Dynamic analysis and application of network structure control in risk conduction in the industrial chain by Xian Xi, Xiangyun Gao, Xiaotian Sun, Huiling Zheng, Congcong Wu

“…In this study, we use the GARCH model, DCC model, and network structure control theory comprehensively to study the price fluctuation risk of the mining stock market from the perspective of the industry chain and network control dynamics and obtain interesting results. (1) Risk conduction among stocks has a prominent industry-driving effect, and the risk conduction ability of upper and middle stocks is stronger. (2) The risk regulation cost, time cost, and node number cost of the whole-industry chain are all higher than those of the two-tier chain, which indicates that the correlation complexity of the network has a positive relationship with risk control. (3) Key risk nodes play an essential role in risk control, so monitoring key stocks from the industrial chain perspective is necessary to control risks in time. …”
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Dual Dynamic Network Polymer Reinforced Carbon Nanotube Foam With Rapid Self‐Healing and Reversible Cycling Capabilities by Huitao Yu, Can Chen, Yanshuai Duan, Linghang Wang, Mengmeng Qin, Wei Feng

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Origin of Dynamic Network Formation of 2D Nanofillers in a Flexible Matrix by Debmalya Roy, Vaishnav B, Subhash Mandal, Ajay Gupta, Benedikt Sochor, Sarathlal Koyiloth Vayalil, Tobias Kraus

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Conductivity hysteresis in MXene driven by structural dynamics of nanoconfined water by Teng Zhang, Katherine A. Mazzio, Ruocun John Wang, Mailis Lounasvuori, Ameer Al-Temimy, Faidra Amargianou, Mohamad-Assaad Mawass, Florian Kronast, Daniel M. Többens, Klaus Lips, Tristan Petit, Yury Gogotsi

“…Here, we show that temperature and confinement drive structural transitions of water within MXene interlayers, including the formation of localized ice clusters, amorphous ice, and dynamic hydrogen-bonded networks. These transformations disrupt stacking order, inducing a reversible metal-to-semiconductor transition and conductivity hysteresis in MXene films. …”
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The Dynamics of Canalizing Boolean Networks by Elijah Paul, Gleb Pogudin, William Qin, Reinhard Laubenbacher

“…Moreover, our results show that, from the standpoint of the attractor structure, high canalizing depth, compared to relatively small positive canalizing depth, has a very modest impact on dynamics. Motivated by these observations, we conduct mathematical study of the attractor structure of a random Boolean network of canalizing depth one (i.e., the smallest positive depth). …”
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Neural Networks in Forecasting Disease Dynamics by A. G. Hasanov, D. G. Shaybakov, S. V. Zhernakov, A. M. Men’shikov, F. F. Badretdinova, I. F. Sufiyarov, J. R. Sagadatova

“…Modern statistical packages and the MATLAB environment were used.Results and discussion.The conducted comparative analysis showed a 3-layer recurring network of direct distribution to be the most suitable neural network architecture. …”
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Injectable ion-coordinated double-network conductive hydrogel for spinal cord injury repair by Huan Yu, Fan Liu, Yaorui Hu, Yaorui Hu, Yaorui Hu, Weikang Wan, Weikang Wan, Weikang Wan, Qing Liu, Qing Liu, Qing Liu, Shuai Zhou, Luping Zhang, Liming Li, Fei Huang

“…This study developed and created an injectable double-network conductive hydrogel, it coordinates iron ions (Fe3+) using dynamic Schiff base bonds and metal ion coordination. …”
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A Generic AI-Based Technique for Assessing Student Performance in Conducting Online Virtual and Remote Controlled Laboratories by Ahmed M. Abd El-Haleem, Mohab Mohammed Eid, Mahmoud M. Elmesalawy, Hadeer A. Hassan Hosny

