Position Of Particle Research Articles

Numerical study on a facing electrode configuration dielectrophoresis microfluidic system for efficient biological cell separation.

Circulating tumor cell separation has been the focus of numerous studies owing to its importance in the diagnosis, prognosis, and therapy of cancer. This study reports a highly efficient microfluidic device that integrates a specialized dielectrophoresis configuration, namely the facing-electrode configuration dielectrophoresis (FEC-DEP) structure, to isolate circulating tumor cells (CTCs) from various blood components, including red blood cells, white blood cells, and platelets. The FEC-DEP design features a bottom-slanted electrode array positioned parallel to a basic rectangular top electrode. A non-homogeneous electric field is produced between these parallel electrodes, generating dielectrophoretic forces acting on cells. Consequently, when the FEC-DEP is integrated into a flow, it can direct various biological objects in the flow along separate trajectories. As a result, cells with comparable characteristics might move together within a similar path. This configuration may simplify the microfabrication process and lessen dependency on particle position within the microchannel. The separation process was numerically analyzed using the finite element method, and device parameters were optimized to obtain high-efficiency and high-purity cell separation. The simulations show that the microfluidic device may effectively enrich tumor cells in a label-free and non-invasive manner, with a high-efficiency rate of almost 80%.

Fluids with power-law repulsion: Hyperuniformity and energy fluctuations.

We revisit the equilibrium statistical mechanics of a classical fluid of point-like particles with repulsive power-law pair interactions, focusing on density and energy fluctuations at finite temperature. Such long-range interactions, decaying with inter-particle distance $r$ as $1/r^s$ in $d$ dimensions, are known to fall into two qualitatively different categories. For $s<d$ (``strongly" long-range interactions) there are screening of correlations and suppression of large-wavelength density fluctuations (hyperuniformity). These effects eliminate density modes with arbitrarily large energy. For $s>d$ (``weakly" long-range interactions) screening and hyperuniformity do not occur. Using scaling arguments, variational analysis, and Monte Carlo simulations, we find another qualitative distinction. For $s\geq d/2$ the strong repulsion at short distances leads to enhanced small-wavelength density fluctuations, decorrelating particle positions. This prevents indefinitely negative entropy and large energy fluctuations. The distinct behaviors for $s\geq d/2$ and $s<d/2$ give rise to qualitatively different dependencies of the entropy, heat capacity, and energy fluctuations on temperature and density. We investigate the effect of introducing an upper cutoff distance in the pair-potential. The effect of the cutoff on energy fluctuations is strong for $s<d/2$ and negligible for $s \geq d/2$.

Soft Actor-Critic Approach to Self-Adaptive Particle Swarm Optimisation

Particle swarm optimisation (PSO) is a swarm intelligence algorithm that finds candidate solutions by iteratively updating the positions of particles in a swarm. The decentralised optimisation methodology of PSO is ideally suited to problems with multiple local minima and deceptive fitness landscapes, where traditional gradient-based algorithms fail. PSO performance depends on the use of a suitable control parameter (CP) configuration, which governs the trade-off between exploration and exploitation in the swarm. CPs that ensure good performance are problem-dependent. Unfortunately, CPs tuning is computationally expensive and inefficient. Self-adaptive particle swarm optimisation (SAPSO) algorithms aim to adaptively adjust CPs during the optimisation process to improve performance, ideally while reducing the number of performance-sensitive parameters. This paper proposes a reinforcement learning (RL) approach to SAPSO by utilising a velocity-clamped soft actor-critic (SAC) that autonomously adapts the PSO CPs. The proposed SAC-SAPSO obtains a 50% to 80% improvement in solution quality compared to various baselines, has either one or zero runtime parameters, is time-invariant, and does not result in divergent particles.

