinate their responses following the existing chain of command and control in their routine. We may expect to observe communities with a sparse connection among organizations. Thus, the way communities emerge in ERNs is an open empirical question that can be answered by examining the entire network.Several countries have experienced novel infectious disease outbreaks over the past decade  and effoWe broadly define a response network as the relations among organizations that can act as critical channels for information, resources, and support. When two organizations engage in any MERS-specific response interactions, they are considered to be related in the response. Examples of interactions include taking joint actions, communicating with each other, or sharing crucial information and resources  related to performing the MERS tasks, as well as having meetings among organizations to establish a collaborative network.4 published between May 20, 2015, and December 31, 2015 (the outbreak period), and (2) a postevent white paper that the Ministry of Health and Welfare published in December 2016. In August 2016, Hanyang university's research center in South Korea provided an online tagging tool for every news article in the country’s news articles database that included the term “MERS (http://naver.com).” A group of researchers at the Korea Institute for Health and Social Affairs wrote the white paper  based on their comprehensive research using multiple data sources and collection methods. The authors of this article and graduate research assistants, all of whom are fluent in Korean, were involved in the data collection process from August 2016 to September 2017.We collected response network data from the following two archival sources: (1) news articles from South Korea’s four major newspapers5Because of the literature’s lack of specific guidance on the data to collect from archival materials to construct interorganizational network data, we collected the data through trial and error. We collected data from news articles through two separate trials . The authors and a graduate assistant then ran a test trial between August 2016 and April 2017. In July 2017, the authors developed a data collection protocol based on the test trial experience collecting the data from the news articles and white paper. Then, we recollected the data from the news articles between August 2017 and September 2017 using the protocol.de facto decision making unit within the context of the response”  multiple persons or other entities, and that acts as a esponse” , p. 6. I6We also categorized individual organizations into different “groups” using the following criteria. First, we distinguished the entities in South Korea from those outside the country . Second, we sorted governmental entities by jurisdiction  and then also by the functions that each organization performs . For example, we categorized local fire stations differently from provincial fire headquarters because these organizations’ scope and role differ within the governmental structure. We categorized nongovernmental entities in the private, nonprofit, or civil society sectors that provide primary services in different service areas . At the end of the data collection process, 69 organizational groups from 1,395 organizations were identified see .6https://gephi.org/). After identifying communities, the network can be reduced to these communities. In generating the reduced graph, each community appears within a circle, the size of which varies according to the number of organizations in the community. The links between communities indicate the connections among community members. The thickness of the lines varies in proportion to the number of pairs of connected organizations. This process improves the ability to understand the network structure drastically and provides an opportunity to analyze the individual communities’ internal characteristics such as the organizations’ diversity and their connectivity for each community.We employed the Leiden algorithm using Python , which wH) is used as a measure of diversity because uncertainty increases as species’ diversity in a community increases . S indicates the total number of species. The fraction of the population that constitutes a species, i, is represented by ip below and then multiplied by the natural logarithm of the proportion  is the proportion of possible edges presented in the graph, which is the ratio between the number of edges present and the maximum possible. It ranges from 0 (no edges) to 1 . A graph’s clustering coefficient (C) is the probability that two neighbors of a node are neighbors themselves. It essentially measures the way a node’s neighbors form a 1-clique. C is 1 in a graph connected fully.While limited, density and the average clustering coefficient can capture the basic idea of a subgraph’s structural cohesion or “cliquishness” . A graphThe MERS response network in the data set consists of 1,395 organizations and 4,801 edges. Organizations coordinated with approximately three other organizations (average degree: 3.44). However, six organizations coordinated with more than 100 others. The country’s health authorities, such as the Ministry of Health and Welfare (MOHW: 595 edges), Central MERS Management Headquarters (CMMH: 551 edges), and Korea Centers for Disease Control and Prevention (KCDC: 253 edges), were found to have a large number of edges. The Ministry of Environment (304 edges) also coordinated with many other organizations in the response. The National Medical Center had 160 edges, and the Seoul Metropolitan City Government had 129.Q) was 0.584, showing that the community detection algorithm partitioned and identified the communities in the network reasonably well. In real-world networks, modularity scores “…typically fall in the range from about 0.30 to 0.70. High values are rare”  at the center of the response network included a large number of organizations, with a knot of intergroup coordination among the groups surrounding those two. These communities included organizations across government jurisdictions, sectors, and geographic locations . The domOther remaining communities were (1) confined geographically, (2) oriented functionally, or (3) both. First, some communities consisted of diverse organizations in the areas where two MERS hospitals are located—Seoul Metropolitan city and Gyeonggi Province (Communities 3 and 5). Organizations in these communities span government levels and sectors within the areas affected. Second, two communities consisted of organizations with different functions and performed supportive