Random Evolution Research Articles

SUPPORT THEOREM FOR THE POSITIVE RANDOM EVOLUTION EQUATION IN H"{O}LDER NORM

Let's consider the stochastic differential equation \[\left\{ \begin{split} dX_t&=\sigma(X_t,V_t )dW_t+b(X_t,R_t)dt\\ X_0&=x>0 \end{split}\right. \] In this paper, we establish the support theorem for this positive random evolution equation type on $C^{\alpha,0}([0;1];\mathbb{R})$. We use the linear interpolations of $W$ for the proof.

Predicting vasospasm risk using first presentation aneurysmal subarachnoid hemorrhage volume: A semi-automated CT image segmentation analysis using ITK-SNAP.

Cerebral vasospasm following aneurysmal subarachnoid hemorrhage (aSAH) is a significant complication associated with poor neurological outcomes. We present a novel, semi-automated pipeline, implemented in the open-source medical imaging analysis software ITK-SNAP, to segment subarachnoid blood volume from initial CT head (CTH) scans and use this to predict future radiological vasospasm. 42 patients were admitted between February 2020 and December 2021 to our tertiary neurosciences center, and whose initial referral CTH scan was used for this retrospective cohort study. Blood load was segmented using a semi-automated random forest classifier and active contour evolution implemented in ITK-SNAP. Clinical data were extracted from electronic healthcare records in order to fit models aimed at predicting radiological vasospasm risk. Semi-automated segmentations demonstrated excellent agreement with manual, expert-derived volumes (mean Dice coefficient = 0.92). Total normalized blood volume, extracted from CTH images at first presentation, was significantly associated with greater odds of later radiological vasospasm, increasing by approximately 7% for each additional cm3 of blood (OR = 1.069, 95% CI: 1.021-1.120; p < .005). Greater blood volume was also significantly associated with vasospasm of a higher Lindegaard ratio, of longer duration, and a greater number of discrete episodes. Total blood volume predicted radiological vasospasm with a greater accuracy as compared to the modified Fisher scale (AUC = 0.86 vs 0.70), and was of independent predictive value. Semi-automated methods provide a plausible pipeline for the segmentation of blood from CT head images in aSAH, and total blood volume is a robust, extendable predictor of radiological vasospasm, outperforming the modified Fisher scale. Greater subarachnoid blood volume significantly increases the odds of subsequent vasospasm, its time course and its severity.

Visual hindsight bias for abnormal mammograms in radiologists.

Hindsight bias-where people falsely believe they can accurately predict something once they know about it-is a pervasive decision-making phenomenon, including in the interpretation of radiological images. Evidence suggests it is not only a decision-making phenomenon but also a visual perception one, where prior information about an image enhances our visual perception of the contents of that image. The current experiment investigates to what extent expert radiologists perceive mammograms with visual abnormalities differently when they know what the abnormality is (a visual hindsight bias), above and beyond being biased at a decision level. experienced mammography readers were presented with a series of unilateral abnormal mammograms. After each case, they were asked to rate their confidence on a 6-point scale that ranged from confident mass to confident calcification. We used the random image structure evolution method, where the images repeated in an unpredictable order and with varied noise, to ensure any biases were visual, not cognitive. Radiologists who first saw an original image with no noise were more accurate in the max noise level condition [area under the curve ] than those who first saw the degraded images (; difference: ), suggesting that radiologists' visual perception of medical images is enhanced by prior visual experience with the abnormality. Overall, these results provide evidence that expert radiologists experience not only decision level but also visual hindsight bias, and have potential implications for negligence lawsuits.

Evolutionary Method of Heterogeneous Combat Network Based on Link Prediction.

