Large quantities of real-time particle data are becoming available from low-cost particle monitors. However, it is crucial to determine the quality of these measurements. The largest network of monitors in the United States is maintained by the PurpleAir company, which offers two monitors: PA-I and PA-II. PA-I monitors have a single sensor (PMS1003) and PA-II monitors employ two independent PMS5003 sensors. We determine a new calibration factor for the PA-I monitor and revise a previously published calibration algorithm for PA-II monitors (ALT-CF3). From the PurpleAir API site, we downloaded 83 million hourly average PM2.5 values in the PurpleAir database from Washington, Oregon, and California between January 1, 2017 and September 8, 2021. Daily outdoor PM2.5 means from 194 PA-II monitors were compared to daily means from 47 nearby Federal regulatory sites using gravimetric Federal Reference Methods (FRM). We find a revised calibration factor of 3.4 for the PA-II monitors. For the PA-I monitors, we determined a new calibration factor (also 3.4) by comparing 26 outdoor PA-I sites to 117 nearby outdoor PA-II sites. These results show that PurpleAir PM2.5 measurements can agree well with regulatory monitors when an optimum calibration factor is found.

This study aims to examine the non-volatile Particle Number (PN) emissions of a retrofitted Heavy-Duty Spark-Ignition (HD-SI) engine powered by liquefied petroleum gas (LPG) under both steady-state and transient conditions. The engine was tested under seven steady-state operating points to investigate the PN behavior and Particle Size Distribution (PSD) upstream and down-stream of the Three-Way Catalyst (TWC). This analysis intends to assess the impact of including particles with diameters ranging from 10 nm to 23 nm in the total particle count, a consideration for future regulations. The study employed the World Harmonized Transient Cycle (WHTC) for transient conditions to encompass the same engine working region used in the steady-state analysis. A Dekati FPS-4000 diluted the exhaust sample to measure the PSD and PN for particle diameters between 5.6 nm and 560 nm using the TSI-Engine Exhaust Particle Sizer (EEPS) 3090. Findings indicate that PN levels tend to increase downstream of the TWC under steady-state conditions in operating points with low exhaust gas temperatures and flows (equal to or less than 500 °C and 120 kg/h). Furthermore, the inclusion of particles with diameters between 10 nm and 23 nm leads to an increase in PN emissions by 17.70% to 40.84% under steady conditions and by an average of 40.06% under transient conditions, compared to measurements that only consider particles larger than 23 nm. Notably, in transient conditions, most PN emissions occur during the final 600 seconds of the cycle, linked to the most intense phase of the WHTC.

The development of computational acoustics allows simulation of sound generation and propagation in complex environment. In particular, meshfree methods are widely used to solve acoustics problems through arbitrarily distributed field points and approximation smoothness flexibility. As a Lagrangian meshfree method, smoothed particle hydrodynamics (SPH) method reduce the difficulty in solving problems with deformable boundaries, complex topologies, or multiphase medium. The traditional SPH method has been applied in acoustic simulation. This study presents the corrective smoothed particle method (CSPM), which is a combination of SPH kernel estimate and Taylor series expansion. The CSPM is introduced as a Lagrangian approach to improve accuracy in solving acoustic wave equations in the time domain. Moreover, a boundary treatment technique based on the hybrid meshfree and finite difference time domain (FDTD) method is proposed to represent different acoustic boundaries with particles. To model sound propagation in pipes with different boundaries, soft, rigid, and absorbing boundary conditions are built with this technique. Numerical results show that the CSPM algorithm is consistent and demonstrates convergence with exact solutions. Main computational parameters are discussed, and different boundary conditions are validated to be effective for benchmark problems in computational acoustics.

Low-cost particle sensors are now used worldwide to monitor outdoor air quality. However, they have only been in wide use for a few years. Are they reliable? Does their performance deteriorate over time? Are the algorithms for calculating PM2.5 provided by the Plantower company for PurpleAir monitors accurate? We investigate these questions using continuous measurements of four monitors (8 sensors) under normal conditions inside and outside a home for 1.5-3 years. A recently-developed algorithm (called ALT-CF3) is compared to the two existing Plantower algorithms. The Plantower CF1 algorithm was shown to lose 25-50% of all indoor data due to the questionable practice of assigning zero to all concentrations below a threshold. None of these data were lost using the ALT-CF3 algorithm. About 92% of all data showed precision better than 20% using the ALT-CF3 algorithm, but only about 45-75% of data achieved that level using the Plantower CF1 algorithm. The limits of detection (LODs) using the ALT-CF3 algorithm were mostly under 1 ug/m3, compared to about 3-10 ug/m3 using the Plantower CF1 algorithm. The percent of observations exceeding the LOD was 53-92% for the ALT-CF3 algorithm, but only 16-44% for the Plantower CF1 algorithm. For indoor air in homes, the Plantower algorithms are inappropriate.

In this paper we report a new development on the numerical model for aluminum-steam combustion. This model is based on diffusion flame of continuum regime and the thermal equilibrium between the particle and the flow field, which can be used to calculate the aluminum particle combustion model for two phase calculation conditions. The model prediction is in good agreement with the experimental data. A new type of vortex combustor was proposed for the combustion of aluminum and steam, and the mathematical model of the two phase reacting flow with in this combustor was established. The turbulence effects are modeled using the Reynolds Stress Model (RSM) with Linear Pressure-Strain approach, and the Eddy-Dissipation model is used to simulate the gas phase combustion. Aluminum particles are injected into the vortex combustor and form a swirling flow around the chamber and their trajectories are traced using the Discrete Phase Model (DPM). The simulation results show that the vortex combustor can achieve high efficient combustion of aluminum and steam. The influencing factors, such as the eccentric distance of the inlet of aluminum particles, particle size and steam inlet diameter, etc., are studied. The work described in this paper represents an attempt to the design of a vortex combustor in order to increase aluminum combustion efficiency.

Effects of oxygen content on microstructure and creep property of FGH96 superalloy were investigated. When oxygen content increased from 135ppm to 341ppm, prior particle boundary (PPB) rose from degree 2 to degree 3, and the size of γ′ phase on PPB enlarged from 1.07μm to 1.27μm, and MC carbide size grew from 77.4nm to 104.0nm. Meanwhile, the steady creep rate accelerated form 4.34×10-3 h-1 to 1.87×10-2 h-1, and the creep rupture life shortened from 176h to 94h, and the creep rupture mode transferred from intergranular and transgranular mixed fracture to along PPB fracture. During creep, the micro-twins formation and gliding will be restrained by ∑3 boundaries. FGH96 superalloy with higher oxygen content contains less ∑3 boundaries and its micro-twins cross slipped instead of single direction slip in lower oxygen content superalloy. Consequently, specimen with higher oxygen content creeped faster and raptured earlier.

Reasonable allocation of production plans and saving the overall electricity cost are crucial for large manufacturing conglomerates, which is an effective way for conglomerates to realize open-source and cost-saving. This paper develops an optimization model of off-site industrial production scheduling for enterprises in response to the problems of high electricity costs due to the irrational allocation of production schedules on the demand side of China's electric power and the difficulty of promoting industrial and commercial distributed photovoltaic (PV) projects in China. The model makes full use of the conditions of different PV resources and different electricity prices in different places to optimize the scheduling of industrial production in different places. The model is embedded with two sub-models of the electricity price prediction model and distributed photovoltaic kWh cost model to complete the model parameters, in which the electricity price prediction model utilizes the LSTM neural network. The integrated model is then solved by the particle swarm algorithm. To verify the effectiveness of the model in solving the problem of off-site production scheduling on the demand side of electricity, the research team researched two off-site pharmaceutical factories belonging to the same large pharmaceutical company. Finally, the research data were substituted into the model for solving, and it was concluded that the optimization model could significantly reduce the cost of electricity consumption of the enterprise by about 7.9 %.

In this paper, we first discuss the research status and application progress of the finite element method and the smoothed particle method. By analyzing the advantages of the smoothed particle method and the finite element method, a new coupling algorithm, namely FEM-SPH algorithm, is proposed. By the method of comparison, it shows that finite element method and SPH method in the simulation of large deformation problems each have advantages and disadvantages, the finite element method smoothed particle coupling algorithm is effective to achieve the performance of high computational efficiency and can naturally simulate large deformation problems across. In the process of calculation, the large deformation unit can be freely into an algorithm to facilitate the calculation accuracy and efficiency of three methods of numerical simulation. Through the study found, FEM-SPH algorithm not only overcome the defect of smooth particle tensile instability, but also overcomes the problem of low efficiency of finite element computation. To further test the FEM-SPH algorithm has advantages in the practical engineering, we have carried out the actual test to the example of the super high speed collision, concluded that, since the target of most of the computational domain is always finite element, smoothed particle focused only in contact with the projectile and target of local area, particle number is not much, the whole calculation process just ten minutes, computational efficiency has been greatly improved, at the same time in the simulation of large deformation, the advantage is very obvious .This provides a criterion for the actual project, depending on the specific material deformation mode and choose a more appropriate conversion algorithm.

Underwater Sensor Networks (UWSNs) can enable a broad range of applications such as resource monitoring, disaster prevention, and navigation-assisted. It is especially relevant for sensor nodes location in UWSNs. Global Positioning System (GPS) is not suitable for using in UWSNs because of the underwater propagation problems. Hence some localization algorithms based on the precise time synchronization between sensor nodes have been proposed which are not feasible for UWSNs. In this paper, we propose a localization algorithm called Two-Phase Time Synchronization-Free Localization Algorithm (TP-TSFLA). TP-TSFLA contains two phases, namely, range-based estimation phase and range-free evaluation phase. In the first phase, we address a time synchronization-free localization scheme base on the Particle Swarm Optimization (PSO) algorithm to decrease the localization error. In the second phase, we propose a Circle-based Range-Free Localization Algorithm (CRFLA) to locate the unlocalized sensor nodes which cannot obtain the location information through the first phase. In the second phase, sensor nodes which are localized in the first phase act as the new anchor nodes to help realize localization. Hence in this algorithm, we use a small number of mobile beacons to help achieve location without any other anchor nodes. Besides, to improve the precision of the range-free method, an extension of CRFLA by designing a coordinate adjustment scheme is updated. The simulation results show that TP-TSFLA can achieve a relative high localization ratio without time synchronization.

The exceptional performance advantages of the fiber optic gyroscope (FOG) position it as a dominant player in middle and high-end inertial navigation systems. To prevent the loss of sensor precision caused by algorithm design and simplify the complex modeling strategy in traditional methods. We gradually demonstrate the significant role of Convolutional Neural Network (CNN) in the navigation system based on FOG, and utilize the particle swarm optimization algorithm (PSO) to expedite the convergence of the network. The experimental results demonstrate that the initial alignment method based on deep learning is more accurate than the traditional method. The attitude angle error is reduced by 81.25%, 92.54% and 36.53% respectively. The research provides support for the future application of deep learning in optical navigation systems.

Although Eulerian approaches are standard in computational acoustics, they are less effective for certain classes of problems like bubble acoustics and combustion noise. A different approach for solving acoustic problems is to compute with individual particles following particle motion. In this paper, a Lagrangian approach to model sound propagation in moving fluid is presented and implemented numerically, using three meshfree methods to solve the Lagrangian acoustic perturbation equations (LAPE) in the time domain. The LAPE split the fluid dynamic equations into a set of hydrodynamic equations for the motion of fluid particles and perturbation equations for the acoustic quantities corresponding to each fluid particle. Then, three meshfree methods, the smoothed particle hydrodynamics (SPH) method, the corrective smoothed particle (CSP) method, and the generalized finite difference (GFD) method, are introduced to solve the LAPE and the linearized LAPE (LLAPE). The SPH and CSP methods are widely used meshfree methods, while the GFD method based on the Taylor series expansion can be easily extended to higher orders. Applications to modeling sound propagation in steady or unsteady fluids in motion are outlined, treating a number of different cases in one and two space dimensions. A comparison of the LAPE and the LLAPE using the three meshfree methods is also presented. The Lagrangian approach shows good agreement with exact solutions. The comparison indicates that the CSP and GFD method exhibit convergence in cases with different background flow. The GFD method is more accurate, while the CSP method can handle higher Courant numbers.