activities . These communities included intergovernmental relations within geographic locations. Secondly, communities of organizations with a specialized function showed relatively high diversity or connectivity. These included organizations from governmental and nongovernmental sectors and had no leading or dominating organizations. For example, Communities 11 and 12 had intersectoral relations but no intergovernmental relations. Thirdly, within each community of fire organizations in different geographic locations, one provincial or metropolitan fire headquarters was linked to multiple local fire stations in a star network. These communities, labeled IGF, had low member diversity and member connectivity, while they were organizationally and functionally coherent.We use the community detection method to gain a better understanding of the patterns of associations among diverse response organizations in an epidemic response network. The large data sets available and increased computational power significantly transform the study of social networks and can shed light on topics such as cohesive subgroups in large networks. Network studies today involve mining enormous digital data sets such as collective behavior online , an e-maWe find two important results. First, the national public health ERN in Korea split largely into two groups. The core response communities’ characteristics were that (1) they were not confined geographically, (2) organizations were heterogeneous across jurisdictional lines as well as sectors, and (3) the community’s internal structure was sparse even if intersectoral relations were present. On the other hand, supportive functional communities’ characteristics were that (1) they were communities of heterogeneous organizations in the areas affected that were confined geographically; (2) the communities of intersectoral, professional organizations were heterogeneous, densely connected, and not confined geographically; and (3) the communities of traditional emergency response organizations  were confined geographically, homogeneous, and connected sparsely in a centralized fashion.These findings show distinct features of the response to emerging infectious diseases. The core response communities suggest that diverse organizations across jurisdictions, sectors, and functions actually performed active and crucial MERS response activities. However, these organizations’ interaction and coordination inside the communities were found to be top down from the key national health authorities to all other organizations. This observation does not speak to the quality of interactions in the centralized top-down structure, but one can also ask how effective such a structure can be in a setting where diverse organizations must share authority, responsibilities, and resources. Second, infectious diseases spread rapidly and can break out in multiple locations simultaneously. The subgroup patterns in response networks to infectious diseases can differ from those of location-bound natural disasters such as hurricanes and earthquakes. While some organizations may not be actively or directly involved in the response, communities of these organizations can be formed to prepare for potential outbreaks or provide support to the core response communities during the event.If intergovernmental relations operate in a community, the community’s member connectivity may be low, regardless of member diversity.professional organization communities’  member connectivity may be dense andemergency response organization communities’  member connectivity may be sparse.If community members are functionally similar,Second, we also find that the communities’ internal characteristics (diversity and connectivity) differed depending upon the types of interorganizational relations that appeared within the communities. Based on these analytical results, two propositions about the community structure in the ERN can be developed:The results suggest that the presence of intergovernmental relations within the communities in ERNs may be associated with low member connectivity. However, this finding does not imply that those communities with intergovernmental relations are not organizationally or functionally cohesive. Instead, we may expect a different correlation between members’ functional similarity and their member connectivity depending upon the types of professions, as seen in 2(a) and (b).Organizations’ concerted efforts during a response to an epidemic is a prevalent issue in many countries . The 201The findings of this study offer a practical implication for public health emergency preparedness and response in the country studied. ERNs’ effective structure has been a fundamental question and a source of continued debate . The ansThis study’s limitations also deserve mention. Several community detection methods have been developed with different assumptions for network partition. Some algorithms take deterministic group finding approaches that partition the network based on betweenness centrality edges  or inforWhile we reviewed the data sources carefully multiple times to capture the response coordination, communication, and collaboration, the process of collecting and cleaning data can never be free from human error. It was a time-consuming, labor-intensive process that required trial and error. Further, the original written materials can have their own biases that reflect the source’s perspective. Government documents may provide richer information about the government’s actions but less so about other social endeavors. Media data, such as newspapers, also have their limitations as information sources to capture rich social networks. Accordingly, our results must be interpreted in the context of these limitations.In conclusion, this article examines the community structure in a large ERN, which is a quite new, but potentially fruitful, approach to the field. We tested a rapidly developing analytical approach to the ERN to generate theoretical insights and find paths to exploit such insights for better public health emergency preparedness and response in the future. Much work remains to build and refine the theoretical propositions on crisis response networks drawn from this rich case study.Click here for additional data file.Supplemental Material, Kim_Kim_Oh_et_al_Online_Supplement for Community Analysis of a Crisis Response Network by Yushim Kim, Jihong Kim, Seong Soo Oh, Sang-Wook Kim, Minyoung Ku and Jaehyuk Cha in Social Science Computer Review