Currently, research on the evolution of heterogeneous combat networks (HCNs) mainly focuses on the modeling process, with little attention paid to the impact of changes in network topology on operational capabilities. Link prediction can provide a fair and unified comparison standard for network evolution mechanisms. This paper uses link prediction methods to study the evolution of HCNs. Firstly, according to the characteristics of HCNs, a link prediction index based on frequent subgraphs (LPFS) is proposed. LPFS have been demonstrated on a real combat network to be superior to 26 baseline methods. The main driving force of research on evolution is to improve the operational capabilities of combat networks. Adding the same number of nodes and edges, 100 iterative experiments demonstrate that the evolutionary method (HCNE) proposed in this paper outperforms random evolution and preferential evolution in improving the operational capabilities of combat networks. Furthermore, the new network generated after evolution is more consistent with the characteristics of a real network.

The long-run analysis of COVID-19 dynamic using random evolution, peak detection and time series.

It is now almost three years that COVID-19 has been the cause of misery for millions of people around the world. Many countries are in process of vaccination. Due to the social complexity of the problem, the future of decisions is not clear. As such, there is a need for the mathematical modeling to predict the long-run behavior of the COVID-19 dynamic for the decision-making with regard to the result of the pandemic on the economy, health, and others. In this paper, we have studied the short and long-run behavior of COVID-19. In a novel way, random evolution (Trichotomous and Dichotomous Markov Noise) is used to model and analyze the long-run behavior of the pandemic in different phases of the pandemic in different countries. On the given conditions, the random evolution model can help us establish the long-run asymptotic behaviour of the pandemic. This allows us to consider different phases of the pandemic as well as the effect of vaccination and other measures taken. The simplicity of the model makes it a practical tool for decision-making based on the long-run behavior of the pandemic. As such, we have established a criterion for the comparison of different regions and countries in different phases. In this regard, we have used real pandemic data from different countries to validate our results.

Reliable Detection Method of Variable Series Arc Fault in Building Integrated Photovoltaic Systems Based on Nonstationary Time Series Analysis

Arc fault characteristics would be influenced by diverse system elements and operation levels in building integrated photovoltaic (BIPV) systems, which would challenge the arc fault detection. This paper aims at improving the detection reliability and speed by applying the time series analysis at feature and classification layers to reflect the random evolution process of arc fault current. According to the arc ignition method in UL1699B, arc fault signals are firstly sampled with a residential storage element and ten DC load elements at three voltage levels in BIPV systems. After evaluating time-frequency information with established indexes, variable and early arc fault are discovered to have no obvious current and harmonic indications due to strong system element noises, sensor precision and low arc energy. Then, sample entropy (SE) feature and gated recurrent unit (GRU) classifier are proposed to improve the detection performance through involving the nonstationary time series correlation of the arc fault current. An arc fault simulation platform is built to generate arc current with low signal-to-noise ratio (SNR), which proves the detection algorithm integrates multi-source information to grasp the appearance of new signal pattern. Next, constrained optimization problems are designed for both feature and classifier layers to achieve the most distinguishable arc fault and fault-like performance. With matched optimization methods, the modified SE and GRU method could improve the detection accuracy by an average of 21.08% after comparing with existing features and classifiers. Finally, hardware implementation results indicate a higher detection accuracy and less runtime of the proposed detection method.

From Semi-Markov Random Evolutions to Scattering Transport and Superdiffusion

We here study random evolutions on Banach spaces, driven by a class of semi-Markov processes. The expectation (in the sense of Bochner) of such evolutions is shown to solve some abstract Cauchy problems. Further, the abstract telegraph (damped wave) equation is generalized to the case of semi-Markov perturbations. A special attention is devoted to semi-Markov models of scattering transport processes which can be represented through these evolutions. In particular, we consider random flights with infinite mean flight times which turn out to be governed by a semi-Markov generalization of a linear Boltzmann equation; their scaling limit is proved to converge to superdiffusive transport processes.