Recycling waste products is an environmental-friendly activity that can bring benefits to accompany, saving manufacturing costs and improving economic efficiency. For the beer industry, recycling bottles can reduce manufacturing costs and reduce the industry's carbon footprint. This paper presents a model for a multi-objective collection-distribution center location and allocation problem in a closed loop supply chain for the beer industry, in which the objective is to minimize total costs and transportation pollution. Uncertainties in the form of randomness and fuzziness are jointly handled in this paper to ensure a more practical problem solution, for which returned bottle sand unusable bottles are considered fuzzy random variables. A heuristic algorithm based on priority-based global-local-neighbor particle swarm optimization (pb-glnPSO) is applied to ensure reliable solutions for this NP-hard problem. A case study on a beer operation company is conducted to illustrate the application of the proposed model and demonstrate the priority-based global-local-neighbor particle swarm optimization.

We have successfully developed curcumin nanosuspension aimed for oral delivery. The main purpose is to improve bioavailability through enhancing its solubility. The nanoparticles were stabilized using various stabilizers including polyvinyl pyrrolidone (PVP), polyvinyl alcohol (PVA), sodium carboxymethylcellulose (Na-CMC), d-alpha tocopheryl polyethylene glycol 1000 succinate (TPGS), and sodium dodecyl sulfate (SDS). The average diameter of particles, microscopic appearance, and sedimentation of each preparation was observed and compared. Each stabilizer demonstrated different degree of inhibition of particle aggregation under electrolyte-containing simulated gastrointestinal (GIT) fluid. Non-ionic stabilizers (PVA, PVP, and TPGS) has shown to preserve the nanosuspension stability against electrolytes. In contrast, strong ionic surfactants such as SDS were found to be very sensitive to electrolytes. The results can provide useful information for the formulators to choose the most suitable stabilizers by considering the nature of stabilizers and physiological characteristics of target site of the drug.

Many protein expression systems are primary utilised to produce a single, specific recombinant protein. In contrast, most biological processes such as virus assembly rely upon a complex of several interacting proteins rather than the activity of a sole protein. The high complexity of the baculovirus genome, coupled with a multiphase replication cycle incorporating distinct transcriptional steps, made it the ideal system to manipulate for high-level expression of a single or co-expression of multiple foreign proteins within a single cell. We have developed and utilised a series of recombinant baculovirus systems to unravel the sequential assembly process of a complex non-enveloped model virus, Bluetongue virus (BTV). The high protein yields expressed by the baculovirus system not only facilitated structure-function analysis of each viral proteins but were also advantageous to crystallography studies and supported the first atomic level resolution of a recombinant viral protein, the major BTV capsid protein. Further, the formation of recombinant double-shelled virus-like particles (VLPs) provided insights into the structure-function relationships among the four major structural proteins of the BTV whilst also representing a potential candidate for a viral vaccine. The baculovirus multi-gene expression system facilitated the study of structurally complex viruses (both non-enveloped and enveloped viruses) and heralded in a new generation of viral vaccines.

We review time expansion wave theory and refine our model showing it to accurately match observed cosmological luminosity data. The theory augments general relativity to create a container within which quantum effects can be explained by treating time as an artefact of a fourth spatial dimension that we observe as the expansion of the universe. This leads us to explain mass creation, the speed of light limit, gravity, black holes without singularities and other macroscopic effects. Our model leads to an expansion velocity of 6.87 0.36 × 106 ms-1, which results in a Hubble expansion rate constant of 71.3 3.7 km/s/Mpc. Because of the finite window of time in which we exist, an extra a degree of freedom is available leading to interesting interpretations at the subatomic level. At this size range, then a spin is allowed within the time dimension explaining why wave particle duality occurs and explains the electromagnetic force and other quantum mechanical properties. It provides a solution to the double slit conundrum and can explain how quantum entangled partners can behave in a quantum way and pass seemingly time-defying information. The potential quantitative origin of the fine structure constant which governs the size of the electromagnetic force is also strongly indicated by this theory.

Motivated by the PEACOC project on metal recovery from solid wastes, an innovative magnetic density separation (MDS) process has been developed for granular material sorting. It has intrinsic advantages over the existing industrialized MDS by eliminating fluid turbulence and particle jamming problems. The new MDS applies an inclined planar magnet and a horizontal basin containing a static magnetic fluid as the separation medium. A particle sliding phenomenon is identified as a feature of the process which could help the separation. The MDS successfully sorted shredded PCBAs to concentrate valuable metals and sorted shredded wires to reduce metallic contaminants in the plastic fractions. A pilot scale facility is introduced to show the design to achieve continuous production and to reduce the consumption of ferrofluid.

We review some properties of a relativistic classical massless charged particle with spin interacting with an external electromagnetic field. We give in particular a proper definition of kinetic energy and total energy, the latter being conserved when the external field is stationary. We find that the particle’s velocity may differ from c as a result of the spin - electromagnetic field interaction, without jeopardizing Lorentz invariance.

A calculation is presented for the mass relationships between electron, muon and the tau particle. It is shown that charged leptons with mass levels beyond the one of the tau particle cannot exist because of insufficient binding energy. The underlying mechanism of the relationship between the leptons is the isospin feature of Dirac particles, which appears being a substantial attribute next to nuclear angular spin. It is further shown that the energy of the elementary electric charge, such as embodied by the mass of an electron, is due to the interaction between the isospins of the pion’s quark and antiquark.

One of the industries with the fastest rate of growth is healthcare, and this industry's enormous data requires extensive cloud storage. The cloud may offer some protection, but there is no assurance that data owners can rely on it for refuge and privacy amenities. Therefore, it is essential to offer security and privacy protection. Increased customer satisfaction with security and privacy preservation is crucial since healthcare big data and its sources are stored in the cloud and contain increasingly private and sensitive information. However, maintaining privacy and security in an untrusted green cloud environment is difficult, thus the data owner should have complete data control. A new work SecPri-BGMPOP(security and privacy of Boost graph Convolutional Network -Pinpointing-Optimization Performance) is suggested that can offer a solution that involves several different steps in order to handle the numerous problems relating to security and protecting privacy. Initially Boost Graph Convolutional Network Clustering (BGCNC) algorithm, which reduces computational complexity in terms of time and memory measurements, is first applied to the input dataset to begin the clustering process. Second, enlarge by employing a piece of the magnifying bit string to generate a safe key, pinpointing based encryption avoids amplify leakage even if a rival or attacker decrypts the key or asymmetric encryption. Finally, to determine the accuracy of the method, an optimal key was created using a meta-heuristic algorithmic framework called Hybrid fragment horde bland lobo Optimization (HFHBLO). Our proposed method currently kept in a cloud environment, allowing analytics users to utilize it without risking their privacy or security.

While the standard model accurately describes data at the electroweak scale without inclusion of gravity, beyond the standard model physics is increasingly intertwined with gravitational phenomena and cosmology. Thus gravity mediated breaking of supersymmetry in supergravity models lead to sparticles masses, which are gravitational in origin, observable at TeV scales and testable at the LHC, and supergravity also provides candidate for dark matter, a possible framework for inflationary models and for models of dark energy. Further, extended supergravity models, and string and D-brane models contain hidden sectors some of which may be feebly coupled to the visible sector resulting in heat exchange between the visible and hidden sectors. Because of the couplings between the sectors both particle physics and cosmology are effected. The above implies that particle physics and cosmology are intrinsically intertwined in the resolution of essentially all of the cosmological phenomena such as dark matter and dark energy and in the resolution of cosmological puzzles such as Hubble tension and EDGES anomaly. Here we give a brief overview of the intertwining and implications for the discovery of sparticles, and the resolution of the cosmological anomalies and identification of dark matter and dark energy as major challenges for the coming decades.

The up-to-date challenge towards clean road transport are non-exhaust emissions. Important advances regarding measurement systems, including well defined characterisation techniques, as well as regulation, will happen in the next few years. In this work we present detailed results of particle emissions from a test bed for wheel suspension and brakes, consisting of aerosol (size distribution, particle number (PN) and mass (PM)) and electron microscopy measurements (EM) under different load conditions. Standard tyres and brakes from series production were tested with a high-load driving cycle while particle measurements were conducted by gravimetric measurements and with a TSI SMPS, a TSI APS and a GRIMM OPS. Furthermore, samples were analyzed by electron microscopy. A bimodal particle size distribution (PSD) was obtained with the SMPS, with peaks at 20nm and around 400nm. EM analysis of >1400 single particles from the electrostatic sampler matches the PSD results. EM analysis also showed ultrafine particles, mainly from O, Fe, Si, Ba, Mg and S and also fractal particles with high C fractions. Our results suggest, in agreement with previously published literature, that particulate emissions are related to brake disc temperature and occur in significant amounts above a threshold temperature.

Meshfree particle method, which is always regarded as a pure Lagrangian approach, is easily represented complicated domain topologies, moving boundaries, and multiphase media. Solving acoustic problems with the mesfree particle method forms a branch of the acoustic wave modeling field, namely, particle-based computational acoustics (PCA). The aim of this paper is to improve the accuracy of using the PCA method to solve two-dimensional acoustic problems, and realize the particle representation with a hybrid meshfree and finite-difference time-domain (FDTD) method for acoustic boundary conditions at both the plane and curved surface. As a widely used Lagrangian meshfree method, the smoothed particle hydrodynamics (SPH) based on the support domain and the kernel function has developed rapidly in recent years. The traditional SPH method is easily implements parallel processing and has been applied in sound wave simulation. As a corrective method with higher accuracy than SPH, the acoustic propagation and scattering in the time domain is simulated with the corrective smoothed particle method (CSPM). Moreover, a hybrid meshfree-FDTD boundary treatment technique is utilized to represent different acoustic boundaries in the Lagrangian approach. In this boundary treatment technique, the parameter value of virtual particles is obtained with the FDTD method, which concerns truncation errors based on the Tayler series expansion. Soft, rigid, and Mur’s absorbing boundary conditions are developed to simulate sound waves in finite and infinite domain. Results of modeling acoustic propagation and scattering show that CSPM is accurate and convergence with exact solutions, and different acoustic boundaries are validated to be effective in the computation.

The fluid flow field at the upper airways is highly complex due to the complex structure of the airway. The inhaled particle flow, the air streamline and the interaction of the continuum and discrete phase could significantly affect the transport behaviour of the inhaled particles. A range of analytical, mathematical and computational fluid dynamics (CFD) models analyzed the airflow and particle transport in different idealized and asymmetric airway models. A precise understanding of the continuum and discrete phase interaction in realistic human airways is missing, and this study aims to develop a CFD-DEM model for particle transport in realistic airways. This study uses the CFD model for the continuum phase and the discrete element method (DEM) for the discrete phase. A soft sphere approach is used for the interaction of the discrete phase. Proper validation is performed for particle transport efficiency. The CFD-DEM model analyzed the particle transport in an idealized and realistic airway model, and different methods are used to analyze the transport behaviour. During the particle-particle interaction, a stagnation point and a high-pressure zone are observed at the airway model's carinal angle. The numerical results report higher deposition efficiency (DE) for particle-particle interaction than without interaction. The flow field becomes highly complex with the spring constant values, and higher DE is found for high spring constant values. The spring dashpot friction-dshf method shows higher deposition at the upper part of the airways than other interaction methods. The findings of this study and more case-specific analysis would improve the knowledge of aerosol transport in airways and the health risk assessment of the patient.

