Assessment of Wet Season Precipitation in the Central United States by the Regional Climate Simulation of the WRFG Member in NARCCAP and Its Relationship with Large-Scale Circulation Biases

Assessment of past-climate simulations of regional climate models(RCMs)is important for understanding the reliability of RCMs when used to project future regional climate.Here,we assess the performance and discuss possible causes of biases in a WRF-based RCM with a grid spacing of 50 km,named WRFG,from the North American Regional Climate Change Assessment Program(NARCCAP)in simulating wet season precipitation over the Central United States for a period when observational data are available.The RCM reproduces key features of the precipitation distribution characteristics during late spring to early summer,although it tends to underestimate the magnitude of precipitation.This dry bias is partially due to the model’s lack of skill in simulating nocturnal precipitation related to the lack of eastward propagating convective systems in the simulation.Inaccuracy in reproducing large-scale circulation and environmental conditions is another contributing factor.The too weak simulated pressure gradient between the Rocky Mountains and the Gulf of Mexico results in weaker southerly winds in between,leading to a reduction of warm moist air transport from the Gulf to the Central Great Plains.The simulated low-level horizontal convergence fields are less favorable for upward motion than in the NARR and hence,for the development of moist convection as well.Therefore,a careful examination of an RCM’s deficiencies and the identification of the source of errors are important when using the RCM to project precipitation changes in future climate scenarios.

FPGA-based position reconstruction method for neutron beam flux spatial distribution measurement in BNCT

A new measurement method for the spatial distribution of neutron beam flux in boron neutron capture therapy(BNCT)is being developed based on the two-dimensional Micromegas detector.To address the issue of long processing times in traditional offline position reconstruction methods,this paper proposes a field programmable gate array based online position reconstruction method utilizing the micro-time projection chamber principle.This method encapsulates key technical aspects:a self-adaptive serial link technique built upon the dynamical adjustment of the delay chain length,fast sorting,a coordinate-matching technique based on the mapping between signal timestamps and random access memory(RAM)addresses,and a precise start point-merging technique utilizing a circular combined RAM.The performance test of the selfadaptive serial link shows that the bit error rate of the link is better than 10-12 at a confidence level of 99%,ensuring reliable data transmission.The experiment utilizing the readout electronics and Micromegas detector shows a spatial resolution of approximately 1.4 mm,surpassing the current method’s resolution level of 5 mm.The beam experiment confirms that the readout electronics system can obtain the flux spatial distribution of neutron beams online,thus validating the feasibility of the position reconstruction method.The online position reconstruction method avoids traditional methods,such as bubble sorting and traversal searching,simplifies the design of the logic firmware,and reduces the time complexity from O(n2)to O(n).This study contributes to the advancement in measuring neutron beam flux for BNCT.

Reactor field reconstruction from sparse and movable sensors using Voronoi tessellation-assisted convolutional neural networks

The aging of operational reactors leads to increased mechanical vibrations in the reactor interior.The vibration of the incore sensors near their nominal locations is a new problem for neutronic field reconstruction.Current field-reconstruction methods fail to handle spatially moving sensors.In this study,we propose a Voronoi tessellation technique in combination with convolutional neural networks to handle this challenge.Observations from movable in-core sensors were projected onto the same global field structure using Voronoi tessellation,holding the magnitude and location information of the sensors.General convolutional neural networks were used to learn maps from observations to the global field.The proposed method reconstructed multi-physics fields(including fast flux,thermal flux,and power rate)using observations from a single field(such as thermal flux).Numerical tests based on the IAEA benchmark demonstrated the potential of the proposed method in practical engineering applications,particularly within an amplitude of 5 cm around the nominal locations,which led to average relative errors below 5% and 10% in the L_(2) and L_(∞)norms,respectively.

