Investigating the elliptic anisotropy of identified particles in p-Pb collisions with a multi-phase transport model

The elliptic azimuthal anisotropy coefficient(v_(2))of the identified particles at midrapidity(|η|<0.8)was investigated in p-Pb collisions at√s_(NN)=5.02 TeV using a multi-phase transport model(AMPT).The calculations of differential v_(2)based on the advanced flow extraction method of light flavor hadrons(pions,kaons,protons,andΛ)in small collision systems were extended to a wider transverse momentum(p_(T))range of up to 8 GeV/c for the first time.The string-melting version of the AMPT model provides a good description of the measured p_(T)-differential v_(2)of the mesons but exhibits a slight deviation from the baryon v_(2).In addition,we observed the features of mass ordering at low p_(T)and the approximate number-of-constituentquark(NCQ)scaling at intermediate p_(T).Moreover,we demonstrate that hadronic rescattering does not have a significant impact on v_(2)in p-Pb collisions for different centrality selections,whereas partonic scattering dominates in generating the elliptic anisotropy of the final particles.This study provides further insight into the origin of collective-like behavior in small collision systems and has referential value for future measurements of azimuthal anisotropy.

MULTI-PHASE ACTIVE CONTOUR MODEL FOR IMAGE SEGMENTATION BASED ON LEVEL SETS

A new multi-phase active contour model is proposed for the image segmentation. It is a generalization of the C-V model with the following characteristics： （1） A key technique, called the technique of painting background （TPBG）, is developed to remove the information of the background, which blocks the detection of weak boundaries in the object; （2） The two-phase level set is applied multiple times for getting the multi-phase segmentation model （n-1 times for the n-phase model, n〉1）; （3） A scaling-based method is introduced to improve the basic model. Experimental results show that the proposed model is effective for detecting weak boundaries.

A new analytical model for thermal stresses in multi-phase materials and lifetime prediction methods

Based on the fundamental equations of the mechanics of solid continuum, the paper employs an analytical model for determination of elastic thermal stresses in isotropic continuum represented by periodically distributed spherical particles with different distributions in an infinite matrix, imaginarily divided into identical cells with dimensions equal to inter-particle distances, containing a central spherical particle with or without a spherical envelope on the particle surface. Consequently, the multi-particle-（envelope）- matrix system, as a model system regarding the analytical modelling, is applicable to four types of multi-phase materials. As functions of the particle volume fraction v, the inter-particle distances dl, d2, d3 along three mutually per- pendicular axes, and the particle and envelope radii, R1 and R2, respectively, the thermal stresses within the cell, are originated during a cooling process as a consequence of the difference in thermal expansion coefficients of phases rep- resented by the matrix, envelope and particle. Analytical-（experimental）-computational lifetime prediction methods for multi-phase materials are proposed, which can be used in engineering with appropriate values of parameters of real multi-phase materials.

THE RATE-INDEPENDENT CONSTITUTIVE MODELING FOR POROUS AND MULTI-PHASE NANOCRYSTALLINE MATERIALS

To determine the time-independent constitutive modeling for porous and multi- phase nanocrystalline materials and understand the effects of grain size and porosity on their mechanical behavior, each phase was treated as a mixture of grain interior and grain bound- ary, and pores were taken as a single phase, then Budiansky＇s self-consistent method was used to calculate the Young＇s modulus of porous, possible multi-phase, nanocrystalline materials, the prediction being in good agreement with the results in the literature. Further, the established method is extended to simulate the constitutive relations of porous and possible multi-phase nanocrystalline materials with small plastic deformation in conjunction with the secant-moduli approach and iso-strain assumption. Comparisons between the experimental grain size and porosity dependent mechanical data and the corresponding predictions using the established model show that it appears to be capable of describing the time-independent mechanical behaviors for porous and multi-phase nanocrystalline materials in a small plastic strain range. Further discussion on the modification factor, the advantages and limitations of the model developed were present.

