Explain systemic risk of commodity futures market by dynamic network

Since inflation of commodities is becoming more and more severe recently caused by many macro events, such as COVID-19 and Russian-Ukrainian conflict, systemic risk of commodity futures market is getting more attention from academic and industrial areas. Instead of using external factors to explain this risk as previous researches, we explain it by internal topology and structures of commodity futures market. This method helps us understand its key driving factors and their different impact to Chinese and international commodity futures markets. © 2023 Elsevier Inc.

The impact of pandemic on dynamic volatility spillover network of international stock markets.

Since the beginning of the twenty-first century, several pandemics, including SARS and COVID-19, have spread faster and on a broader scale. Not only do they harm people's health, but they can also cause significant damage to the global economy within a short period of time. This study uses the infectious disease EMV tracker index to investigate the impact of pandemics on the volatility spillover effects of global stock markets. Spillover index model estimation is conducted using the time-varying parameter vector autoregressive approach, and the maximum spanning tree and threshold filtering techniques are combined to construct the dynamic network of volatility spillovers. The conclusion from the dynamic network is that when a pandemic occurs, the total volatility spillover effect increases sharply. In particular, the total volatility spillover effect historically peaked during the COVID-19 pandemic. Moreover, when pandemics occur, the density of the volatility spillover network increases, while the diameter of the network decreases. This indicates that global financial markets are increasingly interconnected, speeding up the transmission of volatility information. The empirical results further reveal that volatility spillovers among international markets have a significant positive correlation with the severity of a pandemic. The study's findings are expected to help investors and policymakers understand volatility spillovers during pandemics.

Computational analysis of protein stability and allosteric interaction networks in distinct conformational forms of the SARS-CoV-2 spike D614G mutant: reconciling functional mechanisms through allosteric model of spike regulation.

In this study, we used an integrative computational approach to examine molecular mechanisms underlying functional effects of the D614G mutation by exploring atomistic modeling of the SARS-CoV-2 spike proteins as allosteric regulatory machines. We combined coarse-grained simulations, protein stability and dynamic fluctuation communication analysis with network-based community analysis to examine structures of the native and mutant SARS-CoV-2 spike proteins in different functional states. Through distance fluctuations communication analysis, we probed stability and allosteric communication propensities of protein residues in the native and mutant SARS-CoV-2 spike proteins, providing evidence that the D614G mutation can enhance long-range signaling of the allosteric spike engine. By combining functional dynamics analysis and ensemble-based alanine scanning of the SARS-CoV-2 spike proteins we found that the D614G mutation can improve stability of the spike protein in both closed and open forms, but shifting thermodynamic preferences towards the open mutant form. Our results revealed that the D614G mutation can promote the increased number of stable communities and allosteric hub centers in the open form by reorganizing and enhancing the stability of the S1-S2 inter-domain interactions and restricting mobility of the S1 regions. This study provides atomistic-based view of allosteric communications in the SARS-CoV-2 spike proteins, suggesting that the D614G mutation can exert its primary effect through allosterically induced changes on stability and communications in the residue interaction networks.Communicated by Ramaswamy H. Sarma.

The spillover effect between Chinese crude oil futures market and Chinese green energy stock market

With the increasing severe pollution, the new energy industry is greatly favored by the government and investors. Using the static network connectedness method of Diebold and Yilmaz (2009, 2012, 2014) and the dynamic network connectedness approach of Antonakakis et al. (2020), this paper discusses the return and volatility spillover effects between China's crude oil futures market and 7 Chinese green energy stock markets. In terms of return spillover effects, we find firstly that Chinese green energy stock is able to dominate the price changes in the crude oil market, and the dominate role of natural gas stock market is stronger. Secondly, rather than changing the dominant role of the green energy stock market on crude oil futures market price changes, the outbreak of COVID-19 in 2020 strengthened that dominant role. For the volatility spillover effects, the results of static volatility spillover index show that the crude oil futures market volatility is mainly dominated by the green energy equity market, but the dynamic connectedness indices results show that the volatility in the international energy market can strengthen the dominant role of the crude oil futures on the volatility of the green energy stock market. Finally, we can find that both the outbreak of COVID-19 in 2020 and the online operation of the Chinese carbon trading market in 2021 can strengthen the dominant role of the green energy stock market on the crude oil futures market volatility.