Implications of Different Solar Photospheric Flux-transport Models for Global Coronal and Heliospheric Modeling

The concept of surface-flux transport (SFT) is commonly used in evolving models of the large-scale solar surface magnetic field. These photospheric models are used to determine the large-scale structure of the overlying coronal magnetic field, as well as to make predictions about the fields and flows that structure the solar wind. We compare predictions from two SFT models for the solar wind, open magnetic field footpoints, and the presence of coronal magnetic null points throughout various phases of a solar activity cycle, focusing on the months of April in even-numbered years between 2012 and 2020, inclusively. We find that there is a solar-cycle dependence to each of the metrics considered, but there is not a single phase of the cycle in which all the metrics indicate good agreement between the models. The metrics also reveal large, transient differences between the models when a new active region is rotating into the assimilation window. The evolution of the surface flux is governed by a combination of large-scale flows and comparatively small-scale motions associated with convection. Because the latter flows evolve rapidly, there are intervals during which their impact on the surface flux can only be characterized in a statistical sense, thus their impact is modeled by introducing a random evolution that reproduces the typical surface flux evolution. We find that the differences between the predicted properties are dominated by differences in the model assumptions and implementation, rather than the selection of a particular realization of the random evolution.

Absence of logarithmic and algebraic scaling entanglement phases due to the skin effect

Measurement-induced phase transition in the presence of competition between projective measurement and random unitary evolution has attracted increasing attention due to the rich phenomenology of entanglement structures. However, in open quantum systems with free fermions, a generalized measurement with conditional feedback can induce skin effect and render the system short-range entangled without any entanglement transition, meaning the system always remains in the ``area law'' entanglement phase. In this work, we demonstrate that the power-law long-range hopping does not alter the absence of entanglement transition brought on by the measurement-induced skin effect for systems with open boundary conditions. In addition, for the finite-size systems, we discover an algebraic scaling $S(L, L/4)\sim L^{3/2-p}$ when the power-law exponent $p$ of long-range hopping is relatively small. For systems with periodic boundary conditions, we find that the measurement-induced skin effect disappears and observe entanglement phase transitions among ``algebraic law'', ``logarithmic law'', and ``area law'' phases.

The accuracy of determining the coordinates of an unmanned aerial vehicle with a navigation complex integrating an electro-optical positioning system

The article proposes the approaches to updating a strapdown inertial navigation system (SINS) based on data of the airborne electro-optical system (EOS) of an unmanned aerial vehicle (UAV). It is specified that the EOS is presented as a navigation data sensor. The rationale for the feasibility of such an approach is formed, especially in the terms of signal lack or suppression of satellite radio-navigation systems. It is proposed to ensure the accuracy of self-contained navigation by assigning an UAV route, including waypoints with terrestrial references (TRs). Notably, TR-associated image information is preliminarily downloaded into the flight management computer (FMC). The automated TR identification system with denoted coordinates at next waypoints, using airborne data, in fact, allows for alternative global positioning. The reliable operation of such an integrated navigation system over sufficiently extended legs of flight path, first, depends on the accuracy of its constituent elements. Taking into consideration the fact that conventional sensors of navigation information, such as a SINS and an altimeter, are quite well studied in numerous contributions. The article focuses on the UAV airborne electro-optical system and, specifically, on its application features as a navigation sensor. The factors influencing the accuracy of the UAV positioning data determination at waypoints according to the data of the airborne EOS are considered. The developed mathematical model of errors for the UAV inertial optical navigation complex (IONC) is presented. The analysis of the impact of airborne altimeter inaccuracies, earth’s surface features and the shift of the onboard digital camera optical axis, caused by random evolutions of the carrier body in turbulent atmosphere on the positioning accuracy, was conducted. The results of calculating lapses in determining the UAV positioning data, equipped with IONC, are given.