Aiming at effectively generating safe and reliable motion paths for quadruped robots, a hierar-chical path planning approach driven by dynamic 3D point cloud is proposed in this article. The developed path planning model is essentially constituted of two layers: global path planning layer and local path planning layer. At the global path planning layer, a new method is proposed for calculating terrain potential field based on point cloud height segmentation. Variable step size is employed to improve the path smoothness. At the local path planning layer, a real-time prediction method for potential collision area and a strategy for temporary target point selection is developed. Quadruped robot experiments were carried out in an outdoor complex environment. The experi-mental results verified that, for global path planning, the smoothness of the path is improved and the complexity of the passing ground is reduced. The effective step size is increased by a maxi-mum of 13.4 times, and the number of iterations is decreased by up to 1/6, compared with the traditional fixed step size planning algorithm. For local path planning, the path length is short-ened by 20%, more efficient dynamic obstacle avoidance and more stable velocity planning are achieved by using the improved dynamic window approach (DWA).

Tailings generated by mining account for the largest proportion of global waste from industrial activities. Copper is relatively uncommon world-wide, with low concentrations in sediments and waters, yet is very elevated around mining sites. On the Keweenaw Peninsula of Michigan, USA, jutting out into Lake Superior, 140 mines extracted native copper from the Portage Lake Volcanic Series, part of an intercontinental rift system. Between 1901-1932, two mills at Gay (Mohawk, Wolverine) sluiced 22.7 million metric tonnes (MMT) of copper-rich tailings (stamp sands) into Grand (Big) Traverse Bay. Here we examine: 1) 3D coastal elevation and reef bathymetry, 2) particle dispersal, 3) copper concentrations and leaching, plus 4) shoreline toxicity. About 10 MMT of tailings created a migrating coastal beach that stretches 7 km from the Gay pile to the Traverse River Seawall. Another 10 MMT are moving underwater on the coastal shelf, threatening Buffalo Reef, an important lake trout and whitefish breeding ground. Aerial photos, ALS (plane) LiDAR, and relatively inexpensive UAS (drone) surveys characterize shoreline structure and aid initial remediation. Because sands (natural beach quartz, stamp sand basalt) are silicates of similar density, particle dispersal is similar and challenging. However, stamp sand beaches contrast greatly with natural sand beaches in both physical profile and chemistry. Dispersing particles retain copper, and release toxic concentrations. Leaching is elevated by exposure to high DOC and low pH waters. Field experiments, shoreline seining, and benthic sampling confirm serious impacts on aquatic biota, supporting tailings removal.

A new approach was presented in this study to simulate a stable atom along with subatomic particles in it. Subatomic particles in an atom are treated as conventional particles, exhibiting attraction and repulsion between them, giving a stable atom simulation. This new approach leads to developing new potentials for subatomic particles to simulate a stable atom. Subatomic particle dynamics (SPD) simulations were developed for within-atom simulations that give a stable atom structure for infinite time and provide many insights into electron trajectories and atom properties like energies and radius of the atom. The effect of protons and neutrons movement in the nucleus on electron trajectories was captured in SPD. Hydrogen and Carbon atom SPD were reported in this study. The Pilot-wave theory was implemented in this study to simulate the wave nature of subatomic particles in an atom. Electrons motion was guided by the interference pattern produced by the electron’s aether medium waves in SPD. SPD was implemented on molecules, resulting in a stable molecular structure giving insights into electron trajectories shared by multiple atoms.

The non-symmetrical collapse of an empty cylindrical cavity is modelled using Smoothed Particle Hydrodynamics. The presence of a nearby surface produces an anisotropic pressure field generating a high velocity jet that hits the surface. The collapse follows a different dynamic based to the initial distance between the centre of the cavity and the surface. When the distance is greater than the cavity radius (detached cavity) the surface is hit by travelling shock waves. When the distance is less than the cavity radius (attached cavity) the surface is directly hit by the jet and later by other shock waves generated in the last stages of the of the collapse. The results show that the surface is hit by a stronger shock when distance between the centre of the cavity and the surface is zero while showing more complex double peaks behaviour for other distances.

Measurements of optical properties have been used for decades to study particle distributions in the ocean. They have been found useful to constrain suspended mass concentration as well as particle-related properties such as size, composition, packing (particle porosity or density) and settling velocity. Optical properties, however, provide measurements that are biased, as certain particles (based on size, composition, shape or packing) contribute to a specific property more than others. Here we study this issue both theoretically as well as by contrasting different optical properties collected simultaneously in a bottom boundary layer, to highlight the utility of such measurements as well as the biases we are likely to encounter using different optical properties to study suspended particles. In particular, we investigate the possibility to infer settling velocity from vertical profiles of optical measurements, finding that the effects of aggregation dynamics can seldom be ignored.

This study analyzes the correlation between the lifetime and the rest energy of the unstable particle states with a lifetime greater than the zeptosecond (10⁻²¹ s), using data available from the Particle Data Group. This set of states seems to be divided into three groups, in each of which the two quantities can be correlated through a remarkably accurate power law. Although this fact does not represent anything new compared to the predictions of the Standard Model, it nevertheless reveals an unexpected order structure in the set of particle decays, emerging from such predictions.

Polyaniline or PANI is a conductive polymer (CPs) with flexibility in turning into conducting properties. The drawback is mainly in terms of limited mechanical properties improvement which attracted the interest in fabrication of PANI with other polymeric materials. PANI-PVL composites film was synthesized and fabricated in this work with chronological steps according to the objectives aligned in two different phases. The first phase determines the optimum parameters to synthesize nanosized PANI. The ideal initial molar ratio, r was 1, stirring speed of 600 rpm, purified by filtration, synthesis temperature of 25 oC, washed by dopant acid, acetone and distilled water. The usage of non-ionic surfactant, Triton X-100 (TX100) with 0.1 % concentration favours the formation of smaller particle size of PANI and good dispersibility compared to the usage of anionic sodium dodecyl sulphate (SDS). The synthesis of PANI in nanosized dimension has the potential to be applied for antimicrobial applications such as gloves and antimicrobial coating of frequent contact points in the near future.

Double suction centrifugal pump installed along the Yellow River faces the serious sediment erosion due to the high sediment content which cause the poor operation efficiency of pump unit. The particle motion characteristics and erosion characteristics in the pump under different flow rates and different particle concentrations were numerically simulated based on the particle track model of solid-liquid two-phase flow. The results show that the flow rate has a significant effect on the particle tracks and the erosion caused by the particles in the impeller. The total erosion rate is positively correlated with the flow rate, and increases with the increase of flow rate. The vortex and secondary flow in the impeller have obvious influence on the particle trajectory, which increases the particle concentration at the trailing edge of the pressure surface and intensifies the impact erosion in this area. The particles carried by the vortex intensifies the local erosion. The particle concentration mainly affects the erosion rate, but has little effect on the erosion position. The in-fluence of flow rate on the pump erosion is greater than that of the particle properties. These results provide a reference for optimization of the design of anti-erosion blades of the double-suction pump and regulation-operation of pumping station.

We investigate the Friedmann–Lemaitre–Robertson–Walker cosmological models within the framework of Rastall gravity incorporating particle creation. The modified field equations for Rastall gravity are derived and exact solutions are obtained under various choices of scale factors. The qualitative behaviour of our solutions depends on the Rastall coupling parameter ψ=kλ. Following the literature, we have restricted the Rastall coupling parameter ψ(k=1) to the range −0.0001 < ψ < 0.0007 at 68% CL from CMB+BAO data. Further, we have discussed the distinct physical behaviour of the derived models in detail.

We define a spinor-Minkowski metric for SL(4,C). It is not a trivial generalization of the SL(2,C) metric and it involves the Minkowskian one. We define 4x4 version of the Pauli matrices and eight 4-component associated generalized eigenvectors that can be regarded as undotted covariant spinors. The 4-component spinors can be grouped into four categories. Each category transforms in its own way. The outer products of pairwise combinations of 4-component spinors can be associated with 4-vectors. Including the dotted covariant, undotted and dotted contravariant forms totally we have sixteen pairs of spinors. Eight of them live in the conjugate space which has no countepart in SL(2,C).

This study presents a methodology to reveal traces of viral particles, as aerosol with known chemical and molecular structure, in a sample by means of photon and electron interactions. The method is based on Monte Carlo simulations and on the analysis of photon-electron fluxes-spectra through energy channels counts as a function of different aerosol viral concentrations in the air sample and looking at the peculiar photon/electron interactions with the potential abnormal atomic hydrogen (H), oxygen (O), carbon (C), and phosphorus (P) compositions present in the air sample as a function of living and nonliving matter with PO4 group RNA/DNA strands in a cluster configuration.

This study aimed to investigate the interactions between corn particle size (PS) and conditioning temperature (CT) on the performance, carcass traits, intestinal morphology, and immune responses in broilers fed a corn-soybean meal-based diet. A total of 360, one-day-old male broiler chicks (Ross 308) were randomly allocated into six dietary treatments in a 2×3 factorial arrangement, consisting of two corn PS (finely ground with geometric mean diameter (GMD) of 357 µm (PSF) vs. coarsely ground corn with GMD of 737 µm (PSC), and three CT [unconditioned (CTU), conditioned at 75◦C (CT75) and 90◦C (CT90)]. Birds accommodated in 30 pens with five replicates and 12 chicks per each. There was no interaction between corn PS and CT on growth performance and immune response of broilers at any growth phases. However, during the starter (0-10d) period, the average daily weight gain (ADWG) and feed conversion ratio (FCR) of PSF-fed birds were significantly improved compared to those fed PSC (P<0.05). During the starter (0-10d) and grower (11-24d) periods, increasing the conditioning temperature of corn increased the ADWG, while in the starter phase only the CT75 caused a lower FCR (P<0.05). Broilers fed PSF corn showed lowest FCR during the finisher (25-42d) period compared to those fed PSC (P<0.05). Conditioning corn at 75ºC reduced FCR during the finisher (25-42d) period compared to the birds fed CTU and CT90 corn (P<0.05). In whole experimental periods (1-42d), PSF and CT75 treatment increased the ADWG compared to the PSC and CTU (P<0.05). The CT75 treatment improved primary total anti-sheep red blood cell (SRBCs) titer (IgT) and IgM and secondary IgT and IgG responses compared to the other experimental groups (CTU and CT90) (P<0.05). No significant PS × CT interaction was found on Newcastle disease (ND) antibody titer of broiler chickens (P>0.05). Feeding CT75 corn reduced duodenum and jejunum relative length compared to the birds fed diets containing CTU corn. Significant PS×CT interactions (P<0.05) were observed for villus height, villus height to crypt depth, crypt depth, muscle thickness, and absorption surface area of jejunum. The highest carcass yield observed in PSF-CT75-group (P<0.05). In conclusion, the use of finely ground corn (PSF) conditioned at 75℃ (CT75) was beneficial to growth performance, development of the digestive tract, jejunum histomorphometry and the immune responses of broilers.

This study proposes a 3D particle-base (discrete) multiphysics approach for modelling calcification in the aortic valve. Different stages of calcification (from mild to severe) were simulated, and their effect on the cardiac output assessed. The cardiac flow rate decreases with the level of calcification. In particular, there is a critical level of calcification below which the flow rate decreases dramatically. Mechanical stress on the membrane is also calculated. The results show that, as calcification progresses, spots of high mechanical stress appear. Firstly, they concentrate in the regions connecting two leaflets; when severe calcification is reached, then they extend to the area at the basis of the valve.