Research Progress and Improvement Ideas of Anti-Epidemic Resilience in China's Urban Communities.

In the post-epidemic era, China's urban communities are at the forefront of implementing the whole chain of accurate epidemic prevention and control. However, the uncertainty of COVID-19, the loopholes in community management and people's overly optimistic judgment of the epidemic have led to the frequent rebound of the epidemic and serious consequences. Existing studies have not yet formed a panoramic framework of community anti-epidemic work under the concept of resilience. Therefore, this article first summarizes the current research progress of resilient communities from three perspectives, including ideas and perspectives, theories and frameworks and methods and means, and summarizes the gap of the current research. Then, an innovative idea on the epidemic resilience of urban communities in China is put forward: (1) the evolution mechanism of community anti-epidemic resilience is described through the change law of dynamic networks; (2) the anti-epidemic resilience of urban communities is evaluated or predicted through the measurement criteria; (3) a simulation platform based on Multi-Agent and dynamic Bayesian networks simulates the interactive relationship between "epidemic disturbance-cost constraint--epidemic resilience"; (4) the anti-epidemic strategies are output intelligently to provide community managers with decision-making opinions on community epidemic prevention and control.

Endurant Ecosystems: Model-Based Assessment of Resilience of Digital Business Ecosystems

Information Systems (IS) of modern organizations and enterprises often rely on a network of partners’ IS to deliver the services. The resilience of this network is the necessary condition for the operation of such ISs. The Digital Business Ecosystem (DBE) theory has emerged as an approach to ensure functioning and resilience in dynamic and open networks. This paper presents three cases of analysis of resilience of DBEs. The objective of the analysis is to assess the resilience of DBEs during its design phase. During this phase, often, only structural information presented in ISs models is available. In order to assess the resilience, the DBE models are analyzed for the potential for fulfilment of typical ecosystem goals and roles. The three DBE cases analyzed are winter road maintenance, digital vaccine, and Covid-19 testing. The paper evaluates the resilience of the DBEs and formulates the practices for uncovering and strengthening it. © 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.

Dynamic complex network based on status value for infectious disease

COVID-19 plays role in every part of the world;especially, it does harm to lives of people. Thus, COVID-19 sounds the alarm that is very important to build an effective mechanism to help prevent pandemic disease. In this work, dynamic network based on status value is built, which aims to help simulate the added danger level by the addition of infected people or close contacts. First, each node of this network is labelled with different kinds of status which has special value to show its danger degree. Then, the weight of the network represents the relationship of nodes;with the value of each node, average length and average spread of danger level is calculated based on the accumulation of dynamic weight. Thus, epidemic speed and scope of the infectious disease can be simulated. Moreover, the experiments compared to other networks have verified the effectiveness of our model. © The Authors.

Integrating Conformational Dynamics and Perturbation-Based Network Modeling for Mutational Profiling of Binding and Allostery in the SARS-CoV-2 Spike Variant Complexes with Antibodies: Balancing Local and Global Determinants of Mutational Escape Mechanisms.

In this study, we combined all-atom MD simulations, the ensemble-based mutational scanning of protein stability and binding, and perturbation-based network profiling of allosteric interactions in the SARS-CoV-2 spike complexes with a panel of cross-reactive and ultra-potent single antibodies (B1-182.1 and A23-58.1) as well as antibody combinations (A19-61.1/B1-182.1 and A19-46.1/B1-182.1). Using this approach, we quantify the local and global effects of mutations in the complexes, identify protein stability centers, characterize binding energy hotspots, and predict the allosteric control points of long-range interactions and communications. Conformational dynamics and distance fluctuation analysis revealed the antibody-specific signatures of protein stability and flexibility of the spike complexes that can affect the pattern of mutational escape. A network-based perturbation approach for mutational profiling of allosteric residue potentials revealed how antibody binding can modulate allosteric interactions and identified allosteric control points that can form vulnerable sites for mutational escape. The results show that the protein stability and binding energetics of the SARS-CoV-2 spike complexes with the panel of ultrapotent antibodies are tolerant to the effect of Omicron mutations, which may be related to their neutralization efficiency. By employing an integrated analysis of conformational dynamics, binding energetics, and allosteric interactions, we found that the antibodies that neutralize the Omicron spike variant mediate the dominant binding energy hotpots in the conserved stability centers and allosteric control points in which mutations may be restricted by the requirements of the protein folding stability and binding to the host receptor. This study suggested a mechanism in which the patterns of escape mutants for the ultrapotent antibodies may not be solely determined by the binding interaction changes but are associated with the balance and tradeoffs of multiple local and global factors, including protein stability, binding affinity, and long-range interactions.

