Dynamic Evolution Model Research Articles

Prediction of potential conflicts between active targets in airport flight zones based on LSTM

Aiming at the problem of constant conflicts in airport flight zones, a method based on LSTM to predict potential conflicts of active targets in airport flight zones is proposed. Firstly, based on the complex network theory, two types of active targets, aircraft and vehicles, are taken as the research objects, and the flight zone active target network is established. The network dynamic evolution model is set up, and the operation data is input to calculate multiple network characteristic indicators. The principal component analysis of the indicator time series is performed to fit the potential conflict index; then, the Keras framework is used to build a long short-term memory neural network (LSTM) model, and the indicator time series is input into LSTM for training and prediction, and compared with other prediction methods; finally, the actual operation data of Xi'an Xianyang Airport is used for experiments, and the predicted values are compared with the true values. The mean square errors of the prediction results of each indicator are respectively 1.61%、13.13%、0.72%、0.004%、0.014%; The research shows that by establishing the flight zone active target network model, the network characteristic indicators can be used to characterize potential conflicts from different angles; LSTM can effectively predict the potential conflicts of the flight zone active target network, remind relevant personnel to pay attention to preventing conflicts and reducing the probability of conflicts.

Research on the recycling decision of WEEE in rural China under dual supervision

Against the backdrop of promoting green, low-carbon, and high-quality development, this paper aims to promote efficient recycling of waste electrical and electronic equipment (WEEE) in rural China. In this paper, considering the integrated development of urban and rural areas and the standardization of industrial recycling system, under the joint action of the extension of producer responsibility and “dual regulation” of supply and marketing cooperatives, the evolutionary game model and system dynamics model of three-level recycling network of farmers, supply and marketing cooperatives and retailers are established. The mechanism of the participants to promote the recycling of WEEE in rural areas is discussed and the strategic choices and interactive relationships of various entities in the evolutionary process were used to analyze through the evolutionary game method. Meanwhile, using the theory of system dynamics, the main influencing factors of different evolution stages and the dynamic change process of the system are analyzed. The results show that: (1) supply and marketing cooperatives, retailers, and farmers can initially tend to participate in and supervise the recycling of WEEE; however, (2) they can finally achieve strong supervision, actively undertake and participate in the recycling and stabilization stable strategy of rural WEEE depends on their benefits and cost of expenditure expenditures are reasonable. (3) The strategic choice of supply and marketing cooperatives has the most significant impact on the strategic choice of retailers and the strategic choice of retailers has the most significant impact on the strategic choice of farmers.

Nonlinear dynamic modeling and vibration analysis for early fault evolution of rolling bearings

In rotating machinery, the condition of rolling bearings is paramount, directly influencing operational integrity. However, the literature on the fault evolution of rolling bearings in their nascent stages is notably limited. Addressing this gap, our study establishes an innovative nonlinear dynamic model for early fault evolution of rolling bearings based on collision impact. Firstly, considering the fault evolution characteristics, the influence of the rolling element and fault structure, the dynamic model of early fault evolution between the rolling element and the local fault is established. Secondly, according to the Hertzian contact deformation theory, a nonlinear dynamic model of rolling bearings expressed as mass-spring is established. Thirdly, the energy contribution method is used to integrate the fault evolution model and the nonlinear dynamic model of the rolling bearing. A nonlinear dynamic model of early fault evolution of the rolling bearing is proposed by using the Lagrangian equation. Comparing the simulation results of the nonlinear dynamic with the experimental results, it can be seen that the numerical model can effectively predict the evolution process and vibration characteristics of the fault evolution of rolling bearings in the early stage.

