Fitness Sharing Chaotic Particle Swarm Optimization (FSCPSO): A Metaheuristic Approach for Allocating Dynamic Virtual Machine (VM) in Fog Computing Architecture

In recent decades,fog computing has played a vital role in executing parallel computational tasks,specifically,scientific workflow tasks.In cloud data centers,fog computing takes more time to run workflow applications.Therefore,it is essential to develop effective models for Virtual Machine(VM)allocation and task scheduling in fog computing environments.Effective task scheduling,VM migration,and allocation,altogether optimize the use of computational resources across different fog nodes.This process ensures that the tasks are executed with minimal energy consumption,which reduces the chances of resource bottlenecks.In this manuscript,the proposed framework comprises two phases:(i)effective task scheduling using a fractional selectivity approach and(ii)VM allocation by proposing an algorithm by the name of Fitness Sharing Chaotic Particle Swarm Optimization(FSCPSO).The proposed FSCPSO algorithm integrates the concepts of chaos theory and fitness sharing that effectively balance both global exploration and local exploitation.This balance enables the use of a wide range of solutions that leads to minimal total cost and makespan,in comparison to other traditional optimization algorithms.The FSCPSO algorithm’s performance is analyzed using six evaluation measures namely,Load Balancing Level(LBL),Average Resource Utilization(ARU),total cost,makespan,energy consumption,and response time.In relation to the conventional optimization algorithms,the FSCPSO algorithm achieves a higher LBL of 39.12%,ARU of 58.15%,a minimal total cost of 1175,and a makespan of 85.87 ms,particularly when evaluated for 50 tasks.

Mental health in the virtual world:Challenges and opportunities in the metaverse era

Current rates of mental illness are worrisome.Mental illness mainly affects females and younger age groups.The use of the internet to deliver mental health care has been growing since 2020 and includes the implementation of novel mental health treatments using virtual reality,augmented reality,and artificial intelligence.A new three dimensional digital environment,known as the metaverse,has emerged as the next version of the Internet.Artificial intelligence,augmented reality,and virtual reality will create fully immersive,experiential,and interactive online environments in the metaverse.People will use a unique avatar to do anything they do in their“real”lives,including seeking and receiving mental health care.In this opinion review,we reflect on how the metaverse could reshape how we deliver mental health treatment,its opportunities,and its challenges.

Computer vision-aided DEM study on the compaction characteristics of graded subgrade filler considering realistic coarse particle shapes

The compaction quality of subgrade filler strongly affects subgrade settlement.The main objective of this research is to analyze the macro-and micro-mechanical compaction characteristics of subgrade filler based on the real shape of coarse particles.First,an improved Viola-Jones algorithm is employed to establish a digitalized 2D particle database for coarse particle shape evaluation and discrete modeling purposes of subgrade filler.Shape indexes of 2D subgrade filler are then computed and statistically analyzed.Finally,numerical simulations are performed to quantitatively investigate the effects of the aspect ratio(AR)and interparticle friction coefficient(μ)on the macro-and micro-mechanical compaction characteristics of subgrade filler based on the discrete element method(DEM).The results show that with the increasing AR,the coarse particles are narrower,leading to the increasing movement of fine particles during compaction,which indicates that it is difficult for slender coarse particles to inhibit the migration of fine particles.Moreover,the average displacement of particles is strongly influenced by the AR,indicating that their occlusion under power relies on particle shapes.The dis-placement and velocity of fine particles are much greater than those of the coarse particles,which shows that compaction is primarily a migration of fine particles.Under the cyclic load,the interparticle friction coefficientμhas little effect on the internal structure of the sample;under the quasi-static loads,however,the increase inμwill lead to a significant increase in the porosity of the sample.This study could not only provide a novel approach to investigate the compaction mechanism but also establish a new theoretical basis for the evaluation of intelligent subgrade compaction.