Optimization study of a PEM fuel cell performance using 3D multi-phase computational fluid dynamics model

An optimization study using a comprehensive 3D, multi-phase, non-isothermal model of a PEM （proton exchange membrane） fuel cell that incorporates significant physical processes and key parameters affecting fuel cell performance is presented and discussed in detail. The model accounts for both gas and liquid phase in the same computational domain, and thus allows for the implementation of phase change inside the gas diffusion layers. The model includes the transport of gaseous species, liquid water, protons, energy, and water dissolved in the ion-conducting polymer. Water is assumed to be exchanged among three phases： liquid, vapottr, and dissolved, with equilibrium among these phases being assumed. This model also takes into account convection and diffusion of different species in the channels as well as in the porous gas diffusion layer, heat transfer in the solids as well as in the gases, and electrochemical reactions. The results showed that the present multi-phase model is capable of identifying important parameters for the wetting behaviour of the gas diffusion layers and can be used to identify conditions that might lead to the onset of pore plugging, which has a detrimental effect on the fuel cell performance. This model is used to study the effects of several operating, design, and material parameters on fuel cell performance. Detailed analyses of the fuel cell performance under various operating conditions have been conducted and examined.

Further developments of a multi-phase transport model for relativistic nuclear collisions

A multi-phase transport(AMPT)model was constructed as a self-contained kinetic theory-based description of relativistic nuclear collisions as it contains four main components:the fluctuating initial condition,a parton cascade,hadronization,and a hadron cascade.Here,we review the main developments after the first public release of the AMPT source code in 2004 and the corre-sponding publication that described the physics details of the model at that time.We also discuss possible directions for future developments of the AMPT model to better study the properties of the dense matter created in relativistic collisions of small or large systems.

Multi-phase computer modeling and laboratory study of dust capture by an inertial Vortecone scrubber

Dust generated in mining and tunneling activities is hazardous to health of persons and safety of operations. These projects employ pick-milling machines to extract minerals and rock by mechanical breakage.The machines are equipped with flooded-bed scrubbers that encase dust particles within fine water films as particles encounter a flooded wire-mesh screen. A major disadvantage is that the screen gets clogged when particles become trapped within the wire mesh, reducing airflow through the scrubber and increasing ambient dust concentrations. Thus, the system requires frequent maintenance or replacement. The application of a Vortecone scrubber as an improved alternative to conventional fibrous type scrubbers is investigated. A Vortecone forces dust-laden air and water to follow a complex, rapidly swirling motion.The momentum drives dust particles towards the periphery where they are captured by the water film.The operating characteristics of a reduced-scale physical model of a Vortecone, with its primary axis mounted in the horizontal orientation, was analyzed numerically and experimentally. Computational fluid dynamics(CFD) models depicting the spraying action and multi-phase air/water flows using the volume of fraction(VOF) approach, are presented. Experimental results, utilizing an optical particle counting technique to establish the dust-cleaning capabilities of the model, are also described.

GPU parallel computation of dendrite growth competition in forced convection using the multi-phase-field-lattice Boltzmann model

A graphics-processing-unit(GPU)-parallel-based computational scheme is developed to realize the competitive growth process of converging bi-crystal in two-dimensional states in the presence of forced convection conditions by coupling a multi-phase field model and a lattice Boltzmann model.The elimination mechanism in the evolution process is analyzed for the three conformational schemes constituting converging bi-crystals under pure diffusion and forced convection conditions,respectively,expanding the research of the competitive growth of columnar dendrites under melt convection conditions.The results show that the elimination mechanism for the competitive growth of converging bi-crystals of all three configurations under pure diffusion conditions follows the conventional Walton-Chalmers model.When there is forced convection with lateral flow in the liquid phase,the anomalous elimination phenomenon of unfavorable dendrites eliminating favorable dendrites occurs in the grain boundaries.In particular,the anomalous elimination phenomenon is relatively strong in conformation 1 and conformation 2 when the orientation angle of unfavorable dendrites is small,and relatively weak in conformation 3.Moreover,the presence of convection increases the tip growth rate of both favorable and unfavorable dendrites in the grain boundary.In addition,the parallelization of the multi-phase-field-lattice Boltzmann model is achieved by designing the parallel computation of the model on the GPU platform concerning the computerunified-device-architecture parallel technique,and the results show that the parallel computation of this model based on the GPU has absolute advantages,and the parallel acceleration is more obvious as the computation area increases.