Sparse Modal Decomposition Method Addressing Underdetermined Vortex-Induced Vibration Reconstruction Problem for Marine Risers

When investigating the vortex-induced vibration(VIV)of marine risers,extrapolating the dynamic response on the entire length based on limited sensor measurements is a crucial step in both laboratory experiments and fatigue monitoring of real risers.The problem is conventionally solved using the modal decomposition method,based on the principle that the response can be approximated by a weighted sum of limited vibration modes.However,the method is not valid when the problem is underdetermined,i.e.,the number of unknown mode weights is more than the number of known measurements.This study proposed a sparse modal decomposition method based on the compressed sensing theory and the Compressive Sampling Matching Pursuit(Co Sa MP)algorithm,exploiting the sparsity of VIV in the modal space.In the validation study based on high-order VIV experiment data,the proposed method successfully reconstructed the response using only seven acceleration measurements when the conventional methods failed.A primary advantage of the proposed method is that it offers a completely data-driven approach for the underdetermined VIV reconstruction problem,which is more favorable than existing model-dependent solutions for many practical applications such as riser structural health monitoring.

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

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

Large-scale spatial data visualization method based on augmented reality

Background A task assigned to space exploration satellites involves detecting the physical environment within a certain space.However,space detection data are complex and abstract.These data are not conducive for researchers'visual perceptions of the evolution and interaction of events in the space environment.Methods A time-series dynamic data sampling method for large-scale space was proposed for sample detection data in space and time,and the corresponding relationships between data location features and other attribute features were established.A tone-mapping method based on statistical histogram equalization was proposed and applied to the final attribute feature data.The visualization process is optimized for rendering by merging materials,reducing the number of patches,and performing other operations.Results The results of sampling,feature extraction,and uniform visualization of the detection data of complex types,long duration spans,and uneven spatial distributions were obtained.The real-time visualization of large-scale spatial structures using augmented reality devices,particularly low-performance devices,was also investigated.Conclusions The proposed visualization system can reconstruct the three-dimensional structure of a large-scale space,express the structure and changes in the spatial environment using augmented reality,and assist in intuitively discovering spatial environmental events and evolutionary rules.

A Two-Layer Encoding Learning Swarm Optimizer Based on Frequent Itemsets for Sparse Large-Scale Multi-Objective Optimization

Traditional large-scale multi-objective optimization algorithms(LSMOEAs)encounter difficulties when dealing with sparse large-scale multi-objective optimization problems(SLM-OPs)where most decision variables are zero.As a result,many algorithms use a two-layer encoding approach to optimize binary variable Mask and real variable Dec separately.Nevertheless,existing optimizers often focus on locating non-zero variable posi-tions to optimize the binary variables Mask.However,approxi-mating the sparse distribution of real Pareto optimal solutions does not necessarily mean that the objective function is optimized.In data mining,it is common to mine frequent itemsets appear-ing together in a dataset to reveal the correlation between data.Inspired by this,we propose a novel two-layer encoding learning swarm optimizer based on frequent itemsets(TELSO)to address these SLMOPs.TELSO mined the frequent terms of multiple particles with better target values to find mask combinations that can obtain better objective values for fast convergence.Experi-mental results on five real-world problems and eight benchmark sets demonstrate that TELSO outperforms existing state-of-the-art sparse large-scale multi-objective evolutionary algorithms(SLMOEAs)in terms of performance and convergence speed.

Enhancing Evolutionary Algorithms With Pattern Mining for Sparse Large-Scale Multi-Objective Optimization Problems

Sparse large-scale multi-objective optimization problems(SLMOPs)are common in science and engineering.However,the large-scale problem represents the high dimensionality of the decision space,requiring algorithms to traverse vast expanse with limited computational resources.Furthermore,in the context of sparse,most variables in Pareto optimal solutions are zero,making it difficult for algorithms to identify non-zero variables efficiently.This paper is dedicated to addressing the challenges posed by SLMOPs.To start,we introduce innovative objective functions customized to mine maximum and minimum candidate sets.This substantial enhancement dramatically improves the efficacy of frequent pattern mining.In this way,selecting candidate sets is no longer based on the quantity of nonzero variables they contain but on a higher proportion of nonzero variables within specific dimensions.Additionally,we unveil a novel approach to association rule mining,which delves into the intricate relationships between non-zero variables.This novel methodology aids in identifying sparse distributions that can potentially expedite reductions in the objective function value.We extensively tested our algorithm across eight benchmark problems and four real-world SLMOPs.The results demonstrate that our approach achieves competitive solutions across various challenges.

