Sequential Particle Research Articles

Design of highly-sinterable LATP-CNT composite powder by sequential particle assembly for fabrication of highly electrical-conductive composite electrodes

A sequential particle assembling process was developed to fabricate composite particles with multiple components. Cobalt sintering aids and carbon nanotube (CNT) conductive additives were sequentially and electrostatically absorbed on Li1.3Al0.3Ti1.7(PO4)3 (LATP) electrolyte particles under various dispersion conditions. The effect of the particle assembling conditions on the composite body's sintering ability and conductive properties was investigated. By optimizing the microstructure of the composite particle and its particle charging conditions, it became possible to obtain a sintered composite electrode with a finely connected path of each LATP and CNT phase.

Double Power-law Formation by Sequential Particle Acceleration

Spectral double power laws are common in solar high-energy phenomena such as flares and interplanetary energetic-electron events. However, the physical mechanism that produces the changes in power-law index within a single spectrum is unclear. We developed a fully analytical method of forming single power-law spectra from sequential acceleration of particles orbiting inside and hopping between simulated large-scale magnetic islands formed by flare reconnection. Here, we extend the analytical method to the formation of double power-law spectra by assuming sequential acceleration in two successive regions with different acceleration and particle-transport rates. The resulting spectral distribution is continuous and smooth, with a flattening at low energies, two power-law regions at mid-energies, and a steep rollover at high energies. The model provides analytical expressions for the spectral indices, all energy breaks, and normalization constants as functions of just three physical parameters of each acceleration region: (1) the energy gain in each accelerator, (2) the percentage of particles transferred between accelerators, and (3) the number of accelerators visited. One of the most salient predictions of our work is that the spectral index at high (low) energies is determined by the parameters of the first “seed” (second) acceleration region. By constructing the spectral distribution through an iterative analytical process, the evolution toward a double power law is easily characterized and explained. Our analytical model provides tools to interpret space- and ground-based observations from RHESSI, FOXSI, NuSTAR, Solar Orbiter/STIX, EOVSA, and future high-energy missions.

A Geomagnetic/Odometry Integrated Localization Method for Differential Robot Using Real-Time Sequential Particle Filter.

Geomagnetic matching navigation is extensively utilized for localization and navigation of autonomous robots and vehicles owing to its advantages such as low cost, wide-area coverage, and no cumulative errors. However, due to the influence of magnetometer measurement noise, geomagnetic localization algorithms based on single-point particle filters may encounter mismatches during continuous operation, consequently limiting their long-range localization performance. To address this issue, this paper proposes a real-time sequential particle filter-based geomagnetic localization method. Firstly, this method mitigates the impact of noise during continuous operation while ensuring real-time performance by performing real-time sequential particle filtering. Then, it enhances the long-range positioning accuracy of the method by rectifying the trajectory shape of the odometry through odometry calibration parameters. Finally, by performing secondary matching on the preliminary matching results via the MAGCOM algorithm, the positioning error of the method is further minimized. Experimental results show that the proposed method has higher positioning accuracy compared to related algorithms, resulting in reductions of over 28.58%, 37.11%, and 0.77% in RMSE, max error, and error at the end, respectively.

Novel concept of "sequential particle radiotherapy" with atezolizumab plus bevacizumab for hepatocellular carcinoma with portal vein tumor thrombus.

Owing to the high objective response rate of atezolizumab plus bevacizumab (Atez/Bev) for hepatocellular carcinoma (HCC), the concept of sequential conversion to local treatment has recently become mainstream. The conversion concept is mainly applied to Barcelona Clinic for Liver Cancer (BCLC) stage B cases, and radiotherapy is rarely considered as a conversion local treatment. We herein report three patients who were treated with the novel concept of "sequential particle radiotherapy," consisting of Atez/Bev therapy followed by particle radiotherapy (PRT) for HCC with advanced portal vein tumor thrombus (Vp3/4 PVTT). All patients achieved partial response radiologically and were switched to PRT. All patients were recurrence free at 1year after the introduction of Atez/Bev therapy without any additional treatment. This upcoming combination strategy includes the advocacy of sequential concepts for BCLC stage C cases and the introduction of PRT as a local treatment after Atez/Bev.