An analysis of optimal equilibrium in the carbon trading market - From a tripartite evolutionary game perspective

As the climate crisis intensifies, achieving the global consensus of carbon peaking and carbon neutrality has become imperative. Carbon trading is an important financial measure to address the environmental crisis, and the realization of the dual‑carbon goals requires the cooperation and joint efforts of all parties involved in the carbon emissions trading market. This study constructs a dynamic evolutionary game model involving enterprises, government, and financial institutions while considering consumers' influence. By solving for equilibrium points and conducting numerical simulations, we explore optimal strategy choices for each stakeholder. Our findings reveal that the success of enterprise low-carbon transition is contingent upon market dynamics and requires active cooperation from government, financial institutions, and the public. Furthermore, factors such as financial market efficiency and internal governance capacity significantly impact enterprises' transformation decisions by influencing low-carbon transition costs. Public feedback indirectly affects enterprise decisions through its influence on financial institutions' provision of green services. Additionally, gradual reduction of carbon quotas by government entities facilitates progress toward low-carbon transformation objectives.

Optimized patient-specific immune checkpoint inhibitor therapies for cancer treatment based on tumor immune microenvironment modeling.

Enhancing patient response to immune checkpoint inhibitors (ICIs) is crucial in cancer immunotherapy. We aim to create a data-driven mathematical model of the tumor immune microenvironment (TIME) and utilize deep reinforcement learning (DRL) to optimize patient-specific ICI therapy combined with chemotherapy (ICC). Using patients' genomic and transcriptomic data, we develop an ordinary differential equations (ODEs)-based TIME dynamic evolutionary model to characterize interactions among chemotherapy, ICIs, immune cells, and tumor cells. A DRL agent is trained to determine the personalized optimal ICC therapy. Numerical experiments with real-world data demonstrate that the proposed TIME model can predict ICI therapy response. The DRL-derived personalized ICC therapy outperforms predefined fixed schedules. For tumors with extremely low CD8 + T cell infiltration ('extremely cold tumors'), the DRL agent recommends high-dosage chemotherapy alone. For tumors with higher CD8 + T cell infiltration ('cold' and 'hot tumors'), an appropriate chemotherapy dosage induces CD8 + T cell proliferation, enhancing ICI therapy outcomes. Specifically, for 'hot tumors', chemotherapy and ICI are administered simultaneously, while for 'cold tumors', a mid-dosage of chemotherapy makes the TIME 'hotter' before ICI administration. However, in several 'cold tumors' with rapid resistant tumor cell growth, ICC eventually fails. This study highlights the potential of utilizing real-world clinical data and DRL algorithm to develop personalized optimal ICC by understanding the complex biological dynamics of a patient's TIME. Our ODE-based TIME dynamic evolutionary model offers a theoretical framework for determining the best use of ICI, and the proposed DRL agent may guide personalized ICC schedules.

Dynamic Active Sites in Electrocatalysis

In‐depth understanding of the real‐time behaviors of active sites during electrocatalysis is essential for the advancement of sustainable energy conversion. Recently, the concept of dynamic active sites has been recognized as a potent approach for creating self‐adaptive electrocatalysts that can address a variety of electrocatalytic reactions, outperforming traditional electrocatalysts with static active sites. Nonetheless, the comprehension of the underlying principles that guide the engineering of dynamic active sites is presently insufficient. In this review, we systematically analyze the fundamentals of dynamic active sites for electrocatalysis and consider important future directions for this emerging field. We reveal that dynamic behaviors and reversibility are two crucial factors that influence electrocatalytic performance. By reviewing recent advances in dynamic active sites, we conclude that implementing dynamic electrocatalysis through variable reaction environments, correlating the model of dynamic evolution with catalytic properties, and developing localized and ultrafast in‐situ/operando techniques are keys to designing high‐performance dynamic electrocatalysts. This review paves the way to the development of the next‐generation electrocatalyst and the universal theory for both dynamic and static active sites.

Dynamic Active Sites in Electrocatalysis.