Post-disaster spatial reconstruction from the perspective of a rural settlement niche in the upper reaches of the Minjiang River

Post-disaster recovery and reconstruction provide an effective way to reduce the disaster vulnerability of, and promote leapfrog development in, an affected area. To date, studies that have used administrative boundaries to investigate the reconstruction of settlement space have not been able to clearly define the real boundaries of land use changes or quantify the degree of response to the ‘Build-Back-Better’ initiative, and have lacked any consideration of the fourth reconstruction stage–development period(10 years). This study constructed a mountain settlement niche and analyzed the characteristics, spatial reconstruction, and drivers of rural settlements during 2009–2019 in the upper reaches of the Minjiang River, southwest China. The results showed the following:(1) Natural factors were the basis for the formation and development of mountain settlement niches. The scale of the settlement niche and its land use structure depended on the physical geography features and the ethnic farming and grazing traditions. The settlement niche provided a realistic boundary for the spatial reconstruction.(2) The layout of residential land around cropland was the common feature of the mountain settlement niche. Of all the land use types, the roads and rural residential lands showed the most change over the 10 years;13,860 residential patches increased in size and 4,742 patches were abandoned.(3) The area of orchards, planted to reconstruct the economy in the mountains, increased by nearly 2.5 times.(4) Collapses, landslides, and debris flow disasters and the ecological red line influenced the spatial reconstruction. While the main focus of post-disaster recovery is spatial reconstruction, initiatives should include economic and spiritual recovery, and should also achieve sustainable development of the region.

Large-scale model testing of high-pressure grouting reinforcement for bedding slope with rapid-setting polyurethane

Bedding slope is a typical heterogeneous slope consisting of different soil/rock layers and is likely to slide along the weakest interface.Conventional slope protection methods for bedding slopes,such as retaining walls,stabilizing piles,and anchors,are time-consuming and labor-and energy-intensive.This study proposes an innovative polymer grout method to improve the bearing capacity and reduce the displacement of bedding slopes.A series of large-scale model tests were carried out to verify the effectiveness of polymer grout in protecting bedding slopes.Specifically,load-displacement relationships and failure patterns were analyzed for different testing slopes with various dosages of polymer.Results show the great potential of polymer grout in improving bearing capacity,reducing settlement,and protecting slopes from being crushed under shearing.The polymer-treated slopes remained structurally intact,while the untreated slope exhibited considerable damage when subjected to loads surpassing the bearing capacity.It is also found that polymer-cemented soils concentrate around the injection pipe,forming a fan-shaped sheet-like structure.This study proves the improvement of polymer grouting for bedding slope treatment and will contribute to the development of a fast method to protect bedding slopes from landslides.

Quantitative geochemical reconstruction of Eocene paleoenvironment in Fushun Basin,northeast China

The quantitative estimation of key parameters of paleotemperature and paleoprecipitation is crucial for paleoclimate reconstruction.Geochemical data from mod-ern sediments are highly consistent with climate data,and their relationship can provide an important reference for the quantitative reconstruction of the paleoclimate.In this study,detailed inorganic geochemical analysis was carried out using high-precision sampling of the Paleogene(LFD-1 well)Guchengzi,Jijuntun and Xiloutian Formations in the Fushun Basin located in the mid-latitudes of the Northern Hemisphere.The Eocene Guchengzi Formation(54.51–47.8 Ma)and Jijuntun Formation(47.8–41.2 Ma)in the Fushun Basin were found to have been deposited under a humid climate.The lower(41.2–40.1 Ma)and upper(40.1–37.8 Ma)parts of the Xiloutian Formation were character-ized by semiarid and semihumid–semiarid climates,respec-tively,which is very similar to the paleoclimatic information reflected by organic carbon isotopes.The Eocene Thermal Maximum 2(ETM2,~53.7 Ma),Early Eocene Climatic Optimum(EECO,~53.1–46.5 Ma),Eocene Thermal Maxi-mum 3(ETM 3,~52.8 Ma),and Middle Eocene Climatic Optimum(MECO,~40.7–40.1 Ma)events significantly enhanced chemical weathering during these periods.The rapid increase in pCO_(2)concentration leads to an increase in temperature,precipitation,and surface runoff,exhibiting strong chemical weathering.The mean annual temperature(MATa)and mean annual precipitation(MAPa,MAPb,and MAPc)were estimated using parameters,such as the corrosion index without potassium(CIA-K),CaO/Al_(2)O_(3),and(Na_(2)O+K_(2)O)/Al_(2)O_(3).Comparing MAPa,MAPb,and MAPc with the MAP estimated using pollen data,MAPa and MAPb were found to be more sensitive to the climate during high precipitation periods(precipitation>1000 mm,Guchengzi Formation),and the recovered average precipi-tation was similar to MAP.In contrast,MAPc was more sensitive to the climate during low precipitation periods(precipitation<1000 mm,Jijuntun,and Xiloutian Forma-tions),with higher accuracy.To fully consider the influence of soluble inorganic salts Ca^(2+)and Na^(+),multivariate linear equations of CIA-K and CaO/Al_(2)O_(3)with CIA,and CIA-K and CaO/Al_(2)O_(3)with MAP were constructed,namely MAPd and MAPe.The results show that MAPe has the highest per-formance and can be effectively used to estimate the change of paleoprecipitation in Northeast Asia.