The effect of network structure on the purchase of virtual goods on social networking services

Purpose>This paper aims to identify the effect of social structure variables on the purchase of virtual goods. Using field data, it also tests whether their effects on a social networking service are dynamic.Design/methodology/approach>To achieve the research objectives, the authors have applied the random effects panel Tobit model with actual time-series corporate data to explain a link between network structure factors and actual behavior on social networking services.Findings>The authors have found that various network structure variables such as in-degree, in-closeness centrality, out-closeness centrality and clustering coefficients are significant predictors of virtual item sales;while the constraint is marginally significant, out-degree is not significant. Furthermore, these variables are time-varying, and the dynamic model performs better in a model fit than the static one.Practical implications>The findings will help social networking service (SNS) operators realize the importance of understanding network structure variables and personal motivations or the behavior of consumers.Originality/value>This study provides implications in that it uses various and dynamic network structure variables with panel data.

Development of a dynamic network biomarkers method and its application for detecting the tipping point of prior disease development.

The dynamic network biomarker (DNB) method has advanced since it was first proposed. This review discusses advances in the DNB method that can identify the dynamic change in the expression signature related to the critical time point of disease progression by utilizing different kinds of transcriptome data. The DNB method is good at identifying potential biomarkers for cancer and other disease development processes that are represented by a limited molecular profile change between the normal and critical stages. We highlight that the cancer tipping point or premalignant state has been widely discovered for different types of cancer by using the DNB method that utilizes bulk or single-cell RNA sequencing data. This method could also be applied to other dynamic research studies and help identify early warning signals, such as the prediction of a pre-outbreak of COVID-19. We also discuss how the identification of reliable biomarkers of cancer and the development of new methods can be utilized for early detection and intervention and provide insights into emerging paths of the widespread biomarker candidate pool for further validation and disease/health management.

SoftHealth: Softwarized 5G-Driven Network Slicing for Real-time e-Healthcare Applications using ML

The recent years have whiteness the substandard situations of the modern healthcare system due to a fatal pandemic called COVID19. The rapid advancements of modern technology have disseminated the superficial benefits of medical infrastructure, but significant improvements are still extremely necessary over the massive e-healthcare system (mHS). Considering the fact of limited resources and unlimited demands, a highly stable end-to-end optimization model is required. Healthcare also struggles with real-time communication. The next-generation communication networks (e.g 5G and beyond) proficiently influence the network resource distribution for URLLC. In this work, we have envisioned a novel on-demand e-Healthcare dynamic network slice architecture that uses the ML algorithms at the edge server for real-time classification and access of the offloaded data from the central controller (vSDN-Control layer to Data plane layer). The comparative analysis over the datasets of patients consisting of special index parameters shows that our proposed model allows the end-user more efficient data accessibility over the conventional approaches. We have studied the model over the multi-classification ML models (kNN, DT and RF) and we have found an average improvement of 10% to 15% of average data offloading time efficiency from the local machines from the edge servers. This approach can be further extended as the QoS improvement of the healthcare data traffic over the dynamic network slice instances. We have kept the model simple but standard in nature. © 2022 IEEE.

