Neuroinflammation is associated with Parkinson’s disease:Reactive gliosis and neuroinflammation are hallmarks of Parkinson’s disease(PD),a multisystem neurodegenerative disorder characterized by a progressive loss o...Neuroinflammation is associated with Parkinson’s disease:Reactive gliosis and neuroinflammation are hallmarks of Parkinson’s disease(PD),a multisystem neurodegenerative disorder characterized by a progressive loss of dopaminergic neurons.Neuroinflammation has long been considered a mere consequence of neuronal loss,but whether it promotes PD or is a key player in disease progression remains to be determined.Human leukocyte antigen.展开更多
This study presents a comparative analysis of a complex SQL benchmark, TPC-DS, with two existing text-to-SQL benchmarks, BIRD and Spider. Our findings reveal that TPC-DS queries exhibit a significantly higher level of...This study presents a comparative analysis of a complex SQL benchmark, TPC-DS, with two existing text-to-SQL benchmarks, BIRD and Spider. Our findings reveal that TPC-DS queries exhibit a significantly higher level of structural complexity compared to the other two benchmarks. This underscores the need for more intricate benchmarks to simulate realistic scenarios effectively. To facilitate this comparison, we devised several measures of structural complexity and applied them across all three benchmarks. The results of this study can guide future research in the development of more sophisticated text-to-SQL benchmarks. We utilized 11 distinct Language Models (LLMs) to generate SQL queries based on the query descriptions provided by the TPC-DS benchmark. The prompt engineering process incorporated both the query description as outlined in the TPC-DS specification and the database schema of TPC-DS. Our findings indicate that the current state-of-the-art generative AI models fall short in generating accurate decision-making queries. We conducted a comparison of the generated queries with the TPC-DS gold standard queries using a series of fuzzy structure matching techniques based on query features. The results demonstrated that the accuracy of the generated queries is insufficient for practical real-world application.展开更多
Two dimensional(2D) materials based on boron and carbon have attracted wide attention due to their unique properties. BC compounds have rich active sites and diverse chemical coordination, showing great potential in o...Two dimensional(2D) materials based on boron and carbon have attracted wide attention due to their unique properties. BC compounds have rich active sites and diverse chemical coordination, showing great potential in optoelectronic applications. However, due to the limitation of calculation and experimental conditions, it is still a challenging task to predict new 2D BC monolayer materials. Specifically, we utilized Crystal Diffusion Variational Autoencoder(CDVAE) and pre-trained Materials Graph Neural Network with 3-Body Interactions(M3GNet) model to generate novel and stable BCP materials. Each crystal structure was treated as a high-dimensional vector, where the encoder extracted lattice information and element coordinates, mapping the high-dimensional data into a low-dimensional latent space. The decoder then reconstructed the latent representation back into the original data space. Additionally, our designed attribute predictor network combined the advantages of dilated convolutions and residual connections,effectively increasing the model's receptive field and learning capacity while maintaining relatively low parameter count and computational complexity. By progressively increasing the dilation rate, the model can capture features at different scales. We used the DFT data set of about 1600 BCP monolayer materials to train the diffusion model, and combined with the pre-trained M3GNet model to screen the best candidate structure. Finally, we used DFT calculations to confirm the stability of the candidate structure.The results show that the combination of generative deep learning model and attribute prediction model can help accelerate the discovery and research of new 2D materials, and provide effective methods for exploring the inverse design of new two-dimensional materials.展开更多
Recent advances in contrastive language-image pretraining(CLIP)models and generative AI have demonstrated significant capabilities in cross-modal understanding and content generation.Based on these developments,this s...Recent advances in contrastive language-image pretraining(CLIP)models and generative AI have demonstrated significant capabilities in cross-modal understanding and content generation.Based on these developments,this study introduces a novel framework for airfoil design via natural language interfaces.To the authors’knowledge,this study establishes the first end-to-end,bidirectional mapping between textual descriptions(e.g.,“low-drag supercritical wing for transonic conditions”)and parametric airfoil geometries represented by class-shape transformation parameters.The proposed approach integrates a CLIP-inspired architecture that aligns text embeddings with airfoil parameter spaces through contrastive learning,along with a semantically conditioned decoder that produces physically plausible airfoil geometries from latent representations.The experimental results validate the framework’s ability to generate aerodynamically plausible airfoils from natural language specifications and to classify airfoils accurately based on given textual labels.This research reduces the expertise threshold for preliminary airfoil design and highlights the potential for human-AI collaboration in aerospace engineering.展开更多
Concentrated solar thermal power generation has been experimentally tested in advanced countries for a period of time.This paper demonstrates how this technology can be improved by using water molecules as a medium to...Concentrated solar thermal power generation has been experimentally tested in advanced countries for a period of time.This paper demonstrates how this technology can be improved by using water molecules as a medium to drive traditional generator sets for energy conversion,thereby simultaneously improving the energy conversion rate.Additionally,a novel contribution is made by incorporating a magic number 4 to enhance the focusing efficiency of Fresnel lenses,which drives improvements in power generation output and QE(Quantum Efficiency).展开更多
