Qualitative Simulations Research Articles

Aziridination Exploited: A Mechanistic Study of the Divergent Reactivity of Trifluoropropenyl‐iodonium Reagent toward N‐nucleophiles

AbstractIn this study, we use computations and experiments to explore mechanistic scenarios that govern the reaction between secondary amines and trifluoropropenyl iodonium triflate with the aim of rationalizing and predicting experimental outcomes. Based on the results from theory, we have compiled a list of guidelines relevant to synthetic strategies and provide examples for the interpretation of selectivities. To demonstrate the viability of these strategies, we have performed directed experiments applying different reaction conditions to obtain the products predicted by our model. Additionally, qualitative microkinetic simulations based on our computations were carried out for further support.

Distributed task allocation and mission planning of AUVs for persistent underwater ecological monitoring and preservation

This paper presents a robust cooperative distributed task allocation and mission planning system that enables a fleet of a certain class of autonomous underwater vehicles (AUVs) for persistent underwater ecological monitoring and preservation. The proposed system is developed based on a distributed algorithm that optimizes task allocation and mission planning in real-time while considering environmental constraints, sensor capabilities, communication limitations, as well as system failures. A realistic case study of monitoring and control of Crown-Of-Thorns Starfish (COTS) in Australia's Great Barrier is utilized to investigate how the proposed system can contribute to the inspection and eradication of the maximum possible COTS in a limited mission time in the exposure of uncertainty of mission failure caused by internal/external faults. The effectiveness and robustness of our approach are validated through extensive qualitative and quantitative simulations.

First-principles calculations of specular reflection of high-energy electrons during the two-dimensional crystal growth

RHEED_DIFF_2D is an open-source software capable of performing qualitative numerical simulations of changes to the intensity of RHEED oscillations in the function of the layer deposited, employing various models of scattering crystal potential for heteroepitaxial structures of bcc (001) films nucleated on a fcc (001) surface, including the possible existence of various diffuse scattering models through the layer parallel to the surface. This model can be used for interpreting data weakly dependent on the azimuth of the incident electron beam direction and for 2D monolayer-by-monolayer growth modes. The program does not use any external data sources. New version program summaryProgram title: RHEED_DIFF_2DCPC Library link to program files:https://doi.org/10.17632/83ht2kc8xt.1Licensing provisions: GNU General Public License 3Programming language used: C++Journal reference of previous version: Computer Physics Communications 185 (2014) 3001–3009Does the new version supersede the previous version?: No. It is a supplement to the previous version.Reasons for new versionResponding to users' feedback we present a practical procedure of construction of simulation program, which facilitates the calculation of changes to the intensity of RHEED oscillations in the function of the layer deposited for 2D monolayer-by-monolayer growth modes, employing various models of scattering crystal potential for heteroepitaxial structures of bcc (001) Fe thin layers nucleated on the fcc (001) Ag substrate. The forms of these potentials can be freely defined by the user. The proposed approach may be generalized and applied to other metallic bcc/fcc or fcc/bcc interfaces.Nature of problemBulk iron has a bcc structure at room temperature and ferromagnetic below 920 K. It has been noticed that Fe monolayers grown on nonmagnetic substrates show a variety of interesting properties depending on the size of the lattice misfit between the substrate and growing layers, and on the thickness of the deposited material [1,2]. The fundamental scientific problem in such research is to specify both interface type and growth mechanism for subsequent layers. Researchers and technologists manufacturing two-dimensional nanoscale systems frequently use RHEED measurements in the function of the deposited layers to control growth of samples at the atomistic level of accuracy.Experimental data indicate that the iron grows on the Ag(001) surface as a bcc α-Fe film rotated by 450 relative to the underlying lattice [3]. For this reason, the net point spacing in plane (001) of Ag surface is 2.889 Å while that of bulk Fe(001) is 2.8665 Å. This leads to a compressive in-plane misfit (aFe – aAg/√2)/(aAg/√2) ≈-0.78%. This slight mismatch causes that during nucleation and in the early stage of growth, Fe should grow in 2D monolayer-by-monolayer mode on the Ag(001) substrate.Solution methodRHEED intensities are calculated within the general framework described in Ref. [4] with the model of the scattering potential for heterostructures:(1)UCombined(z)=∑nUsubstrate(z)+∑n(Ulayer(z)+Uaddlayer(z)),where the Uaddlaer represents the diffuse scattering on the topmost layer.Summary of revisionsThe presented version RHEED_DIFF_2D of the computer program implements an iterative algorithm for calculating the intensity of the specularly reflected electron beam as a function of growing layers. The pseudocode listed below illustrates the main assumptions of this algorithm.for all growing layers for all coverages of the overlayer for all elements of the scattering matrix Calculating the elements of the scattering matrix for the selected model of the scattering potential of the overlayer end for Calculating the amplitude of the specularly reflected electron beam end forend for