Application of a weakly compressible smoothed particle hydrodynamics multi-phase model to non-cohesive embankment breaching due to flow overtopping

The subject of present study is the application of mesh free Lagrangian two-dimensional non-cohesive sediment transport model applied to a two-phase flow over an initially trapezoidal-shaped sediment embankment. The governing equations of the present model are the Navier-Stocks equations solved using Weakly Compressible Smoothed Particle Hydrodynamics （WCSPH） method. To simulate the movement of sediment particles, the model considers a powerful two-part technique; when the sediment phase has rigid behavior, only the force term due to shear stress in the Navier-Stokes equations is used for simulation of sediment particles＇ movement. Otherwise, all the Navier-Stokes force terms are used for transport simulation of sediment particles. In the present model, the interactions between different phases are calculated automatically, even with considerable difference between the density and viscosity of phases. Validation of the model is performed using simulation of available laboratory experiments, and the comparison between computational results and experimental data shows that the model generally predicts well the flow propagation over movable beds, the induced sediment transport and bed changes, and temporal evolution of embankment breaching.

The impact of heterogeneity and pore network characteristics on single and multi-phase fluid propagation in complex porous media:An X-ray computed tomography study

This study investigates the impact of pore network characteristics on fluid flow through complex and heterogeneous porous media,providing insights into the factors affecting fluid propagation in such systems.Specifically,high-resolution or micro X-ray computed tomography(CT)imaging techniques were utilized to examine outcrop stromatolite samples of the Lagoa Salgada,considered flow analogous to the Brazilian Pre-salt carbonate reservoirs.The petrophysical results comprised two distinct stromatolite depositional facies,the columnar and the fine-grained facies.By generating pore network model(PNM),the study quantified the relationship between key features of the porous system,including pore and throat radius,throat length,coordination number,shape factor,and pore volume.The study found that the less dense pore network of the columnar sample is typically characterized by larger pores and wider and longer throats but with a weaker connection of throats to pores.Both facies exhibited less variability in the radius of the pores and throats in comparison to throat length.Additionally,a series of core flooding experiments coupled with medical CT scanning was designed and conducted in the plug samples to assess flow propagation and saturation fields.The study revealed that the heterogeneity and presence of disconnected or dead-end pores significantly impacted the flow patterns and saturation.Two-phase flow patterns and oil saturation distribution reveal a preferential and heterogeneous displacement that mainly swept displaced fluid in some regions of plugs and bypassed it in others.The relation between saturation profiles,porosity profiles,and the number of fluid flow patterns for the samples was evident.Only for the columnar plug sample was the enhancement in recovery factor after shifting to lower salinity water injection(SB)observed.

Quantitative multi-phase-field modeling of non-isothermal solidification in hexagonal multicomponent alloys

A quantitative multi-phase-field model for non-isothermal and polycrystalline solidification was developed and applied to dilute multicomponent alloys with hexagonal close-packed structures.The effects of Lewis coefficient and undercooling on dendrite growth were investigated systematically.Results show that large Lewis coefficients facilitate the release of the latent heat,which can accelerate the dendrite growth while suppress the dendrite tip radius.The greater the initial undercooling,the stronger the driving force for dendrite growth,the faster the growth rate of dendrites,the higher the solid fraction,and the more serious the solute microsegregation.The simulated dendrite growth dynamics are consistent with predictions from the phenomenological theory but significantly deviate from the classical JMAK theory which neglects the soft collision effect and mutual blocking among dendrites.Finally,taking the Mg-6Gd-2Zn(wt.%)alloy as an example,the simulated dendrite morphology shows good agreement with experimental results.

