Dynamic Approach Research Articles

Deciphering the Cofilin Oligomers via Intermolecular Disulfide Bond Formation: A Coarse-Grained Molecular Dynamics Approach to Understanding Cofilin's Regulation on Actin Filaments.

Cofilin, a key actin-binding protein, orchestrates the dynamics of the actomyosin network through its actin-severing activity and by promoting the recycling of actin monomers. Recent experiments suggest that cofilin forms functionally distinct oligomers via thiol post-translational modifications (PTMs) that promote actin nucleation and assembly. Despite these advances, the structural conformations of cofilin oligomers that modulate actin activity remain elusive because there are combinatorial ways to oxidize thiols in cysteines to form disulfide bonds rapidly. This study employs molecular dynamics simulations to investigate human cofilin 1 as a case study for exploring cofilin dimers via disulfide bond formation. Utilizing a biasing scheme in simulations, we focus on analyzing dimer conformations conducive to disulfide bond formation. Additionally, we explore potential PTMs arising from the examined conformational ensemble. Using the free energy profiling, our simulations unveil a range of probable cofilin dimer structures not represented in current Protein Data Bank entries. These candidate dimers are characterized by their distinct population distributions and relative free energies. Of particular note is a dimer featuring an interface between cysteines 139 and 147 residues, which demonstrates stable free energy characteristics and intriguingly symmetrical geometry. In contrast, the experimentally proposed dimer structure exhibits a less stable free energy profile. We also evaluate frustration quantification based on the energy landscape theory in the protein-protein interactions at the dimer interfaces. Notably, the 39-39 dimer configuration emerges as a promising candidate for forming cofilin tetramers, as substantiated by frustration analysis. Additionally, docking simulations with actin filaments further evaluate the stability of these cofilin dimer-actin complexes. Our findings thus offer a computational framework for understanding the role of thiol PTM of cofilin proteins in regulating oligomerization, and the subsequent cofilin-mediated actin dynamics in the actomyosin network.

A macroscopic model for predicating stepwise crystalline swelling of montmorillonite

Bentonite, as an optimal buffer material in high-level radioactive waste (HLRW) repository, is usually unsaturated and sensitive to relative humidity (RH) conditions. Accurately modelling unsaturated crystalline swelling of bentonite is crucial for estimating the performance of a geological repository. In this study, we attempt to extend Laird model, a classical model for crystalline swelling, from saturated to unsaturated. For this purpose, a binary system that comprising air phase and water phase is introduced into Laird model, then the concept of effective diabattivity for atmosphere with a certain RH is proposed. On the basis, the net hydration repulsions under various RHs conditions can be determined. To calibrate the effective diavattivity, the oriented montmorillonite (Mt) tactoids is prepared and the RH controlled X-ray diffraction test is carried out. Moreover, a coupling approach of molecular dynamics (MD) simulation and fluctuation–dissipation dielectric theorem is employed to gain interlayer dielectric from dipolar fluctuations, which further improves the model accuracy. The novel model is validated by the experimental results for 3 typical buffer materials, including MX-80, Kunipia and FEBEX bentonites. Comparisons between the predicted and measured basal spacings indicate the proposed model can effectively capture the stepwise nature of crystalline swelling. Besides, the model also provides a quantitative approach to track the evolution of interaction energy during hydration process.

Investigating wave solutions and impact of nonlinearity: Comprehensive study of the KP-BBM model with bifurcation analysis.

In this paper, we investigate the (2+1)-dimensional Kadomtsev-Petviashvili-Benjamin-Bona Mahony equation using two effective methods: the unified scheme and the advanced auxiliary equation scheme, aiming to derive precise wave solutions. These solutions are expressed as combinations of trigonometric, rational, hyperbolic, and exponential functions. Visual representations, including three-dimensional (3D) and two-dimensional (2D) combined charts, are provided for some of these solutions. The influence of the nonlinear parameter p on the wave type is thoroughly examined through diverse figures, illustrating the profound impact of nonlinearity. Additionally, we briefly investigate the Hamiltonian function and the stability of the model using a planar dynamical system approach. This involves examining trajectories, isoclines, and nullclines to illustrate stable solution paths for the wave variables. Numerical results demonstrate that these methods are reliable, straightforward, and potent tools for analyzing various nonlinear evolution equations found in physics, applied mathematics, and engineering.