Cooperative Path Planning for Multi-UAVs with Time-Varying Communication and Energy Consumption Constraints

In the field of Unmanned Aerial Vehicle (UAV) path planning, designing efficient, safe, and feasible trajectories in complex, dynamic environments poses substantial challenges. Traditional optimization methods often struggle to address the multidimensional nature of these problems, particularly when considering constraints like obstacle avoidance, energy efficiency, and real-time responsiveness. In this paper, we propose a novel algorithm, Dimensional Learning Strategy and Spherical Motion-based Particle Swarm Optimization (DLS-SMPSO), specifically designed to handle the unique constraints and requirements of cooperative path planning for Multiple UAVs (Multi-UAVs). By encoding particle positions as motion paths in spherical coordinates, the algorithm offers a natural and effective approach to navigating multidimensional search spaces. The incorporation of a Dimensional Learning Strategy (DLS) enhances performance by minimizing particle oscillations and allowing each particle to learn valuable information from the global best solution on a dimension-by-dimension basis. Extensive simulations validate the effectiveness of the DLS-SMPSO algorithm, demonstrating its capability to consistently generate optimal paths. The proposed algorithm outperforms other metaheuristic optimization algorithms, achieving a feasibility ratio as high as 97%. The proposed solution is scalable, adaptable, and suitable for real-time implementation, making it an excellent choice for a broad range of cooperative multi-UAV applications.

Langevin dynamics for a heavy particle immersed within a flow of light particles

A micro-hydrodynamics model based on elastic collisions of light point solvent particles with a heavy solute particle is investigated in the setting where the light particles have velocity distribution corresponding to a background flow. Considering a range of stationary background flows and distributions for the solvent particle velocities, the macroscopic Langevin-type description of the behaviour of the heavy particle is derived in the form of a generalized Ornstein–Uhlenbeck process. At leading order, the drift term in this process depends upon both the geometric structure of the background flow and the size of the heavy particle, while both drift and diffusion terms scale with moments of the light particle velocity distribution. Computational methods for simulating the micro-hydrodynamics model are then designed to confirm the theoretical results. To enable long-time calculations, simulations are performed in a frame co-moving with the heavy particle. Efficient methods for sampling the position and velocity distributions of incoming solvent particles at the boundaries of the co-moving frame are derived for a range of distributions of solvent particles. The simulations show good agreement with the theoretical results.

Inertial active Ornstein–Uhlenbeck particle in a non-linear velocity dependent friction

We explore the self-propulsion of an active Ornstein–Uhlenbeck particle with a non-linear velocity dependent friction. Using analytical approach and numerical simulation, we have exactly investigated the dynamical behaviour of the particle in terms of particle trajectory, position and velocity distribution functions in both underdamped as well as overdamped regimes of the dynamics. Analyzing the distribution functions, we observe that for a confined harmonic particle, with an increase in duration of self-propulsion, the inertial particle prefers to accumulate near the boundary of the confinement rather than the mean position, reflecting an activity induced bistability in the presence of nonlinear friction. On the other hand, in the overdamped or highly viscous regime, where the inertial influence is negligible, the sharp peak structure in the distribution across the mean position of the well reveals as usual trapping of the particle with increase in the persistent duration of activity. Moreover, for a free particle, using perturbation method, we have analytically computed the velocity distribution function in the vanishing limit of noise. The distribution interestingly shows the similar attributes as in case of a harmonic well, thus providing an additional effective confining mechanism that can be explained as a decreasing function of effective temperature. In this limit, the analytically computed distribution agrees well with the simulation results.

Design of a new fiber-optical transmission-based fast ion loss probe on Experimental Advanced Superconducting Tokamak

For the first time, a new probe for fast ion loss has been developed for use in the Experimental Advanced Superconducting Tokamak (EAST) and utilizes optical fiber transmission, the aim of the probe is to study fast ion losses triggered by the ion cyclotron range of frequencies (ICRF) heating and neutral beam injection (NBI) synergy. This probe is used to determine the pitch angle and gyroradii of the ion losses based on the particle positions striking the two-dimensional scintillator. In this paper, the calculation and design of the probe scintillator by using the NLSDETSIM code is discussed. The emitted light from the scintillator is captured by a high-speed camera via an optical lens and high-temperature-resistant fibers and then processed by a signal processing system to determine the real-time energy and angle of the fast ion. Due to the flexible nature of the fiber, the probe is capable of measuring the fast ion losses at various locations within the EAST. Furthermore, the probe can be easily installed by using one of the numerous vacuum feeds on the EAST instead of occupying the entire window. This feature enhances the flexibility of the diagnostic technique.

