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.

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.)

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.

Epidemic dynamics in census-calibrated modular contact network.

Network-based models are apt for understanding epidemic dynamics due to their inherent ability to model the heterogeneity of interactions in the contemporary world of intense human connectivity. We propose a framework to create a wire-frame that mimics the social contact network of the population in a geography by lacing it with demographic information. The framework results in a modular network with small-world topology that accommodates density variations and emulates human interactions in family, social, and work spaces. When loaded with suitable economic, social, and urban data shaping patterns of human connectance, the network emerges as a potent decision-making instrument for urban planners, demographers, and social scientists. We employ synthetic networks to experiment in a controlled environment and study the impact of zoning, density variations, and population mobility on the epidemic variables using a variant of the SEIR model. Our results reveal that these demographic factors have a characteristic influence on social contact patterns, manifesting as distinct epidemic dynamics. Subsequently, we present a real-world COVID-19 case study for three Indian states by creating corresponding surrogate social contact networks using available census data. The case study validates that the demography-laced modular contact network reduces errors in the estimates of epidemic variables.

Optimal Capacity-Constrained COVID-19 Vaccination for Heterogeneous Populations

COVID-19 and the ensuing vaccine capacity constraints have emphasized the importance of proper prioritization during vaccine rollout. This problem is complicated by heterogeneity in risk levels, contact rates, and network topology which can dramatically and unintuitively change the efficacy of vaccination and must be taken into account when allocating resources. This paper proposes a general model to capture a wide array of network heterogeneity while maintaining computational tractability and formulates vaccine prioritization as an optimal control problem. Pontryagin's Maximum Principle is used to derive properties of optimal, potentially highly dynamic, allocation policies, providing significant reductions in the set of candidate policies. Extensive numerical simulations of COVID-19 vaccination are used to corroborate these findings and further illicit optimal policy characteristics and the effects of various system, disease, and population parameters. © 2022 IEEE.

Forecasting district-wide pedestrian volumes in multi-level networks in high-density mixed-use areas

This paper is concerned with improvements in the forecasting of pedestrian flows in multilevel pedestrian networks in high-density urban environments. 3D network topology measures are combined with land-use data, and validated against extensive pedestrian counts, to provide both evidence for the applicability of network analysis in tropical metropolises, as well as a calibrated tool for urban planners. The research focuses on four areas in Singapore. These areas have in common that they all are prominent transport hubs, but differ in surrounding land-use types and dominant network topology (e.g. indoor, outdoor, above ground, below ground, at grade). Multi-level pedestrian networks were drawn based on OpenStreetMap, include sidewalks on both sides of major roads for a radius up to 2 kilometres from the site centroids. Spatial network analysis was performed using sDNA which allows vertical networks to generate measures describing the spatial configuration of the network. Subsequently, pedestrian counts were conducted during three consecutive days. In total, counts were conducted at more than 250 locations in 2018 and 2019, well before the global COVID19 pandemic. Pedestrian flows are set against a series of variables, including pedestrian attractors and generators (e.g. shops, offices, hotels, dwellings), and variables describing the spatial configuration of the network, using advanced regression models. Our results show that betweenness metrics (i.e. space syntax choice) combined with land-use yield high predictive power. Dependent on the study site, network metrics based on angular distance outperform those based on metric distance or perceived link distance. This research demonstrates that is necessary to account for the multi-level nature of networks, and that indoor flows through private developments cannot be neglected, in particular when planning for integrated transport developments. The paper concludes with recommendations and implications for practice. © 2022 Proceedings 13th International Space Syntax Symposium, SSS 2022. All rights reserved.

Shareholder Structure of Major Technology Companies: A Graph Analytics Study during COVID-19 and Beyond

