Hybrid model for BOF oxygen blowing time prediction based on oxygen balance mechanism and deep neural network

The amount of oxygen blown into the converter is one of the key parameters for the control of the converter blowing process,which directly affects the tap-to-tap time of converter. In this study, a hybrid model based on oxygen balance mechanism (OBM) and deep neural network (DNN) was established for predicting oxygen blowing time in converter. A three-step method was utilized in the hybrid model. First, the oxygen consumption volume was predicted by the OBM model and DNN model, respectively. Second, a more accurate oxygen consumption volume was obtained by integrating the OBM model and DNN model. Finally, the converter oxygen blowing time was calculated according to the oxygen consumption volume and the oxygen supply intensity of each heat. The proposed hybrid model was verified using the actual data collected from an integrated steel plant in China, and compared with multiple linear regression model, OBM model, and neural network model including extreme learning machine, back propagation neural network, and DNN. The test results indicate that the hybrid model with a network structure of 3 hidden layer layers, 32-16-8 neurons per hidden layer, and 0.1 learning rate has the best prediction accuracy and stronger generalization ability compared with other models. The predicted hit ratio of oxygen consumption volume within the error±300 m^(3)is 96.67%;determination coefficient (R^(2)) and root mean square error (RMSE) are0.6984 and 150.03 m^(3), respectively. The oxygen blow time prediction hit ratio within the error±0.6 min is 89.50%;R2and RMSE are0.9486 and 0.3592 min, respectively. As a result, the proposed model can effectively predict the oxygen consumption volume and oxygen blowing time in the converter.

Heterophilic Graph Neural Network Based on Spatial and Frequency Domain Adaptive Embedding Mechanism

Graph Neural Networks(GNNs)play a significant role in tasks related to homophilic graphs.Traditional GNNs,based on the assumption of homophily,employ low-pass filters for neighboring nodes to achieve information aggregation and embedding.However,in heterophilic graphs,nodes from different categories often establish connections,while nodes of the same category are located further apart in the graph topology.This characteristic poses challenges to traditional GNNs,leading to issues of“distant node modeling deficiency”and“failure of the homophily assumption”.In response,this paper introduces the Spatial-Frequency domain Adaptive Heterophilic Graph Neural Networks(SFA-HGNN),which integrates adaptive embedding mechanisms for both spatial and frequency domains to address the aforementioned issues.Specifically,for the first problem,we propose the“Distant Spatial Embedding Module”,aiming to select and aggregate distant nodes through high-order randomwalk transition probabilities to enhance modeling capabilities.For the second issue,we design the“Proximal Frequency Domain Embedding Module”,constructing adaptive filters to separate high and low-frequency signals of nodes,and introduce frequency-domain guided attention mechanisms to fuse the relevant information,thereby reducing the noise introduced by the failure of the homophily assumption.We deploy the SFA-HGNN on six publicly available heterophilic networks,achieving state-of-the-art results in four of them.Furthermore,we elaborate on the hyperparameter selection mechanism and validate the performance of each module through experimentation,demonstrating a positive correlation between“node structural similarity”,“node attribute vector similarity”,and“node homophily”in heterophilic networks.

Research on Narrowband Line Spectrum Noise Control Method Based on Nearest Neighbor Filter and BP Neural Network Feedback Mechanism

Thefilter-x least mean square(FxLMS)algorithm is widely used in active noise control(ANC)systems.However,because the algorithm is a feedback control algorithm based on the minimization of the error signal variance to update thefilter coefficients,it has a certain delay,usually has a slow convergence speed,and the system response time is long and easily affected by the learning rate leading to the lack of system stability,which often fails to achieve the desired control effect in practice.In this paper,we propose an active control algorithm with near-est-neighbor trap structure and neural network feedback mechanism to reduce the coefficient update time of the FxLMS algorithm and use the neural network feedback mechanism to realize the parameter update,which is called NNR-BPFxLMS algorithm.In the paper,the schematic diagram of the feedback control is given,and the performance of the algorithm is analyzed.Under various noise conditions,it is shown by simulation and experiment that the NNR-BPFxLMS algorithm has the following three advantages:in terms of performance,it has higher noise reduction under the same number of sampling points,i.e.,it has faster convergence speed,and by computer simulation and sound pipe experiment,for simple ideal line spectrum noise,compared with the convergence speed of NNR-BPFxLMS is improved by more than 95%compared with FxLMS algorithm,and the convergence speed of real noise is also improved by more than 70%.In terms of stability,NNR-BPFxLMS is insensitive to step size changes.In terms of tracking performance,its algorithm responds quickly to sudden changes in the noise spectrum and can cope with the complex control requirements of sudden changes in the noise spectrum.

