Narrow Results

We report on a compact capacitance model that accurately describes both the gate length dependent and the gate length independent frequency dispersion observed in C-V curves for printed thin film transistors with non-ideal contacts. We also show that modeling the drain current with two adjacent subthreshold regions (instead of just one in the previous RPI TFT model) is needed to match the measured current-voltage characteristics. We show that Elmore model which accounts for channel length transit time is not sufficient for describing the frequency dispersion in C-V curves for printed TFTs and present the new Variable Dispersion Model (VDM). VDM reproduces the experimentally observed gate length independent dispersion arising due to the finite time of the electron exchange between the localized states in the mobility gap and the states above the mobility edge in amorphous semiconductors. The combined Elmore-VDM model has been implemented in AIM-Spice and showed good agreement with measured C-V data.

Based on the properties of the heterogeneous traffic flow consisting of car, bicycle and pedestrian, we in this paper present three individual-following models with the consideration of the friction effects, where the individual can here be referred to as car, bicycle or pedestrian in a unified manner. Using the relationship between the micro and macro variables, we obtain a dynamic model for heterogeneous traffic flow with the consideration of the friction effects. The analytical and numerical results show that the bicycle friction effect will reduce the car flow and speed and that the pedestrian friction effect will reduce the bicycle flow and speed, but the reductions are related to the bicycle and pedestrian density.

To simulate passenger train movements on the high-speed rail network, this paper proposes a new dynamic model based on the discrete time method and provides some efficient control policies correspondingly. Besides that, an improved minimum safe headway in the moving-block system on the high-speed rail network is presented. Using the proposed method, the dynamic characteristics of railway traffic flow are analyzed under random interferences on the high-speed rail network. Then, some sensitivity analyses are implemented to investigate the propagation features of delays under different interferences. The results indicate that the proposed dynamic model and control policies for the passenger train movements on the high-speed rail network are effective and can be a fundamental research for subsequent research of delay propagation, rerouting and rescheduling problems.

The development of social networks provides a broad platform for the dissemination of information and also leads to the proliferation of fake news and false information, which we collectively refer to as rumors. The spread of rumors causes unnecessary panic and loss to individuals and society. To reduce the negative impacts of rumors, an appropriate rumor control strategy is necessary. To come up with some reasonable strategies, we need to have a clearer understanding of the spread of rumors. In this paper, we analyze crowd attitudes during the spreading of rumors by setting the misinformation prevalent progress on the social network as a dynamic system. Considering that most people do not have a clear supportive or opposing attitude when exposed to rumor information, we introduce a new group, stiflers who remain neutral, based on the infectious disease model scheme. By deriving the mean-field equation describing the rumor propagation process, we judge the stability of the constructed model. Finally, we use the model to fit the real-world data related to COVID-19, and based on this, we discuss the properties of the model and propose related strategies.

This paper provides panel data evidence on trade liberalization and wage inequality in Bangladesh. Estimates from a dynamic model for five major manufacturing industries spanning the 1975–2002 period suggest that the effect of increased openness to trade is associated with a decrease in wage inequality. The result is in line with the theoretical prediction in that greater openness is expected to reduce wage inequality in developing countries.

This study examines the impact of the 1997 Asian financial crisis and the 2008 Global economic crisis on the capital structures of Korean non-financial listed companies. Using a panel data covering 1,159 Korean listed non-financial firms from 10 industrial sectors over a 31-year period (1985–2015), this study investigates the patterns of firms’ capital structures before and after the crises and identifies their speeds of adjustment toward the optimal leverage. This study finds different effects of the two crises on both capital structures and adjustment speeds. The average debt ratio fell significantly after the 1997 Asian financial crisis. The distance between the optimal and the observed debt ratios shrank after the Asian crisis, while the speed of adjustment increased two-fold. Unlike the Asian financial crisis, the global economic crisis of 2008 had a positive effect on companies’ debt ratios and the speeds of adjustments toward the optimal leverage. Our empirical analysis shows that, on average, the Korean non-financial listed companies decreased their debt ratios over the entire period of observation, with the leverage being the highest before the Asian financial crisis and lowest after the global economic crisis. Our results also show that the debt ratios of Korean chaebols were higher than that of non-chaebols. Moreover, we find that the high level of leverage of Korean firms was associated with tangible assets, income variability, size and age of the firm, non-debt tax shield and uniqueness.

