Dynamic Network Formation Research Articles

Highly Conducting and Ultra-Stretchable Wearable Ionic Liquid-Free Transducer for Wireless Monitoring of Physical Motions.

Wearable strain transducers are poised to transform the field of healthcare owing to the promise of personalized devices capable of real-time collection of human physiological health indicators. For instance, monitoring patients' progress following injury and/or surgery during physiotherapy is crucial but rarely performed outside clinics. Herein, multifunctional liquid-free ionic elastomers are designed through the volume effect and the formation of dynamic hydrogen bond networks between polyvinyl alcohol (PVA) and weak acids (phosphoric acid, phytic acid, formic acid, citric acid). An ultra-stretchable (4600% strain), highly conducting (10 mScm-1), self-repairable (77% of initial strain), and adhesive ionic elastomer is obtained at high loadings of phytic acid (4:1 weight to PVA). Moreover, the elastomer displayed durable performances, with intact mechanical properties after a year of storage. The elastomer is used as a transducer to monitor human motions in a device comprising an ESP32-based development board. The device detected walking and/or running biomechanics and communicated motion-sensing data (i.e., amplitude, frequency) wirelessly. The reported technology can also be applied to other body parts to monitor recovery after injury and/or surgery and inform practitioners of motion biomechanics remotely and in real time to increase convalescence effectiveness, reduce clinic appointments, and prevent injuries.

The in vitro dynamics of pseudo-vascular network formation

Background/ObjectivesPseudo-vascular network formation in vitro is considered a key characteristic of vasculogenic mimicry. While many cancer cell lines form pseudo-vascular networks, little is known about the spatiotemporal dynamics of these formations.MethodsHere, we present a framework for monitoring and characterising the dynamic formation and dissolution of pseudo-vascular networks in vitro. The framework combines time-resolved optical microscopy with open-source image analysis for network feature extraction and statistical modelling. The framework is demonstrated by comparing diverse cancer cell lines associated with vasculogenic mimicry, then in detecting response to drug compounds proposed to affect formation of vasculogenic mimics. Dynamic datasets collected were analysed morphometrically and a descriptive statistical analysis model was developed in order to measure stability and dissimilarity characteristics of the pseudo-vascular networks formed.ResultsMelanoma cells formed the most stable pseudo-vascular networks and were selected to evaluate the response of their pseudo-vascular networks to treatment with axitinib, brucine and tivantinib. Tivantinib has been found to inhibit the formation of the pseudo-vascular networks more effectively, even in dose an order of magnitude less than the two other agents.ConclusionsOur framework is shown to enable quantitative analysis of both the capacity for network formation, linked vasculogenic mimicry, as well as dynamic responses to treatment.

Exploring Dynamic Changes in HIV-1 Molecular Transmission Networks and Key Influencing Factors: Cross-Sectional Study.