Mechanism of spinal cord injury regeneration and the effect of human neural stem cells-secretome treatment in rat model

BACKGROUND Globally,complete neurological recovery of spinal cord injury(SCI)is still less than 1%,and 90%experience permanent disability.The key issue is that a pharmacological neuroprotective-neuroregenerative agent and SCI regeneration mechanism have not been found.The secretomes of stem cell are an emerging neurotrophic agent,but the effect of human neural stem cells(HNSCs)secretome on SCI is still unclear.AIM To investigate the regeneration mechanism of SCI and neuroprotective-neuroregenerative effects of HNSCs-secretome on subacute SCI post-laminectomy in rats.METHODS An experimental study was conducted with 45 Rattus norvegicus,divided into 15 normal,15 control(10 mL physiologic saline),and 15 treatment(30μL HNSCssecretome,intrathecal T10,three days post-traumatic).Locomotor function was evaluated weekly by blinded evaluators.Fifty-six days post-injury,specimens were collected,and spinal cord lesion,free radical oxidative stress(F2-Isoprostanes),nuclear factor-kappa B(NF-κB),matrix metallopeptidase 9(MMP9),tumor necrosis factor-alpha(TNF-α),interleukin-10(IL-10),transforming growth factor-beta(TGF-β),vascular endothelial growth factor(VEGF),B cell lymphoma-2(Bcl-2),nestin,brain-derived neurotrophic factor(BDNF),glial cell line-derived neurotrophic factor(GDNF)were analyzed.The SCI regeneration mechanism was analyzed using partial least squares structural equation modeling(PLS SEM).RESULTS HNSCs-secretome significantly improved locomotor recovery according to Basso,Beattie,Bresnahan(BBB)scores and increased neurogenesis(nestin,BDNF,and GDNF),neuroangiogenesis(VEGF),anti-apoptotic(Bcl-2),anti-inflammatory(IL-10 and TGF-β),but decreased proinflammatory(NF-κB,MMP9,TNF-α),F2-Isoprostanes,and spinal cord lesion size.The SCI regeneration mechanism is valid by analyzed outer model,inner model,and hypothesis testing in PLS SEM,started with pro-inflammation followed by anti-inflammation,anti-apoptotic,neuroangiogenesis,neurogenesis,and locomotor function.CONCLUSION HNSCs-secretome as a potential neuroprotective-neuroregenerative agent for the treatment of SCI and uncover the SCI regeneration mechanism.

Neural-Network-Based Adaptive Finite-Time Control for a Two-Degree-of-Freedom Helicopter System With an Event-Triggering Mechanism

Helicopter systems present numerous benefits over fixed-wing aircraft in several fields of application.Developing control schemes for improving the tracking accuracy of such systems is crucial.This paper proposes a neural-network(NN)-based adaptive finite-time control for a two-degree-of-freedom helicopter system.In particular,a radial basis function NN is adopted to solve uncertainty in the helicopter system.Furthermore,an event-triggering mechanism(ETM)with a switching threshold is proposed to alleviate the communication burden on the system.By proposing an adaptive parameter,a bounded estimation,and a smooth function approach,the effect of network measurement errors is effectively compensated for while simultaneously avoiding the Zeno phenomenon.Additionally,the developed adaptive finite-time control technique based on an NN guarantees finitetime convergence of the tracking error,thus enhancing the control accuracy of the system.In addition,the Lyapunov direct method demonstrates that the closed-loop system is semiglobally finite-time stable.Finally,simulation and experimental results show the effectiveness of the control strategy.

