Dynamic Hybrid Research Articles

Switching mechanism of dynamic hybrid control multi-agent system in manufacturing environment

Switching mechanism of dynamic hybrid control multi-agent system in manufacturing environment

A machine learning and CFD modeling hybrid approach for predicting real-time heat transfer during cokemaking processes

As the development of green energy and smart industries progresses, concerted efforts are being directed towards the transformation of coking industries. Effective management of heat transfer during the coking process is essential to fortify energy consumption management and augment the quality of coke produced. However, the process during cokemaking is a complex thermochemical conversion process where even minor deviations in operating conditions can lead to instability. Additionally, due to the environmental and chemical conditions inside the coke oven, temperature measurements during operation are difficult. As the fields of Industry 4.0 continue to evolve, numerous industries have begun to leverage intelligent strategies to enhance advanced process management. The digital twin (DG) technique stands as a pivotal technology in the realm of industrial transformation, paving the way for a smarter future. However, given the inherent difficulty in real-time data collection for process operations, the challenge of achieving synchronization becomes increasingly prevalent in the development of DG for the smart coking process. As a result, predicting changes in coal temperature during the cokemaking process is crucial. Currently, computer technology allows for the simulation of complex chemical processes. While some numerical models describe the cokemaking process, these models are limited by the time-consuming characteristic and computing resources. To address this issue, this study proposed a Machine Learning (ML) and Computational Fluid Dynamics (CFD) hybrid model for real-time prediction of heat transfer during the cokemaking process. A CFD model was built based on an industrial-scale coke oven, with cokemaking process data generated through CFD simulation serving as input for the ML models. This study used a total of nine ML models, including ensemble and deep learning models. The hyperparameters of each model were optimized by Grey Wolf Optimization (GWO), Particle Swarm Optimization (PSO), and JAYA algorithms to identify the optimal model structure. Both simulation and empirical experimentation using a coke oven, coupled with industrial data verification, have substantiated the superiority of the proposed model. It simulates the heat transfer during the coking process with high accuracy and efficiency, thereby demonstrating its capacity to significantly reduce energy consumption within the coking industry.

MDC-RHT: Multi-Modal Medical Image Fusion via Multi-Dimensional Dynamic Convolution and Residual Hybrid Transformer.

The fusion of multi-modal medical images has great significance for comprehensive diagnosis and treatment. However, the large differences between the various modalities of medical images make multi-modal medical image fusion a great challenge. This paper proposes a novel multi-scale fusion network based on multi-dimensional dynamic convolution and residual hybrid transformer, which has better capability for feature extraction and context modeling and improves the fusion performance. Specifically, the proposed network exploits multi-dimensional dynamic convolution that introduces four attention mechanisms corresponding to four different dimensions of the convolutional kernel to extract more detailed information. Meanwhile, a residual hybrid transformer is designed, which activates more pixels to participate in the fusion process by channel attention, window attention, and overlapping cross attention, thereby strengthening the long-range dependence between different modes and enhancing the connection of global context information. A loss function, including perceptual loss and structural similarity loss, is designed, where the former enhances the visual reality and perceptual details of the fused image, and the latter enables the model to learn structural textures. The whole network adopts a multi-scale architecture and uses an unsupervised end-to-end method to realize multi-modal image fusion. Finally, our method is tested qualitatively and quantitatively on mainstream datasets. The fusion results indicate that our method achieves high scores in most quantitative indicators and satisfactory performance in visual qualitative analysis.

Study on the size effect of dynamic tensile strength in lightweight concrete using Leca aggregates at the mesoscopic level

To investigate the influence of volume fraction of lightweight aggregate concrete on dynamic tensile strength and size effect, theoretical derivation and microscopic numerical simulation are combined in this study, and a dynamic hybrid fracture cohesive zone constitutive model for lightweight aggregate concrete to characterize the tensile behavior considering strain rate at microscopic scale. The research reveals that, for geometrically similar specimens of various sizes, the direct tensile strength with different aggregate volume fractions exhibits differing degrees of improvement with increasing strain rates. The tensile strength gradually decreases as volume fraction increases. The established coupling function incorporating strain rate, specimen size, and aggregate volume fraction accurately characterizes the dynamic tensile strength variation. In addition, a theoretical framework is proposed to provide a microscopic understanding of the static-dynamic size effect. This framework facilitates the estimation of the dynamic tensile strength under specific volume fraction, specimen size and strain rate conditions.

Simulating the efficacy of wolf-dog hybridization management with individual-based modeling.