The issues of seepage in calcareous sand foundations and backfillshave a potentially detrimental effect on the stability and safety of superstructures.Simplifying calcareous sand grains as spheres or ellipsoids in num...The issues of seepage in calcareous sand foundations and backfillshave a potentially detrimental effect on the stability and safety of superstructures.Simplifying calcareous sand grains as spheres or ellipsoids in numerical simulations may lead to significantinaccuracies.In this paper,we present a novel intelligence framework based on a deep convolutional generative adversarial network(DCGAN).A DCGAN model was trained using a training dataset comprising 11,625 real particles for the random generation of three-dimensional calcareous sand particles.Subsequently,3800 realistic calcareous sand particles with intra-particle voids were generated.Generative fidelityand validity of the DCGAN model were well verifiedby the consistency of the statistical values of nine morphological parameters of both the training dataset and the generated dataset.Digital calcareous sand columns were obtained through gravitational deposition simulation of the generated particles.Directional seepage simulations were conducted,and the vertical permeability values of the sand columns were found to be in accordance with the objective law.The results demonstrate the potential of the proposed framework for stochastic modeling and multi-scale simulation of the seepage behaviors in calcareous sand foundations and backfills.展开更多
To ensure an uninterrupted power supply,mobile power sources(MPS)are widely deployed in power grids during emergencies.Comprising mobile emergency generators(MEGs)and mobile energy storage systems(MESS),MPS are capabl...To ensure an uninterrupted power supply,mobile power sources(MPS)are widely deployed in power grids during emergencies.Comprising mobile emergency generators(MEGs)and mobile energy storage systems(MESS),MPS are capable of supplying power to critical loads and serving as backup sources during grid contingencies,offering advantages such as flexibility and high resilience through electricity delivery via transportation networks.This paper proposes a design method for a 400 V–10 kV Dual-Winding Induction Generator(DWIG)intended for MEG applications,employing an improved particle swarmoptimization(PSO)algorithmbased on a back-propagation neural network(BPNN).A parameterized finite element(FE)model of the DWIG is established to derive constraints on its dimensional parameters,thereby simplifying the optimization space.Through sensitivity analysis between temperature rise and electromagnetic loss of the DWIG,the main factors influencing the machine’s temperature are identified,and electromagnetic loss is determined as the optimization objective.To obtain an accurate fitting function between electromagnetic loss and dimensional parameters,the BPNN is employed to predict the nonlinear relationship between the optimization objective and the parameters.The Latin hypercube sampling(LHS)method is used for random sampling in the FE model analysis for training,testing,and validation,which is then applied to compute the cost function in the PSO.Based on the relationships obtained by the BPNN,the PSO algorithm evaluates the fitness and cost functions to determine the optimal design point.The proposed optimization method is validated by comparing simulation results between the initial design and the optimized design.展开更多
Tilted metasurface nanostructures,with excellent physical properties and enormous application potential,pose an urgent need for manufacturing methods.Here,electric-field-driven generative-nanoimprinting technique is p...Tilted metasurface nanostructures,with excellent physical properties and enormous application potential,pose an urgent need for manufacturing methods.Here,electric-field-driven generative-nanoimprinting technique is proposed.The electric field applied between the template and the substrate drives the contact,tilting,filling,and holding processes.By accurately controlling the introduced included angle between the flexible template and the substrate,tilted nanostructures with a controllable angle are imprinted onto the substrate,although they are vertical on the template.By flexibly adjusting the electric field intensity and the included angle,large-area uniform-tilted,gradient-tilted,and high-angle-tilted nanostructures are fabricated.In contrast to traditional replication,the morphology of the nanoimprinting structure is extended to customized control.This work provides a cost-effective,efficient,and versatile technology for the fabrication of various large-area tilted metasurface structures.As an illustration,a tilted nanograting with a high coupling efficiency is fabricated and integrated into augmented reality displays,demonstrating superior imaging quality.展开更多
AIM:To build a functional generalized estimating equation(GEE)model to detect glaucomatous visual field progression and compare the performance of the proposed method with that of commonly employed algorithms.METHODS:...AIM:To build a functional generalized estimating equation(GEE)model to detect glaucomatous visual field progression and compare the performance of the proposed method with that of commonly employed algorithms.METHODS:Totally 716 eyes of 716 patients with primary open angle glaucoma(POAG)with at least 5 reliable 24-2 test results and 2y of follow-up were selected.The functional GEE model was used to detect perimetric progression in the training dataset(501 eyes).In the testing dataset(215 eyes),progression was evaluated the functional GEE model,mean deviation(MD)and visual field index(VFI)rates of change,Advanced Glaucoma Intervention Study(AGIS)and Collaborative Initial Glaucoma Treatment Study(CIGTS)scores,and pointwise linear regression(PLR).RESULTS:The proposed method showed the highest proportion of eyes detected as progression(54.4%),followed by the VFI rate(34.4%),PLR(23.3%),and MD rate(21.4%).The CIGTS and AGIS scores had a lower proportion of eyes detected as progression(7.9%and 5.1%,respectively).The time to detection of progression was significantly shorter for the proposed method than that of other algorithms(adjusted P≤0.019).The VFI rate displayed moderate pairwise agreement with the proposed method(k=0.47).CONCLUSION:The functional GEE model shows the highest proportion of eyes detected as perimetric progression and the shortest time to detect perimetric progression in patients with POAG.展开更多
Recommending personalized travel routes from sparse,implicit feedback poses a significant challenge,as conventional systems often struggle with information overload and fail to capture the complex,sequential nature of...Recommending personalized travel routes from sparse,implicit feedback poses a significant challenge,as conventional systems often struggle with information overload and fail to capture the complex,sequential nature of user preferences.To address this,we propose a Conditional Generative Adversarial Network(CGAN)that generates diverse and highly relevant itineraries.Our approach begins by constructing a conditional vector that encapsulates a user’s profile.This vector uniquely fuses embeddings from a Heterogeneous Information Network(HIN)to model complex user-place-route relationships,a Recurrent Neural Network(RNN)to capture sequential path dynamics,and Neural Collaborative Filtering(NCF)to incorporate collaborative signals from the wider user base.This comprehensive condition,further enhanced with features representing user interaction confidence and uncertainty,steers a CGAN stabilized by spectral normalization to generate high-fidelity latent route representations,effectively mitigating the data sparsity problem.Recommendations are then formulated using an Anchor-and-Expand algorithm,which selects relevant starting Points of Interest(POI)based on user history,then expands routes through latent similarity matching and geographic coherence optimization,culminating in Traveling Salesman Problem(TSP)-based route optimization for practical travel distances.Experiments on a real-world check-in dataset validate our model’s unique generative capability,achieving F1 scores ranging from 0.163 to 0.305,and near-zero pairs−F1 scores between 0.002 and 0.022.These results confirm the model’s success in generating novel travel routes by recommending new locations and sequences rather than replicating users’past itineraries.This work provides a robust solution for personalized travel planning,capable of generating novel and compelling routes for both new and existing users by learning from collective travel intelligence.展开更多