Emerging strategies for nerve repair and regeneration in ischemic stroke:neural stem cell therapy

Ischemic stroke is a major cause of mortality and disability worldwide,with limited treatment options available in clinical practice.The emergence of stem cell therapy has provided new hope to the field of stroke treatment via the restoration of brain neuron function.Exogenous neural stem cells are beneficial not only in cell replacement but also through the bystander effect.Neural stem cells regulate multiple physiological responses,including nerve repair,endogenous regeneration,immune function,and blood-brain barrier permeability,through the secretion of bioactive substances,including extracellular vesicles/exosomes.However,due to the complex microenvironment of ischemic cerebrovascular events and the low survival rate of neural stem cells following transplantation,limitations in the treatment effect remain unresolved.In this paper,we provide a detailed summary of the potential mechanisms of neural stem cell therapy for the treatment of ischemic stroke,review current neural stem cell therapeutic strategies and clinical trial results,and summarize the latest advancements in neural stem cell engineering to improve the survival rate of neural stem cells.We hope that this review could help provide insight into the therapeutic potential of neural stem cells and guide future scientific endeavors on neural stem cells.

Pluggable multitask diffractive neural networks based on cascaded metasurfaces

Optical neural networks have significant advantages in terms of power consumption,parallelism,and high computing speed,which has intrigued extensive attention in both academic and engineering communities.It has been considered as one of the powerful tools in promoting the fields of imaging processing and object recognition.However,the existing optical system architecture cannot be reconstructed to the realization of multi-functional artificial intelligence systems simultaneously.To push the development of this issue,we propose the pluggable diffractive neural networks(P-DNN),a general paradigm resorting to the cascaded metasurfaces,which can be applied to recognize various tasks by switching internal plug-ins.As the proof-of-principle,the recognition functions of six types of handwritten digits and six types of fashions are numerical simulated and experimental demonstrated at near-infrared regimes.Encouragingly,the proposed paradigm not only improves the flexibility of the optical neural networks but paves the new route for achieving high-speed,low-power and versatile artificial intelligence systems.

Metabolic and proteostatic differences in quiescent and active neural stem cells

Adult neural stem cells are neurogenesis progenitor cells that play an important role in neurogenesis.Therefore,neural regeneration may be a promising target for treatment of many neurological illnesses.The regenerative capacity of adult neural stem cells can be chara cterized by two states:quiescent and active.Quiescent adult neural stem cells are more stable and guarantee the quantity and quality of the adult neural stem cell pool.Active adult neural stem cells are chara cterized by rapid proliferation and differentiation into neurons which allow for integration into neural circuits.This review focuses on diffe rences between quiescent and active adult neural stem cells in nutrition metabolism and protein homeostasis.Furthermore,we discuss the physiological significance and underlying advantages of these diffe rences.Due to the limited number of adult neural stem cells studies,we refe rred to studies of embryonic adult neural stem cells or non-mammalian adult neural stem cells to evaluate specific mechanisms.

Pointing fingers at blood contact:mechanisms of subventricular zone neural stem cell differentiation

The limited ability of the central nervous system(CNS)to regenerate in adult mammals after injury or disease is a significant problem.Intriguingly,neural stem/progenitor cells(NSPCs)offer great promise for regenerating the CNS.Endogenous or transplanted NSPCs contribute to repair processes,but their differentiation and function are abnormal in CNS injury and disease.The main reasons for these abnormalities are changes in the extracellular environment in the injured CNS that affect signaling pathways and transcriptional regulation in NSPCs.

