The planar force model of prepreg,initially established based on the principle of minimum potential energy and the Rayleigh-Ritz method,was improved by considering the difference between the tensile and compressive mo...The planar force model of prepreg,initially established based on the principle of minimum potential energy and the Rayleigh-Ritz method,was improved by considering the difference between the tensile and compressive moduli in the direction of the prepreg fibers.Compressivetensile stress distribution coefficients were also established.Combined with tests on the effect of process parameters on interlayer tack,a theoretical prediction model for the turning radius related to process parameters was developed,and the impact of prepreg interlayer tack force on the minimum turning radius was analyzed.A finite element simulation model for prepreg curve placement was created to study the size and distribution patterns of folds generated during the prepreg turning process.A minimum turning radius test was conducted to establish evaluation criteria for surface defects in curve placement and verify the accuracy of the minimum turning radius prediction model.Based on this,a prediction method for the minimum turning radius of prepreg related to process parameters was established,providing constraints for the trajectory design of variable-stiffness placement composites.展开更多
Machine learning-assisted methods for rapid and accurate prediction of temperature field,mushy zone,and grain size were proposed for the heating−cooling combined mold(HCCM)horizontal continuous casting of C70250 alloy...Machine learning-assisted methods for rapid and accurate prediction of temperature field,mushy zone,and grain size were proposed for the heating−cooling combined mold(HCCM)horizontal continuous casting of C70250 alloy plates.First,finite element simulations of casting processes were carried out with various parameters to build a dataset.Subsequently,different machine learning algorithms were employed to achieve high precision in predicting temperature fields,mushy zone locations,mushy zone inclination angle,and billet grain size.Finally,the process parameters were quickly optimized using a strategy consisting of random generation,prediction,and screening,allowing the mushy zone to be controlled to the desired target.The optimized parameters are 1234℃for heating mold temperature,47 mm/min for casting speed,and 10 L/min for cooling water flow rate.The optimized mushy zone is located in the middle of the second heat insulation section and has an inclination angle of roughly 7°.展开更多
Predicting monsoon climate is one of the major endeavors in climate science and is becoming increasingly challenging due to global warming. The accuracy of monsoon seasonal predictions significantly impacts the lives ...Predicting monsoon climate is one of the major endeavors in climate science and is becoming increasingly challenging due to global warming. The accuracy of monsoon seasonal predictions significantly impacts the lives of billions who depend on or are affected by monsoons, as it is essential for the water cycle, food security, ecology, disaster prevention, and the economy of monsoon regions. Given the extensive literature on Asian monsoon climate prediction, we limit our focus to reviewing the seasonal prediction and predictability of the Asian Summer Monsoon (ASM). However, much of this review is also relevant to monsoon predictions in other seasons and regions. Over the past two decades, considerable progress has been made in the seasonal forecasting of the ASM, driven by an enhanced understanding of the sources of predictability and the dynamics of seasonal variability, along with advanced development in sophisticated models and technologies. This review centers on advances in understanding the physical foundation for monsoon climate prediction (section 2), significant findings and insights into the primary and regional sources of predictability arising from feedback processes among various climate components (sections 3 and 4), the effects of global warming and external forcings on predictability (section 5), developments in seasonal prediction models and techniques (section 6), the challenges and limitations of monsoon climate prediction (section 7), and emerging research trends with suggestions for future directions (section 8). We hope this review will stimulate creative activities to enhance monsoon climate prediction.展开更多
A complementary method to determine the vibration source intensity,defined as the weighted vertical acceleration level at the tunnel wall,is needed urgently when comparable measurements or database predictions are una...A complementary method to determine the vibration source intensity,defined as the weighted vertical acceleration level at the tunnel wall,is needed urgently when comparable measurements or database predictions are unavailable in empirical predictions.In this study,we present an analytical model designed to quickly and accurately estimate the vibration source intensity produced by moving metro trains,considering both regular and floating slab tracks.The improved periodic pipe-in-pipe(PiP)model with regular or floating slabs affixed to the tunnel invert was developed.The train loads are represented in the frequency-wavenumber domain to apply in the model.Measured track irregularities were applied and the proposed model was validated against the measured results and verified by a tunnel-soil coupled model.The proposed approach effectively and accurately assessed the vibration source intensity generated by underground trains in a prediction time of just 58 s.Track irregularities significantly affect the vibration source intensity,making them a key factor in comparable measurements or database predictions.A floating slab track can reduce the vibration source intensity by about 14 dB.The proposed approach can serve as an additional method to complement comparable measurements or database predictions for determining the vibration source intensity in empirical predictions.展开更多
Pinus radiata(D.Don)dominates New Zealand's forestry industry,constituting 91%of plantations,and is among the world's most important plantation species.Given the socio-economic and environmental importance of ...Pinus radiata(D.Don)dominates New Zealand's forestry industry,constituting 91%of plantations,and is among the world's most important plantation species.Given the socio-economic and environmental importance of this species,it is important to have accurate and precise projections over time to make efficient decisions for forest management and greenfield investments in afforestation projects,especially for permanent carbon forests.Future projections of any natural resource systems rely on modeling;however,the acceleration of climate change makes future projections of yield less certain.These challenges also impact national expectations of the contribution planted forests will provide to address climate change and meet international commitments under the Paris Agreement.Using a large national-scale set of contemporary ground-measured data(2013–2023),this study investigates the performance of two growth models developed over 30 years ago that are widely used by NZ plantation growers:1)the Pumice Plateau Model 1988(PPM88)and 2)the 300-index(including a model variant of regional drift).Model simulations were made using the FORECASTER modeling suite with geographic boundaries to adjust for drift in space and time.Basal area(BA,m^(2)⋅ha^(-1))and volume(m^(3)⋅ha^(-1))were simulated,and standard errors and goodness-of-fit metrics calculated up to a typical rotation age of 30 years.Model residuals were then separated and analysed for the main plantation growing regions.The models overpredicted observed growth by between 6.8%and 16.2%,but model predictions and errors varied significantly between regions.The results of this study provided clear evidence of divergence between the outputs of both models and the measured data.Finally,this study suggests future measures to address challenges posed by these discrepancies that will provide better information for forest management and investment decisions in a changing climate.展开更多
The F_(1)-ATPase and V_(1)-ATPase are rotary biomotors.Alignment of their amino acid sequences,which originate from bovine heart mitochondria(1BMF)and Enterococcus hirae(3VR6),respectively,demonstrates that the segmen...The F_(1)-ATPase and V_(1)-ATPase are rotary biomotors.Alignment of their amino acid sequences,which originate from bovine heart mitochondria(1BMF)and Enterococcus hirae(3VR6),respectively,demonstrates that the segment forming the ATP catalytic pocket is highly conserved.Single-molecule experiments,however,have revealed subtle differences in efficiency between the F_(1) and V_(1) motors.Here,we perform both atomistic and coarse-grained molecular dynamics simulations to investigate the mechanochemical coupling and coordination in F_(1) and V_(1) ATPase.Our results show that the correlation between conformational changes in F_(1) is stronger than that in V_(1),indicating that the mechanochemical coupling in F_(1) is tighter than in V_(1).Moreover,the unidirectional rotation of F_(1) is more processive than that of V_(1),which accounts for the higher efficiency observed in F_(1) and explains the occasional backward steps detected in single-molecule experiments on V_(1).展开更多
