Development of a convolutional neural network based geomechanical upscaling technique for heterogeneous geological reservoir

Geomechanical assessment using coupled reservoir-geomechanical simulation is becoming increasingly important for analyzing the potential geomechanical risks in subsurface geological developments.However,a robust and efficient geomechanical upscaling technique for heterogeneous geological reservoirs is lacking to advance the applications of three-dimensional(3D)reservoir-scale geomechanical simulation considering detailed geological heterogeneities.Here,we develop convolutional neural network(CNN)proxies that reproduce the anisotropic nonlinear geomechanical response caused by lithological heterogeneity,and compute upscaled geomechanical properties from CNN proxies.The CNN proxies are trained using a large dataset of randomly generated spatially correlated sand-shale realizations as inputs and simulation results of their macroscopic geomechanical response as outputs.The trained CNN models can provide the upscaled shear strength(R^(2)>0.949),stress-strain behavior(R^(2)>0.925),and volumetric strain changes(R^(2)>0.958)that highly agree with the numerical simulation results while saving over two orders of magnitude of computational time.This is a major advantage in computing the upscaled geomechanical properties directly from geological realizations without the need to perform local numerical simulations to obtain the geomechanical response.The proposed CNN proxybased upscaling technique has the ability to(1)bridge the gap between the fine-scale geocellular models considering geological uncertainties and computationally efficient geomechanical models used to assess the geomechanical risks of large-scale subsurface development,and(2)improve the efficiency of numerical upscaling techniques that rely on local numerical simulations,leading to significantly increased computational time for uncertainty quantification using numerous geological realizations.

Impact of effective stress on permeability for carbonate fractured-vuggy rocks

To gain insight into the flow mechanisms and stress sensitivity for fractured-vuggy reservoirs,several core models with different structural characteristics were designed and fabricated to investigate the impact of effective stress on permeability for carbonate fractured-vuggy rocks(CFVR).It shows that the permeability performance curves under different pore and confining pressures(i.e.altered stress conditions)for the fractured core models and the vuggy core models have similar change patterns.The ranges of permeability variation are significantly wider at high pore pressures,indicating that permeability reduction is the most significant during the early stage of development for fractured-vuggy reservoirs.Since each obtained effective stress coefficient for permeability(ESCP)varies with the changes in confining pressure and pore pressure,the effective stresses for permeability of four representative CFVR show obvious nonlinear characteristics,and the variation ranges of ESCP are all between 0 and 1.Meanwhile,a comprehensive ESCP mathematical model considering triple media,including matrix pores,fractures,and dissolved vugs,was proposed.It is proved theoretically that the ESCP of CFVR generally varies between 0 and 1.Additionally,the regression results showed that the power model ranked highest among the four empirical models mainly applied in stress sensitivity characterization,followed by the logarithmic model,exponential model,and binomial model.The concept of“permeability decline rate”was introduced to better evaluate the stress sensitivity performance for CFVR,in which the one-fracture rock is the strongest,followed by the fracture-vug rock and two-horizontalfracture rock;the through-hole rock is the weakest.In general,this study provides a theoretical basis to guide the design of development and adjustment programs for carbonate fractured-vuggy reservoirs.

Targeting gut microbiota in osteoporosis:impact of the microbial based functional food ingredients

Osteoporosis is the most common bone disorder,characterized by low bone mineral density and microarchitectural deterioration of the bone tissue,which increases the susceptibility to fracture.In the past decade,emerging research findings reported the implication of gut microbiota on bone health and osteoporosis pathology.Osteoporotic patients or individuals with a lower bone mineral density exhibit an alteration of the gut microbiota at several taxonomic levels.Additional reports demonstrate that gut microbiota regulates bone metabolism through the modulation of the gut function(mineral availability and absorption,gut integrity),the immune system,and the endocrine system.Thus,based on the vital role of gut microbiota on bone health,it has emerged as a novel therapeutic target for the prevention of bone loss and the treatment of osteoporosis.Microbial-based functional food ingredients,such as probiotics,prebiotics,synbiotics,and fermented foods,have been developed to alter the gut microbiota composition and function and thus,to provide benefits to the host bone health.Despite promising initial results,microbial-based therapies are still under investigation.Moreover,additional animal studies and clinical trials are needed to understand the interactions between gut microbiota and bone metabolism before further applications.