In-depth understanding of the real-time behaviors of active sites during electrocatalysis is essential for the advancement of sustainable energy conversion. Recently, the concept of dynamic active sites has been recognized as a potent approach for creating self-adaptive electrocatalysts that can address a variety of electrocatalytic reactions, outperforming traditional electrocatalysts with static active sites. Nonetheless, the comprehension of the underlying principles that guide the engineering of dynamic active sites is presently insufficient. In this review, we systematically analyze the fundamentals of dynamic active sites for electrocatalysis and consider important future directions for this emerging field. We reveal that dynamic behaviors and reversibility are two crucial factors that influence electrocatalytic performance. By reviewing recent advances in dynamic active sites, we conclude that implementing dynamic electrocatalysis through variable reaction environments, correlating the model of dynamic evolution with catalytic properties, and developing localized and ultrafast in-situ/operando techniques are keys to designing high-performance dynamic electrocatalysts. This review paves the way to the development of the next-generation electrocatalyst and the universal theory for both dynamic and static active sites.

A survey on temporal knowledge graph embedding: Models and applications

Knowledge graph embedding (KGE), as a pivotal technology in artificial intelligence, plays a significant role in enhancing the logical reasoning and management efficiency of downstream tasks in knowledge graphs (KGs). It maps the intricate structure of a KG to a continuous vector space. Conventional KGE techniques primarily focus on representing static data within a KG. However, in the real world, facts frequently change over time, as exemplified by evolving social relationships and news events. The effective utilization of embedding technologies to represent KGs that integrate temporal data has gained significant scholarly interest. This paper comprehensively reviews the existing methods for learning KG representations that incorporate temporal data. It offers a highly intuitive perspective by categorizing temporal KGE (TKGE) methods into seven main classes based on dynamic evolution models and extensions of static KGE. The review covers various aspects of TKGE, including the background, problem definition, symbolic representation, training process, commonly used datasets, evaluation schemes, and relevant research. Furthermore, detailed descriptions of related embedding models are provided, followed by an introduction to typical downstream tasks in temporal KG scenarios. Finally, the paper concludes by summarizing the challenges faced in TKGE and outlining future research directions.

MILESTONES IN THE DEVELOPMENT OF ORGANIZATIONAL MODELS BASED ON THE THEORY OF SPIRAL DYNAMICS

The article examines the milestones of the development of the business environment through the prism of industrial revolutions and the theory of spiral dynamics of organizational development. The past influences the present. The present shapes the future. The world is a complex, interconnected whole that is difficult to represent and understand, especially regarding complex socio-economic processes and organizational systems, including enterprises. The first radical change began precisely with the agrarian revolution when people domesticated animals as a basis for the transition from gathering to agriculture. After the agrarian revolution, radical industrial changes began in the 18th century. The first industrial revolution (from the 1760s to the 1840s) - the invention of the steam engine and the construction of railways. The second industrial revolution (the end of the 19th and the beginning of the 20th century) - the emergence of mass production thanks to the invention of electricity and the introduction of the conveyor. The third industrial revolution, from the 1960s, - was the emergence of semiconductors and their use in large electronic computing machines, personal computers, and the Internet. The fourth industrial revolution began at the beginning of the 21st century and is characterized by the emergence of "smart enterprises". The content and essential components of these periods imposed and imposed their understanding on the development of organizational models and the way of interaction between stakeholders and the economic entities themselves, which arose at the later stages of development and are developing under the influence of global business trends. Deepening the understanding of the nature of modern approaches in management aimed at the development of organizational systems allows turning to the past and investigating issues of organizational models on the way of the history of human development based on the theory of spiral dynamics (Don et al.), which is based on a dynamic model of human development and evolution of her consciousness, change of fundamental values. So, from the standpoint of the concept of systemic perfection of a business organization, based on the theory of spiral dynamics, its consideration can be started in the system-wide understanding of the system state, which corresponds to a greater extent to the content of perfection - from the orange stage and more/less corresponds to the current trends of the fourth industrial revolution. A significant strategic gap occurs based on the mismatch between the stage of development according to the milestones of industrial revolutions and the organization's current state in relation to the theory of spiral dynamics. For example, a red organization that functions in the fourth industrial revolution era. Management of changes in paradigms of thinking is the art of management.

