Deterministic Motion Research Articles

Seismic Risk Assessment for the City of Sofia, Bulgaria

An earthquake is the most destructive natural phenomenon, with its sudden onset and rapid spread impacting large areas. Among the various geohazards, seismic ones dominate in terms of their social and economic effects on human life and the urban environment. In the present study, a deterministic earthquake ground motion scenario for the city of Sofia (the capital of Bulgaria) is presented. The earthquake risk posed to Sofia is quantified by considering the city’s seismic context, which contributes to its hazard and risk. The assessment of seismic hazards and the generation of earthquake scenarios make up the first step of seismic risk evaluations, as risk reduction strategies can only be developed with a better understanding of these threats. Risk assessment and its associated management comprise the most effective approach to estimating the impact of seismic hazards on the city of Sofia, which exhibits high seismic activity. Our findings provide a basis for local governments to review their susceptibility and preparedness. The consideration of earthquake scenarios in the creation of policies for seismic risk reduction will allow us to focus on the prevention of earthquake effects rather than on the activities following these disasters.

The instant probability of object with deterministic motion. Another way to understand quantum mechanics

Usually, the idea of probability is applied only to the random motion in physics. Here, the instant probability concept is obtained by applying the probability approach to objects with deterministic motion trajectories via a coin falling to the ground as an example. Applied this concept to understand quantum mechanics, the following conclusions are inferred: The wave function/quantum state represents an instant probability distribution of physical quantities after taking into account Planck's constant; the collapse of the wave function is a special case of any function on time-dependent probability prediction has to collapse when performing measurement. The arrangement of measurements will affect the observed results; Quantum mechanics reveals the existence of non-local effects, which have nothing to do with the interaction in classical physics. Quantum mechanics cannot answer questions such as whether the cat is dead or alive before opening the box. The correct answer to this kind of question has to be from classic physics.

Thermodynamic Bounds on Generalized Transport: From Single-Molecule to Bulk Observables.

We prove that the transport of any differentiable scalar observable in d-dimensional nonequilibrium systems is bounded from above by the total entropy production scaled by the amount the observation "stretches" microscopic coordinates. The result-a time-integrated generalized speed limit-reflects the thermodynamic cost of transport of observables, and places underdamped and overdamped stochastic dynamics on equal footing with deterministic motion. Our work allows for stochastic thermodynamics to make contact with bulk experiments, and fills an important gap in thermodynamic inference, since microscopic dynamics is, at least for short times, underdamped. Requiring only averages but not sample-to-sample fluctuations, the proven transport bound is practical and applicable not only to single-molecule but also bulk experiments where only averages are observed, which we demonstrate by examples. Our results may facilitate thermodynamic inference on molecular machines without an obvious directionality from bulk observations of transients probed, e.g., in time-resolved x-ray scattering.

On the Existence and Uniqueness of Stationary Distributions for Some Piecewise Deterministic Markov Processes with State-Dependent Jump Intensity

In this paper, we consider a subclass of piecewise deterministic Markov processes with a Polish state space that involve a deterministic motion punctuated by random jumps, occurring in a Poisson-like fashion with some state-dependent rate, between which the trajectory is driven by one of the given semiflows. We prove that there is a one-to-one correspondence between stationary distributions of such processes and those of the Markov chains given by their post-jump locations. Using this result, we further establish a criterion guaranteeing the existence and uniqueness of the stationary distribution in a particular case, where the post-jump locations result from the action of a random iterated function system with an arbitrary set of transformations.

Numerical Modeling of Energetic Charged-particle Transport with SPECTRUM Software: General Approach and Artificial Effects due to Field Discretization

Test-particle simulations are an important tool for magnetospheric and heliophysics research. In this paper, we present the Space Plasma and Energetic Charged particle TRansport on Unstructured Meshes (SPECTRUM) software as a novel tool for performing these types of simulations in arbitrary astrophysical environments, specified either analytically or numerically (i.e., on a grid). We discuss and benchmark SPECTRUM’s interface with meshed magnetohydrodynamic backgrounds, including output from the Block Adaptive Tree Solar-wind Roe-type Upwind Scheme (BATS-R-US) code. We also investigate the effects of field discretization on both deterministic and stochastic particle motion, with emphasis on space science applications, concluding that the discretization error typically enhances the diffusive behavior of the ensemble.

From Brownian to Deterministic Motor Movement in a DNA-Based Molecular Rotor.