Sequential niching particle swarm optimization algorithm for localization of multiple damage locations using fiber bragg grating sensors

Structural Health Monitoring (SHM) systems have a potential to reduce lifecycle costs of structures through maintenance planning and lifetime extension. Hence, there is a lot of active research in the area for SHM of civil and mechanical structures. The SHM system should be low cost, suitable for continuous monitoring, able to detect small levels of damage. Guided waves (GW) based SHM techniques allow monitoring of large thin-walled structures with a few sensors and have been identified as the most promising of techniques for SHM. The GW based techniques allow mapping of damage very easily, and hence are a powerful tool for assessing the damage. The mapping of damage with high resolution is of course computationally expensive and hinders real-time decision making. Hence, methods are being developed to minimize the processing time.In this paper, the authors implement a sequential niching particle swarm optimization (PSO) for multiple damage detection. It is shown that the niching PSO is able to detect multiple damage scenarios effectively. The processing time is significantly better (order of magnitude) hence making it suitable for real-time assessment. The paper also presents some sensitivity studies, for determination of the thresholds. The validation has been conducted experimentally on an aluminum plate instrumented with piezo-actuators for actuation and FBG sensor for sensing. The results indicate that the niching PSO is suitable and indeed a better alternative than the brute-force techniques commonly used in literature.

Optimization of the sensitivity of the magnetoimpedance sensor of small magnetic fields by methods of sequential approximation and particle swarm

Optimization of the sensitivity of the magnetoimpedance sensor of small magnetic fields by methods of sequential approximation and particle swarm

Magnetic-assisted sequential templated self-assembly of hybrid colloid nanoparticle systems.

The assembly of hybrid nanoparticles is a pioneering route for developing nanoscale functional devices, enabling breakthroughs in various fields, including electronics, photonics, energy, sensing, and biomedical applications. Here, we focus on the templated assembly of nano-sized colloidal systems using a combination of silica-coated superparamagnetic beads (MBs) and polymer-coated gold nanoparticles (AuNPs) or silver nanoparticles (AgNPs). These hybrid nanoparticles introduce new functionalities that allow them to be used as nanomachines with numerous possible applications. Using sequential capillarity-assisted particle assembly (sCAPA), we deposit MBs with activating nano-transducers made of PNIPAm@AuNPs, which respond to specific external stimuli such as temperature variations. We deposit MBs with surface-enhanced Raman scattering (SERS) AgNPs, which have the ability to detect molecules at low concentrations. A key achievement of our study is demonstrating the successful CAPA assembly of nanoparticles with ingenious surface chemistry and retrieving these assembled structures from nanotraps in a liquid medium. This ability highlights the potential of hybrid colloids in precisely targeted drug delivery systems and highly effective mobile sensors. It represents a significant leap forward in using nanotechnology to create more complex and responsive nanoscale assemblies.

ЧАСТИНКИ-МАРКЕРИ ВІДПОВІДІ В ЛИТОВСЬКОМУ ДИСКУРСІ І МОВНИХ ЗВОРОТАХ

The paper deals with response particles in Lithuanian conversation. The results of the analysis provide evidence that sequential environments response particles are used in turn out to be central. Thus, the distinctive usages and functions of the particles are investigated in the following sequences: questions-answers, assertions-reactions and directives-reactions. The paper considers similarities and contrasts among the Lithuanian affirmative as well as negative particles. The results of the analysis show that the particles mainly appear in positive responses, thus the inventory of the affirmative particles is much more abundant than that of negative particles. The primary functions of the particles encompass responding to a previous turn: they occur as positive or negative answers to polar (yes-no) questions, as responses to assertions or directives, and as so-called feedback (or back-channel) elements. Affirmative particles firstly operate as confirmation and agreement markers, while negative particles, on their turn, primarily operate as disagreement markers, though at times they have also a capacity of functioning as agreement devices. Sequential contexts appear to have an impact on the emergence of discursive (resp. interactional) meanings of response particles that have not been discussed in Lithuanian grammars.

Precise Control of Two-Dimensional Hexagonal Platelets via Scalable, One-Pot Assembly Pathways Using Block Copolymers with Crystalline Side Chains.