Oral administration of egg white ovotransferrin prevents osteoporosis in ovariectomized rats

Ovotransferrin,an iron-binding glycoprotein,accounting for approximately 12%of egg white protein,is a member of transferrin fam ily.Our previous studies showed that ovotransferrin stimulates the proliferation and differentiation of osteoblasts,while inhibits osteoclastogenesis and resorption activity.The work aims to study the efficacy of orally administered ovotransferrin on the prevention of osteoporosis using ovariectomized(OVX)Sprague-Dawley rats.Oral administration of ovotransferrin showed no negative effect on body weight,food intake and organ weight.After 12-week treatment,feeding ovotransferrin at a dose of 1%(1 g ovotransferrin/100 g diet)prevented OVX-induced bone loss and maintained relatively high bone mineral density and integrated bone microarchitecture.The serum concentration of biomarkers indicating bone formation was increased in ovotransferrin administration groups,while the bone resorption biomarkers were decreased.Ovotransferrin feeding also decreased the production of serum cytokine TNF-αand IL-6,which are two stimulators for osteoclast differentiation.In addition to its direct regulatory role on bone turnover,ovotransferrin supplementation might benefit osteoporosis prevention by inhibiting adipogenesis,and regulating immune response.Our results suggested the potential application of ovotransferrin as a functional food ingredient on the prevention of osteoporosis.

Upper and Lower Bounds of the α-Universal Triple I Method for Unified Interval Implications

Theα-universal triple I(α-UTI)method is a recognized scheme in the field of fuzzy reasoning,whichwas proposed by our research group previously.The robustness of fuzzy reasoning determines the quality of reasoning algorithms to a large extent,which is quantified by calculating the disparity between the output of fuzzy reasoning with interference and the output without interference.Therefore,in this study,the interval robustness(embodied as the interval stability)of theα-UTI method is explored in the interval-valued fuzzy environment.To begin with,the stability of theα-UTI method is explored for the case of an individual rule,and the upper and lower bounds of its results are estimated,using four kinds of unified interval implications(including the R-interval implication,the S-interval implication,the QL-interval implication and the interval t-norm implication).Through analysis,it is found that theα-UTI method exhibits good interval stability for an individual rule.Moreover,the stability of theα-UTI method is revealed in the case of multiple rules,and the upper and lower bounds of its outcomes are estimated.The results show that theα-UTI method is stable for multiple rules when four kinds of unified interval implications are used,respectively.Lastly,theα-UTI reasoning chain method is presented,which contains a chain structure with multiple layers.The corresponding solutions and their interval perturbations are investigated.It is found that theα-UTI reasoning chain method is stable in the case of chain reasoning.Two application examples in affective computing are given to verify the stability of theα-UTImethod.In summary,through theoretical proof and example verification,it is found that theα-UTImethod has good interval robustness with four kinds of unified interval implications aiming at the situations of an individual rule,multi-rule and reasoning chain.

Alternative Method of Constructing Granular Neural Networks

Utilizing granular computing to enhance artificial neural network architecture, a newtype of network emerges—thegranular neural network (GNN). GNNs offer distinct advantages over their traditional counterparts: The ability toprocess both numerical and granular data, leading to improved interpretability. This paper proposes a novel designmethod for constructing GNNs, drawing inspiration from existing interval-valued neural networks built uponNNNs. However, unlike the proposed algorithm in this work, which employs interval values or triangular fuzzynumbers for connections, existing methods rely on a pre-defined numerical network. This new method utilizesa uniform distribution of information granularity to granulate connections with unknown parameters, resultingin independent GNN structures. To quantify the granularity output of the network, the product of two commonperformance indices is adopted: The coverage of numerical data and the specificity of information granules.Optimizing this combined performance index helps determine the optimal parameters for the network. Finally,the paper presents the complete model construction and validates its feasibility through experiments on datasetsfrom the UCIMachine Learning Repository. The results demonstrate the proposed algorithm’s effectiveness andpromising performance.