Complexity Analysis of the Interaction between Government Carbon Quota Mechanism and Manufacturers’ Emission Reduction Strategies under Carbon Cap-and-Trade Mechanism

Based on different carbon quota trading mechanisms, the price and emission reduction strategies of oligopoly manufacturers in the low-carbon market and the government carbon quota mechanism are considered. A dynamic game evolution model of the two oligopoly manufacturers with competitive relations is established. The stability of the equilibrium point of the game model, the price adjustment speed of the decision variable, the impact of carbon emission reduction investment, and the government carbon quota on the system are discussed. Through nonlinear dynamics research, it is found that the advantage of the grandfathering method is that it is conducive to maintaining market stability when the government’s carbon quota decision changes; the advantage of the benchmarking method is that when manufacturers formulate price adjustment strategies, the benchmarking method carbon quota mechanism has a stronger stability range for the market, the manufacturer’s profit price adjustment speed is positively correlated, and the government carbon quota decision and emission reduction investment are also positively correlated. Decision makers need to choose appropriate carbon quota mechanisms and manufacturers’ emission reduction strategies according to actual market changes to maintain supply chain stability.

Dynamic Bond Percolation-Based Reliable Topology Evolution Model for Dynamic Networks

Dynamic Bond Percolation-Based Reliable Topology Evolution Model for Dynamic Networks

Information disclosure, multifaceted collaborative governance, and carbon total factor productivity—An evaluation of the effects of the ‘environmental information disclosure pilot’ policy based on double machine learning

As an environmental institutional arrangement related to the information factor of the diversified participation of the government, enterprises, the media and the public, the environmental information disclosure pilot policy, can and how to affect the carbon emission efficiency through multiple collaborative governance? This study uses the Environmental Information Disclosure Pilot Policy implemented in China in 2007 as a quasi-natural experiment. It examines 284 prefecture-level cities from 2004 to 2021 and A-share listed companies from 2004 to 2021, constructing an evolutionary game dynamic model involving government, public, enterprises, and media. Through mathematical derivation and assignment analysis, it explores how environmental information impacts carbon emission efficiency under multifaceted collaborative governance, assessing the strategic choices and evolutionary paths of stakeholders before and after policy implementation, using methods like double machine learning for empirical testing. The study highlights several key findings: First, the implementation of the Environmental Information Disclosure Pilot Policy significantly enhanced carbon total factor productivity in pilot cities, as revealed through Double Machine Learning (DML) policy effect evaluation. Second, adjustments for potential estimation biases using Doubly Debiased LASSO (DDL) regression indicated that environmental information disclosure impacts carbon productivity via a governance mechanism involving government, public, media, and enterprises. Third, a causal pathway analysis suggested a sequential logic in governance effectiveness, starting from governmental environmental focus to corporate environmental responsibility. Lastly, integrating DML with a moderation effect model revealed a regulatory role for environmental legislation construction, offering new insights for achieving dual carbon goals and enriching empirical evidence on information's impact on carbon emission efficiency.

Programming tumor evolution with selection gene drives to proactively combat drug resistance.

Most targeted anticancer therapies fail due to drug resistance evolution. Here we show that tumor evolution can be reproducibly redirected to engineer therapeutic opportunity, regardless of the exact ensemble of pre-existing genetic heterogeneity. We develop a selection gene drive system that is stably introduced into cancer cells and is composed of two genes, or switches, that couple an inducible fitness advantage with a shared fitness cost. Using stochastic models of evolutionary dynamics, we identify the design criteria for selection gene drives. We then build prototypes that harness the selective pressure of multiple approved tyrosine kinase inhibitors and employ therapeutic mechanisms as diverse as prodrug catalysis and immune activity induction. We show that selection gene drives can eradicate diverse forms of genetic resistance in vitro. Finally, we demonstrate that model-informed switch engagement effectively targets pre-existing resistance in mouse models of solid tumors. These results establish selection gene drives as a powerful framework for evolution-guided anticancer therapy.

