Jump Process Research Articles

Dielectric permittivity and relaxation process investigation of C-doped TlInS2 crystals

In this work, permittivity of TlInS2 and TlInS2 (5%[Formula: see text]C) crystals has been investigated at the AC field with 100–500 K temperatures ranges. [Formula: see text] temperature dependencies of TlInS2 and TlInS2 (5%[Formula: see text]C) crystals are studied at the frequency range of 25–106 Hz. Activation energy and ionic conductivity nature of sample were analyzed. Moreover, permittivity of these samples was studied at the perpendicular direction of axis “[Formula: see text]” of TlInS2 (5%[Formula: see text]C) crystals. The jumping process in the TlInS2 (5%[Formula: see text]C) crystals was studied using [Formula: see text] temperature dependencies.

Interest-Rate Products Pricing Problems with Uncertain Jump Processes

Uncertain differential equations (UDEs) with jumps are an essential tool to model the dynamic uncertain systems with dramatic changes. The interest rates, impacted heavily by human uncertainty, are assumed to follow UDEs with jumps in ideal markets. Based on this assumption, two derivatives, namely, interest-rate caps (IRCs) and interest-rate floors (IRFs), are investigated. Some formulas are presented to calculate their prices, which are of too complex forms for calculation in practice. For this reason, numerical algorithms are designed by using the formulas in order to compute the prices of these structured products. Numerical experiments are performed to illustrate the effectiveness and efficiency, which also show the prices of IRCs are strictly increasing with respect to the diffusion parameter while the prices of IRFs are strictly decreasing with respect to the diffusion parameter.

Acceleration Response Analysis of a Steel-wood Composite Floor System under Human-induced Vibration

To investigate the vibration response of a steel-wood composite floor system under walking and jump excitation. The ABAQUS subroutine Vdload is used to simulate the human walking and jumping process on the structure, while the human-induced excitation is equated to a combination of a Fourier-scale load model and a biomechanical model of mass, stiffness, and damping (MSD) to study the human-induced vibration response under human-structure interaction (HSI). The effects of walking and jumping excitation on the peak acceleration of the structure are also considered. The results show that the peak acceleration of the structure considering the human-structure interaction is significantly smaller than that without the human-structure interaction, and the results obtained from the numerical simulation analysis are more consistent with the experimental situation. In addition, the acceleration response of the steel-wood composite floor system under jump excitation is larger than that of walking excitation, and the peak acceleration increases with the increase of jump frequency. Then: any floor, no matter its structural configuration and material, is prone to vibrate under walking and jumpimg excitation if the excitation frequency is in resonance with one of its main frequencies. The induced vibrations affect the floor serviceability, when the induced peak acceleration exceeds the comfort requirements. The magnitude of the induced peak acceleration is the larger, the larger the acting force and the lower the vibrating floor mass and its damping.

Parametric exploration of a simple model of human jumping on an oscillating structure

Lightweight and long-span civil structures, such as grandstand tiers, are susceptible to vertical vibrations due to jumping or bobbing of human spectators. After a careful review of experimental evidence, including new data, a simple mathematical model is investigated in order to characterise human-structure interactions observed during human rhythmic jumping on a perceptibly moving surface. A passive mass-spring-damper is used to model a human jumper whilst subjected to an input structural oscillation. The coupled system is modelled as a piecewise-smooth contact dynamics problem allowing for the loss of contact during rhythmic jumping. Parametric sweeps are interpreted using methods from non-linear dynamical systems theory, including bifurcation analysis and the use of Poincaré maps. Hysteresis and coexistence of qualitatively different stable periodic motions over a broad range of parameter values of the system are observed. The presence of such coexisting non-intuitive motions is verified by preliminary experimental results that indicate period-doubled jumping behaviour (a repeating sequence of large-jump-small-jump) of test subjects both above and below a structure’s natural frequency. At large amplitudes of structural motion, the model shows that the behaviour can become chaotic. This provides an explanation for experimental findings that it is difficult to jump periodically around the natural frequency of a supporting structure. The simple model provides a detailed map of where different motions occur in parameter space, which should be amenable to further experimental validation.