During financial crises or other unexpected events, investors often seek to include lower-risk assets in their portfolios. Some assets are more sensitive than others to such phenomena. In the equities markets, adjustments tend to be made to the shareholdings of companies that are associated with a higher level of uncertainty. In this work, we explore the evolution of shareholder structure of various well-known companies in the technology sector during the COVID-19 pandemic and beyond. We model, as graphs, shareholder ownership data about twenty US-listed companies between 2020 and 2022. We use freely available tools to explore the bipartite interactions and generate a wide range of topologies that facilitate the identification of how shareholding structures have evolved during the pandemic. In addition, we study the role that some nodes play in the network topology and the process of change that is observed. Our findings include that (1) most investors reduced the amount invested in technology stocks during the pandemic and that these investments tended to bounce back in the post-pandemic era;(2) Vanguard Group, Inc., is the most influential investor in the network;(3) Apple has the highest market capitalization of all technology stocks for all quarters in this study, Microsoft Corp has a significantly lower market capitalization, but a significantly higher number of investors;and (4) While investors for Apple and Microsoft tend to be from London and New York, companies such as Oracle have investors from a variety of locations. © 2022 Copyright for this paper by its authors. Use permitted under Creative Commons License Attribution 4.0 International (CC BY 4.0).

Causal Network Structure Analysis of Pharmaceutical and Biological Industry under the COVID-19

Nowadays, countries in the world are increasingly connected, and major emergencies affect the development of various industries, which makes particularly important to measure industry association.In this paper, we extract ordinary period structure before the outbreak of the COVID-19 and explode pharmaceutical biological industry network, then apply convergence cross mapping causal inference to describe the industry network, further establish the network of industry network topology to measure node and industry system risk. Empirical results show that the network structure of the pharmaceutical and biological industry is similar in the normal period and outbreak period before the epidemic, and the association within the industry was relatively stable. When the epidemic hit the network, the linkage of the pharmaceutical and biological industry is significantly enhanced, and the systemic risks and network efficiency are higher than usual. The network of pharmaceutical and biological industry is of strong robustness and strong ability to deal with emergencies, which provides some reference for grasping the stability of industry network structure and industry risk management under sudden shocks in pharmaceutical and biological industry. © 2021 IEEE.

Design of Wireless Sensor Network Programming Tutorial Module (WSNmesh32)

The use of Internet of Things technology may have a significant impact on changes in online learning systems in the situation of the COVID-19 pandemic. Learners need a laboratory to use and master the necessary tools or components. However, this is difficult to do in a pandemic situation. The use of remote laboratories can overcome the limitations of practicum media, which is an obstacle for students in learning. This research discusses the creation of WSNMesh32 programming tutorial modules as microcontrollers using Wi-Fi-mesh network topology that can be used in Sensor and Microcontroller Practicum courses. This research aims to find out the feasibility and perception of learners towards the WSNMesh32 Programming Tutorial Module as a learning medium in pandemic times. The method used is a quantitative method with the ADDIE model (Analyse, Design, Develop, Implement, Evaluation). Participants consisted of 31 students of the Electrical Engineering Education study program of the Industrial Electronics division 2018. The study was conducted online in light of the ongoing COVID-19 pandemic situation. The feasibility test of the module is carried out by leading discussions and reviews with experts on the material to be presented. Respondents gave a positive response to the module. The results provide the conclusion that the sensor programming tutorial module that has been produced is suitable for use as a tutorial module in sensor and microcontroller practicum courses and can be used in learning in a pandemic situation © 2021 IEEE.

Analysing network structures and dynamics of the Pakistan stock market across the uncertain time of global pandemic (Covid-19)

Purpose - The global pandemic COVID-19 has attracted considerable interest from researchers globally. However, there is very little systematic work on the impact of the COVID-19 crisis on the local stock markets. This paper proposes a complex network method that examines the effects of global pandemic COVID-19 on the Pakistan stock market to fill in these gaps. Methods - Firstly, correlograms are plotted to inspect the correlation matrices of the overall and two sub-sample periods. Secondly, correlation threshold networks and topological properties are examined for different threshold levels. Finally, this paper uses evolving MSTs to construct a dynamical complex network and presents dynamic centrality measures, normalised tree, and average path lengths. Findings - The findings show that COVID-19 related certainty and crisis lead to low volatility and a star-like structure, resulting in a quick flow of information and a strong correlation among the Pakistan stock market. Originality - This paper addresses both classes of the networks. To the best of our knowledge, the static and dynamic evolution of the Pakistan stock market around the global pandemic COVID-19 has not been performed yet.