Remote sensing image super-resolution technology is pivotal for enhancing image quality in critical applications including environmental monitoring,urban planning,and disaster assessment.However,traditional methods ex...Remote sensing image super-resolution technology is pivotal for enhancing image quality in critical applications including environmental monitoring,urban planning,and disaster assessment.However,traditional methods exhibit deficiencies in detail recovery and noise suppression,particularly when processing complex landscapes(e.g.,forests,farmlands),leading to artifacts and spectral distortions that limit practical utility.To address this,we propose an enhanced Super-Resolution Generative Adversarial Network(SRGAN)framework featuring three key innovations:(1)Replacement of L1/L2 loss with a robust Charbonnier loss to suppress noise while preserving edge details via adaptive gradient balancing;(2)A multi-loss joint optimization strategy dynamically weighting Charbonnier loss(β=0.5),Visual Geometry Group(VGG)perceptual loss(α=1),and adversarial loss(γ=0.1)to synergize pixel-level accuracy and perceptual quality;(3)A multi-scale residual network(MSRN)capturing cross-scale texture features(e.g.,forest canopies,mountain contours).Validated on Sentinel-2(10 m)and SPOT-6/7(2.5 m)datasets covering 904 km2 in Motuo County,Xizang,our method outperforms the SRGAN baseline(SR4RS)with Peak Signal-to-Noise Ratio(PSNR)gains of 0.29 dB and Structural Similarity Index(SSIM)improvements of 3.08%on forest imagery.Visual comparisons confirm enhanced texture continuity despite marginal Learned Perceptual Image Patch Similarity(LPIPS)increases.The method significantly improves noise robustness and edge retention in complex geomorphology,demonstrating 18%faster response in forest fire early warning and providing high-resolution support for agricultural/urban monitoring.Future work will integrate spectral constraints and lightweight architectures.展开更多
With the increasing integration of renewable energy,microgrids are increasingly facing stability challenges,primarily due to the lack of inherent inertia in inverter-dominated systems,which is traditionally provided b...With the increasing integration of renewable energy,microgrids are increasingly facing stability challenges,primarily due to the lack of inherent inertia in inverter-dominated systems,which is traditionally provided by synchronous generators.To address this critical issue,Virtual Synchronous Generator(VSG)technology has emerged as a highly promising solution by emulating the inertia and damping characteristics of conventional synchronous generators.To enhance the operational efficiency of virtual synchronous generators(VSGs),this study employs smallsignal modeling analysis,root locus methods,and synchronous generator power-angle characteristic analysis to comprehensively evaluate how virtual inertia and damping coefficients affect frequency stability and power output during transient processes.Based on these analyses,an adaptive control strategy is proposed:increasing the virtual inertia when the rotor angular velocity undergoes rapid changes,while strengthening the damping coefficient when the speed deviation exceeds a certain threshold to suppress angular velocity oscillations.To validate the effectiveness of the proposed method,a grid-connected VSG simulation platform was developed inMATLAB/Simulink.Comparative simulations demonstrate that the proposed adaptive control strategy outperforms conventional VSGmethods by significantly reducing grid frequency deviations and shortening active power response time during active power command changes and load disturbances.This approach enhances microgrid stability and dynamic performance,confirming its viability for renewable-dominant power systems.Future work should focus on experimental validation and real-world parameter optimization,while further exploring the strategy’s effectiveness in improvingVSG low-voltage ride-through(LVRT)capability and power-sharing applications in multi-parallel configurations.展开更多
Predicting molecular properties is essential for advancing for advancing drug discovery and design. Recently, Graph Neural Networks (GNNs) have gained prominence due to their ability to capture the complex structural ...Predicting molecular properties is essential for advancing for advancing drug discovery and design. Recently, Graph Neural Networks (GNNs) have gained prominence due to their ability to capture the complex structural and relational information inherent in molecular graphs. Despite their effectiveness, the “black-box” nature of GNNs remains a significant obstacle to their widespread adoption in chemistry, as it hinders interpretability and trust. In this context, several explanation methods based on factual reasoning have emerged. These methods aim to interpret the predictions made by GNNs by analyzing the key features contributing to the prediction. However, these approaches fail to answer critical questions: “How to ensure that the structure-property mapping learned by GNNs is consistent with established domain knowledge”. In this paper, we propose MMGCF, a novel counterfactual explanation framework designed specifically for the prediction of GNN-based molecular properties. MMGCF constructs a hierarchical tree structure on molecular motifs, enabling the systematic generation of counterfactuals through motif perturbations. This framework identifies causally significant motifs and elucidates their impact on model predictions, offering insights into the relationship between structural modifications and predicted properties. Our method demonstrates its effectiveness through comprehensive quantitative and qualitative evaluations of four real-world molecular datasets.展开更多
The relations among three kinds of structural characteristics of fuzzy measure: (1) pseudometric generating property; (2) pseudometric generating property of type p; (3) null null additivity, and the convergence for ...The relations among three kinds of structural characteristics of fuzzy measure: (1) pseudometric generating property; (2) pseudometric generating property of type p; (3) null null additivity, and the convergence for sequence of measurable function on semi continuous fuzzy measure space are discussed. A set of equivalent conditions for each of these structural characteristics are presented, respectively. It is proved that null null additivity is equivalent to pseudometric generating property for a finite fuzzy measure on S compact space.展开更多