A novel and efficient Hamiltonian dynamic analysis approach for constraint force determination in flexible multibody systems

In the dynamic analysis of flexible multibody systems, Hamiltonian formulations offer advantages in numerical stabilization and systematic handling of systems with varying mass. However, current approaches face challenges. Differential algebraic equations (DAEs) can directly express constraint forces but are computationally inefficient, while ordinary differential equations (ODEs) are more computationally efficient but cannot directly represent constraint forces due to the elimination of the Lagrange multiplier. This paper presents an innovative and efficient dynamic analysis approach based on the Hamiltonian formulation, incorporating velocity transformation and open-constraint coordinate methods. Compared to conventional DAE-based Hamiltonian formulations, our approach conducts analysis efficiently using only independent variables. Compared to conventional ODE-based Hamiltonian formulations, our approach effectively expresses constraint forces through Lagrange multiplier reformulation. Furthermore, when compared to the traditional ODE-based Lagrangian formulation, our approach exhibits superior computational efficiency. Numerical simulations assess our proposed formulation, showing agreement with conventional formulations, shorter calculation time, and alignment with analytical results, confirming the accuracy and usefulness of our approach.

Virtual dynamic interaction games reveal impaired multisensory integration in women with migraine.

In this cross-sectional observational study, we aimed to investigate sensory profiles and multisensory integration processes in women with migraine using virtual dynamic interaction systems. Compared to studies on unimodal sensory processing, fewer studies show that multisensory integration differs in patients with migraine. Multisensory integration of visual, auditory, verbal, and haptic modalities has not been evaluated in migraine. A 12-min virtual dynamic interaction game consisting of four parts was played by the participants. During the game, the participants were exposed to either visual stimuli only or multisensory stimuli in which auditory, verbal, and haptic stimuli were added to the visual stimuli. A total of 78 women participants (28 with migraine without aura and 50 healthy controls) were enrolled in this prospective exploratory study. Patients with migraine and healthy participants who met the inclusion criteria were randomized separately into visual and multisensory groups: Migraine multisensory (14 adults), migraine visual (14 adults), healthy multisensory (25 adults), and healthy visual (25 adults). The Sensory Profile Questionnaire was utilized to assess the participants' sensory profiles. The game scores and survey results were analyzed. In visual stimulus, the gaming performance scores of patients with migraine without aura were similar to the healthy controls, at a median (interquartile range [IQR]) of 81.8 (79.5-85.8) and 80.9 (77.1-84.2) (p = 0.149). Error rate of visual stimulus in patients with migraine without aura were comparable to healthy controls, at a median (IQR) of 0.11 (0.08-0.13) and 0.12 (0.10-0.14), respectively (p = 0,166). In multisensory stimulation, average gaming score was lower in patients with migraine without aura compared to healthy individuals (median [IQR] 82.2 [78.8-86.3] vs. 78.6 [74.0-82.4], p = 0.028). In women with migraine, exposure to new sensory modality upon visual stimuli in the fourth, seventh, and tenth rounds (median [IQR] 78.1 [74.1-82.0], 79.7 [77.2-82.5], 76.5 [70.2-82.1]) exhibited lower game scores compared to visual stimuli only (median [IQR] 82.3 [77.9-87.8], 84.2 [79.7-85.6], 80.8 [79.0-85.7], p = 0.044, p = 0.049, p = 0.016). According to the Sensory Profile Questionnaire results, sensory sensitivity, and sensory avoidance scores of patients with migraine (median [IQR] score 45.5 [41.0-54.7] and 47.0 [41.5-51.7]) were significantly higher than healthy participants (median [IQR] score 39.0 [34.0-44.2] and 40.0 [34.0-48.0], p < 0.001, p = 0.001). The virtual dynamic game approach showed for the first time that the gaming performance of patients with migraine without aura was negatively affected by the addition of auditory, verbal, and haptic stimuli onto visual stimuli. Multisensory integration of sensory modalities including haptic stimuli is disturbed even in the interictal period in women with migraine. Virtual games can be employed to assess the impact of sensory problems in the course of the disease. Also, sensory training could be a potential therapy target to improve multisensory processing in migraine.