Virtual reality for preoperative patient education: Impact on satisfaction, usability, and burnout from the perspective of new nurses

BACKGROUND Traditional paper-based preoperative patient education is a struggle for new nurses and requires extensive training.In this situation,virtual reality technology can help the new nurses.Despite its potential benefits,there are studies on patient satisfaction but there is limited information on the usability of virtual reality(VR)technology for new nurses in giving preoperative education to patients.AIM To investigate the impact on satisfaction,usability,and burnout of a system using VR technology in preoperative patient education.METHODS The study involved 20 nurses from the plastic surgery ward and 80 patients admitted between April and May 2019.Each nurse taught four patients:Two using traditional verbal education and two using virtual reality.The System Usability Scale,After-Scenario Questionnaire,and Maslach Burnout Inventory(MBI)were employed to evaluate the impact of these education methods.RESULTS The VR education groups showed a statistically higher satisfaction than the traditional verbal education groups.Among the three subscales of the MBI,emotional exhaustion and personal accomplishment improved statistically significantly.VR was also better in terms of usability.CONCLUSION This study suggests VR enhances usability and reduces burnout in nurses,but further research is needed to assess its impact on depersonalization and objective measures like stress and heart rate.

Particle agglomeration and inhibition method in the fluidized pyrolysis reaction of waste resin

This work investigated the pyrolysis reaction of waste resin in a fluidized bed reactor.It was found that the pyrolysis-generated ash would adhere to the surface of ceramic particles,causing particle agglomeration and defluidization.Adding kaolin could effectively inhibit the particle agglomeration during the fluidized pyrolysis reaction through physical isolation and chemical reaction.On the one hand,kaolin could form a coating layer on the surface of ceramic particles to prevent the adhesion of organic ash generated by the pyrolysis of resin.On the other hand,when a sufficient amount of kaolin(-0.2%(mass))was added,the activated kaolin could fully contact with the Na+ ions generated by the pyrolysis of resin and react to form a high-melting aluminosilicate mineral(nepheline),which could reduce the formation of low-melting-point sodium sulfate and thereby avoid the agglomeration of ceramic particles.

An improved particle filter indoor fusion positioning approach based on Wi-Fi/PDR/geomagnetic field

The existing indoor fusion positioning methods based on Pedestrian Dead Reckoning(PDR)and geomagnetic technology have the problems of large initial position error,low sensor accuracy,and geomagnetic mismatch.In this study,a novel indoor fusion positioning approach based on the improved particle filter algorithm by geomagnetic iterative matching is proposed,where Wi-Fi,PDR,and geomagnetic signals are integrated to improve indoor positioning performances.One important contribution is that geomagnetic iterative matching is firstly proposed based on the particle filter algorithm.During the positioning process,an iterative window and a constraint window are introduced to limit the particle generation range and the geomagnetic matching range respectively.The position is corrected several times based on geomagnetic iterative matching in the location correction stage when the pedestrian movement is detected,which made up for the shortage of only one time of geomagnetic correction in the existing particle filter algorithm.In addition,this study also proposes a real-time step detection algorithm based on multi-threshold constraints to judge whether pedestrians are moving,which satisfies the real-time requirement of our fusion positioning approach.Through experimental verification,the average positioning accuracy of the proposed approach reaches 1.59 m,which improves 33.2%compared with the existing particle filter fusion positioning algorithms.

Extended wet sieving method for determination of complete particle size distribution of general soils

The traditional standard wet sieving method uses steel sieves with aperture?0.063 mm and can only determine the particle size distribution(PSD)of gravel and sand in general soil.This paper extends the traditional method and presents an extended wet sieving method.The extended method uses both the steel sieves and the nylon filter cloth sieves.The apertures of the cloth sieves are smaller than 0.063 mm and equal 0.048 mm,0.038 mm,0.014 mm,0.012 mm,0.0063 mm,0.004 mm,0.003 mm,0.002 mm,and 0.001 mm,respectively.The extended method uses five steps to separate the general soil into many material sub-groups of gravel,sand,silt and clay with known particle size ranges.The complete PSD of the general soil is then calculated from the dry masses of the individual material sub-groups.The extended method is demonstrated with a general soil of completely decomposed granite(CDG)in Hong Kong,China.The silt and clay materials with different particle size ranges are further examined,checked and verified using stereomicroscopic observation,physical and chemical property tests.The results further confirm the correctness of the extended wet sieving method.