The problems including excessive flow of attemperating water for boiler, failure of butterfly valve at the outlet of circulating water pump, burnt-out of thyristor for excitation regulator, load variation rate of CCS ...The problems including excessive flow of attemperating water for boiler, failure of butterfly valve at the outlet of circulating water pump, burnt-out of thyristor for excitation regulator, load variation rate of CCS not complying with the contract target, etc. occurred during start-up and debugging of two 600 MW generating units in Yangzhou No.2 Thermal Power Plant. Through analysis on these problems. the remedial measures were put forward, to which can be referred for similar units.展开更多
This paper proposes a novel approach for generating 3-dimensional complex geological facies models based on deep generative models.It can reproduce a wide range of conceptual geological models while possessing the fle...This paper proposes a novel approach for generating 3-dimensional complex geological facies models based on deep generative models.It can reproduce a wide range of conceptual geological models while possessing the flexibility necessary to honor constraints such as well data.Compared with existing geostatistics-based modeling methods,our approach produces realistic subsurface facies architecture in 3D using a state-of-the-art deep learning method called generative adversarial networks(GANs).GANs couple a generator with a discriminator,and each uses a deep convolutional neural network.The networks are trained in an adversarial manner until the generator can create "fake" images that the discriminator cannot distinguish from "real" images.We extend the original GAN approach to 3D geological modeling at the reservoir scale.The GANs are trained using a library of 3D facies models.Once the GANs have been trained,they can generate a variety of geologically realistic facies models constrained by well data interpretations.This geomodelling approach using GANs has been tested on models of both complex fluvial depositional systems and carbonate reservoirs that exhibit progradational and aggradational trends.The results demonstrate that this deep learning-driven modeling approach can capture more realistic facies architectures and associations than existing geostatistical modeling methods,which often fail to reproduce heterogeneous nonstationary sedimentary facies with apparent depositional trend.展开更多
Actual sea condition testing and inspection and evaluation method research are carried out for tidal energy devices to provide scientific and effective technical support for the ocean high-tech achievement transformat...Actual sea condition testing and inspection and evaluation method research are carried out for tidal energy devices to provide scientific and effective technical support for the ocean high-tech achievement transformation and marine renewable energy development. By analyzing three core indicators, including the power output characteristics of the tidal current device, the generating capacity, energy conversion efficiency, proposed the test contents and evaluation methods of indicators are proposed in this paper; and based on the research of wind farms, power quality testing and assessment methods of offshore tidal energy device are proposed; given the security access to the test contents of tidal current energy device, tidal current energy device running conditions in the testing ground are comprehensively assessed.展开更多
The shafting vibration is closely related to the rotational angular speed.The angular speed of hydro turbine generating sets(HTGS)is rapidly change in fault transient,it maybe reduce the shafting damage.By means of en...The shafting vibration is closely related to the rotational angular speed.The angular speed of hydro turbine generating sets(HTGS)is rapidly change in fault transient,it maybe reduce the shafting damage.By means of energy analysis,the differential equation of shafting vibration for the HTGS is derived,in which include the equation of generator rotor and hydro turbine runner,it can be applied to transient analysis.Shafting model is transformed into first order differential equation groups,and is combined with the motion equation of HTGS to build integrated model.Various additional forces of shafting are taken as input inspire in proposed model,the generality of model is good.At last,the shafting vibration in emergency stop transient is simulated.展开更多
This paper is one of the series papers about the study on strike-slip earthquake-generating structure in the Chinese mainland. In the first part of this paper, based on the large amount of data from large earthquake i...This paper is one of the series papers about the study on strike-slip earthquake-generating structure in the Chinese mainland. In the first part of this paper, based on the large amount of data from large earthquake investigation and the latest results of focal mechanism, the earthquake-generating structure in the Chinese mainland interior and its neighbouring region is discussed. It is concluded that the absolutely predominated earthquake, not only in number, but also in intensity, as well as in distributing area, is strike slip earthquake, and it is further stressed that the study on the strike slip earthquake-generating structure is significant for seismic risk analysis. In the second part, the characteristics of tectonic stress field about strike slip earthquake-generating structure and the compiled distribution outline of strike slip earthquake-generating fault, normal fault, and thrust fault in the Chinese mainland interior and its neighbouring region, in the light of stress characteristics of fault plane solutions, are also discussed.展开更多
Sequence labelling(SL)tasks are currently widely studied in the field of natural language processing.Most sequence labelling methods are developed on a large amount of labelled training data via supervised learning,wh...Sequence labelling(SL)tasks are currently widely studied in the field of natural language processing.Most sequence labelling methods are developed on a large amount of labelled training data via supervised learning,which is time-consuming and expensive.As an alternative,domain adaptation is proposed to train a deep-learning model for sequence labelling in a target domain by exploiting existing labelled training data in related source domains.To this end,the authors propose a Bi-LSTM model to extract more-related knowledge from multi-source domains and learn specific context from the target domain.Further,the language modelling training is also applied to cross-domain adaptability facilitating.The proposed model is extensively evaluated with the named entity recognition and part-of-speech tagging tasks.The empirical results demonstrate the effectiveness of the cross-domain adaption.Our model outperforms the state-of-the-art methods used in both cross-domain tasks and crowd annotation tasks.展开更多
基金supported by the Spanish Government(ISCIII-FEDER)PI20/01063by Navarra Government(PC 060-061 and PC 192-193)Fundación Gangoiti(to MSA).LA was funded by FPU19/03255.