A semantic vector map-based approach for aircraft positioning in GNSS/GPS denied large-scale environment

Accurate positioning is one of the essential requirements for numerous applications of remote sensing data,especially in the event of a noisy or unreliable satellite signal.Toward this end,we present a novel framework for aircraft geo-localization in a large range that only requires a downward-facing monocular camera,an altimeter,a compass,and an open-source Vector Map(VMAP).The algorithm combines the matching and particle filter methods.Shape vector and correlation between two building contour vectors are defined,and a coarse-to-fine building vector matching(CFBVM)method is proposed in the matching stage,for which the original matching results are described by the Gaussian mixture model(GMM).Subsequently,an improved resampling strategy is designed to reduce computing expenses with a huge number of initial particles,and a credibility indicator is designed to avoid location mistakes in the particle filter stage.An experimental evaluation of the approach based on flight data is provided.On a flight at a height of 0.2 km over a flight distance of 2 km,the aircraft is geo-localized in a reference map of 11,025 km~2using 0.09 km~2aerial images without any prior information.The absolute localization error is less than 10 m.

Online identification and extraction method of regional large-scale adjustable load-aggregation characteristics

This article introduces the concept of load aggregation,which involves a comprehensive analysis of loads to acquire their external characteristics for the purpose of modeling and analyzing power systems.The online identification method is a computer-involved approach for data collection,processing,and system identification,commonly used for adaptive control and prediction.This paper proposes a method for dynamically aggregating large-scale adjustable loads to support high proportions of new energy integration,aiming to study the aggregation characteristics of regional large-scale adjustable loads using online identification techniques and feature extraction methods.The experiment selected 300 central air conditioners as the research subject and analyzed their regulation characteristics,economic efficiency,and comfort.The experimental results show that as the adjustment time of the air conditioner increases from 5 minutes to 35 minutes,the stable adjustment quantity during the adjustment period decreases from 28.46 to 3.57,indicating that air conditioning loads can be controlled over a long period and have better adjustment effects in the short term.Overall,the experimental results of this paper demonstrate that analyzing the aggregation characteristics of regional large-scale adjustable loads using online identification techniques and feature extraction algorithms is effective.

Simulation and reconstruction of particle trajectories in the CEPC drift chamber

The circular electron-positron collider(CEPC)is designed to precisely measure the properties of the Higgs boson,study electroweak interactions at the Z-boson peak,and search for new physics beyond the Standard Model.As a component of the 4th conceptual CEPC detector,the drift chamber facilitates the measurement of charged particles.This study implemented a Geant4-based simulation and track reconstruction for the drift chamber.For the simulation,detector construction and response were implemented and added to the CEPC simulation chain.The development of track reconstruction involves track finding using the combinatorial Kalman filter method and track fitting using the tool of GenFit.Using the simulated data,the tracking performance was studied.The results showed that both the reconstruction resolution and tracking efficiency satisfied the requirements of the CEPC experiment.