Precise forecasts of wildfire danger are crucial for proactive fuel management and emergency responses,yet they pose a challenge at the subseasonal scale due to limitations in prediction capabilities and a gap between...Precise forecasts of wildfire danger are crucial for proactive fuel management and emergency responses,yet they pose a challenge at the subseasonal scale due to limitations in prediction capabilities and a gap between forecast outputs and the needs of decision-makers.This study introduces an innovative hybrid modeling framework that integrates artificial intelligence(AI)with climate dynamic prediction systems to accurately forecast High Fire-Danger Days(HFDDs)for the following month.These HFDDs are derived from historical satellite fire data and the optimum fire danger index,with a particular focus on Southwest China as a case study.The AI module,based on the ResNet-18 neural network model,integrates observational and physically constrained analysis to establish links between HFDDs and optimal predictors of atmospheric circulation from both the concurrent and preceding months.Leveraging climate dynamical forecasting,this hybrid model provides more reliable deterministic predictions for monthly HFDDs than conventional methods that rely solely on terrestrial variables such as precipitation.More importantly,the integration of dynamical ensemble prediction enhances the model’s capability for skillful probabilistic predictions of HFDDs,facilitating the creation of customized fire danger outlooks and emergency action maps tailored to stakeholders’needs.The model’s added economic value was also evaluated,demonstrating its potential to improve decision-making in disaster management and bridge the“last-mile gap”in climate service delivery.This work contributes to the Seamless Prediction and Services for Sustainable Natural and Built Environment(SEPRESS)Program(2025–32),under the United Nations Educational Scientific and Cultural Organization(UNESCO)International Decade of Sciences for Sustainable Development(2024–33).展开更多
Objective:To investigate the impact of targeted nursing interventions based on frailty prediction models on peri-hospitalization clinical outcomes in middle-aged and elderly patients with upper gastrointestinal bleedi...Objective:To investigate the impact of targeted nursing interventions based on frailty prediction models on peri-hospitalization clinical outcomes in middle-aged and elderly patients with upper gastrointestinal bleeding(UGIB).Methods:A prospective cohort study was conducted,and 126 middle-aged and elderly patients with UGIB admitted from August 2024 to August 2025 were selected as the study subjects.The patients were divided into the intervention group(63 cases)and the control group(63 cases)based on whether they received nursing intervention based on frailty prediction models.The control group received routine care,while the intervention group,on the basis of routine care,used the FRAIL scale combined with laboratory indicators(albumin,hemoglobin,etc.)to establish a predictive model to evaluate patients within 24 hours of admission,and implemented multi-dimensional targeted nursing intervention for pre-frailty or frailty patients screened out.The incidence of frailty,rebleeding rate,average length of stay,hospitalization cost,and nursing satisfaction during hospitalization were compared between the two groups.Results:The incidence of frailty during hospitalization in the intervention group was 11.1%(7 cases/63 cases),significantly lower than 31.7%(20 cases/63 cases)in the control group,and the difference was statistically significant(p<0.05).The rebleeding rate of 4.8%vs 12.7%,the average length of stay of(7.2±1.5)days vs(9.1±2.2)days,and the average hospitalization cost of(23,000±6,000)yuan vs(28,000±7,000)yuan in the intervention group were all lower than those in the control group(all p<0.05).The nursing satisfaction score of the intervention group(93.5±4.2)points was higher than that of the control group(86.3±5.8)points(p<0.05).Conclusion:The frailty prediction model applied to the peri-hospitalization care of middle-aged and elderly patients with UGIB can effectively identify frailty risk.Through early targeted intervention,the incidence of frailty and rebleeding rate can be reduced,the length of hospital stay can be shortened,medical expenses can be reduced,and nursing satisfaction can be improved,which has clinical promotion value.展开更多
The rapid advancement of machine learning based tight-binding Hamiltonian(MLTB)methods has opened new avenues for efficient and accurate electronic structure simulations,particularly in large-scale systems and long-ti...The rapid advancement of machine learning based tight-binding Hamiltonian(MLTB)methods has opened new avenues for efficient and accurate electronic structure simulations,particularly in large-scale systems and long-time scenarios.This review begins with a concise overview of traditional tight-binding(TB)models,including both(semi-)empirical and first-principles approaches,establishing the foundation for understanding MLTB developments.We then present a systematic classification of existing MLTB methodologies,grouped into two major categories:direct prediction of TB Hamiltonian elements and inference of empirical parameters.A comparative analysis with other ML-based electronic structure models is also provided,highlighting the advancement of MLTB approaches.Finally,we explore the emerging MLTB application ecosystem,highlighting how the integration of MLTB models with a diverse suite of post-processing tools from linear-scaling solvers to quantum transport frameworks and molecular dynamics interfaces is essential for tackling complex scientific problems across different domains.The continued advancement of this integrated paradigm promises to accelerate materials discovery and open new frontiers in the predictive simulation of complex quantum phenomena.展开更多
The Leafminers,representing a diverse group of insects from various genera within the Agromyzidae family,pose a significant threat to spinach(Spinacia oleracea L.)production.This study aimed to identify single nucleot...The Leafminers,representing a diverse group of insects from various genera within the Agromyzidae family,pose a significant threat to spinach(Spinacia oleracea L.)production.This study aimed to identify single nucleotide polymorphism(SNP)markers associated with leafminer resistance through a genome-wide association study(GWAS)and to evaluate the prediction accuracy(PA)for selecting resistant spinach using genomic prediction(GP).Using a dataset of 84301 SNPs obtained from whole-genome resequencing,seven GWAS models,including BLINK,FarmCPU,MLM,and MLMM in GAPIT 3,as well as MLM,GLM,and SMR in TASSEL 5,were employed to perform GWAS on a panel of 286 USDA spinach germplasm accessions.Three SNP markers,namely 1_115279256_C_T,3_157082529_C_T,and 4_168510908_T_G on chromosomes 1,3,and 4,respectively,were identified as associated with leafminer resistance.In the 30 kb flanking regions of these markers,four candidate genes(SOV1g031330,SOV1g031340,SOV4g047270,and SOV4g047280),encoding LOB domain-containing protein,KH domain-containing protein,were discovered.Nodulin-like domain-containing protein,and SAM domain-containing protein,were discovered.The PA for leafminer resistance selection was estimated using ten different SNP sets,including two GWAS-derived marker sets(three and 51 SNPs)and eight random marker sets(ranging from 51 to 10 K SNPs)analyzed by seven GP models.The findings emphasized the superior performance of GWAS-derived SNP sets,reaching a PA of up to 0.79 using the cBLUP model.Notably,this research marks the pioneering application of GP in the context of insect resistance,providing a significant advancement in the understanding and management of leafminer resistance in spinach cultivation.展开更多
Vitrimers belong to a class of polymeric materials capable of bond exchange reactions,showing great promise for environmental protection and sustainable development.However,studies on the coupling mechanism between th...Vitrimers belong to a class of polymeric materials capable of bond exchange reactions,showing great promise for environmental protection and sustainable development.However,studies on the coupling mechanism between the bond exchange kinetics and segmental dynamics near the glass transition temperature(T_(g))remain scarce.Herein,we employed molecular dynamics simulations to investigate the dynamic heterogeneity of the segment motion and bond exchange in vitrimers.The simulation results revealed that the bond exchange energy barrier exerts a much stronger influence on the bond exchange kinetics than on the segmental dynamics.At lower temperatures,slower segmental relaxation further constraind the bond exchange rate.Additionally,increasing the bond exchange energy barrier markedly enhanced the dynamic heterogeneity of segment motion.A close correlation was observed between heterogeneity and bond exchange.This study elucidated the coupling mechanism between bond exchange and segmental dynamics at the molecular scale,thereby providing a theoretical basis for designing vitrimer materials with tunable dynamic properties.展开更多