The theory studies have showed that, rock burst is a kind of dynamic phenomenon of rock mass in mining, and is a kind of dynamic disaster from mining. The time series of magnitude is a very important exterior behavior of rock burst. The previous studies show that, to model this complicated non-linear time series, the neural network is a very good method. To overcome the shortcomings of traditional neural network, a new kind of evolutionary neural network based on immunized evolutionary programming proposed by author is proposed here. At last, the proposed evolutionary neural network model is verified by a real magnitude series of rock burst. And the result is compared with other method, such as grey system method. The results have showed that, evolutionary neural network model not only has high approaching precision, but also has high predicting precision, and is a good method to construct the non-linear model of rock burst. And this method can be used in a large number of engineering examples.

Complex systems are always composed of many subsystems that exhibit interdependent relationship with each others. It becomes increasingly important across many fields to understand the effect of interdependence among these subsystems. In this paper, we consider a novel flow interaction model on a system comprising two networks with initial line loads and capacities. Once a line in one network is subjected to overload failure, its load will be redistributed to the whole system. Based on this model, we study how load transfer due to redistribution affects the dynamic process of failure propagation and the failure outbreak threshold. Furthermore, we solve an optimal load transfer problem to find the minimum cumulative cost on the low failure level. Our results provide theoretical guidance for optimal load scheduling to suppress cascades in the interdependent networks.

In this paper, a fractional-order dynamic model of the photovoltaic (PV) solar module is introduced. Dynamic modeling of PV solar modules is useful when used in switching circuits and grid-connected situations. The dynamic elements of the proposed model are a fractional-order inductor and capacitor of two independent orders which allow for two extra degrees of freedom over the conventional dynamic model. The step response and transfer function of the load current are investigated for different orders under resistive and supercapacitor loading conditions. Closed-form expressions for the time response of the load current at equal orders of capacitor and inductor are derived. Stability analysis of the load current transfer function is carried out for different orders and loading conditions. The regions for pure real and pure imaginary input admittance scenarios are calculated numerically for both resistive and supercapacitor load cases. It is found that the order of the inductor has a dominant effect on the responses. As a proof of concept, the model is fitted to experimental data to show its flexibility in regenerating the actual response. The fitted fractional-order model response is compared to optimized integer-order ones from literature showing noticeable improvement.

In this paper, the vehicle position points obtained by multi-sensor fusion are taken as the observed values, and Kalman filter is combined with the vehicle kinematics equation to further improve the vehicle trajectory. To realize this, mathematical principles of deep reinforcement learning are analyzed, and the theoretical basis of reinforcement learning is also analyzed. It is proved that the controller based on dynamic model is better than the controller based on kinematics in deviation control, and the performance of the controller based on deep reinforcement learning is also verified. The simulation data show that the proportion integration differentiation (PID) controller has a better tracking effect, but it does not have the constraint ability, which leads to radical acceleration change, resulting in unstable acceleration and deceleration control. Therefore, the deep reinforcement learning controller is selected as the longitudinal velocity tracking controller. The effectiveness of lateral and longitudinal motion decoupling strategy is verified by simulation experiments.

This paper deals with the identification and the qualitative analysis of a dynamic model of a shallow lagoon. The model describes the relations between biotic (phytoplankton, zooplankton) and abiotic (oxygen, nutrients) components of a lagoon. The first step of the paper is to derive estimates for the model parameters through an identification procedure using real data. The second step is to perform a qualitative analysis of the model dynamics, via the introduction of a parameterized reduced order model. The main contribution of the paper is to make an effort in the direction of synergyzing the identification stage with the qualitative analysis of the model dynamics, in order to gain a better understanding of the system behavior and obtain more reliable estimates for the model parameters and exogenous inputs.