The emerging role of exercise as a cancer treatment

Over the past few decades,exercise oncology has emerged as an important subfield within exercise science.Over that time,substantial progress has been made in understanding the role of exercise in people newly diagnosed with cancer,actively being treated for cancer,and recovering after cancer treatments.

Evaluation of soil erosion vulnerability in Hubei Province of China using RUSLE model and combination weighting method

Soil erosion has been recognized as a critical environmental issue worldwide.While previous studies have primarily focused on watershed-scale soil erosion vulnerability from a natural factor perspective,there is a notable gap in understanding the intricate interplay between natural and socio-economic factors,especially in the context of spatial heterogeneity and nonlinear impacts of human-land interactions.To address this,our study evaluates the soil erosion vulnerability at a provincial scale,taking Hubei Province as a case study to explore the combined effects of natural and socio-economic factors.We developed an evaluation index system based on 15 indicators of soil erosion vulnerability:exposure,sensitivity,and adaptability.In addition,the combination weighting method was applied to determine index weights,and the spatial interaction was analyzed using spatial autocorrelation,geographical temporally weighted regression and geographical detector.The results showed an overall decreasing soil erosion intensity in Hubei Province during 2000 and 2020.The soil erosion vulnerability increased before 2000 and then.The areas with high soil erosion vulnerability were mainly confined in the central and southern regions of Hubei Province(Xiantao,Tianmen,Qianjiang and Ezhou)with obvious spatial aggregation that intensified over time.Natural factors(habitat quality index)had negative impacts on soil erosion vulnerability,whereas socio-economic factors(population density)showed substantial spatial variability in their influences.There was a positive correlation between soil erosion vulnerability and erosion intensity,with the correlation coefficients ranging from-0.41 and 0.93.The increase of slope was found to enhance the positive correlation between soil erosion vulnerability and intensity.

Facilitated Prediction of Micropollutant Degradation via UV-AOPs in Various Waters by Combining Model Simulation and Portable Measurement

The degradation of micropollutants in water via ultraviolet(UV)-based advanced oxidation processes(AOPs)is strongly dependent on the water matrix.Various reactive radicals(RRs)formed in UV-AOPs have different reaction selectivities toward water matrices and degradation efficiencies for target micropollutants.Hence,process selection and optimization are crucial.This study developed a facilitated prediction method for the photon fluence-based rate constant for micropollutant degradation(K′_(p,MP))in various UV-AOPs by combining model simulation with portable measurement.Portable methods for measuring the scavenging capacities of the principal RRs(RRSCs)involved in UV-AOPs(i.e.,HO^(·),SO_(4)^(·-),and Cl^(·))using a mini-fluidic photoreaction system were proposed.The simulation models consisted of photochemical,quantitative structure–activity relationship,and radical concentration steady-state approximation models.The RRSCs were determined in eight test waters,and a higher RRSC was found to be associated with a more complex water matrix.Then,by taking sulfamethazine,caffeine,and carbamazepine as model micropollutants,the k′_(p,MP) values in various UV-AOPs were predicted and further verified experimentally.A lower k′_(p,MP) was found to be associated with a higher RRSC for a stronger RR competition;for example,k′_(p,MP) values of 130.9 and 332.5 m^(2) einstein^(–1),respectively,were obtained for carbamazepine degradation by UV/H_(2)O_(2) in the raw water(RRSC=9.47×10^(4) s^(-1))and sand-filtered effluent(RRSC=2.87×10^(4) s^(-1))of a drinking water treatment plant.The developed method facilitates process selection and optimization for UV-AOPs,which is essential for increasing the efficiency and cost-effectiveness of water treatment.