Towards a probabilistic foundation of relativistic quantum theory: the one-body Born rule in curved spacetime

In this work, we establish a novel approach to the foundations of relativistic quantum theory, which is based on generalizing the quantum-mechanical Born rule for determining particle position probabilities to curved spacetime. A principal motivator for this research has been to overcome internal mathematical problems of relativistic quantum field theory (QFT) such as the ‘problem of infinities’ (renormalization), which axiomatic approaches to QFT have shown to be not only of mathematical but also of conceptual nature. The approach presented here is probabilistic by construction, can accommodate a wide array of dynamical models, does not rely on the symmetries of Minkowski spacetime, and respects the general principle of relativity. In the analytical part of this work, we consider the 1-body case under the assumption of smoothness of the mathematical quantities involved. This is identified as a special case of the theory of the general-relativistic continuity equation. While related approaches to the relativistic generalization of the Born rule assume the hypersurfaces of interest to be spacelike and the spacetime to be globally hyperbolic, we employ prior contributions by C. Eckart and J. Ehlers to show that the former condition is naturally replaced by a transversality condition and that the latter one is obsolete. We discuss two distinct formulations of the 1-body case, which, borrowing terminology from the non-relativistic analog, we term the Lagrangian and Eulerian pictures. We provide a comprehensive treatment of both. The main contribution of this work to the mathematical physics literature is the development of the Lagrangian picture. The Langrangian picture shows how one can address the ‘problem of time’ in this approach and, therefore, serves as a blueprint for the generalization to many bodies and the case that the number of bodies is not conserved. We also provide an example to illustrate how this approach can in principle be employed to model particle creation and annihilation.

Deep learning-assisted morphological segmentation for effective particle area estimation and prediction of interfacial properties in polymer composites.

The link between the macroscopic properties of polymer nanocomposites and the underlying microstructural features necessitates an understanding of nanoparticle dispersion. The dispersion of nanoparticles introduces variability, potentially leading to clustering and localized accumulation of nanoparticles. This non-uniform dispersion impacts the accuracy of predictive models. In response to this challenge, this study developed an automated and precise technique for particle recognition and detailed mapping of particle positions in scanning electron microscopy (SEM) micrographs. This was achieved by integrating deep convolutional neural networks with advanced image processing techniques. Following particle detection, two dispersion factors were introduced, namely size uniformity and supercritical clustering, to quantify the impact of particle dispersion on properties. These factors, estimated using the computer vision technique, were subsequently used to calculate the effective load-bearing area influenced by the particles. An adapted micromechanical model was then employed to quantify the interfacial strength and thickness of the nanocomposites. This approach enabled the establishment of a correlation between dispersion characteristics and interfacial properties by integrating experimental data, relevant micromechanical models, and quantified dispersion factors. The proposed systematic procedure demonstrates considerable promise in utilizing deep learning to capture and quantify particle dispersion characteristics for structure-property analyses in polymer nanocomposites.

Double refractive particle tracking and sizing

We present a novel bifocal imaging method enabling three-dimensional particle tracking and size determination employing a single camera only. The method is based on double refraction causing a particle to be imaged twice, each particle image of different blur. From these double images, a linear calibration function can be derived allowing to determine the three-dimensional particle position unambiguously over the entire depth of measurement volume. As this calibration function is independent of the particle size used, the particle size can be determined simultaneously by relating size of the double images and depth position of the particle. To prove the applicability, a co-laminar flow of two particle suspensions with particles of 1.14 μ\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\\upmu$$\\end{document}m and 2.47 μ\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\\upmu$$\\end{document}m in diameter was measured in a Y-shaped microchannel. While the laminar flow field was measured with very low uncertainty and independent of the particle size, the particle size distributions determined reproduced reliably the size distributions expected for the co-laminar flow applied, with a precision of about 98.6 %\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\\%$$\\end{document} regarding the particle size discrimination. The progress for research is a new method readily to implement in common optical setups, promising, for example, valuable insights in polydisperse suspension flows—the vast majority of flows in fundamental research and applications.Graphical abstract

Engineering impurity Bell states through coupling with a quantum bath

We theoretically demonstrate the feasibility of creating Bell states in multicomponent ultracold atomic gases by solely using the ability to control the interparticle interactions via Feshbach resonances. For this we consider two distinguishable impurities immersed in an atomic background cloud of a few bosons, with the entire system being confined in a one-dimensional harmonic trap. By analyzing the numerically obtained ground states we demonstrate that the two impurities can form spatially entangled bipolaron states due to mediated interactions from the bosonic bath. Our analysis is based on calculating the correlations between the two impurities in a two-mode basis, which is experimentally accessible by measuring the particle positions in the left or right sides of the trap. While interspecies interactions are crucial in order to create the strongly entangled impurity states, they can also inhibit correlations depending on the ordering of the impurities and three-body impurity-bath correlations. We show how these drawbacks can be mitigated by manipulating the properties of the bath, namely its size, mass, and intraspecies interactions, allowing one to create impurity Bell states over a wide range of impurity-impurity interactions. Published by the American Physical Society 2024

