The Application of PageRank model in urban epidemic prevention and control

The ongoing COVID-19 epidemic has had a great impact on social activities and the economy. The usage technical analysis tools to provide a more accurate and efficient reference for epidemic control measures is of great significance. This paper analyzes the characteristics and deficiencies of the existing technical methods, such as regression model, simulation calculation, differential equation and so on. By analyzing past outbreak cases and comparing the epidemic prevention measures of different cities, we discuss the importance of early and timely prevention in controlling the epidemic, and the importance of analyzing and formulating plans in advance. We then make the key observation that the spread of the virus is related to the topology of the urban network. This paper further proposes an epidemic analysis model of the optimized PageRank model, and gives a ranking algorithm for virus transmission risk levels based on road nodes, forming a visual risk warning level map, and applies the algorithm to the epidemic analysis of Yuegezhuang area in Beijing. Finally, more in-depth research directions and suggestions for prevention and control measures are put forward. © 2023 SPIE.

Novel Degree-Based Topological Descriptors of Fenofibrate Using M-Polynomial

Chemical graph theory is currently expanding the use of topological indices to numerically encode chemical structure. The prediction of the characteristics provided by the chemical structure of the molecule is a key feature of these topological indices. The concepts from graph theory are presented in a brief discussion of one of its many applications to chemistry, namely, the use of topological indices in quantitative structure-activity relationship (QSAR) studies and quantitative structure-property relationship (QSPR) studies. This study uses the M-polynomial approach, a newly discovered technique, to determine the topological indices of the medication fenofibrate. With the use of degree-based topological indices, we additionally construct a few novel degree based topological descriptors of fenofibrate structure using M-polynomial. When using M-polynomials in place of degree-based indices, the computation of the topological indices can be completed relatively quickly. The topological indices are also plotted. Using M-polynomial, we compute novel formulas for the modified first Zagreb index, modified second Zagreb index, first and second hyper Zagreb indices, SK index, SK1 index, SK2 index, modified Albertson index, redefined first Zagreb index, and degree-based topological indices.

Contact Tracing over Uncertain Indoor Positioning Data

Pandemics often cause dramatic losses of human lives and impact our societies in many aspects such as public health, tourism, and economy. To contain the spread of an epidemic like COVID-19, efficient and effective contact tracing is important, especially in indoor venues where the risk of infection is higher. In this work, we formulate and study a novel query called Indoor Contact Query (<sc>ICQ</sc>) over raw, uncertain indoor positioning data that digitalizes people's movements indoors. Given a query object <inline-formula><tex-math notation="LaTeX">$o$</tex-math></inline-formula>, e.g., a person confirmed to be a virus carrier, an <sc>ICQ</sc> analyzes uncertain indoor positioning data to find objects that most likely had close contact with <inline-formula><tex-math notation="LaTeX">$o$</tex-math></inline-formula> for a long period of time. To process <sc>ICQ</sc>, we propose a set of techniques. First, we design an enhanced indoor graph model to organize different types of data necessary for <sc>ICQ</sc>. Second, for indoor moving objects, we devise methods to determine uncertain regions and to derive positioning samples missing in the raw data. Third, we propose a query processing framework with a close contact determination method, a search algorithm, and the acceleration strategies. We conduct extensive experiments on synthetic and real datasets to evaluate our proposals. The results demonstrate the efficiency and effectiveness of our proposals. IEEE

Topology and evolution of international trade network for fish and fish products

In this study, a complex network method was employed to quantify the changing role of countries in fish trade and the dynamic characteristics of fish globalization. Based on the United Nations Comtrade Database, the International Trade Network for Fish and Fish Products (ITN-Fish) was constructed as a series of weighted-directed networks for each year from 1990 to 2018. Almost all countries and territories worldwide have participated in the fish trade. In 2018, the network identified 229 fish traders. The share of developing countries in imports and exports has increased. Traders actively establish new trade relations, which improve network connectivity. However, these relations only account for a small part of the fish trade. The high connectivity allows risks to spread rapidly in the world through hubs such as the United States and China, which raises concerns about the robustness of these weak links in the Sino-US trade conflict and the outbreak of COVID-19. However, we have optimistic expectations on this issue. The dynamic of network topology property shows that the globalization of fish trade flourished between 1990 and 2018. Although, due to the financial crisis and its subsequent impact, the total amount of fish trade declined in 2009 and 2015, the network structure was not seriously affected, and the trend of topology property remained unchanged. Based on the construction of the international trade network, its node attribute, and its structural attribute, fish trade maintains the trend of globalization. Countries should actively adhere to trade globalization to promote the development of the fish trade.