Pruning digital contact networks for meso-scale epidemic surveillance using foursquare data

With the recent advances in human sensing, the push to integrate human mobility tracking with epidemic modeling highlights the lack of groundwork at the mesoscale (e.g., city-level) for both contact tracing and transmission dynamics. Although GPS data has been used to study city-level outbreaks in the past, existing approaches fail to capture the path of infection at the individual level. Consequently, in this paper, we extend epidemics prediction from estimating the size of an outbreak at the population level to estimating the individuals who may likely get infected within a finite period of time. To this end, we propose a network science based method to first build and then prune the dynamic contact networks for recurring interactions;these networks can serve as the backbone topology for mechanistic epidemics modeling. We test our method using Foursquare's Points of Interest (POI) smart phone geolocation data from over 1.3 million devices to better approximate the COVID-19 infection curves for two major (yet very different) US cities, (i.e., Austin and New York City), while maintaining the granularity of individual transmissions and reducing model uncertainty. Our method provides a foundation for building a disease prediction framework at the mesoscale that can help both policy makers and individuals better understand their estimated state of health and help the pandemic mitigation efforts. © 2021 ACM.

Conversations around organizational risk and insider threat

Organizational risk and resilience as well as insider threat have been studied through the lenses of socio-psychological studies and information and computer sciences. As with all disciplines, it is an area in which practitioners, enthusiasts, and experts discuss the theory, issues, and solutions of the field in various online public forums. Such conversations, despite their public nature, can be difficult to understand and to study, even by those deeply involved in the communities themselves. Who are the key actors? How can we understand and characterize the culture around such communities, the problems they face, and the solutions favored by the experts in the field? Which narratives are being created and propagated, and by whom - and are these actors truly people, or are they autonomous agents, or "bots"? In this paper, we demonstrate the value in applying dynamic network analysis and social network analysis to gain situational awareness of the public conversation around insider threat, nation-state espionage, and industrial espionage. Characterizing public discourse around a topic can reveal individuals and organizations attempting to push or shape narratives in ways that might not be obvious to casual observation. Such techniques have been used to great effect in the study of elections, the COVID-19 pandemic, and the study of misinformation and disinformation, and we hope to show that their use in this area is a powerful way to build a foundation of understanding around the conversations in the online public forum, provide data and analysis for use in further research, and equip counter insider threat practitioners with new insights. © 2021 Owner/Author.

Bayesian dynamic network actor models with application to South Korean COVID-19 patient movement data.

Motivated by the ongoing COVID-19 pandemic, this article introduces Bayesian dynamic network actor models for the analysis of infected individuals' movements in South Korea during the first three months of 2020. The relational event data modelling framework makes use of network statistics capturing the structure of movement events from and to several country's municipalities. The fully probabilistic Bayesian approach allows to quantify the uncertainty associated to the relational tendencies explaining where and when movement events are established and where they are directed. The observed patient movements' patterns at an early stage of the pandemic can provide interesting insights about the spread of the disease in the Asian country.

A LOGIC FOR MONITORING DYNAMIC NETWORKS OF SPATIALLY-DISTRIBUTED CYBER-PHYSICAL SYSTEMS

Cyber-Physical Systems (CPS) consist of inter-wined computational (cyber) and physical components interacting through sensors and/or actuators. Computational elements are networked at every scale and can communicate with each other and with humans. Nodes can join and leave the network at any time or they can move to different spatial locations. In this scenario, monitoring spatial and temporal properties plays a key role in the understanding of how complex behaviors can emerge from local and dynamic interactions. We revisit here the Spatio-Temporal Reach and Escape Logic (STREL), a logic-based formal language designed to express and monitor spatio-temporal requirements over the execution of mobile and spatially distributed CPS. STREL considers the physical space in which CPS entities (nodes of the graph) are arranged as a weighted graph representing their dynamic topological configuration. Both nodes and edges include attributes modeling physical and logical quantities that can evolve over time. STREL combines the Signal Temporal Logic with two spatial modalities reach and escape that operate over the weighted graph. From these basic operators, we can derive other important spatial modalities such as everywhere, somewhere and surround. We propose both qualitative and quantitative semantics based on constraint semiring algebraic structure. We provide an offline monitoring algorithm for STREL and we show the feasibility of our approach with the application to two case studies: monitoring spatio-temporal requirements over a simulated mobile ad-hoc sensor network and a simulated epidemic spreading model for COVID19. © L. Nenzi, E. Bartocci, L. Bortolussi, and M. Loreti.

