Dynamic Diagrams Research Articles

Systems Thinking, Causal Loop Diagram, and Systems Dynamic in Public Health Challenges: Navigating Long COVID Syndrome and Sense of Smell in LGBTQIA+ Communities

ABSTRACTBackgroundThe coronavirus pandemic has profoundly affected global health, economic stability, and environmental sustainability. Despite these challenges, significant gaps in data remain, particularly in effectively assessing and engaging diverse communities such as color, LGBTQIA+ individuals, and low‐income groups. This shortage of comprehensive research limits our capacity to undertake sensitive studies, specifically in dealing with the complexities of long COVID, which some individuals continue to suffer from after their initial recovery.ObjectiveThis review delves into the ongoing repercussions of long‐term COVID‐19, a postinfectious syndrome marked by neurological symptoms such as cognitive deficits and sensory impairments, which may last well beyond the acute phase of the illness. These symptoms frequently overlap with mental health issues (e.g., anxiety and depression), which can aggravate the socioeconomic challenges faced by vulnerable populations, especially within the LGBTQA+ communities.MethodsTo tackle these complex interactions, we have introduced a novel public health framework: model‐based systems thinking (MBST), which incorporates System Dynamics and causal loop diagrams (CLD).Results and DiscussionThe articles were selected on the basis of their discussion of COVID‐19‐associated anosmia, exploration of olfactory dysfunction alongside neurocognitive disorders, and the challenges experienced in LGBQA+ communities. This approach offers a robust framework for dissecting the intricate ties between socioeconomic factors, health outcomes, and the extended recovery trajectories associated with long‐term COVID‐19, with a particular focus on olfactory dysfunction. We also explore strategies to make our models more accessible to healthcare providers and the LGBTQA+ communities, encouraging its broader adoption.ConclusionLong COVID's impact on public health and marginalized communities highlights the urgent need for adopting systems thinking models. Additionally, this article calls for a concerted effort from all experts to foster multidisciplinary, team‐based research and implement effective support measures for COVID‐19 survivors across all communities, mainly focusing on the scientific, social, and behavioral challenges LGBTQIA+ and low‐income individuals face.

Detection of failures in HV surge arrester using chaos pattern with deep learning neural network

AbstractAs a protective component of HV equipment, the primary function of a surge arrester is to mitigate the impact of surge voltages. When a surge arrester fails, the equipment it protects becomes vulnerable to damage. This study integrates chaotic systems with Convolutional Neural Networks (CNN) to diagnose faults in HV surge arresters. The Partial Discharge (PD) test was initially performed on six HV surge arrester fault models. The Discrete Wavelet Transform (DWT) was performed for filtering the PD signals. Subsequently, the Chen‐Lee chaotic system converted the filtered PD signals into a dynamic error scatter diagram, creating a feature map of various fault states. This feature map was then used as the input layer to train the CNN model. The results demonstrate that the proposed CNN achieved an accuracy of 97.0%, outperforming AlexNet and traditional methods using Histograms of Oriented Gradients (HOG) combined with Support Vector Machine (SVM), Decision Tree (DT), Backpropagation Neural Network (BPNN), and K‐Nearest Neighbor (KNN). This study also incorporates the LabVIEW graphic control software with a fault diagnosis system for HV surge arresters. The PD data can identify the fault type in real‐time, enhancing power equipment maintenance efficiency.

Embedding diagrams in stationary spacetimes

We find the spatial and dynamic embedding diagrams in stationary black hole spacetimes. The spatial embeddings include the NUT, pure NUT and Kerr spacetimes. In the case of pure NUT spacetime, the spatial embedding equations are solved in terms of the elliptic integrals. In other cases we obtain the spatial embedding diagrams by numerical integration of the corresponding embedding equations. These embedding diagrams are then compared through their Gaussian and mean curvatures. We also find the dynamic embedding diagrams of NUT and pure NUT spacetimes, and compare them with the dynamic embedding diagram of Schwarzschild spacetime.