Activation Redistribution Based Hybrid Asymmetric Quantization Method of Neural Networks

The demand for adopting neural networks in resource-constrained embedded devices is continuously increasing.Quantization is one of the most promising solutions to reduce computational cost and memory storage on embedded devices.In order to reduce the complexity and overhead of deploying neural networks on Integeronly hardware,most current quantization methods use a symmetric quantization mapping strategy to quantize a floating-point neural network into an integer network.However,although symmetric quantization has the advantage of easier implementation,it is sub-optimal for cases where the range could be skewed and not symmetric.This often comes at the cost of lower accuracy.This paper proposed an activation redistribution-based hybrid asymmetric quantizationmethod for neural networks.The proposedmethod takes data distribution into consideration and can resolve the contradiction between the quantization accuracy and the ease of implementation,balance the trade-off between clipping range and quantization resolution,and thus improve the accuracy of the quantized neural network.The experimental results indicate that the accuracy of the proposed method is 2.02%and 5.52%higher than the traditional symmetric quantization method for classification and detection tasks,respectively.The proposed method paves the way for computationally intensive neural network models to be deployed on devices with limited computing resources.Codes will be available on https://github.com/ycjcy/Hybrid-Asymmetric-Quantization.

Autophagy in neural stem cells and glia for brain health and diseases

Autophagy is a multifaceted cellular process that not only maintains the homeostatic and adaptive responses of the brain but is also dynamically involved in the regulation of neural cell generation,maturation,and survival.Autophagy facilities the utilization of energy and the microenvironment for developing neural stem cells.Autophagy arbitrates structural and functional remodeling during the cell differentiation process.Autophagy also plays an indispensable role in the maintenance of stemness and homeostasis in neural stem cells during essential brain physiology and also in the instigation and progression of diseases.Only recently,studies have begun to shed light on autophagy regulation in glia(microglia,astrocyte,and oligodendrocyte)in the brain.Glial cells have attained relatively less consideration despite their unquestioned influence on various aspects of neural development,synaptic function,brain metabolism,cellular debris clearing,and restoration of damaged or injured tissues.Thus,this review composes pertinent information regarding the involvement of autophagy in neural stem cells and glial regulation and the role of this connexion in normal brain functions,neurodevelopmental disorders,and neurodegenerative diseases.This review will provide insight into establishing a concrete strategic approach for investigating pathological mechanisms and developing therapies for brain diseases.

The underlying mechanism of variety–water–nitrogen–stubble damage interactions on yield formation in ratoon rice with low stubble height under mechanized harvesting

Agronomic measures are the key to promote the sustainable development of ratoon rice by reducing the damage from mechanical crushing to the residual stubble of the main crop, thereby mitigating the impact on axillary bud sprouting and yield formation in ratoon rice. This study used widely recommended conventional rice Jiafuzhan and hybrid rice Yongyou 2640 as the test materials to conduct a four-factor block design field experiment in a greenhouse of the experimental farm of Fujian Agricultural and Forestry University, China from 2018 to 2019.The treatments included fertilization and no fertilization, alternate wetting and drying irrigation and continuous water flooding irrigation, and plots with and without artificial crushing damage on the rice stubble. At the same time, a 13C stable isotope in-situ detection technology was used to fertilize the pot experiment. The results showed significant interactions among varieties, water management, nitrogen application and stubble status.Relative to the long-term water flooding treatment, the treatment with sequential application of nitrogen fertilizer coupled with moderate field drought for root-vigor and tiller promotion before and after harvesting of the main crop, significantly improved the effective tillers from low position nodes. This in turn increased the effective panicles per plant and grains per panicle by reducing the influence of artificial crushing damage on rice stubble and achieving a high yield of the regenerated rice. Furthermore, the partitioning of 13C assimilates to the residual stubble and its axillary buds were significantly improved at the mature stage of the main crop, while the translocation rate to roots and rhizosphere soil was reduced at the later growth stage of ratooning season rice. This was triggered by the metabolism of hormones and polyamines at the stem base regulated by the interaction of water and fertilizer at this time. We therefore suggest that to achieve a high yield of ratoon rice with low stubble height under mechanized harvesting, the timely application of nitrogen fertilizer is fundamental,coupled with moderate field drying for root-vigor preservation and tiller promotion before and after the mechanical harvesting of the main crop.

