The 2025 M_(w)7.7 Myanmar earthquake highlighted the challenge of near-fault seismic intensity field reconstruction due to sparse seismic networks.To address this limitation,a framework was proposed integrating seismi...The 2025 M_(w)7.7 Myanmar earthquake highlighted the challenge of near-fault seismic intensity field reconstruction due to sparse seismic networks.To address this limitation,a framework was proposed integrating seismic wave simulation with a data-constrained finite-fault rupture model.The constraint is implemented by identifying the optimal ground motion models(GMMs)through a scoring system that selects the best-fit GMMs to mid-and far-field China Earthquake Networks Center(CENC)seismic network data;and applying the optimal GMMs to refine the rupture model parameters for near-fault intensity field simulation.The simulated near-fault seismic intensity field reproduces seismic intensities collected from Myanmar’s sparse seismic network and concentrated in≥Ⅷintensity zones within 50 km of the projected fault plane;and identifies abnormal intensity regions exhibiting≥Ⅹintensity along the Meiktila-Naypyidaw corridor and near Shwebo that are attributed to soft soil amplification effects and near-fault directivity.This framework can also be applied to post-earthquake assessments in other similar regions.展开更多
Spiking Neural Network(SNN)inspired by the biological triggering mechanism of neurons to provide a novel solution for plant disease detection,offering enhanced performance and efficiency in contrast to Artificial Neur...Spiking Neural Network(SNN)inspired by the biological triggering mechanism of neurons to provide a novel solution for plant disease detection,offering enhanced performance and efficiency in contrast to Artificial Neural Networks(ANN).Unlike conventional ANNs,which process static images without fully capturing the inherent temporal dynamics,our approach represents the first implementation of SNNs tailored explicitly for agricultural disease classification,integrating an encoding method to convert static RGB plant images into temporally encoded spike trains.Additionally,while Bernoulli trials and standard deep learning architectures likeConvolutionalNeuralNetworks(CNNs)and Fully Connected Neural Networks(FCNNs)have been used extensively,our work is the first to integrate these trials within an SNN framework specifically for agricultural applications.This integration not only refines spike regulation and reduces computational overhead by 30%but also delivers superior accuracy(93.4%)in plant disease classification,marking a significant advancement in precision agriculture that has not been previously explored.Our approach uniquely transforms static plant leaf images into time-dependent representations,leveraging SNNs’intrinsic temporal processing capabilities.This approach aligns with the inherent ability of SNNs to capture dynamic,timedependent patterns,making them more suitable for detecting disease activations in plants than conventional ANNs that treat inputs as static entities.Unlike prior works,our hybrid encoding scheme dynamically adapts to pixel intensity variations(via threshold),enabling robust feature extraction under diverse agricultural conditions.The dual-stage preprocessing customizes the SNN’s behavior in two ways:the encoding threshold is derived from pixel distributions in diseased regions,and Bernoulli trials selectively reduce redundant spikes to ensure energy efficiency on low-power devices.We used a comprehensive dataset of 87,000 RGB images of plant leaves,which included 38 distinct classes of healthy and unhealthy leaves.To train and evaluate three distinct neural network architectures,DeepSNN,SimpleCNN,and SimpleFCNN,the dataset was rigorously preprocessed,including stochastic rotation,horizontal flip,resizing,and normalization.Moreover,by integrating Bernoulli trials to regulate spike generation,ourmethod focuses on extracting themost relevant featureswhile reducingcomputational overhead.Using a comprehensivedatasetof87,000RGB images across 38 classes,we rigorously preprocessed the data and evaluated three architectures:DeepSNN,SimpleCNN,and SimpleFCNN.The results demonstrate that DeepSNN outperforms the other models,achieving superior accuracy,efficient feature extraction,and robust spike management,thereby establishing the potential of SNNs for real-time,energy-efficient agricultural applications.展开更多
This study addresses the pressing challenge of generating realistic strong ground motion data for simulating earthquakes,a crucial component in pre-earthquake risk assessments and post-earthquake disaster evaluations,...This study addresses the pressing challenge of generating realistic strong ground motion data for simulating earthquakes,a crucial component in pre-earthquake risk assessments and post-earthquake disaster evaluations,particularly suited for regions with limited seismic data.Herein,we report a generative adversarial network(GAN)framework capable of simulating strong ground motions under various environmental conditions using only a small set of real earthquake records.The constructed GAN model generates ground motions based on continuous physical variables such as source distance,site conditions,and magnitude,effectively capturing the complexity and diversity of ground motions under different scenarios.This capability allows the proposed model to approximate real seismic data,making it applicable to a wide range of engineering purposes.Using the Shandong Pingyuan earthquake as an example,a specialized dataset was constructed based on regional real ground motion records.The response spectrum at target locations was obtained through inverse distance-weighted interpolation of actual response spectra,followed by continuous wavelet transform to derive the ground motion time histories at these locations.Through iterative parameter adjustments,the constructed GAN model learned the probability distribution of strong-motion data for this event.The trained model generated three-component ground-motion time histories with clear P-wave and S-wave characteristics,accurately reflecting the non-stationary nature of seismic records.Statistical comparisons between synthetic and real response spectra,waveform envelopes,and peak ground acceleration show a high degree of similarity,underscoring the effectiveness of the model in replicating both the statistical and physical characteristics of real ground motions.These findings validate the feasibility of GANs for generating realistic earthquake data in data-scarce regions,providing a reliable approach for enriching regional ground motion databases.Additionally,the results suggest that GAN-based networks are a powerful tool for building predictive models in seismic hazard analysis.展开更多
The integrated simulation and optimization technology of reservoir-wellbore-pipe network is developed to reflect the mutual influence and restriction among reservoir engineering,oil production engineering and surface ...The integrated simulation and optimization technology of reservoir-wellbore-pipe network is developed to reflect the mutual influence and restriction among reservoir engineering,oil production engineering and surface engineering,and to obtain the scheme with minimum conflict and optimal benefit in each step.This technology is based on the concept of global optimization to maximize production and profit,reduce costs and increase benefit.This paper elaborates the current situation of integrated simulation technology of reservoir-wellbore-pipe network both at home and abroad,discusses its correlation with the primary business of Sinopec and its development from three aspects of modeling,cloud platform and intellectualization.Suggestions on its future development are put forward from underlying data,software platform,popularization and application,and cross-border integration to provide means and guidance for the construction of intelligent oil and gas fields.The results show that the integrated simulation of reservoir-wellbore-pipe network can better reflect the optimization requirements of each step,avoid the ineffective operation of field equipment,and effectively improve the efficiency of research and management.Coupling solution,global optimization method and pressure fitting,which can make the simulation results reflect the real situation,are the key technologies for the network.The theoretical technology and main function research of integrated simulation technology have been mature,but the large-scale application and local function improvement of oil and gas fields are yet to be promoted.In the future,the integrated simulation of reservoir-wellbore-pipe network will develop from digitalization to modeling and intellectualization,from local simulation to cloud computing,and from manual intervention to intelligent decision-making.We suggest speeding up the construction of the unified database and model base of the whole underlying platform,strengthening the construction of software integration and integration platform with independent intellectual property rights,speeding up the popularization and application of intelligent oil and gas field demonstration projects,and strengthening the integration of oil and gas industry with artificial intelligence(AI),big data and block chain for its development.展开更多
