Dynamic Network Model Research Articles

Research on a Three-Stage Dynamic Reactive Power Optimization Decoupling Strategy for Active Distribution Networks with Carbon Emissions

The reactive power optimization of an active distribution network can effectively deal with the problem of voltage overflows at some nodes caused by the integration of a high proportion of distributed sources into the distribution network. Aiming to address the limitations in previous studies of dynamic reactive power optimization using the cluster partitioning method, a three-stage dynamic reactive power optimization decoupling strategy for active distribution networks considering carbon emissions is proposed in this paper. First, a carbon emission index is proposed based on the carbon emission intensity, and a dynamic reactive power optimization mathematical model of an active distribution network is established with the minimum active power network loss, voltage deviation, and carbon emissions as the satisfaction objective functions. Second, in order to satisfy the requirement for the all-day motion times of discrete devices, a three-stage dynamic reactive power optimization decoupling strategy based on the partitioning around a medoids clustering algorithm is proposed. Finally, taking the improved IEEE33 and PG&E69-node distribution network systems as examples, the proposed linear decreasing mutation particle swarm optimization algorithm was used to solve the mathematical model. The results show that all the indicators of the proposed strategy and algorithm throughout the day are lower than those of other methods, which verifies the effectiveness of the proposed strategy and algorithm.

DualTrans: A Novel Glioma Segmentation Framework Based on a Dual-Path Encoder Network and Multi-View Dynamic Fusion Model

Segmentation methods based on convolutional neural networks (CNN) have achieved remarkable results in the field of medical image segmentation due to their powerful representation capabilities. However, for brain-tumor segmentation, owing to the significant variations in shape, texture, and location, traditional convolutional neural networks (CNNs) with limited convolutional kernel-receptive fields struggle to model explicit long-range (global) dependencies, thereby restricting segmentation accuracy and making it difficult to accurately identify tumor boundaries in medical imaging. As a result, researchers have introduced the Swin Transformer, which has the capability to model long-distance dependencies, into the field of brain-tumor segmentation, offering unique advantages in the global modeling and semantic interaction of remote information. However, due to the high computational complexity of the Swin Transformer and its reliance on large-scale pretraining, it faces constraints when processing large-scale medical images. Therefore, this study addresses this issue by proposing a smaller network, consisting of a dual-encoder network, which also resolves the instability issue that arises in the training process of large-scale visual models with the Swin Transformer, where activation values of residual units accumulate layer by layer, leading to a significant increase in differences in activation amplitudes across layers and causing model instability. The results of the experimental validation using real data show that our dual-encoder network has achieved significant performance improvements, and it also demonstrates a strong appeal in reducing computational complexity.

Delineating the Heterogeneity of Alzheimer’s Disease and Mild Cognitive Impairment Using Normative Models of Dynamic Brain Functional Networks

BackgroundAlzheimer’s disease (AD), which has been identified as the most common type of dementia, presents considerable heterogeneity in its clinical manifestations. Early intervention at the stage of mild cognitive impairment (MCI) holds potential in AD prevention. However, characterizing the heterogeneity of neurobiological abnormalities and identifying MCI subtypes pose significant challenges. MethodsWe constructed sex-specific normative age models of dynamic brain functional networks and mapped the deviations of the brain characteristics for individuals from multiple datasets, including 295 patients with AD, 441 patients with MCI, and 1160 normal control participants. Then, based on these individual deviation patterns, subtypes for both AD and MCI were identified using the clustering method, and their similarities and differences were comprehensively assessed. ResultsIndividuals with AD and MCI were clustered into 2 subtypes, and these subtypes exhibited significant differences in their intrinsic brain functional phenotypes and spatial atrophy patterns, as well as in disease progression and cognitive decline trajectories. The subtypes with positive deviations in AD and MCI shared similar deviation patterns, as did those with negative deviations. There was a potential transformation of MCI with negative deviation patterns into AD, and participants with MCI had a more severe cognitive decline rate. ConclusionsIn this study, we quantified neurophysiological heterogeneity by analyzing deviation patterns from the dynamic functional connectome normative model and identified disease subtypes of AD and MCI using a comprehensive resting-state functional magnetic resonance imaging multicenter dataset. The findings provide new insights for developing early prevention and personalized treatment strategies for AD.