A stable implicit nodal integration-based particle finite element method(N-PFEM)for modelling saturated soil dynamics

In this study,we present a novel nodal integration-based particle finite element method(N-PFEM)designed for the dynamic analysis of saturated soils.Our approach incorporates the nodal integration technique into a generalised Hellinger-Reissner(HR)variational principle,creating an implicit PFEM formulation.To mitigate the volumetric locking issue in low-order elements,we employ a node-based strain smoothing technique.By discretising field variables at the centre of smoothing cells,we achieve nodal integration over cells,eliminating the need for sophisticated mapping operations after re-meshing in the PFEM.We express the discretised governing equations as a min-max optimisation problem,which is further reformulated as a standard second-order cone programming(SOCP)problem.Stresses,pore water pressure,and displacements are simultaneously determined using the advanced primal-dual interior point method.Consequently,our numerical model offers improved accuracy for stresses and pore water pressure compared to the displacement-based PFEM formulation.Numerical experiments demonstrate that the N-PFEM efficiently captures both transient and long-term hydro-mechanical behaviour of saturated soils with high accuracy,obviating the need for stabilisation or regularisation techniques commonly employed in other nodal integration-based PFEM approaches.This work holds significant implications for the development of robust and accurate numerical tools for studying saturated soil dynamics.

The research progress of an E//B neutral particle analyzer

An E//B neutral particle analyzer(NPA)has been designed and is under development at Sichuan University and Southwestern Institute of Physics.The main purpose of the E//B NPA is to measure the distribution function of fast ions in the HL-2A/3 tokamak.The E//B NPA contains three main units,i.e.the stripping unit,the analyzing unit and the detection unit.A gas stripping chamber was adopted as the stripping unit.The results of the simulations and beam tests for the stripping chamber are presented.Parallel electric and magnetic fields provided by a NdFeB permanent magnet and two parallel electric plates were designed and constructed for the analyzing unit.The calibration of the magnetic and electric fields was performed using a 50 kV electron cyclotron resonance ion source(ECRIS)platform.The detection unit consists of 32lutetium-yttrium oxyorthosilicate(LYSO)detector modules arranged in two rows.The response functions ofα,hydrogen ions(H^(+),H_(2)^(+)and H_(3)^(+))andγfor a detector module were measured with^(241)Am,^(137)Cs and^(152)Eu sources together with the 50 kV ECRIS platform.The overall results indicate that the designed E//B NPA device is capable of measuring the intensity of neutral hydrogen and deuteron atoms with energy higher than 20 keV.

Prediction of corrosion rate for friction stir processed WE43 alloy by combining PSO-based virtual sample generation and machine learning

The corrosion rate is a crucial factor that impacts the longevity of materials in different applications.After undergoing friction stir processing(FSP),the refined grain structure leads to a notable decrease in corrosion rate.However,a better understanding of the correlation between the FSP process parameters and the corrosion rate is still lacking.The current study used machine learning to establish the relationship between the corrosion rate and FSP process parameters(rotational speed,traverse speed,and shoulder diameter)for WE43 alloy.The Taguchi L27 design of experiments was used for the experimental analysis.In addition,synthetic data was generated using particle swarm optimization for virtual sample generation(VSG).The application of VSG has led to an increase in the prediction accuracy of machine learning models.A sensitivity analysis was performed using Shapley Additive Explanations to determine the key factors affecting the corrosion rate.The shoulder diameter had a significant impact in comparison to the traverse speed.A graphical user interface(GUI)has been created to predict the corrosion rate using the identified factors.This study focuses on the WE43 alloy,but its findings can also be used to predict the corrosion rate of other magnesium alloys.

