Dynamic Process Model Research Articles

Carlo method as the basis for in silico-modeling in the study of the pathogenesis of ischemic stroke.

The paper proposes a new mathematical model of dynamic processes in a typical spatially non-uniform biological system. The mathematical problem of modeling the dynamics of the neurovascular units of the brain under conditions of ischemic stroke is formulated and solved. An investigation of this model is conducted, and a numerical and programmatic implementation of the corresponding mathematical problem is proposed.

Adaptive strength: Unveiling a multilevel dynamic process model for organizational resilience

As organizations navigate an increasingly volatile and unpredictable landscape, the concept of organizational resilience has garnered significant attention. Such global disruptions fundamentally alter the course of international business affairs through a range of events spanning from natural disasters to political upheavals, economic downturns, pandemics, and technological advancements. These disruptions are widespread and cause multinational enterprises (MNEs) to face challenges and uncertainties that obstruct their success. The concept of organizational resilience has developed as a critical factor for firms to not only survive but thrive when faced with adversities. This research examines the multilevel elements of organizational resilience through a dynamic process model to enable MNEs to navigate change, adapt to adversity, and sustain success at five levels: (1) individual, (2) organizational, (3) ecosystem, (4) institutional, and (5) global. By embracing a dynamic approach to organizational resilience, MNEs canthrive against uncertainty and seize profitable opportunities in a challenging business environment.

Dynamic processes of mindfulness-based alterations in pain perception.

Mindfulness-based processes have been shown to enhance attention and related behavioral responses, including analgesia, which is discussed as an effective method in the context of pain interventions. In the present review, we introduce the construct of mindfulness, delineating the concepts, factors, and processes that are summarized under this term and might serve as relevant components of the underlying mechanistic pathways in the field of pain. We also discuss how differences in factors such as definitions of mindfulness, study design, and strategies in mindfulness-based attention direction may need to be considered when putting the findings from previous studies into a whole framework. In doing so, we capitalize on a potential dynamic process model of mindfulness-based analgesia. In this respect, the so-called mindfulness-based analgesia may initially result from improved cognitive regulation strategies, while at later stages of effects may be driven by a reduction of interference between both cognitive and affective factors. With increasing mindfulness practice, pathways and mechanisms of mindfulness analgesia may change dynamically, which could result from adaptive coping. This is underlined by the fact that the neural mechanism of mindfulness analgesia is manifested as increased activation in the ACC and aINS at the beginner level while increased activation in the pINS and reduced activation in the lPFC at the expert level.

Modeling of autowave processes in active media with inhomogeneous properties

This paper shows the results of computer modeling of the processes of excitation and propagation of autowaves in distributed active media with inhomogeneous properties. The study of autowaves by the cellular automata method is based on the Wiener–Rosenbluth model, according to which each element of the active environment can be in one of three states: rest, excitation and refractoriness. The software module "AutoWaveModel" has been developed in C++ using the Qt library and OpenGL technology for modeling dynamic processes of excitation of spiral waves and pacemakers. The heterogeneity of the properties of the active environment in the model under consideration is set, in particular, by introducing into the field (volume) of modeling a certain number of inactive elements distributed according to a random law. It is established that the decay of autowaves occurs when about 30-60% of inactive elements are introduced from their total number in the model, moreover, the wave propagation process becomes more stable with an increase in the decay coefficient of the activator of all elements. As one of the factors creating heterogeneity of the active distributed environment, the change in the decay coefficient of the activator in its volume is also considered. In this case, each cell of the model is assigned a random value of the specified coefficient, lying in a given interval from the minimum to the maximum value. With a significant difference in the values of the activator decay coefficient in near areas of the active environment and a sufficiently high excitation threshold, the wave front is curved due to its acceleration or deceleration. In this case, the destruction of the wave is also observed, which is unable to overcome the area with a reduced decay coefficient of the activator.