Towards more valid simulations of slopestyle and big air jumps: Aerodynamics during in-run and flight phase

ObjectivesThis study aimed to investigate air drag and lift during the in-run and flight phase of ski and snowboard slopestyle and big air, to allow more valid modeling of jumps and hence reduce injury risk. DesignWe present an experimental, multiple single athlete study based on wind tunnel measurements of 4 skiers and 3 snowboarders. MethodsMeasurements were carried out in a closed loop wind tunnel, measuring airflow speed and 3D forces acting on the athletes. Athletes performed trials in typical postures at 35, 60 and 85 km/h wearing slim-, regular- and wide fit apparel. Drag and lift area (cDA; cLA) were calculated and analyzed using linear and multiple regression to describe their dependencies on posture, apparel and speed. ResultscDA values were higher than earlier assumed and ranged from 0.3 to 0.95 m2 for skiers and from 0.35 to 0.55 m2 for snowboarders, primarily dominated by posture, and followed by apparel. cLA ranged from −0.1 to 0.45 m2 for skiers and from 0.04 to 0.17 m2 for snowboarders. To facilitate more valid jump modeling posture- and apparel-dependent formulations for air drag coefficients were provided and the consequences of sport specific differences on modeling were highlighted. ConclusionsApplying the air drag coefficients and relationships determined in this study will help to improve validity of jump modeling in big air and slopestyle. The variability in aerodynamic forces in slopestyle and big air is caused by differences between sports, posture and apparel.

Flexible and efficient regulation of coalescence-induced droplet jumping on superhydrophobic surfaces with string

Coalescence-induced droplet jumping on superhydrophobic surfaces has good application potential in the fields of heat transfer enhancement, self-cleaning, water collection, and microfluidics. However, the direction of droplets jumping on the plane is uncontrollable and the energy conversion rate is low, which limits the application of droplets jumping. In this work, we report a flexible approach to efficiently regulate the direction and velocity of droplet jumping on superhydrophobic surfaces using superhydrophobic string. By changing the inclination angle of the string, the jumping direction of the coalescent droplet shows a maximum deviation of 68.1° from the surface normal. The maximum energy conversion rate is 32.1%, which is about 5.4 times higher than that of jumping on the plane. We analyzed the mechanism of the efficient regulation and found that the string affected the hydrodynamic characteristics of the jumping process, thus changing the jumping direction and the speed of the coalescent droplet.

Are Torque-Driven Simulation Models of Human Movement Limited by an Assumption of Monoarticularity?

Subject-specific torque-driven computer simulation models employing single-joint torque generators have successfully simulated various sports movements with a key assumption that the maximal torque exerted at a joint is a function of the kinematics of that joint alone. This study investigates the effect on model accuracy of single-joint or two-joint torque generator representations within whole-body simulations of squat jumping and countermovement jumping. Two eight-segment forward dynamics subject-specific rigid body models with torque generators at five joints are constructed—the first model includes lower limb torques, calculated solely from single-joint torque generators, and the second model includes two-joint torque generators. Both models are used to produce matched simulations to a squat jump and a countermovement jump by varying activation timings to the torque generators in each model. The two-joint torque generator model of squat and countermovement jumps matched measured jump performances more closely (6% and 10% different, respectively) than the single-joint simulation model (10% and 24% different, respectively). Our results show that the two-joint model performed better for squat jumping and the upward phase of the countermovement jump by more closely matching faster joint velocities and achieving comparable amounts of lower limb joint extension. The submaximal descent phase of the countermovement jump was matched with similar accuracy by the two models (9% difference). In conclusion, a two-joint torque generator representation is likely to be more appropriate for simulating dynamic tasks requiring large joint torques and near-maximal joint velocities.

Dynamic stability of bio-inspired biped robots for lateral jumping in rugged terrain

Bio-inspired jumping robots should achieve stable jumping in field environment. Robots easily lose stability because of the complex terrain and large speed change. Lateral jumping of the robot with four-bar jumping legs under rugged terrain was used as the research object, and a method to achieve good dynamic stability by adjusting the leg attitudes of the robot was proposed. The stability criteria were proposed hierarchically on the basis of the analysis of the jumping mechanism of creatures. Then, the dynamic model of the robot jumping on the slope was established, and the effect of different parameters on dynamic stability was analyzed to carry out the primary selection of parameters. Then, the method of maintaining dynamic stability was proposed. On the basis of ensuring large motion distances and stable driving torques, leg attitudes were considered the optimization parameters. Thus, the robot does not overturn or turn over uncontrollably in the take-off, buffering, and flight phases. Analysis results of robot jumping in plane and 3D space under different terrains prove the feasibility of this method. In particular, the dynamic stability of the robot is better if the take-off direction is considered an optimization parameter. The simulation results also proved the accuracy of the analysis results. The research results provide a theoretical basis to realize stable continuous jumping of bio-inspired biped robots.