Complex Contact Network of Patients at the Beginning of an Epidemic Outbreak: An Analysis Based on 1218 COVID-19 Cases in China.

The spread of viruses essentially occurs through the interaction and contact between people, which is closely related to the network of interpersonal relationships. Based on the epidemiological investigations of 1218 COVID-19 cases in eight areas of China, we use text analysis, social network analysis and visualization methods to construct a dynamic contact network of the epidemic. We analyze the corresponding demographic characteristics, network indicators, and structural characteristics of this network. We found that more than 65% of cases are likely to be infected by a strong relationship, and nearly 40% of cases have family members infected at the same time. The overall connectivity of the contact network is low, but there are still some clustered infections. In terms of the degree distribution, most cases' degrees are concentrated between 0 and 2, which is relatively low, and only a few ones have a higher degree value. The degree distribution also conforms to the power law distribution, indicating the network is a scale-free network. There are 17 cases with a degree greater than 10, and these cluster infections are usually caused by local transmission. The first implication of this research is we find that the COVID-19 spread is closely related to social structures by applying computational sociological methods for infectious disease studies; the second implication is to confirm that text analysis can quickly visualize the spread trajectory at the beginning of an epidemic.

Within- and across-day patterns of interplay between depressive symptoms and related psychopathological processes: a dynamic network approach during the COVID-19 pandemic.

BACKGROUND: In order to understand the intricate patterns of interplay connected to the formation and maintenance of depressive symptomatology, repeated measures investigations focusing on within-person relationships between psychopathological mechanisms and depressive components are required. METHODS: This large-scale preregistered intensive longitudinal study conducted 68,240 observations of 1706 individuals in the general adult population across a 40-day period during the COVID-19 pandemic to identify the detrimental processes involved in depressive states. Daily responses were modeled using multi-level dynamic network analysis to investigate the temporal associations across days, in addition to contemporaneous relationships between depressive components within a daily window. RESULTS: Among the investigated psychopathological mechanisms, helplessness predicted the strongest across-day influence on depressive symptoms, while emotion regulation difficulties displayed more proximal interactions with symptomatology. Helplessness was further involved in the amplification of other theorized psychopathological mechanisms including rumination, the latter of which to a greater extent was susceptible toward being influenced rather than temporally influencing other components of depressive states. Distinctive symptoms of depression behaved differently, with depressed mood and anhedonia most prone to being impacted, while lethargy and worthlessness were more strongly associated with outgoing activity in the network. CONCLUSIONS: The main mechanism predicting the amplifications of detrimental symptomatology was helplessness. Lethargy and worthlessness revealed greater within-person carry-over effects across days, providing preliminary indications that these symptoms may be more strongly associated with pushing individuals toward prolonged depressive state experiences. The psychopathological processes of rumination, helplessness, and emotion regulation only exhibited interactions with the depressed mood and worthlessness component of depression, being unrelated to lethargy and anhedonia. The findings have implications for the impediment of depressive symptomatology during and beyond the pandemic period. They further outline the gaps in the literature concerning the identification of psychopathological processes intertwined with lethargy and anhedonia on the within-person level.

Identification of VEGFA-centric temporal hypoxia-responsive dynamic cardiopulmonary network biomarkers.