The HIV-1 molecular network is an innovative tool, using gene sequences to understand transmission attributes and complementing social and sexual network studies. While previous research focused on static network characteristics, recent studies' emphasis on dynamic features enhances our understanding of real-time changes, offering insights for targeted interventions and efficient allocation of public health resources. This study aims to identify the dynamic changes occurring in HIV-1 molecular transmission networks and analyze the primary influencing factors driving the dynamics of HIV-1 molecular networks. We analyzed and compared the dynamic changes in the molecular network over a specific time period between the baseline and observed end point. The primary factors influencing the dynamic changes in the HIV-1 molecular network were identified through univariate analysis and multivariate analysis. A total of 955 HIV-1 polymerase fragments were successfully amplified from 1013 specimens; CRF01_AE and CRF07_BC were the predominant subtypes, accounting for 40.8% (n=390) and 33.6% (n=321) of the specimens, respectively. Through the analysis and comparison of the basic and terminal molecular networks, it was discovered that 144 sequences constituted static molecular networks, and 487 sequences contributed to the formation of dynamic molecular networks. The findings of the multivariate analysis indicated that the factors occupation as a student, floating population, Han ethnicity, engagement in occasional or multiple sexual partnerships, participation in anal sex, and being single were independent risk factors for the dynamic changes observed in the HIV-1 molecular network, and the odds ratio (OR; 95% CIs) values were 2.63 (1.54-4.47), 1.83 (1.17-2.84), 2.91 (1.09-7.79), 1.75 (1.06-2.90), 4.12 (2.48-6.87), 5.58 (2.43-12.80), and 2.10 (1.25-3.54), respectively. Heterosexuality and homosexuality seem to exhibit protective effects when compared to bisexuality, with OR values of 0.12 (95% CI 0.05-0.32) and 0.26 (95% CI 0.11-0.64), respectively. Additionally, the National Eight-Item score and sex education experience were also identified as protective factors against dynamic changes in the HIV-1 molecular network, with OR values of 0.12 (95% CI 0.05-0.32) and 0.26 (95% CI 0.11-0.64), respectively. The HIV-1 molecular network analysis showed 144 sequences in static networks and 487 in dynamic networks. Multivariate analysis revealed that occupation as a student, floating population, Han ethnicity, and risky sexual behavior were independent risk factors for dynamic changes, while heterosexuality and homosexuality were protective compared to bisexuality. A higher National Eight-Item score and sex education experience were also protective factors. The identification of HIV dynamic molecular networks has provided valuable insights into the characteristics of individuals undergoing dynamic alterations. These findings contribute to a better understanding of HIV-1 transmission dynamics and could inform targeted prevention strategies.

Building back better: Modeling decentralized recovery in sociotechnical systems using strategic network dynamics

The ability of sociotechnical systems to recover from disruptions through decentralized, bottom-up processes is a crucial element of their overall resilience, but it is also among the most overlooked in resilience research. This paper proposes a computational framework leveraging dynamic multi-agent networks to conceptualize, model, and quantify multi-agent, decentralized recovery process building upon strategic network formation methods. This innovative approach addresses limitations in conventional strategic network formation models by accounting for the fluidity of structural shifts induced by the strategic behaviors of individual agents, and by acknowledging the inherent heterogeneity within agent populations. The central objective of this study is to highlight the critical role of bottom-up recovery and present a systematic, adaptable framework for modeling these processes in diverse real-world scenarios. We achieve this through four key elements: modeling post-disruption responses, exploring their interdependencies, accommodating multiple equilibria, and generating metrics for scenario comparison and forecasting. Our dynamic strategic network formation model offers valuable insights into crucial trade-offs in decentralized recovery processes and facilitates the integration of both bottom-up and top-down resilience strategies. Furthermore, it could serve as a predictive tool informing initial network architectural decisions by estimating likely post-disruption equilibrium states based on the initial network structure. Ultimately, this study provides a robust platform for analyzing network efficiency and resilience trade-offs, setting the stage for more informed decision-making in complex sociotechnical systems.

Dynamic Network Formation with Ordered Partitioning and Incomplete Information

Dynamic Network Formation with Ordered Partitioning and Incomplete Information

Light‐controlled switchable underwater adhesive

AbstractDespite extensive efforts in designing and preparing switchable underwater adhesives, it is not easy to regulate the underwater adhesion strength locally and remotely. Here, we design and synthesize photoreversible copolymer of poly[dopamine methacrylamide‐co‐methoxyethyl‐acrylate‐co‐7‐(2‐methacryloyloxyethoxy)‐4‐methylcoumarin]. Due to the dynamic formation and breaking of chemical crosslinking networks within the smart adhesives, the material shows widely tunable adhesion strength from ∼150 to ∼450 kPa and long‐range reversible maneuverability under orthogonal 254 and 365 nm ultraviolet light stimulation via the coumarin dimerization and cycloreversion. Moreover, the adhesive exhibits good circulation performance and stability in an acid–base environment. It also demonstrated that the bolt can be coated with the smart adhesive material for on‐demand bonding. This design principle opens the door to the development of remotely controllable high‐performance smart underwater adhesives.