To investigate prognostic values of serum biomarkers of soluble intercellular adhesion molecule 1 (sICAM-1), macrophage migration inhibitor factor (MIF), interleukin 1 From August 2017 to November 2019, 122 consecutive IPF patients treated in our center were classified as stable IPF and AE-IPF based on the newly published international guidelines. Serum levels of four biomarkers at admission were measured by the enzyme-linked immunosorbent assay (ELISA). The primary endpoint was 3-month mortality. The log-rank test and logistic regression analysis were used to evaluate the effects of these biomarkers for survival in patients with AE-IPF. Cox proportional hazards analysis was performed to evaluate the prognostic values of serological biomarkers and clinical data. p < 0.001), IL-1β (p < 0.001), MIF (p < 0.001), and su-PAR (p < 0.001) in patients with IPF were significantly increased compared to those in healthy controls. All the four biomarkers were elevated in patients with AE-IPF compared to those with stable IPF. The 3-month mortality in AE-IPF was 56.1% (23/41). Increased levels of MIF (p = 0.01) and IL-1β  were independent risk factors for 3-month mortality in patients with AE-IPF.  Eighty-one patients were diagnosed with stable IPF, and 41 AE-IPF patients were enrolled in the study. Serum levels of sICAM-1 (β, and MIF had prognostic values for 3-month mortality in AE-IPF. This study provided a clue to promote our understanding in the pathogenesis of the disease. We showed the higher serum levels of IL-1 Idiopathic pulmonary fibrosis (IPF) is a progressive, interstitial lung disease with unknown etiologies , 2. The β (IL-1β) have been reported to be associated with another severe condition, acute respiratory distress syndrome (ARDS), which shares several pathophysiological features with AE-IPF [Increasing evidences indicate that acute inflammation may be responsible for the occurrence of AE-IPF rather than an abrupt fibrosis aggravation. In addition, the activation of abnormal coagulation and fibrinolysis pathways has been implicated in AE-IPF for elevated levels of plasma D-dimer and fibrin degradation product (FDP) observed in patients with AE-IPF . Proinflh AE-IPF , 8. Alsoh AE-IPF , 10. Coah AE-IPF . HoweverIn this study, we measured serum levels of these four cytokines in patients with IPF and age-matched healthy controls. Next, we compared their differential expressions between patients with stable IPF and AE-IPF. And then, we investigated their prognostic values for predicting 3-month mortality in patients with AE-IPF and compared them with the existing clinical data.From August 2017 to November 2019, 122 consecutive patients with IPF were admitted to the Department of Pulmonary and Critical Care Medicine, Nanjing Drum Tower Hospital. The diagnosis criteria of stable IPF and AE-IPF were based on the updated international guidelines and made by a multiple disciplinary team consisted of experienced respiratory physicians and radiologists. Briefly, the criteria for a diagnosis of IPF were as follows: (a) exclusion of other known causes of interstitial lung disease (ILD), (b) the presence of a UIP pattern on HRCT in patients not subjected to surgical lung biopsy, and (c) specific combinations of HRCT and surgical lung biopsy pattern in patients subjected to surgical lung biopsy. The diagnosis of AE-IPF was acute worsening or development of dyspnea typically less than one month with new emerging GGO on the background of the UIP pattern in HRCT. Other conditions leading to a clinical exacerbation such as acute heart failure and pulmonary embolism should be excluded [Secondary interstitial lung fibrosis was excluded through clinical and laboratory investigations. To assess the prognostic value of coagulation dysfunction in patients with IPF, patients who had combination of other thrombotic diseases including acute-stage cerebral infarction and deep vein thrombosis or who had received anticoagulation therapy within 6 months were excluded. The stable patients have to be in a stable phase for at least 3 months before being enrolled in the study.2/FiO2 ratio were collected. Pulmonary function data at admission were available in 61 patients (50 patients with stable IPF and 11 patients with AE-IPF). Variables including forced vital capacity (FVC), FVC% predicted, diffusing capacity of the lung for carbon monoxide (DLCO), and DLCO% predicted were recorded. The Acute Physiology and Chronic Health Evaluation II (APACHE II) score was calculated within 24 hours after the patients were diagnosed with AE. The primary endpoint was 3-month mortality. Survival status was confirmed by reviewing medical documents and telephone follow-up. Survival time of AE-IPF was calculated from the diagnosis of AE to death.Baseline demographic information; clinical characteristics and lab examinations including lactate dehydrogenase (LDH), C-reactive protein (CRP), erythrocyte sedimentation rate (ESR), D-dimer, and albumin (ALB); and blood routine examination including white blood cell count and neutrophil percentage and the PaOForty-five age- and-gender-matched healthy controls were from the Physical Examination Center of Nanjing Drum Tower Hospital. This study was approved by the Ethics Committee at Nanjing Drum Tower Hospital .Peripheral blood samples (5 mL) were collected using standardized venipuncture procedures. Blood samples of patients with stable IPF were collected at the first day of admission. Blood samples of patients with AE-IPF were collected when the diagnosis of AE-IPF was confirmed. Serum samples were separated by centrifugation and stored at −80°C.β , MIF , and su-PAR  according to the manufacturers' instructions, and every sample had a duplicate. The mean minimum detectable dose by the ELISA kits was 8.81 pg/mL.Commercially available enzyme-linked immunosorbent assay (ELISA) kits were adopted to measure the serum levels of sICAM-1 and IL-1U tests. Student's t-test was applied to compare continuous clinical variables when they satisfied a normal distribution. Categorical variables were compared by the chi-square test. Survival of AE and stable IPF patients was evaluated using the logistic regression analysis and log-rank test. The rank correlation analysis was performed to evaluate the associations between cytokines and clinical data. Logistic regression analysis was used to filtrate important confounders regarding the prognosis of AE-IPF. If the regression coefficient of the variables was statistically significant, it will be introduced in multivariate models. Cox proportional hazards analysis was used to identify significant predictors of 3-month mortality in patients with AE-IPF. p values < 0.05 were considered to be significant. Data analyses were performed