Dark Matter Particles May Never Be Directly Detected by Instruments—A Dark Matter Mechanism That Does Not Exceed the Standard Model Framework

A dark matter mechanism within the framework of the standard model (SM) of particle physics is proposed in this article that the essence of dark matter may be the excited virtual particle field by the gravitational field of ordinary matter, which contains virtual photons, virtual positive and negative electron pairs, virtual gluons, virtual positive and negative quark pairs, virtual neutrinos etc. In this mechanism, there are two basic assumptions: 1) the stronger the gravitational field of ordinary matter, the greater the excited energy (mass) density of virtual particle field;2) The excited virtual particle field is generally very weak in self-interaction. The virtual particle field excited by gravity can exhibit the properties of dark matter and may become a dark matter candidate. Based on this new dark matter mechanism, the hydrodynamic equations and cosmic perturbation equations describing cosmic matter are improved, and this may be meaningful for solving the challenges faced by the standard cosmological model (Lambda-CDM or LCDM) and developing and perfecting LCDM model.

Spatial Heterogeneity of Embedded Water Consumption from the Perspective of Virtual Water Surplus and Deficit in the Yellow River Basin,China

Virtual water trade(VWT)provides a new perspective for alleviating water crisis and has thus attracted widespread attention.However,the heterogeneity of virtual water trade inside and outside the river basin and its influencing factors remains further study.In this study,for better investigating the pattern and heterogeneity of virtual water trade inside and outside provincial regions along the Yellow River Basin in 2015 using the input-output model(MRIO),we proposed two new concepts,i.e.,virtual water surplus and virtual water deficit,and then used the Logarithmic Mean Divisia Index(LMDI)model to identify the inherent mechanism of the imbalance of virtual water trade between provincial regions along the Yellow River Basin and the other four regions in China.The results show that:1)in provincial regions along the Yellow River Basin,the less developed the economy was,the larger the contribution of the agricultural sector in virtual water trade,while the smaller the contribution of the industrial sector.2)Due to the large output of agricultural products,the upstream and midstream provincial regions of the Yellow River Basin had a virtual water surplus,with a net outflow of virtual water of 2.7×10^(8) m^(3) and 0.9×10^(8) m^(3),respectively.3)provincial regions along the Yellow River Basin were in a virtual water deficit with the rest of China,and the decisive factor was the active degree of trade with the outside.This study would be beneficial to illuminate the trade-related water use issues in provincial regions along the Yellow River Basin,which has farreaching practical signific-ance for alleviating water scarcity.

Study of deep transportation and plugging performance of deformable gel particles in porous media

Deformable gel particles(DGPs) possess the capability of deep profile control and flooding. However, the deep migration behavior and plugging mechanism along their path remain unclear. Breakage, an inevitable phenomenon during particle migration, significantly impacts the deep plugging effect. Due to the complexity of the process, few studies have been conducted on this subject. In this paper, we conducted DGP flow experiments using a physical model of a multi-point sandpack under various injection rates and particle sizes. Particle size and concentration tests were performed at each measurement point to investigate the transportation behavior of particles in the deep part of the reservoir. The residual resistance coefficient and concentration changes along the porous media were combined to analyze the plugging performance of DGPs. Furthermore, the particle breakage along their path was revealed by analyzing the changes in particle size along the way. A mathematical model of breakage and concentration changes along the path was established. The results showed that the passage after breakage is a significant migration behavior of particles in porous media. The particles were reduced to less than half of their initial size at the front of the porous media. Breakage is an essential reason for the continuous decreases in particle concentration, size, and residual resistance coefficient. However, the particles can remain in porous media after breakage and play a significant role in deep plugging. Higher injection rates or larger particle sizes resulted in faster breakage along the injection direction, higher degrees of breakage, and faster decreases in residual resistance coefficient along the path. These conditions also led to a weaker deep plugging ability. Smaller particles were more evenly retained along the path, but more particles flowed out of the porous media, resulting in a poor deep plugging effect. The particle size is a function of particle size before injection, transport distance, and different injection parameters(injection rate or the diameter ratio of DGP to throat). Likewise, the particle concentration is a function of initial concentration, transport distance, and different injection parameters. These models can be utilized to optimize particle injection parameters, thereby achieving the goal of fine-tuning oil displacement.