Numerical Analysis of Dynamic Stability of Flutter Panels in Supersonic Flow

This paper introduces a novel numerical method for investigating the dynamic stability of a flutter panel exposed to a supersonic gas flow and a fluctuating axial excitation force. Initially, the system equation of motion was derived using Lagrange’s equation, where the first two modes are coupled Mathieu–Hill equations with damping, constituting a system of linear second-order differential equations with periodically variable coefficients. Subsequently, a new numerical method was proposed to analyze the dynamic stability of coupled Mathieu–Hill equations with damping. This method involves breaking down an arbitrary parametric load into discrete segments to approximate the variable excitation function using a step function. The system responses of each segment are then accumulated in matrix form. The proposed numerical method proves particularly effective for dynamic systems whose parameters cannot be treated as small. In practical application, the method allows the construction of instability regions corresponding to natural frequencies, subharmonics, and combination frequencies. Dynamic stability diagrams were generated based on dynamic pressure ratio, air/panel density ratio, Mach number, panel thickness—length ratio, and excitation frequency. The results demonstrated general agreement with those obtained through Hsu’s perturbation method, however, our numerical results have proven more accurate. The paper concludes by offering suggestions for suppressing panel flutter through appropriate parameter combinations.

CURE: A Hierarchical Framework for Multi-Robot Autonomous Exploration Inspired by Centroids of Unknown Regions

In this paper, a novel multi-robot autonomous exploration approach CURE is proposed based on dynamic Voronoi diagrams and centroids of unknown connected regions. Compared with existing approaches, the novelty of this work is twofold: 1) Dynamic Voronoi diagram is used for partition of the space being explored to improve the efficiency of multi-robot exploration, and then a new parameter-insensitive utility function is elaborately designed to evaluate the information of centroids, which helps guide the robot to explore unknown regions. 2) A hierarchical framework consisting of global and local exploration windows for detecting centroids is designed, wherein the global exploration window is activated to find centroids to guide the robot exploration when there are no centroids in any one local exploration window. We validate the feasibility and exploration efficiency of the proposed approach in various complex simulation scenarios and challenging real-world tasks. All test results show that the exploration time consumption and path cost are reduced by up to 50.7% and 34.4%, respectively, compared with an advanced RRT-based multi-robot exploration approach. (Supplementary video link: https://youtu.be/P5jXKlGQOec) <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Note to Practitioners</i> —This paper is motivated by the efficient multi-robot autonomous exploration problem. In some applications such as target search and disaster rescue, the information about the environment is totally unknown to the robots, and thus they are required to explore unknown environments autonomously. In this case, it is necessary to improve the efficiency of multi-robot exploration due to the time limitation of the task and the battery capacity. In this paper, a hierarchical framework is proposed to improve the efficiency of multi-robot autonomous exploration. Each robot only needs to explore the Voronoi partition it is responsible for and is guided to the unknown region by the centroid detected in the global and local exploration windows. Overall, the proposed approach can dramatically reduce the exploration time and path cost.

Dynamical analysis of a novel memristor-type chaotic map

As a unique nonlinear component, the discrete memristor, with its simple structure, is associated with the ability to lead to excellent chaotic performance in the construction of chaotic systems. This characteristic has elevated the discrete memristor to a hot topic in the field of chaos. This paper introduces a cosine hyperchaotic map. Numerical simulations reveal its rich dynamical behaviors. The chaotic map exhibits diverse chaotic control models, including partial amplitude control, total amplitude control, initial boosting, and parameter-offset boosting, with dynamical distribution diagrams plotted for amplitude control to quantify the range of amplitude modulation. Additionally, a localized boosting free region is identified, which exhibits extreme sensitivity to initial values. Dual offset parameters are introduced to control this localized boosting free region, enhancing the flexibility of the system. Finally, the map is implemented on STM32 to validate the numerical simulation results.