Introgressive hybridization between wolves and dogs is a conservation concern due to its potentially deleterious long-term evolutionary consequences. European legislation requires that wolf-dog hybridization be mitigated through effective management. We developed an individual-based model (IBM) to simulate the life cycle of gray wolves that incorporates aspects of wolf sociality that affect hybridization rates (e.g., the dissolution of packs after the death of one/both breeders) with the goal of informing decision-making on management of wolf-dog hybridization. We applied our model by projecting hybridization dynamics in a local wolf population under different mate choice and immigration scenarios and contrasted results of removal of admixed individuals with their sterilization and release. In several scenarios, lack of management led to complete admixture, whereas reactive management interventions effectively reduced admixture in wolf populations. Management effectiveness, however, strongly depended on mate choice and number and admixture level of individuals immigrating into the wolf population. The inclusion of anthropogenic mortality affecting parental and admixed individuals (e.g., poaching) increased the probability of pack dissolution and thus increased the probability of interbreeding with dogs or admixed individuals and boosted hybridization and introgression rates in all simulation scenarios. Recognizing the necessity of additional model refinements (appropriate parameterization, thorough sensitivity analyses, and robust model validation) to generate management recommendations applicable in real-world scenarios, we maintain confidence in our model's potential as a valuable conservation tool that can be applied to diverse situations and species facing similar threats.

Consensus reaching with the dynamic hybrid trust network in group decision making based on an extensive bounded confidence

This study focuses on the consensus-reaching process (CRP) in social networks. Over the years, ample research has been conducted on CRP levels. However, most feedback mechanisms do not consider the decision maker’s (DM) willingness to accept the recommendations provided by these mechanisms. Therefore, this study proposes an extensive bounded confidence-based group consensus feedback mechanism that primarily consists of two parts: (a) the construction of a hybrid dynamic trust network and (b) the construction of an extensive bounded confidence model. In the first part, we propose an algorithm to calculate the importance of network nodes and apply it to the trust propagation and aggregation process. In the second part, we propose an extensive bounded confidence model to address the limitations of existing traditional bounded confidence models. In the proposed extensive bounded confidence model, the trust threshold is no longer a “switch” to determine whether a DM interacts with other DMs but a basis to determine the strength of the DMs’ interaction; moreover, the hybrid dynamic trust relationship will determine the negotiation sets. A practical case of site selection and comparative analysis verify the effectiveness and flexibility of the proposed mechanism.

Enhanced energy harvesting from random excitations: A dynamic amplifier-augmented hybrid bistable piezoelectric energy harvester

In the energy harvesting field, it is critical to efficiently convert ambient vibrations into usable electrical energy, especially under conditions characterized by low amplitude and random excitations. This study introduces a novel hybrid bistable piezoelectric energy harvester (HBPEH) designed to optimize energy conversion in challenging environmental conditions. The HBPEH features a unique piezoelectric cantilever beam integrated with a dynamic amplifier at the fixed end, significantly enhancing both rotational and lateral displacements of the beam. This innovation lowers the threshold for snap-through events, crucial for maximizing energy transfer in bistable systems, and enables superior energy harvesting capabilities. Furthermore, the HBPEH demonstrates significantly higher power outputs when subjected to excitation intensities beyond critical levels, outperforming conventional energy harvesters. Extensive numerical and theoretical analyses confirm that the HBPEH offers a promising solution for applications hindered by suboptimal energy conversion rates. Its ability to operate effectively under diverse and unpredictable excitation conditions underscores the HBPEH's robustness and superiority in advanced energy harvesting scenarios. The design improvements introduced in this study highlight the potential of the HBPEH to transform energy harvesting approaches. By addressing the limitations of traditional harvesters, the HBPEH provides enhanced efficiency and reliability, making it suitable for a broader range of practical applications in variable environmental conditions.

A dynamic hybrid model for efficient tuberculosis incidence rate prediction

ABSTRACT Tuberculosis remains a global health challenge, predicting its incidences is crucial for effective planning and intervention strategies. This study combines AutoRegressive Integrated Moving Average (ARIMA) and Nonlinear AutoRegressive with exogenous input (NARX) models as an innovative approach for TB incidence rate prediction. The performance of the proposed model (ARIMA-NARX) was evaluated using standard metrics (MSE, RMSE, MAE, and MAPE), and it was refined to achieve the best average predictive accuracies with an MSE: 0.0622, RMSE: 0.0851, MAE: 0.07520, and MAPE: 0.05535 followed by NARX 0.1597, 0.3189, 0.2724, and 0.3366, and ARIMA (2,0,0) 0.7781, 0.5959, 0.6524, and 0.6080 Models. These findings are expected to shed light on the TB incidence rate, providing valuable information to policymakers such as the World Health Organization (WHO) and health professionals. The developed model can potentially serve as a predictive tool for proactive TB control and intervention strategies in the region and the world at large.