Green Smart City Intelligent and Cyber-Security-Based IoT Transportation Solutions for Combating the Pandemic COVID-19

This chapter presents the fruit of our research by merging smart transportation and smart health to provide IoT transportation solutions based on green smart city intelligence and safety to fight against the COVID-19 pandemic. For this, we have realized a model that allows transporting citizens via a means of transportation based on electric mobility to reduce energy consumption, reduce CO 2 emission and cost by searching the optimal path that this vehicle will be used. And to transport people, we need a system that allows us to check the health situation of citizens to avoid and prevent the spread of the COVID-19 pandemic. In order to find solutions to this work, we have proposed approaches to calculate the most optimal path that meets our needs, as well as to propose scenarios that allow checking the situation of the citizens. And to complete this work with minimum consumption of memory and time, we made a comparative study on the nodes used for the different IoT network topologies to choose the best one for our platform. Concerning the communication, we chose to use the CoAP protocol to ensure the communication between the nodes, and we used the AES-SHA256 encryption algorithm to compare it with RSA-SHA256 to ensure the elements of security and protection the data from any intrusion. © 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.

Countering Misinformation Through Semantic-Aware Multilingual Models

The presence of misinformation and harmful content on social networks is an emerging problem that endangers public health. One of the most successful approaches for detecting, assessing, and providing prompt responses to this misinformation problem is Natural Language Processing (NLP) techniques based on semantic similarity. However, language constitutes one of the most significant barriers to address, denoting the need to develop multilingual tools for an effective fight against misinformation. This paper presents an approach for countering misinformation through a semantic-aware multilingual architecture. Due to the specificity of the task addressed, which involves assessing the level of similarity between a pair of texts in a multilingual scenario, we built an extension of the well-known Semantic Textual Similarity Benchmark (STSb) to 15 languages. This new dataset allows to fine-tune and evaluate multilingual models based on Transformers with a siamese network topology on monolingual and cross-lingual Semantic Textual Similarity (STS) tasks, achieving a maximum average Spearman correlation coefficient of 83.60%. We validate our proposal using the Covid-19 MLIA @ Eval Multilingual Semantic Search Task. The results reported demonstrate that semantic-aware multilingual architectures are successful at measuring the degree of similarity between pairs of texts, while broadening our understanding of the multilingual capabilities of this type of models. The results and the new multilingual STS Benchmark data presented and made publicly in this study constitute an initial step towards extending methods proposed in the literature that employ semantic similarity to combat misinformation at a multilingual level. © 2021, Springer Nature Switzerland AG.

Bioinformatics/network topology analysis of acupuncture in the treatment of COVID-19: response to methodological issues.

Coronavirus disease 2019 (COVID-19) is a global pandemic and there is an urgent need to discover the therapy for COVID-19. In our original article, we first obtained the target proteins of acupuncture and related target genes of COVID-19 by searching English and Chinese databases, then Gene Ontology biological processes and enrichment analysis were performed on the overlapping targets of acupuncture with COVID-19. Moreover, the compound-target and compound-disease-target network was constructed. This is an innovative attempt to predict the potential benefits of acupuncture treatment for COVID-19. In this letter, we answered reader Zheng's comments.

An Efficient Cost Performance Placement of Macro Sites and Small Cells Under Restricted Topology

The global COVID-19 pandemic leads people to intermittent quarantines and lockdowns. Many large and crowded gatherings were postponed or even cancelled to prevent social distance violation. The paper aims to tackle the placement problem of macro sites, microcells and picocells under a restricted network topology. The cell placement problem is defined based on linear programming. The algorithm named Cost Efficiency algorithm is proposed to construct a network with higher performance and lower cost. Simulation results showed that the proposed algorithm yields higher SINR value and more number of served users over construction cost compared with other planning algorithms. The result of this work is expected to help users have better network service quality when they are isolated in hospital or self-health monitoring at home. © 2021, ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering.

Keeping to the code: How local norms of friendship and dating inform macro-structures of adolescents’ romantic networks

Even though romantic partnerships are often understood as pairwise relationships, there is value in conceptualizing the dating patterns of adolescents as network phenomena, particularly as related to the spread of sexually transmitted infections. The current study adopts this perspective to evaluate how a local norm guiding the coexistence of dating and friendship informs macro-level romantic network structures. Using twelve months of romantic relationship data from the Peers and the Emergence of Adolescent Romance (PEAR) study, we find that the global dating network resembles a chain-like, spanning tree structure consistent with that observed by Bearman and colleagues (2004) in their foundational study. Then, through the application of temporal ERGMs, we uncover evidence that adolescents adhere to a social norm against dating their friends’ previous romantic partners. We use these findings to empirically ground a series of network simulations, which demonstrate that the romantic network’s structure becomes less redundant and more clustered as the norm against dating friends’ previous partners is relaxed. By understanding how local norms shape patterns of friendship and dating, we can better conceptualize the macro-level structural patterns of romantic networks and their implications for infectious disease diffusion.