Development of an Evaluation System for Intelligent Construction Using System Dynamics Modeling

Under the new wave of scientific and technological revolution, the construction industry finds itself with a critical need to alter the traditional and outdated production mode through technological innovation in order to realize industry transformation and move towards a new era characterized by digitalization, informatization, and intelligence. As intelligent construction is the indispensable pathway for the transformation and upgrading of the construction industry, it is of great significance to conduct in-depth research on its evaluation indicators and causality. This paper adopts the system dynamics method, based on the overall structure of intelligent construction, extracts the causality chain and causal feedback loop of intelligent construction, and presents a causality diagram and system dynamics diagram to build a robust system dynamics model for intelligent construction. On this basis, an evaluation index system for intelligent construction is constructed from the five dimensions—investment, design, construction, operation, and environment—for a holistic assessment of the current state of intelligent construction. The research aims to provide a valuable reference for professionals focusing on intelligent construction and the broader development of the industry.

Dynamics and control of a plant-herbivore model incorporating Allee's effect

This research focuses on the interaction between the grape borer and grapevine using a discrete-time plant-herbivore model with Allee's effect. We specifically investigate a model that incorporates a strong predator functional response to better understand the system's qualitative behavior at positive equilibrium points. In the present study, we explore the topological classifications at fixed points, stability analysis, Neimark-Sacker, Transcritical bifurcation and State feedback control in the two-dimensional discrete-time plant-herbivore model. It is proved that for all involved parameters ς1,ϱ1,γ1 and ϒ1, discrete-time plant-herbivore model has boundary and interior fixed points: c1=(0,0), c2=(ς1−1ϱ1,0) and c3=(ϒ1(1−γ1)2γ1−1,γ1(2ς1+ϱ1ϒ1−2)−ϱ1ϒ1+1−ς12γ1−1) respectively. Then by linear stability theory, local dynamics with different topological classifications are investigated at fixed points: c1=(0,0), c2=(ς1−1ϱ1,0) and c3=(ϒ1(1−γ1)2γ1−1,γ1(2ς1+ϱ1ϒ1−2)−ϱ1ϒ1+1−ς12γ1−1). Our investigation uncovers that the boundary equilibrium c2=(ς1−1ϱ1,0) experiences a transcritical bifurcation, whereas the unique positive steady-state c3=(ϒ1(1−γ1)2γ1−1,γ1(2ς1+ϱ1ϒ1−2)−ϱ1ϒ1+1−ς12γ1−1) of the discrete-time plant-herbivore model undergoes a Neimark-Sacker bifurcation. To address the periodic fluctuations in grapevine population density and other unpredictable behaviors observed in the model, we propose implementing state feedback chaos control. To support our theoretical findings, we provide comprehensive numerical simulations, phase portraits, dynamics diagrams, and a graph of the maximum Lyapunov exponent. These visual representations enhance the clarity of our research outcomes and further validate the effectiveness of the chaos control approach.

Fruit–frugivore dependencies are important in Ebolavirus outbreaks in Sub‐Saharan Africa

Ebolaviruses have the ability to infect a wide variety of species, with many African mammals potentially serving either as primary reservoirs or secondary amplifying hosts. Previous work has shown that frugivorous bats and primates are often associated with spillover and outbreaks. Yet the role that patterns of biodiversity, either of mammalian hosts or of common fruiting species such as Ficus (figs, fruit resources used by a wide variety of species), play in driving outbreak risk remains unclear. We investigated what factors most directly influence Ebolavirus outbreak risk in Sub‐Saharan Africa by using a phylogenetically informed path analysis to compare a wide array of potential models (path diagrams) of spatial dynamics. We considered mammalian frugivore richness, cercopithecid and hominid primate richness, richness of pteropodid (fruit) bats, the spatial distribution of species that have tested positive for Ebolavirus antibodies in the wild, Ficus habitat suitability, and environmental conditions (mean annual and variability in temperature and rainfall). The proximate factors that most influenced whether a given host species range contained a site of a previous outbreak event were 1) habitat suitability for Ficus and 2) the diversity of cercopithecid primates. Frugivore richness overall (including bats, primates, and a few other mammals) and the richness of bats in the family Pteropodidae had a strong effect on which species tested positive for Ebolavirus antibodies, but did not influence outbreak risk directly in pathways explored. We interpret this as evidence that foraging around Ficus and frugivorous mammals (such as cercopithecid primates which are commonly hunted for food) play a prominent role in driving outbreaks into human communities, relative to other factors we considered which influence outbreak risk more indirectly.