文摘Neuroinflammation is associated with Parkinson’s disease:Reactive gliosis and neuroinflammation are hallmarks of Parkinson’s disease(PD),a multisystem neurodegenerative disorder characterized by a progressive loss of dopaminergic neurons.Neuroinflammation has long been considered a mere consequence of neuronal loss,but whether it promotes PD or is a key player in disease progression remains to be determined.Human leukocyte antigen.
文摘This study presents a comparative analysis of a complex SQL benchmark, TPC-DS, with two existing text-to-SQL benchmarks, BIRD and Spider. Our findings reveal that TPC-DS queries exhibit a significantly higher level of structural complexity compared to the other two benchmarks. This underscores the need for more intricate benchmarks to simulate realistic scenarios effectively. To facilitate this comparison, we devised several measures of structural complexity and applied them across all three benchmarks. The results of this study can guide future research in the development of more sophisticated text-to-SQL benchmarks. We utilized 11 distinct Language Models (LLMs) to generate SQL queries based on the query descriptions provided by the TPC-DS benchmark. The prompt engineering process incorporated both the query description as outlined in the TPC-DS specification and the database schema of TPC-DS. Our findings indicate that the current state-of-the-art generative AI models fall short in generating accurate decision-making queries. We conducted a comparison of the generated queries with the TPC-DS gold standard queries using a series of fuzzy structure matching techniques based on query features. The results demonstrated that the accuracy of the generated queries is insufficient for practical real-world application.
基金supported by the National Nature Science Foundation of China (Nos. 61671362 and 62071366)。
文摘Two dimensional(2D) materials based on boron and carbon have attracted wide attention due to their unique properties. BC compounds have rich active sites and diverse chemical coordination, showing great potential in optoelectronic applications. However, due to the limitation of calculation and experimental conditions, it is still a challenging task to predict new 2D BC monolayer materials. Specifically, we utilized Crystal Diffusion Variational Autoencoder(CDVAE) and pre-trained Materials Graph Neural Network with 3-Body Interactions(M3GNet) model to generate novel and stable BCP materials. Each crystal structure was treated as a high-dimensional vector, where the encoder extracted lattice information and element coordinates, mapping the high-dimensional data into a low-dimensional latent space. The decoder then reconstructed the latent representation back into the original data space. Additionally, our designed attribute predictor network combined the advantages of dilated convolutions and residual connections,effectively increasing the model's receptive field and learning capacity while maintaining relatively low parameter count and computational complexity. By progressively increasing the dilation rate, the model can capture features at different scales. We used the DFT data set of about 1600 BCP monolayer materials to train the diffusion model, and combined with the pre-trained M3GNet model to screen the best candidate structure. Finally, we used DFT calculations to confirm the stability of the candidate structure.The results show that the combination of generative deep learning model and attribute prediction model can help accelerate the discovery and research of new 2D materials, and provide effective methods for exploring the inverse design of new two-dimensional materials.
基金supported by the National Natural Science Foundation of China(Grant Nos.U23A2069,12372288,12388101,and 92152301)Jilin Province Science and Technology Development Program,China(Grant No.20220301013GX)Aeronautical Science Foundation of China(Grant No.2020Z006058002)。
文摘Recent advances in contrastive language-image pretraining(CLIP)models and generative AI have demonstrated significant capabilities in cross-modal understanding and content generation.Based on these developments,this study introduces a novel framework for airfoil design via natural language interfaces.To the authors’knowledge,this study establishes the first end-to-end,bidirectional mapping between textual descriptions(e.g.,“low-drag supercritical wing for transonic conditions”)and parametric airfoil geometries represented by class-shape transformation parameters.The proposed approach integrates a CLIP-inspired architecture that aligns text embeddings with airfoil parameter spaces through contrastive learning,along with a semantically conditioned decoder that produces physically plausible airfoil geometries from latent representations.The experimental results validate the framework’s ability to generate aerodynamically plausible airfoils from natural language specifications and to classify airfoils accurately based on given textual labels.This research reduces the expertise threshold for preliminary airfoil design and highlights the potential for human-AI collaboration in aerospace engineering.
文摘Concentrated solar thermal power generation has been experimentally tested in advanced countries for a period of time.This paper demonstrates how this technology can be improved by using water molecules as a medium to drive traditional generator sets for energy conversion,thereby simultaneously improving the energy conversion rate.Additionally,a novel contribution is made by incorporating a magic number 4 to enhance the focusing efficiency of Fresnel lenses,which drives improvements in power generation output and QE(Quantum Efficiency).
基金financially supported by the National Natural Science Foundation of China(Grant No.42077232)the National Natural Science Foundation for Excellent Young Scholars of China(Grant No.52222110)the Fundamental Research Funds for the Central Universities(Grant No.14380229).