Fractional Brownian motion in confining potentials: non-equilibrium distribution tails and optimal fluctuations

Abstract At long times, a fractional Brownian particle in a confining external potential reaches a non-equilibrium (non-Boltzmann)&amp;#xD;steady state. Here we consider scale-invariant power-law potentials $V(x)\sim |x|^m$, where $m&gt;0$, and employ the optimal fluctuation method (OFM) to determine the large-$|x|$ tails of the steady-state probability distribution $\mathcal{P}(x)$ of the particle position. The calculations involve finding the optimal (that is, the most likely) path of the particle, which determines these tails, via a minimization of the exact action functional for this system, which has recently become available. Exploiting dynamical scale invariance of the model in conjunction with the OFM ansatz, we establish the large-$|x|$ tails of $\ln \mathcal{P}(x)$ up to a dimensionless factor $\alpha(H,m)$, where $0&lt;H&lt;1$ is the Hurst exponent. We determine $\alpha(H,m)$ analytically (i) in the limits of $H\to 0$ and $H\to 1$, and (ii) for $m=2$ and arbitrary $H$, corresponding to the fractional Ornstein-Uhlenbeck (fOU) process. Our results for the fOU process are in agreement with the previously known exact $\mathcal{P}(x)$ and autocovariance. The form of the tails of $\mathcal{P}(x)$ yields exact conditions, in terms of $H$ and $m$, for the particle confinement in the potential.&amp;#xD;For $H\neq 1/2$, the tails encode the non-equilibrium character of the steady state distribution, and we observe violation of time reversibility of the system except for $m=2$. To compute the optimal paths and the factor $\alpha(H,m)$ for arbitrary permissible $H$ and $m$, one needs to solve an (in general nonlinear) integro-differential equation. To this end we develop a specialized numerical iteration algorithm which&amp;#xD;accounts analytically for an intrinsic cusp singularity of the optimal paths for $H&lt;1/2$.&amp;#xD;&amp;#xD;

Breadth-first search algorithm on the finite element simulation of the electrical resistivity of the carbon black elastomeric pressurized sensor

Resistivity and piezoresistive sensitivity of Carbon Black (CB) elastomeric nanocomposites are studied using a finite element method with a conductive network model. CB spheres are placed into Representative Volume Elements (RVEs) in random positions to perform simulations and obtain the strained state and new position of particles. Numerical results are implemented into a breadth-first search algorithm tailored to find percolation pathways from one end of the RVE to another based on the shortest distance between CBs in the strained regime. Percolation pathways are used by the conductive network model to determine the critical distance for resistivity. Resistivity diminishes as the critical distance increases attributed to a greater number of electrons penetrating the barriers. Critical distance at which tunneling can occur expands with an increase in barrier potential. Smaller CBs that can more efficiently occupy the gaps lead to a reduction in the critical distance range necessary for percolation to happen.

Теоретические исследования процесса смешивания кормов центробежным смесителем

The article presents theoretical studies aimed at understanding the physical processes occurring in the production of compound feeds using a centrifugal cascade mixer, which open up new horizons in optimizing feed production technology. Special attention in these studies is paid to the analysis of the mixing process, which, as it turned out, is closely interrelated with the principles of operation of the dispenser. As a result of the analysis, it was found that the dispenser and mixer should be considered as a single system, regardless of the mixing mechanism used in the production process. This made it possible to develop a cascade mixer of feed materials of centrifugal type and continuous action, which is an innovative solution in the field of feed production. This mixer provides a homogeneous mixture of loose feed components and can be integrated into production lines for the preparation of both feed and combined mineral briquettes. The key advantage of the proposed mixer is the use of a centrifugal force field, which, unlike the gravity field, provides more efficient mixing. In this case, the process of preparing compound feeds can be represented as a sequence of three key stages: the metered supply of components, the introduction of one component into the composition of another, transportation (removal) of the finished product from the working area of the mixer. The research allowed us to obtain analytical dependences of the components of the centrifugal force on the angular velocity of the rotor and its radius. This allows you to optimize the operating parameters of the mixer to achieve maximum mixing efficiency. During the research, a mathematical model of the spatial motion of a particle in a mixer was developed. The model is presented in the form of Lagrange equations of the second kind, the generalized coordinates of which are the displacement of the particle along the cone and the polar angle of the particle position. This makes it possible to solve the optimization problems of centrifugal mixers according to a given objective function. Thanks to the conducted research, it was possible to develop an innovative mixer that provides a homogeneous mixture of components of loose compound feeds. The use of centrifugal forces in the mixer made it possible to increase the mixing efficiency and ensure that the final product meets the requirements of the formulation. The research results are of great practical importance and can be used to improve feed production technologies. Keywords: COMPOUND FEED, FEED MIXTURE, MIXER, DISPENSER, CENTRIFUGAL MIXER, PARTICLE MOTION IN THE MIXER