Comparative analysis of two digestive tract reconstruction methods in total laparoscopic radical total gastrectomy

BACKGROUND The incidence of gastric cancer has significantly increased in recent years.Surgical resection is the main treatment,but the method of digestive tract reconstruction after gastric cancer surgery remains controversial.In the current study,we sought to explore a reasonable method of digestive tract reconstruction and improve the quality of life and nutritional status of patients after surgery.To this end,we statistically analyzed the clinical results of patients with gastric cancer who underwent jejunal interposition double-tract reconstruction(DTR)and esophageal jejunum Roux-en-Y reconstruction(RY).AIM To explore the application effect of DTR in total laparoscopic radical total gastrectomy(TLTG)and evaluate its safety and efficacy.METHODS We collected the relevant data of 77 patients who underwent TLTG at the Fourth Hospital of Hebei Medical University from October 2021 to January 2023.Among them,35 cases were treated with DTR,and the remaining 42 cases were treated with traditional RY.After 1:1 propensity score matching,the cases were grouped into 31 cases per group,with evenly distributed data.The clinical characteristics and short-and long-term clinical outcomes of the two groups were statistically analyzed.RESULTS The two groups showed no significant differences in basic data,intraoperative blood loss,number of lymph node dissections,first defecation time after operation,postoperative hospital stay,postoperative complications,and laboratory examination results on the 1st,3rd,and 5th days after operation.The operation time of the DTR group was longer than that of the RY group[(307.58±65.14)min vs(272.45±62.09)min,P=0.016],but the first intake of liquid food in the DTR group was shorter than that in the RY group[(4.45±1.18)d vs(6.0±5.18)d,P=0.028].The incidence of reflux heartburn(Visick grade)and postoperative gallbladder disease in the DTR group was lower than that in the RY group(P=0.033 and P=0.038).Although there was no significant difference in body weight,hemoglobin,prealbumin,and albumin between the two groups at 1,3 and 6 months after surgery,the diet of patients in the DTR group was better than that in the RY group(P=0.031).CONCLUSION The clinical effect of DTR in TLTG is better than that of RY,indicating that it is a more valuable digestive tract reconstruction method in laparoscopic gastric cancer surgery.

Development of D_α band symmetrical visible optical diagnostic for boundary reconstruction on EAST tokamak

To investigate the potential of utilizing visible spectral imaging for controlling the plasma boundary shape during stable operation of plasma in future tokamak, a D_α band symmetric visible light diagnostic system was designed and implemented on the Experimental Advanced Superconducting Tokamak(EAST). This system leverages two symmetric optics for joint plasma imaging. The optical system exhibits a spatial resolution less than 2 mm at the poloidal cross-section, distortion within the field of view below 10%, and relative illumination of 91%.The high-quality images obtained enable clear observation of both the plasma boundary position and the characteristics of components within the vacuum vessel. Following system calibration and coordinate transformation, the image coordinate boundary features are mapped to the tokamak coordinate system. Utilizing this system, the plasma boundary was reconstructed, and the resulting representation showed alignment with the EFIT(Equilibrium Fitting) results. This underscores the system's superior performance in boundary reconstruction applications and provides a diagnostic foundation for boundary shape control based on visible spectral imaging.

Application of multi-planar reconstruction technique in endovascular repair of aortic dissection

BACKGROUND Endovascular repair of aortic dissection is an effective method commonly used in the treatment of Stanford type B aortic dissection.Stent placement during the operation was one-time and could not be repeatedly adjusted during the operation.Therefore,it is of great significance for cardiovascular physicians to fully understand the branch status,position,angle,and other information regarding aortic arch dissection before surgery.AIM To provide more references for clinical cardiovascular physicians to develop treatment plans.METHODS Data from 153 patients who underwent endovascular repair of aortic dissection at our hospital between January 2021 and December 2022 were retrospectively collected.All patients underwent multi-slice spiral computed tomography angiography.Based on distinct post-image processing techniques,the patients were categorized into three groups:Multiplanar reconstruction(MPR)(n=55),volume reconstruction(VR)(n=46),and maximum intensity projection(MIP)(n=52).The detection rate of aortic rupture,accuracy of the DeBakey classification,rotation,and tilt angles of the C-arm during the procedure,dispersion after stent release,and the incidence of late complications were recorded and compared.RESULTS The detection rates of interlayer rupture in the MPR and VR groups were significantly higher than that in the MIP group(P<0.05).The detection rates of De-Bakey subtypesⅠ,Ⅱ,andⅢin the MPR group were higher than those in the MIP group,and the detection rate of typeⅢin the MPR group was significantly higher than that in the VR group(P<0.05).There was no statistically significant difference in the detection rates of typesⅠandⅡcompared to the VR group(P>0.05).The scatter rate of markers and the incidence of complications in the MPR group were significantly lower than those in the VR and MIP groups(P<0.05).CONCLUSION The application of MPR in the endovascular repair of aortic dissection has improved the detection rate of dissection rupture,the accuracy of anatomical classification,and safety.