using SPSS statistical software version 22.0.Numerical variables were expressed as mean ± SD. Differences of biomarkers among groups were examined by Mann-Whitney 2/FiO2, FVC% predicted, and DLCO% predicted were significantly lower in patients with AE-IPF than in those with stable IPF. An elevated serum D-dimer level was observed in patients with AE-IPF compared to those with stable IPF (p < 0.001).During the study period, 122 consecutive patients with IPF were admitted to the Department of Pulmonary and Critical Care Medicine, Nanjing Drum Tower Hospital. They were 102 males (83.6%) and 20 females (16.4%), with mean age of 65.8 ± 8.6 years old. Among them, 81 patients were diagnosed with stable IPF  and 41 patients with AE-IPF . In this study, 30.3% (37/122) were newly diagnosed with IPF. All AE patients were admitted because of the aggravation of dyspnea and respiratory failure, and 41.5% (17/41) of them also have suspectable pulmonary infections. At admission, 85.4% (35/41) of AE patients have a preceding diagnosis of IPF and 6 patients were newly diagnosed with AE-IPF. The flowchart is described in During hospitalization, 14 patients with AE-IPF  received corticosteroid pulse therapy (methylprednisolone 500-1000 mg/d for 3-5 days). Three patients with AE-IPF  received lung transplantation. In total, there were 35 patients died in the follow-up and 25 of them were AE-IPF. The mean survival time of patients with AE-IPF was 5.4 months from diagnosis of AE-IPF, and 23 of them died within 3 months. The 3-month mortality of AE-IPF was 56.1% (23/41).p < 0.001), IL-1β (p < 0.001), MIF (p < 0.001), and su-PAR (p < 0.001) in patients with IPF were significantly higher than those in healthy controls. Serum levels of sICAM-1 (p < 0.001), IL-1β (p < 0.001), MIF (p = 0.038), and su-PAR (p < 0.001) in patients with AE-IPF were significantly higher than those in patients with stable IPF . The baseline levels of serum IL-1β were correlated with WBC count , CRP , LDH , and ESR . The serum levels of sICAM-1 were correlated with WBC count , not with CRP, ESR, and LDH. The serum levels of MIF were not correlated with any of these clinical parameters. However, in patients with AE-IPF, the serum levels of su-PAR were not correlated with D-dimer. In addition, the serum levels of sICAM-1, IL-1β, and MIF were not correlated with these inflammatory-related laboratory data. Therefore, in patients with AE-IPF, it is meaningful to analyze the impact of these biomarkers on 3-month mortality.In all patients, Spearman's rank correlation coefficient revealed that the serum levels of su-PAR were correlated with D-dimer (p = 0.007), su-PAR (p = 0.033), and IL-1β (p = 0.018) were associated with poor prognosis in patients with AE-IPF. However, higher levels of sICAM-1 were not a risk factor for survival. We performed survival analysis in patients with AE-IPF. Firstly, all these biomarkers were analyzed as continuous variables. Then, categorizations of these variables were taken into consideration when the former were not appropriate. The normal range of IL-1β in our center laboratory was less than 5 pg/mL. The log-rank test was performed according to the clinical cut-off levels and demonstrated that the increased levels of IL-1β (>5 pg/mL) were associated with higher 3-month mortality in patients with AE-IPF (p < 0.001) (p = 0.012) .The mean follow-up time in patients with AE-IPF was 7.6 months (range from 3 to 23 months). Logistic regression analysis revealed that higher serum levels of MIF (< 0.001) . The bas= 0.012) . Logisti2/FiO2 ratio, APACHE II score, and WBC count . The elevated serum levels of IL-1β (>5 pg/mL) were associated with worse survival after adjustment for related markers . After adjustment for demographic information  in multivariate model 2, the above conclusion is appropriate . In addition, only a handful of AE-IPF patients in our study underwent bronchoalveolar lavage, which limited the possibility to compare the levels of these biomarkers in BALF and serum.β, MIF, and su-PAR were observed in AE-IPF. The increased levels of IL-1β and MIF were risk factors for 3-month mortality in patients with AE-IPF. Our study provided evidences that acute inflammatory response and coagulation abnormalities may participate in the pathogenesis of AE-IPF.In conclusion, increased levels of sICAM-1, IL-1


Recordings of Spanish and English sentences by switched-dominance bilingual (SDB) Spanish  and by L1-dominant Spanish and English controls were presented to L1-dominant Spanish and English listeners, respectively. At –4 dB signal-to-noise ratio (SNR), Spanish and English productions by SDBs were equally intelligible with both reaching L1-dominant control levels. At –8 dB SNR, SDB English intelligibility matched that of L1-dominant English controls, yet SDB Spanish intelligibility was significantly lower than that of L1-dominant Spanish controls. These results emphasize that extended (but not early) exposure is both necessary and sufficient for robust speech learning. While by definition typical late bilingual and monolingual populations confound these two factors—earlier onset typically goes with longer duration of acquisition resulting in first-language (L1) dominance—switched-dominance bilinguals (SDBs) dissociate them. With early language exposure being limited to the “home” language (the L1), these bilinguals ultimately have more extensive exposure to the language that predominates in the society-at-large resulting in a switch from L1 to L2 dominance, where language dominance is defined as the language which is used most often.Prior research on speech production by SDBs has demonstrated some benefit of early exposure on speech sound production in the non-dominant L1 relative to late learners of the language . For exIn these prior studies e.g., . Thus, iIn a companion study to the present study of intelligibility of speech produced by SDBs, early exposure on non-dominant L1 production by SDB speakers, the speech-in-noise recognition accuracy evidence .praat (praat script (written by the author). In this script, the average long-term average spectrum (LTAS) of each talker was measured across all sentences and then averaged across all talkers to produce a single LTAS-curve. White noise was then generated in praat and then filtered along this LTAS curve creating speech-shaped noise customized for this experiment .In order to highlight subtle differences in intelligibility of the sentence productions by the three talker groups , all sentence stimuli were mixed with speech-shaped noise. The noise track was created in praat  using a 2.4max/msp software . ListenListeners