The Substructure of Elementary Particles Demonstrated by the I-Theory

Present studies in physics assume that elementary particles are the building blocks of all matter, and that they are zero-dimensional objects which do not occupy space. The new I-Theory predicts that elementary particles do indeed have a substructure, three dimensions, and occupy space, being composed of fundamental particles called I-particles. In this article we identify the substructural pattern of elementary particles and define the quanta of energy that form each elementary particle. We demonstrate that the substructure comprises two classes of quanta which we call “attraction quanta” and “repulsion quanta”. We create a model that defines the rest-mass energy of each elementary particle and can predict new particles. Lastly, in order to incorporate this knowledge into the contemporary models of science, a revised periodic table is proposed.

Optimizing band structure of CoP nanoparticles via rich-defect carbon shell toward bifunctional electrocatalysts for overall water splitting

Transition-metal phosphides(TMPs)with high catalytic activity are widely used in the design of electrodes for water splitting.However,a major challenge is how to achieve the trade-off between activity and stability of TMPs.Herein,a novel method for synthesizing CoP nanoparticles encapsu-lated in a rich-defect carbon shell(CoP/DCS)is developed through the self-assembly of modified polycyclic aromatic molecules.The graft and removal of high-activity C-N bonds of aromatic molecules render the controllable design of crystallite defects of carbon shell.The density functional theory calculation indicates that the carbon defects with unpaired electrons could effectively tailor the band structure of CoP.Benefiting from the improved activity and corrosion resistance,the CoP/DCS delivers outstanding difunctional hydrogen evolution reaction(88 mV)and oxygen evolution reaction(251 mV)performances at 10 mA cm^(−2)current density.Furthermore,the coupled water electrolyzer with CoP/DCS as both the cathode and anode presents ultralow cell voltages of 1.49 V to achieve 10 mA cm^(−2)with long-time stability.This strategy to improve TMPs electrocatalyst with rich-DCS and heterogeneous structure will inspire the design of other transition metal compound electrocatalysts for water splitting.

Effect of particle gradation on pore structure and seepage law of solution in weathered crust elution-deposited rare earth ores

Both CT and Avizo software were used to explore the effect of particle gradation on the evolution characteristics of pore structure and seepage paths in weathered crust elution-deposited rare earth ores during leaching.The results showed that the pore areas in four kinds of ore samples before leaching were mainly concentrated in 10^(4)–10^(7)μm^(2),whose pore quantities accounted for 96.89%,94.94%,90.48%,and 89.45%,respectively,while the corresponding pore volume only accounted for 30.74%,14.55%,7.58%,and 2.84%of the total pore volume.With the decrease of fractal dimension,the average pore throat length increased,but pore throat quantities,the average pore throat radius and coordination number decreased.Compared with that before leaching,the change degree of pore structure during leaching increased with the fractal dimension decreasing.For example,the reduction rate of the average coordination number of ore samples was 14.36%,21.30%,28.00%,and 32.90%,respectively.Seepage simulation results indicated that seepage paths were uniformly distributed before leaching while the streamline density and seepage velocity increased with the fractal dimension decreasing.Besides,the phenomenon of the streamline interruption gradually reduced during leaching while preferential seepage got more obvious with the decrease of the fractal dimension.

Self-Awakened Particle Swarm Optimization BN Structure Learning Algorithm Based on Search Space Constraint

To obtain the optimal Bayesian network(BN)structure,researchers often use the hybrid learning algorithm that combines the constraint-based(CB)method and the score-and-search(SS)method.This hybrid method has the problemthat the search efficiency could be improved due to the ample search space.The search process quickly falls into the local optimal solution,unable to obtain the global optimal.Based on this,the Particle SwarmOptimization(PSO)algorithm based on the search space constraint process is proposed.In the first stage,the method uses dynamic adjustment factors to constrain the structure search space and enrich the diversity of the initial particles.In the second stage,the update mechanism is redefined,so that each step of the update process is consistent with the current structure which forms a one-to-one correspondence.At the same time,the“self-awakened”mechanism is added to prevent precocious particles frombeing part of the best.After the fitness value of the particle converges prematurely,the activation operation makes the particles jump out of the local optimal values to prevent the algorithmfromconverging too quickly into the local optimum.Finally,the standard network dataset was compared with other algorithms.The experimental results showed that the algorithmcould find the optimal solution at a small number of iterations and a more accurate network structure to verify the algorithm’s effectiveness.