Predicting seagrass ecosystem resilience to marine heatwave events of variable duration, frequency and re‐occurrence patterns with gaps

AbstractBackgroundSeagrass, a vital primary producer habitat, is crucial for maintaining high biodiversity and offers numerous ecosystem services globally. The increasing severity and frequency of marine heatwaves, exacerbated by climate change, pose significant risks to seagrass meadows.AimsThis study acknowledges the uncertainty and variability of marine heatwave scenarios and aims to aid managers and policymakers in understanding simulated responses of seagrass to different durations, frequencies and recurrence gaps of marine heatwaves.Materials and MethodsUsing expert knowledge and observed data, we refined a global Dynamic Bayesian Network (DBN) model for a specific case study on Halophila ovalis in Leschenault Estuary, Australia. The model evaluates the potential impact of marine heatwaves on seagrass resilience, examining stress resistance, recovery and extinction risk.ResultsSimulations of different marine heatwave scenarios reveal significant impacts on seagrass ecosystems. Scenarios ranged from 30‐ to 90‐day heatwaves, with longer durations causing more significant biomass decline, reduced resistance, higher extinction risk and prolonged recovery. For instance, recovery time may increase from 18 to 26 months with four 60‐day and from 24 to 47 months with four 90‐day marine heatwave events. Increasing the frequency of marine heatwaves from one to four annual events, with no gaps between occurrences, could raise extinction risk from 11% to 55% for 60‐day events and from 17% to 83% for 90‐day events. However, introducing gaps between heatwaves enhanced resilience, with spaced events showing lower extinction risks and quicker recovery than consecutive yearly events.DiscussionThe study demonstrates the DBN model's utility in simulating the impact of marine heatwaves on seagrass, providing tools for risk‐informed assessment of management and restoration efforts. While these simulations align with existing research on temperature impacts on seagrass, they are not empirical.ConclusionFurther research is necessary to expand our understanding of climate change effects on seagrass ecosystems, guide policy and develop strategies to strengthen marine ecosystem resilience.

Research on entertainment robots based on artificial intelligence interaction for human posture recognition and sports activity monitoring

With the application of artificial intelligence technology in sports activity monitoring, new technologies such as interactive robots and the Internet of Things are being more widely used, which can provide entertainment and interactive guidance for sports training. This article analyzes the research on human pose recognition and sports activity monitoring of entertainment robots based on artificial intelligence interaction. This paper designs and develops an entertainment robot system that can recognize human posture and monitor physical activities. Users can interact with the robot and get real-time feedback about their posture and activities, thereby improving motor skills and physical fitness. After in-depth analysis of the theory of dynamic Bayesian network, we used EM algorithm to build a dynamic Bayesian network model, carried out detailed research on the random variables represented by the nodes of the dynamic model, and used the Internet of Things human posture recognition technology to extract the features of human behavior according to the characteristics of human behavior. The results show that the attitude recognition accuracy can be improved by adding the dynamic Bayesian network based on the random forest algorithm. The entertainment robot can accurately identify human posture and monitor sports activities, and has good real-time and stability. Through interaction with the robot, users can get personalized suggestions for individual postures and activities, improving the training effect and enjoyment of physical activities. Therefore, through the in-depth research and analysis of the dynamic Bayesian network and the human posture recognition of the Internet of Things, it is found that the sports activity monitoring system proposed in this paper can comprehensively monitor students’ sports activities, so as to achieve the goal of comprehensively improving students’ physical fitness.

A dynamic network model predicts the phenotypes of multicellular clusters from cellular properties

Cell division without cell separation produces multicellular clusters in budding yeast. Two fundamental characteristics of these clusters are their size (the number of cells per cluster) and cellular composition: the fractions of cells with different phenotypes. Using cells as nodes and links between mother and daughter cells as edges, we model cluster growth and breakage by varying three parameters: the cell division rate, the rate at which intercellular connections break, and the kissing number (the maximum number of connections to one cell). We find that the kissing number sets the maximum possible cluster size. Below this limit, the ratio of the cell division rate to the connection breaking rate determines the cluster size. If links have a constant probability of breaking per unit time, the probability that a link survives decreases exponentially with its age. Modeling this behavior recapitulates experimental data. We then use this framework to examine synthetic, differentiating clusters with two cell types, faster-growing germ cells and their somatic derivatives. The fraction of clusters that contain both cell types increases as either of two parameters increase: the kissing number and difference between the growth rate of germ and somatic cells. In a population of clusters, the variation in cellular composition is inversely correlated (r2= 0.87) with the average fraction of somatic cells in clusters. Our results show how a small number of cellular features can control the phenotypes of multicellular clusters that were potentially the ancestors of more complex forms of multicellular development, organization, and reproduction.