Deep learning and fatigue life prediction remain focal research areas in rail vehicle engineering.This study addresses the vibration fatigue of wheelset lifting lug in Chengdu Metro Line 1 bogies,aiming to develop a f...Deep learning and fatigue life prediction remain focal research areas in rail vehicle engineering.This study addresses the vibration fatigue of wheelset lifting lug in Chengdu Metro Line 1 bogies,aiming to develop a fatigue life prediction method for critical bogie components using deep learning models and measured track load spectra.Extensive field tests on Chengdu Metro Line 1 were conducted to acquire acceleration and stress response data of the wheelset lifting lug,generating training samples for the neural network system.Component stress responses were calculated via time-domain track acceleration and validated against in-situ stress measurements.Results show that neural network-fitted dynamic stress values exhibit excellent consistency with measured data,with errors constrained within 5%.This study validates the proposed small-sample deep learning approach as an effective and accurate solution for fatigue life prediction of critical bogie components under operational load conditions.展开更多
Excessive blasting-induced vibration during drilling-and-blasting excavation of deep tunnels can trigger geological hazards and compromise the stability of both the rock mass and support structures.This study focused ...Excessive blasting-induced vibration during drilling-and-blasting excavation of deep tunnels can trigger geological hazards and compromise the stability of both the rock mass and support structures.This study focused on the deep double-line Sejila Mountain tunnel to systematically analyze the spatial response of blasting-induced vibration and to develop a prediction model through field tests and numerical simulations.The results revealed that the presence of a cross passage significantly altered propagation paths and the spatial distribution of blasting-induced vibration velocity.The peak particle velocity(PPV)at the cross-passage corner was amplified by approximately 1.92 times due to wave reflection and geometric focusing.Blasting-induced vibration waves attenuated non-uniformly across the tunnel cross-section,where PPV on the blast-face side was 1.54–6.56 times higher than that on the opposite side.We propose an improved PPV attenuation model that accounts for the propagation path effect.This model significantly improved fitting accuracy and resolved anomalous parameter(k and a)estimates in traditional equations,thereby improving prediction reliability.Furthermore,based on the observed spatial distribution of blasting-induced vibration,optimal monitoring point placement and targeted vibration control measures for tunnel blasting were discussed.These findings provide a scientific basis for designing blasting schemes and vibration mitigation strategies in deep tunnels.展开更多
The dense heterogeneous network provides standardized connectivity and access guarantees for 5G communication services.However,the complex network environment and high level of dynamism pose challenges to network sele...The dense heterogeneous network provides standardized connectivity and access guarantees for 5G communication services.However,the complex network environment and high level of dynamism pose challenges to network selection decisions.Existing vertical handover algorithms often overlook the dynamic nature of user mobility and network condition,resulting in problems such as handover failure and frequent handover,ultimately impacting the quality of the user communication service.To address these problems,we propose an intelligent switching method,iMALSTM-DQN,which integrates an improved Multi-level Associative Long Short-Term Memory model(iMALSTM)with Deep Reinforcement Learning(DRL).The algorithm leverages iMALSTM to predict the global network state in the next moment based on the global user movement trajectory and historical network status information within a region,thereby enhancing the prediction accuracy of network states.Subsequently,based on the predicted network state,we employ the Deep Q Network(DON)model to make handover decisions,adaptively determining the optimal switching and network selection strategy through interaction with the environment.Experimental results demonstrate that the proposed algorithm enhances decision timeliness,significantly reduces the number of switch failures,and alleviates the problem of frequent handovers resulting from network dynamics.展开更多
UHMWPE fibers exhibit impressive modulus and strength,but they have not reached their theoretical limits.Researchers focus on molecular weight,orientation,and crystallinity of UHMWPE,yet their contributions to mechani...UHMWPE fibers exhibit impressive modulus and strength,but they have not reached their theoretical limits.Researchers focus on molecular weight,orientation,and crystallinity of UHMWPE,yet their contributions to mechanical properties are unclear.Molecular dynamics simulations are valuable but often limited by computational constraints.Our aim is to simulate higher molecular weights to better represent real UHMWPE fibers.We used Packmol and Polyply methodologies to construct PE systems,with Polyply reproducing more reasonable properties of UHMWPE fibers.Additionally,tensile simulations showed that orientation and crystallinity greatly impact Young's modulus more than molecular weight.Energy decomposition indicated that higher molecular weights lead to covalent bonds that can withstand more energy during stretching,thus increasing breaking strength.Combining simulations with machine learning,we found that orientation has the most significant impact on Young's modulus,contributing 60%,and molecular weight plays the most crucial role in determining the breaking strength,accounting for 65%.This study provides a theoretical basis and guidelines for enhancing UHMWPE's modulus and strength.展开更多
A full-face rock tunnel boring machine(TBM)has two main parameters:cutterhead thrust and cutterhead torque.Cutterhead thrust can be predicted numerically using different methods,such as the rated thrust of the disc cu...A full-face rock tunnel boring machine(TBM)has two main parameters:cutterhead thrust and cutterhead torque.Cutterhead thrust can be predicted numerically using different methods,such as the rated thrust of the disc cutter,the indentation test of the disc cutter,a linear breaking experiment,or applying statistical models based on field data.However,cutterhead torque prediction still lacks the necessary and sufficient theoretical analysis and corresponding research.Consequently,we studied the rock-breaking characteristics of the disc cutter on the cutterhead and found a difference between the lateral friction resistance and the inner and outer rock breaking.We then proposed the cutterhead torque composition of the full-face rock tunnel boring machine,shoveling and transporting ballast torque,rolling resistance torque,lateral friction resistance torque,and disc cutter lateral imbalance torque,and established the corresponding theory.Through practical engineering ver-ification,we found that the relative error between the cutterhead torque value calculated by the theory and the actual value was only 0.51%.If the lateral friction resistance moment of the disc cutter rock breaking and the lateral imbalance moment of the disc cutter are not considered,the relative error between the calculated value of cutterhead torque and the actual value is 7.48%.Therefore,the accuracy of the established cutterhead torque theory is 6.97%higher than that of the disc cutter linear rolling crushing rock test theory.The research results provide an important reference for determining the cutterhead torque of a full-face rock tunnel boring machine.展开更多