Background:Based on network pharmacology and molecular docking,the present study investigated the mechanism of curcumin(CUR)in diabetic retinopathy treatment.Methods:Based on the DisGeNET,Swiss TargetPrediction,GeneCa...Background:Based on network pharmacology and molecular docking,the present study investigated the mechanism of curcumin(CUR)in diabetic retinopathy treatment.Methods:Based on the DisGeNET,Swiss TargetPrediction,GeneCards,Online Mendelian Inheritance in Man,Gene Expression Omnibus,and Comparative Toxicogenomics Database,the intersection core targets of CUR and diabetic retinopathy were identified.The intersection target was imported into the STRING database to obtain the protein-protein interaction map.According to the Database for Annotation,Visualization and Integrated Discovery database,the intersected targets were enriched in Gene Ontology(GO)and Kyoto Encyclopedia of Genes and Genomes pathways.Then Cytoscape 3.9.1 is used to make the drug-target-disease-pathway network.The mechanism of CUR and diabetic retinopathy was further verified by molecular docking and molecular dynamics simulation.Results:There were 203 intersecting targets of CUR and diabetic retinopathy identified.1320 GO entries were enriched for GO functions,which were primarily involved in the composition of cells such as identical protein binding,protein binding,enzyme binding,etc.It was found that 175 pathways were enriched using Kyoto Encyclopedia of Genes and Genomes pathway enrichment methods,which were mainly included in the lipid and atherosclerosis,AGE-RAGE signaling pathway in diabetic complications,pathways in cancer,etc.In the molecular docking analysis,CUR was found to have a good ability to bind to the core targets of albumin,IL-1B,and IL-6.The binding of albumin to CUR was further verified by molecular dynamics simulation.Conclusion:As a result of this study,CUR may exert a role in the treatment of diabetic retinopathy through multi-target and multi-pathway regulation,which indicates a possible direction of future research.展开更多
Objective Traditional Chinese medicine(TCM)constitutes a valuable cultural heritage and an important source of antitumor compounds.Poria(Poria cocos(Schw.)Wolf),the dried sclerotium of a polyporaceae fungus,was first ...Objective Traditional Chinese medicine(TCM)constitutes a valuable cultural heritage and an important source of antitumor compounds.Poria(Poria cocos(Schw.)Wolf),the dried sclerotium of a polyporaceae fungus,was first documented in Shennong’s Classic of Materia Medica and has been used therapeutically and dietarily in China for millennia.Traditionally recognized for its diuretic,spleen-tonifying,and sedative properties,modern pharmacological studies confirm that Poria exhibits antioxidant,anti-inflammatory,antibacterial,and antitumor activities.Pachymic acid(PA;a triterpenoid with the chemical structure 3β-acetyloxy-16α-hydroxy-lanosta-8,24(31)-dien-21-oic acid),isolated from Poria,is a principal bioactive constituent.Emerging evidence indicates PA exerts antitumor effects through multiple mechanisms,though these remain incompletely characterized.Neuroblastoma(NB),a highly malignant pediatric extracranial solid tumor accounting for 15%of childhood cancer deaths,urgently requires safer therapeutics due to the limitations of current treatments.Although PA shows multi-mechanistic antitumor potential,its efficacy against NB remains uncharacterized.This study systematically investigated the potential molecular targets and mechanisms underlying the anti-NB effects of PA by integrating network pharmacology-based target prediction with experimental validation of multi-target interactions through molecular docking,dynamic simulations,and in vitro assays,aimed to establish a novel perspective on PA’s antitumor activity and explore its potential clinical implications for NB treatment by integrating computational predictions with biological assays.Methods This study employed network pharmacology to identify potential targets of PA in NB,followed by validation using molecular docking,molecular dynamics(MD)simulations,MM/PBSA free energy analysis,RT-qPCR and Western blot experiments.Network pharmacology analysis included target screening via TCMSP,GeneCards,DisGeNET,SwissTargetPrediction,SuperPred,and PharmMapper.Subsequently,potential targets were predicted by intersecting the results from these databases via Venn analysis.Following target prediction,topological analysis was performed to identify key targets using Cytoscape software.Molecular docking was conducted using AutoDock Vina,with the binding pocket defined based on crystal structures.MD simulations were performed for 100 ns using GROMACS,and RMSD,RMSF,SASA,and hydrogen bonding dynamics were analyzed.MM/PBSA calculations were carried out to estimate the binding free energy of each protein-ligand complex.In vitro validation included RT-qPCR and Western blot,with GAPDH used as an internal control.Results The CCK-8 assay demonstrated a concentration-dependent inhibitory effect of PA on NB cell viability.GO analysis suggested that the anti-NB activity of PA might involve cellular response to chemical stress,vesicle lumen,and protein tyrosine kinase activity.KEGG pathway enrichment analysis suggested that the anti-NB activity of PA might involve the PI3K/AKT,MAPK,and Ras signaling pathways.Molecular docking and MD simulations revealed stable binding interactions between PA and the core target proteins AKT1,EGFR,SRC,and HSP90AA1.RT-qPCR and Western blot analyses further confirmed that PA treatment significantly decreased the mRNA and protein expression of AKT1,EGFR,and SRC while increasing the HSP90AA1 mRNA and protein levels.Conclusion It was suggested that PA may exert its anti-NB effects by inhibiting AKT1,EGFR,and SRC expression,potentially modulating the PI3K/AKT signaling pathway.These findings provide crucial evidence supporting PA’s development as a therapeutic candidate for NB.展开更多
Accurate prediction of hydraulic fracture propagation is vital for Enhanced Geothermal System(EGS)design.We study the first hydraulic fracturing job at the GR1 well in the Gonghe Basin using field data,where the overa...Accurate prediction of hydraulic fracture propagation is vital for Enhanced Geothermal System(EGS)design.We study the first hydraulic fracturing job at the GR1 well in the Gonghe Basin using field data,where the overall direction of hydraulic fractures does not show a delineated shape parallel to the maximum principal stress orientation.A field-scale numerical model based on the distinct element method is set up to carry out a fully coupled hydromechanical simulation,with the explicit representation of natural fractures via the discrete fracture network(DFN)approach.The effects of injection parameters and in situ stress on hydraulic fracture patterns are then quantitatively assessed.The study reveals that shear-induced deformation primarily governs the fracturing morphology in the GR1 well,driven by smaller injection rates and viscosities that promote massive activation of natural fractures,ultimately dominating the direction of hydraulic fracturing.Furthermore,the increase of in situ differential stress may promote shear damage of natural fracture surfaces,with the exact influence pattern depending on the combination of specific discontinuity properties and in situ stress state.Finally,we provide recommendations for EGS fracturing based on the influence characteristics of multiple parameters.This study can serve as an effective basis and reference for the design and optimization of EGS in the Gonghe basin and other sites.展开更多
OBJECTIVE:To explore the mechanism of Danggui Buxue decoction(当归补血汤,DBD)for the treatment of gastric ulcer(GU),based on network pharmacology and in vivo experiments.METHODS:A network pharmacology strategy was use...OBJECTIVE:To explore the mechanism of Danggui Buxue decoction(当归补血汤,DBD)for the treatment of gastric ulcer(GU),based on network pharmacology and in vivo experiments.METHODS:A network pharmacology strategy was used to predict the main components,candidate targets,and potential signaling pathways.Then,molecular docking was performed to further investigate the interactions and binding affinities between the main components and primary targets.Finally,a mouse model of ethanolinduced gastric ulcers was established to confirm the efficacy and potential therapeutic benefits of DBD,and candidate targets were finally identified.RESULTS:A total of 22 active components and 220 target genes were found to be associated with DBD.In addition,343 GU-related target genes and 57 target genes specific to DBD treatment of GU were identified.The Gene Ontology functional enrichment analysis revealed 510 entries for biological processes,36 entries for cell composition,and 69 entries for molecular functions.In the pathway enrichment analysis,143 signaling pathways were identified.Additionally,the molecular docking results revealed that the main active components of DBD exhibited a strong binding capacity with key proteins,including tumor necrosis factor,AKT serine/threonine kinase 1,interleukin-6,vascular endothelial growth factor,and interleukin-1 Beta.Among these,quercetin,kaempferol,formononetin,isorhamnetin,and beta-sitosterol displayed the strongest binding affinities for these key proteins.in vivo experiments showed that DBD pretreatment effectively protected gastric mucosa,and the benefits might be attributed to the downregulation of above key proteins.CONCLUSIONS:Based on network pharmacology analysis and in vivo experiments,we conclude that DBD leads to the protection and healing of the gastric mucosa by targeting genes and pathways,thus effectively countering the development and progression of GU.展开更多