Learning the mechanisms of network growth

We propose a novel model-selection method for dynamic networks. Our approach involves training a classifier on a large body of synthetic network data. The data is generated by simulating nine state-of-the-art random graph models for dynamic networks, with parameter range chosen to ensure exponential growth of the network size in time. We design a conceptually novel type of dynamic features that count new links received by a group of vertices in a particular time interval. The proposed features are easy to compute, analytically tractable, and interpretable. Our approach achieves a near-perfect classification of synthetic networks, exceeding the state-of-the-art by a large margin. Applying our classification method to real-world citation networks gives credibility to the claims in the literature that models with preferential attachment, fitness and aging fit real-world citation networks best, although sometimes, the predicted model does not involve vertex fitness.

Merging multi-level evidential observations for dynamic reliability assessment of hierarchical multi-state systems: A dynamic Bayesian network approach

Dynamic reliability assessment has offered a new paradigm for engineered systems to integrate multi-level observations to update the reliability measures of a specific running system. The existent work on dynamic reliability assessment can only leverage precise observations and cannot be straightforwardly implemented in scenarios where observations are imprecise. However, the observations are inevitably imprecise owing to the limited accuracy of inspection techniques and vague judgments of the system state. In engineering scenarios, multi-state systems (MSSs) with a hierarchical structure are commonly existent, and the imprecise observations can be collected across multiple physical levels of the system. In this article, the uncertainty associated with imprecise observations is characterized by the evidence theory, and it can be therefore regarded as an evidential form. A dynamic Bayesian network (DBN) model is utilized to evaluate the reliability of hierarchical MSSs with multi-level evidential observations. Subsequently, the evidence theory is implemented to quantify epistemic uncertainties associated with imprecise observations. These observations, sourced from multiple physical levels, are merged by the DBN model using the Dempster rule of combination (DRC) to update the reliability of a specific running system in a dynamic fashion. The feasibility and correctness of the proposed method have been demonstrated through a numerical case and a real engineering case of a kerosene filling control system (KFCS), and the result indicates that the proposed DBN-based algorithm methods have high applicability and generalizability.

Systemic Risk Arising from Shadow Banking and Sustainable Development: A Study of Wealth Management Products in China

Shadow banking is a main way for the financial market to serve the real economy today, and this process is closely related to systemic risk. This study examines the impact of shadow banking associated with sustainable development in China’s banking on systemic risk. We analyze the data obtained from a rich sample of 31 listed commercial banks in China and shadow banking represented by wealth management products (WMPs) by constructing a dynamic complex interbank network model. The results show that the risks and vulnerabilities generated by shadow banking spread out through the interbank network and cause systemic risk to increase. The effect operates through increasing the number of default banks, reducing banks’ survival rate and profit, and forcing central bank bailout funds expansion. However, it has a positive impact in terms of augmenting liquidity and enhancing investment opportunities. Furthermore, the variability in the influence of different categories of shadow banking is assessed, emphasizing that short-term shadow banking exerts a more pronounced impact on systemic risk. In addition, the heterogeneity of the shadow banking effect on different types of commercial banks is explored, revealing that local and rural commercial banks experience a more conspicuous effect compared to state-owned and joint-stock banks. Our findings highlight that improving external supervision, promoting financial internal governance, and constraining credit linkages are vital for alleviating the increase in risks in shadow banking and maintaining the sustainable development of banking.