Research on the spontaneous combustion of the goaf at different advancing distances based on the dynamic evolution of porosity

Spontaneous combustion of residual coal in the goaf is one of the main disasters in coal mines. Understanding the law of spontaneous combustion of residual coal can effectively prevent coal mine fires. This paper establishes a dynamic evolution model of overburden caving and porosity in the goaf using PFC numerical simulation software, and compares and analyzes the dynamic changes in porosity and the spontaneous combustion zone at advancing distances of 50 m, 70 m, and 100 m. The results show that when the working face advances 100 m, the porosity in the caving zone of the goaf decreases from 0–0.6 to 0.16–0.3. The range of the spontaneous combustion zone on the return air side expands from 42m-50 m to 39m–61 m, and on the inlet side, it expands from 0 to 22m–50 m. With increased exposure time, the spontaneous combustion zone range shifts slightly forward to 32m–55 m on the inlet side and 21m–49 m on the return side. This study reveals the dynamic evolution law of the spontaneous combustion zone in the goaf under the influence of different advancing distances and exposure times, which can assist in preventing goaf fires.

A Dynamic Evolution Model for Decentralized Autonomous Car Clusters in a Highway Scene

A Dynamic Evolution Model for Decentralized Autonomous Car Clusters in a Highway Scene

Kalman filter for radio source power and direction of arrival estimation

Images are an important source of information for spacecraft navigation. Based on an image and a known attitude, triangulation techniques (intersection or resection) are often used for positioning and navigation. In the resection problem, an observer estimate its unknown location by using angle measurements to points at known locations (i.e., landmarks), the localization performance depending on the accuracy of the angle measurements. As a contribution to resection for spacecraft navigation, we considers the dynamic image estimation problem based on radio interferometry, i.e., image of radio source power, where the measurements are sample covariance matrices (SCMs). Considering the case where several measurements are available as well as a known dynamic linear model of image evolution, a.k.a a linear state model, the minimum mean-squared error image estimator (MMSE) is given by the Kalman filter (KF) or one of its variants. However standard Kalman-like filters are not a priori suitable for the problem at hand since the measurements (i.e., SCMs) cannot be formulated analytically as a function of state parameters to be estimated (i.e., radio source power). In fact, this lack of analytical formulation can be circumvented by a statistical linear fitting allowing the SCMs to be expressed in terms of the state. This linear fitting introduces an additive residual noise, equivalent to a measurement noise, whose covariance matrix depends on the current state, a non-standard case for a measurement model. The covariance matrix of the residual noise is derived whatever the distributions of the radio sources and of the additive noise at the samples level, unveiling the contribution of their multivariate kurtosis. The proposed method is evaluated on simulated data representative of a dynamic radio interferometry framework. The results show that the proposed method is capable of effectively tracking moving radio sources in complex scenes with theoretical guaranties when the signal multivariate kurtosis is known.

Damage prediction and improvement method based on cutting mode of circular empty hole

Based on the theory of empty hole effect of cutting blasting, the Hopkinson effect and Saint–Venant principle are integrated to establish a two-dimensional calculation model of dynamic stress evolution of the holes wall, and then the dynamic fracture mechanism and damage distribution mode of the rock mass in the cutting area under the action of longitudinal waves are predicted. The results of the calculation and numerical simulation are verified by experiments, and the results show that: The time-varying stress function of the circular cavity wall conforms to the periodic dynamic evolution of the trigonometric function, and the theoretical calculation is consistent with the simulation results. Through the calculation of the round holes cut model and the square empty hole cut model, the change of the shape of the holes in the cut area changes the failure form of the surrounding rock mass. The circular empty hole wall is affected by the stress wave to produce "interval ring" destruction, and the effect of the reflected stretch wave is inhibited. The large range of rock mass in the square empty hole wall produces tensile and shear failure, and the rock mass collapses inward under the influence of the second stage stress. Among them, the empty space utilization rate of the square empty hole model is about 8.5 times that of the circular holes model. Vibration monitoring in the center of the cutting area shows that the vibration effect of the circular empty hole is larger than that of the square empty hole, and the proportion of rock breaking energy is lower.