Aquaporin-4-IgG-seropositive neuromyelitis optica spectrum disorders:progress of experimental models based on disease pathogenesis

Neuromyelitis optica spectrum disorders are neuroinflammatory demyelinating disorders that lead to permanent visual loss and motor dysfunction.To date,no effective treatment exists as the exact causative mechanism remains unknown.Therefore,experimental models of neuromyelitis optica spectrum disorders are essential for exploring its pathogenesis and in screening for therapeutic targets.Since most patients with neuromyelitis optica spectrum disorders are seropositive for IgG autoantibodies against aquaporin-4,which is highly expressed on the membrane of astrocyte endfeet,most current experimental models are based on aquaporin-4-IgG that initially targets astrocytes.These experimental models have successfully simulated many pathological features of neuromyelitis optica spectrum disorders,such as aquaporin-4 loss,astrocytopathy,granulocyte and macrophage infiltration,complement activation,demyelination,and neuronal loss;however,they do not fully capture the pathological process of human neuromyelitis optica spectrum disorders.In this review,we summarize the currently known pathogenic mechanisms and the development of associated experimental models in vitro,ex vivo,and in vivo for neuromyelitis optica spectrum disorders,suggest potential pathogenic mechanisms for further investigation,and provide guidance on experimental model choices.In addition,this review summarizes the latest information on pathologies and therapies for neuromyelitis optica spectrum disorders based on experimental models of aquaporin-4-IgG-seropositive neuromyelitis optica spectrum disorders,offering further therapeutic targets and a theoretical basis for clinical trials.

Landslide Susceptibility Mapping Using RBFN-Based Ensemble Machine Learning Models

This study was aimed to prepare landslide susceptibility maps for the Pithoragarh district in Uttarakhand,India,using advanced ensemble models that combined Radial Basis Function Networks(RBFN)with three ensemble learning techniques:DAGGING(DG),MULTIBOOST(MB),and ADABOOST(AB).This combination resulted in three distinct ensemble models:DG-RBFN,MB-RBFN,and AB-RBFN.Additionally,a traditional weighted method,Information Value(IV),and a benchmark machine learning(ML)model,Multilayer Perceptron Neural Network(MLP),were employed for comparison and validation.The models were developed using ten landslide conditioning factors,which included slope,aspect,elevation,curvature,land cover,geomorphology,overburden depth,lithology,distance to rivers and distance to roads.These factors were instrumental in predicting the output variable,which was the probability of landslide occurrence.Statistical analysis of the models’performance indicated that the DG-RBFN model,with an Area Under ROC Curve(AUC)of 0.931,outperformed the other models.The AB-RBFN model achieved an AUC of 0.929,the MB-RBFN model had an AUC of 0.913,and the MLP model recorded an AUC of 0.926.These results suggest that the advanced ensemble ML model DG-RBFN was more accurate than traditional statistical model,single MLP model,and other ensemble models in preparing trustworthy landslide susceptibility maps,thereby enhancing land use planning and decision-making.

Global Piecewise Analysis of HIV Model with Bi-Infectious Categories under Ordinary Derivative and Non-Singular Operator with Neural Network Approach

This study directs the discussion of HIV disease with a novel kind of complex dynamical generalized and piecewise operator in the sense of classical and Atangana Baleanu(AB)derivatives having arbitrary order.The HIV infection model has a susceptible class,a recovered class,along with a case of infection divided into three sub-different levels or categories and the recovered class.The total time interval is converted into two,which are further investigated for ordinary and fractional order operators of the AB derivative,respectively.The proposed model is tested separately for unique solutions and existence on bi intervals.The numerical solution of the proposed model is treated by the piece-wise numerical iterative scheme of Newtons Polynomial.The proposed method is established for piece-wise derivatives under natural order and non-singular Mittag-Leffler Law.The cross-over or bending characteristics in the dynamical system of HIV are easily examined by the aspect of this research having a memory effect for controlling the said disease.This study uses the neural network(NN)technique to obtain a better set of weights with low residual errors,and the epochs number is considered 1000.The obtained figures represent the approximate solution and absolute error which are tested with NN to train the data accurately.