We address the question of finding the attractors of the extended complex Lorenz model (ℂLM), which is obtained by extending the space from ℝ³ to ℂ³, and defining the model by the same equations as the classical Lorenz model (LM). We have numerical evidence of two strong attractors unrelated to the Lorenz attractor. We calculate its Lyapunov exponents and show that two of them are 0, and the other four are double and negative. Hence the attractors are nonchaotic. We show that they have a quasi-periodic nature. To decipher the structure of these attractors, we introduce the imaginary Lorenz model (𝕀LM), which is defined in the same space ℂ³ by multiplying with the Lorenz equations. Both models locally commute, and with its help we account for the double Lyapunov exponent 0 and show evidence that the basin of attraction of each attractor is a big open set of ℂ³. The chaotic limit set L_ℂ ⊂ ℂ³ obtained from the classical Lorenz attractor L₀ of (LM) by moving it with the (𝕀LM) has two positive Lyapunov exponents, but only captures a set of 6D-volume 0 in its basin of attraction. Hence this attractor may be hyperchaotic in ℝ⁵.

In reality, the dissemination of information about COVID-19 vaccines typically involves a combination of opinion leaders and self-organizing networks, with each node being exposed to information in varying ways. However, conventional models often assume homogeneity in networks, treating all nodes as equal in terms of propagation probabilities within a fixed timeframe, thereby neglecting the inherent heterogeneity of social networks in information dissemination. To address this limitation, we propose a novel semi-directed network model, referred to as the susceptible-forwarding-immune model, which incorporates the complex structure of actual social networks and classifies nodes based on their mode of contact and the number of users they reach within a specific period. We calibrated and validated our model using real data on COVID-19 vaccine information from the Chinese Sina microblog, and our sensitivity analysis yielded insights into optimal strategies for disseminating such information.

A simple compartment model was used to describe the observed HIV/AIDS epidemic in the homo/bisexual male community in Paris (France). In the first step, the model was fitted to the available data, using least squares procedures. The fitting of the model to subsets of the data could perfectly account for the observations, but did not afford a coherent estimation of the model parameters, and was unable to predict the actual development of the disease. These difficulties might result from the fact that many factors have modified the course of the HIV/AIDS epidemic in the recent years. So, a recursive estimation technique known as the Kalman filter has been used. The Kalman filter accounts for stochastic fluctuations both in the model and in the data, and enables to assess the possible progressive adaptation of the model parameters suggested by the new observations. Our study disclosed a noticeable change of some parameters of important epidemiological significance (average transmission rate and mean incubation rate) around the year 1988, most probably due to intervention measures like prevention and/or gradual introduction of early treatment for infected but asymptomatic individuals.

The growth of precipitates in a solution system is studied using the asymptotic method, taking into account the cross diffusion between solutes. The resulting asymptotic solution for the dynamic model of the precipitate growing in the solution reveals that the concentration distribution is significantly changed by the cross diffusion between solutes and the growth of precipitate depends not only on the self diffusion of solutes, but also on the diffusive interaction between solutes. The attractive diffusive interaction between solutes in the solution enhances the solute diffusion and facilitates the growth of precipitate, whereas the repulsive diffusive interaction between solutes depresses the solute diffusion and inhibits the growth of precipitate.

Large multi-flexible-body space structures, such as space solar arrays, comprise of multiple flexible substructures that are connected by joint hinges. Unlike traditional continuous structural models, a noncontinuous multi-flexible-body structural model with joint hinges is set up for the multi-flexible-body structure herein. In contrast to the general multi-body structural models in which each substructure is taken as a rigid body, the elastic deformation of every substructure in the multi-flexible-body structural model is taken into account. Furthermore, the connection stiffness and friction damping of joint hinges are considered as they affect the structural dynamic properties. All of the aforementioned considerations make the dynamic modeling of this multi-flexible-body structural system rather difficult. To solve the problem, an innovative semi-analytical model is developed for each flexible substructure. A four-node massless spring-damper element is built up for each joint hinge, in which the stiffness and damping coefficients of the hinge are calibrated by experiments. By comparing the computed results with experimental results, it can be concluded that the method proposed herein is correct and efficient.