were instructed to type as many words as possible that they heard over the headphones, even if it was just one word. Listeners were required to type something to move on to the next sentence; as such, responses such as “I am not sure” or “yo no sé” (I do not know) were allowed but then considered wholly incorrect. Participants finished within 30 min. The typed output of each listener was then compared to the correct target sentence and with each word being scored as either wholly correct (receiving a score of “1”) or incorrect . Spanish accent marks were ignored and minor spelling errors  were considered “correct.” Scores were then averaged within each sentence  and then averaged for each talker  at each SNR to produce two speech intelligibility scores per talker and per language (SDB only): intelligibility at a relatively easy (–4 dB SNR) and at a relatively difficult SNR (–8 dB SNR). Proportional scores were then log-odd transformed log((p)/(1 − p)) where p is the intelligibility score ranging from 0 to 1 for analysis.3.r  using linear mixed effects regressions (LMERs) with random intercepts for participant. The factors of interest, signal-to-noise ratio (SNR) and participant group , were contrast coded  before building any models. The dependent variable (DV) was the log-odd transformed speech intelligibility score log((p)/(1 − p)), where p is the proportional intelligibility score ranging from 0 to 1. These scores were analyzed as log-odds in order to remove the non-linear relationship of incremental changes in proportions (expressed as percent words correctly identified) at various places on the scale . Significance was assessed using the likelihood ratio test in which the degree to which the data are fit by a full model versus a model excluding a factor (n − 1) or interaction of interest is measured. Final models for both English and Spanish converged with log-odd transformed speech intelligibility as the dependent variable, a two-way interaction, and both main effects for the two factors of interest with random intercepts for participants. Random slopes by SNR were not included as each talker has only one score associated with each level within SNR, causing the model to be over-specified if present.All data were analyzed using the software 3.1β = 0.03, χ2(1) =  86.97, t = 28.79, p < 0.01), indicating that speech intelligibility scores were significantly lower for –8 dB SNR compared to –4 dB SNR. This is a reliable effect and has been shown in many previous speech-in-noise recognition studies , indicating that the impact of SNR on English speech intelligibility scores did not differ between groups. These results are shown along with the Spanish results in Fig. The average log-odds intelligibility score was 0.43 s.d. 0.21) in –8 dB SNR and 1.22 (s.d. 0.2) in –4 dB SNR for L1-dominant English controls. The average log-odds intelligibility score was 0.37 (s.d. 0.21) in –8 dB SNR and 1.07 (s.d. 0.21) in –4 dB SNR for SDBs in English. There was a significant main effect of SNR on speech intelligibility scores   =  67.03, t = 17.47, p < 0.01], indicating that speech intelligibility scores were significantly lower for –8 dB SNR compared to −4 dB SNR. This result is consistent with previous research . These results are show in Fig. The average log-odds intelligibility score was 0.42 (s.d. 0.27) in –8 dB SNR and 1.25 (s.d. 0.34) in –4 dB SNR for L1-dominant Spanish controls. The average log-odds intelligibility score was 0.03 (s.d. 0.14) in –8 dB SNR and 1.1 (s.d. 0.19) in –4 dB SNR for SDBs in Spanish. There was a significant main effect of signal-to-noise ratio (SNR) on speech intelligibility scores [4.The aim of this study was to compare the overall intelligibility of speech produced by SDBs in their non-dominant L1 and in their dominant L2. Building on prior work that demonstrated long-lasting benefits of early exposure for segmental production accuracy, we extended the scope of investigation to intelligibility of words in sentences as presented to L1 listeners of each language under both favorable and unfavorable listening conditions.4.1The main question of this subsection is whether the delayed but extended use of language resulting in L2 (rather than L1) language dominance, was sufficient to overcome any differences that may have been initially present in SDBs' delayed onset (but ultimately dominant) L2 speech. SDB did not show speech production differences in their dominant L2 (English) as the range of speech intelligibility scores for SDB was similar to that of English controls. This result is consistent with other studies that have shown L2-dominant speakers  produce native-like speech sounds in their dominant L2 , L1-dominant Spanish listeners showed similar recognition accuracy for words in sentences produced by SDB and Spanish controls. However, the significant main effect of group  and the significant group by SNR interaction (SDB speech intelligibility was more adversely affected by the more difficult –8 dB SNR compared to that of Spanish controls), suggests that the interrupted L1 acquisition and replacement by a dominant L2 resulted in reduced resistance to signal degradation by high levels of background noise, at least for L1-dominant listeners.It may appear that these results conflict with 5.The goal of the current study was to determine how adult speech production reflects the timing  and amount  of language exposure. The SDBs in this study provided unique insight into the subtle differences in language function not readily available in monolingual or bilingual speakers for whom the first-acquired language (L1) is also the dominant language. Results showed that, for L1-dominant English listeners, intelligibility of dominant L2 English as produced by SDB Spanish speakers was indistinguishable from that of L1-dominant English speakers even under a highly unfavorable (–8 dB) signal-to-noise ratio condition. This indicates that, with respect to speech intelligibility, extended exposure can successfully provide benefit even with delayed exposure to the L2. However, the converse does not appear to hold. That is, early exposure to the L1 does not entirely guard against subtle speech production differences associated with interrupted exposure to the L1. Specifically, the data showed substantially lower intelligibility of non-dominant L1 Spanish for L1-dominant Spanish listeners under a highly unfavorable (–8 dB) signal-to-noise ratio listening condition. These results suggest that with respect to speech production learning, while early exposure has some benefits, extended exposure can compensate for later onset of acquisition.