Photochemically driven one-step triple dynamic network formation in printable tough hydrogel for self-healing tubular sensors

Self-healing hydrogels have attracted widespread attention due to their excellent biocompatibility, mechanical properties, and reparability.

Strategic injustice, dynamic network formation, and social movements

Strategic injustice, dynamic network formation, and social movements

Uncovering the Dynamics of Confined Water Using Neutron Scattering: Perspectives

The main characteristic of liquid water is the formation of dynamic hydrogen bond networks that occur over a broad range of time scales from tens of femtoseconds to picoseconds and are responsible for water’s unique properties. However, in many important processes water does not exist in its bulk form, but in confined nanometer scale environments. The investigation of this confined water dynamics is challenging since the intermediate strength of the hydrogen bonds makes it possible to alter the structure and dynamics of this constrained water. Even if no single experimental technique can give a full picture of such intricate dynamics, it is well established that quasielastic neutron scattering (QENS) is a powerful tool to study the modification of hydrogen bonds in confinement in various materials. This is possible because neutrons tell us where the atoms are and what they are doing, can detect hydrogen, are penetrative and non-destructive. Furthermore, QENS is the only spectroscopic technique that provides information on the dynamics and atomic-motion amplitudes over a predetermined length scale. However scientific value of these data is hardly exploited and never to its full potential. This perspective highlights how new developments on instrumentation and data analysis will lead to appreciable progress in our understanding of the dynamics of complex systems, ranging from biological organisms to cloud formation.

In situ optical spectroscopy of crystallization: One crystal nucleation at a time.

While crystallization is a ubiquitous and an important process, the microscopic picture of crystal nucleation is yet to be established. Recent studies suggest that the nucleation process can be more complex than the view offered by the classical nucleation theory. Here, we implement single crystal nucleation spectroscopy (SCNS) by combining Raman microspectroscopy and optical trapping induced crystallization to spectroscopically investigate one crystal nucleation at a time. Raman spectral evolution during a single glycine crystal nucleation from water, measured by SCNS and analyzed by a nonsupervised spectral decomposition technique, uncovered the Raman spectrum of prenucleation aggregates and their critical role as an intermediate species in the dynamics. The agreement between the spectral feature of prenucleation aggregates and our simulation suggests that their structural order emerges through the dynamic formation of linear hydrogen-bonded networks. The present work provides a strong impetus for accelerating the investigation of crystal nucleation by optical spectroscopy.

Dynamic Network Formation for FSO Satellite Communication

Satellite network optimization is essential, particularly since the cost of manufacturing, launching and maintaining each satellite is significant. Moreover, classical communication optimization methods, such as Minimal Spanning Tree, cannot be applied directly in dynamic scenarios where the satellite constellation is constantly changing. Motivated by the rapid growth of the Star-Link constellation that, as of Q4 2021, consists of over 1600 operational LEO satellites with thousands more expected in the coming years, this paper focuses on the problem of constructing an optimal inter-satellite (laser) communication network. More formally, given a large set of LEO satellites, each equipped with a fixed number of laser links, we direct each laser module on each satellite such that the underlying laser network will be optimal with respect to a given objective function and communication demand. In this work, we present a novel heuristic to create an optimal dynamic optical network communication using an Ant Colony algorithm. This method takes into account both the time it takes to establish an optical link (acquisition time) and the bounded number of communication links, as each satellite has a fixed amount of optical communication modules installed. Based on a large number of simulations, we conclude that, although the underlying problem of bounded-degree-spanning-tree is NP-hard (even for static cases), the suggested ant-colony heuristic is able to compute cost-efficient solutions in semi-real-time.