Preparation and performance evaluation of the slickwater using novel polymeric drag reducing agent with high temperature and shear resistance ability

Slickwater fracturing fluids are widely used in the development of unconventional oil and gas resources due to the advantages of low cost,low formation damage and high drag reduction performance.However,their performance is severely affected at high temperatures.Drag reducing agent is the key to determine the drag reducing performance of slickwater.In this work,in order to further improve the temperature resistance of slickwater,a temperature-resistant polymeric drag reducing agent(PDRA)was synthesized and used as the basis for preparing the temperature-resistant slickwater.The slickwater system was prepared with the compositions of 0.2 wt%PDRA,0.05 wt%drainage aid nonylphenol polyoxyethylene ether phosphate(NPEP)and 0.5 wt%anti-expansion agent polyepichlorohydrindimethylamine(PDM).The drag reduction ability,rheology properties,temperature and shear resistance ability,and core damage property of slickwater were systematically studied and evaluated.In contrast to on-site drag reducing agent(DRA)and HPAM,the temperature-resistant slickwater demonstrates enhanced drag reduction efficacy at 90℃,exhibiting superior temperature and shear resistance ability.Notably,the drag reduction retention rate for the slickwater achieved an impressive 90.52%after a 30-min shearing period.Additionally,the core damage is only 5.53%.We expect that this study can broaden the application of slickwater in high-temperature reservoirs and provide a theoretical basis for field applications.

Exploring impacts of COVID-19 on spatial and temporal patterns of visitors to Canadian Rocky Mountain National Parks from social media big data

COVID-19 posed challenges for global tourism management.Changes in visitor temporal and spatial patterns and their associated determinants pre-and peri-pandemic in Canadian Rocky Mountain National Parks are analyzed.Data was collected through social media programming and analyzed using spatiotemporal analysis and a geographically weighted regression(GWR)model.Results highlight that COVID-19 significantly changed park visitation patterns.Visitors tended to explore more remote areas peri-pandemic.The GWR model also indicated distance to nearby trails was a significant influence on visitor density.Our results indicate that the pandemic influenced tourism temporal and spatial imbalance.This research presents a novel approach using combined social media big data which can be extended to the field of tourism management,and has important implications to manage visitor patterns and to allocate resources efficiently to satisfy multiple objectives of park management.

Exploring the mechanisms of calcium carbonate deposition on various substrates with implications for effective anti-scaling material selection

The unexpected scaling phenomena have resulted in significant damages to the oil and gas industries,leading to issues such as heat exchanger failures and pipeline clogging.It is of practical and fundamental importance to understand the scaling mechanisms and develop efficient anti-scaling strategies.However,the underlying surface interaction mechanisms of scalants(e.g.,calcite)with various substrates are still not fully understood.In this work,the colloidal probe atomic force microscopy(AFM)technique has been applied to directly quantify the surface forces between calcite particles and different metallic substrates,including carbon steel(CR1018),low alloy steel(4140),stainless steel(SS304)and tungsten carbide,under different water chemistries(i.e.,salinity and pH).Measured force profiles revealed that the attractive van der Waals(VDW)interaction contributed to the attachment of the calcium carbonate particles on substrate surfaces,while the repulsive electric double layer(EDL)interactions could inhibit the attachment behaviors.High salinity and acidic p H conditions of aqueous solutions could weaken the EDL repulsion and promote the attachment behavior.The adhesion of calcite particles with CR1018 and4140 substrates was much stronger than that with SS304 and tungsten carbide substrates.The bulk scaling tests in aqueous solutions from an industrial oil production process showed that much more severe scaling behaviors of calcite was detected on CR1018 and 4140 than those on SS304 and tungsten carbide,which agreed with surface force measurement results.Besides,high salinity and acidic p H can significantly enhance the scaling phenomena.This work provides fundamental insights into the scaling mechanisms of calcite at the nanoscale with practical implications for the selection of suitable antiscaling materials in petroleum industries.