From dynamic molecular bridging to achieving Healable, recyclable, photo/magnetic-responsive shape memory EVA@Fe3O4 hybrid network

Dynamic networks show great potential in shape memory polymers (SMPs) not only because they can display excellent shape memory effects (SMEs), but also possess good recyclability and reprocessability due to their dynamic features. Additionally, multiple responsiveness is also an attractive characteristic for SMPs to meet diverse applications. while incorporating functional fillers into SMPs has been explored as a feasible solution, their performance relies heavily on the interface interactions. In this study, we present a facile strategy to construct a dynamic hybrid network that combines excellent multi-responsive SMEs and recyclability. This is achieved by using dihydroxyphenylpropionic acid (CA) to bridge EVA chains and multi-responsive Fe3O4 particles through transesterification and metal-ligand coordination. The hybrid networks exhibited favorable dynamic characteristics. Specifically, EVA-CA@Fe3O410% demonstrates remarkable thermal and light healing efficiency, exhibiting a mechanical strength recovery (ησ) of 102% and elongation at break recovery (ηε) of 95% after healing at 140 °C for 2 h, and a ησ of 107% and ηε of 92% when healing under near-infrared light (NIR) irradiation for 2 min, respectively. Furthermore, good recyclability was demonstrated by maintaining mechanical performance after three reprocessing cycles. Moreover, the hybrid networks exhibited excellent SMEs, enabling multifunctional deformation triggered by heat, NIR and alternating magnetic fields.

Class-Incremental Recognition of Objects in Remote Sensing Images With Dynamic Hybrid Exemplar Selection

Class-Incremental Recognition of Objects in Remote Sensing Images With Dynamic Hybrid Exemplar Selection

Saltation Matrices: The Essential Tool for Linearizing Hybrid Dynamical Systems

Saltation Matrices: The Essential Tool for Linearizing Hybrid Dynamical Systems

Impact of nuclear effects on the ultrafast dynamics of an organic/inorganic mixed-dimensional interface

Electron dynamics at weakly bound interfaces of organic/inorganic materials are easily influenced by large-amplitude nuclear motion. In this work, we investigate the effects of different approximations to the equilibrium nuclear distributions on the ultrafast charge-carrier dynamics of a laser-excited hybrid organic/inorganic interface. By considering a prototypical system consisting of pyrene physisorbed on a MoSe2 monolayer, we analyze linear absorption spectra, electronic density currents, and charge-transfer dynamics induced by a femtosecond pulse in resonance with the frontier-orbital transition in the molecule. The calculations are based on ab initio molecular dynamics with classical and quantum thermostats, followed by time-dependent density-functional theory coupled to multi-trajectory Ehrenfest dynamics. We impinge the system with a femtosecond (fs) pulse of a few hundred GW cm−2 intensity and propagate it for 100 fs. We find that the optical spectrum is insensitive to different nuclear distributions in the energy range dominated by the excitations localized on the monolayer. The pyrene resonance, in contrast, shows a small blue shift at finite temperatures, hinting at an electron-phonon-induced vibrational-level renormalization. The electronic current density following the excitation is affected by classical and quantum nuclear sampling through suppression of beating patterns and faster decay times. Interestingly, finite temperature leads to a longer stability of the ultrafast charge transfer after excitation. Overall, the results show that the ultrafast charge-carrier dynamics are dominated by electronic rather than by nuclear effects at the field strengths and time scales considered in this work.

Exploring interspecific hybridization dynamics in artificial forests of Pinus brutia and P. halepensis: Implications for sustainable afforestation.

Interspecific hybridization increases genetic diversity, which is essential for coping with changing environments. Hybrid zones, occurring naturally in overlapping habitats of closely related species, can be artificially established during afforestation. The resulting interspecific hybridization may promote sustainability in artificial forests, particularly in regions facing degradation due to climate change. Currently, there is limited evidence of hybridization during regeneration of artificial forests. Here, we studied the frequency of Pinus brutia Ten. × P. halepensis Mill. hybridization in five planted forests in Israel in three stages of forest regeneration: seeds before dispersal, emerged seedlings and recruited seedlings at the end of the dry season. We found hybrids on P. brutia, but not on P. halepensis trees due to asynchronous cone production phenology. Using 94 single-nucleotide polymorphism (SNP) markers, we found hybrids at all stages, most of which were hybrids of advanced generations. The hybrid proportions increased from 4.7 ± 2.1 to 8.2 ± 1.4 and 21.6 ± 6.4 per cent, from seeds to emerged seedlings and to recruited seedlings stages, respectively. The increased hybrid ratio implies an advantage of hybrids over P. brutia during forest regeneration. To test this hypothesis, we measured seedling growth rate and morphological traits under controlled conditions and found that the hybrid seedlings exhibited selected traits of the two parental species, which likely contributed to the fitness and survival of the hybrids during the dry season. This study highlights the potential contribution of hybrids to sustainable-planted forests and contributes to the understanding of genetic changes that occur during the regeneration of artificial forests.