A self-attention dynamic graph convolution network model for traffic flow prediction

A self-attention dynamic graph convolution network model for traffic flow prediction

On the development of steady-state and dynamic mass-constrained neural networks using noisy transient data

This paper presents the development of algorithms for mass-constrained neural network models that can exactly satisfy mass conservation laws of chemical process systems, even if the training data violates the same. As opposed to approximately satisfying mass balance constraints of a system by considering additional penalty terms in the objective function, algorithms have been developed to solve an equality-constrained optimization problem, thus ensuring the exact satisfaction of the overall mass conservation laws. For developing dynamic mass-constrained networks, hybrid series and parallel all-nonlinear static-dynamic neural network models are leveraged. The proposed algorithms for solving both the inverse and forward problems are tested by considering both steady-state and dynamic data in presence of varieties of noise characterizations. The proposed structures and algorithms are applied to the development of data-driven models of two nonlinear dynamic chemical processes, namely the Van de Vusse reactor system as well as a solvent-based post-combustion CO2 capture process.

Effective modulation from the ventral medial to the dorsal medial portion of the prefrontal cortex in memory confidence-based behavioral control

Metacognition includes the ability to refer to one’s own cognitive states, such as confidence, and adaptively control behavior based on this information. This ability is thought to allow us to predictably control our behavior without external feedback, for example, even before we take action. Many studies have suggested that metacognition requires a brain-wide network of multiple brain regions. However, the modulation of effective connectivity within this network during metacognitive tasks remains unclear. This study focused on medial prefrontal regions, which have recently been suggested to be particularly involved in metacognition. We examined whether modulation of effective connectivity specific to metacognitive behavioral control is observed using model-based network analysis and dynamic causal modeling (DCM). The results showed that negative modulation from the ventral medial prefrontal cortex to the dorsal medial prefrontal cortex was observed in situations that required metacognitive behavioral control but not in situations that did not require such metacognitive control. Furthermore, this modulation was particularly pronounced in the group of participants who could better use metacognition for behavioral control. These results imply hierarchical properties of metacognition-related brain networks.

Bayesian Structure Learning for Climate Model Evaluation

AbstractA Bayesian structure learning approach is employed to compare and contrast interactions between the major climate teleconnections over the recent past as revealed in reanalyses and climate model simulations from leading Meteorological Centers. In a previous study, the authors demonstrated a general framework using homogeneous Dynamic Bayesian Network models constructed from reanalyzed time series of empirical climate indices to compare probabilistic graphical models. Reversible jump Markov Chain Monte Carlo is used to provide uncertainty quantification for selecting the respective network structures. The incorporation of confidence measures in structural features provided by the Bayesian approach is key to yielding informative measures of the differences between products if network‐based approaches are to be used for model evaluation, particularly as point estimates alone may understate the relevant uncertainties. Here we compare models fitted from the NCEP/NCAR and JRA‐55 reanalyses and Coupled Model Intercomparison Project version 5 (CMIP5) historical simulations in terms of associations for which there is high posterior confidence. Examination of differences in the posterior probabilities assigned to edges of the directed acyclic graph provides a quantitative summary of departures in the CMIP5 models from reanalyses. In general terms the climate model simulations are in better agreement with reanalyses where tropical processes dominate, and autocorrelation time scales are long. Seasonal effects are shown to be important when examining tropical‐extratropical interactions with the greatest discrepancies and largest uncertainties present for the Southern Hemisphere teleconnections.

Modeling the Effects of Cell Stress and Ca2+ Dysregulation on the Crosstalk Between Apoptosis and Autophagy in Neurodegenerative Diseases