Entanglement transitions with free fermions

We use Majorana operators to study entanglement dynamics under random free fermion unitary evolution and projective measurements in one dimension. For certain choices of unitary evolution, namely, those which swap neighboring Majorana operators, and measurements of neighboring Majorana bilinears, one can map the evolution to the statistical model of completely packed loops with crossings (CPLC) and study the corresponding phase diagram. We generalize this model using the language of fermionic Gaussian states to a general free fermion unitary evolution acting on neighboring Majorana operators and numerically compute its phase diagram. We find that both the Goldstone and area-law phases persist in this new phase diagram, but with a shifted phase boundary. One important qualitative aspect of the new phase boundary is that even for the case of commuting measurements, the Goldstone phase persists up to a finite nonzero measurement rate. This is in contrast with the CPLC, in which noncommuting measurements are necessary for realizing the Goldstone phase. We also numerically compute the correlation length critical exponent at the transition, which we find to be near to that of the CPLC, and give a tentative symmetry-based explanation for some differences in the phase transition line between the CPLC and generalized models.

Error-resilient Monte Carlo quantum simulation of imaginary time

Computing the ground-state properties of quantum many-body systems is a promising application of near-term quantum hardware with a potential impact in many fields. The conventional algorithm quantum phase estimation uses deep circuits and requires fault-tolerant technologies. Many quantum simulation algorithms developed recently work in an inexact and variational manner to exploit shallow circuits. In this work, we combine quantum Monte Carlo with quantum computing and propose an algorithm for simulating the imaginary-time evolution and solving the ground-state problem. By sampling the real-time evolution operator with a random evolution time according to a modified Cauchy-Lorentz distribution, we can compute the expected value of an observable in imaginary-time evolution. Our algorithm approaches the exact solution given a circuit depth increasing polylogarithmically with the desired accuracy. Compared with quantum phase estimation, the Trotter step number, i.e. the circuit depth, can be thousands of times smaller to achieve the same accuracy in the ground-state energy. We verify the resilience to Trotterisation errors caused by the finite circuit depth in the numerical simulation of various models. The results show that Monte Carlo quantum simulation is promising even without a fully fault-tolerant quantum computer.

Testing the Efficient Market Hypothesis and the Model-Data Paradox of Chaos on Top Currencies from the Foreign Exchange Market (FOREX)

In this paper, we analyse two interesting applications related to the dynamics of economic phenomena linked to the Efficient Market Hypothesis (EMH), informative surprises, and the Model-Data Paradox of Chaos in certain top currency pairs from the foreign exchange market (FOREX). On the one hand, we empirically show that the FOREX market reacts under the Efficient Market Hypothesis in some cases, creating a significant variation in a short period of time (15, 30, and 60 min) in the quotes of the main currencies from the most important economic regions in the West (the United States, Europe, and the United Kingdom). This variation would depend on the actual deviation of high-impact macroeconomic news reported by these markets in relation to trade balance, unemployment rate, Gross Domestic Product (GDP), retail sales, the Industrial Production Index (IPI), and the Consumer Price Index (CPI). On the other hand, by testing the Model-Data Paradox of Chaos, we empirically verify that if we consider all the information available in the financial markets of currencies (or at least, more desegregated data) instead of daily data, and we apply a robust chaotic behaviour detection method, we can find differences in relation to the detection of chaos on the same series but with different temporal frequencies. This allows us to confirm that behind these financial time series which show an apparently random irregular evolution, there would be a generating system which, although unknown in principle, would be deterministic (and nonlinear), and we could take advantage of that deterministic character to make predictions, even if only in the short term, understanding “short term” as the time it takes for the market to incorporate these informative surprises in the FOREX market analysed.

ATTRACTORS FOR RANDOM LATTICE DYNAMICAL SYSTEMS WITH INFINITE MULTIPLICATIVE COLORED NOISE

In this paper, we establish the existence and uniqueness of random attractor for the first-order random lattice differential equation with a nonlinear colored noise at each node. We first rewrite the equation as a random evolution system and then prove the existence of a unique weak solution. Finally, we obtain the existence of a unique random attractor for the underlying random dynamical system.