“…The study confirmed that Artificial Neural Network (ANN) and Support Vector Machine (SVM) are the best models to be used for automatically evaluating student performance while conducting the online labs with a precision reaching up to 91%.…”
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Network structure influences self-organized criticality in neural networks with dynamical synapses by Yoshiki A. Sugimoto, Hiroshi Yadohisa, Masato S. Abe, Masato S. Abe, Masato S. Abe

“…In this study, we conducted numerical simulations using a simplified neural network model to investigate how network structure may influence SOC. …”
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Interplay of intrinsic and synaptic conductances in the generation of high-frequency oscillations in interneuronal networks with irregular spiking. by Fabiano Baroni, Anthony N Burkitt, David B Grayden

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Rumor Diffusion in an Interests-Based Dynamic Social Network by Mingsheng Tang, Xinjun Mao, Zahia Guessoum, Huiping Zhou

“…To research rumor diffusion in social friend network, based on interests, a dynamic friend network is proposed, which has the characteristics of clustering and community, and a diffusion model is also proposed. …”
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Estimation and prediction in dynamic multi-location multiplex networks by Siddhanth Sabharwal, Yuguo Chen

“…In this paper, we extend dynamic network logistic regression to accommodate MLMNs, improving the estimation of factors driving changes in such networks. …”
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Intelligent predictive networks for MHD nanofluid with carbon nanotubes and thermal conductivity along a porous medium by Hafiz Muhammad Shahbaz, Iftikhar Ahmad

“…It is in this light that recurrent neural networks are well suitable for use in complex areas including fluid dynamics, biological computing, and biotechnology since they are capable of learning patterns. …”
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A dynamic model to sustain the spark: How do network coordinators in Dutch healthcare networks maintain network commitment? by Manon Roest, Doris van der Smissen, Remco Mannak, Leon Oerlemans, Anne Marie Weggelaar-Jansen

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Dynamics of link formation in networks structured on the basis of predictive terms by S. O. Kramarov, O. R. Popov, I. E. Dzhariev, E. A. Petrov

“…In order to model and analyze the information conductivity of complex networks having an irregular structure, it is possible to use percolation theory methods known in solid-state physics to quantify how close the given network is to a percolation transition, and thus to form a prediction model. …”
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Network analysis of memristive device circuits: dynamics, stability and correlations by F Barrows, F C Sheldon, F Caravelli

“…For the case of a purely memresistive network, we derive Lyapunov functions, which can be used to study the stability of the network dynamics. …”
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Host security threat analysis approach for network dynamic defense by Lixun LI, Bin ZHANG, Shuqin DONG

“…Calculating the host security threat in network dynamic defense (NDD) situation has to consider the vulnerabilities’ uncertainty because of dynamic mutation.Firstly,the vulnerabilities’ uncertainty caused by the mutation space and the mutation period was calculated by random sampling model,and combined with the CVSS,the attack success probability formula of single vulnerability was derived.Secondly,to avoid self-loop during the path searching process in multiple vulnerabilities situation,an improved recursive depth first algorithm which combined with node visited queue was proposed.Then,the host security threat was calculated based on attack success probability in the situation of multiple vulnerabilities and paths.Finally,approach’s availability and effectiveness were verified by an experiment conducted in a typical NDD situation.…”
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A Novel Dynamic Weight Neural Network Ensemble Model by Kewen Li, Wenying Liu, Kang Zhao, Mingwen Shao, Lu Liu

“…The paper proposes a novel dynamic weight neural network ensemble model (DW-NNE). …”
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Secure Collaborative Key Management for Dynamic Groups in Mobile Networks by Sukin Kang, Cheongmin Ji, Manpyo Hong

“…We consider secure group key management for peer dynamic groups in mobile wireless networks. Many group based applications have achieved remarkable growth along with increasing use of multicast based services. …”
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Center-Guided Network with Dynamic Attention for Transmission Tower Detection by Xiaobin Li, Zhuwei Liang, Jingbin Yang, Chuanlong Lyu, Yuge Xu

“…To address these problems, we propose the Center-Guided network with Dynamic Attention (CGDA) for detecting TTs from aerial images. …”
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