This work presents a dynamic model of the piston-crankshaft assembly of the Stirling engine with three degrees of freedom combined with the isothermal thermodynamic submodel. The model allowed for consideration of the working gas pressure from the thermodynamic submodel, whose working space was divided into partial volume units and subjected to analysis. Performing the analysis of the physical model and adopting the data from the real object, with the assumption of the static mass reduction, enabled developing of a simulation model of the piston-crankshaft assembly. Subsequently, the model was extended by the part describing pressure changes in the cylinder, theoretical work, theoretical power of the working gas with the assumption of the isothermal heat exchange in the compression and expansion spaces. On the basis of the motion equations shown in the work, the influence of the model’s chosen parameters on the operation of the integrated simulation model was presented and analyzed. The results of the conducted simulations were also additionally derived from an analysis of the displacement, velocity, and piston acceleration curves, as well as the curves of displacement, velocity, and crankshaft angular acceleration. The presented results convey the information about the dynamic operation of the simulated real object working at the preset thermodynamic parameters of the working gas.

In terms of the heavy load and low viscosity operation conditions of the marine rim-driven thruster (RDT), a novel Magnetic Liquid-double-suspension Elastic-supported Thrust Bearing (MLETB) is designed. The MLETB combines the structural features of the Rubber-supported Water-lubricated Thrust Bearing (RWTB) and Permanent Magnet Bearing (PMB). To investigate the axial stiffness and damping of the bearing, the dynamic model of MLETB incorporating a multi-layer structure is developed. Simulation indicates that the dynamic performance of the MLETB is close to that of the RWTB, but the load capacity of the MLETB is better. Besides, the load is not the most important factor to affect the dynamic performance. The parameter identification method is proposed to analyze the experiment results based on the water-lubricated bearing test rig, which show that the MLETB has a dilemma between the load capacity and dynamic performance that its stiffness and damping will be seriously affected by the specific rubber structure.

This paper provides concrete support for business process reengineering (BPR) related decision-making, through analysis of the mechanism of impetus and resistance. We categorize the impetus and resistance according to their domains. We then construct a dynamic model of BPR by using the principles and methods of systems analysis, in conjunction with the theory of mechanical dynamics. On this basis, dynamics analysis of the two forces was carried out and the mechanism of effect was also discussed. In the case study, experimental analysis of BPR in CF Company was carried out with the proposed model. The results showed that the model is applied to analyze the impetus and resistance in BPR as important input into the decision-making for BPR, thus compelling to implement BPR for the enterprise.

Cluster analysis has proven to be a valuable statistical method for analyzing whole genome expression data. Although clustering methods have great utility, they do represent a lower level statistical analysis that is not directly tied to a specific model. To extend such methods and to allow for more sophisticated lines of inference, we use cluster analysis in conjunction with a specific model of gene expression dynamics. This model provides phenomenological dynamic parameters on both linear and non-linear responses of the system. This analysis determines the parameters of two different transition matrices (linear and nonlinear) that describe the influence of one gene expression level on another. Using yeast cell cycle microarray data as test set, we calculated the transition matrices and used these dynamic parameters as a metric for cluster analysis. Hierarchical cluster analysis of this transition matrix reveals how a set of genes influence the expression of other genes activated during different cell cycle phases. Most strikingly, genes in different stages of cell cycle preferentially activate or inactivate genes in other stages of cell cycle, and this relationship can be readily visualized in a two-way clustering image. The observation is prior to any knowledge of the chronological characteristics of the cell cycle process. This method shows the utility of using model parameters as a metric in cluster analysis.