文摘The issues of seepage in calcareous sand foundations and backfillshave a potentially detrimental effect on the stability and safety of superstructures.Simplifying calcareous sand grains as spheres or ellipsoids in numerical simulations may lead to significantinaccuracies.In this paper,we present a novel intelligence framework based on a deep convolutional generative adversarial network(DCGAN).A DCGAN model was trained using a training dataset comprising 11,625 real particles for the random generation of three-dimensional calcareous sand particles.Subsequently,3800 realistic calcareous sand particles with intra-particle voids were generated.Generative fidelityand validity of the DCGAN model were well verifiedby the consistency of the statistical values of nine morphological parameters of both the training dataset and the generated dataset.Digital calcareous sand columns were obtained through gravitational deposition simulation of the generated particles.Directional seepage simulations were conducted,and the vertical permeability values of the sand columns were found to be in accordance with the objective law.The results demonstrate the potential of the proposed framework for stochastic modeling and multi-scale simulation of the seepage behaviors in calcareous sand foundations and backfills.
基金funded by the Science and Technology Projects of State Grid Corporation of China(Project No.J2024136).
文摘To ensure an uninterrupted power supply,mobile power sources(MPS)are widely deployed in power grids during emergencies.Comprising mobile emergency generators(MEGs)and mobile energy storage systems(MESS),MPS are capable of supplying power to critical loads and serving as backup sources during grid contingencies,offering advantages such as flexibility and high resilience through electricity delivery via transportation networks.This paper proposes a design method for a 400 V–10 kV Dual-Winding Induction Generator(DWIG)intended for MEG applications,employing an improved particle swarmoptimization(PSO)algorithmbased on a back-propagation neural network(BPNN).A parameterized finite element(FE)model of the DWIG is established to derive constraints on its dimensional parameters,thereby simplifying the optimization space.Through sensitivity analysis between temperature rise and electromagnetic loss of the DWIG,the main factors influencing the machine’s temperature are identified,and electromagnetic loss is determined as the optimization objective.To obtain an accurate fitting function between electromagnetic loss and dimensional parameters,the BPNN is employed to predict the nonlinear relationship between the optimization objective and the parameters.The Latin hypercube sampling(LHS)method is used for random sampling in the FE model analysis for training,testing,and validation,which is then applied to compute the cost function in the PSO.Based on the relationships obtained by the BPNN,the PSO algorithm evaluates the fitness and cost functions to determine the optimal design point.The proposed optimization method is validated by comparing simulation results between the initial design and the optimized design.
基金supported by National Natural Science Foundation of China(No.52025055 and 52275571)Basic Research Operation Fund of China(No.xzy012024024).
文摘Tilted metasurface nanostructures,with excellent physical properties and enormous application potential,pose an urgent need for manufacturing methods.Here,electric-field-driven generative-nanoimprinting technique is proposed.The electric field applied between the template and the substrate drives the contact,tilting,filling,and holding processes.By accurately controlling the introduced included angle between the flexible template and the substrate,tilted nanostructures with a controllable angle are imprinted onto the substrate,although they are vertical on the template.By flexibly adjusting the electric field intensity and the included angle,large-area uniform-tilted,gradient-tilted,and high-angle-tilted nanostructures are fabricated.In contrast to traditional replication,the morphology of the nanoimprinting structure is extended to customized control.This work provides a cost-effective,efficient,and versatile technology for the fabrication of various large-area tilted metasurface structures.As an illustration,a tilted nanograting with a high coupling efficiency is fabricated and integrated into augmented reality displays,demonstrating superior imaging quality.
基金Supported by the Korea Health Technology R&D Project through the Korea Health Industry Development Institute(KHIDI),funded by the Ministry of Health&Welfare,Republic of Korea(No.HR20C0026)the National Research Foundation of Korea(NRF)(No.RS-2023-00247504)the Patient-Centered Clinical Research Coordinating Center,funded by the Ministry of Health&Welfare,Republic of Korea(No.HC19C0276).
文摘AIM:To build a functional generalized estimating equation(GEE)model to detect glaucomatous visual field progression and compare the performance of the proposed method with that of commonly employed algorithms.METHODS:Totally 716 eyes of 716 patients with primary open angle glaucoma(POAG)with at least 5 reliable 24-2 test results and 2y of follow-up were selected.The functional GEE model was used to detect perimetric progression in the training dataset(501 eyes).In the testing dataset(215 eyes),progression was evaluated the functional GEE model,mean deviation(MD)and visual field index(VFI)rates of change,Advanced Glaucoma Intervention Study(AGIS)and Collaborative Initial Glaucoma Treatment Study(CIGTS)scores,and pointwise linear regression(PLR).RESULTS:The proposed method showed the highest proportion of eyes detected as progression(54.4%),followed by the VFI rate(34.4%),PLR(23.3%),and MD rate(21.4%).The CIGTS and AGIS scores had a lower proportion of eyes detected as progression(7.9%and 5.1%,respectively).The time to detection of progression was significantly shorter for the proposed method than that of other algorithms(adjusted P≤0.019).The VFI rate displayed moderate pairwise agreement with the proposed method(k=0.47).CONCLUSION:The functional GEE model shows the highest proportion of eyes detected as perimetric progression and the shortest time to detect perimetric progression in patients with POAG.
基金supported by the Chung-Ang University Research Grants in 2023.Alsothe work is supported by the ELLIIT Excellence Center at Linköping–Lund in Information Technology in Sweden.