Crystallization-driven self-assembly (CDSA) of block copolymers (BCPs) in selective solvents provides a promising route for direct access to two-dimensional (2D) platelet micelles with excellent uniformity, although significant limitations also exist for this robust approach, such as tedious, multistep procedures, and low yield of assembled materials. Herein, we report a facile strategy for massively preparing 2D, highly symmetric hexagonal platelets with precise control over their dimensions based on BCPs with crystalline side chains. Mechanistic studies unveiled that the formation of hexagonal platelets was subjected to a hierarchical self-assembly process, involving an initial stage of formation of kinetically trapped spheres upon cooling driven by solvophobic interactions, and a second stage of fusion of such spheres to the 2D nuclei to initiate the lateral growth of hexagonal platelets via sequential particle attachments driven by thermodynamically ordered reorganization of the BCP upon aging. Moreover, the size of the developed 2D hexagonal platelets could be finely regulated by altering the copolymer concentration over a broad concentration range, enabling scale-up to a total solids concentration of at least 6% w/w. Our work reveals a new mechanism to create uniform 2D core-shell nanoparticles dictated by crystallization and particle fusion, while it also provides an alternative facile strategy for the design of soft materials with precise control of their dimensions, as well as for the scalability of the derived nanostructures.

Optimization of membrane thickness for proton exchange membrane electrolyzer considering hydrogen production efficiency and hydrogen permeation phenomenon

Reducing the membrane thickness of Proton exchange membrane (PEM) electrolyzer was found to efficiently promote the hydrogen production rate. However, it can also aggravate the phenomenon of hydrogen permeation, leading to an increased hydrogen content on the anode side and posing a risk of explosions. Thus, optimal design of the membrane thickness of PEM electrolyzer systems is crucial to ensure both safe and efficient hydrogen production. In this paper, we propose two optimization problems for the membrane thicknesses, aiming at achieving a balance between the hydrogen content on the anode side and the hydrogen production rate under constant and varying power input conditions. First, the theoretical model of a PEM electrolyzer is established, and a photovoltaic-PEM electrolyzer system is considered to provide varying power input conditions. Then, the two optimization problems are formulated and resolved using the sequential quadratic programming (SQP) and particle swarm optimization (PSO) algorithms, respectively. Our results reveal that the optimal membrane thickness decreases as the constant input power increases. This suggests that high/low power inputs require thin/thick membranes. For varying power input, we collected one year of solar radiation intensity data from four different regions in China. The findings demonstrate that the selection of the optimal membrane thickness depends not only on the average solar radiation intensity but also on its seasonal variations. By applying these strategies, we effectively optimize the membrane thickness in PEM electrolyzer systems, thereby enhancing the efficiency and safety of hydrogen production. The outcomes provide valuable insights for selecting the appropriate membrane thickness in regions with varying solar radiation intensities and accounting for seasonal variations in solar radiation.

Efficient generation of random fiber distribution by combining random sequential expansion and particle swarm optimization algorithms

Representative volume elements (RVEs) with random fiber distribution are widely used in micro-mechanics for determining the properties of unidirectional fiber-reinforced composites from their microstructure. In this paper, the random sequential expansion (RSE) and particle swarm optimization (PSO) algorithms are combined to develop an efficient methodology for generating such RVEs. This methodology can successfully eliminate the biased regions of dense fiber population and unreal matrix-rich corners that usually appear in created RVEs and resolve the complications in selection of the input parameters in the RSE algorithm. Statistical analysis of the generated microstructures and two- and three-dimensional finite element analyses are performed to validate the capability of the proposed algorithms.

Application of trans-dimensional particle filtering for geoacustic inversion

The acquiring geoacoustic model and associated geoacoustic parameters is vital for sound propagation modelling in a range-dependent environment. Conventional sequential methods, e.g., particle filtering and Kalman filtering have been widely used for geoacoustic inversion in range-dependent environment, which encounter challenges when confronted with unknown geoacoustic models that intrinsically varying with range. As an attempt to estimate geoacoustic model and associated geoacoustic parameters as well, a trans-dimensional particle filtering method is presented here. This method integrates birth-death rules into the filtering process, enabling automatic selection of appropriate geoacoustic models and estimating geoacoustic parameters in the same time. Numerical simulations were conducted based on an environment model of the South China Sea, where a sea trial was conducted in 2022. Numerical results demonstrate the efficiency in accurately estimating the geoacoustic model variations and associated parameters. Preliminary results of sea trial data processing are also presented here.