Integrative metabolomic and transcriptomic analyses reveals the accumulation patterns of key metabolites associated with flavonoids and terpenoids of Gynostemma pentaphyllum (Thunb.) Makino.

Gynostemma pentaphyllum (Thunb.) Makino (G. pentaphyllum) is a medicinal and edible plant with multiple functions of liver protection, anti-tumor, anti-inflammation, balancing blood sugar and blood lipids. The nutritional value of the G. pentaphyllum plant is mainly due to its rich variety of biologically active substances, such as flavonoids, terpenes and polysaccharides. In this study, we performed a comprehensive analysis combining metabolomics and root, stem and leaf transcriptomic data of G. pentaphyllum. We used transcriptomics and metabolomics data to construct a dynamic regulatory network diagram of G. pentaphyllum flavonoids and terpenoids, and screened the transcription factors involved in flavonoids and terpenoids, including basic helix-loop-helix (bHLH), myb-related, WRKY, AP2/ERF. Transcriptome analysis results showed that among the DEGs related to the synthesis of flavonoids and terpenoids, dihydroflavonol 4-reductase (DFR) and geranylgeranyl diphosphate synthases (GGPPS) were core genes. This study presents a dynamic image of gene expression in different tissues of G. pentaphyllum, elucidating the key genes and metabolites of flavonoids and terpenoids. This study is beneficial to a deeper understanding of the medicinal plants of G. pentaphyllum, and also provides a scientific basis for further regulatory mechanisms of plant natural product synthesis pathways and drug development.

Measurement of dynamic direction diagrams of phased antenna arrays in process of radar objects tracking

Measurement of dynamic direction diagrams of phased antenna arrays in process of radar objects tracking

Force analysis of the planetary-type mechanisms of the enhanced vibration exciters

The planetary-type vibration exciters are relatively novel and prospective types of actuators for various vibratory technological equipment. Numerous research papers substantiated the possibilities of implementing such exciters for generating circular, elliptical, and rectilinear oscillations of the working members of different technological machines. The present research continues the authors’ previous investigations dedicated to kinematics and dynamics of planetary-type vibration exciters and is focused on the analysis of forces, moments, and torques acting upon the elements of the corresponding actuating mechanisms. The research methodology contains the development of the simplified dynamic diagrams of two different designs of planetary-type mechanisms, derivation of the analytical expressions describing force parameters of the considered mechanical systems, performing the mathematical modeling and computer simulation of the mechanism motion in order to analyze the corresponding forces, moments, and torques. The obtained results present the time dependencies of the mentioned force parameters under different operational conditions and design parameters of the planetary-type mechanism. The major scientific novelty of the paper consists in defining the minimal required torque of the motor needed to be applied for driving the mechanism at different conditions. The research outcomes can be effectively used by engineers while developing new and improving existent vibratory technological machines equipped with the considered planetary-type vibration exciter.

Defect Identification of XLPE Power Cable Using Harmonic Visualized Characteristics of Grounding Current

This paper proposes an online monitoring and defect identification method for XLPE power cables using harmonic visualization of grounding currents. Four typical defects, including thermal aging, water ingress and dampness, insulation scratch, and excessive bending, were experimentally conducted. The AC grounding currents of the cable specimens with different defects were measured during operation. By using the chaotic synchronization system, the harmonic distortion was transformed into a 2D scatter diagram with distinctive characteristics. The relationship between the defect type and the diagram features was obtained. A YOLOv5 (you only look once v5) target recognition model was then established based on the dynamic harmonics scatter diagrams for cable defect classification and identification. The results indicated that the overall shape, distribution range, density degree, and typical lines formed by scatter aggregation can reflect the defect type effectively. The proposed method greatly reduces the difficulty of data analysis and enables rapid defect identification of XLPE power cables, which is useful for improving the reliability of the power system.