The goal of this research is to develop mine-scale discrete fracture network(DFN)models in which the influence of the spatial heterogeneity of fracture distributions may be investigated on the rock wedge stability of ...The goal of this research is to develop mine-scale discrete fracture network(DFN)models in which the influence of the spatial heterogeneity of fracture distributions may be investigated on the rock wedge stability of an open pit slope.For this purpose,spatially conditioned DFN models were developed for the pit walls at Tasiast mine using comprehensive structural data from the mine.Using Sequential Gaussian Simulation(SGS),volumetric fracture intensities(P32)were modeled across the entire mine site in the form of 3D block models.The simulated P32 block models were used as the input constraints for conditional DFN fracture generation,where the DFN grid dimension is the same as the SGS 3D blocks.The spatially constrained DFN models were further calibrated using aerial fracture intensities(P21)data from the pit walls,obtained by a survey of the pit walls using an unmanned aerial vehicle(UAV)and measured traces of joints from 3D point cloud data.The final DFN model is expected to honor the fracture intensities gathered through different means with optimal model accuracy.Finally,bench-scale and interramp scale rock wedge slope stability analyses were conducted using the calibrated conditional DFN models.This work proves the significance of conditioned DFN models in rock wedge stability analysis.Such models provide detailed information regarding rock wedge stability so that site monitoring and prevention plans can be conducted with higher efficiency.展开更多
Ecological network(EN)identification and optimization is an essential research tool for safeguarding regional ecological security patterns and planning territorial space.Especially for the ecologically fragile inland ...Ecological network(EN)identification and optimization is an essential research tool for safeguarding regional ecological security patterns and planning territorial space.Especially for the ecologically fragile inland river basins,EN optimization is of significance in ensuring regional ecological security and virtuous cycle of ecosystems.In addition,EN is a dynamically changing structural system that is more applicable to the regional development by optimizing it from comprehensive future development perspective.EN of Shiyang River basin was constructed on account of the circuit theory,and land use/cover changes(LUCC)of the basin in 2035 was predicted by PLUS model,so as to explore the ecological conservation priorities and formulate optimization strategies.54 ecological sources(ESs)were identified,covering an area of 12,198 km^(2),mainly in the southern basin.133 ecological corridors(ECs)with an area of 3,176.92 km^(2)were extracted.38 ecological pinchpoints(EPs)and 22 ecological barriers(EBs)were identified respectively,which were mainly distributed in the lower basin.To effectively enhance the connectivity of EN in Minqin County,which has the worst ecological environment,we added five stepping stones based on the Ant Forest project.In addition,the optimal EPS is selected according to the development and limitation needs of inland river basins and the threat degree of warning points(WPs)under different scenarios.Scientific and reasonable optimization of future urban layout to prevent WPs can effectively alleviate the contradiction between ecological protection and economic development.The study is intended to provide basis for ecological sustainable development and rational planning territorial space in Shiyang River basin,as well as opinion for EN optimization in inland river basin.展开更多
Background:Bonducellin is one of the bioactive compounds present in Caesalpinia bonduc Roxb(L).It is a homoisoflavonoid recognized for its anti-cancer,anti-androgenic,and anti-estrogenic properties and could potential...Background:Bonducellin is one of the bioactive compounds present in Caesalpinia bonduc Roxb(L).It is a homoisoflavonoid recognized for its anti-cancer,anti-androgenic,and anti-estrogenic properties and could potentially treat polycystic ovary syndrome(PCOS).However,the underlying molecular mechanism remains unexplored.This study aims to elucidate the potential molecular mechanisms of bonducellin in treating PCOS and its associated symptoms through an integrated approach combining network pharmacology,molecular docking,molecular dynamics simulation,and in vivo validation.Methods:Bonducellin-associated and PCOS-related genes were intersected using VENN analysis to determine common gene targets.KEGG pathway analysis was conducted to investigate the biological pathways involving the co-targeted genes.The protein-protein interactions of the target genes were performed to identify the key proteins interacting with bonducellin.Molecular docking and 100 ns molecular simulations were carried out to evaluate the binding affinity and conformational stability of bonducellin with the target proteins.Additionally,the acute toxicity of bonducellin was assessed on zebrafish embryos and in vivo gene expression studies were performed to examine its regulatory effect on the top co-targeted gene.Results:The intersection of bonducellin-associated and PCOS-related genes identified 76 co-targeted genes.KEGG pathway analysis revealed their involvement in 15 critical pathways,including steroid hormone biosynthesis.Protein-protein interaction and pathway enrichment analysis highlighted key targets,including MMP9,AR,KDR,PRKACA,KIT,CYP19A1,HSD11B1,ESR1,STAT3,ESR2,PRKCA,ROCK1,BRAF,HSD17B2,PIK3R1,and RAF1,all of which exhibited strong binding to bonducellin.Molecular simulations confirmed the stability of bonducellin to the top proteins,MMP9 and AR,with high binding scores.Acute toxicity studies in zebrafish embryos determined the LC50 value of bonducellin as 0.8μg/mL at 48 hpf.Gene expression analysis revealed that bonducellin differentially regulates the MMP9 gene that is involved in modulating PCOS-related pathways.Conclusion:This study suggests potential gene pathways and protein interactions through which bonducellin could exert therapeutic effects on PCOS and its associated disorders.This provides valuable insights for future research into understanding and developing bonducellin-based treatments for PCOS.展开更多
This study introduces a hybrid Cuckoo Search-Deep Neural Network(CS-DNN)model for uncertainty quantification and composition optimization of Na_(1/2)Bi_(1/2)TiO_(3)(NBT)-based dielectric energy storage ceramics.Addres...This study introduces a hybrid Cuckoo Search-Deep Neural Network(CS-DNN)model for uncertainty quantification and composition optimization of Na_(1/2)Bi_(1/2)TiO_(3)(NBT)-based dielectric energy storage ceramics.Addressing the limitations of traditional ferroelectric materials—such as hysteresis loss and low breakdown strength under high electric fields—we fabricate(1−x)NBBT8-xBMT solid solutions via chemical modification and systematically investigate their temperature stability and composition-dependent energy storage performance through XRD,SEM,and electrical characterization.The key innovation lies in integrating the CS metaheuristic algorithm with a DNN,overcoming localminima in training and establishing a robust composition-property prediction framework.Our model accurately predicts room-temperature dielectric constant(ε_(r)),maximum dielectric constant(ε_(max)),dielectric loss(tanδ),discharge energy density(W_(rec)),and charge-discharge efficiency(η)from compositional inputs.A Monte Carlo-based uncertainty quantification framework,combined with the 3σ statistical criterion,demonstrates that CSDNN outperforms conventional DNN models in three critical aspects:Higher prediction accuracy(R^(2)=0.9717 vs.0.9382 for ε_(max));Tighter error distribution,satisfying the 99.7% confidence interval under the 3σprinciple;Enhanced robustness,maintaining stable predictions across a 25% composition span in generalization tests.While the model’s generalization is constrained by both the limited experimental dataset(n=45)and the underlying assumptions of MC-based data augmentation,the CS-DNN framework establishes a machine learning-guided paradigm for accelerated discovery of high-temperature dielectric capacitors through its unique capability in quantifying composition-level energy storage uncertainties.展开更多