Federated learning in smart home: A dynamic contract-based incentive approach with task preferences

Federated Learning (FL) is a new type of distributed machine learning paradigm aimed at addressing data privacy and security issues in the Internet of Things (IoT) context. In the training process of federated learning model of smart home, data owners need to constantly update cached data, which lead to resources consumption and service latency. Meanwhile, the update cost of the data owners will change with the passage of time when the model owner requests data update. Therefore, this paper first puts forward an incentive scheme based on two-period dynamic contract theory under the information asymmetry. The scheme can balance the weighted preference of the model owner for age of information (AoI) and service latency, as well as encourage more data owners to participate in model training to increase the utility of model owner. Then we formally prove the feasibility of the proposed dynamic contract, which satisfies the constraints of individual rationality and intertemporal incentive compatibility. The experimental results on MNIST dataset show that the accuracy of the proposed dynamic contract is improved by at least 4% compared with the existing contracts. Additionally, compared with traditional contract and consistent pricing strategy, the model owner obtains more profit from the proposed contract.

Capacity optimization of independent hybrid renewable energy system under different operational strategies based on improved gray wolf algorithm

Renewable energy sources such as wind and solar power exhibit strong stochasticity and volatility, resulting in decreased power supply security and sustainability. A strategically optimized hybrid renewable energy system (HRES) is crucial for maintaining stable load operations and achieving sustainable energy development. This paper introduces an energy optimization management model for an independent HRES consisting of wind turbines, photovoltaic systems, diesel generators, and energy storage units. Operational strategies focus on energy storage-led loads following diesel generator-led load prioritizations. The model aims to optimize objectives to include economic, environmental, and power supply reliability indices. A dynamic adaptive parameter approach balances the parameters of the objective function at various instances. The optimal capacity allocation of the model is solved using the improved gray wolf optimization (IGWO) algorithm. This approach incorporates the golden sine strategy, the levy flight strategy, and the dynamic inverse learning strategy into the traditional GWO algorithm. Analyzing different test functions, evaluation metrics, and actual load data indicates that the proposed algorithm excels in global optimization capabilities and search speeds. The model significantly reduces the economic and environmental costs of the HRES microgrids and improves the sustainable development of renewable energy in various scenarios.

Dynamic control of district heating networks with integrated emission modelling: A dynamic knowledge graph approach

This paper presents a knowledge graph-based approach for the dynamic control of a district heating network with integrated emission dispersion modelling. We propose an interoperable and extensible implementation to forecast the anticipated heat demand of a municipal heating network, minimise associated total generation cost based on a set of available heat sources, and couple it with dispersion modelling of corresponding emissions to provide automatic insights into air quality implications of various heat sourcing strategies. We achieve cross-domain insights in the nexus of energy and air quality via a set of developed ontologies and autonomous software agents, which can be chained together via the World Avatar dynamic knowledge graph to resemble the behaviour of complex systems. Furthermore, we have integrated the City Energy Analyst into this ecosystem to provide building-level insights into energy demand and generation potential to foster strategic analyses and scenario planning. The calculations use building and weather data from the knowledge graph in place of the inherent assumptions in the official software release, facilitating a more data-driven approach. All use cases are implemented for a mid-size town in Germany as a proof-of-concept, and a unified visualisation interface is provided, allowing for the examination of 3D buildings alongside their corresponding energy demand and supply time series, as well as emission dispersion data. With this work, we outline the potential of Semantic Web technologies to connect digital twins for holistic energy modelling in smart cities, thereby addressing the increasing complexity of interconnected energy systems.

Dynamic and spatial approaches to assess the impact of geographical indications on rural areas

The paper explores the role of Geographical Indications (GIs) in promoting the economic development of European regions. We consider all NUTS3 regions of Italy, France, and Spain between 1993 and 2014, and 728 GIs.. Our research aims to empirically assess the impacts of GIs on labor productivity and employment, for the agricultural and industrial sectors. We rely on a dynamic panel model and considere the spatial variation of the data. The main results show that GIs generate a positive impact on employment, both in the short and the long-run. Moreover, we find that the impact of GIs is not limited to the province where they are produced, but also triggers sizable spillover effects. Our results have important policy implications for further economic research.

Ecotourism or ecological concerns? Tracing the impact of economic policy uncertainty and natural resources on tourism-driven ecological footprints.