Analysis of Multi-Stakeholder Behavioral Strategies in the Construction and Demolition Waste Recycling Industry through an Evolutionary Game Theory

Construction and demolition waste (C&DW) recycling products have drawn worldwide attention over the past few decades. There is a general agreement among researchers that C&DW recycling is an important means for curbing the deterioration of the environment. Previous papers mainly focused on the decision-making behavior of dual stakeholders or tripartite stakeholders, as well as the lack of cooperation among multiple stakeholders. This study explored a dynamic evolutionary game model with three different parameter conditions to research the decision-making behaviors and stable strategies of the multi-stakeholders involved in the C&DW recycling product industry, including government departments and some enterprises. This research also investigated how the government’s supervision costs, penalties applied to enterprises, and resource taxes affect the dynamic evolution process of C&DW recycling. This research conducted numerical simulations using Python to analyze stakeholders’ behavioral evolutionarily stable strategy (ESS) and the sensitivity to main factors in each stage of the C&DW recycling process to accelerate the development of construction waste resource utilization. Based on the results of the evolutionary game, this paper proposed that the roles of multi-stakeholders are different at different stages of industry development, and that supervision costs, penalties, and resource tax have their own impacts on the C&DW recycling product industry. The paper suggests a range of discussions and simulation studies to highlight the significance of the government’s refined and moderate adjustments to the regulatory incentive system and the level of government regulation and involvement at different stages of the process. These adjustments are aimed at promoting the sustainable recycling and utilization of construction and demolition waste (C&DW) products within some countries’ construction industry.

Semi-supervised few-shot class-incremental learning based on dynamic topology evolution

Incremental learning of new classes is crucial to developing real-world artificial intelligence systems. However, in some cases, we can only use a limited number of images to train the incremental model due to the difficulty of obtaining them. Few-shot class-incremental learning (FSCIL) is a new paradigm of incremental learning, which continually learns new concepts from a few examples. This setup can quickly lead to catastrophic forgetting and overfitting problems, severely affecting model performance. To address these problems, we propose a dynamic topological evolution (DTE) model that unifies knowledge of old and new categories. In this work, we focus on a novel semi-supervised few-shot class-incremental learning (SSFSCIL) to incrementally learn new categories with a limited number of labeled samples and a large number of unlabeled samples. In detail, we use the Voronoi polygon criterion to divide the feature space and construct a Hebbian graph to represent topological relationships between classes. Then, the corresponding k-nearest neighbor subgraph is constructed for the new incremental task, and label propagation of graph nodes is carried out on the unlabeled set to generate the corresponding pseudo labels. Finally, the new category knowledge is evolved into the actual category knowledge by integrating the Hebbian graph and the k-nearest neighbor subgraph obtained in the new incremental task. Extensive experiments on the mini-ImageNet and CIFAR100 datasets show that the accuracy of the DTE outperforms the baseline and improves by 1.41% and 1.13% on the last incremental task. Furthermore, our methodology achieved a 35.6% time improvement compared to full-supervised methods on the CIFAR100 dataset. In addition to the experiments on the two datasets mentioned above, the validation of our proposed DTE model extends to the engineering application domain of the RESICS-45 aircraft image scene classification dataset.

Analysis of the evolution path of new energy system under polymorphic uncertainty—A case study of China

The exploration and promotion of new energy constitute a significant initiative for numerous countries in their pursuit of sustainable development and efforts to alleviate climate change. However, the influence of a multitude of uncertain factors on the evolutionary path of new energy is currently poorly studied. By establishing a dynamic evolution model for new energy under polymorphic uncertainty, this paper analyses the complex interaction between new energy, economic growth, and carbon trading over the period 2020–2060, using China as a case study. We employ numerical simulation and sensitivity analysis to investigate the evolutionary trajectories of the new energy system under various parameter value scenarios. We show that the evolutionary trajectories will undergo substantial fluctuations before 2030, subsequently stabilizing from 2030 onwards. The study highlights that an excessively rapid pace of new energy development is detrimental to new energy systems, whereas energy storage system deployment has the opposite effect. There exists a significant risk of the system collapsing as a result of slow new energy development, over-building energy storage facilities, or over-intervening in carbon trading. We also find that overdevelopment of energy storage system may have greater negative impacts on the economic system than underdevelopment.