3D particle-in-cell simulations of negative and positive streamers in C4F7N–CO2 mixtures

We investigate negative and positive streamers in C4F7N–CO2 mixtures through simulations. These mixtures are considered to be more environmentally friendly than the insulating gas SF6 that is widely used in high voltage technology. Simulations are performed using a 3D particle-in-cell model. Negative streamers can propagate when the background field is close to the critical field. We relate this to their short conductive channels, due to rapid electron attachment, which limits their field enhancement. Positive streamers also require a background field close to the critical field, and in addition a source of free electrons ahead of them. In our simulations these electrons are provided through an artificial stochastic background ionization process as no efficient photoionization process is known for these gases. In 3D, we can only simulate the early inception stage of positive discharges, due to the extremely high electric fields and electron densities that occur. Qualitative 2D Cartesian simulations show that the growth of these discharges is highly irregular, resulting from incoming negative streamers that connect to existing channels. The inclusion of a stochastic background ionization process also has an interesting effect on negative discharges: new streamers can be generated behind previous ones, thereby forming a chain of negative streamers.

Qualitative analysis and simulations of the biological fouling problem on filtration membranes

The mitigation of fouling formation and development in filtration systems represents the most critical aspect for water treatment, as it naturally affects both the operational costs for management procedures, and the duration of filtration devices. As it constitutes an artificial physical barrier for solid particles retention, membrane systems are perfect environments for the adhesion and development of biological fouling layers, especially in wastewater treatment reactors. Based on recent results, the present work focuses on the qualitative analysis of a mono dimensional continuous model for biofouling dynamics in microfiltration systems. The free boundary problem accounting for the evolution of the biofouling layer during the filtration regimen has been discussed in terms of existence and uniqueness of the solution. The achieved results represent a consistent base for numerical studies related to the correct prediction of transmembrane pressure in membrane systems. Numerical examples related to the heterotrophic–autotrophic interaction occurring in wastewater treatment plants have been presented to highlight the effect of crucial biological aspects, such as extracellular polymeric substances (EPS) accumulation, usually neglected in the classical membrane filtration modeling. The description of biofouling dynamics and membrane performance during the filtration regimen highlight key aspects for microfiltration system management in all industrial applications.

Dynamic modelling and tristability analysis of misfolded α-synuclein degraded via autophagy in Parkinson's disease

The widely-accepted hallmark pathology of Parkinson's disease (PD) is the presence of Lewy bodies with characteristic abnormal aggregated α-synuclein (αSyn). Growing physiological evidence suggests that there is a pivotal role for the autophagy-lysosome pathway (ALP) in the clearance of misfolded αSyn (αSyn∗). This work establishes a mathematical model for αSyn∗ degradation through the ALP. Qualitative simulations are used to uncover the tristable behavior of αSyn∗, i.e., the lower, medium, and upper steady states, which correspond to the healthy, critical, and disease stages of PD, respectively. Time series and codimension-1 bifurcation analysis suggest that the system shows tristability dynamics. Furthermore, variations in the key parameters influence the tristable dynamic behavior, and the distribution of tristable regions is exhibited more comprehensively in codimension-2 bifurcation diagrams. In addition, robustness analysis demonstrates that tristability is a robust property of the system. These results may be valuable in therapeutic strategies for the prevention and treatment of PD.

The domination effect of the intelligent environment in the catastrophe mechanism of investor behavior

In recent years, investors have benefited from the convenience of intelligent recommendation information push, but have exhibited over-dependence on intelligent systems. In some scenarios, investors' trust and behavioral decisions are even dominated by information provided by the intelligent environment, which can lead to polarization and frequent reversal of investor behavior. To explore the mechanism of this frequent change, this work combined qualitative simulations with catastrophe theory, and used text data on investor sentiment, confidence, and behavior changes to construct a catastrophe model of investor behavior. The results show that neutral-type investors are more likely to be affected by information push under the dominance effect of intelligent systems, which results in catastrophe in behavior. Meanwhile, due to the dominance of the intelligent system for some investors, even if the direction of change of the investor's internal trust level is inconsistent with the external situational disturbance, it may still lead to catastrophe behavior. Finally, the catastrophe region of investor behavior is explored through an in-depth analysis of fitted catastrophe model parameters. This can lay the foundation for financial institutions' guidance mechanisms and control strategies for investor behavior in the intelligent era.