The influence of tourism development and economic policy uncertainties on environmental sustainability is substantial. Promoting responsible tourism and using sustainable tourism practises, like offering eco-friendly lodging, is a key part of protecting natural habitats and lowering carbon footprints. Hence, this study tries to examine the relationship between tourism development, economic policy uncertainty, renewable energy, and natural resources on the ecological footprint of India during 1990-2022. This study applies a novel dynamic ARDL simulation approach for long-run and short-run analyses. The study also employs frequency-domain causality to check the causal relationship between the variables. The result reveals that tourism has a positive effect on the ecological footprint. Similarly, economic policy uncertainty has a positive and significant effect on the ecological footprint in India during the sample period. Additionally, natural resource rent shows a positive effect on the ecological footprint or deteriorating environmental quality in the short and long run in the sample period. However, renewable energy consumption indicates a negative effect on the ecological footprint. The results reveal that TDI and EPU have rejected the null hypothesis of no Granger cause in the long, medium, and short term. While renewable energy has a causal relationship with ecological footprints in both the long run and medium run, it is imperative for India to adopt measures that facilitate the advancement of sustainable tourism, with a particular focus on promoting environmentally friendly lodging options, enhancing public transportation systems, and implementing effective waste management strategies.

Hydrodynamic characterization of bubble column using Dynamical High Order Decomposition approach

Bubble columns play a crucial role in a wide range of industries, including chemical and biochemical processes, petrochemicals, and environmental engineering. Understanding the dynamics of bubble columns is essential for optimizing their performance in various applications. This study proposes a data-driven approach for analyzing the dynamics of a two-dimensional bubble column system. We conducted simulations with varying superficial velocities and generated a comprehensive training dataset encompassing the entire velocity and pressure fields to achieve this. We then compared the performance of two approaches, the Fast Fourier Transformation (FFT) and the High-Order Dynamic Mode Decomposition (HODMD), in representing the system’s dynamics. Our findings demonstrate that the conventional FFT approach fails to adequately capture the complex dynamics of the dispersed multiphase flow system. This limitation arises due to the distribution of frequencies along the domain. Conversely, our work highlights the success of the HODMD method in accurately representing the system’s dynamics using only a few arbitrary sampling points within the domain. The implications of this study are significant, as it sheds light on the potential benefits of employing HODMD for analyzing bubble column dynamics. By utilizing this approach, industrial processes can be optimized more effectively across various applications.

Quantification of functional hemodynamics in aortic valve disease using cardiac computed tomography angiography

Background and Objective:Cardiac computed tomography angiography (CTA) is the preferred modality for preoperative planning in aortic valve stenosis. However, it cannot provide essential functional hemodynamic data, specifically the mean transvalvular pressure gradient (MPG). This study aims to introduce a computational fluid dynamics (CFD) approach for MPG quantification using cardiac CTA, enhancing its diagnostic value. Methods:Twenty patients underwent echocardiography, cardiac CTA, and invasive catheterization for pressure measurements. Cardiac CTA employed retrospective electrocardiographic gating to capture multi-phase data throughout the cardiac cycle. We segmented the region of interest based on mid-systolic phase cardiac CTA images. Then, we computed the average flow velocity into the aorta as the inlet boundary condition, using variations in end-diastolic and end-systolic left ventricular volume. Finally, we conducted CFD simulations using a steady-state model to obtain pressure distribution within the computational domain, allowing for the derivation of MPG. Results:The mean value of MPG, measured via invasive catheterization (MPGInv), echocardiography (MPGEcho), and cardiac CTA (MPGCT), were 51.3 ± 28.4 mmHg, 44.8 ± 19.5 mmHg, and 55.8 ± 25.6 mmHg, respectively. In comparison to MPGInv, MPGCT exhibited a higher correlation of 0.91, surpassing that of MPGEcho, which was 0.82. Moreover, the limits of agreement for MPGCT ranged from −27.7 to 18.7, outperforming MPGEcho, which ranged from −40.1 to 18.0. Conclusions:The proposed method based on cardiac CTA enables the evaluation of MPG for aortic valve stenosis patients. In future clinical practice, a single cardiac CTA examination can comprehensively assess both the anatomical and functional hemodynamic aspects of aortic valve disease.