Single-particle inductively coupled plasma mass spectrometry (spICP-MS) has been used for particle size measurement of diverse types of individual nanoparticles and micrometer-sized carbon-based particles, such as microplastics. However, its applicability to the measurement of micrometer-sized non-carbon-based particles such as silica (SiO2) is unclear. In this study, the applicability of spICP-MS to particle size measurement of non-porous/mesoporous SiO2 micro-spheres with a nominal diameter of 5.0 µm or smaller was investigated. Particle sizes of these microspheres were measured using both spICP-MS based on a conventional calibration approach using an ion standard solution and scanning electron microscopy (SEM) as a reference technique and the results were compared. The particle size distributions obtained using both techniques were in agreement within analytical uncertainty. The applicability of this technique to the detection of metal-containing protein-binding mesoporous SiO2 microspheres was also investigated. Bound iron (Fe)-containing proteins (i.e., lactoferrin and transferrin) of mesoporous SiO2 microspheres were detected using Fe as a presence marker for the proteins. Thus, spICP-MS is applicable to the particle size measurement of large-sized and non-porous/mesoporous SiO2 microspheres; it has considerable potential for element-based detection and qualification of bound proteins of mes-oporous SiO2 microspheres in a variety of applications.

In this article various heuristic approaches are discussed to solve the dark matter phenomenon by the concept of vacuum polarization. They are compared with a more fundamental approach, based upon an entropy model of the visible universe. They all make use of some kind of gravitational dipole. These dipoles seem to violate Einstein’s equivalence principle between inertial mass and gravitational mass. It is shown how the paradox can be solved by a quantum mechanical principle.

In this work, we propose a variation of the cellular particle swarm optimization algorithm with differential evolution hybridization (CPSO-DE), to include constrained optimization in it, named Ts-CPD. It is implemented as a kernel of electronic design automation (EDA) tool capable of sizing circuit components considering a single-objective design with restrictions and constraints. The aim is to improve the optimization solutions in the sizing of analog circuits. To evaluate our proposal’s performance, we present the design of three analog circuits: a deferential amplifier, a two-stage operational amplifier, and a folded cascode operational transconductance amplifier. Numerical simulation results indicate that Ts-CPD can find better solutions, in terms of the design objective and the accomplishment of constraints, than those reported in previous works.

The paper presents a new type of hydraulic clutch operating with magnetorheological (MR) fluids and the results achieved from both theoretical analysis and experimental measurement. The hy-draulic clutch system of its own design with MR working fluid and a rotating magnetic field lo-cated is designed. The principle of this clutch is to use a rotating magnetic field created by an al-ternating current electromagnet to set the MR fluid in motion. In the tests of the hydraulic clutch with a rotating magnetic field, MR fluid produced on their own, consisting of solid iron particles of various diameters mixed with a silicone oil, are used as working fluids. The rheological properties of MR fluids are assessed on the basis of tests carried out with a Brookfield DV2T rheometer equipped with a magnetic device for generating the magnetic field of its own design. The char-acteristics of the hydraulic clutch with MR working fluid and the rotating magnetic field are tested on a specially built test stand. As a result of the research, it has been found that the torque transmitted by the clutch is the greater, the higher the rotational speed of the magnetic field and the lower the rotational speed of the beaker in which the working fluid is placed, and that the greatest torque occurs for the working fluid with the highest iron content. Based on the analysis of the structure and characteristics of the clutch in which the magnetic field is used, it has been shown that the design of the developed clutch is similar to that of an induction clutch, and its characteristics correspond to the characteristics of the eddy current clutch. Therefore, the proposed new clutch working with MR fluid and the rotating magnetic field can be applied to stationary power transmission system similarly to an eddy current clutch.

In the study of relativistic jets one of the key open questions is their interaction with the environment on the microscopic level. Here, we study the initial evolution of both electron–proton (e^- - p⁺) and electron–positron (e^±) relativistic jets containing helical magnetic fields, focusing on their interaction with an ambient plasma. We have performed simulations of "global" jets containing helical magnetic fields in order to examine how helical magnetic fields affect kinetic instabilities such as the Weibel instability, the kinetic Kelvin-Helmholtz instability (kKHI) and the Mushroom instability (MI). In our initial simulation study these kinetic instabilities are suppressed and new types of instabilities can grow. In the e^- - p⁺ jet simulation recollimation-like instability occurs and jet electrons are strongly perturbed. In the e^± jet simulation a recollimation-like instability occurs at early times followed by a kinetic instability and the general structure is similar to a simulation without helical magnetic field. Simulations using much larger systems are required in order to thoroughly follow the evolution of global jets containing helical magnetic fields.

We introduce two symmetric concepts to physics: “distance” (space and time in one) and “wavematter” (electromagnetic wave packet and matter in one). We claim that physics has chosen the wrong concept of time: It is not aware that the time of a moving object flows in a direction other than my time. We provide 15 proofs for our claim by solving 15 mysteries of physics graphically! For example, we prove that length contraction and time dilation are actually geometrical effects in a 4D manifold that we call “Euclidean spacetime” (ES). We prove that the discrepancy in calculating the Hubble constant stems from a systematic error in the redshift measurement. We prove that what I deem wave, deems itself matter, which solves wave–particle duality. We even untangle quantum entanglement without the issue of non-locality. We finally understand why alternative theories of Euclidean relativity have failed: They are all based on four dimensions of space rather than distance. A huge amount of energy was injected into ES at a point that we take as origin. Ever since has this energy been moving radially away at the speed of light. We live in the 3D hypersurface of an expanding 4D hypersphere. Hyperspherical coordinates have the great advantage that they reduce all that is ever happening to just one formula. So, it is the Theory of Everything in these coordinates: “Energy is covering radial distance which, divided by Euclidean time, is equal to the speed of light.” Acceleration and force like gravitation emerge from a rotation of objects in ES and a subsequent projection to 3D space. Matching the symmetry simplifies physics.

Magnetic Particle Imaging (MPI) is a new imaging modality that sensitively and specifically detects superparamagnetic iron oxide nanoparticles (SPIONs). Many labs have been developing cellular magnetic resonance imaging (MRI) tools using both SPIONs and fluorine-19 (19F)-based contrast agents for numerous important applications, including tracking of immune and stem cells used for cellular therapies. SPION-based MRI cell tracking has very high sensitivity, but low specificity. SPIONs produce negative contrast in MRI, or signal voids. SPIO is not directly detected by MRI, but indirectly through its relaxation effects on protons, therefore, it is not possible to reliably quantify the local tissue concentration of SPION particles and cell number cannot be determined. 19F based cell tracking uses perfluorocarbons (PFC) to label cells. The number of 19F atoms can be directly measured from 19F MR images and related to cell number. 19F MRI has high specificity, but low sensitivity. MPI cell tracking displays great potential for overcoming the challenges of MRI-based cell tracking allowing for both high cellular sensitivity and high specificity and quantification of SPIO labeled cell number. In this paper we describe nanoparticle and MPI system factors that influence MPI sensitivity and resolution, quantification methods and give our perspective on testing and applying MPI for cell tracking.

The base excitation of mechanical equipment can result in pipeline system vibration which seriously affects the safe operation of the equipment. Particle damping technology is based on the particle collision dissipation principle to realize the structural vibration energy dissipation. This paper introduces the particle damping method into the vibration control of pipeline system. A particle damper has been designed for the structural characteristics of the pipeline. The influence of the material and structure of the pipeline particle damper, the external excitation and other parameters on the energy dissipation characteristics of particle damping are revealed. The energy dissipation mechanism of the pipeline particle collision damper under various parameter is analyzed. A pipeline vibration reduction test system with particle damping has been established. The effects of different excitation conditions, damper materials and number of unit structures of particle damper on the vibration damping effect of pipeline systems are investigated comparatively. The influence of damper parameters on the vibration suppression of pipeline system is obtained. The research in this paper can provide technical references for vibration suppression of pipeline systems.

The production of anatase titanium dioxide particles plays a crucial role in the sulfate process used for manufacturing white pigment. A key factor in improving the quality of white pigments is enhancing the thermal mixing process within the precipitation tank. This improvement ensures the uniform dispersion of seed particles instead of their agglomeration, leading to the formation of particles with uniform sizes. The objective of this study was to enhance three-phase CFD simulations involving the mixing process of H₂SO₄ solution, steam as a gas phase, and solid seed particles. By analyzing the trajectories of the seed particles using CFD, the optimal injection position for the seed particles within the mixing process was determined. Subsequently, lab scale test and real field test were conducted based on the insights gained from the CFD simulations. The particle size distribution of two different types of seed inlets was analyzed and compared using Transmission Electron Microscopy (TEM). The findings of this study demonstrate that the developed multi-phase CFD simulation can be effectively utilized to enhance the precipitation process for the production of anatase titanium dioxide particles.

Acoustic focusing of particle flow in microfluidics has been shown to be an efficient tool for particle separation for various chemical and biomedical applications. The mechanism behind the method is the selective effect of the acoustic radiation force on distinct particles. In this way, they can be selectively focused and separated. The technique can also be applied under stationary conditions, i.e., in the absence of fluid flows. In this study, the manipulation of self-propelled particles, such as Janus particles, in an acoustofluidic setup is investigated. In experiments with self-propelled Janus particles and passive beads, we explore the interplay between self-propulsion and the acoustic radiation force. Our results demonstrate unusual and potentially useful effects such as selective trapping, escape and assisted escape in binary mixtures of active and passive particles. We also analyze various aspects related to the behavior of Janus particles in acoustic traps in the presence and absence of flows.

Container crane has been widely used for port operation. However, the structure of port container crane is large, which always lead to crane damage under the vibration and strong wind. Therefore, a method for vibration reduction of container crane structure based on particle damping technology is proposed. In this investigation, the scale 1:80 crane model is built, the equivalent mechanical model is also established to preliminary verify the effect of vibration suppression. Furthermore, the dependence of vibration suppression effect on the key parameters of the damper, i.e., filling material, filling rate, particle size, and installation position of dampers are analyzed through experiments. The results indicate that the vibration peak of the crane structure displayed in Simulink oscilloscope is weakened and lags behind, the installation of the damper brings the effect of vibration suppression for the crane, the vibration suppression effect reaches 25%; the crane mode obtains the best vibration suppression effect under the condition that the material filled in the damper has a large density and elastic modulus, sufficient collision space and high collision frequency, and the dampers are installed far away from the crane center of gravity. The optimum parameters of the damper obtained from experiments, 12 mm lead beads, filling rate of 60%, installed in the distal ends within the strength range of the forearm of the crane. It is thus concluded that the particle damping technology has provided an effective way in wind and vibration reduction for container crane.

These years have witnessed growing research on applying rheology in grinding and flotation treatment of finely disseminated ores. The slurry rheology has long been identified as the comprehensive effect of inter-particle interactions, including their aggregation (coagulation and flocculation) and dispersion states in slurry, which are more impactive under the fine-particle effect. In this regard, rheology has the potential to play a significant role in interpreting the flowing and deforming phenomenon of inter-particle aggregates, particle-bubble aggregates, and flotation froth. Though much attention has been paid to the rheological effect in industrial suspension, this has not been the case for mineral grinding and flotation for fine particles. The influential mechanism of rheology on the sub-processes of mineral processing has not been systemically concluded and revealed thoroughly, without which the underpinning mechanism for enhancing the processing efficiency has been difficult to discover. This paper reviews the current application and importance of rheology in the fine mineral processing, and the potential research direction in the field is proposed.