Determination of intrinsic kinetic of corncob char gasification with CO2 and steam using multipore diffusion model

The intrinsic heterogeneous reactivity of biochar in CO2 and steam gasification plays an important role in thermochemical reactor design, adjusting operating conditions, and predicting the quality of biomass gasification products, especially when the combined valorization of syngas and biochar with important textural properties is required. In the present work, the intrinsic heterogeneous kinetics of CO2 and steam gasification of corn biochar is estimated by fitting a multimodal pore size distribution (PSD) with random capillary model evolution with respect to the experimental results by thermogravimetric analysis (TGA). As novelty the independence of the initial biochar textural properties was considered, using two samples with different initial pore size distributions (PSDs): A1 sample with an initial surface area of 54.09 m2/g and A2 sample with 22.14 m2/g. The experimental intraparticle gradient effect is considered by using samples with a particle size of 149 µm conventionally larger than those reported to guarantee chemical kinetic control at 60 µm. The apparent kinetics obtained by TGA revealed a difference of 70 kJ/mol for CO2 gasification and 30 kJ/mol for steam gasification in contrast to initial PSD change. The average activation energies and pre-exponential factor obtained by parametric fitting of the model with respect to the evolution of the conversion for CO2 gasification were E = 210.2 kJ/mol and A0 = 1.13*106 g/m2s, while steam revealed E = 136.64 kJ/mol and 7.1*102 g/m2s. Furthermore, the model reduced the activation energy differences with respect to different PSDs by 5 kJ/mol for CO2 and 18 kJ/mol for steam biochar gasification.

Multistrategy Integrated Marine Predator Algorithm Applied to 3D Surface WSN Coverage Optimization

Achieving maximum network coverage with a limited number of sensor nodes is key to node deployment of wireless sensor network (WSN). This paper proposes an improved marine predator algorithm (IMPA) for 3D surface wireless sensor network deployment. A population evolution strategy based on random opposition-based learning and differential evolution operator is proposed to enrich the population diversity and improve the global search capability of the algorithm. The grouping idea of the Shuffled Frog Leaping Algorithm (SFLA) is then introduced. A local search strategy based on the SFLA is proposed to replace the FADs effect of MPA and enhance the ability of the algorithm to escape from the local optimum. A quasireflected opposition-based learning strategy is also presented to improve the optimization accuracy, accelerate the convergence speed of the algorithm, and improve the quality of the solution. Fifteen benchmark functions are selected for testing. The results are compared with seven different algorithms. The results show that the improved algorithm has excellent optimization performances. Finally, the IMPA is applied to optimize WSN coverage on 3D surfaces. The experimental results show that the proposed IMPA has good terrain adaptation and optimal deployment capabilities. It can improve the coverage of the network, reduce the deployment cost, and extend the network life cycle.

That fantastic and mysterious flow of electrons that we call LIFE

The aim of this article is to present a personal conception on the structure and evolution of matter and to examine critically the one theorized by physics and quantum biology. The fundamental questions I am going to try to answer are: · What is a life? · Why is it difficult, elusive to understand its inner molecular mechanism? · Is it possible to formulate a comprehensive theory of the molecular mechanism that guides it, without resorting to abstract mathematical equations, true transcendental transpositions of the real world? · What does mysterious and powerful feeling push us in the search for its essence, despite the limited cognitive tools we have? Is love for knowledge an end in itself? The narrative I’m going to present is based on the assumption that all the elementary particles of matter and the forces with which they interact use a proper means of communication a universal molecular code, that informs them and guides them in the random evolution towards infinite possible solutions. I have also theorized that the evolution of life on earth had been possible thanks to the formation of a barrier, called the cell membrane, capable of containing within it, a set of organized and coordinated molecules, separated from the chaotic external world. To this hypothesis shared by many authors, I have associated the concept that the cell membrane has perfected the process of biological communication. Finally, I have hypothesized as the basis of life processes an incessant flow of electrons, which regulates communication and provide the energy they need. The substrate of this mechanism is formed by electromagnetic waves, the only ones capable to transport electrons and orchestrate the cosmic symphony. I may consider myself sufficiently gratified of the concepts presented here if they find some adhesion and theoretical contribution from the readers.