Reliable and efficient communication is essential for Unmanned Aerial Vehicle(UAV)networks,especially in dynamic and resource-constrained environments such as disaster management,surveillance,and environmental monitor...Reliable and efficient communication is essential for Unmanned Aerial Vehicle(UAV)networks,especially in dynamic and resource-constrained environments such as disaster management,surveillance,and environmental monitoring.Frequent topology changes,high mobility,and limited energy availability pose significant challenges to maintaining stable and high-performance routing.Traditional routing protocols,such as Ad hoc On-Demand Distance Vector(AODV),Load-Balanced Optimized Predictive Ad hoc Routing(LB-OPAR),and Destination-Sequenced Distance Vector(DSDV),often experience performance degradation under such conditions.To address these limitations,this study evaluates the effectiveness of Dynamic Adaptive Routing(DAR),a protocol designed to adapt routing decisions in real time based on network dynamics and resource constraints.The research utilizes the Network Simulator 3(NS-3)platform to conduct controlled simulations,measuring key performance indicators such as latency,Packet Delivery Ratio(PDR),energy consumption,and throughput.Comparative analysis reveals that DAR consistently outperforms conventional protocols,achieving a 20%-30% reduction in latency,a 25% decrease in energy consumption,and marked improvements in throughput and PDR.These results highlight DAR’s ability to maintain high communication reliability while optimizing resource usage in challenging operational scenarios.By providing empirical evidence of DAR’s advantages in highly dynamic UAV network environments,this study contributes to advancing adaptive routing strategies.The findings not only validate DAR’s robustness and scalability but also lay the groundwork for integrating artificial intelligence-driven decision-making and real-world UAV deployment.Future work will explore cross-layer optimization,multi-UAV coordination,and experimental validation in field trials,aiming to further enhance communication resilience and energy efficiency in next-generation aerial networks.展开更多
Investigating the ecological impact of land use change in the context of the construction of national water network project is crucial,as it is imperative for achieving the sustainable development goals of the nationa...Investigating the ecological impact of land use change in the context of the construction of national water network project is crucial,as it is imperative for achieving the sustainable development goals of the national water network and guaranteeing regional ecological stability.Using the Danjiangkou Reservoir Area(DRA),China as the study area,this paper first examined the spatiotemporal dynamics of natural landscape patterns and ecosystem service values(ESV)in the DRA from 2000 to 2018 and then investigated the spatial clustering characteristics of the ESV using spatial statistical analysis tools.Finally,the patch-generating land use simulation(PLUS)model was used to simulate the natural landscape and future changes in the ESV of the DRA from 2018 to 2028 under four different development scenarios:business as usual(BAU),economic development(ED),ecological protection(EP),and shoreline protection(SP).The results show that:during 2000-2018,the construction of water facilities had a significant impact on regional land use/land cover(LULC)change,with a 24830 ha increase in watershed area.ESV exhibited an increasing trend,with a significant and growing spatial clustering effect.The transformation of farmland to water bodies led to accelerated ESV growth,while the transformation of forest land to farmland led to a decrease in the ESV.Normalized difference vegetation index(NDVI)had the strongest effect on the ESV.ESV exhibited a continuous increase from 2018 to 2028 under all the simulation scenarios.The EP scenario had the greatest increase in ESV,while the ED scenario had the smallest increase.The findings suggest that projected land use patterns under different scenarios have varied impacts on ecosystem services(ESs)and that the management and planning of the DRA should balance social,economic,ecological,and security benefits.nomic,ecological,and security benefits.展开更多
The evolution of misfit dislocation network at γ/γ＇ phase interfaces and the stress distribution characteristics of Ni-based single-crystal superalloys under different temperatures of 0, 100 and 300 K are studied b...The evolution of misfit dislocation network at γ/γ＇ phase interfaces and the stress distribution characteristics of Ni-based single-crystal superalloys under different temperatures of 0, 100 and 300 K are studied by molecular dynamics （MD） simulation. It was found that a closed three-dimensional misfit dislocation network appears on the γ/γ＇ phase interfaces, and the shape of the dislocation network is independent of the lattice mismatch. Under the influence of the temperature, the dislocation network gradually becomes irregular, a/2 [110] dislocations in the γ matrix phase emit and partly cut into the γ＇ phase with the increase in temperature. The dislocation evolution is related to the local stress field, a peak stress occurs at γ/γ＇ phase interface, and with the increase in temperature and relaxation times, the stress in the γ phase gradually increases, the number of dislocations in the γ phase increases and cuts into γ＇ phase from the interfaces where dislocation network is damaged. The results provide important information for understanding the temperature dependence of the dislocation evolution and mechanical properties of Ni-based single-crystal superalloys.展开更多
The aim of the research was to create a prediction model for winter rapeseed yield.The constructed model enabled to perform simulation on 30 June,in the current year,immediately before harvesting.An artificial neural ...The aim of the research was to create a prediction model for winter rapeseed yield.The constructed model enabled to perform simulation on 30 June,in the current year,immediately before harvesting.An artificial neural network with multilayer perceptron(MLP) topology was used to build the predictive model.The model was created on the basis of meteorological data(air temperature and atmospheric precipitation) and mineral fertilization data.The data were collected in the period 2008–2017 from 291 productive fields located in Poland,in the southern part of the Opole region.The assessment of the forecast quality created on the basis of the neural model has been verified by defining forecast errors using relative approximation error(RAE),root mean square error(RMS),mean absolute error(MAE),and mean absolute percentage error(MAPE) metrics.An important feature of the created predictive model is the ability to forecast the current agrotechnical year based on current weather and fertilizing data.The lowest value of the MAPE error was obtained for a neural network model based on the MLP network of 21:21-13-6-1:1 structure,which was 9.43%.The performed sensitivity analysis of the network examined the factors that have the greatest impact on the yield of winter rape.The highest rank 1 was obtained by an independent variable with the average air temperature from 1 January to 15 April of 2017(designation by the T1-4_CY model).展开更多
In this paper, neural network control systems for decreasing the spatter of CO2 welding have been created. The Generalized inverse Learning Architecture(GILA), the SPecialized inverse Learning Architecture(SILA)-I &a...In this paper, neural network control systems for decreasing the spatter of CO2 welding have been created. The Generalized inverse Learning Architecture(GILA), the SPecialized inverse Learning Architecture(SILA)-I & H and the Error Back Propagating Model(EBPM) are adopted respectively to simulate the static and dynamic welding control processes. The results of simulation and experiment show that the SILA-I and EBPM have betted properties. The factors affecting the simulating results and the dynamic response quality have also been analyzed.展开更多
Deep neural networks(DNNs)have drawn great attention as they perform the state-of-the-art results on many tasks.Compared to DNNs,spiking neural networks(SNNs),which are considered as the new generation of neural netwo...Deep neural networks(DNNs)have drawn great attention as they perform the state-of-the-art results on many tasks.Compared to DNNs,spiking neural networks(SNNs),which are considered as the new generation of neural networks,fail to achieve comparable performance especially on tasks with large problem sizes.Many previous work tried to close the gap between DNNs and SNNs but used small networks on simple tasks.This work proposes a simple but effective way to construct deep spiking neural networks(DSNNs)by transferring the learned ability of DNNs to SNNs.DSNNs achieve comparable accuracy on large networks and complex datasets.展开更多
基金Scientific Research Fund of Institute of Engineering Mechanics,China Earthquake Administration under Grant No.2023C01National Natural Science Foundation of China under Grant No.52478570Distinguished Young Scholars Program of the Natural Science Foundation of Heilongjiang Province,China under Grant No.JQ2024E002。
文摘The 2025 M_(w)7.7 Myanmar earthquake highlighted the challenge of near-fault seismic intensity field reconstruction due to sparse seismic networks.To address this limitation,a framework was proposed integrating seismic wave simulation with a data-constrained finite-fault rupture model.The constraint is implemented by identifying the optimal ground motion models(GMMs)through a scoring system that selects the best-fit GMMs to mid-and far-field China Earthquake Networks Center(CENC)seismic network data;and applying the optimal GMMs to refine the rupture model parameters for near-fault intensity field simulation.The simulated near-fault seismic intensity field reproduces seismic intensities collected from Myanmar’s sparse seismic network and concentrated in≥Ⅷintensity zones within 50 km of the projected fault plane;and identifies abnormal intensity regions exhibiting≥Ⅹintensity along the Meiktila-Naypyidaw corridor and near Shwebo that are attributed to soft soil amplification effects and near-fault directivity.This framework can also be applied to post-earthquake assessments in other similar regions.