Numerical Simulation Analysis of the Correlation between Hydrodynamic Noise of Hydraulic Turbines and Defects in Runner Blades

To investigate the hydrodynamic noise characterization of hydraulic turbines with runner blade defects, this article establishes the intact machine model and three kinds of models with runner blade defects. Using the Computational Fluid Dynamic (CFD) and Computational Acoustic (CA) hybrid simulation computational methods, the hydrodynamic noise field of the hydraulic turbine is numerically simulated, and the results of the acoustic near field and acoustic far field are shown. 1. The double-row leaf grille and the runner are the primary sound source areas of the hydraulic turbine, and the intensity of sound radiation from these areas is positively correlated with the degree of runner blade defects. 2. As the runner blade defects develop, the sound power level (SWL) increases more significantly in the guide vanes near the nose of the spiral case in the double-row leaf grille. The most pronounced increase in the SWL is observed at the defective craters on the runner blades. 3. The frequency of the defective noise signal is primarily concentrated in the low-frequency band. The dominant frequency amplitude associated with runner blade defects increases and rises after the occurrence of defects. Secondary frequency changes are also observed, and the location of these changes varies at different receiving points.

A Dynamic Hierarchical Improved Tyrannosaurus Optimization Algorithm with Hybrid Topology Structure

Aiming at the problems of the Tyrannosaurus optimization algorithm, of poor search accuracy, insufficient global search capability, and ease of falling into local optimality, a dynamic hierarchical improved Tyrannosaurus optimization algorithm (DHTROA) with hybrid topology structure is proposed. Initially, a chaotic opposition-based learning approach is selected to start the population, ensuring a more uniform distribution of prey across the solution area and boosting population diversity; later, a dynamic hybrid bi-population strategy is introduced to divide the initial population into an ‘advantaged group’ and a ‘disadvantaged group’ to improve the efficiency of individual information exchange. Finally, the ‘advantaged group’ and ‘disadvantaged group’ are hunted synchronously; for the ‘advantaged group’, the position update is carried out using the cellular ring topology strategy, and for the ‘disadvantaged group’, the original algorithm is run in accordance with the main loop process. For the problem of the constant running rate of the Tyrannosaurus in the original algorithm, an adaptive running rate strategy is proposed, which enhances the ability of global optimization, and at the same time, the shortcomings of the original algorithm’s ‘failure’ strategy are improved in order to enhance the original algorithm to jump out of extrema. DHTROA was tested for performance with nine optimization algorithms in different dimensions of the CEC2017 test function. The efficiency of these enhancements was confirmed through the Wilcoxon rank sum test and Friedman test, while DHTROA was utilized for six engineering optimization challenges of differing complexities. The experimental results show that DHTROA has improved greatly in convergence speed, optimality search accuracy, global search ability, and stability, and the excellent engineering optimization performance also proves the excellent robustness of DHTROA.

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

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

Nanoarchitectonic Engineering of Thermal-Responsive Magnetic Nanorobot Collectives for Intracranial Aneurysm Therapy.

Stent-assisted coiling is a main treatment modality for intracranial aneurysms (IAs) in clinics, but critical challenges remain to be overcome, such as exogenous implant-induced stenosis and reliance on antiplatelet agents. Herein, an endovascular approach is reported for IA therapy without stent grafting or microcatheter shaping, enabled by active delivery of thrombin (Th) to target aneurysms using innovative phase-change material (PCM)-coated magnetite-thrombin (Fe3O4-Th@PCM) FTP nanorobots. The nanorobots are controlled by an integrated actuation system of dynamic torque-force hybrid magnetic fields. With robust intravascular navigation guided by real-time ultrasound imaging, nanorobotic collectives can effectively accumulate and retain in model aneurysms constructed in vivo, followed by controlled release of the encapsulated Th for rapid occlusion of the aneurysm upon melting the protective PCM (thermally responsive in a tunable manner) through focused magnetic hyperthermia. Complete and stable aneurysm embolization is confirmed by postoperative examination and 2-week postembolization follow-up using digital subtraction angiography (DSA), contrast-enhanced ultrasound (CEUS), and histological analysis. The safety of the embolization therapy is assessed through biocompatibility evaluation and histopathology assays. This strategy, seamlessly integrating secure drug packaging, agile magnetic actuation, and clinical interventional imaging, avoids possible exogenous implant rejection, circumvents cumbersome microcatheter shaping, and offers a promising option for IA therapy.