Molecular devices that have an anisotropic periodic potential landscape can be operated as Brownian motors. When the potential landscape is cyclically switched with an external force, such devices can harness random Brownian fluctuations to generate a directed motion. Recently, directed Brownian motor-like rotatory movement was demonstrated with an electrically switched DNA origami rotor with designed ratchet-like obstacles. Here, we demonstrate that the intrinsic anisotropy of DNA origami rotors is also sufficient to result in motor movement. We show that for low amplitudes of an external switching field, such devices operate as Brownian motors, while at higher amplitudes, they behave deterministically as overdamped electrical motors. We characterize the amplitude and frequency dependence of the movements, showing that after an initial steep rise, the angular speed peaks and drops for excessive driving amplitudes and frequencies. The rotor movement can be well described by a simple stochastic model of the system.

Modelling non-local cell-cell adhesion: a multiscale approach

Cell-cell adhesion plays a vital role in the development and maintenance of multicellular organisms. One of its functions is regulation of cell migration, such as occurs, e.g. during embryogenesis or in cancer. In this work, we develop a versatile multiscale approach to modelling a moving self-adhesive cell population that combines a careful microscopic description of a deterministic adhesion-driven motion component with an efficient mesoscopic representation of a stochastic velocity-jump process. This approach gives rise to mesoscopic models in the form of kinetic transport equations featuring multiple non-localities. Subsequent parabolic and hyperbolic scalings produce general classes of equations with non-local adhesion and myopic diffusion, a special case being the classical macroscopic model proposed in Armstrong et al. (J Theoret Biol 243(1): 98–113, 2006). Our simulations show how the combination of the two motion effects can unfold. Cell-cell adhesion relies on the subcellular cell adhesion molecule binding. Our approach lends itself conveniently to capturing this microscopic effect. On the macroscale, this results in an additional non-linear integral equation of a novel type that is coupled to the cell density equation.

Deterministic ground motion modeling with target earthquakes and site effects in eastern Azerbaijan

In the context of assessing seismic hazard, accurately predicting ground motion stands out as a crucial task. Achieving precision in ground motion modeling proves valuable in revealing the actual pattern, even when faced with insufficient data on soil structure, provided there is precise information about the seismic source. This study introduces a methodology for calculating local- and near-field ground motion, expressed in peak ground acceleration (PGA) and intensity values. The deterministic approach is employed, incorporating source characteristics and one-dimensional (1D) site effects. For the chosen test area, the Ismayilli-Shamakhi region on the southeastern slope of the Greater Caucasus in Azerbaijan, two seismic scenarios are investigated: the 1902 Shamakhi earthquake (magnitude M = 6.8) and the November 25, 2000 Baku-Caspian earthquake (two shocks with moment magnitude Mw = 6.08 and 6.18). Different soil types are considered to validate the proposed methodological procedures. The analysis involves the computation of peak ground acceleration motion for two scenario earthquakes: a local-field event with M = 6.8 and a near-field event with Mw = 6.5, representing the average magnitude of the 2000 Baku-Caspian earthquake. The computed peak ground acceleration values are then used to derive intensities. Notably, the 1902 Shamakhi earthquake and the 2000 Baku-Caspian earthquake exhibit similar trends on surface PGA values. The local-field scenario estimates PGA values ranging from 77 to 328 gal, corresponding to MSK-64 scale intensity levels of VII-IX. The near-field scenario, with PGA values ranging from 28 to 62 gal, aligns with MSK-64 intensity levels of VI–VII. In the final assessment, the amplification factor in the study area varies between 0.55 and 0.83. The seismic hazard level is identified as high in the southern and southeastern regions, particularly in areas with soft shallow and medium-depth soils, indicating a high potential for ground motion amplification.

Universal dynamics and deterministic motion control of decoherently seeded temporal dissipative solitons via spectral filtering effect

Temporal dissipative solitons have been widely studied in optical systems, which exhibit various localized structures and rich dynamics, and have shown great potential in applications including optical encoding and sensing. Yet, most of the soliton states, as well as the switching dynamics amongst, were fractionally captured or via self-evolution of the system, lacking of control on the soliton motion. While soliton motion control has been widely investigated in coherently seeded optical cavities, such as microresonator-based dissipative solitons, its implementation in decoherently seeded systems, typically the soliton mode-locked lasers, remains an outstanding challenge. Here, we report the universal dynamics and deterministic motion control of temporal dissipative solitons in a mode-locked fibre laser by introducing a scanned spectral filtering effect. We investigate rich switching dynamics corresponding to both the assembly and the disassembly of solitons, revealing a complete and reversible motion from chaotic states to soliton and soliton-molecule states. Significant hysteresis has been recognized in between the redshift and blueshift scan of the motorized optical filter, unveiling the nature of having state bifurcations in dissipative and nonlinear systems. The active soliton motion control enabled by filter scanning highlights the potential prospects of encoding and sensing using soliton molecules.