Prognostic model for esophagogastric variceal rebleeding after endoscopic treatment in liver cirrhosis: A Chinese multicenter study

BACKGROUND Rebleeding after recovery from esophagogastric variceal bleeding(EGVB)is a severe complication that is associated with high rates of both incidence and mortality.Despite its clinical importance,recognized prognostic models that can effectively predict esophagogastric variceal rebleeding in patients with liver cirrhosis are lacking.AIM To construct and externally validate a reliable prognostic model for predicting the occurrence of esophagogastric variceal rebleeding.METHODS This study included 477 EGVB patients across 2 cohorts:The derivation cohort(n=322)and the validation cohort(n=155).The primary outcome was rebleeding events within 1 year.The least absolute shrinkage and selection operator was applied for predictor selection,and multivariate Cox regression analysis was used to construct the prognostic model.Internal validation was performed with bootstrap resampling.We assessed the discrimination,calibration and accuracy of the model,and performed patient risk stratification.RESULTS Six predictors,including albumin and aspartate aminotransferase concentrations,white blood cell count,and the presence of ascites,portal vein thrombosis,and bleeding signs,were selected for the rebleeding event prediction following endoscopic treatment(REPET)model.In predicting rebleeding within 1 year,the REPET model ex-hibited a concordance index of 0.775 and a Brier score of 0.143 in the derivation cohort,alongside 0.862 and 0.127 in the validation cohort.Furthermore,the REPET model revealed a significant difference in rebleeding rates(P<0.01)between low-risk patients and intermediate-to high-risk patients in both cohorts.CONCLUSION We constructed and validated a new prognostic model for variceal rebleeding with excellent predictive per-formance,which will improve the clinical management of rebleeding in EGVB patients.

Stochastic Modeling and Assisted History-Matching Using Multiple Techniques of Multi-Phase Flowback from Multi-Fractured Horizontal Tight Oil Wells

In this paper, the methods developed by?[1] are used to analyze flowback data, which involves modeling flow both before and after the breakthrough of formation fluids. Despite the versatility of these techniques, achieving an optimal combination of parameters is often difficult with a single deterministic analysis. Because of the uncertainty in key model parameters, this problem is an ideal candidate for uncertainty quantification and advanced assisted history-matching techniques, including Monte Carlo (MC) simulation and genetic algorithms (GAs) amongst others. MC simulation, for example, can be used for both the purpose of assisted history-matching and uncertainty quantification of key fracture parameters. In this work, several techniques are tested including both single-objective (SO) and multi-objective (MO) algorithms for history-matching and uncertainty quantification, using a light tight oil (LTO) field case. The results of this analysis suggest that many different algorithms can be used to achieve similar optimization results, making these viable methods for developing an optimal set of key uncertain fracture parameters. An indication of uncertainty can also be achieved, which assists in understanding the range of parameters which can be used to successfully match the flowback data.

Implementation of a Demoisturization and Devolatilization Model in Multi-Phase Simulation of a Hybrid Entrained-Flow and Fluidized Bed Mild Gasifier

A mild gasification process has been implemented to provide an alternative form of clean coal technology called the Integrated Mild Gasification Combined Cycle (IMGCC), which can be utilized to build a new, highly efficient, and compact power plant or to retrofit an existing coal-fired power plant in order to achieve lower emissions and significantly improved thermal efficiency. The core technology of the mild gasification power plant lies on the design of a compact and effective mild gasifier that can produce synthesis gases with high energy volatiles through a hybrid system: utilizing the features of both entrained-flow and fluidized bed gasifiers. To aid in the design of the mild gasifier, a computational model has been implemented to investigate the thermal-flow and gasification process inside this mild gasifier using the commercial CFD (Computational Fluid Dynamics) solver ANSYS/FLUENT. The Eulerian-Eulerian method is employed to model both the primary phase (air) and the secondary phase (coal particles). However, the Eulerian-Eulerian model used in the software does not facilitate any built-in devolatilization model. The objective of this study is therefore to implement a devolatilization model (along with demoisturization) and incorporate it into the existing code. The Navier-Stokes equations and seven species transport equations are solved with three heterogeneous (gas-solid) and two homogeneous (gas-gas) global gasification reactions. Implementation of the complete model starts from adding demoisturization first, then devolatilization, and then adding one chemical equation at a time until finally all reactions are included in the multiphase flow. The result shows that the demoisturization and devolatilization models are successfully incorporated and a large amount of volatiles are preserved as high-energy fuels in the syngas stream without being further cracked or reacted into lighter gases. The overall results are encouraging but require future experimental data for verification.