Which collaborations allow firms to become gatekeepers? A longitudinal analysis of a large-scale collaboration network

Having a gatekeeper position in a collaborative network offers firms great potential to gain competitive advantages. However, it is not well understood what kind of collaborations are associated with such a position. Conceptually grounded in social network theory, this study draws on the resource-based view and the relational factors view to investigate which types of collaboration characterize firms that are in a gatekeeper position, which ultimately could improve firm performance in subsequent periods. The empirical analysis utilizes a unique longitudinal data set to examine dynamic network formation. We used a data crawling approach to reconstruct collaboration networks among the 500 largest companies in Germany over nine years and matched these networks with performance data. The results indicate that firms in gatekeeper positions often engage in medium-intensity collaborations and less likely weak-intensity collaborations. Strong-intensity collaborations are not related to the likelihood of being a gatekeeper. Our study further reveals that a firm's knowledge base is an important moderator and that this knowledge base can increase the benefits of having a gatekeeper position in terms of firm performance.

Intermediate Structural Hierarchy in Biological Networks Modulates the Fractal Dimension and Force Distribution of Percolating Clusters.

Globular protein hydrogels are an emerging class of materials with the potential for rational design, and a generalized understanding of how their network properties emerge from the structure and dynamics of the building block is a key challenge. Here we computationally investigate the effect of intermediate (polymeric) nanoscale structure on the formation of protein hydrogels. We show that changes in both the cross-link topology and flexibility of the polymeric building block lead to changes in the force transmission around the system and provide insight into the dynamic network formation processes. The preassembled intermediate structure provides a novel structural coordinate for the hierarchical modulation of macroscopic network properties, as well as furthering our understanding of the general dynamics of network formation.

Monitoring of random microvessel network formation by in-line sensing of flow rates: A numerical and in vitro investigation

The directed or de novo formation of microvasculature in engineered tissue constructs is essential for accurately replicating physiological function. A limiting factor of a system relying on spontaneous microvessel formation is the inability to precisely quantify the development of the microvascular network and control fluid moving through formed vessels. Herein, we report a strategy to monitor the dynamic formation of microscale fluid networks, which can be translated to the monitoring of microvasculature development in engineered tissue constructs. The non-invasive, non-destructive monitoring and characterization of the fluid network is achieved via in-line sensing of fluid flow rate and correlating this measurement to the hydrodynamic resistance of the fluid network to model the progression of microvessel formation and connectivity. Computational fluid dynamics, equivalent circuit, and experimental models were compared, which simulated multi-generational branching or splitting microvessel networks. The networks simulated vessels with varying cross-sectional area, up to 16 branching vessels, and microvessel network volume ranging from ˜20−30 mm3. In all models, the increasing degree of network complexity and volume corresponded to a decrease in jumper flow-rate measured; however, vessel cross-section also impacted the measured jumper flow rate, i.e. at low vessel height (<200 μm) response was dominated by increased network volume and at higher vessel height (>200 μm) the response was dominated by resistance of narrow channels. An approximately 2% error was exhibited between the models, which was attributed to variation in the geometry of the fabricated models and illustrates the potential to precisely and non-destructively monitor microvessel network development and volumetric changes.

Dynamics and stability of social and economic networks: Experimental evidence

We use a laboratory experiment to test the dynamic formation of networks in a six-subject game where link formation requires mutual consent. First, the game tends to converge to the pairwise-Nash stable (PNS) network when it exists, and to not converge but remain in the closed cycle when no PNS network exists. When two Pareto-rankable PNS networks exist, subjects often coordinate on the high-payoff one. Second, the analysis of single decisions indicates the predominance of myopic rational choices, but it also highlights interesting systematic deviations, especially when actions are more easily reversible and when they involve smaller marginal losses. Third, behavior is heterogeneous across subjects, with varying degrees of sophistication.

Covalent Adaptable Networks Using β-Amino Esters as Thermally Reversible Building Blocks.

In this study, β-amino esters, prepared by the aza-Michael addition of an amine to an acrylate moiety, are investigated as building blocks for the formation of dynamic covalent networks. While such amino esters are usually considered as thermally nondynamic adducts, the kinetic model studies presented here show that dynamic covalent exchange occurs via both dynamic aza-Michael reaction and catalyst-free transesterification. This knowledge is transferred to create β-amino ester-based covalent adaptable networks (CANs) with coexisting dissociative and associative covalent dynamic exchange reactions. The ease, robustness, and versatility of this chemistry are demonstrated by using a variety of readily available multifunctional acrylates and amines. The presented CANs are reprocessed via either a dynamic aza-Michael reaction or a catalyst-free transesterification in the presence of hydroxyl moieties. This results in reprocessable, densely cross-linked materials with a glass transition temperature (Tg) ranging from -60 to 90 °C. Moreover, even for the low Tg materials, a high creep resistance was demonstrated at elevated temperatures up to 80 °C. When additional β-hydroxyl group-containing building blocks are applied during the network design, an enhanced neighboring group participation effect allows reprocessing of materials up to 10 times at 150 °C within 30 min while maintaining their material properties.