BACKGROUND: Apoptosis is a highly regulated and irreversible process of cell death, while autophagy maintains cellular homeostasis by degrading and recycling cytoplasmic components, such as aggregated and damaged proteins (Elmore, 2007; Glick et al., 2010). Dysregulation of both processes is observed in neurodegenerative diseases (NDs) (Ghavami et al., 2014). For example, in Alzheimer’s disease (AD), accumulation of amyloid-β peptides is attributed to dysregulated autophagy (Han et al., 2020). Dysregulated autophagy can further induce apoptosis and neurodegeneration (Chung et al., 2018). Although studies have focused on signaling pathways involved in apoptosis and autophagy extensively in other cell types, an effective framework to clarify their interplay in the nervous system is lacking. Here we develop a simplified network model of the key pathways involved in apoptosis and autophagy to study their interplay and dysregulation in NDs. Specifically, we model triggers such as cell stress and Ca2+ dysregulation implicated in aging and NDs and simulate their effects on cell fate. Although much of the empirical support to model development came from studies in other cell types, our integrative framework of the ubiquitous and non-cell autonomous pathways can offer deeper insights into the pathophysiology in NDs. METHODS: Ordinary differential equations (ODEs) were used to model the network of molecular compartments, and the rates of change in their abundances and interactions. The interconnections between these compartments, for example, the inhibitory role of caspases on autophagy-inducing Beclin-1(BECN1), are validated in empirical work and adopted in previous modeling studies (Bogdał et al., 2013; Fan and Zong, 2013; Mariño et al., 2014; Tavassoly et al., 2015; Wirawan et al., 2010; Yin et al., 2017). MATLAB was used to code and simulate the model; ode45 was the numerical integration method of choice. RESULTS: We developed an integrative framework for the signaling pathways of apoptosis and autophagy. Our model adapted and merged three previously reported dynamic network models (Bogdal et al., 2013; Tavassoly et al., 2015; Yin et al., 2017) which also included pathways relevant to NDs. A hypothetical cell stress input and cytosolic Ca2+ concentration in our network model represent extrinsic and intrinsic triggers respectively. Using such a computational approach, we are investigating the multiplexing role of Beclin 1 protein (part of Bcl-2 protein family), mTOR (mammalian target of rapamycin) kinase and JNK (Jun N-terminal kinase) in the synergy between autophagy and apoptosis. Simulation experiments are underway to predict the mechanisms of cell vulnerability versus resilience implicated in NDs involving these pathways. CONCLUSION: Our network provides a framework based on which experiments can be designed to validate model predictions and better understand the dynamic processes involved in NDs. Although NDs are complex and chronic, understanding the effects of common triggers such as cell stress and disrupted Ca2+ homeostasis on cell fate determination is critical to slow ND progression. Therefore, our study delineating the dynamic crosstalk between apoptosis and autophagy offers crucial insights into ND pathophysiology and suggests specific protein targets within these key common pathways. This is the full abstract presented at the American Physiology Summit 2024 meeting and is only available in HTML format. There are no additional versions or additional content available for this abstract. Physiology was not involved in the peer review process.

Line-of-sight Initial Pointing Model of Space Dynamic Optical Network and Its Verification

In dynamic space networks, achieving high precision and fast initial pointing of the optical line of sight (LOS) is the key goal in developing this technology. It is the premise and basis of realizing optical LOS capture. Based on the composition and working principle of space optical networking systems, and the effect of real-time position and attitude changes on LOS initial pointing between networks, the matrix transformation and transfer principle is used to establish a multi-link LOS initial pointing model and analyze the factors affecting the size of the field of uncertainty (FOU). In a dynamic space optical networking experiment, the “one-to-two” simultaneous LOS pointing test is carried out, which shows that the model can realize the function of multi-link LOS initial pointing. The sizes of the FOU of the test terminal are 8.67 mrad and 8.34 mrad, respectively, with an average capture time of 18.3 s.

Competence-Based Trainings for Psychological Treatments - A Transtheoretical Perspective.

Although in most countries psychotherapy trainings focus on one treatment orientation, such an approach is associated with systematic shortcomings. The priorities from teaching one theoretical framework should be moved to a more rigorous orientation in science and evidence-based practice, and to the needs of patients, even if strategies of different theoretical approaches need to be combined. We discuss whether competence-based trainings in psychological treatments offer a better framework to facilitate the progress of psychological treatments to a professional academic discipline with transtheoretical exchange, and we provide an example of a transtheoretical education in the basic competences of psychological treatments. A transtheoretical education program requires an umbrella model for case formulation and a transtheoretical definition of intervention goals. We provide an adaptation of the traditional model distinguishing vulnerability/resilience, exacerbation, and maintenance of clinical problems for case conceptualization. Dynamic network models offer a further perspective for developing modern, transtheoretical case formulations. Treatment methods should be better classified according to their transtheoretical goals, which offers opportunities to better compare or combine them. We report a case example of how to transform a general competence-based approach in the training of psychological treatments in the academic education system, which found exceptional acceptance from participating students. Thus, a rigorous competence-based approach to training early clinicians in applying psychological treatments helps to bridge the artificial divide between psychotherapeutic traditions. It also supports the evolution of psychological treatments into an academically robust and highly professional, integrative discipline.