Rodeo or Ascot: which hat to wear at the crypto race?

This paper sheds light on the dynamics of the cryptocurrency (CC) sector. By modeling its dynamics via a stochastic volatility with correlated jumps (SVCJ) model in combination with several rolling windows, it is possible to capture the extreme ups and downs of the CC market and to understand its dynamics. Through this approach, we obtain time series for each parameter of the model. Even though parameter estimates change over time and depend on the window size, several recurring patterns are observable which are robust to changes of the window size and supported by clustering of parameter estimates: during bullish periods, volatility stabilizes at low levels and the size and volatility of jumps in mean decreases. In bearish periods though, volatility increases and takes longer to return to its long-run trend. Furthermore, jumps in mean and jumps in volatility are independent. With the rise of the CC market in 2017, a level shift of the volatility of volatility occurred. All codes are available on this http URL.

Stochastic exponential synchronization for delayed neural networks with semi-Markovian switchings: Saturated heterogeneous sampling communication

By implementing heterogeneous sampling communication mechanism, this article addresses the exponential synchronization issue of drive–response chaotic neural networks (CNNs) with interval time-varying delays by simultaneously taking into account the semi-Markovian switchings and saturating actuators. More specifically, a semi-Markovian jumping model whose transition rates (TRs) are not constant but depends on the sojourn time (ST) is introduced to characterize the stochastic changing among the interaction of CNNs, which makes the NNs model under consideration more suitable for some actual circumstances. More particularly, we assume that the sampling intervals are heterogeneous and time-varying, which may be more practical in real-life applications than homogeneous sampling policy. Additionally, by introducing some new terms, one novel time-dependent Lyapunov–Krasovskii function (LKF) is ingeniously constructed, which can fully capture the characteristic information of heterogeneous sampling pattern. Benefitting from the introduced relaxed free-weighting matrices (FWM) and resorting to the formed LKF, some sampling-interval-dependent sufficient conditions for controller design of the resulting semi-MJNNs error system are established and expressed by linear matrix inequalities (LMIs). These LMIs-based constraints can be effectively checked by utilizing the available software packages. Therein, the developed synchronization criteria dependent on both the lower and upper bounds of sampling periods, and the available information about the actual sampling pattern is fully considered. Ultimately, two numerical examples are provided to demonstrate the feasibility and practicability of our theoretical findings.

Universality Classes of Hitting Probabilities of Jump Processes

Quantifying the efficiency of random target search strategies is a key question of random walk theory, with applications in various fields. If many results do exist for recurrent processes, for which the probability of eventually finding a target in infinite space-so called hitting probability-is one, much less is known in the opposite case of transient processes, for which the hitting probability is strictly less than one. Here, we determine the universality classes of the large distance behavior of the hitting probability for general d-dimensional transient jump processes, which we show are parametrized by a transience exponent that is explicitly given.

A multiphase multicomponent flow and transport model for liquid aerosol filtration in coalescing fibrous filters

Pore-scale CFD simulations of liquid aerosol filtration in coalescing fibrous filters are currently limited by severe computational constraints. In this paper, we present a new multiphase multicomponent framework for modeling such dynamic filtration process at the Darcy-scale. Moving from the pore- to the Darcy-scale allows for significant reduction in computational efforts with limited repercussion on the representation of the filter geometry. In our model, two phases coexist within the filter, namely the injected oil-mist and the coalesced oil phase. The oil-mist phase consists of a mixture of a gas phase and oil droplets of different sizes. The governing equations are the standard ones of Darcy flow and transport in porous media, where the filtration processes, such as droplet capture and drainage, are accounted for through ad hoc mass exchange terms, which are coherent with the Jump & Channel model by Kampa et al. (2014). The equations are strongly non-linear and are solved through the IMplicit Pressure Explicit Saturation (IMPES) algorithm. Cell-centered finite volumes are used for discretization in space. The model is validated by replicating experiments of oil-mist filtration available in the literature for both wettable and non-wettable media, as well as for combinations of media with different wettability. Simulations are then performed for a variety of operational conditions, demonstrating both the accuracy and robustness of our implementation.

Roles of host small RNAs in the evolution and host tropism of coronaviruses.