BN-GEPSO:Learning Bayesian Network Structure Using Generalized Particle Swarm Optimization

At present Bayesian Networks(BN)are being used widely for demonstrating uncertain knowledge in many disciplines,including biology,computer science,risk analysis,service quality analysis,and business.But they suffer from the problem that when the nodes and edges increase,the structure learning difficulty increases and algorithms become inefficient.To solve this problem,heuristic optimization algorithms are used,which tend to find a near-optimal answer rather than an exact one,with particle swarm optimization(PSO)being one of them.PSO is a swarm intelligence-based algorithm having basic inspiration from flocks of birds(how they search for food).PSO is employed widely because it is easier to code,converges quickly,and can be parallelized easily.We use a recently proposed version of PSO called generalized particle swarm optimization(GEPSO)to learn bayesian network structure.We construct an initial directed acyclic graph(DAG)by using the max-min parent’s children(MMPC)algorithm and cross relative average entropy.ThisDAGis used to create a population for theGEPSO optimization procedure.Moreover,we propose a velocity update procedure to increase the efficiency of the algorithmic search process.Results of the experiments show that as the complexity of the dataset increases,our algorithm Bayesian network generalized particle swarm optimization(BN-GEPSO)outperforms the PSO algorithm in terms of the Bayesian information criterion(BIC)score.

Effect of variations in the polar and azimuthal angles of coarse particles on the structure of drainage channels in thickened beds

The 3D reconstruction and quantitative characterization of drainage channels and coarse tailings particles in a bed were conducted in this study.The influence of variations in the azimuthal angle(θ)and polar angle(φ)of coarse particles on drainage channel structure was analyzed,and the drainage mechanism of the bed was studied.Results showed that water discharge in the bed reduced the size of pores and throat channels,increasing slurry concentration.The throat channel structure was a key component of the drainage process.Theφandθof particles changed predominantly along the length direction.The changes inφhad a cumulative plugging effect on the drainage channel and increased the difficulty of water discharge.The rake and rod formed a shear ring in the tailings bed with shear,and theθdistribution of particles changed from disorderly to orderly during the rotation process.The drainage channel was squeezed during the shearing process with the change inθ,which broke the channel structure,encouraged water discharge in the bed,and facilitated a further increase in slurry concentration.The findings of this work are expected to offer theoretical guidance for preparing high-concentration underflow in the tailings thickening process.

Carbon Emission Effects Driven by Evolution of Chinese Dietary Structure from 1987 to 2020

Exploring carbon emission effects based on the evolution of residents’ dietary structure to achieve the carbon neutrality goal and mitigate climate change is an important task.This study took China as the research object(data excluding Hong Kong,Macao and Taiwan) and used the carbon emission coefficient method to quantitatively measure the food carbon emissions from 1987–2020,then analyzed the carbon emission effects under the evolution of dietary structure.The results showed that during the study period,the Chinese dietary structure gradually changed to a high-carbon consumption pattern.The dietary structure of urban residents developed to a balanced one,while that of rural residents developed to a high-quality one.During the study period,the per capita food carbon emissions and total food consumption of Chinese showed an increasing trend.The per capita food carbon emissions of residents in urban and rural showed an overall upward trend.The total food carbon emissions in urban increased significantly,while that in rural increased first and then decreased.The influence of beef and mutton on carbon emissions is the highest in dietary structure.Compared with the balanced dietary pattern,the food carbon emissions of Chinese residents had not yet reached the peak,but were evolving to a high-carbon consumption pattern.

Tuning electronic structure of RuO_(2)by single atom Zn and oxygen vacancies to boost oxygen evolution reaction in acidic medium

The poor stability of RuO_(2)electrocatalysts has been the primary obstacles for their practical application in polymer electrolyte membrane electrolyzers.To dramatically enhance the durability of RuO_(2)to construct activity-stability trade-off model is full of significance but challenging.Herein,a single atom Zn stabilized RuO_(2)with enriched oxygen vacancies(SA Zn-RuO_(2))is developed as a promising alternative to iridium oxide for acidic oxygen evolution reaction(OER).Compared with commercial RuO_(2),the enhanced Ru–O bond strength of SA Zn-RuO_(2)by forming Zn-O-Ru local structure motif is favorable to stabilize surface Ru,while the electrons transferred from Zn single atoms to adjacent Ru atoms protects the Ru active sites from overoxidation.Simultaneously,the optimized surrounding electronic structure of Ru sites in SA ZnRuO_(2)decreases the adsorption energies of OER intermediates to reduce the reaction barrier.As a result,the representative SA Zn-RuO_(2)exhibits a low overpotential of 210 mV to achieve 10 mA cm^(-2)and a greatly enhanced durability than commercial RuO_(2).This work provides a promising dual-engineering strategy by coupling single atom doping and vacancy for the tradeoff of high activity and catalytic stability toward acidic OER.