Accurate prediction of rockburst intensity levels is crucial for ensuring the safety of deep hard rock engineering construction.This paper introduced an expert system for rockburst intensity level prediction that empl...Accurate prediction of rockburst intensity levels is crucial for ensuring the safety of deep hard rock engineering construction.This paper introduced an expert system for rockburst intensity level prediction that employs machine learning algorithms as the basis for its inference rules.The system comprises four modules:a database,a repository,an inference engine,and an interpreter.A database containing 1114 rockburst cases was used to construct 357 datasets that serve as the repository for the expert system.Additionally,19 types of machine learning algorithms were used to establish 6783 micro-models to construct cognitive rules within the inference engine.By integrating probability theory and marginal analysis,a fuzzy scoring method based on the SoftMax function was developed and applied to the interpreter for rockburst intensity level prediction,effectively restoring the continuity of rockburst characteristics.The research results indicate that ensemble algorithms based on decision trees are more effective in capturing the characteristics of rockburst.Key factors for accurate prediction of rockburst intensity include uniaxial compressive strength,elastic energy index,the maximum principal stress,tangential stress,and their composite indicators.The accuracy of the proposed rockburst intensity level prediction expert system was verified using 20 engineering rockburst cases,with predictions aligning closely with the actual rockburst intensity levels.展开更多
In recent years,three-dimensional reconstruction technologies that employ multiple cameras have continued to evolve significantly,enabling remote collaboration among users in extended Reality(XR)environments.In additi...In recent years,three-dimensional reconstruction technologies that employ multiple cameras have continued to evolve significantly,enabling remote collaboration among users in extended Reality(XR)environments.In addition,methods for deploying multiple cameras for motion capture of users(e.g.,performers)are widely used in computer graphics.As the need to minimize and optimize the number of cameras grows to reduce costs,various technologies and research approaches focused on Optimal Camera Placement(OCP)are continually being proposed.However,as most existing studies assume homogeneous camera setups,there is a growing demand for studies on heterogeneous camera setups.For instance,technical demands keep emerging in scenarios with minimal camera configurations,especially regarding cost factors,the physical placement of cameras given the spatial structure,and image capture strategies for heterogeneous cameras,such as high-resolution RGB cameras and depth cameras.In this study,we propose a pre-visualization and simulation method for the optimal placement of heterogeneous cameras in XR environments,accounting for both the specifications of heterogeneous cameras(e.g.,field of view)and the physical configuration(e.g.,wall configuration)in real-world spaces.The proposed method performs a visibility analysis of cameras by considering each camera’s field-of-view volume,resolution,and unique characteristics,along with physicalspace constraints.This approach enables the optimal position and rotation of each camera to be recommended,along with the minimum number of cameras required.In the results of our study conducted in heterogeneous camera combinations,the proposed method achieved 81.7%~82.7%coverage of the target visual information using only 2~3 cameras.In contrast,single(or homogeneous)-typed cameras were required to use 11 cameras for 81.6%coverage.Accordingly,we found that camera deployment resources can be reduced with the proposed approaches.展开更多
Because of the developed surface of the Triply PeriodicMinimumSurface(TPMS)structures,polylactide(PLA)products with a TPMS structure are thought to be promising bio soluble implants with the potential for targeted dru...Because of the developed surface of the Triply PeriodicMinimumSurface(TPMS)structures,polylactide(PLA)products with a TPMS structure are thought to be promising bio soluble implants with the potential for targeted drug delivery.For implants,mechanical properties are key performance characteristics,so understanding the deformation and failure mechanisms is essential for selecting the appropriate implant structure.The deformation and fracture processes in PLA samples with different interior architectures have been studied through computer simulation and experimental research.Two TPMS topologies,the Schwarz Diamond and Gyroid architectures,were used for the sample construction by 3D printing.ANSYS software was utilized to simulate compressive deformation.It was found that under the same load,the vonMises stresses in the Gyroid structure are higher than those in the Schwartz Diamond structure,which was associated with the different orientations of the cells in the studied structures in relation to the direction of the loading axis.The deformation process occurs in the local regions of the studied TPMS structures.Maximum von Mises stresses were observed in the vertical parts of the structures oriented along the load direction.It was found that,unlike the Gyroid,the Schwartz Diamond structure contains a frame that forms unique stiffening ribs,which ensures the redistribution of the load under the vertical loading direction.An analysis of the mechanical characteristics of PLA samples with the Schwartz Diamond and Gyroid structures produced by the Fused Deposition Modeling(FDM)method was correlated with computer simulation.The Schwarz Diamond-type structure was shown to have a higher absorption energy than the Gyroid one.A study of the fracture in PLA samples with various cell sizes revealed a particular feature related to the samples’periodic surface topology and the 3D printing process.Scanning electron microscopic(SEM)studies of the samples deformed by compression showed thatwith an increase in the density of the samples,the failure mechanism changes from ductile to quasi-brittle due to the complex participation of both cell deformation and fiber deformation.展开更多
Most predictive maintenance studies have emphasized accuracy but provide very little focus on Interpretability or deployment readiness.This study improves on prior methods by developing a small yet robust system that ...Most predictive maintenance studies have emphasized accuracy but provide very little focus on Interpretability or deployment readiness.This study improves on prior methods by developing a small yet robust system that can predict when turbofan engines will fail.It uses the NASA CMAPSS dataset,which has over 200,000 engine cycles from260 engines.The process begins with systematic preprocessing,which includes imputation,outlier removal,scaling,and labelling of the remaining useful life.Dimensionality is reduced using a hybrid selection method that combines variance filtering,recursive elimination,and gradient-boosted importance scores,yielding a stable set of 10 informative sensors.To mitigate class imbalance,minority cases are oversampled,and class-weighted losses are applied during training.Benchmarking is carried out with logistic regression,gradient boosting,and a recurrent design that integrates gated recurrent units with long short-term memory networks.The Long Short-Term Memory–Gated Recurrent Unit(LSTM–GRU)hybrid achieved the strongest performance with an F1 score of 0.92,precision of 0.93,recall of 0.91,ReceiverOperating Characteristic–AreaUnder the Curve(ROC-AUC)of 0.97,andminority recall of 0.75.Interpretability testing using permutation importance and Shapley values indicates that sensors 13,15,and 11 are the most important indicators of engine wear.The proposed system combines imbalance handling,feature reduction,and Interpretability into a practical design suitable for real industrial settings.展开更多
基金co-supported by the National Natural Science Foundation of China(Nos.U24A20118,52175311,52175133,12472131,52405149)the Fundamental Research Funds for the Central Universities,China(Nos.JZ2023HGPB0289,JZ2023HGQA0475,PA2024GDSK0063)the Anhui Provincial Natural Science Foundation,China(No.2408085QE156)。
文摘The planar force model of prepreg,initially established based on the principle of minimum potential energy and the Rayleigh-Ritz method,was improved by considering the difference between the tensile and compressive moduli in the direction of the prepreg fibers.Compressivetensile stress distribution coefficients were also established.Combined with tests on the effect of process parameters on interlayer tack,a theoretical prediction model for the turning radius related to process parameters was developed,and the impact of prepreg interlayer tack force on the minimum turning radius was analyzed.A finite element simulation model for prepreg curve placement was created to study the size and distribution patterns of folds generated during the prepreg turning process.A minimum turning radius test was conducted to establish evaluation criteria for surface defects in curve placement and verify the accuracy of the minimum turning radius prediction model.Based on this,a prediction method for the minimum turning radius of prepreg related to process parameters was established,providing constraints for the trajectory design of variable-stiffness placement composites.