Dynamic modelling of bacteriophage production process

Dynamic modelling of bacteriophage production process

Confluence simulations based on dynamic channel parameters in the grasslands lacking historical measurements

Technology has greatly promoted ecohydrological model development, but runoff generation and confluence simulations have fallen behind in ecohydrological model development due to limited innovations. To fully understand ecohydrological processes and accurately describe the coupling between ecological and hydrological processes, a distributed ecohydrological model was constructed by integrating multisource information into distributed dynamic process model (DDPM). We mainly describe runoff generation and convergence modules. Based on the improved HBV model and degree-3 h factor method, runoff generation and snow routines were constructed for semiarid grassland basins. In view of meandering and variable steppe river channels and steep hydrological relief characteristics, a confluence module was constructed; the 1-km bend radius equivalent concept was innovatively proposed to unify river channel bend degrees. The daily runoff simulation validation results obtained using two datasets were R2 = 0.947 and 0.932, NSE = 0.945 and 0.905, KGE = 0.971 and 0.680. In the 3-hour flood simulations, the DDPM could better restore small long-distance water flows than the confluence method that did not consider actual river lengths or bend energy losses; the DDPM more accurately simulated the flood peak arrival time than the confluence method that did not consider overflow. The simulated mainstream overflow frequency increased by 0.84/10 years, and significant interaction periods of 10 to 13 years occurred with local precipitation, ecological status, and global climate change. An approximately 2-year lag occurred in the global climate change response. This study helps us further understand and reveal the ecohydrological processes of steppe rivers in semiarid regions.

Evaluating subgradients for convex relaxations of dynamic process models by adapting current tools

Global dynamic optimization problems are often represented as nonlinear optimization problems with embedded parametric ordinary differential equations. Deterministic methods for global optimization typically employ subgradients of convex relaxations to construct crucial lower bounds. Our recent work shows that subgradients in dynamic optimization problems may be obtained by adapting standard forward or adjoint sensitivity approaches; the adjoint approach ought to be computationally favorable except for small problems. However, established adjoint implementations are incompatible with established libraries for subgradient evaluation. At FOCAPO/CPC 2023, we outlined an automated proof-of-concept implementation of adjoint subgradient evaluation in C++, adapting the convexification package MC++, the ODE solver CVODES, and our own differentiation and code generation tools. This article details how these tools were nontrivially adapted, to ultimately implement adjoint ODE sensitivities embedded with either the forward or reverse modes of subgradient automatic differentiation (AD). Numerical examples are presented for illustration.

A PANN-Based Grid Downscaling Technology and Its Application in Landslide and Flood Modeling

The efficiency and accuracy of grid-based computational fluid dynamics methods are strongly dependent on the chosen cell size. The computational time increases exponentially with decreasing cell size. Therefore, a grid coarsing technology without apparent precision loss is essential for various numerical modeling methods. In this article, a physical adaption neural network (PANN) is proposed to optimize coarse grid representation from a fine grid. A new convolutional neural network is constructed to achieve a significant reduction in computational cost while maintaining a relatively accurate solution. An application to numerical modeling of dynamic processes in landslides is firstly carried out, and better results are obtained compared to the baseline method. More applications in various flood scenarios in mountainous areas are then analyzed. It is demonstrated that the proposed PANN downscaling method outperforms other currently widely used downscaling methods. The code is publicly available and can be applied broadly. Computing by PANN is hundreds of times more efficient, meaning that it is significant for the numerical modeling of various complicated Earth-surface flows and their applications.