Blood gene expression predicts intensive care unit admission in hospitalised patients with COVID-19.

Background: The COVID-19 pandemic has created pressure on healthcare systems worldwide. Tools that can stratify individuals according to prognosis could allow for more efficient allocation of healthcare resources and thus improved patient outcomes. It is currently unclear if blood gene expression signatures derived from patients at the point of admission to hospital could provide useful prognostic information. Methods: Gene expression of whole blood obtained at the point of admission from a cohort of 78 patients hospitalised with COVID-19 during the first wave was measured by high resolution RNA sequencing. Gene signatures predictive of admission to Intensive Care Unit were identified and tested using machine learning and topological data analysis, TopMD. Results: The best gene expression signature predictive of ICU admission was defined using topological data analysis with an accuracy: 0.72 and ROC AUC: 0.76. The gene signature was primarily based on differentially activated pathways controlling epidermal growth factor receptor (EGFR) presentation, Peroxisome proliferator-activated receptor alpha (PPAR-α) signalling and Transforming growth factor beta (TGF-ß) signalling. Conclusions: Gene expression signatures from blood taken at the point of admission to hospital predicted ICU admission of treatment naïve patients with COVID-19.

On Analysis of Temperature Based Topological Indices of Some Covid-19 Drugs

A variety of graphical invariants have been described and tested, offering lots of applications in the fields of nanochemistry, computational networks and in different scientific research areas. One commonly studied group of invariants is the topological index, which allows to research the chemical, biological, and physical properties of a chemical structure. Topological indexes are numerical quantities that can be used to describe the properties of the molecular graph. In this article, we draw from the analytically closed formulas of certain molecular structures of coronavirus such as Ribavirin, Sofosbuvir and Oseltamivir by calculating temperature based topological indices.

QSPR Study and Distance-Based New Topological Descriptors of Some Drugs Used in the COVID-19 Treatment

In chemistry and medical sciences, it is essential to study the chemical, biological, clinical, and therapeutic aspects of pharmaceuticals. To save time and money, mathematical chemistry focuses on topological indices used in quantitative structure-property relationship (QSPR) models to predict the properties of chemical structures. The COVID-19 pandemic is widely recognized as the greatest life-threatening crisis facing modern medicine. Scientists have tested various antiviral drugs available to treat COVID-19 disease, and some have found that they help get rid of this viral infection. Antiviral drugs such as Arbidol, chloroquine, hydroxychloroquine, lopinavir, remdesivir, ritonavir, thalidomide, and theaflavin are used to treat COVID-19. In this paper, reformulated leap Zagreb indices are introduced. Then, the reformulated leap Zagreb indices, leap eccentric connectivity indices, and reformulated Zagreb connectivity indices of these antiviral drugs are calculated. Curvilinear and multilinear regression models predicting the physicochemical properties of these antiviral drugs in terms of proposed indices are obtained and analyzed. The findings and models of this study will shed light on new drug discoveries for the treatment of COVID-19.

Analyzing the network structure of risk transmission among renewable, non-renewable energy and carbon markets

The linkage of renewable, non-renewable energy and carbon markets is increasing, and there is a complex network structure for the risk transmission among multiple markets. Based on the methods of network topology analysis and DY spillover index, this paper analyzes the time-varying spillover effect and network structure of risk transmission among renewable, non-renewable energy and carbon markets. The results show that: according to the static spillover index, there are significant spillover effects among renewable, non-renewable energy and carbon markets, and they are asymmetric. Moreover, the total spillover index further shows that the spillover effect between energy and carbon markets is time-varying, especially during the extreme events. Specifically, the net spillover index shows that the spillover effects among renewable, non-renewable energy and carbon markets are bidirectional, asymmetric and time-varying. Additionally, under the influence of various extreme events, the spillover effect and network structure of risk transmission among renewable, non-renewable energy and carbon markets are heterogeneous. Compared with the shale oil revolution and the Sino-US trade dispute, the influence of COVID-19 is more significant and complex, and it is long-term and comprehensive. Finally, some policy implications for preventing risk transmission and optimizing the energy structure to promote emission reduction are put forward. [ FROM AUTHOR] Copyright of Renewable Energy: An International Journal is the property of Pergamon Press - An Imprint of Elsevier Science and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full . (Copyright applies to all s.)