Plant pathogens utilize effectors to subvert host cell biology for facilitating infection. However certain effectors, called as the avirulence proteins, trigger immune responses in the host. AvrPi54 is an avirulence protein from fungus Magnaporthe oryzae which induces the defense reactions in rice cells that contain Pi54 resistance gene. The characterization of such proteins and elucidation of their function facilitate the understanding of the mechanism of disease establishment. In present study, physiochemical properties of AvrPi54 were analysed using computational methods. We found that mature AvrPi54 is a small, hydrophobic and secretary protein with 134 amino acids. It is rich in small amino acids with no significant homology with other proteins in databases. The gene ORF was cloned in pET28a(+) vector and expressed in E. coli BL21(DE3)pLysS. The protein was purified using affinity chromatography. Transient expression of the gene in epidermal cells of onion bulb is a powerful tool to predict the subcellular localization in plant cells. We fused AvrPi54 to eYFP and transformed epidermal cell layer of onion bulb. Fused protein localized to the plasma membrane and nucleus of plant cells. Therefore, AvrPi54 might be functioning in the host cell as transcription factor or chromatin remodelling factor to modify pathogenesis-related processes.

It is shown that the relationship between gravity and quantum physics can be described in terms of the symmetry break of space due to elementary constituents, dubbed as “darks”, which constitute a universal energetic background field that extends from the cosmological level down to the nuclear level. It requires (a) the awareness of the polarisable second elementary dipole moment of a recently discovered third Dirac particle type, next to the electron-type and the Majorana-type, and (b) the awareness that Einstein’s Lambda is not a constant of nature, but, instead, a covariant integration constant with a value that depends on the scope of the cosmological system under consideration, such as solar systems and galaxies, eventually showing up as the Cosmological Constant at the level of the universe. The relationship has been made explicit by relating two major gravitational constants quantaties (the gravitational constant and Milgrom’s acceleration constant) with a single nuclear quantity (the rest mass of the pion derived from the Higgs boson value).

Nature-inspired algorithms are very popular tools for solving optimization problems inspired by nature. However, there is no guarantee that optimal solution can be obtained using a randomly selected algorithm. As such, the problem can be addressed using trial and error via the use of different optimization algorithms. Therefore, the proposed study in this paper analyzes the time-complexity and efficacy of some nature-inspired algorithms which includes Artificial Bee Colony, Bat Algorithm and Particle Swarm Optimization. For each algorithm used, experiments were conducted several times with iterations and comparative analysis was made. The result obtained shows that Artificial Bee Colony outperformed other algorithms in terms of the quality of the solution, Particle Swarm Optimization is time efficient while Artificial Bee Colony yield a worst case scenario in terms of time complexity.

Nature-inspired algorithms are very popular tools for solving optimization problems inspired by nature. However, there is no guarantee that optimal solution can be obtained using a randomly selected algorithm. As such, the problem can be addressed using trial and error via the use of different optimization algorithms. Therefore, the proposed study in this paper analyzes the time-complexity and efficacy of some nature-inspired algorithms which includes Artificial Bee Colony, Bat Algorithm and Particle Swarm Optimization. For each algorithm used, experiments were conducted several times with iterations and comparative analysis was made. The result obtained shows that Artificial Bee Colony outperformed other algorithms in terms of the quality of the solution, Particle Swarm Optimization is time efficient while Artificial Bee Colony yield a worst case scenario in terms of time complexity.

Precipitation processes are technologies commonly used in hydrometallurgical plants to recover metals or to treat wastewaters. Moreover, solid-liquid separation technologies, such as thickening or filtering, are relevant unit operations, included in the precipitation technologies. These methods are strongly dependent on the characteristics of the solid precipitates formed during the specific precipitation reaction. One of these characteristics is the particle size distribution (PSD) of the solid precipitates which are fed into a solid-liquid separation process. Therefore, PSD determination is a typical practice for the characterization of the slurries generated in a precipitation plant. Furthermore, the precipitates generated in these processes have a colloidal or aggregation behavior, depending on the operational conditions. Nevertheless, the conventional methods used to estimate PSD (e.g., laser diffraction and/or ciclosizer) have not been designed to measure particles that tend to aggregate or disaggregate, since they include external forces (e.g., centrifugal, agitation, pumping and sonication). These forces affect the true size of the aggregates formed in a unit operation, thereby losing representativity in terms of aggregates particle size. This study presents an alternative method of measuring the size distribution of particles with aggregation behavior, particularly, by using non-invasive microscopy and image processing and analysis. The samples used have been obtained from an experimental precipitation process by applying sulfidization to treat the cyanide-copper complexes contained in a cyanidation solution. This method has been validated with statistical tools and compared with a conventional analysis based on laser diffraction. Our results show significant differences between the methods analyzed, demonstrating that image processing and analysis by microscopy is an excellent and non-invasive alternative to obtaining size distribution of aggregates in precipitation processes.

In this study, a monoethanolamine aerosol growth model was developed to investigate the aerosol growth factor. Interactions among the internal conditions in an absorber were considered in this aerosol model. Additionally, an experiment was conducted to measure aerosol particle size, for collecting in-house validation data. Sucrose was used as the aerosol nuclei instead of sulfuric acid to prevent the corrosion of equipment used in the experiment. Experimental results showed that the outlet aerosol sizes increased to the same size regardless of the sucrose concentrations. The aerosol growth model was validated using the in-house experimental data. The aerosol growth model efficiently predicted the aerosol size. For investigating aerosol growth effects, particle number concentration was determined to be the primary factor affecting aerosol growth and amine emissions. When the particle number concentration increased, the aerosol size decreased, whereas the MEA emission increased.

Hypoxia, associated with abnormal vessel growth, is a characteristic feature of many solid tumors that increases their metastatic potential and resistance to radiotherapy. Carbon ion radiation therapy, alone or in combination with other treatments, is one of the most promising treatments for hypoxic tumors because the oxygen enhancement ratio decreases with increasing particle LET. Nevertheless, the current clinical practice does not yet fully benefit from using carbon ions for tackling hypoxia. Here we give an overview of the existing experimental and clinical evidence supporting the efficacy of C-ion radiotherapy in overcoming hypoxia-induced radioresistance, followed by a discussion of the strategies proposed to enhance it, including different approaches to maximize the LET in the tumors.

During the last decade, new unusual physical phenomena have been discovered in studying the optics of dielectric mesoscale particles of an arbitrary three-dimensional shape with the Mie size parameter near 10 (q ~ 10). The paper provides a brief overview of these phenomena from optics to terahertz, plasmonic and acoustic ranges. The different particle configurations (isolated, regular or Janus) are discussed, and the possible applications of such mesoscale structures are briefly reviewed herein in relation to the field enhancement, nanoparticle manipulation and super-resolution imaging. The number of interesting applications indicates to a new promising scientific direction emerged in optics, terahertz and acoustic ranges, and plasmonics. In this paper we present the authors' view of these problems.

In this letter, instead of choosing the Einstein Rosen bridge between two black holes as in ER=EPR, we consider a wormhole between a black hole and a closed edge of the wormhole. We assume that information in a black hole travels through a wormhole, turns to mass (dark matter) in the closed region. This study is in contradiction with the existence of the white hole in our Universe. We replace the notion of the white hole with the massive closed region. We prove the metric of the closed region by the Hopf fibration, this new metric generalizes the $AdS_{5}$\ metric.

In this paper, we present a technique to unify the Reissner–Nordstr¨om metric and the Kerr–Newman metric. We construct a specifific model and calculate the entanglement entropy of black horizon. We are interested in the entangled particle and antiparticle spinning on the black hole horizon. The two Reissner-Nordstr¨om horizons r±, are the results of the rotation of several entangled particle-antiparticle on the real horizon. The energy absorbed by a black hole is transformed into a kinetic energy of the entangled particle-antiparticles. This study provides a new type of black hole metric. We show that the rotation of an entangled system of a particle and an antiparticle can create a extremal black hole. We also explore some of the implications of this point of view for the black hole entanglement.

Both macro and microprojectiles (e.g., interplanetary, interstellar and even intergalactic material) are seen as important vehicles for the exchange of potential (bio)material within our solar system as well as between stellar systems in our Galaxy. Accordingly, this requires estimates of the impact probabilities for different source populations of projectiles, including for intergalactic meteor particles which have received relatively little attention since considered as rare events (discrete occurrences that are statistically improbable due to their very infrequent appearance). We employ the simple but comprehensive model of intergalactic microprojectile capture by the gravity of exoplanets which enables us to estimate the map of collisional probabilities for an available sample of exoplanets in habitable zones around host stars. The model includes a dynamical description of the capture adopted from Mautner model of interstellar exchange of microparticles and changed for our purposes. We use statistical and information metrics to calculate probability map of intergalactic meteorite particle capture. Moreover, by calculating the entropy index map we estimate the concentration of these rare events. We further adopted a model from immigration theory, to show that the time dependent distribution of single molecule immigration of material indicates high survivability of the immigrated material taking into account birth and death processes on our planet. At present immigration of material can not be observationally constrained but it seems reasonable to think that it will be possible in the near future, and to use it along other proposed parameters for life sustainability on some planet.

Current numerical methods for simulating biophysical processes in aquatic environments are typically constructed in a grid-based Eulerian framework or as an individual-based model in a particle-based Lagrangian framework. Often, the biogeochemical processes and physical (hydrodynamic) processes occur at different time and space scales, and changes in biological processes do not affect the hydrodynamic conditions. Therefore, it is possible to develop an alternative strategy to grid-based approaches for linking hydrodynamic and biogeochemical models that can significantly improve computational efficiency for this type of linked biophysical model. In this work, we utilize a new technique which links hydrodynamic effects and biological processes through a property-carrying particle model (PCPM) in a Lagrangian/Eulerian framework. The model is tested in idealized cases and its utility is demonstrated in a practical application to Sandusky Bay. Results show the integration of Lagrangian and Eulerian approaches allows for a natural coupling of mass transport (represented by particle movements and random walk) and biological processes in water columns which is described by a nutrient-phytoplankton-zooplankton-detritus (NPZD) biological model. This method is far more efficient than traditional tracer based Eulerian biophysical models for 3-D simulation, particularly for a large domain and/or ensemble simulations.

Transport of hydrophobic drugs in the human body exhibits complications due to the low solubility of these compounds. With the purpose of enhancing the bioavailability and biodistribution of such drugs, recent studies have reported the use of amphiphilic molecules, such as phospholipids, for synthesis of nanoparticles or nanocapsules. Given that phospholipids can self–assemble in liposomes or micellar structures, they are ideal candidates to function as vehicles of hydrophobic molecules. In this work, we report mesoscopic simulations of nanoliposomes, constituted by lecithin and coated with a shell of chitosan. The stability of such structure and the efficiency of encapsulation of capsaicin, as well as the internal and superficial distribution of capsaicin and chitosan inside the nanoliposome were analyzed. The characterization of the system was carried out through density maps and the potentials of mean force for the lecithin–capsaicin, lecithin–chitosan and capsaicin–chitosan interactions. The results of these simulations show that chitosan is deposited on the surface of the nanoliposome, as has been reported in some experimental works. It was also observed that a nanoliposome of approximately 18 nm in diameter is stable during the simulation. The deposition behavior was found to be influenced by pattern of N-acetylation of chitosan.

The interactions of metal ions with fulvic acids resulted in changes in the particle size and charge. Particle size distribution was trimodal for fulvic acids, bimodal for fulvic complexes with calcium and magnesium, and copper-fulvic complexes had only one size fraction. The compensation of negative charge of carboxylic and phenolic functional groups by positive charged metal ions resulted in the increase in zeta potential which was getting closer to zero in the case of copper-fulvic complexes. However, all metal humic complexes behaved as colloidally unstable which resulted in visually observable sedimentation. Calorimetric measurements provided positive values of changes in enthalpy which indicated endothermic processes. In contrast quantum chemical calculations as well as experiments with model compounds provided negative values indicated exothermic processes. Changes in Gibbs energy were determined as negative and changes in entropy as positive which meant spontaneous processes.