Simulation of dendritic–eutectic growth with the phase-field method

Solidification is an important process in many alloy processing routes. The solidified microstructure of alloys is usually made up of dendrites, eutectics or a combination of both. The evolving morphologies are largely determined by the solidification process and thus many materials properties are dependent on the processing conditions. While the growth of either type of microstructure is well-investigated, there is little information on the coupled growth of both microstructures. This work aims to close this gap by formulating a phase-field model capable of reproducing dendritic, eutectic as well as dendritic–eutectic growth. Following this, two-dimensional simulations are conducted which show all three types of microstructures depending on the composition and processing conditions. The effect of the dendritic–eutectic growth on the microstructural lengths, which determine materials properties, is investigated and the morphological hysteresis between eutectic growth and dendritic–eutectic growth is studied by employing solidification velocity jumps. Finally, qualitative three-dimensional simulations are conducted to test for morphological changes in the eutectic.

Effect of gravity on phase transition for liquid–gas simulations

Direct simulations of phase-change and phase-ordering phenomena are becoming more common. Recently, qualitative simulations of boiling phenomena have been undertaken by a large number of research groups. One seldom discussed limitation is that large values of gravitational forcing are required to simulate the detachment and rise of bubbles formed at the bottom surface. The forces are typically so large that neglecting the effects of varying pressure in the system becomes questionable. In this paper, we examine the effect of large pressure variations induced by gravity using pseudopotential lattice Boltzmann simulations. These pressure variations lead to height dependent conditions for phase coexistence and nucleation of either gas or liquid domains. Because these effects have not previously been studied in the context of these simulation methods, we focus here on the phase stability in a one-dimensional system, rather than the additional complexity of bubble or droplet dynamics. Even in this simple case, we find that the different forms of gravitational forces employed in the literature lead to qualitatively different phenomena, leading to the conclusion that the effects of gravity induced pressure variations on phase-change phenomena should be very carefully considered when trying to advance boiling and cavitation as well as liquefaction simulations to become quantitative tools.

Temporally Stochastic Cellular Automata: Classes and Dynamics

This article introduces temporally stochastic cellular automata where a cell of the cellular system can be updated using one of the two elementary cellular automata rules, say [Formula: see text] and [Formula: see text]. The [Formula: see text] is the default rule and [Formula: see text] is temporally applied to the overall system with some probability that acts as noise to the system. As the mathematical analysis of such system is a difficult task, we use qualitative and quantitative simulations to study the dynamics of these temporally stochastic cellular automata. We are specially interested to exploring if it is possible that two periodic (resp. chaotic) rules together depict chaotic (resp. periodic) dynamics. To answer these questions, we fully classify temporally stochastic cellular automata. The study further observes phase transition and various types of class transition in the temporally stochastic cellular automata.

Graphene and graphene oxide energetic interactions with polymers through molecular dynamics simulations

Molecular dynamics simulations of graphene (G) or graphene oxide (GO) plates with epoxy, PDMS, or ABS were separately performed to investigate plate/polymer interactions. The interactions were qualitatively assessed in larger models by visual observation of a G or GO plate’s orientation with respect to polymer surfaces. They were also quantitatively evaluated in smaller models for changes in potential and non-bonding energy during mixing. The quantitative simulations allowed for the determination of energies of interaction that could not be detected in the larger qualitative studies, including aspects of plate alignment with respect to the polymer at the initiation of interactions. Both the qualitative and quantitative simulations independently demonstrated polymer matrix plate composites (PMPCs) doped with GO resulted in greater stabilizing interactions than those doped with G. In epoxy and ABS, GO showed 40–50% stronger interactions than G, while in PDMS, G had a strong de-stabilizing effect.