基金supported in part by the Basic Science Research Program through the National Research Foundation of Korea(NRF),funded by the Ministry of Education(NRF-2021R1A6A1A03039493).
文摘Spiking Neural Network(SNN)inspired by the biological triggering mechanism of neurons to provide a novel solution for plant disease detection,offering enhanced performance and efficiency in contrast to Artificial Neural Networks(ANN).Unlike conventional ANNs,which process static images without fully capturing the inherent temporal dynamics,our approach represents the first implementation of SNNs tailored explicitly for agricultural disease classification,integrating an encoding method to convert static RGB plant images into temporally encoded spike trains.Additionally,while Bernoulli trials and standard deep learning architectures likeConvolutionalNeuralNetworks(CNNs)and Fully Connected Neural Networks(FCNNs)have been used extensively,our work is the first to integrate these trials within an SNN framework specifically for agricultural applications.This integration not only refines spike regulation and reduces computational overhead by 30%but also delivers superior accuracy(93.4%)in plant disease classification,marking a significant advancement in precision agriculture that has not been previously explored.Our approach uniquely transforms static plant leaf images into time-dependent representations,leveraging SNNs’intrinsic temporal processing capabilities.This approach aligns with the inherent ability of SNNs to capture dynamic,timedependent patterns,making them more suitable for detecting disease activations in plants than conventional ANNs that treat inputs as static entities.Unlike prior works,our hybrid encoding scheme dynamically adapts to pixel intensity variations(via threshold),enabling robust feature extraction under diverse agricultural conditions.The dual-stage preprocessing customizes the SNN’s behavior in two ways:the encoding threshold is derived from pixel distributions in diseased regions,and Bernoulli trials selectively reduce redundant spikes to ensure energy efficiency on low-power devices.We used a comprehensive dataset of 87,000 RGB images of plant leaves,which included 38 distinct classes of healthy and unhealthy leaves.To train and evaluate three distinct neural network architectures,DeepSNN,SimpleCNN,and SimpleFCNN,the dataset was rigorously preprocessed,including stochastic rotation,horizontal flip,resizing,and normalization.Moreover,by integrating Bernoulli trials to regulate spike generation,ourmethod focuses on extracting themost relevant featureswhile reducingcomputational overhead.Using a comprehensivedatasetof87,000RGB images across 38 classes,we rigorously preprocessed the data and evaluated three architectures:DeepSNN,SimpleCNN,and SimpleFCNN.The results demonstrate that DeepSNN outperforms the other models,achieving superior accuracy,efficient feature extraction,and robust spike management,thereby establishing the potential of SNNs for real-time,energy-efficient agricultural applications.
基金Funded by the National Key Research and Development Program(2022YFC3003502).
文摘This study addresses the pressing challenge of generating realistic strong ground motion data for simulating earthquakes,a crucial component in pre-earthquake risk assessments and post-earthquake disaster evaluations,particularly suited for regions with limited seismic data.Herein,we report a generative adversarial network(GAN)framework capable of simulating strong ground motions under various environmental conditions using only a small set of real earthquake records.The constructed GAN model generates ground motions based on continuous physical variables such as source distance,site conditions,and magnitude,effectively capturing the complexity and diversity of ground motions under different scenarios.This capability allows the proposed model to approximate real seismic data,making it applicable to a wide range of engineering purposes.Using the Shandong Pingyuan earthquake as an example,a specialized dataset was constructed based on regional real ground motion records.The response spectrum at target locations was obtained through inverse distance-weighted interpolation of actual response spectra,followed by continuous wavelet transform to derive the ground motion time histories at these locations.Through iterative parameter adjustments,the constructed GAN model learned the probability distribution of strong-motion data for this event.The trained model generated three-component ground-motion time histories with clear P-wave and S-wave characteristics,accurately reflecting the non-stationary nature of seismic records.Statistical comparisons between synthetic and real response spectra,waveform envelopes,and peak ground acceleration show a high degree of similarity,underscoring the effectiveness of the model in replicating both the statistical and physical characteristics of real ground motions.These findings validate the feasibility of GANs for generating realistic earthquake data in data-scarce regions,providing a reliable approach for enriching regional ground motion databases.Additionally,the results suggest that GAN-based networks are a powerful tool for building predictive models in seismic hazard analysis.
基金funded by the SINOPEC Science and Technology Project(No.P18080).
文摘The integrated simulation and optimization technology of reservoir-wellbore-pipe network is developed to reflect the mutual influence and restriction among reservoir engineering,oil production engineering and surface engineering,and to obtain the scheme with minimum conflict and optimal benefit in each step.This technology is based on the concept of global optimization to maximize production and profit,reduce costs and increase benefit.This paper elaborates the current situation of integrated simulation technology of reservoir-wellbore-pipe network both at home and abroad,discusses its correlation with the primary business of Sinopec and its development from three aspects of modeling,cloud platform and intellectualization.Suggestions on its future development are put forward from underlying data,software platform,popularization and application,and cross-border integration to provide means and guidance for the construction of intelligent oil and gas fields.The results show that the integrated simulation of reservoir-wellbore-pipe network can better reflect the optimization requirements of each step,avoid the ineffective operation of field equipment,and effectively improve the efficiency of research and management.Coupling solution,global optimization method and pressure fitting,which can make the simulation results reflect the real situation,are the key technologies for the network.The theoretical technology and main function research of integrated simulation technology have been mature,but the large-scale application and local function improvement of oil and gas fields are yet to be promoted.In the future,the integrated simulation of reservoir-wellbore-pipe network will develop from digitalization to modeling and intellectualization,from local simulation to cloud computing,and from manual intervention to intelligent decision-making.We suggest speeding up the construction of the unified database and model base of the whole underlying platform,strengthening the construction of software integration and integration platform with independent intellectual property rights,speeding up the popularization and application of intelligent oil and gas field demonstration projects,and strengthening the integration of oil and gas industry with artificial intelligence(AI),big data and block chain for its development.
