Finite Period Of Time Research Articles

A STUDY ON CASH MANAGEMENT

The project titled “A Study on cash management” for deals with the movement of money into or out of a business, project, or financial product. It is usually measured during a specified, finite period of time. The need for Cash to run the day-to-day business activities cannot be overemphasized. One can hardly find a business firm, which does not require any amount of Cash. Indeed, firms differ in their requirements of the Cash. A firm should aim at maximizing the wealth of its shareholders. In its endeavor to do so, a firm should earn sufficient return from its operation. Earning a steady amount of profit requires successful sales activity. The firm has to invest enough funds in current asset for generating sales. Current asset are needed because sales do not convert into cash instantaneously. There is always an operating cycle involved in the conversion of sales into cash. The objectives are to analyze the Cash management and to determine efficiency in cash, inventories, debtors and creditors.

An improved energy management strategy for hybrid electric powered aircraft based on deep reinforcement learning

Electric airplanes have attracted a great deal of attentions because of their strategic commercial values and low emissions. One of the essentials for efficient control and management of hybrid electric propulsion aircraft is a well-designed energy management strategy (EMS). A new EMS approach based on deep learning is introduced in this study to realize thrust distribution and power distribution, consequently lowering aircraft fuel consumption. A model of hybrid electric propulsion power system including turbine engines and lithium-ion battery pack is constructed to act as an application environment. An intelligent energy management strategy based on deep reinforcement learning (DRL) is then proposed in this paper. Two DRL agents based on twin delayed deep deterministic policy gradient (TD3) are combined in the hierarchical framework. The multi-agents are trained to handle the energy management problem in this paper. In a finite period of time, the training process is accomplished. After training, the simulation result of the test shows that this DRL-based EMS reduces the equivalent fuel consumption by 2.2% compared with that using the existing method, and preserves 99.9% of the battery state of health (SOH). The battery pack's state of charge (SOC) regresses precisely to the reference value at the end. Particularly, the proposed EMS is able to work properly for different flight profiles and various initial SOCs. The results of this paper provide preliminary supports for the feasibility and superiority of deep reinforcement learning applied to the energy management strategy of series hybrid electric aircraft.

Periodization as Metaphor: Transcending Time in Toni Morrison’s Sula and “Recitatif”

Abstract: Toni Morrison spoke and wrote extensively about the primacy of the art of creating fiction, resisting any critical, academic, or social attempts, however well-intentioned, to categorize her texts and wrest their autonomy. Implicit in periodization, one such “lazy labeling” strategy long embraced by critics and pedagogues, is a power dynamic that diminishes the artist, her art, and the reader’s experience of both. This discussion of two related works by Morrison, Sula and “Recitatif,” contextualizes her aesthetic theory by closely examining her defiance of conventional norms for narrative structure, character delineation, and even setting description. As both narratives trace their respective protagonists’ lifelong journeys, which propel forward and revert to earlier times to attain truth, Morrison exposes the mendacity of a singularly linear progression of finite time periods. This consideration of Sula and “Recitatif” focuses on the texts as models of Morrison’s insistence upon connection, ambiguity, and circularity in fiction. As metaphorical argument for a literary canon that transcends classification by time periods or any other limiting, arbitrarily prescribed criteria, Morrison’s narratives resonate timelessly.

Ultrathin, Transferred Layers of Silicon Oxynitrides as Tunable Biofluid Barriers for Bioresorbable Electronic Systems.

Bio/ecoresorbable electronic systems create unique opportunities in implantable medical devices that serve a need over a finite time period and then disappear naturally to eliminate the need for extraction surgeries. A critical challenge in the development of this type of technology is in materials that can serve as thin, stable barriers to surrounding ground water or biofluids, yet ultimately dissolve completely to benign end products. This paper describes a class of inorganic material (silicon oxynitride, SiON) that can be formed in thin films by plasma-enhanced chemical vapor deposition for this purpose. In vitro studies suggest that SiON and its dissolution products are biocompatible, indicating the potential for its use in implantable devices. A facile process to fabricate flexible, wafer-scale multilayer films bypasses limitations associated with the mechanical fragility of inorganic thin films. Systematic computational, analytical, and experimental studies highlight the essential materials aspects. Demonstrations in wireless light-emitting diodes both in vitro and in vivo illustrate the practical use of these materials strategies. The ability to select degradation rates and water permeability through fine tuning of chemical compositions and thicknesses provides the opportunity to obtain a range of functional lifetimes to meet different application requirements.