Enhanced thermally-activated skyrmion diffusion with tunable effective gyrotropic force

Magnetic skyrmions, topologically-stabilized spin textures that emerge in magnetic systems, have garnered considerable interest due to a variety of electromagnetic responses that are governed by the topology. The topology that creates a microscopic gyrotropic force also causes detrimental effects, such as the skyrmion Hall effect, which is a well-studied phenomenon highlighting the influence of topology on the deterministic dynamics and drift motion. Furthermore, the gyrotropic force is anticipated to have a substantial impact on stochastic diffusive motion; however, the predicted repercussions have yet to be demonstrated, even qualitatively. Here we demonstrate enhanced thermally-activated diffusive motion of skyrmions in a specifically designed synthetic antiferromagnet. Suppressing the effective gyrotropic force by tuning the angular momentum compensation leads to a more than 10 times enhanced diffusion coefficient compared to that of ferromagnetic skyrmions. Consequently, our findings not only demonstrate the gyro-force dependence of the diffusion coefficient but also enable ultimately energy-efficient unconventional stochastic computing.

Mooring line failure diagnosis and motion control of semi-submersible platform based on the predictive model

Real-time prediction of deterministic platform motions in the coming future seconds was essential. The long-short-term memory networks (LSTM) and the fully connected neural networks were employed to establish the neural networks for motion predictions. The predicted results were compared with those calculated by AQWA. The results demonstrated that the predictive model could offer a fast and accurate prediction of the motion response of the semi-submersible platform. The sliding window model was built to predict motion without real input and monitor mooring line failure. The results showed that the predictive model based on the sliding window model can also achieve accurate prediction of platform motion. With the help of the predictive model, a control module written in Python was activated on the basis of the identification of mooring line failure. The control results show that the proportional-integral-derivative (PID) controller was very good at recovering the kinematic performance after mooring line failure and suppressing the continuous failure of the remaining mooring lines.

Magnetic Bucket Brigade Transport Networks for Cell Transport

AbstractControlled transport of biological cells in biomedical applications such as sorting, cell sequencing, and assembly of multicellular structures is a technological challenge. Research areas such as drug delivery or tissue engineering can benefit from precise cell location resulting in faster response rates or more complex tissue structures. Using computational methods, different soft magnetic elements with curved edges are designed to form a transport network, enabling transport and all functionalities for the manipulation of microbeads and cells on surfaces by rotational magnetic fields. Building blocks with bimodal functionalities due to segments of differently curved edges permit breakpoints as well as switchable transport via splitting and combining elements. Connecting the elements, networked paths are realized which allow variable movement patterns of magnetic carriers and cells. The direction of magnetic field rotation is altered to direct the beads and cells into different transport lines, and the exact timing is not critical. The networks are used to achieve deterministic movement of microbeads and cells with minimal intervention. Programmed transport over one millimeter with cell transport velocities of several micrometers per s is demonstrated. Based on scalable microchip technology, the networks can be integrated with CMOS‐compatible materials and straightforwardly combined with sensing and diagnostic structures.

Role of the interaction between lumbar kinematics and accelerometer-measured physical activity in bodily pain, physical functioning and work ability among health care workers with low back pain.

The aim of this study was to examine the associations of spinal kinematics and physical activity (PA) with bodily pain, physical functioning, and work ability among health care workers with low back pain (LBP). Spinal kinematics and PA were measured with a wireless Inertial Measurement Unit system (ValedoMotion®) and a waist-worn tri-axial accelerometer (Hookie AM20), respectively. Their association was assessed in relation to Work Ability Index (WAI), bodily pain and physical functioning (RAND-36) in 210 health care workers with recurrent LBP. Greater lumbar movement variability/less deterministic lumbar movement (in angular velocity) during a "Pick Up a Box" functional task was correlated with higher amounts of step counts (r=-0.29, p=0.01) and moderate PA (r=-0.24, p=0.03). A higher amount of PA (p=0.03) as well as less movement control impairment (p=0.04) and movement variability (p=0.03) were associated with greater work ability, whilst greater vigorous PA was the only parameter to explain higher physical functioning (p=0.02). PA and movement variability were relative to each other to explain bodily pain (p=0.01). These findings show the importance of considering the interaction between lumbar kinematics and physical activity while planning strategies to improve bodily pain, physical functioning and work ability among health care workers with LBP.