Fitness-guided particle swarm optimization with adaptive Newton-Raphson for photovoltaic model parameter estimation

This study introduces a new approach for parameter optimization in the four-diode photovoltaic (PV) model, employing a Dynamic Fitness-Guided Particle Swarm Optimization (DFGPSO) algorithm and Enhanced Newton-Raphson (ENR) method. The new DFGPSO algorithm is specifically designed to address the intrinsic challenges in PV modelling, such as local optima entrapment and slow convergence rates that typically hinder traditional optimization methods. By integrating a dynamically evolving fitness function derived from advanced swarm intelligence, the proposed approach significantly enhances global search capabilities. This new fitness function adapts continuously to the search landscape, facilitating rapid convergence towards optimal solutions and effectively navigating the complex, non-linear, and multi-modal parameter space of the PV model. Moreover, the robustness of the DFGPSO algorithm is substantially improved through the strategic incorporation of the ENR method. This integration not only provides accurate initial guesses for the particle positions, thus expediting the convergence process, but also minimizes computational burden, making the method more efficient. Comprehensive simulation studies across various case scenarios demonstrate that the proposed method markedly outperforms existing state-of-the-art optimization algorithms. It delivers faster convergence, enhanced accuracy, and robust performance under diverse environmental conditions, establishing a reliable and precise tool for optimizing PV system performance. This advancement promises significant improvements in energy yield and system reliability for the PV industry.

Simulating 2D Fluid Motion with the Smooth Particle Hydrodynamic Approach

Abstract Virtual two-dimensional (2D) fluid simulation is useful for directly simulating fluids in various situations, including geological simulations for landslides and fluid simulations for teaching. This research aims to simulate the behaviour of three different types of fluids (water, coconut oil, and glycerine) in a 2D container and analyse how these three types of fluids behave under various conditions, including interactions with boundaries. The research used Python programming to simulate fluids and the Wondershare Filmora X application to combine images. The Smooth Particle Hydrodynamics method simulated fluid in a 2D container with 10,000 particles by deriving the force density field directly from the Navier-Stokes equations. The Navier-Stokes equations were utilized to find the acceleration and velocity of fluid particles by considering external forces, internal forces, and gravity through this method. Acceleration and velocity were validated due to wall collisions, collisions with boundaries, and collisions between particles, which caused changes in particle position and velocity. During visualization, the fluid velocity decreased over time due to attenuation caused by interactions between neighbour particles, particles with boundaries, and particles with walls. From the simulation, it was observed that the fluid flowed from a higher place to a lower place, with fluid particles taking the shape of the container and the surface of the fluid forming waves. On the other hand, simulations with boundaries indicated that smaller gap sizes and higher viscosities led to increased difficulty in fluid penetration into the gap within the container.

Peak-CNN: improved particle image localization using single-stage CNNs

AbstractAn important step in the application of Lagrangian particle tracking (LPT) or in general for image-based single particle identification techniques is the detection of particle image locations on the measurement images and their sub-pixel accurate position estimation. In case of volumetric measurements, this constitutes the first step in the process of recovering 3D particle positions, which is usually performed by triangulation procedures. For two-component 2D measurements, the particle localization results directly serve as input to the tracking algorithm. Depending on the quality of the image, the shape and size of the particle images and the amount of particle image overlap, it can be difficult to find all, or even only the majority, of the projected particle locations in a measurement image. Advanced strategies for 3D particle position reconstruction, such as iterative particle reconstruction (IPR), are designed to work with incomplete 2D particle detection abilities but even they can greatly benefit from a more complete detection as ambiguities and position errors are reduced. We introduce a convolutional neural network (CNN) based particle image detection scheme that significantly outperforms current conventional approaches, both on synthetic and experimental data, and enables particle image localization with a vastly higher completeness even at high image densities.