基金financially supported by the National Key Research and Development Program of China (No. 2023YFB3812601)the National Natural Science Foundation of China (No. 51925401)the Young Elite Scientists Sponsorship Program by CAST, China (No. 2022QNRC001)。
文摘Machine learning-assisted methods for rapid and accurate prediction of temperature field,mushy zone,and grain size were proposed for the heating−cooling combined mold(HCCM)horizontal continuous casting of C70250 alloy plates.First,finite element simulations of casting processes were carried out with various parameters to build a dataset.Subsequently,different machine learning algorithms were employed to achieve high precision in predicting temperature fields,mushy zone locations,mushy zone inclination angle,and billet grain size.Finally,the process parameters were quickly optimized using a strategy consisting of random generation,prediction,and screening,allowing the mushy zone to be controlled to the desired target.The optimized parameters are 1234℃for heating mold temperature,47 mm/min for casting speed,and 10 L/min for cooling water flow rate.The optimized mushy zone is located in the middle of the second heat insulation section and has an inclination angle of roughly 7°.
基金supported by the National Natural Science Foundation of China(Grant No.U2342208)support from NSF/Climate Dynamics Award#2025057。
文摘Predicting monsoon climate is one of the major endeavors in climate science and is becoming increasingly challenging due to global warming. The accuracy of monsoon seasonal predictions significantly impacts the lives of billions who depend on or are affected by monsoons, as it is essential for the water cycle, food security, ecology, disaster prevention, and the economy of monsoon regions. Given the extensive literature on Asian monsoon climate prediction, we limit our focus to reviewing the seasonal prediction and predictability of the Asian Summer Monsoon (ASM). However, much of this review is also relevant to monsoon predictions in other seasons and regions. Over the past two decades, considerable progress has been made in the seasonal forecasting of the ASM, driven by an enhanced understanding of the sources of predictability and the dynamics of seasonal variability, along with advanced development in sophisticated models and technologies. This review centers on advances in understanding the physical foundation for monsoon climate prediction (section 2), significant findings and insights into the primary and regional sources of predictability arising from feedback processes among various climate components (sections 3 and 4), the effects of global warming and external forcings on predictability (section 5), developments in seasonal prediction models and techniques (section 6), the challenges and limitations of monsoon climate prediction (section 7), and emerging research trends with suggestions for future directions (section 8). We hope this review will stimulate creative activities to enhance monsoon climate prediction.
基金supported by the Natural Science Foundation of Shandong Province of China(No.ZR2024QE071).
文摘A complementary method to determine the vibration source intensity,defined as the weighted vertical acceleration level at the tunnel wall,is needed urgently when comparable measurements or database predictions are unavailable in empirical predictions.In this study,we present an analytical model designed to quickly and accurately estimate the vibration source intensity produced by moving metro trains,considering both regular and floating slab tracks.The improved periodic pipe-in-pipe(PiP)model with regular or floating slabs affixed to the tunnel invert was developed.The train loads are represented in the frequency-wavenumber domain to apply in the model.Measured track irregularities were applied and the proposed model was validated against the measured results and verified by a tunnel-soil coupled model.The proposed approach effectively and accurately assessed the vibration source intensity generated by underground trains in a prediction time of just 58 s.Track irregularities significantly affect the vibration source intensity,making them a key factor in comparable measurements or database predictions.A floating slab track can reduce the vibration source intensity by about 14 dB.The proposed approach can serve as an additional method to complement comparable measurements or database predictions for determining the vibration source intensity in empirical predictions.
基金funded by Scion's Strategic Science Investment Fund(SSIF)the Forest Growers Levy Trust(FGLT)through the Resilient Forests Programme(Task No.A89220)。
文摘Pinus radiata(D.Don)dominates New Zealand's forestry industry,constituting 91%of plantations,and is among the world's most important plantation species.Given the socio-economic and environmental importance of this species,it is important to have accurate and precise projections over time to make efficient decisions for forest management and greenfield investments in afforestation projects,especially for permanent carbon forests.Future projections of any natural resource systems rely on modeling;however,the acceleration of climate change makes future projections of yield less certain.These challenges also impact national expectations of the contribution planted forests will provide to address climate change and meet international commitments under the Paris Agreement.Using a large national-scale set of contemporary ground-measured data(2013–2023),this study investigates the performance of two growth models developed over 30 years ago that are widely used by NZ plantation growers:1)the Pumice Plateau Model 1988(PPM88)and 2)the 300-index(including a model variant of regional drift).Model simulations were made using the FORECASTER modeling suite with geographic boundaries to adjust for drift in space and time.Basal area(BA,m^(2)⋅ha^(-1))and volume(m^(3)⋅ha^(-1))were simulated,and standard errors and goodness-of-fit metrics calculated up to a typical rotation age of 30 years.Model residuals were then separated and analysed for the main plantation growing regions.The models overpredicted observed growth by between 6.8%and 16.2%,but model predictions and errors varied significantly between regions.The results of this study provided clear evidence of divergence between the outputs of both models and the measured data.Finally,this study suggests future measures to address challenges posed by these discrepancies that will provide better information for forest management and investment decisions in a changing climate.
基金supported by the National Natural Science Foundation of China(Grant Nos.22193032 and 32401033)the Research Fund of Wenzhou Institute,Chinese Academy of Sciences(Grant Nos.WIUCASQD2020009,WIUCASQD2023005,XSZD2024004,2021HZSY0061,and WIUCASICTP2022)。
文摘The F_(1)-ATPase and V_(1)-ATPase are rotary biomotors.Alignment of their amino acid sequences,which originate from bovine heart mitochondria(1BMF)and Enterococcus hirae(3VR6),respectively,demonstrates that the segment forming the ATP catalytic pocket is highly conserved.Single-molecule experiments,however,have revealed subtle differences in efficiency between the F_(1) and V_(1) motors.Here,we perform both atomistic and coarse-grained molecular dynamics simulations to investigate the mechanochemical coupling and coordination in F_(1) and V_(1) ATPase.Our results show that the correlation between conformational changes in F_(1) is stronger than that in V_(1),indicating that the mechanochemical coupling in F_(1) is tighter than in V_(1).Moreover,the unidirectional rotation of F_(1) is more processive than that of V_(1),which accounts for the higher efficiency observed in F_(1) and explains the occasional backward steps detected in single-molecule experiments on V_(1).