On a Problem of Calculating the Solvability Set for a Linear System with Uncertainty

We consider a linear-convex control system defined by a set of differential equations with continuous matrix coefficients. The system may have control parameters, as well as uncertainties (interference) the possible values of which are subject to strict pointwise constraints. For this system, over a finite period of time, taking into account the constraints, we study the problem of guaranteed hitting the target set from a given initial position despite the effect of uncertainty. The main stage of solving the problem is the construction of an alternating integral and a solvability set. To construct the latter, the greatest computational complexity is the calculation of the geometric difference between the target set and the set determined by the uncertainty. A two-dimensional example of this problem is considered for which a method is proposed for finding the solvability set without the need to calculate the convex hull of the difference of the support functions of the sets.

Dissolution of spiral wave's core using cardiac optogenetics.

Rotating spiral waves in the heart are associated with life-threatening cardiac arrhythmias such as ventricular tachycardia and fibrillation. These arrhythmias are treated by a process called defibrillation, which forces electrical resynchronization of the heart tissue by delivering a single global high-voltage shock directly to the heart. This method leads to immediate termination of spiral waves. However, this may not be the only mechanism underlying successful defibrillation, as certain scenarios have also been reported, where the arrhythmia terminated slowly, over a finite period of time. Here, we investigate the slow termination dynamics of an arrhythmia in optogenetically modified murine cardiac tissue both in silico and ex vivo during global illumination at low light intensities. Optical imaging of an intact mouse heart during a ventricular arrhythmia shows slow termination of the arrhythmia, which is due to action potential prolongation observed during the last rotation of the wave. Our numerical studies show that when the core of a spiral is illuminated, it begins to expand, pushing the spiral arm towards the inexcitable boundary of the domain, leading to termination of the spiral wave. We believe that these fundamental findings lead to a better understanding of arrhythmia dynamics during slow termination, which in turn has implications for the improvement and development of new cardiac defibrillation techniques.

Nonlinear Evolution of the Tearing Instability in a Thin Current Sheet

In plane current sheets (foils, or liners), which are thin in comparison with their skin depth, an instability may grow because parallel currents in different areas of these sheets are attracted that leads to compression of these areas and partition of the current sheets into threads. This work describes the study of the nonlinear tearing instability evolution for a 1D case, when all the principal quantities (surface current and mass densities, velocity, and the magnetic field component normal to the sheet surface) depend on the coordinate perpendicular to the current density vector only. A 1D system of equations has been obtained, which describes the magnetic field dynamics and the substance motion in current sheets, and a numerical technique based on the Lagrangian discretization of mass has been developed to solve 1D magnetic hydrodynamics problems. It is shown that, if the tearing instability is considered, small perturbations of currents, velocities and mass densities grow in accordance with the earlier found growth rates of small perturbations and later, when the perturbations are no longer small, a nonlinear growth stage comes. In this stage, the perturbations of the surface current density j and the surface mass density μ increase unlimitedly for a finite time period (apparently, according to power laws j t -t -a,~ ( ) sμ t -t -g s, ts is the time, when these quantities become infinite); however the width of the current and mass density peaks tends to zero, so that the total current and total mass in the peaks of the current density and surface mass density decrease and tend to zero.

Gathering Over Heterogeneous Meeting Nodes

Abstract We consider two finite and disjoint sets of homogeneous robots deployed at the nodes of an infinite grid graph. The grid graph also comprises two finite and disjoint sets of prefixed meeting nodes located over the nodes of the grid. The objective of our study is to design a distributed algorithm that gathers all the robots belonging to the first team at one of the meeting nodes belonging to the first type, and all the robots in the second team must gather at one of the meeting nodes belonging to the second type. The robots can distinguish between the two types of meeting nodes. However, a robot cannot identify its team members. This paper assumes the strongest adversarial model, namely the asynchronous scheduler. We have characterized all the initial configurations for which the gathering problem is unsolvable. For the remaining initial configurations, the paper proposes a distributed gathering algorithm. Assuming the robots are capable of global-weak multiplicity detection, the proposed algorithm solves the problem within a finite time period. The algorithm runs in $\Theta (dn)$ moves and $O(dn)$ epochs, where $d$ is the diameter of the minimum enclosing rectangle of all the robots and meeting nodes in the initial configuration, and $n$ is the total number of robots in the system.