Cautious farsighted stability in network formation games with streams of payoffs

We propose a new notion of farsighted pairwise stability for dynamic network formation which includes two notable features: consideration of intermediate payoffs and cautiousness. This differs from existing concepts which typically consider either only immediate or final payoffs, and which often require that players are optimistic in any environment without full communication and commitment. For arbitrary (and possibly heterogeneous) preferences over the process of network formation, a non-empty cautious path stable set of networks always exists. Furthermore, some general relationships exist between cautious path stability and other farsighted concepts.

Network Formation and Effects: Observations from U.S. Commercial Real Estate Markets

We explore how trading networks among commercial real estate brokerage firms are formed, and the effects of network structure on centrality, assortativity and power in the six largest commercial real estate markets in the United State. Fitting the dynamic network formation model developed by Jackson and Rogers American Economic Review, 97(3), 890-915, (2007) to U.S. commercial real estate transaction data (2003–2016), we calculate the importance of network search relative to random search for commercial real estate brokerage firms. We observe that in general approximately 67% of trades of commercial properties are made through network searches. Network search is more important during the global financial crisis (2008–2010). The trading networks also show geographic heterogeneity as network search is most important in Chicago (79.7%), but relatively less important in Boston and Los Angeles (56.2% and 58.2%). Finally, brokerage firms with many trading partners are more likely to trade with those with fewer partners (negative assortativity) creating conditions for power asymmetry between firms.

Dynamic Formation of Knowledge Networks and Innovating Firm

We document a series of new facts about the very first firms and patents that form new edges in the directed citation networks across patent categories. We call them pathfinder firms and patents. First, the typical pathfinder firms are very larger firms. Second, the average pathfinder patents have higher quality than other patents in different quality measures. Third, firms innovate faster, market value, profit and productivity increase in the future, when they invent a large number of pathfinder patents or are cited by pathfinder patents currently. Fourth, new citation links generate positive externalities to peer firms that innovate in nearby technology space. We then build a dynamic formation model of knowledge network, where new citations form through both quality based preferential attachment, exact and mutated copying of parent patent’s citations. The model rationalizes the firm level empirical facts. After calibrated to the patent citation data, the model sheds lights on the causes of productivity show down: rising number of citation per patent, marginal cost of patent quality and research wage have driven up the cost to innovate since 1976. Effective R&D policies need to decrease these cost factors.

Evolution of Collaborative Governance in the 2015, 2016, and 2018 Myanmar Flood Disaster Responses: A Longitudinal Approach to a Network Analysis

In disaster response, collaboration facilitates interactions among actors, such as the government, the military, nongovernmental organizations, and civil society organizations. This study examined the longitudinal changes in collaborative governance in Myanmar’s disaster responses based on cases of flooding in 2015, 2016, and 2018. To examine the mechanisms underlying this dynamic network formation, the collaborative ties of the actors involved in search and rescue activities were converted into longitudinal relational data sets, and the evolution of collaborative governance was analyzed by relying on the assumptions of social capital, transaction cost, homophily, and resource dependency theories and using a longitudinal social network analysis method. The findings show that the collaborative networks of search and rescue processes in disaster response evolved and changed over time according to the hypothesized patterns of strong, weak, and preferential tie formations. The study also revealed that the collaborative governance system assumes the form of a hierarchy rather than a generalized exchange, and the actors’ reliance on military organizations is not obvious due to the emerging alternative non-military actors and diverse local actors observed in the cases.