Dynamic control of district heating networks with integrated emission modelling: A dynamic knowledge graph approach

This paper presents a knowledge graph-based approach for the dynamic control of a district heating network with integrated emission dispersion modelling. We propose an interoperable and extensible implementation to forecast the anticipated heat demand of a municipal heating network, minimise associated total generation cost based on a previously devised methodology, and couple it with dispersion simulations for induced airborne pollutants to provide automatic insights into air quality implications of various heat sourcing strategies. We create cross-domain interoperability in the nexus of energy and air quality via newly developed ontologies and semantic software agents, which can be chained together via The World Avatar dynamic knowledge graph to resemble the behaviour of complex systems. Furthermore, we integrate the City Energy Analyst into this ecosystem to provide building-level insights into energy demand and renewable generation potential to foster strategic analyses and scenario planning. Underlying calculations use building and weather data from the knowledge graph in place of inherent assumptions in the official software release, facilitating a more data-driven approach. All use cases are implemented for a mid-size town in Germany as a proof-of-concept, and a unified visualisation interface is provided, allowing for the examination of 3D buildings alongside their corresponding energy demand and supply time series, as well as emission dispersion data. With this work, we outline the potential of Semantic Web technologies to connect digital twins for holistic energy modelling in smart cities, thereby addressing the increasing complexity of interconnected energy systems.

Dynamic bargaining game DEA carbon emissions abatement allocation and the Nash equilibrium

Allocating carbon emissions abatement (CEA) is crucial for reducing carbon footprint of organizations (or decision-making units, DMUs) and mitigating global warming. However, discrepancies often arise among DMUs regarding CEA allocation. This study introduces a dynamic bargaining game approach to address the issue under the nonparametric production frontier analysis framework. Our approach employs an iterative process, allowing each DMU to propose its individual preferred CEA allocation proposal in each iteration. Subsequently, dynamic negotiations among the DMUs occur, leading to the eventual consensus on the CEA allocation result. Our theoretical analysis demonstrates that, at the end of the dynamic bargaining game, all DMUs will converge on the same CEA allocation result, termed as the consensus CEA allocation result. This agreement is reached despite each DMU customizing its individual CEA allocation proposal to suit its own interests. Moreover, we prove that this consensus CEA allocation result represents a Nash equilibrium solution, thereby ensuring its stability and acceptance among all DMUs. Finally, we provide a simple numerical example and a case study of CEA allocation across 27 European Union countries to illustrate the usefulness of our approach and compare it with prior CEA allocation approaches.

Race, Racism, and Ethnicity

This article examines the debates over historiographical uses of the terms race, racism, and ethnicity in the study of the premodern past. It begins with an analysis of conventional approaches to defining race, which understand the concept to be fundamentally modern and reject uses of the term in the study of the premodern world. Such conventional approaches to historicizing race are predicated on problematically essentialist definitions of the term that preclude comparisons of varying types of racisms that manifest across different historical periods. The article spotlights some of the arguments in favor of formulations of race and racism that allow for comparative analyses of premodern instances of racism, relying on the ideas of scholars working in race theory, philosophy of race, and premodern critical race studies. It also puts forward a working model of racism useful for analyzing examples of race-thinking found in Christian texts of the late antique period, focusing specifically on negative significations of blackness. Broadly, the article aims to outline more dynamic and fruitful approaches to conceptualizing race, racism, and ethnicity in the study of the ancient world.

Enhancing airless tire performance for military vehicles: natural rubber compound with carbon black Fillers N220 and N550 with dynamic mechanical analysis approach

Enhancing airless tire performance for military vehicles: natural rubber compound with carbon black Fillers N220 and N550 with dynamic mechanical analysis approach

Simulating the corrective actions affecting system availability: a system dynamics approach

Purpose This paper aims to simulate vital corrective actions (CAs) affecting system availability through a system dynamics approach based on the results obtained by analyzing the causal relationships among failure modes and effects analysis elements. Design/methodology/approach A stock and flow diagram has been developed to simulate system behaviors during a timeframe. Some improvement scenarios regarding the most necessary CAs according to their strategic priority and the possibility of eliminating root causes of critical failure modes in a roller-transmission system have been simulated and analyzed to choose the most effective one(s) for the system availability. The proposed approach has been examined in a steel-manufacturing company. Findings Results indicated the most effective CAs to remove or diminish critical failure causes that led to the less reliability of the system. It illustrated the impacts of the selected CAs on eliminating or decreasing root causes of the critical failure modes, lessening the system’s failure rate and increasing the system availability more effectively. Research limitations/implications Results allow managers and decision-makers to consider different maintenance scenarios without wasting time and more cost, choosing the most appropriate option according to system conditions. Originality/value This study innovation would be the dynamic analysis of interactions among failure modes, effects and causes over time to predict the system behavior and improve availability by choosing the most effective CAs through improvement scenario simulation via VENSIM software.