With the aim of achieving a better control, a second path involves the functionalization of the flakes with pillars molecules, bringing a new class of materials with structure partially controlled by the size, shape, and chemical-physical properties of the pillars. We finally outline the first steps on a possible third road, which involves the construction of pillared multi-layers using epitaxial regularly nano-patterned graphene as precursor. While presenting a number of further difficulties, in principle this strategy would allow a complete control on the structural characteristics of the final 3D architecture.Graphene is the prototype of two-dimensional (2D) materials, whose main feature is the extremely large surface-to-mass ratio. This property is interesting for a series of applications that involve interactions between particles and surfaces, such as, for instance, gas, fluid or charge storage, catalysis, and filtering. However, for most of these, a volumetric extension is needed, while preserving the large exposed surface. This proved to be rather a hard task, especially when specific structural features are also required . Here we review the recent experimental realizations and theoretical/simulation studies of 3D materials based on graphene. Two main synthesis routes area available, both of which currently use (reduced) graphene oxide flakes as precursors. The first involves mixing and interlacing the flakes through various treatments , leading to disordered nanoporous materials whose structure can be characterized  Since the experimental confirmation of its existence , graphenWith little exceptions, however, these require some sort of manipulation of the sheet: in general nano-electronics requires doping to increase the density of states at the Fermi energy or to open a gap, which can be achieved by chemical substitutions , introdu2), or electro-chemical energy storage , requiratteries , and theatteries . Clearlyatteries , make itatteries  or hydroatteries , yet maia posteriori.Indeed, preserving the needed properties and possibly enhancing or tailoring them in the 2D to 3D passage has turned out extremely complex. Up to now, two main routes were considered, both using graphene flakes as precursors. In the first, these are created by graphite exfoliation  and suspended in various solvents, resulting in a mixture of flakes with randomly distributed sizes and shapes; upon dehydration, they form 3D scaffolds with random structure and porosity . These tBuilding multi-layered structures separated by molecular “pillars” is considered an alternative to control the properties of the final 3D construct: theoretically, porosity and density in such structures are determined by the size and concentration of pillars, allowing the possibility of engineering the 3D structure via the pillar molecule design. Up to now, this route, has been followed using organic molecules as pillars ,36 couplClearly, the optimal route should involve the control of the pillars positioning at the nano-scale on the precursor sheets, i.e., the combination of controlled chemical nano-patterning with the possibility of stacking the patterned multilayers in a controlled way. In 2/gr [3/g [2/g). The result are 3D structures with randomly distributed sp2 areas interconnected to form a tangled scaffold with pores of size ranging in the nanometer scale . Differuenching  (temperauenching  or densiuenching ), which uenching . Howeveruenching . This meTo the aim of combining a modest computational effort with realism of the final model, a good strategy is using as precursors already formed graphene portions ,78 inste2 adsorption capability, whose low value indicates rather a small SSA value (hundreds of m2/g). The synthesis procedure was recently optimized [2/g and pores size up to 2 nm. At the same time, it was shown that in some cases, in polar solvents these material exhibits reversible swelling, posing doubts on the complete covalent nature of the layer linkage [In disordered nanoporous materials the porosity of the final structures depends on how the flakes interlace during all the phases of the preparation, which introduces a high degree of disorder and stochasticity. In order to reduce this issue and improve the control over the outcome, the idea rose of synthesizing layered structures separated by molecular pillars, i.e., organic molecules suitably designed with given lengths, rigidity and possibly other physic-chemical properties. The size of the pillars determines the inter-layer spacing, and controls the average pore size, together with the relative distance of the pillars on the sheets. The first realization of such structures traces back to almost two decades ago when, inspired by metal-organic frameworks chemistry, layered structures separated by diboronic acid molecules were first proposed . The pilptimized , obtaini linkage . Using a linkage  resulted linkage .ex post [The optimization of these materials depends on their use: for electric energy storage, both a finely tuned pore size and the conductivity are important. Therefore different synthesis routes were explored, involving reduction of GO, either ex post  or direcex post ,85,86. Aex post . A summaAlthough steps forward have been done in the control of the functionalization, the performances of these materials are not better than those of disordered GNM: SSA is at best several hundreds, far below the theoretical limits and below the simulation predictions. In fact, both the carbon-only model systems including nanotube-pillars ,89 and t2 electronic system. Clearly, reactivity can be brought back by reintroducing defects, e.g., by nitrogen sputtering [The reason why the pillars distribution is poorly controllable is encoded in the use of GO (or rGO) flakes as precursors: the covalent bonding of pillars or anchors exploits the presence of epoxy/hydroxy groups or defects, which are reactive sites . Howeveruttering , which cuttering . However2 sites but manifests a preference for graphene on hydrophilic substrates [2 [Indeed, specific kinds of epitaxial graphene offer different possibilities, which exploit the interaction with the substrate. For instance, radicals of diazonium salts are able to attach to spbstrates , due to bstrates ,102, whebstrates , where trates [2  and on trates [2 ). In therates [2 . In factrates [2  produce 