How are artificial intelligence, carbon market, and energy sector connected? A systematic analysis of time-frequency spillovers

The dual role of artificial intelligence (AI) in carbon emissions has come under scrutiny. The feedback mechanism in the “AI-Carbon-Energy” system contains the enlightenment of coordinated development of environment and economy. Based on the dynamic connectedness index and network diagrams, we quantify how the AI industry is connected to the carbon market and the energy sector in the short-term and long-term. Our empirical findings suggest that the information spillover within the system changes over time and across frequency bands. The long-term component drives the overall information spillover. Both the carbon market and the energy sector are closely connected with the AI industry. Specifically, AI industry trading volume is a main information transmitter. Since the release of GPT-4, however, investor attention to the AI industry becomes more important. The carbon market receives a lot of information from the AI industry trading volume and investor attention to the AI industry, particularly since 2023. Nevertheless, the energy sector is only weakly connected to the other two markets. These findings have important implications for policy makers, investors, and producers.

Enhancing Control Room Operator Decision Making

In the dynamic and complex environment of industrial control rooms, operators are often inundated with numerous tasks and alerts, leading to a state known as task overload. This condition can result in decision fatigue and increased reliance on cognitive biases, which may compromise the decision-making process. To mitigate these risks, the implementation of decision support systems (DSSs) is essential. These systems are designed to aid operators in making swift, well-informed decisions, especially when their judgment may be faltering. Our research presents an artificial intelligence (AI)-based framework utilizing dynamic influence diagrams and reinforcement learning to develop a powerful decision support system. The foundation of this AI framework is the creation of a robust, interpretable, and effective DSS that aids control room operators during critical process disturbances. By incorporating expert knowledge, the dynamic influence diagram provides a comprehensive model that captures the uncertainties inherent in complex industrial processes. It excels in anomaly detection and recommending optimal actions. Furthermore, this model is improved through a strategic collaboration with reinforcement learning, which refines the recommendations to be more context-specific and accurate. The primary goal of this AI framework is to equip operators with a live, reliable DSS that significantly enhances their response during process upsets. This paper describes the development of the AI framework and its implementation in a simulated control room environment. Our results show that the DSS can improve operator performance and reduce cognitive workload. However, it also uncovers a trade-off with situation awareness, which may decrease as operators become overly dependent on the system’s guidance. Our study highlights the necessity of balancing the advantages of decision support with the need to maintain operator engagement and understanding during process operations.

Refined composite multiscale dynamic causality diagram: a novel time-series analysis approach and its application in fault diagnosis of rotating machinery

Entropy-based feature extraction methods have been widely used in the fault diagnosis of rotating machinery, but the entropy-based methods still have the defects of poor noise robustness, weak feature extraction, and low computational efficiency. To solve this problem, this article proposes a fault diagnosis method based on refined composite multiscale dynamic causal diagram (RCMSDCD) and local receptive field extreme learning machine (LRFELM). First, a novel feature extraction method, named dynamic causal diagram (DCD), is proposed to comprehensively quantify static and dynamic complexity. DCD is obtained by combining generalized inverse fractional order entropy with complexity–entropy causal plane. Then, combined with the coarse-graining process, DCD is extended to a multiscale analysis called RCMSDCD to complement the feature description at cross-time scales. Third, RCMSDCD features are input into LRFELM classifier for fault recognition of rotating machinery. The effectiveness of the proposed RCMSDCD-LRFELM method is verified by the Paderborn University bearing test and real wind turbine gear signals. The results show that this method has the highest classification accuracy of 100% with high computational efficiency, good stability, and strong generalization ability.

A Big Data and FRAM-Based Model for Epidemic Risk Analysis of Infectious Diseases.

The use of multi-source precursor data to predict the epidemic risk level would aid in the early and timely identification of the epidemic risk of infectious diseases. To achieve this, a new comprehensive big data fusion assessment method must be developed. With the help of the Functional Resonance Analysis Method (FRAM) model, this paper proposes a risk portrait for the whole process of a pandemic spreading. Using medical, human behaviour, internet and geo-meteorological data, a hierarchical multi-source dataset was developed with three function module tags, ie, Basic Risk Factors (BRF), the Spread of Epidemic Threats (SET) and Risk Influencing Factors (RIF). Using the dynamic functional network diagram of the risk assessment functional module, the FRAM portrait was applied to pandemic case analysis in Wuhan in 2020. This new-format FRAM portrait model offers a potential early and rapid risk assessment method that could be applied in future acute public health events.