Bacteria colonization and gene expression related to immune function in colon mucosa is associated with growth in neonatal calves regardless of live yeast supplementation

Background As Holstein calves are susceptible to gastrointestinal disorders during the first week of life,understanding how intestinal immune function develops in neonatal calves is important to promote better intestinal health.Feeding probiotics in early life may contribute to host intestinal health by facilitating beneficial bacteria colonization and developing intestinal immune function.The objective of this study was to characterize the impact of early life yeast supplementation and growth on colon mucosa-attached bacteria and host immune function.Results Twenty Holstein bull calves received no supplementation(CON)or Saccharomyces cerevisiae boulardii(SCB)from birth to 5 d of life.Colon tissue biopsies were taken within 2 h of life(D0)before the first colostrum feeding and 3 h after the morning feeding at d 5 of age(D5)to analyze mucosa-attached bacteria and colon transcriptome.Metagenome sequencing showed that there was no difference inαandβdiversity of mucosa-attached bacteria between day and treatment,but bacteria related to diarrhea were more abundant in the colon mucosa on D0 compared to D5.In addition,q PCR indicated that the absolute abundance of Escherichia coli(E.coli)decreased in the colon mucosa on D5 compared to D0;however,that of Bifidobacterium,Lactobacillus,and Faecalibacterium prausnitzii,which could competitively exclude E.coli,increased in the colon mucosa on D5 compared to D0.RNA-sequencing showed that there were no differentially expressed genes between CON and SCB,but suggested that pathways related to viral infection such as“Interferon Signaling”were activated in the colon mucosa of D5 compared to D0.Conclusions Growth affected mucosa-attached bacteria and host immune function in the colon mucosa during the first 5 d of life in dairy calves independently of SCB supplementation.During early life,opportunistic pathogens may decrease due to intestinal environmental changes by beneficial bacteria and/or host immune function.Predicted activation of immune function-related pathways may be the result of host immune function development or suggest other antigens in the intestine during early life.Further studies focusing on the other antigens and host immune function in the colon mucosa are required to better understand intestinal immune function development.

Elucidating the molecular basis of ATP-induced cell death in breast cancer: Construction of a robust prognostic model

BACKGROUND Breast cancer is a multifaceted and formidable disease with profound public health implications.Cell demise mechanisms play a pivotal role in breast cancer pathogenesis,with ATP-triggered cell death attracting mounting interest for its unique specificity and potential therapeutic pertinence.AIM To investigate the impact of ATP-induced cell death(AICD)on breast cancer,enhancing our understanding of its mechanism.METHODS The foundational genes orchestrating AICD mechanisms were extracted from the literature,underpinning the establishment of a prognostic model.Simultaneously,a microRNA(miRNA)prognostic model was constructed that mirrored the gene-based prognostic model.Distinctions between high-and low-risk cohorts within mRNA and miRNA characteristic models were scrutinized,with the aim of delineating common influence mechanisms,substantiated through enrichment analysis and immune infiltration assessment.RESULTS The mRNA prognostic model in this study encompassed four specific mRNAs:P2X purinoceptor 4,pannexin 1,caspase 7,and cyclin 2.The miRNA prognostic model integrated four pivotal miRNAs:hsa-miR-615-3p,hsa-miR-519b-3p,hsa-miR-342-3p,and hsa-miR-324-3p.B cells,CD4+T cells,CD8+T cells,endothelial cells,and macrophages exhibited inverse correlations with risk scores across all breast cancer subtypes.Furthermore,Kyoto Encyclopedia of Genes and Genomes analysis revealed that genes differentially expressed in response to mRNA risk scores significantly enriched 25 signaling pathways,while miRNA risk scores significantly enriched 29 signaling pathways,with 16 pathways being jointly enriched.CONCLUSION Of paramount significance,distinct mRNA and miRNA signature models were devised tailored to AICD,both potentially autonomous prognostic factors.This study's elucidation of the molecular underpinnings of AICD in breast cancer enhances the arsenal of potential therapeutic tools,offering an unparalleled window for innovative interventions.Essentially,this paper reveals the hitherto enigmatic link between AICD and breast cancer,potentially leading to revolutionary progress in personalized oncology.