A data-driven model of drop size prediction based on artificial neural networks using small-scale data sets

An artificial neural network(ANN)method is introduced to predict drop size in two kinds of pulsed columns with small-scale data sets.After training,the deviation between calculate and experimental results are 3.8%and 9.3%,respectively.Through ANN model,the influence of interfacial tension and pulsation intensity on the droplet diameter has been developed.Droplet size gradually increases with the increase of interfacial tension,and decreases with the increase of pulse intensity.It can be seen that the accuracy of ANN model in predicting droplet size outside the training set range is reach the same level as the accuracy of correlation obtained based on experiments within this range.For two kinds of columns,the drop size prediction deviations of ANN model are 9.6%and 18.5%and the deviations in correlations are 11%and 15%.

Instability mechanism of mining roadway passing through fault at different angles in kilometre-deep mine and control measures of roof cutting and NPR cables

The angle α between the fault strike and the axial direction of the roadway produces different damage characteristics. In this paper, the research methodology includes theoretical analyses, numerical simulations and field experiments in the context of the Daqiang coal mine located in Shenyang, China. The stability control countermeasure of "pre-splitting cutting roof + NPR anchor cable"(PSCR-NPR) is simultaneously proposed. According to the different deformation characteristics of the roadway, the faults are innovatively classified into three types, with α of type I being 0°-30°, α of type II being 30°-60°, and α of type III being 60°-90°. The full-cycle stress evolution paths during mining roadway traverses across different types of faults are investigated by numerical simulation. Different pinch angles α lead to high stress concentration areas at different locations in the surrounding rock. The non-uniform stress field formed in the shallow surrounding rock is an important reason for the instability of the roadway. The pre-cracked cut top shifted the high stress region to the deep rock mass and formed a low stress region in the shallow rock mass. The high prestressing NPR anchor cable transforms the non-uniform stress field of the shallow surrounding rock into a uniform stress field. PSCR-NPR is applied in the fault-through roadway of Daqiang mine. The low stress area of the surrounding rock was enlarged by 3-7 times, and the cumulative convergence was reduced by 45%-50%. It provides a reference for the stability control of the deep fault-through mining roadway.

Deep Neural Network Based Spam Email Classification Using Attention Mechanisms

Spam emails pose a threat to individuals. The proliferation of spam emails daily has rendered traditional machine learning and deep learning methods for screening them ineffective and inefficient. In our research, we employ deep neural networks like RNN, LSTM, and GRU, incorporating attention mechanisms such as Bahdanua, scaled dot product (SDP), and Luong scaled dot product self-attention for spam email filtering. We evaluate our approach on various datasets, including Trec spam, Enron spam emails, SMS spam collections, and the Ling spam dataset, which constitutes a substantial custom dataset. All these datasets are publicly available. For the Enron dataset, we attain an accuracy of 99.97% using LSTM with SDP self-attention. Our custom dataset exhibits the highest accuracy of 99.01% when employing GRU with SDP self-attention. The SMS spam collection dataset yields a peak accuracy of 99.61% with LSTM and SDP attention. Using the GRU (Gated Recurrent Unit) alongside Luong and SDP (Structured Self-Attention) attention mechanisms, the peak accuracy of 99.89% in the Ling spam dataset. For the Trec spam dataset, the most accurate results are achieved using Luong attention LSTM, with an accuracy rate of 99.01%. Our performance analyses consistently indicate that employing the scaled dot product attention mechanism in conjunction with gated recurrent neural networks (GRU) delivers the most effective results. In summary, our research underscores the efficacy of employing advanced deep learning techniques and attention mechanisms for spam email filtering, with remarkable accuracy across multiple datasets. This approach presents a promising solution to the ever-growing problem of spam emails.

Recent research progress in the mechanism and suppression of fusion welding-induced liquation cracking of nickel based superalloys

Nickel-based superalloys are extensively used in the crucial hot-section components of industrial gas turbines,aeronautics,and astronautics because of their excellent mechanical properties and corrosion resistance at high temperatures.Fusion welding serves as an effective means for joining and repairing these alloys;however,fusion welding-induced liquation cracking has been a challenging issue.This paper comprehensively reviewed recent liquation cracking,discussing the formation mechanisms,cracking criteria,and remedies.In recent investigations,regulating material composition,changing the preweld heat treatment of the base metal,optimizing the welding process parameters,and applying auxiliary control methods are effective strategies for mitigating cracks.To promote the application of nickel-based superalloys,further research on the combination impact of multiple elements on cracking prevention and specific quantitative criteria for liquation cracking is necessary.