Communities Detection in Multiplex Networks Using Optimization: Study Case—Employment in Mexico during the COVID-19 Pandemic

The detection of communities in complex networks offers important information about the structure of the network as well as its dynamics. However, it is not an easy problem to solve. This work presents a methodology based of the robust coloring problem (RCP) and the vertex cover problem (VCP) to find communities in multiplex networks. For this, we consider the RCP idea of having a partial detection based onf the similarity of connected and unconnected nodes. On the other hand, with the idea of the VCP, we manage to minimize the number of groups, which allows us to identify the communities well. To apply this methodology, we present the dynamic characterization of job loss, change, and acquisition behavior for the Mexican population before and during the COVID-19 pandemic modeled as a 4- layer multiplex network. The results obtained when applied to test and study case networks show that this methodology can classify elements with similar characteristics and can find their communities. Therefore, our proposed methodology can be used as a new mechanism to identify communities, regardless of the topology or whether it is a monoplex or multiplex network.

NOVEL ACCESSIBILITY METRICS BASED ON HIERARCHICAL DECOMPOSITION OF TRANSPORT NETWORKS

Scientific analysis of public transport systems at the urban, regional, and national levels is vital in this contemporary, highly connected world. Quantifying the accessibility of nodes (locations) in a transport network is considered a holistic measure of transportation and land use and an important research area. In recent years, complex networks have been employed for modeling and analyzing the topology of transport systems and services networks. However, the design of network hierarchy-based accessibility measures has not been fully explored in transport research. Thus, we propose a set of three novel accessibility metrics based on the k-core decomposition of the transport network. Core-based accessibility metrics leverage the network topology by eliciting the hierarchy while accommodating variations like travel cost, travel time, distance, and frequency of service as edge weights. The proposed metrics quantify the accessibility of nodes at different geographical scales, ranging from local to global. We use these metrics to compute the accessibility of geographical locations connected by air transport services in India. Finally, we show that the measures are responsive to changes in the topology of the transport network by analyzing the changes in accessibility for the domestic air services network for both pre-covid and post-covid times. © 2023 Faculty of Transport and Aviation Engineering, Silesian University of Technology. All rights reserved.

Superhighways and roads of multivariate time series shock transmission: Application to cryptocurrency, carbon emission and energy prices

Inferring the heterogeneous connection pattern of a networked system of multivariate time series observations is a key issue. In finance, the topological structure of financial connectedness in a network of assets can be a central tool for risk measurement. Against this, we propose a topological framework for variance decomposition analysis of multivariate time series in time and frequency domains. We build on the network representation of time–frequency generalized forecast error variance decomposition (GFEVD), and design a method to partition its maximal spanning tree into two components: (a) superhighways, i.e. the infinite incipient percolation cluster, for which nodes with high centrality dominate;(b) roads, for which low centrality nodes dominate. We apply our method to study the topology of shock transmission networks across cryptocurrency, carbon emission and energy prices. Results show that the topologies of short and long run shock transmission networks are starkly different, and that superhighways and roads considerably vary over time. We further document increased spillovers across the markets in the aftermath of the COVID-19 outbreak, as well as the absence of strong direct linkages between cryptocurrency and carbon markets. © 2023 Elsevier B.V.

Image Scanning Microscopy to Investigate Polycomb Protein Colocalization onto Chromatin

Super-resolution microscopy has been recently applied to understand the 3D topology of chromatin at an intermediated genomic scale (kilobases to a few megabases), as this corresponds to a sub-diffraction spatial scale crucial for the regulation of gene transcription. In this context, polycomb proteins are very renowned gene repressors that organize into the multiprotein complexes Polycomb Repressor Complex 1 (PRC1) and 2 (PRC2). PRC1 and PRC2 operate onto the chromatin according to a complex mechanism, which was recently recapitulated into a working model. Here, we present a functional colocalization study at 100–140 nm spatial resolution targeting PRC1 and PRC2 as well as the histone mark H3K27me3 by Image Scanning Microscopy (ISM). ISM offers a more flexible alternative to diffraction-unlimited SRMs such as STORM and STED, and it is perfectly suited to investigate the mesoscale of PRC assembly. Our data suggest a partially simultaneous effort of PRC1 and PRC2 in locally shaping the chromatin topology.