The steady-state multiplicity of the porous, non-isothermal, catalyst pellet when two parallel and consecutive chemical reactions take place was analysed in this work. The geometry selected for the catalyst pellet is finite hollow cylinder. A numerical multigrid continuation technique with the preconditioned conjugate gradient squared as coarse grid solver was used. The continuation parameter is the dimensionless adiabatic heat rise (Prater number) for the first chemical reaction. The effect of the other governing parameters was analysed and the results are compared to those provided by the single chemical reaction.

Pharmaceutical aerosols play a key role in the treatment of lung disorders, but also systemic diseases, due to their ability to target specific areas of the respiratory system (RS). This article focuses on identifying and clarifying the influence of various factors involved in the generation of aerosol micro- and nanoparticles on their regional distribution and deposition in the RS. Attention is given to the importance of process parameters during aerosolization of liquids or powders and the role of aerosol flow dynamics in RS. The interaction of deposited particles with the fluid environment of the lung is also pointed out as an important step in the mass transfer of the drug to the RS surface. The analysis presented highlights the technical aspects of preparing the precursors to ensure that the properties of the aerosol are suitable for a given therapeutic target. Analyzing existing technical limitations, several potential strategies for enhancing the efficiency of targeted therapeutic delivery to or through the respiratory system are identified and presented, including the use of smart inhaling devices incorporating AI algorithms.

NUSES is a planned space mission aiming to test innovative observational and technological approaches related to the study of low-energy cosmic rays, gamma rays and high-energy astrophysical neutrinos. Two scientific payloads will be hosted onboard the NUSES space mission: Terzina and Zirè. Terzina will be an optical telescope readout by SiPM arrays, for the detection and study of Cerenkov light emitted by Extensive Air Showers generated by high-energy cosmic rays and neutrinos in the atmosphere. Zirè will focus on the detection of protons and electrons up to a few hundred MeV and to 0.1-10 MeV photons and will include the Low Energy Module (LEM). The LEM will be a particle spectrometer devoted to the observation of fluxes of low-energy electrons in the 0.1-7 MeV range and protons in the 3-50 MeV range along the Low Earth Orbit (LEO) followed by the hosting platform. The detection of Particle Bursts (PBs) in this Physics channel of interest could give new insight into the understanding of complex phenomena such as eventual correlations between seismic events or volcanic activity with the collective motion of particles in the plasma populating van Allen belts. With its compact sizes and limited acceptance, the LEM will allow the exploration of hostile environments such as the South Atlantic Anomaly (SAA) and the inner van Allen belt, in which are expected electron fluxes of the order of 106−7 cm−2 sr−1 s−1. Concerning the vast literature of space-based particle spectrometers, the innovative aspect of the LEM resides in its compactness, within 10x10x10 cm3, and in its “active collimation” approach dealing with the problem of multiple scattering at these very low energies. In this work, the geometry of the detector, its detection concept, its operation modes and the hardware adopted will be presented. Some preliminary results from the Monte Carlo simulation (Geant4) will be shown.

1) Background: Traditionally, complex biological products such as vaccines presented unique challenges to implementation of even rudimentary characterization packages; thus, the product was defined almost exclusively by its manufacturing process. The advances in technology and analytical tools allowed the application of more comprehensive characterization packages for products such as adsorbed combination vaccines, which contain several antigens in a single formulation to protect against more than one disease, and may contain adjuvants and excipients. Aluminum phosphate (AlPO4) is a well-established adjuvant for enhancing the uptake of vaccines and to induce robust immunity against pathogens. During manufacturing, adjuvant is mixed with protein antigens which may in turn impact their higher order structure and stability. 2) Methods: To study the structural changes of protein antigens after adsorption several analytical tools including DLS, FTIR, Fluorescence, LD, and SEM were used. 3) Results: the AlPO4 adjuvant suspension consists of small submicron particles that form a continuous porous surface. Secondary structure alpha-helix and beta-sheet content of DT and TT increased after adsorption to AlPO4 adjuvant, whereas no significant changes were noted for other protein antigens. Interactions were noted between AlPO4 adjuvant and DT, TT, and FHA. 4) Conclusions: here we report for the first time the use of SEM for the visualization of adsorbed multivalent vaccine components. A unique signature profile detected for each multivalent vaccine by FTIR can be used as a lean in-process test to verify vaccine product composition and identity prior to filling.

In this study, we attempted to verify the hypothesis that total metals bound to dust of different particle sizes may reflect pollution levels, but cannot predict its bioavailability and risks in human health assessments. Dust samples were collected using active sampling method; during the dry season months of November, 2014 to March, 2015 at different locations in Akure (7˚10ʹN and 5˚15ʹE). The samples were sorted into different particle sizes (< 10µm, 10 – 50 µm, >50µm), analyzed for some physicochemical properties and assessed for metals bioavailability using two-step physiological extraction method. The amount of metals (Cd, Cu, Cr, Ni, Pb, Zn and Mn) released in each particle sizes were determined using Perkin-Elmer 6000 Inductively Coupled Plasma - Atomic Emission Spectrometry (ICP-AES) analysis. The results showed that bioavailability of some metals (Cd, Ni, Pb, and Zn) decreases with increasing particle sizes, however, the reverse trend was observed for Mn, Cu and Fe concentrations. This may be attributed to some combination of physicochemical characteristics of the dust and metal speciation. Hence, it was concluded that metal bioaccessibility in dust can best be described by the knowledge of physicochemical characteristics. The exposure dose of the metals showed that cancer risks due to inhalation were very high when compared to other exposure routes (ingestion and dermal contact). The calculated non-cancer (HQ) and cancer risk (HI) for humans in the area showed values higher than unity, indicating possibility of the metals’ body burden.

Classical mechanics describes the laws of motion in the macroscopic material world, while quantum mechanics describes the laws of motion in the microscopic material world that classical mechanics cannot explain, and achieves a highly accurate mathematical representation of the laws of microscopic physical motion. But even with such a successful theory, there is confusion about the probability wave. Therefore, this paper attempts to improve the physical definition of the conceptual basis of quantum mechanics, thus solving the confusion of quantum mechanical probability waves, and finally surprisingly discovered another feasible interpretation of the principle of cosmic redshift and the invariance of the speed of light.

In this work, the nearest neighbor distances and Voronoi cell features of the Cu-Ag deposits were analyzed and fitted with Lognormal, Weibull, and Gamma distributions. The nearest neighbor distance distributions of the samples were compared with those of complete spatially random points, showing spatial inhomogenity due to the nucleation exclusion effect. The radial distribution function was calculated, showing both influences from the grain size and the nucleation exclusion effect. Voronoi cells were generated based on the shape of the grains. The size, occupancy, and coordination of the Voronoi cells were examined and fitted. The results show that despite the Cu-Ag deposits seem to be governed by instantaneous nucleation mode, the spatial distribution of the nuclei is more impacted by the nucleation exclusion effect than the Cu-only samples. This behavior is also justified by the grain size distribution generated with Voronoi cell size and occupancy distributions.

Ammonia (NH3), the most prevalent alkaline gas in the atmosphere, plays a significant role in PM2.5 formation, atmospheric chemistry, and new particle formation. This paper reviews quantification of [NH3] through measurements, satellite-remote-sensing, and modeling reported in over 500 publications towards synthesizing current knowledge of [NH3], focusing on spatiotemporal variations, controlling processes, and quantification issues. Most measurements are through regional passive sampler networks. [NH3] hotspots are typically over agricultural regions like the Midwest US and North China Plain, with elevated concentrations reaching monthly averages of 20 and 74 ppbv, respectively. Topographical effects dramatically increase [NH3] over the Indo-Gangetic Plains, North India and San Joaquin Valley, US. Measurements are sparse over oceans, where [NH3] ≈ few tens of ppbv, variations of which can affect aerosol formation. Satellite-remote-sensing (AIRS, CrIS, IASI, TANSO-FTS, TES) provides global [NH3] quantification in the column and at surface since 2002. Modeling is crucial for improving understanding of NH3 chemistry and transport, its spatiotemporal variations, source apportionment, exploring physicochemical mechanisms, and predicting future scenarios. GEOS-Chem (global) and FRAME (UK) models are commonly applied for this. A synergistic approach of measurements↔satellite-inference↔modeling is needed towards improved understanding of atmospheric ammonia, of concern from the standpoint of human health and the ecosystem.

Based on collected power generation data associated with solar irradiance, PV system conversion efficiency and cell temperature, an hourly solar PV power estimation model by a parallel artificial neural network (ANN) and particle swarm optimization (PSO) algorithm is proposed. Weight matrices related to different seasons and geographic areas for estimation power generations have been trained by real measured operation data. The parallel PSO algorithm with heuristic global optimization technique assists the ANN training process to get near optimal solutions precisely. The accuracy and reliability of estimation is audited by actual details of photovoltaic power stations in various regions and scales. The results of estimation model can not only assist the electricity dispatcher to explicitly monitor the trend of solar power generation in different areas, but also coordinate with traditional power plants to meet load demand more accurately. The presented model will bring benefits to power dispatching for larger scales of intermittent and unstable solar power generation.

Stenting is a common method for treating atherosclerosis. A metal or polymer stent is deployed to open the stenosed artery or vein. After the stent is deployed, the blood flow dynamics influence the mechanics by compressing and expanding the structure. If the stent does not respond properly to the resulting stress, vascular wall injury or re-stenosis can occur. In this work, Discrete Multiphysics is used to study the mechanical deformation of the coronary stent and its relationship with the blood flow dynamics. The major parameters responsible for deforming the stent are sort in terms of dimensionless numbers and a relationship between the elastic forces in the stent and pressure forces in the fluid is established. The blood flow and the stiffness of the stent material contribute significantly to the stent deformation and affect the rate of deformation. The stress distribution in the stent is not uniform with the higher stresses occurring at the nodes of the structure.

Particles in classical physics are distinguishable objects, which can be picked out individually on the basis of their unique physical properties. By contrast, in quantum mechanics the standard view is that particles of the same kind (``identical particles'') are completely indistinguishable from each other. This standard view is problematic: Particle indistinguishability is irreconcilable not only with the very meaning of ``particle'' in ordinary language and in classical physical theory, but also with how this term is used in the practice of present-day physics. Moreover, the indistinguishability doctrine prevents a smooth transition from quantum particles to what we normally understand by ``particles'' in the classical limit of quantum mechanics. Elaborating on earlier work, we here discuss an alternative to the standard view that avoids these and similar problems. As it turns out, this alternative approach connects to recent discussions in quantum information theory concerning the question of when identical particles can be considered to be entangled.

The availability of high quality surface observations of precipitation and volume observations by polarimetric operational radars make it possible to constrain, evaluate and validate numerical models with a wide variety of microphysical schemes. In this article, a novel particle-based Monte-Carlo microphysical model (called “McSnow”) is used to simulate the outer rain bands of Hurricane Dorian which traversed the densely instrumented precipitation research facility operated by NASA at Wallops Island, Virginia. The rain bands showed steady stratiform vertical profiles with radar signature of dendritic growth layers near −15 °C and peak reflectivity in the bright band of 55 dBZ along with polarimetric signatures of wet snow with sizes inferred to exceed 15 mm. A 2D-video disdrometer measured frequent occurrences of large drops &gt;5 mm and combined with an optical array probe the drop size distribution was well-documented in spite of uncertainty for drops &lt;0.5 mm due to high wind gusts and turbulence. The 1D McSnow control run and four numerical “experiments” were conducted and compared with observations. One of the main findings is that even at the moderate rain rate of 10 mm/h collisional breakup is essential for the shape of the drop size distribution.

It is shown that gravity and quantum physics can be unified upon the basis of a quark description in terms of a gravitational Dirac particle. It requires the awareness of a second elementary dipole moment (isospin) next to the angular moment (spin) of Dirac particles and the awareness of an (unbroken) omnipresent energetic cosmological background field. The unification has been made explicit by an expression that relates the two major gravitational constants of nature (the gravitational constant and Milgrom’s acceleration constant) with the two major nuclear constants of nature (the weak interaction boson and the Higgs boson).