基金J.YANG was supported by funding from the National Natural Science Foundation of China(Grant Nos.42475022,42261144671)the National Key R&D Program of China(Project No.2024YFC3013100)+2 种基金the Fundamental Research Funds for the Central UniversitiesM.LU was supported by the Otto Poon Centre of Climate Resilience and Sustainability at HKUST and the Hong Kong Research Grant Committee(Project No.16300424)Data processing and storage were supported by the National Key Scientific and Technological Infrastructure project“Earth System Numerical Simulation Facility”(EarthLab).
文摘Precise forecasts of wildfire danger are crucial for proactive fuel management and emergency responses,yet they pose a challenge at the subseasonal scale due to limitations in prediction capabilities and a gap between forecast outputs and the needs of decision-makers.This study introduces an innovative hybrid modeling framework that integrates artificial intelligence(AI)with climate dynamic prediction systems to accurately forecast High Fire-Danger Days(HFDDs)for the following month.These HFDDs are derived from historical satellite fire data and the optimum fire danger index,with a particular focus on Southwest China as a case study.The AI module,based on the ResNet-18 neural network model,integrates observational and physically constrained analysis to establish links between HFDDs and optimal predictors of atmospheric circulation from both the concurrent and preceding months.Leveraging climate dynamical forecasting,this hybrid model provides more reliable deterministic predictions for monthly HFDDs than conventional methods that rely solely on terrestrial variables such as precipitation.More importantly,the integration of dynamical ensemble prediction enhances the model’s capability for skillful probabilistic predictions of HFDDs,facilitating the creation of customized fire danger outlooks and emergency action maps tailored to stakeholders’needs.The model’s added economic value was also evaluated,demonstrating its potential to improve decision-making in disaster management and bridge the“last-mile gap”in climate service delivery.This work contributes to the Seamless Prediction and Services for Sustainable Natural and Built Environment(SEPRESS)Program(2025–32),under the United Nations Educational Scientific and Cultural Organization(UNESCO)International Decade of Sciences for Sustainable Development(2024–33).
基金Construction and Application of Frailty Trajectory Prediction Model for Middle-aged and Elderly Patients with Upper Gastrointestinal Bleeding,Project Source:Sichuan Vocational College of Nursing(Project No.:2024ZRY25)。
文摘Objective:To investigate the impact of targeted nursing interventions based on frailty prediction models on peri-hospitalization clinical outcomes in middle-aged and elderly patients with upper gastrointestinal bleeding(UGIB).Methods:A prospective cohort study was conducted,and 126 middle-aged and elderly patients with UGIB admitted from August 2024 to August 2025 were selected as the study subjects.The patients were divided into the intervention group(63 cases)and the control group(63 cases)based on whether they received nursing intervention based on frailty prediction models.The control group received routine care,while the intervention group,on the basis of routine care,used the FRAIL scale combined with laboratory indicators(albumin,hemoglobin,etc.)to establish a predictive model to evaluate patients within 24 hours of admission,and implemented multi-dimensional targeted nursing intervention for pre-frailty or frailty patients screened out.The incidence of frailty,rebleeding rate,average length of stay,hospitalization cost,and nursing satisfaction during hospitalization were compared between the two groups.Results:The incidence of frailty during hospitalization in the intervention group was 11.1%(7 cases/63 cases),significantly lower than 31.7%(20 cases/63 cases)in the control group,and the difference was statistically significant(p<0.05).The rebleeding rate of 4.8%vs 12.7%,the average length of stay of(7.2±1.5)days vs(9.1±2.2)days,and the average hospitalization cost of(23,000±6,000)yuan vs(28,000±7,000)yuan in the intervention group were all lower than those in the control group(all p<0.05).The nursing satisfaction score of the intervention group(93.5±4.2)points was higher than that of the control group(86.3±5.8)points(p<0.05).Conclusion:The frailty prediction model applied to the peri-hospitalization care of middle-aged and elderly patients with UGIB can effectively identify frailty risk.Through early targeted intervention,the incidence of frailty and rebleeding rate can be reduced,the length of hospital stay can be shortened,medical expenses can be reduced,and nursing satisfaction can be improved,which has clinical promotion value.
基金supported by the Advanced Materials-National Science and Technology Major Project(Grant No.2025ZD0618401)the National Natural Science Foundation of China(Grant No.12504285)+1 种基金the Natural Science Foundation of Jiangsu Province(Grant No.BK20250472)NFSG grant from BITS-Pilani,Dubai campus。
文摘The rapid advancement of machine learning based tight-binding Hamiltonian(MLTB)methods has opened new avenues for efficient and accurate electronic structure simulations,particularly in large-scale systems and long-time scenarios.This review begins with a concise overview of traditional tight-binding(TB)models,including both(semi-)empirical and first-principles approaches,establishing the foundation for understanding MLTB developments.We then present a systematic classification of existing MLTB methodologies,grouped into two major categories:direct prediction of TB Hamiltonian elements and inference of empirical parameters.A comparative analysis with other ML-based electronic structure models is also provided,highlighting the advancement of MLTB approaches.Finally,we explore the emerging MLTB application ecosystem,highlighting how the integration of MLTB models with a diverse suite of post-processing tools from linear-scaling solvers to quantum transport frameworks and molecular dynamics interfaces is essential for tackling complex scientific problems across different domains.The continued advancement of this integrated paradigm promises to accelerate materials discovery and open new frontiers in the predictive simulation of complex quantum phenomena.
基金supported by USDA-SCRI(Grant Nos.2017-51181-26830 and 2023-51181-41321)USDA-AMS SCMP(Grant No.16SCCMAR0001)+1 种基金Arkansas Department of Agriculture SCBGP(Grant No.AM22SCBGPAR1130-00)USDA NIFA Hatch project ARK0VG2018 and ARK02440.
文摘The Leafminers,representing a diverse group of insects from various genera within the Agromyzidae family,pose a significant threat to spinach(Spinacia oleracea L.)production.This study aimed to identify single nucleotide polymorphism(SNP)markers associated with leafminer resistance through a genome-wide association study(GWAS)and to evaluate the prediction accuracy(PA)for selecting resistant spinach using genomic prediction(GP).Using a dataset of 84301 SNPs obtained from whole-genome resequencing,seven GWAS models,including BLINK,FarmCPU,MLM,and MLMM in GAPIT 3,as well as MLM,GLM,and SMR in TASSEL 5,were employed to perform GWAS on a panel of 286 USDA spinach germplasm accessions.Three SNP markers,namely 1_115279256_C_T,3_157082529_C_T,and 4_168510908_T_G on chromosomes 1,3,and 4,respectively,were identified as associated with leafminer resistance.In the 30 kb flanking regions of these markers,four candidate genes(SOV1g031330,SOV1g031340,SOV4g047270,and SOV4g047280),encoding LOB domain-containing protein,KH domain-containing protein,were discovered.Nodulin-like domain-containing protein,and SAM domain-containing protein,were discovered.The PA for leafminer resistance selection was estimated using ten different SNP sets,including two GWAS-derived marker sets(three and 51 SNPs)and eight random marker sets(ranging from 51 to 10 K SNPs)analyzed by seven GP models.The findings emphasized the superior performance of GWAS-derived SNP sets,reaching a PA of up to 0.79 using the cBLUP model.Notably,this research marks the pioneering application of GP in the context of insect resistance,providing a significant advancement in the understanding and management of leafminer resistance in spinach cultivation.