Rough set paradigms via containment neighborhoods and ideals

Imperfect information causes indistinguishability of objects and inability of making an accurate decision. To deal with this type of vague problem, Pawlak proposed the concept of rough set. Then, this concept has been studied from different points of view like topology and ideals. In this manuscript, we use the system of containment neighborhoods to present new rough set models generated by topology and ideals. We discuss their fundamental characterizations and reveal the relationships among them. Also, we prove that the current approximation spaces produce higher accuracy measures than those given by some previous approximation spaces. Ultimately, we provide a medical example to demonstrate that the current approach is one of the preferable and useful techniques to eliminate the ambiguity of the data in practical problems. © 2023, University of Nis. All rights reserved.

Globalization and education

This chapter seeks to explore some of the major theoretical traditions that have informed thinking about globalization and its implication for education research. I particularly situate an understanding of globalization within broader developments in the social sciences around spatial issues, as well as the various theoretical frameworks that have been mobilized to better understand where and to whom education policy is spatially and relationally located. These include (i) scalar approaches, or an understanding of space as nested multi-levelled scales;and (ii) topological approaches, or an understanding of space as constituted through relations between social actors. © 2023 Elsevier Ltd. All rights reserved.

Topological structure and COVID-19 related risk propagation in TFT-LCD supply networks

Thin-film-transistor liquid-crystal displays (TFT-LCDs) have gained popularity due to their widespread use in the production of televisions, laptops, and iPads. TFT-LCD firms' activities that build relationships with suppliers and customers contribute to the emergence of supply networks. A firm's ability to identify risks, however, is complicated, as TFT-LCD supply networks are becoming increasingly global, complex, and interconnected. The extant research on the topological structure of TFT-LCD supply networks is limited, and the risks identified rely on untested assumptions about the topological structure of such networks. To fill these gaps, this study examines the topological structure and COVID-19 related risk propagation in TFT-LCD supply networks from a dynamic perspective. First, the evolution of the topological structure of TFT-LCD supply networks from 2015 to 2020 is explored by constructing a weighted and undirected supply network. Second, the hidden risky sources in TFT-LCD supply networks are revealed by the proposed risk propagation model. The results show that TFT-LCD supply networks are characterised by a ‘hub and spoke' feature and an explicit shift from geographical to global cooperation. Additionally, a ‘robust-yet-fragile' configuration in these supply networks is uncovered, and the hidden risky sources in the main TFT-LCD manufacturers and suppliers and in interfirm cooperations are revealed. These findings will help managers reduce the vulnerability of TFT-LCD supply networks to disruptions and construct more robust and resilient networks.

Non-neighbor Topological Indices on Covid-19 Drugs with QSPR Analysis

Coronavirus (COVID-19) is one of the recent infectious diseases caused by the virus SARS-CoV-2. The virus causes mild to severe respiratory problems which may lead to death in most cases. There is currently no precise or effective medication available to treat COVID-19 patients. Researchers and many pharmaceutical industries are working toward novel therapeutics and repurposed drugs for coronavirus. In this study, we consider some investigational antiviral drugs like Nitazoxanide, Imatinib, Famotidine, Galidesivir, and Artesunate that are used for the treatment of COVID-19. For this purpose, here we define various non-neighbor topological indices over the above aforesaid antiviral drugs to investigate the physicochemical properties associated with the indices. Further QSPR analysis was carried out between seven non-neighbor topological indices and eight physicochemical properties for the above drugs using the Linear regression method. The result obtained could aid in discovering new vaccines and drugs for COVID-19 disease. © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

Feature Extraction Based on GLCM and GLRM Methods on COVID-19 Dataset

COVID-19 epidemic had devastating effects on both the economic and social infrastructures of all countries in the world. Several researches are still being carried out in order to develop effective models for the diagnosis and treatment of COVID-19 patients. A COVID-19 infected person may experience dry cough, muscle ache, brain pain, fever, sore throat, and mild to severe respiratory illness. At the same time, it has a negative impact on the lungs. The severity of COVID-19 contamination over the lungs can be examined using X-Ray and CT scan images of the chest. The examination of the severity of disease is carried out by Manual characterization. However, it may lead to human error. To overcome this drawback, an exact and proficient indicative tool is highly required. Hence, this research provides a new topology for COVID-19 diagnosis using CT scan images. In this topology, features from the images are extracted using Gray Level Co-event Matrix (GLCM), Gray Level Run Length Matrix (GRLM). An automatic classification is carried out with supervised ML algorithms. In order to examine the effectiveness of the proposed model, an experiment was carried out on the COVID-19 Dataset. Various performance evaluation metrics are utilized to identify the best ML method. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