Mathematical model of mechano-sensing and mechanically induced collective motility of cells on planar elastic substrates.

Cells mechanically interact with their environment to sense, for example, topography, elasticity and mechanical cues from other cells. Mechano-sensing has profound effects on cellular behaviour, including motility. The current study aims to develop a mathematical model of cellular mechano-sensing on planar elastic substrates and demonstrate the model's predictive capabilities for the motility of individual cells in a colony. In the model, a cell is assumed to transmit an adhesion force, derived from a dynamic focal adhesion integrin density, that locally deforms a substrate, and to sense substrate deformation originating from neighbouring cells. The substrate deformation from multiple cells is expressed as total strain energy density with a spatially varying gradient. The magnitude and direction of the gradient at the cell location define the cell motion. Cell-substrate friction, partial motion randomness, and cell death and division are included. The substrate deformation by a single cell and the motility of two cells are presented for several substrate elasticities and thicknesses. The collective motility of 25 cells on a uniform substrate mimicking the closure of a circular wound of 200µm is predicted for deterministic and random motion. Cell motility on substrates with varying elasticity and thickness is explored for four cells and 15 cells, the latter again mimicking wound closure. Wound closure by 45 cells is used to demonstrate the simulation of cell death and division during migration. The mathematical model can adequately simulate the mechanically induced collective cell motility on planar elastic substrates. The model is suitable for extension to other cell and substrates shapes and the inclusion of chemotactic cues, offering the potential to complement in vitro and in vivo studies.

Intelligent Trajectory Design for Mobile Energy Harvesting and Data Transmission

Energy harvesting technology enables wireless sensor networks (WSNs) to be self-sustainable, for maintaining long-term key performance indicators, such as the data throughput and sensing coverage. Due to the highly dynamic and complex environment, energy sources (ES) cannot provide stable energy supply, which needs the efficient learning algorithm to enable system adaptations. This article reports on the development of reinforcement learning (RL) methodology to long-term data collection in self-sustainable WSNs. Specifically, we consider the WSN as a 2-D rectangular region, where a mobile sensor (MS) can harvest energy from ambient environments while transmitting the collected data to a fixed sink. Due to the changing environment and the mobility of the MS, the harvested energy by the MS at each slot presents spatiotemporal dynamics within the network, which severely affects the performance of data throughput from the MS to the sink. The MS’s trajectory is investigated to maximize the long-term average MS-to-sink data throughput. Due to the unknown energy arrival information as well as the locations of ESs, we formulate the problem as a Markov decision process, which is then solved with model-free RL. In particular, the deep deterministic policy gradient (DDPG) is applied to tackle the continuous and deterministic movement space. Results show that the MS can learn and optimize the moving trajectory by intelligently tracking the aggregated received energy over slots. Finally, the MS can identify and move to the optimal location where the maximized long-term average MS-to-sink data throughput is achieved. Extensive numerical evaluations are conducted to investigate the impact of various system parameters on the network performance.

Stochastic Synapses Made of Magnetic Domain Walls

The stochastic artificial neural network is widely studied nowadays, since it may address the critical issue of uncertainty quantification demanded in many application scenarios. At the hardware level, it calls for devices that can emulate stochastic weight-modification behaviors of synapses with high efficiency. In this work, we propose a stochastic spintronic synapse based on spin-torque-induced stochastic magnetic domain-wall (DW) dynamics, including stochastic DW generation by spin-transfer torque and deterministic DW motion induced by spin-orbit torque. The former plays the role of updating the synaptic weights stochastically, while the latter gives rise to multistates, namely, the analog property of the synapse. The proposed DW-based stochastic synapse requires few devices to implement sufficient resistance states. The neural-network-level simulation demonstrates that a spiking neural network based on these stochastic synapses is capable of classifying breast cancer data with a high accuracy of 95.7%.