Diabetes is a serious health problem throughout the world, including in Indonesia. The International Diabetes Federation (IDF) reports that the number of adults with diabetes is increasing every year. The Behavioral Risk Factor Surveillance System (BRFSS) is a survey conducted by the Centers for Disease Control and Prevention (CDC) in the United States. Classification methods in data mining techniques are used to classify diabetics and non-diabetics. The data mining process is carried out by preprocessing, feature selection, and dataset classification stages. In the preprocessing stage, data cleaning, data formatting, and data oversampling are carried out using the Synthetic Minority Over-sampling Technique (SMOTE). Next, the feature selection stage is carried out using the Particle Swarm Optimization (PSO) algorithm to find the best attributes. The dataset classification stage is carried out using the CART Model Decision Tree algorithm. The results of the performance evaluation of the CART algorithm are calculated using the confusion matrix and the MAE value, the results obtained for the CART algorithm without SMOTE and PSO obtained the best accuracy of 75.34% and the MAE value of 0.2466, while the CART algorithm using SMOTE and PSO can increase accuracy by 10 .94% to 86.28% and an MAE value of 0.1372.

The COVID-19 pandemic has disrupted the supply chain for personal protective equipment (PPE) for medical professionals, including N95-type respiratory protective masks. To address this shortage, many have looked to the agility and accessibility of additive manufacturing (AM) systems to provide a democratized, decentralized solution to producing respirators with equivalent protection for last-resort measures. However, there are concerns about the viability and safety in deploying this localized download, print, and wear strategy. Several polymer-based AM processes produce porous parts, and inherent process variation between printers and materials also threaten the integrity of tolerances and seals within the printed respirator assembly. The goal of this paper is to quantitatively measure particle transmission through printed respirators of different designs, materials, and AM processes, and assess the viability of printed respirators as N95 equivalents. Results from this study show that respirators printed using desktop/industrial-scale fused filament fabrication processes and industrial-scale powder bed fusion processes have insufficient filtration efficiency at the size of the SARS-CoV-2 virus, even while assuming a perfect seal between the respirator and the user’s face. Almost all printed respirators provided <60% filtration efficiency at the 100-300 nm particle range. Only one respirator, printed on an industrial-scale fused filament fabrication system provided >90% efficiency as-printed. Post-processing procedures including cleaning, sealing surfaces, and reinforcing the filter cap seal generally improved performance, but no respirator sustained the filtration efficiency of an N95 respirator, which filters 95% of SARS-CoV-2 virus particles. Instead, the printed respirators showed similar performance to various cloth masks. While continued optimization of printing process parameters and design tolerances could be implemented to directly print respirators that provide the requisite 95% filtration efficiency, AM processes are not sufficiently reliable for widespread distribution and local production of N95-type respiratory protection without commensurate quality assurance processes in place. Certain design/printer/material combinations may provide sufficient protection for specific users, but the respirators should not be trusted without quantitative filtration efficiency testing. It is currently not advised to expect printed respirators originating from distributed designs to replicate performance across different printers and materials.

This study was aimed at investigating the effects of roasting and cryogenic milling on particle size, water solubility, and amount of bioactive components in ginseng root. Samples were pulverized by cryogenic milling, and one treatment condition was selected for each size range (10-50 μm, and >50 μm). The selected samples were roasted at different temperatures (160-200 °C) followed by cryogenic milling. Powdered samples were analyzed for their physicochemical characteristics. Results revealed that roasted samples exhibited significantly smaller particle size than controls (not roasted, p<0.05), and the particle size of roasted samples decreased with increase in roasting temperature until flocculation occurred around 180-190 °C. With decrease in particle size of ginseng; water solubility index, antioxidant activity, total polyphenol content, and total polysaccharide content were observed to increase. Ginseng samples showed an increasing trend in antioxidant activity with decreasing particle size (p>0.05) with significantly higher values for all roasted samples compared to the controls (p<0.05). Non-polar ginsenosides such as Rg2(S), Rg2(R), Rg3(S), Rg3(R), and Rh1(S) showed an increase in temperature-dependent manner. This study revealed that roasting at certain temperature range with cryogenic milling has positive effects on ginseng by reducing its particle size, and increasing water solubility and bioactive components.

This study aimed to decrease the particle size of ginseng by roasting and cryogenic milling to increase its water solubility and physiological activity. The samples were roasted for different times (9–21 min) and generated in different sizes (10–50, and > 50 μm). All roasted samples revealed significantly smaller particle sizes than did non-roasted samples based on Sauter mean diameter (D [3,2], p < 0.05). Further, the particle sizes of roasted samples decreased till roasting up to 15 min. In terms of the water solubility index (WSI), antioxidant activity, total polyphenol content (TPC), and total polysaccharides according to particle size, 10-20 μm-sized samples showed the highest values when compared with >50 μm-sized samples. Based on roasting time, WSI values of all samples roasted for up to 15 min were higher than those of the control (not roasted) (p < 0.05). Antioxidant activity and TPC also increased with increasing roasting time. Total polysaccharide content was the highest upon roasting for 15 min except for the 10-20 μm sample. Ginsenoside content of roasted samples >20 μm size was higher than that of the control (not roasted) except after 15 min of roasting. Therefore, roasting and cryogenic milling are effective in producing ginseng root powder.

This paper presents research on the application of trajectory design, optimization, and control to an orbital transfer from Mars-Phobos Distant Retrograde Orbits to the surface of Phobos. Given a Distant Retrograde Orbits and a landing location on the surface of Phobos, landing trajectories for which total Δv for a direct 2-burn maneuver is minimized are computed. This is accomplished through the use of Particle Swarm Optimization in which the required Δv and time of flight are optimization parameters. The non-uniform gravitational environment of Phobos is considered in the computation. Results show how direct transfers can be achieved with Δv in the order of ∼30 m/s.

The research introduces the concept of Strong Energy (SE) as a force that encompasses all the different forms of energy in the universe. The study aims to create a mathematical framework that can describe the relationship between these different types of energy and how they can be converted from one form to another. The paper explores the theoretical implications of this model and investigates how it aligns with established physics principles like the conservation of energy and fundamental forces. The paper also discusses potential experiments and the impact this research could have on cosmology, particle physics, and our understanding of energy. This work sets the stage for future research in this area of study.

Flotation of small particles is one of the global challenges facing the mineral raw materials processing industry. Large amounts of non-ferrous and rare metals are lost in the flotation tailings in the form of mineral particles below 15 µm in size as a result of the low effectiveness of their capture by coarse bubbles generated in conventional flotation machines. The method of combined microflotation, which is being developed in recent years, uses not only coarse but also microbubbles (MB) produced in the stand-alone generator of air-in-water microdispersion, which serves as the flotation carriers. Depending on the dose of MB, the effect of their application may be both positive, but also negative. The theoretical analysis of various mechanisms of particle transfer onto the surface of coarse bubbles and further into the froth layer allowed to obtain the formula for the optimal MB dose f=2dddpρp, where dd is MB size; dp and ρp respectively are the size and the density of particles. Experiments performed on the copper ore flotation tailings at the Atalaya Mining (Spain) and Chaarat Kapan (Armenia) concentrators showed that, besides the optimal MB dose in the range of 1-3 ml/g, there is another optimal MB dose in the range of 10-20 ml/g, where the copper recovery increases by several percent compared to the reference test (f= 0). In the area between the optimal MB doses, the deep minimum in copper recovery is observed, which value is by several percent lower compared to the value in the reference test.

Particle Image Velocimetry (PIV) is a widely used experimental technique for measuring flow. In recent years, open-source PIV software has become more popular as it offers researchers and practitioners enhanced computational capabilities. Software development for graphical processing unit (GPU) architectures requires careful algorithm design and data structure selection for optimal performance. PIV software, optimized for central processing units (CPUs), offer an alternative to specialized GPU software. In the present work, an improved algorithm for the OpenPIV-Python software is presented and implemented under a traditional CPU framework. The Python language was selected due to its versatility and widespread adoption. The algorithm was also tested on a supercomputing cluster, a workstation, and Google Colaboratory during the development phase. Using a known velocity field, the algorithm precisely captured the time-average flow, monetary velocity fields, and vortices.

Accurate pose estimation is a fundamental ability that all mobile robots must posses in order to navigate a given environment. Much like a human, this ability is dependent on the robot's understanding of a given scene. For Autonomous Vehicles (AV's), detailed 3D maps created beforehand are widely used to augment the perceptive abilities and estimate pose based on current sensor measurements. This approach however is less suited for rural communities that are sparsely connected and cover large areas. Topological maps such as OpenStreetMaps have proven to be a useful alternative in these situations. However, vehicle localization using these maps is non-trivial, particularly for the global localization task, where the map spans large areas. To deal with this challenge, we propose road descriptors along with an initialization technique for localization that allows for fast global pose estimation. We test our algorithms on (real world) maps and benchmark them against other map based localization as well as SLAM algorithms. Our results show that the proposed method can narrow down the pose to within 50 centimeters of the ground truth significantly faster than the state of the art methods.

This study aimed to improve sensitivity of ferrous particle sensors used in various mechanical systems such as wind power gearboxes to detect abnormalities by measuring the amount of ferrous wear particles generated by metal-to-metal contact. Existing sensors collect ferrous particles using a permanent magnet. However, its ability to detect abnormalities is limited because it only measures amounts of ferrous particles collected on the top of the sensor. To overcome this limitation, this study evaluated sensibility of an existing sensor using a multi-physics analysis method and improved it by changing the core shape inside the sensor. The detection ability of the improved sensor was analytically shown to be better than the existing sensor. This study highlights the use of analytical methods and multi-physics analysis for developing a ferrous particle sensor and presents a method to compare sensibility analytically. Results of the improved sensor are expected to enhance the ability to detect abnormalities in the lubrication system where ferrous wear particles are generated. This study will contribute to the development of more effective ferrous particle sensors, which can potentially improve the performance and reliability of mechanical systems.

The need to regulate the non-exhaust particulate emissions from vehicles has been discussed worldwide due to the toxicity to the human body as well as the atmosphere. In-depth studies have been conducted on precise analysis of the non-exhaust particulates, in particular, accurate measurement of tire-brake-road wear particles, and their proportion in the atmosphere. In this study, the influence of tire and road wear particles (TRWP) on particulate matter (PM) in the atmosphere was investigated through tire and PM samples. PM samples were collected from the atmosphere using a high-volume sampler equipped with a quartz filter. Additionally, polycyclic aromatic hydrocarbons (PAHs) and heavy metals in tire rubber were analyzed as markers by pyrolysis-gas chromatography/mass spectrometry (GC/MS), GC/MS, and inductively coupled plasma/mass spectrometry (ICP/MS). More vinylcyclohexene was detected than dipentene in the markers measured in the samples of tires equipped with vehicles driving on the road with the high-volume sampler installed, while more dipentene was detected in total suspended particles (TSP) samples. Among the PAHs measured in tire samples, pyrene exhibited the highest concentration. In TSP samples, benzo(b)fluoranthene showed the highest concentration. Among the heavy metals, zinc exhibited the highest concentration in all tire samples and calcium in TSP samples.

In this paper, we implement a new method binary Particle Swarm Optimization (PSO) for solving the kidney exchange problem, which will improve the future decisions of kidney exchange programs. Because using a kidney exchange, we can help incompatible patient-donor couples to swap donors to receive a compatible kidney. Kidney paired donation programs provide an innovative approach for increasing the number of available kidneys. Further, we implementing binary particle swarm optimization in parallel with MATLAB with one, two, three and four threads and from the computations point of view, the authors compare the performance to reduce the running time for kidney exchange to match patients as fast as possible to help clinicians. Moreover, implementing binary particle swarm optimization in solving the kidney exchange problem is an effective method. The obtained results indicate that binary PSO outperforms other stochastic-based methods such as genetic algorithm, ant lion optimization, and efficient the number of resulting exchanges.