Topological perturbations on resilience of the world trade competition network

Network resilience refers to a system’s capability to adapt its functions to ensure continuity of essential operations amidst external environmental shifts or internal failures. The resilience of the world trade network faces structural disturbances, such as dynamic changes in the internal and external environments, increasing trade barriers and changes in competition between countries, which is an issue worth exploring. In this study, we introduce a world trade competition network that reflects export competition between two countries. We employ a network dynamics model to assess the resilience of this global trade competition network, focusing on the influence of topological disturbances. The eight distinct types of topological perturbations analyzed include nodes representing countries, links symbolizing inter-country competition, and weights indicative of the intensity of this competition. Our findings reveal that the intensity of export competition between countries significantly influences the resilience of the global trade competition network. Specifically, experimental outcomes indicate that network resilience declines more rapidly when nodes are removed sequentially based on higher weighted degrees than when based on lower ones. Similarly, in link perturbation scenarios, removing links associated with higher competition intensity first leads to a more precipitous decrease in network resilience when the network is otherwise stable. Furthermore, in weight alteration scenarios, networks maintaining a higher ratio of high-intensity competition links demonstrate greater stability compared to those with a reduced proportion of such links. Consequently, sustaining a robust level of export competition between countries is crucial for preserving the stability of the network.

Dynamic Bayesian network-based situational awareness and course of action decision-making support model

In this paper, we developed a dynamic Bayesian network (DBN) model to quantify uncertainties on battlefields. The model consists of the enemy's intention prediction model and the intelligence, surveillance, and reconnaissance (ISR) reliability model quantified by using the sensor detection probability. We calibrated and validated the DBN using a historical dataset based on actual provocations by comprehensively referencing open-source intelligence, including news articles from mass media and official announcements of the Ministry of National Defense. The calibrated model was then used to predict the enemy's intention in the near future, and the accuracy of the model was 84.6%. We suggested an appropriate course of action (COA) based on the enemy's intention to expedite decision-making. We further studied the interaction between the predicted enemy's intention and the selected COA. The main results of this paper are that various information regarding enemy actions, either equality or inequality, can be incorporated into the decision-making process.

Intelligent vehicle trajectory tracking control based on physics-informed neural network dynamics model

In order to solve the accuracy problem of trajectory tracking control method based on data-driven model, an intelligent vehicle trajectory tracking control method based on physics-informed neural network (PINN) vehicle dynamics model is proposed. Aiming at the problem of poor interpretability of data-driven model, a vehicle dynamics model based on the PINN is established, and the physics-driven deep learning method is used instead of the data-driven deep learning method to obtain the dynamic characteristics of the intelligent vehicle, to benefit from both the physical-based method and the data-driven method. A sequential training method is also proposed to solve the coupling problem when training multiple PINNs simultaneously. The model takes the nonlinearity of the neural network model and physical interpretability into consideration compared to the standard neural network model. Then, based on the PINN vehicle dynamics model, a trajectory tracking controller based on the iterative linear quadratic regulator (ILQR) control algorithm is developed. The optimal control law is derived by optimizing the ILQR control algorithm to implement the intelligent vehicle’s precise and stable tracking for the desired trajectory. The Levenberg-Marquardt (LM) algorithm and line search technology are used and damping factor adjustment rules are set up to enhance the convergence performance of the ILQR control algorithm. In order to verify the effectiveness of the proposed method, the simulation is conducted under the condition of double lane change. The simulation results demonstrate that the proposed method can track the reference trajectory accurately under the limited conditions. Its control performance is much better than other algorithms.

Modeling the Propagation of Infectious Diseases across the Air Transport Network: A Bayesian Approach

The COVID-19 pandemic, caused by the SARS-CoV-2 virus, continues to impact the world even three years after its outbreak. International border closures and health alerts severely affected the air transport industry, resulting in substantial financial losses. This study analyzes the global data on infected individuals alongside aircraft types, flight durations, and passenger flows. Using a Bayesian framework, we forecast the risk of infection during commercial flights and its potential spread across an air transport network. Our model allows us to explore the effect of mitigation measures, such as closing individual routes or airports, reducing aircraft occupancy, or restricting access for infected passengers, on disease propagation, while allowing the air industry to operate at near-normal levels. Our novel approach combines dynamic network modeling with discrete event simulation. A real-case study at major European hubs illustrates our methodology.