基金supported by the Hubei Province Research Innovation Team Project(T2021022)Scientific Research Projects of Hubei Health Commission(WJ2023M119).
文摘Background:Based on network pharmacology and molecular docking,the present study investigated the mechanism of curcumin(CUR)in diabetic retinopathy treatment.Methods:Based on the DisGeNET,Swiss TargetPrediction,GeneCards,Online Mendelian Inheritance in Man,Gene Expression Omnibus,and Comparative Toxicogenomics Database,the intersection core targets of CUR and diabetic retinopathy were identified.The intersection target was imported into the STRING database to obtain the protein-protein interaction map.According to the Database for Annotation,Visualization and Integrated Discovery database,the intersected targets were enriched in Gene Ontology(GO)and Kyoto Encyclopedia of Genes and Genomes pathways.Then Cytoscape 3.9.1 is used to make the drug-target-disease-pathway network.The mechanism of CUR and diabetic retinopathy was further verified by molecular docking and molecular dynamics simulation.Results:There were 203 intersecting targets of CUR and diabetic retinopathy identified.1320 GO entries were enriched for GO functions,which were primarily involved in the composition of cells such as identical protein binding,protein binding,enzyme binding,etc.It was found that 175 pathways were enriched using Kyoto Encyclopedia of Genes and Genomes pathway enrichment methods,which were mainly included in the lipid and atherosclerosis,AGE-RAGE signaling pathway in diabetic complications,pathways in cancer,etc.In the molecular docking analysis,CUR was found to have a good ability to bind to the core targets of albumin,IL-1B,and IL-6.The binding of albumin to CUR was further verified by molecular dynamics simulation.Conclusion:As a result of this study,CUR may exert a role in the treatment of diabetic retinopathy through multi-target and multi-pathway regulation,which indicates a possible direction of future research.
文摘Objective Traditional Chinese medicine(TCM)constitutes a valuable cultural heritage and an important source of antitumor compounds.Poria(Poria cocos(Schw.)Wolf),the dried sclerotium of a polyporaceae fungus,was first documented in Shennong’s Classic of Materia Medica and has been used therapeutically and dietarily in China for millennia.Traditionally recognized for its diuretic,spleen-tonifying,and sedative properties,modern pharmacological studies confirm that Poria exhibits antioxidant,anti-inflammatory,antibacterial,and antitumor activities.Pachymic acid(PA;a triterpenoid with the chemical structure 3β-acetyloxy-16α-hydroxy-lanosta-8,24(31)-dien-21-oic acid),isolated from Poria,is a principal bioactive constituent.Emerging evidence indicates PA exerts antitumor effects through multiple mechanisms,though these remain incompletely characterized.Neuroblastoma(NB),a highly malignant pediatric extracranial solid tumor accounting for 15%of childhood cancer deaths,urgently requires safer therapeutics due to the limitations of current treatments.Although PA shows multi-mechanistic antitumor potential,its efficacy against NB remains uncharacterized.This study systematically investigated the potential molecular targets and mechanisms underlying the anti-NB effects of PA by integrating network pharmacology-based target prediction with experimental validation of multi-target interactions through molecular docking,dynamic simulations,and in vitro assays,aimed to establish a novel perspective on PA’s antitumor activity and explore its potential clinical implications for NB treatment by integrating computational predictions with biological assays.Methods This study employed network pharmacology to identify potential targets of PA in NB,followed by validation using molecular docking,molecular dynamics(MD)simulations,MM/PBSA free energy analysis,RT-qPCR and Western blot experiments.Network pharmacology analysis included target screening via TCMSP,GeneCards,DisGeNET,SwissTargetPrediction,SuperPred,and PharmMapper.Subsequently,potential targets were predicted by intersecting the results from these databases via Venn analysis.Following target prediction,topological analysis was performed to identify key targets using Cytoscape software.Molecular docking was conducted using AutoDock Vina,with the binding pocket defined based on crystal structures.MD simulations were performed for 100 ns using GROMACS,and RMSD,RMSF,SASA,and hydrogen bonding dynamics were analyzed.MM/PBSA calculations were carried out to estimate the binding free energy of each protein-ligand complex.In vitro validation included RT-qPCR and Western blot,with GAPDH used as an internal control.Results The CCK-8 assay demonstrated a concentration-dependent inhibitory effect of PA on NB cell viability.GO analysis suggested that the anti-NB activity of PA might involve cellular response to chemical stress,vesicle lumen,and protein tyrosine kinase activity.KEGG pathway enrichment analysis suggested that the anti-NB activity of PA might involve the PI3K/AKT,MAPK,and Ras signaling pathways.Molecular docking and MD simulations revealed stable binding interactions between PA and the core target proteins AKT1,EGFR,SRC,and HSP90AA1.RT-qPCR and Western blot analyses further confirmed that PA treatment significantly decreased the mRNA and protein expression of AKT1,EGFR,and SRC while increasing the HSP90AA1 mRNA and protein levels.Conclusion It was suggested that PA may exert its anti-NB effects by inhibiting AKT1,EGFR,and SRC expression,potentially modulating the PI3K/AKT signaling pathway.These findings provide crucial evidence supporting PA’s development as a therapeutic candidate for NB.
基金support from the National Natural Science Foundation of China(Grant Nos.42320104003,42177175,and 42077247)the Fundamental Research Funds for the Central Universities.
文摘Accurate prediction of hydraulic fracture propagation is vital for Enhanced Geothermal System(EGS)design.We study the first hydraulic fracturing job at the GR1 well in the Gonghe Basin using field data,where the overall direction of hydraulic fractures does not show a delineated shape parallel to the maximum principal stress orientation.A field-scale numerical model based on the distinct element method is set up to carry out a fully coupled hydromechanical simulation,with the explicit representation of natural fractures via the discrete fracture network(DFN)approach.The effects of injection parameters and in situ stress on hydraulic fracture patterns are then quantitatively assessed.The study reveals that shear-induced deformation primarily governs the fracturing morphology in the GR1 well,driven by smaller injection rates and viscosities that promote massive activation of natural fractures,ultimately dominating the direction of hydraulic fracturing.Furthermore,the increase of in situ differential stress may promote shear damage of natural fracture surfaces,with the exact influence pattern depending on the combination of specific discontinuity properties and in situ stress state.Finally,we provide recommendations for EGS fracturing based on the influence characteristics of multiple parameters.This study can serve as an effective basis and reference for the design and optimization of EGS in the Gonghe basin and other sites.