A mathematical-based epidemic model to prevent and control outbreak of corona virus 2019 (COVID-19)

COVID-19 is the seventh member of the Coronaviridae family and this virus will spread quickly in humans, birds and other animals. Human infections are the major source of spreading this virus, it causes mainly respiratory and neurological diseases. In the month of December 2019 there were an increased number of patients reported to hospitals in Wuhan, China. They identified this virus as a novel Corona virus, named as COVID-19. Due to this uncontrollable virus two major challenges are faced by mankind. First, abnormal growth of COVID-19 cases is leading to insufficient medical resources and second, emergency protocols (such as lockdowns) are imposed as preventive measures. we provide a preliminary evolutionary graph theory based mathematical model was designed for control and prevention of COVID-19. In the proposed model, well known technique of social distancing with different variations are implemented. Lockdown by many countries leads to the decrease of Gross Domestic Product (GDP) and increase in mental problems in citizens. These two problems can be solved by the administration of anti virus in some form to the public as a counterpart to the virus. This model works more effectively with high percolation of antiviral nodes in a population and over a period of time. © 2022 Scrivener Publishing LLC.

Customized EfficientNet for Histopathology Image Representation

In recent years, and amplified by the COVID-19 pandemic, the digitization of pathology has gained a considerable attention. Digital pathology provides crucial advantages compared to conventional light microscopy, including more efficient workflows, more accurate diagnosis and treatment planning, and easier collaboration. Despite promising progress, there are some critical challenges related to classifying images in digital pathology, such as huge input sizes (e.g., gigapixels) and expensive processing time. Most of the existing models for classification of histopathology images are very large and accordingly have many parameters to be learned/optimized. In addition, due to the large size of Whole Slide Images (WSIs), e.g., 100,000 × 100,000 pixels, models require enormous computational times to achieve reliable results. In order to address these challenges, we propose a more compact network which is customized to classify cancer subtypes with lower computation time and memory complexity. This model is based on EfficientNet topology, but it is tailored for classifying histopathology images. The utilized model is evaluated over three tumor types brain, lung, and kidney from The Cancer Genome Atlas (TCGA) public repository. Since the pre-trained EfficientNet works properly with the specific size of images, an effective approach is proposed to adjust the size of input images. The proposed model can be trained with a much smaller training set for applications such as image search that require robust and compact representations. The results show that the proposed model, compared to state-of-the-art models, i.e., KimiaNet, can classify cancer subtypes more accurately and provides superior results. In addition, the proposed model achieves memory and computational efficiency in the training phase and is a more compact deep topology compared to KimiaNet. © 2022 IEEE.

SARS-CoV-2 SUD2 and Nsp5 Conspire to Boost Apoptosis of Respiratory Epithelial Cells via an Augmented Interaction with the G-Quadruplex of BclII.

The molecular mechanisms underlying how SUD2 recruits other proteins of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) to exert its G-quadruplex (G4)-dependent pathogenic function is unknown. Herein, Nsp5 was singled out as a binding partner of the SUD2-N+M domains (SUD2core) with high affinity, through the surface located crossing these two domains. Biochemical and fluorescent assays demonstrated that this complex also formed in the nucleus of living host cells. Moreover, the SUD2core-Nsp5 complex displayed significantly enhanced selective binding affinity for the G4 structure in the BclII promoter than did SUD2core alone. This increased stability exhibited by the tertiary complex was rationalized by AlphaFold2 and molecular dynamics analysis. In line with these molecular interactions, downregulation of BclII and subsequent augmented apoptosis of respiratory cells were both observed. These results provide novel information and a new avenue to explore therapeutic strategies targeting SARS-CoV-2. IMPORTANCE SUD2, a unique protein domain closely related to the pathogenesis of SARS-CoV-2, has been reported to bind with the G-quadruplex (G4), a special noncanonical DNA structure endowed with important functions in regulating gene expression. However, the interacting partner of SUD2, among other SARS-CoV-2 Nsps, and the resulting functional consequences remain unknown. Here, a stable complex formed between SUD2 and Nsp5 was fully characterized both in vitro and in host cells. Moreover, this complex had a significantly enhanced binding affinity specifically targeting the Bcl2G4 in the promoter region of the antiapoptotic gene BclII, compared with SUD2 alone. In respiratory epithelial cells, the SUD2-Nsp5 complex promoted BclII-mediated apoptosis in a G4-dependent manner. These results reveal fresh information about matched multicomponent interactions, which can be parlayed to develop new therapeutics for future relevant viral disease.