Correction to: A comprehensive geometrical, metallurgical, and mechanical characteristic dynamic model of laser beam welding process

Correction to: A comprehensive geometrical, metallurgical, and mechanical characteristic dynamic model of laser beam welding process

Verification and validation of software process simulation models: A systematic mapping study

AbstractSoftware process simulation models (SPSMs) that are based on descriptive process models offer the executability that can demonstrate dynamic changes of software processes over time. Verification and validation (V&V) is critical in SPSMs for guaranteeing the quality and reliability of models. V&V of dynamic software process models is more complex and challenging than for static software process models. This work systematically summarizes and maps V&V studies in SPSM to provide guidelines for future research and practice. Specifically, this study aims at identifying the focus of research on V&V, the methods used for V&V, and how to implement V&V of SPSMs in software engineering research. We conducted a systematic mapping study on studies of SPSMs that report on their V&V activities. Under the guidance of a V&V meta‐model for SPSMs, we study four research questions about V&V process. We identified 107 primary studies from a pool of 313 papers on SPSMs until 2021. There are two main results of our study. The first one presents the relationship between quality aspects of SPSMs and the V&V methods to assure them. The second result reveals the relationships among the modeling process, three modeling steps, five quality aspects, and 10 V&V methods. Generally, researchers do not pay sufficient attention to V&V, as 65.8% ( ) failed to mention or elaborate on their V&V process. We systematically summarize and map the state‐of‐the‐art V&V research in software process modeling field to support modelers' practice and improve their V&V process.

A multi-dimensional evolution modeling method for digital twin process model

Digital twin, visually monitor the dynamic evolution process of part cutting in a digital way, which has attracted widespread attention in smart manufacturing. Under this background, creating a high fidelity and evolvable virtual model has become the key to putting digital twin into practice in the manufacturing field. However, the 3D process model pays scant attention on the dynamically machining process, multi-dimensional evolutionary machining feature, and not even the coupling machining semantics. How to realize the evolution process model creation from the perspective of semantics dimension, time dimension, and feature dimension, an evolutionary concurrent modeling method is proposed based on the process knowledge and machining feature. Firstly, the geometric evolution process of typical features is analyzed, and the association mechanism between processing semantics and evolution features is constructed. Secondly, the mathematical model of the evolution feature is constructed based on the part processing information, and the creation method of processing semantics-driven evolutionary feature is proposed. Thirdly, the association mechanism of "machining semantics-process parameters-geometric features" is built, and the dynamic evolution process model which achieve synchronous evolution with the processing is created based on machining semantics. Finally, a case study is carried out with typical processing feature. The results show that the multi-dimensional evolution modeling of the digital twin process model can realize the synchronous visual monitoring and simulation of product processing. The correlation based on semantic features can improve the efficiency and quality of process model construction and provide solutions for dynamic analysis and decision-making of smart process design.

Size-resolved emission rates of episodic indoor sources and ultrafine particle dynamics

Indoor airborne ultrafine particles (UFPs) are mainly originated from occupant activities, such as candle burning and cooking. Elevated exposure to UFPs has been found to increase oxidative stress and cause DNA damage. UFPs originating from indoor sources undergo dynamic aerosol transformation mechanisms. This study investigates the dynamics of UFPs following episodic indoor releases of the six distinct emission sources: 1) candle, 2) gas stove, 3) clothes dryer, 4) tea & toast, 5) broiled fish, and 6) incense. Based on the analytical model of aerosol dynamic processes, this study reports size-resolved source emission rates along with relative contributions of coagulation, deposition, and ventilation to the particle size distribution dynamics. The study findings indicate a significant variation in the geometric mean diameter (GMD) and size-resolved number concentration over time for the sources that emit a substantial amount of UFPs smaller than 10 nm. As the emission progresses, the UFP number concentrations increase in a log-normal distribution, while the GMD shows a tendency to increase over time. The observed result suggests that coagulation can have a considerable impact on UFP number concentration and size, even during the indoor UFP emission. The estimated emission rates of the six indoor sources appear to follow a log-normal distribution while the emission rate ranges from 107 min−1 to 1012 min−1. The indoor UFP concentration and size distribution dynamics are substantially affected by the interplay of the three aerosol loss mechanisms that compete with each other, and this impact varies according to the source type and the indoor environmental conditions. Ultimately, using the aerosol transformation mechanisms examined in this study, researchers can refine exposure assessment for epidemiological studies on indoor ultrafine particles.