基金Supported by the National Natural Science Foundation of China:Mechanism of Danggui Buxue Decoction in Promoting Liver Regulation by Modulating Kupffer Cell Glycolysis-Mediated Histone Lactylation in Hepatocytes(No.82474299)XingLin Scholars Program of Chengdu University of Traditional Chinese Medicine:Study on the Role and Mechanism of Electrospun Astragalus Polysaccharide and Angelica Polysaccharide in Promoting Liver Regeneration(No.YYZX2020036)。
文摘OBJECTIVE:To explore the mechanism of Danggui Buxue decoction(当归补血汤,DBD)for the treatment of gastric ulcer(GU),based on network pharmacology and in vivo experiments.METHODS:A network pharmacology strategy was used to predict the main components,candidate targets,and potential signaling pathways.Then,molecular docking was performed to further investigate the interactions and binding affinities between the main components and primary targets.Finally,a mouse model of ethanolinduced gastric ulcers was established to confirm the efficacy and potential therapeutic benefits of DBD,and candidate targets were finally identified.RESULTS:A total of 22 active components and 220 target genes were found to be associated with DBD.In addition,343 GU-related target genes and 57 target genes specific to DBD treatment of GU were identified.The Gene Ontology functional enrichment analysis revealed 510 entries for biological processes,36 entries for cell composition,and 69 entries for molecular functions.In the pathway enrichment analysis,143 signaling pathways were identified.Additionally,the molecular docking results revealed that the main active components of DBD exhibited a strong binding capacity with key proteins,including tumor necrosis factor,AKT serine/threonine kinase 1,interleukin-6,vascular endothelial growth factor,and interleukin-1 Beta.Among these,quercetin,kaempferol,formononetin,isorhamnetin,and beta-sitosterol displayed the strongest binding affinities for these key proteins.in vivo experiments showed that DBD pretreatment effectively protected gastric mucosa,and the benefits might be attributed to the downregulation of above key proteins.CONCLUSIONS:Based on network pharmacology analysis and in vivo experiments,we conclude that DBD leads to the protection and healing of the gastric mucosa by targeting genes and pathways,thus effectively countering the development and progression of GU.
基金Kinross Gold and MITACS for their financial support(Grant No.FR42880).
文摘The goal of this research is to develop mine-scale discrete fracture network(DFN)models in which the influence of the spatial heterogeneity of fracture distributions may be investigated on the rock wedge stability of an open pit slope.For this purpose,spatially conditioned DFN models were developed for the pit walls at Tasiast mine using comprehensive structural data from the mine.Using Sequential Gaussian Simulation(SGS),volumetric fracture intensities(P32)were modeled across the entire mine site in the form of 3D block models.The simulated P32 block models were used as the input constraints for conditional DFN fracture generation,where the DFN grid dimension is the same as the SGS 3D blocks.The spatially constrained DFN models were further calibrated using aerial fracture intensities(P21)data from the pit walls,obtained by a survey of the pit walls using an unmanned aerial vehicle(UAV)and measured traces of joints from 3D point cloud data.The final DFN model is expected to honor the fracture intensities gathered through different means with optimal model accuracy.Finally,bench-scale and interramp scale rock wedge slope stability analyses were conducted using the calibrated conditional DFN models.This work proves the significance of conditioned DFN models in rock wedge stability analysis.Such models provide detailed information regarding rock wedge stability so that site monitoring and prevention plans can be conducted with higher efficiency.
基金funded by the National Natural Science Foundation of China(Grant No.42101276)。
文摘Ecological network(EN)identification and optimization is an essential research tool for safeguarding regional ecological security patterns and planning territorial space.Especially for the ecologically fragile inland river basins,EN optimization is of significance in ensuring regional ecological security and virtuous cycle of ecosystems.In addition,EN is a dynamically changing structural system that is more applicable to the regional development by optimizing it from comprehensive future development perspective.EN of Shiyang River basin was constructed on account of the circuit theory,and land use/cover changes(LUCC)of the basin in 2035 was predicted by PLUS model,so as to explore the ecological conservation priorities and formulate optimization strategies.54 ecological sources(ESs)were identified,covering an area of 12,198 km^(2),mainly in the southern basin.133 ecological corridors(ECs)with an area of 3,176.92 km^(2)were extracted.38 ecological pinchpoints(EPs)and 22 ecological barriers(EBs)were identified respectively,which were mainly distributed in the lower basin.To effectively enhance the connectivity of EN in Minqin County,which has the worst ecological environment,we added five stepping stones based on the Ant Forest project.In addition,the optimal EPS is selected according to the development and limitation needs of inland river basins and the threat degree of warning points(WPs)under different scenarios.Scientific and reasonable optimization of future urban layout to prevent WPs can effectively alleviate the contradiction between ecological protection and economic development.The study is intended to provide basis for ecological sustainable development and rational planning territorial space in Shiyang River basin,as well as opinion for EN optimization in inland river basin.
文摘Background:Bonducellin is one of the bioactive compounds present in Caesalpinia bonduc Roxb(L).It is a homoisoflavonoid recognized for its anti-cancer,anti-androgenic,and anti-estrogenic properties and could potentially treat polycystic ovary syndrome(PCOS).However,the underlying molecular mechanism remains unexplored.This study aims to elucidate the potential molecular mechanisms of bonducellin in treating PCOS and its associated symptoms through an integrated approach combining network pharmacology,molecular docking,molecular dynamics simulation,and in vivo validation.Methods:Bonducellin-associated and PCOS-related genes were intersected using VENN analysis to determine common gene targets.KEGG pathway analysis was conducted to investigate the biological pathways involving the co-targeted genes.The protein-protein interactions of the target genes were performed to identify the key proteins interacting with bonducellin.Molecular docking and 100 ns molecular simulations were carried out to evaluate the binding affinity and conformational stability of bonducellin with the target proteins.Additionally,the acute toxicity of bonducellin was assessed on zebrafish embryos and in vivo gene expression studies were performed to examine its regulatory effect on the top co-targeted gene.Results:The intersection of bonducellin-associated and PCOS-related genes identified 76 co-targeted genes.KEGG pathway analysis revealed their involvement in 15 critical pathways,including steroid hormone biosynthesis.Protein-protein interaction and pathway enrichment analysis highlighted key targets,including MMP9,AR,KDR,PRKACA,KIT,CYP19A1,HSD11B1,ESR1,STAT3,ESR2,PRKCA,ROCK1,BRAF,HSD17B2,PIK3R1,and RAF1,all of which exhibited strong binding to bonducellin.Molecular simulations confirmed the stability of bonducellin to the top proteins,MMP9 and AR,with high binding scores.Acute toxicity studies in zebrafish embryos determined the LC50 value of bonducellin as 0.8μg/mL at 48 hpf.Gene expression analysis revealed that bonducellin differentially regulates the MMP9 gene that is involved in modulating PCOS-related pathways.Conclusion:This study suggests potential gene pathways and protein interactions through which bonducellin could exert therapeutic effects on PCOS and its associated disorders.This provides valuable insights for future research into understanding and developing bonducellin-based treatments for PCOS.
基金supported by the Postgraduate Education Reform and Quality Improvement Project of Henan Province(Grant Nos.YJS2023JD52 and YJS2025GZZ48)the Zhumadian 2023 Major Science and Technology Special Project(Grant No.ZMD SZDZX2023002)+1 种基金2025 Henan Province International Science and Technology Cooperation Project(Cultivation Project,No.252102521011)Research Merit-Based Funding Program for Overseas Educated Personnel in Henan Province(Letter of Henan Human Resources and Social Security Office[2025]No.37).
文摘This study introduces a hybrid Cuckoo Search-Deep Neural Network(CS-DNN)model for uncertainty quantification and composition optimization of Na_(1/2)Bi_(1/2)TiO_(3)(NBT)-based dielectric energy storage ceramics.Addressing the limitations of traditional ferroelectric materials—such as hysteresis loss and low breakdown strength under high electric fields—we fabricate(1−x)NBBT8-xBMT solid solutions via chemical modification and systematically investigate their temperature stability and composition-dependent energy storage performance through XRD,SEM,and electrical characterization.The key innovation lies in integrating the CS metaheuristic algorithm with a DNN,overcoming localminima in training and establishing a robust composition-property prediction framework.Our model accurately predicts room-temperature dielectric constant(ε_(r)),maximum dielectric constant(ε_(max)),dielectric loss(tanδ),discharge energy density(W_(rec)),and charge-discharge efficiency(η)from compositional inputs.A Monte Carlo-based uncertainty quantification framework,combined with the 3σ statistical criterion,demonstrates that CSDNN outperforms conventional DNN models in three critical aspects:Higher prediction accuracy(R^(2)=0.9717 vs.0.9382 for ε_(max));Tighter error distribution,satisfying the 99.7% confidence interval under the 3σprinciple;Enhanced robustness,maintaining stable predictions across a 25% composition span in generalization tests.While the model’s generalization is constrained by both the limited experimental dataset(n=45)and the underlying assumptions of MC-based data augmentation,the CS-DNN framework establishes a machine learning-guided paradigm for accelerated discovery of high-temperature dielectric capacitors through its unique capability in quantifying composition-level energy storage uncertainties.