Human coronaviruses (CoVs) can cause respiratory infection epidemics that sometimes expand into globally relevant pandemics. All human CoVs have sister strains isolated from animal hosts and seem to have an animal origin, yet the process of host jumping is largely unknown. RNA interference (RNAi) is an ancient mechanism in many eukaryotes to defend against viral infections through the hybridization of host endogenous small RNAs (miRNAs) with target sites in invading RNAs. Here, we developed a method to identify potential RNAi-sensitive sites in the viral genome and discovered that human-adapted coronavirus strains had deleted some of their sites targeted by miRNAs in human lungs when compared to their close zoonic relatives. We further confirmed using a phylogenetic analysis that the loss of RNAi-sensitive target sites could be a major driver of the host-jumping process, and adaptive mutations that lead to the loss-of-target might be as simple as point mutation. Up-to-date genomic data of severe acute respiratory syndrome coronavirus 2 and Middle-East respiratory syndromes-CoV strains demonstrate that the stress from host miRNA milieus sustained even after their epidemics in humans. Thus, this study illustrates a new mechanism about coronavirus to explain its host-jumping process and provides a novel avenue for pathogenesis research, epidemiological modeling, and development of drugs and vaccines against coronavirus, taking into consideration these findings.

An enhanced differential evolution algorithm with a new oppositional-mutual learning strategy

Global optimization has been a hot research topic in various engineering applications, where differential evolution (DE) is one of the most popular approaches. Actually, it is inevitable for DE to trap into local optima when dealing with complex optimization problems. Dynamic opposite learning (DOL), which is a new variant of opposition-based learning (OBL), has the potential for enhancing DE, due to its strong exploration capability contributed by the asymmetric and dynamic search space. To balance exploration and exploitation, an adjustable weight parameter of the search space is adopted, yet adjusting the value needs a lot of tests and expert experience. Instead of relying on the additional weight parameter, a mutual learning (ML) strategy, which leads individuals to learn from each other deterministically, is combined with DOL for raising exploitation. The trade-off between exploration and exploitation is guaranteed by randomly switching DOL and ML in the population initialization process and the generation jumping process. A hybrid strategy, named oppositional-mutual learning (OML), is thereby generated, and it is applied for the performance improvement of DE. Benchmarks from CEC 2014, including unimodal, multi-modal, hybrid and composition functions, were adopted to evaluate the performance of the oppositional-mutual learning DE (OMLDE). Numerical results with the comparisons to the state-of-the-art counterparts show that OMLDE has significant advantages of converging to the global optimum on most functions, which also validates the superiority of the novel OML strategy.

Stochastic SIR Lévy Jump Model with Heavy-Tailed Increments

This paper considers a general stochastic SIR epidemic model driven by a multidimensional Lévy jump process with heavy-tailed increments and possible correlation between noise components. In this framework, we derive new sufficient conditions for disease extinction and persistence in the mean. Our method differs from previous approaches by the use of Kunita’s inequality instead of the Burkholder–Davis–Gundy inequality for continuous processes, and allows for the treatment of infinite Lévy measures by the definition of new threshold \(\mathcal {\bar{R}}_0\). An SIR model driven by a tempered stable process is presented as an example of application with the ability to model sudden disease outbreak, illustrated by numerical simulations. The results show that persistence and extinction are dependent not only on the variance of the processes increments, but also on the shapes of their distributions.

Generalizing HMMs to Continuous Time for Fast Kinetics: Hidden Markov Jump Processes

The hidden Markov model (HMM) is a framework for time series analysis widely applied to single-molecule experiments. Although initially developed for applications outside the natural sciences, the HMM has traditionally been used to interpret signals generated by physical systems, such as single molecules, evolving in a discrete state space observed at discrete time levels dictated by the data acquisition rate. Within the HMM framework, transitions between states are modeled as occurring at the end of each data acquisition period and are described using transition probabilities. Yet, whereas measurements are often performed at discrete time levels in the natural sciences, physical systems evolve in continuous time according to transition rates. It then follows that the modeling assumptions underlying the HMM are justified if the transition rates of a physical process from state to state are small as compared to the data acquisition rate. In other words, HMMs apply to slow kinetics. The problem is, because the transition rates are unknown in principle, it is unclear, a priori, whether the HMM applies to a particular system. For this reason, we must generalize HMMs for physical systems, such as single molecules, because these switch between discrete states in “continuous time”. We do so by exploiting recent mathematical tools developed in the context of inferring Markov jump processes and propose the hidden Markov jump process. We explicitly show in what limit the hidden Markov jump process reduces to the HMM. Resolving the discrete time discrepancy of the HMM has clear implications: we no longer need to assume that processes, such as molecular events, must occur on timescales slower than data acquisition and can learn transition rates even if these are on the same timescale or otherwise exceed data acquisition rates.