Advances, challenges and opportunities of phylogenetic and social network analysis using COVID-19 data.

Coronavirus disease 2019 (COVID-19) has attracted research interests from all fields. Phylogenetic and social network analyses based on connectivity between either COVID-19 patients or geographic regions and similarity between syndrome coronavirus 2 (SARS-CoV-2) sequences provide unique angles to answer public health and pharmaco-biological questions such as relationships between various SARS-CoV-2 mutants, the transmission pathways in a community and the effectiveness of prevention policies. This paper serves as a systematic review of current phylogenetic and social network analyses with applications in COVID-19 research. Challenges in current phylogenetic network analysis on SARS-CoV-2 such as unreliable inferences, sampling bias and batch effects are discussed as well as potential solutions. Social network analysis combined with epidemiology models helps to identify key transmission characteristics and measure the effectiveness of prevention and control strategies. Finally, future new directions of network analysis motivated by COVID-19 data are summarized.

SARS-CoV-2 Molecular Network Structure.

Knowledge about the molecular basis of SARS-CoV-2 infection is incipient. However, recent experimental results about the virus interactome have shown that this single-positive stranded RNA virus produces a set of about 28 specific proteins grouped into 16 non-structural proteins (Nsp1 to Nsp16), four structural proteins (E, M, N, and S), and eight accessory proteins (orf3a, orf6, orf7a, orf7b, orf8, orf9b, orf9c, and orf10). In this brief communication, the network model of the interactome of these viral proteins with the host proteins is analyzed. The statistical analysis of this network shows that it has a modular scale-free topology in which the virus proteins orf8, M, and Nsp7 are the three nodes with the most connections (links). This result suggests the possibility that a simultaneous pharmacological attack on these hubs could assure the destruction of the network and the elimination of the virus.

Network biology to uncover functional and structural properties of the plant immune system.

In the last two decades, advances in network science have facilitated the discovery of important systems' entities in diverse biological networks. This graph-based technique has revealed numerous emergent properties of a system that enable us to understand several complex biological processes including plant immune systems. With the accumulation of multiomics data sets, the comprehensive understanding of plant-pathogen interactions can be achieved through the analyses and efficacious integration of multidimensional qualitative and quantitative relationships among the components of hosts and their microbes. This review highlights comparative network topology analyses in plant-pathogen co-expression networks and interactomes, outlines dynamic network modeling for cell-specific immune regulatory networks, and discusses the new frontiers of single-cell sequencing as well as multiomics data integration that are necessary for unraveling the intricacies of plant immune systems.

Is acupuncture effective in the treatment of COVID-19 related symptoms? Based on bioinformatics/network topology strategy.

Acupuncture is an important part of Chinese medicine that has been widely used in the treatment of inflammatory diseases. During the coronavirus disease 2019 (COVID-19) epidemic, acupuncture has been used as a complementary treatment for COVID-19 in China. However, the underlying mechanism of acupuncture treatment of COVID-19 remains unclear. Based on bioinformatics/topology, this paper systematically revealed the multi-target mechanisms of acupuncture therapy for COVID-19 through text mining, bioinformatics, network topology, etc. Two active compounds produced after acupuncture and 180 protein targets were identified. A total of 522 Gene Ontology terms related to acupuncture for COVID-19 were identified, and 61 pathways were screened based on the Kyoto Encyclopedia of Genes and Genomes. Our findings suggested that acupuncture treatment of COVID-19 was associated with suppression of inflammatory stress, improving immunity and regulating nervous system function, including activation of neuroactive ligand-receptor interaction, calcium signaling pathway, cancer pathway, viral carcinogenesis, Staphylococcus aureus infection, etc. The study also found that acupuncture may have additional benefits for COVID-19 patients with cancer, cardiovascular disease and obesity. Our study revealed for the first time the multiple synergistic mechanisms of acupuncture on COVID-19. Acupuncture may play an active role in the treatment of COVID-19 and deserves further promotion and application. These results may help to solve this pressing problem currently facing the world.