文摘Reliable and efficient communication is essential for Unmanned Aerial Vehicle(UAV)networks,especially in dynamic and resource-constrained environments such as disaster management,surveillance,and environmental monitoring.Frequent topology changes,high mobility,and limited energy availability pose significant challenges to maintaining stable and high-performance routing.Traditional routing protocols,such as Ad hoc On-Demand Distance Vector(AODV),Load-Balanced Optimized Predictive Ad hoc Routing(LB-OPAR),and Destination-Sequenced Distance Vector(DSDV),often experience performance degradation under such conditions.To address these limitations,this study evaluates the effectiveness of Dynamic Adaptive Routing(DAR),a protocol designed to adapt routing decisions in real time based on network dynamics and resource constraints.The research utilizes the Network Simulator 3(NS-3)platform to conduct controlled simulations,measuring key performance indicators such as latency,Packet Delivery Ratio(PDR),energy consumption,and throughput.Comparative analysis reveals that DAR consistently outperforms conventional protocols,achieving a 20%-30% reduction in latency,a 25% decrease in energy consumption,and marked improvements in throughput and PDR.These results highlight DAR’s ability to maintain high communication reliability while optimizing resource usage in challenging operational scenarios.By providing empirical evidence of DAR’s advantages in highly dynamic UAV network environments,this study contributes to advancing adaptive routing strategies.The findings not only validate DAR’s robustness and scalability but also lay the groundwork for integrating artificial intelligence-driven decision-making and real-world UAV deployment.Future work will explore cross-layer optimization,multi-UAV coordination,and experimental validation in field trials,aiming to further enhance communication resilience and energy efficiency in next-generation aerial networks.
基金Under the auspices of National Natural Science Foundation of China(No.42371315,41901213)Natural Science Foundation of Hubei Province(No.2020CFB856)Project of Changjiang Survey,Planning,Design and Research Co.,Ltd(No.CX2022Z23)。
文摘Investigating the ecological impact of land use change in the context of the construction of national water network project is crucial,as it is imperative for achieving the sustainable development goals of the national water network and guaranteeing regional ecological stability.Using the Danjiangkou Reservoir Area(DRA),China as the study area,this paper first examined the spatiotemporal dynamics of natural landscape patterns and ecosystem service values(ESV)in the DRA from 2000 to 2018 and then investigated the spatial clustering characteristics of the ESV using spatial statistical analysis tools.Finally,the patch-generating land use simulation(PLUS)model was used to simulate the natural landscape and future changes in the ESV of the DRA from 2018 to 2028 under four different development scenarios:business as usual(BAU),economic development(ED),ecological protection(EP),and shoreline protection(SP).The results show that:during 2000-2018,the construction of water facilities had a significant impact on regional land use/land cover(LULC)change,with a 24830 ha increase in watershed area.ESV exhibited an increasing trend,with a significant and growing spatial clustering effect.The transformation of farmland to water bodies led to accelerated ESV growth,while the transformation of forest land to farmland led to a decrease in the ESV.Normalized difference vegetation index(NDVI)had the strongest effect on the ESV.ESV exhibited a continuous increase from 2018 to 2028 under all the simulation scenarios.The EP scenario had the greatest increase in ESV,while the ED scenario had the smallest increase.The findings suggest that projected land use patterns under different scenarios have varied impacts on ecosystem services(ESs)and that the management and planning of the DRA should balance social,economic,ecological,and security benefits.nomic,ecological,and security benefits.
基金financially supported by the National Natural Science Foundation of China(Nos.11102139 and 11472195)the Natural Science Foundation of Hubei Province of China(No.2014CFB713)
文摘The evolution of misfit dislocation network at γ/γ＇ phase interfaces and the stress distribution characteristics of Ni-based single-crystal superalloys under different temperatures of 0, 100 and 300 K are studied by molecular dynamics （MD） simulation. It was found that a closed three-dimensional misfit dislocation network appears on the γ/γ＇ phase interfaces, and the shape of the dislocation network is independent of the lattice mismatch. Under the influence of the temperature, the dislocation network gradually becomes irregular, a/2 [110] dislocations in the γ matrix phase emit and partly cut into the γ＇ phase with the increase in temperature. The dislocation evolution is related to the local stress field, a peak stress occurs at γ/γ＇ phase interface, and with the increase in temperature and relaxation times, the stress in the γ phase gradually increases, the number of dislocations in the γ phase increases and cuts into γ＇ phase from the interfaces where dislocation network is damaged. The results provide important information for understanding the temperature dependence of the dislocation evolution and mechanical properties of Ni-based single-crystal superalloys.
文摘The aim of the research was to create a prediction model for winter rapeseed yield.The constructed model enabled to perform simulation on 30 June,in the current year,immediately before harvesting.An artificial neural network with multilayer perceptron(MLP) topology was used to build the predictive model.The model was created on the basis of meteorological data(air temperature and atmospheric precipitation) and mineral fertilization data.The data were collected in the period 2008–2017 from 291 productive fields located in Poland,in the southern part of the Opole region.The assessment of the forecast quality created on the basis of the neural model has been verified by defining forecast errors using relative approximation error(RAE),root mean square error(RMS),mean absolute error(MAE),and mean absolute percentage error(MAPE) metrics.An important feature of the created predictive model is the ability to forecast the current agrotechnical year based on current weather and fertilizing data.The lowest value of the MAPE error was obtained for a neural network model based on the MLP network of 21:21-13-6-1:1 structure,which was 9.43%.The performed sensitivity analysis of the network examined the factors that have the greatest impact on the yield of winter rape.The highest rank 1 was obtained by an independent variable with the average air temperature from 1 January to 15 April of 2017(designation by the T1-4_CY model).
文摘In this paper, neural network control systems for decreasing the spatter of CO2 welding have been created. The Generalized inverse Learning Architecture(GILA), the SPecialized inverse Learning Architecture(SILA)-I & H and the Error Back Propagating Model(EBPM) are adopted respectively to simulate the static and dynamic welding control processes. The results of simulation and experiment show that the SILA-I and EBPM have betted properties. The factors affecting the simulating results and the dynamic response quality have also been analyzed.
基金the National Natural Science Foundation of China(No.61732007)Strategic Priority Research Program of Chinese Academy of Sciences(XDB32050200,XDC01020000).
文摘Deep neural networks(DNNs)have drawn great attention as they perform the state-of-the-art results on many tasks.Compared to DNNs,spiking neural networks(SNNs),which are considered as the new generation of neural networks,fail to achieve comparable performance especially on tasks with large problem sizes.Many previous work tried to close the gap between DNNs and SNNs but used small networks on simple tasks.This work proposes a simple but effective way to construct deep spiking neural networks(DSNNs)by transferring the learned ability of DNNs to SNNs.DSNNs achieve comparable accuracy on large networks and complex datasets.