A comprehensive geometrical, metallurgical, and mechanical characteristic dynamic model of laser beam welding process

A comprehensive geometrical, metallurgical, and mechanical characteristic dynamic model of laser beam welding process

System identification and artificial intelligent (AI) modelling of the molten salt electrolysis process for prediction of the anode effect

NdFeB magnets are widely used in various applications including electric and hybrid vehicles, wind turbines, and computer hard drives. They contain approximately 31–32 wt% Rare Earth Elements (REEs), mainly neodymium (Nd) and praseodymium (Pr), and are produced by molten salt electrolysis using fluoride electrolytes. However, anode passivation or anode effect may occur, generating greenhouse gases if insufficient amounts of metal oxides are available in the system. Therefore, in this study, a dynamic model of the electrochemical process was developed to estimate the system variables and predict the anode effect using several system identification methods. The Transfer Function (TF) estimation, Auto-Regressive with Extra inputs (ARX), Hammerstein-Weiner (HW), and Artificial Neural Network (ANN) models were used, and their results were compared based on the occurrence of the anode effect. The best model achieved an average accuracy of 96% in predicting the process output.

Моделирование динамических режимов работы электроприводов с системой векторного управления синхронным двигателем

Purpose: The purpose of the work is a comparative assessment of dynamic processes in electric drives with a vector control system for permanent magnets synchronous motors and various methods of stator voltage modulation. To achieve this purpose, a mathematical description of a synchronous motor in a rotating dq coordinate system is given, and a mathematical model of a vector control system is developed. The model of the vector control system takes into account the decoupling of stator current control loops and proposes two options for generating a current reference along the d-axis, ensuring the efficiency of energy conversion processes at rotation speeds below the nominal value, depending on the design of the motor rotor. Algorithms for scalar pulse-width and space-vector modulation of motor stator voltage for a frequency converter with a two-level autonomous voltage inverter are presented. Methods: When developing mathematical models and algorithms, methods from the theory of electric drive and the theory of automatic control have been used. The developed mathematical models and algorithms are implemented in the Matlab Simulink software package. Results: The results of modeling dynamic processes in electric drives (mechanical characteristics, stator currents, stator current total harmonic distortion) are presented, which have shown the performance of the developed models and algorithms. Practical significance: The choice of modulation type does not affect the mechanical characteristics of the electric drive, but does affect the harmonic composition of the stator current. The greatest effect from the use of space-vector modulation algorithms is manifested at reduced values of the frequency of the stator current and partial load torque on the rotor shaft.

The dimensions of expansive learning in the workplace: A multicase study from activity theory

AbstractLearning in the workplace is among the most important forms of lifelong learning. This is because the workplace provides an authentic context for meaningful learning. Expansive learning, proposed by Engeström, focuses on learners as a community, the transformation of culture, and the creation of a new theoretical concept. Expansive learning eventually leads to a transformation in collective expertise. The primary aim of the study was to examine the dimensions of expansive learning and their relationships in the workplace. The cases selected were three Chinese consulting firms that focus on professional learning or talent training and development. I used multiple methods for data collection (e.g., semistructured interviews, observation, and document analysis). The successive analytical induction procedure was based on the constant comparison principle of grounded theory, and the four dimensions of expansive learning (distribution, collectiveness, routine, and expansiveness) emerged and were scrutinized. Based on the principle of activity theory, I constructed and integrated an interactive dynamic process model of expansive learning with its related dimensions.