A holistic understanding of optical properties in amorphous H-terminated Si-nanostructures: Combining TD-DFT with AIMD

Silicon, traditionally known as an indirect band gap semiconductor, unveils intriguing properties at the nanoscale, stemming from deviations from k-conservation rules within nanostructures. In our study, we scrutinized four hydrogenated Si 0D-nanostructures—Si10H16, Si14H20, Si18H24, and Si22H28—to unravel their dynamic stability under thermal fluctuations and optical characteristics. We initiated our exploration by employing the TD-DFT framework to generate and analyze the optical properties of these geometrically optimized nanostructures. Simultaneously, we conducted ab initio molecular dynamics simulations to examine the structural robustness and thermal stability of the four structures. Leveraging the Car-Parrinello molecular dynamics approach within the Quantum ESPRESSO open software suite, we observed temperature evolution and stability differences among the nanostructures at targeted temperatures 40 and 300 K. Our subsequent investigation delved into the Turbo-Lanczos time-dependent DFT method, unraveling the optical properties and excited-state dynamics of hydrogenated Si nanostructures. The results unveiled shifts towards higher energy absorption edges E0, accompanied by alterations in the permittivity tensor, complex refractive index, oscillator strength, and reflectivity. Notably, the analysis revealed an enlarged HOMO-LUMO gap, distinctive from bulk Si. Furthermore, our models predicted the elimination of phase-dependent E1/E2 optical transition peaks in the imaginary part of the dielectric function, and a gradual decrease in the low-frequency dielectric response with increased hydrogenation of the amorphous structures. These findings underscore the promising applications of hydrogenating Si nanostructures in diverse technological domains such as optoelectronics, memristors, sensors, and quantum computing. Their tunable optical properties, size-dependent behaviors, and compatibility with existing silicon-based devices make them particularly appealing for next-generation technologies.

Spatiotemporal patterns corresponding to phase synchronization and generalized synchronization states of thermoacoustic instability.

Thermoacoustic instability in turbulent combustion systems emerges from the complex interplay among the flame, flow, and acoustic subsystems. While the onset of thermoacoustic instability exhibits a global order, the characteristics of local interactions between subsystems responsible for this order are not well understood. Here, we utilize the framework of synchronization to elucidate the spatiotemporal interactions among heat release rate fluctuations in the flame, velocity fluctuations in the flow, and acoustic pressure fluctuations in a turbulent combustor, across the bluff-body stabilized flame. We examine two forms of thermoacoustic instability, characterized by phase synchronization and generalized synchronization of the acoustic pressure and global heat release rate oscillations. Despite the presence of global synchrony, we uncover a coexistence of frequency synchrony and desynchrony in the local interaction of these oscillations within the reaction field. In regions of frequency-locked oscillations, various phase-locking patterns occur, including phase synchrony and partial phase synchrony. We observe that the local formation of small pockets of phase synchrony and strong amplitude correlation between these oscillations is sufficient to trigger the state of global phase synchronization. As the global dynamics approach generalized synchronization, these local regions of synchrony expand in the reaction field. Additionally, through coupled analysis of acoustic pressure and local flow velocity fluctuations, we infer that the spatial region of flow-acoustic synchrony plays a significant role in governing thermoacoustic instabilities. Our findings imply that, in turbulent combustors, an intrinsic local balance between order, partial order, and disorder within the coupled subsystems sustains the global order during thermoacoustic instability.

Exploring a pathway to optimise the carbon tax policy in terms of the economy, the environment and health: A scenario-based system dynamics approach

The carbon tax, a pivotal policy instrument in tackling climate change, holds the potential to significantly influence the development of society. To comprehensively analyse the effectiveness of the policy on carbon taxation and explore its possible optimisation path, this study utilizes system dynamics theory to establish a simulation model. A detailed analysis and evaluation of this policy is then conducted from the perspectives of the economy, the environment, and health. To guarantee the precision of the simulation model, a new, comprehensive evaluation method is proposed, which can test the degree of fit of the relative trend and absolute data of the simulation model with reality. The findings reveal that, despite its negative economic implications, a carbon tax policy has positive ramifications for the environment, energy, health, industrial structure, and carbon intensity targets. Furthermore, the synergistic reinforcement effect of R&D and new energy support policies on carbon taxation surpasses the impact of any individual policy alone. Notably, the influence of auxiliary policies has a temporal difference on this policy. Based on these insights, the study concludes with practical policy recommendations.