CyberChain: Cybertwin Empowered Blockchain for Lightweight and Privacy-Preserving Authentication in Internet of Vehicles

Internet of Vehicles (IoVs) presents promising opportunities for vehicle to everything (V2X) applications, wherein authentication acts as the cornerstone to realize trustworthy vehicular context and to support advanced applications. However, existing authentication schemes mainly depend on centralized servers with both security and privacy issues. In this paper, we propose a CyberTwin (CT) empowered blockchain framework for authentication, namely CyberChain, to reduce both the communication and storage cost while maintaining vehicular privacy. By designing a blockchain system in the cyberspace, we decouple the consensus process from the physical world, so that the operation cost of blockchain can be reduced. A Privacy-Preserving Parallel Pedersen Commitment (P4C) algorithm is designed to protect the privacy of vehicles and accelerate the authentication process. To further enhance the operation efficiency of CyberChain, we propose a Diffused Practical Byzantine Fault Tolerance (DPBFT) mechanism to reach consensus in the cyberspace that can reduce consensus latency. The proposed cyberchain framework and the associated mechanisms are evaluated by qualitative analysis and simulations. The evaluation results demonstrated that the proposed cyberchain based framework significantly improves the authentication performance in terms of authentication latency, privacy, communication overhead and storage cost.

A Mathematical Model for the Growth Dynamics of Demand in the Fashion Industry within the Era of the COVID-19 Pandemic

The outbreak of COVID-19 infection and its effects have not spared any economy on the globe. The fourth variant has just announced its appearance with its high death toll and impact on economic activities. The basic reproductive number R 0 , which measures the transmission potential of an infectious disease, is extremely important in the study of epidemiology. The main purpose of this study was to derive R 0 and assess the stability of the model around its equilibrium points. The motivation was to simulate the effect of COVID-19 on the demand for fashion products and how its application has impacted the COVID-19 pandemic. A five-compartment susceptible-infection-recovery-susceptible-based model was formulated in an integrated environment with application of fashion-based personal protective equipment (FPPEs) and government policy regulation, using ordinary differential equations. Solution techniques included a mix of qualitative analysis and simulations with data from various publications on COVID-19. The study revealed that the disease-free equilibrium was both locally and globally asymptotically stable (LAS and GAS) for R 0 ≤ 1 , while the disease-endemic equilibrium was both LAS and GAS for R 0 ≥ 1 . As the demand for FPPEs increases, R 0 decreases, and vice versa. The sensitivity analysis indicated that R 0 was very sensitive to the rate of application of FPPEs. This confirms the significance of high demand for FPPEs in reducing the transmission of COVID-19 infection. Again, the pandemic has had both positive and negative impacts on the demand for fashion products; however, the negative impact outweighed the positive impact. Another discovery was that government policy stringency was significant in increasing demand for FPPEs. The sensitivity analyses suggested prioritization of FPPEs application together with all recommended PPEs. We recommend inter alia that FPPEs be used together with other nonpharmaceutical interventions. Operators in the fashion industry must be dynamic in adjusting to the new trends of taste for fashion products. Finally, governments should maintain high policy stringency.

Morphometric description of strength and degradation in porous media

The influence of the microstructural geometry on the behavior of porous media is widely recognized, particularly in geomaterials, but also in biomaterials and engineered materials. Recent advances in imaging techniques, such as X-ray microcomputed tomography, and in modeling make it possible to capture the exact morphometry of the microstructure with high precision. However, most existing continuum theories only partially account for the morphometry. We propose here a unifying approach to link the strength of porous materials with the necessary and sufficient microstructural information, using Minkowski functionals, as per Hadwiger’s theorem. A morphometric strength law is inferred from synthetic microstructures with a wide range of porosities and heterogeneities, through qualitative 2D phase-field simulations. Namely, damage is modeled at the microstructural level by tracking the solid-pore interfaces under mechanical loading. The strength is found to be best described by an exponential function of the morphometers, thus generalizing early works on metals and ceramics. We then show that the predictiveness of this relationship may extend to real porous media, including rocks, bones, and ceramics.

Global properties of a virus dynamics model with self-proliferation of CTLs

A viral infection model with self-proliferation of cytotoxic T lymphocytes (CTLs) is proposed and its global dynamics is obtained. When the per capita self-proliferation rate of CTLs is sufficient large, an infection-free but immunity-activated equilibrium always exists and is globally asymptotically stable if the basic reproduction number of virus is less than a threshold value, which means that the immune effect still exists though virus be eliminated. Qualitative numerical simulations further indicate that the increase of per capita self-proliferation rate may lead to more severe infection outcome, which may provide insight into the failure of immune therapy.