On the Admissibility and Stability of Multiagent Nonlinear Interconnected Positive Systems With Heterogeneous Delays

Many multi-agent interconnected systems include typical nonlinearities which are highly sensitive to inevitable communication delays. This makes their analysis challenging and the generalization of results from linear interconnected systems theory to those nonlinear interconnected systems very limited. This paper deals with the analysis of Multi-Agent Nonlinear Interconnected Positive Systems (MANIPS). The main contributions of this work are two fold. Based on Perron-Frobenius theorem, we first study the “admissibility” property for MANIPS, and show that it is a fundamental requirement for this category of systems. Then, using admissibility/positivity properties and sequences of functions theory, we propose a suitable Lyapunov function candidate to conduct the analysis of the dynamical behavior of such systems. We show that the stability of MANIPS is reduced to the positiveness property (i.e. negative or positive definiteness) of a new specific matrix-valued function ( <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$\mathcal {Z}$</tex-math></inline-formula> ) that we derive in this paper. Furthermore, the obtained results generalize the existing theory. The quality of the results achieved are demonstrated through the applications of the developed theory on cells with multi-stage maturation process dynamical models.

A novel nonlinear spectrum estimation method and its application in on-line condition assessment of bearing-rotor system

In this paper, a novel approach for nonlinear spectrum estimation is proposed and combined with Support Vector Machine (SVM) classifier to assess the condition of bearing-rotor systems, including fault types and severity. The proposed approach involves extraction and reconstruction of target harmonics, dynamic process modeling of the reconstructed signal based on a data-driven method, and extraction of nonlinear spectrum features in the form of nonlinear output frequency response functions (NOFRFs). The focus is on the influence of model stability and accuracy evaluation on the robustness of NOFRFs. Then the robustness of the NOFRFs based on the proposed estimation approach is compared with the conventional estimation approach using simulation cases and experiments. SVM is used to classify the extracted nonlinear spectrum features. Finally, the classification accuracy of NOFRFs evaluated by the proposed approach is compared with time-domain and frequency-domain features. Neighborhood Component Analysis (NCA) is used to select sensitive features and improve classification accuracy. The proposed condition assessment strategy does not require the selection of sensitive features, ensuring the stability of assessment results, which is also a significant advantage of the proposed strategy.

A reinforcement learning-based transformed inverse model strategy for nonlinear process control

Process control is evolving with a rapid inclusion of artificial intelligence (AI) based methods, which have the potential to significantly boost controller performances. This development is valuable to control engineers, especially for the control of nonlinear processes plagued by higher offsets and larger delays. With this motivation, we develop in this work a new strategy utilizing AI for process control by assimilating reinforcement learning (RL) with a novel approach for inverse model control. This approach involves expressing a dynamic process model in the controlled variable domain to obtain a computationally efficient, transformed inverse model (TIM). In the developed control strategy, the TIM provides baseline control, which is subsequently improved by RL’s deep deterministic policy gradient method in the resultant TIM-RL controller. This controller is examined in three different in-silico case studies of varying nonlinearity and complexity. Relative to nonlinear model predictive control, the TIM-RL controller is observed to have superior performance with more than 30% reduction in integral absolute error, 45% lower settling time, and about an order of magnitude smaller latency.

Spatio-temporal bidirectional redundancy removal for quality prediction of dynamic processes: A novel dynamic feature extraction strategy

Data-driven modeling has been widely recognized as an effective and essential approach for predicting key quality variables in industrial processes. However, due to the long production process and the dynamic fluctuation of operating conditions, it is necessary to integrate data from various spatial and temporal points to comprehensively describe the production process. Furthermore, data is characterized by massive redundancies and high dimensionalities, which pose challenges for modeling dynamic processes. To address these issues, this paper proposes a strategy of spatio-temporal bidirectional redundancy removal to extract dynamic features and applies it to quality prediction. Firstly, it constructs input samples with the spatio-temporal two-dimensional structure. Then, information redundancy is removed by implementing feature extraction in both the spatial and temporal directions, respectively. Additionally, a spatio-temporal weight matrix is used in this process to capture dynamic information which is more related to the output quality variable. Its effectiveness was verified by applying it to the debutanizer column process and the leaching process in zinc smelting. Results show that the proposed strategy generates efficient dynamic features with no information redundancy and low dimensionalities. Moreover, it outperforms other common types of dynamic process modeling methods in terms of quality prediction performance.