Photo-promoted rapid reconstruction induced alterations in active site of Ag@amorphous NiFe hydroxides for enhanced oxygen evolution reaction

The dynamic surface self-reconstruction behavior in local structure correlates with oxygen evolution reaction(OER)performance,which has become an effective strategy for constructing the catalytic active phase.However,it remains a challenge to understand the mechanisms of reconstruction and to accomplish it fast and deeply.Here,we reported a photo-promoted rapid reconstruction(PRR)process on Ag nanoparticle-loaded amorphous Ni-Fe hydroxide nanosheets on carbon cloth for enhanced OER.The photogenerated holes generated by Ag in conjunction with the anodic potential contributed to a thorough reconstruction of the amorphous substrate.The valence state of unsaturated coordinated Fe atoms,which serve as active sites,is significantly increased,while the corresponding crystalline substrate shows little change.The different structural evolutions of amorphous and crystalline substrates during reconstruction lead to diverse pathways of OER.This PRR utilizing loaded noble metal nanoparticles can accelerate the generation of active species in the substrate and increase the electrical conductivity,which provides a new inspiration to develop efficient catalysts via reconstruction strategies.

Scene 3-D Reconstruction System in Scattering Medium

Research on neural radiance fields for novel view synthesis has experienced explosive growth with the development of new models and extensions.The NeRF(Neural Radiance Fields)algorithm,suitable for underwater scenes or scattering media,is also evolving.Existing underwater 3D reconstruction systems still face challenges such as long training times and low rendering efficiency.This paper proposes an improved underwater 3D reconstruction system to achieve rapid and high-quality 3D reconstruction.First,we enhance underwater videos captured by a monocular camera to correct the image quality degradation caused by the physical properties of the water medium and ensure consistency in enhancement across frames.Then,we perform keyframe selection to optimize resource usage and reduce the impact of dynamic objects on the reconstruction results.After pose estimation using COLMAP,the selected keyframes undergo 3D reconstruction using neural radiance fields(NeRF)based on multi-resolution hash encoding for model construction and rendering.In terms of image enhancement,our method has been optimized in certain scenarios,demonstrating effectiveness in image enhancement and better continuity between consecutive frames of the same data.In terms of 3D reconstruction,our method achieved a peak signal-to-noise ratio(PSNR)of 18.40 dB and a structural similarity(SSIM)of 0.6677,indicating a good balance between operational efficiency and reconstruction quality.

Large-Scale Multi-Objective Optimization Algorithm Based on Weighted Overlapping Grouping of Decision Variables

The large-scale multi-objective optimization algorithm(LSMOA),based on the grouping of decision variables,is an advanced method for handling high-dimensional decision variables.However,in practical problems,the interaction among decision variables is intricate,leading to large group sizes and suboptimal optimization effects;hence a large-scale multi-objective optimization algorithm based on weighted overlapping grouping of decision variables(MOEAWOD)is proposed in this paper.Initially,the decision variables are perturbed and categorized into convergence and diversity variables;subsequently,the convergence variables are subdivided into groups based on the interactions among different decision variables.If the size of a group surpasses the set threshold,that group undergoes a process of weighting and overlapping grouping.Specifically,the interaction strength is evaluated based on the interaction frequency and number of objectives among various decision variables.The decision variable with the highest interaction in the group is identified and disregarded,and the remaining variables are then reclassified into subgroups.Finally,the decision variable with the strongest interaction is added to each subgroup.MOEAWOD minimizes the interactivity between different groups and maximizes the interactivity of decision variables within groups,which contributed to the optimized direction of convergence and diversity exploration with different groups.MOEAWOD was subjected to testing on 18 benchmark large-scale optimization problems,and the experimental results demonstrate the effectiveness of our methods.Compared with the other algorithms,our method is still at an advantage.