基金financially supported by the National Natural Science Foundation of China(Nos.52173020 and 52573023)。
文摘Vitrimers belong to a class of polymeric materials capable of bond exchange reactions,showing great promise for environmental protection and sustainable development.However,studies on the coupling mechanism between the bond exchange kinetics and segmental dynamics near the glass transition temperature(T_(g))remain scarce.Herein,we employed molecular dynamics simulations to investigate the dynamic heterogeneity of the segment motion and bond exchange in vitrimers.The simulation results revealed that the bond exchange energy barrier exerts a much stronger influence on the bond exchange kinetics than on the segmental dynamics.At lower temperatures,slower segmental relaxation further constraind the bond exchange rate.Additionally,increasing the bond exchange energy barrier markedly enhanced the dynamic heterogeneity of segment motion.A close correlation was observed between heterogeneity and bond exchange.This study elucidated the coupling mechanism between bond exchange and segmental dynamics at the molecular scale,thereby providing a theoretical basis for designing vitrimer materials with tunable dynamic properties.
基金supported by the CRRC Original Technology TenYear Cultivation Program(Grant No.2022CYY007)。
文摘Deep learning and fatigue life prediction remain focal research areas in rail vehicle engineering.This study addresses the vibration fatigue of wheelset lifting lug in Chengdu Metro Line 1 bogies,aiming to develop a fatigue life prediction method for critical bogie components using deep learning models and measured track load spectra.Extensive field tests on Chengdu Metro Line 1 were conducted to acquire acceleration and stress response data of the wheelset lifting lug,generating training samples for the neural network system.Component stress responses were calculated via time-domain track acceleration and validated against in-situ stress measurements.Results show that neural network-fitted dynamic stress values exhibit excellent consistency with measured data,with errors constrained within 5%.This study validates the proposed small-sample deep learning approach as an effective and accurate solution for fatigue life prediction of critical bogie components under operational load conditions.
基金financially supported by the National Natural Science Foundation of China(Nos.42577209 and U22A20239)the Key R&D Program of Hunan Province(No.2024WK2004)the Key Technologies for Accurate Diagnosis and Intelligent Prevention and Control of Slope Hazards in Open pit Mines,181 Major R&D projects of Metallurgical Corporation of China Ltd。
文摘Excessive blasting-induced vibration during drilling-and-blasting excavation of deep tunnels can trigger geological hazards and compromise the stability of both the rock mass and support structures.This study focused on the deep double-line Sejila Mountain tunnel to systematically analyze the spatial response of blasting-induced vibration and to develop a prediction model through field tests and numerical simulations.The results revealed that the presence of a cross passage significantly altered propagation paths and the spatial distribution of blasting-induced vibration velocity.The peak particle velocity(PPV)at the cross-passage corner was amplified by approximately 1.92 times due to wave reflection and geometric focusing.Blasting-induced vibration waves attenuated non-uniformly across the tunnel cross-section,where PPV on the blast-face side was 1.54–6.56 times higher than that on the opposite side.We propose an improved PPV attenuation model that accounts for the propagation path effect.This model significantly improved fitting accuracy and resolved anomalous parameter(k and a)estimates in traditional equations,thereby improving prediction reliability.Furthermore,based on the observed spatial distribution of blasting-induced vibration,optimal monitoring point placement and targeted vibration control measures for tunnel blasting were discussed.These findings provide a scientific basis for designing blasting schemes and vibration mitigation strategies in deep tunnels.
基金National Key Research and Development Program of China(No.2022YFB3903404,2024YFC3015403)National Natural Science Foundation of China(NSFC No.42271431,42271425)Hubei Province Major Science and Technology Innovation Program(2024BAA011)。
文摘The dense heterogeneous network provides standardized connectivity and access guarantees for 5G communication services.However,the complex network environment and high level of dynamism pose challenges to network selection decisions.Existing vertical handover algorithms often overlook the dynamic nature of user mobility and network condition,resulting in problems such as handover failure and frequent handover,ultimately impacting the quality of the user communication service.To address these problems,we propose an intelligent switching method,iMALSTM-DQN,which integrates an improved Multi-level Associative Long Short-Term Memory model(iMALSTM)with Deep Reinforcement Learning(DRL).The algorithm leverages iMALSTM to predict the global network state in the next moment based on the global user movement trajectory and historical network status information within a region,thereby enhancing the prediction accuracy of network states.Subsequently,based on the predicted network state,we employ the Deep Q Network(DON)model to make handover decisions,adaptively determining the optimal switching and network selection strategy through interaction with the environment.Experimental results demonstrate that the proposed algorithm enhances decision timeliness,significantly reduces the number of switch failures,and alleviates the problem of frequent handovers resulting from network dynamics.
基金financially supported by the National Natural Science Foundation of China(Nos.52303298 and 52233002)。
文摘UHMWPE fibers exhibit impressive modulus and strength,but they have not reached their theoretical limits.Researchers focus on molecular weight,orientation,and crystallinity of UHMWPE,yet their contributions to mechanical properties are unclear.Molecular dynamics simulations are valuable but often limited by computational constraints.Our aim is to simulate higher molecular weights to better represent real UHMWPE fibers.We used Packmol and Polyply methodologies to construct PE systems,with Polyply reproducing more reasonable properties of UHMWPE fibers.Additionally,tensile simulations showed that orientation and crystallinity greatly impact Young's modulus more than molecular weight.Energy decomposition indicated that higher molecular weights lead to covalent bonds that can withstand more energy during stretching,thus increasing breaking strength.Combining simulations with machine learning,we found that orientation has the most significant impact on Young's modulus,contributing 60%,and molecular weight plays the most crucial role in determining the breaking strength,accounting for 65%.This study provides a theoretical basis and guidelines for enhancing UHMWPE's modulus and strength.
基金Supported by National Natural Science Foundation of China(Grant No.51475163)National Hi-tech Research and Development Program of China(Grant No.2012AA041803).