Kinematic accuracy reliability evaluation method of space deployable mechanism based on dynamic model

Space deployable mechanisms are widely used in aerospace missions. The uncertainties in the geometric or physical parameters seriously affect the kinematic accuracy of the mechanism during the deployment process. In this paper, a kinematic accuracy reliability evaluation method of the space deployable mechanism based on dynamic model and adaptive hybrid surrogate models is presented. Firstly, the dynamic model of the space deployable mechanism is established based on the Lagrange method, and the motion error function considering the factors such as the mass, dimension and load is established. In order to improve the efficiency, an adaptive hybrid surrogate model reliability evaluation method based on improved weight measurement method is further proposed. The weight measurement method considers the global errors and local errors of the models. Finally, the Monte Carlo method is used to verify the accuracy of the hybrid surrogate model method. The relative error of the failure probability calculated by the proposed method is within 2.5 %, indicating that the proposed method has high calculation accuracy and efficiency.

A hybrid dynamics modeling method for micro-turbine engine thrust vectoring

Thrust vector control technology is widely used in modern advanced aircrafts, space shuttles and unmanned aerial vehicles, etc. An accurate thrust vector mathematical model is of great significance to enhance the thrust vector control effect. In this paper, the P200 micro-turbine engine with an axisymmetric thrust vector nozzle placed at the rear is taken as an example, A hybrid thrust vectoring dynamics modeling method consisting of engine speed model, engine thrust model, actuator dynamics model, airflow angle model and thrust loss coefficients is proposed. The engine speed model is of Linear Parameter Varying (LPV) model with each segment is characterized by a First Oder Plus Dead Time (FOPDT) model. The engine thrust model is characterized as a cubic polynomial function of engine speed. The actuator’s dynamic model is a FOPDT model. The nozzle airflow deflection angle is proportional to the airflow deflection angle. the thrust loss coefficient is approximated by a cosine relationship with the airflow deflection angle. The experimental results show that the established model can well characterize the dynamics of the thrust vector system.

Mechanical behavior and performance evolution of railway ballast track under dynamic stabiliser based on the hybrid MBD-DEM simulation

The uncertainty in local structure and time-variable compaction of ballast aggregates poses challenges in selecting suitable stabilization methods and parameters for ballast tracks in practical engineering, often resulting in suboptimal operational outcomes. To address this issue comprehensively, the spatial hybrid model of the double dynamic stabilisers-ballast track (MBD-DEM) is proposed in this paper. Subsequent numerical simulations of stabilisation operations are conducted to elucidate ballast vibration, migration behavior and the evolution of ballast track resistance under varying initial conditions of the ballast bed. Initially, leveraging multibody dynamics theory (MBD) and discrete element method (DEM), a MBD model of double stabilisers and a DEM ballast track model containing 6 sleepers were established respectively. Using the coupling interface, the real-time hybrid calculation of these two models is achieved through 6 sleepers in the middle of the ballast bed. Subsequently, the model's accuracy was validated by comparing test data from stabilisation vehicle operations on a Kunming Railway Bureau test line with simulation data from the dynamic double stabilisers-ballast track spatial hybrid model. Finally, utilizing the proposed hybrid model, the dynamic behavior, contact states, and movement rules of ballast particles during stabilisation operations on both loose and initially stable ballast beds (LBB and ISBB) were investigated at the mesoscopic scale. Concurrently, an in-depth analysis of lateral resistance changes in the ballast bed before and after the stabilisation operation is presented. The relevant findings effectively unveil the evolution of dynamic behavior in the ballast bed during stabilisation operations, concluding with practical suggestions for on-site operations. Simulation results illustrate that the rotation of ballast particles in the XY plane beneath each sleeper is predominantly characterized by harmonic rotation. With an increase in the depth of the ballast bed, the range of the rotation axis for combined angular velocity diminishes, leading to the phenomenon of rotation axis deflection. To ensure operational safety and efficiency, a minimum of two-times stabilization operations is recommended for loose ballast beds (LBB), with three-times operations considered optimal. For initially stable ballast beds (ISBB), one-time stabilization operation is basically sufficient.