文摘Recommending personalized travel routes from sparse,implicit feedback poses a significant challenge,as conventional systems often struggle with information overload and fail to capture the complex,sequential nature of user preferences.To address this,we propose a Conditional Generative Adversarial Network(CGAN)that generates diverse and highly relevant itineraries.Our approach begins by constructing a conditional vector that encapsulates a user’s profile.This vector uniquely fuses embeddings from a Heterogeneous Information Network(HIN)to model complex user-place-route relationships,a Recurrent Neural Network(RNN)to capture sequential path dynamics,and Neural Collaborative Filtering(NCF)to incorporate collaborative signals from the wider user base.This comprehensive condition,further enhanced with features representing user interaction confidence and uncertainty,steers a CGAN stabilized by spectral normalization to generate high-fidelity latent route representations,effectively mitigating the data sparsity problem.Recommendations are then formulated using an Anchor-and-Expand algorithm,which selects relevant starting Points of Interest(POI)based on user history,then expands routes through latent similarity matching and geographic coherence optimization,culminating in Traveling Salesman Problem(TSP)-based route optimization for practical travel distances.Experiments on a real-world check-in dataset validate our model’s unique generative capability,achieving F1 scores ranging from 0.163 to 0.305,and near-zero pairs−F1 scores between 0.002 and 0.022.These results confirm the model’s success in generating novel travel routes by recommending new locations and sequences rather than replicating users’past itineraries.This work provides a robust solution for personalized travel planning,capable of generating novel and compelling routes for both new and existing users by learning from collective travel intelligence.
基金This study was supported by:Inner Mongolia Academy of Forestry Sciences Open Research Project(Grant No.KF2024MS03)The Project to Improve the Scientific Research Capacity of the Inner Mongolia Academy of Forestry Sciences(Grant No.2024NLTS04)The Innovation and Entrepreneurship Training Program for Undergraduates of Beijing Forestry University(Grant No.X202410022268).
文摘Remote sensing image super-resolution technology is pivotal for enhancing image quality in critical applications including environmental monitoring,urban planning,and disaster assessment.However,traditional methods exhibit deficiencies in detail recovery and noise suppression,particularly when processing complex landscapes(e.g.,forests,farmlands),leading to artifacts and spectral distortions that limit practical utility.To address this,we propose an enhanced Super-Resolution Generative Adversarial Network(SRGAN)framework featuring three key innovations:(1)Replacement of L1/L2 loss with a robust Charbonnier loss to suppress noise while preserving edge details via adaptive gradient balancing;(2)A multi-loss joint optimization strategy dynamically weighting Charbonnier loss(β=0.5),Visual Geometry Group(VGG)perceptual loss(α=1),and adversarial loss(γ=0.1)to synergize pixel-level accuracy and perceptual quality;(3)A multi-scale residual network(MSRN)capturing cross-scale texture features(e.g.,forest canopies,mountain contours).Validated on Sentinel-2(10 m)and SPOT-6/7(2.5 m)datasets covering 904 km2 in Motuo County,Xizang,our method outperforms the SRGAN baseline(SR4RS)with Peak Signal-to-Noise Ratio(PSNR)gains of 0.29 dB and Structural Similarity Index(SSIM)improvements of 3.08%on forest imagery.Visual comparisons confirm enhanced texture continuity despite marginal Learned Perceptual Image Patch Similarity(LPIPS)increases.The method significantly improves noise robustness and edge retention in complex geomorphology,demonstrating 18%faster response in forest fire early warning and providing high-resolution support for agricultural/urban monitoring.Future work will integrate spectral constraints and lightweight architectures.
基金financially supported by the Talent Initiation Fund of Wuxi University(550220008).
文摘With the increasing integration of renewable energy,microgrids are increasingly facing stability challenges,primarily due to the lack of inherent inertia in inverter-dominated systems,which is traditionally provided by synchronous generators.To address this critical issue,Virtual Synchronous Generator(VSG)technology has emerged as a highly promising solution by emulating the inertia and damping characteristics of conventional synchronous generators.To enhance the operational efficiency of virtual synchronous generators(VSGs),this study employs smallsignal modeling analysis,root locus methods,and synchronous generator power-angle characteristic analysis to comprehensively evaluate how virtual inertia and damping coefficients affect frequency stability and power output during transient processes.Based on these analyses,an adaptive control strategy is proposed:increasing the virtual inertia when the rotor angular velocity undergoes rapid changes,while strengthening the damping coefficient when the speed deviation exceeds a certain threshold to suppress angular velocity oscillations.To validate the effectiveness of the proposed method,a grid-connected VSG simulation platform was developed inMATLAB/Simulink.Comparative simulations demonstrate that the proposed adaptive control strategy outperforms conventional VSGmethods by significantly reducing grid frequency deviations and shortening active power response time during active power command changes and load disturbances.This approach enhances microgrid stability and dynamic performance,confirming its viability for renewable-dominant power systems.Future work should focus on experimental validation and real-world parameter optimization,while further exploring the strategy’s effectiveness in improvingVSG low-voltage ride-through(LVRT)capability and power-sharing applications in multi-parallel configurations.
文摘Predicting molecular properties is essential for advancing for advancing drug discovery and design. Recently, Graph Neural Networks (GNNs) have gained prominence due to their ability to capture the complex structural and relational information inherent in molecular graphs. Despite their effectiveness, the “black-box” nature of GNNs remains a significant obstacle to their widespread adoption in chemistry, as it hinders interpretability and trust. In this context, several explanation methods based on factual reasoning have emerged. These methods aim to interpret the predictions made by GNNs by analyzing the key features contributing to the prediction. However, these approaches fail to answer critical questions: “How to ensure that the structure-property mapping learned by GNNs is consistent with established domain knowledge”. In this paper, we propose MMGCF, a novel counterfactual explanation framework designed specifically for the prediction of GNN-based molecular properties. MMGCF constructs a hierarchical tree structure on molecular motifs, enabling the systematic generation of counterfactuals through motif perturbations. This framework identifies causally significant motifs and elucidates their impact on model predictions, offering insights into the relationship between structural modifications and predicted properties. Our method demonstrates its effectiveness through comprehensive quantitative and qualitative evaluations of four real-world molecular datasets.