Dynamic Equilibrium Strategy of Power Battery Closed-Loop Supply Chain Based on Stochastic Differential Game

The industrialization of new energy vehicles has accelerated, and the recycling industry driven by a large number of retired power batteries has exploded rapidly. How to control and deal with decommissioned batteries and balance economic and environmental benefits has become an urgent problem to be solved. Considering the influence of uncertain interference factors, the dynamic equilibrium strategy of power battery closed-loop supply chain members composed of leading manufacturer, recycler, and cascade utilization enterprise was studied. (1) This paper describes the stochastic evolution process of cascade utilization effort level by using Ito process and constructs a stochastic differential game model of closed-loop supply chain dynamic system combined with cost-sharing coordination mechanism. (2) Based on the stochastic differential game theory, the optimal profit function of the participants is given, and the dynamic equilibrium strategy of each participant is obtained. (3) In order to grasp the statistical characteristics of cascade utilization effort level, the random evolution characteristics of cascade utilization effort level are revealed. (4) Combined with numerical example, the influence of relevant parameters on the dynamic system of power battery closed-loop supply chain is analyzed. The results found that with the increase of the proportion of manufacturer sharing the innovation cost of cascade utilization enterprise, the cascade utilization effort level will improve, the profits of cascade utilization enterprise and recycler will increase significantly, the profits of manufacturer will decrease slightly, and the overall profits of the supply chain will increase. The cost-sharing coordination mechanism is conducive to ensuring the steady development of the industry and effectively improving the utilization efficiency of industrial resources.

Land Use Planning to Reduce Flood Risk: Opportunities, Challenges and Uncertainties in Developing Countries.

Land use planning for flood risk reduction has been significantly addressed in literature. However, a clear methodology for flood mitigation oriented land-use planning and its implementation, particularly in developing countries like Lebanon, is still missing. Knowledge on land use planning is still in its earliest stages in Lebanon. A lack of hazard-informed land use planning coupled to random land cover pattern evolution characterize the country. In response, this study focuses on the opportunities, challenges and uncertainties resulting from the integration of land use planning into efficient Disaster Risk Reduction (DRR). For this purpose, GIS-based analyses were first conducted on the current land use/land cover (LU/LC) of the Assi floodplain. Then, the areas land cover was retraced and its evolution after several flood occurrences was assessed. Subsequently, a flood hazard-informed LU/LC plan was proposed. The latter is mainly based on the spatial allocation of land-uses with respect to different flood hazard levels. This approach resulted in the production of a land use planning matrix for flood risk reduction. The matrix approach can serve as a tool for designing sustainable and resilient land cover patterns in other similar contexts while simultaneously providing robust contributions to decision-making and risk communication.

Full-aperture random polarization smoothing for a low-coherence laser facility.

Two new random polarization smoothing methods using full-aperture elements are proposed on low-coherence lasers, one using birefringent wedge and one using flat birefringent plate. By designing the crystal axis direction and wedge angle of the birefringent plates, the methods can selectively introduce time delay and spatial displacement, so as to obtain fast random evolution of transient polarization by utilizing low spatiotemporal coherence of the laser focal field. Both methods avoid the near field discontinuity and can be used under high fluence. The method using birefringent wedge can slightly improve focal spot uniformity, and the method using flat birefringent plate can obtain non-polarization with DOP lower than 2%. Theoretical studies show that the resulting focal polarization evolves rapidly on sub-picosecond timescales and rapidly covers the entire Poincaré sphere. The method using birefringent wedge is achieved in experiment. The results show that the degree of polarization of the focal spot is reduced from 1 to 0.27, which proves the effectiveness of the full-aperture random polarization smoothing. The full-aperture random polarization smoothing can generate a focal field very close to unpolarized thermal light, which is expected to suppress the laser plasmas instability.