A Structural Approach to Default Modelling with Pure Jump Processes

ABSTRACT We present a general framework for the estimation of corporate default based on a firm’s capital structure, when its assets are assumed to follow a pure jump Lévy processes; this setup provides a natural extension to usual default metrics defined in diffusion (log-normal) models, and allows to capture extreme market events such as sudden drops in asset prices, which are closely linked to default occurrence. Within this framework, we introduce several pure jump processes featuring negative jumps only and derive practical closed formulas for equity prices, which enable us to use a moment-based algorithm to calibrate the parameters from real market data and to estimate the associated default metrics. A notable feature of these models is the redistribution of credit risk towards shorter maturity: this constitutes an interesting improvement to diffusion models, which are known to underestimate short-term default probabilities. We also provide extensions to a model featuring both positive and negative jumps and discuss qualitative and quantitative features of the results. For readers convenience, practical tools for model implementation and GitHub links are also included.

Feature Selection in Jump Models

Jump models switch infrequently between states to fit a sequence of data while taking the ordering of the data into account. We propose a new framework for joint feature selection, parameter and state-sequence estimation in jump models. Feature selection is necessary in high-dimensional settings where the number of features is large compared to the number of observations and the underlying states differ only with respect to a subset of the features. We develop and implement a coordinate descent algorithm that alternates between selecting the features and estimating the model parameters and state sequence, which scales to large data sets with large numbers of (noisy) features. We demonstrate the usefulness of the proposed framework by comparing it with a number of other methods on both simulated and real data in the form of financial returns, protein sequences, and text. The resulting sparse jump model outperforms all other methods considered and is remarkably robust to noise.

Design, Aggregation and Analysis of Power Consumption Data using the Jump Process

This work aims to seek a pragmatic approach to assess electricity consumption at the level of households, buildings and neighborhoods. The main concern consists in proposing aggregation methods based on jump process according to a customer environment that is intrinsically linked to the implementation of a centralized system. The aim of the approach is to present data aggregations that derive their basis from a data model in order to facilitate the processing of electricity data at different scales of analysis. Such a smart meter data management process merits the design of an aggregated database that can store data for a house, a building and a neighborhood. The advantage of this system lies in the facilitation of data interpretation and the ability to guide decision-makers in the management of electricity consumption. An analysis of the behavior of electricity consumption is also proposed based on the monitoring of the electricity consumption of the various devices connected to a smart meter.

Effect and relational analysis of physical parameters on coalescence-induced self-propelled jumping of droplets

Coalescence-induced self-propelled jumping of droplets on superhydrophobic surfaces has been widely concerned because of a great number of potential applications such as in the enhancement of condensation heat transfer, self-cleaning and anti-icing. The droplet jumping phenomenon exists in a gas-liquid two-phase system, and the physical parameters of fluid cannot be ignored. However, there are few reports on the influence of physical parameters on droplet jumping dynamics at present. In this paper, the three-dimensional volume-of-fluid method is used to simulate the coalescence-induced self-propelled jumping behaviors of droplets, then the energy terms are studied, and finally the grey relational analysis method is used to calculate the relation degree of the change of physical parameters (the viscosity and the density) to the real jumping velocity and the real solid-liquid contact time at the droplet departure time, respectively. Based on the changing trend of jumping velocity, the process of coalescence-induced self-propelled jumping can be divided into four stages, namely, the expansion of liquid bridge, the impact between the liquid bridge and the surface, the droplet departure from the surface, and the deceleration and oscillation in the air. Under the condition of dimensionless time, the dynamic characteristics of coalescence and jumping of droplets are affected only by <i>Oh</i> number, which is independent of the viscosity and the density. In addition, the change of <i>Oh</i> number only affects the above third stage of droplet departure from the surface. Under the condition of real time, the varied viscosity has no connection with the real time of droplet coalescence, and it only changes the real time of the third stage before droplet jumping. Meanwhile, the dimensionless jumping velocity decreases with <i>Oh</i> number increasing, while the real jumping velocity increases when the viscosity and the density both descend. According to the calculated results of grey relational degree, the relation between the change of viscosity and the real jumping velocity is greater, while the relation between the change of density and the real contact time is greater. This work not only is favorable for a better understanding of droplet jumping, but also provides more ideas and theoretical bases for follow-up relevant studies.