Mathematical modeling and simulations of stress mitigation by coating polycrystalline particles in lithium-ion batteries

A chemo-mechanical model is developed to investigate the effects on the stress development of the coating of polycrystalline Ni-rich LiNixMnyCo_(z)O_(2)(x≥0.8)(NMC)particles with poly(3,4-ethylenedioxythiophene)(PEDOT).The simulation results show that the coating of primary NMC particles significantly reduces the stress generation by efficiently accommodating the volume change associated with the lithium diffusion,and the coating layer plays roles both as a cushion against the volume change and a channel for the lithium transport,promoting the lithium distribution across the secondary particles more homogeneously.Besides,the lower stiffness,higher ionic conductivity,and larger thickness of the coating layer improve the stress mitigation.This paper provides a mathematical framework for calculating the chemo-mechanical responses of anisotropic electrode materials and fundamental insights into how the coating of NMC active particles mitigates stress levels.

A virtual thermometer for ultrahigh-temperature-pressure experiments in a large-volume press

Ultrahigh-temperature-pressure experiments are crucial for understanding the physical and chemical properties of matter.The recent development of boron-doped diamond(BDD)heaters has made such melting experiments possible in large-volume presses.However,estimates of temperatures above 2600 K and of the temperature distributions inside BDD heaters are not well constrained,owing to the lack of a suitable thermometer.Here,we establish a three-dimensional finite element model as a virtual thermometer to estimate the temperature and temperature field above 2600 K.The advantage of this virtual thermometer over those proposed in previous studies is that it considers both alternating and direct current heating modes,the actual sizes of cell assemblies after compression,the effects of the electrode,thermocouple and anvil,and the heat dissipation by the pressure-transmitting medium.The virtual thermometer reproduces the power-temperature relationships of ultrahigh-temperature-pressure experiments below 2600 K at press loads of 2.8-7.9 MN(~19 to 28 GPa)within experimental uncertainties.The temperatures above 2600 K predicted by our virtual thermometer are within the uncertainty of those extrapolated from power-temperature relationships below 2600 K.Furthermore,our model shows that the temperature distribution inside a BDD heater(19-26 K/mm along the radial direction and<83 K/mm along the longitudinal direction)is more homogeneous than those inside conventional heaters such as graphite or LaCrO_(3) heaters(100-200 K/mm).Our study thus provides a reliable virtual thermometer for ultrahigh-temperature experiments using BDD heaters in Earth and material sciences.

Effects of virtual agents on interaction efficiency and environmental immersion in MR environments

Background Physical entity interactions in mixed reality(MR)environments aim to harness human capabilities in manipulating physical objects,thereby enhancing virtual environment(VEs)functionality.In MR,a common strategy is to use virtual agents as substitutes for physical entities,balancing interaction efficiency with environmental immersion.However,the impact of virtual agent size and form on interaction performance remains unclear.Methods Two experiments were conducted to explore how virtual agent size and form affect interaction performance,immersion,and preference in MR environments.The first experiment assessed five virtual agent sizes(25%,50%,75%,100%,and 125%of physical size).The second experiment tested four types of frames(no frame,consistent frame,half frame,and surrounding frame)across all agent sizes.Participants,utilizing a head mounted display,performed tasks involving moving cups,typing words,and using a mouse.They completed questionnaires assessing aspects such as the virtual environment effects,interaction effects,collision concerns,and preferences.Results Results from the first experiment revealed that agents matching physical object size produced the best overall performance.The second experiment demonstrated that consistent framing notably enhances interaction accuracy and speed but reduces immersion.To balance efficiency and immersion,frameless agents matching physical object sizes were deemed optimal.Conclusions Virtual agents matching physical entity sizes enhance user experience and interaction performance.Conversely,familiar frames from 2D interfaces detrimentally affect interaction and immersion in virtual spaces.This study provides valuable insights for the future development of MR systems.