Modular assembly of microswimmers with liquid compartments.

Artificial microswimmers, i.e. colloidal scale objects capable of self-propulsion, have garnered significant attention due to their central role as models for out of equilibrium systems. Moreover, their potential applications in diverse fields such as biomedicine, environmental remediation, and materials science have long been hypothesized, often in conjunction with their ability to deliver cargoes to overcome mass transport limitations. A very efficient way to load molecular cargoes is to disperse them in a liquid compartment, however, fabricating microswimmers with multiple liquid compartments remains a significant challenge. To address this challenge, we present a modular fabrication platform that combines microfluidic synthesis and sequential capillarity-assisted particle assembly (sCAPA) for microswimmers with various liquid compartments. We demonstrate the synthesis of monodisperse, small polymer-based microcapsules (Ø = 3-6μm) with different liquid cargoes using a flow-focusing microfluidic device. By employing the sCAPA technique, we assemble multiple microcapsules into microswimmers with high precision, resulting in versatile microswimmers with multiple liquid compartments and programmable functionalities. Our work provides a flexible approach for the fabrication of modular microswimmers, which could potentially actively transport cargoes and release them on demand in the future.

Geographical relevance-based multi-period optimization for e-commerce supply chain resilience strategies under disruption risks

With the fierce competition in e-commerce, e-tailers are required to rapid responses to a variety of customised orders with multiple frequencies and strict delivery times. The delay or insufficient supply caused by disruptions might result in lost sales during long-term processes. To address this problem, a two-stage stochastic programming model considering profits, consumer service level (CSL) as well as market priorities is developed to manage long-term disruptions. We analyse multi-period consumer transaction data and formulate geographical relevance (GR) to link each marketplace with historical data in related regions and then prioritise market segments. A GR-based two-stage stochastic programming with multi-period is proposed, which (1) considers both proactive mitigation decisions before disruption and reactive recovery plans after disruption; (2) collaborates three resilience strategies; (3) optimises the e-tailer's profits considering market priorities during long-term disruptions. Using a real case of Chinese e-commerce under the COVID-19 pandemic, it is illustrated (1) the applicability and performance of the proposed GR-based model for multi-period resilience optimisation improving both the CSL and the total profit; (2) the efficiency and robustness of the developed sequential particle swarm optimisation with social structures algorithm. The proposed method could optimise e-tailers' response strategies for managing long-term disruptions in practice.

Single-Step Plasmonic Dimer Printing by Gold Nanorod Splitting with Light.

Optical printing is a flexible strategy to precisely pattern plasmonic nanoparticles for the realization of nanophotonic devices. However, the generation of strongly coupled plasmonic dimers by sequential particle printing can be a challenge. Here, we report an approach to generate and pattern dimer nanoantennas in a single step by optical splitting of individual gold nanorods with laser light. We show that the two particles that constitute the dimer can be separated by sub-nanometer distances. The nanorod splitting process is explained by a combination of plasmonic heating, surface tension, optical forces, and inhomogeneous hydrodynamic pressure introduced by a focused laser beam. This realization of optical dimer formation and printing from a single nanorod provides a means for dimer patterning with high accuracy for nanophotonic applications.

Effect of foundation embedment ratio in suppressing seismic-induced vibrations using optimum tuned mass damper

Tuned mass damper (TMD), which is used in structures to mitigate the dynamic responses caused by environmental loads, is tuned according to the dominant frequency of the structures. The dominant frequency of structure built on soil with low stiffness is significantly affected by the soil-structure interaction (SSI). In previous studies, only the surface foundation case is considered to investigate the design parameters of TMD considering SSI effects and the embedment depth of the foundation is neglected in these studies. Thus, this paper investigates the optimization of TMD for mitigation of responses of a high-rise building considering various depths of embedment and soil properties. The optimum parameters of TMD are investigated using five different optimization algorithms (i.e., simulated annealing, sequential quadratic programming, genetic algorithm, pattern search, and particle swarm optimization). The objective function minimizes the peak acceleration amplitude at the top of the structure. The effectiveness of optimum TMD is verified to use 20 near-fault pulse-like ground motions. The comparisons show that the embedment ratio of the foundation and the soil properties significantly affect the optimization of TMD.