Multilevel sampling based sparse polynomial expansion for efficient uncertainty analysis of mechanical system

The polynomial chaos is widely used in uncertainty analysis of the mechanical system, in which, the samplings hugely affect the efficiency and accuracy. In this paper, a multilevel sampling based sparse polynomial chaos approach is presented for efficient uncertain analysis of the mechanical system, in which, 1) the dynamic responses are approximated with sparse polynomial chaos expansion which enables the sampling number to be much less than that of the polynomial terms, and the number of deterministic systems would be reduced, 2) the multilevel sampling based resampling strategy is applied to weaken the Restricted Isometry Property condition for sparse polynomial chaos expansion based equations solving, and in addition, 3) such multilevel sampling based on evaluating system coherence with generalized coordinates acceleration is used to reduce the motion equations with deterministic parameters for obtaining the dynamic responses accurately and efficiently. Finally, the presented approach is validated with three examples. The results are shown that the sparse polynomial chaos can be utilized to calculate the mean and standard deviation with 1E-5 and 1E-2 accuracy. The sparse polynomial chaos based on multilevel sampling can be used to reduce the sampling size by 25% compared with the Latin Hypercube sampling. Compared with coherence optimal sampling, the convergence of the proposed sampling method is more stable as the sampling size is increased. Lastly, multilevel sampling also can be used to reduce the deterministic motion equations by 20% to obtain 1E-5 accuracy dynamic responses compared with the Latin Hypercube sampling.

A Structure Economic Loss Optimization Method with the Uncertainty of Ground Motion Amplitude for Chinese Masonry Building

In the catastrophe insurance industry, it is impractical for a catastrophe model to simulate millions of sites’ environments in a short time. Hence, the attenuation relation is often adopted to simulate the ground motion on account of calculation speed, and both ground motion expectations and uncertainties must be calculated. Due to the vulnerability curves of our model being based on simulations with a large number of deterministic ground motions, it is necessary but not efficient for loss assessment to analyze all possible ground motion amplitudes and their corresponding loss rates. This paper develops a simplified method to rapidly simulate loss expectations and uncertainties. In this research, Chinese masonry buildings are the focus. The result shows that the modified method gives accurate loss results quickly.

Global dynamics visualisation from Lagrangian Descriptors. Applications to discrete and continuous systems

This paper introduces a new global dynamics and chaos indicator based on the method of Lagrangian Descriptor apt for discriminating ordered and deterministic chaotic motions in multidimensional systems. The selected implementation of this method requires only the knowledge of orbits on finite time windows and is free of the computation of the tangent vector dynamics (i.e.,variational equations are not needed). To demonstrate its ability in visualising different dynamical behaviours, in particular for highlighting chaotic regions, several stability maps of classical systems, obtained with different phase space methods, are reproduced. The benchmark examples are rooted in discrete and continuous nearly-integrable dynamical systems, with prominent features played by resonances. These include the Chirikov standard map, higher dimensional symplectic and volume preserving maps, fundamental models of resonances, and a 3 degrees-of-freedom nearly-integrable Hamiltonian system with a dense web of resonances. The indicator thus appears to be relevant for understanding phase space transport mediated by resonances in nearly-integrable system, as ubiquitous in celestial mechanics or astrodynamics.

FEATURES OF THE ORGANIZATION OF LABORATORY WORKS IN THE STUDY OF PHYSICS BY STUDENTS OF IT SPECIALTIES

In this article, we analyze the problems of organizing laboratory work in physics for students of IT specialties in terms of distance learning. We show the possibility of convenient use of WEB technologies and JavaScript programming language for this purpose. The choice of JavaScript is due to the true platform-independence of this programming language: it is possible to write code in any text editor and run it in any web browser. Unlike specialized environments (Skratch), JavaScript is one of the most common languages of true programming, but the simplicity of syntax is also quite accessible to high school and first-year students. We give two examples: the first with deterministic motion (rectilinear uniform and accelerated) and the second with chaotic motion (Brownian motion). The article describes the structure of a typical mini-project (laboratory work) and shows that this approach in the organization of training is related to the specifics of the IT specialty. Now we consider only “static” cases – that is, without the use of animation. However, JavaScript tools also allow you to implement “dynamics”, i.e. with animation. According to such a scheme, it is possible to implement almost any of the known physical processes. As a result, students acquire skills in programming models of physical processes with visual visualization of the results. Such models make it possible to change the initial conditions for the development of the physical process and see the process of its course, to compare the program-experimental results with the calculated, deeper and more complete understanding of the physical process during its programming. Thus, the foundation of knowledge of the future engineer – CAD designer (computer-aided design systems, which are widely used in real production and especially in its preparation).
 Keywords: educational technologies of distance learning; modeling of motion processes in physics; modeling of Brownian motion; development of simulators of physical processes; JavaScript; WEB technologies in education; laboratory works in physics; virtual learning environment.