Multi-Material Topology Optimization for Spatial-Varying Porous Structures

This paper aims to propose a topology optimization method on generating porous structures comprising multiple materials.The mathematical optimization formulation is established under the constraints of individual volume fraction of constituent phase or total mass,as well as the local volume fraction of all phases.The original optimization problem with numerous constraints is converted into a box-constrained optimization problem by incorporating all constraints to the augmented Lagrangian function,avoiding the parameter dependence in the conventional aggregation process.Furthermore,the local volume percentage can be precisely satisfied.The effects including the globalmass bound,the influence radius and local volume percentage on final designs are exploited through numerical examples.The numerical results also reveal that porous structures keep a balance between the bulk design and periodic design in terms of the resulting compliance.All results,including those for irregular structures andmultiple volume fraction constraints,demonstrate that the proposedmethod can provide an efficient solution for multiple material infill structures.

Estimation-free spatial-domain image reconstruction of structured illumination microscopy

Structured illumination microscopy(SIM)achieves super-resolution(SR)by modulating the high-frequency information of the sample into the passband of the optical system and subsequent image reconstruction.The traditional Wiener-filtering-based reconstruction algorithm operates in the Fourier domain,it requires prior knowledge of the sinusoidal illumination patterns which makes the time-consuming procedure of parameter estimation to raw datasets necessary,besides,the parameter estimation is sensitive to noise or aberration-induced pattern distortion which leads to reconstruction artifacts.Here,we propose a spatial-domain image reconstruction method that does not require parameter estimation but calculates patterns from raw datasets,and a reconstructed image can be obtained just by calculating the spatial covariance of differential calculated patterns and differential filtered datasets(the notch filtering operation is performed to the raw datasets for attenuating and compensating the optical transfer function(OTF)).Experiments on reconstructing raw datasets including nonbiological,biological,and simulated samples demonstrate that our method has SR capability,high reconstruction speed,and high robustness to aberration and noise.

Creating burdock polysaccharide-oleanolic acid-ursolic acid nanoparticles to deliver enhanced anti-inflammatory effects:fabrication,structural characterization and property evaluation

This study explored the potential of polysaccharides from Actium lappa(ALPs)as natural wall materials for producing ALP-based nanoparticles to deliver poorly water-soluble oleanolic acid(OA)and ursolic acid(UA).Encapsulating OA+UA with ALPs(ALP:OA+UA,50:1;OA:UA,1:1)changed the crystalline nature to a more amorphous state through hydrogen bonding and involving O-H/C-O/O-C-O groups.ALP-OA/UA nanoparticles had a particle size and zeta potential(in water)of 199.1 nm/-7.15 mV,with a narrow unimodal size distribution,and excellent pH,salt solution,temperature and storage stability.Compared with ALPs,ALPOA/UA nanoparticles showed enhanced anti-inflammatory activity(especially at a dose of 100μg/mL)in a CuSO-induced zebrafish inflammation model via down-regulating the NF-κB signalling pathway and gene expression of associated transcription factors and cytokines(TNF-α,IL-1βand IL-8).Therefore,ALP-based nanoparticles are natural and anti-inflammatory carriers for hydrophobic bioactive molecules.

A comparison study on structure-function relationship of polysaccharides obtained from sea buckthorn berries using different methods:antioxidant and bile acid-binding capacity

In this study,the structural characters,antioxidant activities and bile acid-binding ability of sea buckthorn polysaccharides(HRPs)obtained by the commonly used hot water(HRP-W),pressurized hot water(HRP-H),ultrasonic(HRP-U),acid(HRP-C)and alkali(HRP-A)assisted extraction methods were investigated.The results demonstrated that extraction methods had significant effects on extraction yield,monosaccharide composition,molecular weight,particle size,triple-helical structure,and surface morphology of HRPs except for the major linkage bands.Thermogravimetric analysis showed that HRP-U with filamentous reticular microstructure exhibited better thermal stability.The HRP-A with the lowest molecular weight and highest arabinose content possessed the best antioxidant activities.Moreover,the rheological analysis indicated that HRPs with higher galacturonic acid content and molecular weight showed higher viscosity and stronger crosslinking network(HRP-C,HRP-W and HRP-U),which exhibited stronger bile acid binding capacity.The present findings provide scientific evidence in the preparation technology of sea buckthorn polysaccharides with good antioxidant and bile acid binding capacity which are related to the structure affected by the extraction methods.