Battery SOH estimation and RUL prediction framework based on variable forgetting factor online sequential extreme learning machine and particle filter

Battery life prediction is of great practical significance to ensure the safety and reliability of equipment. This paper proposes a new framework to realize battery state of health (SOH) estimation and remaining useful life (RUL) prediction. The variable forgetting factor online sequential extreme learning machine (VFOS-ELM) is used to estimate battery SOH, and particle filter (PF) algorithm used to predict battery RUL. To improve the estimation accuracy, a new nonlinear decline method, adaptive weight and Gaussian variation are used to improve the standard whale optimization algorithm (WOA) algorithm. And the improved IWOA algorithm is used for parameter optimization of the VFOS-ELM and PF algorithm. The extremely randomized trees (ERT) algorithm is used to obtain the features with high correlation with the available capacity to reduce the complexity of the model and improve the estimation accuracy. Compared with other methods, the proposed IWOA-VFOS-ELM algorithm has higher estimation performance and noise anti-interference ability. The MAE of APR-3 and APR-4 for SOH estimation are both within 0.12 %, RMSE are within 0.15 %, and IA are both higher than 99.9 %. Compared with PF algorithm, the RUL prediction accuracy obtained by IWOA-PF algorithm is improved by 7.143 %, 6.445 % and 15.094, respectively. In summary, the IWOA-PF algorithm proposed in this paper can be used to predict the battery RUL, and the prediction performance is better than the PF algorithm.

Using sequential particle methods and non-parametric distributions in bayesian evaluations of abundance and catch at age time series

We describe an approach to analyzing time series for two variables connected through the state model with abundance and catch data sets and cohort and catch equations as an example. First, we create a deterministic model with parameters that maximizes the closeness of given data and data generated by a model. Then, we obtain cohort stochastic models using the difference between initial and modeled data. They are represented as hidden Bayesian models with abundances as states and catches as observations. Using these models, one can evaluate posterior densities and calculate averages, deviations, etc. As a general matter, the recursive equations met by posterior densities have no analytic solutions. We describe several particle methods that may be used for density approximations and following calculations of their statistical quantities. All generated sample densities are smoothed with non-parametric kernel density estimation. The Fishmetica package was extended with functions for generating samples and weights for filtering, predicting and smoothing densities. Numerical simulations were conducted for a test data set. Several extensions of the approach are proposed including an additional option for comparing the basic models with the use of a likelihood function.

A novel sequential switching quadratic particle swarm optimization scheme with applications to fast tuning of PID controllers

In this work, a sequential switching quadratic particle swarm optimization (SSQPSO) scheme is investigated, where the velocity update mechanism is improved to enhance the convergence performance. Considering the sequential characteristics (related to evolution factors) of the evolution process, a Markov chain with special probability transition matrix is employed to characterize the switching of evolution state. With the help of the mean distance, the concept of population density is first put forward in the dynamic search region enclosed by all particles. Then, taking into account the change of the population density in different generations, two quadratic acceleration terms are introduced into the velocity update model based on the Hadamard product, where four evolution-state-dependent acceleration coefficients are also adopted. The positivity or negativity of the quadratic acceleration terms is retained by resorting to the matrix sign functions. Several widely utilized benchmark functions (including two unimodal and multimodal functions) are employed to evaluate the search capability of the studied SSQPSO scheme. The experimental consequences illustrate that the performance of the developed SSQPSO scheme is superior to that of some popular particle swarm optimization (PSO) schemes. To further demonstrate the effectiveness in practical engineering, the addressed SSQPSO scheme is successfully applied to achieve the fast parameter tuning of the proportional-integral-derivative controller in a spring-mass-damper system.

A Sequential Sampling-based Particle Swarm Optimization to Control Droop Coefficients of Distributed Generation Units in Microgrid Clusters

A Sequential Sampling-based Particle Swarm Optimization to Control Droop Coefficients of Distributed Generation Units in Microgrid Clusters