Preliminary discussion on the ignition mechanism of exploding foil initiators igniting boron potassium nitrate

Exploding foil initiator(EFI)is a kind of advanced device for initiating explosives,but its function is unstable when it comes to directly igniting pyrotechnics.To solve the problem,this research aims to reveal the ignition mechanism of EFIs directly igniting pyrotechnics.An oscilloscope,a photon Doppler velocimetry,and a plasma spectrum measurement system were employed to obtain information of electric characteristics,impact pressure,and plasma temperature.The results of the electric characteristics and the impact pressure were inconsistent with ignition results.The only thing that the ignition success tests had in common was that their plasma all had a relatively long period of high-temperature duration(HTD).It eventually concludes that the ignition mechanism in this research is the microconvection heat transfer rather than the shock initiation,which differs from that of exploding foil initiators detonating explosives.Furthermore,the methods for evaluating the ignition success of semiconductor bridge initiators are not entirely applicable to the tests mentioned in this paper.The HTD is the critical parameter for judging the ignition success,and it is influenced by two factors:the late time discharge and the energy of the electric explosion.The longer time of the late time discharge and the more energy of the electric explosion,the easier it is to expand the HTD,which improves the probability of the ignition success.

Transplantation of fibrin-thrombin encapsulated human induced neural stem cells promotes functional recovery of spinal cord injury rats through modulation of the microenvironment

Recent studies have mostly focused on engraftment of cells at the lesioned spinal cord,with the expectation that differentiated neurons facilitate recovery.Only a few studies have attempted to use transplanted cells and/or biomaterials as major modulators of the spinal cord injury microenvironment.Here,we aimed to investigate the role of microenvironment modulation by cell graft on functional recovery after spinal cord injury.Induced neural stem cells reprogrammed from human peripheral blood mononuclear cells,and/or thrombin plus fibrinogen,were transplanted into the lesion site of an immunosuppressed rat spinal cord injury model.Basso,Beattie and Bresnahan score,electrophysiological function,and immunofluorescence/histological analyses showed that transplantation facilitates motor and electrophysiological function,reduces lesion volume,and promotes axonal neurofilament expression at the lesion core.Examination of the graft and niche components revealed that although the graft only survived for a relatively short period(up to 15 days),it still had a crucial impact on the microenvironment.Altogether,induced neural stem cells and human fibrin reduced the number of infiltrated immune cells,biased microglia towards a regenerative M2 phenotype,and changed the cytokine expression profile at the lesion site.Graft-induced changes of the microenvironment during the acute and subacute stages might have disrupted the inflammatory cascade chain reactions,which may have exerted a long-term impact on the functional recovery of spinal cord injury rats.

Recent advances in cobalt phosphide-based materials for electrocatalytic water splitting:From catalytic mechanism and synthesis method to optimization design

Electrochemical water splitting has long been considered an effective energy conversion technology for trans-ferring intermittent renewable electricity into hydrogen fuel,and the exploration of cost-effective and high-performance electrocatalysts is crucial in making electrolyzed water technology commercially viable.Cobalt phosphide(Co-P)has emerged as a catalyst of high potential owing to its high catalytic activity and durability in water splitting.This paper systematically reviews the latest advances in the development of Co-P-based materials for use in water splitting.The essential effects of P in enhancing the catalytic performance of the hydrogen evolution reaction and oxygen evolution reaction are first outlined.Then,versatile synthesis techniques for Co-P electrocatalysts are summarized,followed by advanced strategies to enhance the electrocatalytic performance of Co-P materials,including heteroatom doping,composite construction,integration with well-conductive sub-strates,and structure control from the viewpoint of experiment.Along with these optimization strategies,the understanding of the inherent mechanism of enhanced catalytic performance is also discussed.Finally,some existing challenges in the development of highly active and stable Co-P-based materials are clarified,and pro-spective directions for prompting the wide commercialization of water electrolysis technology are proposed.

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.