文摘The relations among three kinds of structural characteristics of fuzzy measure: (1) pseudometric generating property; (2) pseudometric generating property of type p; (3) null null additivity, and the convergence for sequence of measurable function on semi continuous fuzzy measure space are discussed. A set of equivalent conditions for each of these structural characteristics are presented, respectively. It is proved that null null additivity is equivalent to pseudometric generating property for a finite fuzzy measure on S compact space.
文摘The problems including excessive flow of attemperating water for boiler, failure of butterfly valve at the outlet of circulating water pump, burnt-out of thyristor for excitation regulator, load variation rate of CCS not complying with the contract target, etc. occurred during start-up and debugging of two 600 MW generating units in Yangzhou No.2 Thermal Power Plant. Through analysis on these problems. the remedial measures were put forward, to which can be referred for similar units.
文摘This paper proposes a novel approach for generating 3-dimensional complex geological facies models based on deep generative models.It can reproduce a wide range of conceptual geological models while possessing the flexibility necessary to honor constraints such as well data.Compared with existing geostatistics-based modeling methods,our approach produces realistic subsurface facies architecture in 3D using a state-of-the-art deep learning method called generative adversarial networks(GANs).GANs couple a generator with a discriminator,and each uses a deep convolutional neural network.The networks are trained in an adversarial manner until the generator can create "fake" images that the discriminator cannot distinguish from "real" images.We extend the original GAN approach to 3D geological modeling at the reservoir scale.The GANs are trained using a library of 3D facies models.Once the GANs have been trained,they can generate a variety of geologically realistic facies models constrained by well data interpretations.This geomodelling approach using GANs has been tested on models of both complex fluvial depositional systems and carbonate reservoirs that exhibit progradational and aggradational trends.The results demonstrate that this deep learning-driven modeling approach can capture more realistic facies architectures and associations than existing geostatistical modeling methods,which often fail to reproduce heterogeneous nonstationary sedimentary facies with apparent depositional trend.
基金supported by the Implementation Programs for Marine Renewable Energy Special Funds (GHME2012ZC02)
文摘Actual sea condition testing and inspection and evaluation method research are carried out for tidal energy devices to provide scientific and effective technical support for the ocean high-tech achievement transformation and marine renewable energy development. By analyzing three core indicators, including the power output characteristics of the tidal current device, the generating capacity, energy conversion efficiency, proposed the test contents and evaluation methods of indicators are proposed in this paper; and based on the research of wind farms, power quality testing and assessment methods of offshore tidal energy device are proposed; given the security access to the test contents of tidal current energy device, tidal current energy device running conditions in the testing ground are comprehensively assessed.
基金financially supported by the National Natural Science Foundation of China under Grant No.51179079
文摘The shafting vibration is closely related to the rotational angular speed.The angular speed of hydro turbine generating sets(HTGS)is rapidly change in fault transient,it maybe reduce the shafting damage.By means of energy analysis,the differential equation of shafting vibration for the HTGS is derived,in which include the equation of generator rotor and hydro turbine runner,it can be applied to transient analysis.Shafting model is transformed into first order differential equation groups,and is combined with the motion equation of HTGS to build integrated model.Various additional forces of shafting are taken as input inspire in proposed model,the generality of model is good.At last,the shafting vibration in emergency stop transient is simulated.
文摘This paper is one of the series papers about the study on strike-slip earthquake-generating structure in the Chinese mainland. In the first part of this paper, based on the large amount of data from large earthquake investigation and the latest results of focal mechanism, the earthquake-generating structure in the Chinese mainland interior and its neighbouring region is discussed. It is concluded that the absolutely predominated earthquake, not only in number, but also in intensity, as well as in distributing area, is strike slip earthquake, and it is further stressed that the study on the strike slip earthquake-generating structure is significant for seismic risk analysis. In the second part, the characteristics of tectonic stress field about strike slip earthquake-generating structure and the compiled distribution outline of strike slip earthquake-generating fault, normal fault, and thrust fault in the Chinese mainland interior and its neighbouring region, in the light of stress characteristics of fault plane solutions, are also discussed.
基金National Statistical Science Research Project of China,Grant/Award Number:2016LY98Science and Technology Department of Guangdong Province in China,Grant/Award Numbers:2016A010101020,2016A010101021,2016A010101022+1 种基金Characteristic Innovation Projects of Guangdong Colleges and Universities,Grant/Award Number:2018KTSCX049Science and Technology Plan Project of Guangzhou,Grant/Award Numbers:202102080258,201903010013。
文摘Sequence labelling(SL)tasks are currently widely studied in the field of natural language processing.Most sequence labelling methods are developed on a large amount of labelled training data via supervised learning,which is time-consuming and expensive.As an alternative,domain adaptation is proposed to train a deep-learning model for sequence labelling in a target domain by exploiting existing labelled training data in related source domains.To this end,the authors propose a Bi-LSTM model to extract more-related knowledge from multi-source domains and learn specific context from the target domain.Further,the language modelling training is also applied to cross-domain adaptability facilitating.The proposed model is extensively evaluated with the named entity recognition and part-of-speech tagging tasks.The empirical results demonstrate the effectiveness of the cross-domain adaption.Our model outperforms the state-of-the-art methods used in both cross-domain tasks and crowd annotation tasks.