A nonequilibrium statistical mechanics derivation of the hydrodynamic equations of simple fluids using a noncanonical form of the Poisson bracket

The continuum level hydrodynamic equations of a simple fluid were derived by Irving and Kirkwood directly from discrete particle dynamics using statistical mechanics almost 75 years ago. Their elegant derivation demonstrated the fundamental molecular basis of macroscopic fluid flow and culminated in molecular expressions for the stress tensor and heat current density that have since been employed in countless molecular simulations to date. In this article, an alternative derivation is presented, which leads to more general expressions for the fundamental transport relationships and which arrives at them in a more straightforward chain of consistency that ensues directly from the Principle of Least Action. The main point of departure from the Irving–Kirkwood derivation is the application of a transformation mapping of the total momentum of each individual particle onto the sum of its peculiar momentum and its momentum relative to the local velocity field. This mapping provides a phase-space distribution function applicable in the space of particle positions and peculiar momentum, from which a noncanonical Poisson bracket can be derived in terms of the same set of microscopic variables. For a given dynamic variable, expressed in terms of particle positions and peculiar momenta, the expectation value of the noncanonical Poisson bracket of the dynamic variable is shown to correspond to the evolution equation of the expectation value of the dynamic variable. This allows for a direct derivation of all macroscopic density evolution equations (mass, momentum, and energy density fields) using a systematic procedure free of assumptions concerning the macroscopic state of the system. Furthermore, an explicit expression of the time evolution of the entropy density at the hydrodynamic level is derived following the same procedure. Finally, in the limit of short-range interparticle interactions, a molecular-based expression for the local stress tensor as properly defined from continuum mechanics is developed at the hydrodynamic level that elucidates the continuum mechanics connection of the general stress expression of Irving and Kirkwood.

An Improved Quantum Particle Swarm Optimization Algorithm for Target Tracking Deployment in Spatial Sensor Networks

This paper presents a comprehensive review of the current research on spatial sensor networks and the node deployment methods employed for mobile target tracking. The study introduces particle swarm optimization (PSO) and its quantum behavior extension, detailing concepts such as the quantum state wave function and particle position representation. Subsequently, an improved Quantum Particle Swarm Optimization (QPSO) algorithm is proposed. This enhanced algorithm increases population diversity by incorporating quantum rotation gates and quantum mutation mechanisms, expands the search space through the superposition state and interference principles of quantum mechanics, and dynamically adjusts algorithm parameters to balance global exploration and local search. These modifications aim to improve both the convergence speed and accuracy of the algorithm. Simulation results demonstrate that the improved QPSO algorithm surpasses traditional mobile tracking deployment algorithms and the standard quantum behavior particle swarm optimization algorithm in terms of target tracking deployment within spatial sensor networks. Notably, it significantly enhances the tracking success rate and reduces tracking errors.

Active Brownian information engine: Self-propulsion induced colossal performance.

The information engine is a feedback mechanism that extorts work from a single heat bath using the mutual information earned during the measurement. We consider an overdamped active Ornstein-Uhlenbeck particle trapped in a 1D harmonic oscillator. The particle experiences fluctuations from an inherent thermal bath with a diffusion coefficient (D) and an active reservoir, with characteristic correlation time (τa) and strength (Da). We design a feedback-driven active Brownian information engine (ABIE) and analyze its best performance criteria. The optimal functioning criteria, the information gained during measurement, and the excess output work are reliant on the dispersion of the steady-state distribution of the particle's position. The extent of enhanced performance of such ABIE depends on the relative values of two underlying time scales of the process, namely, thermal relaxation time (τr) and the characteristic correlation time (τa). In the limit of τa/τr → 0, one can achieve the upper bound on colossal work extraction as ∼0.202γ(D+Da) (γ is the friction coefficient). The excess amount of extracted work reduces and converges to its passive counterpart (∼0.202γD) in the limit of τa/τr → high. Interestingly, when τa/τr = 1, half the upper bound of excess work is achieved irrespective of the strength of either reservoirs, thermal or active. Finally, we look into the average displacement of active Brownian particles in each feedback cycle, which surpasses its thermal analog due to the broader marginal probability distribution.