3 hybridized. The bonds and corrugations follow a moiré pattern, due to the mismatch of the two lattices, displaying a hexagonal super-lattice of ~3.2 nm side, made of sharp crests and peaks with sp3-like pyramidal configuration [2 graphene can be obtained continuing evaporation: another BL forms under the first one, which is detached and becomes the so-called Mono-Layer graphene (MLG), characterised by a corrugation pattern with the same symmetry as BL, though smother [We now focus on graphene on SiC, to further explore this concept. It is important to observe that graphene on SiC is not a single material but includes different types of 2D carbons  that canguration . Fully s smother . Alterna smother  or metal smother , obtaini smother . This is smother  with ~1. smother . The ele smother .3 cusps at the vertices of the moiré super-lattice are likely to be highly reactive sites in general, not only towards radicals, but possibly also towards e.g., dissociative chemisorption of H2. Conversely, the protruding crests, organized in diene like structures, and the intruding areas, organized in “benzene-like” rings [While all of the different carbon layers on SiC display charge inhomogeneities following a regular nano-pattern induced by the interaction with the substrate, either mediated by the hybridization, by the corrugation or by the vacancies in the intercalation coverage, only the GML was tested on its reactivity, showing selective H adhesion on the crests . On the e” rings  are likee” rings , leadingClearly, the BL functionalization should be viewed as the first, yet fundamental, step of a procedure involving the multilayer formation see c: once tIn summary, we have reported three possible routes to produce graphene-based materials with porosity on the nano-scale, ordered by increasing capability of control and tailoring of the final structure. The first class produces the disordered nanoporous scaffolds from GO or rGO flakes. These can reach large values of SSA and are, up to now, the most interesting for gas storage. However, controlling their final structure is not straightforward, because of the disordered structure of the precursors and of the stochastic nature of their combination during the production procedure. With the aim of controlling at least a part of the variables determining the porosity, the second strategy introduces on the flakes pillars molecules with pre-determined lengths and shapes. This produces a class of materials with average pore sizes at the nano-metric scale, matching with the size of electrolytes and therefore suitable for the use in electric and electro-chemical storage. However, the average value of the SSA of these materials is rather low, and the poor control over the distribution of the pillars on the sheet introduces disorder, preventing a full optimization, not only for supercapacitors and batteries, but also in catalysis and filtering applications.A third route is currently in its infancy, which would provide a full control over the distribution and location of the pillars. This considers as precursors epitaxial graphene and exploits the electronic inhomogeneities of the sheet produced by the interaction with the substrate, typically following a nano-metric moiré pattern, for the controlled chemical functionalization. Although the first timid steps  were demonstrated, the way is long towards the production of multi-layers.The support of computer modeling and simulations is essential in all cases: in the case of disordered scaffolds, the main issue is to create realistic models and to understand the relationship between production procedure and final structure, and between the latter and the adsorption performances; for the pillared (r) GO materials, the challenge is to control the concentration and location of the pillars and predict the properties as a function of the used pillar. Most of all, computer modeling will be of outmost importance in the pillared multi-layers building from epitaxial graphene. In this case, the simulation of the pillaring, stacking and cross-linking would be essential to give indications for the experimental realization of the procedure. Though extremely challenging, this strategy might give a full control over all the structural features of the resulting structure, and–acting on the nature of pillars–might allow to create brand new materials with tailored and unprecedented properties, such as locally tuned elasticity or conductivity, reactivity to external fields, optical response, and others.


We propose the monolayer graphene plasmonic waveguide (MGPW), which is composed of graphene core sandwiched by two graphene metamaterial (GMM) claddings and investigate the properties of plasmonic modes propagating in the waveguide. The effective refraction index of the GMMs claddings takes negative (or positive) at the vicinity of the Dirac-like point in the band structure. We show that when the effective refraction index of the GMMs is positive, the plasmons travel forward in the MGPW with a positive group velocity . In contrast—for the negative refraction index GMM claddings—a negative group velocity of the fundamental mode  appears in the proposed waveguide structure when the core is sufficiently narrow. A forbidden band appears between the negative and positive group velocity regions, which is enhanced gradually as the width of the core increases. On the other hand, one can overcome this limitation and even make the forbidden band disappear by increasing the chemical potential difference between the nanodisks and the ambient graphene of the GMM claddings. The proposed structure offers a novel scheme of on-chip electromagnetic field and may find significant applications in the future high density plasmonic integrated circuit technique. Negative-index materials (NIMs)—a class of photonic structures with simultaneously negative effective dielectric constant and permeability ,2—providFast light is characterized by a group velocity vg = c/ng exceeding the vacuum speed of light c or taking negative values, where ng is the group-velocity index . When thIn this work, we propose a monolayer graphene plasmonic waveguide (MGPW) made up of a graphene core embedded in the graphene plasmonic metamaterial(s) (GMMs) claddings. The GMMs are composed of graphene nanodisks arrays which are immersed in the identical