Implementation of an Ultrasensitive Biomolecular Controller for Enzymatic Reaction Processes With Delay Using DNA Strand Displacement.

In this article, a set of abstract chemical reactions has been employed to construct a novel nonlinear biomolecular controller, i.e, the Brink controller (BC) with direct positive autoregulation (DPAR) (namely BC-DPAR controller). In comparison to dual rail representation-based controllers such as the quasi sliding mode (QSM) controller, the BC-DPAR controller directly reduces the number of CRNs required for realizing an ultrasensitive input-output response because it does not involve the subtraction module, reducing the complexity of DNA implementations. Then, the action mechanism and steady-state condition constraints of two nonlinear controllers, BC-DPAR controller and QSM controller, are investigated further. Considering the mapping relationship between CRNs and DNA implementation, a CRNs-based enzymatic reaction process with delay is constructed, and a DNA strand displacement (DSD) scheme representing time delay is proposed. The BC-DPAR controller, when compared to the QSM controller, can reduce the number of abstract chemical reactions and DSD reactions required by 33.3% and 31.8%, respectively. Finally, an enzymatic reaction scheme with BC-DPAR controller is designed using DSD reactions. According to the findings, the enzymatic reaction process's output substance can approach the target level at a quasi-steady state in both delay-free and non-zero delay conditions, but the target level can only be achieved during a finite-time period, mainly due to the fuel stand depletion.

Distributed adaptive finite-time fault-tolerant formation-containment control for networked Euler–Lagrange systems under directed communication interactions

This paper addresses the finite-time formation-containment control problem for multiple Euler–Lagrange systems with actuator faults and input saturations subject to external disturbances and nonlinear uncertainties under directed communication interactions. Systematic procedures on how to synthesize a distributed control scheme are developed based on adaptive backstepping control, fault-tolerant control, dynamic gain control and finite-time control such that leaders form a desired geometric configuration while tracking the virtual leader, and followers converge to the convex hull spanned by the leaders during a finite-time period with sufficient accuracy. Two auxiliary systems are constructed to facilitate the control scheme design. Finite-time convergence and ease of computation burden in the sense of not using fractional powers of cooperative error information are realized by integrating dynamic gain control, finite-time control and one auxiliary system. Actuator faults, control input saturations, external disturbances and nonlinear uncertainties are coupled together and dealt with by adaptive backstepping control, fault-tolerant control and the other auxiliary system. In the end, the effectiveness of the proposed control scheme is verified with simulation results.

(Invited) Multiscale Diffusion-Reaction Heat Transfer Analysis for Predicting Thermal Runaway in Li-Ion Cells and Battery Packs

Thermal runaway in Li-ion cells and battery packs remains a topic of much importance for ensuring the safety of electrochemical energy conversion and storage systems. Thermal runaway also indirectly affects performance, as conservative overdesign to reduce the risk of thermal runaway affects system-level performance metrics such as energy storage density. Given the considerable challenges in experimental investigation of thermal runaway, theoretical models to predict thermal runaway characteristics of a cell or battery pack are critically needed. Such models can help guide the design of experiments, and ensure that practical systems are reasonably safe without being needlessly overdesigned.This talk will summarize ongoing work on theoretical analysis of multilayer diffusion-reaction heat transfer problems for predicting thermal runaway in Li-ion cells and battery packs. Following a linearization of heat generation terms, the governing energy conservation equation is solved in a multilayer geometry representative of a Li-ion cell comprising multiple electrode and separator layers, or of a battery pack containing several cells. It is mathematically shown that under certain conditions, this problem may admit a finite number of imaginary eigenvalues, which are shown to cause thermal runaway. It is shown that the occurrence of imaginary eigenvalues is governed by a delicate balance between heat generation, thermal conduction within the multilayer body and heat removal from the boundaries, based on which, an explicit threshold condition for thermal runaway is derived. This threshold condition is interpreted in the context of parameter values relevant to Li-ion cells. For example, it is shown that boundary cooling is severely limited in its role to prevent thermal runaway, whereas improving thermal transport within the cell is likely to be a lot more effective. This theoretical model is also extended to a two-layer problem comprising a finite layer adjoined by a semi-infinite body that may model immersion cooling of a Li-ion cell or battery pack. It is shown that while this diffusion-reaction problem is, in principle, unconditionally unstable, the finite time period of any discharge process may allow the temperature field to still remain within a safe envelope, if designed appropriately. Based on this mathematical model, scenarios related to immersion cooling of pouch cells are investigated.The theoretical work described in this talk is broadly relevant to several diffusion-reaction systems. In the particular context of Li-ion cells, these results may form the foundation of techniques to proactively predict and prevent thermal runaway in Li-ion cells and battery packs.