Single particle tracking (SPT) is a powerful class of methods for studying the dynamics of biomolecules inside living cells. The techniques reveal the trajectories of individual particles, with a resolution well below the diffraction limit of light, and from them the parameters defining the motion model, such as diffusion coefficients and confinement lengths. Most existing algorithms assume these parameters are constant throughout an experiment. However, it has been demonstrated that they often vary with time as the tracked particles move through different regions in the cell or as conditions inside the cell change in response to stimuli. In this work, we propose an estimation algorithm to determine time-varying parameters of systems that discretely switch between different linear models of motion with Gaussian noise statistics, covering dynamics such as diffusion, directed motion, and Ornstein-Uhlenbeck dynamics. Our algorithm consists of three stages. In the first stage, we use a sliding window approach, combined with Expectation Maximization (EM) to determine maximum likelihood estimates of the parameters as a function of time. These results are only used to roughly estimate the number of model switches that occur in the data to guide the selection of algorithm parameters in the second stage. In the second stage, we use change detection (CD) techniques to identify where the models switch, taking advantage of the off-line nature of the analysis of SPT data to create non-causal algorithms with better precision than a purely causal approach. Finally, we apply EM to each set of data between the change points to determine final parameter estimates. We demonstrate our approach using experimental data generated in the lab under controlled conditions.

Plastics are naturally hydrophobic materials so, in order to employ flotation for the separation of plastic mixtures, the use of appropriate wetting agents is mandatory. In this work, the effect of pretreatment with alkaline solutions of sodium hydroxide on the floatability of four plastics (PET, PS, PMMA and PVC) was studied. The influence of NaOH concentration, treatment time and temperature of the alkaline solution, and influence of particle size was analyzed. Results showed that alkaline treatment had a strong effect on PET floatability, some effect on floatability of PMMA and PVC and no effect on floatability of PS. Plastics floatability decreased with the increase of NaOH concentration, temperature and treatment time of the alkaline solution. Based on flotation behavior of simple plastics, flotation separation after alkaline treatment of bi-component mixtures of PET with PS and PVC was achieved efficiently. The best separation was obtained for PET/PS mixture, a floated with a grade of 98% in PS and a sunk with a grade of 100% in PET. PET/PMMA mixture led to the worst separation. For PET/PMMA and PET/PVC mixtures, flotation separation improved with the decrease of the particles size.

The Landauer principle supplies the estimation of the minimal mass of a particle which is capable to record/erase information within a thermal bath at the temperature of T. Particles lighter than ${\tilde{m}}_0=\frac{k_{B}Tln2}{c^2}$will not transform the information to the surrounding bodies and are well expected to be undetectable. The relation of the Landauer principle to the problem of “dark matter” is discussed. The maximal informational content of a particle at rest, estimated as $I_{max}=\frac{m_0{c}^2}{k_{B}Tkn2}$ is introduced. The Landauer principle also allows the estimation of minimal energy of the field which is capable to record/erase 1 bit of information in the surrounding at the temperature of T. The relativistic aspects of the Landauer principle and its relation to the relativistic transformation of temperature are addressed.

Mobile robots need an environmental perception ability in order to interact with the surrounding environment. In this paper we present the G-SLAM method, where the map is consisted of a cloud of scattered points in the continuous space and each point is accompanied by an obstacle existence probability. On the other hand the robot's pose (and trajectory) is estimated by an particle filters while the cloud of points is estimated by an adaptive recursive algorithm which is presented. Main feature of the presented method is the adaptive repositioning of the scattered points and their convergence around obstacles. In addition to our previous publications, In this paper a mathematical derivation of the method is presented and real case scenarios are discussed, presented and compared with other methods.

Pollution of sub-surface water reservoirs mainly rivers and streams through contaminated water point sources (CWPS) was studied. The objective was to formulate a discrete time delay mathematical model which describes the dynamics of reservoir pollution using mixing-problem processes that involve single species contaminants such as nitrates, phosphorous and detergents. The concentration of pollutants was expressed as a function of the inflow and outflow rates using the principle for the conservation of mass. Systems of ODEs generated from principles of mixing problems were refined into a system of DDEs so that the concentration of pollutant leaving the reservoir at time would be determined at some earlier instant, for the delay. The formulated model is a mathematical discrete time delay model which would be used to describe the dynamics of sub-surface water reservoir pollution. The results from the validation of the model were analyzed to determine how time delays in the mixing processes affect the rate of particle movement in water reservoirs.

In the past 50 years, calorimeters have become the most important detectors in many particle physics experiments, especially experiments in colliding-beam accelerators at the energy frontier. In this paper, we describe and discuss a number of common misconceptions about these detectors, as well as the consequences of these misconceptions. We hope that it may serve as a useful source of information for young colleagues who want to familiarize themselves with these tricky instruments.

We still do not have satisfactory answers about the inflationary and accelerating expansions of the universe, the missing mass problem, and the origins of the four fundamental forces and the quantum phenomena. Here, we introduce a new model of matter and space and a postulate on a constant of motion, and then show that an equation of motion derived from the constant of motion could provide new understandings of the above problems. For example, we show that there is a natural inflation scenario and that the co-moving distance to the closest accelerating supernova is clearly given as $c/(\sqrt{3} H)$ ($\sim$8.1 billion ly). In addition, we show that, in a system including a proton and an electron, the equation of motion has eight acceleration terms, some of which are suspected to be responsible for the four fundamental forces and the galaxy rotation anomaly. From further discussion, we conjecture that in order to understand the gravitational and strong interactions and the quantum phenomena, it is necessary to consider the expansion of the universe. The lensing mass distribution is explained, based on another postulate on a method of representing superposition.

This paper presents a novel approach for the recognition of multiple sclerosis (MS) using wavelet entropy and a Particle Swarm Optimization (PSO)-based neural network. MS is a complex neurological disorder with diverse clinical manifestations, often challenging to diagnose accurately. In this study, we leverage wavelet entropy (WE) as a feature extraction method to capture intricate patterns within brain imaging data. These extracted features are then employed to train a neural network model optimized through PSO to enhance classification accuracy. Experimental results on a dataset comprising MS and healthy control subjects demonstrate the effectiveness of our proposed approach, viz., WE-PSONN, which achieves a sensitivity of 91.95±1.15, a specificity of 92.36±0.88, a precision of 92.28±0.88, an accuracy of 92.16±0.90. The combination of advanced signal processing techniques and machine learning optimization holds promise for improving the early diagnosis and management of MS, offering potential benefits to both patients and healthcare providers.

In this paper, the effects on the rheological properties, particle size distribution, and fluorescence properties of red wine were investigated under microwave irradiation, and the mechanism of the effects of microwave irradiation on the sensory properties of red wine was discussed. The results showed that the effect of microwave on the rheological properties in red wine could be fitted by the Power-law model and Carson model, through the analysis of the rheological constants, yield stress, and viscosity coefficient of wine, it was found that microwave treatment could improve leg phenomenon and thickening effect by the change of rheological properties; the particle size distribution in wine indicated that microwave irradiation did change the particle size distribution through friction effect and oxidative polymerization, resulting in the wine’s visual effect and mouthfeel; the wine quality could be improved by enhancing the fluorescence intensity under different microwave conditions, which meant that microwave technology could speed up the formation of fluorescent substances, such as the polymerization of flavan-3-ols. In conclusion, microwave treatment could modify the sensory characteristics of wine, and further provide the theoretical basis for the application of microwave in winemaking.

We consider a new model of a branching random walk on a multidimensional lattice with continuous time and one source of particle reproduction and death, as well as an infinite number of sources in which, in addition to the walk, only absorption of particles can occur. The asymptotic behavior of the integer moments of both the total number of particles and the number of particles at a lattice point is studied depending on the relationship between the model parameters. In the case of the existence of an isolated positive eigenvalue of the evolution operator of the average number of particles, a limit theorem is obtained on the exponential growth of both the total number of particles and the number of particles at a lattice point.

Direct georeferencing of airborne pushbroom scanner data usually suffers from the limited precision of navigation sensors onboard of the aircraft. The bundle adjustment of images and orientation parameters, used to perform geocorrection of frame images during the post-processing phase, cannot be used for pushbroom cameras without difficulties: it relies on matching corresponding points between scan lines, which is not feasible in the absence of sufficient overlap and texture information. We address this georeferencing problem by equipping our aircraft with both a frame camera and a pushbroom scanner: the frame images and the navigation parameters measured by a couple GPS/Inertial Measurement Unit (IMU) are input to a bundle adjustment algorithm; the output orientation parameters are used to project the scan lines on a Digital Elevation Model (DEM) and on an orthophoto generated during the bundle adjustement step; using the image feature matching algorithm Speeded Up Robust Features (SURF), corresponding points between the image formed by the projected scan lines and the orthophoto are matched, and through a least-squares method, the boresight between the two cameras is estimated and included in the calculation of the projection; finally, using Particle Image Velocimetry (PIV) on the gradient image, the projection is deformed into a final image that fits the geometry of the orthophoto. We apply this algorithm to five test acquisitions over Lake Geneva region (Switzerland) and Lake Baikal region (Russia). The results are quantified in terms of Root Mean Square Error (RMSE) between matching points of the RGB orthophoto and the pushbroom projection. From a first projection where the Interior Orientation Parameters (IOP) are known with limited precision and the RMSE goes up to 41 pixels, our geocorrection estimates IOP, boresight and Exterior Orientation Parameters (EOP) and produces a new projection which RMSE with the reference orthophoto is around two pixels.

An effective method for coating textile fabrics with porous materials is proposed, and the removal rates of nitrogen oxides (NOx), sulfur oxides (SOx), and fine dust particles in the coated textile fabrics are evaluated. The textile fabrics made of polyester are used to effectively reduce fine dust particles through static electricity. Zeolite and coconut shell activated carbon are used as porous material to reduce SOx and NOx, respectively. The effects of the epoxy content and dilution solution types on the SOx removal rate of textile fabrics coated with zeolite are evaluated to determine the optimum coating conditions. In addition, the effects of external environmental conditions, such as washing and freeze thawing, on the SOx and NOx removal rates of the textile fabrics coated with porous materials using the optimum coating conditions are examined. The test results show that the SOx removal rate of textile fabrics coated with zeolite decreases with the increase in the epoxy content. The decrease is 2.9 times larger for textile fabrics coated using deionized water than those coated using isopropyl alcohol. After one wash, the SOx removal rate decreases dramatically. However, the decrease is reduced by 16% when the epoxy content ratio is increased by 0.5%. The effects of washing and freeze thawing on the SOx and NOx removal rates of textile fabrics coated using the deionized water diluted with the epoxy content ratio of 2% are minimal. Consequently, to maintain stable SOx and NOx removal rates under external environmental conditions such as washing and freeze thawing, 98% deionized water dilution and 2% epoxy content ratio are required for the optimum coating of textile fabrics with zeolite and coconut shell activated carbon.

As the COVID-19 pandemic has caused enormous damage to society, economy and public health security, it is crucial to combat the epidemic and allocate social and health resources effectively. Global optimization algorithms are extensively used in many domains to find optimal solutions to complex problems. Thus, a survey of three typical global optimizations applied on the COVID-19 pandemic is timely. This article is structured into three typical global optimization algorithms - Genetic Algorithm, Particle Swarm Optimization, and Cat Swarm Optimization - and summarizes the framework models and practical applications, as well as other optimization methods that may be applied to COVID-19. Finally, current unresolved challenges and future research opportunities to be explored are summarized.