Structure and immunomodulatory activity of Lentinus edodes polysaccharides modified by probiotic fermentation

Plant-based fermentations provide an untapped source for novel biotechnological applications.In this study,a probiotic named Lactobacillus fermentum 21828 was introduced to ferment Lentinus edodes.Polysaccharides were extracted from fermented and non-fermented L.edodes and purified via DEAE-52 and Sephadex G-100.The components designated F-LEP-2a and NF-LEP-2a were analyzed by FT-IR,HPGPC,HPAEC,SEM,GC-MS and NMR.The results revealed that probiotic fermentation increased the molecular weight from 1.16×10^(4) Da to 1.87×10^(4) Da and altered the proportions of glucose,galactose and mannose,in which glucose increased from 45.94%to 48.16%.Methylation analysis and NMR spectra indicated that F-LEP-2a and NF-LEP-2a had similar linkage patterns.Furthermore,their immunomodulatory activities were evaluated with immunosuppressive mice.NF-LEP and F-LEP improved immune organ indices,immunoglobulin(Ig G and Ig M)and cytokines concentrations;restored the antioxidation capacity of liver;and maintained the balance of gut microbiota.F-LEP displayed better moderating effects on the spleen index,immunoglobulin,cytokines and the diversity of gut microbiota than NF-LEP(200,400 mg/kg).Our study provides an efficient and environment-friendly way for the structural modification of polysaccharides,which helps to enhance their biological activity and promote their wide application in food,medicine and other fields.

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

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

An Original Didactic about Standard Model (Geometric Model of Particle: The Quarks)

This work shows a didactic model representative of the quarks described in the Standard Model (SM). In the model, particles are represented by structures corresponding to geometric shapes of coupled quantum oscillators (GMP). From these didactic hypotheses emerges an in-depth phenomenology of particles (quarks) fully compatible with that of SM, showing, besides, that the number of possible quarks is six.

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

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

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

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

Manipulating the Spatial Structure of Second-Order Quantum Coherence Using Entangled Photons

High-order quantum coherence reveals the statistical correlation of quantum particles. Manipulation of quantum coherence of light in the temporal domain enables the production of the single-photon source, which has become one of the most important quantum resources. High-order quantum coherence in the spatial domain plays a crucial role in a variety of applications, such as quantum imaging, holography, and microscopy. However, the active control of second-order spatial quantum coherence remains a challenging task. Here we predict theoretically and demonstrate experimentally the first active manipulation of second-order spatial quantum coherence,which exhibits the capability of switching between bunching and anti-bunching, by mapping the entanglement of spatially structured photons. We also show that signal processing based on quantum coherence exhibits robust resistance to intensity disturbance. Our findings not only enhance existing applications but also pave the way for broader utilization of higher-order spatial quantum coherence.

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

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

An origami shield with supporting frame structures optimized by a feature-driven topology optimization method

In this paper,the design,manufacture and testing of an origami protective shield with a supporting frame structure are presented.It consists of an origami shield surface and a deployable supporting frame structure that needs to be portable and sufficiently stiff.First,for the design of the shield surface,a threestage origami crease pattern is developed to reduce the shield size in the folded state.The shield surface consists of several stiff modular panels and layered with flexible fabric.The modular panels are made of a multi-layer composite where a ceramic layer is made of small pieces to improve durability as those small pieces enable restriction of crack propagation.Then,the supporting frame structure is designed as a chain-of-bars structure in order to fold into a highly compact state as a bundle of bars and deploy in sequence.Thus,a feature-driven topology structural optimization method preserving component sequence is developed where the inter-dependence of sub-structures is taken into account.A bar with semi-circular ends is used as a basic design feature.The positions of the bar’s end points are treated as design variables and the width of the bars is kept constant.Then,a constraint on the total length of the chain of bars is introduced.Finally,the modular panels made of multi-layer composite and the full-scale prototype of the origami shield are fabricated and tested to verify the bullet-proof performance.