sheet of graphene with different chemical potential. These arrays are arranged in a square-shaped lattice with a lattice constant of a. Negative equivalent refractive indices of the GMMs are obtained by appropriately designed the structure of the graphene plasmonic metamaterials. The plasmonic waveguide is constructed by the graphene core and GMM claddings. We explore the ‘fast’ plasmon effect in the plasmonic waveguide by modifying the width of the graphene core and the chemical potential difference between the graphene nanodisks and the ambient graphene. In contrast to the conventional materials ‘fast-light’ media, the proposed broadly tunable two-dimensional ‘fast’ plasmon device offers the practical on-chip plasmon manipulation, ultracompact footprint and two- dimensional integratively  and may As shown in q is given by0 is the permittivity of the free space. According to the Kubo formula, the surface conductivity of graphene is composed of contributions from intraband electron–phonon scattering intraσ and interband electron transitions interσ, i.e., e is the electron charge, T = 300 K is the temperature, cμ is chemical potential, ω is the angular frequency. The momentum relaxation time is chosen as τ = 1 ps. Utilizing Equation (2), we obtain the complex effective refractive index of SPPs Here, a monolayer of graphene sheet is placed on the x–y plane in r = 0.2573a), where triple accidental degeneracy of the electromagnetic field takes place at the Brillouin zone center (Γ) which is known as the Dirac-like point. kx, ky) in k for simplicity. At a given frequency, the electric (magnetic) field envelope functions of SPPs, ky direction. The effective permittivity, permeability of the s reduce to The proposed graphene metamaterial is shown in I, II in a, we getm is the mode order. eff2(ω) − nGMMs2(ω))/(n(ω)2 − nGMMs2(ω)) represents the normalized refraction index with the effective mode refraction written as neff(ω) = β /k0. When nGMMs(ω) > n(ω), the normalized frequency γ = k0δIn this section, we consider a situation that SPPs travel through the monolayer graphene plasmonic waveguide composed of the graphene core , frequency and graphene’s chemical potential determine the refractive index difference between the core and claddings r = 0.2573a, which allows us to get the Dirac-like point in the band structure. In effn versus ω) for three widths of graphene core of in two different cases, where the graphene core is embedded in the positive index and negative index claddings, respectively. For the MGPW with positive index claddings, the effective mode refraction increases in proportion to the frequency ω when m = 1. Therefore, it corresponds to the normal dispersion. In contrast, in the case of the MGPW with negative index claddings, there exist narrow spectral regions where effn(ω) is a decreasing function of frequency (that is effdn/d ω < 0), which is known as anomalous dispersion. It should be noted that the mode order m takes zero, which corresponds to the fundamental SPPs mode traveling through the plasmonic structure. In addition, the phase velocity of the SPPs sustained by the MGPW is characterized by the pv = ω/β, which is plotted in vg = g, where ng is the group-velocity index. For the MGPW of length L, it takes a propagation time L/vg = ngL/c for SPPs modes. The SPPs modes that enter the proposed MGPW possess at a moment delayed by a time difference ∆T = L/vg-L/c = (ng − 1)L/c, where L/c denotes the vacuum transit time. As shown in m = 0. The delay −∆T = (1−ng)L/c becomes larger than the L/c, meaning that the backward fundamental SPPs mode (fast light) appears with the anomalous dispersion.To demonstrate the negative group velocity of the plasmon, an incident SPPs is launched at the input of the monolayer graphene plasmonic waveguide with the eigen mode. The chemical potential of the ambient graphene is It is important to point out that, as shown in a = 60 nm. The chemical potential of the ambient graphene is r = 0.2023a. The width of the graphene core is δ = 50 nm. To compare the negative and the positive group velocities of the SPPs in the proposed MGPW, we extend the scanning frequency ranged from 55.5 to 60.5 THz. The transmission spectrum is plotted in We numerically investigate the backward (forward) fundamental SPPs in the MGPW with negative(positive) index GMMs claddings. The lattice constant of the GMMs is fs of full width at half maximum (FWHM), are modulated by sinusoidal carrier waves at the entrance of the MGPW (z = 0) and recorded numerically at the rear of the MGPW (z = L). For comparison, the identical light pulses traversing in vacuum with the same distance are also numerically recorded. Because the chemical potential difference between the nanodisks and the ambient graphene limits the “fast” plasmons propagating, we use a relative chemical potential of g of the SPPs Gaussian pulses, which is proportional to the angular frequency. The results are illustrated in g—which is obtained from the analytical Equation (6)—is included in the same diagram. It should be noted the group-velocity index ng obtained from the two methods are consistent with each other in general. The analytical n and the fluctuation of the analytical curve may be resulted from the interpolation to the effective mode refraction.To further verify the negative group velocity nature of the plasmon, we investigate the propagation of the Gaussian pulses through the proposed waveguide illustrated in Comparing with other reported structures of negative group velocity ,17,18,19In conclusion, we propose a monolayer graphene plasmonic waveguide composed of the graphene core and metamaterial claddings. Emulating the propagation waves propagating through the dielectric three-layer waveguide, this structure supports SPPs modes that can be confined in the graphene core. When the refraction index of the GMM takes positive values, the proposed MGPW supports the forward fundamental SPPs mode. In contrast, the MGPW with negative index supports the backward fundamental SPPs mode when the width of graphene is narrow enough. Additionally, a forbidden band lies between the positive and negative group velocity regions. Furthermore, forbidden band gradually enlarges with the width of graphene core. Our proposed structure may offer a novel approach to manipulate electromagnetic fields in nanoscale.