Active control approach to temporal acoustic cloaking

We propose a realization of a transformation-based acoustic temporal cloak using an active closed-loop control approach to an equivalent electromagnetic problem. Unlike the more common spatial cloaks, the goal of which is hiding fixed objects from detection, the goal of the temporal cloak is hiding the occurrence of events during a finite period of time. In electromagnetic systems, in which events represent, for example, leakage of signals from transmission lines or optical fibers, temporal cloaking solutions usually rely on nonlinear phenomena related to the fibers properties or on modulating the properties of the propagation medium itself. In particular, the transformation-based solution requires modulating the constitutive parameters of the medium in both space and time. In acoustic systems, an event may represent an object crossing a propagation channel and temporarily blocking it. Our control approach is fully linear, where the required change in the medium parameters is programmed into the controllers and created by external actuators in real-time. This cloaking system keeps the physical medium unchanged and enables re-programing of the cloaking parameters upon request. We demonstrate our solution in a simulation of a one-dimensional water channel.

Detecting the birth and death of finite‐time coherent sets

AbstractFinite‐time coherent sets (FTCSs) are distinguished regions of phase space that resist mixing with the surrounding space for some finite period of time; physical manifestations include eddies and vortices in the ocean and atmosphere, respectively. The boundaries of FTCSs are examples of Lagrangian coherent structures (LCSs). The selection of the time duration over which FTCS and LCS computations are made in practice is crucial to their success. If this time is longer than the lifetime of coherence of individual objects then existing methods will fail to detect the shorter‐lived coherence. It is of clear practical interest to determine the full lifetime of coherent objects, but in complicated practical situations, for example a field of ocean eddies with varying lifetimes, this is impossible with existing approaches. Moreover, determining the timing of emergence and destruction of coherent sets is of significant scientific interest. In this work we introduce new constructions to address these issues. The key components are an inflated dynamic Laplace operator and the concept of semi‐material FTCSs. We make strong mathematical connections between the inflated dynamic Laplacian and the standard dynamic Laplacian, showing that the latter arises as a limit of the former. The spectrum and eigenfunctions of the inflated dynamic Laplacian directly provide information on the number, lifetimes, and evolution of coherent sets.

Stochastic Modeling of the Epidemic Process Based On a Stage-Dependent Model with Non-Markov Constraints for Individuals

A continuous-discrete stochastic model of the epidemic process is presented. The model takes into account several stages of the development of an infectious disease, as well as the distributions of the durations of stay of individuals in these stages. The variables of the model are integer random variables that denote the quantity of individuals in cohorts, and sets of unique types of individuals that take into account the current state and history of stay of individuals in the stages of development of an infectious disease, distributions of durations of these stages are different from exponential or geometric. The results of an analytical and numerical research of the dynamics of the epidemic process are presented. The probabilities of infection eradication during a finite period of time are examined, depending on the numerical values of the infection spread coefficient and the distributions of the durations of the latent stage of the disease and the stage of preservation of immunity to infection.

Composite Observer-based Finite-time Command Filtered Adaptive Control for Full-state Constraints Nonlinear Systems

In this paper, we look at a type of strict-feedback nonlinear systems that have neural adaptive finite-time command filtered control and full-state constraints. To begin with, indeterminate nonlinear functions are estimated using the NN. The system’s capacity to detect unmeasurable states and disturbances is done while using a composite observer, which significantly boosts the system’s anti-interference performance. Next, the issue of “complex explosion” that arose in the backstepping technique is resolved by adding a modified fractional-order filter. In order to address the full-state constraint issues, the BLF is also introduced. An adaptive finite-time tracking controller is created using Lyapunov’s theory analysis to make sure that all signals are confined to the finite time period. Lastly, a simulation study is conducted to evaluate the performance of the suggested control strategy.