AIMS: Hypoxia, a pathophysiological condition, is profound in several cardiopulmonary diseases (CPD). Every individual's lethality to a hypoxia state differs in terms of hypoxia exposure time, dosage units and dependent on the individual's genetic makeup. Most of the proposed markers for CPD were generally aim to distinguish disease samples from normal samples. Although, as per the 2018 GOLD guidelines, clinically useful biomarkers for several cardio pulmonary disease patients in stable condition have yet to be identified. We attempt to address these key issues through the identification of Dynamic Network Biomarkers (DNB) to detect hypoxia induced early warning signals of CPD before the catastrophic deterioration. MATERIALS AND METHODS: The human microvascular endothelial tissues microarray datasets (GSE11341) of lung and cardiac expose to hypoxia (1% O2) for 3, 24 and 48 h were retrieved from the public repository. The time dependent differentially expressed genes were subjected to tissue specificity and promoter analysis to filtrate the noise levels in the networks and to dissect the tissue specific hypoxia induced genes. These filtered out genes were used to construct the dynamic segmentation networks. The hypoxia induced dynamic differentially expressed genes were validated in the lung and heart tissues of male rats. These rats were exposed to hypobaric hypoxia (simulated altitude of 25,000 or PO2 - 282 mm of Hg) progressively for 3, 24 and 48 h. KEY FINDINGS: To identify the temporal key genes regulated in hypoxia, we ranked the dominant genes based on their consolidated topological features from tissue specific networks, time dependent networks and dynamic networks. Overall topological ranking described VEGFA as a single node dynamic hub and strongly communicated with tissue specific genes to carry forward their tissue specific information. We named this type of VEGFAcentric dynamic networks as "V-DNBs". As a proof of principle, our methodology helped us to identify the V-DNBs specific for lung and cardiac tissues namely V-DNBL and V-DNBC respectively. SIGNIFICANCE: Our experimental studies identified VEGFA, SLC2A3, ADM and ENO2 as the minimum and sufficient candidates of V-DNBL. The dynamic expression patterns could be readily exploited to capture the pre disease state of hypoxia induced pulmonary vascular remodelling. Whereas in V-DNBC the minimum and sufficient candidates are VEGFA, SCL2A3, ADM, NDRG1, ENO2 and BHLHE40. The time dependent single node expansion indicates V-DNBC could also be the pre disease state pathological hallmark for hypoxia-associated cardiovascular remodelling. The network cross-talk and expression pattern between V-DNBL and V-DNBC are completely distinct. On the other hand, the great clinical advantage of V-DNBs for pre disease predictions, a set of samples during the healthy condition should suffice. Future clinical studies might further shed light on the predictive power of V-DNBs as prognostic and diagnostic biomarkers for CPD.

Forecasting the COVID-19 transmission in Italy based on the minimum spanning tree of dynamic region network.

BACKGROUND: Italy surpassed 1.5 million confirmed Coronavirus Disease 2019 (COVID-19) infections on November 26, as its death toll rose rapidly in the second wave of COVID-19 outbreak which is a heavy burden on hospitals. Therefore, it is necessary to forecast and early warn the potential outbreak of COVID-19 in the future, which facilitates the timely implementation of appropriate control measures. However, real-time prediction of COVID-19 transmission and outbreaks is usually challenging because of its complexity intertwining both biological systems and social systems. METHODS: By mining the dynamical information from region networks and the short-term time series data, we developed a data-driven model, the minimum-spanning-tree-based dynamical network marker (MST-DNM), to quantitatively analyze and monitor the dynamical process of COVID-19 spreading. Specifically, we collected the historical information of daily cases caused by COVID-19 infection in Italy from February 24, 2020 to November 28, 2020. When applied to the region network of Italy, the MST-DNM model has the ability to monitor the whole process of COVID-19 transmission and successfully identify the early-warning signals. The interpretability and practical significance of our model are explained in detail in this study. RESULTS: The study on the dynamical changes of Italian region networks reveals the dynamic of COVID-19 transmission at the network level. It is noteworthy that the driving force of MST-DNM only relies on small samples rather than years of time series data. Therefore, it is of great potential in public surveillance for emerging infectious diseases.

Incorporating dynamic flight network in SEIR to model mobility between populations.

Current efforts of modelling COVID-19 are often based on the standard compartmental models such as SEIR and their variations. As pre-symptomatic and asymptomatic cases can spread the disease between populations through travel, it is important to incorporate mobility between populations into the epidemiological modelling. In this work, we propose to modify the commonly-used SEIR model to account for the dynamic flight network, by estimating the imported cases based on the air traffic volume and the test positive rate. We conduct a case study based on data found in Canada to demonstrate how this modification, called Flight-SEIR, can potentially enable (1) early detection of outbreaks due to imported pre-symptomatic and asymptomatic cases, (2) more accurate estimation of the reproduction number and (3) evaluation of the impact of travel restrictions and the implications of lifting these measures. The proposed Flight-SEIR is essential in navigating through this pandemic and the next ones, given how interconnected our world has become.