Mass transfer enhancement and hydrodynamic performance with wire mesh coupling solid particles in bubble column reactor

It is of vital significance to investigate mass transfer enhancements for chemical engineering processes.This work focuses on investigating the coupling influence of embedding wire mesh and adding solid particles on bubble motion and gas-liquid mass transfer process in a bubble column.Particle image velocimetry(PIV)technology was employed to analyze the flow field and bubble motion behavior,and dynamic oxygen absorption technology was used to measure the gas-liquid volumetric mass transfer coefficient(kLa).The effect of embedding wire mesh,adding solid particles,and wire mesh coupling solid particles on the flow characteristic and kLa were analyzed and compared.The results show that the gas-liquid interface area increases by 33%-72%when using the wire mesh coupling solid particles strategy compared to the gas-liquid two-phase flow,which is superior to the other two strengthening methods.Compared with the system without reinforcement,kLa in the bubble column increased by 0.5-1.8 times with wire mesh coupling solid particles method,which is higher than the sum of kLa increases with inserting wire mesh and adding particles,and the coupling reinforcement mechanism for affecting gas-liquid mass transfer process was discussed to provide a new idea for enhancing gas-liquid mass transfer.

Optimizing laser coupling,matter heating,and particle acceleration from solids using multiplexed ultraintense lasers

Realizing the full potential of ultrahigh-intensity lasers for particle and radiation generation will require multi-beam arrangements due to technology limitations.Here,we investigate how to optimize their coupling with solid targets.Experimentally,we show that overlapping two intense lasers in a mirror-like configuration onto a solid with a large preplasma can greatly improve the generation of hot electrons at the target front and ion acceleration at the target backside.The underlying mechanisms are analyzed through multidimensional particle-in-cell simulations,revealing that the self-induced magnetic fields driven by the two laser beams at the target front are susceptible to reconnection,which is one possible mechanism to boost electron energization.In addition,the resistive magnetic field generated during the transport of the hot electrons in the target bulk tends to improve their collimation.Our simulations also indicate that such effects can be further enhanced by overlapping more than two laser beams.

Wear dependent virtual flow rate sensor for progressing cavity pumps with deformable stator

This contribution presents a novel wear dependent virtual flow rate sensor for single stage single lobe progressing cavity pumps. We study the wear-induced material loss of the pump components and the impact of this material loss on the volumetric efficiency. The results are combined with an established backflow model to implement a backflow calculation procedure that is adaptive to wear. We use a laboratory test setup with a highly abrasive fluid and operate a pump from new to worn condition to validate our approach. The obtained measurement data show that the presented virtual sensor is capable of calculating the flow rate of a pump being subject to wear during its regular operation.

State Estimation of Drive-by-Wire Chassis Vehicle Based on Dual Unscented Particle Filter Algorithm

Accurate vehicle dynamic information plays an important role in vehicle driving safety.However,due to the characteristics of high mobility and multiple controllable degrees of freedom of drive-by-wire chassis vehicles,the current mature application of traditional vehicle state estimation algorithms can not meet the requirements of drive-by-wire chassis vehicle state estimation.This paper proposes a state estimation method for drive-by-wire chassis vehicle based on the dual unscented particle filter algorithm,which make full use of the known advantages of the four-wheel drive torque and steer angle parameters of the drive-by-wire chassis vehicle.In the dual unscented particle filter algorithm,two unscented particle filter transfer information to each other,observe the vehicle state information and the tire force parameter information of the four wheels respectively,which reduce the influence of parameter uncertainty and model parameter changes on the estimation accuracy during driving.The performance with the dual unscented particle filter algorithm,which is analyzed in terms of the time-average square error,is superior of the unscented Kalman filter algorithm.The effectiveness of the algorithm is further verified by driving simulator test.In this paper,a vehicle state estimator based on dual unscented particle filter algorithm was proposed for the first time to improve the estimation accuracy of vehicle parameters and states.