Finite-time formation control of nonaffine nonlinear systems under directed communication interactions

This paper studies the problem of finite-time formation tracking control for networked nonaffine nonlinear systems with unmeasured dynamics and unknown uncertainties/disturbances under directed topology. A unified distributed control framework is proposed by integrating adaptive backstepping control, dynamic gain control and dynamic surface control based on finite-time theory and consensus theory. Auxiliary dynamics are designed to construct control gains with non-Lipschitz dynamics so as to guarantee finite-time convergence of formation errors. Adaptive control is used to compensate for uncertain control efforts of the transformed systems derived from original nonaffine systems. It is shown that formation tracking is achieved during a finite-time period via the proposed controller, where fractional power terms are only associated with auxiliary dynamics instead of interacted information among the networked nonlinear systems in comparison with most existing finite-time cooperative controllers. Moreover, the continuity of the proposed controller is guaranteed by setting the exponents of fractional powers to an appropriate interval. It is also shown that the improved dynamic surface control method could guarantee finite-time convergence of formation errors, which could not be accomplished by conventional dynamic surface control. Finally, simulation results show the effectiveness of the proposed control scheme.

Optimal Tuning of Fractional Order Sliding Mode Controller for PMSM Speed Using Neural Network with Reinforcement Learning

An improved fractional-order sliding mode control (FOSMC) for PMSM is presented in this study to set the unavoidable parameters and to improve permanent magnet synchronous motors (PMSMs) drive performance, such as current and speed tracking accuracy. To determine the optimal parameters of the FOSMC for control speed in a PMSM drive, a neural network algorithm with reinforcement learning (RLNNA) is proposed. The FOSMC parameters are set by the ANN algorithm and then adapted through reinforcement learning to enhance the results. The proposed controller using RLNNA based on fractional-order sliding mode control (RLNNA-FOSMC) can drive the motor speed to achieve the referred value in a finite period of time, leading to faster convergence and improved tracking accuracy. For a fair comparison and evaluation, the proposed RLNNA-FOSMC is compared with conventional FOSMC by applying the integral of time multiplied absolute error as an objective function. The most commonly used objective functions in the literature were also compared, including the integral time multiplied square error, integral square error, and integral absolute error. To validate the performance of the RLNNA-FOSMC speed controller, different scenarios with different speeds steps were carried out. The computational results are promising and demonstrate the effectiveness of the proposed controller. Overall, the proposed RLNNA-FOSMC controller for the PMSM speed control system performed better than conventional FOSMC in numerical simulations.

Entropy of Financial Time Series Due to the Shock of War.

The concept of entropy is not uniquely relevant to the statistical mechanics but, among others, it can play pivotal role in the analysis of a time series, particularly the stock market data. In this area, sudden events are especially interesting as they describe abrupt data changes with potentially long-lasting effects. Here, we investigate the impact of such events on the entropy of financial time series. As a case study, we assume data of the Polish stock market, in the context of its main cumulative index, and discuss it for the finite time periods before and after outbreak of the 2022 Russian invasion of Ukraine. This analysis allows us to validate the entropy-based methodology in assessing changes in the market volatility, as driven by the extreme external factors. We show that some qualitative features of such market variations can be well captured in terms of the entropy. In particular, the discussed measure appears to highlight differences between data of the two considered timeframes in agreement with the character of their empirical distributions, which is not always the case in terms of the conventional standard deviation. Moreover, the entropy of cumulative index averages, qualitatively, the entropies of composing assets, suggesting capability for describing interdependencies between them. The entropy is also found to exhibit signatures of the upcoming extreme events. To this end, the role of recent war in shaping the current economic situation is briefly discussed.

Discrete Optimal Control Mathematical Model of Diabetes Population

In this work, nonlinear diabetes controlled model with and without complications in a population is considered. The dynamic behavior of diabetes in a population by including a constant control is studied and investigated. The existence of all its possible fixed points is investigated as well as the conditions of the local stability of the considered model are set. We also find the optimal control strategy in order to reduce the number of people having diabetes with complications over a finite period of time. A numerical simulation is provided and confirmed the theoretical results.

Adaptive finite time smooth nonlinear sliding mode tracking control for surface vessels with uncertainties and disturbances

A study of the tracking control design for surface vessels is presented in this paper. A nonlinear adaptive approximation of the Super-Twisting algorithm (STW) is used to design a control law which provides desired stability properties even in the presence of unknown uncertainties and disturbances. By using this procedure, a desired trajectory is followed successfully and the tracking error is reduced to a small set containing the origin over the course of a finite period of time. An algorithmic solution was developed based on a small number of parameters with physical significance, which simplifies the process of determining the configuration. A numerical example is used to verify the effectiveness and robustness of the controller designed.