Comparative Analysis of Nonlinear Dynamics of an Angular Velocity System of 2-DOF Aerial Manipulator with Different Physical Parameters

The two-degree-of-freedom (2-DOF) aerial manipulator is composed of a quadrotor aircraft and a 2-DOF manipulator, which significantly expands the scope of grabbing and transporting objects. After the manipulator is installed on the quadrotor, the manipulator and the load will cause serious interference to the quadrotor, resulting in difficulty of system control and even instability. This paper presents a mathematical model of the angular velocity system of the 2-DOF aerial manipulator. The model considers the influence of the manipulator and the load on the quadrotor. Based on this model, the nonlinear dynamics of the angular velocity system of the 2-DOF aerial manipulator are analyzed by solving the equilibrium points, calculating the Lyapunov exponents, analyzing the dynamic bifurcation diagram, and drawing the dynamic region distribution map. It is found that angular velocity can produce the dynamic behaviors of sink, period-doubling, and chaos under certain circumstances. By analyzing the nonlinear dynamic behaviors of the angular velocity system under different manipulator postures, different manipulator configurations, different load masses, and different load resistances, the stability of the angular velocity system is analyzed to guide the use of the aerial manipulator more safely and efficiently.

Symmetry, Multistability and Antimonotonicity of a Shinriki Oscillator with Dual Memristors

In this paper, a type of modified dual memristive Shinriki oscillator is constructed with a flux-controlled absolute-type memristor and a voltage-controlled generic memristor, and the proposed oscillator with abundant dynamical behaviors, including the multistability and antimonotonicity, is comprehensively studied through dynamical distribution graphs, bifurcation diagrams, Lyapunov exponents and phase portraits. It is found that the passive/active state of memristor, which means different characteristics in the [Formula: see text]–[Formula: see text] domain with positive and negative parameters of the elements, can affect the state of the oscillator. For example, if the memristor is active, the oscillator will change more frequently in the multistable region. Also, it is noted that, for inherent initial-related symmetry and circuit structures with duality, both phenomena have strong symmetric characteristics and opposite evolution trends modulated by values of corresponding components. Especially, the bubbles, which are symmetric about parameters with duality and own complex evolution laws, have rarely been explored in previous works. In addition, the memristive oscillator is modularized based on field programmable gate array (FPGA) technology, and the multiple coexisting attractors are captured, which verifies the accuracy of the numerical results.

Exploiting the Unexpected

Contemporary imaging technologies have expanded our ability to view contemporary urban space from many vantage points. The camera's portability has meant a departure from any singular ‘ideal’ viewing position previously proposed using linear perspective geometry. This visual and technological shift has brought new opportunities for how urban space is understood and conceived for various modes of inhabitation. By fracturing and multiplying its representation, the recording of dynamic space thus destabilises the traditional relationship between image and form and, in so doing, presents previously unconsidered spatial opportunities. This text-based essay explores the instrumentality of digital image processes in the production of new material and spatial strategies for design and architectural applications. Contextualised within a frame of the digital image and contemporary envisioned space activated by the Internet, it focuses upon the spatial and material properties of three different architectural scales — the residential dwelling, the apartment block, and the public building. The essay demonstrates how repurposed medical imaging software can uncover hidden and unexpected characteristics of different-sized interior spaces by recalibrating image data from captured video footage. The essay draws on a range of generative digital techniques that transform the temporal visual content of interior space — the image stack and its slices or frames — into diagrammatic abstractions of recalibrated colour and form. The various axes of the stack thus serve as a contemporary extrapolation of the traditional orthographic diagrams that currently define architectural production — the plan, section, and elevation. With the visual content of these axes completely redistributed across different scales according to the shifts of the colour- and contrast-based properties of the digital array, the emerging slippages within each slice compose a new dynamic template or diagram that informs new and uncertain strategies of inhabitation.