Exploiting fly models to investigate rare human neurological disorders

Rare neurological diseases,while individually are rare,collectively impact millions globally,leading to diverse and often severe neurological symptoms.Often attributed to genetic mutations that disrupt protein function or structure,understanding their genetic basis is crucial for accurate diagnosis and targeted therapies.To investigate the underlying pathogenesis of these conditions,researchers often use non-mammalian model organisms,such as Drosophila(fruit flies),which is valued for their genetic manipulability,cost-efficiency,and preservation of genes and biological functions across evolutionary time.Genetic tools available in Drosophila,including CRISPR-Cas9,offer a means to manipulate gene expression,allowing for a deep exploration of the genetic underpinnings of rare neurological diseases.Drosophila boasts a versatile genetic toolkit,rapid generation turnover,and ease of large-scale experimentation,making it an invaluable resource for identifying potential drug candidates.Researchers can expose flies carrying disease-associated mutations to various compounds,rapidly pinpointing promising therapeutic agents for further investigation in mammalian models and,ultimately,clinical trials.In this comprehensive review,we explore rare neurological diseases where fly research has significantly contributed to our understanding of their genetic basis,pathophysiology,and potential therapeutic implications.We discuss rare diseases associated with both neuron-expressed and glial-expressed genes.Specific cases include mutations in CDK19 resulting in epilepsy and developmental delay,mutations in TIAM1 leading to a neurodevelopmental disorder with seizures and language delay,and mutations in IRF2BPL causing seizures,a neurodevelopmental disorder with regression,loss of speech,and abnormal movements.And we explore mutations in EMC1 related to cerebellar atrophy,visual impairment,psychomotor retardation,and gain-of-function mutations in ACOX1 causing Mitchell syndrome.Loss-of-function mutations in ACOX1 result in ACOX1 deficiency,characterized by very-long-chain fatty acid accumulation and glial degeneration.Notably,this review highlights how modeling these diseases in Drosophila has provided valuable insights into their pathophysiology,offering a platform for the rapid identification of potential therapeutic interventions.Rare neurological diseases involve a wide range of expression systems,and sometimes common phenotypes can be found among different genes that cause abnormalities in neurons or glia.Furthermore,mutations within the same gene may result in varying functional outcomes,such as complete loss of function,partial loss of function,or gain-of-function mutations.The phenotypes observed in patients can differ significantly,underscoring the complexity of these conditions.In conclusion,Drosophila represents an indispensable and cost-effective tool for investigating rare neurological diseases.By facilitating the modeling of these conditions,Drosophila contributes to a deeper understanding of their genetic basis,pathophysiology,and potential therapies.This approach accelerates the discovery of promising drug candidates,ultimately benefiting patients affected by these complex and understudied diseases.

A promising approach for quantifying focal stroke modeling and assessing stroke progression:optical resolution photoacoustic microscopy photothrombosis

To investigate the mechanisms underlying the onset and progression of ischemic stroke,some methods have been proposed that can simultaneously monitor and create embolisms in the animal cerebral cortex.However,these methods often require complex systems and the effect of age on cerebral embolism has not been adequately studied,although ischemic stroke is strongly age-related.In this study,we propose an optical-resolution photoacoustic microscopy-based visualized photothrombosis methodology to create and monitor ischemic stroke in mice simultaneously using a 532 nm pulsed laser.We observed the molding process in mice of different ages and presented age-dependent vascular embolism differentiation.Moreover,we integrated optical coherence tomography angiography to investigate age-associated trends in cerebrovascular variability following a stroke.Our imaging data and quantitative analyses underscore the differential cerebrovascular responses to stroke in mice of different ages,thereby highlighting the technique's potential for evaluating cerebrovascular health and unraveling age-related mechanisms involved in ischemic strokes.