Sensitivity to Acidification of Forest Soils in Two Watersheds with Contrasting Hydrological Regimes in the Oil Sands Region of Alberta

Input of large amounts of N and S compounds into forest ecosystems through atmospheric deposition is a significant risk for soil acidification in the oil sands region of Alberta.We evaluated the sensitivity of forest soils to acidification in two watersheds(Lake 287 and Lake 185)with contrasting hydrological regimes as a part of a larger project assessing the role of N and S cycling in soil acidification in forest ecosystems.Fifty six forest soil samples were collected from the two watersheds by horizon from 10 monitoring plots dominated by either jack pine(Pinus banksiana)or aspen(Populus tremuloides).Soils in the two watersheds were extremely to moderately acidic with pH(CaCl_2)ranging from 2.83 to 4.91.Soil acid-base chemistry variables such as pH,base saturation,Al saturation,and acid-buffering capacity measured using the acetic acid equilibrium procedure indicated that soils in Lake 287 were more acidified than those in Lake 185. Acid-buffering capacity decreased in the order of forest floor>subsurface mineral soil>surface mineral soil.The most dramatic differences in percent Ca and Al saturations between the two watersheds were found in the surface mineral soil horizon.Percent Ca and Al saturation in the surface mineral soil in Lake 287 were 15% and 70%,respectively;the percent Ca saturation value fell within a critical range proposed in the literature that indicates soil acidification.Our results suggest that the soils in the two watersheds have low acid buffering capacity and would be sensitive to increased acidic deposition in the region.

Upcycling of spent LiCoO_(2) cathodes via nickel- and manganese-doping

Direct recycling has been regarded as one of the most promising approaches to dealing with the increasing amount of spent lithium‐ion batteries(LIBs).However,the current direct recycling method remains insufficient to regenerate outdated cathodes to meet current industry needs as it only aims at recovering the structure and composition of degraded cathodes.Herein,a nickel(Ni)and manganese(Mn)co‐doping strategy has been adopted to enhance LiCoO_(2)(LCO)cathode for next‐generation high‐performance LIBs through a conventional hydrothermal treatment combined with short annealing approach.Unlike direct recycling methods that make no changes to the chemical composition of cathodes,the unique upcycling process fabricates a series of cathodes doped with different contents of Ni and Mn.The regenerated LCO cathode with 5%doping delivers excellent electrochemical performance with a discharge capacity of 160.23 mAh g^(−1) at 1.0 C and capacity retention of 91.2%after 100 cycles,considerably surpassing those of the pristine one(124.05 mAh g^(−1) and 89.05%).All results indicate the feasibility of such Ni–Mn co‐doping‐enabled upcycling on regenerating LCO cathodes.

Strengthening absorption ability of Co-N-C as efficient bifunctional oxygen catalyst by modulating the d band center using MoC

Co-N-C is a promising oxygen electrochemical catalyst due to its high stability and good durability.However,due to the limited adsorption ability improvement for oxygen-containing intermediates,it usually exhibits inadequate catalytic activity with 2-electron pathway and high selectivity of hydrogen peroxide.Herein,the adsorption of Co-N-C to these intermediates is modulated by constructing heterostructures using transition metals and their derivatives based on d-band theory.The heterostructured nanobelts with MoC core and pomegranate-like carbon shell consisting of Co nanoparticles and N dopant(MoC/Co-N-C)are engineered to successfully modulate the d band center of active Co-N-C sites,resulting in a remarkably enhanced electrocatalysis performance.The optimally performing MoC/Co-N-C exhibits outstanding bi-catalytic activity and stability for the oxygen electrochemistry,featuring a high wave-half potential of 0.865 V for the oxygen reduction reaction(ORR)and low overpotential of 370 mV for the oxygen evolution reaction(OER)at 10 mA cm^(-2).The zinc air batteries with the MoC/Co-N-C catalyst demonstrate a large power density of 180 mW cm^(-2)and a long cycling lifespan(2000 cycles).The density functional theory calculations with Hubbard correction(DFT+U)reveal the electron transferring from Co to Mo atoms that effectively modulate the d band center of the active Co sites and achieve optimum adsorption ability with"single site double adsorption"mode.