文摘A full-face rock tunnel boring machine(TBM)has two main parameters:cutterhead thrust and cutterhead torque.Cutterhead thrust can be predicted numerically using different methods,such as the rated thrust of the disc cutter,the indentation test of the disc cutter,a linear breaking experiment,or applying statistical models based on field data.However,cutterhead torque prediction still lacks the necessary and sufficient theoretical analysis and corresponding research.Consequently,we studied the rock-breaking characteristics of the disc cutter on the cutterhead and found a difference between the lateral friction resistance and the inner and outer rock breaking.We then proposed the cutterhead torque composition of the full-face rock tunnel boring machine,shoveling and transporting ballast torque,rolling resistance torque,lateral friction resistance torque,and disc cutter lateral imbalance torque,and established the corresponding theory.Through practical engineering ver-ification,we found that the relative error between the cutterhead torque value calculated by the theory and the actual value was only 0.51%.If the lateral friction resistance moment of the disc cutter rock breaking and the lateral imbalance moment of the disc cutter are not considered,the relative error between the calculated value of cutterhead torque and the actual value is 7.48%.Therefore,the accuracy of the established cutterhead torque theory is 6.97%higher than that of the disc cutter linear rolling crushing rock test theory.The research results provide an important reference for determining the cutterhead torque of a full-face rock tunnel boring machine.
基金Project(42077244)supported by the National Natural Science Foundation of ChinaProject(2020-05)supported by the Open Research Fund of Guangdong Provincial Key Laboratory of Deep Earth Sciences and Geothermal Energy Exploitation and Utilization,China。
文摘Accurate prediction of rockburst intensity levels is crucial for ensuring the safety of deep hard rock engineering construction.This paper introduced an expert system for rockburst intensity level prediction that employs machine learning algorithms as the basis for its inference rules.The system comprises four modules:a database,a repository,an inference engine,and an interpreter.A database containing 1114 rockburst cases was used to construct 357 datasets that serve as the repository for the expert system.Additionally,19 types of machine learning algorithms were used to establish 6783 micro-models to construct cognitive rules within the inference engine.By integrating probability theory and marginal analysis,a fuzzy scoring method based on the SoftMax function was developed and applied to the interpreter for rockburst intensity level prediction,effectively restoring the continuity of rockburst characteristics.The research results indicate that ensemble algorithms based on decision trees are more effective in capturing the characteristics of rockburst.Key factors for accurate prediction of rockburst intensity include uniaxial compressive strength,elastic energy index,the maximum principal stress,tangential stress,and their composite indicators.The accuracy of the proposed rockburst intensity level prediction expert system was verified using 20 engineering rockburst cases,with predictions aligning closely with the actual rockburst intensity levels.
基金supported by the 2024 Research Fund of University of Ulsan.
文摘In recent years,three-dimensional reconstruction technologies that employ multiple cameras have continued to evolve significantly,enabling remote collaboration among users in extended Reality(XR)environments.In addition,methods for deploying multiple cameras for motion capture of users(e.g.,performers)are widely used in computer graphics.As the need to minimize and optimize the number of cameras grows to reduce costs,various technologies and research approaches focused on Optimal Camera Placement(OCP)are continually being proposed.However,as most existing studies assume homogeneous camera setups,there is a growing demand for studies on heterogeneous camera setups.For instance,technical demands keep emerging in scenarios with minimal camera configurations,especially regarding cost factors,the physical placement of cameras given the spatial structure,and image capture strategies for heterogeneous cameras,such as high-resolution RGB cameras and depth cameras.In this study,we propose a pre-visualization and simulation method for the optimal placement of heterogeneous cameras in XR environments,accounting for both the specifications of heterogeneous cameras(e.g.,field of view)and the physical configuration(e.g.,wall configuration)in real-world spaces.The proposed method performs a visibility analysis of cameras by considering each camera’s field-of-view volume,resolution,and unique characteristics,along with physicalspace constraints.This approach enables the optimal position and rotation of each camera to be recommended,along with the minimum number of cameras required.In the results of our study conducted in heterogeneous camera combinations,the proposed method achieved 81.7%~82.7%coverage of the target visual information using only 2~3 cameras.In contrast,single(or homogeneous)-typed cameras were required to use 11 cameras for 81.6%coverage.Accordingly,we found that camera deployment resources can be reduced with the proposed approaches.
文摘Because of the developed surface of the Triply PeriodicMinimumSurface(TPMS)structures,polylactide(PLA)products with a TPMS structure are thought to be promising bio soluble implants with the potential for targeted drug delivery.For implants,mechanical properties are key performance characteristics,so understanding the deformation and failure mechanisms is essential for selecting the appropriate implant structure.The deformation and fracture processes in PLA samples with different interior architectures have been studied through computer simulation and experimental research.Two TPMS topologies,the Schwarz Diamond and Gyroid architectures,were used for the sample construction by 3D printing.ANSYS software was utilized to simulate compressive deformation.It was found that under the same load,the vonMises stresses in the Gyroid structure are higher than those in the Schwartz Diamond structure,which was associated with the different orientations of the cells in the studied structures in relation to the direction of the loading axis.The deformation process occurs in the local regions of the studied TPMS structures.Maximum von Mises stresses were observed in the vertical parts of the structures oriented along the load direction.It was found that,unlike the Gyroid,the Schwartz Diamond structure contains a frame that forms unique stiffening ribs,which ensures the redistribution of the load under the vertical loading direction.An analysis of the mechanical characteristics of PLA samples with the Schwartz Diamond and Gyroid structures produced by the Fused Deposition Modeling(FDM)method was correlated with computer simulation.The Schwarz Diamond-type structure was shown to have a higher absorption energy than the Gyroid one.A study of the fracture in PLA samples with various cell sizes revealed a particular feature related to the samples’periodic surface topology and the 3D printing process.Scanning electron microscopic(SEM)studies of the samples deformed by compression showed thatwith an increase in the density of the samples,the failure mechanism changes from ductile to quasi-brittle due to the complex participation of both cell deformation and fiber deformation.
基金supported by the Deanship of Scientific Research,Vice Presidency for Graduate Studies and Scientific Research,King Faisal University,Saudi Arabia Grant No.KFU253765.
文摘Most predictive maintenance studies have emphasized accuracy but provide very little focus on Interpretability or deployment readiness.This study improves on prior methods by developing a small yet robust system that can predict when turbofan engines will fail.It uses the NASA CMAPSS dataset,which has over 200,000 engine cycles from260 engines.The process begins with systematic preprocessing,which includes imputation,outlier removal,scaling,and labelling of the remaining useful life.Dimensionality is reduced using a hybrid selection method that combines variance filtering,recursive elimination,and gradient-boosted importance scores,yielding a stable set of 10 informative sensors.To mitigate class imbalance,minority cases are oversampled,and class-weighted losses are applied during training.Benchmarking is carried out with logistic regression,gradient boosting,and a recurrent design that integrates gated recurrent units with long short-term memory networks.The Long Short-Term Memory–Gated Recurrent Unit(LSTM–GRU)hybrid achieved the strongest performance with an F1 score of 0.92,precision of 0.93,recall of 0.91,ReceiverOperating Characteristic–AreaUnder the Curve(ROC-AUC)of 0.97,andminority recall of 0.75.Interpretability testing using permutation importance and Shapley values indicates that sensors 13,15,and 11 are the most important indicators of engine wear.The proposed system combines imbalance handling,feature reduction,and Interpretability into a practical design suitable for real industrial settings.