A qualitative study and numerical simulations for a time-delayed dispersive equation

This paper deals with the stability analysis of a nonlinear time-delayed dispersive equation of order four. First, we prove the well-posedness of the system and give some regularity results. Then, we show that the zero solution of the system exponentially converges to zero when the time tends to infinity provided that the time-delay is small and the damping term satisfies reasonable conditions. Lastly, an intensive numerical study is put forward and numerical illustrations of the stability result are provided.

The double fractional Cattaneo model on anomalous transport of compounds in spiny dendrites structure

The double fractional Cattaneo model is first introduced to simulate the anomalous transport of inert compounds in spiny dendrites comb structure. Formulated governing equations with relaxation parameter are solved by Laplace transform and Mellin transform methods. The particle distribution and mean square displacement (MSD) are represented by Fox H-function and Mittag-Leffler function, respectively. We find that MSD has many forms, which can be used to describe complex diffusion in spiny dendrites problems. Considering the application in spinous dendrites, qualitative simulations are made and the physical significance of fractional parameters and delay parameters are obtained. Fractional parameter β represents the density of dendritic spines and relaxation parameter τ reflects the velocity of reaching spines. Besides, the effects of the fractional parameters and relaxation parameter on dynamic characteristics are graphically analyzed and discussed.

Detailed spectra, electronic properties, qualitative non-covalent interaction analysis, solvatochromism, docking and molecular dynamics simulations in different solvent atmosphere of cenobamate

Cenobamate, which is a voltage-gated sodium channel (VGSC) blocker, finds its full application in several anti-epileptic medications. This manuscript tries to look at the structure and other physicochemical properties of this compound with electronic structure methods and molecular mechanics tools. Density functional theory was used to optimise the ground-state geometry of the molecule using dispersion-corrected ω-B97XD functional using aug-cc-pVDZ basis sets, from which frontier molecular orbitals were studied. This study gave a myriad of information about the electronic properties and descriptors that are well enough to predict the bioactivity of the molecule. Electronic excitations between different energy levels, resulting from the interaction between the electrons and electromagnetic radiation, were analysed using time-dependent density functional theory with CAM-B3LYP functional. Numerous parameters like Fukui, ALIE and MESP provided an ample amount of information about the reactivity preferences of the molecule. Molecular docking studies predicted the biological activity of the molecule and its possible use as an anti-depressant. As it was found to show biological activity, its liquid-state behaviour in different solvents was mapped using molecular dynamics simulations.

A Unified Predefined-Time Convergent and Robust ZNN Model for Constrained Quadratic Programming

A variety of realistic industrial problems can be constructed into quadratic programming (QP) problems, especially their time-varying versions. A zeroing neural network (ZNN) as a good approach for dynamic problems can solve QP problems subject to equality constraints in the past. In this article, we propose a unified predefined-time convergent and robust ZNN (PTCR-ZNN) model for solving time-varying QP problems subject to equality or inequality constraints. Compared with the normal ZNN model, the PTCR-ZNN model mainly has advantages in the following three aspects: 1) solving QP problems with or without inequality constraints in a unified model; 2) converging to the optimal solution of QP problems within a predefined time that can be determined in advance; and 3) resisting many external noises with tiny and predictable residual error. These improvements have been rigorously proved in theory. By conducting both qualitative and quantitative simulations with comparisons, the superior properties of the PTCR-ZNN model are further validated. Finally, the application of the PTCR-ZNN model to image fusion task illustrates the efficiency together with its applicability.

Mathematical approach to the analysis of terrorism dynamics

In pursuance of differing goals and ideologies, nations, religions, political groups rising against themselves have characterized human existence over the years. In recent times, such animosities have widened to the point of dehumanization and wide destruction of properties with threat and fear as strong arsenal. Terrorism has become very prevalent especially its many new forms that use drones and chemical and biological weapons. A mathematical model helps to explain a system (mutable natural occurrences such as dynamics in terrorism) by a painstaking study of different components of terrorists’ splinter cells ranging from foot soldiers, intelligent mission units, kidnappers, and suicide bombers, to make predictions about its behaviour. This work argues the use of mathematical models to study terrorism by providing insights into evolving trends. The specific objective of this study is to investigate how to improve the understanding of the policymakers on terrorism mitigation. The study adopted a mathematical modelling and theoretical design. The mathematical equations formulated were based on the underlying principles of terrorism, which include recruitment, desertion from group, counter-terrorism measures, et cetera. The models were sets of ordinary nonlinear differential equations based on assumptions emanating from the literature reviewed on terrorism. Qualitative analysis and relevant numerical simulations of the model were carried out. The results illustrate new approach on which terrorism can be reduced.