A machine learning model to predict unconfined compressive strength of alkali-activated slag-based cemented paste backfill

The unconfined compressive strength(UCS)of alkali-activated slag(AAS)-based cemented paste backfill(CPB)is influenced by multiple design parameters.However,the experimental methods are limited to understanding the relationships between a single design parameter and the UCS,independently of each other.Although machine learning(ML)methods have proven efficient in understanding relationships between multiple parameters and the UCS of ordinary Portland cement(OPC)-based CPB,there is a lack of ML research on AAS-based CPB.In this study,two ensemble ML methods,comprising gradient boosting regression(GBR)and random forest(RF),were built on a dataset collected from literature alongside two other single ML methods,support vector regression(SVR)and artificial neural network(ANN).The results revealed that the ensemble learning methods outperformed the single learning methods in predicting the UCS of AAS-based CPB.Relative importance analysis based on the bestperforming model(GBR)indicated that curing time and water-to-binder ratio were the most critical input parameters in the model.Finally,the GBR model with the highest accuracy was proposed for the UCS predictions of AAS-based CPB.

Nano-additive manufacturing of multilevel strengthened aluminum matrix composites

Nanostructured materials are being actively developed,while it remains an open question how to rapidly scale them up to bulk engineering materials for broad industrial applications.This study propose an industrial approach to rapidly fabricate high-strength large-size nanostructured metal matrix composites and attempts to investigate and optimize the deposition process and strengthening mechanism.Here,advanced nanocrystalline aluminum matrix composites(nanoAMCs)were assembled for the first time by a novel nano-additive manufacturing method that was guided by numerical simulations(i.e.the in-flight particle model and the porefree deposition model).The present nanoAMC with a mean grain size<50 nm in matrix exhibited hardness eight times higher than the bulk aluminum and shows the highest hardness among all Al–Al2O3 composites reported to date in the literature,which are the outcome of controlling multiscale strengthening mechanisms from tailoring solution atoms,dislocations,grain boundaries,precipitates,and externally introduced reinforcing particles.The present high-throughput strategy and method can be extended to design and architect advanced coatings or bulk materials in a highly efficient(synthesizing a nanostructured bulk with dimensions of 50×20×4 mm^(3) in 9 min)and highly flexible(regulating the gradient microstructures in bulk)way,which is conducive to industrial production and application.

Dynamic stability analysis of porous functionally graded beams under hygro-thermal loading using nonlocal strain gradient integral model

We present a study on the dynamic stability of porous functionally graded(PFG)beams under hygro-thermal loading.The variations of the properties of the beams across the beam thicknesses are described by the power-law model.Unlike most studies on this topic,we consider both the bending deformation of the beams and the hygro-thermal load as size-dependent,simultaneously,by adopting the equivalent differential forms of the well-posed nonlocal strain gradient integral theory(NSGIT)which are strictly equipped with a set of constitutive boundary conditions(CBCs),and through which both the stiffness-hardening and stiffness-softening effects of the structures can be observed with the length-scale parameters changed.All the variables presented in the differential problem formulation are discretized.The numerical solution of the dynamic instability region(DIR)of various bounded beams is then developed via the generalized differential quadrature method(GDQM).After verifying the present formulation and results,we examine the effects of different parameters such as the nonlocal/gradient length-scale parameters,the static force factor,the functionally graded(FG)parameter,and the porosity parameter on the DIR.Furthermore,the influence of considering the size-dependent hygro-thermal load is also presented.